Citation
Effects of Subsurface Drip Irrigation Flow Rates and Emitter Spacing on Sugarcane Water Uptake and Production in Florida Alfisols

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Title:
Effects of Subsurface Drip Irrigation Flow Rates and Emitter Spacing on Sugarcane Water Uptake and Production in Florida Alfisols
Creator:
Villalobos Leandro, Jose Eduardo
Place of Publication:
[Gainesville, Fla.]
Florida
Publisher:
University of Florida
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Language:
english
Physical Description:
1 online resource (98 p.)

Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Soil and Water Science
Committee Chair:
MORGAN,KELLY T
Committee Co-Chair:
MCCRAY,JAMES M
Committee Members:
HANLON,EDWARD A,JR
Graduation Date:
5/3/2014

Subjects

Subjects / Keywords:
Canes ( jstor )
Crops ( jstor )
Irrigation ( jstor )
Moisture content ( jstor )
Nutrients ( jstor )
Soil science ( jstor )
Soils ( jstor )
Sugar cane ( jstor )
Water tables ( jstor )
Water usage ( jstor )
Soil and Water Science -- Dissertations, Academic -- UF
alfisols -- drip -- irrigation -- production -- sugarcane -- transpiration
City of Gainesville ( local )
Genre:
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Soil and Water Science thesis, M.S.

Notes

Abstract:
Sugarcane (Saccharum officinarum) production in Florida is the fourth largest commodity with approximately 14 million metric tons of harvested cane per year. Understanding of proper irrigation management in sandy soils is crucial due to the increasingly stringent water management required by the state government given the expected increase of sugarcane planted area. Drip irrigation is an alternative worthy of consideration for sugarcane production to promote higher water use efficiency compared with current seepage irrigation practices. A drip irrigation study was conducted using a commercial sugarcane cultivar (CP-78-1628) planted with three different emitter spacing; 31 cm, 46 cm, and, 61 cm, defined as treatment 1, 2, and 3; respectively. Soil showed a more uniform moisture distribution through the profile for treatments 1 and 2 compared to treatment 3. Moisture uniformity enhanced a higher water uptake when using a shorter emitter spacing Peak water uptake was recorded from 12 pm to 6 pm for all treatments. The root scanning method indicated a high correlation with the intersection method but no differences for RLD were found between treatments due to high root variability. Yield, as well as plant nutrient concentrations, increased for treatments 1 and 2 compared to treatment 3. The results from this study indicate that the Kc for treatments 1 and 2 (31 and 46 cm emitter spacing) were similar to published values for other sugarcane production areas. However, Kc for the widest spaced emitters (61 cm) was lower, indicating reduced water use correlating with lower growth, yield, and nutrient accumulation. Taken together, these results indicate that drip irrigation of sugarcane in sandy soils of south Florida at 31 and 46 cm (12 to 18 inches) between emitters is a viable alternative to surface or seep irrigation. ( en )
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.
Thesis:
Thesis (M.S.)--University of Florida, 2014.
Local:
Adviser: MORGAN,KELLY T.
Local:
Co-adviser: MCCRAY,JAMES M.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2014-11-30
Statement of Responsibility:
by Jose Eduardo Villalobos Leandro.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Embargo Date:
11/30/2014
Classification:
LD1780 2014 ( lcc )

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1 EFFECTS OF SUBSURFACE DRIP IRRIGATION FLOW RATES AND EMITTER SPACING ON SUGARCANE WATER UPTAKE AND PRODUCTION IN FLORIDA ALFISOLS By JOSE E. VILLALOBOS LEANDRO A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2014

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2 2014 Jose E. Villalobos Leandro

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3 To my Family

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4 ACKNOWLEDGMENTS I would like to thank my major advisor, Dr. Kelly Morgan, for his encouragement and guidance throughout the duration of my program. I am extremely grateful to Dr. Edward Hanlon and Dr. Mabry McCray for the support and constant disposition to help and provi de insightful recommendations for my research. The completion of this research would not have been possible without the collaboration of a large group of people that helped me throughout the course of my program. I am extremely grateful to Dr. Davie Kadia mpakeny, Dr. Mihai Giurcanu, Ann Summeralls, and Tony Gallardo for the outstanding assistance and advice. I would also like to thank Dr. Kamal Mahmoud for serving as the Soils Laboratory Manager at SWFREC. I am also obliged to Jose Yaquian, Jorge Leiva, Ju lie McLaughlin, Odiney Alvarez, Miurel Bermudez, Lezcano, Chive and all my Naples Cyclists friends for their invaluable help and exceptional friendship. I will be forever grateful to my family for their support, encouragement, and inspiration to complete this degree. Above all, I thank the almighty God for all the blessings that he has given me including the opportunity to fulfill this dream.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURE S ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................... 12 1 INTRODUCTION ................................ ................................ ................................ .... 14 2 LITERATURE REVIEW ................................ ................................ .......................... 19 Sugarcan e Irrigation ................................ ................................ ............................... 19 Sugarcane Evapotranspiration (ET) and Crop Coefficients (Kc) ............................. 21 Sugarcane Root Length Density ................................ ................................ ............. 22 Soil Physical Properties ................................ ................................ .......................... 24 Nutr ients ................................ ................................ ................................ ................. 25 Objectives and Hypotheses ................................ ................................ .................... 26 Objectives ................................ ................................ ................................ ......... 27 Hypotheses ................................ ................................ ................................ ...... 27 3 MATERIALS AND ME THODS ................................ ................................ ................ 28 Experimental Design ................................ ................................ ............................... 28 Fertilization ................................ ................................ ................................ ............. 31 Root Length and Density Distribution ................................ ................................ ..... 32 Soil Physical Properties ................................ ................................ .......................... 35 Soil Moisture Char acterization ................................ ................................ ................ 36 Estimation of Crop Water Uptake, and Crop Coefficient (Kc) ................................ 38 Sugarcane Biomass Accumulation and Yield ................................ ......................... 40 Soil Nutrients ................................ ................................ ................................ .......... 41 Statistics ................................ ................................ ................................ ................. 43 4 RESULTS AND DISCUSSION ................................ ................................ ............... 44 Soil Physical, Hydraulic Characterization ................................ ................................ 44 Moisture Characterization for the Soil Profile and Sugarcane Water Use ............... 48 Moisture Characterization ................................ ................................ ................. 48 Leaf Measurements ................................ ................................ .......................... 49 Daily Water Use ................................ ................................ ............................... 50 Hourly Water Use ................................ ................................ ............................. 52 Root Distribution ................................ ................................ ................................ ..... 62

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6 Nutrient ................................ ................................ ................................ ................... 69 Tissue Analysis ................................ ................................ ................................ 69 Soil Profile P and K Distribution ................................ ................................ ........ 71 Sugarcane Productivity ................................ ................................ ........................... 79 5 CONCLUSIONS ................................ ................................ ................................ ..... 86 LIST OF REFERENCES ................................ ................................ ............................... 89 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 98

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7 LIST OF TABLES Table page 3 1 Common soils on the unit map ................................ ................................ ................ 29 3 2 Field numbers, area and treatment specifications for each plot .............................. 30 3 3 Treatments specifications and flows per treatment ................................ ................. 30 3 4 Fertilization rates ................................ ................................ ................................ ..... 32 3 5 Positions distances from center emitter ................................ ................................ .. 37 4 1 Soil physical characterization for Malabar fine sand ................................ ............... 47 4 2 Soil Texture for each depth ................................ ................................ ..................... 47 4 3 Soil volumetric water content depth vs. treatment ................................ ................... 55 4 4 Leaf area, leaf area index measurements, water use means, ETo, and estimated Kc ................................ ................................ ................................ ....... 55 4 5 Weather summary table ................................ ................................ .......................... 56 4 6 ANOVA sap flow data ................................ ................................ ............................. 61 4 7 Soil volumetric water content depth vs treatment ................................ .................... 67 4 8 Soil volumetric water content depth vs treatment ................................ .................... 67 4 9 Soil volumetric water content depth vs treatment ................................ .................... 67 4 10 ANOVA RLD ................................ ................................ ................................ ......... 68 4 11 Tissue N, P, and K concentration. ................................ ................................ ......... 74 4 12 Soil phosphorus concentration profile 0 60cm ................................ ...................... 74 4 13 Soil potassium concentration profile 0 60 cm depth ................................ .............. 75 4 14 Overall yield data for each by treatment ................................ ................................ 75 4 15 ANOVA tissue concentration ................................ ................................ ................ 78 4 16 ANOVA yield nutrient concentration ................................ ................................ ..... 78 4 18 Analysis of variance for overall yield data between yield seasons ...................... 83

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8 4 19 Analysis of vari ance for overall yield data for each by treatment. ........................ 83 4 20 Yield data summary by treatment. ................................ ................................ ........ 84 4 21 ANOVA yield data. ................................ ................................ ............................... 84

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9 LIST OF FIGURES Figure page 2 1 Penman Monteith equation ................................ ................................ .................... 22 2 2 The root system of an established sugarcane stool ................................ ............... 23 2 ................................ ................................ ................................ .... 24 3 1 T reatments plots and general layout in the field ................................ ..................... 29 3 ................................ .................. 33 3 3 Root length density equation ................................ ................................ .................. 33 3 ................................ ...................... 35 3 5 Soil profile sampling grid pattern for soil moisture distribution study ...................... 37 3 6 Atmospheric pressure calculation formula ................................ ............................. 38 3 7 Normalized transpiration ................................ ................................ ........................ 39 3 8 1.5 m row stalk counting ................................ ................................ ........................ 40 4 1 Water release curve for Malabar fine sand at three different depths ...................... 46 4 2 Profile volumetric water content distribution for the different emitter spacing for a 0 60 cm soil depth ................................ ................................ ........................... 53 4 3 Profile volumetric water content distribution for the different emitter spacing for a 0 30 cm soil depth ................................ ................................ ........................... 54 4 4 Sap flow and ETo ................................ ................................ ................................ ... 57 4 5 Cumulative sap flow and cumulative ETo ................................ .............................. 58 4 6 Water use mean for treatments for both growth stages ................................ ......... 59 4 7 Hourly water use ................................ ................................ ................................ .... 60 4 8 Regression RLD vs. scanned area ................................ ................................ ......... 65 4 9 Regression RLD LI vs. RLD scanning method ................................ ....................... 66 4 10 Tissue concentration ................................ ................................ ............................ 73 4 11 Profile P distribution for the different emitter spacing for a 0 45 cm soil depth ..... 76

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10 4 12 Profile K distribution for the different emitter spacing for a 0 45 cm soil depth ..... 77 4 13 Yield data for plant cane ................................ ................................ ...................... 81 4 14 Yield data for first ratoon ................................ ................................ ...................... 82

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11 LIST OF ABBREVIATIONS ANOVA BMP CEC DOY E sap ET ET o ICP Kc LA LAI M 1 RLD SDI SWFREC Trt 1, 2 or 3 TRS Analysis of Variance Best Management Practice Cation Exchange Capacity Day of the Year Daily Sapflow per Unit Land Area Evapotranspiration Reference Evapotranspiration Inductively Coupled Plasma Crop coefficient Leaf Area Leaf Area Index Mehlich One Root length density Subsurface drip irriga tion Southwest Florida Research and Education Center Treatment one, two, or tree Total recovery sucrose

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12 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science EFFECTS OF SUBSURFACE DRIP IRRIGATION FLOW RATES AND EMITTER SPACING ON SUGARCANE WATER UPTAKE AND PRODUCTION IN FLORIDA ALFISOLS By Jose E. Villalobos Leandro May 2014 Chair: Kelly T. Morgan Major: Soil and Water Science S ugarcane (Saccharum spp. ) production in Florida is the fourth largest commodity with approximately 14 million metric tons of harvested cane per year. Understanding of proper irrigation management in sandy soils is crucial due to the increasingly stringent water management required by the state government given the expected increase of sugarcane planted area Drip irrigation is an alternative worthy of consideration for sugarcane production to promote higher water use efficiency compared with current seepage irrigation practices A drip irrigation study was conducted using a commercial sugarcane cultivar (CP 78 1628) planted with three different emitter spacing; 31 cm, 46 cm, and, 61 cm, defined as treatment 1, 2, and 3; respectively. Soil showed a more unifo rm moisture distribution through the profile for treatments 1 and 2 compared to treatment 3. Moisture uniformity enhanced a higher water uptake when using a shorter emitter spacing. Peak water uptake was recorded from 12 pm to 6 pm for all treatments. The root scanning method indicated a high correlation with the intersection method but no differences for RLD were found between treatments due to high root variability.

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13 Yield, as well as plant nutrient concentrations, increased for treatments 1 and 2 compared to treatment 3. The results from this study indicate that the Kc for treatments 1 and 2 (31 and 46 cm emitter spacing) were similar to published values for other sugarcane production areas. However, Kc for the widest spaced emitters (61 cm) was lower, ind icating reduced water use correlating with lower growth, yield, and nutrient accumulation. Taken together, these results indicate that drip irrigation of sugarcane in sandy soils of south Florida at 31 and 46 cm (12 to 18 inches) between emitters with the selected cultural practices for this study is a viable alternative to surface or seepage irrigation.

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14 CHAPTER 1 INTRODUCTION Sugarcane ( Saccharum spp. commercial industry is located in the southern part of the state. Most of the planted area is located south of Lake Okeechobee on muck soil (Histosols) with the remaining, approximately 20% grown on sandy mineral soils (Entisols and Alfisols) (Baucum et al., agriculture with an annual yield of $1.07 billion US per year (NASS, 2012). Sugarcane is an economically important crop with a high photosynthetic efficiency, reported to be one of the most efficient carbon fixing crops per unit biomass (Chopart et al., 2008). Sugarcane is a key dietary (nutrition) and industrial (biofuels) sou rce of energy (Crutzen et al., 2007). Current sugarcane prices and renewed interest in the use of sugarcane as a biofuel are expected to encourage an expansion of the sugarcane an extra boost in terms of planted area as a replacement crop. Among the many diseases of citrus that have invaded Florida, Haunglonbing or greening ( Candidatus Liberibacter asiatica indu stry. Farmers are switching from citrus to other crops, and sugarcane is one of the most popular (Spreen, 2008). Irrigation is extensively used for crop production in Florida with more than 810,000 ha of irrigated cropland. Florida's large irrigated hectar age is due to low water holding capacity of its predominantly sandy soils and non uniform temporal rainfall

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15 agricultural area receives from 1200 mm to 1550 mm of rain a yea r (Tucker et al., 2002), the second highest rainfall mean of all states (National Weather Service, 2012). In a state with such an abundant resource, stringent water management seems unnecessary. However, highly variable spatial distribution of water and wa ter quality issues, warrants a proper water management policy (Munson et al., 2005). Most of the sugarcane has been grown in the Everglades Agricultural Area (EAA ) li mestone bedrock (Glaz et al., 2004). Even though many best management practices have been implemented to reduce the oxidation on muck soils in the EAA, the process has only been slowed with current techniques to losses of 1.4 cm yr 1 (Izuno, 1999 and Wrigh t et al., 2009). Depth of Histosol in the EAA varies, but now a number of sugarcane fields have less than 40cm of soil (Shih et al., 1998), due to soil subsidence. Water use in the EAA is complex from a productive and environmental perspective. Various fac tors play an intricate role in water quality management, for instance: microbial oxidation of the soils, P load regulations, shallow soils, and Florida Histosols (muck) convoluted hydrology nature. For instance, these organic soils have low bulk densities and contain more than 20 30% organic matter meaning low weight bearing capacity and subsidence when drained (McDaniel, 2013), which complicates the selection of a proper irrigation method. Thus Florida sugarcane industry is being forced to move away from the shallower organic soil in areas of the EAA to adjacent mineral soil areas. Less information has been produced in sandy soil irrigation for sugarcane compared to the organic soils.

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16 Water use efficiency must be increased to minimize total water use and a t the same time reduce competition from other sectors for this valuable resource. This goal can only be achieved by generating appropriate scientific information, to address the problem systematically. Competition for limited water resources correlates pos itively with population growth. Florida is the sixth fasting growing state in the USA, adding 26 000 new residents each year (USCB, 2012). Improving knowledge as soon as possible of sugarcane and related soil factors that affect crop water uptake is essential to insure water availability for the near future. Sugarcane production has a high water dema nd, particularly on sandy mineral soils with low water retention (Lang et al., 2006). Florida is not the exception to this high water use where sandy Alfisols have water capacity ranging from 0.23 to 0.62 cm m 1 (Obreza et al., 2008). Lack of water causes considerable delays in sugarcane development, reducing biomass accumulation and yield (Subiros, 2000). Most of the area cultivated with sugarcane in Florida is irrigated by subsurface irrigation systems (seepage) because of its cost effectiveness and low maintenance requirement (Haman et al., 1989). This system depends on elevating the water table and upward flux to the soil surface. Not all of the pumped water is available for crop use, depending on the depth of the water table before irrigation. Large vo lumes of water may be required to build and maintain the water table; thus, reducing the application efficiency of seepage irrigation. Other losses occur due to deep percolation below the crop root zone and subsurface lateral flow to surrounding areas ( Sma jstrla et al., 2006) Seepage irrigation application efficiency (E a ) with open ditch system can be as low as 20% ( Smajltra et al., 2006). Loss of fertilizers with the flow of water from agricultural

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17 fields can lead to negative impacts on the water quality of adjacent water bodies (Hurst et al., 2003). However, the focal problem in crop production and irrigation lies in a dwindling supply of fresh water in Florida (Feminella, 2008). To improve crop water use efficiency, the impact of other irrigation system s on sugarcane biomass accumulation, nutrient use, and yield must be understood and compared with the same growth parameters of the relatively inefficient seepage irrigation. Drip irrigation is a promising technology that offers high water use efficiency ( >85%) and is often specified as a best management irrigation practice for reducing groundwater contamination (Evans et al., 2007). Currently, drip irrigation is not commercially used for sugarcane production in Florida. But several studies have demonstrate d economic viability with yield increases of up to 60% in cane wet weight (Yadav, 2012; Camp, 1998; Wiedenfeld, 2004). Soil texture and compaction are two determining factors to be considered for sugarcane drip irrigation, coupled with the flow rate of the emitter, and the size and width of the wetted zone. Irrigation designs should be designed to irrigate the crop with the correct amount of water at high uniformity throughout the field. Understanding of sugarcane irrigation management with systems commonl y used for other crops in sandy soil is crucial. There is increasingly stringent water quality management required by the water management districts in Florida. Thus the expected increase of sugarcane planted area for the upcoming years, and the recent hig h non source pollution levels. Drip irrigation with higher water use efficiency is an alternative worthy of consideration for sugarcane production. The study documented in this thesis will provide measurements regarding the effect of subsurface drip emitte r spacing on

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18 evapotranspiration, root density, biomass accumulation, nutrient uptake, and yield in sandy soil grown sugarcane.

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19 CHAPTER 2 LITERATURE REVIEW Sugarcane Irrigation Worldwide, water is becoming an increasingly scarce resource, therefore, limiting agricultural development in many regions and countries (Qureshi et al., 2001). Soils in South Florida can develop a water table above the spodic horizon of Spodosols, the arg illic horizon of Alfisols, or limestone below the Entisols and Hist o sols to irrigate the cropland. Irrigation by elevation of water tables is usually called seepage irrigation and is used on 158,637 ha of sugarcane ( S accharum spp. ) in south Florida. Adequa te irrigation is achieved by maintaining the water table below the crop root zone and allowing the capillarity of the soil to raise the water up into the root zone (Zotarelli et al., 2013). However, due to the high hydraulic conductivity of sandy soils (2. 5 65 cm h 1 ), there is a consensus between growers and water managers that the conventional rainfall is concentrated in the summer months (June to September). Seepage irrigation can degenerate into flooding conditions when coupled with high seasonal rainfall events, which may have negative effects on sugarcane production. Excessive soil/water conditions have been shown to reduce sugarcane yield (Camp et al., 1883), howe ver, recent development of genotypes has improved the tolerance to flood water regimes (Glaz et al., 2004). Water, itself, does not damage crop roots if drained quickly, but prolonged flooding may cause damage by reducing oxygen in the soil and promoting a build up of carbon dioxide (Erickson, 2012). This condition causes a reduction in the uptake of vital nutrients such as nitrogen and phosphorus (Gayle et al., 1987). Another important factor is the evaporation of water from the soil surface. As documented by

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20 Sun. et al. (1988), the sub irrigation soil moisture was from 39% to 188% higher than in drip. The evaporation rate from soil is directly related to the moisture available in the soil, thus before canopy closure, the soil surface evaporation from the s eepage is greater than from the drip irrigation system. T he spodic horizon or water restrictive layer is commonly uneven; geological factors, such as cracks and holes in the caprock or bedrock, impede the maintenance of an even water table (Lang et al., 1 993). Zotarelli et al. (2013) reported values that indicate a lack of uniformity of the water table in seepage irrigation across large fields due to hydraulic forces. The variable soil moisture can be explained in part by the hydraulic dynamics of the wate r table during irrigation and drainage processes. Soil and environmental conditions in Florida make sugarcane grown with seepage irrigation a high water demanding crop. Using seepage irrigation, sugarcane requires 884 Kg of water for the production of 1 Kg of sugar in plant cane and 1,115 Kg of water in the ratoon cane (Wright et al., 2011). Drip irrigation systems have been broadly used for sugarcane in other countries (Australia, Brazil, South Africa, etc.) (Batchelor, 1990; Wiedenfeld et al., 2004; and S ubiros, 2000). Subsurface drip irrigation (SDI) offers many advantages compare d with other irrigation systems: 1) increased sucrose yield; 2) improved water use efficiency; 3) reduced cost of cane production; 4) reduced labor inputs when automated; 5) incr eased crop nutrient uptake efficiency (NUE); and 6) improved application of water through an even distribution (Ndlovu, 2000). It has been shown by Pitts et al., 1990, and Shih and Gascho, 1980, that reduced irrigation above shallow water tables not only results in

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21 more efficient use of water resources, it also lowers the risk of waterlogging and nutrient losses below the root zone. As noted by Skaggs et al. (2004), realizing the full potential of sub surface drip technologies in sugarcane requires optimizing the operational parameters that are available to irrigators, such as the frequency and duration of irrigation, emitter discharge rate, spacing, and the placement of the drip laterals. Thus, the pro per design and management of SDI systems, requires knowledge of the precise distribution of water around the emitters to determine an optimal distribution of water in the sugarcane root zone. The width of the irrigated soil volume is mainly a function of e mitter discharge, spacing, soil type, and the amount of water (Nakayama and Bucks, 1986). Even though the SDI industry has grown significantly in the United States in the last decade, little research has been conducted and information is needed to develop efficient designs, installation methods, and guidelines for a proper operation according to the crop and environmental conditions (Nakayama and Bucks 1986). Sugarcane Evapotranspiration (ET) and Crop Coefficients (Kc) Evaporation is defined as the loss of liquid water from a surface to the atmosphere as a vapor. On agricultural fields, there are two different types of evaporation: 1) evaporation directly from the soil; and 2) evaporation from the crop tissue surface. Transpiration is defined as the loss of liquid water from plant tissues to the atmosphere as a result of plant physiological activity (Allen et al., 1998). The sum of both sources of water loss are collectively referred to as evapotranspiration (ET), and depend on solar radiation, air temperatu re, relative humidity, and wind speed (Zotarelli et al., 2010).

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22 A number of empirical methods have been developed throughout the last 65 years to estimate evapotranspiration from selected climactic variables; most of these attempts derive from the Penman method (Penman, 1948). The Penman Monteith method (Figure 2 1) estimates accurate ET rates for a well watered reference surface based on physical atmospheric observations (Allen, 1998). In Florida, ET o varies from 1.7 5.3 mm day 1 (Morgan, 2004) Figure 2 1 Penman Monteith equation. ( Allen, 1998) Where, Rn = net radiation G = soil heat flux a = mean air density (e s e a ) = vapor pressure deficit of the air c p = specific heat of the air, = slope of the saturation vapor pressure temperature relationship = psychometric constant, r s and r a = (bulk) surface K c is defined as the ratio of crop evapotranspiration (ET c ) to potential evapotranspiration (ET o ) when soil/water availability is non limiting and is a function of crop type, climate, soil evaporation, and crop growth stage (Fares et al., 2008). There are no K c estimations for sugarcane in South Florida; however, K c values in the tropics range from 0.4 1.25 depending on the crop stage (Allen et al., 1998). Sugarcane Root Length Density There are many below ground constraints on crop growth that are of significant importance for scientific and commercial sugarcane production purposes. Root growth

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23 on sugarcane directly influences sugarca ne productivity by their effects on 1) supply on below ground resources, 2) gas exchange and assimilation, and 3) carbon budget of the plant (Smith et al., 2005). Constraining factors of root growth may be abiotic or biotic. Soil water availability is an a biotic factor whereas, different growth habits among crop cultivars is an example of a biotic factor; both directly influences roots size and distribution (Baran et al., 1974). Sugarcane root architecture can be categorized into 3 major root functional gro ups, 1) superficial roots, 2) buttress roots and 3) rope system. Even though there is evidence that root growth may exceed a depth of 6 m, typically, 50% of the root biomass occurs in the first 20 cm and 85% in the top 60 cm of the soil. (Fig 2 2) Figure 2 2 The root system of an established sugarcane stool (Source: Smith, 2005) Researchers recommend the use of root length density (RLD) for the purposes of better predicting nutrient and water uptake in sugarcane (Chopart et al., 2008). Root length density spatial distribution defines limits to the efficiency of a root system in absorbing water and nutrients (Himmelbauer et al., 2004) The main methods that have

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24 been used for measuring root length are the line intersect method (Newman, 1966); direct measurement ; and opisometer methods ( Reicosky et al ., 1970); photocopying and scanning (Collins et al., 1987; Kirchoff, 1992; Himmelbauer et al. 2004 ) ; and the stereological procedure. Soil Physical Properties The ability of the soil to store and release w ater has a profound influence on the habitat, and atmosphere modifier (Brady and Weil, 2008). Thus, understanding the interaction of soil and water is fundamental to impr ove the irrigation management. The soil physical properties such as hydraulic conductivity and water retention features determine the behavior of the soil water flow (Hillel, 1998). The soil/water relationship plays a corner stone role on sugarcane growth as for any other crop. One of the main characteristics is the hydraulic conductivity of a soil, which depends on pore geometry and the properties of the fluid flowing through or retained in the pores. Soil porosity is a function of soil texture and struct the most commonly used approach to express the hydraulic conductivity for one dimensional vertical flow (Hillel, 1998, Brady and Weil, 2008 and Klute, 1986). Figure 2 Where, q= the volume flux = Gradient H= Hydraulic head

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25 The a mount of water available for plant use is determined through estimation of available water capacity, field capacity, and permanent wilting point. Water release cur ves measure the relation between matric tension and the water content of a given soil. Sandy soils have a lower capacity to retain water content compared to a clay texture soil. Nutrients Improving plant growth by adding mineral nutrients to soils has bee n known as an agriculture practice since ancient times. The effective correlation between production and nutrients is still a popular research topic Nevertheless the understanding of the nutrient soil environment plant relations goes beyond production, en compassing a variety of complex environmental impacts in the system. Nutrient assessment of the soil for sugarcane production is an indispensable best management practice ( McCray et al., 2010) often leading to a more efficient use of nutrients. Improper ir rigation methods, high cost of fertilizer inputs, and inaccurate application rates result in reduced profit margins for farmers (Patil et al., 2013). The rate at which plants uptake nutrients is primarily determined by the plant growth rate, but relying i n other factors such as: soil characteristics, environmental characteristics, crop characteristics, and the management given to the crop (Havlin. et al ., 2005). It is essential to recognize that nutrient uptake is correlated with the crop growth stage. Sugarcane nutrient uptake is not the exception to the rule, and varies depending the growth stage: 1) germination and emergence; 2) tillering and canopy establishme nt; 3) grand growth; and 4) maturation or ripening, which are approximately: 1, 2, 7, and, 2 months in length, respectively (Wiedenfeld, 2004).

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26 Many researchers have attempted to study nutrient leaching to sustain environmental quality and increase the nu trient application efficiency. However, Alfisols in South Florida are sandy with <1% clay and silt at the surface (Anderson et al., 1999) making proper irrigation and nutrient management a challenging task. Sugarcane production on sandy soils is typically influenced by: leaching of nutrients (N, P, K), with undulating limestone parent materials (Anderson et al., 1999). Low cation exchange 18 cmol(+) kg 1 ) (Obreza et al, 2002), tend to augment leaching potential as well. Thus use of more efficient irrigation systems can reduce the nutrient leaching and increase the application efficiency. Objectives and Hypotheses The sugarcane industry is expanding on the sandy m ineral soils of south Florida (Spreen, 2008). Therefore, determining the water requirements and productive responses of the crop to optimize the irrigation management, and water use efficiency is of major importance. The overall objective of this study is to increase the limited Because of the influence SDI emitter spacing has with respect to water availability for crop water and nutrient uptake, the response of sugarca ne production must be understood for different emitter spacing treatments. The information generate d by the study will allow us to expand our understanding of sugarcane water uptake and productivity with each treatment.

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27 Objectives Evaluate sugarcane roo t density at selected depths for each treatment Characterize the soil/water content distribution in the row at selected emitter spacings. Spatial differences in root density and water content with distance in the row, between rows and depth will be determi ned Obtain ET c and calculate K c for sugarcane in the area of study for each emitter spacing based on estimated ET o and plant transpiration Compare the K c obtained in the study to the K c for sugarcane in other production areas and discuss differences in wa ter use and K c based on environmental and soil considerations Define the sugarcane growth and productivity for each emitter spacing treatment to access the effects of drip irrigation of sugarcane on southeast Florida's Alfisols Hypotheses Increased dist ance between drip emitters result in increased root density surrounding drip emitters in sugarcane Shorter distances between emitters will result in a more uniform water content distribution and promote a more even root distribution at a given depth Closer emitter spacing will increase water uptake and reduce stress on the crop Sugarcane water use estimates in Florida will be more accurate with K c determined for the study area than K c developed in sugarcane production areas outside of Florida Longer emitter spacing will decrease sugarcane biomass accumulation, nutrient uptake and yield

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28 CHAPTER 3 MATERIALS AND METHODS Experimental Design Fifteen plots between 1.9 ha and 3.6 ha each were created at Florida Crystal sugarcane farm (Hinson) in Martin County, Indiantown (26 o o on Alfisols (Table 3 1). Soils in the research area are Pineda fine sand (Loamy, siliceous, active, hyperthermic Arenic Glossaqualfs), Riviera fine sands (Loamy, siliceous, active, hyperthermic Arenic Glo ssaqualfs), Pinellas fine sand (Loamy, siliceous, superactive, hyperthermic Arenic Endoaqualfs), Riviera fine sand depressional, and Malabar fine sand (Loamy, siliceous, active, hyperthermic Grossarenic Endoaqualfs) (USDA NRCS, 1998). Annual average precip itation for Martin County is approximately 1320 mm and the temperature ranges between 11C to 35C (NWS, 2012). A block design with psuedo replication was established on a 39.7 ha area with three treatments of five replications each (Figure 3 2). Each tre atment was conducted using different emitter spacing of a SDI system (61 cm, 46 cm, and 30.5 cm) Table 3 3. Irrigation water was supplied by a diesel, self priming, off ditch pump from a perimeter canal, with a maximum capacity of 7.6 m 3 h 1 There was on e drip line for each plantable row placed 0.15 m next from the sugarcane row at a depth of 0.15 m, planting spacing between rows was 1.5 m. Irrigation was timed and scheduled to apply the same amount of water per area regardless the different emitter spaci ng (treatments). The sugarcane planted in early spring, 2012 was variety CP 78 1628.

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29 Table 3 1 Common soils on the unit map Soil Series Area (%) Unit name Soil Taxonomy nomenclature Malabar 64 Malabar fine sand Loamy, siliceous, active, hyperthermic Grossarenic Endoaqualfs Riviera 19 Riviera fine sand Loamy, siliceous, active, hyperthermic Arenic Glossaqualfs Pineda 14 Pineda fine sand Loamy, siliceous, active, hyperthermic Arenic Glossaqualfs Pinellas 3 Pinellas fine sand Loamy, siliceous, superactive, hyperthermic Arenic Endoaqualfs Figure 3 1 Treatments plots and general layout in the field

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30 Table 3 2 Field numbers, area and treatment specifications for each p lot Field Number Spacing between emitters (inches) Spacing between emitters (cm) Treatment Area (ha) 1N 18 46 2 3.5 2N 18 46 2 1.7 3N 18 46 2 2.7 4N 12 30 1 2.6 5N 12 30 1 2.5 6N 12 30 1 2.7 7N 24 61 3 2.7 8N 24 61 3 2.5 9N 24 61 3 2.7 10N 12 30 1 2.6 11N 12 30 1 2.7 6S 18 46 2 2.6 8S 18 46 2 2.5 9S 24 61 3 2.6 10s 24 61 3 2.4 Table 3 3 Treatments specifications and f lows per treatment Treatment Spacing between emitter (cm/inches) Area (ha) Flow (lpm per ha) Flow per emitter (lph) 1 31 / 12 13.1 281 0.78 2 46 / 18 13.1 189 0.79 3 61 / 24 13.0 199 1.11 Area refers to the total area (the sum of the replication plots)

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31 Fertilization For healthy and productive sugarcane, certain elements (nutrients) must be made main nutritional concern s are; N, P, K, Mg, B, Cu, Fe, Mn, Si, and Zn (Rice et al., 2009). The fertilization was provided at commercial rates t o supply the crop demand along the growing stages (Table 3 2). Three months before planting, compost (chicken litter manure) was applied in the field at a rate of 18 353 kg ha 1 Nutrients in the compost are available at a slower rate than what minerals ar e, depending especially on the speed at which mineralization take place. Usually approximately 50% of the total nutrients in the compost is available for plant uptake in the first year (Hochmuth et al ., 2009). Some of the benefits of applying organic amend ments to sandy soils are: Increase water holding capacity, CEC, microbial activity, and improve soil tilth (Hanlon et al., 2011). For pla n t cane due to political/management decision in the farm, fertigation was only applied to Trt 3. The other two treatmen ts (Trt1 and Trt2) received the fertilized as a ground application. The application was not according with the experiment methodology, where fertigation was expected for all of the treatments. For first ratoon fertilization rate applied was all the same among the three treatments. Fertigation was divided in three applications, once each month for April, May, and June. No potassium (K) was provided by drip application for the first ratoon. K was supplied by grou nd, and by previous compost application.

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32 Table 3 4 Fertilization rates Section Date Formula Application Rate N P 2 O 5 K 2 O ---------------------kg ha 1 ----------------------------Plant Cane All 9/17/2011 1 1.5 0.5 Ground calmaine* 18356 184 275 92 All 11/21/2011 9 9 23 Roma Ground 37 3 3 8 All 5/1/2012 9 17 12 Roma Ground 28 3 5 3 All 5/4/2012 11 52 0 Roma Ground 11 1 6 0 All Roma Ground 8 12 19 Trt 3 Drip System 30 15 28 1 st Ratoon All 2/13/2013 15 9 17 Roma Ground 55 8 5 9 All 4/1/2013 11 52 0 Drip System 7 1 4 0 All 5/1/2013 32 0 0 Drip System 34 11 0 0 All 6/10/2013 9 0 23 Perry farms 69 6 0 16 All 6/19/2013 32 0 0 Drip System 34 11 0 0 All 6/20/2013 22 0 0 Roma Air 10 2 0 0 All 8/8/2013 12 0 8 Drip System 28 3 0 2 Root Length and Density D istribution Root length and root length density was estimated by sampling soil with a 430 cm 3 bucket auger in each plot. Three soil core sets were collected from each plot during spring 2013 at two locations per plot from 100 m of the north and south ends. Two of the core sets were taken 10 cm from the drip line towards the center of the planted r ow, one of them in front of the emitter and the other between emitters, the third core set was collected 25 cm from the emitter (Figure 3 2) perpendicular from the planted row. Two depths (0 20 cm and 20 40 cm) for each set for a total of 260 samples. The samples were collected one week after crop cane was harvested. The study carried out by Ball Coelho et al. (1992) found evidence that live root density starts decreasing 30 days after harvest, so representative root density from late growth stage is expect ed to be

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33 recorded in the study. All the soil cores were labeled and stored in a refrigerator <4C o before being analyzed. Figure 3 Soil was removed from the roots by rinsing the sample with water on a 2 mm and then a 600 mm sieve. Any other debris not passing through the 600 micrometer sieve was manually removed. The roots were soaked in water for 10 minutes to dehydrate them, and dri ed on a paper towel for 5 minutes to a final diameter. The roots were divided according to diameter: <0.5 mm, 0.5 1.0 mm, and >3 mm before using the line intersection method (adapted from Morgan, 2004). Figure 3 3 Root length density equation Where, G= length of the gridded section N= number of intersections across vertical and horizontal lines V= the volume of the soil core in cm 3

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34 Resulting root length densities determined by the line intersection method developed by Tennant (1975) was adjusted and calibrated with the scanning method to reduce the time required to estimate root length (Kadyampakeni et al., 2012). The grid used for the intersection method consisted of a rectangle 21.4 cm x 16 cm with 1 cm x 1 cm grid. The roots were spread avoiding overlapping in between the rectangle grid glass in the bottom and a transparent glass in the top to ensure the roots were leveled with the grid (Figure 3 3). Subsequently the intersections were counted for each for 30 sets of samples. Thereafter, all roots were scanned with a HP 4860 ( Palo Alto, CA ) flatbed scanner at a resolution of 200 dots per inch (dpi) and dimension of 1716 x 2464 pixels. Imaging software (Image J, http://rsb.info.nih.gov/ij/ ) was utilized to determine the area (mm 2 ) covered by the roots. Afterwards a correlation was done between the covered area determined by the imaging software and the root length density (cm cm 3 ) obtained with the inters ection method (Figure 3 3). The correlation curve was obtained using 2 greater than 0.85 (Kadyampakeni, et al., 2012).

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35 Figure 3 (Source: Jose Villalobos) Soil Physical P roperties Soil cores were taken at 6 locations in the field at three depths (0 15 cm, 15 30 cm, 30 45 cm). The plots sampled were: 1N north, 3N south, 5N north, 8S north, 9N north and 11N north (Figure 3 1). The s ampling was conducted during spring 2012 and summer 2013 for a total of 18 cores. The samples were collected using a 135 cm 3 cylinder from each soil type within the plots. Soil water retention curves were determined in the laboratory according to the proc ess described by Klute (1986) using Tempi Cells, from this procedure, bulk density b fc s r ) were determined. In the field, the samples were covered with plastic wrap and place d in

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36 a cooler with ice for transportation. To avoid changes in the properties, samples were kept refrigerated until analyzed. Soil/water release curves were determined by using pressure (Klute, 1986). The soil cores were placed on the 0.5 bar porous cerami c plate and saturated by immersing them in water for 24 hours before the pressure was applied. The weight was recorded each time soil water equilibrated at pressures of 2.8, 5.5, 8.3, 10.3, 12.4, 13.8, 16.5, 19.3, 26.2, 37.9, 48.3, 68.3, and 100 kPa. Satur ated hydraulic conductivities were determined by the constant head method (Elrick, 1992). The core was transferred to the hydraulic conductivity apparatus in which water was applied to the top cylinder with water level kept at a constant height. Once a ste ady flow was established for each sample, the drainage water was collected for a known period of time. The volumes of drained water and time were recorded to determine the saturated hydraulic conductivities of each sample. Other soil morphological characte ristics (texture, structure, taxonomical description, etc.) were recorded from the Website for Soil Survey (http.websoilsurvey.nrcs.usda.gov/app/websoilsurvey.aspx). Soil texture was determined by the method described by (Bouyoucos, 1962) Soil Moisture C ha racterization Pits were dug adjacent to a drip line for each treatment (total 3 pits). Samples were collected 26 th April 2013, to expose a vertical soil profile below three emitters of each treatment for gravimetric soil water content to quantify the water movement and uniformity. In each case 30 soil samples were taken with a scoop in a grid pattern (Figure 3 5), minutes after the irrigation cycle was finished.

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37 Table 3 5 Positions distances from center emitter _______________________________Position_____________________ 1 2 3 4 5 6 Treatment Distance from emitter in the middle (cm) 1 31 20.6 10.3 10.3 20.6 31 2 46 30.6 15.3 15.3 30.6 46 3 61 40.3 20.3 20.3 40.6 61 Figure 3 5 Soil profile sampling grid pattern for soil moisture distribution study (Source: Jose Villalobos) The 1, 2, 3, 4, 5, and 6 positions were taken in the same location with respect to the central emitter for each treatment (Table 3 5) depths followed the same pattern for

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38 all treatments (0 cm, 10 cm, 20cm, 30 cm, 40 cm, 50cm, and 60 cm) (Figure 3 5). Soil samples were packed in properly labeled sampling plastic inner layer bags and placed in a cooler. Subsequently the soil was weighed at field moisture and dried at 105 C o for 24 h to obtain the dry weight. Volumetric water contents were determined by multip lying the gravimetric water contents with the bulk density measured as explained above in the soil physical properties materials and methods section. A 2D image was obtained showing the moisture distribution in volumetric moisture content through the profi le with Sigmaplot 10.0. Estimation of Crop Water Uptake, a nd Crop Coefficient ( Kc ) Weather data were collected with a portable weather station (HOBO U30/NRC; Cape Cod, MA), located on the periphery of the research area. The weather variables collected were : 1) rainfall, 2) relative humidity, 3) wind speed, 4) temperature, and 5) solar radiation. Atmospheric pressures were calculated using elevation determined by a GPS device (Garmin 62S) using the formula below (Figure 3 6). Results for atmospheric pressure were compared with the national environmental satellite data and information service (NESDIS, 2012). Figure 3 6 Atmospheric pressure calculation formula (Zotarelli, 2010) Where z = elevation above sea level (m), P = atmospheric pressure (kPa).

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39 Sugarcane transpiration was obtained using a heat balance method employing the Dynagage Flow32 1K Sap Flow System (Houston, TX). Sap flow for four stalks per treatment were measure for 19 days at the beginning of the grand gro wth stage (June 21 to July 9 2013), and 16 days before maturation (October 8 to October 23) Before installation, stalk diameters were recorded to select the proper sensor size. Sensor sizes were SGB 13 (12 16mm diameter) SGB 16(15 19mm diameter), SGB 19(1 8 23mm diameter), and SGB 25 (24 32mm diameter). The sap flow sensors were connected to a datalogger (CR 1000, Cam p bell Scientific Inc., Logan UT) to record data at 1 hr intervals with the proper software calibration using a thermal conductance constant (K sh) of 0.54 for sugarcane (Dynamax, 2007). Leaf area (cm 2 ) was determined with a portable leaf area meter (Model LI 3000A LI COR, Lincoln, NE, USA) for all functional leaves in each stalk. And leaf area index was estimated dividing the leaf area (m 2 ) by th e ground area per stalk (m 2 ). Stem flow measurements from individual plants suggested by Ham et al. (1990) and adapted by Lascano et al. (1992) was estimated (Figure 3 7). Figure 3 7 Normalized transpiration (Lascano et.al., 1992) Where, T1 =mean transpiration (kg m 2 s 1 ), f i = measured stem flow (kg s 1 ), x i = leaf area (m 2 ) of plant i, LAI= leaf area index of the plot (m 2 ). The transpiration was converted to mm d 1 by dividing T 1 by the 1000 kg m 3 (water density) and multiplying by 86,400 s (seconds in one day) as suggested by Kadyampakeni et al. (2013).

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40 Sugarcane Biomass Accumulation and Y ield Stalk counts were performed before each harvest. Counts included all sugarcane taller than 1.5 m to the l eaf sheath. Data were collected approximately 50m from the end of each plot for both the north and south ends of each plot (Figure 3 8) to avoid the border effect on the samples. A 1.5 m linear row count with three replications was done in each of the 30 s ampling sites per treatment for a total of 90 samples of 1.5 m row counts in each treatment for each harvest season. Figure 3 8 1.5 m row stalk counting (Source: Jose Villalobos) To determine the biomass accumulation, ten mature sugarcane stalks wer e cut at the soil surface from both the north and south side of each plot (total of 20 stalks per plot). The leaves, the top four internodes (crown), and stalks were weighed separately to determine the accumulated cane biomass. The stalks with leaves and t ops removed were passed through the milling process to extract sap. Brix and pols were calculated from the sugarcane sap previously extracted. Sap was clarified (Octopol) and filtered for

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41 its subsequence analysis with a Saccharimeter. Brix, which is the unit that expresses the concentration of solids in aqueous solution was estimated using a refractometer (Index PTR 46x). Pols refers to the sucrose content in the stalk and is expressed as a percentage For instance, s ugar with 98 pol (sometimes expressed as 98 degrees pol) would contain approximately 98% sucrose The name derives from the machine that is used, the polarimeter. Theoretical recoverable sucrose (TRS) was calculated using the procedure described by Legendre (1992) and Birkett (1998) using th e following equation (Figure 3 9). Figure 3 9 Sucrose estimation Soil N utrients Soil samples were collected for N P K, which are considered essential minerals for plant growth and development (Havlin et al., 2005). Six soil samples per replicate per treatment where taken in August 2012 (plant cane), March 2013 and June 2013 (first ratoon). Soil samples were taken at 3 different depths (0 to 15 cm), (15 to 30 cm), and (30 to 45cm) depths at which most of functional roots for sugarcane are located (Chopart et al. 2008 and Otto et al., 2009) Total of soil samples = 3 treatments x 5 replicates x 3 samples per replicate x 2 locations per replicates= 90 soil samples per date. All the soil cores were proper ly labeled and stored in a refrigerator <4C o before being analyzed. In February 2013, samples collected in three different positions per location for soil physical characterization were used for nutrient analysis. Two of the samples were

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42 taken 10 cm from t he drip line in the center of the planted row, one of them adjacent to the emitter and the other between emitters, the third sample set was 35 cm perpendicular of the emitter between planted rows (Figure 3 2). The soil cores were taken at 2 depths (0 20 cm and 20 40 cm) at each location. In April 2014, three pits per treatment were dug to obtain a profile perpendicular to the drip line. Samples were collected in a grid pattern as explained in the soil moisture characterization methodology. Samples were tak en at five depths (0 cm, 10 cm, 20cm, 30 cm, 40 cm, 50cm, and 60 cm) vertically and six horizontal positions based on the relative distance from the emitter for each treatment (Figure 3 5). Samples were collected to give a 2D image (Sigmaplot 11.0) of nutr ient distribution in the soil profile for the different emitters spacing treatments in the drip line. Ammonium nitrogen (NH 4 N) and NO 3 N for all soil samples where extracted using 2M KCL in a 1 to 10 ratio (soil: solution). 40ml of 2M KCL was added to 5 g of wet soil collected from the field (4 cm 3 approximate ly for mineral soil) of soil in test tube. The solution was shake for 30 minutes as instructed by (Hanlon et al 1997). After settle the soil was filtered. The soil extracts were analyzed using a Flo w Analyzer (Quich Chem 8500, Lachat Co.) at 660 nm and 520 nm, NH 4 N and NO 3 N respectively. For all soil samples collected, Mehlich 1 (M 1) extraction procedure was used to determine P and K. Mehlich 1 is recommended for soils with low organic matter (Me hlich, 1953 ; Ming Huang, 2012). Samples were dried and 5 g of dry soil was shaken with M 1 solution for 5 minutes. The extract was filtered with Whatman filter paper #42. Phosphorus and potassium concentrations were measured by an Inductively Coupled

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43 Plasm a (ICP) (Perkin Elmer Co.) using blank and standards for P and K at 2, 6, and 12 ppm) for calibration. Statistics In general the design of the experiment could be expected to be a block design with pseudo replication with 5 replications for each treatment. The experiment layout was conceded according to the best/easier practical irrigation system design in terms of cost and operation. The reason for the pseudo replication is due to the zoned irrigation application. Samples were taken only in the Malabar ser ies soil to reduce variability. Malabar series comprised more than 64% of the total study area. Analysis of variance (ANOVA) with a generalized linear mixed model procedure was used (Proc Glimix ) SAS 9.3. All treatment effect is significant at p<0.05. The pairwise comparison used was Tukey Kramer method. As for the different studies sampling methodology in the same project, diverse experimental design approaches where used. The design used were; complete randomized block design (CRBD), block design with pseudo replication, and an observational study approach due to low randomization. In the observational study approach inferences can be draw n using multivariate stats. Graphical representation with error bars differences were made.

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44 CHAPTER 4 RESULTS AND DISCUSSION Soil Physical, Hydraulic Characterization Soil is a system of three interrelated and interdependent phases (Hillel, 1998) which are: soil matrix, solution, and atmosphere. Those phases are measured by basic parameters such as volume, and mass to physically characterize it. In this study those parameters were measured to generate useful information in understanding soil/water relations. Saturated hydraulic conductivity for the site (Mala bar fine sand) ranged from 8.15 to 11.21 cm h 1 with no statistical differences among the three depths (p<0.05). These data agreed with the average sandy soil hydraulic conductivities presented by Hillel (1998). Results indicate high hydraulic conductivity thus fast water drainage. The Argillic horizon is usually found below the 20 cm depth (Collins, 1997). There were no statistical differences between depths suggesting no water restrictive layer in the top 60 cm and if an Argillic horizon exists that woul d restrict water movement. Field capacity as suggested by Obreza et al. (1997) is at 10 kPa, volumetric water content at this pressure ranged from 0.13 to 0.09 cm 3 cm 3 The available water capacity (AWC) was previously calculated to be 0.06 cm 3 cm 3 for t he Malabar fine sand by NRCS (2012). to 0.03 cm 3 cm 3 for all depths. The bulk density ( Table 4 1) values ranged from 1530 to 1630 kg m 3 of soil. No statistical textural differences were found within the sampled profile to a depth of 60 cm (Table 4 2). The predominant fraction in the soil sampled was sand, with values from 91% to 94%. Textur al class for the soil is Sand.

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45 High hydraulic conductivity values and low AWC point out the importance of proper water management, to avoid water misuse and potential nutrient leaching below the root zone (Evans et al., 2007). The evaluation of those physi cal properties yield valuable site specific data to describe water movement and aid decision making for better irrigation management based on objective environmental characteristics.

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46 Figure 4 1 Water release curve for Malabar fine sand at three different depths

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47 Table 4 1 Soil physical characterization for Malabar fine sand Order Series Depth (cm) *k sat (cm h 1 ) (g cm 3 ) sat (cm 3 cm 3 ) §10kPa (cm 3 cm 3 ) 100kPa (cm 3 cm 3 ) +AWC (cm 3 cm 3 ) Alfisol Malabar 0 15cm 11.21a 1.53a 0.31 0.09 0.03 0.06 Alfisol Malabar 15 30cm 8.15a 1.62a 0.32 0.13 0.07 0.06 Alfisol Malabar 30 45cm 10.75a 1.63a 0.37 0.10 0.04 0.06 Tukey Kramer n=9 Ksat Saturated hydraulic conductivity Bulk density Saturated moisture content §10kPa Volumetric water content at 10 kPa 100kPa Volumetric water content at 100Kpa .Calculated from NRCS AWC values +AWC Available water content. Obtained from NRCS soil survey 2014 Table 4 2 Soil Texture for each de pth Depth Sand (%) Silt (%) Clay (%) Textural Class 0 93.671.76 1.000.86 5.331.16 Sand 15 93.640.75 2.470.71 3.890.19 Sand 30 93.521.53 1.751.04 4.740.95 Sand 45 93.750.86 1.500.91 4.750.50 Sand 60 91.523.34 2.501.30 5.992.72 Sand one standard deviation n=4

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48 Moisture Characterization for the Soil Profile and Sugarc ane Water Use Moisture C haracterization The approach used assumes the SDI system as a two dimensional planar model. The Figure 4 2 shows measured soil volum etric water content distribution in a vertical plane between three emitters for each treatment in a Malabar fine sand profile. Note that in Figure 4 2, the emitter location is indicated for each 2 D image by a star. For treatment 1 (emitter spacing of 12 i nches), the water was uniformly distributed across each different depth, with decreasing water content between emitters as the spacing enough to distribute the water uniforml y between emitters for treatment 3 (emitter spacing=24 inches) for the given flow of 199 Lpm ha 1 Therefore, the dry area in treatment 3 was greater compared to treatments 1 and 2 (Table 3 4). Measurements taken at the 60 cm soil depth showed that samples had additional water content compared to the 45 cm depth. This finding was due to its proximity to the water table, and capillary rise brought the water to 60 cm. Water table level was difficult to control for the large commercial farm site. Differ ent dynamics can be observed when comparing the different spacing treatments (Figure 4 2). The water flows per emitter were 0.78, 0.79, and 1.11, L h 1 for treatments 1, 2, and 3, respectively. Table 4 3 shows the differences among treatments through the f ive soil depths. Otto et al. (2009) found more than 50% of the root system is in the top 50 cm of the soil and 59% of the RLD in our study was found in the first 15 cm of the 30 sampled cm (Table 4 6). No statistical differences in water content were found among treatments in the top 15 cm (Table 4 3). At 30 cm depth, water content for treatment 1 was similar to treatment 2

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49 and both were 7.5% higher than treatment 3. All treatments showed significant differences in water content at the 45 cm depth. In Figu re 4 3, the pattern shows a visible drying slope for treatment 3 towards the far side from the emitter, this slope is less visible for treatments 1 and 2. This graph illustrates moisture dynamics for a non exposed profile (Figure 4 3). Pattern differ from Figure 4 2 to Figure 4 3 due to different sampling procedure: (fewer sampling points, only 4 points per 2D figure with n=10 for each point), and rain of 96.26mm was recorded in the 5 days previous to the sample collection day (9/19/2013). Nevertheless bot h figures (Figure 4 2 and Figure 4 3) visually agree that the 24 inch emitter spacing (treatment 3) promoted a less uniform water distribution in the soil profile. It is important to remember that the movement of solute through this experiment was in unsa turated conditions and is the reason why the speed of water movement might differ from the saturated hydraulic conductivity measurements in Table 4 1. Leaf M easurements Significant differences among treatments were found for leaf area (LA) and leaf area index (LAI) at the grand growth stage in June 2013 (first ratoon) and at maturity in October 2013 (Table 4 3). For June 2013, LA and LAI were greater (p<0.05) for treatmen t 1 than for treatments 2 and 3, which had similar values. Fertiigation applied treatment 1 for first ratoon. Again observations in October 2013 revealed that LAI for treatm ents 1, was significantly higher than treatment 3 by 73. %. No significant differences were found in LAI between treatment 1 and 2. LA in October 2013 for treatment 2 was greater than treatment 3 but treatment 1 was similar to both treatments 2 and 3.

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50 Over all mean LAI and LA increased from July to October by 45.7% and 42.9%, respectively. Sandhu et al ( 2012) found similar results with LAI increasing with time during the current season and also that LAI decreases with each yield season, i.e. plant cane> fi rst ratoon> second ratoon. Also the results indicated that LA and LAI were influenced by the spacing to water stress as one of the main reason s Additionally, nutrient uptake is further impaired by dry soi ls (Marschner, 2012), thus lower biomass growth such as leaf area is expected using the selected treatment conditions. Daily Water U se The mean ETo for October data was 3.31 0.96 mm day 1 which was not statistically different than the mean ETo for June July (3.05 0.81 mm day 1 ) (Table 4 4 and Table 4 6). Data agree with other crops indicating ETo is affected only by climatic parameters (Zotarelli et al., 2010). The temperature was 9% higher for June July than for October (Table 4 5). In October no rain w as recorded at all, contrasting with 26 mm of rain recorded June July, and in both cases irrigation was supplied as needed. Table 4 4 indicates that higher LAI is found in October, but transpiration is statistically the same due to less accelerate d growth compare d with June July (grand growth stage). Data during the grand growth stage indicates that overall reduced water availability in treatment 3 resulted in reduced water uptake and LAI, ultimately resulting in lower potential biomass and sugar yields (T able 4 4). Evapotranspiration measured for 35 days in two periods of time, 19 days during the grand growth stage (June 21 to July 9) showed a lower transpiration for treatment 3 (p<0.05) (Figure 4 6). Mean water use for treatments 1 and 2 was 54% and 28% higher

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51 than treatment 3. The increase in water uptake can be attributed to water distribution by the irrigation system (Figure 4 2) T reatments 1 and 2 showed a more uniform volumetric water content distribution after the irrigation event. The water use fo r June July data ranged from 1.24 mm day 1 to 5.75 mm day 1 Statistical differences were found for days of the year (DOYs) 175, 178, 180, 181, 182, 183, 186, and, 188 (Figure 4 Sep tember (Omary and Izuno, 1995). Inman Bamber and McGlinchey (2003) suggest Kc values of 0.4 during the initial stage, 1.25 in mid stage and 0.7 for drying off phase. As shown in Table 4 4, there were differences for estimated Kc among all treatments for th e June July data collection. Treatment 1 mean suggests a Kc 8.0% higher than published Kc using the FAO 56 calculated reference ET (ETo), treatment 2 Kc indicated a value 8% lower than the published Kc, and treatment 3 showed a 29% lower Kc than suggested by FAO 56(Table 4 4). Thus, the Kc estimated for treatment 1 and treatment 2 would be close to the suggested mid stage value (1.25) by McGlinchey (2003). Lower water use and resulting Kc values for treatment 3 indicate water stress. Data from October show ed similar water use between treatment means (p<0.05) with values ranging from 1.95 mm day 1 to 5.5 mm day 1 (Figure 4 6). No significant differences were found for any DOY among treatments (Figure 4 4). Estimated Etc for October did not show statistical d ifferences among treatments (Table 4 4), with means in the decreasing order of treatment 1 > treatment 2 > treatment 3 (Table 4 4). Overall mean Kc was 1.16 for October showing to be 7% smaller than FAO 56 estimated Kc. In the cumulative Etc (Fig 4 5), we can see that for both dates, treatment 1 and 2 are located in the uppermost part of the Y axis in the graph.

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52 Hourly Water U se Using normalized LAI according to the procedure by Lascano et al. (1992) hourly water use for June July ranged from non water us e to 0.41 mm hour 1 An increase in transpiration rate may enhance translocation and uptake of nutrient (Marschner, 2012). Results clearly revealed a diurnal water uptake for all treatments with peaks between 1300 1500 HR. (Figure 4 7). There were no diffe rences between treatments (Figure 4 7). High variability between hours was due to variation in transpiration among days for both dates. In October hourly water use showed similarities between treatments w ith the water use peak situated in 1400 1500 HR. M ax average water use per hour measurements were 39%, 51%, and 15% higher for treatment 1, 2, and 3 in October, compared to the respective treatment for June July. Higher October readings can be explained by larger LA (Table 4 4). The ETo was lower for October (Table 4 4) but greater crop development compensates the ETo change. The shape of the graph for both dates (Figure 4 7) differ s according to the time in the year that the data was collected. Longer periods of daylight (longer days) in June explain the more extended transpiration range throughout the day. June transpiration was recorded from 5000HR to 21000 HR compared with October (10000 21000HR) (Figure 4 7). In October transpiration peaked higher values (STDV) than in June (Figure 4 7). Higher peak transpiration values in October are caused by the lower rain thus clear sky as well as higher solar radiation compared with June (Table 4 5).

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53 Figure 4 2 Profile volumetric water content distribution fo r the different emitter spacing for a 0 60 cm soil depth Color scales represent volumetric water content (cm 3 cm 3 ). Emitter location illustrated by a star Positions refer to Table 3 4

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54 Figure 4 3 Profile volumetric water content distribution fo r the different emitter spacing for a 0 30 cm soil depth. Two positions and 2 depths Color scales represent volumetric water content (cm 3 cm 3 ). Emitter location illustrated by a star

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55 Table 4 3 Soil volumetric water content depth vs. treatment Treatment 0 cm depth 15 cm depth 30 cm depth 45 cm depth 60 cm depth __ ______________ _________________ cm 3 cm 3 ________________________________________ 1 0.15a 0.18a 0.14a 0.06a 0.10a 2 0.15a 0.15a 0.11ab 0.0b 0.08a 3 0.10a 0.16a 0.08b 0.02b 0.08a SE 0.01 0.01 0.01 0.01 0.01 p values ns ns *** ns Tukey Kramer test n=225 ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Table 4 4 Leaf area, leaf area index measurements, water use means, ETo, and estimated Kc Mo./Yr Trt Leaf area (m 2 ) Leaf area Index Water Use (mm day 1 ) Estimated Kc ETo (mm day 1 ) Jun. 2013 1 0.270.04a 2.22 0.31a 4.191.03a 1.37 3.050.81 2 0.190.03b 1.52 0.25b 3.480.9a 1.14 3 0.180.04b 1.49 0.34b 2.711.0b 0.88 mean 0.210.05 1.770.48 3.470.98 1.14 Coeff. variance 24.46 17.27 31.08 Oct. 2013 1 0.290.09ab 2.950.79a 3.970.83a 1.19 3.310.96 2 0.380.07a 3.030.58a 3.790.93a 1.14 3 0.210.08b 1.750.64b 3.720.63a 1.12 mean 0.300.09 2.580.864 3.310.13 1.16 Coeff. variance 17.34 26.26 44.02 Tukey Kramer test n=12 one standard deviation Treatment 1 (31cm spacing), treatment 2 (46cm spacing), and treatment 3 (61 cm)

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56 Table 4 5 Weather summary table Mo./Yr §T max (C o ) o ) *T mean (C o ) Wind speed (m s 1 ) Rain (mm) Solar radiation (Mj m 2 ) RH (%) Jun Jul 2013 36.1 21.3 0.70.9 26.23.1 19.8030.8 8911 Oct. 2013 34.6 16.9 24.04.2 0.50.8 0 14.421.9 8515 §T max=maximum temperature *T mean=average temperature

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57 Figure 4 4 Sap flow and ETo Error bars equal to one standard deviation

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58 Figure 4 5 Cumulative s ap flow and cumulative ETo Error bars equal to one cumulative standard deviation

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59 Figure 4 6 Water use mean for treatments for both growth stages Tukey Kramer test (p<0.05). Error bars as one standard deviation (n=30)

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60 Figure 4 7 Hourly water u se Error bars as one standard deviation (n=30)

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61 Table 4 6 ANOVA sap flow data Sou rce DF Pr > F LA (m 2 ) Pr > F LAI Pr > F Water use (mm day 1 ) Trt 2 ** ** *** DOY 36 N/A N/A *** Season 1 ** ** ns Season*Trt 2 ns ns ** DOY*Trt 70 N/A N/A ns ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 N/A not apply

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62 Root Distribution Several RLD measurements methods have been described, but their implementation in the field are often costly and generate data that are not always representative of field conditions (Chopart, 2008). Root length densities (RLD) measured using the line intersection (LI) method developed by Tennant (1975) is the standard RLD measurement method and correlated well with the scanning area method (Figure 4 8 ). Agreement between the new scanning method and line intersection method shows the precision of using a scanning method previously calibrated with the line intersection method. Method accuracy for all diameters of root was r 2 adj >0.94 There was also close agreement in the validation set between RLD estimated using the line intersection method and the RLD calculated by the calibrated scanning method (Figure 4 9). Correlation showed for the validation set: r 2 adj > 0.92 for the root diameter<0.5 mm, r 2 adj > 0.95 for diameter 0.5 3.0 mm and for roots with diameter >3.0 mm the r 2 adj .> 0.97. Adjusted coefficients of determinations are even higher than values obtained in other similar studies (Kadyampakeni et al., 2013). The scanning method is a viable alt ernative that reduces time required to measure RLD compare d with the line intersection method O ther researchers had re commend ed reprography for RLD determination as a reliable method that could accomplish accurate results in a short time(Collins et al., 1 987, and Kirchoff, 1992). Additionally, the required software ( image j, http://rsb.info.nih.gov/ij/ ) is a free use software There were no differences in RLD among treatments for any of the root diameters (Table 4 7 and Table 4 10). The addition of fertigation to only treatment 3 early in the plant cane stages could have influenced the high variability in the root distribution. More than 95% of the roots for all treatments were >0.5 mm. Uptake of

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63 water by roots i s driven by difference in water potential between soil and plant. Smaller diameter roots have higher capacity for water uptake than larger roots at the same potential gradient (Smith, 2005). Shoot roots growth rates of 40 mm day 1 had been reported (Smith, 2005). Likewise, root distribution and resistance to water stress is directly related to the sugarcane variety (Subiros, 1995). High similarity in RLD across treatments might be due the sugarcane planting method. The buds of the sugarcane are randomly dis tributed in the planting row with an average density of 11.70.7 stalk pieces per m of row, which enhances uniform root distribution. Increased RLD in the top soil was caused by more favorable physical, chemical, and biological conditions compared to deep er soil depths (Lynch, 2012). According to Blackburn (1984), 50 % of the sugarcane root biomass is found in the top 20 cm. RLD values as high as 0.6 cm cm 3 were found in the top 15 cm of the soil. Data from this study agreed with the statement above, show ing higher RLD values in the top 15cm of soil compare d with 15 30 cm (Table 4 8) accounting for 69% of the root length in the 0 15 cm soil layer. Comparatively the <0.5 mm diameter roots and the 0.5 3.0 mm diameter roots at 0 15 cm depth were 153% and 190% higher than the same root diameters at the 15 30 cm depth. RLD for root diameters 0.5 3 mm (Table 4 9) were 73% lower than RLD values collected in the row, However, RLD between emitters and adjacent to the emitters were similar. Evans (1964) suggested tha t highly branched superficial roots improve drought tolerance, so the similarities of small diameter root densities between in row positions are evidence that the plant compensates for differences in moisture distribution (Figure 4 2).

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64 Root distribution f or sugarcane is not a common topic of study. More and accurate information for different cultivars are needed. Likewise, root distribution understanding is important for better crop management. Metabolic cost and relevance of the root system is substantial As an example Lynch (2012) reported that depending on environmental conditions and plant species from 15% to 50% of the daily photosynthesis is allocated to roots for growth and soil exploration.

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65 Figure 4 8 Regression RLD vs. s canned area n=30

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66 Figure 4 9 Regression RLD LI vs. RLD s canning method LI= Line intersection method n=30

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67 Table 4 7 Soil volumetric water content depth vs treatment Treatment RLD for root diameter <0.5 mm RLD for root diameter 0.5 3.0 mm RLD for root diameter >3.0 mm _____________________cm cm 3_____________________ 1 0.84 0.03 0.004 2 0.76 0.03 0.005 3 0.68 0.02 0.005 Pr>/t/ ns ns ns Tukey Kramer test n=180 RLD Root length density ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Table 4 8 Soil volumetric water content depth vs treatment Soil Depth (cm) RLD for root diameter <0.5 mm RLD for root diameter 0.5 3.0 mm RLD for root diameter >3.0 mm _____________________cm cm 3_____________________ 0 15 0.109 0.037 0.0054 15 30 0.043 0.018 0.0049 Pr>/t/ *** *** ns Tukey Kramer test n=180 RLD Root length density ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Table 4 9 Soil volumetric water content depth vs treatment Position RLD for root diameter <0.5 mm RLD for root diameter 0.5 3.0 mm RLD for root diameter >3.0 mm _____________________cm cm 3_____________________ M 0.072 0.019b 0.0053 B 0.080 0.033a 0.0052 § E 0.077 0.029ab 0.005 Pr>/t/ ns ns Tukey Kramer test n=180 RLD Root length density Middle of the row Between Emitters §E In front of the Emitter ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001

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68 Table 4 10 ANOVA RLD Source Num DF Den Df Pr>F (0 0.5mm) Pr>F (0.5 3mm) Pr>F >3mm) Subsample 1 148 ns ns ns Position 2 148 ns ns ns Subsample*position 2 148 ns ns ns Depth 1 148 *** ** ns Sub sample*Depth 1 148 ns ns ns Position*Depth 2 148 ns ns ns SubsamPositio*Depth 2 148 ns ns ns Trt 2 148 ns ns ns Trt*subsample 2 148 ns ns ns Trt*Position 4 148 ns ns ns Trt*Subsam*Position 4 148 ns ns ns Trt*Depth 2 148 ns ns ns Trt*Subsamp*Depth 2 148 ns ns ns Trt* Positio*Depth 4 148 ns ns ns ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001

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69 Nutrient Plant growth is determined by various factors such as light, CO 2 water and nutrients. Providing enough supply of all this factors is important to obtain optimal yields. In the case of nutrients, the application has to be precise in time and amount. Yield curves are strongly modulated by the interaction of nutrients and other growth factors (Marschner, 2012).A consistent soil testing program is a valuable best management practice (BMP) that allows sugarcane growers to make appropriate fertilization decisions. However, soil testing in Florida has a limitation, soil samples are routinely taken only before sugarcane is planted and rarely are soil samples collected be coupled with tissue analysis. Tissue analysis has been used intensively by a limi ted number of Florida sugarcane growers and has the potential for an expanded role in growers' fertility programs. Although is important to know that tissue analysis only reflects what happens in a particular moment. Therefore the proper interpretation of the tissue analysis upon the time the samples are collected is a corner stone in nutrient management. Tissue A nalysis Leaf collection was carried out in the plant cane grand growth period (June 2012) and the second one 8 weeks later. Data showed a 23% hig her concentration in June for K in treatment 1 compared to treatment 2; treatment 3 was similar to both of the other treatments. For P and N, no differences were identified among treatments for June 2012 (Figure 4 10). According to McCray (2011), sugarcan e sufficiency leaf nutrient ranges, were between deficient and high for June samplings (Table 4 11). Nitrogen was the element

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70 with lowest concentration. Nitrogen is still the most limiting nutrient to crop production worldwide (Marschner, 2012). This limit ation is especially a problem in Florida soils w h ere soil testing is not that useful for sandy soils. Recommendations for sand soils for nitrogen is of 202 Kg N ha 1 (Erickson, 2012, and Rice. et. al, 2010). Samples collected in August showed an overall d ecrease in nutrient concentrations for N, P, and K compared to the previous date (Table 4 11) with differences between dates (Table 4 15). No differences were shown in the data among treatments (Table 4 15). In August, nutrient status ranged from deficient to marginal according to McCray (2011). Results suggest that the best date to collect tissue samples may be close to June, before the plant growth slows and relocate s the nutrients away from leaf tissue. To confirm the statement more data must be collecte d in future research. For the plant cane yield the potassium concentration in debris was significantly higher for treatment 3 than treatment 1, no differences were found between treatment 2 and 3 (Table 4 12). No statistical differences for P concentration s were found. The sucrose content per cane was lower in treatment 3 (Table 4 19), which suggests a slower maturation process. For the plant cane season, fertilizer was applied by mistake only in treatment 3, which can affect the K nutrients concentration i n the biomass of the plant. There is evidence that excessive K concentrations can lead to slower sucrose formation (Subiros, 2000) In the first ratoon, no statistical difference for K and P concentrations in the debris were found Average nutrients concent statistically decrease d between the first ratoon and the plant cane (Table 4 16). This indicates that

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71 the amount of P per hectare that is removed in harvest is lower in the first ratoon. These d ata provide growers with objective information to take in account the amount of P and K that is removed from the field by debris and cane crush ed in each harvest cycle. The data explained above may lead to bias ed conclusions due to the uncertainty created by the mistaken application of fertigation for treatment 3. And more differences extra fertilization. Soil P rof ile P and K D istribution Phosphorus distribution across the profile showed d ifferences among treatments in the 0 15 cm depth. Data collected in 2013 indicates treatment 2 as having the highest P concentration. Mean values were more than 30 mg Kg 1 for all treatments. UF/IFAS suggest that above 30 mg Kg soil 1 M 1 as an optimum va lue for P (Sartain, 2012 and Mylavarapu, 2011). At the 15 30 cm depth, no differences were found between treatments. But for the 30 45 cm depth, treatments 2 and 3 had a higher concentration than did treatment 1. At the 45 60 cm depth, treatment 3 had a P concentration 180% higher than treatment 1. The reason why the P is higher in the profile for treatment 3 than in treatment 1 can be attributed to the higher water flow from the emitters. The nutrient (P and K) distribution finding shows that wider emitter spacing can enhance P leaching when applying the required amount of water through fewer emitters. UF/IFAS recommends an optimum range of K extracted with M 1 to be between 36 to 60 mg kg 1 (Morgan et al 2012). The K concentrations (Figure 4 12) indicate that the only treatment within the optimum range in the top layer was treatment 2. The optimum range of K extracted with M 1 is K>60 mg kg 1 (Morgan et al 2012). In the 45 60 cm depth, K data agree (Table 4 13) in pattern with the P data (Table 4 12), where

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72 the wider spacing treatment (treatment 3) has a higher nutrient concentration. Again the leaching of the nutrient is evidenced by the data from the wider spacing. There is a lower overall P concentration in the profile for treatment 3 suggesti ng leaching loss (Figure 4 12). Data for both nutrients P and K show higher concentrations of the nutrient below the 45 cm depth for treatment 3 than for the other treatments. Those results are a finding that indicates higher leaching potential for both n utrients with the larger emitter spacing. The fact that leaching is higher indicates lower nutrient uptake efficiency by the crop and higher ground water pollution risk.

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73 Figure 4 10 Tissue concentration Tukey Kramer test n=30 Different letters indicate (p<0.05)

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74 Table 4 11 Tissue N, P, and K concentration. Date Treatment P% mean *Status § K% mean Status N% mean Status 6/5/2012 1 0.22 Sufficient 1.13 High 1.52 Deficient 2 0.19 Marginal 0.92 Marginal 1.73 Deficient 3 0.22 Sufficient 1.08 High 1.83 Marginal p value ns ns 7/30/2012 1 0.16 Deficient 0.82 Deficient 1.57 Deficient 2 0.17 Deficient 0.94 Marginal 1.45 Deficient 3 0.18 Deficient 0.94 Marginal 1.7 Deficient p value ns ns ns Tukey Kramer test n=30 Status Leaf concentration status (McCray, 2011) P% mean Phosphorus tissue concentration mean § K% mean Potassium tissue concentration mean N% mean Nitrogen tissue concentration mean ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Table 4 12 Soil phosphorus concentration profile 0 60cm Treatment n 0 15 cm depth S.E 15 30 cm depth S.E 30 45 cm depth S.E 45 60 cm depth S.E _______ ______________________ P (mg Kg 1 )______________________________ ___ 1 18 76.3b 6.9 21.8a 6.9 7.3b 2.3 5.1b 0.01 2 18 134.7a 6.9 32.2a 6.9 17.5a 2.3 8.8ab 0.01 3 18 30.7c 6.9 14.2a 6.9 11.1ab 2.3 14.7a 0.01 p values n s *** Tukey Kramer test n=225 ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001

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75 Table 4 13 Soil potassium concentration profile 0 60 cm depth Treatment n 0 15 cm depth S.E 15 30 cm depth S.E 30 45 cm depth S.E 45 60 cm depth S.E ____________________________________ K (mg Kg 1 )___________________________________ 1 18 16.2 b 3.3 3.7 b 0.96 3.2 a 2.7 2.6 b 0.92 2 18 39.9a 3.3 6.1 ab 0.96 4.6 a 2.7 0.1 b 0.92 3 18 24.9 b 3.3 9.2 a 0.96 7.1 a 2.7 7.9a 0.92 p values ** ** *** Tukey Kramer test n=225 ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Table 4 14 Overall yield data for each by treatment Season Treatment 1 ) K in Debris (g ha 1 ) §P in Stalks (Kg ha 1 ) 1 ) Plant Cane 1 965 7325b 24.5 87.1 2 746 6124b 22.4 87.9 3 1255 12147a 20.3 83.2 p value ns ** ns ns First Ratoon 1 684 4496 17.9 50.0 2 900 6668 16.3 47.0 3 1526 10271 14.7 42.1 p value ns ns ns ns Tukey Kramer test n=60 ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001

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76 Figure 4 11 Profile P distribution for the different emitter spacing for a 0 45 cm soil depth Color scales represent nutrient concentration (mg Kg soil 1 ). Emitter location illustrated by a star Positions refer to Table 3 4

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77 Figure 4 12 Profile K distribution for the different emitter spacing for a 0 45 cm soil depth Color scales represent nutrient concentration (mg Kg soil 1 ). Emitter location illustrated by a star Positions refer to Table 3 4

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78 Table 4 15 ANOVA t issue concentration Source DF P % K % N % Trt 2 ns ns Rep 4 ns ns ns Season 1 ns ** *** Season*Trt 2 ns ns ns Season*Subsample 1 ns ns ns Season*Subsam*Trt 2 ns ns ns Subsampl*Trt*Rep 12 ns ns ns Subsample 1 ns ns ns Subsample*Trt 2 ns ns ns Trt*Rep 8 ns ns ns ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Table 4 16 ANOVA y ield nutrient concentration Source DF P debri (g ha 1 ) K debri (g ha 1 ) P stalks (kg ha 1 ) K stalks (kg ha 1 ) Trt 2 ns ns ns Rep 4 ns ns ns ns Season 1 ns ns ** ** Season*Trt 2 ns ns ns ns Season*Subsample 1 ns ns ns ns Season*Subsam* Trt 2 ns ns ns ns Subsampl*Trt*Rep 12 ns ns ns ns Subsample 1 ns ns ns ns Subsample*Trt 2 ns ns ns ns Trt*Rep 8 ns ns ns ns ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Table 4 17 ANOVA p rofile nutrient concentration Source DF Pr>F K (mg kg 1 ) Pr>F P (mg kg 1 ) Position 2 ns ns Depth 1 *** ** Trt 2 * Position*Depth 2 ns ns Trt*Position 4 ns ns Trt*Depth 2 ns ns Trt*Positio*Depth 4 ns ns ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001

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79 Sugarcane P roductivity Sugar field variability may be observed as much as 25% (Hanlon et al., 2005). For the plant cane (first time harvest crop) differences were found in cane yield, and sugar content per cane (Figure 4 14) (Table 4 21). First ratoon fresh biomass weight for treatment 2 was greater than treatment 1 by 4.9% and tr eatment 3 by 7.9%. There were no differences between treatment 1 and 3. Treatment 2 fresh cane biomass was consistently higher compare d with other treatments for both yield seasons (Table 4 21). Fresh yield biomass decreased considerably between yield seas ons for all treatments (Table 4 19 and Table 4 21). For treatment 1 cane biomass decreased 31%, for treatment 2 the cane biomass f e ll 25% and for treatment 3 was 24% (Table 4 20). The overall cane biomass average yield for the first ratoon was of 65 Mg ha 1 (29 short tons acre 1 ). Fresh cane biomass ranged from 51.5 to 121.9 Mg ha 1 for plant cane. Florida reported an average of 80.47 Mg ha 1 in 2013 (NASS USDA, 2013). Yield was close to commercial averages, even though tissue concentration appears to be lo w in August (Table 4 11). Treatment 1 fresh cane biomass was 14% higher than treatment 3, Treatment 2 was not significantly different than treatments 1 and 3 (Table 4 20). Sugar content in the form of TRS (total recovery sucrose) (Legendre 1992) per can e was higher for treatment 2 than for treatments 1 and 3 for plant cane yield. There is evidence that sucrose may feedback negatively on photosynthesis (Inman Bamber et al., 2011, and McCormick et al., 2009). The sucrose negative effect in photosynthetic p rocesses may explain why the treatment 2 had a lower weight per stalk than treatment 1 and 3 but higher sugar content (Table 4 19). Results indicate that stalk weight for

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80 treatment 2 was 9.8% and 8.6% lighter than treatments 1 and 3, respectively. Water de ficit stress modifies a diversity of physiological processes, such as stomata conductance, transpiration rate, leaf temperature, respiration, photochemical electron transport, and photosynthesis (Zhao, 2013). These physiological processes are directly or i ndirectly associated with crop growth and yields (Silva et al., 2007). Sugarcane physiological and morphological traits responsible for improved yield, sucrose content, and resource use are still poorly understo od (Inman Banmber et al., 2005). E ven though the study points a clear proportional correlation between the measured transpiration for the crop and the fresh cane biomass for the plant cane (Figure 4 12) where treatment 1 had the highest transpiration, as well the highest fresh cane biomass productio n. The juice per stalk (kg stalk 1 ) and Pols was similar for all treatments (Table 4 19). The mistaken fertigation application in plant cane for treatment 3 is a factor that may explain the similarities between treatments. Sugarcane production results are typically higher in sugar yields for the plant cane crop with declining sugar yields in the consecutive ratoon crops (Muchovej and Newman, 2004). Average fresh weight biomass was 35% higher in the plant cane than the first ratoon (Table 4 21). Thus, the sa me declining trend for all the dependent variables resulted in a significant decrease in productivity between the plant cane and first ratoon (Table 4 18).

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81 Figure 4 13 Yield data for plant cane Error bars stand for Standard Error Tukey Kramer test (p<0 .05) n=30

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82 Figure 4 14 Yield data for f irst ratoon Error bars stand for Standard Error Tukey Kramer test (p<0.05) n=30

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83 Table 4 18 Analysis of variance for overall yield data between yield seasons Harvest Season Juice (kg stalk 1 ) Pols (%) Fresh weight stalk (kg stalk 1 ) Fresh cane biomass (Mg ha 1 ) Sugar per area (Mg ha 1 ) Sugar per cane (kg Mg cane 1 ) Trt1 Trt2 Trt3 Trt1 Trt2 Trt3 Trt1 Trt2 Trt3 Trt1 Trt2 Trt3 Trt1 Trt2 Trt3 Trt1 Trt2 Trt3 Plant cane 0.46 a 0.42a 0.50a 83.6a 86.4a 83.2a 1.2a 1.0a 1.2a 95.9a 85.8a 86.5a 13.6a 12.8a 12.1a 142.9a 145.7a 142.2a First Ratoon 0.27b 0.30b 0.26b 72.6b 73.8b 75.2b 0.9b 0.9a 0.8b 70.1b 66.5b 59.0b 8.3b 7.9b 7.4b 117.3b 120.0b 124.6b S.E 0.03 0.02 0.03 0.9 1.4 1.3 0.05 0.06 0.07 5.5 4.8 4.5 1.5 1.5 1.5 6.1 5.8 7.5 P> value *** *** *** *** *** *** *** ns ** *** *** ** ** ** ** *** Tukey Kramer test n=60 Trt1 Treatment 1 Treatment 2 §Trt3 Treatment 3 ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Table 4 19 Analysis of variance for overall yield data for each by treatment. Tukey Kramer test n=30 ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Treatment Sugar per cane Sugar per area Cane w eight Weight per stalk Juice per stalk Pols Kg Mg cane 1 Mg ha 1 Mg ha 1 kg stalk 1 kg stalk 1 % Plant Cane 1 142.8b 10.9a 94.7a 1.18a 0.45a 84.1a 2 145.6a 10.3a 90.6ab 1.07b 0.44a 83.6a 3 142.2b 9.74a 83.0b 1.17a 0.47a 85.5a SE 0.38 0.17 3.0 0.02 0.01 1.7 p value *** ns *** ns ns

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84 Table 4 20 Yield data summary by treatment. Tukey Kramer test n=30 ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Table 4 21 ANOVA y ield data. Source DF Pr > F Sugar per area cane (Kg Mg cane 1 ) Pr > F Sugar per area (Mg ha 1 ) Pr > F Biomass (Mg ha 1 ) Pr > F Fresh weight stalk (kg stalk 1 ) Trt 2 ns ** ** Rep 4 ns ns ns ns Season 1 *** *** *** *** Season*Trt 2 ns ns ns ns Season*Subsample 1 ns ns ns ns Season*Subsam*Trt 2 ns ns ns ns Subsampl*Trt*Rep 12 ns ns ns ns Subsample 1 ns ns ns ns Subsample*Trt 2 ns ns ns ns Trt*Rep 8 ns ns ns ns ns Not significant. Significant at the p< 0.05** Significant at the p< 0.01 *** Significant at the p<0.001 Treatment Sugar per cane Sugar per area Cane weight Weight per stalk Juice per stalk Pols Kg Mg cane 1 Mg ha 1 Mg ha 1 kg stalk 1 kg stalk 1 % First Ratoon 1 117.34a 8.25a 64.7b 0.87b 0.27b 73.74a 2 120.00a 7.95a 67.9a 0.90a 0.30a 73.19a 3 124.64a 7.95a 62.9b 0.87b 0.27b 74.78a SE 6.1 1.5 0.8 0.01 0.00004 0.36 p value ns ns *** ** *** ns

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85 Figure 4 15 Pictures Harvest data collection (Source: Jose Villalobos) 1 Harvest of cane in the field 2 Milling process 3 Clarification of juice for Pols quantification

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86 CHAPTER 5 CONCLUSIONS Water is one of the most precious natural resources in the world and in a state like Florida with an abundant water resource, stringent water management seems unnecessary. However, highly variable temporal distribution of rainfall and water quality issues warrants proper water management to conserve this precious resource and to use nutrients efficiently During the 2012 and 2013 seasons, sugarcane drip irrigation was studied in Martin County, Indiantown Florida (26 o o at the Hinson Farm (Florida Crystals). The study, evaluated the effect of three different emitter spacing s on sugarcane ( CP 78 1628) productivity, nutrition accumulation, and water use. The treatments were drip irrigation at 31cm (treatment 1), 46cm (tre atment 2), and 61cm (treatment 3) between emitters. Only water was applied through the irrigation system, fertilizer was applied as dry soluble material on the ground. The physical characterization of the soil at the study site exhibited high hydraulic con ductivity, low water holding capacity, and a sandy texture. The determination of the hydraulic conductivity and water retention characteristics yielded important site specific parameters like saturated and residual moisture contents, and hydraulic conducti vity to aid decision making for future irrigation management. Soil moisture content in the soil profile for the treatment 3 indicated dry conditions between the emitters at the 61 cm spacing. Due to lower water availability, plants demonstrated lower LAI in fertilized treatment 3 than in the other treatments, which led to reduced water uptake. As a consequence, decreased transpiration resulted

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87 in a reduction in overall water use. Results clearly revealed a diurnal water uptake for all treatments peaking be tween 1300 1500 HR. During that time growers should ensure that water is available for in the root zone to increase the water use efficiency and reduce losses of water or nutrients. The Kc obtained for treatments 1 and 2 were similar to the FAO Kc recomme ndations, but the value for treatment 3 was lower indicating a smaller amount of water uptake. RLD was highly variable in the study, resulting in no remarkable differences between treatments and positions. However the RLD values in the middle of the row we re lower than near the emitter and between emitters for 0.5 3 mm diameter roots. It was found that more than 95% of the roots for all treatments were >0.5 mm in diameter, and more than 69% of the total RLD were in the first 15 cm depth of the 30cm. The lin e intersection method demonstrated a positive correlation with the scanned root area method. Thus, this study also proved that the scanning method is a viable alternative, which reduces time and money required to measure RLD in sugarcane compare d with Tenn ant method and is a worthy of consideration alternative method for growers to assess the root distribution of sugarcane. Data for both P and K showed greater concentrations below the 45 cm depth for fertilized treatment 3 compared with the other treatmen ts. These data indicate higher leaching potential for both nutrients with the larger emitter spacing. The potential for nutrient movement to the ground water is increase by the larger spacing treatment. Other finding s of the study propose that collect ion o f leaf tissue samples is best accomplished approximately in June before the plant slows growth and relocate s nutrients away from le af tissue. June data compare with August yield a better nutrient

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88 concentration parameter as reported by Coale et al 1993. The findings in the study combined with future research may provide growers with a better date range to accurately assess leaf tissue concentration. The study generated basic initial information to better understand drip irrigation dynamics for sugarcane i future research.

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96 Smajltra A., B. Boman, G. Clark, D. Haman, D. Harrison, F. Izuno, D. Pitts and F. Zazueta. 2006. Efficiencies of Florida Agricultural Irrigation Systems. UF/IFAS Extension Cooperative Servic e, Adv. Bul 247. University of Florida, Gainesville, FL. Smith, D.M., N.G. Inman Bamber, and P.J. Thorburn. 2005. Growth and function of the sugarcane root system. Field Crops Res. 92(2 3): 169 183. Spreen, T., Brown, M., Jauregui, C., 2008. Production a nd price effects of new diseases and other challenges confronting the processed orange industry. Paper presented at the Southern Agricultural Economic Association, Dallas TX. February 2008. Subiros, F. 2000. Sugarc ane Crop. (In Spanish). 467 p. UNED pres s, San Jos Costa Rica. Sun. F, 1988. Drip irrigation and sub irrigation of sugarcane. J. Irrig. and Drain. Eng., Vol.114, No. 1. Tennant, D. 1975. Test of a modified line intersect method of estimating root length. J.Ecol. 63:995 1001. Tucker, D., B. Water and Florida citrus: use, regulation, irrigation, systems and management. Chapter 1:1 8. United States Census Bureu (USCB). 2012. Information center [Online] available at http: ht tp://www.census.gov/popest/data/national/totals/2012/index.html. (Verified 22 December 2012). Wiedenfeld, B. 2004. Scheduling water application on drip irrigated sugarcane. Agric. Water Manage. 64: 169 181. Wright, A. L., and E. A. Hanlon. 2009. Measuring organic matter in everglades wetlands and the everglades agricultural area UF/IFAS Extension Cooperative Service, SL 285, University of Florida, Gainesville, FL. Wrigth, D.L, Whitty, E.B and Chambliss, C.G. 2011. Water use and irrigation manag ement of agronomic crops (FAO 56 Method). UF/IFAS Extension Cooperative Service, SS AGR 155. University of Florida, Gainesville, FL. Yadav B.S2012. Proc. India Water Week 2012, New Delhi 10 14 April 2012. AICRP on Water Management, Agricultural Research Station, Rajastha. Yu, C., and C. Zheng. 2010. HYDRUS: S oftware for flow and transport modeling in variably saturated media Ground Water 48:787 791.

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97 Zhao, D., B. Claz, and J. Comstock. 2013. Sugarcane leaf photosynthesis and growth characters during de velopment of water deficit stress Crop Sci 53: 1066 1075. Zotarelli, L., M. Dukes, C. Romero, K. Migliaccio, and Morgan. K.T, 2010. Step by step calculation of the penman monteith evapotranspiration (FAO 56 Method). UF/IFAS Extension Cooperative Servic e, AE459. University of Florida, Gainesville, FL. Zotarelli, L., L. Rens, C. Barrett, D. Cantliffe, D. Dukes, M. Clark and S. Lands. 2013. Subsurface drip irrigation for enhanced water distribution seepage hybrid syst em. UF/IFAS Extension Cooperative Service, HS1217. University of Florida, Gainesville, FL.

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98 BIOGRAPHICAL SKETCH Jos E Villalobos L, was born in La Garita, Alajuela, Costa Rica in 1991. He grew up e. Jose received a Bachelor of Science in agronomy from EARTH University in Costa Rica in 2011. He continued further studies in the Soil and Water Science Department at the University of Florida ree in the spring of 2014, Jose would like to work as a research scientist to gain more experience.