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Crop Water Requirements and Irrigation Management of Southern Highbush Blueberries

Permanent Link: http://ufdc.ufl.edu/UFE0021504/00001

Material Information

Title: Crop Water Requirements and Irrigation Management of Southern Highbush Blueberries
Physical Description: 1 online resource (84 p.)
Language: english
Creator: Dourte, Daniel R
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: balance, blueberries, coefficient, crop, irrigation, lysimetry, management, water
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
Genre: Agricultural and Biological Engineering thesis, M.E.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Climate-neutral measures of crop water use for mature blueberry plantings could offer improved irrigation management by growers, reducing irrigation diversions. In contribution to improved irrigation management practices for Florida blueberry growers, our research presents crop coefficients for mature southern highbush blueberry plants. Measures of crop water requirements were made using a water balance enabled by drainage lysimeters. Grower-controlled irrigation management was compared with a researcher-managed (UF), timer-controlled schedule. Grower?s event lengths and frequency were determined from experience and visual observations of field conditions; daily events in the UF plot were of length determined from the water balance results of the previous week. The UF plot on a commercial blueberry farm was irrigated independently of the grower?s fields, enabling comparison of crop evapotranspiration, fruit yield, and plant size between grower-controlled and researcher-controlled irrigation management. Four lysimeters in the quarter acre researcher-managed plot and four lysimeters in a six acre plot of the grower?s field were used to measure crop water use. Initiation of a second water balance experiment measuring crop water use of young southern highbush blueberry plants was completed. Three irrigation treatments are compared on plants grown in two soils: pine bark mulch and a soil and mulch incorporation. Mean water balance duration was 11 days; lysimeter water withdrawal was terminated at a chosen minimum flow rate. Irrigation inputs were measured by flowmeters, application rate and uniformity tests enabling accurate depth conversion. Precipitation inputs were measured by an onsite weather station that is also used to generate reference evapotranspiration using the American Society of Civil Engineers standardized reference evapotranspiration equation with daily time step and short reference surface. Crop coefficients for mature plants ranged from 0.67 (May 2006) to 1.05 (August 2006) with a mean of 0.84; crop coefficients responded to plant development, but monthly crop coefficients were not significantly different (alpha = 0.05) from each other. Crop coefficient values were higher in this experiment (average of 1.69) than were measured with the mature plants due to increased evaporation from exposed soil and mulch surfaces from limited canopy cover and because of Kc calculation based only on irrigated areas.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Daniel R Dourte.
Thesis: Thesis (M.E.)--University of Florida, 2007.
Local: Adviser: Haman, Dorota Z.
Local: Co-adviser: Williamson, Jeffrey G.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021504:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021504/00001

Material Information

Title: Crop Water Requirements and Irrigation Management of Southern Highbush Blueberries
Physical Description: 1 online resource (84 p.)
Language: english
Creator: Dourte, Daniel R
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: balance, blueberries, coefficient, crop, irrigation, lysimetry, management, water
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
Genre: Agricultural and Biological Engineering thesis, M.E.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Climate-neutral measures of crop water use for mature blueberry plantings could offer improved irrigation management by growers, reducing irrigation diversions. In contribution to improved irrigation management practices for Florida blueberry growers, our research presents crop coefficients for mature southern highbush blueberry plants. Measures of crop water requirements were made using a water balance enabled by drainage lysimeters. Grower-controlled irrigation management was compared with a researcher-managed (UF), timer-controlled schedule. Grower?s event lengths and frequency were determined from experience and visual observations of field conditions; daily events in the UF plot were of length determined from the water balance results of the previous week. The UF plot on a commercial blueberry farm was irrigated independently of the grower?s fields, enabling comparison of crop evapotranspiration, fruit yield, and plant size between grower-controlled and researcher-controlled irrigation management. Four lysimeters in the quarter acre researcher-managed plot and four lysimeters in a six acre plot of the grower?s field were used to measure crop water use. Initiation of a second water balance experiment measuring crop water use of young southern highbush blueberry plants was completed. Three irrigation treatments are compared on plants grown in two soils: pine bark mulch and a soil and mulch incorporation. Mean water balance duration was 11 days; lysimeter water withdrawal was terminated at a chosen minimum flow rate. Irrigation inputs were measured by flowmeters, application rate and uniformity tests enabling accurate depth conversion. Precipitation inputs were measured by an onsite weather station that is also used to generate reference evapotranspiration using the American Society of Civil Engineers standardized reference evapotranspiration equation with daily time step and short reference surface. Crop coefficients for mature plants ranged from 0.67 (May 2006) to 1.05 (August 2006) with a mean of 0.84; crop coefficients responded to plant development, but monthly crop coefficients were not significantly different (alpha = 0.05) from each other. Crop coefficient values were higher in this experiment (average of 1.69) than were measured with the mature plants due to increased evaporation from exposed soil and mulch surfaces from limited canopy cover and because of Kc calculation based only on irrigated areas.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Daniel R Dourte.
Thesis: Thesis (M.E.)--University of Florida, 2007.
Local: Adviser: Haman, Dorota Z.
Local: Co-adviser: Williamson, Jeffrey G.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021504:00001


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CROP WATER REQUIREMENTS AND IRRIGATION MANAGEMENT OF
SOUTHERN HIGHBUSH BLUEBERRIES





















By

DANIEL R. DOURTE


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

2007






































O 2007 Daniel R. Dourte













TABLE OF CONTENTS


page

LI ST OF T ABLE S ............ ...... ._ ...............5....


LIST OF FIGURES .............. ...............6.....


AB S TRAC T ......_ ................. ............_........8


CHAPTER


1 INTRODUCTION AND REVIEW OF LITERATURE ................. ......_._ ...............10O


Introducti on ........._..._.._ ...._._. ...............10.....
Review of Literature ........._..._.._ ...............12.._.._._......

Irrigation ........._..._.. .... .._._.. ...............12......
Purposes of irrigation systems. ....._.. ........._..... ......._.__ ....... .........12
Classifications of irrigation systems ................. ...._._ ....__ ................13
Irrigation efficiency ................. ...............14.................
W ater use fractions ................. ...............15........... ....

Irrigation management .............. ...............18....
Evap otran spi rati on ................. ...............19........... ...
Measuring and Modeling ET .............. ...............21....
W ater balance ................. ...............21.................

Lysim etry .............. .. .... ........ .........2
Energy balance and microclimatology ................. ...............23................
Modeling evapotranspiration............... ...........2
Crop coefficient............... ..............2
Soil Water Measurement .............. ...............30....
Blueberry Production............... ...............3
S oil ................ ...............3.. 3..............


2 WATER BALANCE EXPERIMENTS: THE MATERIALS AND METHODS OF
MEASURING CROP ET .............. ...............37....


Experiment Design and Irrigation Systems Description .............. ...............37....
Experiment 1: Island Grove Ag Products............... ...............37
Irrigation uniformity testing ................. ......... ...............38......
Measuring water balance flows ................. ...............38................
Blueberry cultivation................. .. ... ...................4
Experiment 2: University of Florida Plant Science Research and Education Unit.........40
Irrigation uniformity testing ................. ......... ...............42......
Measuring water balance components .............. ...............42....
Blueberry cultivation.................. ...............4
Lysimeter Design, Construction, Installation, and Operation .............. .....................4
Soil Moisture Sensor Calibration............... ..............4











Plant Growth and Yield .............. ...............46....


3 RESULTS OF WATER BALANCE EXPERIMENTS .............. ...............57....


Experiment 1: Island Grove Ag Products ........._._._....._. ... ...............57..
Crop Coefficients and Crop and Reference ET ..............._ .........._.._ ..... 57....._ ..
Irrigation, Precipitation, and Deep Percolation .............. ...............58....
Irrigation System Performance .........._.... ......... ...............61....
Plant Growth and Yield ................ .. ....._. ... ..... .... ...... ... ............6

Experiment 2: University of Florida Plant Science Research and Education Unit ................62
Crop Coefficients and Crop and Reference ET ....._____ ..... ...___ ........._.......6
Irrigation, Precipitation, and Deep Percolation .............. ...............64....
Plant Growth and Yield ............ _...... ._ ...............64...


4 CONCLUSIONS AND APPLICATIONS OF WATER BALANCE RESULTS..................79


Discussion of Water Balance Experiment Results ............_...... .__ .. .....___........7
Applications of Results ............... .. ......_ ...............79...
Recommendations for Proj ect Continuation. ................. ....___.....__ ...........8

LIST OF REFERENCES ............_...... .__ ...............82...

BIOGRAPHICAL SKETCH .............. ...............84....










LIST OF TABLES


Table page

1-1 Constants for use in ASCE Standardized Reference Evapotranspiration Equation from
EWRI of ASCE, 2002 ........._. ...... .___ ...............34...

1-2 Irrigation water use categorization adapted from Allen et al., 2005 .............. ...................35

2-1 Calibration coefficients for use with Campbell ScientificO CS616 TDR soil moisture
sensors in pine bark mulch and mulch/soil incorporation .............. .....................5

3-1 Crop ET and Kc annual averages: June 2006 to May 2007 .............. .....................6

3-2 Annual depth totals (June 2006 to May 2007) of rainfall, irrigation, deep percolation,
and crop ET ............... ...............65...

3-3 Irrigation application rates and uniformity coefficients (CU) in UF plot and Grower's
field .............. ...............65....

3-4 Beneficial evaporation fraction (BEF) means and standard deviations in UF plot and
Grower' s plot ................. ...............65................

3-5 Effective precipitation, irrigation depths, and crop water requirement (ETc)........................66

3-6 Plant size means and summaries of analyses of variance for Grower and UF plots .............67

3-7 Mean yield and berry size of mature southern highbush blueberries: 2007 harvest ........._....68

3-8 Application rate and coefficient of uniformity of microsprinkler irrigation at UFPSREU....68

3-9 Summaries of plant sizes (m3) in June and October 2006 by treatment and analysis of
variance between treatments for October 2006 sizes ................. ................. ..........69

3-10 Yield of young southern highbush blueberries in response to soil type and irrigation
treatment: 2007 harvest............... ...............69

3-11 Yield comparison (tons/ha) of young southern highbush blueberries considering soil
system as the main effect: 2007 harvest .............. ...............70....

4-1 Annual irrigation volume, time, and energy comparisons using Kc of 1.00 and 0.84 for
irrigation scheduling .............. ...............81....










LIST OF FIGURES


Figure page

1-1 W ater balance diagram .............. ...............35....

1-2 Shallow root system (~12 cm) of mature (8 years) southern highbush blueberry plant
removed for lysimeter installation .............. ...............36....

2-1 Location of Island Grove Ag Products (green dot) ........._.._. ....._... ........_............48

2-2 Island Grove Ag products lower box corresponding to UF planting, upper box
corresponding to location oflysimeters in Grower' s field ................. .....__ ............48

2-3 Plant varieties and lysimeter locations at Island Grove Agricultural Products ......................49

2-4 Collectors positioned for irrigation uniformity testing ........._._.._......_.. ......._.._.......50

2-5 Location of UFPSREU (green arrow) .............. ...............51....

2-6 Location of field experiment at UFPSREU ................. ...............51........... ..

2-7 Lysimeters at UFPSREU ........... ......._ ...............52..

2-8 Lysimeter section view ............... ...............53..

2-9 Mature blueberry plant removal for lysimeter installation A) Blueberry plant at
Experiment 1 prepared for removal; B) Root system in pine bark mulch can be easily
separated from the soil below the mulch; C) Transplanting tray positioned for plant
rem oval .............. ...............54....

2-10 Lysimeter installation at UFPSREU ................. ...............55........... ..

2- 11 Lysimeter water withdrawal at UFP SREU ................. ...............56........... .

3-1 Daily averages of ETc and ETo for May through December 2006 ..........._..................70

3 -2 Daily averages of ETc and ETo for January through June 2007.............__ ........._ ......71

3-3 Monthly crop coefficients for June 2006 through May 2007 for mature southern
highbush blueberry plants ........... ......._ ...............71...

3-4 Monthly depth totals of irrigation and rainfall from May through December of 2006 ..........72

3-5 Monthly depth totals of irrigation and rainfall from January through June of 2007 .............72

3-6 Cumulative irrigation depths from May 2006 to June 2007............... ...............73..

3-7 Deep percolation depths at each water balance from May 2006 to June 2007 ......................73










3-8 Soil moisture, volumetric water content (VWC, m3/m3), in UF plot and Grower's field:
A) dates from 7 April to 26 July 2007; B) dates from 21 April to May 5 2007 ................74

3-9 Mean yield and berry size of mature southern highbush blueberries with 0.95
confidence intervals: 2007 harvest............... ...............75

3-10 Daily averages of crop ET and reference ET for microsprinkler-irrigated young
southern highbush blueberry plants: March to June 2007 .............. .....................7

3-11 Crop coefficients and applied water (daily average of sum of irrigation and rainfall) for
March to June 2007............... ...............77..

3-12 Total monthly ETc depth (March to June 2007) in response to total applied water ...........77

3-13 Total monthly rainfall depths and irrigation depths for each treatment .............. ................78

3-14 Yield and 0.95 confidence intervals of young southern highbush blueberries in
response to soil type and irrigation treatment: 2007 harvest .............. .....................7









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 Engineering

CROP WATER REQUIREMENTS AND IRRIGATION MANAGEMENT OF
SOUTHERN HIGHBUSH BLUEBERRIES

By

Daniel R. Dourte

December 2007

Chair: Dorota Z. Haman
Major: Agricultural and Biological Engineering

Climate-neutral measures of crop water use for mature blueberry plantings could offer

improved irrigation management by growers, reducing irrigation diversions. In contribution to

improved irrigation management practices for Florida blueberry growers, our research presents

crop coefficients for mature southern highbush blueberry plants. Measures of crop water

requirements were made using a water balance enabled by drainage lysimeters. Grower-

controlled irrigation management was compared with a researcher-managed (UF), timer-

controlled schedule. Grower' s event lengths and frequency were determined from experience

and visual observations of field conditions; daily events in the UF plot were of length determined

from the water balance results of the previous week. The UF plot on a commercial blueberry

farm was irrigated independently of the grower' s fields, enabling comparison of crop

evapotranspiration, fruit yield, and plant size between grower-controlled and researcher-

controlled irrigation management. Four lysimeters in the quarter acre researcher-managed plot

and four lysimeters in a six acre plot of the grower' s field were used to measure crop water use.

Initiation of a second water balance experiment measuring crop water use of young

southern highbush blueberry plants was completed. Three irrigation treatments are compared on










plants grown in two soils: pine bark mulch and a soil and mulch incorporation. Mean water

balance duration was 11 days; lysimeter water withdrawal was terminated at a chosen minimum

flow rate. Irrigation inputs were measured by flowmeters, application rate and uniformity tests

enabling accurate depth conversion. Precipitation inputs were measured by an onsite weather

station that is also used to generate reference evapotranspiration using the American Society of

Civil Engineers standardized reference evapotranspiration equation with daily time step and

short reference surface. Crop coefficients for mature plants ranged from 0.67 (May 2006) to

1.05 (August 2006) with a mean of 0.84; crop coefficients responded to plant development, but

monthly crop coefficients were not significantly different (a = 0.05) from each other. Crop

coefficient values were higher in this experiment (average of 1.69) than were measured with the

mature plants due to increased evaporation from exposed soil and mulch surfaces from limited

canopy cover and because of Kc calculation based only on irrigated areas.









CHAPTER 1
INTRODUCTION AND REVIEW OF LITERATURE

Introduction

The area under blueberry cultivation has more than doubled in the last 10 years. This

growth trend is recently evidenced by the yield increase from about 1.5 million pounds in 1999

to greater than 3.5 million pounds in 2003, corresponding to a value increase of 250% for

Florida' s blueberry industry for this period (Williamson and Lyrene, 2004a). The current trend

among growers is to plant early yielding (mid-April to early May) southern highbush blueberry

cultivars, interspecific hybrids of Vaccinium corymbosum x y. a~shei, K. darrowi (Williamson

and Lyrene, 2004b). With 80% of Florida' s blueberries being shipped fresh (Williamson and

Lyrene, 2004a), early yields realize much higher prices; in the last ten years, the average price of

fresh blueberries shipped from Florida before May 20 was $4/pound. In early April, prices of

$15/pound are seen by growers. After June 1, prices drop to $1i/pound or less as Florida growers

compete with growers from cooler climates (Williamson et al., 2004).

In an effort to benefit from the higher values of early yields, some growers have shifted

from the conventionally cultivated low-lying areas characterized by soils high in organic matter

and water content to cultivating higher areas with sandy soils and less susceptibility to cold

damage. Accompanying this recent shift is the practice of growing blueberries in a pine bark

culture that is typically deposited to a depth of six inches on top of the existing soil (Williamson

and Lyrene, 2004). The pine bark culture offers the benefits of low pH and fast root system

establishment; it creates a soil system that is more similar to the forest soils, having high organic

matter content, which blueberries are native to. However, the low water storage capacity of the

pine bark requires modified irrigation and fertilization management. Additionally, the root zone










of the plants is largely limited to the pine bark culture, reducing root zone effectiveness at water

extraction.

The obj ective of our research was to improve irrigation management by determining the

total water requirement for mature southern highbush blueberries and to compare potential water

savings from different irrigation scheduling. This was done by using a water balance to measure

the water requirements of blueberries grown in pine bark and a pine bark and soil incorporation.

The water balance results (plant water use), plant size data, and fruit yield data were used to

compare various irrigation scheduling strategies on blueberries grown in pine bark and in a pine

bark and soil incorporation.

An excellent starting point in realizing well-managed irrigation is information about crop

water use. Ultimately, this crop water use information comes from Hield measurements, and data

have been used to develop models capable of simulating crop water use. Being dependent on

climatic parameters, crop water use, or crop evapotranspiration (ETc), is best presented as a

function of reference evapotranspiration (ETo). This is done by defining a crop coefficient as the

ratio of ETc of a well-watered crop to ETo, the water use of hypothetical reference crop, which

can be determined if sufficient climate data are available. With adequate instrumentation, ETo

can be measured anywhere, evidencing the utility of the crop coefficient.

In places with adequate rainfall, there is still an incentive to replace irrigation water

diversions with better irrigation management, increasing the amount of irrigation water that is

benefieially used for crop production. The incentives to growers: lower energy costs, lower

water costs, and reduced fertilizer inputs. Additional benefits are improved health of water

resources and increases in water available for municipal and industrial purposes and for










ecosystem function. The goal of our work is to improve the available information about crop

water use of blueberries.

Review of Literature

Irrigation

Agriculture is responsible for about 75% of global freshwater diversions (Shiklomanov,

1991), and 40% of the world' s food is provided by irrigated agriculture on 20% of the world' s

cultivated land area. These data serve to highlight the importance of irrigation in the context of

global food production. As population pressures decrease the world's per capital cultivable area,

agricultural yield / land area ratios must continue to increase, requiring greater performance from

irrigated agriculture (IPTRID, 1999). FAOSTAT reports that agricultural land area per capital

(ha / capital) has fallen from 1.24 in 1970 to 0.80 in 2002, a 65% decrease in per capital

agricultural land area. This was accompanied by a 60% increase in the land area that was

irrigated (FAOSTAT statistical database. 2004. Food and Agriculture Organization of the

United Nations. Available at http://faostat.fao.org/ accessed March 2006).

Burgeoning populations will place even greater demands on the shoulders of irrigated

agriculture, and if acute, local and regional water shortages are to be avoided, water use

efficiency in agriculture must continue its increase. This effort is concisely stated by Dr. Marvin

Jensen: "The greatest challenge for agriculture is to develop the technology for improving water

use efficiency (Karasov, 1982)." Here, water use efficiency means the ratio of mass of crop

biomass or marketable crop to the volume of water applied to produce it.

Purposes of irrigation systems

There are numerous justifications for irrigation systems; some common purposes are listed

below:

*Reduced vulnerability: moderating irregularities of precipitation










Increased yield: improving the yield (mass or $) / land (area) ratio
Environmental management and protection against cold damage
Fertilizer application
Dust control

Classifications of irrigation systems

Cuenca (1989) suggests broadly dividing irrigation systems in two classifications:

nonpressurized and pressurized. Nonpressurized describes the original irrigation systems, known

as gravity systems in many cases. Water flows along excavated soil contours near or among

crops to apply water to plant root zones. Water losses from the crop root zone and surface

storage (conveyance) to infiltration and evaporation are high, labor investments for excavation

are high, but investments in materials are very low. Nonpressurized systems are presently

finding increasing application in plant nurseries. Water is diverted to flood an area where

container plants are positioned, and capillarity draws water into the containers through holes in

container bottoms. The flooded area is drained and the water is stored for reuse. Subirrigation,

or water table control, is a second type of nonpressurized irrigation that is feasible on appropriate

soils, sandy or muck, and hydrologic conditions, shallow, restrictive subsurface layer, (Smaj strla

et al., 1992). Water is applied in subirrigation by elevating the water table near the plant root

zones and allowing capillarity to supply water. Surface ditches or subsurface pipes may be used

to apply water to control water tables.

Pressurized irrigation systems can be divided between microirrigation and sprinklers.

Microirrigation systems can be further divided by differentiating between point source and line

source emitters. A point source emitter provides a contained distribution of water, typically

below plant vegetation. Point source emitters are often used on crops with wide spacing (trees,

vineyards, berries); a system with point source emitters can compensate for pressure variations in

lateral. A line source emitter is characterized by a lateral that is porous or has small orifices










along its length; this type is generally used for closely spaced crops. Sprinkler irrigation systems

can be categorized by differences in nozzle or lateral movement designs: solid-set, hand move,

side roll (manually or motorized), big gun (moved by line coil rotation or sprinkler vehicle),

linear move, and center pivot. Smaj strla et al. (1992) offer comprehensive reviews and

explanations of all the above described irrigation systems: nonpressurized (surface and

subirrigation) and pressurized (microirrigation and sprinkler).

Irrigation efficiency

An indicator of irrigation performance is necessary if some improvement is to be made in

the operation of an irrigation system. Some commonly used indicators (Haman et al., 1996) are

defined:

Irrigation conveyance efficiency. This is the ratio of the volume of water delivered by
the irrigation system to the volume of water input to the irrigation system.

Irrigation application efficiency. This is the ratio of the volume of water that is
evaporated or transpired to the volume of water delivered by the irrigation system. This
value can never reach the desired ratio of 1 because of advective flows, runoff, wind drift,
and deep percolation losses.

Crop water use efficiency. This is the ratio of crop yield, marketable yield, or total crop
biomass to volume of water delivered to the crop.

Irrigation water use efficiency. This is the ratio of the volume of water evaporated and
transpired to the volume of water delivered by the irrigation system or the ratio of the
difference between irrigated and nonirrigated crop yield to the volume of water delivered
by an irrigation system.

The application of a quantification of evapotranspiration (ET) should be an improvement

in some type of irrigation efficiency. Some appropriate reasons for increasing some measure of

irrigation efficiency are given (Allen et al., 2005):

Reduce water conveyance costs

Reduce leaching of fertilizers and other chemicals and limit degradation of groundwater










Reduce nonevaporated components of diverted water that flow into a saline system
(ocean, saline lake, or brackish groundwater) and are therefore not recoverable or
contaminate streams downgradient

Reduce water diversions from deep, confined aquifers, in instances where the
nonevaporated components of diverted water percolate to a shallower unconfined aquifer,
thus changing the distribution of water between the aquifers in an undesirable way

Reduce waterlogging and improve salinity control

Maximize the total fraction of water delivered to crops to increase crop yields

Reduce soil erosion.

Water use fractions

There is presently some interest in replacing the commonly used efficiency terms by water

use fractions (Willardson et al., 1994; Allen et al., 2005). Efficiency can be misleading to those

in other disciplines when it is used in the context of irrigation, as it may imply a loss of water.

The use of fractional terms better describes water' s conservation; water is moved, it changes

phase, and it changes quality, but it is not lost. It should be considered that irrigation water uses

that are defined as consumptive, accounting for water storage in plant biomass and fruit, phase

change from evaporation and transpiration, or detrimental water quality change, can be judged as

being either beneficial or nonbeneficial. Beneficial water uses are those that contribute to crop

development and yield production. Nonbeneficial uses are those that are judged to not be aiding

in crop production. Water uses that are noncomsumptive include flows that are reusable (deep

percolation and runoff of water of good quality to water bodies where it can be withdrawn

again). A helpful figure (Figure 1-1) was adapted from (Allen et. al., 2005) to categorize

irrigation water use.

The following water use fractions can be used in place of efficiencies to describe irrigation

water use (Willardson, et al., 1994 and Allen et al., 2005). The beneficial evaporated fraction

(BEF) describes the irrigation diversion that is used beneficially and consumptively by the crop,









and it is the measure used in this research to compare irrigation performance. Essentially this is

the fraction of water transpired by a crop or evaporated from soil surfaces immediately

surrounding the crop.

BEF = QET Qdiv -,

where QET is the net irrigation water requirement: the fraction of crop water use provide by

irrigation. Qdiv is the total quantity of water diverted for irrigation. BEF correlates most closely

with irrigation application efficiency. It is this fraction that can be described as both

consumptive and beneficial water use. The nonbeneficial evaporated fraction (NEF) is defined

as shown in Equation 1-2:

NEF = QE Qdiv (-)

where QE includes the quantity of water that evaporates from irrigation water storage reservoirs

or conveyance paths (including surface and sprinkler flows). QE alSO includes water evaporated

from excess wet soil (soil beyond the area of the crop root zone or the plant vegetative area,

whichever is larger). The reusable fraction (RF) is written as shown in Equation 1-3:

RF = QR Qdiv (-)

where QR is the quantity of water that is available for use after irrigation application. This water

is returned, through natural or artificial flow paths, to a freshwater system. The nonreusable

fraction (NRF) is given in Equation 1-4:

NRF = QNR Qdiv (-)

where QNR is the quantity of water that cannot by other water users who require fresh water of

good quality. This includes water discharged to saline systems and water that has declined in

quality below that which is economically recoverable. The consumed fraction (CF) is defined by

Equation 1-5:









CF = (QNR + ET + E + exp)/ div ~ BEF + NEF + NRF (-)

where Qex is the water that is exported beyond the hydrologic basin from which it is was

withdrawn. The water contained in blueberries that are exported to distant watersheds is an

example of Qexp-

In determining these irrigation water use fractions, it is important to account for water

contributions from precipitation. This is done in the net irrigation water requirement term, QET:

defined QET = CTOp evapotranspiration effective precipitation. Failure to do so may result in

inflated irrigation water use fractions. Below (Wallace and Batchelor, 1997) are four categories

into which efforts at increasing the beneficial evaporated fraction (BEF) can be organized:

agronomic, engineering, managerial, and institutional.

Agronomic improvements relating to water use include: improved crop management,

introduction of higher-yielding varieties, adoption of cropping strategies that maximize cropped

area during periods of low potential evaporation and periods of high rainfall. Increasing the

fraction of water a crop uses benefieially through engineering methods includes: laser leveling of

flood irrigation schemes to improve irrigation uniformity, adoption of practices that increase

effectiveness of rainfall, introduction of more efficient irrigation methods, such as drip irrigation

and subsurface irrigation, which reduce soil evaporation, improve uniformity, and reduce

drainage. Managerial means of improving irrigation includes human decisions and the tools to

aid in decision-making: adoption of demand-based irrigation scheduling systems, use of deficit

scheduling, better use and management of saline and waste water, improved maintenance of

equipment. Related to managerial approaches to improvements in irrigation are institutional

methods including: user involvement in scheme operation and maintenance, introduction of

water pricing and legal frameworks to provide incentives for efficient water use and









disincentives for inefficient use, introduction of integrated catchment management, improved

training and extension.

The starting point to enter into any attempt to increase BEF, or another chosen irrigation

performance indicator, is the acquisition or measurement of a reliable crop water requirement.

Estimations or measurements of evapotranspiration are crucial methods for crop water

requirement determination, enabling improvements in irrigation technology and management.

The research presented here of southern highbush blueberry water requirements likely finds its

application in managerial and institutional efforts to increase BEF.

Irrigation management

Management of irrigation describes the interaction between the people responsible for

irrigating and the equipment that administers irrigation water. Those responsible for managing

irrigation gather information to support their decisions about irrigation frequency, timing, and

amount. Information gathering may be done by human senses, in which the feel of the soil,

appearance of the crop, and consideration of weather are observed to help make irrigation

management decisions. Alternatively, irrigation managers may use instruments to more

accurately gather relevant information about soil moisture, weather, and crop development.

Postel (1999) reports on some of the more famous information systems, sensing technology, and

datasets available to growers for supporting decisions about irrigation, most of them being

automated, agricultural weather station networks. Growers using the California Irrigation

Management Information Service were shown in 1995 to be realizing economic savings from

$99/ha for alfalfa to $927/ha for lettuce (Postel, 1999). Measurements of crop water use serve to

improve or validate the irrigation requirements suggested by the instruments employed to help

manage irrigation. Allen et al. (1998) carefully detail the combination of crop coefficients and

simulated reference evapotranspiration for the purpose of managing irrigation.










Evapotranspiration

Evapotranspiration (ET) is the sum of the water that evaporates from the soil and plant

surfaces and the water that is transpired by a plant (from soil, through roots, to leaves where it is

vaporized and nearly all of it is removed through plant stomata). Falkenmark and Rockstroim

(2004) describe the presence of the term this way:

Until recently it has been very cumbersome to distinguish between productive transpiration
and non-productive evaporation. This has led to the combining of two thermodynamically
similar but ecologically very different processes into the awkward notion of
evapotranspiration.

While the combination of productive and non-productive evaporation terms may be awkward for

some purposes, it is a sensible combination for the purposes of irrigation management and

allocation of water resources to growers because both productive transpiration and non-

productive evaporation are return water flows to the atmosphere that must be replaced by

irrigation.

Transpiration constitutes a colossal water use when compared to the plant development it

yields. In the production of 1 kg of plant biomass, 200 to 1000 kg of water are transpired. The

transpirati on/production ratio at least doubles when fruit yields are the denominator of interest

(Seckler, 2003). This has prompted David Seckler and his colleagues at the International Water

Management Institute to ask, "Why do plants need so much water for transpiration?"

Unconvinced by the incomplete explanation that transpiration is simply a consequence of carbon

dioxide intake through stomatal openings, Seckler offers three beneficial functions of

transpirati on.

Transpiration prevents stress to plants from high temperatures. Plant surfaces would reach

very high temperatures without the phase change on vegetative surfaces that facilitates heat

transfer. A second purpose of transpiration is to raise water from plant roots to plant leaves.









This water carries nutrients required for plant food production, and some of the water is required

for photosynthesis. Nearly all of the water absorbed by roots exits a plant by the mechanism of

transpiration, resulting in a tensile force in the water in the narrow plant vessels that provides

some of the force required to lift water through the height of a plant. The hydraulic lifting force

provided by transpiration accounts for a third purpose, one that occurs outside of the plant: flow

of water and nutrients in the soil surrounding plant roots. The withdrawal of water and nutrients

from the soil by roots creates a moisture gradient that results in additional water and nutrient

flows through the soil. These three services of transpiration provide some justification for

plants' large water requirements.

The energy for this phase change both on plant vegetative surfaces and soil surfaces is

provided by radiation from the sun. ET corresponds to a crop water requirement, the amount of

water needed for plant development. Evapotranspiration is a function of climate, crop physical

environment, and crop physiology, as detailed by the lists:

Climate conditions

Energy supply (radiation and air temperature)
Vapor pressure gradient (air humidity)
Wind (wind speed)


Environmental conditions

Soil water content and distribution
Soil hydraulic conductivity
Soil water salinity
Soil tilth (level of cultivation), mulch


Crop characteristics

Type, variety, development stage
Size of vegetative surface and root zone
Roughness and reflectivity of vegetation












Measuring and modeling ET

Evapotranspiration cannot be measured directly. Indirect measurements have been

developed to enable estimation of crop water requirements. A water balance and an energy

balance are common ways of measuring ET, and estimations of ET can be computed through the

use of equations driven by the climatic, environmental and physiological variables and

parameters affecting ET.

Water balance

A water balance can be used to measure ET by recording the mass or volume of water that

enters and leaves a system, and computing ET to satisfy the water balance equation (equation 6).

The law of conservation of mass requires that all water flows across the system boundaries sum

to zero. Equation 1-6 and the diagram of Figure 1-1 illustrate the concept of balancing water

flows into and out of a system.

ET = I + P RO DP + CR & ASF & ASW (-)
where,
I = irrigation
P = precipitation
RO = runoff
DP = deep percolation
CR = capillary rise
SF = subsurface flow
SW = soil water content

If the control surface is the surface perimeter surrounding the plant and its root zone and all

fluxes can be determined (some can usually be neglected as their contributions are small) and

soil water content can be measured, ET can be found by subtraction. Change in soil water

content can be measured gravimetrically or by using soil moisture sensors. Units of ET are

typically given as depth over time.










Lysimetry

A lysimeter creates a control volume in which water balance terms can be measured. In

order to account for deep percolation and capillary rise, lysimeters may record the mass of water

lost by ET (weighing lysimeter) or they may be used to measure the drainage water (drainage

lysimeter) that flows below the root zone. To find ET using a weighing lysimeter, the change in

mass measured is divided by the density of water and then again by the evaporative area of the

lysimeter. For drainage lysimeters, the water volume collected is divided by the evaporative area

to compute ET. Drainage lysimeters may be either gravimetric, in which water is drained by

gravity into a collection tank at an elevation less than that of the lysimeter bottom, or they may

be negative pressure, in which a pump is used to create a partial vacuum to withdraw water.

If all the terms in the above water balance can be accounted for and if soil water content

can be measured, ET can be determined. Lysimetry has few weaknesses, but disturbed soil

structure, restricted root zone, and high cost are often cited by detractors. A disturbed soil

structure can be avoided by installing a lysimeter monolithically; this is done by pressing a

lysimeter frame into the soil and then removing the soil core to affix a bottom surface to the

lysimeter.

For cropped lysimeters to accurately measure ET, some environmental requirements must

be satisfied (Allen et al., 1991). Crop elevation and crop density must be the same as that of the

surrounding crop. An elevated lysimeter will overestimate crop ET because of increased wind

speed resulting from the loss of bordering crops. Underestimation of ET can result from

encroachment of vegetative surfaces into the lysimeter area, increasing the shaded area in the

lysimeter's evaporative area. Conversely, if the vegetative area that extends beyond the

lysimeter is not included in the total evaporative area, ET will be overestimated. For above-










ground lysimeters and subsurface lysimeters with exposed walls, the lysimeter surfaces may

reflect or radiate heat to the crop and soil, inflating ET values.

Lysimeter studies are generally the reference to which modeled ET is compared. A recent

evaluation (Mutziger et al., 2005) of the ability of the FAO-56 Penman-Monteith equation to

predict evaporation from bare soil used recorded lysimeter data to compare evaporation from

different soil types. Ventura et al. (1999) provide another of the many comparisons of ET

models in the literature that rely on lysimeter data as a benchmark. Modeling of crop water use

is an excellent tool, and the applications of modeled ETc will continue to increase; but actual

measures of ETc, provided by lysimetry, are required if models are to be calibrated and

validated.

Cuenca (1989) reviewed improvement of the Blaney-Criddle method of ET estimation, a

simple, empirically-derived, temperature-based relationship, of the Soil Conservation Service

(SCS). The SCS was able to improve the accuracy of the Blaney-Criddle method by using

measured crop water use, from lysimeter studies, to determine more accurate proportionality

constants describing the ET and temperature correlation.

Energy balance and microclimatology

Being limited by the available energy, evapotranspiration can be found by measuring the

terms in the equation that describes the balance of energy present in the ET process. The energy

balance is given (Allen et al., 1998):

LE = ET= RN G H (-)
where,
LE = latent heat flux from ET that is leaving the system
ET = evaporative and transpirative water loss
RN = net solar radiation that is entering the system
G = soil heat flux
H = canopy heat flux









The units for the terms of Equation 1-7 are typically measured in W-m-2 (1 mm of

ET-day =28.36 W-m-2). Only energy flows in the vertical direction are considered; lateral

energy flows are neglected, making this method inadequate for cropping systems having

considerable exposed surfaces perpendicular to the ground surface. Therefore, this method is not

appropriate for citrus, apples, blueberries, or other tree or bush crops that are grown in rows with

considerable vertical areas of the crop exposed. The quantities in the above model can be

estimated from climatic variables or they can be measured with sensing technologies that are

able to record the required radiation input and heat flux outputs.

Finding the mass transfer by quantifying the microclimate near a plant of interest is

another way to estimate ET. This method also considers only vertical flows (of mass in this

case), and like the energy balance is effective only above large homogenous cropping systems.

Measurements of temperature, water vapor, and wind speed gradients can be used to

approximate the mass transfer of water from the system (ET) through the use of a eddy

covariance methods. Extensive instrumentation is required to measure the gradients of

temperature, water vapor, and wind speed, limiting microclimatology's application.

Modeling evapotranspiration

The meteorological factors affecting ET can be used to develop models capable of

estimating ET. Relationships between ET and climatic and atmospheric parameters have been

determined experimentally and numerous models have related ET to various climatic variables.

These models combine energy balance and mass transfer methodologies with crop-descriptive

parameters to determine ET. The Penman-Monteith model is often regarded as the most accurate

predictor of ET in a wide range of climates. Forms of the Penman-Moneith equation are used as

the international and U. S. standard estimators of ET. A recent comparison (Temesgen et al.,

2005) of some ET equations (CIMIS, Hargreaves, Penman-Monteith) demonstrated the utility of









the Penman-Monteith combination equation, needing no local calibration when all input data are

available.

In its general form the model describes potential evapotranspiration as (Allen et al., 1998):


A(R, )+Pc
ET =



where,
ET evapotranspiration [mm day- ],
Rn = net radiation at the crop surface [MJ m-2 day ]~,
G = soil heat flux density [MJ m-2 day- ],
es = saturation vapour pressure [kPa],
ea = actual vapour pressure [kPa],
es ea = saturation vapour pressure deficit [kPa],
A slope vapour pressure curve [kPa oC- ],
y psychrometric constant [kPa oC- ],
rs and ra (bulk) surface and aerodynamic resistances [sm ]~,
pa mean air density at constant pressure[kg m-3,
c, = specific heat of the air [MJ kg-1 OC- ].

A theoretical grass reference surface was defined to avoid the determination of parameters

(rs and ra) for countless crop types and stages of development. With the reference surface, the

Penman-Monteith method is effective at estimating the evapotranspiration of a reference crop if

climate data can be measured. ETo is useful to compare evapotranspiration demands of different

environments and to estimate the ET of other crops. The reference ET equation of FAO-56

(Allen et al., 1998) is:

900
0.408A(R, G) + u2 se a ,
E T, =T+273
A + y(1 + 0.34u12) (-)
where,
ETo reference evapotranspiration [mm day- ],
Rn = net radiation at the crop surface [MJ m-2 day- ],
G = soil heat flux density [MJ m-2 day- ],
T = mean daily air temperature at 2 m height [oC],
U2 = wind speed at 2 m height [ms ]~,
es = saturation vapour pressure [kPa],









ea = actual vapour pressure [kPa],
es ea = saturation vapour pressure deficit [kPa],
A slope vapour pressure curve [kPa oC- ],
y = psychrometric constant [kPa oC- ].

This is the international standard for daily reference evapotranspiration determination, as

declared by the United Nations Food and Agriculture Organization (UN FAO) in Irrigation and

Drainage paper 56. In 1999, the Irrigation Association requested that a standardized ET equation

be decided on or developed to be used in the United States "to establish and define a benchmark

reference evapotranspiration equation. The purpose of the benchmark equation is to standardize

the calculation of reference evapotranspiration that can be used to improve transferability of crop

coefficients." The American Society of Civil Engineers (ASCE) developed the standardized

Penman-Monteith equation (Eq. 1-10).

C,
0.408A(Rn G) +7 u2 s a U
ET = T + 273
A ~+ y(1+ Cd2,)(10)
where
ETsz = standardized reference crop evapotranspiration for short (ETos) or tall (ETrs)
surfaces [mm/day for daily time steps or mm/hour for hourly time steps],
Rn= calculated net radiation at the crop surface [MJ m-2 d-l for daily time steps or
MJ m-2 h-l for hourly time steps],
G soil heat flux density at the soil surface [MJ m-2 d-l for daily time steps or MJ m-2 h-l
for hourly time steps],
T = mean daily or hourly air temperature at 1.5 to 2.5-m height [oC],
U2 = mean daily or hourly wind speed at 2-m height [m/s],
es = saturation vapor pressure at 1.5 to 2.5-m height [kPa], calculated for daily time
steps as the average of saturation vapor pressure at maximum and minimum air
temperature,
ea = mean actual vapor pressure at 1.5 to 2.5-m height [kPa],
A = slope of the saturation vapor pressure-temperature curve [kPa / OC],
y = psychrometric constant [kPa / OC],
Cn = numerator constant that changes with reference type and calculation time step,
Cd = denominator constant that changes with reference type and calculation time
step.

Table 1-1 provides appropriate values for Cn and Cd depending on reference surface and

time step. The differences from the FAO-56 equation are that a tall grass (alfalfa) or a short










grass (clipped surface) reference can be used with hourly or daily time steps. Cn and Cd are

chosen to match the reference grass type and time step; different Cn and Cd values are chosen

for hourly estimates corresponding to daytime and nighttime. It should be noted that ASCE's

standardized and FAO 56's Penman-Monteith equations are identical if a daily time step and

short reference surface are considered.

Crop coefficient

Calculated values of ETo enable ETc to be determined in various climates using a crop

coefficient. A crop coefficient is defined as the ratio of actual crop evapotranspiration to

reference evapotranspiration when the crop has access to adequate soil water and is not stressed

by water quality constraints, pests, or inadequate soil fertility. Crop coefficients have been

determined empirically and are tabulated for many different types of crops to facilitate

determination of crop water requirements, or crop evapotranspiration, by calculating the ETo

from measured climatic data. Use of a single crop coefficient combines soil evaporation and

crop transpiration into one value, Kc. If a reliable value of the crop coefficient is available, a

climate-specific estimate of ETc, the evapotranspiration of a specific crop, can be found by the

relationship below if it is decided that combining crop transpiration and soil evaporation into one

coefficient is valid. This provides a time-averaged representation of crop evapotranspiration, as

soil surface moisture will vary considerably.


ET, = K, ETo 11)
where
ET, = crop evapotranspiration [mm d- ],
K, = crop coefficient dimensionlesss],
ETo reference crop evapotranspiration [mm d- ].

If it is decided that two crop coefficients are needed to have a more accurate representation

of crop evapotranspiration over shorter time intervals or if it is decided that the transpiration and









evaporation components of ET are to be separately determined, the following expression can be

used (Eq. 1-12). The basal crop coefficient (Kcb) is the ratio ETc/ETo when the top surface of

the soil is dry and there is adequate moisture in the root zone for maximum plant transpiration.

The soil water evaporation coefficient (Ke) is determined from a water balance of the top layer

of soil.

ET, = (Kcb + Ke) ETo (1-12)

Ke can be separated from the total Kc by using a valid (considering the crop of interest)

model to determine the transpiration to evapotranspiration ratio (T/ET); these models are usually

driven by leaf area index (LAI) and require a measure of ETc to determine transpiration. Also,

plants can be instrumented with stem-flow gages that use heat transfer principles to estimate

transpiration, which could provide one with a Kcb value if ETo is available; Ke could be

determined by subtraction using Equation 1-12. Additionally, microlysimetry can be reliably

used to create a water balance of the soil near the surface (Kang et al., 2003), providing one with

a measurement of soil evaporation that can be used to compute Ke. Microlysimeters should be

installed in the area of the crop's evaporative area, and installation may proceed as follows. A

length of PVC pipe at least as long as the maximum depth of evaporation (approximately 15 cm)

can make a suitable microlysimeter. This can be done by capping the bottom end with some type

of drainage screen, excavating a hole that situates the microlysimeter rim at the same level as the

soil surface, inserting a larger diameter pipe section to allow for easy insertion and removal of

the microlysimeter, and filling the microlysimeter with enough soil (packing as it is filled) to

achieve the desired bulk density. To find Ke, the microlysimeter must be removed and weighed

at chosen intervals following an irrigation or rain event. The amount of water evaporated

divided by ETo is the Ke. Kang et al. used 4 cylinders under winter wheat and maize crops,









measuring masses three times a day for 4 consecutive days. The procedure was repeated several

times during their experiment finding ET, of the winter wheat and maize. They used their results

to calibrate a model (Stroosnij der 1987) that relates ETo to soil evaporation, Es. The relation is

given in Equation 1-13:

CE, = tzElo +A (1-13)
where,
CEs = cumulative soil evaporation [mm],
t = time elapsed since evaporation started [day],
ETo = the reference evapotranspiration [mm/ day],
tl = duration of the linear phase of soil evaporation [day],
A = soil parameter [mm / 0.5 day].

Once calibrated at chosen crop development stages, the model of Equation 1-13 can

provide predictions of soil evaporation given calculated ETo values. Microlysimetry can be

automated in manner similar to the weighing lysimeters of larger size by installing scales to

regularly measure and record the mass of microlysimeters without requiring their removal.

Crop coefficients are tabulated for fixed climatic parameters as functions of crop type and

crop development stage. Adjustments can be made according to the model of Equation 1-14

(Allen et al., 1998) to account for different levels of relative humidity and wind speed. Kc

values for crops at the initial development stage (from the date of planting or the arrival of

vegetative buds to time when ground cover is 10%), at the middle development stage (from full

vegetative cover to the start of fluit maturity), and at the final development stage (from the start

of fluit maturity to the time of harvest) have been empirically determined and are tabulated.


Kmd =Kcmu(Tab)j+[0.04(u 2 -2)-0.004(RHnn -45)1 0. (11)

where,
Kcmid (Tab) tabulated value for development-stage-specific crop coefficient (middle in
above model)
U2 mean ValUe for daily wind speed at 2 m height over grass during the development
stage under consideration [m s ], for 1 m sl < u2 < 6 ms ~,










RHmin = mean value for daily minimum relative humidity during the development stage
under consideration [%], for 20% < RHmin < 80%,
h = mean plant height during the development stage under consideration [m], for 0. 1 m < h
< 10 m.

There is little information available in the literature on measured values of Kc for

blueberries, and this research helps remedy this literature weakness. Haman et al. (1997) used

drainage lysimeters to measure ETc of microirrigated young blueberries under three soil water

tension levels. Kc values were calculated and found to reach 0.5 for plants of age 3. These crop

coefficients were modified to be based only on water use (ETc) of irrigated areas (immediately

below plants) to aid in management of microirrigation; Kc was not determined on the field level.

The Pacific Northwest Cooperative Agricultural Weather Network reports a crop coefficient

curve for blueberries with Kc values ranging from 0. 17 before leaf appearance to 1.00 when fully

vegetated (Pacific Northwest Cooperative Agricultural Weather Network. AgriMet Crop

Coefficients: Blueberries. Available at

http ://www.usbr.gov/pn/agrimet/cropcurves/BLUc1 hm accessed July 2007)

Soil water measurement

Accurate measures of soil moisture are needed for a water balance to accurately provide a

value for ET,; change in soil moisture being one of the fluxes of a water balance. Dielectric-

based sensors have seen wide application since their development. A large disparity in dielectric

constants of soil (E = 3-5), air (E = 1), and water (E = 81), provides dielectric-based soil moisture

sensors with the benefit of being somewhat insensitive to differences in soil composition and

texture (Dasbar and Or, 1999). Sensors of this type can be broadly divided into those that

estimate the dielectric constant of a medium by measuring propagation time of an

electromagnetic pulse (time domain reflectometry) or by measuring the rate of voltage change in

response to an excitation voltage (capacitance probe).









Time Domain Reflectometry (TDR) is an effective way to indirectly and nondestructively

measure the volumetric water content of soils. TDR works by sending high frequency

electromagnetic pulses through the soil. The waves propagates down the wave guides of the

TDR probe and reflect back to the probe with a velocity that is inversely proportional the

dielectric constant of the soil-water matrix. Higher water content corresponds to lower wave

velocity and longer period. The time of this wave travel can be used to determine volumetric

water content by calibrating a probe or datalogger for a soil type with known dielectric constant

and using a function that relates wave period to volumetric water content. Increasing application

of TDR can be attributed to low calibration requirements, high accuracy and repeatability, and

high spatial and temporal resolution (Mufioz-Carpena, 2004).

A means of measuring volumetric soil moisture that is more affordable and simpler than

TDR is the capacitance method. A capacitance probe measures the time it takes to charge a

capacitor in the soil when a known excitation voltage is applied. Dielectric constant and charge

time are inversely proportional; a large dielectric constant (that of water) being accompanied by

a short charge time. An empirically derived function relating charge time or charge voltage to

volumetric water content is used to interpret probe output.

Time domain transmission (TDT) soil moisture sensors work much like TDR sensors,

wave propagation time being used to determine volumetric water content of a soil. However,

TDT sensors measure a one-way propagation time rather than a two-way reflected propagation

time recorded by a TDR sensor. TDT measures wave travel time with sensing electronics at the

end of probe opposite of the wave generator, meaning equipment is needed at both ends of a

sensor or the wave guides must be bent to return the electromagnetic pulse to the electronics.

This provides a sensor that is more cumbersome to install than a TDR sensor; usually requiring









that a place be excavated for installation and that the sensor be permanently installed. Acclima

TDT soil moisture sensors and irrigation controllers are being used to control irrigation in two

treatments at our water balance experiment 2 (see Chapter 2). Plauborg et al. (2005) compare the

performance of TDR and TDT sensors with standard calibration in sandy soils, finding that the

TDT sensors (Aquaflex, Streat Instruments) underestimated volumetric water content by up to

0.1 m3/m3 COmpared to the TDR (CS 616, Campbell Scientific). TDR' s were found to perform

well with standard calibration and showed 0.04 m3/m3 Variability among 5 sensors.

Mufioz-Carpena (2004) gives a thorough review of available techniques and technologies

for measuring soil moisture. Reported accuracies of the three sensors mentioned above are 0.01

m3/m3 for TDR with standard calibration, 0.01 m3/m3 for capacitance probes with soil-specific

calibration, and 0.01 to 0.02 m3/m3 for TDT with standard calibration (Mufioz-Carpena, 2004).

Blueberry Production

Blueberries are native to the eastern United States, making them among the minority of

fruit crops that are native to and presently commercially cultivated in the United States

(Williamson and Lyrene, 2004a). Blueberry production in Florida has been steadily increasing

as growers try to realize the greater returns afforded by early harvests. The climate in Florida

allows early ripening blueberry varieties to be grown. The market responds to this early fruit by

offering fruit prices that are four to five times higher in early Spring than average Summer prices

(Williamson and Lyrene, 2004a and Haman et al., 1994).

Florida blueberry cultivation is divided between rabbiteve varieties and southern highbush

varieties. The rise in southern highbush cultivation is motivated by the early maturity and fast

fruit development of the variety, enabling the high prices for early Spring fruit. Blueberries

grow best in soils with high organic content and low pH. Shallow root zones (10 to 20 cm: see

Figure 1-2) and the low water-holding capacity of Florida' s sandy soils, and the pine bark mulch









beds used to grow most of the newer plantings of blueberries, make irrigation a requisite for

successful blueberry cultivation (Haman et al., 1997).

As noted in the section discussing crop coefficients, literature on crop water use and crop

coefficients for blueberries is limited, but some recent research has begun to provide some of

these data. Irrigation depth on blueberries has been found to effect number and size of fruit, and

type of irrigation was shown to affect yields under equal application depths (Holzapfel et al.,

2004). Blueberries under microspray irrigation were found to yield significantly more fruit than

blueberries under drip irrigation that were irrigated to the same depth. It was supposed that the

improved distribution of the water by microsprinkler was responsible for the greater yield

(Holzapfel et al., 2004). General guidelines on expected ETc of northern highbush blueberries

are given by the Northwest Berry and Grape Information Network to be between 3.6 and 5.5

mm/day for blueberries grown in the Pacific Northwest (Northwest Berry and Grape Information

Network. Water Management for Blueberry Fields. Available at

http://berrygrape. oregonstate. edu/water-management-for-blueberry-fields accessed July 2007).

Climate differences from the Pacific Northwest mean that southern highbush blueberries grown

in the Southeastern United States can be expected to require more water.

Soil

Soil texture, the size of soil particles, and soil structure, the arrangement of soil particles,

determine the storage capacity and mobility of water in soil. In the context of irrigation, three

soil-water content levels of interest are saturation, field capacity, and wilting point. Saturation is

the soil-water content marked by rapid gravitational drainage. This is the point at which nearly

all pore spaces in the soil matrix are filled with water. Field capacity is the soil-water content

after the rate of drainage has decreased considerably. This is often considered as an upper limit

of the water available to a crop. The wilting point is often considered as the lower limit of water









available to crops, the point at which plants cannot recover from wilting induced by the low soil-

water content. This point depends not only on soil parameters but also on plant type.

Soil-water potential describes the ability for work to be done, governing water movement

in a system. Water moves from areas of high potential to areas of low potential. There are three

soil-water potentials that determine the availability of soil-water (adapted from Cuenca, 1989):

Gravitational soil water is water that drains through a saturated soil matrix. This water
generally spends little time in the crop root zone.

Pressure potential describes the pressure exerted on soil-water is dependent on soil-water
content and soil texture (particle size and particle pore size). Positive soil-water pressure
indicates a compression state (high water availability), occurring only at saturation;
negative soil-water pressure indicates that water is under tension as a result of capillary
forces exerted by soil pores.

Osmotic potential is function of salinity of the soil-water. The water available to the
plant decreases as the concentration of salt in the soil water increases.

Soil information is necessary for irrigation managers because frequency and duration of

irrigation events depends on water holding capacity of the soil.



Table 1-1. Constants for use in ASCE Standardized Reference Evapotranspiration Equation
from EWRI of ASCE, 2002.

Cal cul ati on Short Tall Units for Units for
Time Step Reference, ETos Reference, ETrs ETos, ETrs Rn, G
Cn Cd Cn Cd
Daily 900 0.34 1600 0.38 mm/day MJ/m2/day
Hourly, daytime 37 0.24 66 0.25 mm/hour MJ/m2/hour
Hourly, nighttime 37 0.96 66 1.7 mm/hour MJ/m2/hour









Table 1-2. Irrigation water use categorization adapted from Allen et al., 2005

Consumptive use Nonconsumptive use

Evaporated fraction Nonreusable fraction Reusable fraction
a Crop ET o Nonreusable o Reusable
o Landscape ET deep percolation deep percolation
Beneficial uses o Evaporation for for salt leaching for salt leaching
climate control o Water exported
from basin
a Phreatophyte ET o Nonreusable o Reusable
o Sprinkler evaporation deep percolation excess deep
.o Reservoir evaporation due to contamination percolation
Nonbeneficial uses.
o Excess wet soil o Excess deep o Reusable runoff,
evaporation percolation, runoff canal overflows
to saline sinks


I Runoff
Evaporation~1


Root zone
Subsurface flow in
Subsurfap~e flow out





Capillary rise Deep percolation

Figure 1-1. Water balance diagram


Transpiration


Precipitation




Irrigation









































r 1 .-.
).;*5- ~s~d
~c~-~ i c~p ?
L
~GL


-r ~u~n
f
.3


Figure 1-2. Shallow root system (~12 cm) of mature (8 years) southern highbush blueberry plant
removed for lysimeter installation









CHAPTER 2
WATER BALANCE EXPERIMENTS: THE MATERIALS AND METHODS OF
MEASURING CROP ET

Experiment Design and Irrigation Systems Description

Experiment 1: Island Grove Ag Products

Water balance data collected at a commercial blueberry farm, Island Grove Ag Products

(IGAP), in Island Grove, Florida, was used to determine crop water use and crop coefficients of

mature southern highbush blueberries. Additionally, grower-controlled irrigation management

was compared with researcher-managed irrigation schedule. Lysimeters were used to facilitate a

water balance and the determination of ETc. The duration of a single water balance was the time

between lysimeter withdrawals (about 7 to 14 days). The Horticultural Sciences department of

the University of Florida maintains a 0. 12 hectare plot of Southern Highbush blueberries at the

blueberry farm. This plot is irrigated independently of the grower' s fields, enabling comparison

of ETc, yield, and plant size between grower-controlled and UF's timer-controlled irrigation

management. Both the irrigation systems, the grower's and UF's, use overhead impact

sprinklers at a height of 2 meters and riser spacing of 12 by 12 meters. Nozzles on the grower's

system were Nelson F32, 4 mm. diameter. In the UF-managed plot Rainbird pat. no. 4182494

nozzles were used; nozzle pressures in both plots were measured throughout the irrigation

network using a pitot tube pressure gage and were found to be consistently 207 kPa. A rain

switch interrupts an irrigation event of a day in the UF plot if more than 6 mm. of rain falls

during the time since the last irrigation.

The information used to manage irrigation summarizes the irrigation management

distinction between the grower' s plot and the UF plot. Grower' s irrigation depths and frequency

were decided on by the grower using their experience and attention to plant health and weather.

Irrigation of the UF plot consisted of a daily timer-controlled event with lengths updated based










on lysimeter withdrawals, ensuring the crop is well-watered, but attempting to minimize deep

percolation losses to 10% of the daily crop water requirement determined from the water balance

results. The Figures 2-1, 2-2, and 2-3 show the location of the blueberry farm in Island Grove,

Florida and the locations of lysimeters in the Hields.

Irrigation uniformity testing

Irrigation application rates and uniformity were determined experimentally through

distribution uniformity tests on the grower's irrigation system and in UF's plot. Collectors of

known diameter were positioned in 6 aisles of the grower' s Hield in the area where the lysimeters

were installed, and the grower irrigated for 60 minutes. The volumes of water in the 48

collectors were measured and used to calculate the uniformity of irrigation and the application

intensity. The uniformity measure was the uniformity coefficient (CU) of Christiansen (1942);

defined as CU = 1 (average absolute deviations from mean depth) / (average collected depth).

Application intensity was calculated by dividing the average collected depth of water by the

length of the irrigation event. Similarly, irrigation uniformity was measured in the UF plot by

gridding 80 collectors in the aisle between the two rows of the plot (see Figure 2-4). After one

hour of irrigation application, collector volumes were measured and uniformity calculated.

Measuring water balance flows

The measured irrigation application rates from uniformity testing were used to convert

flow volumes obtained from the flowmeters into irrigation depths for each water balance.

Rainfall was measured and recorded at the field using a tipping bucket rain gage. Effective

precipitation, the portion of rainfall that remains in the plant root zone and contributes to

satisfying the crop water requirement, was calculated on a monthly basis from Equation 2-1; this

is required for accurate BEF beneficiall evaporation fraction) determination.

Peff = ETc I when ETc I < P, (2-1)









Peff = P when ETc I > P,
Peff = 0 when ETc I < 0,
where
Peff = effective precipitation [mm/month]
ETc = crop water requirement [mm/month]
I = irrigation depth applied [mm/month]

Changes in soil moisture were recorded from measurements by TDR soil moisture

sensors. Six TDR' s were inserted diagonally into the pine bark mulch to the depth of plant root

zones (20 cm) at both the UF and Grower plots. The mean volumetric water contents given by

the soil moisture sensors were used to determine the change in soil water between the start and

end of a water balance in each plot. Water that percolates below plant root zones was collected

by the lysimeter and was extracted and measured; mean water balance duration was 11 days.

With lysimeters to measure deep percolation water, all the necessary water balance terms were

measured (neglecting lateral flows). This enabled ETc to be determined by subtraction from the

water balance equation (equation 6, neglecting subsurface flows, runoff, and capillary rise): ETc

= I + P DP & ASW.

Lysimeters were installed in the UF plot and in a 6 six acre plot of the grower' s field, 4 in

each location, and irrigation inputs were measured by reading flowmeters that were installed in

each of the 2 laterals of UF's plot and in 2 risers in the grower' s plot. Lysimeter withdrawals

from row 17 of the Grower' s field (see Figure 2-3, above) were occasionally excluded due to the

drainage differences between the two rows that increased deep percolation depths regularly by as

much as twice the depths observed in row 25. Water table depth was observed to be as low as 10

cm. in row 17, causing lysimeters to fill to their maximum capacity, but depth to water table in

row 25 was observed to be approximately 5 cm. greater than that of row 17. Shallow water table

conditions were only problematic following large rainfall or irrigation events. The water

balances from lysimeters in row 25 yielded ETc values that were more reasonable, having values









much nearer to ETo, hence these data were retained and the data from row 17 were sometimes

excluded. Also, in August of 2006, the plant above lysimeter 5 (in row 17) became unhealthy

and required additional pruning; though the plant recovered, it was considerably smaller than the

other plants, requiring that it always be excluded from water balance data.

The neglect of lateral flows, including runoff of rainfall, in this experiment and

experiment two (described below) is a reasonable assumption for two reasons. First, the Hields

are level; second, the high inHiltration capacity of the coarse pine bark mulch allows for large

inHiltration rates, making the zero runoff assumption sound except in extraordinary storms.

Humidity, wind speed, temperature, and solar radiation were measured and recorded by an on-

site weather station, providing the information needed to calculate ETo (see section on

Measuring and modeling ET), which was used to compute Kc values.

Blueberry cultivation

The grower-controlled area and researcher-controlled area both used pine bark systems

and equal plant spacing, but had single row and paired row plantings, respectively. Therefore,

three guard plants in the paired row adj acent to the plant above a lysimeter were removed. This

avoided ETc disparities between the two areas resulting from wind and radiation blocking from

the paired row. Plants were 8 years of age and are of 3 commonly grown southern highbush

varieties: Star, Misty, and Jewel. Plant and lysimeter locations can be seen in Figure 2-3.

Experiment 2: University of Florida Plant Science Research and Education Unit

Located at the University of Florida Plant Science Research and Education Unit

(UFPSREU) in Citra, this experiment consisted of three irrigation treatments and two soil types,

making six experimental units. The irrigation treatments included a once daily soil moisture

sensor controlled treatment, and two timer-controlled treatments, a once daily schedule and a

twice daily schedule with event lengths updated, as described above, from lysimeter









withdrawals, ensuring the crop is well-watered but aiming to minimize deep percolation losses to

10% of daily ETc. Each of the 6 units was replicated 3 times, making 18 total units; therefore,

lysimeters were installed below 18 plants. Each unit contains four southern highbush blueberry

plants and was bordered by four guard plants on both sides of the unit in the row. Guard plants

were irrigated once a day with event length equal to that of the timer-controlled schedules. A

rain switch interrupted an irrigation event if more than 6 mm. of rain occurred during the time

since the last irrigation.

Acclima TDT soil moisture sensors were used to control irrigation scheduling in the soil

moisture sensor controlled treatment. The Acclima system consists of the sensor and a

controller; the controller allows communication with the user and interrupts a scheduled

irrigation event if soil moisture is above a chosen threshold. The sensor measures volumetric

water content (VWC) of the soil and is read by the controller; the user inputs the desired

volumetric water content above which they want scheduled irrigation to be interrupted. Two

Acclima systems were installed, one in the pine bark mulch and one in the mulch/soil

incorporation. Sensors were installed diagonally to the depth of plant root zones by removing

soil and mulch, inserting the sensors, and packing soil and mulch around the sensors. The

system in the bark mulch was set to irrigate unless VWC was greater than 22% and the system in

the mulch/soil incorporation was set to irrigate unless VWC was greater than 12%. These values

were selected to be 3% above the average readings in the evenings. Calibration of the sensors

during continuation of the proj ect will likely adjust these threshold values.

Irrigation application was by MaxiJet microsprinklers using Max-14 emitters (part

number MAU36E1) and flow controllers at each emitter (part number MCTBXBB). Pressure

regulators of 138 kPa (20 psi) were used. Emitters were positioned to limit the wetted area to the









evaporative area of a plant, which here is equal to the area of a lysimeter (1.022 m2). Each

replication of an experimental unit, four adj acent plants in the same soil system and under the

same irrigation treatment, was irrigated with five microsprinklers: two half emitters at the ends

of the unit and three full emitters between the four plants.

Irrigation uniformity testing

Irrigation application intensity and uniformity were measured and calculated. A

graduated cylinder was used to measure flow volume from the emitters in each replication of

each experimental unit. Flow rate from 54 full emitters and 36 half emitters were measured by

recording the volume emitted and time of flow using a graduated cylinder and stopwatch. This

information was used to determine application uniformity and intensity. The coefficient of

uniformity was calculated as described previously, and application rate was calculated by

dividing the average flow rate by the lysimeter area and adjusting units to mm/hour. A much

greater application rate was seen from the microsprinklers compared to the overhead sprinklers

of the IGAP experiment (33.4 mm/hour compared to 6.4 mm/hour) because the application rate

of microsprinklers was based only on the irrigated areas or rows of blueberries, not on the whole

field level.

Measuring water balance components

Rainfall was measured at the field and checked against local weather stations.

Flowmeters were installed to measure irrigation inputs to each of the 6 experimental groups.

Total flow was divided by the number of plants in each group (24) and by the evaporative area of

a plant (1.022 m2) to provide the depth of irrigation applied to each plant during a water balance

duration (mean water balance duration: 11 days). The map of Figure 2-5 shows the orientation

of the field and the placement of the lysimeters. Effective precipitation was calculated as

described above from Equation 2-1.










Changes in soil moisture were recorded from TDR soil moisture sensor measurements.

Three TDR' s were inserted diagonally to the depth of plant root zones (20 cm) in the pine bark

mulch and in the mulch and soil incorporation. The mean volumetric water contents given by

the soil moisture sensors were used to determine the change in soil water between the start and

end of a water balance in each plot. Water that percolated below plant root zones was collected

by the lysimeter and was extracted and measured; mean water balance duration was 11 days.

With lysimeters to measure deep percolation water, all the necessary water balance terms were

measured (neglecting lateral flows). This enabled ETc to be determined by subtraction from the

water balance equation (equation 6, neglecting subsurface flows, runoff, and capillary rise): ETc

= I + P DP & ASW.

The neglect of lateral flows, including runoff of rainfall, remains reasonable for the

reasons given above: level fields and high infiltration capacity of pine bark mulch. Similar to the

IGAP experiment, humidity, wind speed, temperature, and solar radiation were measured and

recorded by a local weather station, providing the information needed to calculate ETo; ETo was

used to compute Kc values. The Figures 2-5 and 2-6 give the location of UFPSREU and the

experiment location.

Blueberry cultivation

Eighteen lysimeters were installed (three replications for each experimental unit) prior to

transplanting of young plants into the field. Container-grown plants were transplanted at age 1

year in a single row planting system. Plant varieties were Emerald and Jewel; their locations can

be seen in Figure 2-7. At commencement of water balance, plants were 2 years of age; these are

young blueberry plants, but plants of this maturity are still highly productive (mean yield in 2007

was 8.34 tons/ha). Soils consisted of the pine bark culture of 20 cm depth and an incorporation

of 10 cm of pine bark into the top 30 cm of soil.










Lysimeter Design, Construction, Installation, and Operation

Our lysimeter design emphasizes simplicity and economy. Fabrication time for one

lysimeter is approximately V/2 Of a person-hour. Our design is a drainage-type lysimeter in which

collected deep percolation water is withdrawn from the lysimeter by applying negative pressure

in the lysimeter and by having a means to convey the water to a container. The lysimeter is

constructed by longitudinally halving a 210 liter plastic barrel. The barrel halves are fastened

together along longitudinal edges with silicone sealant and steel fasteners, producing a lysimeter

of 91.5 cm length (in the direction of planted row) and 1 12 cm width. Capped sections of 50 mm

diameter PVC well screen were placed in the bottom of each barrel half and fitted with 50 cm

lengths of flexible vacuum tubing that connect at a tee. From the tee an additional 70 cm length

of tubing is attached to facilitate a connection above the soil to the water collection system (see

the diagram of Figure 2-8).

To prepare for excavation for lysimeter installation at Experiment 1, eight mature

southern highbush blueberry plants were removed from rows with minimal root system

disruption, extraction being aided by large, wooden transplanting trays that were inserted below

the root zone and used to lift and move the plants (see Figure 2-9), shallow root zones largely

confined to the bark mulch beds aid in this step. Lysimeters were installed in the sites of plant

removal: four in the grower's field and four in UF's plot. Excavation for lysimeters was done to

install the lysimeters at a depth of 30 cm. (from soil surface to lysimeter rim). Soil removal and

repacking into lysimeter was done in a manner that minimized soil layer disruptions. Large trays

were used to separate soil types as they were removed from the lysimeter holes. Lysimeter holes

were leveled and lysimeters were inserted and repacked with soil in the appropriate soil-type

order. The mature plants were transplanted above the lysimeters in the pine bark mulch from

which they were removed. The depth of the mulch beds was approximately 20 cm.










Lysimeter installation at Experiment 2 was completed prior to planting of the field. Upon

completion of the installation, young plants were transplanted above lysimeters and in the rest of

the rows as shown in Figure 2-7. Lysimeter depth, site excavation, and repacking are the same

as described above.

Withdrawing water from the lysimeters was accomplished using a vacuum pump and

vacuum tank. At Experiment 1 in the grower's field, where electricity was not available, a

generator was used to provide power for the pump. Vacuum bottles of 20 liter capacity were

used collect and measure water withdrawn from the lysimeters. Tubing from the well screen in

the lysimeter was connected to a vacuum bottle, and tubing from the vacuum pump was

connected to the bottle to create a partial vacuum (pressure of -40 to -55 kPa) in the bottle.

Withdrawal from the lysimeters was terminated when excessive air started to enter the collection

bottle. To limit the subj activity of terminating lysimeter water collection, it was decided to end

pumping when flow into the bottle dropped below 0.5 1/min, or when pressure gages showed less

than -35 kPa. This could quickly be measured using the graduations on the bottles and a

stopwatch. At the IGAP experiment, two bottles were connected together with tubing and a tee

to allow the withdrawal of water from two lysimeters at the same time. Six bottles were

connected together and two pumps used at the UFPSREU experiment to allow the withdrawal of

water from six lysimeters at the same time (Figure 2-11), accelerating the withdrawal process.

Valves were connected inline in the tubing that j oined the bottles together; this enabled a bottle

to be disconnected from the vacuum pump if the lysimeter a bottle was collecting water had been

emptied. Volumes of extracted water were measured for each lysimeter using the graduations on

the bottles.









Soil Moisture Sensor Calibration

Campbell ScientificO CS616 TDR soil moisture sensors were calibrated for use in pine

bark mulch and in the pine bark mulch and soil incorporation. Calibration was done in the field

with 6 TDR' s. Sensors were inserted horizontally into the soil; 3 sensors were positioned in the

pine bark, and 3 were positioned in the bark/soil incorporation. Three soil moisture levels were

created by applying water to the soil in the area of 2 of the sensors for each soil. Sufficient time

was allowed for percolation of water, and just before soil sampling, water was applied to the soil

in the area of 1 of the sensors for each soil. A soil sampler was used to extract 3 soil samples of

6 cm diameter and 8 cm length along the length of each of the 6 sensors. At the time of

sampling, each sensor was queried for period average (in Cps). The samples for a single sensor

were combined into 1 container, and were taken to the lab for gravimetric determination of soil

moisture. Samples were weighed and dried and weighed following the drying. Drying was done

in a 105 OC oven for 48 hours.

A plot of volumetric soil moisture vs. period average was assembled from the 3 moisture

levels for each soil type. A regression was used to fit a second-order polynomial and a line to the

plot of volumetric soil moisture vs. period average. The in-field calibration has an advantage

over calibration in a laboratory: accurate soil compaction and root structure are difficult to

achieve in a laboratory setting.

Plant Growth and Yield

Measurements of plant growth and yield were conducted to determine the effect of

different irrigation management on crop development and fruit yield. Plant size was measured

by hand in three directions, length in the direction of the row, width, and height; the product of

the three measurements provided an estimate of plant volume (m3). At the IGAP experiment, in









both the Grower and UF plots, plant sizes were recorded for 40 plants following pruning in July

of 2006, after regrowth (late October 2006), and before pruning in May of 2007.

Yield was measured in 2007 by harvesting ripe fruit each Monday, Wednesday, and Friday

(4/5/07 to 5/21/07) from the 4 plants above lysimeters in each plot. Fruit mass was recorded

separately for the 4 plants in each plot. Total yield per plant was calculated and used to find

yield per field area in tons/ha, having 3500 plants/ha. A random sample of 10 berries was

chosen from each planted being harvested to measure berry size (kg/10 berries).

Plant sizes were recorded at the UFPSREU experiment for 24 plants in each soil/irrigation

group (6 groups x 24 plants) in June of 2006 and October of 2006. Yield was measured in 2007

by harvesting ripe fruit each Monday, Wednesday, and Friday (4/3/07 to 6/8/07) from 12 plants

in each group; total yield per plant was calculated and used to find yield per field area in tons/ha,

again assuming 3500 plants/ha. Berry size was not evaluated as done at the IGAP experiment.














ap Satellite


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Figure 2-1. Location of Island Grove Ag Products (green dot)


Figure 2-2. Island Grove Ag products lower box corresponding to UF planting, upper box

corresponding to location oflysimeters in Grower' s field


-~ O





i le L .. L w
Leanrr.

pnLin ~


L 110,54
Lau a


















Row 2-5


Figure 2-3. Plant varieties and lysimeter locations at Island Grove Agricultural Products


out Jewel
out Jewel
Jewel Jewel
Jewel Jewel
Jewel Jewel
Star Star
Jewel Jewel Lysimeter 3
Star Msy
Jewel Lyimeter I Jewel
Star Misty
Jew~el Jewel
MisyMsy
Jewel Lyimeter 2 Jewel Lyimeter 4
Star Star
Jewel Jewel
Misty Star
Jewel Jewel
Mist Star
Jewel Jewel
Misty Misty
Jewel Jewel
Mist Misty
Jewel Jewel
Misty Star
Jewel Jewel
Mist Star
Star Star
Star Misty


out x
small x
x x
x x
x x
x xv
x x
x x
x x
x x
x x
x x
x x
x xv
Jewel Lysimeter 5s x
x x
x x
x x
xv x
x x
x x
Jewel Lyimeter 6 s
1; Jewel Lysimeter 7
x x
x x
x x
x x
x Jewel Lysimeter 8


Ly-simetry- at Island Grove Ag Products


ULF planting


Grower's field


er, two center rows
Rowi 2


NW corn~
Plant &
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27


NW' corner
Plant &
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
322
23
24
25
26
27
28


Row 3


Row 17






































Figure 2-4. Collectors positioned for irrigation uniformity testing















SMao Satellite?

. Ganqe
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Figure 2-5. Location of UFPSREU (green arrow)


Figure 2-6. Location of field experiment at UFPSREU












Lysimetry at Uofnitvest of Forida Plant Science Research and Education Unit

Cultivar: J= Jewel E = Emerald
Water Collection: L =Ly'suneltr
Treatment: G= Guard Plant
S= Soil moisture sensor controlled schedule
1= Once daily schedule
2= Twice daily schedule
Soil Type: I= Bark and soil incorporation; B = Bark bed

Plant # Row I Row 2 Row 3 Row 4 Row 5 Row 6
I B B I B I
5 IE S E 1 E 'E 1 E S E 1
6 E LIS E 1 E 2 E 1IELI3SE 1
7 IE S E L4 IIE 2 E 1 E S E Ll6I
8 IE S E 1 E 2E 1 E S E 1
9 J G IJ G IJ G J G J G IJ G
10 J GIJ G J GJ G J GIJ G
11 J G IJ G IJ G J G J G IJ G
12 J G J G J GJ GJ G J G
13 IE 2 E L E 1E S E 1 E S
14 E L' 2 E Le E 1 E S E 1 IE S
15 IE 2 IE 2 IE 1 E S ELl4 IIE Ll S
16 IE 2 E 2 E 1 E S E 1 E S
17 J G J G J GJ GJ G J G
18 J GIJ G J GJ G J GIJ G
19 J G J G J GJ GJ G J G
20 J G IJ G IJ G J G J G IJ G
21 IE 1 E S E S E 2E 2 E 2
22 E I IE L6 S E i E IE LlI; 2 E I
23 E L3 IIE 5 E S E 2 IE 2 ELl8 2
24 IE 1 E S E S E 2E 2 E 2
25 J G IJ G IJ G J G J G IJ G
26 J G IJ G IJ G J G J G J G
27 J G IJ G IJ G J G J G IJ G
28 J G IJ G IJ G J G J G IJ G
29 IE 2 E S E 2E SE 1 E S
30 E IE i E 2 IE LIOS E E 5
31 E 2 E 5 E L' 2 E i E 1 IE 5
32 IE 2 E S E 2E SE 1 E S
33 J G IJ G IJ G J G J G IJ G
34 J G IJ G IJ G J G J G J G
35 J G IJ G IJ G J G J G IJ G
36 J G IJ G IJ G J G J G IJ G
37 IE S E S E 1 E 1 E 2 E 2
38 E 5 E i E L8 I E I IE E I
39 E 5 E 5 E 1 E~LIll E 2 E
40 E S E S E 1 E 1 E 2 E 2
41 J G IJ G IJ G J G J G IJ G
41 J G IJ G IJ G J G J G J G
43 J G IJ G IJ G J G J G IJ G
44 J G IJ G IJ G J G J G IJ G
45 IE 1 IE 1 E S E 2E S E 1
46 E 1 IE l E S E 1E S E 1
47 E I IE 1E L9 S ELI 21 E 5 E
48 E 1 E l E S E 2E S E 1


Figure 2-7. Lysimeters at UFPSREU

















































Figure 2-8. Lysimeter section view


30 5 cm depth below soil














































Figure 2-9. Mature blueberry plant removal for lysimeter installation A) Blueberry plant at
Experiment 1 prepared for removal; B) Root system in pine bark mulch can be easily
separated from the soil below the mulch; C) Transplanting tray positioned for plant
removal













































Figure 2-10. Lysimeter installation at UFPSREU










































Table 2-1. Calibration coefficients for use with Campbell ScientificO CS616 TDR soil moisture
sensors in pine bark mulch and mulch/soil incorporation

Calibration coefficients for CS616 TDR
linear quadratic
standard
CO Cl CO Cl C2
-0.468 0.0283 -0.0663 -0.00630 0.000700

calibrated: pine bark mulch
CO Cl CO Cl C2
0.0321 -0.486 0.00559 0.203 1.980

calibrated: pine bark mulch and soil incorporation
CO Cl CO Cl C2
0.0271 -0.299 -0.000140 0.0334 -0.368


Figure 2-11. Lysimeter water withdrawal at UFPSREU









CHAPTER 3
RESULTS OF WATER BALANCE EXPERIMENTS

Experiment 1: Island Grove Ag Products

Crop Coefficients and Crop and Reference ET

Crop evapotranspiration and crop coefficients are the most important results of this study.

These data have considerable utility for the purposes of managing irrigation more effectively and

allocating water more accurately. In Figure 3 -1, the seasonal and physiological response of ETc

can be seen. Seasonal response can be noticed by observing the correlation of ETc and ETo;

most noticeably in late fall as crop water use declines with the falling ETo values. The

physiological response is best observed from Figure 3-3; the large Kc value in August 2006 (Kc

= 1.05) is likely a result of the flush of young vegetation following the pruning that occurred in

June. By August, the young vegetation is considerable and has not matured to develop improved

stomatal control of transpiration. High cuticular transpiration may be responsible for this large

Kc. In 2007, April's relatively large Kc of 0.92 may be a response to the high fruit load during

this month. April is the month of most of the harvest and sees the most ripening of fruit for

southern highbush blueberries. The photosynthetic response to the sink demand of ripening fruit

would result in a transpiration increase. Figure 3-3 does show some response of Kc to plant

development, as mentioned, however, small sample inference reports that there is not a monthly

Kc that is significantly different from the annual mean (June 2006 to May 2007 of Kc = 0.84) of

all the months at an alpha level of 0.05. Also, 0.95 confidence intervals developed using

Tukey's method of multiple comparison are shown in Figure 3-3 and evidence no significantly

different Kc's in any month. Small sample sizes (number of water balances per month) result in

few degrees of freedom, resulting in significantly different Kc values only when Kc differences









are larger than 0.4, using qoos(12, 41) = 4.82, for 12 treatment means and 41 error degrees of

freedom. Table 3-1 shows annual mean values of Kc and daily ETc in the UF and Grower plots.

Figures 3-1 and 3-2 show similar ETc in the UF plot and the Grower's plot, and an

analysis of variances shows there is no significant daily ETc difference annually between the two

plots at an alpha level of 0.05. However, differences in monthly averages of daily ETc were

shown to be statistically significant at the level of a = 0.05 for the months in 2006 of June,

October, November, and December and in 2007 of May and June. Insufficient data were

available for statistical analysis of ETc in May of 2006, and it is supposed that the ETc

difference in May and June 2007 is likely caused by greater irrigation depth applied in the UF

plot. The cause for any difference in ETc between the two plots, whether statistically significant

or just visibly evident, is likely applied irrigation depths. Figures 3-4 and 3-5 show the

differences in irrigation management between the UF plot and the Grower' s plot, and suggest

ETc differences are caused by irrigation depth differences. In May 2006 the Grower' s irrigation

depth was approximately double the irrigation depth in the UF plot. This was the month of the

start of water balance data collection, and an insufficient amount of water was being applied in

the UF plot, providing a lower ETc than under well-watered conditions.

Irrigation, Precipitation, and Deep Percolation

The purposes of presenting irrigation and precipitation depths (Figures 3-4 and 3-5) are to

evidence differences in irrigation depths between the UF plot and Grower's plot and to show that

both types of irrigation management respond to rainfall. The Grower knows if it rains by

checking a rain gage each morning, and in the UF plot, the irrigation controller detects rain by

communication with a rain sensor with 6 mm. threshold. Again, showing effectiveness of both

types of irrigation management, a seasonal response is seen in irrigation depths.









Cold protection water (UF plot: 88 and 97 mm; Grower plot: 120 and 125 mm in January

and February, respectively) inflates the irrigation depths of January and February beyond what is

required to supply sufficient water for ETc (Figure 3-5); however, the cold protection water is

necessary for protection of flowers and fruit to ensure maximum marketable yield. Cold

protection events resulted in highly inflated ETc values in the Grower's Hield (from lysimeter

overtopping) for January and February; these values (and their corresponding Kc's) were

replaced with results to match ETc in the UF plot.

Table 3-2 presents annual total depths, from June 2006 to May 2007, of precipitation,

irrigation, deep percolation, and crop evapotranspiration. Cold protection water has been

excluded from the irrigation total in this table, and the deep percolation depth was adjusted to

satisfy the water balance. Cumulative average irrigation depths, meaning cumulative total

irrigation depth divided by total days of experiment in mm/day, were significantly different since

May 2006. Annually, from June 2006 to May 2007, cumulative daily average irrigation depths

were significantly different between the UF and Grower plots (p-value < 0.001). However,

monthly significantly different (a = 0.05) irrigation was observed only in June, October, and

December of 2006. Insufficient data were available for statistical analysis in May of 2006.

The differing values in the cumulative irrigation depth of Figure 3-6 have several

explanations. In May of 2006, irrigation depths in the UF plot were below what was required to

ensure well-watered conditions (daily irrigation application was approximately 50% of daily

ETo). The reason for this was that irrigation managers in the UF plot were trying to demonstrate

more efficient irrigation than the Grower, forgetting the importance of maintaining well-watered

conditions so that the measure of ETc is not skewed downward. After harvest and pruning of

blueberry plants in June 2006, the Grower's irrigation applications decreased. In November and









December 2006, the Grower's irrigation was minimal due to irrigation system maintenance. The

large timescale of plot of cumulative irrigation depths masks the monthly differences; these are

best seen from the previous two figures. Comparisons of deep percolation depths in the

Grower' s Hield and in the UF plot are seen in Figure 3-7, showing the vastly larger deep

percolation depths in the Grower' s Hield. While shown here to illustrate irrigation management

differences, some of these are excluded in the calculation of ETc due to suspected lysimeter

overtoppmng.

The plotted and analyzed data of irrigation in the Grower' s Hield and in the UF plot have

only considered irrigation amounts, but timing and frequency have great importance in irrigation

management also. Soil moisture sensors in each plot provide the most complete view of

frequency and timing comparisons between the two plots. Figure 3-8 shows very similar trends

in soil water content, evidencing little timing and frequency differences in irrigation; careful

inspection of the plot in Figure 3-8 A does show higher regularity of irrigation in the UF plot.

When the timescale is decreased these differences become more noticeable: during the two week

period shown in Figure 3-8 B, Hyve differences in irrigation can be seen (noted by arrows). The

conclusion from this and the above analysis showing statistically significant difference in

cumulative average daily irrigation depths is that the Grower irrigates less frequently and with

larger irrigation events. This serves to explain the larger deep percolation depths seen in the

Grower' s Hield. The mean VWC difference seen in Figure 3-8 should be known to be resultant

of soil moisture sensor response in different soils. The newer bark mulch in the UF plot results

in larger than actual VWC readings. Gravimetric measurements of field capacity of soil in both

plots showed field capacity to be 6 to 7 % VWC.










Irrigation System Performance

Similar results were found in the UF and Grower Hields in measurement of irrigation

system uniformity and application rate. In both locations, it was decided that uniformity was

sufficiently high to not require more spatially precise measures (at the locations of lysimeters) of

irrigation application rates (Table 3-3).

The calculation of BEF, as described in the review of literature in Chapter 1 (equation 1),

is made by dividing the net irrigation water requirement, QET = ETc Peff (effective

precipitation), by the total irrigation diversion. A monthly crop water requirement was

determined from the mean daily ETc values from each plot, and effective precipitation values

were calculated as explained in Chapter 2 (equation 15). There was variation in effective

precipitation values between the UF plot and the Grower's Hield due to the different irrigation

amounts and timing of irrigation in each plot. Beneficial evaporation fraction (BEF) annual

means and standard deviations (June 2006 to May 2007) were calculated and are reported in

Table 3-4. Grower's irrigation management, despite employing little technological aids,

performs comparatively well to the irrigation management of the UF plot. As expected, BEF in

the Grower' s plot is found to have greater variance from the mean than in the UF plot due to the

Grower' s less-instrumented irrigation scheduling.

Plant Growth and Yield

In order to evaluate the horticultural effect of different irrigation schedules between the

Grower' s Hield and the UF plot plant size measurements were recorded for approximately 40

plants in each plot. Measurements were recorded on 1 July 2006, soon after pruning, on 12

October 2006, following the vegetation flush that occurs during the summer, and on 22 May

2007, just before pruning. The results shown in Table 3-6 are more illustrative of cultural

management difference than irrigation management differences, meaning that pruning strategies









in the Grower's field and the UF plot are very different. The Grower prunes much more heavily

using horizontal and vertical bar mowers to prune the sides and tops of the plants; large canes are

removed by hand-pruning. In the UF plot, less biomass is removed during pruning, evidenced by

the mean plant size after pruning that is 2.5 times that of the mean plant size in the Grower' s

field. However, this does not prevent a conclusion from being made about the irrigation

scheduling effect on plant growth. In May of 2007, following harvest, no significant difference

(a = 0.05) in plant size is observed between the two plots, suggesting that pruning differences,

and not irrigation differences, are responsible for the significantly different plant sizes between

the two plots in July and October 2006. Visual observations that were recorded concerning plant

health and development offered no evidence that there were differences in plant growth and

vigor between the UF and Grower plots.

Yield comparisons between the UF and Grower plots showed no significant differences (a

= 0.05) in mean yield (Figure 3-9: overlapping confidence intervals), but fruit size was

significantly different between the UF and Grower plots. It is expected that this is explained by

cultural differences in cold protection and pruning between the two plots, not by effects of

irrigation differences. Confidence intervals were developed for small sample (nUF = Hrowe = 3)

inference using Satterthwaite's estimated degrees of freedom and the Student t distribution.

Experiment 2: University of Florida Plant Science Research and Education Unit

Crop Coefficients and Crop and Reference ET

The large ETc values (Figure 3-10) and Kc values (Figure 3-11) are difficult to explain

with certainty. However, Figures 3-11 and 3-12 support a probable the explanation that soil

water evaporation is making a more substantial contribution to ETc than it does when mature

plants are considered due to the exposed soil surfaces allowed by the small plants. Figures 3-11

and 3-12 show the response of Kc and ETc to applied water (irrigation rainfall); ETc values of










Figure 3-12 are averages from all 6 units. Though only 4 months of data were considered

(microsprinklers were installed in December of 2006 and emitters were properly positioned to

limit irrigation to the lysimeter areas by February 2007), the high correlation coefficient of the

response of ETc to applied water, (r = 0.998) seen in Figure 3-12, supports the conclusion that

soil water evaporation is inflating ETc. Kc and ETc values were calculated from irrigated areas

only for the purpose of aiding in management of microirrigation, this helps to explain the larger

than expected ETc values. On the whole field level, ETc and Kc values would be approximately

half the reported values. The treatment codes seen in Figure 3-10, 3-11, 3-13, 3-14 and Table 3-

10 are: I for incorporated soil (bark and mulch mix), B for bark mulch, 1 for once-daily

irrigation, 2 for twice-daily irrigation, S for soil moisture sensor controlled irrigation.

Soil evaporation is a function of soil type, soil cover, available energy, and available water.

With data showing little response to available energy (ETc response to ETo in Figure 3-10) and

considerable response to applied water, it is reasonable to conclude that soil evaporation is

responsible for the high ETc values, considering the wealth of data showing that transpiration

responds mostly to weather (energy) and not applied water (assuming well-watered conditions).

As described in Chapter 1, it is difficult to accurately measure or estimate the partitioning of

evaporation and transpiration, but some models may be helpful in achieving this partitioning.

Measuring leaf area index (LAI) at various development stages and using lysimeters to measure

ETc and microlysimeters to measure soil evaporation, Kang et al. (2003) developed an empirical

model relating LAI to the ratio of transpiration to evapotranspiration (T/ETc) for winter wheat

and maize. The model for maize is given (Eq. 3-1) from Kang et al. (2003):

T 1.0593LAI
(3-1)
ETc LAI +0.9032









Irrigation, Precipitation, and Deep Percolation

Similar irrigation depth totals for March to June 2007 are seen in Figure 3-13 for all

treatments, and deep percolation responds to changes in irrigation depths. Figures 3-10 (ETc)

and 3-13 (irrigation depths) further display the response of ETc to irrigation depths applied.

Table 3-8 displays the high uniformity of irrigation achieved at the UFPSREU experiment. The

application rate was calculated from summing the mean flow of two half emitters (5.30 ml/s/half

emitter) and three full emitters (9.12 ml/s/full emitter), making up a 4-plant experimental unit,

and dividing the flow by the evaporative area of 4 plants, or the area of 4 lysimeters (1.022

m2/lySim eter).

Plant Growth and Yield

Plant sizes were measured to establish baseline mean plant sizes by treatment for the newly

established plants (transplanted March 2006). A summary of these data is shown in Table 3-9.

It should be noticed that in October 2006, after pruning and regrowth, there is no significant

difference (a = 0.05) in treatment plant-size means. Continuing plant measurements and yield

data will help strengthen comparisons of soils (pine bark mulch and mulch/soil incorporation)

and irrigation treatments.

Figure 3-14 and Table 3-10 present yield information for plants of the two soil groups

and three irrigation treatments. Tukey's test was used to compare means, and it was found that

only the mean yields of the plants in the sensor-controlled irrigation treatment in the bark mulch

(B2) and the plants of the twice-daily treatment in the bark/soil incorporation (I2) were found to

be significantly different. The brevity (5 months) of establishment of irrigation schedules

weakens the dependence of yield differences on irrigation scheduling, and yield data in the

coming years will serve to determine the strength of this dependence. Interestingly, Table 3-11

shows a very convincing (p-value = 0.002) difference in yield between plants in the bark mulch



















UTF plot Grower's plot
ETc (mm/day) Kc ETc (mm/day) Kc
mean st. dev. mean st. dev. mean st. dev. mean st. dev.
4.00 1.38 0.86 0.17 3.99 1.50 0.81 0.15




Table 3-2. Annual depth totals (June 2006 to May 2007) of rainfall, irrigation, deep percolation,
and crop ET

Rain Irrigation Deep percolation ETc
UF plot Grower plot UF plot Grower plot UF plot Grower plot
783 1028 1346 351 673 1461 1456


Application Uniformity
Location rate (mm/hour) CU
UF plot 5.8 0.80
Grower's field 6.4 0.90


UJF plot Grower plot
mean st. dev. mean st. dev.
1.00 0.014 0.94 0.10


and plants in the mulch and soil incorporation, plants in the mulch and soil incorporation having


significantly higher yield.




Table 3-1. Crop ET and Kc annual averages: June 2006 to May 2007


Table 3-3. Irrigation application rates and uniformity coefficients (CU) in UF plot and Grower' s
Hield


Table 3-4. Benefieial evaporation fraction (BEF) means and standard deviations in UF plot and
Grower' s plot












Table 3-5. Effective precipitation, irrigation depths, and crop water requirement (ETc)

Monthly Effective Precipitation Monthly Irrigation Totals Crop Water Requirement
Rain Totals UF plot Grower plot UF plot Grower UF plot Grower
Month (mm) (mm) (mm) (mm) (mm) (mm) (mm)
May-06 9.7 2.5 0.0 93.0 195.3 95.5 181.7
June-06 122.0 26.7 0.0 90.0 249.0 116.7 179.4
July-06 178.0 109.4 92.0 54.7 75.6 164.1 167.7
August-06 117.0 117.0 87.3 44.8 86.8 173.8 174.1
September-06 74.4 61.8 69.6 47.6 67.5 109.4 137.1
October-06 39.9 25.5 0.0 61.7 169.4 87.2 169.0
November-06 34.0 22.7 0.0 68.4 88.6 91.1 59.5
December-06 40.9 15.5 29.5 63.1 16.8 78.6 46.3
January-07 53.9 0.0 0.0 148.3 186.7 71.2 71.2
February-07 45.0 0.0 0.0 167.6 249.0 91.8 91.8
March-07 30.5 16.7 0.0 92.3 145.5 109.0 123.8
April-07 29.0 1.3 0.1 185.9 123.9 187.2 124.0
May-07 18.5 0.0 0.0 188.9 132.0 180.4 112.3
June-07 101.6 49.2 53.3 122.8 81.9 172.0 135.2
Totals: 783.0 396.6 278.4 1213.3 1590.8 1460.5 1456.1
June-06 to May 07










Table 3-6. Plant size means and summaries of analyses of variance for Grower and UF plots


July 2006 plant size (m3), after pruning


SUMMARY
Groups
UF
Grower


Count
39
39



SS
23.363
20.506


Sum
69.456
26.767


Average
1.781
0.686



MS
23.363
0.270


Variance P-value
0.485 3.479E-14
0.0542


ANOVA
Source of Variation
Between Groups
Within Groups

Total


F
86.590


P-value F crit
3.479E-14 3.967


43.869 77


October 2006 plant size (m3), after Fall vegetation regrowth
SUMMARY


Groups
UJF
Grower


Count
43
40



SS
4.234
53.407


Sum
95.379
70.643



df
1
81


Average
2.218
1.766



MS
4.234
0.659


Variance
0.960
0.336



F
6.422


P-value
0.0132


ANOVA
Source of Variation
Between Groups
Within Groups

Total


P-value
0.0132


F crit
3.959


57.642 82


May 2007 plant size (m3), before pruning
SUMMARY


Groups
UF
Grower


Count Sum
43 111.719
40 92.456


Average
2.598
2.311



MS
1.704
0.792


Variance P-value
0.953 0.146
0.617


ANOVA
Source of Variation
Between Groups
Within Groups


SS
1.704
64.120


P-value
0.146


F crit
3.959


F
2.152


Total


65.824 82














Average yield (tons/ha) Average berry size (kg/10 berries)
UF Grower UF Grower
18.95 14.43 0.955 1.143


Application Uniformity
rate (mm/hour) CU
33.4 0.97


Table 3-7. Mean yield and berry size of mature southern highbush blueberries: 2007 harvest


Table 3-8. Application rate and coefficient of uniformity of microsprinkler irrigation at
UFP SREU










Table 3-9. Summaries of plant sizes (m3) in June and October 2006 by treatment and analysis of
variance between treatments for October 2006 sizes

Plant Size (m3) Summary
6/17/2006


Table 3-10. Yield of young southern highbush blueberries in response to soil type and irrigation
treatment: 2007 harvest

group
BS B2 B1 IS Il I2
yield (tons/ha) 6.34 7.02 8.48 8.72 9.39 10.09


SUNIMARY
Groups


Count Sum
24 4.318
23 4.217
23 3.747
24 3.177
24 3.616
24 3.033


Average
0.180
0.183
0.163
0.132
0.151
0.126


Variance
0.004
0.003
0.006
0.002
0.002
0.003


Plant Size (m3) Summary and ANOVA
10/26/2006


SU1V1VARY
Groups
IS
II
12
BS
Bl
B2


Count
24
23
23
24
24
24


Sum
9.812
10.052
9.393
8.486
9.888
8.701


Average
0.409
0.437
0.408
0.354
0.412
0.363



M~S
0.0245
0.0112


Variance
0.012
0.008
0.024
0.006
0.010
0.007


ANOVA
Source of Variation
Between Groups
Within Groups


SS df
0.122 5
1.524 136

1.647 141


P-value F crit
0.0596 2.281


F
2.183


Total





Table 3-11. Yield comparison (tons/ha) of young southern highbush blueberries considering soil
system as the main effect: 2007 harvest


SUMMARY
Groups
Bark mulch
Incorporation


Count
18
18


Sum
131.027
169.23


Average
7.27929
9.40168



M~S
40.541
3.57195


Variance
2.96414
4.17975


ANOVA
Source of Variation
Between Groups
Within Groups

Total


SS
40.541
121.446


F P-value F crit
11.3498 0.00189 4.13002


161.987 35


H UF ETc
H Grower's ETc
O ETo


g

7

6

h5




2


May-06


Jun-06


Jul-06


Aug-06 Sep-06
month


Nov-06


Dec-06


Oct-06


Figure 3-1. Daily averages of ETc and ETo for May through December 2006
















UF ETc

Grower's ETc

0 ETo


Jan-07 Feb-07 Mar-07 Apr-07 May-07

month


Figure 3-2. Daily averages of ETc and ETo for January through June 2007


Jun-07


fruit developmentI
peak harvest
leaf drop


1.1.4 puig


1.0




S0.6

0.4

0.2

0.0


Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07
month



Figure 3-3. Monthly crop coefficients for June 2006 through May 2007 for mature southern
highbush blueberry plants
















300 5UF
SGrower
~250 O Rainfall

S200

-J150

.P 100

5 -



May-06 Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06
month



Figure 3-4. Monthly depth totals of irrigation and rainfall from May through December of 2006


H UF
Grower
O Rainfall
0 UF cold protection
Grower cold protection


300

S250

S200


-J 150




.t 50


Jan-07 Feb-07 Mar-07


Apr-07


May-07


Jun-07


month


Figure 3-5. Monthly depth totals of irrigation and rainfall from January through June of 2007
































'I,


2000-

1800-

E 1600-

a 1400 UF plot
S1200 Grower plot


S800-

.g 600-









100



80~~`6 Flyietr






60 -
50



40
S30
20
10





water balance dates


Figure 3-7. Deep percolation depths at each water balance from May 2006 to June 2007


~o,












0.240 0.100

0.220-
-Grower 0.090 -
S0.200 -

0.180 0.080




0.120 -I~

0. 100 --0.050

A



0.180 0.080

0.~~~~~ ~ ~ ~ -7 1 hI l h n 0.075
0.17 -.7
0.160 5


--0.060
0.140 -1- U .5
Grower
0. 130' -0.050




Figure 3-8. Soil moisture, volumetric water content (VWC, m3/m3), in UF plot and Grower's
field: A) dates from 7 April to 26 July 2007; B) dates from 21 April to May 5 2007













-1.4

-1.2




-0.8 ~

-0.6

-0.4 L

-0.2

-0


Grower berry size


UF berry size


Figure 3-9. Mean yield and berry size of mature southern highbush blueberries with 0.95
confidence intervals: 2007 harvest


UF yield


Grower yeld













H IS
II
O I2
14.0 O BS
SB1
12.0 -1 B2
HETo
S10.0-





8.0-







Mar-07 Apr-07 May-07 Jun-07

month




Figure 3-10. Daily averages of crop ET and reference ET for microsprinkler-irrigated young
southern highbush blueberry plants: March to June 2007













MMApplied Water
-*-Kc IS
- Kc II
Kc I2
Kc BS
SKc Bl


2.50 -



2.00 -



1.50 -



1.00 -



0.50 -


-14.0


-12.0




_ 0 0 C


-6.0 a

-4.0 4


-e- Kc B2


S0.0


Mar-07


Apr-07


May-07


Jun-07


Figure 3-11. Crop coefficients and applied water (daily average of sum of irrigation and rainfall)
for March to June 2007


400 .



350 -



300 -



250 -



200 -


S= 1.125x -52.176
R2 = 0.996


150 -R
200


220 240 260 280 300 320 340 360 380 400


Sum of Irrigation and Rainfall (mm)




Figure 3-12. Total monthly ETc depth (March to June 2007) in response to total applied water


month









M IS

I I2S
0 BS

400 M B2
SRainfall
350 -9-deep percolation


-50
-45
-40 ~




-20
-15
- 0a

-0


1I
Apr-07


May-07


Mar-07


Jun-07


month


Figure 3-13. Total monthly rainfall depths and irrigation depths for each treatment





13
12
11
Cd10 01
oO II
r Bl I
8


6 O BS




soil and irrigation treatment


Figure 3-14. Yield and 0.95 confidence intervals of young southern highbush blueberries in
response to soil type and irrigation treatment: 2007 harvest









CHAPTER 4
CONCLUSIONS AND APPLICATIONS OF WATER BALANCE RESULTS

Discussion of Water Balance Experiment Results

The primary information provided by the Island Grove water balance experiment is a

reliable crop coefficient (Kc = 0.84) for mature southern highbush blueberries. Additionally, the

design of the experiment allowed some comparisons to be made between a grower' s irrigation

management and the more-instrumented irrigation management of University of Florida

researchers. As expected, the grower irrigated more than the UF-managed plot (1346 mm

compared to 1028 mm annual total: June 2006 to May 2007). This difference could have been

increased, but the necessity to supply irrigation beyond estimated crop water requirements, to

ensure well-watered conditions for the purpose of Kc determination, weakened the irrigation

depth distinctions between the two plots.

The high ETc values seen at the UFPSREU experiment site require further investigation to

determine if it is bare soil evaporation or subsurface flows (or their combination) that are

elevating the crop water requirement. Much care was used in orienting the microsprinklers to

ensure all irrigation water was applied inside the lysimeter area. The ETc and Kc data presented

are from irrigated areas only, since aisles receive no irrigation; they are not determined on a field

level .

Applications of Results

There are three likely uses of our measurements from these experiments. The first is the

use of measured crop coefficients for the purposes of scheduling irrigation. Growers would use

the Kc value in combination with values of ETo that they lookup or have provided by some

service to determine their crop water requirement. It may seem that employing Kc data may not

provide much advantage over just using ETo values (and an assumed Kc of 1.00) to schedule









irrigation, but Table 4-1 illustrates the water diversion and energy savings afforded through the

use of a more accurate Kc. The assumptions involved in assembling the table: 22% of average

annual rainfall is effective, the previous year's (May 2006 to April 2007) simulated monthly ETo

values are representative of typical values, irrigated area is 405,000 m2 (100 acres), irrigation

volumes and energy needs are for one year, and there are about 10 kWh in a liter of diesel fuel

(38 kWh/gallon of diesel fuel).

The second application of our experiment results: those responsible for determining water

allocations could use the measured Kc value in determining the allowed irrigation withdrawals of

southern highbush blueberry growers. Limiting growers' irrigation rights can be ill-received by

growers, but the goal (and usually the result) is not to limit crop production; rather, the goal is to

maintain adequate fresh water supplies for proper ecosystem functions. Thirdly, these data have

utility in the context of modeling crop water use and crop development, serving as measures with

which to calibrate or validate some model of interest. Models of crop water use and growth

cannot be used for decision making unless calibrated and validated against reliable

measurements of model outputs.

Recommendations for Project Continuation

It is suggested that the next phase of this water balance proj ect involves the

implementation of our measured Kc for the purposes of irrigation management at Island Grove.

The irrigation of the UF-managed plot could be updated daily from the previous day' s ETo; this

could be done by employing an ET controller and inputting our measured Kc. This would allow

a more rigorous comparison of irrigation management between the two plots at Island Grove and

would allow for demonstration of the use of our Kc for management purposes. ETc and Kc in

both plots could continue to be measured along with plant size and yield. It is recommended that

pruning be done in the UF-managed plot to more closely match that of the grower if yield
























Kc Difference
1 0.84
Total irrigation volume (m ) 543610.0 456632.4 86977.6
Total irrigation volume (acre-ft) 440.7 370.2 70.5
Irrigation time, hours 223.9 188.1 35.8
Pump energy, kWh 44602.9 37466.5 7136.5
Fuel requirement, liters 22243.5 18684.5 3559.0
Fuel requirement, gallons 5876.1 4935.9 940.2


comparisons are to be reliable (see Table 3-6 of post-harvest pruning sizes). Fertilizer

applications in the UF plot at the Island Grove experiment site must match those of the Grower

for plant growth and yield data to be of good integrity for demonstration of irrigation scheduling

using our measured Kc.



Table 4-1. Annual irrigation volume, time, and energy comparisons using Kc of 1.00 and 0.84
for irrigation scheduling










LIST OF REFERENCES


Allen, R.G., A.J. Clemmens, L. S. Willardson. 2005. Agro-Hydrology and Irrigation Efficiency.

Allen, R.G., L.S. Pereria, D. Raes, and M. Smith. 1998. Crop evapotranspiration Guidelines for
computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. Rome,
Italy: United Nations, Food and Agriculture Organization.

Allen, R.G., W. O. Pruitt, M. E. Jensen. 1991. Environmental Requirements for Lysimeters. In:
Lysinteters for evapotr~rt~rt~rt~ranspration~ and' environmental nzeasurentents, 170-18 1. New
York, NY: American Society of Civil Engineers.

Cuenca, Richard H. 1989. In: Brrigation System Design: An Engineering Approach, 123, 60.
Englewood Cliffs, NJ: Prentice Hall.

Dasber, S., D. Or. 1999. In: Drip irrigation, 114. New York, NY: Springer-Verlag.

Environmental and Water Resources Institute of the American Society of Civil Engineers and the
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Standardized Reference Evapotranspiration Equation, 1-5.

Falkenmark, M. and J. Rockstroim. 2004. Water Availability Expanding the Perspective. In:
Balancing Water for H~umans and Nature: the New Approach in Ecohydrology, 25-44.
Sterling, VA: Earthscan.

Haman, D. Z., R. T. Pritchard, A. G. Smaj strala, F. S. Zazueta and P. M. Lyrene. 1997.
Evapotranspiration and crop coefficients for young blueberries in Florida. Appl. Eng.
Agric. 13: 209-216.

Haman, D. Z., A. G. Smajstrla, R. T. Pritchard, F. S. Zazueta and P. M. Lyrene. 1994. Water
use in establishment of young blueberry plants. Bulletin 296. Gainesville, Fla.: Univ. of
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Holzapfel, E.A., R. F., Hepp, M.A. Marino. 2004. Effect of irrigation on fruit production in
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Kang, Shaozhong, B. Guc, T. Dua, and J. Zhang. 2003. Crop coefficient and ratio of
transpiration to evapotranspiration of winter wheat and maize in a semi-humid region.
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FAO-56 modeled evaporation from bare soil. ASCE Journal oflrrigation and Drainage
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Moisture Sensors in Drip Irrigated Sandy Soils, Vadose Zone 1 4: 1037-1047.

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Last, 179-182. New York, NY: Worldwatch Norton & Company.

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Symposium to commemorate 25 years of the IHP, 93- 126. Paris, France: UNESCO/IHP.

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Netherlands J. Agric. Sci. 35: 417-426.

Temesgen, B., S. Eching, B. Davidoff, and K. Frame. Comparison of Some Reference
Evapotranspiration Equations for California. ASCE Journal oflrrigation and Drainage
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Ventura, F., D. Spano, P. Duce, and R.L. Snyder. 1999. An evaluation of common
evapotranspiration equations. Irrigation Science 18: 163-170.

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Trans. R. Soc. London Ser. B(352): 937-947.

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BIOGRAPHICAL SKETCH

Daniel Dourte graduated from Messiah College in 2004 with a Bachelor of Science in

Mechanical Engineering. He has volunteered his engineering skills in West Africa, working on

developing improved means of mobility for physically disabled persons. A year of experience

on a vegetable farm in Pennsylvania, USA provided Dourte with experience in on-farm

management of irrigation and crops. He plans to use his experience gained at the University of

Florida in the area of water resources and crop management to work in developing areas to help

growers realize more secure livelihoods and less vulnerable farming systems.





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1 CROP WATER REQUIREMENTS AND IRRIGATION MANAGEMENT OF SOUTHERN HIGHBUSH BLUEBERRIES By DANIEL R. DOURTE 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 2007

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2 2007 Daniel R. Dourte

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3 TABLE OF CONTENTS page LIST OF TABLES ...........................................................................................................................5 LIST OF FIGURES .........................................................................................................................6 ABSTRACT .....................................................................................................................................8 CHAPTER 1 INTRODUCTION AND REVIEW OF LITERATURE ........................................................10 Introduction .............................................................................................................................10 Review of Literature ...............................................................................................................12 Irrigation ..........................................................................................................................12 Purposes of irrigation systems ..................................................................................12 Classifications of irrigation systems ........................................................................13 Irrigation efficiency ..................................................................................................14 Water use fractions ...................................................................................................15 Irrigation management .............................................................................................18 Evapotranspiration ...........................................................................................................19 Measuring and Modeling ET ..........................................................................................21 Water balance ...........................................................................................................21 Lysimetry .................................................................................................................22 Energy balance and microclimatology .....................................................................23 Modeling evapotranspiration ....................................................................................24 Crop coefficient ........................................................................................................27 Soil Water Measurement ................................................................................................30 Blueberry Production .......................................................................................................32 Soil ...................................................................................................................................33 2 WATER BALANCE EXPERIMENTS: THE MATERIALS AND METHODS OF MEASURING CROP ET .......................................................................................................37 Experiment Design and Irrigation Systems Description ........................................................37 Experiment 1: Island Grove Ag Products ........................................................................37 Irrigation uniformity testing .....................................................................................38 Measuring water balance flows ................................................................................38 Blueberry cultivation ................................................................................................40 Experiment 2: University of Florida Plant Science Research and Education Unit .........40 Irrigation uniformity testing .....................................................................................42 Measuring water balance components .....................................................................42 Blueberry cultivation ................................................................................................43 Lysimeter Design, Construction, Installation, and Operation ................................................44 Soil Moisture Sensor Calibration ............................................................................................46

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4 Plant Growth and Yield ..........................................................................................................46 3 RESULTS OF WATER BALANCE EXPERIMENTS .........................................................57 Experiment 1: Island Grove Ag Products ...............................................................................57 Crop Coefficients and Crop and Reference ET ...............................................................57 Irrigation, Precipitation, and Deep Percolation ...............................................................58 Irrigation System Performance ........................................................................................61 Plant Growth and Yield ...................................................................................................61 Experiment 2: University of Florida Plant Science Research and Education Unit ................62 Crop Coefficients and Crop and Reference ET ...............................................................62 Irrigation, Precipitation, and Deep Percolation ...............................................................64 Plant Growth and Yield ...................................................................................................64 4 CONCLUSIONS AND APPLICATIONS OF WATER BALANCE RESULTS ..................79 Discussion of Water Balance Experiment Results .................................................................79 Applications of Results ...........................................................................................................79 Recommendations for Project Continuation ...........................................................................80 LIST OF REFERENCES ...............................................................................................................82 BIOGRAPHICAL SKETCH .........................................................................................................84

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5 LIST OF TABLES Table page 1-1 Constants for use in ASCE Standardized Reference Evapotranspiration Equation from EWRI of ASCE, 2002. .......................................................................................................34 1-2 Irrigation water use categorization adapted from Allen et al., 2005 ......................................35 2-1 Calibration coefficients for use with Campbell Scientific CS616 TDR soil moisture sensors in pine bark mulch and mulch/soil incorporation .................................................56 3-1 Crop ET and Kc annual averages: June 2006 to May 2007 ...................................................65 3-2 Annual depth totals (June 2006 to May 2007) of rainfall, irrigation, deep percolation, and crop ET ........................................................................................................................65 3-3 Irrigation application rates and uniformity coefficients (CU) field ....................................................................................................................................65 3-4 Beneficial evaporation fraction (BEF) means and standard deviations in UF plot and .....................................................................................................................65 3-5 Effective precipitation, irrigation depths, and crop water requirement (ETc) ........................66 3-6 Plant size means and summaries of analyses of variance for Grower and UF plots ..............67 3-7 Mean yield and berry size of mature southern highbush blueberries: 2007 harvest ..............68 3-8 Application rate and coefficient of uniformity of microsprinkler irrigation at UFPSREU ....68 3-9 Summaries of plant sizes (m3) in June and October 2006 by treatment and analysis of variance between treatments for October 2006 sizes .........................................................69 3-10 Yield of young southern highbush blueberries in response to soil type and irrigation treatment: 2007 harvest ......................................................................................................69 3-11 Yield comparison (tons/ha) of young southern highbush blueberries considering soil system as the main effect: 2007 harvest ............................................................................70 4-1 Annual irrigation volume, time, and energy comparisons using Kc of 1.00 and 0.84 for irrigation scheduling ..........................................................................................................81

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6 LIST OF FIGURES Figure page 1-1 Water balance diagram ...........................................................................................................35 1-2 Shallow root system (~12 cm) of mature (8 years) southern highbush blueberry plant removed for lysimeter installation .....................................................................................36 2-1 Location of Island Grove Ag Products (green dot) ................................................................48 2-2 Island Grove Ag products lower box corresponding to UF planting, upper box ................................................48 2-3 Plant varieties and lysimeter locations at Island Grove Agricultural Products ......................49 2-4 Collectors positioned for irrigation uniformity testing ...........................................................50 2-5 Location of UFPSREU (green arrow) ....................................................................................51 2-6 Location of field experiment at UFPSREU ............................................................................51 2-7 Lysimeters at UFPSREU ........................................................................................................52 2-8 Lysimeter section view ...........................................................................................................53 2-9 Mature blueberry plant removal for lysimeter installation A) Blueberry plant at Experiment 1 prepared for removal; B) Root system in pine bark mulch can be easily separated from the soil below the mulch; C) Transplanting tray positioned for plant removal ..............................................................................................................................54 2-10 Lysimeter installation at UFPSREU .....................................................................................55 2-11 Lysimeter water withdrawal at UFPSREU ...........................................................................56 3-1 Daily averages of ETc and ETo for May through December 2006 ........................................70 3-2 Daily averages of ETc and ETo for January through June 2007 ............................................71 3-3 Monthly crop coefficients for June 2006 through May 2007 for mature southern highbush blueberry plants ..................................................................................................71 3-4 Monthly depth totals of irrigation and rainfall from May through December of 2006 ..........72 3-5 Monthly depth totals of irrigation and rainfall from January through June of 2007 ..............72 3-6 Cumulative irrigation depths from May 2006 to June 2007 ...................................................73 3-7 Deep percolation depths at each water balance from May 2006 to June 2007 ......................73

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7 3-8 Soil moisture, volumetric water content (VWC, m3/m3A) dates from 7 April to 26 July 2007; B) dates from 21 April to May 5 2007 ................74 3-9 Mean yield and berry size of mature southern highbush blueberries with 0.95 confidence intervals: 2007 harvest.....................................................................................75 3-10 Daily averages of crop ET and reference ET for microsprinkler-irrigated young southern highbush blueberry plants: March to June 2007 .................................................76 3-11 Crop coefficients and applied water (daily average of sum of irrigation and rainfall) for March to June 2007 ............................................................................................................77 3-12 Total monthly ETc depth (March to June 2007) in response to total applied water ............77 3-13 Total monthly rainfall depths and irrigation depths for each treatment ...............................78 3-14 Yield and 0.95 confidence intervals of young southern highbush blueberries in response to soil type and irrigation treatment: 2007 harvest .............................................78

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8 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 Engineering CROP WATER REQUIREMENTS AND IRRIGATION MANAGEMENT OF SOUTHERN HIGHBUSH BLUEBERRIES By Daniel R. Dourte December 2007 Chair: Dorota Z. Haman Major: Agricultural and Biological Engineering Climate-neutral measures of crop water use for mature blueberry plantings could offer improved irrigation management by growers, reducing irrigation diversions. In contribution to improved irrigation management practices for Florida blueberry growers, our research presents crop coefficients for mature southern highbush blueberry plants. Measures of crop water requirements were made using a water balance enabled by drainage lysimeters. Grower-controlled irrigation management was compared with a researcher-managed (UF), timer-controlled schedule. and visual observations of field conditions; daily events in the UF plot were of length determined from the water balance results of the previous week. The UF plot on a commercial blueberry farm was evapotranspiration, fruit yield, and plant size between grower-controlled and researcher-controlled irrigation management. Four lysimeters in the quarter acre researcher-managed plot were used to measure crop water use. Initiation of a second water balance experiment measuring crop water use of young southern highbush blueberry plants was completed. Three irrigation treatments are compared on

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9 plants grown in two soils: pine bark mulch and a soil and mulch incorporation. Mean water balance duration was 11 days; lysimeter water withdrawal was terminated at a chosen minimum flow rate. Irrigation inputs were measured by flowmeters, application rate and uniformity tests enabling accurate depth conversion. Precipitation inputs were measured by an onsite weather station that is also used to generate reference evapotranspiration using the American Society of Civil Engineers standardized reference evapotranspiration equation with daily time step and short reference surface. Crop coefficients for mature plants ranged from 0.67 (May 2006) to 1.05 (August 2006) with a mean of 0.84; crop coefficients responded to plant development, but monthly crop coefficients from each other. Crop coefficient values were higher in this experiment (average of 1.69) than were measured with the mature plants due to increased evaporation from exposed soil and mulch surfaces from limited canopy cover and because of Kc calculation based only on irrigated areas.

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10 CHAPTER 1 INTRODUCTION AND REVIEW OF LITERATURE Introduction The area under blueberry cultivation has more than doubled in the last 10 years. This growth trend is recently evidenced by the yield increase from about 1.5 million pounds in 1999 to greater than 3.5 million pounds in 2003, corresponding to a value increase of 250% for 4a). The current trend among growers is to plant early yielding (mid-April to early May) southern highbush blueberry cultivars, interspecific hybrids of Vaccinium corymbosum x V. ashei, V. darrowi (Williamson and Lyrene, 2004b)Lyrene, 2004a), early yields realize much higher prices; in the last ten years, the average price of fresh blueberries shipped from Florida before May 20 was $4/pound. In early April, prices of $15/pound are seen by growers. After June 1, prices drop to $1/pound or less as Florida growers compete with growers from cooler climates (Williamson et al., 2004). In an effort to benefit from the higher values of early yields, some growers have shifted from the conventionally cultivated low-lying areas characterized by soils high in organic matter and water content to cultivating higher areas with sandy soils and less susceptibility to cold damage. Accompanying this recent shift is the practice of growing blueberries in a pine bark culture that is typically deposited to a depth of six inches on top of the existing soil (Williamson and Lyrene, 2004). The pine bark culture offers the benefits of low pH and fast root system establishment; it creates a soil system that is more similar to the forest soils, having high organic matter content, which blueberries are native to. However, the low water storage capacity of the pine bark requires modified irrigation and fertilization management. Additionally, the root zone

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11 of the plants is largely limited to the pine bark culture, reducing root zone effectiveness at water extraction. The objective of our research was to improve irrigation management by determining the total water requirement for mature southern highbush blueberries and to compare potential water savings from different irrigation scheduling. This was done by using a water balance to measure the water requirements of blueberries grown in pine bark and a pine bark and soil incorporation. The water balance results (plant water use), plant size data, and fruit yield data were used to compare various irrigation scheduling strategies on blueberries grown in pine bark and in a pine bark and soil incorporation. An excellent starting point in realizing well-managed irrigation is information about crop water use. Ultimately, this crop water use information comes from field measurements, and data have been used to develop models capable of simulating crop water use. Being dependent on climatic parameters, crop water use, or crop evapotranspiration (ETc), is best presented as a function of reference evapotranspiration (ETo). This is done by defining a crop coefficient as the ratio of ETc of a well-watered crop to ETo, the water use of hypothetical reference crop, which can be determined if sufficient climate data are available. With adequate instrumentation, ETo can be measured anywhere, evidencing the utility of the crop coefficient. In places with adequate rainfall, there is still an incentive to replace irrigation water diversions with better irrigation management, increasing the amount of irrigation water that is beneficially used for crop production. The incentives to growers: lower energy costs, lower water costs, and reduced fertilizer inputs. Additional benefits are improved health of water resources and increases in water available for municipal and industrial purposes and for

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12 ecosystem function. The goal of our work is to improve the available information about crop water use of blueberries. Review of Literature Irrigation Agriculture is responsible for about 75% of global freshwater diversions (Shiklomanov, cultivated land area. These data serve to highlight the importance of irrigation in the context of global food pragricultural yield / land area ratios must continue to increase, requiring greater performance from irrigated agriculture (IPTRID, 1999). FAOSTAT reports that agricultural land area per capita (ha / capita) has fallen from 1.24 in 1970 to 0.80 in 2002, a 65% decrease in per capita agricultural land area. This was accompanied by a 60% increase in the land area that was irrigated (FAOSTAT statistical database. 2004. Food and Agriculture Organization of the United Nations. Available at http://faostat.fao.org/ accessed March 2006). Burgeoning populations will place even greater demands on the shoulders of irrigated agriculture, and if acute, local and regional water shortages are to be avoided, water use efficiency in agriculture must continue its increase. This effort is concisely stated by Dr. Marvin use efficiency (Karasov, 1means the ratio of mass of crop biomass or marketable crop to the volume of water applied to produce it. Purposes of irrigation systems There are numerous justifications for irrigation systems; some common purposes are listed below: Reduced vulnerability: moderating irregularities of precipitation

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13 Increased yield: improving the yield (mass or $) / land (area) ratio Environmental management and protection against cold damage Fertilizer application Dust control Classifications of irrigation systems Cuenca (1989) suggests broadly dividing irrigation systems in two classifications: nonpressurized and pressurized. Nonpressurized describes the original irrigation systems, known as gravity systems in many cases. Water flows along excavated soil contours near or among crops to apply water to plant root zones. Water losses from the crop root zone and surface storage (conveyance) to infiltration and evaporation are high, labor investments for excavation are high, but investments in materials are very low. Nonpressurized systems are presently finding increasing application in plant nurseries. Water is diverted to flood an area where container plants are positioned, and capillarity draws water into the containers through holes in container bottoms. The flooded area is drained and the water is stored for reuse. Subirrigation, or water table control, is a second type of nonpressurized irrigation that is feasible on appropriate soils, sandy or muck, and hydrologic conditions, shallow, restrictive subsurface layer, (Smajstrla et al., 1992). Water is applied in subirrigation by elevating the water table near the plant root zones and allowing capillarity to supply water. Surface ditches or subsurface pipes may be used to apply water to control water tables. Pressurized irrigation systems can be divided between microirrigation and sprinklers. Microirrigation systems can be further divided by differentiating between point source and line source emitters. A point source emitter provides a contained distribution of water, typically below plant vegetation. Point source emitters are often used on crops with wide spacing (trees, vineyards, berries); a system with point source emitters can compensate for pressure variations in lateral. A line source emitter is characterized by a lateral that is porous or has small orifices

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14 along its length; this type is generally used for closely spaced crops. Sprinkler irrigation systems can be categorized by differences in nozzle or lateral movement designs: solid-set, hand move, side roll (manually or motorized), big gun (moved by line coil rotation or sprinkler vehicle), linear move, and center pivot. Smajstrla et al. (1992) offer comprehensive reviews and explanations of all the above described irrigation systems: nonpressurized (surface and subirrigation) and pressurized (microirrigation and sprinkler). Irrigation efficiency An indicator of irrigation performance is necessary if some improvement is to be made in the operation of an irrigation system. Some commonly used indicators (Haman et al., 1996) are defined: Irrigation conveyance efficiency. This is the ratio of the volume of water delivered by the irrigation system to the volume of water input to the irrigation system. Irrigation application efficiency. This is the ratio of the volume of water that is evaporated or transpired to the volume of water delivered by the irrigation system. This value can never reach the desired ratio of 1 because of advective flows, runoff, wind drift, and deep percolation losses. Crop water use efficiency. This is the ratio of crop yield, marketable yield, or total crop biomass to volume of water delivered to the crop. Irrigation water use efficiency. This is the ratio of the volume of water evaporated and transpired to the volume of water delivered by the irrigation system or the ratio of the difference between irrigated and nonirrigated crop yield to the volume of water delivered by an irrigation system. The application of a quantification of evapotranspiration (ET) should be an improvement in some type of irrigation efficiency. Some appropriate reasons for increasing some measure of irrigation efficiency are given (Allen et al., 2005): Reduce water conveyance costs Reduce leaching of fertilizers and other chemicals and limit degradation of groundwater

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15 Reduce nonevaporated components of diverted water that flow into a saline system (ocean, saline lake, or brackish groundwater) and are therefore not recoverable or contaminate streams downgradient Reduce water diversions from deep, confined aquifers, in instances where the nonevaporated components of diverted water percolate to a shallower unconfined aquifer, thus changing the distribution of water between the aquifers in an undesirable way Reduce waterlogging and improve salinity control Maximize the total fraction of water delivered to crops to increase crop yields Reduce soil erosion. Water use fractions There is presently some interest in replacing the commonly used efficiency terms by water use fractions (Willardson et al., 1994; Allen et al., 2005). Efficiency can be misleading to those in other disciplines when it is used in the context of irrigation, as it may imply a loss of water. is moved, it changes phase, and it changes quality, but it is not lost. It should be considered that irrigation water uses that are defined as consumptive, accounting for water storage in plant biomass and fruit, phase change from evaporation and transpiration, or detrimental water quality change, can be judged as being either beneficial or nonbeneficial. Beneficial water uses are those that contribute to crop development and yield production. Nonbeneficial uses are those that are judged to not be aiding in crop production. Water uses that are noncomsumptive include flows that are reusable (deep percolation and runoff of water of good quality to water bodies where it can be withdrawn again). A helpful figure (Figure 1-1) was adapted from (Allen et. al., 2005) to categorize irrigation water use. The following water use fractions can be used in place of efficiencies to describe irrigation water use (Willardson, et al., 1994 and Allen et al., 2005). The beneficial evaporated fraction (BEF) describes the irrigation diversion that is used beneficially and consumptively by the crop,

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16 and it is the measure used in this research to compare irrigation performance. Essentially this is the fraction of water transpired by a crop or evaporated from soil surfaces immediately surrounding the crop. BEF = QET/Qdiv (1-1), where QET is the net irrigation water requirement: the fraction of crop water use provide by irrigation. Qdiv is the total quantity of water diverted for irrigation. BEF correlates most closely with irrigation application efficiency. It is this fraction that can be described as both consumptive and beneficial water use. The nonbeneficial evaporated fraction (NEF) is defined as shown in Equation 1-2: NEF = QE/Qdiv (1-2), where QE includes the quantity of water that evaporates from irrigation water storage reservoirs or conveyance paths (including surface and sprinkler flows). QE also includes water evaporated from excess wet soil (soil beyond the area of the crop root zone or the plant vegetative area, whichever is larger). The reusable fraction (RF) is written as shown in Equation 1-3: RF = QR/Qdiv (1-3), where QR is the quantity of water that is available for use after irrigation application. This water is returned, through natural or artificial flow paths, to a freshwater system. The nonreusable fraction (NRF) is given in Equation 1-4: NRF = QNR/Qdiv (1-4), where QNR is the quantity of water that cannot by other water users who require fresh water of good quality. This includes water discharged to saline systems and water that has declined in quality below that which is economically recoverable. The consumed fraction (CF) is defined by Equation 1-5:

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17 CF = (QNR + QET + QE + Qexp)/ Qdiv (1-5), where Qexp is the water that is exported beyond the hydrologic basin from which it is was withdrawn. The water contained in blueberries that are exported to distant watersheds is an example of Qexp. In determining these irrigation water use fractions, it is important to account for water contributions from precipitation. This is done in the net irrigation water requirement term, QET: defined QET = crop evapotranspiration effective precipitation. Failure to do so may result in inflated irrigation water use fractions. Below (Wallace and Batchelor, 1997) are four categories into which efforts at increasing the beneficial evaporated fraction (BEF) can be organized: agronomic, engineering, managerial, and institutional. Agronomic improvements relating to water use include: improved crop management, introduction of higher-yielding varieties, adoption of cropping strategies that maximize cropped area during periods of low potential evaporation and periods of high rainfall. Increasing the fraction of water a crop uses beneficially through engineering methods includes: laser leveling of flood irrigation schemes to improve irrigation uniformity, adoption of practices that increase effectiveness of rainfall, introduction of more efficient irrigation methods, such as drip irrigation and subsurface irrigation, which reduce soil evaporation, improve uniformity, and reduce drainage. Managerial means of improving irrigation includes human decisions and the tools to aid in decision-making: adoption of demand-based irrigation scheduling systems, use of deficit scheduling, better use and management of saline and waste water, improved maintenance of equipment. Related to managerial approaches to improvements in irrigation are institutional methods including: user involvement in scheme operation and maintenance, introduction of water pricing and legal frameworks to provide incentives for efficient water use and

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18 disincentives for inefficient use, introduction of integrated catchment management, improved training and extension. The starting point to enter into any attempt to increase BEF, or another chosen irrigation performance indicator, is the acquisition or measurement of a reliable crop water requirement. Estimations or measurements of evapotranspiration are crucial methods for crop water requirement determination, enabling improvements in irrigation technology and management. The research presented here of southern highbush blueberry water requirements likely finds its application in managerial and institutional efforts to increase BEF. Irrigation management Management of irrigation describes the interaction between the people responsible for irrigating and the equipment that administers irrigation water. Those responsible for managing irrigation gather information to support their decisions about irrigation frequency, timing, and amount. Information gathering may be done by human senses, in which the feel of the soil, appearance of the crop, and consideration of weather are observed to help make irrigation management decisions. Alternatively, irrigation managers may use instruments to more accurately gather relevant information about soil moisture, weather, and crop development. Postel (1999) reports on some of the more famous information systems, sensing technology, and datasets available to growers for supporting decisions about irrigation, most of them being automated, agricultural weather station networks. Growers using the California Irrigation Management Information Service were shown in 1995 to be realizing economic savings from $99/ha for alfalfa to $927/ha for lettuce (Postel, 1999). Measurements of crop water use serve to improve or validate the irrigation requirements suggested by the instruments employed to help manage irrigation. Allen et al. (1998) carefully detail the combination of crop coefficients and simulated reference evapotranspiration for the purpose of managing irrigation.

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19 Evapotranspiration Evapotranspiration (ET) is the sum of the water that evaporates from the soil and plant surfaces and the water that is transpired by a plant (from soil, through roots, to leaves where it is vaporized and nearly all of it is removed through plant stomata). Falkenmark and Rockstrm (2004) describe the presence of the term this way: Until recently it has been very cumbersome to distinguish between productive transpiration and non-productive evaporation. This has led to the combining of two thermodynamically similar but ecologically very different processes into the awkward notion of evapotranspiration. While the combination of productive and non-productive evaporation terms may be awkward for some purposes, it is a sensible combination for the purposes of irrigation management and allocation of water resources to growers because both productive transpiration and non-productive evaporation are return water flows to the atmosphere that must be replaced by irrigation. Transpiration constitutes a colossal water use when compared to the plant development it yields. In the production of 1 kg of plant biomass, 200 to 1000 kg of water are transpired. The transpiration/production ratio at least doubles when fruit yields are the denominator of interest (Seckler, 2003). This has prompted David Seckler and his colleagues at the International Water Unconvinced by the incomplete explanation that transpiration is simply a consequence of carbon dioxide intake through stomatal openings, Seckler offers three beneficial functions of transpiration. Transpiration prevents stress to plants from high temperatures. Plant surfaces would reach very high temperatures without the phase change on vegetative surfaces that facilitates heat transfer. A second purpose of transpiration is to raise water from plant roots to plant leaves.

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20 This water carries nutrients required for plant food production, and some of the water is required for photosynthesis. Nearly all of the water absorbed by roots exits a plant by the mechanism of transpiration, resulting in a tensile force in the water in the narrow plant vessels that provides some of the force required to lift water through the height of a plant. The hydraulic lifting force provided by transpiration accounts for a third purpose, one that occurs outside of the plant: flow of water and nutrients in the soil surrounding plant roots. The withdrawal of water and nutrients from the soil by roots creates a moisture gradient that results in additional water and nutrient flows through the soil. These three services of transpiration provide some justification for The energy for this phase change both on plant vegetative surfaces and soil surfaces is provided by radiation from the sun. ET corresponds to a crop water requirement, the amount of water needed for plant development. Evapotranspiration is a function of climate, crop physical environment, and crop physiology, as detailed by the lists: Climate conditions Energy supply (radiation and air temperature) Vapor pressure gradient (air humidity) Wind (wind speed) Environmental conditions Soil water content and distribution Soil hydraulic conductivity Soil water salinity Soil tilth (level of cultivation), mulch Crop characteristics Type, variety, development stage Size of vegetative surface and root zone Roughness and reflectivity of vegetation

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21 Measuring and modeling ET Evapotranspiration cannot be measured directly. Indirect measurements have been developed to enable estimation of crop water requirements. A water balance and an energy balance are common ways of measuring ET, and estimations of ET can be computed through the use of equations driven by the climatic, environmental and physiological variables and parameters affecting ET. Water balance A water balance can be used to measure ET by recording the mass or volume of water that enters and leaves a system, and computing ET to satisfy the water balance equation (equation 6). The law of conservation of mass requires that all water flows across the system boundaries sum to zero. Equation 1-6 and the diagram of Figure 1-1 illustrate the concept of balancing water flows into and out of a system. ET = I + P RO (1-6), where, I = irrigation P = precipitation RO = runoff DP = deep percolation CR = capillary rise SF = subsurface flow SW = soil water content If the control surface is the surface perimeter surrounding the plant and its root zone and all fluxes can be determined (some can usually be neglected as their contributions are small) and soil water content can be measured, ET can be found by subtraction. Change in soil water content can be measured gravimetrically or by using soil moisture sensors. Units of ET are typically given as depth over time.

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22 Lysimetry A lysimeter creates a control volume in which water balance terms can be measured. In order to account for deep percolation and capillary rise, lysimeters may record the mass of water lost by ET (weighing lysimeter) or they may be used to measure the drainage water (drainage lysimeter) that flows below the root zone. To find ET using a weighing lysimeter, the change in mass measured is divided by the density of water and then again by the evaporative area of the lysimeter. For drainage lysimeters, the water volume collected is divided by the evaporative area to compute ET. Drainage lysimeters may be either gravimetric, in which water is drained by gravity into a collection tank at an elevation less than that of the lysimeter bottom, or they may be negative pressure, in which a pump is used to create a partial vacuum to withdraw water. If all the terms in the above water balance can be accounted for and if soil water content can be measured, ET can be determined. Lysimetry has few weaknesses, but disturbed soil structure, restricted root zone, and high cost are often cited by detractors. A disturbed soil structure can be avoided by installing a lysimeter monolithically; this is done by pressing a lysimeter frame into the soil and then removing the soil core to affix a bottom surface to the lysimeter. For cropped lysimeters to accurately measure ET, some environmental requirements must be satisfied (Allen et al., 1991). Crop elevation and crop density must be the same as that of the surrounding crop. An elevated lysimeter will overestimate crop ET because of increased wind speed resulting from the loss of bordering crops. Underestimation of ET can result from encroachment of vegetative surfaces into the lysimeter area, increasing the shaded area in the lysimeter is not included in the total evaporative area, ET will be overestimated. For above-

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23 ground lysimeters and subsurface lysimeters with exposed walls, the lysimeter surfaces may reflect or radiate heat to the crop and soil, inflating ET values. Lysimeter studies are generally the reference to which modeled ET is compared. A recent evaluation (Mutziger et al., 2005) of the ability of the FAO-56 Penman-Monteith equation to predict evaporation from bare soil used recorded lysimeter data to compare evaporation from different soil types. Ventura et al. (1999) provide another of the many comparisons of ET models in the literature that rely on lysimeter data as a benchmark. Modeling of crop water use is an excellent tool, and the applications of modeled ETc will continue to increase; but actual measures of ETc, provided by lysimetry, are required if models are to be calibrated and validated. Cuenca (1989) reviewed improvement of the Blaney-Criddle method of ET estimation, a simple, empirically-derived, temperature-based relationship, of the Soil Conservation Service (SCS). The SCS was able to improve the accuracy of the Blaney-Criddle method by using measured crop water use, from lysimeter studies, to determine more accurate proportionality constants describing the ET and temperature correlation. Energy balance and microclimatology Being limited by the available energy, evapotranspiration can be found by measuring the terms in the equation that describes the balance of energy present in the ET process. The energy balance is given (Allen et al., 1998): LE = ET= RN G H (1-7), where, LE = latent heat flux from ET that is leaving the system ET = evaporative and transpirative water loss RN = net solar radiation that is entering the system G = soil heat flux H = canopy heat flux

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24 The units for the terms of Equation 1-7 are typically measured in Wm (1 mm of ETday28.36 Wm). Only energy flows in the vertical direction are considered; lateral energy flows are neglected, making this method inadequate for cropping systems having considerable exposed surfaces perpendicular to the ground surface. Therefore, this method is not appropriate for citrus, apples, blueberries, or other tree or bush crops that are grown in rows with considerable vertical areas of the crop exposed. The quantities in the above model can be estimated from climatic variables or they can be measured with sensing technologies that are able to record the required radiation input and heat flux outputs. Finding the mass transfer by quantifying the microclimate near a plant of interest is another way to estimate ET. This method also considers only vertical flows (of mass in this case), and like the energy balance is effective only above large homogenous cropping systems. Measurements of temperature, water vapor, and wind speed gradients can be used to approximate the mass transfer of water from the system (ET) through the use of a eddy covariance methods. Extensive instrumentation is required to measure the gradients of temperature, water vapor, and wind speed, limiting Modeling evapotranspiration The meteorological factors affecting ET can be used to develop models capable of estimating ET. Relationships between ET and climatic and atmospheric parameters have been determined experimentally and numerous models have related ET to various climatic variables. These models combine energy balance and mass transfer methodologies with crop-descriptive parameters to determine ET. The Penman-Monteith model is often regarded as the most accurate predictor of ET in a wide range of climates. Forms of the Penman-Moneith equation are used as the international and U.S. standard estimators of ET. A recent comparison (Temesgen et al., 2005) of some ET equations (CIMIS, Hargreaves, Penman-Monteith) demonstrated the utility of

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25 the Penman-Monteith combination equation, needing no local calibration when all input data are available. In its general form the model describes potential evapotranspiration as (Allen et al., 1998): (1-8), where, ET = evapotranspiration [mm day-1], Rn = net radiation at the crop surface [MJ m-2 day-1], G = soil heat flux density [MJ m-2 day-1], es = saturation vapour pressure [kPa], ea = actual vapour pressure [kPa], es ea = saturation vapour pressure deficit [kPa], -1], -1], rs and ra = (bulk) surface and aerodynamic resistances [s m-1], a = mean air density at constant pressure[kg m-3], cp = specific heat of the air [MJ kg-1 C-1]. A theoretical grass reference surface was defined to avoid the determination of parameters (rs and ra) for countless crop types and stages of development. With the reference surface, the Penman-Monteith method is effective at estimating the evapotranspiration of a reference crop if climate data can be measured. ETo is useful to compare evapotranspiration demands of different environments and to estimate the ET of other crops. The reference ET equation of FAO-56 (Allen et al., 1998) is: (1-9), where, ETo = reference evapotranspiration [mm day-1], Rn = net radiation at the crop surface [MJ m-2 day-1], G = soil heat flux density [MJ m-2 day-1], T = mean daily air temperature at 2 m height [C], u2 = wind speed at 2 m height [m s-1], es = saturation vapour pressure [kPa],

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26 ea = actual vapour pressure [kPa], es ea = saturation vapour pressure deficit [kPa], -1], -1]. This is the international standard for daily reference evapotranspiration determination, as declared by the United Nations Food and Agriculture Organization (UN FAO) in Irrigation and Drainage paper 56. In 1999, the Irrigation Association requested that a standardized ET equation reference evapotranspiration equation. The purpose of the benchmark equation is to standardize the calculation of reference evapotranspiration that can be used to improve transferability of crop veloped the standardized Penman-Monteith equation (Eq. 1-10). (1-10), where ETsz = standardized reference crop evapotranspiration for short (ETos) or tall (ETrs) surfaces [mm/day for daily time steps or mm/hour for hourly time steps], Rn = calculated net radiation at the crop surface [MJ m-2 d-1 for daily time steps or MJ m-2 h-1 for hourly time steps], G = soil heat flux density at the soil surface [MJ m-2 d-1 for daily time steps or MJ m-2 h-1 for hourly time steps], T = mean daily or hourly air temperature at 1.5 to 2.5-m height [C], u2 = mean daily or hourly wind speed at 2-m height [m/s], es = saturation vapor pressure at 1.5 to 2.5-m height [kPa], calculated for daily time steps as the average of saturation vapor pressure at maximum and minimum air temperature, ea = mean actual vapor pressure at 1.5 to 2.5-m height [kPa], por pressure-temperature curve [kPa / C], Cn = numerator constant that changes with reference type and calculation time step, Cd = denominator constant that changes with reference type and calculation time step. Table 1-1 provides appropriate values for Cn and Cd depending on reference surface and time step. The differences from the FAO-56 equation are that a tall grass (alfalfa) or a short

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27 grass (clipped surface) reference can be used with hourly or daily time steps. Cn and Cd are chosen to match the reference grass type and time step; different Cn and Cd values are chosen for hourly estimates corresponding to daytime and nighttime. standardized and F-Monteith equations are identical if a daily time step and short reference surface are considered. Crop coefficient Calculated values of ETo enable ETc to be determined in various climates using a crop coefficient. A crop coefficient is defined as the ratio of actual crop evapotranspiration to reference evapotranspiration when the crop has access to adequate soil water and is not stressed by water quality constraints, pests, or inadequate soil fertility. Crop coefficients have been determined empirically and are tabulated for many different types of crops to facilitate determination of crop water requirements, or crop evapotranspiration, by calculating the ETo from measured climatic data. Use of a single crop coefficient combines soil evaporation and crop transpiration into one value, Kc. If a reliable value of the crop coefficient is available, a climate-specific estimate of ETc, the evapotranspiration of a specific crop, can be found by the relationship below if it is decided that combining crop transpiration and soil evaporation into one coefficient is valid. This provides a time-averaged representation of crop evapotranspiration, as soil surface moisture will vary considerably. ETc = Kc ETo (1-11), where ETc = crop evapotranspiration [mm d-1], Kc = crop coefficient [dimensionless], ETo = reference crop evapotranspiration [mm d-1]. If it is decided that two crop coefficients are needed to have a more accurate representation of crop evapotranspiration over shorter time intervals or if it is decided that the transpiration and

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28 evaporation components of ET are to be separately determined, the following expression can be used (Eq. 1-12). The basal crop coefficient (Kcb) is the ratio ETc/ETo when the top surface of the soil is dry and there is adequate moisture in the root zone for maximum plant transpiration. The soil water evaporation coefficient (Ke) is determined from a water balance of the top layer of soil. ETc = (Kcb + Ke) ETo (1-12) Ke can be separated from the total Kc by using a valid (considering the crop of interest) model to determine the transpiration to evapotranspiration ratio (T/ET); these models are usually driven by leaf area index (LAI) and require a measure of ETc to determine transpiration. Also, plants can be instrumented with stem-flow gages that use heat transfer principles to estimate transpiration, which could provide one with a Kcb value if ETo is available; Ke could be determined by subtraction using Equation 1-12. Additionally, microlysimetry can be reliably used to create a water balance of the soil near the surface (Kang et al., 2003), providing one with a measurement of soil evaporation that can be used to compute Ke. Microlysimeters should be ve area, and installation may proceed as follows. A length of PVC pipe at least as long as the maximum depth of evaporation (approximately 15 cm) can make a suitable microlysimeter. This can be done by capping the bottom end with some type of drainage screen, excavating a hole that situates the microlysimeter rim at the same level as the soil surface, inserting a larger diameter pipe section to allow for easy insertion and removal of the microlysimeter, and filling the microlysimeter with enough soil (packing as it is filled) to achieve the desired bulk density. To find Ke, the microlysimeter must be removed and weighed at chosen intervals following an irrigation or rain event. The amount of water evaporated divided by ETo is the Ke. Kang et al. used 4 cylinders under winter wheat and maize crops,

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29 measuring masses three times a day for 4 consecutive days. The procedure was repeated several times during their experiment finding ETc of the winter wheat and maize. They used their results to calibrate a model (Stroosnijder 1987) that relates ETo to soil evaporation, Es. The relation is given in Equation 1-13: (1-13), where, s = cumulative soil evaporation [mm], t = time elapsed since evaporation started [day], ETo = the reference evapotranspiration [mm/ day], tl = duration of the linear phase of soil evaporation [day], A = soil parameter [mm / 0.5 day]. Once calibrated at chosen crop development stages, the model of Equation 1-13 can provide predictions of soil evaporation given calculated ETo values. Microlysimetry can be automated in manner similar to the weighing lysimeters of larger size by installing scales to regularly measure and record the mass of microlysimeters without requiring their removal. Crop coefficients are tabulated for fixed climatic parameters as functions of crop type and crop development stage. Adjustments can be made according to the model of Equation 1-14 (Allen et al., 1998) to account for different levels of relative humidity and wind speed. Kc values for crops at the initial development stage (from the date of planting or the arrival of vegetative buds to time when ground cover is 10%), at the middle development stage (from full vegetative cover to the start of fruit maturity), and at the final development stage (from the start of fruit maturity to the time of harvest) have been empirically determined and are tabulated. (1-14), where, Kcmid (Tab) = tabulated value for development-stage-specific crop coefficient (middle in above model) u2 = mean value for daily wind speed at 2 m height over grass during the development stage under consideration [m s-1], for 1 m s-1 < u2 < 6 m s-1,

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30 RHmin = mean value for daily minimum relative humidity during the development stage under consideration [%], for 20% < RHmin < 80%, h = mean plant height during the development stage under consideration [m], for 0.1 m < h < 10 m. There is little information available in the literature on measured values of Kc for blueberries, and this research helps remedy this literature weakness. Haman et al. (1997) used drainage lysimeters to measure ETc of microirrigated young blueberries under three soil water tension levels. Kc values were calculated and found to reach 0.5 for plants of age 3. These crop coefficients were modified to be based only on water use (ETc) of irrigated areas (immediately below plants) to aid in management of microirrigation; Kc was not determined on the field level. The Pacific Northwest Cooperative Agricultural Weather Network reports a crop coefficient curve for blueberries with Kc values ranging from 0.17 before leaf appearance to 1.00 when fully vegetated (Pacific Northwest Cooperative Agricultural Weather Network. AgriMet Crop Coefficients: Blueberries. Available at http://www.usbr.gov/pn/agrimet/cropcurves/BLUBcc.html accessed July 2007) Soil water measurement Accurate measures of soil moisture are needed for a water balance to accurately provide a value for ETc; change in soil moisture being one of the fluxes of a water balance. Dielectric-based sensors have seen wide application since their development. A large disparity in dielectric -5)-based soil moisture sensors with the benefit of being somewhat insensitive to differences in soil composition and texture (Dasbar and Or, 1999). Sensors of this type can be broadly divided into those that estimate the dielectric constant of a medium by measuring propagation time of an electromagnetic pulse (time domain reflectometry) or by measuring the rate of voltage change in response to an excitation voltage (capacitance probe).

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31 Time Domain Reflectometry (TDR) is an effective way to indirectly and nondestructively measure the volumetric water content of soils. TDR works by sending high frequency electromagnetic pulses through the soil. The waves propagates down the wave guides of the TDR probe and reflect back to the probe with a velocity that is inversely proportional the dielectric constant of the soil-water matrix. Higher water content corresponds to lower wave velocity and longer period. The time of this wave travel can be used to determine volumetric water content by calibrating a probe or datalogger for a soil type with known dielectric constant and using a function that relates wave period to volumetric water content. Increasing application of TDR can be attributed to low calibration requirements, high accuracy and repeatability, and high spatial and temporal resolution (Muoz-Carpena, 2004). A means of measuring volumetric soil moisture that is more affordable and simpler than TDR is the capacitance method. A capacitance probe measures the time it takes to charge a capacitor in the soil when a known excitation voltage is applied. Dielectric constant and charge time are inversely proportional; a large dielectric constant (that of water) being accompanied by a short charge time. An empirically derived function relating charge time or charge voltage to volumetric water content is used to interpret probe output. Time domain transmission (TDT) soil moisture sensors work much like TDR sensors, wave propagation time being used to determine volumetric water content of a soil. However, TDT sensors measure a one-way propagation time rather than a two-way reflected propagation time recorded by a TDR sensor. TDT measures wave travel time with sensing electronics at the end of probe opposite of the wave generator, meaning equipment is needed at both ends of a sensor or the wave guides must be bent to return the electromagnetic pulse to the electronics. This provides a sensor that is more cumbersome to install than a TDR sensor; usually requiring

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32 that a place be excavated for installation and that the sensor be permanently installed. Acclima TDT soil moisture sensors and irrigation controllers are being used to control irrigation in two treatments at our water balance experiment 2 (see Chapter 2). Plauborg et al. (2005) compare the performance of TDR and TDT sensors with standard calibration in sandy soils, finding that the TDT sensors (Aquaflex, Streat Instruments) underestimated volumetric water content by up to 0.1 m3/m3 compared to the TDR (CS 616, Campbell Scientific). well with standard calibration and showed 0.04 m3/m3 variability among 5 sensors. Muoz-Carpena (2004) gives a thorough review of available techniques and technologies for measuring soil moisture. Reported accuracies of the three sensors mentioned above are 0.01 m3/m3 for TDR with standard calibration, 0.01 m3/m3 for capacitance probes with soil-specific calibration, and 0.01 to 0.02 m3/m3 for TDT with standard calibration (Muoz-Carpena, 2004). Blueberry Production Blueberries are native to the eastern United States, making them among the minority of fruit crops that are native to and presently commercially cultivated in the United States (Williamson and Lyrene, 2004a). Blueberry production in Florida has been steadily increasing as growers try to realize the greater returns afforded by early harvests. The climate in Florida allows early ripening blueberry varieties to be grown. The market responds to this early fruit by offering fruit prices that are four to five times higher in early Spring than average Summer prices (Williamson and Lyrene, 2004a and Haman et al., 1994). Florida blueberry cultivation is divided between rabbiteye varieties and southern highbush varieties. The rise in southern highbush cultivation is motivated by the early maturity and fast fruit development of the variety, enabling the high prices for early Spring fruit. Blueberries grow best in soils with high organic content and low pH. Shallow root zones (10 to 20 cm: see Figure 1-2) and the low water-holding and the pine bark mulch

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33 beds used to grow most of the newer plantings of blueberries, make irrigation a requisite for successful blueberry cultivation (Haman et al., 1997). As noted in the section discussing crop coefficients, literature on crop water use and crop coefficients for blueberries is limited, but some recent research has begun to provide some of these data. Irrigation depth on blueberries has been found to effect number and size of fruit, and type of irrigation was shown to affect yields under equal application depths (Holzapfel et al., 2004). Blueberries under microspray irrigation were found to yield significantly more fruit than blueberries under drip irrigation that were irrigated to the same depth. It was supposed that the improved distribution of the water by microsprinkler was responsible for the greater yield (Holzapfel et al., 2004). General guidelines on expected ETc of northern highbush blueberries are given by the Northwest Berry and Grape Information Network to be between 3.6 and 5.5 mm/day for blueberries grown in the Pacific Northwest (Northwest Berry and Grape Information Network. Water Management for Blueberry Fields. Available at http://berrygrape.oregonstate.edu/water-management-for-blueberry-fields/ accessed July 2007). Climate differences from the Pacific Northwest mean that southern highbush blueberries grown in the Southeastern United States can be expected to require more water. Soil Soil texture, the size of soil particles, and soil structure, the arrangement of soil particles, determine the storage capacity and mobility of water in soil. In the context of irrigation, three soil-water content levels of interest are saturation, field capacity, and wilting point. Saturation is the soil-water content marked by rapid gravitational drainage. This is the point at which nearly all pore spaces in the soil matrix are filled with water. Field capacity is the soil-water content after the rate of drainage has decreased considerably. This is often considered as an upper limit of the water available to a crop. The wilting point is often considered as the lower limit of water

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34 available to crops, the point at which plants cannot recover from wilting induced by the low soil-water content. This point depends not only on soil parameters but also on plant type. Soil-water potential describes the ability for work to be done, governing water movement in a system. Water moves from areas of high potential to areas of low potential. There are three soil-water potentials that determine the availability of soil-water (adapted from Cuenca, 1989): Gravitational soil water is water that drains through a saturated soil matrix. This water generally spends little time in the crop root zone. Pressure potential describes the pressure exerted on soil-water is dependent on soil-water content and soil texture (particle size and particle pore size). Positive soil-water pressure indicates a compression state (high water availability), occurring only at saturation; negative soil-water pressure indicates that water is under tension as a result of capillary forces exerted by soil pores. Osmotic potential is function of salinity of the soil-water. The water available to the plant decreases as the concentration of salt in the soil water increases. Soil information is necessary for irrigation managers because frequency and duration of irrigation events depends on water holding capacity of the soil. Table 1-1. Constants for use in ASCE Standardized Reference Evapotranspiration Equation from EWRI of ASCE, 2002.

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35 Table 1-2. Irrigation water use categorization adapted from Allen et al., 2005 Figure 1-1. Water balance diagram

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36 Figure 1-2. Shallow root system (~12 cm) of mature (8 years) southern highbush blueberry plant removed for lysimeter installation

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37 CHAPTER 2 WATER BALANCE EXPERIMENTS: THE MATERIALS AND METHODS OF MEASURING CROP ET Experiment Design and Irrigation Systems Description Experiment 1: Island Grove Ag Products Water balance data collected at a commercial blueberry farm, Island Grove Ag Products (IGAP), in Island Grove, Florida, was used to determine crop water use and crop coefficients of mature southern highbush blueberries. Additionally, grower-controlled irrigation management was compared with researcher-managed irrigation schedule. Lysimeters were used to facilitate a water balance and the determination of ETc. The duration of a single water balance was the time between lysimeter withdrawals (about 7 to 14 days). The Horticultural Sciences department of the University of Florida maintains a 0.12 hectare plot of Southern Highbush blueberries at the enabling comparison of ETc, yield, and plant size between grower--controlled irrigation sprinklers at a height of 2 meters and riser spacing of 12 by 12 system were Nelson F32, 4 mm. diameter. In the UF-managed plot Rainbird pat. no. 4182494 nozzles were used; nozzle pressures in both plots were measured throughout the irrigation network using a pitot tube pressure gage and were found to be consistently 207 kPa. A rain switch interrupts an irrigation event of a day in the UF plot if more than 6 mm. of rain falls during the time since the last irrigation. The information used to manage irrigation summarizes the irrigation management s plot and the UF plot. Grower irrigation depths and frequency were decided on by the grower using their experience and attention to plant health and weather. Irrigation of the UF plot consisted of a daily timer-controlled event with lengths updated based

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38 on lysimeter withdrawals, ensuring the crop is well-watered, but attempting to minimize deep percolation losses to 10% of the daily crop water requirement determined from the water balance results. The Figures 2-1, 2-2, and 2-3 show the location of the blueberry farm in Island Grove, Florida and the locations of lysimeters in the fields. Irrigation uniformity testing Irrigation application rates and uniformity were determined experimentally through distollectors of in the area where the lysimeters were installed, and the grower irrigated for 60 minutes. The volumes of water in the 48 collectors were measured and used to calculate the uniformity of irrigation and the application intensity. The uniformity measure was the uniformity coefficient (CU) of Christiansen (1942); defined as CU = 1 (average absolute deviations from mean depth) / (average collected depth). Application intensity was calculated by dividing the average collected depth of water by the length of the irrigation event. Similarly, irrigation uniformity was measured in the UF plot by gridding 80 collectors in the aisle between the two rows of the plot (see Figure 2-4). After one hour of irrigation application, collector volumes were measured and uniformity calculated. Measuring water balance flows The measured irrigation application rates from uniformity testing were used to convert flow volumes obtained from the flowmeters into irrigation depths for each water balance. Rainfall was measured and recorded at the field using a tipping bucket rain gage. Effective precipitation, the portion of rainfall that remains in the plant root zone and contributes to satisfying the crop water requirement, was calculated on a monthly basis from Equation 2-1; this is required for accurate BEF (beneficial evaporation fraction) determination. Peff = ETc I when ETc I < P, (2-1)

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39 Peff = P when ETc I > P, Peff = 0 when ETc I < 0, where Peff = effective precipitation [mm/month] ETc = crop water requirement [mm/month] I = irrigation depth applied [mm/month] Changes in soil moisture were recorded from measurements by TDR soil moisture zones (20 cm) at both the UF and Grower plots. The mean volumetric water contents given by the soil moisture sensors were used to determine the change in soil water between the start and end of a water balance in each plot. Water that percolates below plant root zones was collected by the lysimeter and was extracted and measured; mean water balance duration was 11 days. With lysimeters to measure deep percolation water, all the necessary water balance terms were measured (neglecting lateral flows). This enabled ETc to be determined by subtraction from the water balance equation (equation 6, neglecting subsurface flows, runoff, and capillary rise): ETc = I + P Lysimeters were installed in the UF plot and in a 6 six a, 4 in each location, and irrigation inputs were measured by reading flowmeters that were installed in each of the 2 l 2 Lysimeter withdrawals -3, above) were occasionally excluded due to the drainage differences between the two rows that increased deep percolation depths regularly by as much as twice the depths observed in row 25. Water table depth was observed to be as low as 10 cm. in row 17, causing lysimeters to fill to their maximum capacity, but depth to water table in row 25 was observed to be approximately 5 cm. greater than that of row 17. Shallow water table conditions were only problematic following large rainfall or irrigation events. The water balances from lysimeters in row 25 yielded ETc values that were more reasonable, having values

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40 much nearer to ETo, hence these data were retained and the data from row 17 were sometimes excluded. Also, in August of 2006, the plant above lysimeter 5 (in row 17) became unhealthy and required additional pruning; though the plant recovered, it was considerably smaller than the other plants, requiring that it always be excluded from water balance data. The neglect of lateral flows, including runoff of rainfall, in this experiment and experiment two (described below) is a reasonable assumption for two reasons. First, the fields are level; second, the high infiltration capacity of the coarse pine bark mulch allows for large infiltration rates, making the zero runoff assumption sound except in extraordinary storms. Humidity, wind speed, temperature, and solar radiation were measured and recorded by an on-site weather station, providing the information needed to calculate ETo (see section on Measuring and modeling ET), which was used to compute Kc values. Blueberry cultivation The grower-controlled area and researcher-controlled area both used pine bark systems and equal plant spacing, but had single row and paired row plantings, respectively. Therefore, three guard plants in the paired row adjacent to the plant above a lysimeter were removed. This avoided ETc disparities between the two areas resulting from wind and radiation blocking from the paired row. Plants were 8 years of age and are of 3 commonly grown southern highbush varieties: Star, Misty, and Jewel. Plant and lysimeter locations can be seen in Figure 2-3. Experiment 2: University of Florida Plant Science Research and Education Unit Located at the University of Florida Plant Science Research and Education Unit (UFPSREU) in Citra, this experiment consisted of three irrigation treatments and two soil types, making six experimental units. The irrigation treatments included a once daily soil moisture sensor controlled treatment, and two timer-controlled treatments, a once daily schedule and a twice daily schedule with event lengths updated, as described above, from lysimeter

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41 withdrawals, ensuring the crop is well-watered but aiming to minimize deep percolation losses to 10% of daily ETc. Each of the 6 units was replicated 3 times, making 18 total units; therefore, lysimeters were installed below 18 plants. Each unit contains four southern highbush blueberry plants and was bordered by four guard plants on both sides of the unit in the row. Guard plants were irrigated once a day with event length equal to that of the timer-controlled schedules. A rain switch interrupted an irrigation event if more than 6 mm. of rain occurred during the time since the last irrigation. Acclima TDT soil moisture sensors were used to control irrigation scheduling in the soil moisture sensor controlled treatment. The Acclima system consists of the sensor and a controller; the controller allows communication with the user and interrupts a scheduled irrigation event if soil moisture is above a chosen threshold. The sensor measures volumetric water content (VWC) of the soil and is read by the controller; the user inputs the desired volumetric water content above which they want scheduled irrigation to be interrupted. Two Acclima systems were installed, one in the pine bark mulch and one in the mulch/soil incorporation. Sensors were installed diagonally to the depth of plant root zones by removing soil and mulch, inserting the sensors, and packing soil and mulch around the sensors. The system in the bark mulch was set to irrigate unless VWC was greater than 22% and the system in the mulch/soil incorporation was set to irrigate unless VWC was greater than 12%. These values were selected to be 3% above the average readings in the evenings. Calibration of the sensors during continuation of the project will likely adjust these threshold values. Irrigation application was by MaxiJet microsprinklers using Max-14 emitters (part number MAU36E1) and flow controllers at each emitter (part number MCTBXBB). Pressure regulators of 138 kPa (20 psi) were used. Emitters were positioned to limit the wetted area to the

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42 evaporative area of a plant, which here is equal to the area of a lysimeter (1.022 m2). Each replication of an experimental unit, four adjacent plants in the same soil system and under the same irrigation treatment, was irrigated with five microsprinklers: two half emitters at the ends of the unit and three full emitters between the four plants. Irrigation uniformity testing Irrigation application intensity and uniformity were measured and calculated. A graduated cylinder was used to measure flow volume from the emitters in each replication of each experimental unit. Flow rate from 54 full emitters and 36 half emitters were measured by recording the volume emitted and time of flow using a graduated cylinder and stopwatch. This information was used to determine application uniformity and intensity. The coefficient of uniformity was calculated as described previously, and application rate was calculated by dividing the average flow rate by the lysimeter area and adjusting units to mm/hour. A much greater application rate was seen from the microsprinklers compared to the overhead sprinklers of the IGAP experiment (33.4 mm/hour compared to 6.4 mm/hour) because the application rate of microsprinklers was based only on the irrigated areas or rows of blueberries, not on the whole field level. Measuring water balance components Rainfall was measured at the field and checked against local weather stations. Flowmeters were installed to measure irrigation inputs to each of the 6 experimental groups. Total flow was divided by the number of plants in each group (24) and by the evaporative area of a plant (1.022 m2) to provide the depth of irrigation applied to each plant during a water balance duration (mean water balance duration: 11 days). The map of Figure 2-5 shows the orientation of the field and the placement of the lysimeters. Effective precipitation was calculated as described above from Equation 2-1.

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43 Changes in soil moisture were recorded from TDR soil moisture sensor measurements. Three to the depth of plant root zones (20 cm) in the pine bark mulch and in the mulch and soil incorporation. The mean volumetric water contents given by the soil moisture sensors were used to determine the change in soil water between the start and end of a water balance in each plot. Water that percolated below plant root zones was collected by the lysimeter and was extracted and measured; mean water balance duration was 11 days. With lysimeters to measure deep percolation water, all the necessary water balance terms were measured (neglecting lateral flows). This enabled ETc to be determined by subtraction from the water balance equation (equation 6, neglecting subsurface flows, runoff, and capillary rise): ETc = I + P DP The neglect of lateral flows, including runoff of rainfall, remains reasonable for the reasons given above: level fields and high infiltration capacity of pine bark mulch. Similar to the IGAP experiment, humidity, wind speed, temperature, and solar radiation were measured and recorded by a local weather station, providing the information needed to calculate ETo; ETo was used to compute Kc values. The Figures 2-5 and 2-6 give the location of UFPSREU and the experiment location. Blueberry cultivation Eighteen lysimeters were installed (three replications for each experimental unit) prior to transplanting of young plants into the field. Container-grown plants were transplanted at age 1 year in a single row planting system. Plant varieties were Emerald and Jewel; their locations can be seen in Figure 2-7. At commencement of water balance, plants were 2 years of age; these are young blueberry plants, but plants of this maturity are still highly productive (mean yield in 2007 was 8.34 tons/ha). Soils consisted of the pine bark culture of 20 cm depth and an incorporation of 10 cm of pine bark into the top 30 cm of soil.

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44 Lysimeter Design, Construction, Installation, and Operation Our lysimeter design emphasizes simplicity and economy. Fabrication time for one lysimeter is approximately of a person-hour. Our design is a drainage-type lysimeter in which collected deep percolation water is withdrawn from the lysimeter by applying negative pressure in the lysimeter and by having a means to convey the water to a container. The lysimeter is constructed by longitudinally halving a 210 liter plastic barrel. The barrel halves are fastened together along longitudinal edges with silicone sealant and steel fasteners, producing a lysimeter of 91.5 cm length (in the direction of planted row) and 112 cm width. Capped sections of 50 mm diameter PVC well screen were placed in the bottom of each barrel half and fitted with 50 cm lengths of flexible vacuum tubing that connect at a tee. From the tee an additional 70 cm length of tubing is attached to facilitate a connection above the soil to the water collection system (see the diagram of Figure 2-8). To prepare for excavation for lysimeter installation at Experiment 1, eight mature southern highbush blueberry plants were removed from rows with minimal root system disruption, extraction being aided by large, wooden transplanting trays that were inserted below the root zone and used to lift and move the plants (see Figure 2-9), shallow root zones largely confined to the bark mulch beds aid in this step. Lysimeters were installed in the sites of plant install the lysimeters at a depth of 30 cm. (from soil surface to lysimeter rim). Soil removal and repacking into lysimeter was done in a manner that minimized soil layer disruptions. Large trays were used to separate soil types as they were removed from the lysimeter holes. Lysimeter holes were leveled and lysimeters were inserted and repacked with soil in the appropriate soil-type order. The mature plants were transplanted above the lysimeters in the pine bark mulch from which they were removed. The depth of the mulch beds was approximately 20 cm.

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45 Lysimeter installation at Experiment 2 was completed prior to planting of the field. Upon completion of the installation, young plants were transplanted above lysimeters and in the rest of the rows as shown in Figure 2-7. Lysimeter depth, site excavation, and repacking are the same as described above. Withdrawing water from the lysimeters was accomplished using a vacuum pump and generator was used to provide power for the pump. Vacuum bottles of 20 liter capacity were used collect and measure water withdrawn from the lysimeters. Tubing from the well screen in the lysimeter was connected to a vacuum bottle, and tubing from the vacuum pump was connected to the bottle to create a partial vacuum (pressure of -40 to -55 kPa) in the bottle. Withdrawal from the lysimeters was terminated when excessive air started to enter the collection bottle. To limit the subjectivity of terminating lysimeter water collection, it was decided to end pumping when flow into the bottle dropped below 0.5 l/min, or when pressure gages showed less than -35 kPa. This could quickly be measured using the graduations on the bottles and a stopwatch. At the IGAP experiment, two bottles were connected together with tubing and a tee to allow the withdrawal of water from two lysimeters at the same time. Six bottles were connected together and two pumps used at the UFPSREU experiment to allow the withdrawal of water from six lysimeters at the same time (Figure 2-11), accelerating the withdrawal process. Valves were connected inline in the tubing that joined the bottles together; this enabled a bottle to be disconnected from the vacuum pump if the lysimeter a bottle was collecting water had been emptied. Volumes of extracted water were measured for each lysimeter using the graduations on the bottles.

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46 Soil Moisture Sensor Calibration Campbell Scientific CS616 TDR soil moisture sensors were calibrated for use in pine bark mulch and in the pine bark mulch and soil incorporation. Calibration was done in the field Sensors were inserted horizontally into the soil; 3 sensors were positioned in the pine bark, and 3 were positioned in the bark/soil incorporation. Three soil moisture levels were created by applying water to the soil in the area of 2 of the sensors for each soil. Sufficient time was allowed for percolation of water, and just before soil sampling, water was applied to the soil in the area of 1 of the sensors for each soil. A soil sampler was used to extract 3 soil samples of 6 cm diameter and 8 cm length along the length of each of the 6 sensors. At the time of were combined into 1 container, and were taken to the lab for gravimetric determination of soil moisture. Samples were weighed and dried and weighed following the drying. Drying was done in a 105 C oven for 48 hours. A plot of volumetric soil moisture vs. period average was assembled from the 3 moisture levels for each soil type. A regression was used to fit a second-order polynomial and a line to the plot of volumetric soil moisture vs. period average. The in-field calibration has an advantage over calibration in a laboratory: accurate soil compaction and root structure are difficult to achieve in a laboratory setting. Plant Growth and Yield Measurements of plant growth and yield were conducted to determine the effect of different irrigation management on crop development and fruit yield. Plant size was measured by hand in three directions, length in the direction of the row, width, and height; the product of the three measurements provided an estimate of plant volume (m3). At the IGAP experiment, in

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47 both the Grower and UF plots, plant sizes were recorded for 40 plants following pruning in July of 2006, after regrowth (late October 2006), and before pruning in May of 2007. Yield was measured in 2007 by harvesting ripe fruit each Monday, Wednesday, and Friday (4/5/07 to 5/21/07) from the 4 plants above lysimeters in each plot. Fruit mass was recorded separately for the 4 plants in each plot. Total yield per plant was calculated and used to find yield per field area in tons/ha, having 3500 plants/ha. A random sample of 10 berries was chosen from each planted being harvested to measure berry size (kg/10 berries). Plant sizes were recorded at the UFPSREU experiment for 24 plants in each soil/irrigation group (6 groups x 24 plants) in June of 2006 and October of 2006. Yield was measured in 2007 by harvesting ripe fruit each Monday, Wednesday, and Friday (4/3/07 to 6/8/07) from 12 plants in each group; total yield per plant was calculated and used to find yield per field area in tons/ha, again assuming 3500 plants/ha. Berry size was not evaluated as done at the IGAP experiment.

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48 Figure 2-1. Location of Island Grove Ag Products (green dot) Figure 2-2. Island Grove Ag products lower box corresponding to UF planting, upper box

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49 Figure 2-3. Plant varieties and lysimeter locations at Island Grove Agricultural Products

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50 Figure 2-4. Collectors positioned for irrigation uniformity testing

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51 Figure 2-5. Location of UFPSREU (green arrow) Figure 2-6. Location of field experiment at UFPSREU

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52 Figure 2-7. Lysimeters at UFPSREU

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53 Figure 2-8. Lysimeter section view

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54 A B C Figure 2-9. Mature blueberry plant removal for lysimeter installation A) Blueberry plant at Experiment 1 prepared for removal; B) Root system in pine bark mulch can be easily separated from the soil below the mulch; C) Transplanting tray positioned for plant removal

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55 Figure 2-10. Lysimeter installation at UFPSREU

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56 Figure 2-11. Lysimeter water withdrawal at UFPSREU Table 2-1. Calibration coefficients for use with Campbell Scientific CS616 TDR soil moisture sensors in pine bark mulch and mulch/soil incorporation

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57 CHAPTER 3 RESULTS OF WATER BALANCE EXPERIMENTS Experiment 1: Island Grove Ag Products Crop Coefficients and Crop and Reference ET Crop evapotranspiration and crop coefficients are the most important results of this study. These data have considerable utility for the purposes of managing irrigation more effectively and allocating water more accurately. In Figure 3-1, the seasonal and physiological response of ETc can be seen. Seasonal response can be noticed by observing the correlation of ETc and ETo; most noticeably in late fall as crop water use declines with the falling ETo values. The physiological response is best observed from Figure 3-3; the large Kc value in August 2006 (Kc = 1.05) is likely a result of the flush of young vegetation following the pruning that occured in June. By August, the young vegetation is considerable and has not matured to develop improved stomatal control of transpiration. High cuticular transpiration may be responsible for this large relatively large Kc of 0.92 may be a response to the high fruit load during this month. April is the month of most of the harvest and sees the most ripening of fruit for southern highbush blueberries. The photosynthetic response to the sink demand of ripening fruit would result in a transpiration increase. Figure 3-3 does show some response of Kc to plant development, as mentioned, however, small sample inference reports that there is not a monthly Kc that is significantly different from the annual mean (June 2006 to May 2007 of Kc = 0.84) of all the months at an alpha level of 0.05. Also, 0.95 confidence intervals developed using -3 and evidence no significantly Small sample sizes (number of water balances per month) result in few degrees of freedom, resulting in significantly different Kc values only when Kc differences

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58 are larger than 0.4, using q0.05(12, 41) = 4.82, for 12 treatment means and 41 error degrees of freedom. Table 3-1 shows annual mean values of Kc and daily ETc in the UF and Grower plots. Figures 3-1 and 3-, and an analysis of variances shows there is no significant daily ETc difference annually between the two plots at an alpha level of 0.05. However, differences in monthly averages of daily ETc were October, November, and December and in 2007 of May and June. Insufficient data were available for statistical analysis of ETc in May of 2006, and it is supposed that the ETc difference in May and June 2007 is likely caused by greater irrigation depth applied in the UF plot. The cause for any difference in ETc between the two plots, whether statistically significant or just visibly evident, is likely applied irrigation depths. Figures 3-4 and 3-5 show the and suggest ETc differences are caused by irrigation depth differences. depth was approximately double the irrigation depth in the UF plot. This was the month of the start of water balance data collection, and an insufficient amount of water was being applied in the UF plot, providing a lower ETc than under well-watered conditions. Irrigation, Precipitation, and Deep Percolation The purposes of presenting irrigation and precipitation depths (Figures 3-4 and 3-5) are to both types of irrigation management respond to rainfall. The Grower knows if it rains by checking a rain gage each morning, and in the UF plot, the irrigation controller detects rain by communication with a rain sensor with 6 mm. threshold. Again, showing effectiveness of both types of irrigation management, a seasonal response is seen in irrigation depths.

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59 Cold protection water (UF plot: 88 and 97 mm; Grower plot: 120 and 125 mm in January and February, respectively) inflates the irrigation depths of January and February beyond what is required to supply sufficient water for ETc (Figure 3-5); however, the cold protection water is necessary for protection of flowers and fruit to ensure maximum marketable yield. Cold protection events resulted in highly inflated ETc values (from lysimeter replaced with results to match ETc in the UF plot. Table 3-2 presents annual total depths, from June 2006 to May 2007, of precipitation, irrigation, deep percolation, and crop evapotranspiration. Cold protection water has been excluded from the irrigation total in this table, and the deep percolation depth was adjusted to satisfy the water balance. Cumulative average irrigation depths, meaning cumulative total irrigation depth divided by total days of experiment in mm/day, were significantly different since May 2006. Annually, from June 2006 to May 2007, cumulative daily average irrigation depths were significantly different between the UF and Grower plots (p-value < 0.001). However, December of 2006. Insufficient data were available for statistical analysis in May of 2006. The differing values in the cumulative irrigation depth of Figure 3-6 have several explanations. In May of 2006, irrigation depths in the UF plot were below what was required to ensure well-watered conditions (daily irrigation application was approximately 50% of daily ETo). The reason for this was that irrigation managers in the UF plot were trying to demonstrate more efficient irrigation than the Grower, forgetting the importance of maintaining well-watered conditions so that the measure of ETc is not skewed downward. After harvest and pruning of In November and

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60 The large timescale of plot of cumulative irrigation depths masks the monthly differences; these are best seen from the previous two figures. Comparisons of deep percolation depths in the are seen in Figure 3-7, showing the vastly larger deep percoldifferences, some of these are excluded in the calculation of ETc due to suspected lysimeter overtopping. s field and in the UF plot have only considered irrigation amounts, but timing and frequency have great importance in irrigation management also. Soil moisture sensors in each plot provide the most complete view of frequency and timing comparisons between the two plots. Figure 3-8 shows very similar trends in soil water content, evidencing little timing and frequency differences in irrigation; careful inspection of the plot in Figure 3-8 A does show higher regularity of irrigation in the UF plot. When the timescale is decreased these differences become more noticeable: during the two week period shown in Figure 3-8 B, five differences in irrigation can be seen (noted by arrows). The conclusion from this and the above analysis showing statistically significant difference in cumulative average daily irrigation depths is that the Grower irrigates less frequently and with larger irrigation events. This serves to explain the larger deep percolation depths seen in the The mean VWC difference seen in Figure 3-8 should be known to be resultant of soil moisture sensor response in different soils. The newer bark mulch in the UF plot results in larger than actual VWC readings. Gravimetric measurements of field capacity of soil in both plots showed field capacity to be 6 to 7 % VWC.

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61 Irrigation System Performance Similar results were found in the UF and Grower fields in measurement of irrigation system uniformity and application rate. In both locations, it was decided that uniformity was sufficiently high to not require more spatially precise measures (at the locations of lysimeters) of irrigation application rates (Table 3-3). The calculation of BEF, as described in the review of literature in Chapter 1 (equation 1), is made by dividing the net irrigation water requirement, QET = ETc Peff (effective precipitation), by the total irrigation diversion. A monthly crop water requirement was determined from the mean daily ETc values from each plot, and effective precipitation values were calculated as explained in Chapter 2 (equation 15). There was variation in effective precipitation values between amounts and timing of irrigation in each plot. Beneficial evaporation fraction (BEF) annual means and standard deviations (June 2006 to May 2007) were calculated and are reported in Table 3-performs comparatively well to the irrigation management of the UF plot. As expected, BEF in due to the -instrumented irrigation scheduling. Plant Growth and Yield In order to evaluate the horticultural effect of different irrigation schedules between the plants in each plot. Measurements were recorded on 1 July 2006, soon after pruning, on 12 October 2006, following the vegetation flush that occurs during the summer, and on 22 May 2007, just before pruning. The results shown in Table 3-6 are more illustrative of cultural management difference than irrigation management differences, meaning that pruning strategies

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62 and the UF plot are very different. The Grower prunes much more heavily using horizontal and vertical bar mowers to prune the sides and tops of the plants; large canes are removed by hand-pruning. In the UF plot, less biomass is removed during pruning, evidenced by field. However, this does not prevent a conclusion from being made about the irrigation scheduling effect on plant growth. In May of 2007, following harvest, no significant difference () in plant size is observed between the two plots, suggesting that pruning differences, and not irrigation differences, are responsible for the significantly different plant sizes between the two plots in July and October 2006. Visual observations that were recorded concerning plant health and development offered no evidence that there were differences in plant growth and vigor between the UF and Grower plots. Yield comparisons between the UF and Grower plots showed no significant diffe= 0.05) in mean yield (Figure 3-9: overlapping confidence intervals), but fruit size was significantly different between the UF and Grower plots. It is expected that this is explained by cultural differences in cold protection and pruning between the two plots, not by effects of irrigation differences. Confidence intervals were developed for small sample (nUF = nGrower = 3) t distribution. Experiment 2: University of Florida Plant Science Research and Education Unit Crop Coefficients and Crop and Reference ET The large ETc values (Figure 3-10) and Kc values (Figure 3-11) are difficult to explain with certainty. However, Figures 3-11 and 3-12 support a probable the explanation that soil water evaporation is making a more substantial contribution to ETc than it does when mature plants are considered due to the exposed soil surfaces allowed by the small plants. Figures 3-11 and 3-12 show the response of Kc and ETc to applied water (irrigation rainfall); ETc values of

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63 Figure 3-12 are averages from all 6 units. Though only 4 months of data were considered (microsprinklers were installed in December of 2006 and emitters were properly positioned to limit irrigation to the lysimeter areas by February 2007), the high correlation coefficient of the response of ETc to applied water, (r = 0.998) seen in Figure 3-12, supports the conclusion that soil water evaporation is inflating ETc. Kc and ETc values were calculated from irrigated areas only for the purpose of aiding in management of microirrigation, this helps to explain the larger than expected ETc values. On the whole field level, ETc and Kc values would be approximately half the reported values. The treatment codes seen in Figure 3-10, 3-11, 3-13, 3-14 and Table 3-10 are: I for incorporated soil (bark and mulch mix), B for bark mulch, 1 for once-daily irrigation, 2 for twice-daily irrigation, S for soil moisture sensor controlled irrigation. Soil evaporation is a function of soil type, soil cover, available energy, and available water. With data showing little response to available energy (ETc response to ETo in Figure 3-10) and considerable response to applied water, it is reasonable to conclude that soil evaporation is responsible for the high ETc values, considering the wealth of data showing that transpiration responds mostly to weather (energy) and not applied water (assuming well-watered conditions). As described in Chapter 1, it is difficult to accurately measure or estimate the partitioning of evaporation and transpiration, but some models may be helpful in achieving this partitioning. Measuring leaf area index (LAI) at various development stages and using lysimeters to measure ETc and microlysimeters to measure soil evaporation, Kang et al. (2003) developed an empirical model relating LAI to the ratio of transpiration to evapotranspiration (T/ETc) for winter wheat and maize. The model for maize is given (Eq. 3-1) from Kang et al. (2003): (3-1)

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64 Irrigation, Precipitation, and Deep Percolation Similar irrigation depth totals for March to June 2007 are seen in Figure 3-13 for all treatments, and deep percolation responds to changes in irrigation depths. Figures 3-10 (ETc) and 3-13 (irrigation depths) further display the response of ETc to irrigation depths applied. Table 3-8 displays the high uniformity of irrigation achieved at the UFPSREU experiment. The application rate was calculated from summing the mean flow of two half emitters (5.30 ml/s/half emitter) and three full emitters (9.12 ml/s/full emitter), making up a 4-plant experimental unit, and dividing the flow by the evaporative area of 4 plants, or the area of 4 lysimeters (1.022 m2/lysimeter). Plant Growth and Yield Plant sizes were measured to establish baseline mean plant sizes by treatment for the newly established plants (transplanted March 2006). A summary of these data is shown in Table 3-9. It should be noticed that in October 2006, after pruning and regrowth, there is no significant plant-size means. Continuing plant measurements and yield data will help strengthen comparisons of soils (pine bark mulch and mulch/soil incorporation) and irrigation treatments. Figure 3-14 and Table 3-10 present yield information for plants of the two soil groups only the mean yields of the plants in the sensor-controlled irrigation treatment in the bark mulch (B2) and the plants of the twice-daily treatment in the bark/soil incorporation (I2) were found to be significantly different. The brevity (5 months) of establishment of irrigation schedules weakens the dependence of yield differences on irrigation scheduling, and yield data in the coming years will serve to determine the strength of this dependence. Interestingly, Table 3-11 shows a very convincing (p-value = 0.002) difference in yield between plants in the bark mulch

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65 and plants in the mulch and soil incorporation, plants in the mulch and soil incorporation having significantly higher yield. Table 3-1. Crop ET and Kc annual averages: June 2006 to May 2007 Table 3-2. Annual depth totals (June 2006 to May 2007) of rainfall, irrigation, deep percolation, and crop ET Table 3-field Table 3-4. Beneficial evaporation fraction (BEF) means and standard deviations in UF plot and

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66 Table 3-5. Effective precipitation, irrigation depths, and crop water requirement (ETc)

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67 Table 3-6. Plant size means and summaries of analyses of variance for Grower and UF plots

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68 Table 3-7. Mean yield and berry size of mature southern highbush blueberries: 2007 harvest Table 3-8. Application rate and coefficient of uniformity of microsprinkler irrigation at UFPSREU

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69 Table 3-9. Summaries of plant sizes (m3) in June and October 2006 by treatment and analysis of variance between treatments for October 2006 sizes Table 3-10. Yield of young southern highbush blueberries in response to soil type and irrigation treatment: 2007 harvest

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70 Table 3-11. Yield comparison (tons/ha) of young southern highbush blueberries considering soil system as the main effect: 2007 harvest Figure 3-1. Daily averages of ETc and ETo for May through December 2006

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71 Figure 3-2. Daily averages of ETc and ETo for January through June 2007 Figure 3-3. Monthly crop coefficients for June 2006 through May 2007 for mature southern highbush blueberry plants pru ning fruit development peak harvest leaf drop

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72 Figure 3-4. Monthly depth totals of irrigation and rainfall from May through December of 2006 Figure 3-5. Monthly depth totals of irrigation and rainfall from January through June of 2007

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73 Figure 3-6. Cumulative irrigation depths from May 2006 to June 2007 Figure 3-7. Deep percolation depths at each water balance from May 2006 to June 2007

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74 A B Figure 3-8. Soil moisture, volumetric water content (VWC, m3/m3field: A) dates from 7 April to 26 July 2007; B) dates from 21 April to May 5 2007

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75 Figure 3-9. Mean yield and berry size of mature southern highbush blueberries with 0.95 confidence intervals: 2007 harvest

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76 Figure 3-10. Daily averages of crop ET and reference ET for microsprinkler-irrigated young southern highbush blueberry plants: March to June 2007

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77 Figure 3-11. Crop coefficients and applied water (daily average of sum of irrigation and rainfall) for March to June 2007 Figure 3-12. Total monthly ETc depth (March to June 2007) in response to total applied water

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78 Figure 3-13. Total monthly rainfall depths and irrigation depths for each treatment Figure 3-14. Yield and 0.95 confidence intervals of young southern highbush blueberries in response to soil type and irrigation treatment: 2007 harvest

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79 CHAPTER 4 CONCLUSIONS AND APPLICATIONS OF WATER BALANCE RESULTS Discussion of Water Balance Experiment Results The primary information provided by the Island Grove water balance experiment is a reliable crop coefficient (Kc = 0.84) for mature southern highbush blueberries. Additionally, the management and the more-instrumented irrigation management of University of Florida researchers. As expected, the grower irrigated more than the UF-managed plot (1346 mm compared to 1028 mm annual total: June 2006 to May 2007). This difference could have been increased, but the necessity to supply irrigation beyond estimated crop water requirements, to ensure well-watered conditions for the purpose of Kc determination, weakened the irrigation depth distinctions between the two plots. The high ETc values seen at the UFPSREU experiment site require further investigation to determine if it is bare soil evaporation or subsurface flows (or their combination) that are elevating the crop water requirement. Much care was used in orienting the microsprinklers to ensure all irrigation water was applied inside the lysimeter area. The ETc and Kc data presented are from irrigated areas only, since aisles receive no irrigation; they are not determined on a field level. Applications of Results There are three likely uses of our measurements from these experiments. The first is the use of measured crop coefficients for the purposes of scheduling irrigation. Growers would use the Kc value in combination with values of ETo that they lookup or have provided by some service to determine their crop water requirement. It may seem that employing Kc data may not provide much advantage over just using ETo values (and an assumed Kc of 1.00) to schedule

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80 irrigation, but Table 4-1 illustrates the water diversion and energy savings afforded through the use of a more accurate Kc. The assumptions involved in assembling the table: 22% of average (May 2006 to April 2007) simulated monthly ETo values are representative of typical values, irrigated area is 405,000 m2 (100 acres), irrigation volumes and energy needs are for one year, and there are about 10 kWh in a liter of diesel fuel (38 kWh/gallon of diesel fuel). The second application of our experiment results: those responsible for determining water allocations could use the measured Kc value in determining the allowed irrigation withdrawals of southern highbush blueberry growers. can be ill-received by growers, but the goal (and usually the result) is not to limit crop production; rather, the goal is to maintain adequate fresh water supplies for proper ecosystem functions. Thirdly, these data have utility in the context of modeling crop water use and crop development, serving as measures with which to calibrate or validate some model of interest. Models of crop water use and growth cannot be used for decision making unless calibrated and validated against reliable measurements of model outputs. Recommendations for Project Continuation It is suggested that the next phase of this water balance project involves the implementation of our measured Kc for the purposes of irrigation management at Island Grove. The irrigation of the UF-could be done by employing an ET controller and inputting our measured Kc. This would allow a more rigorous comparison of irrigation management between the two plots at Island Grove and would allow for demonstration of the use of our Kc for management purposes. ETc and Kc in both plots could continue to be measured along with plant size and yield. It is recommended that pruning be done in the UF-managed plot to more closely match that of the grower if yield

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81 comparisons are to be reliable (see Table 3-6 of post-harvest pruning sizes). Fertilizer applications in the UF plot at the Island Grove experiment site must match those of the Grower for plant growth and yield data to be of good integrity for demonstration of irrigation scheduling using our measured Kc. Table 4-1. Annual irrigation volume, time, and energy comparisons using Kc of 1.00 and 0.84 for irrigation scheduling

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82 LIST OF REFERENCES Allen, R.G., A.J. Clemmens, L. S. Willardson. 2005. Agro-Hydrology and Irrigation Efficiency. Allen, R.G., L.S. Pereria, D. Raes, and M. Smith. 1998. Crop evapotranspiration Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. Rome, Italy: United Nations, Food and Agriculture Organization. Allen, R.G., W. O. Pruitt, M. E. Jensen. 1991. Environmental Requirements for Lysimeters. In: Lysimeters for evapotranspiration and environmental measurements, 170-181. New York, NY: American Society of Civil Engineers. Cuenca, Richard H. 1989. In: Irrigation System Design: An Engineering Approach, 123, 60. Englewood Cliffs, NJ: Prentice Hall. Dasber, S., D. Or. 1999. In: Drip Irrigation, 114. New York, NY: Springer-Verlag. Environmental and Water Resources Institute of the American Society of Civil Engineers and the Standardization of Reference Evapotranspiration Task Committee. 2002. The ASCE Standardized Reference Evapotranspiration Equation, 1-5. Falkenmark, M. and J. Rockstrm. 2004. Water Availability Expanding the Perspective. In: Balancing Water for Humans and Nature: the New Approach in Ecohydrology, 25-44. Sterling, VA: Earthscan. Haman D. Z., R. T. Pritchard, A. G. Smajstrala, F. S. Zazueta and P. M. Lyrene. 1997. Evapotranspiration and crop coefficients for young blueberries in Florida. Appl. Eng. Agric. 13: 209216. Haman, D. Z., A. G. Smajstrla, R. T. Pritchard, F. S. Zazueta and P. M. Lyrene. 1994. Water use in establishment of young blueberry plants. Bulletin 296. Gainesville, Fla.: Univ. of Florida Coop. Ext. Ser. Holzapfel, E.A., R. F., Hepp, M.A. Marino. 2004. Effect of irrigation on fruit production in blueberry. Agric. Water Mmgt. 67: 173-184. International Programme for Technology and Research in Irrigation and Drainage. 1999. Poverty Reduction and Irrigated Agriculture. Issues Paper No. 1, January, p. i. Kang, Shaozhong, B. Guc, T. Dua, and J. Zhang. 2003. Crop coefficient and ratio of transpiration to evapotranspiration of winter wheat and maize in a semi-humid region. Agricultural Water Management 59(3): 239-254. Karasov, C.G. 1982. Irrigation efficiency in water delivery. Technology 2(2): 6274. Muoz-Carpena, R. 2004. Field Devices for Monitoring Soil Water Content. Bulletin 343. Gainesville, Fla.: Univ. of Florida Coop. Ext. Ser.

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83 Mutziger, A. J., C. M. Burt, D. J. Howes, R. G. Allen. 2005. Comparison of measured and FAO-56 modeled evaporation from bare soil. ASCE Journal of Irrigation and Drainage Engineering 131(1): 59-72. Plauborg, F., B. Iversen, and P. Lrke. 2005. In Situ Comparison of Three Dielectric Soil Moisture Sensors in Drip Irrigated Sandy Soils, Vadose Zone J. 4: 1037-1047. Postel, Sandra. 1999. The Productivity Frontier. In: Pillar of Sand: Can the Irrigation Miracle Last, 179-182. New York, NY: Worldwatch Norton & Company. Seckler, D. 2003. Appendix A: A Note on Transpiration. In: Water Productivity in Agriculture: Limits and Opportunities for Improvement, 311-318. Cambridge, MA: CABI. Shiklomanov, I. A. 1991. The World's Water Resources. In: Proceedings of the International Symposium to commemorate 25 years of the IHP, 93-126. Paris, France: UNESCO/IHP. Smajstrla, A. G., G. A. Clark, and D. Z. Haman. 1992. Florida Irrigation Systems. Circular 1035. Gainesville, Fla.: Univ. of Florida Coop. Ext. Ser. Stroosnijder, L. 1987. Soil evaporation: test of a practical approach under semi-arid conditions. Netherlands J. Agric. Sci. 35: 417426. Temesgen, B., S. Eching, B. Davidoff, and K. Frame. Comparison of Some Reference Evapotranspiration Equations for California. ASCE Journal of Irrigation and Drainage Engineering 131(1): 73-84. Ventura F., D. Spano, P. Duce, and R.L. Snyder. 1999. An evaluation of common evapotranspiration equations. Irrigation Science 18: 163170. Wallace, J.S., and C.H. Batchelor. 1997. Managing water resources for crop production. Philos. Trans. R. Soc. London Ser. B(352): 937947. Willardson, L.S., R.G. Allen, H.D. Frederiksen. 1994. Elimination of Irrigation Efficiencies. Question 47, Irrigation Planning and Management Measures in Harmony with the Environment. USCID 13th Technical Conference. Denver, CO: USCID. Williamson, J.G. and P.M. Lyrene. 2004a. Blueberry Varieties for Florida. Bulletin HS967. Gainesville, Fla.: Univ. of Florida Coop. Ext. Ser. Williamson, J.G. and P.M. Lyrene. 2004b. Florid. Bulletin HS742. Gainesville, Fla.: Univ. of Florida Coop. Ext. Ser. Williamson, J. G., P. M. Lyrene, T. D. Hewitt, and K. C. Ruppert. 2004. Alternative Opportunities for Small Farms: Blueberry Production Review. Bulletin RF-AC008. Gainesville, Fla.: Univ. of Florida Coop. Ext. Ser.

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84 BIOGRAPHICAL SKETCH Daniel Dourte graduated from Messiah College in 2004 with a Bachelor of Science in Mechanical Engineering. He has volunteered his engineering skills in West Africa, working on developing improved means of mobility for physically disabled persons. A year of experience on a vegetable farm in Pennsylvania, USA provided Dourte with experience in on-farm management of irrigation and crops. He plans to use his experience gained at the University of Florida in the area of water resources and crop management to work in developing areas to help growers realize more secure livelihoods and less vulnerable farming systems.


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