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Assessing Nutrient Leaching under Different Nutrient and Irrigation Management Practices

Permanent Link: http://ufdc.ufl.edu/UFE0041957/00001

Material Information

Title: Assessing Nutrient Leaching under Different Nutrient and Irrigation Management Practices
Physical Description: 1 online resource (177 p.)
Language: english
Creator: Kiggundu, Nicholas
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: adsorption, avocado, beta, bmp, bucket, calcareous, calibration, ceramic, desorption, fast, fertilizer, freundlich, global, irrigation, isotherms, krome, langmuir, leaching, leachm, lysimeter, modeling, morris, nitrogen, nutrient, parameter, phosphorus, sensitivity, simmonds, simulation, sorption, uncertainty, validation, variance
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
Genre: Agricultural and Biological Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Leaching of nitrogen (N) and phosphorus (P) from agricultural fields is one cause of water resource impairment. My study was designed to 1) explore if ceramic tension lysimeters interfere with the chemical composition of leachates containing P and to compare leached concentrations of N and P estimated using tension and gravitational lysimeters, 2) determine the effect of seven irrigation and fertilizer management practices (IFMP) on nutrient leaching and avocado (Persea Americana Mill.) yield, and 3) apply the Leaching Estimation and Chemistry Model (LEACHM) to refine the identified management practice. An evaluation of three commercially available ceramic lysimeters (i.e., Ceramic A, Ceramic B, and Ceramic C) indicated that all three ceramics contained various chemical elements that may influence P estimation of the leachate. The elements identified that may influence P estimation depending on the pH and concentration of the soil water being sampled were Fe, Al, Si, and Ca. The lower the ceramic?s P adsorption maximum ( ) the more accurate the sampler was in estimating PO4-P concentration of a known stock solution. A protocol to be followed to determine the efficacy of ceramic lysimeters for water quality monitoring was proposed. Evaluation of the seven IFMP indicated that irrigating 'Simmonds' avocado trees based on soil water at 15 kPa (SW) with fertilizer at a standard rate (FSR) saved 87% of the water volume applied and resulted in average annual reductions of 70 and 75% in NO3 N and TP leaching respectively campared to the set schedule irrigation with FSR method. Since ?Beta? avocado is genetically predisposed to higher fruit yield than ?Simmonds? the SW with FSR may not the best IFMP for the production of ?Beta?. Global techniques that involved use of Morris' screening and eFAST methods were used to perform LEACHM model sensitivity and uncertainty analyses. The eFAST sensitivity method showed that total first-order effects explained 89, 87, and 89% of the output variability in drainage water volume, leached NO3-N, and leached TP. The proposed refined best IFMP (BMP) would lead to further water and irrigation use efficiency and a sustainable management environment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Nicholas Kiggundu.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Migliaccio, Kati W.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041957:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041957/00001

Material Information

Title: Assessing Nutrient Leaching under Different Nutrient and Irrigation Management Practices
Physical Description: 1 online resource (177 p.)
Language: english
Creator: Kiggundu, Nicholas
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: adsorption, avocado, beta, bmp, bucket, calcareous, calibration, ceramic, desorption, fast, fertilizer, freundlich, global, irrigation, isotherms, krome, langmuir, leaching, leachm, lysimeter, modeling, morris, nitrogen, nutrient, parameter, phosphorus, sensitivity, simmonds, simulation, sorption, uncertainty, validation, variance
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
Genre: Agricultural and Biological Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Leaching of nitrogen (N) and phosphorus (P) from agricultural fields is one cause of water resource impairment. My study was designed to 1) explore if ceramic tension lysimeters interfere with the chemical composition of leachates containing P and to compare leached concentrations of N and P estimated using tension and gravitational lysimeters, 2) determine the effect of seven irrigation and fertilizer management practices (IFMP) on nutrient leaching and avocado (Persea Americana Mill.) yield, and 3) apply the Leaching Estimation and Chemistry Model (LEACHM) to refine the identified management practice. An evaluation of three commercially available ceramic lysimeters (i.e., Ceramic A, Ceramic B, and Ceramic C) indicated that all three ceramics contained various chemical elements that may influence P estimation of the leachate. The elements identified that may influence P estimation depending on the pH and concentration of the soil water being sampled were Fe, Al, Si, and Ca. The lower the ceramic?s P adsorption maximum ( ) the more accurate the sampler was in estimating PO4-P concentration of a known stock solution. A protocol to be followed to determine the efficacy of ceramic lysimeters for water quality monitoring was proposed. Evaluation of the seven IFMP indicated that irrigating 'Simmonds' avocado trees based on soil water at 15 kPa (SW) with fertilizer at a standard rate (FSR) saved 87% of the water volume applied and resulted in average annual reductions of 70 and 75% in NO3 N and TP leaching respectively campared to the set schedule irrigation with FSR method. Since ?Beta? avocado is genetically predisposed to higher fruit yield than ?Simmonds? the SW with FSR may not the best IFMP for the production of ?Beta?. Global techniques that involved use of Morris' screening and eFAST methods were used to perform LEACHM model sensitivity and uncertainty analyses. The eFAST sensitivity method showed that total first-order effects explained 89, 87, and 89% of the output variability in drainage water volume, leached NO3-N, and leached TP. The proposed refined best IFMP (BMP) would lead to further water and irrigation use efficiency and a sustainable management environment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Nicholas Kiggundu.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Migliaccio, Kati W.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041957:00001


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ASSESSING NUTRIENT LEACHING UNDER DIFFERENT NUTRIENT AND
IRRIGATION MANAGEMENT PRACTICES





















By

NICHOLAS KIGGUNDU


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2010


































2010 Nicholas Kiggundu



































To my daughter, Gabriella and wife, Mary Louise









ACKNOWLEDGMENTS

I give Glory and thanks to God for the gift of life and for the many blessings that have I

received during my Ph. D. training. I thank my parents for teaching me many important aspects

of life that have enabled me to keep working hard, believing, and having faith that I can attain a

Ph. D. degree. My sincere thanks go to Dr. Kati W. Migliaccio my major advisor for teaching me

how to conduct research and how to write scientific manuscripts. Her patience, insight,

dedication, thoughtfulness, and constant guidance were cornerstones in the timely completion of

my Ph. D. training. I am grateful to the supervisory services received from the members of my

Ph. D. committee: Dr. Yuncong Li for guidance during the laboratory experiments and the

sections that required knowledge of soil and water chemistry; Dr. Bruce Schaffer for guidance on

data statistical analysis and knowledge related to crop physiology; Dr. Jonathan Crane for his

support on the orchard management, interpretation of yield results, and for ensuring that the trees

remained healthy during the time of the experiment; Dr. James W. Jones for his guidance on

model global sensitivity analysis; and Dr. Kirk Hatfield for his guidance on the use ofLEACHM

model. I wish to acknowledge the fruitful discussions I had with Dr. Mufioz-Carpena on

hydrological modeling. I have benefited from the discussions I have had with my colleagues in

the Agricultural Biological Engineering Department on handling model simulation problems and

I wish to convey my sincere thanks to Kofikuma Dzotsi, Oscar Perez-Ovilla, Zuzanna Zajac,

Anna Cathey, Stuart Muller, and Gareth Lagerwall. I am grateful to my friend Girish

Ravunnikutty for his assistance with coding in C++. The advices of my colleague Dr. Gregory

Hendricks on managing the many challenges of life in graduate school were very invaluable.

I wish to thank the technicians at Tropical Research and Education Center (TREC) in

Homestead, FL namely; Tina Dispenza, Michael Gutierrez, Wanda Montas, and Harry Trafford

who constantly helped me with data collection especially during the time I was at the main









campus in Gainesville working on my course work. The following colleagues were instrumental

in data collection too: Dr. Richard Carey, Isaya Kisekka, Luis Barquin, Brigette Castro,

Chunfang Li "Daisey", David H Boniche, and Paulina Sabbagh and I greatly appreciate their

support. I am grateful to the workshop and field crew team at the TREC for their role in

managing the avocado orchard that included mowing; applying pesticides, freeze protection, and

tree staking.

Analyzing water, soil, and tissue samples for various chemical elements was a big portion

of my research. I wish to thank Dr. Qingren Wang the manager of the Soil and Water Science

laboratory at TREC for his guidance and patience during the time I was learning how to analyze

samples for the various chemical elements. I am indebted to Ms. Quigin Yu and Ms. Rosado

Laura for their help with sample analysis and for putting in extra hours to ensure that I got my

results on time. I benefited from the guidance of Dr. Guodong Liu and Dr. Fan Xiang on how to

analyze and interpret nitrogen content from the different sources.

I am very grateful for the following financial support; the College of Agriculture and Life

Sciences at the University of Florida for awarding me the Alumni Fellowship that paid fo my

tution and stipend, USDA-CSREES for funding my research work, and to Makerere University

Administration for granting me a study leave and for the financial support.

Finally I wish to thank my wife and daughter for their understanding, patience, love and

support for the many days I was away from home working on my research and writing the

dissertation. To my sisters, brother, and cousins I love you and I miss you all.









TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S .................................................................................................................... 4

LIST OF TABLES... ..................... 9

L IS T O F F IG U R E S ............... ................................................................................................ ...... 12

L IST O F A B B R E V IA T IO N S .............................................................................. ........................ 14

A B STR A CT ...................... ...................... .......... ................ 15

CHAPTER

1 IN T R O D U C T IO N ......... ... ...... .. ........... .................................................................... 17

Nitrogen and Phosphorus Leaching from Agricultural Fields ........................................... 17
Nitrogen and Phosphorus Forms and their Reactions............... ............ ............... 19
Irrigation and Nutrient Best Management Practices (BMPs)................................................20
E T -based Irrigation ...................................................................... ................. 2 1
Soil W ater-based Irrigation ..................................................... ...................................... 22
Irrigation and N utrient B M Ps ........................... ......... .............................. ............... 23
A ssessing N utrient L teaching ..................... ........ .................... .... .............................. 24
Field Scale Simulation Modeling of Nutrient Transport .................. ............................... 27
Model Sensitivity and Uncertainty Analysis...... .................... .. .................. 31
State ent of Problem ........................................................................... ........... .............. ....... 32
G oal and O objectives .................................................................................................... ............... 33
Rationale for Tropical Fruit BMPs in South Florida ...................................................... 34
Stu dy A rea an d S cop e ...................................................................... ..................................... 3 5

2 MONITORING NUTRIENT LEACHING USING TENSION AND GRAVITATION
LY SIM E TER S ......................................................... ....... .......... ...... 43

Introduction .................................... ......................... 43
M materials and M eth od s................................................................ .......................................... 4 6
S tu d y A rea ........................................................................................................ .... 4 6
Chem ical Com position of Ceram ic Cups...................................................... ..................47
Phosphorus Adsorption and Desorption Potential of Commercial Ceramic
L ysim eters ....................................................... ........ .......... ...... 48
Sorption Isotherm s........................... ......... ... ..... ...... ....... ............ ............ 49
Estimation of Known P Stock Solutions by Different Ceramic Lysimeters................. 50
Comparison of Leachate Concentrations from Bucket and Ceramic Lysimeters
Under a Controlled Environment............. ................. ... .............................52
Comparison of Leachate Concentrations from Bucket and Ceramic Lysimeters
from an A avocado O orchard .......................... ............................................ ..................... 54
R e su lts a n d D iscu ssio n ............................................................................. ............................ 5 5









Constituents of Elem ents in Ceram ic Cups............................................. ..................... 55
Adsorption-desorption Potential of the Ceramic Cups............... .... ............... 56
Phosphorus Estimation by the Ceramic Lysimeters...................................... 57
Nutrient Concentrations Sampled from a Controlled Environment...............................59
Comparison of Nutrient Concentrations Sampled form an Avocado Orchard................ 61
Conclusions ....... ........................................... ........... 61

3 EVALUATION OF IRRIGATION AND NUTRIENT MANAGEMENT PRACTICES
IN A YOUNG AVOCADO ORCHARD ............................................. ......................... 73

Introduction ........................................ ..................... 73
M materials and M ethod s................................................................................... ........................ 77
Stu dy A rea .................. .................................................................... .... 7 7
Avocado Orchard Layout and Experimental Design............................................ 78
Irrigation M anagem ent Practices.................................................................................. 78
P lan t M ea su rem ents..................................................................... .......... .............. ....... 8 1
Soil Sam pling and A analysis .. .................. ........................................ .............................. 82
M easuring Nutrient Loads Leached ................................... ...... ............................. 83
Effect of Fertilizer Amount and Irrigation Water Volume on Avocado Yield ................ 85
R esu lts an d D iscu ssion ............................................................................. ............................ 8 6
W after A application ...................... .. ............... ................. ... ....... ....... 86
Nitrogen and Phosphorus Loads Leached .............................. ..................... 87
Soil A naly sis ................. .. ................ .......................... .................. 88
Avocado Yield Analyzed as Treatment Main Effects....................................................90
Avocado Yield Analyzed as 2-levels of Irrigation by 3-levels of Fertilizer Factorial
D design ............................................. ................. ............................. 93
Plant Nutrient Assessment and Tree Growth.......................... ................... ...............94
Recom m endations from m y Study ............... ............................................................ 95
Conclusions .................................... ......................... 96

4 MODEL SIMULATION OF NITROGEN AND PHOSPHORUS LEACHING IN
CALCAREOUS SOILS OF SOUTH FLORIDA...................................... ............... 119

Introdu action ... ................ ....................................... ........ .......... ...... 119
M materials and M eth od s.............................................................. .......................................... 12 3
E xperim ental D esign ............... .................................................................. ............ 123
L E A C H M M odel ..................... ............................................................... ........................... 124
Model Set-up and Parameterization ................................ 127
Global Sensitivity and Uncertainty Analyses .................................... ............... 128
M odel Calibration and Validation ...................................................... 130
R defining the Selected B M P ........................... ......... ................................ ............... 132
R esu lts an d D iscu ssion ................................................... .................................................... 13 3
Global Sensitivity Analysis: Morris'. ............................... 133
Global Sensitivity Analysis: eFAST..................................................... 134
M odel Calibration .............. ...... .. ......... ..................... 136
Constraints with M odel Validation................................................ 136
M modeling of the B M P ......... ...................... ..................................................... 137


7









Recommendations from my Study ................................ 138
Conclusions ..................................................... ........ 140

5 S U M M A R Y ....... .......... ................. ......................... ............................................ 15 5

Objective 1 ...................................................... ......... 155
O bje ctiv e 2 ................... ...............................................5 6
O objective 3 .......... ..................................... 157

LIST OF REFERENCES .......................................................................... 162

BIOGRAPHICAL SKETCH ......................................... 177









LIST OF TABLES


Table page

1-1 Parameters used for sensitivity analysis in LEACHM for water and nitrate leaching ......37

1-2 Parameters used for calibration of LEACHM for water and nitrate leaching....................38

2-1 Composition of ceramic cups considering major elements reported by chemical
analyses, n=3. ............................................. ..................... ............... 64

2-2 Sorption parameters for the ceramic materials included in the experiment considering
two isotherms and a P concentration range of 0 to 20 mg L1.......................................... 64

2-3 Estimation of a known concentration of PO4-P by different lysimeters, n=3 .................... 65

2-4 Estimation of a known concentration of PO4-P by ceramics B and C after
conditioning, n=3. ............................................. 65

2-5 Mass of P retained by lysimeter ceramic cups, n=3 .......................................................66

2-6 Status of the cleaned lysimeters after sampling a known P concentration, n=3................67

2-7 Leachate concentration comparison of bucket and ceramic lysimeters for the
sam pling of 7/23/2008, n= 3 .......................................................................... ....... ........... 68

2-8 Leachate concentration comparison of bucket and ceramic lysimeters for the
sam pling of 8/25/2008, n= 3 .......................................................................... ....... ........... 68

2-9 Leachate concentration comparison of bucket and ceramic lysimeters for the
sam pling of 5/20/2009, n= 3 .......................................................................... ....... ........... 68

2-10 Leachate concentration comparison of bucket and ceramic lysimeters for the
sam pling of 6/17/2009, n= 3 .......................................................................... ....... ........... 69

3-1 Fertilizer at a standard rate management scheme used for the 'Simmonds' and 'Beta'
avocado trees ..... ... ........ .............................. 98

3-2 The ETo and k, values used to compute water application rates for the ET-based
irrigation m anagem ent m ethod..... ...................................................................... 98

3-3 Amount of water applied (x 10- 3 3tree-day-1) by the different management
practices ....... ................................... ........... .......... 98

3-4 Nutrient leaching reduction percentage of' Simmonds' avocado trees as compared
to set schedule with FSR treatm ent..................................................................... 99

3-5 Total nutrient load leached kg ha-ffrom Nov 2007 to Oct 2009 for 'Simmonds'
av o cad o trees .... ............... .......................................... ........... ...... 99









3-6 'Simmonds' and 'Beta' soil analysis for irrigation and fertilizer management
treatm ents 09/0 5/2006, n= 3 ........................................... .................................................. 99

3-7 'Simmonds' and 'Beta' soil analysis for irrigation and fertilizer management
treatm ents 7/6/2 0 0 7 n= 3 ..................................................................................................... 10 0

3-8 'Simmonds' and 'Beta' soil analysis for irrigation and fertilizer management
treatm ents 9/22/0 8, n= 3 .............................................. ................................................... 10 0

3-9 'Simmonds' and 'Beta' soil analysis for irrigation and fertilizer management
treatm ents 9/7/0 9 n = 3 ................................................... ................................................ 10 0

3-10 'Beta' soil inorganicN for different sampling dates, n=3 .............................................. 101

3-11 'Simmonds' soil inorganic N for different sampling dates, n=3 ...................................... 101

3-12 Soil's factorial analysis of 2-levels of irrigation by 3-levels of fertilizer from 2006 to
2009.................. ..................................... 101

3-13 'Simmonds' avocado yield (kg ha-1) by harvest date per treatment for 2008 and 2009,
n=12 ................................................ 102

3-14 'Simmonds' avocado fruit number and weight per treatment for 2008 and 2009, n=12.. 102

3-15 'Beta' avocado yield (kg ha-1) by harvest date per treatment for 2008 and 2009, n=4 ... 103

3-16 'Beta' avocado fruit number and weight per treatment for two years, n=4 .................. 103

3-17 Means fruit weight per cubic meter of water applied water applied (kg m-3)................ 103

3-18 Means fruit weight per kg of fertilizer applied (kg kg) ................................................... 104

3-19 Leaf tissue analysis for irrigation and fertilizer management treatments 08/03/2006,
n= 4 ................................................... ..... 104

3-20 Leaf tissue analysis for irrigation and fertilizer management treatments 12/06/2006,
n= 4 .................................................... ..... 104

3-21 Leaf tissue analysis for irrigation and fertilizer management treatments 04/30/2007,
n = 3 ................... ................... ........................................................... 1 0 5

3-22 Leaf tissue analysis for irrigation and fertilizer management treatments 08/23/2007,
n = 3 ................... ................... ........................................................... 1 0 5

3-23 Leaf tissue analysis for irrigation and fertilizer management treatments 12/26/2007,
n = 3 ................... ................... ........................................................... 1 0 5

3-24 Leaf tissue analysis for irrigation and fertilizer management treatments 04/08/2008,
n = 3 ................... ................... ........................................................... 1 0 6









3-25 Leaf tissue analysis for irrigation and fertilizer management treatments 08/13/2008,
n = 3 ................... ................... ........................................................... 1 0 6

3-26 Leaf tissue analysis for irrigation and fertilizer management treatments 12/12/2008,
n = 3 ................... ................... ........................................................... 1 0 6

3-27 Leaf tissue analysis for irrigation and fertilizer management treatments 04/20/2009,
n = 3 ................... ................... ........................................................... 1 0 7

3-28 Leaf tissue analysis for irrigation and fertilizer management treatments 08/26/2009,
n = 3 ................... ................... ........................................................... 1 0 7

3-29 Tissue factorial analysis of 2-levels of irrigation by 3-levels of fertilizer for different
sam pling dates from 2006 to 2009 ........................................................ ............. 108

4-1 Sensitivity analysis parameters used for in LEACHM for water flow and nitrate
L teaching .................. ...................................... ........................... 142

4-2 Calibration parameters identified for LEACHM for water flow and nitrate leaching..... 143

4-3 LEACHM water, nitrate, and phosphorus input factors and their probability density
functions .............................................. .. ................... ............... 145

4-4 Selected sensitive param eters by M orris' m ethod .......................... ....... ................... 147

4-5 Extended Fourier Amplitude Sensitivity Test (FAST) results for LEACHM.................. 148

4-6 Uncertainty analysis statistic for the 3 selected output probability distributions
obtained from Extended FAST results, n=13300............................... 149

4-7 Rainfall and irrigation amounts for selected months in 2008 and 2009......................... 149

4-8 Modeled leached nutrient loads for the selected BMP. .......................................... 149









LIST OF FIGURES


Figure page

1-1 The nitrogen cycle: the different transformations are numbered 1 to 7 (adapted from
H av lin et al. 2 0 0 4 )........................................................................................... .......... 3 9

1-2 Illustration of the interactions between the different forms of P in the soil (adapted
from H av lin et al. 2 0 04 ) ...................................................................................... ..... .... 4 0

1-3 Bucket lysimeter. A) elevation view and, B) inside view (credits. Harry Trafford). ........41

1-4 Porous ceramic lysimeters and relevant sampling devices. A) ceramic lysimeter, B)
pump used to apply suction, C) syringe used to expel leachate form the collection
tubing (Adapted from Irrometer Company, Inc.) .......... ......................... ...........41

1-5 Map of Miami-Dade County, south Florida and the surrounding water bodies.................42

2-1 Arrangement of the bucket and ceramic lysimeters in each container. A) cross
section view of each container, B) arrangement of the four boxes under a rainfall
simulator ....... .......................... ........ ..................... 70

2-2 Sorption-desorption curves of P. A) Ceramic A, B) Ceramic B, C) Ceramic C................71

2-3 Elution curves of nutrient concentrations sampled by the lysimeters under a
controlled environment. A) ceramic and B) bucket. ........................................................72

3-1 Orchard layout. A) Treatments and their replicates where the first number is the
treatment and second number is the replicate, and B) tree cultivar and number and
type of device installed beside the tree. ............................... 109

3-2 Automated switching tensiometers were set at 15 kPa .............................................. 109

3-3 Amount of water applied as daily average per month by the Set schedule, ET and SW
irrig action m an ag em ent. .................................................. ............................................... 1 10

3-4 Mean water volume applied per day for ET-based, SW-based, and Set schedule
based irrigation during wet and dry season. ........................................... ............... ..111

3-5 Correlation of historical and real time ETo (R2 = 0.913)................................................. 111

3-6 Leached nitrate for 'Simmonds' over a two year period.. A) November 2007 to
October 2008 and B) November 2008 to October 2009................................................... 112

3-7 Leached total phosphorus for 'Simmonds' over a two year period. A) November
2007 to October 2008 and B) November 2008 to October 2009............... ................ 113

3-8 Effect of irrigation and fertilizer management treatments on mean fruit weight of
avocados for 2008 and 2009. A) 'Simmonds', and B) 'Beta'............... ................. 114









3-9 'Simmonds' yield analyzed as a factorial design with 2-levels of irrigation and
3-levels of fertilizer input. A) 2008, and B) 2009 ....................................................... 115

3-10 'Beta' yield analyzed as factorial design with 2-levels of irrigation and 3-levels of
fertilizer input. A) 2008, and B) 2009.................................. .................... 116

3-11 Effect of irrigation and fertilizer management treatments on mean tree diameter for
4-year. A) 'Simm onds', and B) 'Beta'..................... ... .. ............................... 117

3-12 Effect of irrigation and fertilizer management treatments on mean SPAD value for
reading collected from 2006 to 2009. A) 'Simmonds', and B) 'Beta' ............ ............. 118

4-1 Global sensitivity analysis results obtained from the Morris (1991) screening using
the LEACHM model as monthly average values for the period May to December
2006. A) drainage, B) nitrate, and C) phosphorus. Input factors separated from the
origin of the u*- plane were considered important. Labels of less important or
unimportant parameters factors (close to the p -o plane origin) have been removed
for clarity. Factor definitions are given in Tables 4.3 and 4.4. ....................................... 150

4-2 Global uncertainty analysis results obtained from the eFAST variance-based method
expressed in form of probability distribution function (PDF) and cumulative
distribution function (CDF) for monthly average for the period May to December
2006. A) drainage, B) nitrate, and C) phosphorus. ............................................... 151

4-3 Global uncertainty analysis results obtained from the extended FAST variance-based
method expressed in form of probability of exceedance for monthly average for the
period May to December 2006. A) drainage, B) nitrate, and C) phosphorus................. 152

4-4 LEACHM model calibration results on a monthly basis. A) drainage, B) nitrate, and
C ) p h o sp h oru s ............................................................................ ................ 153

4-5 Simulated monthly leached nutrients at different irrigation volumes and fertilizer
rates. A) drainage, B) nitrate, and C) phosphorus............................... ............... 154
















ASCE-EWRI


BMP

CP-FUE

CP-WUE

eFAST

EPA

ET

FAO

FAWN

FDACS

FDEP

FSR

IFMP

LEACHM

SPAD

SW


LIST OF ABBREVIATIONS

American Society of Civil Engineers-Environmental and Water Resources
Institute

Best management practice

Crop production fertilizer use efficiency

Crop production water use efficiency

Extended Fourier amplitude sensitivity test

Environmental Protection Agency

Evapotranspiration

Food and Agricultural Organization of the United Nations

Florida Automatic Weather Network

Florida Department of Agriculture and Consumer Services

Florida Department of Environmental Protection

Fertilizer at a standard rate

Irrigation and fertilizer management practices

Leaching estimation and chemistry model

Soil plant analysis development

Soil water









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

ASSE S SING NUTRIENT LEACHING UNDER DIFFERENT NUTRIENT AND
IRRIGATION MANAGEMENT PRACTICES

By

Nicholas Kiggundu

August 2010

Chair: Kati W. Migliaccio
Major: Agricultural and Biological Engineering

Leaching of nitrogen (N) and phosphorus (P) from agricultural fields is one cause of water

resource impairment. My study was designed to 1) explore if ceramic tension lysimeters interfere

with the chemical composition of leachates containing P and to compare leached concentrations

ofN and P estimated using tension and gravitational lysimeters, 2) determine the effect of seven

irrigation and fertilizer management practices (IFMP) on nutrient leaching and avocado (Persea

Americana Mill.) yield, and 3) apply the Leaching Estimation and Chemistry Model (LEACHM)

to refine the identified management practice.

An evaluation of three commercially available ceramic lysimeters (i.e., Ceramic A,

Ceramic B, and Ceramic C) indicated that all three ceramics contained various chemical

elements that may influence P estimation of the leachate. The elements identified that may

influence P estimation depending on the pH and concentration of the soil water being sampled

were Fe, Al, Si, and Ca. The lower the ceramic's P adsorption maximum (Smx ) the more

accurate the sampler was in estimating PO4-P concentration of a known stock solution. A

protocol to be followed to determine the efficacy of ceramic lysimeters for water quality

monitoring was proposed.









Evaluation of the seven IFMP indicated that irrigating 'Simmonds' avocado trees based on

soil water at 15 kPa (SW) with fertilizer at a standard rate (FSR) saved 87% of the water volume

applied and resulted in average annual reductions of 70 and 75% in N03-N and TP leaching

respectively compared to the set schedule irrigation with FSR method. Since 'Beta' avocado is

genetically predisposed to higher fruit yield than 'Simmonds' the SW with FSR may not the best

IFMP for the production of 'Beta'.

Global techniques that involved use of Morris' screening and eFAST methods were used to

perform LEACHM model sensitivity and uncertainty analyses. The eFAST sensitivity method

showed that total first-order effects explained 89, 87, and 89% of the output variability in

drainage water volume, leached N03-N, and leached TP. The proposed refined best IFMP (BMP)

would lead to further water and irrigation use efficiency and a sustainable management

environment.









CHAPTER 1
INTRODUCTION


Nitrogen and Phosphorus Leaching from Agricultural Fields

Nutrient leaching from agricultural fields (primarily nitrogen [N] and phosphorus [P]) is a

water quality concern in many areas of the world (Burkitt et al., 2004; Quifiones et al., 2007;

Eulenstein et al., 2008) due to site-specific (e.g., groundwater nitrate concentrations) and

downstream (e.g., increased eutrophication) water supply ramifications. While identified as a

nutrient source, agricultural related nutrient leaching is fairly complex and is difficult to quantify

as each situation and production system is very unique. Nutrient leaching, or the downward

movement of dissolved nutrients in the soil profile with percolating water (Havlin et al., 2004), is

influenced by hydrologic and soil characteristics. Hydrologic characteristics of a location such as

rainfall patterns (occurrence, intensity, duration, and amount) and infiltration characteristics of

the soil influence nutrient leaching loads. For example, soils with high water infiltration rates and

low nutrient retention capacity (such as sandy soil or highly porous soils and well structured

ferrallitic soils with low activity clays and low organic matter contents) are particularly

conducive to nutrient leaching (Lehmann and Schroth, 2003). Nutrient leaching is also effected

by fertilization practices, irrigation practices, crop characteristics, and production system

management.

Agricultural nutrient leaching is mainly attributed to fertilizers (which often include N, P,

potassium [K]) that are applied to enhance plant growth and yields. Although the intent is for

these fertilizers to be used by the crop, some fertilizers may leach into groundwater (Schaffer,

1998; Tischner et al., 1998; Schroder et al., 2005) and contribute to increased downstream

eutrophication (Li et al., 1999). The residual amount of N and P in the soil after crop harvest and

the rate of N and P mineralization for the decomposing plant residue also affect the nutrient loads









leached (Jiao et al., 2004). Nutrient leaching may also occur due to over irrigation and heavy

rainfall events that result in increased infiltration and drainage (He et al., 2000; Mufioz-Carpena

et al., 2002). Fertilizer utilization efficiency practices such as timing of nutrient application,

amount of nutrients applied, formulations of the nutrients, and method of application are some of

the control measures used to reduce nutrient leaching from agricultural fields. For example, split

application of fertilizers as fertigation has shown reduction in nutrient leaching loads as opposed

to applying fertilizers by broadcasting in one or two applications followed by water application

(Nakamura et al., 2004; Quifiones et al., 2007; Worthington et al., 2007).

The nutrient leaching potential of many elements, especially N (Tagliavini et al., 1996;

Jalali, 2005; Evanylo et al., 2008) has been evaluated extensively. However, P related nutrient

leaching has received less attention historically as the high P-fixation capacity in many mineral

soils led researchers to believe that P leaching did not occur in an amount that would cause an

environmental threat (Akinremi and Chou, 1991; Sims et al., 1998). This was further exacerbated

by the traditional method of quantifying P movement in soil based on extractable soil P as

function of depth rather than sampling soil water using tile drains or lysimeters. Extractable soil

P shows lower P concentration than the concentrations captured in tile drains or lysimeters due to

macro pore or preferential flow (Brye et al., 2002). More current studies that focus on P have

attributed P leaching to land-use practices such as over fertilization (Bryan, 1933; Nelson et al.,

2005; Zhao et al., 2009), excessive animal waste applications (Sims et al., 1998; Elliott et al.,

2002), over irrigation (Geohring et al., 2001; Maguire and Sims, 2002; Djodjic et al., 2004) and

other factors like soil properties (Byre et al., 2002; Godlinski et al., 2008) and climatic

conditions (Granlund et al., 2007; Rankinen et al., 2009; Sharpley and Moyer, 2000).









Nitrogen and Phosphorus Forms and their Reactions

Plants absorb both nitrate (NO3-) and ammonium (NH4 ) although the uptake of NO- is

usually greater than NH4+ (Fig. 1-1). The concentration of NO3 is usually greater than NH4+ in

moist, warm, aerated soils. Since NH4+ is positively charged, it tends to leach less due to the

negative charges associated with the soil's cation exchange capacity. Nitrate being negatively

charged, is repelled by soil colloids, and is easily leached out of the root zone (Lehmann and

Schroth, 2003).

To protect humans from NO3s contamination, the U.S. Environmental Protection Agency

(EPA) set a maximum contaminant level for N03-N of 10 mg L-1 (equivalent to 45 mg L-1 as

NO3) for discharges into water bodies. Given that NO3s is mobile and can be transformed into

other forms ofN depending on soil water content, temperature, soil pH, and microbial activity;

NOs3 concentration in the soil profile varies widely. High N concentrations in soil water and

groundwater samples have been documented in agricultural settings. Stanley et al. (2009)

reported values of 10 to 20 N03-N mg L-1 in leachate samples from citrus orchards in Manatee

County, Florida. Such high N concentrations were attributed to unsuitable denitrification

conditions caused by low soluble carbon content in sandy soils and low microbial counts.

Similarly, Yates et al. (1992) observed high NO3s concentrations (i.e., 50 to 200 mg L1) in water

samples collected using suction lysimeters from avocado orchards in Corona, California.

Phosphorus dynamics in soil water differ depending on the chemistry and

sorption/desorption characteristics of the soil. While the primary form of P in soil solution is

orthophosphate (P04-3), P chemistry in soil solution is driven by pH (Fig. 1-2) such that at low

soil pH (< 7.2) P exists as H2PO4- and at high pH (> 7.2) it exists as HP04-2 (Havlin et al., 2004).

In acidic conditions P reacts with Al, Fe oxide, and hydroxide minerals through sorption and

precipitation to form insoluble amorphous precipitates such as variscite (A1PO4.2H20) and









strengite (FePO4.2H20) (Reddy and DeLaune, 2008). In soils that are alkaline due to the

existence of CaCO3, small amount of HP04-2 are retained through sorption at low P

concentrations. At high P concentrations, much of P retention is by precipitation of HP04-2 to

form insoluble compounds such as like monocalcium phosphate [Ca(H2P04)2], dicalcium

phosphate (CaHPO4), and hydroxyl apatite [(Ca3(P04)2)3Ca(OH)2] (Freeman and Rowell, 1981).

However, once the P sorption capacity is reached additional P results in weaker bonding between

the adsorbent and phosphate (Wisawapipat et al., 2009) which may subsequently be available for

leaching. Akinremi and Chou (1991) observed that the major form of P retention in most

calcareous soils (with less than 300,000 mg CaCO3 kg of soil) is through precipitation rather that

sorption since the sorption capacity of such soils is less than 25 mg kg-1. Thus P sorption is

limited to low concentration of P; and at high P concentration, P is removed from the solution

through precipitation. Although the P compounds formed with mineral elements are insoluble, in

conditions of prolonged water saturation, dissolution occurs and P may leach out of the system.

Elevated levels of P leaching from agricultural fields have been observed by several researchers.

Coale et al. (1994) observed P leachate of 0.2 to 1.4 mg L-1 from a sugarcane field in the Florida

Everglades. Hooda et al. (1999) recorded leachate with P concentration in the range of 0.45 to

0.67 mg L-1 from a grassland in southwestern Scotland. While, Sui et al. (1999) reported a P

leaching concentration of 0.05-2 mg L-1 from a switchgrass field in Ames, Iowa.

Irrigation and Nutrient Best Management Practices (BMPs)

Nutrient leaching may be minimized by implementing different nutrient and irrigation

management practices. The ability of the practice or set of practices to reduce leaching depends

on many factors including soil properties, production system characteristics, irrigation

requirements, and weather. Specific irrigation management practices that may be implemented to

reduce nutrient leaching are ET based irrigation and soil water based irrigation.









ET-based Irrigation

ET-based irrigation has been used for computing crop water requirement for many years

(Penman, 1948; Turc, 1961; Kisekka et al., 2010). The most commonly used method involves

computing the reference evapotranspiration (ETo) using weather data (e.g., temperature, solar

radiation, relative humidity, wind speed, and sun shine hours). Due to climatic variability in time

and space, several equations have been developed to estimate ETo for different locations.

Applicability of these equations depends on data availability. Methods used to compute ETo that

require one or two input variables include: class-A-pan (Doorenbos and Pruitt, 1977)

(evaporation); Turc, 1961 (solar radiation and temperature); Food and Agricultural Organization

of the United Nations (FAO) Blaney-Criddle (Allen and Pruitt, 1986) (temperature and day time

hours); and Hargreaves and Samani (1985) (temperature and radiation). While other methods

such as Priestley and Taylor (1972); FAO Penman-Monteith (Allen et al., 1998); UF IFAS

(1984) Penman (Jones et al., 1984); and the American Society of Civil Engineers-Environmental

and Water Resources Institute (ASCE-EWRI, 2005) require several input variables to estimate

ETo. Each of these equations was developed to estimate ETo under specific weather conditions

and therefore their use in different climatic conditions may yield inaccurate estimations (Yodar

et al., 2005). However, where data is available, the FAO Penman-Monteith and the ASCE-EWRI

methods have been shown to most accurately estimate ETo (Attarod et al., 2009; Pereira et al.,

2009; Sahoo et al., 2009). The actual crop evapotranspiration (ETa) is computed as:

ET ET x k (1-1)

where k, is the crop coefficient (unitless).

The availability of crop coefficients is one of the limitations of the ET based irrigation

method, since these coefficients require time and financial resources to be developed and once









developed they remain site and cultivar specific. The k, accounts for four aspects that

differentiate the reference grass (alfalfa) that was used in the development of the ETo equation to

the crop of interest to be irrigated (Allen et al., 1998). These k, aspects are: 1) crop height since

it influences the aerodynamic resistance, 2) albedo which influences the net radiation of the soil

surface, 3) crop canopy resistance to vapor transfer that influences surface resistance, and 4)

evaporation from the bare soil in the field. Kisekka et al. (2010) evaluated ET-based irrigation

scheduling in a carambola (Averrhoa carambola) orchard in Homestead, FL. The authors

observed that ET-based irrigation resulted in 70% water saving in comparison to set schedule

irrigation (calendar based irrigation) with no significant difference (p < 0.05) between yields for

the two irrigation methods. Migliaccio et al. (2010) assessed plant response in the production of

papaya (Caricapapaya) using ET-based irrigation and set schedule irrigation on Krome very

gravelly loam soils of south Florida. The authors observed water savings of 66% from ET-based

irrigation in comparison to set schedule irrigation (calendar based irrigation). However, for the

ET-based irrigation and set schedule irrigation there was no significant differences (p < 0.05)

between plant nutrient content, growth, photosynthetic rates, and fruit yields. Others (Meyer and

Marcum, 1998; Silva et al., 2009; Spreer et al., 2009) have conducted research on the ET-based

irrigation method and have reported water savings (13 to 46%) and increased yields (6 to 11%)

as opposed to using a set schedule irrigation based on calendar days or time and frequency of

irrigation.

Soil Water-based Irrigation

Soil water sensors measure the soil water content and can be linked with irrigation control

equipment to automate irrigation scheduling. Zotarella et al. (2008) reported that using soil water

based drip irrigation reduced water application by 33 to 80% compared to a scheduled drip

irrigation method at Citra, Florida. In a study on assessing irrigation BMPs using tensiometers in









a royal palm (Roystonea elata) field nursery in Homestead, Florida, Migliaccio et al. (2008)

found that an automated irrigation system that irrigated at soil suctions of 5 and 15 kPa reduced

water volumes applied by 75 and 96% when compared to standard irrigation scheduling without

reducing tree quality. Meron et al. (2001) reported that irrigating at a soil suction of 15 to 25 kPa

resulted in water savings of 500 to 650 mm in comparison to ET based irrigation water use of

700 to 850 mm per season in an apple (Malus domestic) orchard in Upper Galilee, Israel, which

has a Mediterranean climate. Kukal et al. (2005) reported for rice (Oryza sativa) in Ludhiana,

India, that irrigating at a soil suction of 16 kPa resulted in water saving of 30 to 35% in

comparison to the traditional practices of a 2-day irrigation set schedule interval. Working with

avocado in South Africa, du Plessus (1991) reported that tensiometer irrigation scheduling at 30

kPa on sandy soil and at 50 kPa on clay soils at a depth of 300 mm gave optimum yields as

opposed to irrigation based on ET. du Plessus (1991) observed that although irrigating based on

ET is easy to apply, the crop factors do not reflect the water status of the soil or plant and the

accuracy of this method is further limited by the requirement for conducting extensive research

to develop the crop factors that depict local conditions. Others (Enciso et al., 2009; Fuentes et al.,

2008; McCready et al., 2009; Migliaccio et al., 2010) have also conducted irrigation research

using soil water sensors and have reported water savings 25 to 74% compared to set schedule

irrigation practices.

Irrigation and Nutrient BMPs

Nutrient BMPs in combination with irrigation BMPs, either through use of ET-based

irrigation (Yates et al., 1992; Diez et al., 1997; Doltra et al., 2008; Paulino-Paulino et al., 2008)

or soil water sensor based irrigation (Paramasivam et al., 2000; Lao and Jimenez, 2004; Alva et

al., 2006) have been evaluated for the impact of the combined irrigation and nutrient BMPs on

nutrient leaching reduction and water savings. Alva et al. (2003) conducted a study on sandy









soils in Lake Alfred, Florida, for over five years where they monitored the effects of different N

and irrigation BMPs on orange yields (Citrus sinensis) and N03-N concentrations in

groundwater. The authors reported water savings and N leaching reductions with irrigation based

on soil suctions of 10 and 15 kPa and split N fertigation in comparison to non-systematic

irrigation scheduling with broadcasting ofN fertilizer. Likewise, a study was conducted by

Quifiones et al. (2007) on eight-year old citrus trees in Moncada, Spain, on sandy-loam textured

soil to assess fertilizer and irrigation management on N uptake and seasonal distribution of N in

the soil profile. Quifiones et al. (2007) reported split application ofN irrigation at soil water

suction of 10 kPa reduced N leaching and significantly improved tree N use efficiency in

comparison to flood irrigation with two equal applications ofN. Yates et al. (1992) reported that

split application of granular fertilizers in avocado orchards 8 times during the year reduced

nutrient leaching as opposed to applying the fertilizers twice a year. The authors did not detect

the difference in nutrient load leached by irrigating at 80%, 100%, and 120% of ET.

Assessing Nutrient Leaching

Methods used to evaluate nutrient leaching vary and there is no single device that will

perfectly sample soil solution in all conditions encountered in the field (Litaor, 1988). Although

sampling strategies have varied widely, zero-tension (Fig. 1-3) and tension (Fig. 1-4) lysimeters

have primarily been used to sample soil water (Kosugi and Katsuyama, 2004; Gehl et al., 2005;

Weihermiller et al., 2005; Amador et al., 2007). However, each technique for measuring nutrient

leaching has limitations.

Tension lysimeters with porous ceramic cups are popularly used to collect soil water due to

their ease of use and low cost as compared to other soil water collection methods (Wagner, 1962;

Van der Ploeg and Beese, 1977; Litaor, 1988; Swistock et al., 1990). These samplers are used to

obtain soil water samples from both saturated and unsaturated soils and from varying soil depths.









Tension lysimeters do not destroy the soil structure or the rooting system (Grossmann and

Udluft, 1991); however, some disadvantages of tension lysimeters include physical sampling and

chemical interference factors. The small area of influence of a tension lysimeter is one physical

factor that limits its ability to adequately sample leachate (Talsma et al., 1979). The sampling

zone is limited to the volume immediately surrounding the ceramic tip. In addition, the structure

of the soil may preclude representative soil water sampling during saturated conditions due to

bypass flow (Shaffer et al., 1979). This problem may be exacerbated in soils with a large

proportion of macropores or heterogeneities, or where the sampler is not in contact with the

macropores (Grossmann and Udluft, 1991).

Chemical factors can also influence the composition of the soil water collected by ceramic

tension lysimeters depending on the extraction time used for sample collection (Van der Ploeg

and Beese, 1977; Swistock et al., 1990) and the chemical composition of the ceramic (Van der

Ploeg and Beese, 1977; Litaor, 1988) that is made from formulations of kaolin, talc, alumina,

ball clay, and other feldspathic materials (Soilmoisture Equipment Corporation, 2007). Such

materials are often assumed to be inert and not to interfere with the chemical composition of the

collected water sample. However, sorption of certain solute ion(s) has presented a problem with

these lysimeters (Hansen and Harris, 1975; Nagpal, 1982; Grossmann and Udluft, 1991).

Chemical reactions between solutions and ceramic materials including solute ion adsorption and

precipitation may be influenced by solution composition and pH, cup sorption capacity, applied

suction, and sampling rate (Grossmann and Udluft, 1991; Hansen and Harris, 1975; Nagpal,

1982). Precipitation of compounds may result in underestimation of soil water sample

concentration (Hansen and Harris, 1975) and the volume of sample collected due to pore

blocking (Grossmann and Udluft, 1991).









The literature provides contradictory results concerning ceramic samplers influence on

soil water sample chemical composition, particularly of P in the sample. Beier and Hansen

(1992) who compared ceramic and polytetrafluoroethene (PTFE) cup lysimeters for soil water

sampling of sodium (Na), K, calcium (Ca), aluminum (Al), NH4, hydrogen (H), and

non-purgeable organic carbon (NPOC) reported that neither sampler contaminated the soil water

samples nor retained any substances. However, Vandenbruwane et al. (2008) observed that

ceramic lysimeters adequately sampled most cations except Al. Levin and Jackson (1977) who

compared micro-hollow fiber and porous ceramic cup lysimeters in sampling soil water

containing Ca, magnesium (Mg), and orthophosphate-phosphorus (PO4-P), found that neither

extractor altered the chemical composition of the leachate. Haines et al. (1982) compared

ceramic and zero tension lysimeters to measure various nutrients including PO4-P from a forest

ecosystem in southern Appalachia, USA; PO4-P concentrations to a depth of 30 cm were not

different for the two measuring devices. Other studies, however, have shown significant P

adsorption in ceramic tension lysimeters (Hansen and Harris, 1975; Severson and Grigal, 1976;

Zimmermann et al., 1978; Nagpal, 1982; Bottcher at al., 1984; Andersen, 1994). In a laboratory

experiment, Hansen and Harris (1975) observed that P sorption increased with P concentration

with up to 110 mg of P being sorbed by a single ceramic cup. Litaor (1988) suggested that

ceramic soil water samplers are not suitable for use in studies involving P due to its adsorption;

however, no quantitative values were given. A possible cause of these contradictions may arise

from the chemical composition/source of the ceramic material used in the manufacture of the

ceramic cups in various ceramic tension lysimeters marketed by different companies worldwide

(Hughes and Reynolds, 1990).









Zero-tension lysimeters are devices used to collect gravitational water percolating through

the soil profile under saturated flow (Wood, 1973; Litaor, 1988) in order to measure the chemical

characteristics of water which has leached (Migliaccio et al., 2006). Such lysimeters are

constructed with a collection container and two flexible tubes. The tubes provide the ability to

collect the water sample from the collection container; one tube serves as an air vent while the

other is connected to a peristaltic pump. The soil infiltration rate, rainfall and irrigation

characteristics, and the capacity of the collection container are used to determine lysimeter

sampling interval. The major limitation of the zero-tension lysimeter is the failure to collect

leachate under unsaturated soil conductions due to pass by flow (Vandenbruwane et al., 2008).

Zotarelli et al. (2007) observed that anaerobic conditions may develop inside the lysimeters

followed by denitrification of the collected leachate which may result in underestimation of the

NOs3 load leached. In my study, to control denitrification, bucket lysimetes were incorporated

with an aeration tube and emptied each month.

Field Scale Simulation Modeling of Nutrient Transport

Due to resource constraints it is unrealistic to field test all possible BMP combinations that

may involve nutrients levels and nutrient application methods and irrigation volumes and

application methods. Thus, models are commonly used in planning, management, and decision

making due to their advantage for giving an insight on how systems function and interact. Many

models that simulate N and P leaching are available. Deterministic physically based models (e.g.,

The Decision Support System for Technology Transfer [DSSAT] (Jones et al., 2003),

Groundwater Loading Effects of Agricultural Management Systems [GLEAMS] (Leonard et al.,

1987), Field Hydrologic and Nutrient Transport Model [FHANTM] (Fraisse and Campbell,

1997), and Leaching Estimation and Chemistry Model [LEACHM] (Hutson and Wagenet, 1992)

that simulate nutrient leaching have been used and reported to give satisfactory results (Sogbedji









et al., 2001; Webb et. al., 2001; Asadi and Clemente, 2003). Nutrient leaching has been

simulated in fields of various crops including maize, wheat, millet, potato, cassava, bahia grass,

and brachiaria grass (Jones et al., 2003). However, such models are less often applied to fruit

orchards (Gary et al., 1998).

Of the process-based field scale leaching models, LEACHM has been widely used to

simulate water flow and solute transport with satisfactory results (Jemison and Fox, 1992; Jabro

et al., 1995; Sogbedji et al., 2001; Contreras et al., 2009). LEACHM was developed by Hutson

and Wagenet (1992) to simulate vertical water and solute transport both in field and laboratory

columns using numerical routines. It has been revised and tested over the years by different

researchers (Borah and Kalita, 1999; Sogbedji et al., 2001; Jabro et al., 2006) and the current

LEACHM (ver. 4.0) is a suit of three models. The three simulation models include LEACHP for

pesticides, LEACHN for N and P, and LEACHC for salinity in calcareous soils (Hutson, 2005).

The model incorporates C, N, and P pools and pathways.

The Addiscot tipping bucket or Richard's equation is used in LEACHM to predict water

content, water fluxes, and potentials. Richard's equation, the soil water flow equation for

transient vertical flow derived from Darcy's law and continuity equation, is:

az K(0)H -U(-z,t) (1-2)
Qt Sz L 0Z I- z

where 0 is the volumetric water content of a specific soil layer segment (m3 m-3), H is the

hydraulic head (mm), K is the hydraulic conductivity (mm d-1), t is time (d), z is the soil depth

below a reference point (mm), and U is a sink term representing water lost per unit time by

transpiration (d-1).

Defining the differential water capacity, C(o), as;










C(o)= (1-3)
Oh

where h is soil water pressure head, enables transformation of equation 1-2 to an equation where

pressure potential is the only dependent variable.

hC() = aK() -U(z,t) (1-4)
at oz oz

The model considers water depth and water potential in terms of mm per day. The solution to

equation 1-4 by normal finite differencing methods is accomplished by dividing the soil profile

into a finite number (k) of equally spaced horizontal layers of size Az, and dividing the total

time period into small time intervals, At. Utilizing the Crank-Nicolson implicit method reduces

equation 1-4 to:

a/hl + P/h/ +y7/ h/ = (1-5)

where c; f/ / and S' contain constants (h, K, C, U, Az, At) for the time increment, which

have known or estimated values, and h/1 the soil water matric potential at the start of the time

interval is known. The k equations developed (one at each depth node) form a tridiagonal matrix

that may be solved for h/ h/l, h/ using a rapid Gaussian elimination method. The

convective-dispersion equation is used to describe solute transport and flux is usually represented

as;


JcL = -0D (q)C + qc, (1-6)
dz

where 0 is the volumetric water content of a specific soil layer segment (m3 m-3), q is the

macroscopic water flux, z is soil depth below a reference point (mm), CL is the solute

concentration (mg L-1), and DM (q) is the mechanical dispersion coefficient that describes mixing

between large and small pores as the result of local variations in mean water flow velocity.









The value of DM (q) can be estimated from:

D (v)= Av (1-7)

where v is the pore water velocity v = q/O and A is the dispersivity, limited in LEACHM to the

range 0.5Az to 2Az.

The LEACHN model can be used to predict both nutrient uptake by a crop and leaching

below the root zone up to a depth of 2 m. The model inputs include daily maximum and

minimum temperatures, precipitation and/or irrigation, evapotranspiration, soil texture, organic

carbon, and amount ofN and P applied. Model calibration requires soil hydraulic data (e.g.,

water retention curve parameters), N and P rate reaction parameters, and sorption coefficients.

The model uses a daily time step and can simulate a growing season or several years (Hutson,

2005).

Ng et al. (2000) used the LEACHN model to identify management practices (i.e., water

table management, conservation tillage, and intercropping) that would reduce nitrate leaching

from a corn (Zea mays L.) field fertilized with urea in Ontario, Canada. They reported that the

LEACHM model gave better predictions for nitrate leaching in plots under controlled

drainage/subsurface irrigation systems than in plots under free drainage. Using LEACHN model

to develop BMPs for potato (Solanum tuberosum) production at Nevsehir, Turkey, Unlii et al.

(1999) reported that nutrient leaching could be reduced significantly by reducing the

irrigation/rain water applied from 1100 mm to 650 mm, reducing ammonium sulfate fertilizer

input from 900 kg ha-1 to 400 kg ha-1, and applying the fertilizers after most of the supplemental

irrigations were complete. The authors further recommend that rotating potato with wheat could

further reduce the residual NO3- leaching since half of the applied NH4-N in the fertilizer was

converted to NO3- during the growing season. Jabro et al. (2006) compared the simulation









accuracy and performance of LEACHN in predicting N dynamics in a soil-water-plant system to

two other field scale models: NCSWAP (Nitrogen and Carbon cycling in Soil Water And Plant)

(Molina and Richards, 1984) and SOILN (SOIL-SOILN) (Eckersten and Jansson, 1991). They

reported that LEACHN and NCSWAP estimated nitrate leaching more accurately than SOILN,

from a corn field fertilizer with ammonium nitrate and manure in Rock Springs, Pennsylvania.

Previous application of the LEACHN model has resulted in identification of parameters

used for sensitivity analysis (Table 1-1) and for calibration (Table 1-2). Although LEACHN can

simulate P leaching, studies where P leaching in the field were simulated could not be identified

in available refereed literature.

Model Sensitivity and Uncertainty Analysis

Global sensitivity and uncertainty analysis are tools used with model applications due to

uncertainties associated with all predictive deterministic models and measured data to improve

the interpretation and thus the application of modeling results (Shirmohammadi et al., 2006). The

large number of input factors or parameters that control the variation of simulated model results

is one source of uncertainty in models. Sensitivity analysis provides the strength of the

relationship between a given uncertain input factor and the model simulation output while

uncertainty analysis propagates uncertainties onto the model output of interest. Traditionally,

model sensitivity has been quantified by computing local indices (Saltelli et al., 2005). However,

hydrological models are non-linear and global techniques are therefore more appropriate as they

explore the entire model parametric space. Global sensitivity analysis provides parameter

ranking and information about first and higher order effects of parameters by specified outputs.

One way of accounting for uncertainty in model inputs is through the development of

probability density functions (PDFs) of the target model outputs (Shirmohammadi et al., 2006;

Saltelli et al., 2008; Muioz-Carpena et al., 2010a). The output PDFs are then used to evaluate









uncertainty in the model predictions by placing confidence intervals on the outputs either as

margin of safety component or by calculating probability of exceedance of a threshold value

(Morgan and Henrion, 1992). A common approach to sampling distributions for simulating

model outputs is the use of Monte-Carlo sampling which consists of multivariate random

sampling from model input probability density distributions in order to conduct a large number

of model simulations. Due to high computational costs of the Monte-Carlo type of uncertainty

analysis, it is convenient to use a sensitivity screening method first to identify the subset of

important inputs factors controlling the model output variability (Saltelli et al., 2004;

Shirmohammadi et al., 2006; Mufioz-Carpena et al., 2010a). Thereafter, model uncertainty is

then efficiently assessed with a faster computation time with the subset of important model

inputs.

Statement of Problem

Nutrient leaching from agricultural fields is a major source of water body impairment in

many parts of the world including Florida (Burkitte et al., 2004; Stanley et al., 2009). Nutrient

leaching is attributed to improper matching of the optimal fertilizer requirements and water

needs to the crop and production environment. Nutrient leaching may have adverse effects in

south Florida due to the interaction between surface water and groundwater arising from a

shallow water table (Noble et al., 1996), and the existence of naturally sensitive water bodies like

the Biscayne Bay and the Everglades (Browder et al., 2005; Reddy et al., 2006) (Fig. 1-5).

Solute leaching measurement techniques primarily include use of tension ceramic

lysimeters and zero tension lysimeter. Tension lysimeters with porous ceramic cups are

popularly used to collect soil water due to their ease of use and low cost as compared to other

soil water collection methods (Wagner, 1962; Van der Ploeg and Beese, 1977; Litaor, 1988;

Swistock et al., 1990). However contradictory cases are cited in literature on the efficacy of









tension ceramic lyismeters to sample a leachate containing P (Litaor, 1988; Hughes and

Reynolds, 1990).

Nutrient leaching can be controlled by implementing irrigation and nutrient BMPs.

Although several BMPs have been develop for various agricultural crops in Florida, no irrigation

and nutrient BMP has been developed, tested, and documented for tropical fruit tree crops. The

BMP interests of my study were to reduce irrigation water applied and thus save energy input

and nutrient leaching while maintaining tropical fruit yields (fruit number and weight).

Measurement of these quantities can be challenging, particularly regarding fate and transport of

solutes in calcareous gravelly soils. The LEACHM model was selected due to its capability to

simulate N and P, available documentation, and worldwide application.

Literature review revealed research gaps that would hinder development of BMPs for

tropical fruits. These research gaps included: 1) lack of a tested monitoring methodology for soil

water sampling from orchards on gravelly calcareous soil, 2) inadequate documentation on the

extent of nutrient leaching from orchards in south Florida, and 3) inadequate documentation on

how different avocado cultivars respond to irrigation and fertilizer management practices.

Therefore, my study focused on identifying 1) appropriate methods for sampling soil pore water

from gravelly calcareous soils and 2) field testing and assessment of BMPs for tropical fruit

production (with avocado as reference crop) with regard to water savings and reduction ofN and

P leaching into groundwater.

Goal and Objectives

The overall goal of my study was to evaluate techniques for estimating nutrient (N and P)

leaching as a method for assessing irrigation and nutrient BMPs. The objectives include:

1) Determine if ceramic tension lysimeters interfere with the chemical composition of
sampled water containing P.









a) Compare leached concentration of N and P estimated using tension and gravitational
lysimeters in gravelly calcareous soils.

2) Determine the effect of nutrient load and irrigation scheduling on nutrient leaching, water
volume applied, and avocado yield in calcareous soils.

3) Apply the LEACHM model (Hutson and Wagenet, 1992) to evaluate production
management scenarios and identify scenarios that achieve optimum fruit production while
saving water volumes applied and reducing nutrient leaching.

Rationale for Tropical Fruit BMPs in South Florida

To prevent pollution from point and nonpoint sources, the U.S. Congress passed the

Federal Clean Water Act in 1972 (amended in 1987) to restore and maintain the chemical,

physical, and biological integrity of the nation's waters. Subsequently, the U.S. Environmental

Protection Agency (EPA) passed on the regulations to states to identify impaired water bodies

and develop Total Maximum Daily Loads (TMDLs) (Migliaccio and Boman, 2006). The Florida

Department of Environmental Protection (FDEP) develops TMDLs for specific pollutants in

impaired water bodies and develops Basin Management Action Plans (BMAPs). Agricultural

BMPs are required by law in areas where FDEP develops a BMAP that includes agriculture (e.g.

Lake Okeechobee Watershed). BMPs are defined as a set of on farm practices designed to reduce

nutrient loss and improve water quality while sustaining economically viable farming operations

for the grower.

The Florida Agricultural BMP program is aimed at reducing movement of N and P from

agricultural fields to water bodies (Simonne and Hutchinson, 2005). Due to shallow groundwater

aquifers and sensitive surface water bodies, water and fertilizer management in Florida is

interrelated. Several BMPs manuals have been developed or are being developed for enterprises

such as silviculture, ridge citrus, container nursery, vegetable and agronomic crops among

others. The BMP manuals have been developed and adopted in Florida through a program based

on commodity and region due to climatic and soil diversity (FDACS and FDEP, 1998; Simonne









et al., 2003). The BMPs identified in the manuals were selected based on the best available

knowledge and not necessarily research specific to a commodity, location, or production system.

Results of my study will help fill this gap for tropical fruit production in Krome soils of south

Florida.

Study Area and Scope

The study was conducted at the University of Florida, Tropical Research and Education

Center, Homestead, Miami-Dade County, Florida (25o20'21" N, 80o20'01" W) (Fig. 1-5).

Homestead has a humid subtropical climate with hot, humid summers where high temperatures

average between 31 to 330C. The soil at the site is very gravelly loam calcareous soil (Krome

very gravelly loam), very shallow up to 20 cm deep, well drained, moderately permeable and

underlined by limestone. The water table ranges between 1 to 2 m. The wet season in Florida

spans from May to October and 80% of the rainfall occurs during this period.

Laboratory tests were conducted to test the suitability of ceramic lysimeters to sample a

leachate that contain P by: 1) assessing the chemical composition of ceramic cups, 2)

determining PO4-P adsorption and desorption potentials of the three commercially available

ceramic water samplers, 3) comparing PO4-P concentrations in water samples collected using the

three ceramic lysimeters to that of a known P stock solution, and 4) comparing leached

concentration of N and P estimated using ceramic and bucket lysimeters sampled from gravelly

calcareous soils. Monthly (November 2007 to October 2009) leached water samples containing

N and P, which were collected from an avocado orchard using bucket lysimeter from each of the

seven nutrient and irrigation management practices being evaluated. The seven irrigation and

nutrient management practices evaluated were: 1) irrigation based on crop evapotranspiration

(ET) irrigation with fertilizer at a standard rate (FSR) typically used in avocado production in the

area; 2) ET irrigation with 50% FSR; 3) ET irrigation with 200% FSR; 4) soil water suction at









15 kPa (SW) with FSR; 5) irrigation at a set schedule (based on timing and frequency typically

used in the production of avocado in the area) with FSR; 6) SW with 50% FSR; and 7) SW with

200% FSR. The data collected were analyzed for differences among treatment means using

statistical ANOVA and means separated by Waller-Duncan K ratio. Treatment effects on tissue

N, C, and TP; soil nutrient status parameters including organic carbon, C:N and C:P ratios, and

inorganic N; tree growth; and fruit yield were analyzed using ANOVA and means separated by

Waller-Duncan K ratio. The identified nutrient and irrigation BMPs that resulted in water saving

and reduced nutrient leaching were fine tuned using the LEACHM model by employing global

sensitivity analysis tools.









Table 1-1. Parameters used for sensitivity analysis in LEACHM for water and nitrate leaching
Parameter Ng et al., 2000 Mahmood et al., 2002
value/range sensitivity ratio


Air entry value (AEV)
Exponent for Campbell equation
(BCAM)
Volumetric water content (m3 m-3)
Soil organic carbon (%)
Diffusion coefficient (mm2 day-1)
Dispersivity (mm)
Bulk density (g cm-3)
Hydraulic conductivity (mm day-1)
Urea hydrolysis (day- )
Nitrification (day-1)
Denitrification (day-1)
Humus mineralization (day-1)
Base temperature (C)
Qio factor


-0.22
-0.30


0.04
0.06


60-120.0
60-150.0
1-1.3
10-100.0
0.4
0.3
0.1


0.04
-0.024
0.62
-0.67
0.40


Adapted from: Ng et al., 2000. Study conducted at Woodslee, Ontario, Canada from 1991-1994
using corn (Zea mays L., Pioneer 3573), Mahmood et al., 2002. Study conducted at Carterton,
New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species (two-year-old
Eucalyptus nitens and Eucalyputs ovata) and pasture.









Table 1-2. Parameters used for calibration of LEACHM for water and nitrate leaching
Parameter Borah et al., Ng et al., Mahmood et Jabro et al.,
1999 2000 al., 2002 2006
value/range value value/range value/range
Air entry value (AEV) -0.1 -1.00 -0.3-3.00
Exponent for Campbell 3.0 3-5.00 7.8-18.70
equation (BCAM)
Clay particles% 20.00
Silt particles% 12.00
Volumetric water 0.5 0.26-0.29
content (m3 m3)
Soil organic carbon (%) 2.50-4.50
Dispersivity (mm) 120.0
Bulk density (g cm-3) 1.00-1.44
Saturated Hydraulic 100-5100.00 40-2184.00
conductivity (mm day-1)
Water potential, kPa 10-35.00
C:N ratio 10:1.00 10.00
Nitrification (day-1) 0.20 0.1 0.2-0.40
Denitrification (day-1) 0.10 0.1 0.02-0.08
Humus mineralization 7x 10-5
(day-1)
Qio factor 3.0 2.00
Kd for NO3-N (L kg-1) 0.05 0.00
Litter mineralization 0.01 0.01
(day-1)
Manure mineralization 0.02 0.02
(day-1)
Humus mineralization 0.70 3x 105
(day-1)
N Plant uptake (kg ha-) 102.0
NH4-N (mg N kg-1) 2.63-4.00 3.00
NO3-N (mg N kg-1) 3.85-6.38 0.00
Adapted from: Borah et al., 1999. Study conducted at Manhattan, Kansas, USA form 1996-1997
using corn (Zea mays L.); Ng et al., 2000. Study conducted at Woodslee, Ontario, Canada from
1991-1994 using corn (Zea mays L., Pioneer 3573); Mahmood et al., 2002. Study conducted at
Carterton, New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species
(two-year-old Eucalyptus nitens and Eucalyputs ovata) and pasture; Jabro et al., 2006. Study
conducted at Rock Springs, Pennsylvania, USA form 1988-1991 using corn (Zea mays L.).









N2 Fixation

0


Figure 1-1. The nitrogen cycle: the different transformations are numbered 1 to 7 (adapted from
Havlin et al. (2004)).


Lightning
Rainfall








ttT


Plant and animal residues


Leaching


Figure 1-2. Illustration of the interactions between the different forms of P in the soil (adapted
from Havlin et al. (2004)).


Fertilizer































A B


Figure 1-3. Bucket lysimeter. A) elevation view and, B) inside view (credits. UF/IFAS Harry
Trafford).


Figure 1-4. Porous ceramic lysimeters and relevant sampling devices. A) ceramic lysimeter, B)
pump used to apply suction, C) syringe used to expel leachate form the collection
tubing (Adapted from Irrometer Company, Inc.).




































I0 Biscayne Bay Aquatic Preserve
Biscayne Bay National Park
94 Everglades National Park
,Florida Keys
Figure 1-5. Map of Miami-Dade County, south Florida and the surrounding water bodies.









CHAPTER 2
MONITORING NUTRIENT LEACHING USING TENSION AND GRAVITATION
LYSIMETERS1

Introduction

Monitoring nutrient leaching is essential in order to quantify the effect of different

irrigation and nutrient practices on surface water and groundwater quality (de Vos, 2001; Herzog

et al., 2008; Fonder et al., 2010). Methods used to evaluate nutrient leaching vary as there is no

single device that will perfectly sample soil solution in all conditions encountered in the field

(Litaor, 1988). Although sampling strategies have varied widely, zero-tension and tension

lysimeters have primarily been used to sample soil water (Kosugi and Katsuyama, 2004; Gehl et

al., 2005; Weihermiiller et al., 2005; Amador et al., 2007). However, each technique for

measuring nutrient leaching has limitations.

Tension lysimeters with porous ceramic cups are popularly used to collect soil water due to

their ease of use and low cost as compared to other soil water collection methods (Wagner, 1962;

Van der Ploeg and Beese, 1977; Litaor, 1988; Swistock et al., 1990). These samplers are used to

obtain soil water samples from both saturated and unsaturated soils and from varying soil depths.

Tension lysimeters do not destroy the soil structure or the root system (Grossmann and Udluft,

1991); however, some disadvantages of tension lysimeters include physical sampling and

chemical interference factors. The small area of influence of a tension lysimeter is one physical

factor that limits its ability to adequately sample leachate (Talsma et al., 1979). The sampling

zone is limited to the volume immediately surrounding the ceramic tip. In addition, the structure

of the soil may preclude representative soil water sampling during saturated conditions due to

bypass flow (Shaffer et al., 1979). This problem may be exacerbated in soils with a large



Manuscript titled, "Phosphorus adsorption by ceramic suction lysimeters" is under review with the Vadose Zone
Journal: Authors include Nicholas Kiggundu, Yuncong Li, and Kati W. Migliaccio.
43









proportion of macropores or inhomogeneities, or where the sampler is not in contact with the

macropores (Grossmann and Udluft, 1991).

Chemical factors can also influence the composition of the soil water collected by ceramic

tension lysimeters depending on the extraction time used for sample collection (Van der Ploeg

and Beese, 1977; Swistock et al., 1990) and the chemical composition of the ceramic (Van der

Ploeg and Beese, 1977; Litaor, 1988) that is made from formulations of kaolin, talc, alumina,

ball clay, and other feldspathic materials (Soilmoisture Equipment Corporation, 2007). Such

materials are often assumed to be inert and not to interfere with the chemical composition of the

collected water sample. However, sorption of certain solute ion(s) has presented a problem with

these lysimeters (Hansen and Harris, 1975; Nagpal, 1982; Grossmann and Udluft, 1991).

Chemical reactions between solutions and ceramic materials including solute ion adsorption and

precipitation may be influenced by solution composition and pH, cup sorption capacity, applied

suction, and sampling rate (Grossmann and Udluft, 1991; Hansen and Harris, 1975; Nagpal,

1982). Precipitation of compounds may result in underestimation of soil water sample

concentration (Hansen and Harris, 1975) and the volume of sample collected due to pore

blockage (Grossmann and Udluft, 1991).

The literature provides contradictory results concerning ceramic samplers influence on

chemical composition of soil water sampled, particularly ofP in the sample. Beier and Hansen

(1992), who compared ceramic and polytetrafluoroethene (PTFE) cup lysimeters for soil water

sampling of sodium (Na), K, calcium (Ca), aluminum (Al), NH4, hydrogen (H), and

non-purgeable organic carbon (NPOC), reported that neither sampler contaminated the soil water

samples nor retained any substances. However, Vandenbruwane et al. (2008) observed that

ceramic lysimeters adequately sampled most cations except Al. Levin and Jackson (1977)









compared micro-hollow fiber and porous ceramic cup lysimeters in sampling soil water

containing Ca, magnesium (Mg), and orthophosphate-phosphorus (P04-P), and found that

neither extractor altered the chemical composition of the leachate. Haines et al. (1982) compared

ceramic and zero tension lysimeters to measure various nutrients including P04-P from a forest

ecosystem in southern Appalachia, USA; P04-P concentrations to a depth of 30 cm were not

different for the two measuring devices. In contrast, other studies have shown significant P

adsorption in ceramic tension lysimeters (Hansen and Harris, 1975; Severson and Grigal, 1976;

Zimmermann et al., 1978; Nagpal, 1982; Bottcher at al., 1984; Andersen, 1994). In a laboratory

experiment, Hansen and Harris (1975) observed that P sorption increased with P concentration

with up to 110 mg of P being sorbed by a single ceramic cup. Litaor (1988) suggested that

ceramic soil water samplers are not suitable for use in studies involving P due to its adsorption;

however, no quantitative values were given. A possible cause of these contradictions may arise

from the chemical composition/source of the ceramic material used in the manufacture of the

ceramic cups in various ceramic tension lysimeters marketed by different companies worldwide

(Hughes and Reynolds, 1990).

Zero-tension lysimeters are devices used to collect gravitational water percolating through

the soil profile under saturated flow (Wood, 1973; Litaor, 1988) in order to measure the chemical

characteristics of leached water (Migliaccio et al., 2006). Such lysimeters are constructed with a

collection container and two flexible tubes. The tubes provide the ability to collect the water

sample from the collection container; one tube serves as an air vent while the other is connected

to a peristaltic pump. The soil infiltration rate, rainfall and irrigation characteristics, and the

capacity of the collection container are used to determine lysimeters sampling interval. The

major limitation of the zero-tension lysimeter is the failure to collect leachate under unsaturated









soil conductions due to by- pass flow (Vandenbruwane et al., 2008). Zotarelli et al. (2007)

observed that anaerobic conditions may develop inside the lysimeters followed by denitrification

of the collected leachate which may result in underestimation of the N03-N load leached.

The continued use of ceramic tension lysimeters to sample leachate containing P (Brye et

al., 2002; Bajracharya and Homagain, 2006; Lentz, 2006) requires a detailed evaluation of these

lysimeters to reduce the uncertainties in the data collected and provide some validation of

sampling results. Thus, the goal of my study was to test a methodology for evaluating ceramic

tension lysimeters for sampling soil water P04-P which consisted of the following objectives: 1)

to evaluate the chemical composition of three commercially available ceramic cups (referred to

here as Ceramics A, B, and C); 2) to determine P adsorption and desorption potential of the three

ceramic water samplers; 3) to compare P concentrations in water samples collected using the

three ceramic lysimeters; and 4) to investigate gravitational (bucket) and ceramic tension

lysimeters in order to compare concentrations ofNO3-N and P04-P sampled by these devices for

application in leachate studies.

Materials and Methods

Study Area

The study was conducted in Homestead, Miami-Dade County, FL, at the University of

Florida's Tropical Research and Education Center (TREC) (25o20'21" N, 80o20'01" W). The

elevation of TREC is about 4 m above sea level. The annual rainfall is 1.44 m, maximum and

minimum daily annual averages of 31.5C and 11.6C respectively (considering available data

from 1998 to 2007) for Homestead, FL, from the Florida Automatic Weather Network,

http://fawn.ifas.ufl.edu/data/reports). Homestead has a humid subtropical climate with hot,

humid summers where high temperatures average between 310 to 330C. Winters are mild, but on

average cooler than the nearby coastal areas. The wet season in Florida spans from May to

46









October and 80% of the rainfall occurs during this period (Mulholland et al., 1997). The soils at

the site were gravelly, loamy-skeletal, carbonatic, hyperthermic lithic udorthents, and are

classified as Krome very gravelly loam (Noble et al., 1996). Krome soils are very shallow up to

20 cm deep, well drained, moderately permeable soils underline by limestone. Mufioz-Carpena et

al. (2002) reported that Krome soil is 51% coarse material, 36% sand, 40% silt, 24% clay has

and a bulk density of 1.42 g cm-3. Krome soil has a high pH of 7.4-8.4 (Zhou and Li, 2001) and

soil organic carbon content of 8.47% (Chin et al., 2007).

Chemical Composition of Ceramic Cups

Methods similar to those used to assay a sample of soil, sediment, or plant tissue by ashing

for total P were used to determine the chemical composition of the ceramic cups (Davies, 1974;

Ben-Dor and Banin, 1989). Three different types of ceramic lyismeters from two companies

were used. The ceramic samplers are referred to as 1) Ceramic A (22 mm diam. x 60 mm length)

with an air entry value of 100 kPa (1 bar) (Irrometer Company, Inc., Riverside, CA); 2) Ceramic

B (22 mm diam. x 60 mm length) with an air entry value of 100 kPa (1 bar) (Soilmoisture

Equipment Corp., Santa Barbara, CA); and 3) Ceramic C (48 mm diam. x 57 mm length) with an

air entry value of 200 kPa (2 bar) (Soilmoisture Equipment Corp., Santa Barbara, CA). Three

lyismeters of each type were acquired and used in this experiment representing three replicates.

The contents of P, K, Ca, Mg, zinc (Zn), manganese (Mn), copper (Cu), Fe, Al, cadmium

(Cd), nickel (Ni), lead (Pb), and silicon (Si) were measured by ashing 0.5 g of the ceramic

materials in a 50-ml beaker (with three replicates) initially at 2500C for 30 min. and then at

550C for 4 h. The ashed ceramic materials were then moistened with deionizied and distilled

(DDI) water after which 20 mL of 6 M HC1 was added. The beakers were then placed on a hot

plate in the fume hood and heated at 1000C until dry. Once dry, the hot plate temperature was set

to high for 30 min. After cooling, the samples were moistened with 2 to 3 mL of DDI water and

47









2.25 mL 6 M HC1. The beakers were returned to the hot plate, set on high, and allowed to reach a

near boiling state. After cooling, the samples were filtered through Whatman No. 42 filter paper

into 50 mL volumetric flasks and stored at room temperature for analysis. Estimated mean

separation for each element in the samplers was computed using proc GLM of SAS 9.1 statistical

software (SAS Institute, Cary, NC, USA).

Phosphorus Adsorption and Desorption Potential of Commercial Ceramic Lysimeters

A P adsorption/desorption study of ceramic tension lysimeters was conducted using

Ceramics A, B, and C. For this experiment, new ceramic lysimeters were acquired. The ceramic

cups were broken and ground separately and particles ranging in size from 0.5 to 1.0 mm were

collected. For the adsorption study, six different concentrations of P04-P concentrations of 0,

0.5, 1.0, 5, 10, and 20 mg L-1 were used. A ceramic sample of 3 g was weighed into a 50 mL

centrifuge tube and 30 mL of the desired P04-P solution was added. The tubes were shaken at

room temperature for 24 h, centifuged at 200 xg for 15 min., and filtered through Whatman No.

42 filter paper. The P04-P retained by the ceramic was calculated as follows:

S = [(C C24)xV]/M (2-1)


Where Co is the concentration (mg L-1) of P in the added solution, C24 the concentration (mg L1)

of P in solution after 24 h equilibration period, V the volume (L) of the P solution added, M the

mass of ceramic material (kg), and S, the amount (mg kg-1) of added P adsorbed by ceramic.

The desorption study was performed by adding 15 mL of 0.050 M KC1 solution to each

tube containing the ceramic material with sorbed P (Zhou and Li, 2001), shaking for 24 h,

centrifuging at 200 xg for 15 min., and filtering through Whatman No. 42 filter paper. The

phosphate desorbed by the ceramic was calculated as follows:









Sd = [C48 x(V3 + V V) C24 (V- V)]/M (2-2)


Where C48 is the concentration (mg L-1) of P in the added solution KC1 solution after

24 h equilibration, V the volume (L) of the KC1 solution added, V the volume (L) of the P

solution that was added for adsorption, V2 the volume (L) of the P solution that was recovered

by filtration after adsorption, M the mass of ceramic material (kg), and Sd the amount (mg kg-1)

ofP desorbed by ceramic in KCL solution. P04-P was analyzed by the Murphy-Riley

colorimetric method on a SEAL AQ2 discrete analyzer (SEAL Analytical, Inc., Mequon, WI).

Sorption Isotherms

P sorption parameters were computed using linear, Freundlich, and Langmuir isotherms

(Zhou and Li, 2001).

The Freudlich equation is:

S = KC" (2-3)

where Kf is the Freundlich adsorption coefficient (mg1-n Ln kg-1), which is the ratio between log

P sorbed on solid phase and log phosphorus in solution, C is the P concentration in solution

(mg L1), and n is an empirical constants related to sorption (n < 1). Given that Freudlich

equation is empirical in nature, it does not give information about amount ofP adsorption.

The Langmuir equation gives information on the amount of P ions in solution that are

adsorbed by solid particles assuming that the solid particles have a finite capacity to adsorb P.

The Langmuir equation is:

C 1 C (2-4)
-= -b + (2-4)
bS
max max









where S is the amount of P in adsorbed phase (mg kg-1), C is the concentration of P in solution

(mg L-1), Smax is the P adsorption maximum (mg kg-1) which is widely used to estimate the

capacity of a solid surface to adsorb P, and b is a constant related to the bonding energy between

phosphate and the solid surface (mL kg-1).

Estimation of Known P Stock Solutions by Different Ceramic Lysimeters

A laboratory study to test the concentration of water samples collected from commercially

available ceramic tension lysimeters to sample a known P04-P solution was conducted. Three

lyismeters of each type were acquired and used in this experiment representing three replicates.

The ceramic lysimeters were cleaned using deionized (DI) water and diluted HC1 in separate

containers as described in the following steps. 1) The lysimeters were soaked in DI water for

24 h in a clean plastic container with the ceramic cups fully immersed, after which the water that

collected in the lysimeter tubing was discarded. 2) The lysimeters were immersed in 1000 mL of

0.1 M HC1 for 8 h. During this period, suction was applied to the lysimeters twice. A suction of

60 kPa was applied to Ceramics A and B while a suction of 50 kPa was applied on Ceramic C.

This was to ensure that adequate volumes of the solution were drawn in the lysimeter tubing. 3)

The lysimeters were then soaked in fresh 0.1 M HC1 for 24 h and suction was applied twice.

Next, the lysimeters were thoroughly rinsed with DI water inside and outside. 4) The lysimeters

were then soaked in DI water for 24 h and suction was applied twice. This step was repeated

using fresh DI water. The solution collected in the lysimeters after the last suction event was

collected in clean bottles and analyzed for P concentration and pH, to serve as background

information on the status of each cleaned lysimeter. After cleaning, the lyismeters were dried at

room temperature for at least 36 h.









Stock solutions of 0.10, 0.50, 1, and 20 mg P L-1 were prepared with potassium

dihydrogen phosphate (KH2PO4) standard solution (1 mL = 1 mg P) and DDI water. For each

stock solution, 3250 mL was prepared. Ceramics A and B each required 1000 mL to conduct the

experiment over a 24 h period while Ceramic C needed a volume of 1250 mL for the same

period. The stock solutions were evaluated in ascending order of concentration. The lysimeters

were placed in clean containers and the proper solution was added at 8 am. The exact volume of

stock solution used in each experiment was weighed to enable accurate computation of P mass.

Suction was applied to each of the lysimeters (60 kPa for Ceramics A and B and 50 kPa for

Ceramic C). The first sample was collected after 2 h from the start of the experiment, from each

of the lysimeters and from the solution in the three containers. A second suction was applied and

samples were drawn after 4 h from the start of the experiment. A third suction was applied and

samples were collected 24 h from the start of the experiment. The samples collected from the

lysimeters and the containers were weighed to allow for the computation of P mass. At the end of

each experiment with a given solution, the lysimeters were thoroughly cleaned following the

procedure outlined above to avoid P residue carryover to another concentration level.

The effect of conditioning ceramic lysimeters to improve estimation accuracy as suggested

by Grossmann and Udluft (1991) was evaluated using Ceramics B and C which had greater Smax

The lysimeters were used to estimate concentration of 0.1, 0.2, 0.3, and 0.5 mg P L-1. The

purpose was to equilibrate the cups cation exchange capacity with adequate P sorption to reduce

the amount of P in solution that would be adsorbed on the ceramic cups. This was also intended

to mimic field conditions since lysimeters are used to sample soil water of varying

concentrations without being cleaned after each sampling event.









P04-P concentrations were measured by the Murphy-Riley colorimetric method (von

Wandruszka, 2006) on an Auto analyzer 3 (Bran-Luebbe, Hamberg, Germany). Estimated mean

concentration for each sampler type and sampling time and mean percentage deviations from the

known concentrations were computed using proc GLM of SAS 9.1 statistical software (SAS

Institute, Cary, NC, USA).

Comparison of Leachate Concentrations from Bucket and Ceramic Lysimeters Under a
Controlled Environment

Four containers of dimensions 0.756 m in height with a surface area of 0.45 m2 were

fabricated of wood. The container dimensions were selected since they could accommodate the

installation of the bucket and ceramic lysimeters. Using the design ofMigliaccio et al. (2006),

four bucket lysimeters were fabricated using 5-gallon buckets, 12.7 mm hose pipes, and plastic

catch pans. In my study, to control denitrification, bucket lysimetes were incorporating with an

aeration tube and emptied each month. Each bucket lysimeter was placed in the container after

which the container was filled with Krome soil. The soil used was collected from a depth of up

to 30 cm. Ceramic lysimeters were installed in the wooden container adjacent to the bucket

lysimeter with the ceramic tip of the lysimeter placed in line with the top of the bucket lysimeter

(Fig. 2-1a). The containers were then placed under a rainfall simulator, which had a spray area of

6.65 m2 (Fig. 2-1b). The soil was left to settle for a week. During this week, equipment was

tested using the rainfall simulator to ensure the integrity of all sampling devices. Pressure gauge

tensiometers were installed in each container to monitor water suction within the theoretical root

zone (0.15 m deep) and to ensure soil water content equality among the four containers.

Prior to the application of the stock solution, the rainfall simulator was used to apply water

and water samples from the bucket and ceramic lysimeters were collected to determine the initial

concentration of N03-N, P04-P, and Br in the soil. Bromide is usually present in soils at very









low concentrations and is not subjected to chemical or biological transformation. Thus,

movement of Br in soils has been used widely to evaluate nitrate mobility because of the

similarity of NO3-N and Br mobility (Li et al., 1995).

After initial sampling, a stock solution was prepared using 88 g of KNO3, 54 g of KH2PO4,

and 18 g ofKBr and applied at a rate of 1 L per container as a pulse. This volume permitted

uniform coverage of the entire container surface area of 0.45 m2 with minimum penetration of

the soil profile. The amounts ofNO3-N, P04-P, and Br in the stock solution were exaggerated

from annual application rate of 98 kg ha-1 to allow for the detection of these compounds in

leachate after five pore volumes.

Simulated rainfall events began two days after nutrient application. Considering total

porosity of 0.45 and an effective root depth of 0.31 m, water applications equivalent to half a

pore volume (67.5 mm of rainfall) were used. The rainfall simulator delivered water at a rate of

0.013 m3 min1. Hence, the simulator was operated for approximately 34 minutes to simulate half

pore volume of rainfall.

During data collection, rainfall was delivered by the simulator starting at 8:00 AM and

leachate was collected the same day at 5:00 PM. A motorized pump was used to draw leachate

from the bucket lysimeter and a 60 ml syringe was used to expel the leached from the soil water

sampler. The leachate fraction was filtered through filter paper (0.45tim) for N03-N and

Whatman filter paper No. 42 for P04-P. Nitrate and Br concentrations were analyzed using an

ion chromatograph (Dionx LC20, Dionex Corporation, Sunnyvale, CA) and P04-P concentration

was determined using the Murphy-Riley colorimetric method on Auto analyzer 3 (Bran-Luebbe,

Hamberg, Germany). Data was analyzed using the one-way analysis of variance (ANOVA) and









means separated using Waller-Duncan K-ratio Test using SAS statistical software (SAS Institute,

Cary, NC, USA).

Comparison of Leachate Concentrations from Bucket and Ceramic Lysimeters from an
Avocado Orchard

Bucket lysimeters based on the design presented by Migliaccio et al. (2006) were

fabricated and installed in the avocado orchard on the second 'Simmonds' tree of each replicate

see chapter 3 for orchard description). Each lysimeter was composed of a collection container

(20 L) and two flexible tubes. The bucket lysimeters were installed 0.3 m away from the tree

trunk and 0.3 m below the ground so as not to interfere with the development of the roots. The

flexible tubes on the lysimeters were left protruding on the ground after installation. The tubes

provided the ability to collect the water samples from the collection container; one tube served as

an air vent while the other tube was connected to a peristaltic pump to draw the leachate.

Ceramic A lysimeters were installed on each of the third 'Simmonds' trees of each

treatment replicate. The lysimeters were installed 0.15 m away from the tree trunk and 0.3 m

below the ground. The lysimeters had two tubes, a shorter one and a longer one. The short tube

was used to apply a suction of about 50 kPa on the sampling day. The longer tube was used to

draw the leached from the lysimeter.

Leachate sampling was collected monthly from November 2007 to October 2009. The

amount of water collected from each lysimeters was measured and recorded. A sample from each

lysimeter was collected in 270 mL plastic bottles and taken to the laboratory for analysis. A

portion of each sample which had been filtered through Whatman No. 42 filter paper into a

20 mL bottle vial was stored for the determination of NO3-N and PO4-P. The samples were

frozen at -4 C until they were analyzed. Nitrate was determined spectrophotometrically by first

reducing NO3- to NO2- using a cadmium coil and the resulting NO2- concentration was then









determined by USEPA method 353.2 (USEPA, 1993 a). P04-P was determined using

ascorbic-acid method (USEPA method 365.1). All sample analysis was done using SEAL AQ2

discrete analyzer (SEAL Analytical, Inc. Mequon, WI). Data were analyzed for significant

different (p < 0.05) among treatment means and between sampling devices using a one-way

analysis of variance (ANOVA).

Results and Discussion

Constituents of Elements in Ceramic Cups

Analysis of the ceramic cups composition revealed existence of several elements in

varying amounts (Table 2-1), which included P, Fe, Al, Ca, K, Mg, Zn, and Si. However, Mg

was only detected in Ceramics A and B. The elements Pb, Mn, Cd, Cu, and Ni were either

detected in very low amounts or could not be detected in the three ceramic materials (date not

shown). The presence of substantial amounts of Fe, Al, Si, and Ca in the three ceramic materials

may influence P estimation through adsorption or precipitation depending on the pH and

concentration of the soil water being sampled. Ceramic A had significantly greater amounts of

the eight elements analyzed than Ceramics B and C apart from Zn. The similarity in composition

between Ceramic B and C was expected as those samplers were obtained from the same

manufacturer. Given that tension ceramic lysimeters are normally used in leachate studies of

different constituents, the obtained leachate may be influenced by the ceramic cup chemistry. For

example, Swistock et al. (1990) found that water samples from tension lysimeters were

significantly greater in sulfate (S04-2), Ca, Mg, Mn, and K than the water samples collected from

pan lysimeters. They suggested the difference in the elements was attributed to the collection of

micro flow by the tension lysimeters compared to macro flow collected by pan lysimeters. Micro

flow has a greater residence time in the soil profile, which increases the concentration of mineral

ions compared to macro flow, which has a shorter interaction time with the elements in the soil

55









medium. Results from my study show that the ceramic cups could also have been a likely source

for Ca, Mg, and K due to their abundance in the ceramic materials. Thus, suction lysimeters

should be selected as a sampling tool with caution depending on the sampling goals (macro pore

versus micro pore) and potential chemical interference.

Adsorption-desorption Potential of the Ceramic Cups

Phosphorus sorption characteristics were best described by Freundlich isotherms for

Ceramics A and C while Ceramic B was best described by a Langmuir isotherm (Table 2-2).

Ceramics B and C had higher sorption maxima than Ceramic A. Ceramic B had the lowest

desorption rate while Ceramic C had the highest desorption rate (Fig. 2-2). The amount of P

required to reach maximum sorption (Smax) was 5.69, 11.22, and 16.56 mg P kg-1 for Ceramics

A, B, and C, respectively. The low Smax for Ceramic A could be attributed to its high P

composition (Table 2-1) which contributed to meeting the P sorption demand of this ceramic

material. The bonding energies for the three ceramics were low with values of 0.31, 0.17, and

0.19 ml kg-1 for Ceramics A, B, and C respectively, implying that the sorped P may be released

overtime. Data presented in Fig. 2-2 suggest that a solution of KCl could not desorb the entire P

amount that was retained through sorption in each of the ceramic materials within a period of

24 h. The hysteresis effect in all three graphs indicates P diffusion into the reactive ceramic

layers. The hysteresis effect was highest in Ceramic B where no P was desorbed. The differences

in the behavior of the three ceramic types concur with data in Table 2-1 that the ceramic cups

contained varying amounts of different elements which play a role in P adsorption and

desorption. Similarly, others have studied P sorption and found a hysteresis effect of the sorbed

P; they explained this sorption to be a function of solid-phase physicochemical characteristics,

P-loading rate, and residence time (Reddy and DeLaune, 2008). The Reddy and DeLaune (2008)

study and our results indicate that isotherm evaluations provide valuable information that can be

56









used to better assess sampling results by understanding sorption and desorption processes and

hysteresis effects, particularly for P.

Phosphorus Estimation by the Ceramic Lysimeters

Results from the three different ceramic lysimeters suggested variability in collected water

sample composition when sampling four different P04-P stock solutions (Table 2-3). Sampling

4 h from the start of the experiment generally resulted in concentrations that were closer to the

stock solution value for Ceramics A and C lysimeters than sampling after 2 or 24 h. Water

samples collected from Ceramic A were most similar to the P04-P stock solutions, while water

samples collected using Ceramics B and C underestimated P04-P concentrations especially for

lower concentrations (0.1 and 0.5 mg P L-1). We observed that Ceramic A overestimated water

samples with a concentration of 0.1 mg P L-1 by 8% at the 4 h sampling time and by 12% at the

24 h sampling time. Generally the estimation of P04-P concentration in water samples by

Ceramics B and C were not significantly different (p < 0.05).

For accurate P measurement, the ceramic's P adsorption capacity has to be met. Thus for

lower P04-P concentrations, Ceramics B and C adsorbed P from the solution, which resulted in

underestimation of the concentration. This P adsorption from solution was possibly due to the

formation of phosphates of Fe and Al. The higher P04-P concentration solutions were reasonably

estimated since there was enough P mass to reach adsorption potential and still maintain

adequate P in solution. Data show that the accuracy of the P04-P concentrations in water samples

collected using Ceramics B and C increases as the concentration of the stock solution increases.

A similar phenomenon was reported by Nagpal (1982) and was attributed to the material

retaining less P in proportion to the amount of P in the stock solution at greater P04-P

concentrations. Thus the accuracy of Ceramics B and C improved from about 10% with

0.1 mg P L-1 solution to about 97% with 20 mg P L1.

57









For the 0.5, 1, and 20 mg P L-1 stock solutions, the P04-P concentrations measured from

samples extracted using Ceramic A varied by 3% or less from the stock solution concentrations.

Although Ceramic A had the greatest amount of P, it estimated P04-P of a known solution more

accurately than Ceramics B and C for the sampled stock solution. The higher P mass in Ceramic

A could be beneficial in meeting the P sorption requirements of the ceramic cups, thus resulting

in a more accurate estimate. Likewise, the higher P mass in Ceramic A may have contributed to

the overestimation ofP for the 0.1 mg P L-1 stock solution.

At the 4 h sampling, the difference in sampled concentrations for the 0.5 and 1.0 mg P L-1

stock solutions by Ceramic B and Ceramic C was at least 16% (Table 2-3). The difference

between P04-P concentrations collected by these two ceramics reduced to at most 13% for water

samples collected after 24 h. In a separate experiment where Ceramics B and C were used to

estimate solutions of 0.1, 0.2, 0.3, and 0.5 mg P L-1 without the lysimeters being cleaned (i.e.,

conditioning the ceramics), the estimation of 0.5 mg P L-1 solution at 4 h improved (Table 2-4)

compared to the estimates given in Table 3. However, estimates for 0.1 and 0.5 mg P L-1 stock

solutions measured for Ceramic A were still more accurate than the respective results from the

conditioned ceramic lysimeters.

Although Ceramics B and C were from the same manufacturer, Ceramic B estimations

were closer to the concentrations of the stock solutions than the estimates of Ceramic C. This

was attributed to the difference in the P sorption maximum values as shown in Table 2-2. Thus,

with conditioning the maximum sorption values of Ceramic B (11.22 mg kg-1), which was lower

than that of C, would be fulfilled faster than that of Ceramic C (16.56 mg kg-1) which was

greater. For the tested samplers, the lower the ceramic's Smax the more accurate the sampler was

in estimating P04-P concentration of a known stock solution.









The mass of P retained in the ceramic cups for each lysimeter type was estimated after

24 h, for each of the four stock solutions used (Table 2-5). Ceramic A retained the lowest P

amount in the range of 2 to 8% (of the initial P mass) for the four stock solutions. Ceramic B

retained between 5 to 36% (of the initial P mass) for the four stock solutions while Ceramic C

retained between 8 to 65% (of the initial P mass). Such P retention in ceramic cups was also

observed by others (Hansen and Harris, 1975; Zimmermann et al., 1978). The lower PO4-P

concentrations measured with the cleaned lysimeters (Table 2-6) implies that P adsorption of the

ceramics was reversible in a solution of HCl and the lysimeters can be cleaned for reuse. Similar

studies by Nagpal (1982) and Bottcher at al. (1984) showed that P sorbed on ceramic cups can be

desorbed in a solution of HC1, however, with low bonding energies (Table 2-2) P desorption will

occur gradually under field condition.

The higher P content in Ceramic A explains why this lysimeter had more P recovered after

the cleaning protocol (Table 2-6). The results suggest that the three lysimeter types used would

not provide accurate estimation of soil water whose PO4-P concentration was less than

0.1 mg P L-1. In such a situation, Ceramic A would likely over estimate the concentration by

releasing P into the collected samples, while Ceramics B and C would likely underestimate the

concentration due to P adsorption. Overall, of the three evaluated ceramic lysimeters, Ceramic A

would be more suitable to sample a leachate with PO4-P concentrations within the range of my

study. Ceramics B and C would be considered for a PO4-P concentration of 20 mg P L-1 or

greater since their results would be more uncertain for lower P concentration.

Nutrient Concentrations Sampled from a Controlled Environment

The elution curves of NO3-N obtained from the average leached concentration for the

bucket and ceramic lysimeters showed that NO3-N and Br followed similar leaching patterns in

Krome soil (Fig. 2-3). Elution curves showed that the peak NO3-N and Br elutions occurred at

59









2.5 pore volumes (equivalent to 338 mm of rainfall) for both bucket lysimeters and ceramic

lysimeters. Thus, N03-N and Br were quickly leached from the soil after each pore volume.

Similar results were obtained by Li et al. (1995) using 3 layers of spodosols soils, whereby 4.5

pore volumes leached out 85 to 87% of the N03-N applied across a soil depth of 0.7 m. There

were no significant differences (p < 0.05) between the concentrations ofNO3-N and Br sampled

by the two devices, although bucket lysimeters had higher concentrations than ceramic

lysimeters. Swistock et al. (1990) observed no significant difference (p < 0.05) between NO3-N

in the leachate sampled by tension ceramic lysimeters and non- zero tension lysimeters, although

data from ceramic lysimeters showed a higher concentration.

The P04-P elution curves indicated that very little phosphorus was leached from the

system when compared to the amount applied. There were no significant differences (p < 0.05)

between the concentrations of P04-P sampled by the two devices, although the bucket lysimeters

had a higher concentration (Fig. 2-3). Haines et al. (1982) observed similar P04-P concentration

between tension ceramic and zero-tension lysimeters for a leacheate collected at a depth of 0.3 m

from a forest ecosystem over a period of one year. The lesser concentration of P04-P in leacheate

observed in my study was attributed to the reaction of HP04-2 with calcium carbonate (calcite)

through precipitation resulting in the formation of monocalcium phosphate as observed by

Freeman and Rowell (1981).

The similarity in the concentration ofNO3-N and P04-P sampled by ceramic and bucket

lysimeters could be attributed to both devices sampling under saturated flow conditions since

suction was applied to the ceramic lysimeter immediately after water application. Whereby, both

devices had a representative sample of the leachate from macro pore flow. Hence, either device









could be used in the monitoring of nutrient leaching in Krome soils under such soil water

conditions.

Comparison of Nutrient Concentrations Sampled form an Avocado Orchard

Due to high water percolation rate of Krome soil attributed to 51% gravel fractions, an

adequate sample of about 50 ml required for laboratory analysis of the major nutrient elements

could only be sampled by ceramic lysimeters when the soil water content was near saturation.

Such sampling conditions for ceramic lysimeters occurred four times out the 24 sampling events.

Although N03-N concentrations for bucket lysimeters samples were higher they were not

significantly different (p < 0.05) from concentrations sampled using ceramic lysimeters (Table

2-7 to 2-10). Swistock et al. (1990) observed similar results between these sampling devices.

Bucket lysimeters' samples had significantly higher (p < 0.05) P04-P concentrations than

concentrations for samples from ceramic lysimeters (Tables 2-7 to 2-10). The differences

between P04-P concentrations sampled by the two devices may be attributed to bucket

lysimeters collecting a cumulative leachate over a longer duration while ceramic lysimeters

sampled a "snap-shot" of the leachate that was available on the day of sampling. Probably

sampling more events using the ceramic lysimeters during the wet season would have provided

more representative concentrations. In the study by Haines et al. (1982) where P04-P

concentrations between tension ceramic and zero-tension lysimeters were similar, the ceramic

lysimeter was equipped to collect a cumulative leacheate over a the sampling period.

Conclusions

Methods used in my study provide an outline for testing ceramic lysimeters for their

appropriate application for measuring soil water of different chemical constituents. The

particular ceramic lysimeters (Ceramics A, B, and C) used in my study outlined the variability

among ceramic tension lysimeters. The analysis of the ceramic material composition indicated

61









that all three types of ceramic lysimeters contained substantial amounts of Fe, Al, Si, and Ca that

may influence P estimation depending on the pH and concentration of the soil water being

sampled. The adsorption-desorption study and the fitting of the Freundlich and Langmuir

isotherms provided information that assisted in interpreting the different results. For the tested

samplers, the lower the ceramic's Smax the more accurate the sampler was in estimating P04-P

concentration of a known stock solution. Thus, Ceramic A with the greatest P mass and lesser

Smax estimated P04-P concentration for the four concentrations more accurately than Ceramics B

and C which had a lower P mass but greater Smx. The comparison of the leachate concentration

ofNO3-N and P04-P in a controlled environment showed no significant differences (p < 0.05)

between bucket and ceramic lysimeters due the soil's saturated flow conditions during sampling.

The difference in P04-P concentration in the orchard samples was due to the difference in the

leachate volume sampled. The bucket lysimetes captured a cumulative leachate from macro flow

yet the ceramic lysimeters represented a snap-shot of micro flow. More sampling events from the

ceramic lysimeters to time saturate flow conditions would most likely have improved the

ceramic lysimeter estimation since P04-P leaching is more influenced by water flow volume

through dissolution than the amount P added to the soil (see Chapter 3).

Findings from my study suggest that before ceramic samplers are used in soil water

sampling studies the following three steps should be completed: 1) determination of the chemical

composition of the ceramic cups; 2) development of sorption isotherms; and 3) testing of ceramic

sampler with known stock solutions. Depending on the results obtained, a decision to use

ceramic lysimeters or to explore other soil water collecting methods can be scientifically made.

Further research is needed to test if more sampling events using ceramic lysimeters targeting









saturate flow field conditions would give similar results as sampling using bucket lysimeters in

Krome soil.









Table 2-1. Composition of ceramic cups considering major elements reported by chemical
analyses, n=3.
Elementy Ceramic A Ceramic B Ceramic C
------------------------------mg kg-------------------------
P 52 a 12.0 c 18.0 b
Fe 436 a 4.9 b 54.0 b
Al 8309 a 951.0 b 729.0 b
Ca 8063 a 275.0 b 71.0 b
K 1212 a 13.0 b 31.0 b
Mg 4468 a 149.0 b Zn 23 a 11.0 a 9.5 a
Si 472 a 30.0 b 10.2 b
y, Element with same letter not significantly different (p < 0.05) by row.
z,

Table 2-2. Sorption parameters for the ceramic materials included in the experiment considering
two isotherms and a P concentration range of 0 to 20 mg L1.
Ceramic Freundlich Langmuir
type
Kf n R2 Smax b R2
L kg- mg kg- mL kg-
A 1.365 0.464 0.979 5.69 0.310 0.974
B 1.891 0.515 0.886 11.22 0.167 0.901
C 2.942 0.524 0.946 16.56 0.192 0.837
Kf, is the Freundlich adsorption coefficient (L kg-1); n, is an empirical constants related to
sorption (n < 1); R2, is the coefficient of determination; Smx, the phosphorus adsorption
maximum (mg kg-1); b, is a constant related to the bonding energy between phosphate and the
solid surface (ml kg-1).









Table 2-3. Estimation of a known concentration of P04-P by different lysimeters, n=3.
Stock Ceram Average concentration drawn by the samplers
solution ic type Sampling timey


mg L-1 -----------------------------mg L ---------------------------
0.100 A 0.109 a 0.108 a 0.112 a
B 0.003 c 0.011 b 0.018 b
C 0.011 b 0.011 b 0.026 b
0.514 A 0.489 a 0.506 a 0.524 a
B 0.029 b 0.159 b 0.270 b
C 0.016 b 0.228 b 0.292 b
1.069 A 1.024 a 1.038 a 1.031 a
B 0.301 b 0.699 c 0.797 b
C 0.249 b 0.834 b 0.695 b
20.503 A 20.449 a 20.520 a 20.585 a
B 17.984 b 19.993 b 20.107 b
C 17.968 b 19.825 b 19.539 b
y, Sampling time in hours after the start of the experiment.
z, Sampled concentrations with same letter not significantly different (p < 0.05)
stock solution concentration.


by column and


Table 2-4. Estimation of a known concentration of P04-P by ceramics B and C after
conditioning, n=3.
Stock Ceram Average concentration drawn by the samplers
solution ic type Sampling timey
2 4 24
mg-1 mg-1
mg L-1 -------------------------mg L-1 -------------------------
0.100 B 0.017az 0.026a 0.047a
C 0.014a 0.018a 0.032a
0.200 B 0.089a 0.144a 0.127a
C 0.046a 0.107a 0.093a
0.300 B 0.155a 0.243a 0.243a
C 0.092a 0.214a 0.172a
0.500 B 0.364a 0.480a 0.451a
C 0.324a 0.389b 0.331b
y, Sampling time in hours after the start of the experiment.
z, Sampled concentrations with same letter not significantly different (p < 0.05) by column and
stock solution concentration.









Table 2-5. Mass of P retained by lysimeter ceramic cups, n=3.
Starting Ceramic Mass Mass Mass Mass % mass
mass type extracted in extracted in remaining retained retained in
water samples water g in in ceramic
drawn sampled from container ceramic cups
through the container after
lysimetersz sampling

mg-- ---- ------------------------------mg------------------
mg
0.097 A 0.051 0.007 0.037 0.002 1.6
0.096 B 0.003 0.005 0.054 0.034 35.5
0.120 C 0.018 0.007 0.018 0.078 65.1
0.502 A 0.286 0.025 0.176 0.015 3.0
0.497 B 0.043 0.028 0.291 0.134 27.0
0.624 C 0.218 0.033 0.054 0.318 51.0
1.039 A 0.576 0.061 0.323 0.080 7.7
1.020 B 0.169 0.058 0.583 0.211 20.7
1.276 C 0.648 0.070 0.039 0.519 40.7
19.740 A 11.515 0.959 6.876 0.390 2.0
19.912 B 5.426 1.355 12.140 0.994 5.0
25.031 C 20.491 1.094 1.439 2.007 8.0
z, Water samples were collected after 2, 4, and 24 h.









Table 2-6. Status of the cleaned lysimeters after sampling a known P concentration, n=3.
Ceramic Concentration of Concentration pH ofDI pH of solution in Remark
type P04-P in DI water of solution in water container number
collected from container collected
lysimetersy from
lysimeters
-------------mg L-------------

A 0.030 a 0.006 5.11 a 5.44
B 0.004 b 0.002 4.75 a 5.28 1V
C 0.006 b NAz 4.68 a NAz

A 0.035 a 0.004 4.89 a 5.62
B 0.007 b 0.002 4.54 b 5.37 2
C 0.010 b 0.003 4.21 c 5.08

A 0.036 a 0.004 4.75 a 5.88
B 0.015 b 0.002 4.65 b 5.47
C 0.008 b 0.003 4.61 c 5.68
y, Concentration and pH with same letter not significantly different (p < 0.05) by column.
z, NA= No solution was available to measure get a measurement.
v, Lysimeters cleaned to remove factory residues and composition of DI water sampled
thereafter.
w, Lysimeters cleaned after use with stock solution of 0.5 mg P L-1 and composition of DI water
sampled thereafter.
x, Lysimeters cleaned after use with stock solution of 1.0 mg P L-1 and composition of DI water
sampled thereafter.









Table 2-7. Leachate concentration comparison of bucket and ceramic lysimeters for the sampling
of 7/23/2008, n=3.


Treatment
1, ET+FSR
2, ET + 50% FSR
3, ET + 200% FSR
4, SW + FSR
5, Set sch. + FSR
6, SW + 50% FSR
7, SW + 200% FSR


Bucket
8.0
10.0
15.0
2.0
0.3
0.1
18.0 a


N03-N
Ceramic P-value Bucket
8.0 0.9726 0.328 a
1.0 0.2268 0.474 a
3.0 0.2355 0.389 a
0.1 0.2867 0.636 a
0.3 0.8653 0.235 a
0.03 0.6666 0.467 a
0.3 b 0.0043 0.496 a


PO4-Pz
Ceramic P-value


0.015 b
0.060 b
0.032 b
0.093 b
0.011 b
0.015 b
0.020 b


0.0002
0.0111
0.0167
0.0070
0.0006
0.0422
0.1088


z, Sampled concentrations with same letter not significantly different (p < 0.05) by treatment.


Table 2-8. Leachate concentration comparison of bucket and ceramic lysimeters for the sampling
of 8/25/2008, n=3.


Treatment
1, ET+FSR
2, ET + 50% FSR
3, ET + 200% FSR
4, SW + FSR
5, Set sch. + FSR
6, SW + 50% FSR
7, SW + 200% FSR


z, Sampled concentrations with


N03-N
Bucket Ceramic P-value
0.646 0.063 0.3539
1.156 0.027 0.1584
1.483 0.012 0.2993
2.129 0.053 0.2653
0.180 0.005 0.1341
0.093 0.051 0.2715
7.330 0.047 0.2489


Bucket
0.224 a
0.275 a
0.269 a
0.421 a
0.157 a
0.300 a
0.339 a


P04-Pz
Ceramic
0.034 b
0.019b
0.019b
0.027 b
0.031 b
0.019b
0.075 b


same letter not significantly different (p < 0.05)


P-value
0.0007
0.0087
0.0064
0.0014
0.0016
0.0041
0.0148
by treatment.


Table 2-9. Leachate concentration comparison of bucket and ceramic lysimeters for the sampling
of 5/20/2009, n=3.


Treatment
1, ET+FSR
2, ET + 50% FSR
3, ET + 200% FSR
4, SW + FSR
5, Set sch. + FSR
6, SW + 50% FSR
7, SW + 200% FSR


Bucket
138
124
270
50
139
87
160


N03-N
Ceramic P-value Bucket
40 0.2836 0.142
63 0.2526 0.133 a
251 0.9433 0.100
43 0.8288 0.241
1 0.1240 0.137
50 0.4669 0.193 a
117 0.6180 0.254


P04-Pz
Ceramic
0.049
0.035 b
0.029
0.112
0.035
0.024 b
0.173


P-value
0.1689
0.0321
0.1259
0.0934
0.2079
0.0126
0.5270


z, Sampled concentrations with same letter not significantly different (p < 0.05) by sampling
device.









Table 2-10. Leachate concentration comparison of bucket and ceramic lysimeters for the
sampling of 6/17/2009, n=3.


Treatmentz
1, ET+FSR
2, ET + 50% FSR
3, ET + 200% FSR
4, SW + FSR
5, Set sch. + FSR
6, SW + 50% FSR
7, SW + 200% FSR


N03-N
Bucket Ceramic P-value Bucket
60 57 0.9620 0.184 a
36 a 10 b 0.0363 0.280 a
137 a 10b 0.0313 0.162
12 4 0.3803 0.359
5 1 0.3288 0.180
19 ? 03381 0.196 a


0.3993


P04-P
Ceramic
0.039 b
0.002 b
0.009 b
0.052
0.010


P-value
0.0316
0.0380
0.0012
0.0530
0.0810


0.000 b 0.0009


0.213 0.059 0.4827


z, Sampled concentrations with same letter not significantly different (p < 0.05) by sampling
device.


















Soil sface
I'


Water
sampler


lysimeter


--- 0.67m
A B

Figure 2-1. Arrangement of the bucket and ceramic lysimeters in each container. A) cross
section view of each container, B) arrangement of the four boxes under a rainfall
simulator.


\ ~~7















^-

-- Adsorption
0 o Desorption


/
So O


Adsorption
o Desorption


Ceramic C


-- Adsorption
o Desorption


5 10 15


Equilibrium P concentration (mg/L)


Figure 2-2. Sorption-desorption curves of P. A) Ceramic A, B) Ceramic B, C) Ceramic C.


12

. 10

E 8

. 6
I=-


2

0










400 0.16
aa N-N03-N
E
300 -Br 0.12
SO P04-P
Z 0

0 200 0.08
z C


o 100 0.04 I


c 0 I 0.00
0
Ic 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Pore volume

A


400 0.16
---N03-N
E -
-Br
300 0.12 E
-P04-P
0I \\ 4 aQ

z 200 0.08 t



S100 0.04
aJ 0 0


0 '- 0.00
Ic 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Pore volume B


Figure 2-3. Elution curves of nutrient concentrations sampled by the lysimeters under a
controlled environment. A) ceramic and B) bucket.









CHAPTER 3
EVALUATION OF IRRIGATION AND NUTRIENT MANAGEMENT PRACTICES IN A
YOUNG AVOCADO ORCHARD

Introduction

Nutrient leaching of nitrogen (N) and phosphorus (P) from agricultural fields is a water

quality concern in many areas of the world due to increased nitrate (NO3) concentrations and

eutrophication of water supplies (Burkitt et al., 2004; Quifiones et al., 2007; Eulenstein et al.,

2008). Nutrient leaching, or the downward movement of dissolved nutrients in the soil profile

with percolating water (Havlin et al., 2004), is influenced by hydrologic and soil characteristics.

Hydrologic characteristics of a location such as rainfall patterns (frequency, intensity, duration,

and amount) and infiltration characteristics of the soil influence nutrient leaching. Nutrient

leaching is also effected by fertilization practices, irrigation practices, crop characteristics, and

production system management.

Leaching ofN and P is mainly attributed to fertilizers that are applied to enhance plant

growth and yields. Although the intent is for these fertilizers to be used by the crop, some

fertilizers may leach into groundwater (Schaffer, 1998; Tischner et al., 1998; Schroder et al.,

2005) and contribute to increased downstream eutrophication (Li et al., 1999). The residual

amount of N and P in the soil after crop harvest and the rate of N and P mineralization of the

decomposing plant residue also affect nutrient leaching (Jiao et al., 2004). Nutrient leaching may

also occur due to over irrigation and heavy rainfall events that result in increased infiltration and

drainage (He et al., 2000; Mufioz-Carpena et al., 2002).

Irrigation and fertilizer best management practices (BMPs) have been reported to minimize

nutrient leaching (Yates et al., 1992; Paramasivam et al., 2000; Doltra et al., 2008) and reduce

water volume applied without affecting yields (Zotarelli et al., 2008; Migliaccio et al., 2010).

Practices that enhance fertilizer utilization efficiency include appropriate timing of fertilizer

73









application, formulation of the fertilizer material, amount and rate of fertilizer applied, and

methods used to apply fertilizers. For example, split application of fertilizers as fertigation has

been shown to reduce nutrient leaching loads as opposed to applying fertilizers by broadcasting

in one or two applications followed by water application (Nakamura et al., 2004; Quifiones et al.,

2007; Worthington et al., 2007). Efficient irrigation methods such as irrigating based on crop

evapotranspiration (ET) demand or soil water sensors minimize over irrigation and thus water

volumes applied without affecting yields (McCready et al., 2009; Silva et al., 2009; Migliaccio et

al., 2010) and subsequently reduce nutrient leaching.

ET-based irrigation has been a method of computing crop water requirements for many

years (Penman, 1948; Turc, 1961; Kisekka et al., 2010). The ET estimation methods involve

computing the reference evapotranspiration (ETo) using weather data (e.g., temperature, solar

radiation, relative humidity, and wind speed). Two widely accepted methods of calculating ETo

are the Food and Agricultural Organization of the United Nations (FAO) Penman-Monteith

(Allen et al., 1998) and the American Society of Civil Engineers-Environmental and Water

Resources Institute (ASCE-EWRI) 2005 (Attarod et al., 2009; Pereira et al., 2009; Sahoo et al.,

2009). The concept of a reference crop was introduced to prevent the need to define unique

evaporation parameters for each crop and stage of crop growth. Thus, crop coefficients (k,) serve

the purpose of relating evapotransipration from the reference crop (ETo) to evapotranspiration

rates (ETa) of a crop of interest (i.e., ETa is the product of ETo and kc). According to Allen et al.

(1998) k, serves the purpose of distinguishing a crop of interest from the reference grass used to

compute ETo. Thus k, factors are an aggregation of the physical and physiological differences

between the crop of interest and the reference grass (alfalfa). The availability of k values is one

limitation of using ET-based irrigation methods, since these coefficients require time and









financial resources to be developed and once developed they remain site, stage of crop growth,

plant size, and cultivar specific. Even so, ET-based irrigation research has reported water savings

(13 to 46%) and corresponding increased yields (6 to 11%) as opposed to set schedule irrigation

for potato, and mango crops (Meyer and Marcum, 1998; Silva et al., 2009; Spreer et al., 2009).

Soil water sensors estimate the soil water content and can be can be linked with irrigation

control equipment to automate irrigation scheduling. Soil water sensors commonly used for

irrigation scheduling include gypsum blocks, dielectric probes, time domain reflectometry (TDR)

probes, and automated switching tensionmeters. Kukal et al. (2005) reported for rice (Oryza

sativa) in Ludhiana, India, that irrigating at a soil suction of 16 kPa resulted in water saving of 30

to 35% in comparison to the traditional practices of a 2-day irrigation set schedule interval. In a

study to assess irrigation BMPs using tensiometers in a royal palm (Roystonea elata) field

nursery in Homestead, FL, Migliaccio et al. (2008) found that automating irrigation at soil

suctions of 5 and 15 kPa reduced water volumes applied by 75 and 96% when compared to

standard irrigation scheduling without sacrificing tree crop quality. Meron et al. (2001) reported

that irrigating at a soil suction of 15 to 25 kPa resulted in water saving of 500 to 650 mm in

comparison to ET-based irrigation water use of 700 to 850 mm per season in an apple (Malus

domestic) orchard in Upper Galilee, Israel. Others (Enciso et al., 2009; Fuentes et al., 2008;

McCready et al., 2009; Migliaccio et al., 2010) have also conducted irrigation research using soil

water sensors and have reported water savings from 25 to 74% of set schedule irrigation

practices.

Irrigation BMPs in combination with nutrient BMPs, either through use of ET-based

irrigation (Yates et al., 1992; Diez et al., 1997; Doltra et al., 2008; Paulino-Paulino et al., 2008)

or soil water sensor based irrigation (Paramasivam et al., 2000; Lao et al., 2004; Alva et al.,









2006) have been evaluated for the impact of the combined irrigation and nutrient BMPs on

nutrient leaching reduction and water savings. Alva et al. (2003) conducted a study on sandy

soils in Lake Alfred, FL, for over five years where they monitored the effects of different N and

irrigation BMPs on orange yields (Citrus sinensis) and N03-N concentrations in groundwater.

The authors reported water savings and N leaching reductions with irrigation based on soil

suctions at 10 and 15 kPa and split N fertigation in comparison to non-systematic irrigation

scheduling with broadcasting ofN fertilizer. Likewise, a study was conducted by Quifiones et al.

(2007) on eight-year old citrus trees in Moncada, Spain, on sandy-loam soil to assess irrigation

and fertilizer management on N uptake and seasonal distribution ofN in the soil profile.

Quifiones et al. (2007) reported split application of N irrigation at soil water suction of 10 kPa

reduced N leaching and significantly improved tree N use efficiency compared to flood irrigation

with two equal application of N. Yates et al. (1992) reported that split application of granular

fertilizers in avocado orchards 8 times during the year reduced nutrient leaching as opposed to

applying the fertilizers twice a year. The authors did not detect a significant difference in nutrient

load leached by irrigating at 80%, 100%, and 120% of ET.

The BMPs in my study focus on water savings and reduction of nutrient leaching while

maintaining crop yield of avocado (Persea americana Mill) in the environmentally sensitive

ecosystems of south Florida. The BMPs consisted of an irrigation management

evapotranspirationn [ET] or soil water tension at 15 kPa [SW]) and a fertilizer rate management.

Previous studies have not evaluated avocado response to irrigation and nutrient management

practices in south Florida. Likewise the effects of the different irrigation and nutrient

management practices on nutrient leaching, volume of water applied, tissue nutrient status, and

fruit yield of avocado grown on gravelly calcareous soils have not been adequately documented









so that irrigation and nutrient use efficiency may be optimized. The specific objectives of the

study were to determine the effect of nutrient load and irrigation scheduling in calcareous soils

on: (1) nutrient leaching of N and P; (2) tissue nutrient status, growth, and yield of' Simmonds'

and 'Beta' avocado cultivars; and (3) soil nutrient indicators such as (soil organic carbon, C:N

and C:P ratios, and soil inorganic N).

Materials and Methods

Study Area

The study was conducted in Homestead, Miami-Dade County, FL, at the University of

Florida's Tropical Research and Education Center (TREC) (25o20'21" N, 8020'01" W). The

elevation of TREC is about 4 m above sea level. The annual rainfall is 1.44 m with maximum

and minimum daily annual average temperatures of 31.5C and 11.6C respectively (considering

available data from 1998 to 2007) for Homestead, FL (Florida Automatic Weather Network

[FAWN], 2010). Homestead has a humid subtropical climate with hot, humid summers where

high temperatures average between 31 to 330C. Winters are mild, but on average cooler than the

nearby coastal areas. The wet season in Florida spans from May to October and 80% of the

rainfall occurs during this period (Mulholland et al., 1997). The soils at the site are gravelly,

loamy-skeletal, carbonatic, hyperthermic lithic udorthents, and are classified as Krome very

gravelly loam (Noble et al., 1996). Krome soils are very shallow (up to 20 cm deep), well

drained, moderately permeable and underline by limestone. Krome soils have 51% coarse

material, 36% sand, 40% silt, 24% clay,a bulk density of 1.42 g cm-3, (Mufioz-Carpena et al.

(2002) a high pH of 7.4 to 8.4 (Zhou and Li, 2001) and an organic carbon content of 8.47%

(Chin et al., 2007). To provide space for root development in a mechanically rock plowed soil,

tropical fruit trees are planted 50 cm deep at the intersection of perpendicular trenches

(Nufinez-Elisea et al., 2001).









Avocado Orchard Layout and Experimental Design

The experiment was conducted in an avocado orchard composed of 28 'Beta' trees and 84

'Simmonds' trees. The trees were planted in four rows at spacing of 6 m between rows and 4.5 m

inter row. The orchard was planted on 26 February 2006. The trees were irrigated with similar

water volume for 2 months until they were considered established. The trees received specified

irrigation and nutrient management practices beginning in August 2006. The seven irrigation and

nutrient management practices evaluated were: 1) irrigation based on crop evapotranspiration

(ET) irrigation with fertilizer at a standard rate (FSR) typicalfor avocado production in the area;

2) ET irrigation with 50% FSR; 3) ET irrigation with 200% FSR; 4) soil water suction at 15 kPa

(SW) with FSR; 5) irrigation at a set schedule (based on timing and frequency typically used in

the production of avocado in the area) with FSR; 6) SW with 50% FSR; and 7) SW with 200%

FSR. The experiment was conducted from August 2006 to October 2009. Each treatment was

replicated four times and each replicate included 1 'Beta' tree and 3 'Simmonds' trees in a

completely randomized design (Fig. 3-1). The fertilizer at a standard rate (FSR) was modified

during the experiment based on tree size (Table 3-1). The strategy of the fertilizer program

during the first two years (2006 to 2007) was for tree development and fruits were removed

immediately after fruit set. The fertilizer strategy during the following two years (2008 to 2009)

was for fruit production. The trees were fertilized by broadcasting the nutrients under the tree

canopy. For each treatment replicate, a tensiometer (Irrometer, CA) was installed on the first

'Simmonds' tree and a bucket lysimeter was installed on the second 'Simmonds' tree (Fig 3-1).

Irrigation Management Practices

Evapotranspiration (ET) irrigation volumes (m3) were computed as follows:









1) The average monthly daily reference evapotranspiration (ETo) was calculated using the FAO

Penman-Monteith equation and historical weather data from the Florida Automatic Weather

Network (FAWN) website (http://fawn.ifas.ufl/edu/data/reports) for Homestead, FL.

900
0.408A(R-G)+y 900 u2(e -e)
ET = +237 (3-1)
A + y(+ 0.34u2)

Where ETo represents reference evapotranspiration [mm day-1], Rn is net radiation at the

crop surface [MJ m-2 day-1], G is the soil heat flux density [MJ m-2 day-1], T is the mean daily

air temperature at 2 m height [C], u2 is the wind speed at 2 m height [m s-1], es is the

saturation vapor pressure [kPa], ea is the actual vapor pressure [kPa], es ea is the saturation

vapor pressure deficit [kPa], A is the slope of vapor pressure curve [kPa C-1], and y is the

psychrometric constant [kPa C-1].

2) Actual crop evapotranspiration (ETa) [mm] was calculated as

ET = ETx k (3-2)

where k, is the crop coefficient (unit less)

3) The length of irrigation per day (Itd) was calculated in hours as

ETl (mm) x (m / mm) micro sprinkler delivery area (m2)
Id 1000
td irrigation delivery rate (m3 /hr)

(3-3)

4) Water volume applied per tree per day (Wvd) was calculated in cubic meters as

Wd= irrigation time perday(hr) x micro sprinkler rate(m3 /hr) (3-4)









To allow for proper root development, the trees receiving ET-based irrigation were irrigated

three times each week at 8:05 am (Monday, Wednesday, and Friday). The ETo and crop

coefficient (k,) used are given in Table 3-2.

For SW-based irrigation, switching tensiometers (Irrometer, CA) were used to monitor soil

suction in the orchard (Fig. 3-2). Krome soil is considered dry for most crops when the soil water

suction exceeds 15 kPa (Mufioz-Carpena et al., 2002). The irrigation was scheduled at 8:30 AM

and 1:00 PM each day and trees were irrigated when the soil water suction of 15 kPa was

exceeded.

Trees irrigated on a set schedule were irrigated twice a week for two hours during each

irrigation event. Trees were irrigated every Tuesday and Friday starting at 6:00 AM. The

irrigation time was kept constant for the four-year study period.

For all treatments, irrigation was delivered through micro sprinkler (Maxijet, Inc., Dundee,

FL) sprinkler system connected with polytubes. Each avocado tree had one micro-jet sprinkler

with an application rate of 0.079 m3 h-1. The micro-jets were placed beside tree trunks (on the

east side) and the irrigated area per sprinkler had a diameter of 1.57 m. Each treatment replicate

was monitored using a water meter (Daniel L. Jerman Co., NJ) to record the volume of water

applied. Irrigation was controlled with Nelson solenoids (Forth Worth, TX) and a Toro controller

(Ecxtra, Model 53768, Riverside, CA). Data were analyzed for differences among treatment

means ofET, SW, and set schedule based irrigation using SAS statistical software (SAS

Institute, Cary, NC, USA). A one-way analysis of variance (ANOVA) was performed and

treatment means separated using and Waller-Duncan K-ratio Test.









Plant Measurements

Tree diameter was measured once each year in August on each 'Beta' tree and the third

'Simmonds' tree of each treatment replicate. Diameters were measured 0.15 m above the ground.

Tree heights were not collected because trees were pruned.

Leaf greenness expressed as SPAD units was estimated (SPAD-502). According to Pestana

et al. (2004) leaf color is correlated linearly and positively with leaf chlorophyll content.

Measurements were made on three fully expanded recently mature leaves from each of the 'Beta'

tree and the third 'Simmonds tree. The average value for the three leaves was computed and

recorded as the SPAD reading for the sampled tree. According the method proposed by Morales

et al. (1990) and Abadia et al. (1991), the developed equations that correlate SPAD and

chlorophyll was equation 3-6 for 'Beta' which had a coefficient of determination of 0.86 and

equation 3-6 for 'Simmonds' which had a coefficient of determination of 0.76.

chlorophyll = 1.036 x SPAD 8.253 (3-5)

chlorophyll = 0.969 x SPAD 0.808 (3-6)

Three to five fully expanded recently mature leaves were picked from the 'Beta' tree and

the third 'Simmonds' tree of each treatment replicate. The leaves were placed in labeled bags and

transferred to the laboratory for washing. The leaves were first washed with distilled water (DI)

and then washed in a detergent prepared using 30 ml of soap (Liqui-nox, Alconox Inc., White

Plains, NY) and 2500 ml of DI. The leaves were washed in acid prepared with 60 ml of 6 N HC1

and 2500 ml of DI. The acid was washed off with DI water and the leaves were put in marked

paper bags. The leaves were then placed in an oven at 75C until they reached a constant weight.

The dried leaves were ground in a Wiley mill (Thomas-Wiley Co., Philadelphia, PA) with a 1

mm mesh sieve. The ground samples were placed in clean labeled plastic bags and stored until









they were analyzed for Total N (TN), Total carbon (TC), and Total P (TP). Sampling was done

3 times each year: April, August, and December.

The process of determining TN and TC in leaves involved measuring 0.2 g of the ground

tissue sample in crucibles. Both TN and TC in the tissue were measured by the combustion

method using a Vario Max Elemental CNS Analyzer (Elementar Analysensysteme GmbH,

Germany). Solutions used to analyze TP were extracted using the ashing and ignition method

(Davis, 1974; Ben-Dor and Banin, 1989). Samples were then analyzed for TP (USEPA method

365.1). Data were analyzed for differences among treatment means for SPAD, tree diameter, TN,

and TC using SAS statistical software (SAS Institute, Cary, NC, USA). A one-way analysis of

variance (ANOVA) was performed and treatment means separated using a Waller-Duncan

K-ratio Test. Where data were not normally distributed, the Box-Cox (1964) method was used to

normalize data distribution before the statistical analysis. Data were further analyzed as factorial

design with 2-levels of irrigation and 3-levels of fertilizer to explore the influence of different

rates of irrigation and fertilizer on tissue nutrient indicators.

Soil Sampling and Analysis

Soil samples were collected three times each year to determine the nutrient concentration

before fertilizers were applied. However, the first sampling events of 2006 was not conducted

since the trees were not established and 2008 the first sampling event was skipped due to labor

constraint. The dry matter and any fertilizer residue covering the soil were removed before

collecting the soil. Samples were collected from four equidistant positions around the 'Beta' tree

and the third 'Simmonds' tree of each treatment replicate. Each soil sample was placed in a clean

labeled paper bag. The soil samples were dried and sieved to pass through 2 mm mesh sieve.

Each year the samples collected at the end of the harvest season (September) were analyzed for

soil TN, TC, NH4-N, N03-N, TP, and inorganic carbon (IC). The samples collected at the other

82









time of the year were analyzed only for NH4-N and N03-N. TN and TC were analyzed by the

combustion method (Vario Max Elemental CNS Analyzer, Elementar Analysensysteme GmbH,

Germany). For the analysis of NH4-N and N03-N an extraction was made by weighing 2 g of the

soil sample in a 50 ml bottle to which 20 ml of 2 N KC1 solution was added. The bottles were

shaken for 30 min at 180 rpm. Solutions used to analyze TP were extracted using the ashing and

ignition method (Davis, 1974; Ben-Dor and Banin, 1989). The extracted solutions were then

analyzed for TP (USEPA method 365.1). The soil inorganic C was analyzed by the modified

pressure-calcimeter method (Sherrod et al., 2002). Soil organic carbon was determined as the

difference between TC (from CNS analyzer) and soil inorganic carbon. Data were analyzed for

differences among treatment means ofNO3-N, NH3-N, TN, and TC using SAS statistical

software (SAS Institute, Cary, NC, USA). A one-way analysis of variance (ANOVA) was

performed and treatment means separated using and Waller-Duncan K-ratio Test. Where data

was not normally distributed, the Box-Cox (1964) method was used to normalize data

distribution before the statistical analysis. Data were further analyzed by a two way ANOVA

with 2-levels of irrigation (ET and SW) and 3-levels of fertilizer (50% FSR, FSR, and 200%

FSR) to explore the influence of different rates of irrigation and fertilizer on soil nutrient

indicators.

Measuring Nutrient Loads Leached

Bucket lysimeters based on the design presented by Migliaccio et al. (2006) were

fabricated and installed in the avocado orchard on the second 'Simmonds' tree of each treatment

replicate. Each lysimeter was composed of a collection container (20 L) and two flexible tubes.

The bucket lysimeters were installed 0.3 m away from the tree trunk and 0.3 m below the ground

so as not to interfere with the development of the roots. The flexible tubes on the lysimeters were

left protruding on the ground after installation. The tubes provided the ability to collect the water

83









samples from the collection container; one tube served as an air vent while the other tube was

connected to a peristaltic pump to draw the leachate. Leachate samples were collected monthly,

from November 2007 to October 2009. The amount of water collected from each lysimeters was

measured and recorded. A sample from each lysimeter was collected in a 270 mL plastic bottle

and all samples were taken to the laboratory for analysis.

A portion of each sample was filtered through Whatman No. 42 filter paper into a 20 mL

bottle vial and stored at -40C until the determination ofNO3-N, NH4-N, and PO4-P. The

procedure for determining TP in water samples involves first the conversion of particulate

organic and condensed phosphates into orthophosphates under a high acidic, oxidizing

conditions, and high temperature environment (Li et al., 2005b). Thus, a 30 mL portion of each

water sample was put into line-in-disposable vials unfiltered. A pipette was used to measure

0.6 ml of 5 N H2SO4. The acid was then added to the 30 ml of unfiltered water sample, followed

by the addition of about 0.24 g of (NH4)2S208 (ammonium persulfate). The water samples were

covered and shaken to allow (NH4)2S208 to dissolve in the water. The samples were digested in

an autoclave (Consolidated Stills & Sterilizers, Boston, MA) for 30 minutes at a pressure of

about 103.4 kPa. After digestion, the samples were stored at room temperature until they were

analyzed for TP.

Nitrate was determined spectrophotometrically by first reducing NO3- to NO2 using a

cadmium coil and the resulting NO2- concentration was then determined by USEPA method

353.2 (USEPA, 1993a). Ammonium was determined spectrophotometrically by USEPA method

350.1 based on the Berthelot reaction (USEPA, 1993b). In the reaction NH4-N was converted to

chloramine that reacted with phenol under basic conditions to create an intensely blue

indophenol dye, whose color was directly proportional to the NH4-N concentration. Both PO4-P









and TP were determined using ascorbic-acid method (USEPA method 365.1, USEPA, 1993c).

All sample analyses were completed using SEAL AQ2 discrete analyzer (SEAL Analytical, Inc.

Mequon, WI). The amount of nutrient load leached for N03-N, NH4-N, P04-P, and TP was

computed using equation 3.7.

N, = V, x Ce (3-7)

where Ce is the concentration (mg L1) of any of the four nutrient leached elements, VT the total

volume of water leached in a month (L), and NL the load of nutrient element leached (mg). The

total volume of water leached VT was determined using equation 3.8.

V = V, xAs / Ac (3-8)

where VY is the volume of water pumped from the bucket lysimeter (L), As is the area irrigated

by the micro sprinkler (m2), and Ac the area of the bucket lysimeter's catch pan (m2).

Data were analyzed for differences among treatment means ofNO3-N, NH4-N, P04-P, and

TP using SAS statistical software (SAS Institute, Cary, NC, USA). A one-way analysis of

variance (ANOVA) was performed and treatment means separated using and Waller-Duncan

K-ratio Test. Where data were not normally distributed, the Box-Cox (1964) method was used to

normalize data distribution before the statistical analysis.

Effect of Fertilizer Amount and Irrigation Water Volume on Avocado Yield

Avocado yields (fruit number and weight) for 2008 and 2009 were harvested in accordance

to the shipping schedule of the Florida Avocado Administrative Committee (Hatton and Reeder,

1965). The 'Simmonds' fruit harvesting dates were June 23, July 7 and 21, and August 4 with the

corresponding diameter of fruits to be harvested of 90 mm (3.56 in), 87 mm (3.44 in), 78 mm

(3.06 in), and no size (pick all remaining fruits). The 'Beta' fruit harvesting dates were August

11 and 18 and September 1 and 8 with the corresponding diameter of fruits to be harvested of

85









89 mm (3.50 in), 84 mm (3.31in), 81 mm (3.19 in), and no size (pick all remaining fruits). Fruits

harvested from each tree were counted and weighed. Fruit number and weight data were

analyzed for significant differences (p < 0.05) among treatment means using a one-way analysis

of variance (ANOVA). Where significance differences among treatment were detected, the

Waller-Duncan K-ratio Test was used to separate treatment means (p < 0.05). Both ANOVA and

mean separation was done with SAS statistical software (SAS Institute, Cary, NC, USA). Where

data were not normally distributed, the Box-Cox (1964) method was used to normalize data

distribution before the statistical analysis.

Results and Discussion

Water Application

Significant differences (p < 0.05) in volume of water applied were observed among the

irrigation methods (Fig. 3-3; Table 3-3). The water volume applied for the ET-based treatment

and SW-based treatment increased annually to match water volume applied with tree growth and

subsequent root development and expansion (Table 3-3). The historic ET values used to compute

irrigation volumes were compared to the real time ET to explore the accuracy of using historical

data (Fig. 3-5). It was observed that the historical ET values computed using weather data from

1998 to 2005 reasonably estimated the real time ET (R2 = 0.913). The data suggest that climate

variables such as temperature, radiation, relative humidity, and wind speed that were used to

compute historical ET by the FAO Penman-Monteith method were fairly stable during the study

period. For each irrigation practice, there was no significant difference between the volumes of

water applied in the wet and dry seasons during the four years of the study (Fig. 3-4). This was

attributed to a dry and cold season (November to April) and a wet and hot season (May to

October) in south Florida (Mulholland et al., 1997). However, the volume of water applied by

the three management practices in each of the two seasons differed significantly (p < 0.05).

86









Water savings of 93 and 87% were achieved by using ET and SW-based irrigation

practices respectively, in comparison to the set schedule irrigation management. The slight

difference in water applied based on ET and SW could be attributed to the k, values used. The k,

values used were estimated based on expert knowledge of avocado k, values developed from

similar climatic conditions elsewhere and not on measured data. The high water savings in my

study are attributed to water being applied based on historic weather patterns or estimated soil

water content.

Similar high water saving with SW-based irrigation has been reported by others. Zotarelli

et al. (2008) indicated water saving ranged from 33 to 80% in zucchini squash (Cucurbitapepo

L.) and Migliaccio et al. (2008) reported water saving of 96% while irrigating at 15 kPa soil

suction in royal palm (Roystonea elata) in comparison to set scheduled irrigation. Water saving

from ET-based irrigation observed by Silva et al. (2009) and Spreer et al. (2009) for mango

ranged from 13 to 46% which were lower than the water saving achieved in the current study.

Nitrogen and Phosphorus Loads Leached

There were no significant differences (p < 0.05) among treatments for N03-N load leached

(Table 3-5). Although no significant differences were detected, treatments where the fertilizer

rate was doubled (i.e. ET with 200% FSR [treatments 3] and SW with 200% FSR [treatments 7])

leached greater N03-N loads (Fig. 3-6, Table 3-5). ET with 50% FSR (treatment 2) resulted in

the least N03-N load leached. Analyzing leaching by seasons showed over 80% reduction in

NO3-N load in the dry season using ET or SW-based irrigation methods in comparison to set

schedule (Table 3-4). In the wet season, 7 and 38% more N03-N load was leached from

treatments 3 and 7, respectively, compared to treatment 5. This was likely due to N03-N buildup

in these treatments that did not occur in treatment 5 due to its high irrigation rate. However, the

reduction in N03-N load in the wet season for the remaining treatments ranged between 29 to

87









68% in comparison to treatment 5 (Table 3-4). Thus, the amount of NO3-N leached in my study

was more closely correlated with the amount of water applied than the fertilizer rate. Others

(Sexton et al., 1996; Mufioz-Carpena et al., 2008; Gheysari et al., 2009) observed that NO3-N

leaching was more influenced by the amount ofN applied than water volume applied. Results

from my study suggest that efficient irrigation methods have a potential to reduce NO3-N

leaching. This is because under saturated flow NO3s ions move at similar speed as water

molecules (Havlin et al., 2004). Thus by implementing ET or SW-based irrigation practices,

tropical fruit producers can save on irrigation energy costs and also save on fertilizer costs.

Set schedule with FSR (treatment 5) leached a significantly (p < 0.05) greater TP load than

the other treatments (Fig 3.7, Table 3.5). The higher TP leached for the ET and SW-based

treatments (Fig 3.7) coincided with the months when more than 100 mm of rainfall was received.

ET or SW-based irrigation methods reduced TP leaching in the dry season by over 80% while

the TP reduction in the wet season ranged between 58 to 70% compared to the set schedule

method (treatment 5) (Table 3-4). The lesser TP reduction in the wet season was attributed to

more P being dissolved due to extra water from the rainfall resulting in leaching. Nelson et al.

(2005) reported that excessive P leaching was attributed to over application of P, low P sorption

capacity of the soil, and rainfall exceeding evaporation. The P leaching in the current study was

attributed to the high P content of Krome soil (3500 mg kg-1), such that with excess irrigation P

would be dissolved and becomes available for leaching as observed by others (Coale et al., 1994;

He et al., 2000; Nelson et al., 2005). Thus efficient water application reduced fertilizer lost

through nutrient leaching.

Soil Analysis

Significant differences (p < 0.05) among treatments for soil inorganic N were observed

once out of the 10 sampling dates for both avocado cultivars (Table 3-6 to 3-11). Generally set

88









schedule with FSR (treatment 5), ET with 50% FSR (treatments 2), and SW with 50% FSR

(treatment 6) had the least soil inorganic N. This implies that applying less fertilizer with

optimum irrigation resulted in the same soil inorganic N status as doubling the fertilizer amount

with excessive water application. Thus, the excessive water volume in treatment 5 resulted in

flushing of the nutrients from the root zone. Consistent values of soil inorganic N were observed

on all sampling dates apart from 19 May 2009 sampling. The greater magnitude in value of soil

inorganic N on 19 May 2009 was attributed to a higher fertilizer rate application on 21 April

2009 of 1950 kg ha-1 (for the FSR) and little rainfall (46 mm) in the subsequent days prior to soil

sampling. Water from irrigation and rainfall dissolved the fertilizer but was not adequate to leach

inorganic N out of the root zone to be captured in the bucket lysimeters. This is evidenced by a

small load (less than 3%) of NH4-N leached in comparison to N03-N load leached during May

2009 for each of the treatments. Analyzing treatment effects as 2-levels of irrigation and 3-levels

of fertilizer factorial design (with treatment 5 omitted) showed that fertilizer level significantly

(p < 0.05) affected soil inorganic N of 'Beta' in 2008 and 2009 (Table 3-13) with no significant

interaction effect between irrigation and fertilizer levels. There were no significant effects

detected for 'Simmonds'.

Soil organic C, C:N ratio, and C:P ratio were measured annually with no significant

differences (p < 0.05) among main treatments over the four year period (Table 3-6 to 3-9). Set

schedule with FSR (treatment 5) generally had a lower soil organic C and a higher C:N ratio in

comparison to other treatments which was attributed to leaching of organic carbon as reported by

Roose and Barthes (2001). There was a significant decrease (p < 0.05) in C:N and C:P ratios

between the values observed in the first year and those values observed in the fourth the year

(Tables 3-6 to 3-9). This implies an increase in the soil N and P content over the study period









possibly attributed to inorganic fertilizer input. The C:N reported in my study was about twice

that observed by others (Wang et al., 2005; Munoz-Carpena et al., 2008; Wang et al., 2010) in

vegetable production fields on Krome very gravel loam soil of south Florida. Likewise the soil

organic C was 2 to 3 times less than that observed by Chin et al. (2007) and Wang et al. (2010)

in vegetable production fields. The difference in the C:N ratio and soil organic C was attribute to

high use of fertilizer in vegetable production compared to the level of fertilizer input in tropical

fruit production.

Analyzing treatment effects as 2-levels of irrigation and 3-levels of fertilizer in a factorial

design (omitting treatment 5) resulted in detection of a few cases of significant effects attributed

to either irrigation or fertilizer level, but there were no general trend over the four years (Table

3-13). Irrigation level only caused a significant (p < 0.05) affect in the C:P ratio for 'Simmonds'

in 2007. Fertilizer level significantly affected the C:P ratio of 'Beta' in 2008 and 'Simmonds'

C:N ratio in 2009 (Table 3-13). In all cases where significant effects were detected, there were

no interactions between irrigation and fertilizer levels.

Avocado Yield Analyzed as Treatment Main Effects

The first 'Simmonds' fruit harvest of June 23 (per Florida Avocado Administrative

Committee guidelines) was skipped as few fruits had attained the harvesting diameter of 90 mm.

The greatest number of fruit was recorded on July 21 of each year while the least fruit number

was recorded on August 4 (Table 3-14). Significant differences (p < 0.05) in total 'Simmonds'

yields (fruit number and weight) were observed among treatments, with ET with FSR (treatment

1) SW with FSR (treatment 4), Set sch. with FSR (treatment 5), and SW with 200% FSR

(treatment 7) recording the greatest fruit number in 2008 (Table 3-15). Although not significant,

there was a trend for these treatments to produce more fruit in 2009. SW with FSR (treatment 4)









and SW with 200% FSR (treatment 7) also were measured with greater yields in 2009 compared

to other treatments although the differences were not significant.

This implies that 'Simmonds' trees were responsive to both water volume applied and

fertilizer rate. Interestingly in both years, the trend was for greater to lesser fruit production for

SW+FSR > SW+200%FSR > ET+FSR > Set sch.+FSR suggesting that over irrigation and

nutrient leaching in the Set. sch.+FSR reduced crop yields and that perhaps limiting water (i.e.,

ET based irrigation treatments) and/or fertilizer rates in the other treatments reduced yields.

Given that SW-based irrigation treatments produced more fruit than ET-based treatments suggest

k, values were underestimated.

The greatest fruit number for 'Beta' was recorded on August 11 in 2008 and on August 18

in 2009 while the least fruit numbers were recorded on September 1 in 2008 and September 8 in

2009 (Table 3-16). This is in contrast to 'Simmonds' where largest yields occurred during the

second harvest. This may be due to the effect of low average ambient late winter and spring

temperatures on 'Simmonds' fruit development compared to hot average ambient temperatures

during 'Beta' fruit development. In 2008 the 'Beta' fruit harvest of September 1 and 8 was

combined since the fruits had to be removed from the field in preparation for hurricane Gustav.

No significant differences were observed among treatments for all harvesting dates both in 2008

and 2009 (Table 3-16). Likewise no significant differences were observed among treatments for

the total yield (fruit number and weight) for the two years (Table 3-17). Since fewer fruits were

harvested on 8 September 2009, fruits of this harvest date may be combined with those of

September 1 to save on harvesting and shipping costs (Evans, 2006). SW with 200% FSR

(treatment 7) had the greatest yield in 2008 while set schedule with FSR (treatment 5) had a

greater yield in 2009 in comparison to other treatments. Since 'Beta' trees are known to









genetically predisposed to greater production than 'Simmonds' trees, this implies that the range

in irrigation and fertilizer treatments was large enough to encompass and have an effect on fruit

yield of'Simmonds' but not 'Beta' (J.H. Crane, Homestead, 2010, personal communication).

Further research is needed to investigate if a lower or higher main treatment would have caused

an effect in 'Beta' fruit yield.

Evaluation of crop production water use efficiency (CP-WUE) as a ratio of fruit weight per

volume of water applied for 'Simmonds' fruits and 'Beta' fruits resulted in significant

differences (p < 0.05) among treatments in 2008 (Table 3-18). Set schedule with FSR (treatment

5) had the least CP-WUE in comparison to other treatments in both 2008 and 2009. In 2008 there

were significant differences in CP-WUE (p < 0.05) between ET and SW-based irrigation

treatments for both 'Simmonds' and 'Beta', yet in 2009 such differences were not observed.

Although SW with FSR (treatment 4) had the greatest yield in 2008, it had a lower CP-WUE in

comparison to the ET-based treatments. This was attributed to a leak in the treatment 4 water line

that resulted in excess water flowing through the water meters. In 2009 where no system leaks

occurred, there was no significant difference (p < 0.05) between SW and ET-based irrigation

treatments of CP-WUE 'Simmonds'.

Analysis of crop production fertilizer use efficiency (CP-FUE) as a ratio of fruit weight per

amount of fertilizer applied resulted in significant differences (p < 0.05) among treatments

(Table 3-19). For 'Simmonds', treatments where fertilizer rate was halved corresponded to a

greater CP-FUE while treatments where the fertilizer rate was doubled resulted in the least

CP-FUE. For 'Beta' no significant differences (p < 0.05) were observed among treatments

although treatments with a double fertilizer rate had the lowest CP-FUE (Table 3-19). The low

CP-FUE for 'Simmonds' and 'Beta' in 2009 was attributed to a rise in FSR from 340 kg ha-1 in









2008 to 1950 kg ha-1 in 2009. Thus the FSR increased by about 600% fertilizer, yet the fruit yield

did not change for 'Simmonds' and slightly increased for 'Beta'. The CP-FUE ratio assists with

identifying the treatments with the greatest yield returns per unit of fertilizer input. Such

information may be beneficial in deciding the fertilizer rate to apply when performing an

economic analysis for avocado production.

The mean fruit weight of 'Simmonds' and 'Beta' were not significantly different (p < 0.05)

in 2008 and 2009 (Fig. 3-8). However, 'Simmonds' fruits in 2009 were larger than those in 2008

for all treatments although the yield was similar in the two years. This may be attributed to the

trees being bigger in 2009 and capable of manufacturing and allocating more carbohydrates for

fruit development than in 2008. Generally the 'Beta' fruits of 2008 were larger than those of

2009 with the SW with 50% FSR (treatment 6) producing the smallest fruits in 2009. For the

'Beta' trees, the fruit number was at least 50% more in 2009 than in 2008. The carbohydrates

produced in 2009 had to be divided among more fruits produced, which resulted in smaller fruits

(Genard et al., 2008). Fruit mean weight for 'Simmonds' and 'Beta' corresponded to values

observed by Hatton and Reeder (1965) among avocado cultivars.

Avocado Yield Analyzed as 2-levels of Irrigation by 3-levels of Fertilizer Factorial Design

The results of mean fruit weight per volume of water applied showed that set schedule with

FSR (treatment 5) had the lowest CP-WUE compared to other treatments (Table 3-18). Therefore

treatment 5 was removed from the subsequent analysis to create a 2 x 3 factorial design to

explore the effect of irrigation and nutrient management on fruit yield. In this analysis the

2-levels of irrigation were ET and SW while the 3-levels of fertilizer rate were 50% FRS (half),

100% FSR (standard), and 200% FSR (double). The factorial analysis showed that both

irrigation and fertilizer rate significantly (p < 0.05) affected 'Simmonds' yield in 2008 (Fig. 3-9).

However, in 2009 it was only the irrigation level that significantly (p < 0.05) affected

93









'Simmonds' fruit yield. SW-based irrigation recorded greater fruit yield than ET-based irrigation

except at half fertilizer rate in 2008 (Fig. 3-9). The failure to detect a significant difference

(p 0.05) due to fertilizer rate in 2009 was attributed to a sharp rise in the FSR of about 600%

suggesting that an effect may have been detected again had a lower FSR been used in 2009. The

data implies that doubling the fertilizer rate was not beneficial in influencing 'Simmonds' fruit

yields in both production years. For 'Beta'neither irrigation nor fertilizer rate had a significant

effect on fruit yield in both 2008 and 2009, with no interactions between irrigation volume and

fertilizer rate (Fig. 3-10). This observation was in agreement with the early finding from the

main irrigation and nutrient treatment effects on 'Beta' fruit yield. Further research is required to

explore if'Beta' fruit yield would be influenced by either a lesser or greater irrigation and

nutrient treatment.

Plant Nutrient Assessment and Tree Growth

Generally there were no significant differences (p < 0.05) among treatments for

'Simmonds' trees or 'Beta' trees for leaf concentrations of TN, TC, and TP observed from 2006

to 2009 (Table 3-20 to 3-29). The treatment means for these three elements ranged as follows;

TN (1.48 to 2.33%), TC (44.8 to 47.7%), and TP (1090 to 2847 mg kg1). An increase in the FSR

amount over the years did not increase TN content in the leaves as observed by Embleton et al.

(1958). The range of TN (1.48 to 2.33%) in my study was similar to that observed by Lahav and

Kadman (1980) for other avocado cultivars. Analysis of treatment effects as a factorial design of

2 levels of irrigation with 3-levels of fertilizers did not reveal information that was contrary the

main treatment effects on TN, TC and TP (Table 3-30).

Evaluation of plant tree trunk diameter showed no significant differences (p <0.05) among

treatments for the four years for either 'Simmonds' trees or 'Beta' trees (Fig 3-11). The pooled

mean (with standard deviations) for 'Simmonds' trees showed that the tree diameter increased

94









from 1.67 0.52 cm in 2006 to 24.45 + 3.52 cm in 2009. For 'Beta' trees, the pooled mean (with

standard deviations) tree trunk diameter increased from 1.60 + 0.47 cm in 2006 to 24.14 4.40

cm in 2009. For 'Simmonds' trees, SW with FSR (treatment 4) had larger tree diameters in 2008

and 2009 than the other treatments. For 'Beta' trees, treatments where fertilizer rate was halved

had the smallest tree truck diameter in 2008 and 2009 in comparison to other treatments.

Factorial analysis of tree diameter as influenced by 2-levels of irrigation and 3-levels of fertilizer

also failed to detected differences among treatments. This suggests that the smaller tree diameter

observed for 'Beta' during 2008 and 2009 may be attributed to other factors other than irrigation

or fertilizer input levels.

Leaf color expressed in SPAD units was not significantly different (p <0.05) among

treatments for 'Simmonds' trees and 'Beta' trees. Mean values for each treatment for the various

sampling dates are shown in Fig 3-12. This implies that the trees had the same level of leaf color

which corresponds to the same level of chlorophyll among the different treatments (Porro, 2001).

Chlorophyll level is directly related to N status of leaves. The SPAD units observed were within

the range reported for avocado by Neaman and Espinoza (2010). The SPAD values were in

agreement with tissue TN values which showed that there were no differences among treatments.

Recommendations from my Study

Based on the greatest avocado fruit yield, CP-WUE, and CP-FUE values SW with FSR

(treatment 4) ranked higher than the other treatments and thus is proposed as the BMP for the

production of 'Simmonds' avocado. Although ET-based irrigation would be a good alternative to

SW-based irrigation, this methods requires developing or modifying available avocado k, values

to meet the climatic conditions of south Florida. The amount of P in fertilizer applied was

reduced by half during fruit production years, yet the C:P ratio decreased over the years. This

implies that the P formulation in the fertilizer could be lowered without affecting the fruit yield.

95









Research is needed to explore how no P fertilizer application may affect avocado growth and

production on calcareous soil with high P content. Likewise research is needed to explore how

the different irrigation and nutrient management practices would affected nutrient leaching in

'Beta'. The yield difference between 'Simmonds' and 'Beta' suggest differences in nutrient

uptake between the two cultivars. Performing a nutrient uptake analysis would provide insight

into fertilizer use efficiency between the two cultivars and an opportunity to perform a fertilizer

mass balance for avocado production.

Conclusions

Irrigating young avocado trees based on ET or SW saved 93 and 87% of the water volume

applied in comparison to set schedule over the four year period. No significant differences

(p < 0.05) were observed in the water volume applied during the wet or dry season from each of

the irrigation methods. Irrigating based on SW with FSR (treatment 4) resulted in average annual

reductions of 70 and 75% in N03-N and TP leaching compared to the set schedule irrigation

method over the two years of leachate sampling. While irrigating based on ET with FSR

(treatment 1) resulted in average annual reductions of 51 and 84% in N03-N and TP leaching

compared to the set schedule irrigation method over the two years of leachate sampling. Such

high reductions were attributed to nutrient leaching being more influenced by the irrigation

management than fertilizer rate. Based on avocado fruit yield, CP-WUE, and CP-FUE values

SW with FSR (treatment 4) and ET with FSR (treatment 1) had higher fruit yield for 'Simmonds'

than the other treatments and should be explored further as trees mature to determine if similar

results occur. Yield results for 'Simmonds' suggest that this avocado cultivar is responsive to

well maintained soil water regime in the root zone. Since 'Beta' genetically predisposed to

greater production than 'Simmonds' the FSR may not be the best nutrient management practices

for the production of'Beta' avocado. Generally no significant differences (p <0.05) were

96









observed among treatments for SPAD value; leaf TN, TC, and TP; trunk diameter; and soil

organic carbon, C:N and C:P ratios, and soil inorganic N.

Considering that water is becoming scarce due to climatic variability and increasing

demand by other uses and the need for more sustainable agriculture, irrigation BMPs should be

beneficial for avocado producers. By applying lower water volumes, lower fertilizer rates are

needed to support crop growth since nutrient leaching is reduced. Although the current practice

is to apply the same amount of fertilizer to all avocado cultivars, some avocado cultivars respond

differently to fertilizer rates. This would save producers the extra cost incurred in applying

surplus fertilizer for cultivars that may be predisposed to greater production at lower fertilizer

rates. The amount of P in fertilizer applied was reduced by half during fruit production years, yet

the C:P ratio declined over the years. This implies that the P formulation in the fertilizer could be

lowered without affecting the fruit yield. Research is needed to explore how the different

irrigation and nutrient management practices affected nutrient leaching and uptake in 'Beta'. The

yield difference between 'Simmonds' and 'Beta' suggest differences in nutrient uptake between

the two cultivars.









Table 3-1. Fertilizer at a standard rate management scheme used for the 'Simmonds' and 'Beta'
avocado trees
Development Year No. of Amount Nutrient Element content (%)
stage fertilizer applied N P K Mg
applications each time
(year-1) (kg tree-1)
Orchard
alish 2006-2007 6 0.045 6 6 6 2
establishment
Fruit bearing trees 2008 4 0.227 8 3 9 3
(production) 2009 4 1.361



Table 3-2. The ETo and k, values used to compute water application rates for the ET-based
irrigation management method
Month ETo crop coefficient
(mm month-1) (kc)z
Jan 1.85 0.50
Feb 2.46 0.50
Mar 3.53 0.80
Apr 3.99 0.80
May 4.55 0.68
Jun 4.52 0.68
Jul 3.89 0.68
Aug 3.51 0.68
Sep 3.51 0.68
Oct 3.07 0.68
Nov 2.49 0.50
Dec 1.93 0.50
z, Crane, Homestead, 2006, personal communication.


Table 3-3. Amount of water applied (x 103 m3 tree-'day-1) by the different management practices
Year Irrigation managementy
ETz Soil water Set schedule
2006 1.55 a 2.24 b 40.61 c
2007 1.70 a 4.49 b 40.36 c
2008 2.12 a 6.13 b 40.56 c
2009 4.33 a 5.73 b 39.85 c
y, Irrigation managements with same letter within rows are not significantly different (p < 0.05)

z, To get amount of water applied per hectare (x 103 m3/tree/day) multiple value by 358 (trees).









Table 3 -4. Nutrient leaching reduction percentage of 'Simmonds' avocado trees as compared to
set schedule with FSR treatment
Dry season Wet season
Treatment TP NO3-N TP NO3-Ny
1, ET+FSR 86 94 70 29
2,ET + 50% FSR 84 99.5 59 68
3, ET + 200% FSR 85 84 69 -38
4, SW + FSR 86 86 62 61
5, Set sch. + FSR -
6, SW + 50% FSR 91 99.8 58 30
7, SW+200%FSR 87 83 64 -7
y, Negative values imply that greater nutrients were leached from that treatment than set
schedule with FSR.


Table 3-5. Total nutrient load leached kg ha-lfrom Nov 2007 to Oct 2009 for 'Simmonds'
avocado trees
Treatment NO3-N TPz
1, ET+FSR 53 0.470
2,ET + 50% FSR 24 0.624
3, ET + 200% FSR 119 0.533
4, SW + FSR 33 0.732
5, Set sch. + FSR 108 2.899
6, SW + 50% FSR 58 0.702
7, SW + 200% FSR 121 0.685


Table 3-6. 'Simmonds' and 'Beta' soil analysis for irrigation and fertilizer management
treatments 09/05/2006, n=3
Simmonds Beta
Treatment C:N Organi C:P Inorg Nz C:N Organic C:P Inorg N
ratio c C % ratio (mg kg-1) ratio C % ratio (mg kg-1)
1, ET+FSR 39 5.11 15 24 ab 43 4.66 11 22
2, ET + 50% FSR 52 4.55 13 24 ab 53 4.53 14 30
3, ET +200% FSR 39 4.69 14 39 a 38 5.27 17 39
4, SW + FSR 40 5.04 14 31 ab 40 4.50 12 47
5, Set sch. + FSR 46 3.70 12 20 b 58 3.37 12 33
6, SW + 50% FSR 43 4.32 11 25 ab 39 4.12 11 29
7, SW + 200% FSR 48 3.76 11 24 ab 38 4.82 15 26
p-value 0.7265 0.3776 0.5439 0.1916 0.2896 0.4786 0.2434 0.4992
z, Inorganic N with same letter within columns are not significantly different (p < 0.05) by
column.










Table 3-7. 'Simmonds' and 'Beta' soil analysis for irrigation and fertilizer management


treatme

Treatment

1, ET+FSR
2, ET + 50% FSR
3, ET + 200% FSR
4, SW + FSR
5, Set sch. + FSR
6, SW + 50% FSR
7, SW + 200% FSR
p-value


:nts 7/6/2007, n=3
Simmonds
C:N Organi
ratio cC %


40
48
37
40
46
44
40
0.6287


4.20
3.98
3.77
3.56
2.99
3.59
3.78
0.5061


Beta
C:P Inorg N C:N
ratio (mg kg-1) ratio


17 49
16 29
13 54
12 39
12 30
12 30
13 41
0.3737 0.1845


Organic
C%
3.40
4.96
4.12
4.08


C:P
ratio
12
19
14
14


48 3.14 14
40 3.59 11
36 4.21 12
0.5632 0.105 0.1


Inorg N
(mg kg-1)
30
47
55
59


59
59
53
113 0.7378


Table 3-8. 'Simmonds' and 'Beta' soil analysis for irrigation and fertilizer management
treatments 9/22/08, n=3
Simmonds Beta
Treatment C:N Organi C:P Inorg N C:N Organi C:P Inorg N
ratio cC % ratio (mg kg-1) ratio cC % ratio (mg kg1)
1, ET+FSR 29 4.90 13 20 32 4.88 13 16.83 b
2, ET + 50% FSR 33 3.95 10 16 29 4.67 14 22.27 ab
3, ET + 200% FSR 25 5.62 10 29 22 5.83 10 36.90 a
4, SW + FSR 26 5.54 11 23 25 5.26 13 24.03 ab
5, Set sch. + FSR 41 4.43 14 17 34 4.77 15 16.53 b
6, SW + 50% FSR 28 5.05 14 23 32 4.61 14 21.20 b
7, SW + 200% FSR 28 5.01 11 26 25 5.45 11 30.00 ab
p-value 0.0822 0.3056 0.1161 0.5768 0.1740 0.4677 0.1055 0.0433
z, Inorganic N with same letter not significantly different (p < 0.05) by column.


Table 3-9. 'Simmonds' and 'Beta' soil analysis for irrigation and fertilizer management
treatments 9/7/09, n=3
Simmonds Beta
Treatment C:N Organi C:P Inorg N C:N Organi C:P Inorg N
ratio cC % ratio (mg kg-1) ratio cC % ratio (mg kg-1)
1, ET+FSR 33 4.03 8 43 42 3.55 9 34
2, ET + 50% FSR 39 3.80 9 36 50 3.12 10 36
3,ET + 200% FSR 36 3.88 9 46 33 3.70 9 57
4, SW + FSR 30 4.80 10 50 43 3.49 10 36
5, Set sch. + FSR 41 3.37 10 35 41 2.95 8 43
6, SW + 50% FSR 40 4.13 10 38 40 3.88 11 41
7, SW + 200% FSR 32 4.29 11 43 32 4.18 8 49
p-value 0.0524 0.3324 0.9193 0.6925 0.5823 0.5568 0.9065 0.2116










Table 3-10. 'Beta' soil inorganic N for different sampling dates, n=3
Treatment Nov 1, 06 Mar 13, 07 Nov 19, 07 May 6, 08 Jan 14, 09 May 19, 09
1, ET+FSR 31 27 22 30 34 271
2, ET + 50% FSR 27 36 41 29 23 200
3, ET +200% FSR 60 42 37 26 32 743
4, SW + FSR 31 42 43 33 30 318
5, Set sch. + FSR 19 24 22 32 24 42
6, SW + 50% FSR 29 37 37 30 33 159
7, SW + 200% FSR 42 78 44 31 42 321
p-value 0.5106 0.0546 0.3773 0.9902 0.3772 0.1414

Table 3-11. 'Simmonds' soil inorganic N for different sampling dates, n=3
Treatment Nov 1, 06 Mar 13, 07 Nov 19, 07 May 6, 08 Jan 14, 09 May 19, 09


1, ET+FSR
2, ET + 50% FSR
3, ET + 200% FSR
4, SW + FSR
5, Set sch. + FSR
6, SW + 50% FSR
7, SW + 200% FSR
p-value


35
34
41
27
21
22
34
0.3481


40 abc
24 c
51 a
53 a
23 c
32 c
49 ab
0.0062


25
13
39
43
42
38
59
0.3121


21
20
41
35
30
39
31
0.1819


28
26
36
35
25
33
36
0.7733


255
241
283
313
68
258
267
0.4181


z, Inorganic N with same letter within columns not significantly different (p < 0.05) by
column.

Table 3-12. Soil's factorial analysis of 2-levels of irrigation by 3-levels of fertilizer from 2006 to
2009
'Simmonds' 'Beta'
Year Source C:N Organic C:P Inorg N C:N Organic C:P Inorg N
ratio C % ratio (mg kg-1) ratio C % ratio (mg kg-1)
2006 Irrig 0.9497 0.3825 0.1547 0.5180 0.2686 0.5296 0.2854 0.6270
Fert 0.4322 0.3134 0.3966 0.4000 0.5156 0.5425 0.0828 0.8399
Irrigx Fert 0.3675 0.7204 0.8905 0.1299 0.4171 0.9685 0.6380 0.1370
2007 Irrig 0.9127 0.3384 0.0453 0.2756 0.4916 0.5765 0.1222 0.2942
Fert 0.3145 0.9638 0.6495 0.1100 0.8015 0.4516 0.3704 0.7629
Irrigx Fert 0.7759 0.7366 0.4280 0.6737 0.7022 0.0874 0.0569 0.5734
2008 Irrig 0.5849 0.3643 0.1688 0.6286 0.8835 0.9580 0.6888 0.9466
Fert 0.6131 0.2320 0.3287 0.3359 0.1723 0.1572 0.0254 0.0276
Irrigx Fert 0.5368 0.2398 0.1027 0.7062 0.3271 0.7371 0.9294 0.3348
2009 Irrig 0.4376 0.1711 0.7741 0.3109 0.5497 0.2864 0.8942 0.9029
Fert 0.0369 0.5597 0.4298 0.8715 0.1845 0.6076 0.6787 0.0433
Irrigx Fert 0.5845 0.8577 0.7894 0.9934 0.6400 0.7043 0.8150 0.6486









Table 3-13. 'Simmonds' avocado yield (kg ha-1) by harvest date per treatment for 2008 and
2009, n=12


Treatments 2008
July 7' July 21y Aug 4y
1, ET+FSR 479 be 1333 280
2, ET + 50% FSR 199 d 1137 107
3, ET +200% FSR 766 b 1074 143
4, SW + FSR 506 b 1664 257
5, Set sch. + FSR 783 ab 1054 890
6, SW + 50% FSR 284 cd 1139 273
7, SW + 200% FSR 1232 a 1065 124
p-value < 0.0001 0.5333 0.5371
y, Data was first normalized using the Box-Cox method


2009
July 7y July 21Y Aug 4y
391 c 1302 253
469 be 1047 194
839 ab 785 279
608 abc 1292 389
914 ab 941 387
492 be 993 485
1184 a 1061 357
0.012 0.299 0.1025
before performing ANOVA


z, Inorganic N with same letter within columns are not significantly different (p < 0.05) by
column.

Table 3-14. 'Simmonds' avocado fruit number and weight per treatment for 2008 and 2009, n=12
Treatment 2008 2009
Number of Weight Number of Weight
fruits (ha-l)y (kg ha-l)y fruits (ha-l)y (kg ha-1)
1, ET+FSR 4349 ab 1854 ab 4261 1761
2, ET +50% FSR 3487 b 1464 b 3532 1496
3, ET +200% FSR 3794 b 1759 b 3882 2058
4, SW+ FSR 5894 a 2607 a 4963 2485
5, Set sch. + FSR 3978 ab 1845 ab 3891 2346
6, SW + 50% FSR 3304 b 1389 b 3106 2019
7, SW + 200% FSR 5693 a 2560 a 4753 2450
p-value 0.0108 0.0020 0.2352 0.1493
y, Data was first normalized using the Box-Cox method before performing ANOVA
z, fruit yield with same letter within columns are not significantly different (p < 0.05) by
column.









Table 3-15. 'Beta' avocado yield (kg ha-1) by harvest date per treatment for 2008 and 2009, n=4
Treatment 2008 2009
Aug 11 Aug 18 Sept 1y Aug 11 Aug 18 Sept 1z Sept 8y


1, ET+FSR
2, ET + 50% FSR
3, ET + 200% FSR
4, SW + FSR
5, Set sch. + FSR
6, SW + 50% FSR
7, SW + 200% FSR
p-value


2418
1326
2266
2813
3207
2212
4013
0.3923


1250
537
2759
1594
836
812
1577
0.07


822
216
1067
412
322
544
609
0.5950


3072
1084
1111
2150
4559
3511
1851
0.0712


2974
3153
5136
2633
3431
1684
1980
0.1182


y, Data was first normalized using the Box-Cox method before performing


1159c
1565 be
3607 ab
1971 abc
1541 be
1230 c
3870 a
0.0363
ANOVA


162
497
537
179
343
322
0.7618


z, Fruit yield with same letter within columns are not significantly different (p < 0.05) by

column.

Table 3-16. 'Beta' avocado fruit number and weight per treatment for two years, n=4
Treatment 2008 2009
Number of Weight Number of Weight
fruits (ha-1) (kg ha-') fruits (ha-1) (kg ha-1)


1, ET+FSR 8599
2, ET + 50% FSR 4031
3, ET + 200% FSR 8868
4, SW + FSR 7076
5, Set sch. + FSR 7703
6, SW + 50% FSR 5374
7, SW + 200% FSR 8868
p-value 0.523


4416
1894
4197
3559
3985
3159
4988
0.552


13883
9764
15407
10122
17915
10838
10122
0.482


7280
4926
7892
4766
9430
5213
5462
0.367


Table 3-17. Means fruit weight per cubic meter of water applied water applied (kg m3)
Treatment 'Simmonds' 'Beta'
2008y 2009 2008y 2009
1, ET+FSR 12.22 a 6.40 a 28.48 a 26.89
2, ET + 50% FSR 9.16b 5.46 a 10.66 bcd 18.20
3, ET +200% FSR 11.60 ab 7.49 a 20.87 ab 29.15
4, SW + FSR 4.94 c 7.00 a 5.97 cd 13.55
5, Set sch. + FSR 0.71 e 0.89 b 1.15 e 3.63
6, SW + 50% FSR 3.20 b 5.97 a 6.71 cd 15.65
7, SW + 200% FSR 9.69 ab 7.55 a 18.33 abc 16.96
p-value <0.0001 <0.0001 0.0004 0.0683
y, Data was first normalized using the Box-Cox method before performing ANOVA
z, Mean fruit weight with same letter within columns are not significantly different (p < 0.05) by
column.









Table 3-18. Means fruit weight per kg of fertilizer applied (kg kg-1)
Treatmentz 'Simmonds' 'Beta'
2008y 2009y 2008 2009
1, ET+FSR 5.337 ab 0.836 cd 12.949 ab 3.658
2, ET + 50% FSR 8.041 a 1.449 ab 11.112 ab 4.340
3, ET +200% FSR 2.521 d 0.498 e 6.154 b 1.635
4, SW + FSR 7.450 ab 1.225 be 10.437 ab 2.292
5, Set sch. + FSR 5.236 c 1.170 bc 11.685 ab 4.795
6, SW + 50% FSR 8.272 a 1.933 a 18.526 a 4.831
7, SW+200% FSR 3.799 cd 0.605 de 7.313 ab 1.315
p-value <0.0001 <0.0001 0.1624 0.0987
y, Data was first normalized using the Box-Cox method before performing ANOVA
z, Mean fruit weight with same letter within columns are not significantly different (p < 0.05) by
column.


Table 3-19. Leaf tissue analysis for irrigation and fertilizer management treatments 08/03/2006,
n=4
Simmonds Beta
Treatment TN % TC % TP TN % TC % TP mg kg-1
mg kg-1
1, ET+FSR 2.03 46.68 1912.00 1.96 46.63 1702.00
2, ET + 50% FSR 2.01 47.24 1709.00 2.03 48.30 1974.00
3, ET +200% FSR 1.93 47.37 1554.00 2.14 47.00 2053.00
4, SW + FSR 1.90 47.69 1542.00 2.27 47.71 1917.00
5, Set sch. + FSR 1.84 47.63 1512.00 2.05 46.99 1541.00
6, SW + 50% FSR 1.88 47.57 1601.00 2.06 46.77 1632.00
7, SW + 200% FSR 1.81 47.09 1604.00 2.23 46.84 1803.00
p-value 0.85 0.51 0.57 0.30 0.25 0.63


Table 3-20. Leaf tissue analysis for irrigation and fertilizer management treatments 12/06/2006,
n=4
Simmonds Beta
Treatment TN % TC % TP TN % TC % TP
(mg kg-1) (mg kg-1)
1, ET+FSR 1.97 47.11 1888.00 2.17 47.28 2230.00
2, ET + 50% FSR 2.00 47.17 1828.00 1.88 46.98 2268.00
3, ET + 200% FSR 2.11 47.34 2036.00 2.05 47.37 1787.00
4, SW + FSR 2.13 46.94 1759.00 2.24 47.08 1853.00
5, Set sch. + FSR 1.72 47.71 1968.00 1.99 47.49 2285.00
6, SW + 50% FSR 2.20 47.54 2099.00 2.02 47.10 1817.00
7, SW + 200% FSR 2.17 47.09 1862.00 2.48 47.30 2065.00
p-value 0.22 0.45 0.91 0.07 0.59 0.31









Table 3-21. Leaf tissue analysis for irrigation and fertilizer management treatments 04/30/2007,
n=3


Treatment

1, ET+FSR
2, ET + 50% FSR
3, ET + 200% FSR
4, SW + FSR
5, Set sch. + FSR
6, SW + 50% FSR
7, SW + 200% FSR
p-value


Simmonds
TN % TC %


1.89
1.71
2.11
1.89
1.77
1.86
1.97
0.23


46.41
46.35
46.28
46.17
46.58
46.20
46.24
0.35


Betaz
TN %


(mg kg-1)
1857.00
1922.00
1871.00
2087.00
2089.00
1930.00
1907.00
0.82


2.04 abc
1.99 abc
2.34 a
1.79 c
1.83 c
1.93 be
2.28 ab
0.58


TC % TP


46.41
46.02
46.43
46.27
46.23
46.51
46.37
0.57


(mg kg-1)
1928.00
1915.00
1847.00
1917.00
1755.00
1854.00
1571.00
0.19


z, TN% with same letter within column are not significantly different (p < 0.05) by column.

Table 3-22. Leaf tissue analysis for irrigation and fertilizer management treatments 08/23/2007,
n=3


Simmonds


Beta


Treatment TN % TC % TP TN % TC % TP
(mg kg-1) (mg kg-1)
1, ET+FSR 1.80 46.02 1799 1.75 46.21 1593
2, ET +50% FSR 1.88 45.81 1939 1.82 46.41 1768
3, ET +200% FSR 1.89 45.92 1730 1.85 46.10 1601
4, SW+ FSR 1.95 45.83 1847 1.93 45.96 1752
5, Set sch. + FSR 1.94 45.78 1934 1.84 45.40 1920
6, SW+ 50% FSR 1.64 45.78 2231 1.85 45.96 1839
7, SW + 200% FSR 2.10 45.93 1885 1.81 46.26 1615
p-value 0.0507 0.9682 0.3699 0.8584 0.1259 0.5442


Table 3-23. Leaf tissue analysis for irrigation and fertilizer management treatments 12/26/2007,
n=3


Treatment


Simmonds
TN %


TC % TP


Beta
TN %


TC %


(mg kg-1) (mg kg-1)
1, ET+FSR 1.56 46.75 1988 1.65 46.45 2768
2, ET + 50% FSR 1.48 46.69 1097 1.55 46.09 1597
3, ET +200% FSR 1.80 46.59 1164 1.68 46.34 1518
4, SW + FSR 1.79 47.04 1090 1.63 46.15 1559
5, Set sch. + FSR 1.52 45.78 1497 1.52 45.83 2356
6, SW + 50% FSR 1.51 46.47 2253 1.69 46.60 1926
7, SW + 200% FSR 1.71 46.08 1213 1.69 45.90 1957
p-value 0.3438 0.2211 0.0851 0.9320 0.5971 0.3201









Table 3-24. Leaf tissue analysis for irrigation and fertilizer management treatments 04/08/2008,
n=3
Simmonds Beta
Treatment TN % TC % TP TN % TC % TP
(mg kg-1) (mg kg-1)
1, ET+FSR 1.72 45.20 2422 1.91 45.08 2555
2, ET +50% FSR 1.99 45.81 2589 1.86 45.28 2588
3, ET +200% FSR 2.17 45.61 2088 2.13 45.65 2029
4, SW+ FSR 2.13 46.11 2175 1.93 45.83 2109
5, Set sch. + FSR 2.01 45.58 2159 1.97 43.97 2430
6, SW+ 50% FSR 2.07 45.37 2847 1.94 45.56 2621
7, SW + 200% FSR 2.07 45.56 1917 2.14 45.73 1965
p-value 0.1483 0.8488 0.5865 0.4943 0.7897 0.5055


Table 3-25. Leaf tissue analysis for irrigation and fertilizer management treatments 08/13/2008,
n=3
Simmonds Betaz
Treatment TN % TC % TP TN % TC % TP
(mg kg-1) (mg kg-1)
1, ET+FSR 1.56 45.21 2181 2.04 a 46.09 1629
2, ET + 50% FSR 1.85 46.45 1677 1.59 b 45.54 1422
3, ET +200% FSR 1.79 45.88 1593 1.72 ab 45.73 1295
4, SW+ FSR 1.83 45.89 1635 1.81 ab 45.67 1513
5, Set sch. + FSR 1.58 45.91 1768 1.92 ab 46.38 1595
6, SW + 50% FSR 1.76 45.69 2218 1.92 ab 45.95 1359
7, SW + 200% FSR 1.77 45.35 1586 1.82ab 45.13 1182
p-value 0.2559 0.7078 0.1447 0.2014 0.7428 0.7990
z, TN% with same letter within column are not significantly different (p < 0.05) by column.

Table 3-26. Leaf tissue analysis for irrigation and fertilizer management treatments 12/12/2008,
n=3
Simmonds Betaz
Treatment TN % TC % TP TN % TC % TP
(mg kg-1) (mg kg-1)
1, ET+FSR 1.89 46.63 2805 1.91 ab 46.42 a 2370
2, ET + 50%FSR 1.80 46.57 2294 2.00 ab 46.11 ab 2932
3, ET +200% FSR 2.00 46.50 1716 2.21 a 46.70 a 2356
4, SW+ FSR 1.78 46.89 1652 1.85 ab 46.63 a 2018
5, Set sch. + FSR 1.85 45.95 2152 1.85 ab 45.36 b 2573
6, SW + 50% FSR 1.90 46.64 2547 1.70 b 46.45 a 2881
7, SW + 200% FSR 1.95 46.38 1675 2.10 ab 46.42 a 2233
p-value 0.9034 0.4470 0.1731 0.1563 0.0487 0.6825
z, Elements with same letter within columns are not significantly different (p < 0.05) by column.









Table 3-27. Leaf tissue analysis for irrigation and fertilizer management treatments 04/20/2009,
n=3
Simmonds Beta
Treatment TN % TC % TP TN % TC % TP
(mg kg-1) (mg kg-1)
1, ET+FSR 2.03 46.05 2634 1.88 46.13 1928
2, ET + 50% FSR 2.01 46.21 2328 1.97 46.41 2084
3, ET +200% FSR 2.17 46.28 2087 2.03 46.78 1794
4, SW + FSR 2.14 46.46 2131 2.08 46.56 2045
5, Set sch. + FSR 2.09 46.14 2300 1.77 46.29 1729
6, SW + 50% FSR 2.18 46.22 2433 1.95 46.66 2139
7, SW + 200% FSR 2.33 45.99 2324 2.03 46.16 1798
p-value 0.0795 0.7946 0.5665 0.1329 0.0982 0.2481



Table 3-28. Leaf tissue analysis for irrigation and fertilizer management treatments 08/26/2009,
n=3
Simmonds Beta
Treatment TN % TC % TP TN % TC % TP
(mg kg-1) (mg kg-1)
1, ET+FSR 1.80 45.09 1908 1.83 45.02 1495
2, ET +50% FSR 1.84 44.81 1876 1.86 45.55 1539
3, ET +200% FSR 1.98 45.28 1783 2.10 46.06 1629
4, SW + FSR 1.98 45.52 1752 2.12 46.30 1462
5, Set sch. + FSR 2.00 45.11 1966 1.87 45.60 1430
6, SW + 50% FSR 1.90 45.39 1736 1.88 46.54 1328
7, SW+200% FSR 2.03 45.31 1850 2.17 46.23 1512
p-value 0.2871 0.0567 0.7221 0.0621 0.1520 0.2907










Table 3-29. Tissue factorial analysis of 2-levels of irrigation by 3-levels of fertilizer for different
sampling dates from 2006 to 2009
Simmonds Beta
Sampling Source TP (mg kg-1) TN% TC% TP (mg kg-1) TN% TC%
date
8/3/2006 Irrig 0.2980 0.2438 0.2763 0.4650 0.0890 0.6257
Fert 0.6674 0.7480 0.8273 0.7465 0.4106 0.4918
Irrigx Fert 0.4451 0.9992 0.2703 0.4250 0.3590 0.0586
12/6/2006 Irrig 0.9337 0.2493 0.9419 0.2657 0.0558 0.7389
Fert 0.6343 0.7992 0.4562 0.7896 0.0585 0.3601
Irrigx Fert 0.3363 0.8802 0.4468 0.1564 0.3394 0.7203
4/30/2007 Irrig 0.3978 0.9828 0.2250 0.1275 0.1681 0.4956
Fert 0.8103 0.0750 0.9715 0.0991 0.0027 0.7697
Irrigx Fert 0.6501 0.3778 0.7769 0.4662 0.6875 0.2518
8/23/2007 Irrig 0.2761 0.4160 0.6681 0.2431 0.3656 0.3347
Fert 0.1380 0.2291 0.5547 0.2626 0.6098 0.7253
Irrigx Fert 0.6924 0.2950 0.9437 0.3652 0.0924 0.8498
12/26/2007 Irrig 0.6456 0.6363 0.6234 0.5868 0.6295 0.7925
Fert 0.2096 0.1783 0.3233 0.5088 0.6244 0.7031
Irrigx Fert 0.0077 0.5047 0.5468 0.1864 0.7616 0.7474
4/8/2008 Irrig 0.8814 0.1873 0.7171 0.7228 0.8992 0.2899
Fert 0.1857 0.2794 0.9877 0.7000 0.2749 0.6263
Irrigx Fert 0.7702 0.1191 0.3627 0.9741 0.9510 0.6901
8/13/2008 Irrig 0.9792 0.5012 0.6473 0.6989 0.4431 0.8674
Fert 0.1740 0.4950 0.5837 0.7174 0.6571 0.1622
Irrigx Fert 0.0496 0.1985 0.3737 0.9579 0.4029 0.7939
12/12/2008 Irrig 0.2772 0.8607 0.6843 0.7239 0.1138 0.6425
Fert 0.1251 0.5524 0.3462 0.7578 0.3155 0.9891
Irrigx Fert 0.1312 0.7273 0.6661 0.9651 0.1425 0.4856
4/20/2009 Irrig 0.7558 0.0319 0.8259 0.6315 0.3482 0.8917
Fert 0.6256 0.0802 0.8477 0.3768 0.6773 0.0185
Irrigx Fert 0.2024 0.8913 0.2898 0.7399 0.5437 0.0074
8/26/2009 Irrig 0.4271 0.0995 0.0100 0.0634 0.1053 0.0431
Fert 0.9790 0.1424 0.2885 0.2752 0.1477 0.6904
Irrigx Fert 0.5636 0.5875 0.1641 0.1445 0.3639 0.7890











4-3 2-3 3-4


1-3 3-3 7-3 2-4
6-3 3-2 *

4-4 1-4 6-4 7-4



6-1 4-2 1-1 2-2
4-1 7-2 3-1 1-2


7-1 2-1 o-


Figure 3-1. Orchard layout. A) Treatments and their replicates where the first number is the
treatment and second number is the replicate, and B) tree cultivar and number and
type of device installed beside the tree.


Figure 3-2. Automated switching tensiometers were set at 15 kPa.


S'Simmonds' 3


O 'Simmonds' 2 (bucket lysimeter)


O 'Simmonds' 1 tensiometerr)


'Beta'













0.05
--


S 0.04
a)
E
o

' 0.03 -

-a
C.0
C 0.02 -

CU
-4-

0
S0.01 -
1--
E


0.00


Rainfall
Soil water
ET
Set schedule


SIl


*i


"le** o61 --. o.o.....................



:1



I I *
I1 :1
IS I:


.
*:1 :1
I: I:

1 I ::





I ** .S
si' \ : \! .


I .


:1 I:00


oooooooooooooooooo000000000000000000000000000000000000000
d z > 6) Cn'-0 '->'C5 Cg-i; > 6 s '- '- CTOEL- > 0 C- >'C-5 ODI-T6
<(O ZOQ-) Q

Month and year




Figure 3-3. Amount of water applied as daily average per month by the Set schedule, ET, and
SW irrigation management.

























110


i



St a


500



400


E

300 _
r-


200 -
o
O

100



0


""
..


A












& 0.05

C,
- 0.04
CO
E
-o-

0.03

CU
0.02
CU
4-
O 0.01
U,
E
O 0.00


Wet
Dry









ET Soil water Set schedule


Irrigation management practice

Figure 3-4. Mean water volume applied per day for ET-based, SW-based, and Set schedule
based irrigation during wet and dry season.


* Data
S Regression line


Monthly Historical ETo (mm/day)


Figure 3-5. Correlation of historical and real time ETo (R2 = 0.913).





















.- iki l .


m 1, ET+FSR
2, ET+0.5FSR

3, ET+2FSR
S4, SW+FSR

5, Set sch.+FSR

I 6, SW+O.5FSR

S*7,SW+2FSR


Month


. I


I _L .


* 1, ET+FSR

* 2, ET+0.5FSR

* 3, ET+2FSR


S4, SW+FSR

5, Set sch.+FSR

6, SW+0.5FSR

.| 7, SW+2FSR


Month


Figure 3-6. Leached nitrate for 'Simmonds' over a two year period. A) November 2007 to
October 2008 and B) November 2008 to October 2009.


20


S16
- 12
12 -
--

1
8

4
z


50
-c
0 40

S30
(-

S20
4--
10

0-


I I I I I __I I I I


<$ zb Ci q 4 5
^ ^ S^ k^ ^ ^ ^ S^ ^ ^ ^ 0B












m 1, ET+FSR


0.16



S0.12

-c

m0.08



0.04



0.00

0~5g%



Month






0.16



S0.12
-c



0.08
U
m


0.04




0.00 16011


S2, ET+0.5FSR


S3, ET+2FSR


* 4, SW+FSR


* 5,Set sch.+FSR


* 6,SW+0.5FSR


S7, SW+2 FSR


* 1, ET+FSR

* 2, ET+O.5FSR

E 3, ET+2FSR

S4, SW+FSR

S5, Set sch.+FSR

H 6, SW+0.5FSR

7, SW+2FSR


Month



Figure 3-7. Leached total phosphorus for 'Simmonds' over a two year period. A) November
2007 to October 2008 and B) November 2008 to October 2009.


0.20













0.56


- 0.52

4.1
--
0.48 -
aF

CO
(D 0.44 -
--

L- 0.40


0.36



0.60



2 0.56
-I-
-a
0)

S0.52
--
Cr

E

2 0.48
LL-


0.44


0 0


2008
2009


A- x x


Treatment


Figure 3-8. Effect of irrigation and fertilizer management treatments on mean fruit weight of
avocados for 2008 and 2009. A) 'Simmonds' and B) 'Beta'


I I I I I I I











3000


o0
. 2500
c Source P-Value
irrigation 0.0072
SFertilizer 0.0009
o 2000 Irrig*Fert 0.2610
0--.
0 0
c
01500


A
1000

3000



2500 Source P-Value
E irrigation 0.0272
Fertilizer 0.2190
SIrrig*Fert 0.8531
0 2000 -
Qc-
n


G 1500 -


B
1000
Half Standard Double

Fertilizer level

ET Irrigation
o SW Irrigation


Figure 3-9. 'Simmonds' yield analyzed as a factorial design with 2-levels of irrigation and
3-levels of fertilizer input. A) 2008 and B) 2009.










6000


S5000
-C
)Source P-Value
irrigation 0.6422
a 4000 -
S000Fertilizer 0.1512
0 Irrig*Fert 0.5714
S3000

a)
2 2000 -
A
1000

9000


S8000
Source P-Value
S* irrigation 0.2933
a 7000 -Fertilizer 0.6607
c Irrig*Fert 0.6684
0
0 6000
C0
(a 0
2 5000 -
0
B
4000
Half Standard Double
Fertilizer level

ET Irrigation
o SW Irrigation


Figure 3-10. 'Beta' yield analyzed as factorial design with 2-levels of irrigation and 3-levels of
fertilizer input. A) 2008 and B) 2009.












30


25 -
A A A

o 20 -
(,

E 15
-3
(D 10 -
1-
5 0 0 0 0 0 0 A

6 *
0 I 0 2006
o 2007
30 v 2008
A 2009
A
25 A A A A
E v v a
o 20 A
1V
E 15
CO
(0
(D 10
1-



t-t
O \
5- 0 0 0 0 o B

0 ----------------------







Treatment


Figure 3-11. Effect of irrigation and fertilizer management treatments on mean tree diameter for
4-year. A) 'Simmonds' and B) 'Beta'.


















V T
0 o

0


Q 7
V


50


0D 45 -
Q-
45

40

o 35


30

25

55


50

a?
I 45n


S40 -


35


30


A







* 8/2/2006
o 10/31/2006
v 2/19/2007
A 8/3/2007
* 11/14/2007
o 2/27/2008
* 5/27/2008
o 8/13/2008
A 12/12/2008
v 3/18/2009
* 6/10/2009
a 8/14/2009







B


Treatment

Figure 3-12. Effect of irrigation and fertilizer management treatments on mean SPAD value for
reading collected from 2006 to 2009. A) 'Simmonds' and B) 'Beta'.


55 -


VV y


S0



I g









CHAPTER 4
MODEL SIMULATION OF NITROGEN AND PHOSPHORUS LEACHING IN
CALCAREOUS SOILS OF SOUTH FLORIDA

Introduction

To prevent pollution from point and nonpoint sources, the U.S. Congress passed the

Federal Clean Water Act in 1972 (amended in 1987) to restore and maintain the chemical,

physical, and biological integrity of the nation's waters. Nutrient leaching of mainly nitrogen (N)

and phosphorus (P) from agricultural fields is one source of surface water and groundwater

impairment. Nutrient leaching is attributed to improper matching of the optimal fertilizer

requirements and water needs to the crop and production environment. Nutrient leaching may

have adverse effects in south Florida due to the interaction of surface water and groundwater

resulting from a shallow water table (Noble et al., 1996), and the existence of naturally sensitive

water bodies like the Biscayne Bay and the Everglades (Brower et al., 2005; Reddy et al., 2006).

In Florida, the agricultural best management practice (BMP) program is aimed at reducing

movement ofN and P from agricultural fields to water bodies (Simonne and Hutchinson, 2005).

BMPs are defined as a set of on farm practices designed to reduce nutrient loss and improve

water quality while sustaining economically viable farming operations. Although several BMPs

have been developed for various agricultural crops in Florida (FDACS and FDEP, 1998;

Simonne et al., 2003; Obreza and Schumann, 2010), no irrigation and nutrient BMP has been

developed, tested, and documented for tropical fruit tree crops.

Due to resource constraints it is unreasonable to field test all possible BMP combinations

that involve nutrient levels and nutrient application methods and irrigation volumes and

application methods. Thus, field-tested computer models are commonly used in research,

planning, management, and decision making due to their advantage of giving an insight on how

systems function and interact. Many models that simulate N and P leaching are available.

119









Deterministic physically based models (e.g., Decision Support System for Technology Transfer

[DSSAT] (Jones et al., 2003), Groundwater Loading Effects of Agricultural Management

Systems [GLEAMS] (Leonard et al., 1987), Field Hydrologic and Nutrient Transport Model

[FHANTM] (Fraisse and Campbell, 1997), and Leaching Estimation and Chemistry Model

[LEACHM] (Wagenet and Hutson, 1989) that simulate nutrient leaching have been used and

reported to give satisfactory results (Sogbedji et al., 2001; Webb et al., 2001; Asadi and

Clemente, 2003). Nutrient leaching has been simulated in fields of various crops including

maize, wheat, millet, potato, cassava, bahia grass, and brachiaria grass (Jones et al., 2003).

However, such models are less often applied to fruit orchards (Gary et al., 1998).

Of the process-based field scale leaching models, LEACHM has been widely used to

simulate water flow and solute transport with satisfactory results (Jemison and Fox, 1992; Jabro

et al., 1995; Sogbedji et al., 2001; Contreras et al., 2009). LEACHM is a comprehensive,

deterministic-mechanistic, one-dimensional finite difference model that was developed by

Hutson and Wagenet (1989) to simulate vertical water and solute transport both in field and

laboratory columns using numerical routines. It has been revised and tested over the years by

different researchers (Borah and Kalita, 1999; Sogbedji et al., 2001; Jabro et al., 2006) and the

current LEACHM (ver. 4.0) is a suit of three models. The three simulation models include

LEACHP for pesticides, LEACHN for N and P, and LEACHC for salinity in calcareous soils

(Hutson, 2005). The model incorporates carbon (C), N, and P pools and pathways.

Ng et al. (1999) used the LEACHN model to identify management practices (i.e., water

table management, conservation tillage, and intercropping) that would reduce nitrate (NO3)

leaching from a corn (Zea mays L.) field fertilized with urea in Ontario, Canada. The authors

reported that LEACHN model gave better predictions for NO3 leaching on plots under controlled









drainage/subsurface irrigation systems than on plots under free drainage. Using LEACHN model

to develop BMPs for potato (Solanum tuberosum) production in Nevsehir, Turkey, Unli et al.

(1999) reported that nutrient leaching could be reduced significantly by reducing the

irrigation/rain water applied from 1100 mm to 650 mm, reducing ammonium sulfate fertilizer

input from 900 kg ha-1 to 400 kg ha-1, and applying fertilizers after most of the supplemental

irrigations were completed. The authors further recommend that rotating potato with wheat could

further reduce the residual NO3- leaching since half of the applied NH4-N in the fertilizer was

converted to NO3 during the growing season. Jabro et al. (2006) compared the simulation

accuracy and performance of LEACHN in predicting N dynamics in a soil-water-plant system to

two other field scale models: Nitrogen and Carbon Cycling in Soil Water And Plant (NCSWAP)

(Molina and Richards, 1984) and SOIL-SOILN (SOILN) (Eckersten and Jansson, 1991). The

authors reported that LEACHN and NCSWAP estimated NO3 leaching more accurately than

SOILN, from a corn field fertilized with ammonium nitrate and manure in Rock Springs,

Pennsylvania. Ng et al. (1999) and Mahmood et al. (2002) identified parameters used in

sensitivity analyses (Table 4-1) while calibration parameters were identified by Borah et al.

(1999), Ng et al. (1999), Mahmood et al. (2002), and Jabro et al. (2006) (Table 4-2). Although

LEACHN can simulate P leaching, such studies could not be identified in available refereed

literature.

Global sensitivity and uncertainty analyses are tools used with model applications due to

uncertainties associated with all predictive deterministic models and measured data to improve

the interpretation and thus the application of modeling results (Shirmohammadi et al., 2006). The

input factors or parameters that control the variation of the simulated model are one source of

uncertainty. Sensitivity analysis provides a measure of the relationship between a given uncertain









input factor and a model simulation output while uncertainty analysis propagates uncertainties

onto the model output of interest. Traditionally, model sensitivity has been quantified by

computing local indices (Saltelli et al., 2005). However, hydrological models are non-linear and

global techniques are therefore more appropriate as they explore the entire model parametric

space. Global sensitivity analysis provides parameter ranking and information about first and

higher order effects of parameters by specified outputs.

One way of accounting for uncertainty in model inputs is through the development of

probability density functions (PDFs) of the target model outputs (Shirmohammadi et al., 2006;

Saltelli et al., 2008; Mufioz-Carpena et al., 2010). The output PDFs are then used to evaluate

uncertainty in the model predictions by placing confidence intervals on the outputs either as

margin of safety component or by calculating probability of exceedance of a threshold value

(Morgan and Henrion, 1992). A common approach to sampling distributions for simulating

model outputs is the use of Monte-Carlo sampling which consists of multivariate random

sampling from model input probability density distributions in order to conduct a large number

of model simulations. Due to high computational costs of the Monte-Carlo type of uncertainty

analysis, it is convenient to use a sensitivity screening method first to identify the subset of input

factors as having the most influence on model output variability (Saltelli et al., 2004;

Shirmohammadi et al., 2006; Mufioz-Carpena et al., 2010). Model uncertainty is then efficiently

assessed at a lesser computation time with the subset of model inputs.

In my study, the use of global sensitivity and uncertainty analyses of Morris' and eFAST

methods to simulate NO3-N and TP leaching in calcareous soils were evaluated. A previous

study involving use of global modeling technique with LEACHC (a sub-suite of LEACHM for

modeling pesticide leaching) was limited to sensitivity analysis and did not involve a factor









screening step (Soutter and Musy, 1999). Although LEACHM can simulate P leaching, field case

studies where the model has been applied to simulate P leaching could not be identified in

available refereed literature. The specific objectives of the study were to: (1) perform global

sensitivity and uncertainty analyses of the LEACHM model and (2) apply LEACHM to refine

the identified BMPs in an avocado (Persea americana Mill.) orchard while reducing water

volumes applied and nutrient leaching.

Materials and Methods

Experimental Design

The study site was in Homestead, Miami-Dade County, FL, at the University of Florida's

Tropical Research and Education Center (TREC) (25o20'21" N, 8020'01" W). The elevation of

TREC is about 4 m above sea level with groundwater generally 1000 to 2200 mm below the

surface. The wet season in Florida is from May to October and 80% of the rainfall occurs during

this period (Mulholland et al., 1997). Data were collected in an avocado orchard from

'Simmonds' cultivar trees. The trees were planted in four rows at spacing of 6 m between rows

and 4.5 m inter row. The orchard was considered flat with gravelly, loamy-skeletal, carbonatic,

hyperthermic lithic udorthents, soils classified as Krome very gravelly loam (Noble et al., 1996).

Krome soils are very shallow up to 20 cm deep, well drained, moderately permeable and

underline by limestone. Mufioz-Carpena et al. (2002) reported that Krome soils are 51% coarse

material, 36% sand, 40% silt, 24% clay and a bulk density of 1.42 g/cm3). To provide space for

root development in a mechanically rock plowed soil, tropical fruit trees are planted 50 cm deep

at the intersection of perpendicular trenches (Nufinez-Elisea et al., 2001).

The irrigation and nutrient BMPs to be refined through computer simulations were

developed based on field data collected from 2006 to 2009 in an avocado orchard. The BMPs

involved irrigating the trees based on soil water suction at 15 kPa (SW) and fertilizing at a

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standard rate (FSR) (see Chapter 3). The fertilizers were simulated as NH4, NO3, and TP

(Hutson, 2005). Input weather data such as mean temperature, temperature's amplitude,

precipitation, and potential evapotranspiration (ETp) were obtained from Florida Automatic

Weather Network (FAWN) (2010). FAWN uses a modified Penman equation referred to as UF

IFAS (1984) Penman (Jones et al., 1984) method to compute ETo.

LEACHM Model

LEACHM model is a deterministic model that uses finite difference form of the one

dimensional Richard's transient flow or Addiscot tipping bucket equation to predict water fluxes

and soil moisture distribution with time. Water retention and conductivity functions are

calculated by the methods proposed by Campbell (1974). A one dimensional

convection-diffusion equation (CDE) is solved numerically to estimate chemical fluxes and

distribution in the profile. Crop growth subroutines used in LEACHM are based upon empirical

equations and the effects of water content, soil strength, and nutrient concentration on root and

shoot developments are not considered. However, the distribution of roots with depth partly

determines the water and chemical uptake terms within each soil segment. According to Hutson

(2005), N transformations in LEACHM are described as fluxes between soil organic N pools, in

addition to inorganic fertilizer application in the form of urea, NH4-N, and N03-N. The major

nutrient pathways considered are nutrient leaching (N03-N and TP) and plant nutrient uptake

(N03-N, NH4-N, P). The description of the equations used to model N transformations,

parameters used, and the initial and boundary conditions are given in the LEACHM manual

(Hutson, 2005).

Sensitivity analyses of the LEACHM model indicated that N03-N leaching was affected

by changes in bulk density, saturated hydraulic conductivity, initial water content, air entry









value, Campbell exponent coefficient, organic carbon content, and mineralization rate constants

(Wagenet and Hutson, 1992; Ng et al., 1999; Mahmood et al., 2002).

The model simulates water and solute transport within a soil profile divided into

segments ranging from 12 to 25. Fewer segments are recommended for use with Richards'

equation in order to reduce on the model simulation time. The nodes used in the

finite-differencing using the Crank-Nicolson implicit method are in the center of the segments.

LEACHM automatically computes extra nodes at the boundaries to maintain the correct

boundary conditions. The model requires specifying the number of rainfall and/or irrigation days

in the simulation period. Other inputs include daily maximum and minimum temperatures,

evapotranspiration, soil texture, organic carbon, and amount of N and P applied, soil hydraulic

data (e.g., water retention curve parameters), N and P rate reaction parameters, and sorption

coefficients.

The model is equipped with C, N, and P pools and pathways that are used to simulate

flow between these pools in each soil segment as well as on the soil surface. The C and N

cycling are based on the procedures described by Johnsson et al. (1987), but with additional

pools and pathways. The inorganic P model was based on concepts described by Shaviv and

Shachar (1989) but modified to represent bound P pool as either a precipitate or a sorption

isotherm. The labile pool is always in local equilibrium, but sorption to or desorption from the

bound pool is kinetic. LEACHM predicts both nutrient uptake by a crop and leaching below the

root zone up to a depth of 2 m. The model uses a daily time step and can simulate a growing

season or several years (Hutson, 2005). A default maximum simulation time interval of 0.1 day

is used to reduce on the computation time while the outputs may be given at a desired time step

e.g. daily, weekly, monthly, etc.









Richard's equation, the soil water flow equation for transient vertical flow, derived from

Darcy's law and the continuity equation, is:

[z K(0)aH -U(z,t) (4-1)
dt dz L dz I

where 0 is the volumetric water content of a specific soil layer segment (m3 m-3), H is the

hydraulic head (mm), K is hydraulic conductivity (mm d-1), t is time (d), z is soil depth below a

reference point (mm), and U is a sink term representing water lost per unit time by transpiration

(d-1).

Defining the differential water capacity, C(o), as


C(0)= (4-2)
Oh

where h is soil water pressure head, enables transformation of equation 4-1 to an equation where

pressure potential is the only dependent variable:



aC(h ) K(O) U(z, t) (4-3)
at dz z aZ

The model considers water depth and water potential in terms of mm per day. The solution to

equation (4-3) by normal finite differencing methods is accomplished by dividing the soil profile

into a finite number (k) of equally spaced horizontal layers of size Az, and dividing the total

time period into small time intervals, At. Utilizing the Crank-Nicolson implicit method reduces

equation (4-3) to:

aJh/,1 +P h +y7/hI = 5/ (4-4)

where c; f/ / and 5/ contain constants (h, K, C, U, Az, At) for the time increment, which

have known or estimated values, and h/I the soil water matric potential at the start of the time

126









interval is known. The k equations developed (one at each depth node) form a tridiagonal matrix

that may be solved for h h/1, h,1 using a rapid Gaussian elimination method. The

convective-dispersion equation is used to describe solute transport and flux is usually represented

as:


JcL = -ODM (q) d + qc, (4-5)
dz

where q is the macroscopic water flux, CL is solute concentration (mg L-1), and DM(q) is the

mechanical dispersion coefficient that describes mixing between large and small pores as the

result of local variations in mean water flow velocity (Wagenet, 1983).The value of DM(q) can be

estimated from

DM(v)= Av (4-6)

where v is the pore water velocity v = q/O and A is the dispersivity, limited in LEACHM to the

range 0.5Az to 2Az.

Model Set-up and Parameterization

Simulating water flow, N03-N, and TP in LEACHM requires physico-chemical input

factors that depend mostly on water input, soil properties, and fertilizer mineralization rates. The

effective root depth of avocado trees was set at 0.30 m due to the existence of a limestone layer

below this depth. Soil homogeneity was assumed within the simulated 0.30 m soil profile.

Twelve layers of 0.025 m each were considered as individual segments. A separate file was used

for irrigation input using the number of days irrigation came on in the orchard for the identified

BMP. Although fertilizers were applied four times each year, in the model each fertilizer

application was further split into two parts to account for the 25% slow release portion. The

simulated model outputs of drainage and leached N03-N and TP were output on a monthly time









step to coincide with measure data. The lower boundary condition of the model simulation was

selected to be free drainage. The selected input factors are given Table 4-3. Nutrient uptake

parameters of N and P were fixed at 14 and 2.3 kg ha-1 assuming that 50% of their applied

fertilizer (N and P) was taken up by the trees. Nutrient uptake data ofN and P could not be

collect since it would involve sacrificing at least three trees from each treatment replicate at the

end of each fruiting season (2008 and 2009).

To perform global sensitivity and uncertainty analyses, the input factors were assigned

average values, ranges, and probability PDFs as shown in Table 4-3. For data obtained from

literature and where a PDF was not given for that variable, a uniform distribution was assumed.

The range of the uniform distribution was assigned as 30% of the mean value except for bulk

density where a 20% was used. Easyfit software (MathWave Technologies, 2010) was used to

generate PDFs for the input variables/parameters where data was either measured or could be

generated. The selected LEACHM model outputs for model validation of the BMP were

drainage (mm), leached N03-N, and leached TP (kg ha-1).

Global Sensitivity and Uncertainty Analyses

A qualitative global sensitivity analysis screening method of Morris (1991), which was

later modified by Campolongo et al. (2007), was applied first. This was intended to obtain a

ranking of 28 inputs factors shown in Table 4-3 on the effect of the desired LEACHM outputs.

Several elementary effects for each of the 28 factors were obtained and averaged producing a

statistic p whose magnitude when compared to all model input factors described the order of

importance of this factor to a desired model output. Campolongo et al. (2007) proposed to use a

statistic u (an absolute value of the elementary effects) instead of t to avoid the possibility of

canceling effects due to opposing signs. Since elementary effects are the same for a given factor









where there are no factor interactions, the standard deviation of P (C) can be used as a statistic

indicating a measure of the sum of all interactions between the input factors and all its nonlinear

effects. The number of simulation (N) required for the Morris' method is computed as:

N = r(k + 1) (4-7)

where r is the sampling size for the search trajectory (r = 10) produces satisfactory results and k

is the number of factors.

Once the most relevant factors were identified by the Morris' methods, Monte-Carlo

simulations were run using multivariate pseudo-random samples drawn from the input factors

distributions using the eFAST (Saltelli et al., 1999) sampling method. The number of validations

required for the analysis is expressed as:

N = m(k + 2) (4-8)

where m is a number between 500 and 1000, and k is the number of factors. The eFAST

variance-based method provides a quantitative measure of sensitivity of the model output with

respect to each input factor, using first order sensitivity index, (S,) (Saltelli 1999). S, is defined

as the fraction of the total output variance attributed to a single input factor. In a rare situation of

an additive model, with no interactions, -S, = 1. The eFAST method also calculates first and

all higher order indices (interactions) for a given input factor which results in a total sensitivity

index, S,, expressed as:

Sr, = S, + S, + S1jk... + S1. (4-9)

From equation 4-9 the interaction effects is calculated as S, S,.

In my study, the screening method of Morris (1991) and eFAST variance-based method

were applied to investigate the input factors that influence drainage, leached NO3-N, and leached









TP in LEACHM. The analysis involved five steps: (1) identification of model input factors and

construction of the PDFs; (2) generation of different factor sets by pseudo-random sampling of

the input factors PDFs using the statistical pre-processor module of the Simlab v2.2 software

(Saltelli et al., 2004); (3) generation of simulation results for each input factor set and outputs

from each simulation collected; (4) performing a global sensitivity analysis according to the

selected method e.g. Morris' screening method to identify a subset of factors and repeating steps

2 and 4 for the factors using the eFAST method; and (5) assessing uncertainty based on the

outputs from the eFAST simulations by constructing PDFs/CDFs and other statistical outputs.

An interface program was written in C++ language to: (a) feed the generated set of factor

PDFs from Simlab v2.2 software pre-processor to LEACHM, and (b) prepare an output file from

LEACHM that was fed back in Simlab to perform sensitivity and uncertainty analyses. The

statistical post-processor module of Simlab used input and output matrices to calculate the

sensitivity indexes of the Morris and the eFAST methods. Data analysis software was used to

construct output probability distributions and to quantify the uncertainty based on the set of

eFAST simulation outputs.

Model Calibration and Validation

Legates and McCabe (1999) and Wallach (2006) recommend use of several statistical

measures to calibrate and validate model performance. The statistical measures used for model

calibration and validation were: 1) plots of simulated and observed values, 2) the mean absolute

error (MAE), 3) the root mean square error (RMSE), 4) the Nash-Sutcliffe coefficient (E), and

5) the Index of agreement, (d).

The RMSE and the MAE are absolute error goodness-of-fit indicators that describe

differences in observed and predicted values in measured units.










RMSE= N Z(O,- P)2 (4-10)


MAE= N 1 O1 -P (4-11)
i=1

where O, is the observed value, P is the predicted value, and O is the mean of the observed

values and N is the total number of observations.

The E, also known as the Nash-Sutcliffe coefficient (NS-coefficient), is another

commonly applied statistical measure for evaluating the performance of hydrological and water

quality models (Nash and Sutcliff, 1970). The value of E has the range -oo < E < 1.

N
Z(0,p)2
E= -, (4-12)
(o, -O)


where O, is the observed values, P is the predicted value, and O is the mean of the observed

values. Values of E closer to 1 indicate better agreement between observed 0, and predicted P

values. However, ifE < 0, then the mean of the observed values O, is a better predictor than the

model's predicted values (Legates and McCabe, 1999). This indicates that the model error is so

large that its predictive value is no better than the mean of the observed value.

Index of agreement (d) is another statistical measure for the validation of hydrologic

models. It is a measure of the degree to which a model's predictions are free of errors (Harmel

and Smith, 2007). Similar to E, dis also considered an improvement over R2. However, d is

sensitive to extreme values of observed and predicted values due to squaring of the difference

terms (Legates and McCabe, 1999). Values range from 0 to 1 and d is calculated as:










(0,-P)2
d = l- (4-13)
t$p,-o,+o,-o\


where O, is the observed values, P is the predicted value, and O is the mean of the observed

values. Higher values ofd indicate a better agreement of predicted and observed values.

Refining the Selected BMP

The months of March, April, May, and July of 2008 were selected for use in the model to

refine the identified BMP. The target model outputs were nutrient leached loads ofNO3-N and

TP. LEACHM model was used to refine the BMP considering reductions of 10, 15, and 20% in

irrigation water depth for each irrigation event and fertilizer input for 2008. For the identified

BMP to be refined each irrigation event was equivalent to 9.5 mm of water depth and each

irrigation event lasted for 14 minutes. The irrigation application water depth reductions used in

model simulations were 8.6, 8.1, and 7.6 mm which corresponded to irrigation duration per each

event of 13, 12, and 11 minutes, respectively. The field application of such water depth

reductions would be to set irrigation to come on when soil water suctions exceeded 16.5 kPa (for

the 8.6 mm which corresponds to a volumetric water content of 0.21 cm3 cm-3), 18 kPa (for the

8.1 mm which corresponds to a volumetric water content of 0.205 cm3 cm-3), and 20 kPa (for the

7.6 mm which corresponds to a volumetric water content of 0.20 cm3 cm-3). All the three

irrigation volumes were higher than the amount of water of irrigated based on ET where each

irrigation event was 9 minutes (irrigating when the soil water suctions exceeded 25 kPa which

corresponds to a volumetric water content of 0.19 cm3 cm-3). The reduction in fertilizer

application of 10, 15, and 20% of the FSR corresponded to 0.207, 0.193, and 0.182 kg per tree

per application. The fertilizers were applied four times during 2008.









Results and Discussion


Global Sensitivity Analysis: Morris'

Factors that influence simulated drainage, leached N03-N, and leached TP using the

LEACHM as identified by the Morris' method are shown in Fig. 4-1 and Table 4-4 which shows

the ranking of the factors as they influence variability of the three desired model outputs.

Application of the Morris' method resulted in identification of factors and separated them from

the origin of the u *-c plane. Values of u are greater than a in all three plots (Fig. 4-1) which

may be interpreted as there being small interactions between factors. Thus, first order factor

effects dominated model responses. The number of factors for further analysis was reduced from

28 to 17, representing a 40% reduction. The factors whose u value was close to the origin were

identified as non-influential for the desired model output (Campolongo et al., 2006) and thus

were fixed at their mean value (Saltelli, 1999).

The most influential factors for drainage were hydraulic conductivity (K) and pore

interaction parameter (PIP) since they govern water flow and subsequently percolated water

volume below the root zone (Fig. 4-1; Table 4-4). Hydraulic conductivity (K) was identified by

Borah et al. (1999) and Jabro et al. (2006) as a factor influencing water flow prediction in

LEACHM model. PIP was an important factor due to the gravelly nature of the soils. The other

secondary factors influencing drainage were Campbell's b exponent (BCAM), Campbell's air

entry value (AEV), and bulk density (Pb) which are related to hydraulic conductivity.

Campbell's coefficients AEV and BCAM were identified and they describe the shape of the

water retention curve. This curve describes the range of soil water content that is used to

determine the mobile and immobile fractions of the liquid phase and is also useful in

computation of water flux between two adjacent cells (Huston, 2005). Bulk density was









influential since it is used to define the saturated water content which is the upper limit of soil

water content (Hillel, 1998). Surprisingly drainage was insensitive to soil properties like clay and

silt content (%).

Factors identified to influence variability of leached NO3-N load were K, C:N, OC, pb, and

PIP (Fig. 4-1; Table 4-4). The C:N ratio and OC influenced leached NO3-N through secondary

effects since these factors influence N mineralization rates. Organic carbon and the C:N ratio

relate to NO3-N leaching through the mineralization of N in biomass to form ammonium which

eventually gets oxidized to NO3-N. The higher the C:N ratio the lower the amount of N in

biomass that is available for mineralization (Havlin et al., 2005; Huston, 2005). In such a

situation N will be immobilized from the soil by the microbes and less NO3-N will be available

for leaching.

The variability in leached TP was mostly influenced by Kd, Lab-P, K, Fe, and PIP

(Fig. 4-1; Table 4-4). The P sorption coefficients Kd and Fe were identified as they determine the

amount P retained by the soil. Factor Lab-P was influential as it is constantly in equilibrium with

solution P (the form of P available for leaching) and non-labile P. In that if the amount of

solution P decreases, Lab-P is dissolved to maintain the equilibrium (Li et al., 2005). Factors K

and PIP were identified since they influence water flow and subsequently the amount of water

drained below the root zone.

Global Sensitivity Analysis: eFAST

The identified 17 factors by Morris' method were used to conduct 13,300 simulations

using eFAST method to generate data for model uncertainty analysis. The eFAST global

sensitivity analysis (Table 4-5) was in agreement with qualitative sensitivity results obtained

with Morris' method for the first-order effects (S,) and interactions (Sf S,). The total









first-order effects explained 89, 87, and 89% of the output variability in drainage water volume,

leached N03-N, and leached TP. Although Morris' method had identified K as an important

factor for leached N03-N, eFAST was able to rank it as the most influential contribution 44% of

the variability in the output. This is attributed to the fact water molecules and NO3s move at a

similar rate within the soil profile (Johnsson et al., 1987; Havlin, 2004). The variability in

leached P was explained by Kd (72%) and Lab-P (16%). This was attributed to the high amount

of initial labile P content in calcareous soil. This implies that for leached TP the sorption

coefficient and the initial labile P amount outweigh the water transport contributions from other

sources (i.e., fertilizer). Results of global sensitivity analysis using Morris' and eFAST methods

imply that time and financial resources should be spent on measuring the six input factors (K,

PIP, OC, C:N, Lab-P, and Kd) that mostly influenced variability in drainage, leached N03-N, and

leached TP in calcareous soils of south Florida (Table 4-5). All the remaining input factors

would then be estimated from literature since they would not influence simulation results. Global

Uncertainty Analysis: eFAST

The global uncertainty analysis provided ranges and other statistical values for expected

drainage water volume and leached nutrient loads of NO3-N and TP (Fig. 4-2; Table 4-6). The

drainage exhibits a near normal distribution in comparison to the distributions of leached N03-N

and TP. The predication of NO3-N load indicated lesser uncertainty in input factors due to a

smaller 95% confidence interval in the output in comparison to the 95% confidence intervals in

drainage volume and TP load outputs. Similarly uncertainty results were presented as probability

of exceedance of a desired target values or percentage (Fig 4-3). In that regard, the amount

expected to be exceeded at 50% probably were 137 mm, 3.4 kg ha-1, and 1.5 kg ha-lfor drainage,

leached N03-N, and leached TP, respectively.









Model Calibration

The LEACHM model was calibrated for leached water volume (drainage), leached N03-N,

and leached TP using 2008 data. However, bucket lysimeters could not capture all the drainage

generated during big rainfall events of more than 10 mm due to high water percolation rates in

Krome soil. Thus the drainage calibration was completed using four months (March, April, May,

and July) were storms of less than 10 mm where recorded. For the four month used for model

calibration on average each month received about 100 mm from irrigation and 95 mm from

rainfall. The model calibration indicators for drainage were: MAE is 1.750, RMSE is 3.255, the

Nash-Sutcliffe (E) is 0.953, and Index of agreement (d) is 0.990 (Fig 4-4a).

To achieve the slow release effect (of 25% for N) of the fertilizer applied, monthly

fertilizer inputs were split into two parts to coincide with the months when peak N03-N

concentrations were sampled. This is because in LEACHM inorganic fertilizers in form of NH4

or NO3 are subject to immediate local equilibrium (Hutson, 2005). The model calibration

indicators for N03-N were: MAE is 0.054, RMSE is 0.140, The Nash-Sutcliffe (E) is 0.853, and

Index of agreement (d) is 0.950 (Fig 4-4b).

LEACHM considers a slow dissolution of fertilizer P to be proportional to the difference

between the concentration of P in the soil solution and the solubility of the fertilizer P (Hutson,

2005). However, the amount of fertilizer dissolved P cannot exceed the amount of soluble P in

the soil. The model calibration indicators for TP were: MAE is 0.003, RMSE is 0.007, the

Nash-Sutcliffe (E) is 0.874, and Index of agreement (d) is 0.958 (Fig 4-4c).

Constraints with Model Validation

The LEACHM model could not be validated using the 2009 data. This is because the

selected months of March, April, May, and July received varying amounts of rainfall. The

average rainfall for the four months was 118 mm with a standard deviation of 139 mm, the same

136









months in 2008 had an average rainfall of 95 mm with a standard deviation of 47 mm (Table

4-7). It was also observed that for the selected months in 2008, there was no rainfall event that

exceeded 10 mm, yet for the same months in 2009 there were 15 high rainfall events with an

average of 28 mm and standard deviation of 21 mm. Therefore, due to bucket lysimeter physical

limitations an adequate leachate volume could not be collected during high rainfall events of

more than 10 mm. The difference in the rainfall amount between the two years is attributed to

part of 2008 being a La Nina year and 2009 being a neutral year (NOAA, 2010). La Nina years

are typically every 2 to 7 years in south Florida based on NOAA's historical records since 1903.

La Nifia brings different weather conditions in several parts of the worlds. In south Florida, La

Nifia conditions are associated with a wamer and drier spring season compared to the weather

experienced during a normal year. This implies that the spring total rainfall during La Nifia years

is less than the amount rainfall received in a neutral year. Thus, since the model could not be

validated for water flow, it could not be validated for nutrient leaching since nutrients move with

water.

Modeling of the BMP

The model outputs for nutrient leaching (Fig. 4-5) show that reducing the FSR by 20% did

not significantly (p < 0.05) affect the leached load ofNO3-N and TP (Table 4-8). Given that

avocado yield was responsive to volume of water applied and fertilizer input in 2008 and only

responsive to irrigation in 2009, the suggested refined BMP which is to to apply 8.6 mm of water

per each irrigtione event with 90% of the FSR would be appropriate for only water application in

2008 and only fully applicable to the 2009 production (second year of fruiting). The justification

in water reduction is based on the 8.6 mm water depth being more that 40% of the ET irrigation

water depth applied yet there were no signifant difference (p < 0.05) between 'Simmonds' yields

for SW with FSR and ET with FSR. The fertilizer reduction is justified by the fact that the yields

137









of 2008 and 2009 were similar despite an increase in the FSR of 600% in 2009. The field

application of 8.6 mm of irrigation water depth would be achieved by by setting irrigation to

come on when soil water suctions exceeded 16.5 kPa (which corresponds to a volumetric water

content of 0.21 cm3 cm-3). Each irrigation event would be time to run for 13 minutes. For the

fertilizer input the amount applied per tree would be reduced from 1.361 kg per tree per

application (1950 kg ha-1 yr-1) to 1.225 kg per tree per application (1755 kg ha-1 yr-1). Thus the

refined BMP would lead to further savings in volume of water applied and fertilizer amount

applied in avocado production during the second year of fruiting.

Recommendations from my Study

Modeling tools provide an opportunity to explore different irrigation and nutrient

management scenarios that cannot be tested in the field due to limitations such as field space,

time, and funds. Global sensitivity analysis reduced the number of input factors that mostly

influenced variability in drainage, leached N03-N, and leached TP in calcareous soils of south

Florida to only six. These factors were K, PIP, OC, C:N Lab-P, and Kd. This is important in

saving time and funds that would be spent on estimating/measuring the entire set of 28 factors

initially identified. If LEACHM model is to be used to investigate other irrigation and nutrient

BMP for avocado or other tropical fruits in south Florida these are the factors that require

accurate measurements. The remaining model input factors can be estimated from literature

without influencing the simulation results of drainage, leached N03-N, and leached TP. The

recommended practice for production of' Simmonds' avocado in young orchards is to apply

8.6 mm of water depth for each irrigation event (with soil water suction set at 16.5 kPa) and 90%

of the FSR from the second year of fruiting on wards. Although the refined BMP would lead to

further savings in volume of water and amount of fertilizer applied in avocado production, it

would not result into significant reduction in nutrient leaching. This is because nutrient leaching

138









was more influenced by rainfall amount rather than irrigation amount (see chapter 3). The

findings from my study form a basis for developing irrigation and nutrient BMPs for other

tropical fruit crops grown in south Florida. However, refining the identified BMPs using

LEACHM would require measuring soil water content at two proposed soil depths of 15 and

30 cm every 2 hours for a period of about 3 years. This would be beneficial in having a wide

range of data available for model calibration and validation of water flow. A good calibration

and validation for water flow is essential since nutrients move water. Likewise, leachate

sampling should be done more frequently during the month using other sampling devices like

ceramic lysimeters instead of bucket Isyimeters.

Although parameters like initials soil temperature, crop uptake of N and P are likely to

influence leached N03-N and leached TP loads, these parameters were not considered during the

sensitivity analysis. It would be interesting to investigate how these parameters influence the

variability in simulated leached N03-N and leached TP. Likewise before performing sensitivity

analysis, dependence of the input factors was not evaluated. Much as factors that are expected

to be correlated such as percentage clay and percentage sand did not influence the simulated

outputs of drainage, leached N03-N and leached TP, future work should incorporate rank

correlation analysis before performing sensitivity analysis. Accounting for covariance and

interaction between the input factors is important to minimize uncertainties in the simulated

results. Likewise, the results of the simulated BMP could be strengthened by including

uncertainty analysis. The analysis may include constructing confidence intervals on the model

outputs or determining expected probabilities of attaining a certain level of nutrient reduction.

Since in my study seven treatments (irrigation and nutrient management practices) were









investigated, it is worth exploring if the model could be validated using another treatment e.g. ET

with FSR (treatment 1) with the 2008 data.

LEACHM does not account for the slow release effect ofN in the fertilizer since inorganic

N fertilizers are subjected to an immediate local equilibrium. Exploring field dissolution rates of

slow release fertilizer and making the necessary modification in the way LEACHM model

simulates N would be a good research topic. Another way of refining irrigation and nutrient

BMPs would be to collect data on nutrient uptake of' Simmonds' avocado since the LEACHM

model is capable of simulating nutrient uptake. Nutriet loss through volatilaztion was on

considered inmy study. Future research should explore if the volatilization rate of N fertilizer

affects leaching of NO3-N in calcerous soils of south Florida.

Conclusions

Twenty-eight input factors were identify as influential in simulating drainage, N03-N, TP

and global techniques were used to perform LEACHM model sensitivity and uncertainty

analyses. The Morris' screening methods reduced the number of influential factors to 17. The

eFAST sensitivity method showed that total first-order effects explained 89, 87, and 89% of the

output variability in drainage water volume, leached N03-N, and leached TP. Hydraulic

conductivity (K) was the most important factor which accounted for 56 and 44% variability in

the outputs of drainage and leached N03-N. The variability in the leached TP output was

influenced by labile P (16%) and Kd (72%). Uncertainty analysis performed using eFAST

showed that there was minimal uncertainty in predicting N03-N load leached in comparison to

the uncertainty involved in predicting drainage or Leached TP. The amount expected to be

exceeded at 50% probability were 137 mm, 3.4 and 1.5 kg ha-1 for drainage, leached N03-N and

TP respectively. Since bucket lysimeters experienced by-flow during high intensity rainfall

events due to the high water percolation rate of Krome soil, only four months with lesser rainfall

140









in 2008 were used to calibrate the model. The model was successfully calibrated with

Nash-Sutcliff and index of agreement coefficients in the range of 0.87 to 0.98 and minimal

RMSE less than 4 (mm or kg ha-1). However the months used to calibrate the model received

varying rainfall amounts in 2009 compared to the amounts received in 2008, making it

impossible to validate the model. Therefore due to climatic variability the model was used to

refine the BMPs without being validated. The proposed refined BMP is to irrigate for 13 minutes

for each event instead of 14 minutes however, to the application of 90% of the fertilizer at a

standard rate would be applicable in the second year of tree fruiting (2009) since in 2008

fertilizer rate affected yield. Since nutrient leaching was mainly driven by rainfall the refined

BMP would not result in significant (p < 0.05) reductions of leached loads of N03-N and TP in

compared to SW with FSR.










Table 4-1. Sensitivity analysis parameters used for in LEACHM for water flow and nitrate
Leaching.
Parameter Ng et al., 1999z Mahmood et al., 2002
value/range sensitivity ratio
Air entry value (AEV) -0.22
Exponent for Campbell equation -0.30
(BCAM)
Volumetric water content (m3 m-3) 0.04
Soil organic carbon (%) 0.06
Diffusion coefficient (mm2 day-1) 60-120
Dispersivity (mm) 60-150
Bulk density (g cm-3) 1.00-1.30
Hydraulic conductivity (mm day-1) 10-100
Urea hydrolysis (day-1) 0.36
Nitrification (day-1) 0.3 0.04
Denitrification (day-1) 0.1 -0.024
Humus mineralization (day-1) 0.62
Base temperature (C) -0.67
Qio factor 0.4
Adapted from: Ng et al., 2000. Study conducted at Woodslee, Ontario, Canada from 1991-1994
using corn (Zea mays L., Pioneer 3573); Mahmood et al., 2002. Study conducted at Carterton,
New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species (two-year-old
Eucalyptus nitens and Eucalyputs ovata) and pasture.









Table 4-2. Calibration parameters identified for LEACHM for water flow and nitrate leaching.
Parameter Borah et al., Ng et al., Mahmood et al., Jabro et al., 2006
1999 1999 2002
value/range value value/range value/range
Air entry value (AEV) -0.1 -1.0 -0.3 -3
Exponent for Campbell equation (BCAM) 3.0 3-5 7.8-18.7
Clay particles% 20
Silt particles% 12
Volumetric water content (m3 m-3) 0.5 0.26-0.29
Soil organic carbon (%) 2.50-4.50
Dispersivity (mm) 120
Bulk density (g cm-3) 1.00-1.44
Saturated Hydraulic conductivity (mm day-1) 100-5100 40-2184
Water potential, kPa 10-35
C:N ratio 10:1 10
Nitrification (day-1) 0.2 0.1 0.2-0.4
Denitrification (day-1) 0.1 0.1 0.02-0.08
Adapted from: Borah et al., 1999. Study conducted at Manhattan, Kansas, USA form 1996-1997 using corn (Zea mays L.); Ng et al.,
2000. Study conducted at Woodslee, Ontario, Canada from 1991-1994 using corn (Zea mays L., Pioneer 3573); Mahmood et al., 2002.
Study conducted at Carterton, New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species (two-year-old
Eucalyptus nitens and Eucalyputs ovata) and pasture; Jabro et al., 2006. Study conducted at Rock Springs, Pennsylvania, USA form
1988-1991 using corn (Zea mays L.).









Table 4-2. Continued
Parameter Borah et al., Ng et al., Mahmood et al., Jabro et al., 2006
1999z 1999 2002
value/range value value/range value/range
Humus mineralization (day- ) 7x10-
Qio factor 3.0 2.0
Kd for NO3-N (L kg-1) 0.05 0.00
Litter mineralization (day-1) 0.01 0.01
Manure mineralization (day-1) 0.02 0.02
Humus mineralization (day-) 0.7 3 x 10
N Plant uptake (kg ha-1) 102
NH4-N (mg N kg-1) 2.63-4.00 3
NO3-N (mg N kg-1) 3.85-6.38 0
Adapted from: Borah et al., 1999. Study conducted at Manhattan, Kansas, USA form 1996-1997 using corn (Zea mays L.); Ng et al.,
2000. Study conducted at Woodslee, Ontario, Canada from 1991-1994 using corn (Zea mays L., Pioneer 3573); Mahmood et al., 2002.
Study conducted at Carterton, New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species (two-year-old
Eucalyptus nitens and Eucalyputs ovata) and pasture; Jabro et al., 2006. Study conducted at Rock Springs, Pennsylvania, USA form
1988-1991 using corn (Zea mays L.).









Table 4-3. LEACHM water, nitrate, and phosphorus input factors and their probability density functions.
No Factor Symbol PDF Mean Range Source
value


1 Initial water table (mm)
2 Clay %
3 silt %
4 Organic carbon %
5 Bulk density (g/cm3)
6 Air-entry value (KPa)
7 Campbell exp b coefficient

8 Hydraulic Conductivity
(mm day-1)
9 Matric potential (KPa)
10 Pore interaction parameter
11 Dispersivity (mm)
12 Initial NH4 in soil
(mg N kg-1)
13 Initial NO3 in soil
(mg N kg-1)
14 Initial Residue Carbon
(g C kg-1)


IWT
C%
S%
OC
Pb
AEV
BCAM


MP
PIP
D
NH4


Uniform
Uniform
Uniform
Normal
Uniform
Uniform
Uniform


Exponential

Uniform
Discrete
Uniform
Exponential


NO3 Lognormal


Normal


1600
25
24
4.270.97
1.42
-0.54
3.77

36703607

26
1
31.25
7.67+3.0


[1000-2200]
[17-31]
[28-52]
[5.6-10.4]
[1.136-1.704]
[-0.7 -0.38]
[2.64-4.90]

[1.25-25438]

[2-50]
[0-3]
[12.5-50]
[4.71-16.6]


21.27.9 [4.78-63]


101.1+7.9


[86.2-118.2]


Unpublished well survey data
Munoz-Carpena et al., 2002
Munoz-Carpena et al., 2002
Measured
Munoz-Carpena et al., 2002
Al-Yahyai et al., 2006 and value
computed using Van Genuchten' s
equation
Al-Yahyai et al., 2006 data, and
Mualem 1976 equation
Measured
Hutson, 2005
Estimate based on measured data
Estimate based on measured data

Estimate based on measured data

Estimate based on measured data











Table 4-3. Continued
No Factor Symbol


15 Initial Labile P in soil
(mg P kg-1)
16 Initial Residue P in soil
(mg P kg-1)
17 Freundlich Kd (L kg-1)
18 Exponent
19 Synthesis efficiency factor
20 Humification fraction
21 C:N ratio
22 C/P ratio
23 High end opt water
Relative transformation Rate
24 (day-1)
25 NH4->N03 rate (day-1)
26 N03->N rate (day-1)
27 Denitrification half
saturation (mg L-1)
28 Limiting N03/NH4 ratio


PDF


Lab-P Normal

Res-P Normal


Kd
Fe
Se
H
CN
CP
How
Tr

Knitri
Kdenit
KO.5denitr


Uniform
Uniform
Uniform
Uniform
Lognormal
Normal
Uniform
Uniform

Triangular
Triangular
Uniform


Mean value
34.56.14

103.718.3

32.5
0.56
0.5
0.2
3810.6
123.19
0.443
0.6

0.19
0.036
10


Ldenit Uniform 8


Range
[29-52]

[60-157]

[15-50]
[0.50-0.63]
[0.35-0.65]
[0.14-0.26]
[24-57]
[7-17]
[0.361-0.524]
[0.42-0.78]

[0.1-0.8]
[0.001-0.1]
[7-13]

[5.6-10.4]


Source


Estimate based on measured data

Estimate based on measured data

Zhou and Li, 2001

Johnsson et al., 1987

Measured
Measured
Estimate based from data
Johnsson et al., 1987

Munoz-Carpena et al., 2010
(manuscript under review)
Johnsson et al., 1987

Johnsson et al., 1987









Table 4-4. Selected sensitive parameters by Morris' method
No Parameter Symbol Drainage LeachedNO3 LeachedP
1 Organic carbon % OC 2 9
2 Bulk density (g/cm3) pb 5 4 7
3 Air-entry value (KPa) AEV 3 10 6
4 Campbell exp b coefficient BCAM 4 8 8
5 Hydraulic Conductivity (mm day-1) K 1 3 3
6 Pore interaction parameter PIP 2 5 5
7 Dispersivity D 12
8 Initial NH4 in soil (mg N kg- ) NH4 7
9 Initial Labile P in soil (mg P kg-1) Lab-P 2
10 Freundlich Kd (L kg-1) Kd 1
11 Exponent (empirical constants <1) Fe 4
12 C:N ratio CN 1
13 C/P ratio CP 10
14 High end optimum water How 9
15 Relative transformation Rate (day-1) Tr 6
16 NH4->NO3 rate Kniti 11
17 NO3->N rate Kdenit 13

Numbers in the table represent the parameter ranking in decreasing order of importance
where "1" = most important and "-" not relevant.









4-5. Extended Fourier Amplitude Sensitivity Test (FAST) results for LEACHM.
No. Factor symbol First order sensitivity index, Si (%) Interactions, STi-Si (%)
Drainage Leached Leached Drainage Leached Leached
N03-N P N03-N P
1 OC 0 12 0 1 7 2
2 Pb 5 4 0 1 4 2
3 AEV 2 0 0 1 3 2
4 BCAM 6 1 0 2 3 1
5 K 56 44 0 10 11 2
6 PIP 20 8 0 8 6 2
7 D 0 0 0 2 4 2
8 NH4 0 1 0 2 3 2
9 Lab-P 0 0 16 2 3 9
10 Kd 0 0 72 2 4 10
11 Fe 0 0 0 2 2 3
12 CN 0 17 0 2 8 2
13 CP 0 0 0 2 5 2
14 How 0 0 0 2 4 2
15 Tr 0 0 0 2 5 2
16 Knitri 0 1 0 2 5 2
17 Kdenit 0 0 0 2 2 2
Total 89 87 89









Table 4-6. Uncertainty analysis statistic for the 3 selected output probability distributions
obtained from Extended FAST results, n=13300.
Statistical Drainage (mm) Leached N03-N Leached P
indicator (kg ha-1) (kg ha-1)


Range
Max
Min
Median


Mean
Std Dev
Std. Error
95% Confidence interval
Skewness
Kurtosis


100.4


138
6.34
0.054
127-151
0.584
0.407


3.4
3.3
3.5
3.5
0.17
0.001
3.2-3.9
1.391
3.43


11
11.2
0.17
1.5
1
2.6
2
1.46
0.013
0.4-5.9
1.682
3.275


Table 4-7. Rainfall and irrigation amounts for selected months in 2008 and 2009.
Calibration Validation
Month Rain Irrigation Total Month Rain Irrigation Total
------------mm--------------- ----------------mm---------


Mar.2008
Apr. 2008
May. 2008
Jul. 2008


71.9
111
45.7
152.7


97.5
104.5
104.5
66.5


169.4
215.5
149.2
219.2


Mar. 09
Apr. 09
May. 09
Jul. 09


55.4 76 131.4


9.7
320.3
86.3


114
85.5
114


123.7
405.8
200.3


Table 4-8. Modeled leached nutrient loads for the selected BMP.
Irrigation amount for each event NO3-N TP
and nutrient management -----------kg ha-1----------


9.5 mm with FSR
8.6 mm with 90%FSR
8.1 mm with 85%FSR
7.6 mm with 80%FSR
P-value


0.9258
0.7575
0.6815
0.6093
0.8286


0.05925
0.05350
0.05100
0.04850
0.9539























AEV
Pb, PIP
* BCAM


0 10


20 30 40 50 60


.oc


CN


Tr PIP
SBeAM Pb
A BCAM


U.I


|AEV
e. PIP

0

Absolute value


Lab-P
0


1 2 3

of mean elementary effects, p*


Figure 4-1. Global sensitivity analysis results obtained from the Morris (1991) screening
using the LEACHM model as monthly average values for the period May to
December 2006. A) drainage, B) nitrate, and C) phosphorus. Input factors
separated from the origin of the u *-c plane were considered important. Labels
of less important or unimportant parameters factors (close to the p *-Cr plane
origin) have been removed for clarity. Factor definitions are given in Tables 4.3
and 4.4.













0.06



0.04


0.02



0.00 -
110


0.08


0.06


0.04


0.02


0.00


Ii 0
120 130 140 150 160 170
Drainage (mm)
100

80

60 LI
40
40 0


3.2 3.6 4.0

Leached N03-N (kg ha-1)


0.08


0.06


0.04


0.02


0.00


0 2 4 6 8
Leached TP (kg ha-1)


-4 0
4.4


100

80

60

40

20

0


10 12


Figure 4-2. Global uncertainty analysis results obtained from the eFAST variance-based
method expressed in form of probability distribution function (PDF) and
cumulative distribution function (CDF) for monthly average for the period May
to December 2006. A) drainage, B) nitrate, and C) phosphorus.


151























0 -
120

100

80

60

40

20

0
3.0


130 140 150 160 170


Drainage (mm)


3.5 4.0 4.5

Leached N03-N (kg ha1)


0 2 4 6 8 10

Leached P (kg ha-1)
Figure 4-3. Global uncertainty analysis results obtained from the extended FAST variance-
based method expressed in form of probability of exceedance for monthly
average for the period May to December 2006. A) drainage, B) nitrate, and C)
phosphorus.


100



















* Observed
Simulated


* Observed
- Simulated


20

2.5

S2.0


1.5




0 0.5
---






0.0

0.12


C-
0.08
-o
C)
O

. 0.04
0-
I-


Mar 08 Apr08 May 08 Jul08
Month


Figure 4-4. LEACHM model calibration results on a monthly basis. A) drainage, B)
nitrate, and C) phosphorus.


120

100

80

60

40


* Observed
S Simulated


0.00


0



















l// \ \
:/ w
\ i i


---- 9.5 mm+FSR
-o 8.6 mm+90%FSR
----- 8.1 mm+85%FSR
---- 7.6 mm+80%FSR










B


,


Month


Figure 4-5. Simulatedmonthly leached nutrients at different irrigation volumes and
fertilizer rates. A) drainage, B) nitrate, and C) phosphorus.




A


00
0.12


0.08



0.04


0.00









CHAPTER 5
SUMMARY

Nutrient leaching from agricultural fields, a major source of water body impairment in

many parts of the worlds including Florida (Burkitte et al., 2004), is attributed to improper

matching of the optimal fertilizer and water to the crop and production needs. Nutrient leaching

is of particular concern in south Florida due to the interaction between surface water and

groundwater arising from a shallow water table and the existence of naturally sensitive water

bodies such as the Biscayne Bay and the Everglades (Browder et al., 2005; Reddy et al., 2006).

Nutrient leaching measurement techniques primarily include use of tension ceramic lysimeters

and zero tension lysimeter. However, contradictory results are cited in literature on the efficacy

of using tension ceramic lyismeters to sample a leachate containing P (Litaor, 1988). Although

nutrient leaching can be controlled through efficient use of water and fertilizer application, no

irrigation and nutrient BMP has been developed, tested, and documented for tropical fruit tree

crops. The overall goal of my study was to evaluate techniques for estimating nutrient (N and P)

leaching as a method for assessing irrigation and nutrient BMPs. The specific objectives and

summary of results are outlined below.


Objective 1

Determine if ceramic tension lysimeters interfere with the chemical composition of

sampled water containing P and to compare leached concentration of N and P estimated using

tension and gravitational lysimeters in gravelly calcareous soils. The specific objectives were: 1)

to evaluate the chemical composition of three commercially available ceramic cups (referred to

as Ceramics A, B, and C); 2) to determine P adsorption and desorption potential of the three

ceramic water samplers; 3) to compare P concentrations in water samples collected using the

three ceramic lysimeters; and 4) to investigate gravitational (bucket) and ceramic tension









lysimeters in order to compare concentrations ofNO3-N and P04-P sampled by these devices for

application in leachate studies.


Analysis of the ceramic material composition indicated that all three types of ceramic
lysimeters contained substantial amounts ofFe, Al, Si, and Ca that may influence P
estimation depending on the pH and concentration of the soil water being sampled.

The adsorption-desorption study and fitting of Freundlich and Langmuir isotherms
provided information that assisted in interpreting the different results in that the lower the
ceramic's Smax the more accurate the sampler was in estimating P04-P concentration of a
known stock solution.

A protocol to be followed before deciding to use ceramic samplers in nutrient leaching
and monitoring studies was proposed to give authenticity of the reported data. The
suggested protocol explored if the ceramic material would interfere with the element to
be sampled and it involved the following three steps: 1) determining the chemical
composition of the ceramic cups; 2) developing sorption isotherms; and 3) testing the
efficiency of ceramic sampler to sample a stock solution of a known concentration.

The comparison of the leachate concentrations ofNO3-N or P04-P in a controlled
environment showed no significant differences (p < 0.05) between the two devices due
the soil's saturated flow conditions during sampling. However, since bucket lysimetes
captured a cumulative leachate from macro flow and the ceramic lysimeters represented a
"snap-shot" of micro flow, a significant difference was observed in P04-P concentration
from the orchard sampled between the two devices.

Objective 2

Evaluate the effect of nutrient load and irrigation scheduling on water volume applied,

nutrient leaching, and fruit yield of avocado trees in calcareous soils. The specific objectives

were to determine the effect of nutrient load and irrigation scheduling on: 1) nutrient leaching of

N and P; 2) tissue nutrient status, growth, and yield of 'Simmonds' and 'Beta' avocado cultivars;

and 3) soil nutrient indicators (soil organic carbon, C:N and C:P ratios, and soil inorganic N).


*Irrigating young avocado trees based on ET or SW saved 93 and 87% respectively of the
water volume applied compared to irrigation based on a set schedule over the four year
period. No significant differences (p < 0.05) were observed between the water volume
applied during the wet or dry season from each of the irrigation methods.









Irrigating based on SW with FSR resulted in the highest average annual reductions of 70
and 75% in N03-N and TP leaching respectively compared to the set schedule irrigation
method over the two years of leachate sampling. Such high reduction were attribute to
nutrient leaching being more influenced by the irrigation management than fertilizer rate.

Based on the highest avocado fruit yield of 'Simmonds' avocado, CP-WUE, and
CP-FUE values SW with FSR (treatment 4) ranked higher than the other treatments and
thus is proposed as the BMP for the production of' Simmonds' avocado. ET with FSR
(treatment 1) was the second best irrigation and fertilizer management practice. Yield
results for 'Simmonds' suggest that this avocado cultivar is responsive to well maintained
soil water regime in the root zone.

For 'Beta' cultivar, both irrigation and fertilizer rate had no effect on avocado fruit yield
in both 2008 and 2009, with no interactions between irrigation volume and fertilizer rate.
Further research is required to explore if 'Beta' fruit yield would be influenced by either
a lesser or greater irrigation and nutrient treatment.

Generally no significant differences (p <0.05) were observed among treatments for
SPAD value; leaf TN, TC, and TP; trunk diameter; and soil organic carbon, C:N and C:P
ratios, and soil inorganic N.

Objective 3

Apply modeling techniques to refine the identified BMP for avocado production. The

specific objectives were: 1) perform a global sensitivity and uncertainty analysis of LEACHM

model (Hutson and Wagenet, 1992), and (2) apply LEACHM to refine the identified BMPs in an

avocado orchard while saving water volumes applied and reducing nutrient leaching.


From the 28 input factors initially identified, the Morris' screening methods reduced the
number of factors that influence drainage, N03-N, and TP to 17.

The eFAST sensitivity method showed that total first-order effects explained 89, 87, and
89% of the output variability in drainage water volume, leached N03-N, and leached TP,
respectively.

Hydraulic conductivity (K) was the highest ranked factor which accounted for 56 and
44% variability in the outputs of drainage and leached N03-N respectively. The
variability in the output in leached TP was influenced by labile P (16%) and Kd (72%).

Uncertainty analysis performed using eFAST showed that there was minimal uncertainty
in predicting N03-N load leached in comparison to the uncertainty involved in predicting









drainage or leached TP. The amount expected to be exceeded at 50% probability were
137 mm and 3.4 and 1.5 kg ha-1 for drainage and leached N03-N and TP, respectively.

The LEACHM model was successfully calibrated using data for 2008 with the
Nash-Sutcliff and index of agreement coefficients in the range of 0.87 to 0.98 and
minimal RMSE less than 4 (mm or kg ha-1). The model could not be validated because
the methods used to sample drainage were not able to capture an adequate leachate
volume for the amount of rainfall received in 2009.

The proposed refined BMP is to apply 8.6 mm (soil water suction set at 16.5 kPa which
corresponds to a volumetric water content of 0.21 cm3 cm3) and to apply 90% of the FSR
in the second year of fruiting. Since nutrient leaching was mainly driven by rainfall the
refined BMP would not result in a significant (p < 0.05) reduction in leached loads of
NO3-N and TP in comparison to SW with FSR.

Study implications and contributions

The findings from my study form a basis for developing irrigation and nutrient BMPs for

other tropical fruit crops grown in south Florida and elsewhere. Modeling tools provide an

opportunity to refine the identified BMPs which may lead to further saving in water volume

applied and fertilizer rate applied and subsequently reduce nutrient leaching. The following are

the major contributions from my study:


The determination of the ceramic cup composition, the P adsorption-desorption study,
and the fitting of the Freundlich and Langmuir isotherms provided information that
assisted in interpreting the different results of sampled P04-P stock solution. The lower
the ceramic's Smax the more accurate the sampler was in estimating P04-P concentration
of a known stock solution. This is the first time that such a detailed working involve
exploring the efficacy of ceramic lysimeters to sample a leachate containing P had been
done. The protocol developed to test the suitability of ceramic lysimeter to sample
elements in a leachate would be beneficial to other researchers intending to use ceramic
lysimeters.

Irrigating young avocado trees based on SW with FSR saved 87% of the water volume
applied resulted in annual reductions of 55 and 75% in N03-N and TP leaching in
comparison to the set schedule irrigation with FSR. Such high reductions in water volume
did not affect avocado yield and were attributed to use of microsprinklers and nutrient
leaching being more influenced by the irrigation management rather than the fertilizer
rate.









Although the current practice in south Florida is to apply the same fertilizer amount to all
avocado cultivars, some avocado cultivars respond differently to the fertilizer rate. This
would save producers the extra cost incurred in applying surplus fertilize amount for
cultivars that may be predispose to greater production at lesser fertilizer rates.

Previous use of global modeling techniques by Soutter and Musy (1999) with LEACHC
(a sub-suite ofLEACHM for modeling pesticide leaching) was limited to sensitivity
analysis and did not involve a factor screening process. This is the first time that a global
sensitivity and uncertainty analysis has been applied to LEACHN (a sub-suite of
LEACHM for modeling N and P leaching) and also to simulate P leaching using
LEACHN. The global analysis tools proved to be effective in factor screening,
quantifying each factors contribution to variability in the desired model output,
identifying and quantifying factor interactions, and accounting for uncertainty in model
output. This resulted in performing model simulations at a lesser computational cost and
obtaining model results with less uncertainty.

Research Synthesis

Considering that water is becoming scarce due to climatic variability and increasing

demand by uses other than agricultural, irrigation BMPs are beneficial for 'Simmonds' avocado

producers. By applying less water volumes, lower fertilizer rates are needed to support crop

growth since nutrient leaching is reduced. The identified irrigation BMP involves using a

tensiometer to trigger irrigation. However, tensiometers may require biweekly checkups to

ensure their functionality and this may be a drawback for adopting this technology since tropical

fruit producers in south Florida typically use a set schedule which has no maintenance

requirements. An economic analysis of the proposed BMP should be performed and a survey

conducted to assess the opinion of the tropical fruit producers about this BMP. ET-based

irrigation, a suitable alternative with no maintenance requirements, requires research to develop

local k, coefficients. The kc values used in my study were developed based on best professional

judgment and resulted into lesser irrigation water volume and a lesser fruit yield in comparison

to SW-based irrigation.









Based on avocado fruit yield, CP-WUE, and CP-FUE values SW with FSR (treatment 4)

and ET with FSR (treatment 1) had higher fruit yield for 'Simmonds' than the other treatments

and should be explored further as trees mature to determine if similar results occur. Although the

current practice is to apply the same fertilizer amount to all avocado cultivars, my study results

showed that some avocado cultivars may respond differently to the fertilizer rate. This would

save producers the extra cost incurred in applying surplus fertilizer amount for cultivars that may

be predisposed to greater production at lesser fertilizer rates. The amount of P in fertilizer

applied was reduced by half during fruit production years, yet the C:P ratio reduced over the

years. This implies that the P formulation in the fertilizer grade could be lowered. Although the

fertilizer application was similar between 'Simmonds' and 'Beta' their yields were different. In

my study 'Beta' fruit yield did not respond to variations in both irrigation water volumes and

fertilizer rates during the fruiting years of 2008 and 2009. Research is needed to explore other

factors that may influence yield in avocado or whether a lesser or greater irrigation and nutrient

treatment would influence yields.

Modeling tools provide an opportunity to explore different irrigation and nutrient

management scenarios that cannot be tested in the field due to limitations such as field space,

time, and funds. In my study the measured data corroborated the simulated data during model

calibration; however, the same agreement could not be attained during model validation due to

the limitations with the method used to measure drainage. The field measured data was collected

using 20 L bucket lysimeters installed 0.3 m below the ground. One of the drawbacks of bucket

lysimeters is the failure to collect leachate under unsaturated soil conditions due to by-pass flow.

By-pass flow could occur during high intensity rainfall events due to high water percolation rate

of Krome soil. Research is needed to explore if increasing the water inlet area (which was 840









mm2) on the bucket catch pan (which was 51,945 mm2) affects the leached volume collected

during rainfall events of varying intensities and magnitudes. The physical sampling limitations

associated with ceramic tension lysimeters could be minimized by equipping the sampler with a

cumulative leachate sampling device similar to the one used by Haines et al. (1982). This would

reduce uncertainty associated with leachate concentrations data since both unsaturated and

saturated water flow would be sampled. Since nutrient leaching is mostly reported as a load,

sampling using tension lysimeters would require a drainage measurement method such as

measuring soil water content at two soil depths of 15 and 30 cm at desired time interval using a

data logger. Having data collected on a shorter time interval is beneficial in exploring other

LEACHM model outputs formats e.g. weekly, 10-day or 15-day time periods. This would

provide data with minimized uncertainty for model calibration and validation of water flow. A

good calibration and validation for water flow is essential since nutrients move with water.

LEACHM model simulates inorganic N fertilizers in form of Urea, NH4 or NO3 by

subjecting them to an immediate local equilibrium (Hutson, 2005). However, this analysis does

not account for the slow release effect in the granular fertilizers used in production of tropical

fruits. To achieve the slow release effect (of 25% for N) of the fertilizer applied in model

simulation, monthly fertilizer inputs were split into two parts to coincide with the months when

peak N03-N concentrations were sampled. Exploring field dissolution rates of slow release

fertilizer and making the necessary modification in the way LEACHM model simulates N would

be a good research topic. Another way of refining irrigation and nutrient BMPs would be to

collect data on nutrient uptake of avocado since the LEACHM model is capable of simulating

nutrient uptake.









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BIOGRAPHICAL SKETCH

Nicholas Kiggundu was born in Uganda. He spent the first years of his youth with his

paternal grandparents and during that time he was exposed to various agricultural activities. He

attended St. Henry's College Kitovu, Masaka, Uganda for both his ordinary level and high

school education. He graduated with a bachelor's degree in agricultural engineering from

Makerere University and was awarded a fellowship from the African Academy of Sciences to

pursue a Master of Science degree in agricultural Engineering at the University of Nairobi.

Nicholas possesses two postgraduate diplomas in Groundwater Resources Exploration

Exploitation and Management from The Hebrew University of Jerusalem, Rehovot, Israel and

River Basin Hydraulics from the Hydraulic Research Institute, Delta Barrage, Egypt. Nicholas

has served his country, Uganda, as a Water Officer for a Non Government Organization, a

Lecturer at Makerere University, a Chairperson of Uganda Rainwater Association, and as a

writer of revision mathematics pamphlets for high school students. Nicholas realized that to be

able to answer some of the challenges Uganda faced in the areas of training and research he had

to return to school to obtain a PhD. His goal is to be able to contribute to the sustainable use of

water for production by both the small and large scale growers through teaching and research.





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1 ASSESSING NUTRIENT LEACHING UNDER DIFFERENT NUTRIENT AND IRRIGATION MANAGEMENT PRACTICES By NICHOLAS KIGGUNDU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQ UIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

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2 2010 Nicholas Kiggundu

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3 To my daughter, Gabriella and wife M ary Louise

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4 ACKNOWLEDGMENTS I give Glory and thanks to God f or the gift of life and for the many blessings that have I received during my Ph. D. training. I thank my parents for teaching me many important aspects of life that have enabled me to keep working hard, believing, and having faith that I can attain a Ph. D. degree. My sincere thanks go to Dr. Kati W. Migliaccio my major advisor for teaching me how to conduct research and how to write scientific manuscripts. Her patience, insight, dedication, thoughtfulness, and constant guidance were corner stones in the ti mely completion of my Ph. D. training. I am grateful to the supervisory services received from the members of my Ph. D. committee: Dr. Yuncong Li for guidance during the laboratory experiments and the sections that required knowledge of soil and water chem istry; Dr. Bruce Schaffer for guidance on data statistical analysis and knowledge related to crop physiology; Dr. Jonathan Crane for his support on the orchard management, interpretation of yield results, and for ensur ing that the trees remained healthy du ring the time of the experiment ; Dr James W Jones for his guidance on model global sensitivity analysis ; and Dr Kirk Hatfield for his guidance on the use of LEACHM model I wish to acknowledge the fruitful discussions I had with Dr Muoz -Carpena on hy drological modeling. I have benefited from the discussions I have had with my colleagues in the A gricultural B iological E ngineering Department on handling model simulation problems and I wish to c onvey my sincere thanks to Kofikuma Dz ot s i Oscar Perez Ovil la Zuzanna Zaja c Anna Cathe y Stuart Muller and Gareth Lag erwa ll I am grateful to my friend Girish Ravunnikutty for his assistance with coding in C++. The advices of my colleague Dr. Gregory Hendricks on managing the many challenges of life in gradua te school were very invaluable I wish to thank the technicians at Tropical Research and Education Center (TREC) in Homestead, FL namely; Tina Dispenza Michael Gutierrez, Wanda Montas and Harry Trafford who constantly helped me with data collection especi ally during the time I was at the main

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5 campus in Gainesville working on my course work. The follow ing colleagues were instrumental in data collection too: Dr. Richard Carey Isaya Kisekka Luis Barquin Brigette Castro Chunfang Li "Daisey", David H Boniche and Paulina Sabbagh and I greatly appreciate their support. I am grateful to the workshop and field crew team at the T REC for their role in managing the avocado orchard that included mowing; applying pesticides, freeze protection, and tree staking. Ana lyzing water, soil, and tissue samples for various chemical elements was a big portion of my research. I wish to thank Dr. Qingren Wa ng the manager of the Soil and Water S cience laboratory at TREC for his guidance and patience during the time I was learning how to analyze samples for the various chemical elements. I a m indebted to Ms. Quigin Yu and Ms. Rosado Laura for their help with sample analysis and for putting in extra hours to ensure that I got my results on time. I benefited from the guidance of Dr. Guodong Liu and Dr. Fan Xiang on how to analyze and interpret nitrogen content from the different sources. I am very grateful for the following financial support; the College of Agriculture and Life Sciences at the Univer s ity of Florida for awarding me t he Alumni Fellowship that paid fo my tution and stipend, USDA -CSREES for funding my research work, and to Makerere University Administration for granting me a study leave and for the financial support. Finally I wish to thank my wife and daughter for their understanding, patience, love and support for the many days I was away from home working on my research and writing the dissertation. To my sisters brother and cousins I love you and I miss you all.

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6 TABLE OF CONTENTS ACKNOWLEDGMENTS .................................................................................................................... 4 page LIST OF TABLES ................................................................................................................................ 9 LIST OF FIGURES ............................................................................................................................ 12 LIST OF ABBREVIATIONS ............................................................................................................ 14 ABSTRACT ........................................................................................................................................ 15 CHAP T E R 1 INTRODUCTION ....................................................................................................................... 17 Nitrogen and Phosphorus Leaching from Agricultural Fields ................................................. 17 Nitrogen and Phosphorus Forms and their Reactions ............................................................... 19 Irrigation and Nutrient Best Management Practices (BMPs) ................................................... 20 ET based Irrigation .............................................................................................................. 21 Soil Water -based Irrigation ................................................................................................. 22 Irrigation and Nutrient BMPs ............................................................................................. 23 Assessing Nutrient Leaching ...................................................................................................... 24 Field Scale Simulation Modeling of Nutrient Transport .......................................................... 27 Model Sensitivity and Uncertainty Analysis ............................................................................. 31 Statement of Problem .................................................................................................................. 32 Goal and Objectives .................................................................................................................... 33 Rationale for Tropical Fruit BMPs in South Florida ................................................................ 34 Stu dy Area and Scope ................................................................................................................. 35 2 MONITORING NUTRIENT LEACHING USING TENSION AND GRAVITATION LYSIMETERS ............................................................................................................................ 43 Introduction ................................................................................................................................. 43 Materials and Methods ................................................................................................................ 46 Study Area ............................................................................................................................ 46 Chemical Composition of Ceramic Cups ........................................................................... 47 Phosphorus Adsorption and Desorption Potential of Commercial Ceramic Lysimeters ........................................................................................................................ 48 Sorption Isotherms ............................................................................................................... 49 Estimation of Known P Stock Solutions by Different Ceramic Lysimeters .................... 50 Comparison of Leachate Concentrations from Bucket and Ceramic Lysimeters Under a Controlled Environment .................................................................................... 52 Comparison of Leachate Concentrations from Bucket and Ceramic Lysimeters from an Avocado Orchard ............................................................................................... 54 Results and Discussion ............................................................................................................... 55

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7 Constituents of Elements in Ceramic Cups ........................................................................ 55 Adsorption -desorption Potential of the Ceramic Cups ...................................................... 56 Phosphorus Estimation by the Ceramic Lysimeters .......................................................... 57 Nutrient Concentrations Sampled from a Controlled Environment ................................. 59 Comparison of Nutrient Concentrations Sampled form an Avocado Orchard ................ 61 Conclusions ................................................................................................................................. 61 3 EVALUATION OF IRRIGATION AND NUTRIENT MANAGEMENT PRACTICES IN A YOUNG AVOCADO ORCHARD .................................................................................. 7 3 Introduction ................................................................................................................................. 73 Materials and Methods ................................................................................................................ 77 Study Area ............................................................................................................................ 77 Avocado Orchard Layout and Experimental Design......................................................... 78 Irrigation Management Practices ........................................................................................ 78 Plant Measurements ............................................................................................................. 81 Soil Sampling and Analysi s ................................................................................................ 82 Measuring Nutrient Loads Leached ................................................................................... 83 Effect of Fertilizer Amount and Irrigation Water Volume on Avocado Yield ................ 85 Results and Discussion ............................................................................................................... 86 Water Application ................................................................................................................ 86 Nitrogen and Phosphorus Loads Leached .......................................................................... 87 Soil Analysis ........................................................................................................................ 88 Avocado Yield Analyzed as Treatment Mai n Effects ....................................................... 90 Avocado Yield Analyzed as 2 levels of Irrigation by 3 levels of Fertilizer Factorial Design ............................................................................................................................... 93 Plant Nutrient Assessment and Tree Growth ..................................................................... 94 Recommendations from my Study ..................................................................................... 95 Conclusions ................................................................................................................................. 96 4 MODEL SIMULATION OF NITROGEN AND PHOSPHORUS LEACHING IN CALCAREOUS SOILS OF SOUTH FLORIDA ................................................................... 119 Introduction ............................................................................................................................... 119 Materials and Methods .............................................................................................................. 123 Experimental Design ......................................................................................................... 123 LEACHM Model ............................................................................................................... 124 Model Set up and Parameterization ................................................................................. 127 Global Sensitivity and Uncertainty Analyses .................................................................. 128 Model Calibration and Validation .................................................................................... 130 Refinin g the Selected BMP ............................................................................................... 132 Results and Discussion ............................................................................................................. 133 Global Sensitivity Analysis: Morris ................................................................................ 133 Global Sensitivit y Analysis: eFAST ................................................................................. 134 Model Calibration .............................................................................................................. 136 Constraints with Model Validation................................................................................... 136 Modeling of the BMP ........................................................................................................ 137

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8 Recommendations from my Study ................................................................................... 138 Conclusions ............................................................................................................................... 140 5 SUMMARY ............................................................................................................................... 155 Objective 1 ................................................................................................................................. 155 Objective 2 ................................................................................................................................. 156 Objective 3 ................................................................................................................................. 157 LIST OF REFERENCES ................................................................................................................. 162 BIOGRAPHICAL SKETCH ........................................................................................................... 177

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9 LIST OF TABLES Table page 1 1 Parameters used for sensitivity analysis in LEACHM for water and nitrate leaching ...... 37 1 2 Parameters used for calibration of LEACHM for water and nitrate leaching .................... 38 2 1 Composition of ceramic cups considering major eleme nts reported by chemical analyses, n=3. ......................................................................................................................... 64 2 2 Sorption parameters for the ceramic materials included in the experiment considering two isotherms and a P concentration range of 0 to 20 mg L1. ............................................ 64 2 3 Estimation of a known concentration of PO4P by different lysimeters, n=3. ................... 65 2 4 Estimation of a known concentration of PO4P by ceramics B and C after conditioning, n=3. .................................................................................................................. 65 2 5 Mass of P retained by lysimeter ceramic cups, n=3. ............................................................ 66 2 6 Status of t he cleaned lysimeters after sampling a known P concentration, n=3. ............... 67 2 7 Leachate concentration comparison of bucket and ceramic lysimeters for the sampling of 7/23/2008, n=3. .................................................................................................. 68 2 8 Leachate concentration comparison of bucket and ceramic lysimeters for the sampling of 8/25/2008, n=3. .................................................................................................. 68 2 9 Leachate concentration com parison of bucket and ceramic lysimeters for the sampling of 5/20/2009, n=3. .................................................................................................. 68 2 10 Leachate concentration comparison of bucket and ceramic lysimeters for the sampling of 6/17/2009, n=3. .................................................................................................. 69 3 1 Fertilizer at a standard rate management scheme used for the Simmonds and Beta avocado trees .......................................................................................................................... 98 3 2 The ETo and kc values used to compute water application rates for the ET -based irrigation management method .............................................................................................. 98 3 3 Amount of water applied (103 m3 tree1day1) by the different management practices .................................................................................................................................. 98 3 4 Nutrient leaching reduction percentange of Simmonds avocado trees as compared to set schedule with FSR treatment ....................................................................................... 99 3 5 Total nutrie nt load leached kg ha1from Nov 2007 to Oct 2009 for Simmonds avocado trees .......................................................................................................................... 99

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10 3 6 Simmonds and Beta soil analysis for irrigation and fertilizer management treatments 09/05/2006, n=3 ................................................................................................... 99 3 7 Simmonds and Beta soil analysis for irrigation and fertilizer management treatments 7/6/2007, n=3 ..................................................................................................... 100 3 8 Simmonds and Beta soil analysis for irrigation and fertilizer management treatments 9/22/08, n=3 ....................................................................................................... 100 3 9 Simmonds and Beta soil analysis for irrigation and fertilizer management treatments 9/7/09, n =3 ......................................................................................................... 100 3 10 Beta soil inorganic N for different sampling dates n=3 ................................................. 101 3 11 Simmonds soil inorganic N for different samplin g dates n=3 ....................................... 101 3 12 Soils factorial analysis of 2 levels of irrigation by 3 levels of fertilizer from 2006 to 2009 ....................................................................................................................................... 101 3 13 Simmonds avocado yield (kg ha1) by harvest date per treatment for 2008 and 2009, n=12 ...................................................................................................................................... 102 3 14 'Simmonds' avocado fruit number and weight per treatment for 2008 and 2009, n=12 .. 102 3 15 Beta avocado yield (kg ha1) by harvest date per treatment for 2008 and 2009, n=4 ... 103 3 16 'Beta' avocado fruit number and weight per treatment for two years, n=4 ....................... 103 3 17 Means fruit weight per cubic meter of water applied water applied (kg m3) .................. 103 3 18 Mea ns fruit weight per kg of fertilizer applied (kg kg1) ................................................... 104 3 19 Leaf tissue analysis for irrigation and fertilizer management treatments 08/03/2006, n=4 ......................................................................................................................................... 104 3 20 Leaf tissue analysis for irrigation and fertilizer management treatments 12/06/2006, n=4 ......................................................................................................................................... 104 3 21 Leaf tissue analysis for irrigation and fertilizer managemen t treatments 04/30/2007, n=3 ......................................................................................................................................... 105 3 22 Leaf tissue analysis for irrigation and fertilizer management treatments 08/23/2007, n=3 ......................................................................................................................................... 105 3 23 Leaf tissue analysis for irrigation and fertilizer management treatments 12/26/2007, n=3 ......................................................................................................................................... 105 3 24 Leaf tissue analysis for irrigation and fertilizer management treatments 04/08/2008, n =3 ......................................................................................................................................... 106

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11 3 25 Leaf tissue analysis for irrigation and fertilizer management treatments 08/13/2008, n=3 ......................................................................................................................................... 106 3 26 Leaf tissue analys is for irrigation and fertilizer management treatments 12/12/2008, n=3 ......................................................................................................................................... 106 3 27 Leaf tissue analysis for irrigation and fertilizer management treatments 04/20/2009, n=3 ......................................................................................................................................... 107 3 28 Leaf tissue analysis for irrigation and fertilizer management treatments 08/26/2009, n=3 ......................................................................................................................................... 107 3 29 Tissue factorial analysis of 2 -levels of irr igation by 3 levels of fertilizer for different sampling dates from 2006 to 2009 ...................................................................................... 108 4 1 Sensitivity analysis parameters used for in LEACHM for water flow and nitrate Leaching. ............................................................................................................................... 142 4 2 Calibration parameters identified for LEACHM for water flow and nitrate leaching. .... 143 4 3 LEACHM water, nitrate, and phosphorus input f actors and their probability density functions. ............................................................................................................................... 145 4 4 Selected sensitive parameters by Morris method .............................................................. 147 4 5 Extended Fourier Amplitude Sensitivity Test (FAST) results for LEACHM. ................. 148 4 6 Uncertainty analysis statistic for the 3 selected output probability distributions obtained from Extended FAST results, n=13300. .............................................................. 149 4 7 Rainfall and irrigation amounts for selected months in 2008 and 2009. .......................... 149 4 8 Modeled leached nutrient loads for the sel ected BMP. ..................................................... 149

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12 LIST OF FIGURES Figure page 1 1 The nitrogen cycle: the different transformations are numbered 1 to 7 (adapted from Havlin et al. 20 04). ................................................................................................................. 39 1 2 Illustration of the interactions between the different forms of P in the soil (adapted from Havlin et al. 2004). ........................................................................................................ 40 1 3 Bucket lysimeter. A) elevation view and, B) inside view (credits. Harry Trafford). ........ 41 1 4 Porous ceramic lysimeters and relevant sampling devices. A) ceramic lysimeter, B) pump used to apply suction, C) syringe used to expel leachate form the collection tubing (Adapted from Irrometer Company, Inc.). ................................................................ 41 1 5 Map of Miami -Dade County, south Florida and the surrounding water bodies ................ 42 2 1 Arrangement of the bucket and ceramic lysimeters in each container. A) cross section view of each container, B) arrangement of the four boxes under a rainfall simulator. ................................................................................................................................ 70 2 2 Sorption -desorption curves of P. A) Ceramic A, B) Ceramic B, C) Ceramic C. ............... 71 2 3 Elution curves of nutrient concentrations sampled by the lysimeters under a controlled environment. A) ceramic and B) bucket. ............................................................ 72 3 1 Orchard layout. A) Treatments and their replicates where the firs t number is the treatment and second number is the replicate, and B) tree cultivar and number and type of device installed beside the tree. .............................................................................. 109 3 2 Automated switching tensiometers were set at 15 kPa. ..................................................... 109 3 3 Amount of water applied as daily average per month by the Set s cheduls, ET and SW irrigation manageme n t. ........................................................................................................ 110 3 4 Mean water volume applied per day for ET ba sed, SW -based, and Set schedule based irrigation during wet and dry season. ....................................................................... 111 3 5 Correlation of historical and real time ETo (R2 = 0.913). .................................................. 111 3 6 Leached nitrate for Simmonds over a two year period.. A) November 2007 to October 2008 and B) November 2008 to October 2009. ................................................... 112 3 7 Leached total phosphorus for Simmonds over a two year period. A) November 2007 to October 2008 and B) November 2008 to October 2009. ..................................... 113 3 8 Effect of irrigation and fertilizer management treatments on mean fruit weight of av ocados for 2008 and 2009. A) Simmonds, and B) Beta ........................................... 114

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13 3 9 Simmonds yield analyzed as a factorial design with 2 levels of irrigation and 3 levels of fertilizer input. A) 2008, and B) 2009. ............................................................. 115 3 10 Beta yield analyzed as factorial design with 2 -levels of irrigation and 3 levels of fertilizer input. A) 2008, and B) 2009. ................................................................................ 116 3 11 Effect of irrigation and fertilizer management treatments on mean tree diameter for 4 year. A) Simmonds, and B) Beta. ............................................................................... 117 3 12 Effect of irrigation and fertilizer manag ement treatments on mean SPAD value for reading collected from 2006 to 2009. A) Simmonds, and B) Beta. ............................ 118 4 1 Global sensitivity analysis results obtained from the Morris (1991) screening using the LEACHM model as monthly average values for the period May to December 2006. A) drainage, B) nitrate, and C) phosphorus. Input factors separated from the origin of the plane were considered important. Labels of less import ant or unimportant parameters factors (close to the plane origin) have been removed for clarity. Factor definitions are given in Tables 4.3 and 4.4. ......................................... 150 4 2 Global uncer tainty analysis results obtained from the eFAST variance-based method expressed in form of probability distribution function (PDF) and cumulative distribution function (CDF) for monthly average for the period May to December 2006. A) drainage, B) nitrate, and C) phosphorus. ............................................................ 151 4 3 Global uncertainty analysis results obtained from the extended FAST variance based method expressed in form of probability of exceedance for monthly average for the period May to December 2006. A) drainage, B) nitrate, and C) phosphorus. .................. 152 4 4 LEACHM model calibration results on a monthly basis. A) drainage, B) nitrate, and C) phosphorus. ...................................................................................................................... 153 4 5 Simulated monthly leached nutrients at different irrigation volumes and fertilizer rates. A) drainage, B) nitrate, and C) phosphorus. ............................................................. 154

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14 LIST OF ABBREVIATIONS ASCE -EWRI American Society of Civil Engineers -Environmental and Water Resources Institute BMP Best management practice CP FUE Crop production fertilizer use efficiency CP WUE Crop production water use efficiency eFAST Extended Fourier amplitude sensitivit y test EPA Environmental Protection Agency ET Evapotranspiration FAO Food and Agricultural Organization of the United Nations FAWN Florida Automatic Weather Network FDACS Florida Department of Agriculture and Consumer Services FDEP Florida Department of En vironmental Protection FSR Fertilizer at a standard rate IFMP Irrigation and fertilizer management practices LEACHM Leaching estimation and chemistry model SPAD Soil plant analysis development SW Soil water

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15 Abstract of Dissertation Presented to the Gra duate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ASSESSING NUTRIENT LEACHING UNDER DIFFERENT NUTRIENT AND IRRIGATION MANAGEMENT PRACTICES By Nicholas Kiggundu Aug ust 2010 Chair: Kati W. Migliaccio Major: Agricultural and Biological Engineering Leaching of nitrogen (N) and phosphorus (P) from agricultural fields is one cause of water resource impairment. My study was designed to 1) explore if ceramic tension lysim eters interfere with the chemical composition of leachates containing P and to compare leached concentrations of N and P estimated using tension and gravitational lysimeters, 2) determine the effect of seven irrigation and fertilizer management practices ( IFMP) on nutrient leaching and avocado ( Persea Americana Mill. ) yield, and 3) apply the Leaching Estimation and Chemistry Model (LEACHM) to refine the identified management practice. A n evaluation of three commercially available ceramic lysimeters (i.e., C eramic A, Ceramic B, and Ceramic C) indicated that all three ceramic s contain ed various chemical elements that may influence P estimation of the leachate. The elements identified that may influence P estimation depending on the pH and concentration of the soil water being sampled were Fe, Al, Si, and Ca T he lower the ceramics P adsorption maximum ( maxS ) the more accurate the sampler was in estimating PO4P concentration of a known stock solution. A protocol to be followed to determine the efficacy of ceramic lysimeters for water quality monitoring was proposed.

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16 Evaluation of the seven IFMP indicated that irrigating 'Simmonds' avocado trees based on soil water at 15 kPa (SW) with fertilizer at a standard rate (FSR) saved 87% of the water volume applied and resulted in average annual reductions of 70 and 75% in NO3-N and TP leaching respectively campared t o the set schedule irrigation with FSR method. Since Beta avocado is genetically predisposed to higher fruit yield than Simmonds the SW with FSR may not the best IFMP for the production of Beta. Global techniques that involved use of Morris' screening and eFAST methods were used to perform LEACHM model sensitivity and uncertainty analyses. The eFAST sensitivity method showed that tot al first order effects explained 89, 87, and 89% of the output variability in drainage water volume, leached NO3-N, and leached TP. The proposed refined best IFMP (BMP) would lead to further water and irrigation use efficiency and a sustainable management environment

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1 7 CHAPTER 1 INTRODUCTION Nitrogen and Phosphorus Leaching from Agricultural F ields Nutrient leaching from agricultural fields (primarily nitrogen [N] and phosphorus [P]) is a water quality concern in many areas of the world (Burkitt et al., 2004; Qui ones et al., 2007; Eulenstein et al., 2008) due to site -specific (e.g., groundwater nitrate concentrations) and downstream (e.g., increased eutrophication) water supply ramifications. While identified as a nutrient source, agricultural related nutrient leaching is fairly complex and is difficult to quantify as each situation and production system is very unique. Nutrient leaching, or the downward movement of dissolved nutrients in the soil profile with perc olating water (Havlin et al., 2004) is influenced by hydrologic and soil characteristics. Hydrologic characteristics of a locati on such as rainfall patterns (occurrence, intensity, duration, and amount) and infiltration characteristics of the soil influence nutrient leaching loads For example, s oils with high water infiltration rates and low nutrient retention capacity ( such as sa ndy soil or highly porous soils and well structured ferrallitic soils with low activity clays and low organic matter contents) are particularly conducive to nutrient leaching (Lehmann and Schroth, 2003) Nutrient leaching is also effected by fertilization practices, irrigation practices, crop characteristics, and production system management. Agricultural nutrient leaching is mainly attributed to fertilizers (which often include N P, potassium [K]) that are applied to enhance plant growth and yields. Although the intent is for these fertilizers to be used by the crop, some fertilizer s may leach into groundwater (Schaffer, 1998; Tischner et al., 1998; Schroder et al., 2005) and contribute to increased downstream eutrophication (Li et al., 1999) The residual amount of N and P in the soil after crop harvest and the rate of N and P mineralization for the decomposing plant residue also affect the nutrient loads

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18 leached (Jiao et al., 2004). Nutrient leaching may also occur due to over irrigation and heavy rainfall events that result in increased infiltration and drainage (He et al 2000; Muoz -Carpena et al., 2002) Fertilizer utilization efficiency practices such as timing of nutrient application, amount of nutrients applied, formulations of the nutrients, and method of application are some of the control measures used to reduce nutrient leaching from agricultural f ields. For example, split application of fertilizers as fertigation has shown reduction in nutrient leaching loads as opposed to applying fertilizers by broadcasting in one or two applications followed by water application (Nakamura et al., 2004; Quiones et al., 2007; Worthington et al., 2007). The nutrient leaching potential of many elements, especially N ( Tagliavini et al., 1996; Jalali, 2005 ; Evanylo et al., 2008 ) has been evaluated extensively. However, P related nutrient leaching has received less attention historically as the high P -fixation capacity in many mineral soils led researchers to believe that P leaching did not occur in an amount that would cause an environment al threat (Akinremi and Chou, 1991; Sims et al., 1998). This was further exacerbated by the traditional method of quantify ing P movement in soil based on extractable soil P as function of depth rather than sampling soil wat er using tile drains or lysimeters. Extractable soil P shows lower P concentration than the concentrations captured in tile drains or lysimeters due to macro pore or preferential flow (Brye et al., 2002). More current studies that focus on P ha ve attribute d P leaching to land use practices such as over fertilization (Bryan, 1933; Nelson et al., 2005; Zhao et al., 2009), excessive animal waste applications (Sim s et al., 1998; Elliott et al., 2002), over irrigation (Geohring et al., 2001; Maguire and Sims, 2002; Djodjic et al., 2004 ) and other factors like soil properties (Byre et al., 2002; Godlinski et al., 2008) and climatic conditions ( Granlund et al., 2007; Rankinen et al., 2009; Sharpley and Moyer 2000).

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19 Nitrogen and Phosphorus Forms and their Reactions Plants absorb both nitrate (NO3 -) and ammonium ( NH4 +) although the uptake of NO3 is usually greater than NH4 + (Fig. 1 1). The concentration of NO3 is usually greater than NH4 + in moist, warm, aerated soils. Since NH4 + is positively charged, it tends to leach less due to the negative charges associated with the soils cation exchange capacity. N itrate being negatively charged is repelled by soil colloids and is easily leached out of the root zone (Lehmann and Schroth, 2003). To protect humans from NO3 contamination, t he U.S. Enviro nmental Protection Agency (EPA) set a maximum contaminant level for NO3N of 10 mg L1 (equivalent to 45 mg L1 as NO3 -) for discharges into water bodies. Given that NO3 is mobile and can be transformed into other forms of N depending on soil water content, temperature, soil pH, and microbial activity; NO3 concentration in the soil profile varies widely. High N concentrations in soil water and groundwater samples have been documented in agricultural settings. Stanley et al. (2009) reported values of 10 to 20 NO3N mg L1 in leachate samples from citrus orchards in Manatee County, Florida. Such high N concentrations were attributed to unsuitable denitrification conditions caused by low soluble carbon content in sandy soils and low microbial counts. Similarl y, Yates et al. (1992) observed high NO3 concentrations (i.e., 50 to 200 mg L1) in water samples collected using suction lysimeters from avocado orchards in Corona, California Phosphorus dynamics in soil water differ depending on the chemistry and sorp tion/desorption characteristics of the soil. While the primary form of P in soil solution is orthophosphate (PO4 3), P chemistry in soil solution is driven by pH (Fig. 1 -2) such that at low soil pH (< 7.2) P exists as H2PO4 and at high pH (> 7.2) it exist s as HPO4 2 (Havlin et al. 2004). In acidic conditions P reacts with Al, Fe oxide, and hydroxide minerals through sorption and precipitation to form insoluble amorphous precipitates such as variscite (AlPO4.2H2O) and

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20 strengite (FePO4.2H2O) (Reddy and DeLa une, 2008). In soils that are alkaline due to the existence of CaCO3, small amount of HPO4 2 are retained through sorption at low P concentrations. At high P concentrations, much of P retention is by precipitation of HPO4 2 to form insoluble compounds such as like monocalcium phosphate [Ca(H2PO4)2], dicalcium phosphate (CaHPO4), and hydroxyl apatite [(Ca3(PO4)2)3Ca(OH)2] (Freeman and Rowell, 1981). However, once the P sorption capacity is reached additional P results in weaker bonding between the adsorbent and phosphate ( Wisawapipat et al., 2009) which may subsequently be available for leaching. Akinremi and Chou (1991) observed that the major form of P retention in most calcareous soils (with less than 300,000 mg CaCO3 kg of soil) is through precipitation rather that sorption since the sorption capacity of such soils is less than 25 mg kg1. Thus P sorption is limited to low concentration of P; and at high P concentration, P is removed from the solution through precipitation. Although the P compounds formed with mineral elements are insoluble, in conditions of prolonged water saturation, dissolution occurs and P may leach out of the system. Elevated lev els of P leaching from agricultural fields have been observed by several researchers. Coale et al. (1994) observed P leachate of 0.2 to 1.4 mg L1 from a sugarcane field in the Florida Everglades. Hooda et al. (1999) recorded leachate with P concentration in the range of 0.45 to 0.67 mg L1 from a grassland in southwestern Scotland. While, Sui et al. (1999) reported a P leaching concentration of 0.052 mg L1 from a switchgrass field in Ames, Iowa. Irrigation and Nutrient Best Management Practices (BMPs) N utrient leaching may be minimized by implementing different nutrient and ir rigation management practices. The ability of the practice or set of practices to reduce leaching depends on many factors including soil properties, production system characteristic s, irrigation requirements, and weather. Specific irrigation management practices that may be implemented to reduce nutrient leaching are ET based irrigation and soil water based irrigation.

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21 ET-based I rrigation ET based irrigation has been used for comput ing crop water requirement for many year s (Penman, 1948; Turc, 1961; Kisekka et al., 2010). The most commonly used method involves computing the reference evapotranspiration (ETo) using weather data (e.g., temperature, solar radiation, relative humidity, w ind speed, and sun shine hours). Due to climatic variability in time and space, several equations have been developed to estimate ETo for different locations. Applicability of these equations depends on data availability M ethods used to compute ETo that require one or two input variables include: class -A pan (Doorenbos and Pruitt, 1977) (evaporation); Turc, 1961 ( solar radiation and temperature) ; Food and Agricultural Organization of the United Nations ( FAO ) Blaney Criddle (Allen and Pruitt, 1986) (tempera ture and day time hours ); and Hargreaves and Samani (1985) (temperature and radiation ). While other methods such as Priestley and Taylor (1972); FAO Penman -Monteith (Allen et al., 1998); UF IFAS (1984) Penman (Jones et al., 1984); and the American Society of Civil Engineers -Environmental and Water Resources Institute ( ASCE -EWRI 2005) require several input variables to estimate ETo. Each of t hese equations was developed to estimate ETo under specific weather conditions and therefore the i r use in different c limatic conditions may yield inaccurate estimations ( Yodar et al., 2005). However, where data is available, the FAO Penman -Monteith and the ASCE EWRI methods have been shown to most accurately estimate ETo (Attarod et al., 2009; Pereira et al., 2009; Sahoo et al., 2009). The actual crop evapotranspiration (ETa) is computed as: c o ak ET ET (1 1) where kc is the crop coefficient (unitless). The availability of crop coefficients is one of the limitations of the ET based irrigation method, since these coefficients require time and financial resources to be devel oped and once

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22 developed they remain site and cultivar specific. The kc accounts for four aspects that differentiate the reference grass (alfalfa) that was used in the development of the ETo equation to the crop of interest to be irrigated (Allen et al. 1998). These kc aspects are: 1) crop height since it influences the aerodynamic resistance, 2) albedo which influences the net radiation of the soil surface, 3) crop canopy resistance to vapor transfer that influences surface resistance and 4) evaporation f rom the bare soil in the field. Kisekka et al. (2010) evaluated ET based irrigation scheduling in a c arambola ( Averrhoa carambola) orchard in Homestead, F L. The authors observed that ET based irrigation resulted in 70% water saving in comparison to set schedule irrigation (calendar based irrigation) with no significant difference (p < 0.05) between yields for the two irrigation methods Migliaccio et al. (2010) assessed plant response in the production of papaya ( Carica papaya) using ET based irrigation and set schedule irrigation on Krome very gravelly loam soils of south Florida. The authors observed water savings of 66% from ET -based irrigation in comparison to set schedule irrigation (calendar based irrigation) However, for the ET based irrigation and s et schedule irrigation there was no significant difference s (p < 0.05) between plant nutrient content, growth, photosynthetic rates, and fruit yields. Others ( Meyer and Marcum, 1998; Silva et al., 2009; Spreer et al., 2009) have conducted research on the E T -based irrigation method and have reported water savings (13 to 46%) and increased yields (6 to 11%) as opposed to using a set schedule irrigation based on calendar days or time and frequency of irrigation. Soil Water -based I rrigation S oil water sensors m easure the soil water content and can be linked with irrigation control equipment to automate irrigation scheduling. Zotarella et al. (2008) reported that using soil water based drip irrigation reduced water application by 33 to 80% compared to a scheduled drip irrigation method at Citra, Florida. In a study on assessing irrigation BMPs using tensiometers in

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23 a royal palm ( Roystonea elata) field nursery in Homestead, Florida Migliaccio et al. (2008) found that an automat ed irrigation system that irrigate d at soil suctions of 5 and 15 kPa reduced water volumes applied by 75 and 96% when compared to standard irrigation scheduling without reducing tree quality. Meron et al. (2001) reported that irrigating at a soil suction of 15 to 25 kPa resulted in water sav ing s of 500 to 650 mm in comparison to ET based irrigation water use of 700 to 850 mm per season in an apple ( Malus domestica) orchard in Upper Galilee, Israel, which has a Mediterranean climate. Kukal et al. (2005) reported for rice ( Oryza sativa ) in Ludh iana, India, that irrigati ng at a soil suction of 16 kPa resulted in water saving of 30 to 35% in comparison to the traditional practices of a 2 -day irrigation set schedule interval. Working with avocado in South Africa, du Plessus (1991) reported that t en siometer irrigation scheduling at 30 kPa on sandy soil and at 50 kPa on clay soils at a depth of 300 mm gave optimum yields as opposed to irrigation based on ET. du Plessus (1991) observed that although irrigating based on ET is easy to apply, the crop fac tors do not reflect the water status of the soil or plant and t he accuracy of this method is further limited by the requirement for conducting extensive research to develop the crop factors that depict local conditions Others (Enciso et al., 2009; Fuente s et al., 2008; McCready et al., 2009; Migliaccio et al., 2010) have als o conducted irrigation research using soil water sensors and have reported water savings 25 to 7 4% compared to set schedule irrigation practices Irrigation and N utrient BMPs Nutrient BMPs in combination with irrigation BMPs, either through use of ET based irrigation ( Yates et al., 1992; Diez et al., 1997; Doltra et al., 2008; Paulino -Paulino et al., 2008) or soil water sensor based irrigation ( Paramasivam et al., 2000; Lao and Jimenez 2004; Alva et al., 2006) ha ve been evaluated for the impact of the combined irrigation and nutrient BMPs on nutrient leaching reduction and water savings. Alva et al. (2003) conducted a study on sandy

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24 soils in Lake Alfred, Florida, for over five years where they monitored the effects of different N and irrigation BMPs on orange yields (Citrus sinensis ) and NO3N concentrations in groundwater. The authors reported water savings and N leaching reductions with irrigation based on soil suctions of 10 and 15 kPa and split N fertigation in comparison to non-systematic irrigation scheduling wi th broadcasting of N fertilizer. Likewise, a study was conducted by Qui ones et al. (2007) on eight year old citrus trees in Moncada, Spain, on sandy loam textured soil to assess fertilizer and irrigation management on N uptake and seasonal distribution of N in the soil profile. Qui ones et al. (2007) reported split application of N irrigation at soil water suction of 10 kPa reduced N leaching and significantly improved tree N use efficiency in comparison to flood irrigation with two equal application s of N Yates et al. (1992) reported that split application of granular fertilizers in avocado orchards 8 times during the year reduced nutrient leaching as opposed to applying the fertilizers twice a year. The authors did not detect the difference in nutrient l oad leached by irrigating at 80%, 100%, and 120% of ET. Assessing Nutrient Leaching Methods used to evaluate nutrient leaching vary and there is no single device that will perfectly sample soil solution in all conditions encountered in the field (Litaor, 1988). Although sampling strategies have varied widely, zero-tension (Fig. 1 3) and tension (Fig. 1 4) lysimeters have primarily been use d to sample soil water (Kosugi and Katsuyama, 2004; Gehl et al., 2005; Weihermller et al., 2005; Amador et al., 2007) However each technique for measuring nutrient leaching has limitations. Tension lysimeters with porous ceramic cups are popularly used to collect soil water due to their ease of use and low cost as compared to other soil water collection methods (Wagner 1962; Van der Ploeg and Beese 1977; Litaor, 1988; Swistock et al., 1990) These samplers are used to obtain soil water samples from both saturated and unsaturated soils and from varying soil depth s

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25 Tension lysimeters do not destroy the soil structure or the rooting system (Grossmann and Udluft, 1991); however, some disadvantages of tension lysimeters include physical sampling and chemical interference factors. T he small area of influence of a tension lysimeter is o ne physical factor that limits its ability to adequately sample leachate (Talsma et al. 1979). The sampl ing zone is limited to the volume imm ediately surrounding the ceramic tip. In addition, the structure of the soil may preclude representative soil water sampling during saturated conditions due to bypass flow (Shaffer et al., 1979) Th is problem may be exacerbated in soils with a large propor tion of macropores or hetero geneities or where the sampler is not in contact with the macropore s (Grossmann and Udluft, 1991). Chemical factors can also influence the composition of the soil water collected by ceramic tension lysimeter s depending on the extraction time used for sample collection (Van der Ploeg and Beese, 1977; Swistock et al., 1990) and the chemical composition of the ceramic (Van der P loeg and Beese, 1977; Litaor, 1988 ) that is made from formulations of kaolin, talc, alumina, ball clay, and other fel d spathic material s (Soilmoisture Equipment Corporation, 2007). Such materials are often assumed to be inert and not to interfere with the chemical composition of the collected water sample. However, sorption of certain solute ion(s) has presen ted a problem with these lysimeters (Hansen and Harris, 1975; Nagpal, 1982; Grossmann and Udluft, 1991). Chemical reactions between solutions and ceramic materials includ ing solute ion adsorption and precipitation may be influence d by solution composition and pH, cup sorption capacity, applied suction, and sampling rate (Grossmann and Udluft, 1991; Hansen and Harris, 1975; Nagpal, 1982) Precipitation of compounds may result in underestimation of soil water sample c oncentration (Hansen and Harris 1975) and the volume of sample collected due to pore blocking (Grossmann and Udluft, 1991).

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26 The l iterature provides contradict ory results concerning ceramic samplers influence on soil water sample chemical composition p articularly of P in the sample Beier and Hansen (1992) who compared ceramic and polytetrafluoroethene (PTFE) cup lysimeters for soil water sampling of sodium (Na), K, calcium ( Ca ), aluminum ( Al ), NH4, hydrogen (H) and non -purgeable organic carbon (NPOC) reported that neither sampler contaminated the soil water samples nor retained any substance s However, Vandenbruwane et al. (2008) observed that ceramic lysimeters adequately sampled most cations except Al. Levin and Jackson (1977) who compared micro -hollow fiber and porous ceramic cup lysimeters in s ampl ing soil water containing Ca, magnesium (Mg), and ortho phosphate -phosphorus ( PO4P ) found that neither extractor altered the chemical composition of the leachate. Haines et al. (1982) compared ceramic and zero tension lysimeters to measure various nut rients includ ing PO4P from a forest ecosystem in southern Appalachia, USA ; PO4-P concentrations to a depth of 30 cm w ere not different for the two measuring devices. Other studies, however, have shown significant P adsorption in ceramic tension lysimeters (Hansen and Harris, 1975; Severson and Grigal, 1976; Zimmermann et al., 1978 ; Nagpal, 1982; Bottcher at al., 1984; Andersen, 1994). In a laboratory experiment, Hansen and Harris (1975) observed that P sorption increase d with P concentration with up to 110 mg of P being sorbed by a single ceramic cup. Lita or (1988) suggested that ceramic soil water samplers are not suitable for use in studies involving P due to its adsorption; however, no quantitative values were given. A possible cause of the se contradicti ons may arise from the chemical composition/source of the ceramic material used in the manufacture of the ceramic cups in various ceramic tension lysimeters marketed by different companies worldwide (Hughes and Reynolds, 1990)

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27 Zero tension lysimeters are devices used to collect gravi tational water percolating through the soil profile under saturated flow (Wood, 1973; Litaor, 1988 ) in order to measure the chemical characteristics of water which has leached ( Migliaccio et al. 2006). Such lysimeters are constructed with a collection con tainer and two flexible tubes The tubes provide the ability to collect the water sample from the collection container; one tube serves as an air vent while the other is connected to a peristaltic pump. The soil infiltration rate rainfall and irrigation c haracteristics and the capacity of the collection container are used to determin e lysimeter sampling interval The major limitation of the zero tension lysimeter is the failure to collect leachate under unsaturated soil conductions due to pass by flow (Vandenbruwane et al., 2008) Zotarelli et al. (2007) observed that anaerobic conditions may develop inside the lysimeters followed by denitrification of the collected leachate which may result in underestimation of the NO3 load leached. In my study, to control denitrification, bucket lysimetes were incorporated with an aeration tube and emptied each month. Field Scale Simulation Modeling of Nutrient Transport Due to resourc e constraints it is un realistic to field test all possible BMP combinations that may involve nutrients levels and nutrient application methods and irrigation volumes and application methods. Thus, models are commonly used in planning, management, and decis ion making due to their advantage for giving an insight on how systems function and interact. Many m odel s that simulate N and P leaching are available Deterministic physically based models (e.g., The Decision Support System for Technology Transfer [DSSAT] (Jones et al., 2003), Groundwater Loading E ffects of Agricultural Management S ystems [ GLEAMS ] (Leonard et al., 1987), Field Hydrologic and N utrient Transport Model [FHANTM] (Fraisse and Campbell, 1997), and Leaching Estimation and Chemistry Model [LEACHM] (Hutson and Wagenet, 1992) that simulate nutrient leaching have been used and reported to give satisfactory results (Sogbedji

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28 et al., 2001; Webb et. al., 2001; Asadi and Clemente 2003). Nutrient leaching has been simulated in fields of various crops incl uding maize, wheat, millet, potato, cassava, bahia grass, and brachiaria grass (Jones et al., 2003) However, such models are less often applied to fruit orchards (Gary et al., 1998). Of the process -based field scale leaching models, LEACHM has been widel y used to simulate water flow and solute transport with satisfactory results (Jemison and Fox 1992; Jabro et al., 1995; Sogbedji et al., 2001; Contreras et al., 2009). LEACHM was developed by Hutson and Wagenet (1992) to simulate vertical water and solute transport both in field and laboratory columns using numerical routines. It has been revised and tested over the years by different researchers ( Borah and Kalita, 1999; Sogbedji et al., 2001; Jabro et al. 2006) and the current LEACHM ( ver. 4.0 ) is a suit of three models. The three simulation models include LEACHP for pesticides, LEACHN for N and P, and LEACHC for salinity in calcareous soils (Hutson, 2005). The model incorporates C, N, and P pool s and pathways. The Addiscot tipping bucket or Richards equation is used in LEACHM to predict water content, water fluxes and potentials Richard's equation, the soil water flow equation for transient vertical flow derived from Darcy's law and continuity equation, is: t z U z H K z t (1 2) where is the volumetric water content of a specific soil layer segment (m3 m3), H is the hydraulic head (mm), K is the hydraulic conductivity (mm d1), t is time (d), z is the soil depth below a reference point (mm), and U is a sink term representing water lost per unit time by transpiration (d1). Defining the differential water capacity, C as ;

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29 h C (1 3) where h is soil water pressure head, enables transformation of equation 1 2 to an equa tion where pressure potential is the only dependent variable. t z U z H K z C t h (1 4) The model considers water depth and water potential in terms of mm per day. The solution to equation 1 4 by normal finite differencing methods is accomplished by dividing the soil profile into a finite number (k) of equally spaced horizontal layers of size z and dividing the total time period into small time intervals, t Utilizing the Crank Nicolson implicit method reduces equation 1 4 to: j i j i j i j i j i j i j ih h h 1 1 (1 5) where j i j i j i and j i contain constants (h, K, C, U, z t ) for the time increment, which have known or estimated values, and j ih1 the soil water matric potential at the start of the time interval is known. The k equations developed (one at each depth node) form a tridiagonal matrix that may be solved for j ih j ih1 j ih1 using a rapid Gaussian elimination method. T he convective -dispersion equation is used to describe solute transport and flux is usually represented as ; L L M CLqc dz dC q D J (1 6) where is the volumetric water content of a specific soil layer segment (m3 m3), q is the macroscopic water flux z is soil depth below a reference point (mm) CL is the solute concentration (mg L1), and DM (q) is the mechanical di spersion coefficient that d escribes mixing between large and small pores as the result of local variations in mean water flow velocity

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30 The value of DM (q) can be estimated from : v v DM (1 7) where v is the p ore water velocity q v and is the dispersivity, limited in LEACHM to the range z 5 0 to z 2 The LEACHN model can be used to predict both nutrient uptake by a crop and leaching b elow the root zone up to a depth of 2 m. The model inputs include daily maximum and minimum temperatures, precipitation and/or irrigation, evapotranspiration, soil texture, organic carbon, and amount of N and P applied. Model calibration requires soil hydr aulic data (e.g., water retention curve parameters), N and P rate reaction parameters, and sorption coefficients. The model uses a daily time step and can simulate a growing season or several years (Hutson, 2005). Ng et al. (2000) used the LEACHN model to i dentify management practices (i.e. water table management, conservation tillage and intercropping) that would reduce nitrate leaching from a corn (Zea mays L. ) field fertilized with urea in Ontario Canada. The y reported that the LEACHM model gave bett er predictions for nitrate leaching i n plots under controlled drainage/subsurface irrigation systems t han in plots under free drainage Using LEACHN model to develop BMPs for potato ( Solanum tuberosum ) production at Nevsehir, Turkey, nl et al. (1999) rep orted that nutrient leaching could be reduced significantly by reducing the irrigation/rain water applied from 1100 mm to 650 mm, reducing ammonium sulfate fertilizer input from 900 kg ha1 to 400 kg ha1, and applying the fertilizers after most of the supplemental irrigations were complete. The authors further recommend that rotating potato with wheat could further reduce the residual NO3 leaching since half of the applied NH4N in the fertilizer was converted to NO3 during the growing season. Jabro et a l. (2006) compared the simulation

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31 accuracy and performance of LEACH N in predict ing N dynamics in a soil -water -plant system to two other field scale models: NCSWAP (Nitrogen and Carbon cycling in Soil Water And Plant) (Molina and Richards, 1984) and SOILN ( SOIL -SOILN) ( Eckersten and Jansson 1991). The y reported that LEACHN and NCSWAP estimated nitrate leaching more accurately than SOILN, from a corn field fertilizer with ammonium nitrate and manure in Rock Springs, Pennsylvania. Previous application of the LEACHN model has resulted in identification of parameters used for sensitivity analysis (Table 1 1) and for calibration (Table 1 2) Although LEACHN can simulate P leaching, studies where P leaching in the field were simulated could not be identified in a vailable refereed literature. Model Sensitivity and Uncertainty Analysis Global sensitivity and uncertainty analysis are tools used with model application s due to uncertainties associated with all predictive deterministic models and measured data to improv e the interpretation and thus the application of modeling results (Shirmohammadi et al., 2006). Th e large number of input factors or parameters that control the variation of simulated model results is one source of u ncertainty in models. Sensitivity analys is provides the strength of the relationship between a given uncertain input factor and the model simulation output while uncertainty analysis propagates uncertainties onto the model output of interest. Traditionally model sensitivity has been quantified by computing local indices (Saltelli et al., 2005). However, hydrological models are non -linear and global techniques are therefore more appropriate as they explore the entire model parametric space. G lobal sensitivity analysis provides parameter ranking a nd information about first and higher order effects of parameters by specified outputs. One way of accounting for uncertainty in model inputs is through the development of probability density functions (PDFs) of the target model outputs (Shirmohammadi et al., 2006; Saltelli et al., 2008; Muoz Carpena et al., 2010a ). The output PDFs are then used to evaluate

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32 uncertainty in the model predictions by placing confidence intervals on the outputs either as margin of safety component or by calculating probability of exceedance of a threshold val ue (Morgan and Henrion, 1992). A common approach to sampling distributions for simulating model outputs is the use of Monte Carlo sampling which consists of multivariate random sampling from model input probability density distributions in order to conduct a large number of model simulations. Due to high c omputational costs of the Monte Carlo type of uncertainty analysis, it is convenient to use a sensitivity screening method first to identify the subset of important inputs factors controlling the model out put variability (Saltelli et al., 2004; Shirmohammadi et al. 2006; Muoz Carpena et al., 2010a) Thereafter, model uncertainty is then efficiently assessed with a faster computation time with the subset of important model inputs. Statement of Problem Nut rient leaching from agricultural fields is a major source of water body impairment in many parts of the world including Florida (Burkitte et al., 2004; Stanley et al., 2009). Nutrient leaching is attributed to improper matching of the optimal fertilizer re quirements and water needs to the crop and production environment. Nutrient leaching may have adverse effects in south Florida due to the interaction between surface water and groundwater arising from a shallow water table (Noble et al., 1996), and the exi stence of naturally sensitive water bodies like the Biscayne Bay and the Everglades (Browder et al., 2005; Reddy et al., 2006) (Fig. 1 5). Solute leaching measurement techniques primarily include use of tension ceramic lysimeters and zero tension lysimete r. Tension lysimeters with porous ceramic cups are popularly used to collect soil water due to their ease of use and low cost as compared to other soil water collection methods (Wagner, 1962; Van der Ploeg and Beese, 1977; Litaor, 1988; Swistock et al., 1990) However contradictory cases are cited in literature on the efficacy of

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33 tension ceramic lyismeters to sample a leachate containing P ( Litaor, 1988; Hughes and Reynolds, 1990). Nutrient leaching can be controlled by implement ing irrigation and nutrient BMPs. Although several BMPs have been develop for various agricultural crops in Florida, no irrigation and nutrient BMP has been developed, tested, and documented for tropical fruit tree crops. The BMP interests of my study we re to reduce irrigation water appli ed and thus save energy input and nutrient leaching while maintaining tropical fruit yields (frui t number and weight) Measurement of these quantities can be c hallenging particularly regarding fate and transport of solutes in calcareous gravel ly soils. The L EACHM model was selected due to its capability to simulate N and P, available document ation, a nd worldwide application. Literature review revealed research gaps that would hinder development of BMPs for tropical fruits. These research gaps included: 1) lack of a tested monitoring methodology for soil water sampling from orchards on gravelly calcare ous soil, 2) inadequate documentation on the extent of nutrient leaching from orchards in south Florida, and 3) inadequate documentation on how different avocado cultivars respond to irrigation and fertilizer management practices. Therefore, my study focus ed on identifying 1) appropriate methods for sampling soil pore water from gravelly calcareous soils and 2) field testing and assessment of BMPs for tropical fruit production (with avocado as reference crop) with regard to water saving s and reduction of N and P leaching into groundwater. Goal and Objectives T he overall goal of my study wa s to evaluate techniques for estimating nutrient (N and P) leaching as a method for assessing irrigation and nutrient BMPs. The objectives include: 1 ) Determine if ceramic tension lysimeters interfere with the chemical composition of sampled water containing P

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34 a) Compare leached concentration of N and P estimated using tension and gravitational lysimeters in gravelly calcareous soils 2 ) Determine the effect of nutrient load an d irrigation scheduling on nutrient leaching, water volume applied, and avocado yield in calcareous soils. 3 ) Apply the LE ACHM model (Hutson and Wagenet, 1992) to evaluate production management scenarios and identify scenarios that achieve optimum fruit prod uction while saving water volumes applied and reducing nutrient leaching. Rationale for Tropical Fruit BMPs in South Florida To prevent pollution from point and nonpoint sources, the U.S. Congress passed the Federal Clean Water Act in 1972 (amended in 1987) to restore and maintain the chemical, physical, and biological integrity of the nations waters. Subsequently, the U.S. Environmental Protection Agency (EPA) passed on the regulations to states to identify impaired water bodies and develop Total Maximum Daily Loads (TMDLs) (Migliaccio and Boman, 2006) The Florida Department of Environmental Protection (FDEP) dev elops TMDLs for specific pollutants in impaired water bodies and develops Basin Management Action Plans (BMAPs). Agricultural BMPs are required by law in areas where FDEP develops a BMAP that includes agriculture (e.g. Lake Okeechobee Watershed). BMPs are defined as a set of on farm practices designed to reduce nutrient loss and improve water quality while sustaining economically viable farming operations for the grower. T he Florida Agricultural BMP program is aimed at reducing movement of N and P from agr icultural fields to water bodies ( Simonne and Hutchinson, 2005). Due to shallow groundwater aquifers and sensitive surface water bo dies, water and fertilizer management in Florida is interrelated. Several BMPs manuals have been developed or are being developed for enterprises such as s ilviculture, ridge citrus, c ontainer n ursery, v egetable and a gronomic crops among others. The BMP man uals have been developed and adopted in Florida through a program based on commodity and region due to climatic and soil diversity ( FDACS and FDEP, 1998; Simonne

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35 et al. 2003). The BMPs identified in the manuals were selected based on the best available kn owledge and not necessarily research specific to a commodity, location, or production system Results of my study will help fill this gap for tropical fruit production in Krome soils of south Florida. Study Area and Scope The study was conducted at the Un iversity of Florida, Tropical Research and Education Center, Homestead, Miami Dade County, Florida (25o2021 N 80o2001 W ) ( Fig. 1 5). Homestead has a hum id subtropical climate with hot, humid summers where high temperature s average between 31 to 33C. The soil at the site is very gravelly loam calcareous soil (Krome very gravelly loam) very shallow up to 20 cm deep, well drained, moderately permeable an d underlined by limestone. The water table ranges between 1 to 2 m. The wet season in Florida spans from May to October and 80% of the rainfall occurs during this period. Laboratory tests were conducted to test the suitability of ceramic lysimeters to sam ple a leachate that contain P by: 1) assessing the chemical composition of ceramic cups, 2) determin ing P O4-P adsorption and desorption potentials of the three commercially available ceramic water samplers, 3) compar ing P O4-P concentrations in water sample s col lected using the three ceramic lysimeters to that of a known P stock solution, and 4) c ompar ing leached concentration of N and P estimated using ceramic and bucket lysimeters sampled from gravelly calcareous soils Monthly (November 2007 to October 2009) leached water samples containing N and P, which were collected from an avocado orchard using bucket lysimeter from each of the seven nutrient and irrigation management practices being evaluated The s even irrigation and nutrient management practices ev aluated were : 1) irrigation based on crop evapotranspiration (ET) irrigation with fertilizer at a standard rate (FSR) typically used in avocado production in the area ; 2) ET irrigation with 50% FSR; 3) ET irrigation with 200% FSR; 4) soil water suction at

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36 15 kP a (SW) with FSR; 5) irrigation at a set schedule (based on timing and frequency typically used in the production of avocado in the area) with FS R; 6) SW with 50% FSR; and 7) SW with 200% FSR. The data collected were analyzed for differences among treatment means using statistical ANOVA and means separated by Waller Duncan K ratio. Treatment effects on tissue N, C, and TP; soil nutrient status parameters including organic carbon, C:N and C:P ratios, and inorganic N; tree growth; and fruit yield were ana lyzed using ANOVA and means sepa rated by Waller Duncan K ratio. The identified nutrient and irrigation BMPs that resulted in water saving and reduced nutrient leaching were fine tuned using the LEACHM model by employing global sensitivity analysis tools.

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37 Table 1 1. Parameters used for sensitivity analysis in LEACHM for water and nitrate leaching Parameter Ng et al., 2000 Mahmood et al., 2002 value/range sensitivity ratio Air entry value (AEV) 0.22 Exponent for Campbell equation (BCAM) 0.30 Volum etric water content (m 3 m 3 ) 0.04 Soil organic carbon (%) 0.06 Diffusion coefficient (mm 2 day 1 ) 60 120 .0 Dispersivity (mm) 60 150 .0 Bulk density (g cm 3 ) 1 1.3 Hydraulic conductivity (mm day 1 ) 10 100 .0 Urea hydrolysis (day 1 ) 0. 4 Nitrificat ion (day 1 ) 0.3 0.04 Denitrification (day 1 ) 0.1 0.024 Humus mineralization (day 1 ) 0.62 Base temperature ( o C) 0.67 Q 10 factor 0.4 0 Adapted from: Ng et al., 2000. Study conducted at Woodslee, Ontario, Canada from 19911994 using corn (Zea mays L ., Pioneer 3573) Mahmood et al., 2002. Study conducted at Carterton, New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species (two -year old Eucalyptus nitens and Eucalyputs ovata) and pasture.

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38 Table 1 2. Parameters used for calibrati on of LEACHM for water and nitrate leaching Parameter Borah et al., 1999 Ng et al., 2000 Mahmood et al., 2002 Jabro et al., 2006 value/range value value/range value/range Air entry value (AEV) 0.1 1.0 0 0.3 3 .00 Exponent for Campbell equation (BCAM) 3.0 3 5 .00 7.8 18.7 0 Clay particles% 20 .00 Silt particles% 12 .00 Volumetric water content (m3 m3) 0.5 0.26 0.29 Soil organic carbon (%) 2.50 4.50 Dispersivity (mm) 120 .0 Bulk density (g cm 3 ) 1.00 1.44 Saturated Hydraulic conduct ivity (mm day1) 100 5100 .00 40 2184 .00 Water potential, kPa 10 35 .00 C:N ratio 10:1 .00 10 .00 Nitrification (day 1 ) 0.2 0 0.1 0.2 0.4 0 Denitrification (day 1 ) 0.1 0 0.1 0.02 0.08 Humus mineralization (day 1 ) 7 0 5 Q 10 factor 3.0 2.0 0 Kd for NO 3 N (L kg 1 ) 0.05 0.00 Litter mineralization (day1) 0.01 0.01 Manure mineralization (day 1 ) 0.02 0.02 Humus mineralization (day 1 ) 0.7 0 310 5 N Plant uptake (kg ha 1 ) 102 .0 NH 4 N (mg N kg 1 ) 2.63 4.00 3 .00 NO 3 N (mg N kg 1 ) 3. 85 6.38 0 .00 Adapted from: Borah et al., 1999. Study conducted at Manhattan, Kansas, USA form 19961997 using corn (Zea mays L.) ; Ng et al., 2000. Study conducted at Woodslee, Ontario, Canada from 19911994 using corn (Zea mays L., Pioneer 3573) ; Mahmood et al., 2002. Study conducted at Carterton, New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species (two -year old Eucalyptus nitens and Eucalyputs ovata) and pasture ; Jabro et al., 2006. Study conducted at Rock Springs, Pennsylvania, USA form 19881991 using corn (Zea mays L.).

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39 Figure 1 1 The nitrogen cycle: the different transformations are numbered 1 to 7 (adapted from Havlin et al. (2004)).

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40 Figure 1 2. Illustrat ion of the interactions between the different forms of P in the soil (adapted from Havlin et al. (2004)).

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41 Figure 1 3 Bucket lysimeter. A ) e levation view and B ) inside view (credits. UF/IFAS Harry Trafford) Figure 1 4 Porous ceramic lysimeters and relevant sa mpling devices A ) ceramic lysimeter B) pump used to apply suction, C ) syringe used to expel leachate form the collection tubing (Adapted from Irrometer Company, Inc.) A B A C B

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42 Figure 1 5 Map of Miami Dade County, south Florida and the surrounding water bodies.

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43 CHAPTER 2 MONITORING NUTRIENT LEACHING USING TENSI ON AND GRAVITATION LYSIMETERS1Introduction Monitoring nutrient leaching is essential in order to quantify the effect of different irrigation and nutrient practices on surf ace water and groundwater quality (de Vos, 2001; Herzog et al., 2008; Fonder et al., 2010). Methods used to evaluate nutrient leaching vary as there is no single device that will perfectly sample soil solution in all conditions encountered in the field (Litaor, 1988). Although sampling strategies have varied widely, zero -tension and tension lysimeters have primarily been use d to sample soil water ( Kosugi and Katsuyama, 2004; Gehl et al., 2005; Weihermller et al., 2005; Amador et al., 2007). However, each t echnique for measuring nutrient leaching has limitations. Tension lysimeters with porous ceramic cups are popularly used to collect soil water due to their ease of use and low cost as compared to other soil water collection methods (Wagner, 1962; Van der P loeg and Beese 1977; Litaor, 1988; Swistock et al., 1990) These samplers are used to obtain soil wate r samples from both saturated and unsaturated soils and from varying soil depth s Tension lysimeters do not destroy the soil structure or the root system (Grossmann and Udluft, 1991); however, some disadvantages of tension lysimeters include physical sampl ing and chemical interference factors. T he small area of influence of a tension lysimeter is o ne physical factor that limits its ability to adequately sample leachate (Talsma et al. 1979). The sampl ing zone is limited to the volume immediately surrounding the ceramic tip. In addition, the structure of the soil may preclude representative soil water sampling during saturated conditions due to bypass flow (Shaffer et al., 1979) Th is problem may be exacerbated in soils with a large 1 Manuscript titled, Phosphorus adsorption by ceramic suction l ysimeters is under review with the Vadose Zone Journal: Authors include Nicholas Kiggundu, Yuncong Li, and Kati W. Migliaccio.

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44 proportion of macropores o r inhomogeneities, or where the sampler is not in contact with the macropores (Grossmann and Udluft, 1991). Chemical factors can also influence the composition of the soil water collected by ceramic tension lysimeter s depending on the extraction time use d for sample collection (Van der Ploeg and Beese, 1977; Swistock et al., 1990) and the chemical composition of the ceramic (Van der P loeg and Beese, 1977; Litaor, 1988 ) that is made from formulations of kaolin, talc, alumina, ball clay, and other fel d spath ic material s (Soilmoisture Equipment Corporation, 2007). Such materials are often assumed to be inert and not to interfere with the chemical composition of the collected water sample. However, sorption of certain solute ion(s) has presented a problem with these lysimeters (Hansen and Harris, 1975; Nagpal, 1982; Grossmann and Udluft, 1991). Chemical reactions between solutions and ceramic materials includ ing solute ion adsorption and precipitation may be influence d by solution composition and pH, cup sorption capacity, applied suction, and sampling rate (Grossmann and Udluft, 1991; Hansen and Harris, 1975; Nagpal, 1982) Precipitation of compounds may result in underestimation of soil water sample concentration (Hanse n and Harris 1975) and the volume of sample collected due to pore block age (Grossmann and Udluft, 1991). The l iterature provides contradict ory results concerning ceramic samplers influence on chemical composition of soil water sampled particularly of P in the sample Beier and Hansen (1992) who compared ceramic and polytetrafluoroethene (PTFE) cup lysimeters for soil water sampling of sodium (Na), K, calcium ( Ca ), aluminum ( Al ), NH4, hydrogen (H) and non -purgeable organic carbon (NPOC) reported that neither sampler contaminated the soil water samples nor retained any substance s However, Vandenbruwane et al. (2008) observed that ceramic lysimeters adequately sampled most cations except Al. Levin and Jackson (1977)

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45 compared micro -hollow fiber and porous ceramic cup lysimeters in sampl ing soil water containing Ca, magnesium (Mg), and ortho phosphate -phosphorus ( PO4P ) and found that neither extractor altered the chemical composition of the leachate. Haines et al. (1982) compared ceramic and zero tension lysimeters to measure various nutrients includ i ng PO4P from a forest ecosystem in southern Appalachia, USA ; PO4-P concentrations to a depth of 30 cm w ere not different for the two measuring devices. In contrast, o ther studies have shown significant P adsorption in ceramic tension lysimeters (Hansen an d Harris, 1975; Severson and Grigal, 1976; Zimmermann et al., 1978 ; Nagpal, 1982; Bottcher at al., 1984; Andersen, 1994). In a laboratory experiment, Hansen and Harris (1975) observed that P sorption increase d with P concentration with up to 110 mg of P be ing sorbed by a single ceramic cup. Lita or (1988) suggested that ceramic soil water samplers are not suitable for use in studies involving P due to its adsorption; however, no quantitative values were given. A possible cause of the se contradictions may ari se from the chemical composition/source of the ceramic material used in the manufacture of the ceramic cups in various ceramic tension lysimeters marketed by different companies worldwide (Hughes and Reynolds, 1990) Zero tension lysimeters are devices used to collect gravitational wa ter percolating through the soil profile under saturated flow (Wood, 1973; Litaor, 1988 ) in order to measure the chemical characteristics of leached water ( Migliaccio et al. 2006). Such lysimeters are constructed with a collection container and two flexib le tubes The tubes provide the ability to collect the water sample from the collection container; one tube serves as an air vent while the other is connected to a peristaltic pump. The soil infiltration rate rainfall and irrigation characteristics and t he capacity of the collection container are used to determin e lysimeter s sampling interval The major limitation of the zero -tension lysimeter is the failure to collect leachate under unsaturated

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46 soil conductions due to bypass flow (Vandenbruwane et al., 2008) Zotarelli et al. (2007) observed that anaerobic conditions may develop inside the lysimeters followed by denitrification of the collected leachate which may resul t in underestimation of the NO3N load leached. The continued use of ceramic tension lysimeters to sample leachate containing P ( Brye et al., 2002; Bajracharya and Homagain, 2006; Lentz, 2006) requires a detailed evaluation of these lysimeters to reduce the uncertainties in the data collected and provide some validation of sampling results Thus, the goal of my study was to test a methodology for evaluating ceramic tension lysimeters for sampling soil water P O4-P which consisted of the following objectives: 1) to evaluate the chemical composition of three commercial ly available c eramic cup s (referred to here as C eramic s A, B, and C ); 2) to determine P adso rption and desorption potential of the three ceramic water s amplers; 3) to compare P concentrations in water samples col lected using the three ceramic lysimeters; and 4) to investigate gravitational ( bucket ) and ceramic tension lysimeters in order to compare concentrations of NO3N and PO4-P sampled by these device s for application in leachate studies Ma terials and M ethods Study Area The study was conducted in Homestead, Miami Dade County, FL, at the University of Floridas Tropical Research and Education Center (TREC) (25o2021 N 80o2001 W ). The elevation of T REC is about 4 m above sea level. The annual rainfall is 1.44 m, maximum and minimum daily annual averages of 31.5oC and 11.6oC respectively (considering available data from 1998 to 2007) for Homestead, FL, from the Florida A utomatic W eather Network, http://fawn.ifas.ufl.edu/data/reports ). Homestead has a humid subtropical climate with hot, humid summers where high temperature s average between 31 to 33C. Winters are mild, but on average cooler than th e nearby coastal areas. The wet season in Florida spans from May to

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47 October and 80% of the rainfall occurs during this period ( Mulholland et al., 1997). The soils at the site we re gravelly, loamy -skeletal, carbonatic, hyperthermic lithic udorthents, and ar e classified as Krome very gravelly loam (Noble et al., 1996) Krome soils are very shallow up to 20 cm deep, well drained, moderately permeable soils underline by limestone. Mu oz Carpena et al. (2002) reported that Krome soil is 51% coarse material, 36% sand, 40% silt, 24% clay has and a bulk density of 1.42 g cm3. Krome soil ha s a high pH of 7.4 8.4 (Zhou and Li 2001) and soil organic carbon content of 8.47% (Chin et al. 2007). Chemical Composition of Ceramic Cups Methods similar to those used to ass ay a sample of soil, sediment, or plant tissue by ashing for total P w ere used to determine the chemical composition of the ceramic cups (Davi e s, 1974; Ben Dor and Banin, 1989). T hree different types of ceramic lyismeters from two companies were used. The ceramic samplers are referred to as 1) Ceramic A (22 mm diam. x 60 mm length) with an air entry value of 100 kPa (1 bar) (Irrometer Company, Inc. Riverside, CA ); 2) Ceramic B (22 mm diam. x 60 mm length) with an air entry value of 100 kPa (1 bar) (Soilmoi sture Equipment Corp., Santa Barbara, CA); and 3) Ceramic C (48 mm diam. x 57 mm length) with an air entry value of 2 00 kPa ( 2 bar) (Soilmoisture Equipment Corp., Santa Barbara, CA) Three lyismeters of each type were acquired and used in this experiment r epresenting three replicates. The content s of P, K, Ca, Mg, zinc (Zn), manganese (Mn), copper (Cu), Fe, Al, cadmium (Cd), nickel (Ni), lead (Pb), and silicon ( Si ) were measured by ashing 0.5 g of the ceramic materials in a 50 -ml beaker (with three replicat es ) initially at 250oC for 30 min. and then at 550oC for 4 h. The ashed ceramic material s w ere then moistened with deionizied and distilled (DDI ) water after which 20 m L of 6 M HCl was added. The beakers were then placed on a hot plate in the fume hood and heated at 100oC until dry. Once dry, the hot plate temperature was set to high for 30 min. After cool ing, t he samples w ere moistened with 2 to 3 m L of DDI water and

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48 2.25 m L 6 M HCl. The beakers were returned to the hot plate, set on high, and allowed to r each a near boiling state. After cool ing, t he samples were filtered through Whatman No. 42 filter paper in to 50 m L volumetric flasks and stored at room temperature for analysis. Estimated mean separation for each element in the samplers was computed using proc GLM of SAS 9.1 statistical software (SAS Institute, Cary, NC, USA). Phosphorus Adsorption and Desorption Potential of Commercial Ceramic Lysimeters A P adsorption/desorption study of ceramic tension lysimeters was conducted using Ceramic s A, B, and C For this experiment, new ceramic lysimeters were acquired. The ceramic cups were broken and ground separately and particle s ranging in size from 0.5 to 1.0 mm were collected For the adsorption study, six different concentration s of PO4-P concentrations of 0, 0.5, 1.0, 5, 10, and 20 mg L1 were used. A ceramic sample of 3 g was weighed into a 50 m L centrifuge tube and 30 m L of the desired PO4-P solution was added. The tub es were sh a k en at room temperature for 24 h, centifuged at 200 xg for 15 min., and fi ltered through Whatman No. 42 filter paper. The PO4-P retained by the ceramic was calculated as follows: M V C C Sa/24 0 (2 1 ) Where 0C is the concentration (mg L1) of P in the added solution, 24C the c oncentration (mg L1) of P in solution after 24 h equilibration period, V the volume (L) of the P solution added, M the mass of ceramic material (kg), and aS the amount (mg kg1) of added P adsorbed by ceramic. The d esorption study was perform ed by adding 15 m L of 0.050 M KCl solution to each tube containing the ceramic material with sorbed P (Zhou and Li, 2001 ), shaking for 24 h, centrifuging at 200 xg for 15 min., and filtering through Whatman No. 42 filter paper. The phosphate desorbed by the ceramic was calculated as follows:

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49 M V V C V V V C Sd/2 24 2 3 48 (2 2 ) Where 48C is the concentration (mg L1) of P in the added solution KCl solution after 24 h equilibration, 3V the volum e (L) of the KCl solution added, V the volume (L) of the P solution that was added for adsorption, 2V the volume (L) of the P solution that was recovered by filtration after adsorption, M the mass of ceramic material (kg), and dS the amount (mg kg1) of P desorbed by ceramic in KCL solution. PO4-P was analyzed by the MurphyRiley colorimetric method on a SEAL AQ2 discrete analyzer ( SEAL Analytical, Inc. Mequon, WI ). Sorption Isotherms P sorption parameters we re computed using linear, Freundlich, and Langmuir isotherms (Zhou and Li 2001) The Freudlich equation is: n fC K S (2 3 ) w here fK is the Freundlich adsorption coefficient ( mg1n Ln kg1), which i s the ratio between log P sorbed on solid phase and log phosphorus in solution, C is the P concentration in solution (mg L1), and n is an empirical constants related to sorption ( n < 1) Given that Freudlich equation is empirical in nature, it does not gi ve information about amount of P adsorption. The Langmuir equation gives information on the amount of P ions in solution that are adsorbed by solid particles assuming that the solid particles have a finite capacity to adsorb P. The Langmuir equation is : max max1 S C bS S C (2 4 )

PAGE 50

50 w here S is the amount of P in adsorbed phase (mg kg1), C is the concentration of P in solution (mg L1), maxS is the P ads orption maximum (mg kg1) which is wi dely used to estimate the capacity of a solid surface to adsorb P and b is a constant related to the bonding energy between phosphate and the solid surface (mL kg1) Estimation of Known P Stock Solutions by Different Ceramic Lysimeters A laboratory study to test the concentration of water samples collected from commercially available ceramic tension lysimeters to sample a known PO4-P solution was conducted Three lyismeters of each type were acquired and used in this experiment representing three replicat es. The ceramic lysimeters were cleaned using deionized (DI) water and diluted HCl in separate containers as described in the following steps. 1) The lysimeters were soaked in DI water for 24 h in a clean plastic container with the ceramic cups fully immer sed, after which the water that collected in the lysimeter tubing was discarded. 2) The lysimeters were immersed in 1000 mL of 0.1 M HCl for 8 h. During this period, suction was applied to the lysimeters twice. A suction of 60 kPa was applied to Ceramics A and B while a suction of 50 kPa was applied on Ceramic C. This was to ensure that adequate volumes of the solution were drawn in the lysimeter tubing. 3) The lysimeters were then soaked in fresh 0.1 M HCl for 24 h and suction was applied twice. Next, the lysimeters were thoroughly rins ed with DI water inside and outside. 4) The lysimeters were then soaked in DI water for 24 h and suction was applied twice. This step was repeat ed using fresh DI water. The solution collected in the lysimeters after the last suction event was collected in clean bottles and analyzed for P concentration and pH, to serve as background information on the status of each cleaned lysimeter. After cleaning, the lyismeters were dried at room temperature for at least 36 h.

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51 S tock solut ions of 0.10, 0. 5 0, 1 and 20 mg P L1 were prepared with potassium dihydrogen phosphate (KH2PO4) standard solution (1 mL = 1 mg P) and DDI water For each stock solution, 3250 mL was prepared. Ceramics A and B each required 1000 mL to conduct the experime nt over a 24 h period while Ceramic C needed a volume of 1250 mL for the same period. The stock solutions were evaluated in ascending order of concentration. The lysimeters were placed in clean containers and the proper solution was added at 8 am. The exac t volume of stock solution used in each experiment was weighed to enable accurate computation of P mass. Suction was applied to each of the lysimeters (60 kPa for Ceramics A and B and 50 kPa for Ceramic C). The first sample was collected after 2 h from the start of the experiment, from each of the lysimeters and from the solution in the three containers. A second suction was applied and samples were drawn after 4 h from the start of the experiment. A third suction was applied and samples were collected 24 h from the start of the experiment. The samples collected from the lysimeters and the containers were weighed to allow for the computation of P mass. At the end of each experiment with a given solution, the lysimeters were thoroughly cleaned following the p rocedure outlined above to avoid P residue carryover to another concentration level. The effect of conditioning ceramic lysimeters to improve estimation accuracy as suggested by Grossmann and Udluft (1991) was evaluated using Ceramics B and C which had gre ater maxS The lysimeters were used to estimate concentration of 0.1, 0.2, 0.3, and 0.5 mg P L1. The purpose wa s to equilibrate the cups cation exchange capacity with adequate P sorption to reduce the amount of P in solution that would be adsorbed on the ceramic cups. This was also intended to mimic field conditions since lysimeters are used to sample soil water of varying concentrations without being cleaned after each sampling event.

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52 PO4P concentrations were measured by the MurphyRiley colorimetric method (v on Wandruszka, 2006) on an Auto analyzer 3 (BranLuebbe, Hamberg, Germany). Estimated mean concentration for each sampler type and sampling time and mean percentage deviations from the known concentrations were computed using proc GLM of SAS 9.1 statistic al software (SAS Institute, Cary, NC, USA). Comparison of Leac hate Concentrations from Bucket and Ceramic Lysimeters Under a Controlled Environment Four containers of dimensions 0.756 m in height with a surface area of 0.45 m2 were fabricated of wood. The container dimensions were selected since they could accommodate the installation of the bucket and ceramic lysimeters. Using the design of Migliaccio et al. (2006) four bucket lysimeters were fabr icated using 5 -gallon buckets, 12.7 mm hose pipes and plastic catch pans. In my study, to control denitrification, buck et lysimetes were incorporating with an aeration tube and emptied each month. Each bucket lysimeter was placed in the container after which the container was filled with Krome soil. The soil used was collected from a depth of up to 30 cm. Ceramic lysimeter s were installed in the wooden container adjacent to the bucket lysimeter with the ceramic tip of the lysimeter placed in line with the top of the bucket lysimeter (Fig. 2 1a). The c ontainers were then placed under a rainfall simulator which had a spray a re a of 6.6 5 m2 (Fig. 2 1b). The soil was left to settle for a week. During this week, equipment was tested using the rainfall simulator to ensure the inte grity of all sampling devices. Pressure gauge tensiometer s were installed in each container to monitor water suction within the theoretical root zone (0.15 m deep ) and to ensure soil water content equality among the four containers. Prior to the application of the stock solution, the rainfall simulator was used to apply water and water samples from the b ucket and ceramic lysimeter s were collected to determine the initial concentration of NO3N, PO4P, and Br in the soil. Bromide is usually present in soils at very

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53 low concentrations and is not subjected to chemical or biological transformation. Thus, move ment of Br in soils has been used widely to evaluate nitrate mobility because of the similarity of NO3-N and Br mobility (Li et al. 1995). After initial sampling, a stock solution was prepared using 88 g of KNO3, 54 g of KH2PO4, and 18 g of KBr and appli ed at a rate of 1 L per container as a pulse This volume permitted uniform coverage of the entire container surface area of 0.45 m2 with minimum pe netration of the soil profile. The amounts of NO3N, PO4-P, and Br in the stock solution were exaggerated fr om annual application rate of 98 kg ha1 to allow for the detection of these compounds in leachate after five pore volumes. Simulated rainfall events began two days after nutrient application. Considering total porosity of 0.45 and an effective root depth of 0.31 m water application s equivalent to half a pore volume ( 67.5 mm of rainfall) were used. The rainfall simulator delivered water at a rate of 0.013 m3 min1. Hence, the simulator was operated for approximately 34 minutes to simulate half pore volume of rainfall. During data collection, rainfall was delivered by the simulator starting at 8:00 AM and leachate was collected the same day at 5:00 PM. A motorized pump was used to draw leachate from the bucket lysimeter and a 60 ml syringe was used to expe l the leached from the soil water sampler. The leachate fraction was filtered through filter paper (0.45m) for N O3N and W hatman filter paper No. 42 for PO4-P. Nitrate and Br concentrations were analyzed using an ion chromatograph (Dionx LC20, Dionex Corporation, Sunnyvale, CA) and PO4P concentration was determined using the Murphy-Riley colorimetric method on Auto analyzer 3 (Bran Luebbe, Hamberg, Germany). D ata was analyz e d using the one -way analysis of variance (ANOVA) and

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54 means separated using Waller Duncan K ratio Test using SAS statistical software (SAS Institute, Cary, NC, USA). Comparison of Leachate Concentrations from Bucket and Ceramic Lysimeters from an Avocado Orchard Bucket lysimeters based o n the design presented by Migliaccio et al. (2006) were fabricated and installed in the avocado orchard on the second Simmonds tree of each replicate see chapter 3 for orchard description). Each lysimeter was composed of a collection container (20 L) and two flexible tubes The bucket lysimeters were in stalled 0.3 m away from the tree trunk and 0.3 m below the ground so as not to interfere with the development of the roots The flexible tubes on the lysimeters we re left protruding on the ground after installation. The tubes provided the ability to collec t the water samples from the collection container; one tube serve d as an air vent while the other tube was connected to a peristaltic pump to draw the leachate Ceramic A lysimeters were installed on each of the third Simmonds trees of each treatment re plicate. The lysimeters were installed 0.15 m a way from the tree trunk and 0.3 m below the ground. The lysimeters had two tubes, a shorter one and a longer one. The short tube was used to apply a suction of about 50 kPa on the sampling day. The longer tube was used to draw the leached from the lysimeter. Leachate sampling was collected monthly from November 2007 to October 2009. The amount of water collected from each lysimeters was measured and recorded. A sample from each lysimeter was collected in 270 mL plastic bottles and taken to the laboratory for analysis. A portion of each sample which had been filtered through Whatma n No. 42 filter paper into a 20 mL bottle vial was stored for the determination of NO3N and PO4-P. The samples were frozen at 4 oC u ntil they were analyzed. Nitrate was determined spectrophotometrically by first reducing NO3 to NO2 using a cadmium coil and the resulting NO2 concentration was then

PAGE 55

55 determined by USEPA method 353.2 (USEPA, 1993a) PO4P was determined using ascorbic ac id method (USEPA method 365.1). All sample analysis was done using SEAL AQ2 discrete analyzer ( SEAL Analytical, Inc. Mequon, WI ). Data were analyzed for significant different (p < 0.05) among treatment means and between sampling devices using a one -way ana lysis of variance ( ANOVA). Results and Discussion Constituents of Elements in Ceramic Cups Analysis of the ceramic cups composition revealed existence of several elements in varying amounts (Table 2 1), which included P, Fe, Al, Ca, K, Mg, Zn and Si. Howe ver, Mg was only detected in Ceramic s A and B The elements Pb, Mn, Cd, Cu, and Ni were either detected in very low amounts or could not be detected in the three ceramic materials (date not shown) The presence of substantial amounts of Fe, Al, Si, and Ca in the three ceramic materials may influence P estimation through adsorption or precipitation depending on the pH and concentration of the soil water being sampled. Ceramic A had significantly greater amounts of the eight elements analyzed than Ceramics B and C apart from Zn. The similarity in composition between Ceramic B and C was expected as th o se samplers were obtained from the same manufacturer. Given that tension ceramic lysimeters are normally used in leachate studies of different constituents the o btained leachate may be influence d by the ceramic cup chemistry For example, Swistock et al. (1990) found that water samples from tension lysimeters were significant ly greater in sulfate (SO4 2), Ca, Mg, Mn, and K than the water samples collected from pan lysimeters. The y suggested the difference in the elements was attributed to the collection of micro flow by the tension lysimeters compared to macro flow collected by pan lysimeters. Micro flow has a greater residence time in the soil profile, which incre ases the concentration of mineral ions compared to macro flow, which has a shorter interaction time with the elements in the soil

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56 medium. Results from my study show that the ceramic cups could also have been a likely source for Ca, Mg, and K due to their a bundance in the ceramic materials. Thus, suction lysimeters should be selected as a sampling tool with caution depending on the sampling goals (macro pore versus micro pore) and potential chemical interferences. Adsorptiondesorption Potential of the Cera mic Cups Phosphorus sorption characteristics were best described by Freundlich isotherms for Ceramics A and C while Ceramic B was best described by a Langmuir isotherm (Table 2 2) Ceramic s B and C had highe r sorption maxima than C eramic A Ceramic B had t he lowest desorption rate while C eramic C had the highest desorption rate (Fig. 2 2) The amount of P required to reach m aximum sorption (Smax) was 5. 69, 11.2 2 and 16. 56 mg P kg1 for C eramic s A, B, and C respectively. The low Smax for Ceramic A could be attributed to its high P composition (Table 2 1) which contributed to meeting the P sorption demand of this ceramic material. The bonding energ ies for the three c eramic s w ere low with values of 0.31, 0.17, and 0.19 ml kg1 for Ceramic s A, B, and C respect ively, implying that the sorped P may be released overtime Data presented in Fig. 2 2 suggest that a solution of KCl could not desorb the entire P amount that was retained through sorption in each of the ceramic materials within a period of 24 h The hyst eresis effect in all three graphs indicates P diffusion into the reactive ceramic layers. The hysteresis effect was highest in Ceramic B where no P was desorbed. T he differences in the behavior of the three ceramic types concur with data in Table 2 1 that the ceramic cups contained varying amounts of different elements which play a role in P adsorption and desorption. Similarly, others have studied P sorption and found a hysteresis effect of the sorbed P; they explained this sorption to be a function of sol id -phase physicochemical characteristics, P loading rate, and residence time (Reddy and DeLaune, 2008). The Reddy and DeLaune (2008) study and our results indicate that isotherm evaluations provide valuable information that can be

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57 used to better assess sam pling results by understanding sorption and desorption processes and hysteresis effects, particularly for P. Phosphorus Estimation by the Ceramic Lysimeters Results from the t hree different ceramic lysimeters suggested variability in collected water sample composition when sampling four different PO4P stock solu tions (Table 2 3). Sampling 4 h from the start of the experiment generally resulted in concentrations that were closer to the stock solution value for Ceramics A and C lysimeters than sampling after 2 or 24 h Water samples collected from Ceramic A were most similar to the PO4-P stock solutions, while water samples collected using Ceramics B and C underestimated PO4-P concentrations especially for lower concentrations (0.1 and 0.5 mg P L1). We obser ved that Ceramic A overestimated water samples with a concentration of 0.1 mg P L1 by 8% at the 4 h sampling time and by 12% at the 24 h sampling time Generally the estimation of PO4P concentration in water samples by Ceramics B and C were not significa ntly different (p < 0.05). For accurate P measurement, the ceramics P adsorption capacity has to be met. Thus for lower PO4-P concentrations, Ceramics B and C adsorbed P from the solution, which resulted in underestimation of the concentration. This P ads orption from solution was possibly due to the formation of phosphates of Fe and Al. The higher PO4P concentration solutions were reasonably estimated since there was enough P mass to reach adsorption potential and still maintain adequate P in solution. D a ta show that the accuracy of the PO4P concentrations in water samples collected using Ceramics B and C increase s as the concentration of the stock solution increases. A similar phenomenon was reported by Nagpal (1982) and was attributed to the material re tain ing less P in proportion to the amount of P in the stock solution at greater PO4-P concentrations. Thus the accuracy of Ceramics B and C improved from about 10% with 0.1 mg P L1 solution to about 97% with 20 mg P L1.

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58 For the 0.5, 1, and 20 mg P L1 stock solutions, the PO4-P concentrations measured from samples extracted using Ceramic A varied by 3% or less from the stock solution concentrations. Although Ceramic A had the greatest amount of P it estimated PO4P of a known solution more accurately t han Ceramics B and C for the sampled stock solution. The higher P mass in Ceramic A could be beneficial in meeting the P sorption requirements of the ceramic cups, thus resulting in a more accurate estimate. Likewise the high er P mass in Ceramic A may hav e contributed to the overestimation of P for the 0.1 mg P L1 stock solution. At the 4 h sampling, the difference in sampled concentrations for the 0.5 and 1.0 mg P L1 stock solutions by Ceramic B and Ceramic C was at least 16% (Table 2 3). The differenc e between PO4-P concentrations collected by these two ceramics reduced to at most 13% for water sample s collected after 24 h. In a separate experiment where Ceramics B and C were used to estimate solutions of 0.1, 0.2, 0.3, and 0.5 mg P L1 without the lys imeters being cleaned (i.e., conditioning the ceramics), the estimation of 0.5 mg P L1 solution at 4 h improved (Table 2 4) compared to the estimates given in Table 3. However, estimates for 0.1 and 0.5 mg P L1 stock solutions measured for Ceramic A were still more accurate than the respective results from the conditioned ceramic lysimeters. Although Ceramics B and C were from the s a me manufacturer, Ceramic B estimations were closer to the concentrations of the stock solutions than the estimates of Cerami c C. This was attributed to the difference in the P sorption maximum values as shown in Table 2 2. Thus, with conditioning the maximum sorption values of Ceramic B (11.22 mg kg1), which was lower than that of C would be fulfilled faster than that of Cera mic C (16.56 mg kg1) which was greater. For the tested samplers, the lower the ceramics maxS the more accurate the sampler was in estimating PO4-P concentration of a known stock solution.

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59 The mass of P retained in the ceramic cups for ea ch lysimeter type was estimated after 24 h for each of the four st ock solutions used (Table 25). Ceramic A retained the lowest P amount in the range of 2 to 8% (of the initial P mass) for the four stock solutions. Ceramic B retained between 5 to 36% (of the initial P mass) for the four stock solutions while Ceramic C retained between 8 to 65% (of the initial P mass). Such P retention in ceramic cups was also observed by others ( Hansen and Harris, 1975; Zimmermann et al., 1978) The lower PO4-P concentrati ons measured with the cleaned lysimeters (Table 2 6) implies that P adsorption of the ceramics was reversible in a solution of HCl and the lysim eters can be cleaned for reuse. Similar studies by Nagpal (1982) and Bottcher at al. (1984) showed that P sorbed on ceramic cups can be desorbed in a solution of HCl, however, wit h low bonding energies (Table 22) P desorption will occur gradually under field condition. The higher P content in Ceramic A explain s why this lysimeter had more P recovered after the cleaning protocol (Table 2 6). The results suggest that the three lysimeter types used would not provide accurate estimation of soil water whose PO4P concentration was less than 0.1 mg P L1. In such a situation, Ceramic A would likely over estimate the conc entration by releasing P into the collected samples, while Ceramics B and C would likely underestimate the concentration due to P adsorption Overall, of the three evaluated ceramic lysimeters, Ceramic A would be more suitable to sample a leachate with PO4-P concentration s within the range of my study Ceramics B and C would be considered for a PO4P concentration of 2 0 mg P L1 or greater since their results would be more uncertain for lower P concentration. Nutrient Concentrations Sampled from a Controll ed Environment The elution curves of NO3N obtained from the average leached concentration for the bucket and ceramic lysimeters showed that NO3-N and Br follow ed similar leaching patterns in Krome soil (Fig. 2 3). Elution curves showed that the peak NO3N and Br elution s occurred at

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60 2.5 pore volume s (equivalent to 338 mm of rainfall ) for both bucket lysimeters and ceramic lysimeters Thus, NO3N and Br were quickly leached from the soil after each pore volume. Similar results were obtained by Li et al. (19 95) using 3 layers of spodosols soils, whereby 4.5 pore volumes leached out 85 to 87% of the NO3N applied across a soil depth of 0.7 m. There were no significant difference s (p < 0.05) between the concentrations of NO3N and Br sampled by the two devices, although bucket lysimeters had higher concentrations than ceramic lysimeters. Swistock et al. (1990) observed no significant difference (p < 0.05) between NO3N in the leachate sampled by tension ceramic lysimeter s and non zero tension lysimeters, althou gh data from ceramic lysimeters showed a higher concentration. The PO4P elution curves indicated that very little phosphorus was leached from the system when compared to the amount applied. There were no significant difference s (p < The similarity in the concentration of NO3N and PO4-P sampled by ceramic and bucket lysimeters could be attributed to both devices sampling under saturated flow conditions since suct ion was applied to the ceramic lysimeter immediately after water application. Whereby, both devices had a representative sample of the leachate from macro pore flow. Hence either device 0.05) between the con centrations of PO4-P sampled by the two devices, although the bucket lysimeters had a higher concentration (Fig. 2 3). Haines et al. (1982) observed similar PO4P concentration between tension ceramic and zero tension lysimeters for a leacheate collected a t a depth of 0.3 m from a forest ecosystem over a period of one year. T he lesser concentration of PO4P in leacheate observed in my study was attributed to the reaction of HPO4 2 with calcium carbonate (calcite) through precipitation resulting in the forma tion of monocalcium phosphate as observed by Freeman and Rowell (1981).

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61 could be used in the monitoring of nutrient leaching in Krome soils u nder such soil water conditions. Comparison of Nutrient Concentrations Sampled form an Avocado Orchard Due to high water percolation rate of Krome soil attributed to 51% gravel fractions, an adequate sample of about 50 ml required for laboratory analysis of the major nutrient elements could only be sampled by ceramic lysimeters when the soil water content was near saturation. Such sampling conditions for ceramic lysimeters occurred four times out the 24 sampling events. Although NO3-N concentrations for bucket lysimeters samples were higher they were not significantly different (p < 0.05) from concentrations sampled using ceramic lysimeters (Table 2 7 to 2 10). Swistock et al. (1990) observed similar results between these sampling devices. Bucket lysimeter s samples had significantly higher (p < 0.05) PO4-P concentrations than concentrations for samples from ceramic lysimeters (Tables 2 7 to 2 10). The differences between PO4-P concentrations sampled by the two devices may be attributed to bucket lysimeters collecting a cumulative leachate over a longer duration while ceramic lysimeters sampled a snap -shot of the leachate that was available on the day of sampling. Probably sampling more events using the ceramic lysimeters during the wet season would have provided more representative concentrations. In the study by Haines et al. (1982) where PO4-P concentrations between tension ceramic and zero-tension lysimeters were similar, the ceramic lysimeter was equipped to collect a cumulative leacheate over a the sa mpling period. Conclusions Methods used in my study provide an outline for testing ceramic lysimeters for their appropriate application for measuring soil water of different chemical constituents. The particular ceramic lysimeters (Ceramics A, B, and C) u sed in my study outlined the variability among ceramic tension lysimeters The analysis of the ceramic material composition indicated

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62 that all three types of ceramic lysimeters contained substantial amounts of Fe, Al, Si, and Ca that may influence P estima tion depending on the pH and concentration of the soil water being sampled. The adsorptiondesorption study and the fitting of the Freundlich and Langmuir isotherms provided information that assisted in interpreting the different results. For the tested sa mplers, the lower the ceramics maxS the more accurate the sampler was in estimating PO4P concentration of a known stock solution. Thus, Ceramic A with the greatest P mass and lesser maxS estimated PO4-P concentration fo r the four concentrations more accurately than Ceramics B and C which had a lower P mass but greater maxS The comparison of the leachate concentration of NO3-N and PO4-P in a controlled environment showed no significant differences (p < 0.05) between bucket and ceramic lysimeters due the soils saturated flow conditions during sampling. The difference in PO4P concentration in the orchard samples was due to the difference in the leachate volume sampled. The bucket lysimetes captured a cumulative leachate from macro flow yet the ceramic lysimeters represented a snap -shot of micro flow. More sampling events from the ceramic lysimeters to time saturate flow conditions would most likely have improved the ceramic lysimeter estimation since PO4P leaching is more influenced by water flow volume through dissolution than the amount P added to the soil (see Chapter 3). Findings from my study suggest that before ceramic samplers are used in soil water sampling studies the following thr ee steps shoul d be completed: 1) determination of the chemical composition of the ceramic cups; 2) development of sorption isotherms; and 3) testing of ceramic sampler with known stock solutions Depending on the results obtained, a decision to use ceramic lysimeter s or to explore other soil water collecting methods can be scientifically made. Further research is need ed to test if more sampling events using ceramic lysimeters targeting

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63 saturate flow field conditions would give similar results as sampling using bucket lys imeters in Krome soil.

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64 Table 2 1. Composition of ceramic cups considering major elements reported by chemical analyses, n=3. Element y Ceramic A Ceramic B Ceramic C -----------------------------mg kg 1 ----------------------P 52 a 12 .0 c 18 .0 b Fe 436 a 4.9 b 54 .0 b Al 8309 a 951 .0 b 729 .0 b Ca 8063 a 275 .0 b 71 .0 b K 1212 a 13 .0 b 31 .0 b Mg 4468 a 149 .0 b
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65 Table 2 3. Estimation of a known con centration of PO4P by different lysimeters, n=3. Stock solution Ceram ic type Average concentration drawn by the samplers Sampling time y 2 z 4 24 mg L 1 --------------------------------mg L 1 ------------------------0.100 A 0.109 a 0.108 a 0.112 a B 0.003 c 0.011 b 0.018 b C 0.011 b 0.011 b 0.026 b 0.514 A 0.489 a 0.506 a 0.524 a B 0.029 b 0.159 b 0.270 b C 0.016 b 0.228 b 0.292 b 1.069 A 1.024 a 1.038 a 1.031 a B 0.301 b 0.699 c 0.797 b C 0.249 b 0.834 b 0.695 b 20.503 A 20.449 a 20.520 a 20.585 a B 17.984 b 19.993 b 20.107 b C 17.968 b 19.825 b 19.539 b y, Sampling time in hours after the start of the experiment. z, Sampled concentrations with same letter not significantly different ( p < 0.05) by column and stock solution concentration. Table 2 4 Estimation of a known concentration of PO4P by ceramics B and C after conditioning, n=3. Stock solution Ceram ic type Average concentration drawn by the samplers Sampling time y 2 4 24 mg L 1 ------------------------mg L 1 ------------------------0.100 B 0.017a z 0.026a 0.047a C 0.014a 0.018a 0.032a 0.200 B 0.089a 0.144a 0.127a C 0.046a 0.107a 0.093a 0.300 B 0.155a 0.243a 0.243a C 0.092a 0.214a 0.172a 0.500 B 0.364a 0.480a 0.451a C 0.324a 0.389b 0.331b y, Sampling time in hours after the start of the experiment. z, Sampled concentrations with same letter not significantly different ( p < 0.05) by column and stock solution concentration.

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66 Table 2 5 Mass of P retained by ly simeter ceramic cups n= 3 Starting mass Ceramic type Mass extracted in water samples drawn through lysimetersz Mass extracted in water sampled from the container Mass remainin g in container after sampling Mass retained in ceramic % mass retained in ceramic cups mg --------------------------------mg ------------------------------0.097 A 0.051 0.007 0.037 0.002 1.6 0.096 B 0.003 0.005 0.054 0.034 35.5 0.120 C 0.018 0.007 0.018 0.078 65.1 0.502 A 0.286 0.025 0.176 0.015 3.0 0.497 B 0.043 0.028 0.291 0.134 27.0 0.624 C 0.218 0.033 0.054 0.318 51.0 1.039 A 0.576 0.061 0.323 0.080 7.7 1.020 B 0.169 0.058 0.583 0.211 20.7 1.276 C 0.648 0.070 0.039 0.519 40.7 19.740 A 11.515 0.959 6.876 0.390 2.0 19.912 B 5.426 1.355 12.14 0 0.994 5.0 25.031 C 20.491 1.094 1.439 2.007 8.0 z, Water samples were collected after 2, 4, and 24 h.

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67 Table 2 6. Status of the cleaned lysimeters after sampling a known P concentration, n=3. Ceramic type Concentration of PO4P in DI water collected from lysimetersy Conce ntration of solution in container pH of DI water collected from lysimeters pH of solution in container Remark number ---------------mg L1-------------A 0.030 a 0.006 5.11 a 5.44 1v B 0.004 b 0.002 4.75 a 5.28 C 0.006 b NAz 4.68 a NAz A 0. 035 a 0.004 4.89 a 5.62 2w B 0.007 b 0.002 4.54 b 5.37 C 0.010 b 0.003 4.21 c 5.08 A 0.036 a 0.004 4.75 a 5.88 3x B 0.015 b 0.002 4.65 b 5.47 C 0.008 b 0.003 4.61 c 5.68 y, Concentration and pH with same letter not significantly different ( p < 0 .05 ) by column. z, NA = No solution was available to measure get a measurement. v, Lysimeters cleaned to remove factory residues and composition of DI water sampled thereafter. w, Lysimeters cleaned after use with stock solution of 0.5 mg P L1 and composi tion of DI water sampled thereafter. x, Lysimeters cleaned after use with stock solution of 1.0 mg P L1 and composition of DI water sampled thereafter.

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68 Table 2 7 L each ate concentration comparison of bucket and ceramic lysimeters for the sampling o f 7/23/2008, n=3. NO 3 N PO 4 P z Treatment Bucket Ceramic P value Bucket Ceramic P value 1, ET+FSR 8 .0 8 .0 0.9726 0.328 a 0.015 b 0.0002 2, ET + 50% FSR 10 .0 1 .0 0.2268 0.474 a 0.060 b 0.0 111 3, ET + 200% FSR 15.0 3 .0 0.2355 0.389 a 0.032 b 0.0 167 4, SW + FSR 2 .0 0.1 0.2867 0.636 a 0.093 b 0.00 70 5, Set sch. + FSR 0.3 0.3 0.8653 0.235 a 0.011 b 0.00 06 6, SW + 50% FSR 0.1 0.03 0.6666 0.467 a 0.015 b 0.0 422 7, SW + 200% FSR 18 .0 a 0.3 b 0.0043 0.496 a 0.020 b 0.1088 z, Sampled c oncentrations with same letter not significantly different ( p < 0.05) by treatment. Table 2 8 L each ate concentration comparison of bucket and ceramic lysimeters for the sampling of 8 /2 5 /2008, n=3. NO 3 N PO 4 P z Treatment Bucket Ceramic P value Bucket Ceramic P value 1, ET+FSR 0.646 0.063 0.3539 0.224 a 0.034 b 0.0007 2, ET + 50% FSR 1.156 0.027 0.1584 0.275 a 0.019 b 0.00 8 7 3, ET + 200% FSR 1.483 0.012 0.2993 0.269 a 0.019 b 0.00 64 4, SW + FSR 2.129 0.053 0.2653 0.421 a 0.027 b 0.00 14 5, Set sch. + FSR 0.180 0.005 0 .1341 0.157 a 0.031 b 0.00 16 6, SW + 50% FSR 0.093 0.051 0.2715 0.300 a 0.019 b 0.00 41 7, SW + 200% FSR 7.330 0.047 0.2489 0.339 a 0.075 b 0.0 148 z, Sampled c oncentrations with same letter not significantly different ( p < 0.05) by treatment Table 2 9 L each ate concentration comparison of bucket and ceramic lysimeters for the sampling of 5 /2 0 /2009 n=3. NO 3 N PO 4 P z Treatment Bucket Ceramic P value Bucket Ceramic P value 1, ET+FSR 138 40 0.2836 0.142 0.049 0.1689 2, ET + 50% FSR 124 63 0.2526 0. 133 a 0.035 b 0.0 321 3, ET + 200% FSR 270 251 0.9433 0.100 0.029 0. 1259 4, SW + FSR 50 43 0.8288 0.241 0.112 0.0 934 5, Set sch. + FSR 139 1 0.1240 0.137 0.035 0. 2079 6, SW + 50% FSR 87 50 0.4669 0.193 a 0.024 b 0.0 126 7, SW + 200% FSR 160 117 0.6180 0.254 0.173 0. 5270 z, Sampled c oncentrations with same letter not significantly different ( p < 0.05) by sampling device

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69 Table 2 10. L each ate concentration comparison of bucket and ceramic lysimeters for the sampling of 6 / 17/2009 n=3 NO 3 N PO 4 P Tr eatment z Bucket Ceramic P value Bucket Ceramic P value 1, ET+FSR 60 57 0.9620 0.184 a 0.039 b 0.0316 2, ET + 50% FSR 36 a 10 b 0.0363 0.280 a 0.002 b 0.0 380 3, ET + 200% FSR 137 a 10 b 0.0313 0.162 0.009 b 0. 0012 4, SW + FSR 12 4 0.3803 0.359 0.052 0. 0 530 5, Set sch. + FSR 5 1 0.3288 0.180 0.010 0. 0810 6, SW + 50% FSR 12 5 0.3381 0.196 a 0.000 b 0.0 009 7, SW + 200% FSR 115 7 0.3993 0.213 0.059 0.4827 z, Sampled c oncentrations with same letter not significantly different ( p < 0.05) by sampling device

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70 Figure 2 1 Arrangement of the bucket and ceramic lysimeters in each container. A) cross section view of each container, B) arrangement of the four boxes under a rainfall simulator. A B

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71 Figure 2 2 Sorption desorption curv es of P A) C eramic A, B) Ceramic B, C) C eramic C. A B C

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72 A B Figure 2 3 Elution curves of nutrient concentrations sampled by the lysimeters under a controlled environment. A) ceramic and B) bucket.

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73 CHAPTER 3 EVALUATION OF IRRIGATION AND NUTRIENT MA NAGEMENT PRACTICES IN A YOUNG AVOCADO ORCHARD Introduction Nutrient leaching of nitrogen (N) and phosphorus (P) from agricultural fields is a water quality concern in many areas of the world due to increased nitrate (NO3) concentrations and eutrophication of water supplies (Burkitt et al., 2004; Quiones et al ., 2007; Eulenstein et al., 2008) Nutrient leaching, or the downward movement of dissolved nutrients in the soil profile with percolating water (Havlin et al., 2004) is influenced by hydrologic and soil characteristics. Hydrologic characteristics of a location such as rainfall pattern s ( frequency intensity, duration, and amount) and infiltration characteristics of the soil influence nutrient leaching Nutrient leaching is also effected by fertilization practices, irrigation practices, crop characteristics, and production system manage ment. Leaching of N and P is mainly attributed to fertilizers that are applied to enhance plant growth and yields. Although the intent is for these fertilizers to be used by the crop, some fertilizer s may leach into groundwater (Schaffer, 1998; Tischner et al., 1998; Schroder et al., 2005) and contribute to increased downstream eutrophication (Li et al., 1999) The residual amount of N and P in the soil after crop harvest and the rate of N and P mineraliz ation of the decomposing plant residue also affect nutrient leaching (Jiao et al., 2004). Nutrient leaching may also occur due to over irrigation and heavy rainfall events that result in increased infiltration and drainage (He et al 2000; Muoz -Carpena et al., 2002) Irrigation and fertilize r best management practices (BMPs) have been reported to minimize nutrient leaching (Yates et al., 1992; Paramasivam et al., 2000 ; Doltra et al., 2008) and reduce water volume applied without affecting yields (Zotarelli et al., 2008; Migliaccio et al., 201 0). Practices that enhance fertilizer utilization efficiency include appropriate timing of fertilizer

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74 application, formulation of the fertilizer material, amount and rate of fertilizer applied, and methods used to apply fertilizers. For example, split appl ication of fertilizers as fertigation has been shown to reduce nutrient leaching loads as opposed to applying fertilizers by broadcasting in one or two applications followed by water application (Nakamura et al., 2004; Qui ones et al., 2007; Worthington e t al., 2007). Efficient irrigation methods such as irrigating based on crop evapotranspiration (ET) demand or soil water sensors minimize over irrigation and thus water volumes applied without affecting yields ( McCready et al., 2009; Silva et al., 2009; Migliaccio et al., 2010) and subsequently reduce nutrient leaching. ET bas ed irrigation has been a method of computing crop water requirements for many years (Penman, 1948; Turc, 1961; Kisekka et al., 2010). The ET estimation methods involve computing the reference evapotranspiration (ETo) using weather data ( e.g., temperature, solar radiation, relative humidity, and wind speed). Two widely accepted methods of calculating ETo are the Food and Agricultural Organization of the United Nations (FAO) Penman -Monteith (Allen et al., 1998) and the American Society of Civil Engineers Envi ronmental and Water Resources Institute (ASCE -EWRI) 2005 (Attarod et al., 2009; Pereira et al., 2009; Sahoo et al., 2009). The concept of a reference crop was introduced to prevent the need to define unique evaporation parameters for each crop and stage of crop growth. Thus, crop coefficients (kc) serve the purpose of relating evapotransipration from the reference crop (ETo) to evapotranspiration rates (ETa) of a crop of interest (i.e., ETa is the product of ETo and kc). According to Allen et al. (1998) kc serves the purpose of distinguishing a crop of interest from the reference grass used to compute ETo. Thus kc factors are an aggregation of the physical and physiological differences between the crop of interest and the reference grass (alfalfa). The a vail ability of kc values is one limitation of using ET -based irrigation methods, since these coefficients require time and

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75 financial resources to be developed and once developed they remain site, stage of crop growth, plant size, and cultivar specific. Even so ET -based irrigation research has reported water savings (13 to 46% ) and corresponding increased yields (6 to 11% ) as opposed to set schedule irrigation for potato, and mango crops ( Meyer and Marcum, 1998; Silva et al., 2009; Spreer et al., 2009). S oil wa ter sensors estimate the soil water content and can be can be linked with irrigation control equipment to automate irrigation scheduling Soil water sensors commonly used for irrigation scheduling include gypsum blocks, dielectric probes, time domain refle ctometry (TDR) probes, and automated switching tension meters Kukal et al. (2005) reported for rice ( Oryza sativa ) in Ludhiana, India, that irrigati ng at a soil suction of 16 kPa resulted in water saving of 30 to 35% in comparison to the traditional practi ces of a 2 -day irrigation set schedule interval. In a study to assess irrigation BMPs using tensiometers in a royal palm ( Roystonea elata) field nursery in Homestead, F L, Migliaccio et al. (2008) found that automating irrigation at soil suctions of 5 and 15 kPa reduced water volumes applied by 75 and 96% when compared to standard irrigation scheduling without sacrificing tree crop quality Meron et al. (2001) reported that irrigating at a soil suction of 15 to 25 kPa resulted in water saving of 500 to 650 m m in comparison to ET -based irrigation water use of 700 to 850 mm per season in an apple ( Malus domestica ) orchard in Upper Galilee, Israel. Others (Enciso et al., 2009; Fuentes et al., 2008; McCready et al., 2009; Migliaccio et al., 2010) have also conduc ted irrigation research using soil water sensors and have reported water savings from 25 to 7 4% of set schedule irrigation practices Irrigation BMPs in combination with nutrient BMPs, either through use of ET based irrigation ( Yates et al., 1992; Diez et al., 1997; Doltra et al., 2008; Paulino-Paulino et al., 2008) or soil water s ensor based irrigation (Paramasivam et al., 2000; Lao et al., 2004; Alva et al.,

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76 2006) have been evaluated for the impact of the combined irrigation and nutrient BMPs on nutrient leaching reduction and water savings. Alva et al. (2003) conducted a study on sandy soils in Lake Alfred, FL, for over five years w here they monitored the effects of different N and irrigation BMPs on orange yields ( Citrus sinensis ) and NO3N concentrations in groundwater. The authors reported water savings and N leaching reductions with irrigation based on soil suctions at 10 and 15 kPa and split N fertigation in comparison to non -systematic irrigation scheduling with broadcasting of N fertilizer. Likewise, a study was conducted by Qui ones et al. (2007) on eight -year old citrus trees in Moncada, Spain, on sandy loam soil to assess irrigation and fertilizer management on N uptake and seasonal distribution of N in the soil profile. Qui ones et al. (2007) reported split application of N irrigation at soil water suction of 10 kPa reduced N leaching and significantly improved tree N use efficiency compared to flood irrigation with two equal application of N. Yates et al. (1992) reported that split application of granular fertilizers in avo cado orchards 8 times during the year reduced nutrient leaching as opposed to applying the fertilizers twice a year. The authors did not detect a significant difference in nutrient load leached by irrigating at 80%, 100%, and 120% of ET. The BMPs in my stu dy focus on water savings and reduction of nutrient leaching while maintaining crop yield of avocado ( Persea americana Mill ) in the environmentally sensitive ecosystems of south Florida. The BMPs consisted of an irrigation management (evapotranspiration [ E T ] or soil water tension at 15 kPa [SW]) and a fertilizer rate management Previous studies have not evaluated avocado response to irrigation and nutrient management practices in south Florida Likewise the effects of the different irrigation and nutrient management practices on nutrient leaching, volume of water applied, tissue nutrient status, and fruit yield of avocado grown on gravelly calcareous soils have not been adequately documented

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77 so that irrigation and nutrient use efficiency may be optimized. T he specific objectives of the study were to d etermine the effect of nutrient load and irrigation scheduling in calcareous soils on: (1) nutrient leaching of N and P; (2) tissue nutrient status, growth, and yield of Simmonds and Beta avocado cultivars ; and (3) soil nutrient indicators such as (soil organic carbon, C:N and C:P ratios, and soil inorganic N) Materials and Methods Study Area The study was conducted in Homestead, Miami Dade County, FL, at the University of Floridas Tropical Research and Edu cation Center (TREC) (25o2021 N 80o2001 W ). The elevation of TREC is about 4 m above sea level. The annual rainfall is 1.44 m with maximum and minimum daily annual average temperatures of 31.5oC and 11.6oC respectively (considering available data from 1998 to 2007) for Homestead, FL ( Florida A utomatic W eather Network [FAWN] 2010). Homestead has a humid subtropical climate with hot, humid summers where high temperature s average between 31 to 33C. Winters are mild, but on average cooler than the nearb y coastal areas. The wet season in Florida spans from May to October and 80% of the rainfall occurs during this period ( Mulholland et al., 1997). The soils at the site are gravelly, loamy -skeletal, carbonatic, hyperthermic lithic udorthents, and are classi fied as Krome very gravelly loam (Noble et al., 1996) Krome soils are very shallow (up to 20 cm deep ) well drained, moderately permeable and underline by limestone. Krome soil s have 51% coarse material, 36% sand, 40% silt, 24% clay a bulk density of 1.42 g cm3, ( Mu oz Carpena et al. (2002) a high pH of 7.4 to 8.4 (Zhou and Li 2001) and an organic carbon content of 8.47% (Chin et al. 2007). To provide space for root development in a mechanically rock plowed soil, tropical fruit trees are planted 50 cm d eep at the intersection of perpendicular trenches (N nez Elisea et al., 2001 ).

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78 Avocado Orchard Layout and Experimental Design The experiment was conducted in an avocado orchard composed of 28 B eta trees and 84 S immonds trees. The trees were planted i n four rows at spacing of 6 m between rows and 4.5 m inter row. The orchard was planted on 26 February 2006. The trees were irrigated with similar water volume for 2 months until they were considered established. The trees received specified irrigation and nutrient management practices beginning in August 2006. The seven irrigation and nutrient management practices evaluated were : 1) irrigation based on crop evapotranspiration (ET) irrigation with fertilizer at a standard rate (FSR) typicalfor avocado production in the area ; 2) ET irrigation with 50% FSR; 3) ET irrigation with 200% FSR; 4) soil water suction at 15 kP a (SW) with FSR; 5) irrigation at a set schedule (based on timing and frequency typically used in the production of avocado in the area) with FS R; 6) SW with 50% FSR; and 7) SW with 200% FSR. The experiment was conducted from August 2006 to October 2009. Each treatment was replicated four times and each replicate included 1 Beta tree and 3 Simmonds trees in a completely randomized design (Fig. 3 1). The fertilizer at a standard rate (FSR) was modified during the experiment based on tree size (Table 3 1). The strategy of the fertilizer program during the first two years (2006 to 2007) was for tree development and fruits were removed immediately after fruit set. The fertilizer strategy during the following two years (2008 to 2009) was for fruit production. The trees were fertilized by broadcasting the nutrients under the tree canopy. For each treatment replicate, a tensiometer (Irrometer, CA) was installed on the first Simmonds tree and a bucket lysimeter was installed on the second Simmonds tree (Fig 3 1). Irrigation Management Practices Evapotranspiration (ET) irrigation volumes (m3) were computed as follows:

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79 1 ) The average monthly daily refer ence evapotranspiration (ETo) was calculated using the FAO Penman -Monteith equation and historical weather data from the Florida Automatic Weather Network (FAWN) website ( http://fawn.ifas.ufl/edu/data/re ports ) for Homestead, FL. 2 234 .0 1 237 900 408 0 u e e u T G R ETa s n o (3 1) Where ETo represents reference evapotranspiration [mm day1], Rn is net radiation at the crop surface [MJ m2 day1], G is the soil heat flux density [MJ m2 day1], T is the mean daily air temperature at 2 m height [C], u2 is the wind speed at 2 m height [m s1], es is the saturation vapor pressure [kPa], ea is the actual vapor pressure [kPa], es ea is the saturation vapor pressure deficit [kPa], is the slope of vapor pressure curve [kPa C1], and is the psychrometric constant [kPa C1]. 2 ) Actual crop evapotranspiration (ETa) [mm] was calculated as c o ak ET ET (3 2) where kc is the crop coefficient (unit less) 3 ) The length of irrigation per day (Itd) was calculated in hours as ) / ( ) ( ) / ( 1000 1 ) (3 2hr m rate delivery irrigtion m area delivery sprinkler micro mm m mm ET Ia td (3 3) 4 ) Water volume applied per tree per day (Wvd) was calculated in cubic meters as ) / ( ) (3hr m rate sprinkler micro hr perday time irrigtion Wvd (3 4)

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80 To allow for proper root development, the trees receiving ET -based irrigation were irrigated three times each week at 8:05 am ( Monday, Wednesday, and Friday). The ETo and crop coefficient ( kc) used are given in Table 3 2. For SW based i rrigation, switching tensiometers ( Irrometer, CA ) were used to monitor soil suction in the orchard (Fig. 3 2). Krome soil is cons idered dry for most crops when the soil water suction exceeds 15 kP a (Muoz Carpena et al., 2002). The irrigation was scheduled at 8:30 AM and 1:00 PM each day and trees were irrigated when the soil water suction of 15 kP a was exceeded. Trees irrigated on a set schedule were irrigated twice a week for two hours during each irrigation event. Trees were i rrigat ed every Tuesday and Friday starting at 6:00 AM. The irrigation time was kept constant for the four -year study period. For all treatments, irrigation was delivered through micro sprinkler (Maxijet Inc., Dundee, FL ) sprinkler system connected with polytubes Each avocado tree had one micro jet sprinkler with an application rate of 0.079 m3 h1. The micro jet s were placed beside tree trunks (on the east side) and the irrigated area per sprinkler had a diameter of 1.57 m. Each treatment replicat e was monitored using a water meter ( Daniel L. Jerman Co., NJ ) to record the volume of water applied. Irrigation was controlled with Nelson solenoids ( Forth Worth TX) and a Toro c ontroller (Ecxtra, Model 53768, Riverside, CA ). Data were analyzed for differences among treatment means of ET SW, and set schedule based irrigation using SAS statistical software (SAS Institute, Cary, NC, USA). A one -way analysis of varia nce (ANOVA) was performed and treatment means separated using and Waller -Duncan K ratio Test

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81 Plant Measurements Tree diameter w as measured once each year in August on each Beta tree and the third Simmonds tree of each treatment replicate. Diameters w ere measured 0.15 m above the ground. Tree heights were not collected because trees were pruned. Leaf greenness expressed as SPAD units was estimated (SPAD 502). According to Pestana et al. (2004) leaf color is correlated linearly and positively with leaf chlorophyll content. Measurements were made on three fully expanded recently mature leaves from each of the Beta tree and the third Simmonds tree The average value for the three leaves was computed and recorded as the SPAD reading for the sampled tree According the method proposed by Morales et al. (1990) and Abadia et al. (1991), the developed equations that correlate SPAD and chlorophyll was equation 3 6 for Beta which had a coefficient of determination of 0.86 and equation 3 6 for Simmonds whic h had a coefficient of determination of 0.76. 253 8 036. 1 SPAD l chlorophyl (3 5) 808 0 969 0 SPAD l chlorophyl (3 6) Three to five fully expanded recently mature leaves were picked from the Beta tree and the third Simmonds tree of each treatment replica te. The leaves were placed in labeled bags and transferred to the laboratory for washing. The leaves were first washed with distilled water (DI) and then washed in a detergent prepared using 30 ml of soap (Liqui nox, Alconox Inc., White Plains, NY) and 250 0 ml of DI. The leaves were washed in acid prepared with 60 ml of 6 N HCl and 2500 ml of DI. The acid was washed off with DI water and the leaves were put in marked paper bags. The leaves were then placed in an oven at 75oC until they reached a constant weight The dried leaves were ground in a Wiley mill (Thomas -Wiley Co., Philadelphia, PA) with a 1 mm mesh sieve. The ground samples were placed in clean labeled plastic bags and stored until

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82 they were analyzed for Total N (TN), Total carbon (TC), and Total P (TP). Sampling was done 3 times each year: April, August, and December. The process of determining TN and TC in leaves involved measuring 0.2 g of the ground tissue sample in crucibles. Both TN and TC in the tissue were measured by the combustion method using a Vario Max Elemental CNS Analyzer (Elementar Analysensysteme GmbH, Germany ). Solutions used to analyze TP were extracted using the ashing and ignition method (Davis, 1974; Ben-Dor and Banin, 1989 ). Samples were then analyzed for TP ( US EPA method 365.1) Data were analyzed for differences among treatment means for SPAD tree diameter TN and TC using SAS statistical software (SAS Institute, Cary, NC, USA). A one -way analysis of variance (ANOVA) was performed and treatment means separated using a Wall er Duncan K ratio Test Where data were not normally distributed, the Box Cox (1964) method was used to normalize data distribution before the statistical analysis. Data were further analyzed as factorial design with 2 levels of irrigation and 3-levels of fertilizer to explore the influence of different rates of irrigation and fertilizer on tissue nutrient indicators. Soil S ampling and Analysis Soil samples were collected three times each year to determine the nutrient concentration before fertilizers were applied. However, the first sampling events of 2006 was not conducted since the trees were not established and 2008 the first sampling event was skipped due to labor constraint. The dry matter and any fertilizer residue covering the soil were removed befo re collecting the soil. Samples were collected from four equidistant positions around the Beta tree and the third Simmonds tree of each treatment replicate. Each soil sample was placed in a clean labeled paper bag. The soil samples were dried and sieve d to pass through 2 mm mesh sieve. Each year the samples collected at the end of the harvest season (September) were analyzed for soil TN, TC, NH4N, NO3N, TP, and inorganic carbon (IC). The samples collected at the other

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83 time of the year were analyzed on ly for NH4N and NO3-N. TN and TC were analyzed by the combustion method (Vario Max Elemental CNS Analyzer, Elementar Analysensysteme GmbH, Germany ). For the analysis of NH4-N and NO3N an extraction was made by weighing 2 g of the soil sample in a 50 ml b ottle to which 20 ml of 2 N KCl solution was added. The bottles were shaken for 30 min at 180 rpm. Solutions used to analyze TP were extracted using the ashing and ignition method (Davis, 1974; BenDor and Banin, 1989 ). The extracted solutions were then an alyzed for TP ( US EPA method 365.1) The soil inorganic C was analyzed by the modified pressure -calcimeter method (Sherrod et al., 2002). Soil organic c arbon was determined as the difference between TC ( from CNS analyzer) and soil inorganic carbon. Data wer e analyzed for differences among treatment means of NO3-N, NH3-N, TN and TC using SAS statistical software (SAS Institute, Cary, NC, USA). A one -way analysis of variance (ANOVA) was performed and treatment means separated using and Waller Duncan K ratio T est Where data was not normally distributed, the BoxCox (1964) method was used to normalize data distribution before the statistical analysis. Data were further analyzed by a two way ANOVA with 2 -levels of irrigation (ET and SW) and 3 levels of fertilize r (50% FSR, FSR, and 200% FSR) to explore the influence of different rates of irrigation and fertilizer on soil nutrient indicators. Measuring Nutrient Loads Leached Bucket lysimeters based o n the design presented by Migliaccio et al. (2006) were fabricat ed and installed in the avocado orchard on the second Simmonds tree of each treatment replicate. Each lysimeter was composed of a collection container ( 20 L) and two flexible tubes The bucket lysimeters were installed 0.3 m away from the tree trunk and 0.3 m below the ground so as not to interfere with the development of the roots The flexible tubes on the lysimeters we re left protrud ing on the ground after installation. The tubes provided the ability to collect the water

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84 samples from the collection con tainer; one tube serve d as an air vent while the other tube was connected to a peristaltic pump to draw the leachate Leachate samples were collected monthly, from November 2007 to October 2009. The amount of water collected from each lysimeters was measur ed and recorded. A sample from each lysimeter was collected in a 270 mL plastic bottle and all samples were taken to the laboratory for analysis. A portion of each sample was filtered through Whatma n No. 42 filter paper into a 20 mL bottle vial and stored at 4oC until the determination of NO3N, NH4N, and PO4P. The procedure for determining TP in water samples involves first the conversion of particulate organic and condensed phosphates into orthophosphates under a high acidic, oxidizing conditions, and high temperature environment (Li et al., 2005b). Thus, a 30 mL portion of each water sample was put into line -in -disposable vials unfiltered. A pipette was used to measure 0.6 ml of 5 N H2SO4. The acid was then added to the 30 ml of unfiltered water sampl e, followed by the addition of about 0.24 g of (NH4)2S2O8 (a mmonium persulfate). The water samples were covered and shaken to allow (NH4)2S2O8 to dissolve in the water. The samples were digested in an autoclave (Consolidated Stills & Sterilizers, B oston, M A ) for 30 minutes at a pressure of about 103.4 kPa. After digestion, the samples were stored at room temperature until they were analyzed for TP. Nitrate was determined spectrophotometrically by first reducing NO3 to NO2 using a cadmium coil and the resu lting NO2 concentration was then determined by USEPA method 353.2 (USEPA, 1993a) A mmonium was determined spectrophotometrically by USEPA method 350.1 based on the Berthelot reaction ( USEPA, 1993b ). In the reaction NH4N was converted to chloramine that reacted with phenol under basic conditions to create an intensely blue indophenol dye, whose color was directly proportional to the NH4N concentration. Both PO4P

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85 and TP were determined using ascorbic acid method (USEPA method 365.1, USEPA, 1993c). All sa mple analyses were completed using SEAL AQ2 discrete analyzer (SEAL Analytical, Inc. Mequon, WI ). The amount of nutrient load leached for NO3N, NH4N, PO4-P, and TP was computed using equation 3. 7 e T LC V N (3 7 ) w here eC is the concentration (mg L1) of any of the four nutrient leached elements, TV the total volume of water leached in a month ( L ), and LN the load of nutrient element leached (mg). The total volume of water le ached TV was determined using equation 3. 8 C S B TA A V V / (3 8 ) w here BV is the volume of water pumped from the bucket lysimeter ( L ), SA is the area irrigated by the micro sprinkler ( m2), and CA the area of the bucket lysimeters catch pan ( m2). Data were analyzed for differences among treatment means of NO3N, NH4-N, PO4-P, and TP using SAS statistical software (SAS Institute, Cary, NC, USA). A one -way analysis o f variance (ANOVA) was performed and treatment means separated using and Waller Duncan K ratio Test Where data w ere not normally distributed, the Box Cox (1964) method was used to normalize data distribution before the statistical analysis. Effect of Fert ilizer Amount and I rrigation Water Volume on Avocado Yield Avocado yields (fruit number and weight) for 2008 and 2009 were harvested in accordance to the shipping schedule of the Florida Avocado Administrative Committee ( Hatton and Reeder, 1965). The Simm onds fruit harvesting dates were June 23, July 7 and 21, and August 4 with the corresponding diameter of fruits to be harvested of 90 mm (3.56 in), 87 mm (3.44 in), 78 mm (3.06 in), and no size (pick all remaining fruits). The Beta fruit harvesting date s were August 11 and 18 and September 1 and 8 with the corresponding diameter of fruits to be harvested of

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86 89 mm (3.50 in), 84 mm (3.31in), 81 mm (3.19 in), and no size (pick all remaining fruits). Fruits harvested from each tree were counted and weighed. Fruit number and weight data were analyzed for significant differences (p among treatment means using a one -way analysis of variance (ANOVA). Where significance differences among treatment were detected, the Waller Duncan K ratio Test was used to separate treatment means (p 0.05) Both ANOVA and mean separation was done with SAS statistical software (SAS Institute, Cary, NC, USA). Where data w ere not normally distributed, the Box -Cox (1964) method was used to normalize data distribution before the statistical analysis. Results and Discussion Water A ppli cation Signifi cant differences (p < 0.05) in volume of water applied were observed among the irrigation methods (Fig. 3 3; Table 3 3). The water volume applied for the ET based treatment and SW -based treatment increased annually to match water volume applied with tree growth and subsequent root development and expansion (Table 3 3) The historic ET values used to compute irrigation volumes were compared to the real time ET to explore the accuracy of using historical data (Fig. 3 5). It was observed that the historical ET values computed using weather data from 1998 to 2005 reasonably estimated the real time ET (R2 = 0.913). The data suggest that climate variable s such as temperature, radiation, relative humidity, and wind speed that were used to compute historical ET by t he FAO Penman Monteith method were fairly stable during the study period. For each irrigation practice, there was no significant difference between the volumes of water applied in the wet and dry seasons during the four years of the study (Fig. 3 4). This was attributed to a dry and cold season (November to April) and a wet and hot season (May to October) in south Florida (Mulholland et al., 1997). However, the volume of water applied by the three management practices in each of the two seasons differed sig nificantly (p < 0.05).

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87 Water savings of 93 and 87% were achieved by using ET and SW -based irrigation practices respectively, in comparison to the set schedule irrigation management. The slight difference in water applied based on ET and SW could be attributed to the kc values used. The kc values used were estimated based on expert knowledge of avocado kc values developed from similar climatic conditions elsewhere and not on measured data. The high water savings in my study are attributed to water being app lied based on historic weather pattern s or estimated soil water content. Similar high water saving with SW -based irrigation has been reported by others. Zotarelli et al. (2008) indicated water saving ranged from 33 to 80% in zucchini squash ( Cucurbita pepo L .) and Migliaccio et al. (2008) reported water saving of 96% while irrigating at 15 kPa soil suction in royal palm (Roystonea elata) in compar ison to set scheduled irrigation. Water saving from ET -based irrigation observed by Silva et al. (2009) and Spreer et al. (2009) for mango ranged from 13 to 46% which were lower than the water saving achieved in the current study. Nitrogen and Phosphorus Loads Leached There were no significant differences (p 0.05) among treatments for NO3-N load leached (Tab le 3 5) Although no significant differences were detected, treatments where the fertilizer rate was doubled (i.e. ET with 200% FSR [ treatments 3 ] and SW with 200% FSR [ treatments 7]) leached greater NO3N loads (Fig. 3 6, Table 3 5). ET with 50% FSR (trea tment 2) resulted in the least NO3N load leached. Analyzing leaching by seasons showed over 8 0 % reduction in NO3N load in the dry season using ET or SW -based irrigation methods in comparison to set schedule (Table 3 4). In the wet season 7 and 38% more NO3-N load was leached from treatments 3 and 7 respectively, compared to treatment 5 This was likely due to NO3N buildup in these treatments that did not occur in treatment 5 due to its high irrigation rate. However, the reduction in NO3N load in the w et season for the remaining treatments ranged between 29 to

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88 68% in comparison to treatment 5 (Table 3 4). Thus, t he amount of NO3-N leached in my study was more closely correlated with the amount of water applied than the fertilizer rate. Others (Sexton et al., 1996; Muoz Carpena et al., 2008; Gheysari et al., 2009) observed that NO3N leaching was more influenced by the amount of N applied than water volume applied. Results from my study suggest that efficient irrigation methods have a potential to reduce NO3N leaching. This is because under saturated flow NO3 ions move at similar speed as water molecules ( Havlin et al., 2004). Thus by implementing ET or SW based irrigation practices, tropical fruit producers can save on irrigation energy costs and also save on fertilizer costs. Set schedule with FSR (treatment 5) leached a significantly (p 0.05) greater TP load than the other treatments (Fig 3. 7 Table 3.5). The higher TP leached for the ET and SW based treatments (Fig 3. 7 ) coincided wit h the months w hen more than 100 mm of rainfall was received. ET or SW -based irrigation methods reduce d TP leaching in the dry season by over 8 0% while the TP reduction in the wet season ranged between 58 to 70% compared to the set schedule method (treatment 5) (Table 3 4). The lesser TP reduction in the wet season was attributed to more P being dissolved due to extra water from the rainfall result ing in leaching. Nelson et al. (2005) reported that excessive P leaching was attributed to over application of P, low P sorpti on capacity of the soil, and rainfall exceeding evaporation. The P leaching in the current study was attributed to the high P content of Krome soil (3500 mg kg1), such that with excess irrigation P would be dissolved and becomes available for leaching as observed by others (Coale et al., 1994; He et al., 2000; Nelson et al., 2005). Thus efficient water application reduced fertilizer lost through nutrient leaching. Soil Analysis Significant differences (p < 0.05) among treatments for soil inorganic N were observed once out of the 10 sampling dates for both avocado cultivars (Table 3 6 to 3 1 1 ). Generally set

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89 schedule with FSR (treatment 5), ET with 50% FSR (treatments 2), and SW with 50% FSR (treatment 6) had the least soil inorganic N. This implies that ap plying less fertilizer with optimum irrigation resulted in the same soil inorganic N status as doubling the fertilizer amount with excessive water application. Thus, the excessive water volume in treatment 5 resulted in flushing of the nutrients from the r oot zone. Consistent values of soil inorganic N were observed on all sampling dates apart from 19 May 2009 sampling. The greater magnitude in value of soil inorganic N on 19 May 2009 was attributed to a higher fertilizer rate application on 21 April 2009 of 1950 kg ha1 (for the FSR) and little rainfall (46 mm) in the subsequent days prior to soil sampling. Water from irrigation and rainfall dissolved the fertilizer but was not adequate to leach inorganic N out of the root zone to be captured in the bucket lysimeters. This is evidenced by a small load (less than 3%) of NH4N leached in comparison to NO3N load leached during May 2009 for each of the treatments. Analyzing treatment effects as 2 levels of irrigation and 3 levels of fertilizer factorial design (with treatment 5 omitted) showed that fertilizer level significantly (p < 0.05) affected soil inorganic N of Beta in 2008 and 2009 (Table 3 13) with no significant interaction effect between irrigation and fertilizer levels. There were no significant e ffects detected for Simmonds. Soil organic C, C:N ratio, and C:P ratio were measured annually with no significant differences (p < 0.05) among main treatments over the four year period (Table 3 6 to 3 9 ). S et schedule with FSR (treatment 5) generally ha d a lower soil organic C and a higher C:N ratio in comparison to other treatments which was attributed to leaching of organic carbon as reported by Roose and Barthes (2001). There was a significant decrease (p < 0.05) in C:N and C:P ratios between the valu es observed in the first year and those values observed in the fourth the year (Tables 3 6 to 3 9) This implies an increase in the soil N and P content over the study period

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90 possibly attributed to inorganic fertilizer input. The C:N reported in my study w as about twice that observed by others ( Wang et al., 2005; Munoz -Carpena et al., 2008; Wang et al., 2010) in vegetable production fields on Krome very gravel loam soil of south Florida. Likewise the soil organic C was 2 to 3 times less than that observed b y Chin et al. (2007) and Wang et al. (2010) in vegetable production fields. The difference in the C:N ratio and soil organic C was attribute to high use of fertilizer in vegetable production compared to the level of fertilizer input in tropical fruit produ ction. Analyzing treatment effects as 2 -levels of irrigation and 3 levels of fertilizer in a factorial design (omitting treatment 5) resulted in detection of a few cases of significant effects attributed to either irrigation or fertilizer level, but there were no general trend over the four years (Table 3 13). Irrigation level only caused a significant (p < 0.05) affect in the C:P ratio for Simmonds in 2007. Fertilizer level significantly affected the C:P ratio of Beta in 2008 and Simmonds C:N ratio in 2009 (Table 3 13). In all cases where significant effects were detected there were no interaction s between irrigation and fertilizer levels. Avocado Yield Analyzed as Treatment Main Effects The first Simmonds fruit harvest of June 23 (per Florida Avocado Administrative Committee guidelines) was skipped as few fruits had attained the harvesting diameter of 90 mm. The greatest number of fruit was recorded on July 21 of each year while the least fruit number was recorded on August 4 (Table 3 14). Significant differences (p < 0.05) in total Simmonds yields (fruit number and weight ) were observed among treatments, with ET with FSR (treatment 1) SW with FSR (treatment 4) Set sch. with FSR (treatment 5), and SW with 200% FSR (treatment 7) recording the gre atest fruit number in 2008 (Table 3 15). Although not significant, there was a trend for these treatments to produce more fruit in 2009. S W with FSR (treatment 4)

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91 and SW with 200% FSR (treatment 7) also were measured with greater yields in 2009 compared to other treatments although the differences were not significant. This implies that Simmonds trees were responsive to both water volume applied and fertilizer rate. Interestingly in both years, the trend was for greater to lesser fruit production for SW+ FSR > SW+200%FSR > ET+FSR > Set sch.+FSR suggesting that over irrigation and nutrient leaching in the Set. sch.+FSR reduced crop yields and that perhaps limiting water (i.e., ET based irrigation treatments) and/or fertilizer rates in the other treatments r educed yields. Given that SW -based irrigation treatments produced more fruit than ET -based treatments suggest kc values were underestimated. The greatest fruit number for Beta was recorded on August 11 in 2008 and on August 18 in 2009 while the least fr uit numbers were recorded on September 1 in 2008 and September 8 in 2009 (Table 3 16). This is in contrast to Simmonds where largest yields occur red during the second harvest. This may be due to the effect of low average ambient late winter and spring te mperatures on Simmonds fruit development compared to hot average ambient temperatures during Beta fruit development. In 2008 the Beta fruit harvest of September 1 and 8 was combined since the fruits had to be removed from the field in preparation for hurricane Gustav. No significant differences were observed among treatments for all harvesting dates both in 2008 and 2009 (Table 3 16) Likewise no significant differences were observed among treatments for the total yield (fruit number and weight) for the two years (Table 31 7 ). Since fewer fruits were harvested on 8 September 2009, fruits of this harvest date may be combined with those of September 1 to save on harvesting and shipping costs (Evans, 2006). SW with 200% FSR (treatment 7) had the greatest yield in 2008 while set schedule with FSR (treatment 5) had a greater yield in 2009 in comparison to other treatments. Since Beta trees are known to

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92 genetically predispose d to greater production than Simmonds trees, this implies that the range in irri gation and fertilizer treatments was large enough to encompass and have an effect on fruit yield of Simmonds but not Beta (J.H. Crane, Homestead, 20 10, personal communication ). Further research is needed to investigate if a lower or higher main treatme nt would have caused an effect in Beta fruit yield. Evaluation of crop production water use efficiency (CP WUE) as a ratio of fruit weight per volume of water applied for Simmonds fruits and Beta fruits resulted in significant differences (p < 0.05) among treatments in 2008 (Table 3 1 8 ). Set schedule with FSR (treatment 5) had the least CP WUE in comparison to other treatments in both 2008 and 2009. In 2008 there were significant differences in CP -WUE (p < 0.05) between ET and SW based irrigation trea tments for both Simmonds and Beta, yet in 2009 such differences were not observed. Although SW with FSR (treatment 4) had the greatest yield in 2008, it had a lower CP -WUE in comparison to the ET -based treatments. This was attributed to a leak in the t reatment 4 water line that resulted in excess water flowing through the water meters. In 2009 where no system leaks occured there was no significant difference (p < Analysis of cro p production fertilizer use efficiency (CP FUE) as a ratio of fruit weight per amount of fertilizer applied resulted in significant differences (p 0.05) between SW and ET -based irrigation treatments of CP -WUE Simmonds. < 0.05) among treatments (Table 3 1 9 ). For Simmonds, treatments where fertilizer rate was halved correspond ed to a greater CP FUE while treatments where the fertilizer rate was doubled resulted in the least CP FUE. For Beta no significant differences (p < 0.05) were observed among treatments although treatments with a double f ertilizer rate had the lowest CP FUE (Table 3 1 9 ). The low CP FUE for Simmonds and Beta in 2009 was attributed to a rise in FSR from 340 kg ha1 in

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93 2008 to 1950 kg ha1 in 2009. Thus the FSR increased by about 600% fertilizer, yet the fruit yield did not change for Simmonds and slig htly increased for Beta. The CP FUE ratio assists with identifying the treatments with the greatest yield returns per unit of fertilizer input. Such information may be beneficial in deciding the fertilizer rate to apply when performing an economic analys is for avocado production. The mean fruit weight of Simmonds and Beta were not significantly different (p < 0.05) in 2008 and 2009 (Fig. 3 8 ). However, Simmonds fruits in 2009 were larger than those in 2008 for all treatments although the yield was similar in the two years. This may be attributed to the trees being bigger in 2009 and capable of manufacturing and allocating more carbohydrates for fruit development than in 2008. Generally the Beta fruits of 2008 were larger than those of 2009 with t he SW with 50% FSR (treatment 6) producing the smallest fruits in 2009. For the Beta trees, the fruit number was at least 50% more in 2009 than in 2008. The carbohydrates produced in 2009 had to be divided among more fruits produced, which resulted in sm aller fruits (Genard et al., 2008). Fruit mean weight for Simmonds and Beta corresponded to values observed by Hatton and Reeder (1965) among avocado cultivars Avocado Yield Analyzed as 2-levels of Irrigation by 3-levels of Fertilizer Factorial Desig n The results of mean fruit weight per volume of water applied showed that set schedule with FSR (treatment 5) had the lowest CP WUE compared to other treatments (Table 3 1 8 ). Therefore treatment 5 was removed from the subsequent analysis to create a 2 3 factorial design to explore the effect of irrigation and nutrient management on fruit yield. In this analysis the 2 levels of irrigation were ET and SW while the 3 -levels of fertilizer rate were 50% FRS (half), 100% FSR (standard), and 200% FSR (double). The factorial analysis showed that both irrigation and fertilizer rate significantly (p < 0.05) affected Simmonds yield in 2008 (Fig. 39 ). However, in 2009 it was only the irrigation level that significantly (p < 0.05) affected

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94 Simmonds fruit yield. S W -based irrigation recorded greater fruit yield than ET based irrigation except at half fertilizer rate in 2008 (Fig. 3 9 ). The failure to detect a significant difference (p < Plant Nutrient Assessment and Tree Growth 0.05) due to fertilizer rate in 2009 was attributed to a sharp rise in the FSR of about 600% suggesting that an effect may have been detected again had a lower FSR been used in 2009. The data implies that doubling the fertilizer rate was not beneficial in influencing Simmonds fruit yields in both production years. For Beta neither irrigation nor fertilize r rate had a significant effect on fruit yield in both 2008 and 2009, with no interactions between irrigation volume and fertilizer rate (Fig. 3 10). This observation was in agreement with the early finding from the main irrigation and nutrient treatment effects on Beta fruit yield. Further research is required to explore if Beta fruit yield would be influenced by either a lesser or greater irrigation and nutrient treatment. Generally the re were no significant differences (p < 0.05) among treatments for Simmonds trees or Beta trees for leaf concentrations of TN, TC, and TP observed from 2006 to 2009 (Table 320 to 3 29). The treatment means for these three elements ranged as follows; T N (1.48 to 2.33%), TC (44.8 to 47.7%), and TP (1090 to 2847 mg kg1). An increase in the FSR amount over the years did not increase T N content in the leaves as observed by Embleton et al. (1958) The range of T N (1.48 to 2.33% ) in my study was similar to that observed by Lahav and Kadman (1980) for other avocado cultivars Analysis of treatment effects as a factorial design of 2 levels of irrigation with 3 levels of fertilizers did not reveal information that was contrary the main treatment ef fects on TN, T C and TP (Table 3 30). Evaluation of plant tree trunk diameter showed no significant differences (p < 0.05) among treatments for the four years for either Simmonds trees or Beta trees (Fig 3 1 1 ). The pooled mean (with standard deviations) for Simmonds trees showed that the tree diameter increased

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95 from 1.67 0.52 cm in 2006 to 24.45 3.52 cm in 2009. For Beta trees, the pooled mean (with standard deviations) tree trunk diameter increased from 1.60 0.47 cm in 2006 to 24.14 4.40 cm in 2009. For Simmonds trees, SW with FSR (treatment 4) had larger tree diameters in 2008 and 2009 than the other treatments. For Beta trees, treatments where fertilizer rate was halved had the smallest tree truck diameter in 2008 and 2009 in comparison to other trea tments. Factorial analysis of tree diameter as influenced by 2 -levels of irrigation and 3levels of fertilizer also failed to detected differences among treatments. This suggests that the smaller tree diameter observed for Beta during 2008 and 2009 may b e attributed to other factors other than irrigation or fertilizer input levels. Leaf color expressed in SPAD units was not significantly different (p < Recommend ation s from m y Study 0.05) among treatments for Simmonds trees and Beta trees. Mean values for each treatment for the var ious sampling dates are shown in Fig 3 1 2 This implies that the trees had the same level of leaf color which corresponds to the same level of chlorophyll among the different treatments (Porro, 2001). Chlorophyll level is directly related to N status of le aves. The SPAD units observed were within the range report ed for avocado by Neaman and Espinoza (2010). The SPAD values were in a gre ement with tissue TN values which showed that there were no differences among treatments. Bas ed on the greatest avocado fruit yield, CP WUE, and CP -FUE values SW with FSR (treatment 4) ranked higher than the other treatments and thus is proposed as the BMP for the production of Simmonds avocado. Although ET based irrigation would be a good alter native to SW -based irrigation this methods requires developing or modifying available avocado kc values to meet the climatic condition s of south Florida. The amount of P in fertilizer applied was reduced by half during fruit production years, yet the C:P ratio decreased over the years. This implies that the P formulation in the fertilizer could be lowered without affecting the fruit yield.

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96 Research is needed to explore how no P fertilizer application may affect avocado growth and production on calcareous s oil with high P content. Likewise research is needed to explore how the different irrigation and nutrient management practices would affected nutrient leaching in Beta. The yield difference between Simmonds and Beta suggest differences in nutrient up take between the two cultivars Performing a nutrient uptake analysis would provide insight into fertilizer use efficiency between the two cultivars and an opportunity to perform a fertilizer mass balance for avocado production. Conclusions Irrigati ng young avocado trees based on ET or SW saved 93 and 87% of the water volume applied in comparison to set schedule over the four year period No significant differences (p < 0.05) were observed in the water volume applied during the wet or dry season from each of the irrigation method s Irrigating based on SW with FSR (treatment 4) resulted in average annual reductions of 70 and 75% in NO3N and TP leaching compared to the set schedule irrigation method over the two years of leachate sampling While irrigating ba sed on ET with FSR (treatment 1) resulted in average annual reductions of 51 and 84% in NO3N and TP leaching compared to the set schedule irrigation method over the two years of leachate sampling Such high reduction s were attribute d to nutrient leaching being more influenced by the irrigation management than fertilizer rate. Based on avocado fruit yield, CP WUE, and CP FUE values SW with FSR (treatment 4) and ET with FSR (treatment 1) had higher fruit yield for Simmonds than the other treatments and sh ould be explored further as trees mature to determine if similar results occur. Yield results for Simmonds suggest that this avocado cultivar is responsive to well maintained soil water regime in the root zone. Since Beta genetically predisposed to gre ater production than Simmonds the FSR may not be the best nutrient management practices for the production of Beta avocado. Generally no significant differences (p < 0.05) were

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97 observed among treatments for SPAD value ; leaf TN, TC, and TP; trunk diame ter; and soil organic carbon, C:N and C:P ratios, and soil inorganic N. Considering that water is becoming scar c e due to climatic variability and increasing demand by other uses and the need for more sustainable agriculture, irrigation BMPs should be bene ficial for avocado producers. By apply ing lower water volumes, lower fertilizer rates are needed to support crop growth since nutrient leaching is reduced. Although the current practice is to apply the same amount of fertilizer to all avocado cultivars so me avocado cultivars respond differently to fertilizer rate s This would save producers the extra cost incurred in applying surplus fertilize r for cultivars that may be predispose d to greater production at lower fertilizer rates. The amount of P in fertili zer applied was reduced by half during fruit production years, yet the C:P ratio declined over the years. This implies that the P formulation in the fertilizer could be lowered without affecting the fruit yield. Research is needed to explore how the differ ent irrigation and nutrient management practices affected nutrient leaching and uptake in Beta. The yield difference between Simmonds and Beta suggest differences in nutrient uptake between the two cultivars

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98 Table 3 1. F ertilizer at a standard rat e management scheme used for the Simmonds and Beta avocado trees Development stage Year No. of fertilizer applications (year1) Amount applied each time (kg tree1) Nutrient Element content (%) N P K Mg Orchard establishment 20062007 6 0.045 6 6 6 2 Fruit bearing trees (production) 2008 4 0.227 8 3 9 3 2009 4 1.361 Table 3 2 The ETo and kc values used to compute water application rates for the ET -based irrigation management method Month ET O ( mm month 1 ) crop coefficient (k c ) z Jan 1 .85 0.5 0 Feb 2.46 0.5 0 Mar 3.53 0.8 0 Apr 3.99 0.8 0 May 4.55 0.68 Jun 4.52 0.68 Jul 3.89 0.68 Aug 3.51 0.68 Sep 3.51 0.68 Oct 3.07 0.68 Nov 2.49 0.5 0 Dec 1.93 0.5 0 z, Crane, Homestead, 2006, personal communication. Table 3 3. Amount of water a pplied ( 103 m3 tree1day1) by the different management practices Year Irrigation management y ET z S oil water S et schedule 2006 1.5 5 a 2.2 4 b 40.6 1 c 2007 1. 70 a 4.49 b 40.36 c 2008 2.1 2 a 6.1 3 b 40.56 c 2009 4.3 3 a 5.7 3 b 39.8 5 c y, Irrigation managements with same letter within rows are not significantly different (p 0.05) z To get amount of wate r applied per hectare (103 m3/tree/day) multiple value by 358 (trees).

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99 Table 3 4 Nutrient leaching reduction percentange of Simmonds avocado trees as compared to set schedule with FSR t reatment Dry season Wet season Treatment TP NO 3 N TP NO 3 N y 1, ET+FSR 86 94 70 29 2, ET + 50% FSR 84 99.5 59 68 3, ET + 200% FSR 85 84 69 38 4, SW + FSR 86 86 62 61 5, Set sch. + FSR 6, SW + 50% FSR 91 99.8 58 30 7, SW + 200% FSR 87 83 64 7 y, Negative values imply that greater nutrients were leac hed from that treatment than set schedule with FSR. Table 3 5. Total nutrient load leached kg ha1from Nov 2007 to Oct 2009 for Simmonds avocado trees Treatment N O 3 N TP z 1, ET+FSR 53 0.470 2, ET + 50% FSR 24 0.624 3, ET + 200% FSR 119 0.533 4, S W + FSR 33 0.732 5, Set sch. + FSR 108 2.899 6, SW + 50% FSR 58 0.702 7, SW + 200% FSR 121 0.685 Table 3 6 Simmonds and Beta s oil analysis for irrigation and fertilizer management treatments 09/05/2006, n=3 Simmonds Beta Treatment C:N ratio Or gani c C % C:P ratio Inorg N z (mg kg 1 ) C:N ratio Organic C % C:P ratio Inorg N ( mg kg 1 ) 1, ET+FSR 39 5.11 15 24 ab 43 4.66 11 22 2, ET + 50% FSR 52 4.55 13 24 ab 53 4.53 14 30 3, ET + 200% FSR 39 4.69 14 39 a 38 5.27 17 39 4, SW + FSR 40 5.04 14 31 ab 40 4.50 12 47 5, Set sch. + FSR 46 3.70 12 20 b 58 3.37 12 33 6, SW + 50% FSR 43 4.32 11 25 ab 39 4.12 11 29 7, SW + 200% FSR 48 3.76 11 24 ab 38 4.82 15 26 p value 0.7265 0.3776 0.5439 0.1916 0.2896 0.4786 0.2434 0.4992 z, Inorganic N with same l etter within columns are not significantly different (p 0.05) by column

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100 Table 3 7 Simmonds and Beta s oil analysis for irrigation and fertilizer management treatments 7/6/2007, n=3 Simmonds Beta Treatment C:N ratio Organi c C % C:P ratio Inorg N ( mg kg 1 ) C:N ratio Organic C % C:P ratio Inorg N ( mg kg 1 ) 1, ET+FSR 40 4.20 17 49 43 3.40 12 30 2, ET + 50% FSR 48 3.98 16 29 40 4.96 19 47 3, ET + 200% FSR 37 3.77 13 54 36 4.12 14 55 4, SW + FSR 40 3.56 12 39 34 4.08 14 59 5 Set sch. + FSR 46 2.99 12 30 48 3.14 14 59 6, SW + 50% FSR 44 3.59 12 30 40 3.59 11 59 7, SW + 200% FSR 40 3.78 13 41 36 4.21 12 53 p value 0.6287 0.5061 0.3737 0.1845 0.5632 0.105 0.1113 0.7378 Table 3 8 Simmonds and Beta s oil analysis for irrigation and fertilizer management treatments 9/22/08, n=3 Simmonds Beta Treatment C:N ratio Organi c C % C:P ratio Inorg N ( mg kg 1 ) C:N ratio Organi c C % C:P ratio Inorg N ( mg kg 1 ) 1, ET+FSR 29 4.90 13 20 32 4.88 13 16.83 b 2, ET + 50% FSR 33 3 .95 10 16 29 4.67 14 22.27 ab 3, ET + 200% FSR 25 5.62 10 29 22 5.83 10 36.90 a 4, SW + FSR 26 5.54 11 23 25 5.26 13 24.03 ab 5, Set sch. + FSR 41 4.43 14 17 34 4.77 15 16.53 b 6, SW + 50% FSR 28 5.05 14 23 32 4.61 14 21.20 b 7, SW + 200% FSR 28 5.01 11 26 25 5.45 11 30.00 ab p value 0.0822 0.3056 0.1161 0.5768 0.1740 0.4677 0.1055 0.0433 z, Inorganic N with same letter not significantly different (p 0.05) by column Table 3 9 Simmonds and Beta s oil analysis for irrigation and fertilizer management treatments 9/7/09, n=3 Simmonds Beta Treatment C:N ratio Organi c C % C:P ratio Inorg N (mg kg1) C:N ratio Organi c C % C:P ratio Inorg N (mg kg1) 1, ET+FSR 33 4.03 8 43 42 3.55 9 34 2, ET + 50% FSR 39 3.80 9 36 50 3.12 1 0 36 3, ET + 200% FSR 36 3.88 9 46 33 3.70 9 57 4, SW + FSR 30 4.80 10 50 43 3.49 10 36 5, Set sch. + FSR 41 3.37 10 35 41 2.95 8 43 6, SW + 50% FSR 40 4.13 10 38 40 3.88 11 41 7, SW + 200% FSR 32 4.29 11 43 32 4.18 8 49 p value 0.0524 0.3324 0. 9193 0.6925 0.5823 0.5568 0.9065 0.2116

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101 Table 3 1 0 Beta soil inorganic N for different sampling dates n=3 Treatment Nov 1, 06 Mar 13, 07 Nov 19, 07 May 6, 08 Jan 14, 09 May 19, 09 1, ET+FSR 31 27 22 30 34 271 2, ET + 50% FSR 27 36 41 29 23 200 3, ET + 200% FSR 60 42 37 26 32 743 4, SW + FSR 31 42 43 33 30 318 5, Set sch. + FSR 19 24 22 32 24 42 6, SW + 50% FSR 29 37 37 30 33 159 7, SW + 200% FSR 42 78 44 31 42 321 p value 0.5106 0.0546 0.3773 0.9902 0.3772 0.1414 Table 3 1 1 Simmonds soil inorganic N for different sampling dates n=3 Treatment z Nov 1, 06 Mar 13, 07 Nov 19, 07 May 6, 08 Jan 14, 09 May 19, 09 1, ET+FSR 35 40 abc 25 21 28 255 2, ET + 50% FSR 34 24 c 13 20 26 241 3, ET + 200% FSR 41 51 a 39 41 36 283 4, SW + FSR 27 53 a 43 35 35 313 5, Set sch. + FSR 21 23 c 42 30 25 68 6, SW + 50% FSR 22 32 c 38 39 33 258 7, SW + 200% FSR 34 49 ab 59 31 36 267 p value 0.3481 0.0062 0.3121 0.1819 0.7733 0.4181 z, Inorganic N with same letter within columns not signif icantly different (p 0.05) by column Table 3 1 2 Soils factorial analysis of 2 levels of irrigation by 3 levels of fertilizer from 2006 to 2009 Simmonds Beta Year Source C:N ratio Organic C % C:P ratio Inorg N (mg kg1) C:N ratio Organic C % C:P ratio Inorg N (mg kg1) 2006 Irrig 0.9497 0.3825 0.1547 0.5180 0.2686 0.5296 0.2854 0.6270 Fert 0.4322 0.3134 0.3966 0.4000 0.5156 0.5425 0.0828 0.8399 Irrig Fert 0.3675 0.7204 0.8905 0.1299 0.4171 0.9685 0.6380 0.1370 2007 Ir rig 0.9127 0.3384 0.0453 0.2756 0.4916 0.5765 0.1222 0.2942 Fert 0.3145 0.9638 0.6495 0.1100 0.8015 0.4516 0.3704 0.7629 Irrig Fert 0.7759 0.7366 0.4280 0.6737 0.7022 0.0874 0.0569 0.5734 2008 Irrig 0.5849 0.3643 0.1688 0.6286 0.8835 0.9580 0.6888 0. 9466 Fert 0.6131 0.2320 0.3287 0.3359 0.1723 0.1572 0.0254 0.0276 Irrig Fert 0.5368 0.2398 0.1027 0.7062 0.3271 0.7371 0.9294 0.3348 2009 Irrig 0.4376 0.1711 0.7741 0.3109 0.5497 0.2864 0.8942 0.9029 Fert 0.0369 0.5597 0.4298 0.8715 0.1845 0.6076 0 .6787 0.0433 Irrig Fert 0.5845 0.8577 0.7894 0.9934 0.6400 0.7043 0.8150 0.6486

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102 Table 3 1 3 Simmonds avocado y ield ( kg ha1) by harvest date p er treatment for 2008 and 2009, n=12 Treatment z 2008 2009 July 7 y July 21 y Aug 4 y July 7 y July 21 y Aug 4 y 1, ET+FSR 479 bc 1333 280 391 c 1302 253 2, ET + 50% FSR 199 d 1137 107 469 bc 1047 194 3, ET + 200% FSR 766 b 1074 143 839 ab 785 279 4, SW + FSR 506 b 1664 257 608 abc 1292 389 5, Set sch. + FSR 783 ab 1054 890 914 ab 941 387 6, SW + 50% FSR 284 cd 1139 273 492 bc 993 485 7, SW + 200% FSR 1232 a 1065 124 1184 a 1061 357 p value < 0.0001 0.5333 0.5371 0.012 0.299 0.1025 y, Data was first normalized using the Box Cox method before performing ANOVA z, Inorganic N with same letter within columns are not significantly different (p 0.05) by column Table 3 1 4 'Simmonds' avocado f ruit number and weight per treatment for 2008 and 2009, n=12 Treatment z 2008 2009 Number of fruits ( ha1)y Weight (kg ha1)y Number of fruits (ha1)y Weight (kg ha1) 1, ET+FSR 4349 ab 1854 ab 4261 1761 2, ET + 50% FSR 3487 b 1464 b 3532 1496 3, ET + 200% FSR 3794 b 1759 b 3882 2058 4, SW + FSR 5894 a 2607 a 4963 2485 5, Set sch. + FSR 3978 ab 1845 ab 3891 2346 6, SW + 50% FSR 3304 b 1389 b 3106 2019 7, SW + 200% FSR 5693 a 2560 a 4753 2450 p value 0.0108 0.0020 0.2352 0.1493 y, Data was first normalized using the BoxCox method before performing ANOVA z, fruit yield with same letter within columns are not significantly different (p 0.05) by column

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103 Table 3 1 5 Beta avocado y ield ( kg ha1) by harvest date p er treatment for 2008 and 2009, n=4 Treatment 2008 2009 Aug 11 Aug 18 Sept 1 y Aug 11 Aug 18 Sept 1 z Sept 8 y 1, ET+FSR 2418 1250 822 3072 2974 1159 c 2, ET + 50% FSR 1326 537 216 1084 3153 1565 bc 162 3, ET + 200% FSR 2266 2759 1067 1111 5136 3607 ab 497 4, SW + FSR 2813 1594 412 2150 2633 1971 abc 537 5, Set sch. + FSR 3207 8 36 322 4559 3431 1541 bc 179 6, SW + 50% FSR 2212 812 544 3 511 1684 1230 c 343 7, SW + 200% FSR 4013 1577 609 1851 1980 3870 a 322 p value 0.3923 0.07 0.5950 0.0712 0.1182 0.0363 0.7618 y, Data was first normalized using the BoxCox method before performing ANOVA z, Fruit yield with same letter within col umns are not significantly different (p 0.05) by column Table 3 1 6 'Beta' avocado f ruit number and weight per treatment for two years n=4 Treatment 2008 2009 Number of fruits ( ha 1 ) Weight ( kg ha 1 ) Number of fruits ( ha 1 ) Wei ght ( kg ha 1 ) 1, ET+FSR 8599 4416 13883 7280 2, ET + 50% FSR 4031 1894 9764 4926 3, ET + 200% FSR 8868 4197 15407 7892 4, SW + FSR 7076 3559 10122 4766 5, Set sch. + FSR 7703 3985 17915 9430 6, SW + 50% FSR 5374 3159 10838 5213 7, SW + 200% FSR 8868 4988 10122 5462 p value 0.523 0.55 2 0.48 2 0.36 7 Table 3 1 7 Means fruit weight per cubic meter of water applied water applied ( kg m3) Treatment z Simmonds Beta 2008 y 2009 2008 y 2009 1, ET+FSR 12.2 2 a 6.40 a 28.48 a 26. 89 2, ET + 50% FSR 9.16 b 5.46 a 10.66 bcd 18. 20 3, ET + 200% FSR 11.60 ab 7.49 a 20.8 7 ab 29.15 4, SW + FSR 4.9 4 c 7.00 a 5.97 cd 13.55 5, Set sch. + FSR 0.7 1 e 0.89 b 1.1 5 e 3.6 3 6, SW + 50% FSR 3.20 b 5.97 a 6.7 1 cd 15.6 5 7, SW + 200% FSR 9.6 9 ab 7.55 a 18.33 abc 16.96 p value <0.0001 <0.0001 0.0004 0.0683 y, Data was first normalized using the Box Cox method before performing ANOVA z, Mean fruit weight with same letter within columns are not significantly different (p 0.05) by column

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104 Table 3 1 8 Means fruit weight per kg of fertilizer applied ( kg kg1) Treatment z Simmonds Beta 2008 y 2009 y 2008 2009 y 1, ET+FSR 5.337 ab 0.836 cd 12.949 ab 3.658 2, ET + 50% FSR 8.041 a 1.449 ab 11.112 ab 4.340 3, ET + 200% FSR 2.521 d 0.498 e 6.154 b 1.635 4, SW + FSR 7.450 ab 1.225 bc 10.437 ab 2.292 5, Set sch. + FSR 5.236 c 1.170 bc 11.685 ab 4.795 6, SW + 50% FSR 8.272 a 1.933 a 18.526 a 4.831 7, SW + 200% FSR 3.799 cd 0.605 de 7.313 ab 1.315 p value < 0.0001 < 0.000 1 0.1624 0.0987 y, Data was first normalized using the Box Cox method before performing ANOVA z, Mean fruit weight with same letter within columns are not significantly different (p 0.05) by column Table 3 19. Leaf t issue analysi s for irrigation and fertilizer management treatments 08/03/2006, n=4 Simmonds Beta Treatment T N % T C % TP mg kg 1 T N % T C % TP mg kg 1 1, ET+FSR 2.03 46.68 1912 .00 1.96 46.63 1702 .00 2, ET + 50% FSR 2.01 47.24 1709 .00 2.03 48.30 1974 .00 3, ET + 200 % FSR 1.93 47.37 1554 .00 2.14 47.00 2053 .00 4, SW + FSR 1.90 47.69 1542 .00 2.27 47.71 1917 .00 5, Set sch. + FSR 1.84 47.63 1512 .00 2.05 46.99 1541 .00 6, SW + 50% FSR 1.88 47.57 1601 .00 2.06 46.77 1632 .00 7, SW + 200% FSR 1.81 47.09 1604 .00 2.23 46. 84 1803 .00 p value 0.8 5 0.5 1 0.5 7 0.30 0.2 5 0.63 Table 3 2 0 Leaf t issue analysis for irrigation and fertilizer management treatments 12/06/2006, n=4 Simmonds Beta Treatment T N % T C % TP ( mg kg 1 ) T N % T C % TP ( mg kg 1 ) 1, ET+FSR 1.97 47.11 1888 .00 2.17 47.28 2230 .00 2, ET + 50% FSR 2.00 47.17 1828 .00 1.88 46.98 2268 .00 3, ET + 200% FSR 2.11 47.34 2036 .00 2.05 47.37 1787 .00 4, SW + FSR 2.13 46.94 1759 .00 2.24 47.08 1853 .00 5, Set sch. + FSR 1.72 47.71 1968 .00 1.99 47.49 2285 .00 6, SW + 50% FSR 2.20 47.54 2099 .00 2.02 47.10 1817 .00 7, SW + 200% FSR 2.17 47.09 1862 .00 2.48 47.30 2065 .00 p value 0.22 0.4 5 0.9 1 0.07 0.59 0.3 1

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105 Table 3 2 1 Leaf t issue analysis for irrigation and fertilizer management treatments 04/30/2007, n=3 Simmonds Beta z Tr eatment T N % T C % TP ( mg kg 1 ) T N % T C % TP ( mg kg 1 ) 1, ET+FSR 1.89 46.41 1857 .00 2.04 abc 46.41 1928 .00 2, ET + 50% FSR 1.71 46.35 1922 .00 1.99 abc 46.02 1915 .00 3, ET + 200% FSR 2.11 46.28 1871 .00 2.34 a aa 46.43 1847 .00 4, SW + FSR 1.89 46.17 2087 .0 0 1.79 c aa 46.27 1917 .00 5, Set sch. + FSR 1.77 46.58 2089 .00 1.83 c aa 46.23 1755 .00 6, SW + 50% FSR 1.86 46.20 1930 .00 1.93 bc a 46.51 1854 .00 7, SW + 200% FSR 1.97 46.24 1907 .00 2.28 ab a 46.37 1571 .00 p value 0.23 0.3 5 0.8 2 0.5 8 aaa 0.57 0.19 z, T N% with same letter within column are not significantly different (p 0.05) by column Table 3 2 2 Leaf t issue analysis for irrigation and fertilizer management treatments 08/23/2007, n=3 Simmonds Beta Treatment T N % T C % TP (mg kg1) T N % T C % TP (mg kg1) 1, ET+FSR 1.80 46.02 1799 1.75 46.21 1593 2, ET + 50% FSR 1.88 45.81 1939 1.82 46.41 1768 3, ET + 200% FSR 1.89 45.92 1730 1.85 46.10 1601 4, SW + FSR 1.95 45.83 1847 1.93 45.96 1752 5, Set sch. + FSR 1.94 45.78 1934 1. 84 45.40 1920 6, SW + 50% FSR 1.64 45.78 2231 1.85 45.96 1839 7, SW + 200% FSR 2.10 45.93 1885 1.81 46.26 1615 p value 0.0507 0.9682 0.3699 0.8584 0.1259 0.5442 Table 3 2 3 Leaf t issue analysis for irrigation and fertilizer management treatments 12/26/2007, n=3 Simmonds Beta Treatment T N % T C % TP (mg kg1) T N % T C % TP (mg kg1) 1, ET+FSR 1.56 46.75 1988 1.65 46.45 2768 2, ET + 50% FSR 1.48 46.69 1097 1.55 46.09 1597 3, ET + 200% FSR 1.80 46.59 1164 1.68 46.34 1518 4, SW + FSR 1.79 47.0 4 1090 1.63 46.15 1559 5, Set sch. + FSR 1.52 45.78 1497 1.52 45.83 2356 6, SW + 50% FSR 1.51 46.47 2253 1.69 46.60 1926 7, SW + 200% FSR 1.71 46.08 1213 1.69 45.90 1957 p value 0.3438 0.2211 0.0851 0.9320 0.5971 0.3201

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106 Table 3 2 4 Leaf t issue an alysis for irrigation and fertilizer management treatments 04/08/2008, n=3 Simmonds Beta Treatment T N % T C % TP ( mg kg 1 ) T N % T C % TP ( mg kg 1 ) 1, ET+FSR 1.72 45.20 2422 1.91 45.08 2555 2, ET + 50% FSR 1.99 45.81 2589 1.86 45.28 2588 3, ET + 200% F SR 2.17 45.61 2088 2.13 45.65 2029 4, SW + FSR 2.13 46.11 2175 1.93 45.83 2109 5, Set sch. + FSR 2.01 45.58 2159 1.97 43.97 2430 6, SW + 50% FSR 2.07 45.37 2847 1.94 45.56 2621 7, SW + 200% FSR 2.07 45.56 1917 2.14 45.73 1965 p value 0.1483 0.848 8 0.5865 0.4943 0.7897 0.5055 Table 3 2 5 Leaf t issue analysis for irrigation and fertilizer management treatments 08/13/2008, n=3 Simmonds Beta z Treatment T N % T C % TP (mg kg1) T N % T C % TP (mg kg1) 1, ET+FSR 1.56 45.21 2181 2.04 a 46.09 1629 2, ET + 50% FSR 1.85 46.45 1677 1.59 b 45.54 1422 3, ET + 200% FSR 1.79 45.88 1593 1.72 ab 45.73 1295 4, SW + FSR 1.83 45.89 1635 1.81 ab 45.67 1513 5, Set sch. + FSR 1.58 45.91 1768 1.92 ab 46.38 1595 6, SW + 50% FSR 1.76 45.69 2218 1.92 ab 45.95 1359 7, SW + 200% FSR 1.77 45.35 1586 1.82 ab 45.13 1182 p value 0.2559 0.7078 0.1447 0.2014 0.7428 0.7990 z, T N% with same letter within column are not significantly different (p 0.05) by column Table 3 2 6 Leaf t issue analysis for i rrigation and fertilizer management treatments 12/12/2008, n=3 Simmonds Beta z Treatment T N % T C % TP (mg kg1) T N % T C % TP (mg kg1) 1, ET+FSR 1.89 46.63 2805 1.91 ab 46.42 a 2370 2, ET + 50% FSR 1.80 46.57 2294 2.00 ab 46.11 ab 2932 3, ET + 200% FSR 2.00 46.50 1716 2.21 a 46.70 a 2356 4, SW + FSR 1.78 46.89 1652 1.85 ab 46.63 a 2018 5, Set sch. + FSR 1.85 45.95 2152 1.85 ab 45.36 b 2573 6, SW + 50% FSR 1.90 46.64 2547 1.70 b 46.45 a 2881 7, SW + 200% FSR 1.95 46.38 1675 2.10 ab 46.42 a 2233 p va lue 0.9034 0.4470 0.1731 0.1563 0.0487 0.6825 z, Elements with same letter within columns are not significantly different (p 0.05) by column

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107 Table 3 2 7 Leaf t issue analysis for irrigation and fertilizer management treatments 04/20/2009, n=3 Simmonds Beta Treatment T N % T C % TP ( mg kg 1 ) T N % T C % TP ( mg kg 1 ) 1, ET+FSR 2.03 46.05 2634 1.88 46.13 1928 2, ET + 50% FSR 2.01 46.21 2328 1.97 46.41 2084 3, ET + 200% FSR 2.17 46.28 2087 2.03 46.78 1794 4, SW + FSR 2. 14 46.46 2131 2.08 46. 56 2045 5, Set sch. + FSR 2.09 46.14 2300 1.77 46.29 1729 6, SW + 50% FSR 2.18 46.22 2433 1.95 46.66 2139 7, SW + 200% FSR 2.33 45.99 2324 2.03 46.16 1798 p value 0.0795 0.7946 0.5665 0.1329 0.0982 0.2481 Table 3 2 8 Leaf t issue analysis for irrigation and fertilizer management treatments 08/26/2009, n=3 Simmonds Beta Treatment T N % T C % TP ( mg kg 1 ) T N % T C % TP ( mg kg 1 ) 1, ET+FSR 1.80 45.09 1908 1.83 45.02 1495 2, ET + 50% FSR 1.84 44.81 1876 1.8 6 45.55 1539 3, ET + 200% FSR 1.98 45.28 1783 2.10 4 6.06 1629 4, SW + FSR 1.98 45.52 1752 2.12 46.30 1462 5, Set sch. + FSR 2.00 45.11 1966 1.87 45.60 1430 6, SW + 50% FSR 1.90 45.39 1736 1.88 46.54 1328 7, SW + 200% FSR 2.03 45.31 1850 2.17 46.23 1512 p value 0.2871 0.0567 0.7221 0.0621 0.1520 0.2907

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108 Table 3 29. Tissue factorial analysis of 2 levels of irrigation by 3 levels of fertilizer for different sampling dates from 2006 to 2009 Simmonds Beta Sampling date S ource TP ( mg kg 1 ) TN% TC% TP ( mg kg 1 ) TN% TC% 8/3/2006 Irrig 0.2980 0.2438 0.2763 0.4650 0.0890 0.6257 Fert 0.6674 0.7480 0.8273 0.7465 0.4106 0.4918 Irrig Fert 0.4451 0.9992 0.2703 0.4250 0.3590 0.0586 12/6/2006 Irrig 0.9337 0.2493 0.9419 0.2657 0.0558 0.7389 Fert 0.6343 0.7992 0.4562 0.7896 0.0585 0.3601 Irrig Fert 0.3363 0.8802 0.4468 0.1564 0.3394 0.7203 4/30/2007 Irrig 0.3978 0.9828 0.2250 0.1275 0.1681 0.4956 Fert 0.8103 0.0750 0.9715 0.0991 0.0027 0.7697 Irrig Fert 0.6501 0.3778 0 .7769 0.4662 0.6875 0.2518 8/23/2007 Irrig 0.2761 0.4160 0.6681 0.2431 0.3656 0.3347 Fert 0.1380 0.2291 0.5547 0.2626 0.6098 0.7253 Irrig Fert 0.6924 0.2950 0.9437 0.3652 0.0924 0.8498 12/26/2007 Irrig 0.6456 0.6363 0.6234 0.5868 0.6295 0.7925 Fer t 0.2096 0.1783 0.3233 0.5088 0.6244 0.7031 Irrig Fert 0.0077 0.5047 0.5468 0.1864 0.7616 0.7474 4/8/2008 Irrig 0.8814 0.1873 0.7171 0.7228 0.8992 0.2899 Fert 0.1857 0.2794 0.9877 0.7000 0.2749 0.6263 Irrig Fert 0.7702 0.1191 0.3627 0.9741 0.9510 0.6901 8/13/2008 Irrig 0.9792 0.5012 0.6473 0.6989 0.4431 0.8674 Fert 0.1740 0.4950 0.5837 0.7174 0.6571 0.1622 Irrig Fert 0.0496 0.1985 0.3737 0.9579 0.4029 0.7939 12/12/2008 Irrig 0.2772 0.8607 0.6843 0.7239 0.1138 0.6425 Fert 0.1251 0.5524 0.34 62 0.7578 0.3155 0.9891 Irrig Fert 0.1312 0.7273 0.6661 0.9651 0.1425 0.4856 4/20/2009 Irrig 0.7558 0.0319 0.8259 0.6315 0.3482 0.8917 Fert 0.6256 0.0802 0.8477 0.3768 0.6773 0.0185 Irrig Fert 0.2024 0.8913 0.2898 0.7399 0.5437 0.0074 8/26/2009 I rrig 0.4271 0.0995 0.0100 0.0634 0.1053 0.0431 Fert 0.9790 0.1424 0.2885 0.2752 0.1477 0.6904 Irrig Fert 0.5636 0.5875 0.1641 0.1445 0.3639 0.7890

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109 A B Figure 3 1. Orchard layout A ) Treatments and their replicates where the first number is the treatment and second number is the replicate, and B) tree cultivar and number and type of device installed beside the tree. Fig ure 3 2. Automated switching tensiometers were set at 15 kPa

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110 Figure 3 3 Amount of water appli ed as daily average per month by the Set s chedul e ET and SW irrigation managemen t

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111 Irrigation management practice ET Soil water Set scheduleVolume of water applied (m 3 /day/tree) 0.00 0.01 0.02 0.03 0.04 0.05 Wet Dry Figure 3 4. Mean water volume applied per day for ET -based, SW -based, and Set schedule based irrigation during we t and dry season. Figure 3 5 Correlation of historical and real time ETo (R2 = 0.913)

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112 A B Figure 3 6. Leached nitrate for Simmonds over a two year period. A) November 2007 to October 2008 and B) November 2008 to October 2009.

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113 A B Figure 3 7. Leached total phosphorus for Simmonds over a two year period. A) November 2007 to October 2008 and B) November 2008 to October 2009.

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114 Figure 3 8 Effect of irrigation and fertilizer management treatments on m ean fruit weight of avocados for 2008 and 2009. A) Simmonds and B) Bet a A B

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115 Figure 3 9 Simmonds yield analyzed as a factorial design with 2 level s of irrigation and 3 levels of fertilizer input. A) 2008 and B) 2009. A B

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116 Figure 3 10. Beta yield analyzed as factorial de sign with 2 level s of irrigation and 3 -levels of fertilizer input. A) 2008 and B) 2009. A B

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117 Figure 3 11. Effect of irrigation and fertilizer management treatments on mean tree diameter for 4 year. A) Simmonds and B) Beta. A B

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118 Figure 3 12. Effect of irrigation and fertilizer management treatments on mean SPAD value for reading collected from 2006 to 2009. A) Simmonds and B) Beta. B A

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119 CHAPTER 4 MODEL SIMULATI ON OF NITROGEN AND PHOSPHORUS LEACHING IN CALCAREOUS SOILS OF SOUTH FLORIDA Intr oduction To prevent pollution from point and nonpoint sources, the U.S. Congress passed the Federal Clean Water Act in 1972 (amended in 1987) to restore and maintain the chemical, physical, and biological integrity of the nations waters. Nutrient leaching of mainly nitrogen (N) and phosphorus (P) from agricultural fields is one source of surface water and groundwater impairment. Nutrient leaching is attributed to improper matching of the optimal fertilizer requirements and water needs to the crop and produ ction environment. Nutrient leaching may have adverse effects in south Florida due to the interaction of surface water and groundwater resulting from a shallow water table (Noble et al., 1996), and the existence of naturally sensitive water bodies like the Biscayne Bay and the Everglades (Brower et al., 2005; Reddy et al., 2006). In Florida, the agricultural best management practice (BMP) program is aimed at reducing movement of N and P from agricultural fields to water bodies ( Simonne and Hutchinson, 2005). BMPs are defined as a set of on farm practices designed to reduce nutrient loss and improve water quality while sustaining econom ically viable farming operations. Although several BMPs have been develop ed for various agricultural crops in Florida ( FDACS and FDEP, 1998; Simonne et al., 2003; Obreza and Schumann, 2010), no irrigation and nutrient BMP has been developed, tested, and documented for tropical fruit tree crops. Due to resource constraints it is unreasonable to field test all possible BMP combinations that involve nutrient levels and nutrient application methods and irrigation v olumes and application methods. Thus, field -te sted computer models are commonly used in research, planning, management, and decision making due to their advantage of giving an insight on how systems function and interact. Many models that simulate N and P leaching are available.

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120 Deterministic physical ly based models (e.g., Decision Support System for Technology Transfer [DSSAT] (Jones et al., 2003), Groundwater Loading Effects of Agricultural Management Systems [GLEAMS] (Leonard et al., 1987), Field Hydrologic and N utrient Transport Model [FHANTM] ( Fra isse and Campbell, 1997), and Leaching Estimation and Chemistry Model [LEACHM] (Wagenet and Hutson, 1989) that simulate nutrient leaching have been used and reported to give satisfactory results ( Sogbedji et al., 2001; Webb et al., 2001; Asadi and Clemente 2003). Nutrient leaching has been simulated in fields of various crops including maize, wheat, millet, potato, cassava, bahia grass, and brachiaria grass (Jones et al., 2003). However, such models are less often applied to fruit orchards (Gary et al., 19 98). Of the process -based field scale leaching models, LEACHM has been widely used to simulate water flow and solute transport with satisfactory results (Jemison and Fox 1992; Jabro et al., 1995; Sogbedji et al., 2001; Contreras et al., 2009). LEACHM is a comprehensive, deterministic -mechanistic, one -dimensional finite difference model that was developed by Hutson and Wagenet (1989) to simulate vertical water and solute transport both in field and laboratory columns using numerical routines. It has been r evised and tested over the years by different researchers (Borah and Kalita, 1999; Sogbedji et al., 2001; Jabro et al., 2006) and the current LEACHM (ver. 4.0 ) is a suit of three models. The three simulation models include LEACHP for pesticides, LEACHN for N and P, and LEACHC for salinity in calcareous soils (Hutson, 2005). The model incorporates carbon (C) N, and P pool s and pathways. Ng et al. (1999) used the LEACHN model to identify management practices (i.e., water table management, conservation till age, and intercropping) that would reduce nitrate (NO3) leaching from a corn ( Zea mays L.) field fertilized with urea in Ontario, Canada. The authors reported that LEACH N model gave better predictions for NO3 leaching on plots under controlled

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121 drainage/sub surface irrigation systems than on plots under free drainage. Using LEACHN model to develop BMPs for potato ( Solanum tuberosum ) production in Nevsehir, Turkey, nl et al. (1999) reported that nutrient leaching could be reduced significantly by reducing th e irrigation/rain water applied from 1100 mm to 650 mm, reducing ammonium sulfate fertilizer input from 900 kg ha1 to 400 kg ha1, and applying fertilizers after most of the supplemental irrigations were completed The authors further recommend that rotat ing potato with wheat could further reduce the residual NO3 leaching since half of the applied NH4N in the fertilizer was converted to NO3 during the growing season. Jabro et al. (2006) compared the simulation accuracy and performance of LEACHN in predic ting N dynamics in a soil -water -plant system to two other field scale models: Nitrogen and Carbon C ycling in Soil Water And Plant (NCSWAP) (Molina and Richards, 1984) and SOIL -SOILN (SOILN) (Eckersten and Jansson, 1991). The authors reported that LEACHN an d NCSWAP estimated NO3 leaching more accurately than SOILN, from a corn field fertilized with ammonium nitrate and manure in Rock Springs, Pennsylvania. Ng et al. (1999) and Mahmood et al. (2002) identified parameters used in sensitivity analys e s (Table 4 1) while calibration parameters were identified by Borah et al. (1999), Ng et al. (1999), Mahmood et al. (2002), and Jabro et al. (2006) (Table 4 2) Although LEACHN can simulate P leaching, such studies could not be identified in available refereed litera ture. Global sen sitivity and uncertainty analyse s are tools used with model application s due to uncertainties associated with all predictive deterministic models and measured data to improve the interpretation and thus the application of modeling results ( Shirmohammadi et al., 2006). Th e input factors or parameters that control the variation of the simulated model are one source of u ncertainty. Sensitivity analysis provides a measure of the relationship between a given uncertain

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122 input factor and a model sim ulation output while uncertainty analysis propagates uncertainties onto the model output of interest. Traditionally model sensitivity has been quantified by computing local indices (Saltelli et al., 2005). However, hydrological models are non-linear and g lobal techniques are therefore more appropriate as they explore the entire model parametric space. G lobal sensitivity analysis provides parameter ranking and information about first and higher order effects of parameters by specified outputs. One way of ac counting for uncertainty in model inputs is through the development of probability density functions (PDFs) of the target model outputs (Shirmohammadi et al., 2006; Saltelli et al., 2008; Muoz Carpena et al., 2010). The output PDFs are then used to evaluate uncertainty in the model predictions by placing confidence intervals on the outputs either as margin of safety component or by calculating probability of exceedance of a threshold valu e (Morgan and Henrion, 1992). A common approach to sampling distributions for simulating model outputs is the use of Monte Carlo sampling which consists of multivariate random sampling from model input probability density distributions in order to conduct a large number of model simulations. Due to high c omputational costs of the Monte Carlo type of uncertainty analysis, it is convenient to use a sensitivity screening method first to identify the subset of input factors as having the most influence on model output variability (Saltelli et al., 2004; Shirmohammadi et al. 2006; Muoz Carpena et al., 2010). M odel uncertainty is then efficiently assessed a t a lesser computation time with the subset of model inputs. In my study the use of global sensitivity a nd uncertainty analyses of Morris and eFAST methods to simulate NO3N and TP leaching in calcareous soils were evaluated. A p revious stud y involving use of global modeling technique with LEACHC (a sub-suite of LEACHM for modeling pesticide leaching) was l imited to sensitivity analysis and did not involve a factor

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123 screening s tep (Soutter and Musy, 1999). Although LEACHM can simulate P leaching, field case studies where the model has been applied to simulate P leaching could not be identified in available refereed literature. The specific objectives of the study were to: (1) perform global sensitivity and uncertainty analys e s of the LEACH M model and (2) apply LEACHM to refine the identified BMPs in an avocado ( Persea american a Mill .) orchard while reducing wa ter volumes applied and nutrient leaching. Materials and M ethods Experimental Design The study site was in Homestead, Miami Dade County, FL, at the University of Floridas Tropical Research and Education Center (TREC) (25o2021 N, 80o2001 W). The elevat ion of TREC is about 4 m above sea level with groundwater generally 1000 to 2200 m m below the surface. The wet season in Florida is from May to October and 80% of the rainfall occurs during this period ( Mulholland et al., 1997). Data were collected in an a vocado orchard from Simmonds cultivar trees. The trees were planted in four rows at spacing of 6 m between rows and 4.5 m inter row. The orchard was considered flat with gravelly, loamy -skeletal, carbonatic, hyperthermic lithic udorthents, soils classifi ed as Krome very gravelly loam (Noble et al., 1996). Krome soils are very shallow up to 20 cm deep, well drained, moderately permeable and underline by limestone. Muoz Carpena et al. (2002) reported that Krome soil s are 51% coarse material, 36% sand, 40% silt, 24% clay and a bulk density of 1.42 g/cm3). To provide space for root development in a mechanically rock plowed soil, tropical fruit trees are planted 50 cm deep at the intersection of perpendicular trenches (Nnez Elisea et al., 2001). The irrigati on and nutrient BMPs to be refined through computer simulations were developed based on field data collected from 2006 to 2009 in an avocado orchard. The BMPs involved irrigating the trees based on soil water su ction at 15 kPa (SW) and fertilizing at a

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124 sta ndard rate (FSR) (see Chapter 3). The fertilizers were simulated as NH4, NO3, and TP (Hutson, 2005). Input weather data such as mean temperature, temperatures amplitude, precipitation, and potential evapotranspiration (ETp) were obtained from Florida Auto matic Weather Network (FAWN) (2010). FAWN uses a modified Penman equation referred to as UF IFAS (1984) Penman (Jones et al., 19 84) method to compute ETo. LEACHM Model LEACHM model is a deterministic model that uses finite difference form of the one dimen sional Richards transient flow or Addiscot tipping bucket equation to predict water fluxes and soil moisture distribution with time. Water retention and conductivity functions are calculated by the methods proposed by Campbell (1974). A one dimensional co nvection-diffusion equation (CDE) is solved numerically to estimate chemical fluxes and distribution in the profile. Crop growth subroutines used in LEACHM are based upon empirical equations and the effects of water content, soil strength, and nutrient concentration on root and shoot developments are not considered. However, the distribution of roots with depth partly determines the water and chemical uptake terms within each soil segment. According to Hutson (2005), N transformations in LEACHM are describe d as fluxes between soil organic N pools, in addition to inorganic fertilizer application in the form of urea NH4N, and NO3N. T he major nutrient pathways considered are nutrient leaching (NO3-N and T P) and plant nutrient uptake (NO3-N, NH4-N, P). The de scription of the equations used to model N transformations, parameter s used, and the initial and boundary conditions are given in the LEACHM manual (Hutson, 2005). Sensitivity analyses of the LEACHM model indicated that NO3N leaching was affected by cha nges in bulk density, saturated hydraulic conductivity, initial water content, air entry

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125 value, Campbell exponent coefficient, organic carbon content, and mineralization rate constants (Wagenet and Hutson, 1992; Ng et al., 1999; Mahmood et al., 2002). The model simulates water and solute transport within a soil profile divided into segments ranging from 12 to 25. Fewer segments are recommended for use with Richards equation in order to reduce on the model simulation time. The nodes used in the finite -diff erencing using the Crank Nicolson implicit method are in the cente r of the segments. LEACHM automatically computes extra nodes at the boundaries to maintain the correct boundary conditions. The model requires specifying the number of rainfall and/or irriga tion days in the simulation period. Other inputs include daily maximum and minimum temperatures, evapotranspiration, soil texture, organic carbon, and amount of N and P applied, soil hydraulic data (e.g., water retention curve parameters), N and P rate rea ction parameters, and sorption coefficients The model is equipped with C, N, and P pools and pathways that are used to simulate flow between these pools in each soil segment as well as on the soil surface. The C and N cycling are based on the procedures described by Johnsson et al. (1987), but with additional pools and pathways. The inorganic P model was based on concepts described by Shaviv and Shachar (1989) but modified to represent bound P pool as either a precipitate or a sorption isotherm. The labi le pool is always in local equilibrium, but sorption to or desorption from the bound pool is kinetic. LEACH M predicts both nutrient uptake by a crop and leaching below the root zone up to a depth of 2 m. The model uses a daily time step and can simulate a growing season or several years (Hutson, 2005). A default maximum simulation time interval of 0.1 day i s used to reduce on the computation time while the outputs may be given at a desired time step e.g. daily, weekly, monthly, etc.

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126 Richard's equation, th e soil water flow equation for transient vertical flow derived from Darcy's law and the continuity equation, is: t z Uz H K z t (4 1) where is the volumetric water content of a specific soil layer segment (m3 m3), H is the hydraulic head (mm), K is hydraulic conductivity (mm d1), t is time (d), z is soil depth below a reference point (mm), and U is a sink term representing water lost per unit time by transpiration (d1). Defining the differential water capacity, C as h C (4 2) where h is soil water pressure head, enables transformation of equation 4 1 to an equation where pressure potential is the only dependent variable: t z U z H K z C t h (4 3) The model considers water depth and water potential in terms of mm per day. The solution to equation ( 4 3) by normal finite differencing methods is accomplished by dividing the soil profile into a finite number (k) of equally spaced horizontal layers of size z and dividing the total time period into small time intervals, t Utilizing the Crank Nicolson implicit method reduces equation ( 4 3 ) to: j i j i j i j i j i j i j ih h h 1 1 (4 4) where j i j i j i and j i contain constants (h, K, C, U, z t ) for the time increment, which have known or estimated values, and j ih1 the soil water matric potentia l at the start of the time

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127 interval is known. The k equations developed (one at each depth node) form a tridiagonal matrix that may be solved for j ih j ih1 j ih1 using a rapid Gaussian elimination met hod. The convective -dispersion equation is used to describe solute transport and flux is usually represented as : L L M CLqc dz dC q D J (4 5) where q is the macroscopic water flux, CL is solute concentration (mg L1), and DM(q) is the mechanical d ispersion coefficient that describes mixing between large and small pores as the result of local variations in mean water flow velocity (Wagenet, 1983).The value of DM(q) can be estimated from v v DM (4 6 ) where v is the pore water velocity q v and is the dispersivity, limited in LEACHM to the range z 5 0 to z 2 M odel Set -up and P arameter ization Simulating water flow, N O3N and T P i n LEACHM requires physico-chemical input factors that depend mostly on water input, soil propert ies and fertilizer mineralization rates. The effective root depth o f avocado trees was set at 0.30 m due to the existence of a limestone layer below this depth. Soil homogeneity was assumed within the simulated 0.30 m soil profile Twelve layers of 0.025 m each were considered a s individual segment s A separate file was used for irrigation input using the number of days irrigation came on in the orchard for the identified BMP. Although fertilizers were applied four times each year, in the model each fertilizer application was further split into two parts to account for the 25% slow release portion The simulated model outputs of drainage and leached NO3N and TP were output on a monthly time

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128 step to coincide with measure data. The lower boundary condition of the model simulation was selected to be free drainage. The selected input factors are given Table 4 3 Nutrient uptake parameters of N and P were fixed at 14 and 2.3 kg ha1 assuming that 50% of ther applied fertilizer (N and P) was taken up by the trees. Nutrient uptake data of N and P could not be collect since it would involve sacrif ic ing at least three trees from each treatment replicate at the end of each fruiting season (2008 and 2009). To perform global sensitivity and uncertainty analyses, the input factors were assigned average values, ranges, and probability PDFs as shown in Table 4 3 For data obtained from literature and where a PDF was not given for that variable a uniform distribution was assumed. The range of the uniform distribution was assigned as 30% of the mean value except for bulk density where a 20% was used Easyfit software (MathWave Technologies, 2010) was used to generate PDFs for th e input variables/parameters where data was either measured or could be generated. The selected LEACHM model outputs for model validation of the BMP were drainage (mm), leached NO3N and leached T P (kg ha1). Global Sensitivity and U ncertainty A nalyses A qualitative global sensitivity analysis screening method of Morris (1991), which was later modifie d by Campolongo et al. (2007), was applied first This was intended to obtain a ranking of 28 inputs factors shown in Table 4 3 on the effect of the desired LEACHM outputs Several elementary effects for each of the 28 factors were obtained and averaged producing a statistic whose magnitude when compared to all model input factors described the order of importance of this factor to a des ired model output. Campolongo et al. (2007) proposed to use a statistic canceling effects due to opposing signs. Since elementary effects are the same for a given factor

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129 where there are no factor interactions, the standard deviation of indicating a measure of the sum of all interactions between the input factors and all its nonlinear effects. The number of simulation (N) required for the Morris method is computed as: ) 1 ( k r N (4 7 ) where r is the sampling size for the search trajectory (r = 10) produces satisfactory results and k is the number of factors. Once the most rel evant factors were identified by the Morris methods, Monte Carlo simulations were run using multivariate pseudo random samples drawn from the input factors distributions using the eFAST (Salte ll i et al., 1999) sampling method. The number of validation s re quired for the analysis is expressed as: ) 2 ( k m N (4 8 ) where m is a number between 500 and 1000, and k is the number of factors. The eFAST variance -based method provides a quantitative measure of sensitivity of the model output with respect to each input factor, using first order sensitivity index, ( iS ) (Saltelli 1999) iS is defined as the fraction of the total output variance attributed to a single input factor. In a r are situation of an addi tive model, with no interactions, 1iS The eFAST method also calculates first and all higher order indices (interactions) for a given input factor which results in a total sensitivity index, TiS expressed as: n jk i i TiS S S S S... 1 ... 1 1 (4 9 ) From equation 49 the interaction effects is calculated as i TiS S In my study the screening method of Morris (1991) and eFAST variance based method were applied to investigate the input factors that influence d rainage, leached NO3N and leached

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1 30 T P in LEACHM. The analysis involve d five steps: (1) identification of model input factors and construction of the PDFs; (2) generation of different factor sets by pseudorandom sampling of the input factors PDFs using the statistical p re p rocessor module of the Simlab v2.2 software (Saltelli et al., 2004); (3) generation of simulation results for each input factor set and outputs from each simulation collected; (4) performing a global sensitivity analysis according to the selected method e.g. Morris screening method to identify a subset of factors and repeating steps 2 and 4 for the factors using the eFAST method; and ( 5 ) assessing uncertainty based on the outputs from the eFAST simulations by constructing PDFs/CDFs and other statistical outputs. An interface program was written in C++ language to: (a) feed the generated set of factor PDFs from Simlab v2.2 software pre processor to LEACHM, and (b) prepare an output file from LEACHM that was fed back in Simlab to perform sensitivity and uncertainty analys e s. The statistical post -processor module of Simlab use d input and output matrices to calculate the sensitivity indexes of the Morris and the eFAST methods. Data analysis software was used to construct output probability d istributions and to quantify the uncertainty based on the set of eFAST simulation outputs. Model C alibration and Validation Legates and McCabe (1999) and Wallach (2006) recommend use of several statistical measures to calibrate and validate model performan ce T he statistical measures used for model calibration and validation were: 1) plots of simulated and observed values, 2) the mean absolute error (MAE), 3) the root mean square error (RMSE), 4) the Nash -Sutcliffe coefficient (E), and 5) the Index of agree ment, (d). The RMSE and the MAE are absolute error goodness of -fi t indicators that describe differences in observed and predi cted values in measured units.

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131 N i i iP O N RMSE1 2 1 (4 10) N i iiP O N MAE1 1 (4 11) w here iO is the observed value, iP is the predicted value, and O is the mean of the observed values and N is the total number of observations. The E also known as the Nash -Sutcliffe coefficient (NS coefficient), is ano ther commonly applied statistic al measure for evaluating the performance of hydrological and water quality models (Nash and Sutcliff, 1970). The value of E has the range < E 1. N ii N i i iO O P O E1 2 1 21 (4 12) w here iO is the observed values, iP is the predicted value, and O is the mean of the observed values. Values of E closer to 1 indi cate better agreement between observed iO and predicted iP values. However, if E 0, then the mean of the observed values iO is a better predictor than the models predicted values (Legates and M cCabe, 1999). This indicates that the model error is so large that its predictive value is no better than the mean of the observed value. Index of agreement (d ) is another statistical measure for the validation of hydrologic models. It is a measure of the degree to which a models predictions are free of errors (Harmel and Smith, 2007). Similar to E d is also considered an improvement over R2. H owever, d is sensitive to extreme values of observed and predicted values due to squaring of the difference term s (Legates and McCabe, 1999). Values range from 0 to 1 and d is calculated as:

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132 n i i i i n i i iO O O P P O d1 2 1 21 (4 13) w here iO is the observed values, iP is the predicted value, and O is the mean of the observed values. Higher values of d indicate a better agreement of predicted and observed values. Refining the Selected BMP The months of March, April, May, and July of 2008 were selected for use in the model to refine the identified BMP. The target m odel outputs were nutrient leached loads of NO3N and TP. LEACHM model was used to refine the BMP considering reductions of 10, 15, and 20% in irrigation water depth for each irrigation event and fertilizer input for 2008. For the identified BMP to be refi ned each irrigation event was equivalent to 9.5 mm of water depth and each irrigation event lasted for 14 minutes. The irrigation application water depth reductions used in model simulations were 8.6, 8.1, and 7.6 mm which corresponded to irrigation durati on per each event of 13, 12, and 11 minutes respectively The field application of such water depth reductions would be to set irrigation to come on when soil water suction s exceeded 16.5 kPa ( for the 8.6 mm which corresponds to a volumetric water content of 0.21 cm3 cm3), 18 kPa ( for the 8.1 mm which corresponds to a volumetric water content of 0.205 cm3 cm3), and 20 kPa ( for the 7.6 mm which corresponds to a volumetric water content of 0.20 cm3 cm3) All the three irrigation volumes were higher than t he amount of water of irrigated based on ET where each irrigation event was 9 minutes (irrigating when the soil water suction s exceeded 25 kPa which corresponds to a volumetric water content of 0. 19 cm3 cm3) The reduction in fertilizer application of 10, 15, and 20% of the FSR corresponded to 0.207, 0.193, and 0.182 kg per tree per application. The fertilizers were applied four times during 2008.

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133 Results and D iscussion Global Sensitivity A nalysis: Morris Factors that influence simulated drainage, leache d NO3N, and leached T P using the LEACHM as identified by the Mor ris method are shown in Fig. 4 1 and Table 4 4 which show s the ranking of the factors as they influence variability of the three desired model outputs. Application of the Morris method resu lted in identification of factors and separated them from the origin of the plane. Val u es of are 4 1) which may be interpreted as there being small interactions between factors. Thus, first order factor effects dominated model responses. The number of factors for further analysis was reduced from 28 to 17, repr esenting a 40% reduction. The factors whose value was close to the origin were identified as noninfluential for the desired model output (Campolongo et al., 2006) and thus were fixed at their mean value (Saltelli, 1999). The most i nfluen tial factors for drainage were hydraulic conductivity ( K ) and pore interaction parameter ( PIP) since they govern water flow and subsequently percolated water volume below the root zone (Fig. 4 1; Table 4 4) Hydraulic conductivity (K) w as identified by Borah et al. (1999) and Jabro et al. (2006) as a factor influencing water flow prediction in LEACHM model. PIP was an important factor due to the gravelly nature of the soils. The other secondary factors influencing drainage were Campbells b exponent ( BCAM), Campbells air entry value (AEV), and bulk density ( b ) which are related to hydraulic conductivity. Campbell's coefficients AEV and BCAM were identified and they describe the shape of the water retention curve. This curve descri bes the range of soil water content that is used to determine the mobile and immobile fractions of the liquid phase and is also useful in computation of water flux between two adjacent cells (Huston, 2005). Bulk density was

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134 influential since it is used to define the saturated water content which is the upper limit of soil water content (Hillel, 1998). Surprisingly drainage was insensitive to soil properties like clay and silt content (%). Factors identified to influence variability of leached NO3N load wer e K C:N OC, b and PIP (Fig. 4 1; Table 4 4 ). The C:N ratio and OC influenced leached NO3N through secondary effects since these factors influence N mineralization rates. Organic carbon and the C:N ratio relate to NO3N leaching through the minera liza tion of N in biomass to f o r m a mmonium which eventually gets oxidized to NO3N The higher the C:N ratio the lower the amount of N in biomass that is available for min e ra lization (Havlin et al., 2005; Huston, 2005). In such a situation N w ill be immobilized from the soil by the microbes and less NO3-N will be available for leaching The variability in leached TP was mostly influenced by Kd, Lab P K, Fe, and PIP (Fig. 4 1; Table 4 4 ). The P sorption coefficients Kd and Fe were identified as they determine the amount P retained by the soil. Factor Lab P was influential as it is constantly in equilibrium with solution P (the form of P available for leaching) and non -labile P. In that if the amount of solution P decreases, Lab P is dissolved to maintain the equilibrium (Li et al., 2005). Factors K and PIP were identified since they influence water flow and subsequently the amount of water drained below the root zone. Global Sensitivity A nalysis: eFAST The indentified 17 factors by Morris metho d were used to conduct 13, 300 simulations using eFAST method to generate data for model uncertainty analysis. The eFAST global sensitivity analysis (Table 4 5 ) was in agreement with qualitative sensitivity results obtained with Morris method for the first -order effects ( iS ) and interactions ( i TiS S ). The total

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135 first order effects explained 89, 87, and 89% of the output variability in drainage water volume, leached NO3-N, and leached T P. A lthough Morris method had identi fied K as an important factor for leached NO3N, e FAST was able to rank it as the most influential contribution 44% of the variability in the output. This is attributed to the fact water molecules and NO3 move at a similar rate within the soil profile ( Jo hnsson et al., 1987; Havlin, 2004). The variability in leached P was explained by Kd (72%) and Lab-P (16%). This was attributed to the high amount of initial labile P content in calcareous soil. This implies that for leached TP the sorption coefficient and the initial labile P amount outweigh the water transport contributions from other sources (i.e., fertilizer). Results of global sensitivity analysis using Morris and eFAST methods imply that time and financial resources should be spent on measuring the s ix input factors (K, PIP, OC, C:N Lab P, and Kd) that mostly influenced variability in drainage, leached NO3N, and leached TP in calcareous soils of south Florida (Table 4 5). All the remaining input factors would then be estimated from literature since they would not influence simulation results. Global Uncertainty A nalysis: eFAST The global uncertainty analysis provided ranges and other statistical values for expected drainage water volume and leached nutrient loads of NO3N and T P (Fig 4 -2; Table 4 6 ). The drainage exhibit s a near normal distribution in comparison to the distributions of leached NO3-N and T P. The predication of NO3N load indicated lesser uncertainty in input factors due to a smaller 95% confidence interval in the output in comparison to the 95% confidence intervals in drainage volume and T P load outputs Similarly uncertainty results were presented as probability of exceedance of a desired target values or percentage (Fig 4 3) In that regard, the amount expected to be ex ceeded at 50% probably were 137 mm, 3.4 kg ha1, and 1.5 kg ha1for drainage, leached NO3-N and leached T P respectively.

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136 Model C alibration The LEACHM model was calibrated for leached water volume (drainage), leached NO3N and leached TP using 2008 data. However, buc ket lysimeters could not capture all the drainage generated during big rainfall events of more than 1 0 mm due to high water percolation rates in Krome soil. Thus the drainage calibration was completed using four months (March, April, May, and July) were st orms of less than 1 0 mm w h ere recorded. For the four month used for model calibration on averag e each month received about 100 mm from irrigation and 95 mm from rainfall. The model calibration indicators for drainage were: MAE is 1.750, RMSE is 3.255, t he Nash -Sutcliffe (E) is 0.953, and Index of agreement (d) is 0.990 (Fig 44a) T o achieve the slow release effect (of 25% for N) of the fertilizer applied, monthly fertilizer inputs were split into two parts to coincide with the months when peak NO3N conce ntrations were sampled. This is because in LEACHM inorganic fertilizers in form of NH4 or NO3 are subject to immediate local equilibrium (Hutson, 2005). The model calibration indicators for NO3N were: MAE is 0.054, RMSE is 0.140, The Nash Sutcliffe (E) is 0.853, and Index of agreement (d) is 0.950 (Fig 4 4b) LEACHM considers a slow dissolution of fertilizer P to be proportional to the difference between the concentration of P in the soil solution and the solubility of the fertilizer P (Hutson, 2005). How ever, the amount of fertilizer dissolved P cannot exceed the amount of soluble P in the soil. The model calibrat ion indicators for TP were: MAE is 0.003, RMSE is 0.007, t he Nash -Sutcliffe (E) is 0.874, and Index of agreement (d) is 0.958 (Fig 44c) Const raints with Model Validation The LEACHM m odel could not be validat ed using the 2009 data. This is because the selected months of March, April, May, and July received varying amounts of rainfall. The average rainfall for the four months was 118 mm w ith a st andard deviation of 139 mm, the same

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137 months in 2008 had an average rainfall of 95 mm with a standard deviation of 47 mm (Table 4 7). It was also observed that for the selected months in 2008, there was no rainfall event that exceeded 10 mm, yet for the sam e months in 2009 there were 15 high rainfall events with an average of 28 mm and standard deviation of 21 mm. Therefore, due to bucket lysimeter physical limitations an adequate leachate volume could not be collected during high rainfall events of more tha n 10 mm The difference in the rainfall amount between the two years is attributed to part of 2008 being a La Nina year and 2009 being a neutral year (NOAA, 2010). La Nina years are typically every 2 to 7 years in south Florida based on NOAAs historical r ecords since 1903 La Ni a brings different weather conditions in several parts of the worlds In south Florida, La Nia conditions are associated with a wamer and drier spring season compared to the weather experienced during a normal year. This implies that the spring total r ainfall during La Nia years is less than the amount rainfall received in a n eutral year. Thus, since the model could not be validated for water flow, it could not be validated for nutrient leaching since nutrients move with water. Modeling of the BMP T he mode l outputs for nutrient leaching (Fig. 4 5) show that reducing the FSR by 20% did not significantly ( p 0.05) affect the leached load of NO3-N and TP (Table 4 -8). Given that avocado yield was responsive to volume of water applie d and fertilizer input in 2008 and only responsive to irrigation in 2009, the suggested refined BMP which is to to apply 8.6 mm of water per each irrigtione event with 90% of the FSR would be appropriate for only water application in 2008 and only fully ap plicable to the 2009 production (second year of fruiting) The justification in water reduction is based on the 8.6 mm water depth being more that 40% of the ET irrigation water depth applied yet there were no signifant difference ( p 0.05) between Simmonds yields for SW with FSR and ET with FSR. The fertilizer reduction is justified by the fact that the yields

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138 of 2008 and 2009 were similar despite an increase in the FSR of 600% in 2009. The field application of 8.6 mm of irrigation water depth would be achieved by by set ting irrigation to come on when soil water suction s exceeded 16.5 kPa (which corresponds to a volumetric water content of 0.21 cm3 cm3). Each irrigation event would be time to run for 13 minutes. For the fertilizer i nput the amount applied per tree would be reduced from 1.361 kg per tree per application ( 1950 kg ha1 yr1) to 1.225 k g per tree per application ( 1755 kg ha1 yr1). Thus the refined BMP would lead to further saving s in volume of water applied and fertili zer amount applied in avocado production during the second year of fruiting Recommend ation s from m y Study Modeling tools provide an opportunity to explore different irrigation and nutrient management scenarios that cannot be tested in the field due to li mitations such as field space, time, and funds. Global sensitivity analysis reduced the number of input factors that mostly influence d variability in drainage, leached NO3N and leached TP in calcareous soils of s outh Florida to only six These factors were K, PIP, OC, C:N Lab -P, and Kd. This is important in saving time and funds that would be spent on estimating/measuring the entire set of 28 factors initially identified. If LEACHM model is to be used to investigate other irrigation and nutrient BMP for a vocado or other tropical fruits in s outh Florida these are the factors that require accurate measurements. The remaining model input factors can be estimated from literature without influencing the simulation results of drainage, leached NO3N, and leached TP The recommended practice for production of Simmonds avocado in young orchards is to apply 8.6 mm of water depth for each irrigation event (wi th soil water suction set at 16.5 kPa) and 90% of the FSR from the second year of fruiting on wards Although the refined BMP would lead to further savings in volume of water and amount of fertilizer applied in avocado production, it would not result into significant reduction in nutrient leaching. This is because nutrient leaching

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139 was more influenced by rainfal l amount rather than irrigation amount (see chapter 3). The findings from my study form a basis for developing irrigation and nutrient BMPs for other tropical fruit crops grown in south Florida. However, refining the identified BMPs using LEACHM would requ ire measuring soil water content at two pr oposed soil depths of 15 and 30 cm every 2 hours for a period of about 3 years. This would be beneficial in having a wide range of data available for model calibration and validation of water flow. A good calibrati on and validation for water flow is essential since nutrients move water. Likewise, leachate sampling should be done more frequently during the month using other sampling devices like ceramic lysimeters instead of bucket lsyimeters. Although parameters li ke initials soil temperature, crop uptake of N and P are likely to influence leached NO3N and leached TP loads these parameters were not considered during the sensitivity analysis. It would be interesting to investigate how these parameters influence the variability in simulated leached NO3N and leached TP. Likewise before performing sensitivity analysis, depen den ces of the input factors was not evaluated. Much as factors that are expected to be correlated such as percent age clay and perc en tage sand did not influence the simulated outputs of drainage, leached NO3N and leached TP, future work should incorporate rank correlation analysis before performing sensitivity analysis. Accounting for covariance and interaction between the input factors is important to minimize uncertaint ies in the simulated results. Likewise the results of the simulated BMP could be strengthened by including uncertainty analysis The analysis may include constructing confidence intervals on the model outputs or determining expected probabilit ies of attaining a certaint level of nutrient reduction. Since in my study seven treatments (irrigation and nutrient management practices) were

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140 investigated, it is worth exploring if the model could be validat ed using another treatment e.g. ET w ith FSR (treatment 1) with the 2008 data. LEACHM does not account for the slow release effect of N in the fertilizer since inorganic N fertilizers are subjected to an immediate local equilibrium E xploring field dissolution rates of slow release fertilizer and making the necessary modification in the way LEACHM model simulates N would be a good research topic. Another way of refining irrigation and nutrient BMPs would be to collect data on nutrient uptake of Simmonds avocado since the LEACHM model is capa ble of simulating nutrient uptake. Nutriet loss through volatilaztion was on considered inmy study. Future research should explore if the volatilization rate of N fertilizer affects leaching of NO3N in calcerous soils of south Florida. Conclusions Twenty -eight input factors were indentify as influential in simulating drainage, NO3-N, TP and global techniques were used to perform LEACHM model sensitivity and uncertainty analys e s. The Morris screening methods reduced the number of influential factors to 17. The eFAST sensitivity method showed that total first -order effects explained 89, 87, and 89% of the output variability in drainage water volume, leached NO3-N, and leached TP. Hydraulic conductivity (K) was the most important facto r which accounted for 56 and 44% variability in the output s of drainage and leached NO3-N. The variability in the l eached TP output was influenced by labile P ( 16%) and Kd (72% ). Uncertainty analysis performed using eFAST showed that there was minimal uncertainty in predicting NO3N load leached in comparison to the uncertainty involved in predicting drainage or Leached TP. The amount expected to be exceeded at 50% probability were 137 mm, 3.4 and 1.5 kg ha1 for drainage, leached NO3N and TP respectively. Since bucket lysimeters experienced by -flow during high intensity rainfall events due to the high water percolation rate of Krome soil, only four months with lesser rainfall

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141 in 2008 were used to calibrate the model. The model was successful ly calibrate d with Nash -Sutcliff and in dex of agreement coefficients in the range of 0.87 to 0.98 and minimal RMSE less than 4 (mm or kg ha1). However the months used to calibrate the model received varying rainfall amounts in 2009 compar ed to the amounts received in 2008, making it impossible to validate the model. Therefore due to climatic variability the model was used to refine the BMPs without being validated. The proposed refined BMP is to irrigate for 13 minutes for each event instead of 14 minutes however, to the application of 90% of t he fertilizer at a standard rate would be applicable in the second year of tree fruiting (2009) since in 2008 fertilizer rate affected yield Since nutrient leaching was mainly driven by rainfall the refined BMP would not result in significant ( p 0.05) reduction s of leached loads of NO3N and TP in compar ed to SW with FSR.

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142 Table 4 1. Sensitivity analysis parameters used for in LEACHM for water flow and nitrate Leaching. Parameter Ng et al., 1999 z Mahmood et al., 2002 value/range s ensitivity ratio Air entry value (AEV) 0.22 Exponent for Campbell equation (BCAM) 0.30 Volumetric water content (m 3 m 3 ) 0.04 Soil organic carbon (%) 0.06 Diffusion coefficient (mm 2 day 1 ) 60 120 Dispersivity (mm) 60 150 Bulk density (g cm 3 ) 1.00 1.30 Hydraulic conductivity (mm day 1 ) 10 100 Urea hydrolysis (day 1 ) 0.36 Nitrification (day 1 ) 0.3 0.04 Denitrification (day 1 ) 0.1 0.024 Humus mineralization (day 1 ) 0.62 Base temperature ( o C) 0.67 Q 10 factor 0.4 Adapted from: Ng et al., 2000. Study conducted at Woodslee, Ontario, Canada from 19911994 using corn (Zea mays L., Pioneer 3573); Mahmood et al., 2002. Study conducted at Carterton, New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species (two-year ol d Eucalyptus nitens and Eucalyputs ovata) and pasture.

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143 Table 4 2 Calibration parameters identified for LEACHM for water flow and nitrate leaching. Parameter Borah et al., 1999 Ng et al., 1999 Mahmood et al., 2002 Jabro et al., 2006 value/range valu e value/range value/range Air entry value (AEV) 0.1 1.0 0.3 3 Exponent for Campbell equation (BCAM) 3.0 3 5 7.8 18.7 Clay particles% 20 Silt particles% 12 Volumetric water content (m 3 m 3 ) 0.5 0.26 0.29 Soil organic carbon (%) 2.50 4. 50 Dispersivity (mm) 120 Bulk density (g cm 3 ) 1.00 1.44 Saturated Hydraulic conductivity (mm day 1 ) 100 5100 40 2184 Water potential, kPa 10 35 C:N ratio 10:1 10 Nitrification (day 1 ) 0.2 0.1 0.2 0.4 Denitrification (day 1 ) 0.1 0.1 0.02 0.08 Adapted from: Borah et al., 1999. Study conducted at Manhattan, Kansas, USA form 19961997 using corn (Zea mays L.); Ng et al., 2000. Study conducted at Woodslee, Ontario, Canada from 19911994 using corn (Zea mays L., Pioneer 3573); Mahmood et al., 2002. Study conducted at Carterton, New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species (twoyear -old Eucalyptus nitens and Eucalyputs ovata) and pasture ; Jabro et al., 2006. Study conducted at Rock Springs, Pennsylvania, USA form 19881991 using corn (Zea mays L.).

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144 Table 4 2 Continued Parameter Borah et al., 1999 z Ng et al., 1999 Mahmood et al., 2002 Jabro et al., 2006 value/range value value/range value/range Humus mineralization (day 1 ) 7 0 5 Q 10 factor 3.0 2.0 Kd for NO 3 N (L kg 1 ) 0.05 0.00 Litter mineralization (day 1 ) 0.01 0.01 Manure mineralization (day 1 ) 0.02 0.02 Humus mineralization (day 1 ) 0.7 310 5 N Plant uptake (kg ha 1 ) 102 NH 4 N (mg N kg 1 ) 2.63 4.00 3 NO 3 N (mg N kg 1 ) 3.85 6.38 0 Adapted from: Borah et al., 1999. Study conducted at Manhattan, Kansas, USA form 19961997 using corn (Zea mays L.); Ng et al., 2000. Study conducted at Woodslee, Ontario, Canada from 19911994 using corn (Zea mays L., Pioneer 3573); Mahm ood et al., 2002. Study conducted at Carterton, New Zealand from Dec 1997 to Aug 1998 on a plot planted with two tree species (twoyear -old Eucalyptus nitens and Eucalyputs ovata) and pasture ; Jabro et al., 2006. Study conducted at Rock Springs, Pennsylvan ia, USA form 19881991 using corn (Zea mays L.).

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145 Table 4 3 LEACHM water, nitrate, and phosphorus input factors and their probability density functions No Factor Symbol PDF Mean value Range Source 1 Initial water table (mm) IWT Unifo rm 1600 [1000 2200] Unpublished w ell survey data 2 Clay % C% Uniform 25 [ 17 31 ] Munoz Carpena et al., 2002 3 silt % S% Uniform 24 [ 28 52 ] Munoz Carpena et al., 2002 4 Organic carbon % OC Normal 4.270.97 [5.6 10.4] Measured 5 Bulk density (g/cm 3 ) b Uniform 1. 42 [ 1.136 1. 704 ] Munoz Carpena et al., 2002 6 Air entry value (KPa) AEV Uniform 0.54 [ 0.7 0.38] Al Yahyai et al., 2006 and value computed using Van Genuchtens equation 7 Campbell exp b coefficient BCAM Uniform 3.77 [2.6 4 4.90] 8 Hydraulic Conductivity (mm day 1 ) K Exponential 36703607 [1.25 25438] Al Yahyai et al., 2006 data, and Mualem 1976 equation 9 Matric potential (KPa) MP Uniform 26 [2 50] Measured 10 Pore interaction parameter PIP Discrete 1 [0 3] Hutson, 20 05 11 Dispersivity (mm) D Uniform 31.25 [12.5 50] Estimate based on measured data 12 Initial NH 4 in soil (mg N kg1) NH4 Exponential 7.673.0 [4.71 16.6] Estimate based on measured data 13 Initial NO 3 in soil (mg N kg 1 ) NO3 Lognormal 21.27.9 [4.78 6 3] Estimate based on measured data 14 Initial Residue Carbon (g C kg1) I C Normal 101.17.9 [86.2 118.2] Estimate based on measured data

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146 Table 4 3. Continu ed No Factor Symbol PDF Mean value Range Source 15 Initial Labile P in soil (mg P kg 1 ) Lab P Normal 34.56.14 [29 52] Estimate based on measured data 16 Initial Residue P in soil (mg P kg 1 ) Res P Normal 103.718.3 [60 157] Estimate based on measured data 17 Freundlich Kd (L kg 1 ) K d Uniform 32.5 [15 50] Zhou and Li, 2001 18 Exp onent F e Uniform 0.56 [0.50 0.63] 19 Synthesis efficiency factor S e Uniform 0.5 [0.35 0.65] Johnsson et al., 1987 20 Humification fraction H Uniform 0.2 [0.14 0.26] 21 C:N ratio CN Lognormal 3810.6 [24 57 ] Measured 22 C/P ratio CP Normal 123.19 [ 7 1 7 ] Measured 23 High end opt water H ow Uniform 0.443 [0.361 0.524] Estimate based from data 24 Relative transformation Rate (day1) Tr Uniform 0.6 [0.42 0.78] Johnsson et al., 1987 25 NH4 >NO3 rate (day 1 ) K nitri Triangular 0.19 [0.1 0.8] Munoz Carpena et al., 2010 (manuscript under review) 26 NO3 >N rate (day 1 ) K denit Triangular 0.036 [0.001 0.1] 27 Denitrification half saturation (mg L 1 ) K 0.5denitr Uniform 10 [7 13] Johnsson et al., 1987 28 Limiting NO3/NH4 ratio L denit Uniform 8 [5.6 10.4] Joh nsson et al., 1987

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147 Table 4 4 Selected sensitive parameters by Morris method No Parameter Symbol Drainage LeachedNO3 LeachedP 1 Organic carbon % OC 2 9 2 Bulk density (g/cm 3 ) b 5 4 7 3 Air entry value (KPa) AEV 3 10 6 4 Campbell exp b co efficient BCAM 4 8 8 5 Hydraulic Conductivity (mm day 1 ) K 1 3 3 6 Pore interaction parameter PIP 2 5 5 7 Dispersivity D 12 8 Initial NH 4 in soil (mg N kg 1 ) NH4 7 9 Initial Labile P in soil (mg P kg 1 ) Lab P 2 10 Freundlich Kd (L kg 1 ) K d 1 11 Exponent (empirical constants <1) F e 4 12 C:N ratio CN 1 13 C/P ratio CP 10 14 High end optimum water H ow 9 15 Relative transformation Rate (day 1 ) Tr 6 16 NH4 >NO3 rate K nitri 11 17 NO3 >N rate K denit 13 Numb er s in the table represent the parameter ranking in decreasing order of importance where 1 = most important and not relevant.

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148 4 5 Extended Fourier Amplitude Sensitivity Test (FAST) results for LEACHM No. Factor symbol First order sens itivity index, S i (%) Interactions, S Ti S i (%) Drainage Leached NO3N Leached P Drainage Leached NO3N Leached P 1 OC 0 12 0 1 7 2 2 b 5 4 0 1 4 2 3 AEV 2 0 0 1 3 2 4 BCAM 6 1 0 2 3 1 5 K 56 44 0 10 11 2 6 PIP 20 8 0 8 6 2 7 D 0 0 0 2 4 2 8 NH4 0 1 0 2 3 2 9 Lab P 0 0 16 2 3 9 10 K d 0 0 72 2 4 10 11 F e 0 0 0 2 2 3 12 CN 0 17 0 2 8 2 13 CP 0 0 0 2 5 2 14 H ow 0 0 0 2 4 2 15 Tr 0 0 0 2 5 2 16 K nitri 0 1 0 2 5 2 17 K denit 0 0 0 2 2 2 Total 89 87 89

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149 Table 4 6 Uncertainty analysis statistic for the 3 selected output probability distributions obtained from Extended FAST results, n=13300. Statistical indicator Drainage (mm) Leached NO 3 N ( kg ha 1 ) Leached P ( kg ha 1 ) Range 63 1.7 11 Max 1 64 4.7 11.2 Min 100.4 3 0.17 Median 137 3.4 1.5 Q1 133 3.3 1 Q3 142 3.5 2.6 Mean 138 3.5 2 Std Dev 6.34 0.17 1.46 Std. Error 0.054 0.001 0.013 95% Confidence interval 127 151 3.2 3.9 0.4 5.9 Skewness 0.584 1.391 1.682 Kurtosis 0.407 3.43 3.275 Table 4 7 Rainfall and irrigation amounts for selected months in 2008 and 2009. Calibration Validation Month Rain Irrigation Total Month Rain Irrigation Total ---------------mm -----------------------------mm ---------------Mar.2008 71.9 97.5 169.4 Mar. 09 55.4 76 131.4 Apr. 2008 111 104.5 215.5 Apr. 09 9.7 114 123.7 May. 2008 45.7 104.5 149.2 May. 09 320.3 85.5 405.8 Jul. 2008 152.7 66.5 219.2 Jul. 09 86.3 114 200.3 Table 4 8 Modeled leached nutrient loads for the selected BMP. Irrigation amount for each event and nutrient management NO 3 N TP ----------kg ha 1 ----------9.5 mm with FSR 0.9258 0.05925 8.6 mm with 90% FSR 0.7575 0.05350 8.1 mm with 85% FSR 0.6815 0.05100 7.6 mm with 80% FSR 0.6093 0.04850 P value 0.8286 0.9 539

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150 Figure 4 1. Global sensitivity analysis results obtained from the Morris (1991) screening using the LEACHM model as monthly average values for the period May to December 2006. A) drainage B) nitrate and C) phosphorus Input factors separate d from the origin of the plane were considered important. Labels of less important or unimportant parameters factors (close to the plane origin) have been removed for clarity. Factor definitions are given in Ta bles 4. 3 and 4. 4 A B C

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151 Figure 4 2. Global uncertainty analysis results obtained from the eFAST variance -based method expressed in form of probability distribution function (PDF) and cumulative distribution function (CDF) for monthly average for the pe riod May to December 2006. A) drainage B) nitrate and C) phosphorus A A B C

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152 Figure 4 3. Global uncertainty analysis results obtained from the extended FAST variancebased method expressed in form of probability of exceedance for monthly average for the p eriod May to December 2006. A) drainage B) nitrate and C) phosphorus A B C

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153 Figure 4 4. LEACHM model calibration results on a monthly basis A) drainage B) nitrate and C) phosphorus A A B C

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154 Figure 4 5. Simulated monthly leached nutrients at diffe rent irrigation volumes and fertilizer rates. A) drainage B) nitrate and C) phosphorus A B A B

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155 CHAPTER 5 SUMMARY Nutrient leaching from agricultural fields, a major source of water body impairment in many parts of the worlds including Florida (Burkitte et al. 2004), is attributed to improper matching of the optimal fertilizer and water to the crop and production needs Nutrient leaching is of particular concern in south Florida due to the interaction between surface water and groundwater arising from a shallo w water table and the existence of naturally sensitive water bodies such as the Biscayne Bay and the Everglades (Browder et al., 2005; Reddy et al., 2006). Nutrient leaching measurement techniques primarily include use of tension ceramic lysimeters and zer o tension lysimeter. However, contradictory results are cited in literature on the efficacy of using tension ceramic lyismeters to sample a leachate containing P (Litaor, 1988). Although nutrient leaching can be controlled through efficient use of water and fertilizer application, no irrigation and nutrient BMP has been developed, tested, and documented for tropical fruit tree crops. The overall goal of my study was to evaluate techniques for estimating nutrient (N and P) leaching as a method for assessing irrigation and nutrient BMPs. The specific objectives and summary of results are outlined below. Objective 1 Determine if ceramic tension lysimeters interfere with the chemical composition of sampled water containing P and to compare leached concentratio n of N and P estimated using tension and gravitational lysimeters in gravelly calcareous soils. The specific objectives were: 1) to evaluate the chemical composition of three commercially available ceramic cups (referred to as Ceramics A, B, and C); 2) to determine P adsorption and desorption potential of the three ceramic water samplers; 3) to compare P concentrations in water samples collected using the three ceramic lysimeters; and 4) to investigate gravitational (bucket) and ceramic tension

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156 lysimeters i n order to compare concentrations of NO3N and PO4-P sampled by these devices for application in leachate studies. A nalysis of the ceramic material composition indicated that all three types of ceramic lysimeters contained substantial amounts of Fe, Al, S i, and Ca that may influence P estimation depending on the pH and concentration of the soil water being sampled. The adsorption -desorption study and fitting of Freundlich and Langmuir isotherms provided information that assisted in interpreting the differ ent results in that the lower the ceramics maxS the more accurate the sampler was in estimating PO4-P concentration of a known stock solution. A protocol to be followed before deciding to use ceramic samplers in nutrient leaching and mo nitoring studies was proposed to give authenticity of the reported data. The suggested protocol explore d if the ceramic material would interfere with the element to be sampled and it involve d the following three steps: 1) determining the chemical compositi on of the ceramic cups; 2) developing sorption isotherms; and 3) testing the efficiency of ceramic sampler to sample a stock solution of a known concentration. The comparison of the leachate concentration s of NO3N or PO4-P in a controlled environment showed no significant differences (p < 0.05) between the two devices due the soils saturated flow conditions during sampling. However, since bucket lysimetes captured a cumulative leachate from macro flow and the ceramic lysimeters represented a snap -shot of micro flow, a significant difference was observed in PO4-P concentration from the orchard sampled between the two devices. Objective 2 Evaluate the effect of nutrient load and irrigation scheduling on water volume applied, nutrient leaching, and fruit yield of avocado trees in calcareous soils. The specific objectives were to determine the effect of nutrient load and irrigation scheduling on: 1) nutrient leaching of N and P; 2) tissue nutrient status, growth, and yield of Simmonds and Beta avocado c ultivars ; and 3) soil nutrient indicators (soil organic carbon, C:N and C:P ratios, and soil inorganic N). Irrigating young avocado trees based on ET or SW saved 93 and 87% respectively of the water volume applied compar ed to irrigation based on a set sch edule over the four year period. No significant differences (p < 0.05) were observed between the water volume applied during the wet or dry season from each of the irrigation method s

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157 Irrigating based on SW with FSR resulted in the highest average annual reductions of 70 and 75% in NO3-N and TP leaching respectively compar ed to the set schedule irrigation method over the two years of leachate sampling. Such high reduction were attribute to nutrient leaching being more influenced by the irrigation management than fertilizer rate. Based on the highest avocado fruit yield of Simmonds avocado CP -WUE, and CP FUE values SW with FSR (treatment 4) ranked higher than the other treatments and thus is proposed as the BMP for the production of Simmonds avocado. ET with FSR (treatment 1) was the second best irrigation an d fertilizer management practice. Yield results for Simmonds suggest that this avocado cultivar is responsive to well maintained soil water regime in the root zone. For Beta cultivar both irr igation and fertilize r rate had no effect on avocado fruit yield in both 2008 and 2009, with no interactions between irrigation volume and fertilizer rate. Further research is required to explore if Beta fruit yield would be influenced by either a lesser or greater irrigation and nutrient treatment. Generally no significant differences (p < 0.05) were observed among treatments for SPAD value ; leaf TN, TC, and TP; trunk diameter; and soil organic carbon, C:N and C:P ratios, and soil inorganic N. Objectiv e 3 Apply modeling techniques to refine the identified BMP for avocado production. The specific objectives were: 1) perform a global sensitivity and uncertainty analysis of LEACHM model (Hutson and Wagenet, 1992), and (2) apply LEACHM to refine the identif ied BMPs in an avocado orchard while saving water volumes applied and reducing nutrient leaching. From the 28 input factors initial ly indentif ied the Morris screening methods reduced the number of factor s that influence drainage, NO3N, and TP to 17. T he eFAST sensitivity method showed that total first -order effects explained 89, 87, and 89% of the output variability in drainage water volume, leached NO3N, and leached TP respectively Hydraulic conductivity (K) was the highest ranked factor which acc ounted for 56 and 44% variability in the outputs of drainage and leached NO3-N respectively The variability in the output in leached TP was influenced by labile P (16%) and Kd (72%). Uncertainty analysis performed using eFAST showed that there was minima l uncertainty in predicting NO3N load leached in comparison to the uncertainty involved in predicting

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158 drainage or leached TP. The amount expected to be exceeded at 50% probability were 137 mm and 3.4 and 1.5 kg ha1 for drainage and leached NO3-N and TP, respectively. The LEACHM model was successful ly calibrate d using data for 2008 with the Nash -Sutcliff and index of agreement coefficients in the range of 0.87 to 0.98 and minimal RMSE less than 4 (mm or kg ha1). The model could not be validated because t he methods used to sample drainage were not able to capture an adequate leachate volume for the amount of rainfall received in 2009. The proposed refined BMP is to apply 8.6 mm (soil water suction set at 1 6.5 kPa which corresponds to a volumetric water c ontent of 0.21 cm3 cm3) and to apply 90% of the FSR in the second year of fruiting Since nutrient leaching was mainly driven by rainfall the refined BMP would not result in a significant (p 0.05) reduction in leached loads of NO3N and TP in comparison to SW with FSR. Study implications and contributions The findings from my study form a basis for developing irrigation and nutrient BMPs for other tropical fruit crops grown in south Florida and elsewhere. Modeling tools provide an o pportunity to refine the identified BMPs which may lead to further saving in water volume applied and fertilizer rate applied and subsequently reduce nutrient leaching. T he following are the major contributions from my study : The determination of the cera mic cup composition, the P adsorption-desorption study and the fitting of the Freundlich and Langmuir isotherms provided information that assisted in interpreting the different results of sampled PO4P stock solution The lower the ceramics maxS the more accurate the sampler was in estimating PO4-P concentration of a known stock solution. This is the first time that such a detailed working involve exploring the efficacy of ceramic lysimeters to sample a leachate containing P had been done The protocol developed to test the suitability of ceramic lysimeter to sample elements in a leachate would be beneficial to other researchers intending to use ceramic lysimeters. Irrigating young avocado trees based on SW with FSR saved 87% of the water volume applied resulted in annual reductions of 55 and 75% in NO3N and TP leaching in comparison to the set schedule irrigation with FSR. Such high reductions in water volume did not affect avocado yield and were attributed to use of microsprinkler s and nutrient leaching being more influenced by the irrigation management rather than the fertilizer rate.

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159 Although the current practice in south Florida is to apply the same fertilizer amount to all avocado cultivars some avocado cultivars respond differentl y to the fertilizer rate. This would save producers the extra cost incurred in applying surplus fertilize amount for cultivars that may be predispose to greater production at lesser fertilizer rates. P revious us e of global modeling techniques by Soutter a nd Musy (1999) with LEACHC (a sub -suite of LEACHM for modeling pesticide leaching) was limited to sensitivity analysis and did not involve a factor screening process. This is the first time that a global sensitivity and uncertainty analysis has been applie d to LEACHN (a sub -suite of LEACHM for modeling N and P leaching) and also to si mulate P leaching using LEACHN. The global analysis tools proved to be effective in factor screening, quantifying each factors contribution to variability in the desired model output, identifying and quantifying factor interactions, and accounting for uncertainty in model output. This resulted in performing model simulations at a lesser computational cost and obtaining model results with less uncertainty. Research Synthesis Co nsidering that water is becoming scarce due to climatic variability and increasing demand by uses other than agricultural irrigation BMPs are beneficial for Simmonds avocado producers. By apply ing less water volumes, lower fertilizer rates are needed t o support crop growth since nutrient leaching is reduced. The identified irrigation BMP involves using a tensiometer to trigger irrigation. However, tensiometers may require biweekly checkups to ensure their functionality and this may be a drawback for ado pting this technology since tropical fruit producers in south Florida typically use a set schedule which has no maintenance requirements. An economic analysis of the proposed BMP should be performed and a survey conducted to assess the opinion of the tropi cal fruit producers about this BMP. ET -based irrigation, a suitable alternative with no maintenance requirements, requires research to develop local kc coefficients. The kc values used in my study were developed based on best professional judgment and resu lted into lesser irrigation water volume and a lesser fruit yield in comparison to SW -based irrigation.

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160 Based on avocado fruit yield, CP WUE, and CP -FUE values SW with FSR (treatment 4) and ET with FSR (treatment 1) had higher fruit yield for Simmonds t han the other treatments and should be explored further as trees mature to determine if similar results occur. Although the current practice is to apply the same fertilizer amount to all avocado cultivars my study results showed that some avocado cultivar s may respond differently to the fertilizer rate. This would save producers the extra cost incurred in applying surplus fertilize r amount for cultivars that may be predispose d to greater production at lesser fertilizer rates. The amount of P in fertilizer applied was reduced by half during fruit production years, yet the C:P ratio reduced over the years. This implies that the P formulation in the fertilizer grade could be lowered. Although the fertilizer application was similar between Simmonds and Beta their yields were different. In my study Beta fruit yield did not respond to variations in both irrigation water volumes and fertilize r rates during the fruiting years of 2008 and 2009. Research is needed to explore other factors that may influence yiel d in avocado or whether a lesser or greater irrigation and nutrient treatment would influence yields Modeling tools provide an opportunity to explore different irrigation and nutrient management scenarios that cannot be tested in the field due to limitat ions such as field space, time, and funds. In my study the measured data corroborated the simulated data during model calibration ; however, the same agreement could not be attained during model validation due to the limitations with the method used to meas ure drainage. The field measured data was collected using 20 L bucket lysimeters installed 0.3 m below the ground. One of the drawbacks of bucket lysimeters is the failure to collect leachate under unsaturated soil cond i tions due to by-pass flow. By -pass f low could occur during high intensity rainfall events due to high water percolation rate of Krome soil. Research is needed to explore if increasing the water inlet area (which was 840

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161 mm2) on the bucket catch pan (which was 51,945 mm2) affects the leached volume collected during rainfall events of varying intensities and magnitudes. The physical sampling limitations associated with ceramic tension lysimeters could be minimized by equipping the sampler with a cumulative leachate sampling device similar to th e one used by Haines et al. (1982). This would reduce uncertainty associated with leachate concentrations data since both unsaturated and saturated water flow would be sampled. Since nutrient leaching is mostly report ed as a load, sampling using tension lysimeters would require a drainage measurement method such as measuring soil water content at two soil depths of 15 and 30 cm at desired time interval using a data logger. Having data collected on a shorter time interval is beneficial in exploring other LEA CHM model outputs formats e.g. weekly, 10 -day or 15 -day time periods. This would provide data with minimized uncertainty for model calibration and validation of water flow. A good calibration and validation for water flow is essential since nutrients move with water. LEACHM model simulates inorganic N fertilizers in form of Urea, NH4 or NO3 by subject ing them to an immediate local equilibrium (Hutson, 2005). However, this analysis does not account for the slow release effect in the granular fertilizers used in production of tropical fruits. T o achieve the slow release effect (of 25% for N) of the fertilizer applied in model simulation, monthly fertilizer inputs were split into two parts to coincide with the months when peak NO3-N concentrations were sampled. Exploring field dissolution rate s of slow release fertilizer and making the necessary modification in the way LEACHM model simulates N would be a good research topic. Another way of refining irrigation and nutrient BMPs would be to collect data on nutrien t uptake of avocado since the LEACHM model is capable of simulating nutrient uptake.

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177 BIOGRAPHICAL SKETCH Nicholas Kiggundu was born in Uganda. He spent the first years of his youth with his paternal grandparents and during that time he was exposed to various agricultural activities. He attended St. Henrys College Kitovu, Masaka, Uganda for both his o rdinary le vel and high school education. He graduated with a b achelors d egree in a gricultural e ngineering from Makerere University and was awarded a fellowship f rom the African Academy of Science s to pursue a Master of Science d egree in a gricultural Engineering at the University of Nairobi. Nicholas possesses two postgraduate d iplomas in Groundwater Resources Exploration Exploitation and Management from The Hebrew University of Jerusalem, Rehovot, Israel and River Basin Hydraulics from the Hydraulic Research Institute, Delta Barrage, Egypt. Nicholas has served his country, Uganda, as a Water Officer for a Non Government Organization, a Lecturer at Makerere Universi ty, a Chairperson of Uganda Rainwater Association, and as a writer of revision mathematics pamphlets for high school students. Nicholas realized that to be able to answer some of the challenges Uganda faced in the areas of training and research he had to r eturn to school to obtain a PhD. His goal is to be able to contribute to the sustainable use of water for production by both the small and large scale growers through teaching and research.