Crop Wateruse Watermelon

Water Use and Crop Coefficient for Watermelon in Southwest Florida

Sanjay Shukla

Fouad Jaber

Saurabh Srivastava

James Knowles

Agricultural and Biological Engineering Department

September 2007

Southwest Florida Research and Education Center, Immokalee

Institute of Food and Agricultural Sciences (IFAS)

University of Florida

Immokalee, FL 34142

FINAL REPORT

Report No. WRP-LY-0009

Deliverable 9

Submitted to:

Southwest Florida Water Management District

Brooksville, Florida

2

Table of Contents

Table of Contents ................................................................................................................ 2

List of Figures ..................................................................................................................... 5

List of Tables ...................................................................................................................... 6

Executive Summary ............................................................................................................ 8

Introduction ....................................................................................................................... 10

Objective ........................................................................................................................... 11

Literature Review.............................................................................................................. 12

Evapotranspiration ........................................................................................................ 12

Reference evapotranspiration (ETo) .............................................................................. 12

Crop evapotranspiration ................................................................................................ 14

Crop coefficient ............................................................................................................ 14

Crop coefficient estimation ........................................................................................... 15

Design considerations for lysimeters ............................................................................ 16

Lysimeter-based crop coefficients ................................................................................ 17

Fetch and buffer area requirements ............................................................................... 19

Material and Methods ....................................................................................................... 20

Study Area .................................................................................................................... 20

Experimental Design ..................................................................................................... 20

3

Survey of crop production practices ............................................................................. 21

Lysimeter Water Balance .............................................................................................. 21

Lysimeter Design, Construction, and Installation......................................................... 22

Design and Construction ........................................................................................... 22

Lysimeter Body ..................................................................................................... 22

Drainage and Runoff Collection and Discharge ................................................... 26

Field Layout .......................................................................................................... 27

Installation............................................................................................................. 29

Irrigation Systems ................................................................................................. 31

Monitoring System........................................................................................................ 31

Irrigation, Drainage, and Runoff ............................................................................... 31

Soil moisture monitoring system .............................................................................. 31

Data collection .............................................................................................................. 32

Reference Evapotranspiration Computation ................................................................. 33

FAO-Penman-Monteith method ............................................................................... 33

Modified-modified Blaney-Criddle Method ............................................................. 33

Development of Crop Coefficient ............................................................................. 34

Crop Production Practices............................................................................................. 35

Spring 2003 ............................................................................................................... 36

Spring 2004 ............................................................................................................... 36

4

Spring 2005 ............................................................................................................... 37

Results and Discussion ..................................................................................................... 37

Water Input, Output, and Storage ................................................................................. 37

Spring 2003 ............................................................................................................... 37

Spring 2004 ............................................................................................................... 45

Spring 2005 ............................................................................................................... 52

Crop Coefficient (Kc) and Evapotranspiration (ETc) ........................................................ 63

FAO-Penman-Monteith Crop Coefficient .................................................................... 63

Modified modified Blaney-Criddle crop coefficient .................................................... 65

Summary and Conclusion ................................................................................................. 66

References ......................................................................................................................... 66

5

List of Figures

Figure 1. Study location at southwest Florida Research and Education Center

(SWFREC), Immokalee, Fl....................................................................................... 20

Figure 2. Lysimeter layout for the watermelon crop. ....................................................... 23

Figure 3. Soil profile inside the lysimeter. ........................................................................ 24

Figure 4. Sloped shape of the lysimeter base. ................................................................... 25

Figure 5. Lysimeter placement in the pit. ......................................................................... 26

Figure 6. Layout of the experimental field for the lysimeter study. ................................. 29

6

List of Tables

Table 1. Irrigation* (mm) for the four lysimeters during the spring 2003 season. ........... 38

Table 2. Drainage* (mm) for the four lysimeters during the spring 2003 season. ............ 39

Table 3. Soil moisture (%) in the bed in lysimeter D1 during the spring 2003 season. ... 39




Table 7. Daily rainfall (mm) during the spring 2003 Season. .......................................... 44

Table 8. Daily irrigation* (mm) for all lysimeters during the Spring 2004 season. ......... 46

Table 9. Soil moisture (%) in the bed for lysimeter D1 during Spring 2004. ................... 47

Table 10. Soil moisture (%) in the bed for lysimeter D2 during Spring 2004. ................. 48

Table 11. Soil moisture (%) in the bed for lysimeter D3 during Spring 2004 .................. 49


Table 13. Rainfall (mm) events during Spring 2004. ....................................................... 52

Table 14. Daily irrigation (mm) for all lysimeters during Spring 2005 season. ............... 53





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Table 19. Drainage (mm) events in all lysimeters during Spring 2005. ........................... 62

Table 20. Runoff (mm) events in all lysimeters during Spring 2005. .............................. 62

Table 21. Rainfall (mm) events during Spring 2005 ........................................................ 62

Table 22. Average monthly crop evapotranspiration (ETc), FAO-Penman-Monteith

reference evapotranspiration (ETo), and crop coefficient (Kc) for 2003, 2004 and

2005 for watermelon in southwest Florida ............................................................... 64

Table 23. Monthly crop evapotranspiration (ETc), FAO-Penman-Monteith reference

evapotranspiration (ETo), and crop coefficient (Kc) for watermelon in southwest

Florida. ...................................................................................................................... 64

Table 24. Average monthly crop evapotranspiration (ETc), modified-modified Blaney-

Criddle reference evapotranspiration (ETo), and crop coefficient (Kc) for 2003, 2004

and 2005 for watermelon in southwest Florida......................................................... 65

Table 25. Monthly crop evapotranspiration (ETc), modified-modified Blaney-Criddle

reference evapotranspiration (ETo), and crop coefficient (Kc) for watermelon in

southwest Florida. ..................................................................................................... 65

8

Executive Summary

Increasing population growth coupled with dwindling water resources makes water

conservation in Florida a state priority. Conservation measures should be implemented

for all water uses (industrial, urban and agricultural). As agriculture is the single largest

water user in Florida (Marella, 1999), improved irrigation management could result in

large water savings. Determining crop water requirements is the first step in reducing

water used while maintaining profitable production. Vegetable crops constitute a large

portion of the crops grown in Florida. A large fraction of the vegetables crops are

irrigated using drip irrigation. Drip irrigation is one of the most efficient irrigation

systems available to growers. Crop water requirements for several vegetables have not

been quantified for southwest Florida, including watermelon, one of the most abundant

vegetable crops in the region.

A three-year field study was conducted in the Southwest Florida Research and Education

Center (SWFREC), to quantify drip irrigated watermelons water requirements and to

develop crop coefficients (Kc) that will allow the SWFWMD and vegetable growers to

estimate water requirements based on the crop growth stage and climatic data. Four large

lysimeters (4.85 m x 3.65 m x 1.35 m), large metal tanks buried in the ground within an

agricultural field, were built and installed at SWFREC. These lysimeters were

instrumented to measure water input (rainfall and irrigation), output (drainage and

runoff), and storage (soil moisture). By applying a water balance, the crop

evapotranspiration (ETc) from the lysimeters can then be calculated. By dividing the

estimated ETc by a weather-based reference evapotranspiration (ETo), watermelon Kc

values were calculated. In this study, two estimates of monthly Kc values were made,

using two ETo equations. The first is the FAO-Penman-Monteith (FAO-PM) method,

while the other is the modified-modified Blaney-Criddle method (BC).

Three-year averaged monthly Kc values for each of the two methods were developed.

Number of replications for this study were four except during 2005 when it was three due

to erroneous data from one of the lysimeters. When compared with the suggested FAO-

9

PM based Kc values, the crop coefficient from this study were higher for the initial

growth period; 0.57 (this study) compared to 0.4 (Allen et al., 1996). For the two

remaining months, the Kc values from both studies were comparable; 0.89 and 0.76 (this

study) compared to 1.00 and 0.75 (Allen at al., 1996). The high initial Kc value from this

study was due to the high water table at the beginning of the season, which is typical for

southwest Florida. High water table is maintained in southwest Florida to wet the top soil

for bed preparation. This wetness results in higher evaporation from the bare soil between

the beds, thus increasing total ETc. For BC, the crop coefficients were found to be 0.44,

0.71, and 0.61 for the three month of growth respectively. This is the first BC Kc estimate

for watermelons using experimental data.

10

Introduction

Florida has been endowed with abundant water resources comprising over 1700 streams

and rivers, 7800 fresh water lakes and an annual rainfall of 1145 - 1520 mm (Marella,

1999). However, with population growth rate of nearly 23% (BEBR-UF, 2001) and

blooming economic development, demand for water is increasing continuously. Even

with its vast resources, water is in short supply in the state. In addition, contamination

from the industrial and the agricultural activities are putting further constraints on the

surface and groundwater resources. Conserving water and preserving its quality are two

challenges faced by the state.

Agriculture is the single largest user of fresh water in the state, accounting for 45% of

total fresh water withdrawals in 1995 (Marella, 1999). Vegetable production constitutes a

large part of southwest Floridas agriculture industry. Sub-tropical climate in southwest

Florida makes the area conducive for vegetable production. Watermelon is one of the

main vegetable crops grown in the state. Watermelon production in the state is carried out

on highly sandy soils, which are characterized by low water holding capacity and organic

matter content. Water and nutrients can easily be lost from these soils. Therefore,

watermelon is grown on raised soil beds covered with plastic mulch. These beds help in

conserving the soil moisture and reduce nutrient losses. Although southwest Florida

receives large amounts of rainfall annually, nearly 70% of the total is received during the

non-growing season of June - October. Temporal variability, coupled with the spatially

variable nature of rainfall, makes irrigation a necessity for the states agriculture. While

under-application of water could lead to plant stress and increase the salinity of soil

especially during the beginning of the season, over-application leads to wastage of water

and leaching of nutrients from the root zone. Sound irrigation scheduling and the use of

efficient irrigation systems is a key for optimum plant growth and can also help in

conserving water quantity and quality.

To develop an effective irrigation management strategy, it is important to estimate crop

water use. Knowledge of crop coefficient (Kc) is essential for the estimation of water use.

It helps in determining the water requirement of the crops according to their growth stage

11

and environmental factors. Kc is the ratio of crop evapotranspiration (ETc) and reference

evapotranspiration (ETo). While ETo is estimated from weather parameters only, ETc is

affected by crop type, growth stage and cultural practices. If Kc is known for a given

crop, then ETc can be calculated from ETo. Studies have found that Kc for the same crop

may vary from place to place based on factors such as climate and soil evaporation (Kang

et al., 2003 and Allen et al., 1998). Doorenboss and Pruitt (1977) and Kang et al. (2003)

emphasized the need to develop regional Kc for accurate estimation of water use, under a

specific climatic condition. Studies over the years have developed Kc for tomato,

strawberry (Clark et al., 1996) and blueberries (Haman et al., 1997) under the warm and

humid climate of southwest Florida. However, regional Kc values for watermelon still

need to be developed.

One method to measure ETc in order to estimate Kc is by using drainage lysimeters.

Lysimeters are containers used to study the optimization of water management for any

crop if they are adequately designed to approximate the physical system (Chow, 1964).

Lysimeters provide a direct estimation of ETc (Clark et al., 1996; Haman et al., 1997;

Steele et al., 1997; Simon et. al, 1998), which is used to develop Kc.

Drip irrigation systems are increasingly being used in watermelon production in

southwest Florida. Drip irrigation systems apply water directly to the root zone with high

efficiency, thereby minimizing water loss. Studies have shown that drip irrigation

systems reduce the water use of tomato by 50% compared to that under seepage system

in southwest Florida (Pitts and Clark, 1991). Moreover, drip systems provide the

opportunity to apply fertilizer mixed with irrigation water, on as needed basis through

fertigation.

Objective

The goal of this study was to develop monthly Kc values for drip irrigated watermelon

grown on the raised beds covered with plastic mulch in southwest Florida region.

12

Literature Review

EVAPOTRANSPIRATION

Evaporation (Ea) and transpiration (Tp) are the two most important processes governing

removal of water from the land into the atmosphere. These processes occur

simultaneously, and are hard to distinguish from each other (Allen et al., 1998). Stanhill

(1973) found considerable interaction between the two processes. The term

evapotranspiration (ET) was coined to define the total loss of water from an area. While

occurring simultaneously, Ea is governed by the availability of water in the topsoil and

the fraction of solar radiations reaching soil surface. Amount of solar radiation reaching

soil surface varies with the degree of crop shading. Transpiration (Tp) on the other hand is

a function of crop canopy and soil water status. Ea has been found to dominate the ET by

as much as 100% during early stages of crop growth while Tp contributes to nearly 90%

of the ET for a fully matured crop (Allen et al., 1998). Liu et al. (1998) reported that soil

Ea constitutes nearly 30% of the total ETcfor winter wheat. A similar study by Kang et al.

(2003) found that Tp accounted for 67% and 74% of seasonal ETc for wheat and maize

respectively, grown under semi humid conditions. ET can be classified into: reference

evapotranspiration (ETo) and crop evapotranspiration (ETc) (Allen et al, 1998).

REFERENCE EVAPOTRANSPIRATION (ETO)

ETo is a representation of the Ea demand of atmosphere, independent of crop growth and

management factors (Allen et al., 1998). It can be estimated from the weather data. Allen

et al. (1994) define ETo as the rate of ET from a hypothetical reference crop with an

assumed crop height of 0.12 m, a fixed surface resistance of 70 sec/m and an albedo of

0.23, closely resembling the evapotranspiration from an extensive surface of green grass

of uniform height, actively growing, well-watered, and completely shading the ground.

ETo determines the loss of water from a standardized vegetated surface, which helps in

fixing the base value of ET specific to a site.

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ETo can be estimated by measuring the open water surface evaporation from an

evaporation pan. Open water Ea incorporates the effects of temperature, humidity, wind

speed and solar radiation. Pan evaporation coupled with the use of a calibrated pan

coefficient (Kp) to relate Ea with the standard vegetative surface, can provide good

estimates of ETo, provided that soil water is readily available to the crop (James, 1988).

Some of the commonly used pans are: Class-A Evaporation pan and Sunken Colorado

pan. However, pan evaporation method requires regular maintenance of the evaporation

pan and the vegetation around it. Also, unavailability of regional pan coefficient can limit

the accuracy of ETo estimates.

Alternatively ETo can be estimated from meteorological data using empirical and semi-

empirical equations. Numerous empirical methods have been developed to estimate

evapotranspiration from different climatic variables. Examples of such methods include

Penman-Monteith (Monteith, 1965) and Blaney-Criddle (Blaney and Criddle, 1950).

One of the most important factors governing the selection of a method is the data

availability. For instance, Blaney-Criddle only requires the temperature data while the

Penman-Monteith requires additional parameters such as wind speed, humidity, solar

radiation. In addition, since the Blaney-Criddle method is used to calculate monthly Kc

values as compared to daily, less data is needed for this method.

Several studies have been conducted over the years to evaluate the accuracy of different

ETo methods. Most of these studies have concluded that Penman-Montieth equation in its

different forms provides the best ETo estimates under most conditions. Therefore, the

Food and Agricultural Organization (FAO) recommended FAO-Penman Monteith (FAO-

PM) method as the sole standard method for computation of ETo (Allen et al., 1998).

FAO-PM can provide accurate ETo estimates for weekly or even hourly periods. In some

instances, a specific method has been modified to better suit a region or a specific type of

use such as a water allocation tool by water management districts. One such example is

the use of modified modified Blaney Criddle method (Shih et al., 1981) that is used by

the Southwest Florida Water Management District (SWFWMD) within the district

boundaries for the purpose of water allocations.

14

CROP EVAPOTRANSPIRATION

The actual crop water use depends on climatic factors, crop type and crop growth stage.

While ETo provides the climatic influence on crop water use, the effect of crop type and

management is addressed by ETc. Factors affecting ETc such as ground cover, canopy

properties and aerodynamic resistance for a crop are different from the factors affecting

reference crop (grass or alfalfa); therefore, ETc differs from ETo.

The characteristics that distinguish field crops from the reference crop are integrated into

a crop factor or crop coefficient (Kc) (Allen et al., 1998). Kc is used to determine the

actual water use for any crop in conjunction with ETo (Equation 1).

occ ETKET (1)

CROP COEFFICIENT

The crop coefficient (Kc) is computed as the ratio of reference and crop ET (Equation 1).

Factors affecting Kc include crop type, crop growth stage, climate, soil moisture. Kc is

commonly expressed as a function of time. However, Kc as a function of time does not

take into account environmental and management factors that influence the rate of

canopy development (Grattan et al., 1998). Therefore, most researchers have reported Kc

as a function of days after transplanting (DAT) which helps to reference Kc on crop

development stage (Allen et al., 1998; Tyagi et al., 2000; Kashyap and Panda, 2001;

Sepashkah and Andam, 2001).

Accurate prediction of crop water use is the key to develop efficient irrigation

management practices making it imperative to develop Kc for a specific crop. Numerous

studies have been conducted over the years to develop the Kc for different agricultural

crops. Since most of the studies have been specific to one or two crops, Doorenbos and

Pruitt (1977) prepared a comprehensive list of Kc for various crops under different

climatic conditions by compiling results from different studies. Similar list of Kc was also

given by Allen et al. (1998) and Doorenbos and Kassam (1979). However, Kc for a crop

15

may vary from one place to another, depending on factors such as climate, soil, crop type,

crop variety, irrigation methods (Kang et al., 2003). Thus, for an accurate estimation of

the crop water use, it is imperative to use a regional Kc. Researchers have emphasized the

need for regional calibration of Kc under a given climatic conditions (Doorenbos and

Pruitt, 1977; and Kang et al., 2003). Therefore, the reported values of Kc should be used

only in situations when regional data are not available. For example, the southwest

Florida region that has unique conditions compared to other regions of the world. Sandy

soils with high water table and subtropical weather conditions, can result in large error in

estimating the ETc using the Kc developed in other parts of U.S. and the world.

In summary, there is a need to develop regional Kc for a realistic estimation of water use

to better schedule irrigation.

CROP COEFFICIENT ESTIMATION

Brouwe and Heibloem (1986) outlined the steps for development of Kc as: determination

of total growing period of the crop, identifying the length of different growth stages, and

determination of Kc values for each growth stage. However, Kc cannot be measured

directly, but is estimated as a ratio (Equation 1). While ETo can be estimated using one of

several available methods, ETc can be estimated by a lysimeter study (Gratten et al.,

1998).

A lysimeter is essentially a container that isolates soil and water hydrologically from its

surroundings, but still represents the adjoining soil as closely as possible. Lysimeters can

be used as a research tool to study plant-water relationships if they are designed

adequately to approximate the physical system (Chow, 1964). Lysimeters provide a

controlled soil-water or nutrient environment system for precise measurement of water

and nutrient use and movement (Chalmers et al., 1992). Non-weighing or drainage

lysimeters are used to estimate ET by computing the water balance. The water balance

involves measuring all the water inputs and outputs to and from the lysimeter and the

change in storage (soil moisture) over a stipulated period of time. These lysimeters

16

provide viable estimates of ETc for longer periods such as weekly or monthly

Aboukhaled et al. 1982).

DESIGN CONSIDERATIONS FOR LYSIMETERS

One of the most important factors controlling the accuracy of a lysimeter is its size

(Gangopadhyaya et al., 1966). Clark and Reddell (1990) noted that the lysimeter surface

area and its depth should be large enough to minimize root restrictions. Gangopadhyaya

et al. (1966) reported that miniature lysimeters (10 cm diameter and 10 cm deep) were

sensitive but not reliable due to distortions in thermal properties. They concluded that

the accuracy of lysimeters increases with an increase in their surface area. Boast and

Robertson (1982) reported that shallow lysimeters tend to retain more water per unit

depth than the actual field and thus introduce a bias by overestimating ET. Yang et al.

(2000) reported that groundwater evaporation contributes up to 56% of total ET.

Therefore, authors suggested that lysimeters measuring ET should be deep enough to

account for soil-water and groundwater exchanges and water table fluctuations.

Another debatable topic concerning design of lysimeters is the use of a rain shelter. To

avoid unwanted water from entering the lysimeter system via precipitation, rain shelters

have been employed at some of the lysimeter sites around the world. By keeping

unwanted rainfall away from the system, rain shelters reduce the uncertainty in ET

estimation especially, during the times soon after rainfall when extremely wet soil

conditions trigger high ET rate. However, their use in field studies also has attracted

some criticism. Dugas and Upchurch (1984) reported that the sides of rain shelter could

restrict the wind movement under the shelter causing excessive heat. Authors further

noted that rain shelter lowered the radiation reaching the plants by 30 - 40%. Clark and

Reddell (1990) noted that permanent rain shelters excessively heated the crop due to

improper ventilation.

17

LYSIMETER-BASED CROP COEFFICIENTS

Lysimeters have been successfully used by researchers to measure the ETc and develop

Kc for various fruits and vegetables (Haman et al., 1997; Clark et al., 1996) and field

crops (Steele et al., 1997; Simon et. al, 1998; Tyagi et al., 2000).

Steele et al. (1997) developed mean crop curves for corn as a function of DAT and

CGDD based on Jensen and Haise (1963) and modified Penman equation (Allen 1986)

ETo methods. Using 11 years of data from four drainage lysimeters, they developed fifth

order crop curves for corn using both ETo methods.

Steele et al. (1997) revealed that the lack of accuracy in determining soil moisture,

measured by neutron attenuation method, was the most important source of variability in

their study. They noted that the lack of soil moisture monitoring at the bottom 0.3 m

region of lysimeter added to the uncertainty in the results. Another complicating part of

their study was negative Kc for periods when lysimeters were drained after rainfall.

Authors did not discuss the reasons for negative Kc, but, they noted that it can be avoided

by increasing the time step for estimating ETc to two or more periods (each water balance

period in their study was 10 days). Steele et al. (1997) also found that Kc should be

referenced to the middle of time step (t) for periods longer than daily such as weekly,

bi-weekly or monthly periods. They noted that referencing Kc to the beginning or end of

the growing period could change the shape, amplitude and position of the crop curve

significantly, thereby, reducing its accuracy.

Haman et al. (1997) used drainage lysimeters to study ET and develop Kc for two

varieties of young blueberries for Florida. They used cylindrical tanks as lysimeters (1.6

m diameter and 1.8 m deep) equipped with porous plates to extract drainage water. The

ETc in their study included Tp and Ea from the surface wetted by the irrigation system, but

did not include water loss from the grassed alleys. They noted that their computed Kc was

different from the standard Kc, but it provided information for actual crop water use.

Although Kc for both the varieties followed the same general trend, Kc values for the two

18

varieties were different from each other. Differences in Kc values of the two varieties

were attributed to the differences in plant development of the two varieties.

Clark et al. (1996) used drainage lysimeters to compute ETc and develop Kc for drip

irrigated strawberry in Florida. They used 16 drainage lysimeters 2.4 m 0.6 m 0.6 m

equipped with rain shelters for their study. Since drip irrigation applies water directly to

the root zone, actual crop water use can be different from the seepage irrigation system

which has high water table and wet row middles. To study differences due to high water

table and wet row middles, Clark et al. (1996) used two types of plant arrangements: first

arrangement estimated ETc only from the plants while second estimated ETc from the

plants and the exposed row middles. They reported monthly Kc based on modified

Penman (PENET) (Burman et al., 1980), modified Blaney-Criddle (BCRAD) (Shih et al.,

1977) and pan evaporation (PANET) (Doorenboss and Pruitt, 1977). Their results

indicated that for lysimeters with plants and exposed row middles, ETc and Kc were

higher than those with plants only. They estimated that 25 - 35% of ETc was Ea from

exposed row middles. Using linear regression, they observed high R2 for their Kc curves

(PENET =0.97, PANET = 0.94, BCRAD = 0.94.). They recommended that Kc developed

from their study was useful for irrigation scheduling and developing water budgeting

procedures for drip irrigated strawberry production in a humid region.

Simon et al. (1998) conducted a study to develop regional Kc for maize in Trinidad. They

used 2 m 2 m 1.2 m drainage lysimeter for three seasons to develop Kc. The effects of

dry and wet season (temporal variability of climate) on Kc were also discussed. They

found that Kc during a wet season (Kc =1.13 to 1.41) was greater than during a dry

season. (Kc = 0.73 to 0.94). They attributed the differences between the wet and dry

season Kc to lower ETo during the wet season. Mean Kc for maize was found to be greater

than the reported values by Doorenboss and Pruitt (1977). Therefore, the authors stressed

on the importance of developing regional Kc for accurate irrigation scheduling.

Sepaskhah and Andam (2001) used drainage lysimeters to estimate Kc for sesame for

semi arid regions of Iran. They developed Kc based on modified Penman-Monteith

(Jensen et al., 1990) and FAO- PM, as a function of DAT. Authors reported that their

19

observed Kc was different from those given by Doorenboss and Pruitt (1977) and Allen et

al. (1998) for similar crops. In a similar study, Lie et al. (2003) used cylindrical drainage

lysimeter (diameter = 1 m; depth = 0.8 m) to develop Kc for watermelon and honey dew

melons in China using ETo from pan evaporation. Their reported Kc for watermelon

varied from 0.35 - 2.43. These values were considerably higher than the Kc (0.4 - 1.0) as

reported by Allen et al. (1998). Study by Kang et al. (2003) reported Kc for wheat and

maize for semi-humid conditions of northwestern China. They used three 3 m 2 m 2

m drainage lysimeters equipped with rain shelters. Average Kc was developed from 10

years of measured data. Although, their Kc matched well with the Kc given by

Doorenboss and Pruitt (1977) during the initial growth period for both the crops, it was

higher during the mid and late season.

FETCH AND BUFFER AREA REQUIREMENTS

For reliable estimates of crop water use, a lysimeter should be surrounded by a buffer

area of the same crop that is of the same age, growth stage, and density. Aboukhaled et

al. (1982) suggested that a buffer area approximately 400 times the lysimeter area should

be used. However, for humid and sub-humid conditions, a smaller area may be used

(Fougerouze, 1966). In a discussion on the fetch requirement to minimize the border and

boundary effect, Rosenburg et al. (1983) gave a height of crop to fetch ratio of 1:100 as

being sufficient for agricultural crops. However, Mather (1959) noted that the fetch

requirements may be reduced under humid conditions such as those in southwest Florida

(Sadler and Camp, 1986).

In summary, literature review presented in this chapter indicated the need to develop

regional Kc for watermelon to better schedule irrigation in southwest Florida. In addition,

it provided the guidelines to plan, design, and construct the experiment and analyze the

data .

20

Material and Methods

STUDY AREA

The study was conducted at the research farm of the UF/IFAS Southwest Florida

Research and Education Center (SWFREC) located in Immokalee, Florida (Figure 1).

Average maximum and minimum temperatures for the region are 29 oC and 17

oC,

respectively. Southwest Florida receives an annual rainfall nearly of 1,370 mm. Soils in

the area are typically poorly drained, hydric and highly sandy in characteristics. These

soils, also known as flatwood soils, have a subsurface spodic horizon, which acts as a

hard pan that maintains a high water table. Seasonal high water table levels vary from 15

cm to 45 cm.

Figure 1. Study location at southwest Florida Research and Education Center (SWFREC),

Immokalee, Fl.

EXPERIMENTAL DESIGN

A set of four drainage lysimeters were used to quantify the ETc and develop Kc for bell

pepper and watermelon. The four lysimeters were irrigated with drip system (designated

as D1, D2, D3 and D4). Vegetables in southwest Florida are grown on raised, pressed soil

beds covered with plastic mulch with fixed row-to-row (r - r) and plant-to-plant (p - p)

spacing. The r - r and p - p spacing was an important factor in designing the size of

21

lysimeters. To emulate the actual crop management practices, few vegetable farms were

surveyed in June-July 2002.

SURVEY OF CROP PRODUCTION PRACTICES

A vegetable production survey covering six large vegetable producers in southwest

Florida revealed considerable variability in crop production practices. Typical crop

rotation in southwest Florida includes tomato or pepper grown in fall season followed by

watermelon, eggplant or tomato during the spring season. The survey showed that

watermelon had the largest r - r spacing among all vegetable crops. The r - r spacing for

watermelon varied from 1.8 m to 2.75 m. This was considered as the basis of the

lysimeters design. Survey further revealed considerable variability in field layouts and

other production practices including fertilizer application rates, pesticide use and plant

density. Production practices data (e.g. plant density, area) from the survey and the

University of Florida/Institute of Food and Agricultural Sciences (UF/IFAS)

recommendation for watermelon (Maynard et al., 2001) were considered as the basis for

determining the size of the lysimeters.

LYSIMETER WATER BALANCE

For water use studies, the mass balance for the drainage lysimeter can be written as:

Input Output = Change in storage (S) (2)

For quantification of evapotranspiration, equation 2 can be written as:

ETc = Kc x ETo = P + I D R S (3)

where, ETc is the crop evapotranspiration (mm), Kc is the crop coefficient (unitless), ETo

is the reference evapotranspiration (mm), P is precipitation (mm), I is irrigation (mm), D

is the water drained (mm), R is the runoff (mm), and S is the change in the soil water

storage during the period for which ETc and Kc is computed (mm). Precipitation can be

measured with a rain gage at the site. Irrigation (I), D, and R for the lysimeter can be

22

measured with accurate flow meters. Change in soil moisture (S) can be estimated with

soil moisture measurements taken at different depths. ETo can be estimated using the

weather data in one of the several available ET models such as the modified Penman

(Allen, 1986). Measured values of all the terms on the right hand side of the Equation 2

can be used to compute ETc as well as Kc.

LYSIMETER DESIGN, CONSTRUCTION, AND INSTALLATION

Design and Construction

Lysimeter Body

Factors considered in designing the lysimeters included: typical vegetable production

practices in southwest Florida; size and material for the lysimeter; buffer area; and

measurements of water input and output. The most important consideration in designing

the lysimeter was vegetable production practices in southwest Florida. Typical

watermelon production in southwest Florida involves growing the crop on a raised bed

that is covered with polyethylene mulch. Drip and/or seepage irrigation systems are

typically used to apply water. Drip irrigation systems in southwest Florida also use the

seepage irrigation system during bed preparation to raise the water table close to the

surface (e.g. 30 cm). The high water table provides sufficient moisture to make the soil

workable to form beds and to cover them with plastic mulch using tractor driven

equipment.

The part of a vegetable field emulated in the lysimeter included two beds with a ditch

between the beds (Figure 2). Large plant (1.2 m) and bed (1.8 m) spacing posed a

challenge with regards to the size of the lysimeter. To ensure the success of the

experiment in light of prevalence of diseases in this humid region, it was deemed

necessary to have at least six plants per lysimeter (three plants per bed). Typical soil

characteristics of the Flatwoods region accounting for a majority of southwest Floridas

vegetable production, include A and E horizons (down to 1.0 m) underlain by a low

conductivity soil layer (spodic horizon, Bh). The low hydraulic conductivity of the spodic

layer results in perched water table conditions and allows for maintaining a high water

23

table (0.4 m) for bed preparation. The design depth of the lysimeters was chosen to

include the entire E horizon (down to 1 m; Figure 3). The lysimeter depth was further

extended to accommodate 0.18 m layer of coarse sand to facilitate drainage. The final

dimensions of the lysimeter were 4.85 m x 3.65 m x 1.35 m.

Figure 2. Lysimeter layout for the watermelon crop.

A total of six lysimeters were constructed. Each lysimeter was constructed from 3.175

mm thick mild steel sheets. The sheets were welded together using gas metal arc welding

techniques. A frame was constructed from 5.08 cm x 5.08 cm x 0.64 cm mild steel angle

iron to support the steel sheets making up the sides and bottom of the lysimeter. The steel

angle iron was welded to the sheets at the joints and corners.

A drainage capture and discharge system was designed to facilitate drainage from the

lysimeters. To collect percolation, the lysimeter bottoms sloped towards the center

(Figures 3 and 4) and were similar to a face generated by cutting one of the sides of a

dodecahedron (Figure 4). To drain percolation collected at the bottom, a 1.22 m long and

5.1 cm diameter intake screen made of stainless steel wire-wrapped well screen (screen

size = 0.25 mm) was used. The screen was welded to a 5.1 cm mild steel pipe (Figures 3

24

and 4) extending through the bottom of the tank to a cleanout Tee. The drainage screen

assembly through-connection to the outside of the tank was welded.

Figure 3. Soil profile inside the lysimeter.

After constructing the lysimeter bottom and drainage pipe, lysimeter walls were welded

to final dimensions (Figure 3). The exterior of the lysimeter was reinforced with 6.4 cm

vertical angle iron braces at each corner and at every 1.2 m around the perimeter of the

lysimeter (14 total) (Figure 4). A 6.4 cm angle iron brace was welded horizontally around

the inside perimeter of each tank at 46 cm below the top of the tank to provide extra

support for the tank body and to prevent sidewall flow (Figure 5). A 2.5 cm square steel

tube was welded at 5 cm from the top to provide additional strength to the upper part of

the lysimeter (Figure 5). Eight 30.5 cm-long supporting legs with 10 cm x10 cm square

flat steel plates at the bottom end were welded to the lower end of the steel angle iron

frame to reduce point loads during installation (Figure 4). To capture runoff from rainfall

events, two runoff catchments, were made from the same steel sheets used in making the

lysimeter body and welded to the exterior of the lysimeter. Each runoff catchment was

0.46 x 0.46 x 0.46 m with an adjustable steel gate mechanism that could be aligned with

the soil surface in the lysimeter to enable free runoff flow.

25

Figure 4. Sloped shape of the lysimeter base.

26

Figure 5. Lysimeter placement in the pit.

The inside and the outside lysimeter surfaces were painted with two coats of multi-

purpose epoxy paint followed by two coats of anti-corrosive chemical to prevent rusting

of the lysimeter container. The paint was chemically non-sorptive/reactive. Two

additional coats of elastomeric coal-tar free paint was applied to the inside surface of the

lysimeter tanks. Before installation, each lysimeter was tested for leaks by filling with

water. Any observed leaks were sealed by welding, followed by painting the affected

area.

Drainage and Runoff Collection and Discharge

The lysimeters were gravity drained. The end of the steel pipe (Figure 4) connected to the

stainless steel drainage screen was connected to a sump with 3.8-cm diameter marine

sanitation hose. The sump was made from a 20 cm diameter PVC pipe with a PVC

bottom plate. The elevation of the sump bottom was the same as the well screen

elevation. A 12 V DC self-priming diaphragm pump with a flow rate of 6.25 liters per

minute was used to drain each lysimeter. The pump was triggered by a water-level sensor

27

installed in the sump at the same height as the desired water table level in that lysimeter.

Pumped drainage was measured by a 1.9-cm flow meter installed in an instrument

enclosure (Figure 3). Water from the two runoff catchments was routed through the same

sump-pump-flowmeter-splitter setup that was used for the drainage.

Field Layout

Results of the survey were used to configure the experimental field to be characteristic of

the vegetable farms in southwest Florida. Field layout with locations of the drip and

seepage lysimeters is shown in Figure 6. For this study, a buffer area of 0.83 ha (399

times the lysimeter area) was used. The field was designed to have eight blocks of crop

rows with each block having four crop rows. The drip lysimeters were installed in the

fourth block of the field (Figure 6). The minimum fetch (in the direction of prevailing

wind) to watermelon plant height (12 cm) ratio for all the six lysimeters was almost 3

times the fetch requirement of 1:100 noted by Rosenberg et al. (1983). The plant and row

spacing inside the lysimeter were the same as in the surrounding field.

29

Figure 6. Layout of the experimental field for the lysimeter study.

Installation

The experimental field was surveyed to mark the precise location of each lysimeter with

respect to the location of crop rows. Installation was completed in February, 2003.

Designated areas for the four drip lysimeters were excavated to make two 1.4 m deep soil

pits (Figure 5). A large trackhoe was used to remove the soil in 15 cm increments from

each of the top two horizons (A and E). The excavated soil for the A and E horizons was

stored separately on plastic at two different locations to avoid mixing of soils from the

two horizons.

An 8-cm thick gravel layer was placed in the pit to provide a stable foundation for the

lysimeters. Cement blocks (20 x 30 x 10 cm) were placed on the gravel layer to support

the eight lysimeter legs. A laser level was used to ensure that all cement blocks were

level. A commercial crane (lifting capacity = 0.91 tons) was used to lower and place the

lysimeters in the pit. A dewatering pump was used to drain the water from the pit to a

nearby canal to keep the water table low during the installation. Immediately after placing

the lysimeter on the cement blocks, each lysimeter was filled with water to avoid floating

of lysimeters in case ground water filled the pit. A 5.5 m x 4.3 m form was constructed

around each lysimeter using wooden boards. Flowable fill cement was poured in the form

to fill the area between the lysimeter bottom and the gravel. Two weeks were allowed to

ensure the hardening of the cement. The resulting cement foundation provided a solid

base for the lysimeter.

The stockpiled soil (Immokalee fine sand, the native soil series at the research site) was

used to fill the lysimeters. Soil characterization, including characterizing the soil profile

and bulk density measurements in the research field, was performed before lysimeter

installation. The horizons observed at the site were typical of the Immokalee fine sand

soil: two horizons A and E (Figure 5). The thickness of the A horizon was approximately

at 0.30 m while for E horizon it was 0.70 m. Measured bulk density (field soil) of the A

and E horizons were 1.49 gm/cm3 and 1.57 gm/cm

3, respectively.

30

To prevent sand particles from flowing out with the drainage water, it is important to use

a filtering layer of a coarser material (Xu et al., 1998). A 5-cm layer of coarse sand

overlain with a geo-textile sheet made from a woven fabric of monofilament

polypropylene yarn (average mesh size of 0.21 mm) was placed at the bottom of each

lysimeter to act as a filtering mechanism and facilitate drainage. The soil profile inside

the lysimeter was rebuilt similar to that observed in the field in increments of 15 cm by

compaction of each increment. The E-horizon soil (0.70 m) was placed on top of the geo-

textile filter cloth (Figure 3). The soil layer was alternatively saturated and drained until

the bulk density inside the lysimeter was close to the field soil. After draining the excess

water, the soil in the lysimeters was allowed to dry for two days. The same process of

saturation and drainage was repeated for the A-horizon (topsoil). The top of the A-

horizon was 10 cm from the top of the lysimeter. The soil around the lysimeter in the

excavation was also reconstructed by using the same procedure as for the lysimeters

(except for the wetting/drainage process).

Bed and plastic mulch forming is normally accomplished with a tractor-mounted

equipment. Under field conditions, the soil is cut and thrown into a loose bed after which

the soil is firmed with a tractor driven mechanical bed press. However, given the small

area of the lysimeters compared to the field, this equipment could not be used inside the

lysimeter. A wooden mold, 1.82 m x 0.9 2 m x 0.22 m, was fabricated for making the

plastic mulch beds inside the lysimeter. This mould was accurately positioned in the

lysimeter and filled with soil in 5.0 cm increments. The non-bedded area within each

lysimeter is level, which is similar to the actual field conditions in southwest Florida. The

soil in the bed was compacted lightly as necessary to bring the bulk density close to the

bulk density of the soil as observed in the field. A soil compaction meter was used to

assess the bulk density of soil within the bed inside the lysimeter in the field as

compaction progressed. Standard bulk density measurement techniques were used to

verify that the bulk density of the lysimeter soils (bulk density = 1.55 and 1.53 gm/cm3

for A and E horizons) were close to field conditions.

31

Irrigation Systems

Four separate irrigation lines were designed for the research field: lysimeter drip

irrigation, lysimeter seepage irrigation, field drip irrigation, and field seepage irrigation.

The drip and seepage irrigation lines for the lysimeters were further subdivided to allow

measurements of irrigation volumes (seepage and drip) for each lysimeter. The drip and

seepage irrigation lines for each lysimeter were controlled using a hydraulic actuator

switch at the main pump station. The emitter spacing for the drip tape (T-Systems

International Inc., flow rate = 0.34 L/h/100 m) used in the lysimeters as well as in the

field was 0.30 m. The fertilization for the lysimeter experiment included pre-plant

application in the bed as well as fertigation for the drip lysimeters. Part of the fertilizer

was applied through fertigation. The fertilization schedule for the lysimeters and the field

was based on the University of Florida Institute of Food and Agriculture Sciences (UF-

IFAS) fertilizer recommendations for watermelon (Maynard et al., 2001). The UF-IFAS

recommendations are expressed on lb/acre basis and consider the actual cropped area

by taking into account the distance between the plant beds.

MONITORING SYSTEM

Irrigation, Drainage, and Runoff

A flow meter (Model DLJ S50, 1.3 cm, DLJ Company, NJ) was installed on the drip and

seepage irrigation lines (pressure 0.069 MPa) at each lysimeter site for measuring

irrigation volumes applied to each lysimeter. The flow meter is a single-jet horizontal

impeller type meter with accuracy of 95% or greater (DLJ Company, 2006). Drainage

and runoff volumes were also measured using flow meters. The flow meter readings were

taken before and after each irrigation, drainage, and runoff event.

Soil moisture monitoring system

Accurate soil moisture data for the entire soil profile in the lysimeters are essential to

account for changes in soil water storage (S) for the drainage lysimeter (Equation 2).

Each lysimeter was equipped with soil moisture measurement devices in each bed and

32

one between the ditch and the bed. Capacitance-based soil moisture sensors were used for

an accurate estimation of soil moisture at different depths and locations. The Diviner

2000 (Sentek Sensors Technologies, Australia) was used for measuring the soil moisture.

The Diviner 2000 is a portable unit and measures the soil moisture at each 10 cm depth.

Two access pipes (5 cm I.D.) for the portable type sensor were installed in each

lysimeter. The first access pipe was installed near a plant and the second access pipe was

installed away from the bed and close to the seepage ditch in the lysimeter (Figure 2).

The access tube was located between the plant and the emitter. The distance of the access

tube from the plant and the emitter was 5 cm. Daily soil moisture readings at 10-cm depth

increments from 10 to 70 cm were taken manually from these two locations. Soil

moisture measurements were undertaken before irrigation. To assess the accuracy of the

Diviner 2000 for the study site, 24 Diviner observations taken from the lysimeter field

were compared to the gravimetric soil moisture values (Pandey and Shukla, unpublished

data). The average absolute error (percent difference between the Diviner and

gravimetric) was 13%. The soil moisture readings taken from the soil moisture sensors

were used to schedule irrigation by maintaining the soil moisture between field capacity

(FC = 9%) and 33% depletion of plant available water (PAW = 6%, wilting point = 3%)

to avoid plant stress. At times, occurrence of rainfall resulted in soil moisture exceeding

the field capacity.

DATA COLLECTION

The data on irrigation, drainage, soil moisture, and runoff were used to compute the water

balance. All flow meters and SDI-12 soil moisture devices were connected to a CR205

(CSI, 2003a) wireless datalogger that was housed in an instrument shelter. Each of the

lysimeters has one CR-205 data logger, which recorded the irrigation volume and soil

moisture data. The data from each of these loggers were wirelessly transmitted to the

main pump station located adjacent to the field. A CR10X datalogger equipped with a

RF400 radio (CSI, 2003b) was installed at this location, and was used to store and

transmit the data to the University of Florida network for later access by the research

personnel in the office. Weather parameters, including rainfall, air temperature, wind

33

speed, relative humidity, and solar radiation data, were also collected at the UF-IFAS

Florida Automated Weather Network (FAWN) weather station located 50 m from the

research field. The weather parameters were used to compute the ETo using the FAO-

Penman model (Allen, 1986) and the modified modified-Blaney Criddle Equation (Shih.,

1981).

REFERENCE EVAPOTRANSPIRATION COMPUTATION

For the purpose of developing Kc curves, two different ETo methods were used: FAO

Penman-Monteith method (FAO-PM) and modified-modified Blaney-Criddle method

(BC).

FAO-Penman-Monteith method

The FAO-PM (Allen et al., 1998) is the standard method of ETo estimation. Allen et al.

(1998) described the methodology of estimating ETo using FAO-PM (equation 4).

)34.01(

)()273(

900)(408.0

2

2

u

eeuT

GR

ETasn

o (4)

where Rn is the net radiation at the crop surface [MJ m-2

day-1

],

G is the soil heat flux density [MJ m-2

day-1

],

T is the mean daily air temperature at 2 m height [C],

u2 is the wind speed at 2 m height [m s-1

],

es is the saturation vapor pressure [kPa],

ea is the actual vapor pressure [kPa],

es-ea is the saturation vapor pressure deficit [kPa],

is the slope vapor pressure curve [kPa C-1], is the psychometric constant [kPa C-1].

Modified-modified Blaney-Criddle Method

Blaney-Criddle method (BC) is commonly used by water management districts in Florida

for the purpose of water allocations. BC was developed to estimate ET losses in the

34

western United States by SCS (SCS, 1967). The BC equation has been modified several

times and a form developed by Shih (1981) is used by the SWFWMD and is termed

modified-modified Blaney-Criddle equation. The equation consists of the following

equations:

fKET to (5)

314.00173.0 tK t (6)

100

tpf (7)

where

p is monthly percentage of annual incoming solar radiation

t is the mean monthly temperature

Development of Crop Coefficient

The monthly Kc values were developed for bell pepper and watermelon using ETo

estimates from FAO-PM and BC methods. The Kc was calculated using equation 9

o

cc

ET

ETK (8)

To compute Kc based on crop development stage, it is important to establish the length of

different crop growth stages. Allen et al. (1998) divided the crop cycle into four stages:

initial stage (marked with about 10% of plant cover), middle stage (marked with the

growth of plant from 10% to 100% canopy cover), and an end stage (from maturity to

harvesting).

35

CROP PRODUCTION PRACTICES

The lysimeters were covered with Visqueen plastic cover for 21 days to emulate the

plastic mulch in the field. The beds inside the lysimeters were constructed. A wooden

mold, 12 ft x 3 ft x 0.8 ft, fabricated for making the plastic mulch beds inside the

lysimeter, was accurately positioned in the lysimeter and filled with soil in 5.0 cm

increments. The soil in the bed was compacted lightly as necessary to bring the bulk

density close to the bulk density of the soil as observed in the field. A soil compaction

meter was used to assess the bulk density of soil within the bed inside the lysimeter in the

field as compaction progressed. The beds were then manually covered with plastic mulch

and holes were punched for the transplants. Watermelon transplants were obtained from a

commercial nursery. To avoid the occurrence of fungal disease, preventive fumigant (K-

pam HL, application rate = 250 l/ha) was applied in the lysimeters prior to planting

during each spring season. The rest of the experimental field was fumigated with Telone,

which was added to the soil at the time of bed preparation. Watermelons were

transplanted in late February to early March and were harvested in late May except for

Spring 2003 when the crop failed.

For computing water balances, the monitoring data on irrigation, drainage, soil moisture

and runoff were used. Weather parameters including rainfall, air temperature, wind

speed, relative humidity, and solar radiation data were collected at the UF-IFAS Florida

Automated Weather Network (FAWN) weather station located 50 m from the research

field. The weather parameters are to be used to compute PNET and BNET.

Yield data were collected from the lysimeters as well as the outside field for each season.

To compare the lysimeter yield with rest of the field, six check plots were established.

Each check plot had the same number of plants as any lysimeter. Harvesting of lysimeters

and rest of the field was done at the same time. Only fruits of marketable quality were

harvested to compute the yield. Yield data collection included fruit count and weight.

Specifics for each season are detailed below:

36

Spring 2003

During Spring 2003, the first transplants showed symptoms of a fungal disease caused by

Pythium spp. As a result, the lysimeters were replanted. However, successive

transplanting failed within one week of transplanting. Fifth transplants drenched in

recommended preventive chemical Rodomil Gold 4 EC (Maynard et al., 2001) survived

till 6th

week after transplantation. The crop became infected with Fusarium wilt caused by

Fusarium oxysporum during the 6th

week, which damaged the entire crop by 8th

week.

Crop failure did not allow for a full season of soil, water, and yield data to be collected.

However, watermelon crop takes 60 to 90 days to maturity from transplants (Olson and

Simonne, 2005). A survey conducted in southwest Florida for this study indicates that the

first harvest occurs 65-75 days after transplant (approximately 10 weeks). Data were

collected for this crop for 6 weeks. Since Kc data are calculated on a bi-weekly basis, 3

data points out of 5 possible will be available from the spring 2003 crop experiment.

Since this experiment is replicated both in space (4 lysimeters) and in time (3 years), the

data from spring 2003 were included in computing the average of 3 replications (2003,

2004, and 2005) in time for the first 3 Kc data points, while the last 2 data points we had

only two replications (2004 and 2005).

Spring 2004

The crop showed signs of gummy stem blight in some parts of the field during the 2nd

week after planting. The disease was caused by infected seedlings that did not show signs

of the disease at transplant time. To avoid spreading of the disease, new transplants of

watermelon were replanted on 03/08/2004. The crop showed signs of a disease known as

vine decline during the 11th week after transplanting. Foliar symptoms of vine decline

included yellowing, wilting of the vines, scorched and brown leaves, and rapid mature

vine collapse. Frequently, the interior fruit rind appeared greasy with a brown

discoloration, rendering the fruit non-marketable. Disease progress was very rapid. Vine

decline increased from 10% affected plants to greater than 80% within a week. Research

at the SWFREC is underway to determine the cause of vine decline in order to manage or

avoid it in the future. Due to the spread of the disease in the research field, the crop

37

season was ended after 81 days after transplant (DAT). The season was limited to two

harvests on 5/25/2004 and 05/28/2004. As the crop loss occurred at the end of the

experiment at harvest, it could be assumed that it will not have any effect on Kc which is

almost constant after the first harvest.

Spring 2005

Watermelon transplants were planted on 03/01/2005 in spring 2005, the same day they

were brought from the nursery. The plant growth was normal and no disease was

reported. The crop was harvested two times. The first time was on 05/20/2005 and the

second time was on 05/31/2005.

Results and Discussion

WATER INPUT, OUTPUT, AND STORAGE

Spring 2003

Irrigation and drainage data collected during the spring 2003 season are presented in

Tables 2 and 3, respectively for the four lysimeters. On April 9, 13.53 mm were applied

in D4 and on April 16, 21.69 mm of irrigation were applied to D1 (Table 1), which were

higher than other lysimeters. On both occasions this was caused by leaks in the drip

system, which resulted in excess irrigation. As the water table was relatively low, most of

the excess irrigation infiltrated to the groundwater. Excess water did not have a large

effect on ETc and Kc calculations. D1 and D4 were not irrigated on the next day to

compensate for the excess irrigation. One of the notable events of the season was the

unusually high rainfall and the subsequent drainage during the last week f September.

Due to unusually large rainfall on 26 and 27 September (total rainfall = 85.34 mm) (Table

7), a large volume was drained (average drainage = 37.5 mm) from all lysimeters on

September 28 (Table 2). Soil moisture data are presented for each lysimeter in Tables 4,

5, 6 and 7 for D1, D2, D3 and D4, respectively. Rainfall data, recorded at FAWN weather

station, are presented in Table 8.

38

Table 1. Irrigation* (mm) for the four lysimeters during the spring 2003 season.

Date Irrigation (mm) D1 D2 D3 D4

03-Apr-03 2.95 3.96 3.77 2.50

04-Apr-03 1.78 2.33 2.48 3.03

05-Apr-03 2.42 3.60 3.54 4.09

06-Apr-03 1.48 2.10 2.20 2.93

07-Apr-03 1.55 2.08 2.18 2.73

08-Apr-03 0.76 1.10 1.12 1.42

09-Apr-03 0.55 0.00 0.91 13.53

10-Apr-03 0.00 0.00 0.00 0.00

11-Apr-03 0.42 0.74 0.76 1.00

12-Apr-03 2.18 2.20 2.35 3.18

13-Apr-03 1.00 1.95 2.08 2.52

14-Apr-03 0.98 2.27 2.12 2.61

15-Apr-03 0.51 2.40 2.59 2.90

16-Apr-03 21.69 8.01 6.42 5.19

17-Apr-03 0.00 1.34 1.36 1.36

18-Apr-03 4.63 6.28 6.61 6.38

19-Apr-03 1.31 4.24 4.07 4.45

20-Apr-03 2.33 3.39 3.07 3.52

21-Apr-03 2.96 3.12 3.14 2.92

22-Apr-03 1.32 5.06 3.61 3.47

23-Apr-03 3.02 3.80 3.44 4.18

24-Apr-03 6.85 8.49 8.20 9.71

25-Apr-03 1.06 1.12 1.44 1.51

26-Apr-03 0.00 0.00 0.00 0.00

27-Apr-03 0.00 0.00 0.00 0.00

28-Apr-03 1.40 1.17 1.26 1.50

29-Apr-03 0.00 0.00 0.00 0.00

30-Apr-03 0.00 0.00 0.00 0.00

01-May-03 0.00 0.00 0.00 0.00

02-May-03 5.91 6.03 4.51 4.67

03-May-03 2.95 3.33 3.74 3.91

04-May-03 3.04 3.36 3.48 3.48

05-May-03 2.55 2.53 2.68 3.44

06-May-03 0.00 0.00 0.00 0.00

07-May-03 5.51 6.02 4.23 4.71

08-May-03 6.35 4.34 4.66 4.94

09-May-03 3.85 3.01 4.87 4.22

10-May-03 2.55 2.34 2.45 2.75

11-May-03 0.00 0.00 0.00 0.00

12-May-03 7.82 7.25 7.54 7.21

13-May-03 3.37 2.88 4.00 4.32

14-May-03 2.17 1.48 1.88 1.67

15-May-03 0.00 0.00 0.00 0.00

16-May-03 3.69 3.55 4.38 3.70

17-May-03 4.05 4.08 4.02 6.06

18-May-03 4.26 3.47 3.81 3.91

19-May-03 3.97 4.07 4.19 3.36

20-May-03 0.00 0.00 0.00 0.00

39

Date Irrigation (mm) D1 D2 D3 D4

21-May-03 4.62 4.70 4.29 3.82

22-May-03 3.37 3.30 3.78 3.50

23-May-03 0.00 0.00 0.00 0.00

24-May-03 0.00 0.00 0.00 0.00

25-May-03 3.11 4.08 3.78 3.17

26-May-03 2.63 2.96 3.10 2.92

27-May-03 0.00 0.00 0.00 0.00

28-May-03 0.00 0.00 0.00 0.00

29-May-03 0.00 0.00 0.00 0.00

30-May-03 0.00 0.00 0.00 0.00

*The water depths were calculated as total irrigation input divided by the total area of the

lysimeters of 17.83 m2 (192 ft

2). 1mm = 0.0394 in.

Table 2. Drainage* (mm) for the four lysimeters during the spring 2003 season.

DATE Drainage (mm)

D1 D2 D3 D4

10-Apr-03 0.00 0.00 0.00 12.49

26-Apr-03 14.57 1.35 1.54 11.35

27-Apr-03 9.69 2.79 3.78 2.78

28-Apr-03 14.70 11.90 12.23 12.33

29-Apr-03 6.65 0.00 2.00 6.51

30-Apr-03 8.65 1.46 7.25 7.68

1-May-03 10.60 9.44 9.53 10.60

14-May-03 14.89 7.45 7.55 10.58

23-May-03 17.92 16.67 17.46 17.82

28-May-03 48.35 32.32 31.67 44.96

29-May-03 12.46 12.18 12.45 12.58

30-May-03 11.72 0.00 0.00 8.48

*The water depths were calculated as total drainage output divided by the total area of the

lysimeters of 17.83 m2 (192 ft

2). 1 mm = 0.0394 in.

Table 3. Soil moisture (%) in the bed in lysimeter D1 during the spring 2003 season.

Date Soil moisture (% vol) 10 cm (3.93 in) 20 cm (7.87 in) 30 cm (11.8 in)

3-Apr-03 8.50 8.00 16.12

4-Apr-03 8.82 11.64 16.12

5-Apr-03 8.03 11.33 16.12

6-Apr-03 8.08 11.33 15.09

7-Apr-03 7.97 10.95 15.26

8-Apr-03 8.14 11.12 15.26

9-Apr-03 7.91 10.92 15.01

10-Apr-03 7.77 10.62 14.62

11-Apr-03 7.80 10.16 14.03

40


12-Apr-03 8.46 10.32 13.88

13-Apr-03 8.23 10.29 13.88

14-Apr-03 8.64 10.39 13.65

15-Apr-03 8.67 10.35 13.92

16-Apr-03 9.55 11.92 16.49

17-Apr-03 10.13 14.30 21.16

18-Apr-03 11.16 14.93 21.64

19-Apr-03 10.03 14.38 21.21

20-Apr-03 9.55 13.73 20.40

21-Apr-03 10.03 14.89 21.69

22-Apr-03 10.29 14.50 21.49

23-Apr-03 10.39 14.42 21.11

24-Apr-03 10.89 15.91 23.21

25-Apr-03 10.82 16.12 22.51

26-Apr-03 13.57 26.67 30.09

27-Apr-03 12.42 26.41 30.66

28-Apr-03 11.68 25.51 29.92

29-Apr-03 13.80 27.48 31.29

30-Apr-03 15.91 27.86 30.43

1-May-03 11.40 23.16 30.49

2-May-03 11.89 21.93 30.38

3-May-03 12.07 22.27 30.66

4-May-03 10.20 22.27 30.66

5-May-03 11.92 20.45 31.47

6-May-03 11.92 20.45 31.47

7-May-03 11.92 20.45 31.47

8-May-03 11.92 20.45 31.47

9-May-03 11.43 18.84 30.72

10-May-03 9.67 18.84 30.72

11-May-03 10.17 18.39 29.92

12-May-03 10.17 18.39 29.92

13-May-03 8.83 18.39 29.92

14-May-03 11.54 18.39 29.92

15-May-03 10.59 18.04 30.09

16-May-03 10.49 16.83 29.58

17-May-03 10.62 17.56 30.38

18-May-03 9.90 15.58 29.19

19-May-03 10.69 17.17 29.81

20-May-03 11.36 18.53 30.38

21-May-03 9.90 15.54 29.25

22-May-03 9.84 15.26 29.02

23-May-03 11.89 19.84 31.12

24-May-03 11.43 19.16 30.72

25-May-03 10.72 18.39 31.00

26-May-03 10.52 17.51 30.60

27-May-03 10.52 17.51 30.60

28-May-03 19.52 29.75 31.76

29-May-03 19.52 29.75 31.76

30-May-03 18.30 29.53 31.58

41



3-Apr-03 9.00 11.00 N/A

4-Apr-03 7.06 10.59 14.86

5-Apr-03 6.85 10.55 14.82

6-Apr-03 6.93 10.75 15.05

7-Apr-03 7.01 10.92 15.30

8-Apr-03 6.93 10.75 15.09

9-Apr-03 6.98 10.75 14.97

10-Apr-03 6.85 10.69 14.89

11-Apr-03 6.62 10.13 13.84

12-Apr-03 6.77 10.13 13.65

13-Apr-03 6.62 10.16 13.65

14-Apr-03 6.64 9.97 13.35

15-Apr-03 6.42 10.00 13.46

16-Apr-03 7.36 11.36 16.03

17-Apr-03 8.26 12.57 18.13

18-Apr-03 8.26 12.97 18.79

19-Apr-03 8.00 12.72 18.39

20-Apr-03 8.05 12.94 18.88

21-Apr-03 8.34 13.54 19.98

22-Apr-03 8.26 13.38 19.80

23-Apr-03 8.55 13.92 20.73

24-Apr-03 9.00 14.78 23.16

25-Apr-03 9.12 15.13 23.11

26-Apr-03 12.75 26.73 28.25

27-Apr-03 11.09 25.83 29.75

28-Apr-03 10.35 24.07 28.41

29-Apr-03 13.54 27.05 29.13

30-Apr-03 13.80 27.10 28.86

1-May-03 10.42 24.12 28.52

2-May-03 11.54 24.27 28.91

3-May-03 11.00 24.27 28.91

4-May-03 10.00 24.27 28.91

5-May-03 12.46 26.04 29.75

6-May-03 12.46 26.04 29.75

7-May-03 12.46 26.04 29.75

8-May-03 12.46 26.04 29.75

9-May-03 11.61 25.93 29.36

10-May-03 9.33 25.93 29.36

11-May-03 9.00 24.68 28.86

12-May-03 9.00 24.68 28.86

13-May-03 8.67 24.68 28.86

14-May-03 11.12 24.68 28.86

15-May-03 10.69 25.72 28.80

16-May-03 10.42 24.58 27.97

17-May-03 10.89 25.72 29.25

18-May-03 9.97 24.42 28.80

19-May-03 10.65 24.63 28.86

20-May-03 11.40 25.77 28.97

42


21-May-03 10.00 24.47 28.69

22-May-03 9.87 23.51 28.30

23-May-03 10.89 24.47 27.76

24-May-03 10.62 24.37 28.69

25-May-03 9.93 23.41 28.36

26-May-03 9.81 22.76 27.92

27-May-03 9.81 22.76 27.92

28-May-03 9.81 22.76 27.92

29-May-03 11.19 24.12 28.19

30-May-03 11.19 24.12 28.19



3-Apr-03 8.50 8.50 N/A

4-Apr-03 9.43 10.49 13.54

5-Apr-03 7.80 9.84 13.27

6-Apr-03 8.20 10.26 13.73

7-Apr-03 8.03 10.16 13.35

8-Apr-03 8.20 10.35 13.76

9-Apr-03 8.23 10.26 13.73

10-Apr-03 7.91 9.81 13.35

11-Apr-03 8.03 9.71 12.94

12-Apr-03 9.93 9.71 12.50

13-Apr-03 8.79 9.68 12.68

14-Apr-03 8.52 9.65 12.50

15-Apr-03 8.67 9.43 12.46

16-Apr-03 13.92 11.50 14.82

17-Apr-03 11.43 12.10 16.41

18-Apr-03 12.03 12.53 17.30

19-Apr-03 10.55 11.64 16.37

20-Apr-03 9.77 11.61 16.74

21-Apr-03 10.13 12.21 17.65

22-Apr-03 11.82 12.17 17.51

23-Apr-03 12.07 13.12 16.96

24-Apr-03 11.57 13.31 19.66

25-Apr-03 11.19 13.61 19.75

26-Apr-03 16.49 19.25 31.06

27-Apr-03 13.46 17.86 31.70

28-Apr-03 12.61 16.70 31.18

29-Apr-03 14.11 23.26 31.64

30-Apr-03 15.30 23.76 31.18

1-May-03 10.89 19.94 31.00

2-May-03 13.50 19.34 30.89

43


3-May-03 8.67 19.34 30.89

4-May-03 11.80 19.34 30.89

5-May-03 11.96 18.84 31.24

6-May-03 11.96 18.84 31.24

7-May-03 11.96 18.84 31.24

8-May-03 11.96 18.84 31.24

9-May-03 10.72 17.73 30.89

10-May-03 11.17 17.73 30.89

11-May-03 9.67 17.39 29.92

12-May-03 9.67 17.39 29.92

13-May-03 9.50 17.39 29.92

14-May-03 11.12 17.39 29.92

15-May-03 10.95 18.22 30.20

16-May-03 12.64 17.73 30.09

17-May-03 15.66 17.99 30.66

18-May-03 12.75 16.58 30.03

19-May-03 12.57 17.82 30.20

20-May-03 12.39 18.66 30.55

21-May-03 10.45 16.83 30.32

22-May-03 9.93 16.20 30.20

23-May-03 12.46 18.26 30.38

24-May-03 11.54 17.51 30.15

25-May-03 9.87 17.00 30.38

26-May-03 9.90 16.45 30.15

27-May-03 9.90 16.45 30.15

28-May-03 12.64 21.30 30.49

29-May-03 12.79 25.77 29.13

30-May-03 11.06 18.62 29.75



3-Apr-03 8.00 8.00 N/A

4-Apr-03 10.65 12.61 14.78

5-Apr-03 14.54 14.11 15.13

6-Apr-03 9.46 12.28 15.05

7-Apr-03 9.81 12.39 15.01

8-Apr-03 9.68 12.86 15.46

9-Apr-03 9.65 12.61 15.70

10-Apr-03 9.30 12.14 15.22

11-Apr-03 9.18 11.75 14.54

12-Apr-03 12.79 12.50 14.34

13-Apr-03 10.16 11.50 14.11

14-Apr-03 10.42 12.07 13.84

15-Apr-03 9.52 11.57 14.07

16-Apr-03 14.78 14.34 17.47

17-Apr-03 12.03 14.74 18.35

18-Apr-03 12.50 14.86 18.88

19-Apr-03 11.54 14.23 18.35

44


20-Apr-03 10.95 14.38 18.97

21-Apr-03 11.43 15.01 20.12

22-Apr-03 13.57 15.30 19.89

23-Apr-03 13.42 16.03 19.94

24-Apr-03 13.08 18.26 23.61

25-Apr-03 12.46 17.47 24.07

26-Apr-03 17.30 28.25 29.47

27-Apr-03 13.57 27.43 30.55

28-Apr-03 12.46 26.25 29.75

29-Apr-03 16.20 28.30 31.29

30-Apr-03 16.70 26.70 30.30

1-May-03 11.78 23.86 29.58

2-May-03 13.76 23.66 29.36

3-May-03 10.00 23.66 29.36

4-May-03 10.40 23.66 29.36

5-May-03 10.40 23.66 29.36

6-May-03 10.40 23.66 29.36

7-May-03 10.40 23.66 29.36

8-May-03 13.35 22.66 29.75

9-May-03 10.83 20.45 27.38

10-May-03 12.83 20.45 27.38

11-May-03 10.33 17.13 27.97

12-May-03 10.33 17.13 27.97

13-May-03 8.17 17.13 27.97

14-May-03 11.43 17.13 27.97

15-May-03 9.33 17.39 28.47

16-May-03 14.30 17.39 28.47

17-May-03 16.66 14.89 28.08

18-May-03 11.78 16.53 28.25

19-May-03 12.72 17.51 27.92

20-May-03 11.54 18.93 29.36

21-May-03 10.79 14.97 27.92

22-May-03 12.97 14.11 27.10

23-May-03 14.50 26.40 29.50

24-May-03 11.50 17.00 29.02

25-May-03 11.33 15.50 28.97

26-May-03 11.33 15.50 28.97

27-May-03 11.33 15.50 28.97

28-May-03 11.33 15.50 28.97

29-May-03 11.33 15.50 28.97

30-May-03 10.25 16.21 28.35

Table 7. Daily rainfall (mm) during the spring 2003 Season.

Date Rainfall (mm)

9-Apr-03 3.05

15-Apr-03 13.72

26-Apr-03 36.83

45

28-Apr-03 24.64

30-Apr-03 9.91

1-May-03 3.30

14-May-03 19.81

19-May-03 5.84

22-May-03 11.43

23-May-03 14.48

26-May-03 41.40

27-May-03 43.94

28-May-03 2.03

29-May-03 24.38

1 mm = 0.0394 in.

Spring 2004

Irrigation data for spring 2004 growing season are presented in Table 9. No runoff

occurred during the monitoring period and only one drainage event on the 13th

of April,

2004. The total volume drained was 5.29, 4.67, 5.26, and 4.41 mm for D1, D2, D3, and

D4, respectively. Soil moisture data are presented for each lysimeter in Tables 10, 11, 12

and 13 for D1, D2, D3 and D4, respectively. Rainfall data are presented in Table 14. The

lysimeters produced 20,300 kg/ha marketable yield. Yield was particularly low in 2004

due to the vine decline disease that infested the field. As mentioned previously, harvest

was reduced to two events (instead of the usual three) and plants carried less fruit. This

had limited effect on the peak Kc values, because by the time the disease infected the

lysimeters the plants had reached maturity.

46

Table 8. Daily irrigation* (mm) for all lysimeters during the Spring 2004 season.

DATE Irrigation (mm)

D1 D2 D3 D4

08-Mar-04 4.24 4.46 3.81 4.32

09-Mar-04 2.13 2.17 1.90 1.77

10-Mar-04 0.00 0.00 0.00 0.00

11-Mar-04 0.00 0.00 0.00 0.00

12-Mar-04 0.00 0.00 0.00 0.00

13-Mar-04 2.12 2.06 1.91 1.97

14-Mar-04 2.10 1.98 1.75 1.69

15-Mar-04 0.51 0.51 0.48 0.46

16-Mar-04 1.05 1.07 0.93 1.03

17-Mar-04 2.33 2.49 2.26 2.09

18-Mar-04 1.30 1.01 0.93 0.70

19-Mar-04 2.83 2.87 2.72 3.29

20-Mar-04 0.00 0.00 0.00 0.00

21-Mar-04 1.58 1.45 1.46 1.27

22-Mar-04 2.69 2.08 1.78 1.80

23-Mar-04 1.18 1.15 2.14 1.06

24-Mar-04 1.50 1.52 2.28 1.35

25-Mar-04 1.19 1.25 1.26 1.38

26-Mar-04 1.24 1.23 1.81 1.09

27-Mar-04 0.64 0.66 0.70 0.65

28-Mar-04 1.20 1.12 1.16 1.02

29-Mar-04 2.49 2.86 2.58 2.32

30-Mar-04 0.92 0.93 0.94 0.84

31-Mar-04 2.06 2.13 2.06 1.81

01-Apr-04 2.49 2.53 2.38 2.09

02-Apr-04 3.76 3.94 3.68 3.16

03-Apr-04 1.09 1.04 0.93 0.88

04-Apr-04 0.95 0.89 0.86 0.81

05-Apr-04 1.99 1.91 1.68 1.61

06-Apr-04 2.16 2.15 2.20 1.74

07-Apr-04 1.50 1.45 1.38 1.21

08-Apr-04 2.36 2.40 2.20 2.01

09-Apr-04 2.53 2.58 2.46 2.01

10-Apr-04 1.33 1.48 1.33 1.26

11-Apr-04 0.00 0.00 0.00 0.00

12-Apr-04 0.00 0.00 0.00 0.00

13-Apr-04 0.00 0.00 0.00 0.00

14-Apr-04 0.69 0.71 0.70 0.64

15-Apr-04 0.92 0.92 0.93 0.82

16-Apr-04 0.81 0.82 0.76 0.73

17-Apr-04 1.37 1.54 1.50 1.13

18-Apr-04 1.12 1.12 1.05 0.95

19-Apr-04 1.90 1.94 1.81 1.64

20-Apr-04 2.80 2.76 2.56 2.24

21-Apr-04 2.76 2.83 2.52 2.29

22-Apr-04 2.72 2.82 2.57 2.23

23-Apr-04 1.50 1.49 1.35 1.21

24-Apr-04 1.81 2.02 1.79 1.59

47

DATE Irrigation (mm)

D1 D2 D3 D4

25-Apr-04 0.00 0.00 0.00 0.00

26-Apr-04 1.59 1.64 1.19 1.42

27-Apr-04 2.53 2.48 2.36 2.11

28-Apr-04 2.64 2.76 2.48 2.30

29-Apr-04 1.92 2.04 1.88 1.62

30-Apr-04 1.00 1.09 0.96 0.85

01-May-04 0.00 0.00 0.00 0.00

02-May-04 1.01 1.13 0.99 0.89

03-May-04 0.30 0.33 0.29 0.28

04-May-04 0.24 0.18 0.16 0.20

05-May-04 2.50 2.66 2.40 2.11

06-May-04 1.23 1.36 1.22 1.05

07-May-04 2.79 2.96 2.70 2.42

08-May-04 2.98 3.31 2.94 2.70

09-May-04 0.59 0.68 0.61 0.52

10-May-04 2.03 2.54 1.90 1.69

11-May-04 1.46 1.98 1.33 1.33

12-May-04 1.22 1.85 1.26 1.10

13-May-04 2.35 3.18 2.12 1.92

14-May-04 0.00 0.00 0.00 0.00

15-May-04 2.47 2.49 2.29 2.13

16-May-04 0.64 0.63 0.59 0.54

17-May-04 1.28 1.32 1.20 1.06

18-May-04 1.42 1.64 1.47 1.38

19-May-04 1.82 1.82 1.68 1.57

20-May-04 1.30 1.44 1.36 1.24

21-May-04 1.16 1.28 1.49 1.07

22-May-04 1.16 1.28 1.49 1.07

23-May-04 0.59 0.65 0.93 0.57

24-May-04 0.60 0.61 0.89 0.53

25-May-04 1.31 1.28 1.66 1.07

26-May-04 1.15 1.27 1.62 1.09

27-May-04 0.42 0.58 0.78 0.52

*The water depths were calculated as total irrigation input divided by the total area of the

lysimeters of 17.84 m, while the actual wetted area was only in the beds. 1 mm = 0.0394

in.

Table 9. Soil moisture (%) in the bed for lysimeter D1 during Spring 2004.

Date Soil moisture (% vol)

10 cm (3.93 in) 20 cm (7.87 in) 30 cm (11.8 in)

08-Mar-04 5.71 12.14 23.61

09-Mar-04 11.68 13.35 23.86

10-Mar-04 10.09 13.01 22.51

13-Mar-04 7.41 11.68 21.83

16-Mar-04 9.42 18.94 23.27

17-Mar-04 10.38 15.15 28.82

48


10 cm (3.93 in) 20 cm (7.87 in) 30 cm (11.8 in)

18-Mar-04 11.09 16.64 29.55

19-Mar-04 9.67 12.81 28.93

22-Mar-04 10.35 13.11 23.83

24-Mar-04 10.06 12.19 27.65

25-Mar-04 11.03 18.22 27.49

26-Mar-04 10.35 12.66 35.19

29-Mar-04 11.76 14.70 37.25

30-Mar-04 12.55 20.95 30.87

31-Mar-04 10.55 12.89 28.76

02-Apr-04 10.75 12.74 21.24

06-Apr-04 10.55 12.26 19.35

08-Apr-04 9.13 12.15 21.00

09-Apr-04 9.86 12.96 20.52

12-Apr-04 10.96 17.33 29.78

14-Apr-04 9.83 16.99 33.00

15-Apr-04 9.42 14.74 30.99

19-Apr-04 11.30 13.07 24.24

21-Apr-04 9.42 12.26 23.12

22-Apr-04 13.19 14.54 21.19

26-Apr-04 13.61 15.06 17.99

27-Apr-04 10.96 12.26 18.17

28-Apr-04 13.68 13.88 19.58

29-Apr-04 14.74 13.57 16.47

30-Apr-04 13.26 13.49 25.92

04-May-04 9.04 12.22 21.19

06-May-04 15.43 13.07 18.99

07-May-04 11.06 12.55 14.94

10-May-04 12.12 11.79 12.74

11-May-04 10.42 10.15 12.08

12-May-04 9.96 10.19 10.75

13-May-04 9.32 9.29 10.06

14-May-04 13.53 10.09 10.59

17-May-04 8.62 8.56 9.01

18-May-04 10.15 9.67 9.51

19-May-04 10.99 10.86 10.22

20-May-04 9.99 11.09 12.52

25-May-04 10.55 11.34 12.01

26-May-04 10.96 11.51 12.52

28-May-04 9.57 10.52 11.94

Table 10. Soil moisture (%) in the bed for lysimeter D2 during Spring 2004.


10 cm (3.93 in) 20 cm (7.87 in) 30 cm (11.8 in)

08-Mar-04 8.37 12.68 21.98

09-Mar-04 11.19 13.76 21.73

10-Mar-04 9.03 12.86 21.49

13-Mar-04 8.05 11.89 21.25

16-Mar-04 8.53 13.30 23.73

17-Mar-04 9.54 14.34 24.03

49


10 cm (3.93 in) 20 cm (7.87 in) 30 cm (11.8 in)

18-Mar-04 9.86 13.11 23.67

19-Mar-04 7.86 12.12 22.66

22-Mar-04 10.19 12.08 21.29

24-Mar-04 9.76 12.26 24.14

25-Mar-04 9.73 13.99 28.88

26-Mar-04 9.51 11.65 31.99

29-Mar-04 12.08 14.07 35.25

30-Mar-04 9.83 13.41 33.12

31-Mar-04 9.64 12.22 25.07

02-Apr-04 11.90 13.15 25.33

06-Apr-04 10.55 11.44 20.81

08-Apr-04 9.42 11.30 22.52

09-Apr-04 9.80 12.37 23.47

12-Apr-04 12.41 16.14 30.76

14-Apr-04 9.70 13.99 29.84

15-Apr-04 9.26 12.26 28.09

19-Apr-04 11.41 12.01 23.93

21-Apr-04 9.80 12.01 25.49

22-Apr-04 12.81 13.00 21.43

26-Apr-04 14.15 15.76 18.89

27-Apr-04 10.96 11.94 21.48

28-Apr-04 13.19 13.76 21.10

29-Apr-04 14.46 13.22 16.69

30-Apr-04 12.96 14.78 20.24

04-May-04 9.17 12.96 20.00

06-May-04 20.10 16.43 17.29

07-May-04 11.79 12.52 19.08

10-May-04 12.44 12.59 14.42

11-May-04 10.19 10.42 12.12

12-May-04 9.38 10.75 11.87

13-May-04 9.13 8.23 10.42

14-May-04 12.55 9.26 10.25

17-May-04 9.04 8.59 8.89

18-May-04 10.59 10.38 9.29

19-May-04 11.83 12.59 10.59

20-May-

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