UNDERSTANDING AND SIMULATING SPATIAL SOIL WATER AND …ufdcimages.uflib.ufl.edu/UF/00/10/07/07/00001/nijbroek.pdf · TDR1 plot. 0-30 cm layer. Arrows indicate the point when the drained

UNDERSTANDING AND SIMULATING SPATIAL SOIL WATERAND YIELD VARIABILITY IN AN IRRIGATED SOYBEAN FIELD

ByRAVIC NIJBROEK

A THESIS PRESENTED TO THE GRADUATE SCHOOLOF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF ENGINEERING

UNIVERSITY OF FLORIDA

1999

Copyright 1999

by

Ravic Nijbroek

iii

ACKNOWLEDGEMENTS

This thesis work would not have been completed without the help of several

people whom I wish to thank. First, my utmost appreciation goes to my advisor and

mentor, Dr. Jim Jones. He accepted me into his environment and guided me through with

unlimited patience and energy. He has always been ready to discuss new ideas and his

wisdom has time and again helped me get on the right track. My respect for him as a

scientist and human being is unparalleled. I could never have dreamed of a better advisor.

I am grateful to Dr. Gerrit Hoogenboom, who was there from the beginning to

assist me with technical issues and help me get off on a good start, which is one of the

most important factors that led to the success of this work. I thank him for applying much

needed pressure during the final stages of this research. Dr. Peter Kizza’s eye for detail

and our conversations about life on a larger scale kept me going when my focus was

blurred. I would like to give my special thanks to Dr. Dorota Haman for her support,

interest, and knowledge on irrigation engineering and for being able to share both a social

and professional relationship.

Furthermore, I wish to recognize Tony Smith for giving me unlimited access to

his farm during the 1998 summer crop season. Much of the research in precision

agriculture would not be possible without farmers like him who are interested in current

research and allow the use of their fields. I wish to acknowledge the personnel of the soil

analysis laboratories, Larry Schwandes and Dave Cantlin, for taking the time to teach me

the necessary skills and sharing their equipment. I am very grateful to Wayne Williams

iv

for assisting me during all phases of the data collection process and for ensuring my

safety during our many hours on the road.

Special thanks go to my friends and family who were always ready to help me go

through the rough times by helping me carry the burden and maximized my joy by

sharing the little triumphs. My colleagues always provided a healthy, productive, and

stimulating work atmosphere and refreshing discussions during coffee breaks. Finally, I

greatly appreciated the company of my friend Dave who gave me courage to continue

and complete this study through his extraordinary talents.

v

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...............................................................................................iii

LIST OF TABLES ...........................................................................................................viii

LIST OF FIGURES............................................................................................................. x

LIST OF SYMBOLS .......................................................................................................xiv

CHAPTERS

1 INTRODUCTION........................................................................................................... 1

2 COMPARISON OF SOIL WATER ESTIMATION TECHNIQUES............................ 3

Introduction ............................................................................................................. 3Materials and Methods ............................................................................................ 7

Research Location and Field Conditions .................................................... 7Time Domain Reflectometry....................................................................... 8

TDR measured drained upper limit and lower limit ....................... 9Comparison of TDR and gravimetric measurements.................... 11

Bulk Density and Soil Water Content by Gravimetric Sampling ............. 11Comparison of Soil Parameter Estimation Techniques............................. 13

DSSAT Soil/Create method .......................................................... 13SWLIMITS method....................................................................... 14Saxton method............................................................................... 15Rawls method................................................................................ 16

Comparison of Soil Parameter Estimation Methods ................................. 16Results and Discussion.......................................................................................... 18

Time Domain Reflectometry Data ............................................................ 18TDR measured drained upper limit and lower limit ..................... 18Comparison of TDR and gravimetric measurements.................... 23

Bulk Density and Soil Water Content by Gravimetric Sampling ............. 26Comparison of Soil Parameter Estimation Methods ................................. 28

Conclusions ........................................................................................................... 34

3 INVESTIGATING SPATIALLY VARIABLE IRRIGATION AND RAINFALL ..... 36

Introduction ........................................................................................................... 36

vi

Materials and Methods .......................................................................................... 38Research Location and Field Conditions .................................................. 38Crop Simulation Model............................................................................. 40Field Experiments ..................................................................................... 42

Spatial distribution of rainfall ....................................................... 42Spatial distribution of irrigation .................................................... 43Gravimetric soil water content ...................................................... 43Comparison of observed and simulated yield ............................... 43

Simulation Experiments ............................................................................ 44Optimization of the irrigation threshold factor ............................. 45Management based on zone with earliest sign of stress................ 46Management based on highest yielding zone................................ 46Management based on the largest zone......................................... 47Management based on optimal irrigation by zone ........................ 47

Economic Analysis.................................................................................... 47Results and Discussion.......................................................................................... 48

Field Experiments ..................................................................................... 48Spatial distribution of rainfall ....................................................... 50Spatial distribution of irrigation .................................................... 52Gravimetric soil water content ...................................................... 54Comparison of observed and simulated yield ............................... 57

Simulation Experiments ............................................................................ 59Management based on earliest sign of stress ................................ 60Management based on the highest yielding zone.......................... 61Management based on the largest zone......................................... 61Management based on optimal irrigation by zone ........................ 61

Economic Analysis.................................................................................... 65Conclusions ........................................................................................................... 68

4 SUMMARY AND CONCLUSIONS............................................................................ 70

APPENDICES

A SOIL WATER CONTENT DRAINAGE RATES FOR THE DETERMINATION OFDRAINED UPPER LIMIT VALUES IN THE UPPER SOIL LAYERS................... 74

B TIME DOMAIN REFLECTOMETRY DATA FROM IRRIGATED AND NON-IRRIGATED LOCATIONS........................................................................................ 87

C COMPARISON OF SIMULATED SWC VALUES FROM RAWLS INPUTPARAMETERS, TDR MEASUREMENTS, AND OBSERVED SWC IN THEIRRIGATED ZONE 1............................................................................................... 130

D IRRIGATION AND RAINFALL DATA FROM RAIN GAGES AND THEWEATHER STATION ............................................................................................. 134

vii

E COMPARISON OF SIMULATED SOIL WATER CONTENT VALUES FROMRAWLS INPUT PARAMETERS AND OBSERVED SWC VALUES .................. 138

F MANAGEMENT AND SOIL INPUT PARAMETERS FOR 1998SIMULATIONS........................................................................................................ 147

REFERENCES................................................................................................................ 148

BIOGRAPHICAL SKETCH........................................................................................... 151

viii

LIST OF TABLES

Table page

2-1. Drained upper limit values measured from TDR data. ............................................ 21

2-2. Particle size analysis and lower limit estimations for soil samples collected betweenTDR-1 and TDR-2 Locations. ................................................................................. 21

2-3. Predicted and measured soil water limits................................................................. 27

2-4. Bulk density and SWC from gravimetric measurements and from TDR1 and TDR2locations in Field 10. ............................................................................................... 27

2-5. Index of Agreement (d) and the root mean square difference (RMSD) between soillimits derived from TDR and soil parameter estimation methods. n=6. ................. 29

3-1. Particle size distribution in management zones in Field 10..................................... 49

3-2. Predicted drained upper limit (DUL), lower limit (LL), and plant available soilwater (PASW) in all management zones in Field 10, using the Rawls (Rawls andBrakensiek, 1982) method. ...................................................................................... 49

3-3. Irrigation amounts in management zones in 1998. .................................................. 53

3-4. Root mean square difference of simulated versus observed soil water contentvalues. ...................................................................................................................... 57

3-5. Irrigation threshold factors that resulted in maximum gross margins for allmanagement zones with respective percentages sand, clay, and silt. ...................... 59

3-6. Simulations of irrigation starting dates and yields under automatic and non-irrigatedconditions for five management zones in Field 10.................................................. 60

ix

3-7. Twenty-five year averages and standard deviations of total production and wateruse for five management zones under different irrigation treatments. .................... 62

3-8. Simulated gross margins of five management zones measured over 25 years using asoybean price of $6 per bushel (approximately $222.40 per 1000 kg). .................. 66

B-1. Time domain reflectometry data for irrigated zone. ............................................... 87

B-2. Time domain reflectometry data for non-irrigated zone. ...................................... 108

D-1. Irrigation and rainfall data (mm) collected from rain gages (RG) and a weatherstation from the Georgia Automated Environmental Monitoring Network(http://www.griffin.peachnet.edu/bae/) ................................................................. 134

F-1. Management and soil information for the 1998 simulation................................... 147

x

LIST OF FIGURES

Figure page

2-1. Placement of time domain reflectometry equipment in the field. a) Top view of theTDR set up in the non-irrigated and irrigated (TDR-1 and TDR-2) parts of the field;b) Side view of the locations of the probes in the irrigated and non-irrigated parts ofthe field. ..................................................................................................................... 8

2-2. Index of agreement (d) for fits of TDR-1 vs. gravimetric measurements ofvolumetric SWC for (a) each layer and (b) the profile (no 60-90 cm values)........ 25

2-3. Time domain reflectometry soil water data collected every two hours in six layersat the TDR-1 irrigated location................................................................................ 19

2-4. Soil water content from irrigated TDR-1 and rainfall late in the season. ................ 22

2-5. Plant available soil water predictions from four soil parameter estimation methodsusing particle size data from the five different locations in field 10. ...................... 28

2-6. Plant available soil water for each layer compared to the observed TDR andpressure plate analysis results. The total SWC values for the soil parameterestimation methods are indicated on top of the bars................................................ 30

2-7. CROPGRO-Soybean simulated yield using predicted and observed soil inputparameters for three irrigation management practices............................................. 31

2-8. Sensitivity analysis of CROPGRO-Soybean using soil limits from different soilparameter estimation methods. ................................................................................ 32

2-9. SWC estimated by the CROPGRO-Soybean water balance using Rawls inputparameters versus actual in-field SWC measurements from gravimetric readingsand TDR. ................................................................................................................. 33

3-1. Management zones in Field 10, Crestview, GA. ..................................................... 39

xi

3-3. Spatially variable rainfall (irrigation excluded) measured in Field 10 and theweather station. ........................................................................................................ 51

3-4. Spatially variable irrigation amounts (rainfall excluded) in six management zones.................................................................................................................................. 52

3-5. Simulated and observed SWC values in the soil profile of Zone 2. 0-30 cm (a) and30-60 cm (b). ........................................................................................................... 55

3-6. Observed versus predicted dry seed weight............................................................. 58

3-7. Production differences between a spatially variable irrigated production (zero line)and three other irrigation management options. ...................................................... 63

3-8. Differences in water use between spatially variable irrigation (zero line) andirrigation by the water demands of different zones. ................................................ 64

3-9. Differences in cumulative drainage between spatially variable irrigation (zero line)and irrigation by the water demands of different zones. ......................................... 64

3-10. Differences in gross margin between spatially variable irrigation (zero line) andirrigation by the water demands of different zones. ................................................ 66

3-11. Lower and upper quartiles (box) of the gross margin from different managementstrategies based on: spatially variable irrigation (A), zone with earliest stress sign(B), zone 3 (C), zone 4 (D), largest zone (E), and highest yielding zone (F).Whiskers and black areas indicate gross margin range and confidence interval(p=0.05) of median respectively. ............................................................................. 67

A-1. Average drainage rates of volumetric SWC for three time periods in the irrigatedTDR1 plot. 0-30 cm layer. Arrows indicate the point when the drained upper limitequilibrium was reached. ......................................................................................... 74


xii





A-7. Average drainage rates of volumetric SWC for three time periods in the non-irrigated TDR1 plot. 0-30 cm layer. Arrows indicate the point when the drainedupper limit equilibrium was reached. ...................................................................... 80






xiii

C-1. Simulated volumetric SWC using Rawls soil input parameters versus observedvolumetric SWC values from gravimetric measurements and TDR in the 30-60 cmlayer. ........................................................................................................................ 87

C-2. Simulated volumetric SWC using Rawls soil input parameters versus observedvolumetric SWC values from gravimetric measurements and TDR in the 60-90 cmlayer. ...................................................................................................................... 131

C-3. Simulated volumetric SWC using Rawls soil input parameters versus observedvolumetric SWC values from gravimetric measurements and TDR in the 90-120 cmlayer. ...................................................................................................................... 132

E-1. Zone 3: Simulated and observed soil water content in the soil profile: 0-30 cm (a)and 30-60 cm (b).................................................................................................... 138




xiv

LIST OF SYMBOLS

Symbol Definition

BD Bulk density (g cm-3)

d Index of agreement (range: 0 - 1)

DUL Drained upper limit (cm3 cm-3)

FC Field capacity (cm3 cm-3)

LL Lower limit (cm3 cm-3)

MD Mean difference

N Number of samples

OAVG Average of all observed values

Oi The i-th observed value

OM Organic matter

PASW Plant available soil water (cm3 cm-3)

PWP Permanent wilting point (cm3 cm-3)

Pi The i-th predicted value

RMSD Root mean square difference

SPE Soil parameter estimation

SWC Soil water content (cm3 cm-3)

TDR Time domain reflectometry

xv

Abstract of Thesis Presented to the Graduate Schoolof the University of Florida in Partial Fulfillment of theRequirements for the Degree of Master of Engineering

UNDERSTANDING AND SIMULATING SPATIEL SOIL WATER AND YIELDVARIABILITY IN AN IRRIGATED SOYBEAN FIELD

By

Ravic Nijbroek

December 1999

Chairperson: James W. JonesMajor Department: Agricultural and Biological Engineering

When investigating how agricultural profits can be maximized while minimizing

the impact on the environment, it may be necessary to study management practices from

a system’s approach. In this research, five different irrigation management strategies

were analyzed using the CROPGRO-Soybean crop simulation model. The 12.6 ha

irrigated soybean research site was located in Crestview, Georgia.

One of the most critical factors when using simulation models is the accuracy of

input parameters used. The most yield-limiting factor for soybeans in many locations is

drought stress. Therefore, the most critical model input parameters are the soil water

holding limits and amounts of water applied during the growing season. In a selected

location the soil water holding limits were measured using time domain reflectometry and

1500 kPa pressure plate chambers. The time domain reflectometry time series data were

used to estimate the drained upper limits in the middle of the growing season when plant

xvi

roots were actively removing soil water. This new method was based on nighttime

drainage rates in consecutive soil layers. The pressure plate analysis was used for the

estimation of the lower limits.

The field observations were used to select a soil parameter estimation method that

best predicted the soil water holding limits. This method was used to estimate the water

holding limits in five predetermined management zones in the remainder of the field. The

simulated irrigation schedules of those management zones with the earliest sign of stress,

largest area, and highest yield were used to simulate irrigation over the entire field. In

addition, the field was irrigated according to an optimal irrigation for each zone. The total

field production, water used, water drained, and gross margins were calculated to

compare the irrigation management options.

The time domain reflectometry data were successfully used with the pressure

plate analysis to estimate 84 mm of plant available soil water in the soil profile. The

Rawls soil parameter estimation method best approximated these results (78 mm of plant

available water). The 25-year field scale simulations indicated that spatially variable

irrigation resulted in the highest average field production (33.5 metric tons). Irrigation

management according to the highest yielding zone used the least amount of irrigation

water and had the lowest drainage as well (23.4 and 14.6 million liters respectively). The

spatially variable irrigation management strategy resulted in the highest average gross

margin ($6919 using $6 per bushel and $2 per hectare-cm). However, this gross margin

value was statistically different from the other management options for this field.

1

CHAPTER 1INTRODUCTION

No field is spatially or temporally uniform. Pierce and Nowak (1999) stated that

“managing soils and crops in space and time is the sustainable management principle of

the 21st century.” If sustainable farming is our future objective, i.e. using a minimal

amount of resources and chemicals assuring equal opportunities for future generations

while optimizing profits in the present time, one must fully understand this spatial and

temporal variability. The science of managing soil and crop systems variable in space and

time is known as precision agriculture. A more complete definition of precision

agriculture is the application of technologies and principles to manage spatial and

temporal variability associated with all aspects of agricultural production for the purpose

of improving crop performance and environmental quality (Pierce and Nowak, 1998).

In many locations, the most critical yield-limiting factor for a nitrogen-fixing

legume, i.e. soybeans, is drought stress (Shen et al., 1998). The spatial patterns of plant

available soil water thus determine much of the spatial variability of soybean yield.

Therefore, the ability to predict the spatial variability of plant available soil water is

required to understand the spatial variability of soybean yield. This understanding may

result in optimal management practices of available water resources. These management

practices include irrigation scheduling such that a minimal amount of water is lost

through drainage, which could potentially pollute the ground water.

2

The main goal of this study was to investigate different irrigation management

strategies in a Southwest Georgia soybean field. In order to achieve this goal, a soil

parameter estimation (SPE) equation that successfully approximated the plant available

soil water was selected. The selection was made from four commonly used SPE

techniques. These were compared to time domain reflectometry and laboratory derived

data in chapter 2. The best SPE equation resulting from this comparison was then applied

to pre-determined management zones.

The resulting spatially varying soil water holding limits were used to investigate

four different irrigation schedules in chapter 3. The average total field production, water

use, and drainage were simulated for 25 years using a process oriented crop model. The

optimal management strategy was defined by the management practice resulting in

maximum gross margin (high production and low water use) and the least impact on the

environment (low drainage).

3

CHAPTER 2COMPARISON OF SOIL WATER ESTIMATION TECHNIQUES

Introduction

One of the most important soil factors for determining crop production is its

ability to retain water. Crop yields can only be maximized if plant available soil water

(PASW) supply remains high throughout the growing season (Hillel, 1980). PASW is

defined as the difference between the drained upper limit (DUL) and the lower limit (LL)

(Ritchie, 1990). Factors that may affect the PASW are: soil texture, type of clay present,

organic matter content, depth of wetting and antecedent moisture and the presence of

impeding layers (Hillel, 1980). Some of these factors are spatially and temporally

variable and they depend upon the properties of meteorological and plant conditions

(Hillel, 1980). The management of these spatial and temporal soil and weather variations

for the purpose of optimal crop performance and environmental quality may require

precision agriculture.

Crop simulation models are potentially important for use in precision agriculture

because they allow us to understand the impact of soil and weather patterns on crop

production and its variability. For optimal model performance it is important to have

accurate input data reflecting the variability of these properties. However, it is not

practical to monitor the variability of all the aforementioned parameters over space and

4

time. Crop variety data are often known and weather data can be obtained either directly

through the placement of inexpensive rain gages in the field, an existing weather station,

via the Internet, or from companies that provide these data inexpensively for use on farms

(Welch et al., 1999). Therefore, the quantification of the spatial variability of soil

properties, in particular, is critical. However, soil properties are difficult and expensive to

measure.

The DUL and LL values can be measured in the field or in the laboratory, or they

may be estimated using soil parameter estimation (SPE) techniques that require soil

properties such as texture, organic matter content, and bulk density. It is important to

have a clear understanding of the similarities and differences between these techniques to

express the soil water holding limits and simulate yield.

Common laboratory techniques to estimate the soil upper limit include

equilibration of pre-saturated soils with a centrifugal force 1000 times the gravity force or

with a matric suction value of 10 or 33 kPa. However, Hillel (1980) argues that such

measurements can by no means be generalized and can “at best be correlated” with the

actual DUL. Soil upper water holding limits derived from laboratory methods ignore

several variables that influence field conditions such as: soil profile heterogeneity,

preferential water flow, soil surface evaporation and plant uptake during drainage, root

distribution, and plant species (Ritchie and Amato, 1990). The field capacity is often used

to describe laboratory-measured data. Ritchie (1980) preferred using the term DUL,

defined as the highest field-measured water content of a soil after it has been thoroughly

wetted and allowed to drain until drainage has become practically negligible.

5

Ritchie (1980) refers to the lower limit as the lowest field-measured volumetric

water content of a soil after plants stop extracting water due to premature death or

dormancy as a result of water deficit. This parameter is not exclusively dependent on the

soil, because it may vary with the crop root extraction ability. However, Ratliff et al.

(1983) showed that there was no significant difference between laboratory-measured and

field-measured LL values for loamy sands and sandy loams. Common laboratory

estimation of LL is conducted by applying 1500 kPa of suction to the soil samples and

measuring the remaining soil water content (SWC). These limits may be underestimated

by 1.0% or more for sands and sandy clay loams (Ratliff et al., 1983).

Ideal sensors to measure soil water limits in the farmer’s field should allow low

labor requirements and electronic data acquisition. Yoder et al. (1998) made an

evaluation of sensors that meet these requirements: tensiometers, neutron gauges,

electrical resistance sensors, electrical capacitance sensors, heat dissipation sensors, and

time domain reflectometry (TDR). Additionally, the sensor should be able to collect

completely automated time series data and work independently for weeks at a time

because data retrieval may be possible infrequently in many studies.

Soil parameter estimation equations, as opposed to in-field measurement

techniques, may be useful in precision agriculture. They can estimate the soil water limits

for virtually any location in the field from readily available soil particle size data. It is

therefore necessary to first select one (or more) best SPE equation(s) for the specific soil

types in a field before precision agriculture techniques, such as crop models, can be used

as a management tool.

6

Tietje and Tapkenhinrichs (1993) evaluated 13 different soil parameter estimation

methods for their applicability to a broad range of different soils, the mean difference

(MD) and root mean square difference (RMSD) of the predicted and observed values, and

the predicted and observed soil parameters. They concluded that the Saxton equations

were applicable for 98% of the soil samples and had a low MD (-0.005 m3 m-3), but large

RMSD (0.068 m3 m-3). The Rawls equations could be used for 100% of the available soil

samples. However, this method generally underestimated the water content for the soils

in the data set, which resulted in a large RMSD (0.073). They found that five additional

methods could be used to estimate the soil water limits for 98% or more of the soil

samples, while the six remaining methods were applicable for 88% or less of the total

available soil samples. The DSSAT (Ritchie, 1980) and SWLIMITS (Ritchie et al., 1998)

equations were not evaluated in this study.

The hypothesis of this research is that soil water holding limits and available soil

water of typical sandy soils in the Southeast United States can be estimated using readily

available methods. In order to accurately evaluate the selected SPE methods, it was

necessary to measure in-field soil water retention parameters. A TDR system was

selected to complete this task. However, TDR only measures volumetric water content,

which had to be manipulated to obtain DUL, LL and PASW values. Accuracy of the

resulting soil parameters was determined by: comparison of total potential plant available

soil water, the coefficients of agreement (d), and the lowest root mean square difference

(RMSD) of the estimated and measured LL, DUL, and PASW. The specific objectives of

this chapter were:

7

1. To develop a method for deriving drained upper limit values from time domain

reflectometry.

2. To evaluate different soil parameter estimation equations by comparing their DUL

and LL estimates to those obtained from TDR and 1500 kPa pressure plate analysis

data respectively.

Materials and Methods

Research Location and Field Conditions

The study site was located in Crestview, Georgia, on the border of Baker and

Early Counties. The latitude and longitude coordinates of the field are 31.330 and –

84.630 degrees, respectively. The nearly 12.6 hectare field, also known as Field 10, is

farmed by Tony Smith. The Soil Conservation Service maps indicate that the study site

lies in the Goldsboro series and has mostly loamy sands in the upper layers and sandy

loams in the lower layers (U.S. Dept. Agr., 1985).

The field was irrigated with a center pivot, except for a small 0.7 ha area that was

out of the reach of the center pivot system. The field has slopes of less than 2%, which

makes it ideal for this study because lateral water flow most likely had a minimal effect

on the variability of plant available soil water. Therefore, the localized textural

differences are expected to have the greatest influence on the determination of the SWC

for a given location. A winter crop of canola (Brassica napus L.) was harvested from this

field about two to three weeks before soybeans were planted.

8

1502B

0-30 cm30-60 cm60-90 cm

90-120 cm120-150 cm150-180 cm

TDRProbes

30 cm

IRRIGATED NON-IRRIGATED

GravimetricSamplingLocations

TDR-1 ElectromagneticTransmission LinesTotal Span: 16.5 m.

1502B

a.

b.

TDR-2

Figure 2-1. Placement of time domain reflectometry equipment in the field. a) Top viewof the TDR setup in the non-irrigated and the TDR-1 and TDR-2 irrigated sections of thefield; b) Side view of the locations of the probes in the irrigated and non-irrigated areas ofthe field.

Time Domain Reflectometry

Time domain reflectometry is based on measuring the dielectric constant of soils

from the propagation velocity of a pulse travelling along an electromagnetic transmission

line embedded in the soil. Topp and Davis (1984) showed that a third-order polynomial

describes the relationship between the dielectric constant and volumetric SWC. A 1502B

Metallic TDR unit (Tektronix) was placed in the field. Cassel et al. (1994) used a similar

TDR system with a cable length of 25 m. The recommendations from Campbell

Scientific Inc. were to not exceed a total distance of 25 feet (8.2 m) from the probes to the

1502B cable tester. Thus, data collection was limited to one area in the field.

9

The TDR probes were placed in four locations between the irrigated and non-

irrigated sections of the field, with a total span of 16.5 meters between the top probes (see

Figure 2-1). An auger was first used to make a hole to the top of the layer of interest. A

set of probes was then placed vertically in each undisturbed layer. A probe guide was

designed and constructed for placing the probes with relative ease at these depths while

maintaining a spacing of 50 mm between the probes. The probes were not inserted

horizontally because our research could not disturb the crop, which had already been

planted. The soil that was removed in order to install the probes was replaced in small

amounts while applying pressure to approximate its original undisturbed density.

The entire TDR system was powered by a marine deep cycle battery recharging

constantly with a MSX15R 15 Watt solar panel from Campbell Scientific. This design

allowed for the collection of volumetric SWC measurements every four hours for the first

two weeks and every two hours for the remainder of the season on a Campbell Scientific

Inc. CR-10 data logger. The data logger had the capacity to store TDR data for three

weeks. Data were collected from June 23 until October 2. Measurements were taken at

two positions in the irrigated and non-irrigated zones each. The TDR data that were

collected in the non-irrigated zone were not used due to the lack of soybean plant

emergence in this section. The two positions in the irrigated section of the field were

labeled TDR1 and TDR2.

TDR measured drained upper limit and lower limit

I developed a new technique to determine the DUL in the upper layers using time

domain reflectometry data. It was assumed that the spatial variability between the TDR1

and TDR2 irrigated locations was insignificant for the purposes of estimating the DUL in

10

the small area containing the TDR probes. Under ideal circumstances, the DUL for all

layers can be determined from TDR data by installing the probes before planting and

observing the SWC after the soil is saturated while covering the soil (Ritchie and Amato,

1990). Unfortunately, the TDR system could not be installed until after planting. DUL

values were thus derived from TDR data that were collected in the soybean field while

the crop was actively extracting soil water.

The decrease in SWC in a given layer is a result of either water movement

(mostly gravity induced downward drainage and some upward movement) or root water

uptake. First, it was assumed that root uptake became negligibly small at night after

several hours, which were needed to re-hydrate the plants, except in the uppermost layer

where a low nighttime soil evaporation water loss may have occurred. The nighttime

drainage continued in each soil layer until a drainage equilibrium had been reached. The

rate of drainage was calculated using the bi-hourly TDR measurements during the

nighttime only. Therefore, the SWC during nights when the average drainage rate first

became zero, after it was wet and showed drainage, was used to estimate the DUL. This

method thus allowed for the determination of DUL from TDR data collected over a

relatively short time period during the growing season, when the roots may have been

actively removing soil water during daytime hours. Five criteria were taken into account

in applying this approach:

1. The SWC must have reached a value that was above the expected DUL value.

2. The DUL must be determined after the soil had been draining at night (negative flux

due to gravity).

11

3. The DUL was estimated at the earliest instant after criteria 1 and 2 were established,

since the nighttime flux would remain zero (or positive due to upward flow) when the

SWC was below the DUL.

4. The DUL was estimated during rainfall, because the net inflow and outflow of soil

water into a layer may have resulted in a water flux of zero.

Additionally, a higher degree of confidence was obtained if the different layers

were analyzed simultaneously. This was done to make certain that the bordering layers

were not draining or saturating simultaneously, thus creating a false equilibrium.

The daily drainage rates were calculated for three 8-hour periods: 10pm-6am,

6am-2pm, and 2pm-10pm. The nighttime interval (10pm-6am) was used to calculate the

DUL values. Positive nighttime SWC rates show an increase in SWC due to irrigation,

rainfall, or upward water movement and negative values represent a decrease in SWC due

to drainage or root uptake.

Comparison of TDR and gravimetric measurements

The TDR data were used as the basis for choosing the best soil parameter

estimation method. Therefore these data were validated by comparison with

gravimetrically sampled volumetric SWC data from the irrigated zone. The gravimetric

and TDR volumetric SWC data were analyzed for each layer and the overall fit of the

data in all layers was calculated.

Bulk Density and Soil Water Content by Gravimetric Sampling

I measured soil water content (SWC) levels 11 times throughout the growing

season by collecting gravimetric soil samples. On two occasions the samples were

12

collected with an undisturbed core sampling kit to measure the bulk density (BD). All BD

samples were taken at a position between the two TDR locations in the irrigated zone

(see Figure 2-1, page 8). Soil samples were immediately placed in pre-weighed aluminum

cans and sealed with tape to minimize moisture loss through evaporation. After all

samples were collected, the cans were weighed in the field on a Mettler PC 440 precision

balance. Upon return to Gainesville, the soil samples were dried at 105 oC for 24 hours

and weighed again to determine the percentage water based on weight. The resulting

weight-based SWC was multiplied by BD to obtain the volumetric SWC. The TDR was

installed two weeks after gravimetric sampling had started.

In addition, I performed a particle size analysis on soil samples taken at a position

between the TDR1 and TDR2 locations from all six layers (same as BD sampling

locations). The pipet method with the mechanical analysis technique (U.S. Dept. Agr.,

1996) was used to obtain the particle size distribution. The same soil samples were used

for both the particle size analysis and the determination of the LL values.

The LL values were determined under laboratory conditions by measuring the

volumetric soil water content after saturating the disturbed soil samples and having

placed them under 1500 kPa of pressure for several days. Each day the pressure chambers

were observed for drainage and leaks. After drainage ceased, the samples were quickly

weighed. The procedure was performed in duplicate. The average SWC was then

calculated for samples from each layer.

13

Comparison of Soil Parameter Estimation Techniques

There are many equations for the estimation of general soil parameters based on

different criteria. In this research, four SPE methods were selected for estimating DUL

and LL: Rawls (Rawls and Brakensiek, 1982), SWLIMITS (Ritchie et al., 1998), DSSAT

(Ritchie, 1980), and Saxton (Saxton et al., 1986). The best method was selected by

comparing derived soil water retention limits with the soil water limits computed from

TDR data in the field (DUL) and estimated from pressure plate data in the lab (LL). The

selection was based on several factors:

• Ease of access to input data. Particle size distribution data are inexpensive.

• The representation of a large range of soil classes by the method.

• The target audience of crop modelers, specifically users of DSSAT.

Where necessary, organic carbon content values were obtained from the Soil

Conservation Service database. A brief explanation of each SPE method is given below.

DSSAT Soil/Create method

The DSSAT soil/create program (Ritchie, 1980) was specifically developed for

use in the IBSNAT crop models and can be found in the DSSAT v3 Volume 1 User’s

Guide (Tsuji et al., 1994). Data from 61 soil profiles representing six soil orders and

collected from 15 states throughout the U.S. were used to develop the empirical equations

used in DSSAT v3.0 and v3.5 (Ratliff et al., 1983). However, this data set did not contain

samples with large variation in bulk density and organic matter such that this method

should not be used for organic or volcanic soils (Ratliff et al., 1983). The DSSAT

equations use the following input parameters to obtain DUL and LL: bulk density and

percentages sand, clay, silt, organic carbon, and coarse fractions greater than 2 mm.

14

Separate equations have been developed for various ranges of soil textures for the best

approximation of available soil water limits.

For sand > 75%

For Sand < 75% and Silt < 70%

Modified versions of these equations have also been developed to account for

soils with unusually high amounts of organic matter and/or rock fragments and can be

found in Ritchie and Crum (1988).

SWLIMITS method

A newer method developed by Ritchie et al. (1998) can be downloaded from the

World Wide Web at http://nowlin.css.msu.edu/. The DUL and LL were calculated using

the bulk density and percentages of sand and clay. Data from 312 soils from 15 U.S.

states were used to develop this method. Histosols, Oxisols, and Spodosols were not

represented and SWLIMITS should not be used for clayey Oxisols, as the DUL will be

overestimated (Ritchie et al., 1998).

For Sand > 65%

( ) ( )[ ] 100/%168.08.18 SandLL ∗−= [2-1]

( ) ( )[ ] 100/%381.03.42 SandPASW ∗−= [2-2]

( ) ( )[ ] 100/%444.062.3 ClayLL ∗−=

( ) ( )[ ] 100/%05004.01079 SiltPASW ∗−=

[2-3]

[2-4]

( ) BDClay

SandDUL ∗

∗=

− 146.0

188.0

( ) ( ) ( )[ ]SandEXPPASW ∗∗∗−= − 105.0105.2132.0 6

[2-5]

[2-6]

15

For Sand < 65%

Saxton method

Saxton et al. (1986) developed an interactive soil triangle model which is

available online and can be downloaded from http://www.bsyse.wsu.edu/~saxton/.

Saxton used sand and clay percentages to estimate two coefficients using stepwise

multiple nonlinear regression techniques. The objective was to find a best fit to water

potential-water content curves from the Rawls data set (Rawls and Brakensiek, 1982).

For the complete representation of these curves, three sets of equations were found for (i)

potentials greater than 10 kPa, (ii) air entry to 10 kPa, and (iii) saturation to air entry

potentials. Only the first set of equations, valid within 5% � clay �60% and sand � 5%,

represent potential in the DUL and LL range and were used in this study.

These equations assume that field capacity (FC) and permanent wilting point (PWP)

occur at 33 and 1500 kPa, respectively.

132.0=PASW [2-7]

( )( ) ( )( )( )( ) ( )

∗−

∗−−−=

−

−

ClaySand

SandClayEXPacoef

25

24

1028.4

1088.40715.0396.4

( )( ) ( )( ) ( )ClaySandClaybcoef 2523 1048.31022.214.3 −− ∗−∗−−=

[2-8]

[2-9]

=

bcoef

acoefFC

1

3333.0

=

bcoef

acoefPWP

1

15

[2-10]

[2-11]

http://www.bsyse.wsu.edu/~saxton/soilwatr/

16

Rawls method

Rawls and Brakensiek (1982) used an extensive database of 500 soils consisting

of 2,543 soil horizons from 18 states to predict soil water retention values for 12 matric

potentials from 4 to 1500 kPa. Three levels of regression equations were used to develop

twelve sets of coefficients.

The Rawls method allows the user to select coefficients for the computation of the

DUL at both 10 kPa and 33 kPa. Equations [2-12] and [2-13] show the coefficients used

to obtain the DUL with 10 and 33 kPa potentials respectively. There are no set guidelines

for choosing the proper DUL water potential. In general, the 10 kPa potential is used to

define the DUL for very sandy soils (sand > 85% and clay < 10%) (Saxton et al., 1986).

The 33 kPa coefficients were selected for the soils in this study (loamy sands and sandy

loams).

Comparison of Soil Parameter Estimation Methods

The methods were compared to the observed TDR derived DUL approximations

and laboratory derived LL values by measuring the index of agreement (d), the root mean

square difference (RMSD) of the estimated and measured LL, DUL, and PASW, and the

total plant available water in all layers. The coefficient of determination (r2) is often used

for comparing observed and predicted values. However, the r2 is a misleading statistical

method, because it provides little information beyond the correlation of the measurements

[2-12]

[2-13]

[2-14]

( ) ( ) ( )OMClaySandDUL ∗+∗+∗−+= 0299.00036.0002.02576.0

( ) ( )OMClayLL ∗+∗+= 0158.0005.0026.0

( ) ( ) ( )OMClaySandDUL *0317.00023.0003.04128.0 +∗+∗−+=

17

to the one-to-one line (Willmont, 1982). The RMSD summarizes the average difference

of the observed and predicted values. However, this statistical measure does not give a

clear indication of the correlation between the observed and predicted values. Willmont

(1982) recommended that researchers report both the RMSD and the average relative

error represented by the index of agreement (d). This index can be described as a measure

of the correlation between the observed and predicted values with respect to the one-to-

one line. It is calculated by equation [2-15],

where Pi’ = P i – O AVG and Oi’ = O i – O AVG. Clearly, graphical displays are still one of

the best ways to show the relative ability of a SPE method to make an accurate

prediction.

The CROPGRO-Soybean model (Hoogenboom et al., 1994 and Boote et al.,

1998) was used to evaluate the sensitivity of the different PASW values on simulated

yield. The resulting simulated soil water values were also compared to the actual SWC

measurements from gravimetric sampling and TDR. This was done by obtaining the

SWC values for seven different layers (5, 15, 30, 45, 60, 90, and 120 cm) in the simulated

soil water balance. The weighted average of the SWC in first three layers (5, 15, and 30

cm) was compared to the measured SWC values in the top layer (0-30 cm). An average

SWC value in the next two simulated layers (45 and 60 cm) was calculated for

comparison with the measured SWC in the 30-60 cm layer. The remaining simulated

values were compared directly with the measured values. The CROPGRO-Soybean

model is discussed in more detail in the next chapter.

( ) ( ) 10,’’/11 1

22 ≤≤

+−−= ∑ ∑

= =dOPOPd

N

i

N

iiiii

[2-15]

18

Using CROPGRO-Soybean, simulated yields were obtained for three different

irrigation schemes: no irrigation, actual irrigation, and automatic irrigation. Automatic

irrigation fills the soil profile to the DUL when the SWC reaches a set percentage of the

PASW (default = 50%). The model requires initial SWC conditions as well. These were

calculated for each method based on the fraction of the total available water in the

observed profile at the start of the season on day of year (DOY) 156.

Results and Discussion

Time Domain Reflectometry Data

TDR measured drained upper limit and lower limit

DUL values in the upper layers were estimated by analyzing the TDR data using a

new approach. TDR values from the ten-day period starting on day of year (DOY) 197

were used to estimate most of the DUL values because this period had a large rain event

(DOY 197) followed by a relatively dry long period, which allowed the soils to drain.

The average rates of soil water drainage were calculated and presented along with the

SWC and the normalized rainfall in Appendix A. The actual TDR data are in Appendix

B. All calculations, except for the non-irrigated TDR-2, 60-90 cm layer, indicated when

the average flux at night first reached zero. The SWC increase in the 60-90 cm layer was

not large enough, which resulted in an insignificant amount of drainage and no assurance

that the DUL had been reached for this layer.

The daytime SWC drainage rates in the 0-30 cm layer were the most dynamic of

any layer. In this layer, a decrease in SWC can be the result of evaporation or root water

19

0.00

0.03

0.06

0.09

0.12

0.15

0.18

0.21

0.24

0.27

0.30

193

193

194

195

196

197

198

199

200

201

202

203

204

204

205

206

207

208

209

210

211

Day of Year

Volu

met

ric S

WC

cm

3 /cm

-3

0

10

20

30

40

50

60

70

80

Rai

nfal

l and

Irrig

atio

n (m

m)

0-30 cm 30-60 cm 60-90 cm 90-120 cm120-150 cm 150-180 cm

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

Figure 2-2. Time domain reflectometry soil water data collected every two hours in sixlayers at the TDR-1 irrigated location.

0.00

0.03

0.06

0.09

0.12

0.15

0.18

0.21

0.24

0.27

0.30

193

193

194

195

196

197

198

199

200

201

202

203

204

204

205

206

207

208

209

210

211

Day of Year

Volu

met

ric S

WC

cm

3 /cm

-3

0

10

20

30

40

50

60

70

80

Rai

nfal

l and

Irrig

atio

n (m

m)

0-30 cm 30-60 cm 60-90 cm 90-120 cm 120-150 cm

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

Figure 2-3. Time domain reflectometry soil water data collected every two hours in sixlayers at the TDR-2 irrigated location. The TDR probe in the 150-180 cm layermalfunctioned.

20

uptake and an increase in SWC can be the result of a rain or irrigation event. A different

pattern was observed in the next lower layer. The daytime rates in the 30-60 cm layer did

not show much deviation from the nighttime drainage rates because the conductivity in

that layer was low due to a hard pan. I used a penetrometer to measure the resistance of

this pan on DOY 315. An average force of 37 kg/cm2 (n=3) was necessary to penetrate

this soil layer in comparison with forces of only 15 kg/cm2 to penetrate the top 30 cm of

soil. The influence of the hard pan on the 30-60 cm layer can also be seen in Figures 2-2

and 2-3, where the intra-daily fluctuations were much smaller than those in the remaining

top layers.

There was a positive flux in the 60-90 cm layers in the irrigated zone after the

DUL had been reached (see Figures A-3 and A-6). This increase in SWC most likely was

the result of the upward movement of water from the next lower layer because the layer

above (30-60 cm) did not show a simultaneous decrease during these nights. The high

demand of the soybean plant roots during the day likely created a high nighttime suction

gradient resulting in a positive flux after the DUL had been reached.

DUL values of the deeper layers (90-120 cm) were determined from TDR data

late in the season (DOY 271) after the plants reached physiological maturity (see Figures

2-4 and 2-5). At this time the soils had drained as a result of gravity after the DUL had

been reached. In addition, the plant roots were not removing water from the soil as the

plants had lost their leaves. Thus, the SWC reached equilibrium at this time. A summary

of DUL values determined by TDR is presented in Table 2-1. The LL values obtained

from the 1500 kPa analysis and the particle size data are presented in Table 2-2.

21

Soil water holding limits from the different methods are presented in Table 2-3.

The measured PASW in the lowest layer (90-120 cm) was unusually low. The sharp

increase in clay content from the 60-90 cm to the 90-120 cm layer (11.68% to 19.44%)

should have sharply increased the limits of both the LL and DUL. The LL did increase

accordingly (0.070 to 0.139 cm3 cm-3), but the DUL in this layer was determined from

TDR measurements at the end of the season (see Figure 2-4) and did not increase as was

expected (0.135 to 0.186 cm3 cm-3). The result was an uncharacteristically low PASW

value (0.047 cm cm-1), which may be due to incomplete wetting of this layer as was

assumed.

Table 2-1. Drained upper limit values measured from TDR data.Drained Upper Limit

(irrigated zone)Drained Upper Limit

(non-irrigated)DepthTDR-1 TDR-2 TDR-1 TDR-2

DUL(avg)

cm cm3 cm-3 cm3 cm-3 cm3 cm-3 cm3 cm-3 cm3 cm-3

0-30 0.144 0.157 0.144 0.132 0.155 0.14630-60 0.135 0.162 0.140 0.133 0.14360-90 0.135 0.141 0.139 0.127 N/A 0.14090-120 0.197 0.172 N/A 0.190 0.186

Table 2-2. Particle size analysis and lower limit estimations for soil samples collectedbetween TDR-1 and TDR-2 Locations.

Depth Sand Clay Silt LL1 LL2 Average LL

(cm) (%) (%) (%) (cm3cm-3) (cm3cm-3) (cm3cm-3)

0 – 30 85.34 5.04 9.62 0.044 0.047 0.04530 – 60 81.20 10.24 8.56 0.077 0.080 0.07960 – 90 79.80 11.68 8.52 0.079 0.072 0.07590 – 120 72.76 19.44 7.80 0.142 0.136 0.139

22

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

253

254

255

256

256

257

258

259

260

261

261

262

263

264

265

266

266

267

268

270

271

272

272

Day of Year

Volu

met

ric S

WC

cm

3 /cm

-3

0

10

20

30

40

50

60

70

80

90

100

Rai

nfal

l and

Irrig

atio

n (m

m)

0-30 cm 30-60 cm 60-90 cm 90-120 cm120-150 cm 150-180 cm

25

3

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

Figure 2-4. Soil water content from irrigated TDR-1 and rainfall late in the season.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

253

254

255

256

256

257

258

259

260

261

261

262

263

264

265

266

266

267

268

270

271

272

272

Day of Year

Volu

met

ric S

WC

cm

3 /cm

-3

0

10

20

30

40

50

60

70

80

90

100

Rai

nfal

l and

Irrig

atio

n (m

m)

0-30 cm 30-60 cm 90-120 cm 120-150 cm

25

3

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

Figure 2-5. Soil water content from irrigated TDR-2 and rainfall late in the season. TDRprobes in 60-90 cm and 150-180 cm layers did not collect any data.

23

Comparison of TDR and gravimetric measurements

The TDR1 and gravimetric measurements of volumetric SWC were plotted in

Figure 2-6. The index of agreement (d) was 0.82 for the irrigated TDR zone designated as

TDR1. However, when the data were plotted showing each depth separately (see Figures

4a), the measurements at 60-90 cm show a trend different from the overall pattern. When

these data points were omitted (60-90 cm layer excluded) the d-values increased to 0.84

(see Figure 4b). Similar calculations were completed for the TDR2 irrigated plot. These

fits resulted in d-values of 0.87 for the entire profile and 0.90 when the 60-90 cm layer

was excluded. However, these calculations were completed for the 0-180 cm soil profile.

The focus in this study was on the 0-120 cm soil profile where the d-values were 0.89 and

0.91 for TDR1 and TDR2, respectively, (60-90 cm layer excluded).

The measurements taken immediately after a rain or irrigation event (189, 196,

210 and 230 day of year) showed a higher degree of error in the 60-90 cm layer in both

TDR1 and TDR2 locations. This may be the result of the TDR readings being too low,

the gravimetric measurements being too high, or a combination of both. Three

explanations are given below for the trend in the 60-90 cm layer.

Baker and Lascano (1989) and Knight (1993) gave an explanation for the possible

difference between gravimetric and TDR measurements. They suggested that the sampled

volume was more heavily weighted along the length of the TDR probes in the region

closer to the transmission line elements. In this TDR setup, it represents the region closer

to the top of the probes. The TDR measurements were averaged over a 30 cm layer

because the TDR probes were placed in the soil vertically. The gravimetric samples were

collected at 30 cm intervals at positions halfway between the top and bottom of these

24

probes. Throughout the season there was an average increase in SWC of 6.0% between

the 60-90 cm layer and the next lower layer, at both TDR locations. Given this relatively

large increase in SWC, one can expect the aforementioned differences to be amplified at

these depths. Two other factors that may further amplify this trend are: (i) the measured

SWC decreased with depth in the upper layers but increased with depth in the lower

layers and (ii) the SWC difference between layers is larger in the lower layers than in the

upper layers. However, Baker and Lascano (1989) did not give an indication of the

magnitude of the bias.

A more likely source of the encountered differences between gravimetrically and

TDR measured volumetric SWC may be the result of errors in gravimetric sampling. Soil

samples were collected with a six-inch diameter auger and a tape measure. The headpiece

of the auger is 1 foot (30.5 cm) long and can hold a large soil sample to be removed from

the ground. The actual amount of soil collected in the headpiece is not constant,

depending on the density of the soil, which is loosened during sampling from the turning

motion of the auger. Therefore, the auger head collected anywhere from 15 cm to almost

the full length of 30 cm (in sandy soils) layer. To avoid the collection of soil that had

fallen back into the hole during previous sample collections, samples were removed from

the bottom of the headpiece. To collect samples from the 60-90 cm layer, for example, a

hole was dug 60 cm deep. The actual soil sample was then removed from the bottom of

the headpiece of the next auger collection, which was visually monitored to ensure that

the auger was not too deep. I could therefore only be certain that a sample was collected

in a range of 10 to 25 cm below the 60-cm depth. This can make a difference when both

the upper and lower layers have a higher SWC than the layer being sampled. Any

25

Figure 2-6. Index of agreement (d) for fits of TDR-1 vs. gravimetric measurements ofvolumetric SWC for (a) each layer and (b) the profile (no 60-90 cm values).

day 196

day 189

day 203day 210

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.05 0.10 0.15 0.20 0.25 0.30 0.35

Volu

met

ric S

WC

from

TD

R, c

m3 cm

-3

0-3030-6060-9090-120120-150150-180

d = 0.82

d = 0.84

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.05 0.10 0.15 0.20 0.25 0.30 0.35

Volumetric SWC from Gravimetric Measurements, cm3 cm-3

Volu

met

ric S

WC

from

TD

R, c

m3 cm

-3

a.

b.

26

gravimetric sample taken not exactly at 15 cm could have indicated a higher SWC. More

than likely the samples were taken below the midpoint, which probably had a relatively

higher than average SWC, given the 6.0% increase in the layer below 90 cm.

Thirdly, a closer look at the SWC trends for all layers clearly shows the spatial

variability in SWC within such small distances. The gravimetric measurements were not

collected immediately next to the TDR probes. Instead, Figure 2-1 (page 8) shows how

one set of gravimetric readings was taken at a point between the TDR probes and used for

comparison with both TDR readings. In addition, at each TDR location the probes in each

consecutive layer were placed at approximate 30-cm horizontal intervals to allow for

digging a new hole for the next lower set of TDR probes. The different trend in the 60-90

cm layer can at best be explained by a combination of all the above mentioned

explanations. Regardless of the discrepancies found in the 60-90 cm layer, it was

concluded that the TDR data were reliable and were therefore used for further analysis.

Bulk Density and Soil Water Content by Gravimetric Sampling

Eleven gravimetric measurements were made during the season. Bulk density

values and the measured volumetric SWC from gravimetric sampling are shown in Table

2-4. An attempt was made to measure the BD of all layers in triplicate. However, it was

only measured twice because the layers below 30 cm were saturated late in the season

during the third attempt. The crop had already been harvested at this time and the deeper

soil layers would not have likely drained allowing for the collection of undisturbed soil

samples within a reasonable time frame.

27

Table 2-3. Predicted and measured soil water limits.

LayerDepth TDR Press. Plate

DUL LL PASW DUL LL PASW DUL LL PASW DUL LL PASW DUL LL PASW

cm cm3 cm-3 cm3 cm-3 cm3 cm-3 cm3 cm-3 cm3 cm-3

0-30 0.215 0.102 0.113 0.140 0.060 0.080 0.142 0.045 0.098 0.135 0.067 0.068 0.146 0.045 0.10130-60 0.222 0.103 0.119 0.170 0.087 0.083 0.165 0.052 0.114 0.147 0.085 0.062 0.143 0.079 0.06460-90 0.219 0.098 0.121 0.178 0.094 0.083 0.173 0.054 0.119 0.155 0.092 0.063 0.135 0.075 0.06090-120 0.247 0.121 0.127 0.213 0.129 0.084 0.234 0.123 0.112 0.197 0.131 0.066 0.186 0.139 0.047

Measured Soil Water Limits

SWLIMITS SAXTON DSSAT

Predicted Soil Water Limits

RAWLS

Table 2-4. Bulk density and SWC from gravimetric measurements and from TDR1 and TDR2 locations in Field 10.Layer Average Day of yearDepth Bulk Density 156 162 168 174 182 189 196 210 218 223 230

cm g cm-3 Water Content (cm3 cm-3)Irrigated 0-30 1.71 0.060 0.070 0.068 0.073 0.087 0.096 0.158 0.171 0.106 0.071 0.143

30-60 1.55 0.083 0.086 0.101 0.107 0.113 0.110 0.103 0.142 0.138 0.135 0.13160-90 1.51 0.137 0.129 0.115 0.129 0.135 0.117 0.126 0.130 0.151 0.167 0.13790-120 1.60 0.178 0.178 0.152 0.208 0.182 0.183 0.191 0.171 0.207 0.210 0.228

Non- 0-30 1.67 0.071 0.099 0.137 0.136 0.081 0.144 0.179 0.190 0.111 0.110 0.160Irrigated 30-60 1.64 0.147 0.169 0.132 0.134 0.116 0.142 0.169 0.173 0.149 0.127 0.148

60-90 1.66 0.212 0.236 0.143 0.134 0.129 0.145 0.182 0.170 0.191 0.265 0.18490-120 1.68 0.282 0.252 0.211 0.205 0.179 0.177 0.227 0.224 0.268 0.307 0.221

28

Comparison of Soil Parameter Estimation Methods

The SPE equations were first studied by examining the overall trends of predicted

PASW values. Data points from five other locations in the field collected from six layers,

each 30 cm thick (see Chapter 3) are plotted in Figure 2-7. The DSSAT method did not

provide a continuous trend. It uses two sets of equations for soils containing less than and

greater than 75 % sand. This creates a discontinuity at the point where the percentage of

sand equals 75 %. This data set did not contain soil samples where the percentage of sand

is approximately equal to 75 %. Therefore, the true shift in the SWC prediction was not

noticeable but could potentially be more than 2 %. This method should be tested for other

sandy soils and probably not be used for sandy looms, loamy sands, or sandy clay looms,

which all potentially have sand percentages in the range of 75%.

4

6

8

10

12

14

16

45 50 55 60 65 70 75 80 85 90Percent Sand

Avai

labl

e So

il W

ater

, %

SW LIMITS SAXTON DSSAT RAW LS

Figure 2-7. Plant available soil water predictions from four soil parameter estimationmethods using particle size data from the five different locations in field 10.

29

The SWLIMITS technique consistently predicted the highest PASW. It assumes

no variability in PASW for soils with less than 65% sand, in which case it estimates a

constant PASW value of 0.132 cm3 cm-3. Although the developers of this method

emphasized the simplicity of the equations and inputs (Ritchie et al., 1998), it should

probably not be used for sandy soils because the PASW estimations in soils with more

than 65% sand were relatively high. The Saxton and Rawls equations predicted the

lowest PASW in the selected field.

For comparison purposes two statistical measures, the index of agreement (d) and

the root mean square difference (RMSD), were used to evaluate the goodness of the

predicted soil parameters relative to values estimated from TDR and 1500 kPa pressure

plate analysis. The results are presented in Table 2-5. From these figures one can observe

that the method with the best prediction of the DUL and the LL values (Rawls) did not

necessarily have the best prediction of the PASW, even though this parameter is directly

obtained by subtracting LL from DUL. The lack of agreement between PASW

estimations is most likely a result of the limited number of data points (n=6). Another

standard for determining the best SPE method is the RMSD. The RMSD values

consistently indicated that the Rawls equations had the least error for the DUL, LL, and

PASW values (0.02 for all).

Table 2-5. Index of Agreement (d) and the root mean square difference (RMSD) betweensoil limits derived from TDR and soil parameter estimation methods. n=6.

D RMSDMethod

LL DUL PASW LL DUL PASWSWLIMITS 0.74 0.66 0.22 0.04 0.07 0.07Saxton 0.94 0.92 0.34 0.02 0.03 0.03DSSAT 0.93 0.92 0.23 0.03 0.03 0.05Rawls 0.96 0.97 0.20 0.02 0.02 0.02

30

The best soil parameter estimation technique was selected by comparing the total

measured PASW with the predicted (see Figure 2-8). The observed total PASW in the

four top layers (0-120 cm) was 84 mm. The SWLIMITS, Saxton, DSSAT, and Rawls

methods predicted 144, 99, 133, and 78 mm of available soil water in the 0-120 cm soil

profile respectively. The Rawls technique had the smallest the absolute difference of the

predicted and observed total available water of the four methods (6 mm). However, it

underpredicted the available soil water in the top layer by 10 mm. The DSSAT and

SWLIMITS methods more closely approximated the available water in the top layer, but

overpredicted the total available water by 49 and 60 mm respectively.

3825 34

20 14

3625

3619 18

3625

3419 19

3424 29 20

30

SWLIMITS SAXTON DSSAT RAWLS TDR-15bar

Avai

labl

e So

il W

ater

, mm

0-30 cm

30-60 cm

60-90 cm

90-120 cm

144 mm 99 mm 133 mm 78 mm 84 mm

Figure 2-8. Plant available soil water for each layer compared to the observed TDR andpressure plate analysis results. The total SWC values for the soil parameter estimationmethods are indicated on top of the bars.

31

The effect of using different SPE techniques to estimate soil input data is evident

in the differences in simulated yields and water balances of crop models. The simulated

yield differences between the worst and best SPE methods were 380 and 106 kg/ha for

non-irrigated and irrigated simulations respectively (see Figure 2-9). These yield

differences were simulated at a single location in the field. The impact of using a

different SPE method on simulated yield may be even greater when applied to the entire

field. Among the SPE methods used, only the Rawls technique showed variations in

simulated yield among the three simulated irrigation options.

2800

2900

3000

3100

3200

3300

3400

3500

3600

3700

SWLIMITS SAXTON DSSAT RAWLS TDR-1500 kPa

Seed

Yie

ld (k

g/ha

dry

wei

ght)

Non-Irrigated

Farmer'sPractice

Auto-Irrigated

Figure 2-9. CROPGRO-Soybean simulated yield using predicted and observed soil inputparameters for three irrigation management practices.

A sensitivity analysis was completed to better understand the yield simulation

differences between the use of input parameters from the four methods. The sensitivity

analysis was performed by incrementally increasing the total PASW by 5 mm and

32

comparing simulated yield versus PASW for each method. These results are presented in

Figure 2-10. The crop model showed little difference in sensitivity from input parameters

for different SPE equations. When the total PASW reached approximately 95 mm,

simulated yield ceased to increase. This explains why only the Rawls method showed

variation between non-irrigated, irrigated, and auto-irrigated yield simulations; it

estimated a PASW of 78 mm for the 120 cm profile while the other methods estimated

values greater than 95 mm. These results showed that the major differences among SPE

methods were due to total soil profile available water.

3100

3200

3300

3400

3500

3600

3700

75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150

Absolute Plant Available Soil Water in 0-120 cm Profile (mm)

Seed

Yie

ld (k

g/ha

dry

wei

ght)

SWLIMITS

SAXTON

DSSAT

RAWLS

TDR

Figure 2-10. Sensitivity analysis of CROPGRO-Soybean using soil limits from differentsoil parameter estimation methods.

The simulated SWC values from the crop model’s water balance were also

compared with the field-measured SWC values. Both the gravimetric readings and the

actual TDR measurements were plotted for the top 30 cm in zone 1 (see Figure 2-11)

33

against the crop model’s predicted SWC values using soil input parameters estimated by

the Rawls method. The observed values were first measured on DOY 156 while the TDR

was set up on DOY 174 and the simulations started on DOY 161. The remaining figures

can be viewed in Appendix C. These figures indicate how the SWC in the deeper layers

remained constant when it was less than the DUL until the root started removing soil

water.

Although I concluded that the TDR data were correct and may be more reliable

than the gravimetric measurements, the initial condition for the simulations were obtained

form the latter source. This was done because the TDR equipment was installed after the

first planting date. When the initial conditions were derived from the TDR data, the

simulated SWC values better fit the TDR data in the first 30 days of the simulations. For

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

150 180 210 240 270 300

Day of Year

Volu

met

ric S

oil W

ater

Con

tent

, cm

3 cm

-3

Rawls Simulations Using Gravimetric Initial SWCGravimetric MeasurementsTDR MeasurementsRawls Simulations Using TDR Initial SWC

Figure 2-11. SWC estimated by the CROPGRO-Soybean water balance using Rawlsinput parameters versus actual in-field SWC measurements from gravimetric readingsand TDR.

34

the purpose of using input parameters, the actual gravimetric measurements appeared to

be a better option than the extrapolated TDR measurements.

Conclusions

A new method to estimate the drained upper limit (DUL) from time series TDR

data was developed. This method allows the determination of DUL over a relatively short

period of time in the middle of the growing season when roots are actively removing soil

water. The basis for the analysis is the nighttime soil water drainage or lack thereof in

consecutive soil layers. The DUL is reached when the soil water drainage rate due to

gravity approaches zero. Since the drainage rate is critical, this technique cannot be

applied to soils with a high water table.

Four soil parameter estimation methods were compared with observed soil water

limits. All methods may effectively estimate the soil water limits, but some may be better

(or worse) suited for particular soils. The Rawls equations best predicted the plant

available soil water (PASW) for the research site. The root mean square difference values

were the lowest for this method when comparing the estimated and observed DUL, LL,

and PASW (0.02 for all). It also had the highest index of agreement (d) values when

comparing estimated and predicted DUL and LL (0.96 and 0.97 respectively). The

selected method had the lowest absolute difference in total predicted soil water in the soil

profile (6 mm).

No one soil water limit estimation technique can be labeled optimal for all soils

and crops. The Rawls method showed the best results for this site in the 1998 year with

35

soybeans, but the Saxton equations were also reasonably close. Other users should

evaluate these methods for their specific sites and crops before using them.

36

CHAPTER 3INVESTIGATING SPATIALLY VARIABLE IRRIGATION AND RAINFALL

Introduction

The existence of spatial and temporal yield variability of any crop has been well

documented. In particular, the spatial variability of soil properties that influence soil

water holding limits is a specific factor that affects crop yield and is a major source of

uncertainty in crop management (Bresler et al., 1981). Paz et al. (1998) showed that 69%

of the variability in soybean yield over three years within a 16-ha field in Iowa could be

explained by variations in soil water holding characteristics and crop drought stress. Other

sources of field variability, such as environmental (climatic) and genetic factors are more

random (Morkoc et al., 1985; Dagan and Bresler, 1988).

Plant extractable soil water is dependent on the variability of the soil and plant

rooting characteristics, and water application. Water application occurs either through

irrigation or rainfall. The temporal variability of weather parameters cannot be controlled;

hence historical rainfall patterns are studied to improve prediction of future climatic

conditions. However, irrigation management is critical because under-irrigation can cause

yield reduction and over-irrigation can waste natural resources (Aboitiz et al., 1986).

The importance of irrigation management as part of site-specific crop management

has not been researched in much detail. Or and Hanks (1992) studied the effect of

variable irrigation on crop yield by inducing spatial variability in soil water by applying

37

non-uniform drip irrigation in an otherwise near-uniform Utah corn field. They concluded

that spatially variable crop yield was more distinct in areas where water was applied in

frequent and smaller amounts as opposed to scheduled large amounts. Ritchie and Amato

(1990) conducted a comparison study of appropriate strategies for irrigation scheduling.

They studied the uniform irrigation of an entire field along with several variable rate

irrigation options. All irrigation strategies were applied to three different management

zones that were derived from soil color imagery: regions with the lowest, highest, and

intermediate plant available soil water (PASW). This simulation study was conducted

using 30 years of weather data. Variable rate irrigation was the best irrigation

management option in terms of yield. They carried out an analysis of the total water usage

but refrained from selecting a best management option because no comparison of

maximum production and minimal water use was done.

Relationships between rooting depth and distribution, soil moisture content, and

water table depth play an important role in determining the extent of water stress,

especially late in the season when seed filling dominates root growth in terms of sink

demand (Paz et al., 1998). Ritchie and Amato (1990) also concluded that water stress

early in the season during vegetative growth followed by adequate water supplies later in

the season may increase production because it enhances the root density in the lower part

of the profile during the water stressed periods. In either case, the ability to understand

and simulate the effects of water stress on crop production is critical for irrigation

management.

The main hypothesis of this research is that variability in soil water holding limits,

irrigation, and rainfall contribute to the spatial variability of PASW and crop yield in an

38

agricultural field. These factors need to be analyzed first to obtain a better understanding

of this variability. Optimal irrigation requirements for maintaining high yields are directly

related to PASW. Therefore different irrigation management strategies should be

analyzed to determine if water use can be decreased and yields increased in spatially

variable fields. Thus, the objectives of this study were as follows:

1. To analyze the spatial variability of soil water limits, irrigation amounts, and rainfall

patterns in an irrigated soybean field, and

2. To investigate different irrigation management practices, including, spatially variable

irrigation, in a field with pre-determined management zones using a crop simulation

model.

Materials and Methods

Research Location and Field Conditions

The 12.6 hectare field, referred to as Field 10, is located in Southwest Georgia

(31.330 north, 84.630 west). The previous winter crop was canola (Brassica napus L.)

and this crop had depleted most of the PASW by prior to planting soybeans. The

irrigation system was a center pivot. The field was usually irrigated immediately before

planting and, if necessary, during critical crop development stages later in the season

(Smith, personal communication). The field was furthermore irregularly shaped causing

the center pivot not to reach all corners of the field. The result was that a small section

(0.7 ha) remained un-irrigated.

39

This study site was selected for its high spatial variability (Smith, personal

communication) and access to detailed historical and current weather information. In

addition, several studies have been conducted in the past in this field, resulting in a map

with specific management zones for precision management. This map was a critical part

of this study. Stuart Pocknee from the National Environmentally Sound Production and

Agriculture Laboratory (NESPAL) conducted research on Field 10 for the partial

completion of his Ph.D. research (personal communication). His studies resulted in the

identification of nine management zones (see Figure 3-1).

Figure 3-1. Management zones in Field 10, Crestview, GA. Shaded zones were not usedin the analysis. Mark indicates location of Rain Gage 1B.

50 m

4

3

2

5 7 6

8

9

1

40

The management zones were defined on the basis of several sources of

information:

• 1995 and 1996 corn yield maps

• Enhanced imagery of remotely sensed soil color aerial photographs

• Normalized Difference Vegetation Index (NDVI) of a 1996 corn crop image

• Farmer’s knowledge and experience

• US Soil Conservation Service Maps

The Georgia Automated Environmental Monitoring Network located on the World

Wide Web (http://www.griffin.peachnet.edu/bae/) has collected weather data near Field

10 with a weather station. A dataset consisting of 25 years was acquired for this study. It

was not recorded at Field 10 however, but in Arlington, the nearest town approximately

25 miles Northwest from Field 10.

Crop Simulation Model

A process oriented crop model, CROPGRO-Soybean (Boote et al., 1998;

Hoogenboom et al., 1994), was used for the simulation study. The model requires input

data including management practices and environmental conditions e.g. soil type, daily

maximum and minimum temperatures, rainfall, and solar radiation to simulate growth,

development, and yield on homogeneous spatial units. The carbon balance, development,

and soil water balance are updated on a daily timestep.

The soil water balance developed by Ritchie (1985) for the CERES-Wheat crop

model was modified for use in CROPGRO-Soybean. This water balance allows the user

to specify different soil layers. Four horizontal layers were defined in the profile used for

http://www.griffin.peachnet.edu/bae/

41

Field 10. The water balance decreases the water content in these layers through root

absorption, flow to an adjacent layer, and evaporation (top layer).

Several critical input parameters are required for each layer to successfully use the

water balance in the model. The lower limit of extractable plant water (LL), drained upper

limit (DUL), saturated soil water content, saturated hydraulic conductivity, and proportion

of the four layers that have active roots are necessary. The soil water in each layer will

start to drain when its soil water content (SWC) exceeds the DUL and the next lower

layer is not saturated. The drainage coefficient (fraction of water that can drain from a

layer in a day under free drainage conditions) and the saturated hydraulic conductivity

control the maximum water movement between layers.

When running the CROPGRO-Soybean model using the DSSAT interface, the

soil water limits can be estimated from measured particle size and bulk density (BD) soil

data. This soil parameter estimation model was described and compared with other

methods in the previous chapter. Another option allows the user to define the soil water

limits using a different soil parameter estimation technique. The results from the previous

chapter indicated that the Rawls method (Rawls and Brakensiek, 1982) performed best

when estimating soil water limits in this selected Southwest Georgia research site. This

soil parameter estimation (SPE) method was used to define soil water holding limits for

each individual management zone of the field and these values were then used as input

data for the CROPGRO-Soybean model.

42

Field Experiments

Seven TE778 tipping bucket rain gages (Campbell Scientific, Inc.) were placed in

the management zones to measure irrigation and rainfall patterns. The rain gage buckets

were placed as close as possible to the ground at the beginning of the season and

gradually adjusted in the course of the crop growth such that they were always slightly

higher than the canopy. Although there were nine management zones, there were only

seven rain gages available for placement in the field. Therefore the rainfall and irrigation

were not measured in every zone. One gage was used for both zones 3 and 4. Two gages

were placed near the edge of the center pivot between zone 1 and 5 to compare the

irrigated and non-irrigated zones (see Figure 3-1). An additional gage was used for zone

5. The gage in zone 8 failed soon after installation. In addition, soybeans did not emerge

in the non-irrigated zone 1 due to excessive dryness at the beginning of the growing

season. Water application measurements were stopped in this area as well. As a result,

water distribution was measured in five locations representing six zones: management

zones 1, 2, 3 (and 4), 5, and 6. HOBO electronic event dataloggers, manufactured by

Onset Computers, recorded every 1/100 inch (approximately 0.25 mm) with the

corresponding time. These data were stored on the HOBO’s for two weeks at a time

before being downloaded into a portable computer for analysis.

Spatial distribution of rainfall

The rain gages were individually calibrated before placement in the field. The

rainfall amounts collected by the weather station were used as a reference to analyze the

rainfall collected in these rain gages. This technique also allowed for the analysis of in-

field rainfall patterns.

43

Spatial distribution of irrigation

Center pivots are designed and calibrated according to the irrigation requirements

of a specific field. These systems are supposed to apply uniform amounts of water in each

location of the field, but this may not always be the case. If the center pivot in Field 10

applied variable rates of water, it could have influenced the spatial variability in SWC.

Therefore, the irrigation variability in the five management areas (zones 2, 3, 4, 5, and 6)

was studied. Any differences between rainfall and irrigation patterns, if present, were

studied as well.

Gravimetric soil water content

The SWC was collected every week for ten weeks by gravimetric sampling. This

was done to compare the measured values with the crop model’s simulated water balance.

The collections were made at 30 cm intervals up to 180 cm deep inside a 2 meter radius

around each rain gage. BD was also measured in duplicate on DOY 168, 196, and 203.

An attempt late in the season to measure the BD a third time failed because most of the

lower layers in the field were saturated. Volumetric SWC values were computed by

multiplying gravimetric SWC by BD. The measured SWC values were fitted against the

simulated volumetric SWC values in each layer in all zones. The root mean square

difference (RMSD) was calculated to measure the fits.

Comparison of observed and simulated yield

A comparison of observed and simulated yields was made. The observed yields

were measured by collecting plant samples 12 days before harvest, which occurred on

DOY 301. The samples were collected from two 1 m2 plots in each zone. The weight and

44

count of the stems, seeds, and pods, and the plant population were recorded. No below

ground plant samples were collected.

The actual total production of the field was not measured, but an estimate was

obtained from the farmer. This estimate was not used in our analysis because the farmer

acknowledged that the crop had a very low emergence as a result of “bad seed” or the

unusually dry weather early in the season. In addition he re-planted the field, except for

parts of zone 6, two weeks after the original planting date.

Simulation Experiments

Simulation experiments were conducted to determine a strategy that would

maximize expected profit over a 25-year period and the corresponding yields and water

uses. Three different irrigation management options are available when using the

CROPGRO-Soybean model: non-irrigated option, user provided irrigation schedule, and

the automatic irrigation option. The non-irrigated option simulates crop growth using only

rainfall data from each zone. The automatic irrigation option allows the computer to

decide when and how much to irrigate. When the simulated SWC falls below a set

threshold, one has the option to either apply a fixed amount of water or let the soil profile

be filled to the DUL. The second option was used to determine the threshold factor.

The importance of the automatic irrigation option is that it provides an irrigation

schedule that minimizes water stress. This schedule can be determined in one zone. Those

data and irrigation amounts can then be used to irrigate other zones. This option thus

allows one to simulate spatially variable or uniform irrigation of a field that has different

management zones. All simulation experiments were completed using 25 years of

45

weather data. Although the previous section discussed spatially variable rainfall in Field

10, this type of detailed historical rainfall data were not available for 25 years. Thus,

rainfall was assumed to be uniformly distributed across the field for these simulation

experiments.

Optimization of the irrigation threshold factor

One can manipulate the aforementioned irrigation threshold (ITHRL) factor. This

parameter reflects a percentage of the PASW when an irrigation event is triggered. The

optimization of the ITHRL factor for each zone was necessary because of the spatial

variability of the soil. Therefore, the required optimal irrigation amounts are related to the

ITHRL factor.

The optimization of the ITHRL factor involved simulating the crop using 25 years

of weather data while incrementally increasing the threshold factor by 2 % over a specific

range (15% - 65%). The gross margin was then calculated for all years using equation 3-1

and maximized to determine the optimal ITHRL factor for each zone. The units were

expressed in dollars per hectare ($/ha).

Gross Margin = Soybean Price * Yield – Irr. Cost * Irr. Amount – Fixed Cost

The fixed cost in this equation is independent of the management strategy and

remains constant regardless of the irrigation strategy. The final gross margin amounts

reported were all relative to the fixed cost. A value of $0.0 was assigned to the fixed cost

because it was not necessary for comparing different methods in this paper. It was also

assumed that it did not exceed the total of the remaining terms in Equation 3-1.

[3 – 1]

46

Management based on zone with earliest sign of stress

The management zone with the earliest sign of stress was selected by investigating

which zone was first irrigated in each season under the auto irrigation option using 25

years of historical weather data. The initial SWC conditions used will influence the

earliest irrigation date. However, the initial SWC conditions for the past 25 years were

not available. Therefore, the DUL values for each zone were used as the initial conditions

for the historical simulated SWC. For the 1998 simulations, the actual SWC values at the

beginning of the season (DOY 156) were used as initial conditions. From these results, an

average first irrigation date was calculated. After the zone with the earliest sign of stress

was selected, the irrigation schedule for each year from this field was used to simulate

yield in the other zones. Finally, total field production, water use, drainage, and gross

margin were computed using the irrigation schedule of the zone with the earliest sign of

stress.

Management based on highest yielding zone

The suggested optimal irrigation schedule for the management zone with the

highest yield was analyzed as well. The determination of the highest yielding zone was

simulated in the same manner as with the earliest stress zone. The yield was simulated for

the different management zones for the last 25 years and I then compared average yields

for all zones. The non-irrigation option was used for these simulations because the actual

irrigation schedules were not available for each year and the automatic irrigation option

would not likely have shown yield differences between the zones.

As with the analysis of the zone with the earliest stress, the irrigation schedules

generated for this zone using the automatic irrigation option were recorded for each of the

47

25 years and used to simulate the rest of the field. Similarly, the total production, water

use, and drainage were recorded. The selected highest yielding management zone may not

be the zone with the highest production because this is dependent on the total area of the

zone as well.

Management based on the largest zone

The largest management area was zone 5. As with the first two irrigation plans,

the simulated auto irrigation schedules for 25 years for zone 5 were uniformly applied to

the rest of the field for each year. Average production, water use, drainage, and gross

margin values were computed and compared to the additional management options.

Management based on optimal irrigation by zone

This spatially variable irrigation option was investigated by independently

simulating each zone using the automatic irrigation option. Although this option would

likely result in the highest total field production, it may not necessarily result in the

highest gross margin because this is dependent on the total volume of water used as well.

At the time of this study, the variable rate irrigation option was not a possibility for this

field. However, this option was investigated because the calculations may show that the

increase in the gross margin justifies investments in the necessary equipment.

Economic Analysis

The electricity cost for the center pivot irrigation system and an average soybean

market price were used to complete a simple economic analysis. The actual electricity

cost was not available. Instead, the irrigation cost data were based on information from a

1989 Florida Cooperative Extension Service bulletin (Pitts and Smajstrla, 1989). The

48

prices from the bulletin were adjusted to better represent today’s electricity cost. These

adjustments were made by incrementally increasing the irrigation cost, thus calculating

multiple possible cost scenarios.

Several factors were considered when the economic analysis was conducted.

Spatially variable irrigation requires a significant initial investment. This start up cost was

not taken into account in this study because the type of economic analysis required for

investment decisions went beyond the scope of this study. In addition, Georgia farmers

have unlimited and unregulated access to the groundwater supply at the present time.

Many farmers in the area use private wells connected to a center pivot irrigation system.

However, the Georgia state laws regarding water use may change in the near future.

Similarly, the market soybean price reached a ten year low in 1998 after having been

above average for many years (Good et al., 1998). It is therefore not necessary to spend

too much time on estimating precise irrigation cost data when the soybean market prices

behave unpredictably and the irrigation laws may soon change. Although the total water

drained from the soil profile was simulated, I did not include an environmental cost as a

result of nutrient leaching.

Results and Discussion

Field Experiments

In chapter 2 it was found that the Rawls method (Rawls and Brakensiek, 1982)

was the best soil parameter estimation technique for the selected location. I calculated the

particle size distribution for all management zones in Field 10, using this method (Table

49

Table 3-1. Particle size distribution in management zones in Field 10.LayerDepth

Sand Clay Silt Sand Clay Silt Sand Clay Silt Sand Clay Silt Sand Clay Siltcm % % % % %

0-30 87.82 3.12 9.06 78.28 3.92 17.80 83.50 5.44 11.06 82.24 5.68 12.08 78.50 5.12 16.3830-60 85.44 5.76 8.80 87.08 2.08 10.84 61.20 25.68 13.12 70.60 19.52 9.88 79.42 8.00 12.5860-90 85.30 6.16 8.54 83.50 8.08 8.42 60.28 26.48 13.24 65.76 24.40 9.84 72.42 17.68 9.9090-120 69.20 24.32 6.48 85.44 6.96 7.60 57.84 28.40 13.76 66.40 25.12 8.48 69.36 22.88 7.76

Particle Size DistributionZONE 2 ZONE 3 ZONE 4 ZONE 5 ZONE 6

Table 3-2. Predicted drained upper limit (DUL), lower limit (LL), and plant available soil water (PASW) in all management zones inField 10, using the Rawls (Rawls and Brakensiek, 1982) method.LayerDepth

DUL LL PASW DUL LL PASW DUL LL PASW DUL LL PASW DUL LL PASWcm cm3 cm-3 cm3 cm-3 cm3 cm-3 cm3 cm-3 cm3 cm-3

0-30 0.123 0.057 0.066 0.145 0.061 0.084 0.140 0.069 0.071 0.143 0.070 0.073 0.149 0.067 0.08230-60 0.122 0.063 0.060 0.106 0.044 0.062 0.243 0.162 0.080 0.202 0.132 0.070 0.143 0.074 0.06960-90 0.124 0.065 0.059 0.135 0.074 0.060 0.247 0.166 0.081 0.229 0.156 0.073 0.191 0.122 0.06990-120 0.222 0.156 0.066 0.127 0.069 0.058 0.259 0.176 0.083 0.230 0.160 0.071 0.216 0.148 0.068

ZONE 2 ZONE 3 ZONE 4 ZONE 5 ZONE 6

50

3-1). In addition, the Rawls method was used to simulate the soil water holding limits of

the remaining zones (Table 3-2). Zone 4, which had the highest clay content in the soil

profile, also had the highest PASW. Zones 2 and 3 had the least PASW and highest sand

contents.

Spatial distribution of rainfall

The crop model rainfall and irrigation input data were acquired from two sources.

The rainfall data from the weather station were used for the weather input and the

irrigation data from each zone were measured for the simulation of crop growth in the

field. These data are provided in Appendix D.

The rainfall data measured in the field with rain gages were compared to the

weather station data in Figure 3-3. The days when the farmer irrigated and no rainfall was

collected by the weather station were excluded in this figure with one exception. On day

of year (DOY) 170, the rain gages in the management zones collected significantly more

water than the weather station. The detailed data file from the HOBO’s indicated that

water was collected twice on this day. The first event occurred at 10:35 am and lasted 15

minutes. The second event started at 8:12 pm and lasted roughly two hours. Most likely, it

rained an insufficient amount in the morning and the farmer irrigated again in the evening

hours.

The rainfall amounts were not uniformly distributed across the field. The forest

growth on the south and west sides of the field may have contributed to the rainfall

variability as well. This forest line could have affected the wind dynamics (speed and

direction) creating perhaps another source of variability. The rain gages 1B, 2, and 3 were

approximately 45, 42, and 25 meters from the forest border, respectively. I did not expect

51

0

5

10

15

20

25

30

35

156 157 170 176 184 189 190 194 195 197 208

Day of Year

Rai

nfal

l Am

ount

s (m

m)

RG 2 RG 3 RG 5

RG 6 RG 1B WS

Figure 3-3. Spatially variable rainfall (irrigation excluded) measured in Field 10 and theweather station.

gages 5 and 6 and the weather station to be affected by the forest line since these were

located 150 meters or more from the trees. The rainfall amounts after DOY 208 were not

included in this graph because these amounts were very high relative to the remaining

data and had similar patterns. As expected, the average difference between the rain gages

in zones 5 and 6 was 1.6 mm (st. dev. = 2.2 mm, n=26). Similarly the average rainfall

amounts of gages 1B and 2, which were equally distanced from the forest line, had an

absolute difference of 2.1 mm (st. dev. = 1.9 mm). Next, I compared the average rainfall

amounts of the three gages closest to the forest line with those averages from the gages

away from the forest line. The three gages closer to the forest line collected an average of

4.6 mm more water than the gages away from the forest line (st. dev. = 4.6 mm).

52

Spatial distribution of irrigation

The rainfall and irrigation distribution patterns did not follow the same trends in

the field. The irrigation amounts of only those days when there was no rainfall collected

are presented in Figure 3-4. Since the center pivot usually completed two rotations around

the field when irrigating (Smith, personal communication), it was necessary to add

irrigation amounts from consecutive irrigation events in order to better analyze the spatial

irrigation distribution in certain cases. There were several instances, however, when the

pivot was in a different position when I returned to the field for data collection. Therefore,

some of the gages likely collected more or less water than other gages on several

occasions (DOY 243).

0

5

10

15

20

25

30

35

40

45

162 165+166 167+168 181+182 187+188 206+207 240+241 243 260

Day of Year

Irrig

atio

n Am

ount

s (m

m)

RG 2 RG 3

RG 5 RG 6

RG 1B

Figure 3-4. Spatially variable irrigation amounts (rainfall excluded) in six managementzones throughout the crop season.

53

The rain gage in zones 3 and 4 (RG3) consistently measured the highest amount of

irrigation, except on DOY 206 and 260. This gage also measured the most rainfall, which

lead to the conclusion that there could have been a problem with its calibration. This

could also change the results of the previous section (spatially variable rainfall). Rain

gage 2 collected relatively low amounts of irrigation water throughout the season. The

irrigation amounts collected on DOY 243 by this gage were unusually high with no

obvious reason. In addition, RG3 and RG6 did not collect irrigation water on this day

because the center pivot likely did not complete a full rotation.

Small amounts of rainfall were collected by the weather station on DOY 165 and

166. However, the field was irrigated as well. The amount presented on these days in

Figure 3-4 represent the water collected by the rain gages after the rainfall amounts from

the weather station were subtracted. The gage in zone 6 had the most fluctuations. During

two irrigation periods (DOY 167/168 and 206/207) it collected the least amount of

applied irrigation water while it measured significantly higher amounts of water during

the 181/182 and 240/241 periods. Again, this could be the result of incomplete pivot

rotations. The rain gages 1B and 5 did not show any significant fluctuations with respect

to the other irrigation data. The total irrigation amounts for each zone are presented in

Table 3-3.

Table 3-3. Irrigation amounts in management zones in 1998.Zone Zone2 Zone 3 and 4 Zone 5 Zone 6 Zone 1B

Rain Gage RG2 RG3 RG5 RG6 RG1B

1998 IrrigationAmount 103 mm 169 mm 132 mm 125 mm 127 mm

54

Gravimetric soil water content

The simulated SWC values in the soil profile of zone 2 are plotted in Figure 3-5

with the observed SWC values. The first observed measurement was taken on DOY 156

and the simulations started on the day of planting (DOY 161). The simulations for the

remaining zones (3, 4, 5, and 6) are located in Appendix E. In general, the simulated

SWC values in the two upper layers of all the zones fitted the observed values well. In the

lower zones, where there were fewer roots simulated, the gravimetric measurements were

more scattered than the simulated values. It was expected that the SWC in the deeper

layers was constant when this value was below the DUL and roots were not yet developed

at those depths. In addition, the model under-predicted the SWC values in the lower zones

relative to the gravimetrically measured values. Considering the possible problems with

our gravimetric measurements (chapter 2) there is no reason to further speculate about the

correctness of one method versus another.

The RMSD of the simulated fits of all zones are presented in Table 3-4. The errors

of the simulated fits in the upper layer were relatively high, except in zones 5 and 6.

Aside from zone 6, the simulations had the closest fit in the 30-60 cm layer. The greatest

errors were found in the 90-120 cm layer, where the simulated SWC remained relatively

constant at the DUL, while the observed values varied. In general, the observed SWC

values were higher than the simulated values in the lower layers, resulting in higher errors

in these layers. On the other hand, the large errors in the top layer can partially be

accounted to the daily timestep in the water balance while the actual SWC was more

dependent on the time of the day when the samples were collected.

55

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

Volu

met

ric S

oil W

ater

Con

tent

, cm

3 cm

-3Simulated

Actual

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

150 180 210 240 270 300

Day of Year

Volu

met

ric S

oil W

ater

Con

tent

, cm

3 cm

-3

Figure 3-5. Simulated and observed SWC values in the soil profile of Zone 2. 0-30 cm (a)and 30-60 cm (b).

a

b

56

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Volu

met

ric S

oil W

ater

Con

tent

, cm

3 cm

-3

Simulated

Grav.

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

150 180 210 240 270 300Day of Year

Volu

met

ric S

oil W

ater

Con

tent

, cm

3 cm

-3

Figure 3-5 Continued.

c

d

57

Table 3-4. Root mean square difference of simulated versus observed soil water contentvalues.

ZONE 2 ZONE 3 ZONE 4 ZONE 5 ZONE 6Depth(cm) ROOT MEAN SQUARE DIFFERENCE (dimensionless)

0-30 0.051 0.051 0.052 0.036 0.02930-60 0.014 0.029 0.034 0.032 0.04760-90 0.024 0.057 0.073 0.051 0.05790-120 0.047 0.077 0.077 0.064 0.044

Comparison of observed and simulated yield

The ability of a crop model to approximate actual yield is one of its most

important functions. This can only be achieved after the crop model has been calibrated

with sufficient actual yield data and correct input parameters. Unfortunately the time span

of this study only allowed for the acquisition of a single year of yield data.

The observed soybean yields are presented in Table 3-5 and plotted in Figure 3-6.

The low yield could have been a result of the extreme dry weather conditions that were

exhibited at the beginning of the 1998 season. In addition, the farmer experienced a poor

plant stand (Smith, personal correspondence) causing him to plant a second time two

weeks after the first. The second planting had a low plant population as well, resulting in

an overall average actual plant population in five management zones of 10 plants per m2.

This value is usually in the range of 20 to 30 plants per m2 (Jones and Ritchie, 1990).

Other important management input parameters are presented in Appendix F.

A sensitivity analysis showed the influence of total PASW and plant population

(POP) on yield. A POP of 30 plants per m2 was used for the historical simulations.

However, this was not representative for the 1998 conditions. The average absolute error

between observed and simulated yields was very large (762 kg/ha). In addition, when the

58

2000

2400

2800

3200

3600

4000

2000 2400 2800 3200 3600 4000PREDICTED Dry Seed Weight (kg/ha)

OBS

ERVE

D D

ry S

eed

Wei

ght (

kg/h

a)

0-110 cm, Actual POP0-100 cm, Actual POP0-120 cm, Actual POP0-120 cm, 30 POP

ZONE 2

ZONE 3

ZONE 5

ZONE 4

ZONE 6

ONE-TO-ONE LINE

Figure 3-6. Observed versus predicted dry seed weight.

soybean was simulated with the actual POP, it became apparent that the roots may have

penetrated only part of this layer because the simulated yields between different zones

were relatively small. A sensitivity analysis showed that the simulated yields for zones 2

and 5 decreased more than the remaining zones, as there was less water available due to a

lack of root penetration.

Additional data are needed to determine if this bias is consistent before modifying

the soybean crop model or its inputs to more closely simulate observed field yields. For

the purpose of this study, however, the model could not be calibrated to the 1998

conditions because historical simulation results would not likely be representative of the

field. In addition, only a small plant sample was harvested at the request of the farmer.

The probable error during the collection of plant samples, for example in zone 2, likely

59

resulted in the large difference between simulated and observed yield as well. Therefore,

results in this paper were based on the soybean model without calibration to this field.

Optimization of the irrigation threshold factor

The irrigation threshold was optimized using the automatic irrigation option. The results

are presented in Table 3-5. These were used for the 25-year simulations of yield, water

use, and water drainage in Field10. These thresholds varied from 41% for the zone with

highest clay content (zone 4) to 51% for zone 2, which had the highest percentage of sand

down to 90 cm.

Table 3-5. Irrigation threshold factors that resulted in maximum gross margins for allmanagement zones with respective percentages sand, clay, and silt.

ManagementZone ITHRL (%) % Sand in Top

90 cm% Clay in Top

90 cm% Silt in Top

90 cm

2 51 86.19 5.01 8.803 47 82.95 4.69 12.354 41 68.33 19.20 12.475 49 72.87 16.53 10.606 41 76.78 10.27 12.95

Simulation Experiments

After the 1998 field conditions were simulated, I completed simulations using 25

years of historical weather data for management based on the zone with the earliest stress

sign and with the highest yield. The results of simulations conducted to determine the

zone with earliest stress and highest non-irrigated yield shown in Table 3-6. A more

detailed analysis is given in the following sections.

60

Table 3-6. Simulations of irrigation starting dates and yields under automatic and non-irrigated conditions for five management zones in Field 10.

Earliest Auto-Irrigation Dates Non-Irrigated*

Initial ConditionsEqual to DUL*

Actual 1998 InitialConditions

Area

Earliest Average Earliest Average

AverageYield St.Dev.

Management

Zone

(ha) (day of year) (kg/ha)2 2.04 182 196 164 168 1492 7513 0.43 182 198 179 197 1727 8484 1.04 185 201 164 169 1848 7925 4.78 182 196 182 196 1685 7646 1.65 185 201 185 201 1785 838

* Simulations were conducted for 25 years.

Management based on earliest sign of stress

Management zones 2 and 4 had the lowest actual 1998 initial SWC conditions,

which resulted in the simulated earliest irrigation dates. Both zones showed a need to be

irrigated first on DOY 164 in 1998 for optimal irrigation of these zones. However, when

the DUL values for each zone were used as the initial SWC conditions, zones 2 and 5

showed the earliest signs of stress. Zone 2 was selected as the zone with the earliest

average irrigation date. The irrigation schedules from each year’s automatic irrigation

simulation of zone 2 were applied to the remaining four zones. The total water use and

production of the entire field were then calculated and are presented in Table 3-7.

The irrigation schedules from zone 2 for 25 years produced an average of 33.1

tons (3332 kg/ha) and used 27.2 million liters of water (274 mm). Although this

production value is the second highest (after spatially variable irrigation), it used the most

water of any management practice and had the most water drained out of the profile

(Table 3-7).

61

Management based on the highest yielding zone

The average simulated yield under non-irrigated conditions was the highest in

zone 4 (1848 kg/ha). However, both the simulated yield using the actual 1998 irrigation

schedule (3565 kg/ha) and the measured yield from plant samples (3149 kg/ha) indicated

that zone 6 was the highest yielding zone. Personal observations in the field suggested

that zone 6 had more organic matter in the upper layers and it had a better plant stand as

well. Therefore, zone 6 was selected as the highest yielding zone.

When management of the entire field was based on the irrigation schedule for zone 6, an

average annual water use over 25 years of 20.4 million liters (205 mm) was simulated.

Additionally, the total water drained was 13.7 million liters. Although this was the lowest

amount of water necessary among the management strategies analyzed, it resulted in the

second lowest average production as well (31.8 tons, 3201 kg/ha).

Management based on the largest zone

Management zone 5 had the largest area in Field 10 (4.78 hectares). When whole

field irrigation management was scheduled according to this zone, production was 33.1

tons (3333 kg/ha). Although this amount was similar to that of the earliest stress

management practice, it used much less water (23.4 million liters, 236 mm). However, the

total amount of water drained was the second highest (14.8 million liters).

Management based on optimal irrigation by zone

Spatially varying irrigation required an annual average of 23.4 million liters of

water (236 mm) for irrigation and had the highest total average production of 33.5 tons

per year (3374 kg/ha). The cumulative drainage averaged 14.6 million liters per season.

Figures 3-7, 3-8, and 3-9 show the differences in total soybean production, water use,

62

Table 3-7. Twenty-five year averages and standard deviations of total production and water use for five management zones underdifferent irrigation treatments.

WATER USE WATER DRAINAGE*TOTALPRODUCTION

AVG.YIELD TOTAL AVG. TOTAL AVG.

Irrigation managementbased on:

(tons) St. Dev. (kg/ha) (liters 106) St. Dev. (mm) (liters 106) St. Dev. (mm)Earliest Stress (Zone 2) 33.1 3.0 3332 27.2 8.8 274 16.1 7.9 162Zone 3 Schedule 32.2 2.6 3246 21.8 7.7 220 14.1 8.2 142Zone 4 Schedule 31.5 2.6 3176 21.8 7.9 219 14.0 8.2 141Largest Area (Zone 5) 33.1 2.9 3333 23.4 7.7 236 14.8 8.1 149Highest Yield (Zone 6) 31.8 2.6 3201 20.4 7.6 205 13.7 8.1 138Variable Rate Irrigation 33.5 3.0 3374 23.4 7.9 236 14.6 8.2 147

* Cumulative drainage from the bottom of the soil profile

63

and drainage respectively between spatially variable irrigation and the remaining

irrigation management strategies for each simulated year. The general trend shows that

both the irrigation schedule based on the zone with the earliest sign of stress and the

highest yielding zones produced less than the variable irrigation practice, except under

the 1982 weather conditions. The water use and drainage trends were similar;

management according to the earliest stressed zone irrigated and drained more water

while the practice following the highest yielding zone used and drained less water. The

management option derived from the largest zone was most similar to the spatially

variable irrigation practices.

-4500

-4000

-3500

-3000

-2500

-2000

-1500

-1000

-500

0

500

74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98Year

Tota

l Pro

duct

ion

(kg)

Earliest Stress

Largest Zone

Highest Yield

Figure 3-7. Production differences between a spatially variable irrigated production(zero line) and three other irrigation management options.

64

-7.0

-5.0

-3.0

-1.0

1.0

3.0

5.0

7.0

9.0

74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98Year

Wat

er U

se (m

ilion

liter

s)

Earliest Stress Largest Zone Highest Yield

Figure 3-8. Differences in water use between spatially variable irrigation (zero line) andirrigation by the water demands of different zones.

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98Year

Cum

ulat

ive

Dra

inag

e (m

ilion

liter

s)


Figure 3-9. Differences in cumulative drainage between spatially variable irrigation(zero line) and irrigation by the water demands of different zones.

65

Unfortunately no soil data were collected in zones 7, 8 and 9 to complete a

similar analysis on all the zones. The simulated production and water use represented

only 79% of the actual farm area. However, from field observations it did not appear as

if any of the remaining zones had properties resulting in more extreme conditions. The

same selections would probably have been made for the different irrigation practices.

The total area would have increased possibly, resulting in amplified differences among

the management practices.

Economic Analysis

The economic analysis was based on average soybean market prices and

estimated electricity costs. The costs were based on $6 per bushel (1000 kg

approximates 37 bushels) for soybeans and $1.50, $2.00 and $2.50 per hectare per cm

for the irrigation pumping expense (Pitts and Smajstrla, 1989). The total income and

expenditure for the five zones (9.93 ha.) were calculated and compared. The year-by-

year differences are shown in Figure 3-10, while a summary is presented in Table 3-8.

Spatially variable irrigation resulted in the highest potential gross margin.

Among the options currently available to the farmer, irrigation management according to

the largest zone gave the highest economic return. The practices can be ranked as

follows: management based on the (1) optimal irrigation by zone, (2) largest

management area, zone 5, (3) schedule of zone 2, the area with the earliest sign of stress,

(4) schedule of zone 3, (5) schedule of the highest yielding area, zone6, and (6) schedule

of zone 4. The irrigation management according to zone 5 ranked high on the list

because it constituted 48% of the total study area.

66

Table 3-8. Simulated gross margins of five management zones measured over 25 yearsusing a soybean price of $6 per bushel (approximately $222.40 per 1000 kg).Irrigation cost ($/ha-cm) 1.50 2.00 2.50

GrossMargin St.Dev. Gross

Margin St.Dev. GrossMargin St.Dev.Irrigation schedule

according to:($)

Zone with earliest stress (2) 6886 644 6750 640 6614 640Zone 3 schedule 6768 543 6659 540 6550 539Zone 4 schedule 6644 565 6535 566 6426 570Zone with largest area (5) 6971 592 6823 576 6706 570Highest yielding zone (6) 6700 548 6598 545 6496 545Variable rate irrigation 7037 608 6919 596 6802 587

-900

-700

-500

-300

-100

100

74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98Year

Gro

ss M

argi

n (U

S$)


Figure 3-10. Differences in gross margin between spatially variable irrigation (zero line)and irrigation by the water demands of different zones.

The differences in gross margins of the irrigation options were small. This is

better expressed in Figure 3-13 where the range, median, and upper and lower quartiles

67

are shown in a box plot. The first quartiles of the irrigation management strategies were

not different from one another because they overlapped. This was most likely the result

of the generally high rainfall in Southwest Georgia resulting in a general lack of

significant water stress. In addition, the study site was relatively small. An increase in

field size will likely increase the differences among the economic significance of the

different methods. Nevertheless, the difference between the average gross margin of the

worst and best option was still more than 5% of the latter value.

6000

6500

7000

7500

8000

A B C D E F

Management

Gro

ss M

argi

n (U

S$)

Figure 3-11. Lower and Upper Quartiles (box) of the Gross Margin from differentmanagement strategies based on: Spatially Variable Irrigation (A), Zone with EarliestStress Sign (B), Zone 3 (C), Zone 4 (D), Largest Zone (E), and Highest Yielding Zone(F). Whiskers and black areas indicate gross margin range and confidence interval(p=0.05) of median respectively.

68

Conclusions

A series of analyses were completed to study the spatial variability of irrigation

and rainfall distribution in a Southwest Georgia site with predetermined management

zones. In addition, an economic analysis was conducted to study how this variability

could best be managed through variable rate irrigation or one of four other irrigation

strategies. These irrigation management practices were all based on management zones.

Water application from irrigation was not uniform across the field as was

intended by the farmer. Rainfall was not spatially uniform either. It appeared to be

related to the distance to the bordering forest line. Rainfall amounts increased as the

distance to the forested border decreased. Further studies are necessary to validate this

finding and to understand the cause and its impact on spatial variability of yield and soil

water better.

Twenty-five years of weather data were used to analyze and compare four

different irrigation management practices: irrigation according to the water demands of

the zone with the earliest signs of stress, the zone with the highest yield, the largest

zone, and the optimal irrigation based on the demands of each individual zone. Variable

rate irrigation was the best irrigation management option for this field in terms of gross

margin when considering only operation cost and assuming that a variable rate irrigation

system is in the field. However, the operating center pivot was not set up for this type of

management. The second best option was the irrigation of the entire field according to

irrigation demands of the largest management area, zone 5.

The method of analysis that was used to study the potential value of spatially

variable irrigation management can be applied to other fields where farmers may be

69

considering investments in equipment. The actual value of spatially variable irrigation

would have to take into account investment, maintenance, depreciation, and other costs

not considered in this study. However, the method used in this study provides average

and yearly estimates of gross margin above costs of applying water so that uncertainty

and risk can be considered. Because crop models are used to simulate the responses to

different ways of managing fields, the approach can be applied to any field if spatial

variability in soil properties is known or can be measured.

70

CHAPTER 4SUMMARY AND CONCLUSIONS

This study presented an analysis of the spatial variability in a Southwest Georgia

field in order to prescribe better management techniques. The emphasis was placed on

plant available soil water and irrigation. The overall objective was to investigate

different irrigation management techniques based on pre-determined management zones

for optimal gross margin.

The logic was that a process oriented crop model was necessary to evaluate

different irrigation options. Crop models are very sensitive to input parameters and

soybean models especially are very sensitive to soil water input information. The work

was completed on a field scale. Therefore, it was necessary to first find a soil parameter

estimation method that best predicts the water holding limits in the entire research field

based on soil texture and bulk density information. The drained upper limit (DUL)

values and lower limit (LL) values from several models were compared to DUL values

obtained from field measured time domain reflectometry data and LL values estimated

from laboratory pressure plate analysis. This chapter summarizes briefly the main

conclusions of the different phases outlined above.

A new method for estimating DUL values from time domain reflectometry

(TDR) data was developed. This method is based on the nighttime drainage rates

measured at two-hour intervals. It was successfully used in the upper layers in the

middle of the growing season while plants were actively removing soil water during

71

daytime hours and the field remained undisturbed. The resulting DUL values were used

to select a best soil parameter estimation technique. This method needs to be validated

with multiple years of TDR data, preferably from different sites.

The Saxton, DSSAT, SWLIMITS, and Rawls methods were selected for

comparison based on their readily available input data, representation of a large range of

soil classes, and the target audience of crop modelers. The selection of the best method

was related to its ability to estimate the total plant available water in the profile relative

to the values obtained from TDR and pressure plate measurements. The Rawls method

resulted in the best DUL and LL estimates the Southwest Georgia study site. It was used

to estimate soil water holding limits for the different management zones, which were

then used as soil input parameters in the CROPGRO-Soybean model.

Rainfall and irrigation data measured in the 1998 season showed significant

variations in the field. Rainfall amounts were generally larger in areas close to the forest

border. The center pivot showed variations in the water application amount as well.

Although the existence of spatially variable rainfall was evident in the field, only

uniform historical weather data were available for simulation purposes.

Several simulation experiments were completed to select the best irrigation

management option. First, the irrigation threshold factor for automatic irrigation in the

model was optimized for each zone with respect to a maximum gross margin. Next, the

zones with the earliest signs of stress, highest yield, and largest area were selected from

the 1998 simulations. The recommended irrigation schedules from these zones for 25

years were applied to the entire field. In addition, spatially variable irrigation,

72

determined by the water demands of each individual zone, was applied over a 25 year

period as well.

Spatially variable irrigation had the highest gross margin for the farmer. In

addition, the total difference in gross margin between this option and the second best

strategy was relatively small. Uniform irrigation of the field according to the water

demands of the largest management zone was the best option under the currently

available center pivot configuration.

The total drainage at the bottom of the soil profile was calculated but its impact

on the environment was not taken into account during the selection of the best

management strategy. However, it was shown that the CROPGRO-Soybean model can

be used successfully for the management of water resources. In the future, such process-

oriented models will likely play an important role in sustainable farming and precision

agriculture.

APPENDIX ASOIL WATER CONTENT DRAINAGE RATES FOR THE DETERMINATION OF

DRAINED UPPER LIMIT VALUES IN THE UPPER SOIL LAYERS

-0.006

-0.004

-0.002

0.000

0.002

0.004

0.006

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3 /c

m3 ) a

nd R

ainf

all/4

00 (m

m)

Normalized Rainfall 10pm - 6am 6am - 2pm2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207

Figure A-1. Average drainage rates of volumetric SWC for three time periods in the irrigated TDR1 plot in the 0-30 cm layer. Arrowsindicate the point when the Drained Upper Limit equilibrium was reached.

74

75

-0.004

-0.003

-0.002

-0.001

0.000

0.001

0.002

0.003

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)

Normalized Rainfall 10pm - 6am 6am - 2pm

2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207

Figure A-2. Average drainage rates of volumetric SWC for three time periods in the irrigated TDR1 plot in the 30-60 cm layer.Arrows indicate the point when the Drained Upper Limit equilibrium was reached.

76

-0.004

-0.003

-0.002

-0.001

0.000

0.001

0.002

0.003

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)

Normalized Rainfall 10pm - 6am 6am - 2pm2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207


77

-0.006

-0.005

-0.004

-0.003

-0.002

-0.001

0.000

0.001

0.002

0.003

0.004

0.005

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)


2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207

Figure A-4. Average drainage rates of volumetric SWC for three time periods in the irrigated TDR2 plot in the 0-30 cm layer. Arrowsindicate the point when the Drained Upper Limit equilibrium was reached.

78

-0.004

-0.003

-0.002

-0.001

0.000

0.001

0.002

0.003

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)


2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207


79

-0.004

-0.003

-0.002

-0.001

0.000

0.001

0.002

0.003

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)


2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207


80

-0.006

-0.005

-0.004

-0.003

-0.002

-0.001

0.000

0.001

0.002

0.003

0.004

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)


2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207

Figure A-7. Average drainage rates of volumetric SWC for three time periods in the non-irrigated TDR1 plot in the 0-30 cm layer.Arrows indicate the point when the Drained Upper Limit equilibrium was reached.

81

-0.003

-0.003

-0.002

-0.002

-0.001

-0.001

0.000

0.001

0.001

0.002

0.002

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)


2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207


82

-0.003

-0.002

-0.001

0.000

0.001

0.002

0.003

0.004

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)


2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207


83

-0.006

-0.005

-0.004

-0.003

-0.002

-0.001

0.000

0.001

0.002

0.003

0.004

0.005

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)


2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207


84

-0.003

-0.002

-0.001

0.000

0.001

0.002

0.003

0.004

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)


2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207


85

-0.002

-0.001

0.000

0.001

0.002

Day of Year

Flux

(SW

C/h

r)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

TDR

SW

C (c

m3/

cm3)

and

Rai

nfal

l/400

(mm

)


2pm - 10pm TDR

19

4

195

196

197

198

199

200

201

202

203

204

205

206

207

Figure A-12. Average drainage rates of volumetric SWC for three time periods in the non-irrigated TDR2 plot in the 60-90 cm layer.The SWC likely did not exceed the Drained Upper Limit.

APPENDIX BTIME DOMAIN REFLECTOMETRY DATA FROM IRRIGATED AND NON-

IRRIGATED LOCATIONS

87

Table B-1. Time domain reflectometry data for irrigated zone.Irrigated Zone, TDR1 Irrigated Zone, TDR2

Layer (cm) Layer (cm)

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

174 1600 0.13 0.14 0.12 0.17 0.18 0.23 0.12 0.11 0.11 0.16 0.21 0.03174 2000 0.13 0.14 0.12 0.17 0.19 0.23 0.12 0.11 0.11 0.16 0.21 0.03175 0 0.13 0.14 0.12 0.17 0.18 0.23 0.12 0.10 0.11 0.16 0.21 0.03175 400 0.13 0.14 0.23 0.17 0.18 0.23 0.11 0.10 0.11 0.16 0.21 0.02175 800 0.13 0.14 0.12 0.17 0.18 0.23 0.11 0.10 0.11 0.16 0.21 0.04175 1200 0.13 0.14 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.03175 1600 0.13 0.14 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.01175 2000 0.13 0.14 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21176 0 0.13 0.14 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.01176 400 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.00176 800 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.03176 1200 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.01176 1600 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.00176 2000 0.17 0.13 0.12 0.17 0.19 0.23 0.14 0.10 0.11 0.16 0.21177 0 0.17 0.13 0.12 0.17 0.19 0.23 0.15 0.10 0.11 0.16 0.21177 400 0.18 0.14 0.12 0.17 0.19 0.23 0.15 0.10 0.11 0.16 0.21 0.03177 800 0.17 0.14 0.12 0.17 0.19 0.23 0.15 0.10 0.11 0.16 0.21177 1200 0.17 0.14 0.12 0.17 0.19 0.23 0.14 0.10 0.11 0.16 0.21177 1600 0.16 0.14 0.12 0.17 0.19 0.23 0.14 0.10 0.11 0.16 0.21177 2000 0.15 0.14 0.12 0.17 0.19 0.23 0.14 0.10 0.11 0.16 0.21 0.01178 0 0.15 0.14 0.12 0.17 0.19 0.23 0.13 0.10 0.11 0.16 0.21 0.01178 400 0.15 0.14 0.12 0.17 0.19 0.23 0.14 0.10 0.11 0.16 0.21 0.02178 800 0.15 0.14 0.12 0.17 0.19 0.23 0.13 0.10 0.11 0.16 0.21 0.03178 1200 0.15 0.14 0.12 0.17 0.19 0.23 0.13 0.10 0.11 0.16 0.21 0.04178 1600 0.14 0.14 0.12 0.17 0.19 0.23 0.13 0.10 0.11 0.16 0.21 0.01178 2000 0.14 0.14 0.12 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.03179 0 0.14 0.14 0.12 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.02179 400 0.14 0.14 0.12 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.03179 800 0.14 0.14 0.12 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.01179 1200 0.14 0.14 0.12 0.17 0.19 0.23 0.12 0.10 0.11 0.17 0.21 0.02179 1600 0.13 0.14 0.12 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.04179 2000 0.13 0.14 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.02

88

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

180 0 0.13 0.14 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21180 400 0.13 0.13 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.00180 800 0.13 0.14 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.02180 1200 0.13 0.14 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.02180 1600 0.12 0.14 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.04180 2000 0.13 0.14 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.03181 0 0.13 0.14 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.00181 400 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.02181 800 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.09 0.11 0.16 0.21 0.02181 1200 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.02181 1600 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.02181 2000 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.03182 0 0.15 0.13 0.12 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.00182 400 0.15 0.13 0.12 0.17 0.19 0.23 0.12 0.09 0.11 0.16 0.21182 800 0.15 0.13 0.12 0.17 0.19 0.23 0.12 0.09 0.11 0.16 0.21 0.04182 1200 0.14 0.13 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.01182 1400 0.13 0.13 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.02182 1600 0.13 0.13 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.00182 1800 0.13 0.13 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21182 2000 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21182 2200 0.13 0.13 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.05183 0 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.09 0.11 0.16 0.21183 200 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.09 0.11 0.16 0.21 0.02183 400 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.17 0.21 0.04183 600 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21183 800 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21183 1000 0.13 0.13 0.12 0.17 0.19 0.23 0.10 0.09 0.11 0.16 0.21 0.03183 1200 0.12 0.13 0.12 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.02183 1400 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.02183 1600 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.02183 1800 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.05183 2000 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21183 2200 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.04184 0 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.01184 200 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.09 0.11 0.16 0.21 0.03184 400 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.00184 600 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.09 0.11 0.16 0.21 0.01184 800 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.03

89

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

184 1000 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.09 0.11 0.16 0.21 0.03184 1200 0.12 0.13 0.12 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21184 1400 0.15 0.13 0.12 0.17 0.19 0.23 0.13 0.10 0.11 0.16 0.21 0.01184 1600 0.15 0.13 0.15 0.17 0.19 0.23 0.13 0.10 0.11 0.16 0.21 0.03184 1800 0.15 0.13 0.12 0.17 0.19 0.23 0.13 0.10 0.11 0.16 0.21184 2000 0.15 0.13 0.12 0.17 0.19 0.23 0.13 0.10 0.11 0.17 0.21184 2200 0.15 0.13 0.12 0.17 0.19 0.23 0.12 0.09 0.11 0.17 0.21 0.01185 0 0.15 0.13 0.12 0.17 0.19 0.23 0.12 0.09 0.11 0.17 0.21 0.02185 200 0.15 0.13 0.12 0.17 0.19 0.23 0.12 0.09 0.11 0.16 0.21 0.01185 400 0.15 0.13 0.11 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21185 600 0.15 0.13 0.12 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.02185 800 0.15 0.13 0.12 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.03185 1000 0.15 0.13 0.12 0.17 0.19 0.23 0.12 0.09 0.11 0.16 0.21 0.02185 1200 0.14 0.13 0.12 0.17 0.19 0.23 0.12 0.09 0.11 0.16 0.21 0.01185 1400 0.14 0.13 0.11 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21185 1600 0.13 0.13 0.11 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.06185 1800 0.13 0.13 0.15 0.17 0.19 0.23 0.12 0.10 0.11 0.16 0.21 0.01185 2000 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.02185 2200 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21186 0 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.01186 200 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.09 0.11 0.16 0.21186 400 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.00186 600 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.02186 800 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.03186 1000 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.09 0.11 0.16 0.21 0.03186 1200 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.11186 1400 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.17 0.21 0.02186 1600 0.12 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.01186 2000 0.12 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.00186 2200 0.12 0.13 0.11 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21187 0 0.13 0.13 0.11 0.17 0.19 0.23 0.11 0.10 0.11 0.16 0.21 0.00187 200 0.12 0.13 0.11 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.00187 400 0.13 0.13 0.11 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.01187 600 0.13 0.13 0.11 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.02187 800 0.12 0.13 0.11 0.17 0.19 0.23 0.10 0.09 0.11 0.16 0.21 0.03187 1000 0.12 0.13 0.11 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.01187 1200 0.12 0.13 0.11 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.03187 1400 0.12 0.13 0.10 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.03

90

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

187 1600 0.12 0.13 0.10 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21187 1800 0.12 0.13 0.10 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21187 2000 0.11 0.13 0.10 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.03187 2200 0.12 0.13 0.11 0.17 0.19 0.23 0.10 0.10 0.11 0.16 0.21 0.02188 0 0.16 0.13 0.11 0.17 0.19 0.23 0.14 0.09 0.11 0.16 0.21 0.03188 200 0.16 0.13 0.11 0.17 0.19 0.23 0.14 0.09 0.11 0.16 0.21188 400 0.16 0.13 0.11 0.17 0.19 0.23 0.14 0.09 0.11 0.16 0.21 0.01188 600 0.16 0.13 0.11 0.17 0.19 0.23 0.14 0.09 0.11 0.16 0.21 0.02188 800 0.16 0.13 0.11 0.17 0.19 0.23 0.14 0.09 0.11 0.16 0.21188 1000 0.16 0.13 0.11 0.17 0.19 0.23 0.13 0.09 0.11 0.16 0.21 0.01188 1200 0.15 0.13 0.10 0.17 0.19 0.23 0.13 0.10 0.11 0.16 0.21 0.02188 1400 0.15 0.13 0.10 0.17 0.19 0.23 0.13 0.10 0.11 0.16 0.21 0.02188 1600 0.18 0.13 0.10 0.17 0.19 0.23 0.16 0.10 0.11 0.16 0.21 0.04188 1800 0.19 0.13 0.10 0.17 0.19 0.23 0.16 0.10 0.11 0.16 0.21 0.02188 2000 0.19 0.14 0.10 0.17 0.19 0.23 0.16 0.10 0.10 0.16 0.21 0.00188 2200 0.18 0.14 0.10 0.17 0.19 0.23 0.16 0.10 0.11 0.17 0.21 0.03189 0 0.18 0.14 0.10 0.17 0.19 0.23 0.16 0.10 0.11 0.17 0.21 0.02189 200 0.18 0.14 0.10 0.17 0.19 0.23 0.16 0.11 0.11 0.16 0.21 0.03189 400 0.18 0.14 0.10 0.17 0.19 0.23 0.16 0.11 0.11 0.16 0.21 0.02189 600 0.18 0.14 0.10 0.17 0.19 0.23 0.16 0.11 0.11 0.16 0.21 0.01189 800 0.18 0.14 0.11 0.17 0.19 0.23 0.16 0.11 0.11 0.16 0.21 0.02189 1000 0.17 0.14 0.11 0.17 0.19 0.23 0.16 0.11 0.11 0.16 0.21 0.01189 1200 0.17 0.14 0.10 0.17 0.19 0.23 0.16 0.11 0.11 0.16 0.21 0.02189 1400 0.17 0.14 0.10 0.17 0.19 0.23 0.16 0.11 0.11 0.16 0.21 0.02189 1600 0.16 0.14 0.10 0.17 0.19 0.23 0.15 0.11 0.11 0.16 0.21 0.02189 1800 0.20 0.15 0.10 0.17 0.19 0.23 0.20 0.12 0.11 0.16 0.21 0.02189 2000 0.20 0.15 0.10 0.17 0.19 0.23 0.20 0.13 0.11 0.16 0.21 0.04189 2200 0.20 0.16 0.10 0.17 0.19 0.23 0.20 0.13 0.11 0.16 0.21 0.01190 0 0.20 0.16 0.10 0.17 0.19 0.23 0.20 0.13 0.11 0.16 0.21190 200 0.20 0.16 0.10 0.17 0.19 0.23 0.19 0.13 0.11 0.16 0.21 0.03190 400 0.20 0.16 0.10 0.17 0.19 0.23 0.19 0.13 0.11 0.16 0.21 0.02190 600 0.19 0.16 0.10 0.17 0.19 0.23 0.19 0.13 0.11 0.16 0.21 0.03190 800 0.19 0.16 0.10 0.17 0.19 0.23 0.19 0.14 0.11 0.16 0.21 0.01190 1000 0.19 0.16 0.11 0.17 0.19 0.23 0.19 0.14 0.11 0.16 0.21190 1200 0.19 0.16 0.10 0.17 0.19 0.23 0.19 0.14 0.11 0.16 0.21190 1400 0.18 0.16 0.10 0.17 0.19 0.23 0.18 0.14 0.10 0.16 0.21 0.03190 1600 0.18 0.16 0.10 0.17 0.19 0.23 0.18 0.14 0.11 0.16 0.21 0.01190 1800 0.18 0.16 0.10 0.17 0.19 0.23 0.17 0.14 0.11 0.16 0.21 0.09

91

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

190 2000 0.18 0.16 0.10 0.17 0.19 0.23 0.18 0.14 0.11 0.16 0.21 0.02190 2200 0.20 0.16 0.10 0.17 0.19 0.23 0.19 0.14 0.11 0.16 0.21 0.03191 0 0.20 0.16 0.11 0.17 0.19 0.23 0.19 0.14 0.11 0.16 0.21 0.02191 200 0.19 0.16 0.10 0.17 0.19 0.23 0.19 0.15 0.11 0.16 0.21 0.01191 400 0.19 0.16 0.11 0.17 0.19 0.23 0.19 0.15 0.11 0.16 0.21 0.02191 600 0.19 0.16 0.11 0.17 0.19 0.23 0.19 0.15 0.11 0.16 0.21 0.01191 800 0.19 0.17 0.11 0.17 0.19 0.23 0.18 0.15 0.11 0.16 0.21191 1000 0.19 0.16 0.11 0.17 0.19 0.23 0.18 0.15 0.11 0.16 0.21 0.02191 1200 0.18 0.17 0.11 0.17 0.19 0.23 0.18 0.14 0.11 0.16 0.21191 1400 0.18 0.16 0.11 0.17 0.19 0.23 0.17 0.15 0.11 0.16 0.21191 1600 0.17 0.17 0.10 0.17 0.19 0.23 0.17 0.15 0.11 0.16 0.21 0.04191 1800 0.17 0.16 0.10 0.17 0.19 0.23 0.17 0.15 0.11 0.16 0.21191 2000 0.17 0.16 0.10 0.17 0.19 0.23 0.16 0.14 0.11 0.16 0.21 0.01191 2200 0.17 0.16 0.10 0.17 0.19 0.23 0.16 0.14 0.11 0.16 0.21 0.02192 0 0.17 0.16 0.10 0.17 0.19 0.23 0.16 0.14 0.11 0.16 0.21 0.02192 200 0.17 0.16 0.10 0.17 0.19 0.23 0.16 0.14 0.11 0.16 0.21 0.02192 400 0.17 0.16 0.10 0.17 0.19 0.23 0.16 0.14 0.11 0.16 0.21192 600 0.17 0.16 0.10 0.17 0.19 0.23 0.16 0.14 0.11 0.16 0.21 0.03192 800 0.17 0.16 0.11 0.17 0.19 0.23 0.16 0.14 0.11 0.16 0.21 0.03192 1000 0.16 0.16 0.11 0.17 0.19 0.23 0.15 0.14 0.11 0.16 0.21192 1200 0.16 0.16 0.11 0.17 0.19 0.23 0.15 0.14 0.11 0.16 0.21 0.02192 1400 0.15 0.16 0.10 0.17 0.19 0.23 0.15 0.14 0.11 0.16 0.21192 1600 0.15 0.16 0.10 0.17 0.19 0.23 0.15 0.14 0.12 0.16 0.21 0.01192 1800 0.15 0.16 0.10 0.17 0.19 0.23 0.15 0.14 0.11 0.16 0.21 0.04192 2000 0.15 0.16 0.10 0.17 0.19 0.23 0.14 0.14 0.12 0.17 0.21 0.02192 2200 0.15 0.16 0.10 0.17 0.19 0.23 0.14 0.14 0.12 0.17 0.21 0.03193 0 0.15 0.15 0.10 0.17 0.19 0.23 0.14 0.14 0.12 0.16 0.21 0.01193 200 0.14 0.15 0.10 0.17 0.19 0.23 0.14 0.13 0.12 0.16 0.21193 400 0.14 0.15 0.11 0.17 0.19 0.23 0.14 0.13 0.12 0.16 0.21 0.01193 600 0.14 0.15 0.11 0.17 0.19 0.23 0.14 0.13 0.12 0.16 0.21 0.03193 800 0.14 0.15 0.11 0.17 0.19 0.23 0.14 0.13 0.12 0.16 0.21 0.02193 1000 0.14 0.15 0.11 0.17 0.19 0.23 0.14 0.13 0.12 0.17 0.21 0.01193 1200 0.14 0.15 0.11 0.17 0.19 0.23 0.14 0.13 0.12 0.17 0.21 0.07193 1400 0.14 0.15 0.10 0.17 0.19 0.23 0.13 0.13 0.12 0.17 0.21 0.01193 1600 0.14 0.15 0.11 0.17 0.19 0.23 0.13 0.13 0.12 0.17 0.21193 1800 0.14 0.15 0.11 0.17 0.19 0.23 0.13 0.13 0.12 0.17 0.21 0.01

92

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

193 2000 0.14 0.15 0.11 0.17 0.19 0.23 0.13 0.13 0.12 0.17 0.21 0.01193 2200 0.13 0.15 0.11 0.17 0.19 0.23 0.13 0.13 0.12 0.17 0.21194 0 0.13 0.15 0.11 0.17 0.19 0.23 0.13 0.13 0.12 0.17 0.21 0.03194 200 0.14 0.15 0.11 0.17 0.19 0.23 0.13 0.13 0.12 0.17 0.21194 400 0.14 0.15 0.11 0.17 0.19 0.23 0.13 0.13 0.12 0.16 0.21194 600 0.16 0.15 0.11 0.17 0.19 0.23 0.15 0.12 0.12 0.16 0.21194 800 0.16 0.15 0.11 0.17 0.19 0.23 0.15 0.13 0.12 0.17 0.21194 1000 0.16 0.15 0.11 0.17 0.19 0.23 0.15 0.13 0.12 0.17 0.21194 1200 0.16 0.15 0.11 0.17 0.19 0.23 0.15 0.12 0.12 0.17 0.21194 1400 0.16 0.14 0.11 0.17 0.19 0.23 0.15 0.13 0.12 0.17 0.21 0.01194 1600 0.16 0.15 0.11 0.17 0.19 0.23 0.15 0.13 0.12 0.17 0.21 0.12194 1800 0.16 0.15 0.11 0.17 0.19 0.23 0.15 0.13 0.12 0.17 0.21 0.03194 2000 0.18 0.15 0.11 0.17 0.19 0.23 0.16 0.12 0.12 0.17 0.21 0.05194 2200 0.18 0.15 0.12 0.17 0.19 0.23 0.17 0.13 0.12 0.17 0.21195 0 0.18 0.15 0.12 0.17 0.19 0.23 0.17 0.13 0.12 0.17 0.21 0.00195 200 0.18 0.15 0.12 0.17 0.19 0.23 0.17 0.13 0.12 0.17 0.21 0.02195 400 0.18 0.15 0.12 0.17 0.19 0.23 0.17 0.13 0.12 0.17 0.21 0.01195 600 0.18 0.15 0.12 0.17 0.19 0.23 0.17 0.13 0.12 0.17 0.21 0.03195 800 0.18 0.15 0.12 0.18 0.19 0.23 0.17 0.13 0.12 0.17 0.21 0.01195 1000 0.18 0.15 0.12 0.17 0.19 0.23 0.17 0.14 0.12 0.17 0.21 0.00195 1200 0.18 0.15 0.12 0.17 0.19 0.23 0.17 0.14 0.12 0.17 0.21195 1400 0.17 0.15 0.12 0.17 0.19 0.23 0.17 0.14 0.12 0.17 0.21 0.00195 1600 0.17 0.15 0.11 0.17 0.19 0.23 0.16 0.13 0.12 0.17 0.21 0.02195 1800 0.17 0.15 0.12 0.17 0.19 0.23 0.16 0.14 0.12 0.17 0.21 0.01195 2000 0.20 0.15 0.11 0.17 0.19 0.23 0.19 0.13 0.12 0.17 0.21195 2200 0.20 0.16 0.11 0.17 0.19 0.23 0.20 0.15 0.12 0.17 0.21 0.02196 0 0.20 0.16 0.12 0.17 0.19 0.23 0.19 0.15 0.12 0.17 0.21 0.05196 200 0.20 0.16 0.12 0.17 0.20 0.23 0.19 0.15 0.12 0.17 0.21196 400 0.20 0.16 0.12 0.17 0.19 0.23 0.19 0.15 0.12 0.17 0.21 0.02196 600 0.19 0.16 0.12 0.18 0.19 0.23 0.19 0.15 0.12 0.17 0.21 0.02196 800 0.19 0.16 0.12 0.18 0.19 0.23 0.19 0.15 0.12 0.17 0.21 0.02196 1000 0.19 0.16 0.12 0.17 0.19 0.23 0.19 0.15 0.12 0.17 0.21 0.02196 1200 0.19 0.16 0.12 0.17 0.19 0.23 0.19 0.15 0.12 0.17 0.21 0.01196 1400 0.18 0.16 0.12 0.17 0.19 0.23 0.18 0.15 0.12 0.17 0.21 0.02196 1600 0.18 0.16 0.11 0.17 0.20 0.23 0.18 0.15 0.12 0.17 0.21 0.00196 1800 0.17 0.16 0.11 0.17 0.19 0.23 0.17 0.15 0.12 0.17 0.21 0.01

93

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

196 2000 0.17 0.16 0.11 0.17 0.19 0.23 0.17 0.15 0.12 0.17 0.21 0.02196 2200 0.17 0.16 0.11 0.17 0.19 0.23 0.17 0.15 0.13 0.17 0.21197 0 0.17 0.16 0.12 0.17 0.19 0.23 0.17 0.16 0.12 0.17 0.21 0.02197 200 0.17 0.16 0.12 0.18 0.19 0.23 0.17 0.16 0.12 0.17 0.21 0.01197 400 0.17 0.16 0.12 0.18 0.19 0.23 0.17 0.16 0.12 0.17 0.21 0.02197 600 0.17 0.16 0.12 0.18 0.19 0.23 0.17 0.16 0.13 0.17 0.21 0.01197 800 0.17 0.16 0.12 0.18 0.19 0.23 0.16 0.16 0.13 0.17 0.21 0.06197 1000 0.17 0.16 0.12 0.18 0.20 0.23 0.16 0.16 0.13 0.17 0.21 0.02197 1200 0.17 0.15 0.12 0.18 0.19 0.23 0.16 0.16 0.13 0.17 0.21 0.00197 1400 0.18 0.16 0.12 0.17 0.19 0.23 0.17 0.16 0.13 0.17 0.21 0.03197 1600 0.19 0.16 0.12 0.17 0.19 0.23 0.18 0.16 0.13 0.17 0.21 0.02197 1800 0.21 0.16 0.11 0.17 0.20 0.23 0.20 0.16 0.13 0.17 0.21 0.02197 2000 0.21 0.19 0.11 0.18 0.19 0.23 0.21 0.18 0.13 0.17 0.21197 2200 0.21 0.19 0.12 0.18 0.20 0.23 0.21 0.19 0.13 0.17 0.21 0.01198 0 0.21 0.19 0.12 0.18 0.20 0.23 0.20 0.19 0.13 0.17 0.21 0.03198 200 0.21 0.19 0.12 0.18 0.20 0.23 0.20 0.19 0.13 0.17 0.21 0.01198 400 0.20 0.19 0.12 0.17 0.20 0.23 0.20 0.19 0.13 0.17 0.21 0.01198 600 0.20 0.19 0.12 0.18 0.20 0.23 0.20 0.18 0.14 0.17 0.22 0.02198 800 0.20 0.19 0.13 0.18 0.20 0.23 0.19 0.18 0.14 0.17 0.21 0.03198 1000 0.20 0.19 0.13 0.17 0.20 0.23 0.19 0.19 0.14 0.17 0.21 0.01198 1200 0.19 0.19 0.13 0.18 0.20 0.23 0.19 0.18 0.14 0.18 0.22 0.01198 1400 0.19 0.18 0.13 0.18 0.20 0.23 0.18 0.18 0.14 0.18 0.21 0.01198 1600 0.18 0.18 0.13 0.18 0.20 0.23 0.18 0.18 0.14 0.18 0.21 0.03198 1800 0.18 0.18 0.14 0.18 0.20 0.23 0.17 0.18 0.15 0.18 0.21 0.02198 2000 0.18 0.18 0.14 0.18 0.20 0.23 0.17 0.18 0.15 0.18 0.22 0.03198 2200 0.18 0.18 0.14 0.18 0.20 0.23 0.17 0.17 0.15 0.18 0.22 0.02199 0 0.18 0.18 0.14 0.18 0.20 0.23 0.17 0.17 0.15 0.18 0.21 0.03199 200 0.18 0.18 0.14 0.18 0.20 0.23 0.17 0.17 0.15 0.18 0.22 0.01199 400 0.18 0.18 0.14 0.18 0.20 0.23 0.17 0.17 0.15 0.18 0.21 0.02199 600 0.18 0.17 0.14 0.18 0.20 0.23 0.17 0.17 0.15 0.19 0.21 0.03199 800 0.18 0.17 0.14 0.19 0.20 0.23 0.17 0.17 0.15 0.18 0.21 0.03199 1000 0.18 0.17 0.14 0.19 0.20 0.23 0.17 0.17 0.15 0.19 0.22 0.02199 1200 0.17 0.17 0.14 0.19 0.20 0.23 0.16 0.17 0.15 0.19 0.21 0.02199 1400 0.16 0.17 0.14 0.18 0.20 0.24 0.16 0.17 0.15 0.19 0.22 0.03199 1600 0.16 0.17 0.14 0.18 0.21 0.24 0.16 0.16 0.15 0.19 0.22 0.00199 1800 0.16 0.17 0.14 0.18 0.21 0.24 0.15 0.16 0.15 0.19 0.22 0.02199 2000 0.16 0.17 0.14 0.19 0.20 0.24 0.15 0.17 0.15 0.19 0.22 0.01

94

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

199 2200 0.16 0.17 0.14 0.18 0.21 0.24 0.15 0.16 0.15 0.19 0.22200 0 0.16 0.17 0.14 0.19 0.21 0.24 0.15 0.16 0.15 0.19 0.22 0.01200 200 0.16 0.17 0.14 0.18 0.20 0.24 0.15 0.16 0.15 0.19 0.22 0.03200 400 0.16 0.16 0.14 0.19 0.21 0.24 0.15 0.16 0.15 0.19 0.22 0.02200 600 0.16 0.16 0.14 0.19 0.21 0.24 0.15 0.16 0.15 0.19 0.22 0.05200 800 0.16 0.16 0.14 0.19 0.21 0.24 0.15 0.16 0.14 0.19 0.22 0.02200 1000 0.15 0.16 0.14 0.19 0.21 0.24 0.15 0.16 0.15 0.19 0.22 0.02200 1200 0.15 0.16 0.14 0.19 0.21 0.24 0.14 0.16 0.14 0.19 0.22200 1400 0.15 0.16 0.14 0.19 0.21 0.24 0.14 0.16 0.14 0.19 0.22200 1600 0.14 0.16 0.14 0.19 0.21 0.24 0.13 0.15 0.14 0.19 0.22 0.02200 1800 0.14 0.16 0.14 0.19 0.21 0.24 0.13 0.16 0.14 0.19 0.22 0.02200 2000 0.14 0.16 0.14 0.18 0.21 0.24 0.13 0.16 0.14 0.19 0.22200 2200 0.14 0.16 0.14 0.19 0.21 0.24 0.13 0.15 0.14 0.19 0.22 0.02201 0 0.14 0.16 0.14 0.19 0.21 0.24 0.13 0.15 0.14 0.19 0.22 0.02201 200 0.14 0.16 0.14 0.19 0.21 0.24 0.13 0.15 0.14 0.19 0.22 0.02201 400 0.14 0.16 0.14 0.19 0.21 0.24 0.13 0.15 0.14 0.19 0.22201 600 0.14 0.15 0.14 0.19 0.21 0.24 0.13 0.15 0.14 0.19 0.22201 800 0.14 0.15 0.14 0.19 0.21 0.24 0.13 0.15 0.14 0.19 0.22 0.02201 1000 0.14 0.15 0.14 0.19 0.21 0.24 0.13 0.15 0.14 0.19 0.22 0.01201 1200 0.13 0.16 0.14 0.19 0.21 0.24 0.13 0.14 0.14 0.19 0.23 0.03201 1400 0.13 0.16 0.13 0.19 0.21 0.24 0.13 0.14 0.14 0.19 0.22 0.01201 1600 0.14 0.15 0.13 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.22 0.02201 1800 0.14 0.15 0.13 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.23 0.01201 2000 0.13 0.15 0.13 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.22 0.02201 2200 0.13 0.15 0.14 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.23 0.01202 0 0.13 0.15 0.13 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.23 0.00202 200 0.13 0.15 0.14 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.23 0.04202 400 0.13 0.15 0.14 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.23 0.01202 600 0.13 0.15 0.14 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.23 0.02202 800 0.13 0.15 0.14 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.23 0.03202 1000 0.13 0.15 0.14 0.19 0.21 0.24 0.12 0.14 0.14 0.19 0.23 0.01202 1200 0.13 0.15 0.13 0.19 0.21 0.24 0.12 0.14 0.13 0.19 0.23 0.03202 1400 0.12 0.15 0.13 0.19 0.21 0.24 0.11 0.13 0.13 0.19 0.23 0.03202 1600 0.12 0.15 0.12 0.18 0.21 0.24 0.11 0.13 0.13 0.19 0.23 0.03202 1800 0.12 0.15 0.12 0.18 0.21 0.24 0.11 0.13 0.13 0.18 0.23202 2000 0.11 0.15 0.12 0.18 0.21 0.24 0.11 0.13 0.13 0.19 0.23 0.02202 2200 0.12 0.15 0.12 0.18 0.21 0.24 0.10 0.13 0.13 0.19 0.23 0.03203 0 0.12 0.15 0.13 0.18 0.21 0.24 0.11 0.13 0.13 0.19 0.23

95

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

203 200 0.11 0.15 0.13 0.19 0.21 0.24 0.10 0.13 0.13 0.19 0.23 0.02203 400 0.12 0.15 0.13 0.19 0.21 0.24 0.10 0.13 0.13 0.19 0.23 0.02203 600 0.12 0.15 0.13 0.19 0.21 0.24 0.10 0.13 0.13 0.19 0.23 0.01203 800 0.12 0.14 0.13 0.19 0.21 0.24 0.10 0.13 0.13 0.19 0.23 0.02203 1000 0.12 0.14 0.13 0.19 0.21 0.24 0.10 0.13 0.13 0.19 0.23 0.03203 1200 0.11 0.15 0.12 0.18 0.21 0.25 0.10 0.12 0.13 0.19 0.23 0.02203 1400 0.11 0.14 0.12 0.18 0.21 0.24 0.10 0.12 0.13 0.18 0.23 0.03203 1600 0.11 0.14 0.11 0.18 0.21 0.25 0.09 0.12 0.12 0.18 0.23 0.01203 1800 0.11 0.14 0.11 0.18 0.21 0.24 0.09 0.12 0.12 0.18 0.23 0.02203 2000 0.10 0.14 0.11 0.18 0.21 0.24 0.09 0.12 0.12 0.18 0.23 0.02203 2200 0.10 0.14 0.11 0.18 0.21 0.24 0.09 0.12 0.12 0.18 0.23204 0 0.11 0.14 0.12 0.18 0.21 0.24 0.09 0.12 0.12 0.18 0.23204 200 0.10 0.14 0.12 0.18 0.21 0.24 0.09 0.11 0.13 0.18 0.23 0.03204 400 0.11 0.14 0.12 0.19 0.21 0.24 0.09 0.12 0.13 0.18 0.23 0.02204 600 0.11 0.14 0.12 0.19 0.21 0.24 0.09 0.11 0.13 0.19 0.23 0.03204 800 0.11 0.14 0.12 0.19 0.21 0.24 0.09 0.12 0.13 0.19 0.23 0.02204 1000 0.10 0.14 0.12 0.18 0.21 0.24 0.09 0.12 0.13 0.18 0.23204 1200 0.10 0.14 0.11 0.18 0.21 0.25 0.09 0.11 0.12 0.18 0.23 0.02204 1400 0.10 0.14 0.10 0.18 0.21 0.25 0.08 0.11 0.12 0.18 0.23 0.02204 1600 0.10 0.13 0.10 0.18 0.21 0.24 0.08 0.10 0.11 0.18 0.23204 1800 0.10 0.13 0.10 0.18 0.21 0.24 0.08 0.10 0.12 0.18 0.23 0.02204 2000 0.10 0.13 0.10 0.18 0.21 0.24 0.08 0.10 0.12 0.18 0.23 0.01204 2200 0.10 0.13 0.11 0.18 0.21 0.24 0.08 0.10 0.12 0.18 0.23205 0 0.10 0.14 0.11 0.18 0.21 0.25 0.08 0.10 0.12 0.18 0.23205 200 0.10 0.14 0.11 0.18 0.21 0.24 0.08 0.10 0.13 0.18 0.23 0.00205 400 0.10 0.14 0.11 0.18 0.21 0.24 0.08 0.10 0.12 0.18 0.23 0.02205 600 0.10 0.13 0.12 0.18 0.21 0.24 0.08 0.10 0.12 0.18 0.23205 800 0.10 0.14 0.12 0.18 0.21 0.24 0.08 0.10 0.12 0.18 0.23 0.03205 1000 0.10 0.13 0.12 0.18 0.21 0.25 0.08 0.10 0.12 0.18 0.23 0.01205 1200 0.09 0.13 0.11 0.18 0.21 0.25 0.08 0.10 0.12 0.18 0.23 0.03205 1400 0.09 0.13 0.10 0.18 0.21 0.24 0.07 0.10 0.11 0.18 0.23 0.02205 1600 0.09 0.13 0.09 0.18 0.21 0.24 0.07 0.09 0.11 0.17 0.22 0.01205 1800 0.09 0.12 0.09 0.17 0.21 0.24 0.07 0.09 0.10 0.17 0.23 0.03205 2000 0.09 0.13 0.09 0.18 0.21 0.24 0.07 0.09 0.11 0.17 0.23205 2200 0.09 0.13 0.09 0.18 0.21 0.24 0.07 0.09 0.11 0.17 0.23206 0 0.09 0.13 0.09 0.18 0.21 0.24 0.07 0.09 0.11 0.18 0.23 0.02206 200 0.09 0.13 0.09 0.18 0.21 0.24 0.07 0.10 0.11 0.18 0.23 0.03

96

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

206 400 0.09 0.12 0.09 0.18 0.21 0.24 0.07 0.10 0.12 0.18 0.23 0.03206 600 0.09 0.12 0.09 0.18 0.21 0.24 0.07 0.09 0.12 0.18 0.23 0.00206 800 0.09 0.13 0.10 0.18 0.21 0.24 0.07 0.09 0.12 0.17 0.23206 1000 0.09 0.13 0.10 0.18 0.21 0.24 0.07 0.09 0.11 0.18 0.23 0.04206 1200 0.09 0.12 0.10 0.18 0.21 0.25 0.07 0.09 0.12 0.18 0.23 0.02206 1400 0.09 0.12 0.10 0.18 0.21 0.24 0.07 0.09 0.11 0.17 0.23 0.01206 1600 0.08 0.12 0.09 0.17 0.21 0.24 0.07 0.09 0.10 0.17 0.22 0.02206 1800 0.08 0.12 0.09 0.17 0.21 0.24 0.06 0.09 0.10 0.17 0.23 0.00206 2000 0.08 0.12 0.09 0.17 0.21 0.24 0.07 0.09 0.10 0.17 0.23 0.00206 2200 0.12 0.12 0.09 0.17 0.21 0.24 0.09 0.09 0.11 0.17 0.23 0.02207 0 0.12 0.12 0.09 0.18 0.21 0.24 0.10 0.08 0.10 0.17 0.23207 200 0.13 0.12 0.08 0.18 0.21 0.24 0.10 0.09 0.11 0.18 0.23 0.01207 400 0.12 0.12 0.09 0.18 0.21 0.24 0.10 0.09 0.11 0.18 0.23 0.02207 600 0.13 0.12 0.09 0.18 0.21 0.24 0.10 0.09 0.11 0.18 0.23 0.02207 800 0.12 0.12 0.09 0.18 0.21 0.24 0.10 0.09 0.11 0.18 0.23207 1000 0.12 0.12 0.09 0.18 0.21 0.24 0.10 0.09 0.11 0.18 0.23 0.03207 1200 0.12 0.12 0.09 0.18 0.21 0.24 0.09 0.09 0.10 0.17 0.23 0.03207 1400 0.15 0.12 0.09 0.17 0.21 0.24 0.11 0.09 0.10 0.17 0.23 0.02207 1600 0.14 0.12 0.08 0.17 0.21 0.24 0.11 0.09 0.10 0.17 0.23207 1800 0.14 0.12 0.09 0.17 0.21 0.24 0.11 0.09 0.10 0.17 0.22 0.04207 2000 0.14 0.12 0.08 0.17 0.21 0.24 0.11 0.09 0.10 0.17 0.22 0.02207 2200 0.14 0.12 0.09 0.18 0.21 0.24 0.11 0.09 0.10 0.17 0.23 0.01208 0 0.14 0.12 0.08 0.17 0.21 0.24 0.11 0.09 0.10 0.17 0.23208 200 0.14 0.12 0.09 0.18 0.21 0.24 0.11 0.08 0.10 0.17 0.23 0.03208 400 0.14 0.12 0.09 0.18 0.21 0.24 0.11 0.08 0.10 0.17 0.23 0.04208 600 0.14 0.12 0.09 0.18 0.21 0.24 0.11 0.09 0.11 0.17 0.23 0.01208 800 0.14 0.12 0.09 0.18 0.21 0.25 0.11 0.09 0.11 0.17 0.23 0.01208 1000 0.14 0.12 0.09 0.18 0.21 0.24 0.11 0.09 0.11 0.17 0.23 0.00208 1200 0.14 0.12 0.09 0.18 0.21 0.24 0.11 0.09 0.10 0.17 0.23 0.03208 1400 0.14 0.12 0.09 0.18 0.21 0.24 0.10 0.09 0.09 0.17 0.22 0.02208 1600 0.15 0.12 0.08 0.18 0.21 0.24 0.12 0.08 0.10 0.17 0.23208 1800 0.15 0.12 0.09 0.17 0.21 0.24 0.12 0.09 0.10 0.17 0.23 0.02208 2000 0.17 0.13 0.09 0.18 0.21 0.24 0.14 0.08 0.10 0.17 0.23 0.02208 2200 0.18 0.14 0.09 0.18 0.21 0.25 0.15 0.09 0.10 0.17 0.23 0.01209 0 0.18 0.14 0.09 0.18 0.21 0.25 0.15 0.10 0.10 0.17 0.23 0.03209 200 0.18 0.14 0.09 0.18 0.21 0.24 0.16 0.11 0.11 0.17 0.23 0.02

97

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

209 400 0.19 0.15 0.09 0.18 0.21 0.25 0.16 0.12 0.11 0.17 0.23 0.01209 600 0.19 0.15 0.10 0.18 0.21 0.25 0.16 0.13 0.11 0.17 0.23 0.03209 800 0.18 0.15 0.10 0.18 0.21 0.25 0.17 0.13 0.11 0.17 0.23 0.02209 1000 0.18 0.15 0.10 0.18 0.21 0.24 0.17 0.13 0.11 0.17 0.23 0.03209 1200 0.18 0.15 0.09 0.18 0.21 0.24 0.16 0.13 0.10 0.17 0.23 0.02209 1400 0.17 0.15 0.09 0.18 0.21 0.24 0.16 0.13 0.10 0.17 0.22209 1600 0.17 0.15 0.09 0.17 0.21 0.24 0.16 0.13 0.10 0.17 0.23 0.02209 1800 0.18 0.14 0.09 0.17 0.21 0.24 0.16 0.13 0.10 0.17 0.22 0.04209 2000 0.20 0.17 0.09 0.18 0.21 0.24 0.20 0.16 0.10 0.17 0.23209 2200 0.20 0.18 0.09 0.18 0.21 0.24 0.20 0.15 0.10 0.17 0.23 0.03210 0 0.20 0.18 0.09 0.18 0.21 0.25 0.20 0.16 0.10 0.17 0.23 0.03210 200 0.20 0.18 0.09 0.18 0.21 0.24 0.20 0.16 0.10 0.17 0.23 0.01210 400 0.20 0.18 0.09 0.18 0.21 0.25 0.20 0.16 0.10 0.17 0.23 0.01210 600 0.20 0.18 0.09 0.18 0.21 0.25 0.19 0.16 0.10 0.17 0.23210 800 0.20 0.18 0.09 0.18 0.21 0.25 0.19 0.17 0.10 0.17 0.23 0.01210 1000 0.19 0.18 0.09 0.18 0.21 0.24 0.19 0.17 0.10 0.17 0.23 0.03210 1200 0.19 0.18 0.09 0.18 0.21 0.25 0.19 0.16 0.10 0.17 0.22 0.02210 1400 0.19 0.18 0.09 0.18 0.21 0.24 0.18 0.16 0.10 0.17 0.23 0.03210 1600 0.19 0.17 0.09 0.17 0.21 0.24 0.18 0.16 0.10 0.17 0.22 0.01210 1800 0.22 0.21 0.09 0.18 0.21 0.24 0.26 0.26 0.13 0.17 0.23210 2000 0.22 0.21 0.09 0.18 0.21 0.24 0.25 0.24 0.15 0.17 0.23210 2200 0.22 0.21 0.09 0.18 0.21 0.25 0.24 0.24 0.17 0.17 0.23 0.02211 0 0.21 0.09 0.18 0.21 0.24 0.23 0.23 0.18 0.17 0.23 0.03211 200 0.21 0.20 0.10 0.18 0.21 0.25 0.23 0.22 0.18 0.17 0.23 0.04211 400 0.21 0.20 0.11 0.18 0.21 0.25 0.22 0.22 0.18 0.17 0.23 0.04211 600 0.20 0.20 0.11 0.18 0.21 0.25 0.22 0.21 0.18 0.18 0.23 0.03211 800 0.20 0.20 0.12 0.18 0.22 0.25 0.22 0.21 0.18 0.18 0.23211 1000 0.20 0.20 0.13 0.18 0.22 0.25 0.21 0.21 0.18 0.19 0.23 0.00211 1200 0.19 0.19 0.14 0.18 0.21 0.25 0.21 0.20 0.18 0.19 0.23 0.04211 1400 0.19 0.19 0.14 0.18 0.22 0.25 0.21 0.20 0.17 0.19 0.23 0.01211 1600 0.19 0.19 0.14 0.18 0.22 0.25 0.20 0.20 0.17 0.19 0.23 0.02211 1800 0.18 0.19 0.14 0.18 0.22 0.25 0.19 0.19 0.17 0.19 0.23 0.02211 2000 0.18 0.19 0.14 0.18 0.22 0.25 0.19 0.19 0.17 0.20 0.23 0.04211 2200 0.18 0.19 0.15 0.19 0.22 0.25 0.19 0.19 0.17 0.19 0.23 0.04212 0 0.18 0.18 0.15 0.19 0.22 0.19 0.19 0.17 0.20 0.23 0.01212 200 0.18 0.18 0.15 0.19 0.22 0.25 0.19 0.19 0.17 0.20 0.23

98

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

212 400 0.18 0.18 0.15 0.19 0.22 0.25 0.18 0.18 0.17 0.20 0.23 0.02212 600 0.18 0.18 0.15 0.19 0.22 0.25 0.18 0.17 0.20 0.23 0.03212 800 0.18 0.18 0.15 0.19 0.22 0.25 0.18 0.18 0.17 0.20 0.23 0.02212 1000 0.18 0.18 0.15 0.20 0.22 0.26 0.18 0.18 0.17 0.20 0.23 0.00212 1200 0.17 0.18 0.15 0.19 0.22 0.25 0.18 0.18 0.16 0.20 0.23 0.01212 1400 0.17 0.17 0.15 0.19 0.22 0.25 0.17 0.18 0.16 0.20 0.23212 1600 0.17 0.17 0.14 0.19 0.22 0.25 0.17 0.17 0.16 0.20 0.23 0.02212 1800 0.17 0.17 0.14 0.19 0.22 0.26 0.16 0.17 0.16 0.20 0.23 0.02212 2000 0.16 0.17 0.15 0.19 0.22 0.26 0.16 0.17 0.16 0.20 0.23 0.04212 2200 0.16 0.15 0.19 0.22 0.26 0.16 0.17 0.16 0.20 0.23213 0 0.16 0.17 0.15 0.19 0.22 0.26 0.16 0.17 0.16 0.20 0.23 0.03213 200 0.16 0.17 0.15 0.20 0.22 0.26 0.16 0.17 0.16 0.20 0.24 0.00213 400 0.22 0.18 0.15 0.20 0.22 0.26 0.23 0.20 0.16 0.20 0.24 0.02213 600 0.22 0.20 0.15 0.20 0.22 0.26 0.23 0.21 0.16 0.20 0.24 0.04213 800 0.25 0.21 0.15 0.20 0.22 0.26 0.24 0.23 0.17 0.20 0.24 0.02213 1000 0.23 0.22 0.16 0.20 0.22 0.26 0.23 0.23 0.18 0.20 0.24213 1200 0.22 0.22 0.16 0.20 0.22 0.26 0.22 0.22 0.18 0.21 0.24 0.03213 1400 0.21 0.21 0.17 0.20 0.22 0.26 0.22 0.22 0.18 0.21 0.24 0.04213 1600 0.21 0.21 0.17 0.20 0.22 0.26 0.22 0.21 0.18 0.21 0.24 0.03213 1800 0.21 0.20 0.17 0.20 0.22 0.26 0.21 0.21 0.18 0.21 0.24 0.00213 2000 0.21 0.20 0.17 0.20 0.22 0.27 0.21 0.20 0.18 0.21 0.24 0.03213 2200 0.20 0.20 0.17 0.20 0.23 0.27 0.21 0.20 0.18 0.22 0.24 0.03214 0 0.20 0.20 0.17 0.20 0.23 0.27 0.21 0.20 0.18 0.21 0.24 0.03214 200 0.20 0.20 0.17 0.21 0.23 0.27 0.20 0.20 0.18 0.22 0.24214 400 0.20 0.20 0.17 0.21 0.23 0.27 0.20 0.20 0.17 0.22 0.24214 600 0.20 0.19 0.17 0.21 0.23 0.27 0.20 0.19 0.18 0.22 0.24 0.03214 800 0.20 0.19 0.17 0.21 0.23 0.27 0.20 0.19 0.17 0.22 0.24 0.04214 1000 0.20 0.19 0.17 0.21 0.23 0.27 0.20 0.19 0.17 0.22 0.24 0.02214 1200 0.19 0.19 0.17 0.21 0.23 0.27 0.19 0.19 0.17 0.22 0.24 0.03214 1400 0.19 0.19 0.16 0.21 0.23 0.27 0.19 0.19 0.17 0.21 0.25 0.03214 1600 0.18 0.19 0.16 0.21 0.23 0.27 0.18 0.18 0.16 0.22 0.24 0.02214 1800 0.18 0.18 0.16 0.23 0.27 0.18 0.18 0.16 0.22 0.24224 1600 0.13 0.11 0.10 0.18 0.21 0.25 0.13 0.09 0.09 0.17 0.23 0.02224 1800 0.13 0.11 0.09 0.18 0.21 0.25 0.13 0.09 0.10 0.17 0.24224 2000 0.12 0.11 0.09 0.18 0.21 0.26 0.13 0.09 0.09 0.17 0.24224 2200 0.12 0.11 0.10 0.18 0.21 0.26 0.13 0.09 0.09 0.17 0.24

99

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

225 0 0.12 0.11 0.10 0.18 0.21 0.26 0.13 0.09 0.10 0.17 0.24 0.02225 200 0.12 0.11 0.09 0.18 0.21 0.26 0.13 0.08 0.10 0.17 0.24 0.01225 400 0.12 0.11 0.10 0.18 0.21 0.26 0.13 0.09 0.09 0.17 0.24 0.03225 600 0.12 0.11 0.10 0.18 0.21 0.26 0.13 0.09 0.09 0.17 0.24 0.03225 800 0.12 0.11 0.10 0.18 0.21 0.26 0.13 0.09 0.09 0.17 0.24 0.03225 1000 0.12 0.11 0.10 0.18 0.21 0.26 0.13 0.09 0.10 0.18 0.24 0.02225 1200 0.12 0.11 0.10 0.18 0.21 0.26 0.12 0.08 0.10 0.17 0.24 0.02225 1400 0.12 0.11 0.09 0.18 0.21 0.26 0.12 0.08 0.09 0.17 0.24 0.03225 1600 0.12 0.11 0.09 0.18 0.21 0.25 0.12 0.08 0.09 0.17 0.23 0.03225 1800 0.12 0.11 0.09 0.18 0.21 0.25 0.12 0.09 0.09 0.17 0.24 0.03225 2000 0.12 0.11 0.09 0.18 0.21 0.25 0.12 0.08 0.09 0.17 0.24 0.02225 2200 0.12 0.08 0.09 0.17 0.24 0.02226 0 0.12 0.11 0.09 0.18 0.21 0.25 0.12 0.09 0.09 0.17 0.24226 200 0.12 0.11 0.10 0.18 0.21 0.25 0.12 0.09 0.09 0.17 0.24 0.01226 400 0.12 0.11 0.10 0.18 0.21 0.25 0.12 0.09 0.09 0.17 0.24 0.03226 600 0.12 0.11 0.09 0.18 0.21 0.12 0.09 0.09 0.17 0.24 0.03226 800 0.11 0.11 0.10 0.18 0.21 0.26 0.12 0.09 0.09 0.17 0.24 0.02226 1000 0.11 0.11 0.10 0.18 0.21 0.25 0.12 0.09 0.10 0.17 0.24 0.02226 1200 0.11 0.11 0.09 0.18 0.21 0.25 0.12 0.08 0.09 0.17 0.24 0.01226 1400 0.11 0.11 0.09 0.18 0.21 0.25 0.11 0.08 0.09 0.17 0.24226 1600 0.11 0.10 0.09 0.18 0.21 0.25 0.08 0.09 0.17 0.24 0.02226 1800 0.11 0.11 0.09 0.18 0.21 0.25226 2000 0.11 0.11 0.09 0.18 0.21 0.25 0.11 0.08 0.09 0.17 0.24 0.04226 2200 0.11 0.11 0.09 0.18 0.21 0.25 0.11 0.08 0.09 0.17 0.24 0.01227 0 0.11 0.11 0.09 0.18 0.21 0.25 0.11 0.08 0.09 0.17 0.24 0.04227 200 0.11 0.11 0.10 0.18 0.21 0.25 0.11 0.09 0.09 0.17 0.24 0.04227 400 0.11 0.11 0.10 0.18 0.21 0.26 0.11 0.08 0.09 0.17 0.24 0.01227 600 0.11 0.11 0.10 0.18 0.21 0.25 0.11 0.08 0.09 0.17 0.24 0.02227 800 0.11 0.11 0.10 0.18 0.21 0.11 0.08 0.09 0.17 0.24 0.04227 1000 0.11 0.11 0.09 0.18 0.21 0.25 0.11 0.09 0.17 0.24 0.04227 1200 0.10 0.11 0.09 0.21 0.25 0.11 0.08 0.09 0.17 0.24 0.03227 1400 0.10 0.10 0.09 0.18 0.21 0.25 0.11 0.08 0.09 0.17 0.24 0.04227 1600 0.10 0.10 0.09 0.18 0.21 0.25 0.10 0.08 0.09 0.16 0.23 0.03227 1800 0.16 0.10 0.09 0.18 0.21 0.25 0.16 0.08 0.09 0.16 0.23 0.04227 2000 0.15 0.11 0.09 0.18 0.21 0.25 0.16 0.08 0.09 0.17 0.24 0.03227 2200 0.15 0.10 0.09 0.18 0.21 0.25 0.16 0.08 0.09 0.17 0.24 0.02

100

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

228 0 0.15 0.11 0.09 0.18 0.21 0.25 0.16 0.08 0.09 0.17 0.24 0.02228 200 0.15 0.11 0.09 0.18 0.21 0.25 0.16 0.08 0.09 0.17 0.24 0.03228 400 0.15 0.11 0.09 0.18 0.21 0.25 0.17 0.08 0.09 0.17 0.23 0.03228 600 0.15 0.10 0.09 0.18 0.21 0.25 0.17 0.09 0.17 0.23228 800 0.15 0.11 0.09 0.18 0.21 0.25 0.17 0.09 0.09 0.17 0.23 0.03228 1000 0.15 0.11 0.09 0.18 0.21 0.25 0.17 0.09 0.09 0.17 0.23 0.03228 1200 0.15 0.11 0.09 0.18 0.21 0.25 0.17 0.09 0.09 0.17 0.24 0.01228 1400 0.15 0.10 0.09 0.18 0.21 0.25 0.17 0.09 0.09 0.00 0.17 0.18228 1600 0.19 0.11 0.09 0.18 0.17 0.09 0.09 0.17 0.24 0.00228 1800 0.19 0.11 0.09 0.18 0.17 0.09 0.09 0.17 0.24 0.00228 2000 0.20 0.13 0.09 0.18 0.21 0.25 0.22 0.15 0.09 0.17 0.23 0.04228 2200 0.20 0.13 0.09 0.18 0.21 0.25 0.21 0.09 0.17229 0 0.20 0.13 0.09 0.18 0.21 0.25 0.21 0.15 0.09 0.17 0.24 0.03229 200 0.20 0.13 0.09 0.18 0.21 0.25 0.21 0.15 0.09 0.17 0.23 0.01229 400 0.20 0.13 0.09 0.18 0.21 0.25 0.21 0.15 0.09 0.17 0.23 0.03229 600 0.20 0.14 0.09 0.18 0.21 0.25 0.21 0.15 0.09 0.17 0.23 0.03229 800 0.19 0.14 0.09 0.18 0.21 0.25 0.21 0.15 0.09 0.17 0.23 0.04229 1000 0.19 0.14 0.10 0.18 0.21 0.25 0.20 0.15 0.09 0.17 0.23 0.03229 1200 0.19 0.14 0.10 0.18 0.21 0.25 0.20 0.15 0.09 0.17 0.03229 1400 0.19 0.14 0.09 0.18 0.21 0.25 0.20 0.15 0.09 0.17 0.23 0.03229 1600 0.19 0.14 0.09 0.18 0.21 0.25 0.20 0.15 0.09 0.17 0.23 0.02229 1800 0.19 0.14 0.09 0.18 0.21 0.25 0.20 0.15 0.09 0.17 0.24 0.03229 2000 0.19 0.14 0.09 0.18 0.21 0.25 0.19 0.15 0.09 0.17 0.23 0.04229 2200 0.19 0.14 0.09 0.18 0.17230 0 0.18 0.14 0.09 0.18 0.21 0.25 0.19 0.15 0.09 0.17 0.23 0.04230 200 0.19 0.14 0.09 0.18 0.21 0.25 0.19 0.15 0.09 0.17 0.23 0.00230 400 0.18 0.15 0.10 0.18 0.21 0.19 0.15 0.09 0.17 0.23230 600 0.18 0.15 0.10 0.18 0.21 0.25 0.19 0.15 0.09 0.17 0.23 0.04230 800 0.18 0.14 0.09 0.21 0.25 0.19 0.16 0.09 0.17 0.23230 1000 0.18 0.14 0.09 0.21 0.25 0.19 0.16 0.09 0.17 0.23230 1200 0.18 0.14 0.10 0.18 0.25 0.19 0.15 0.09 0.17 0.24 0.03230 1400 0.18 0.14 0.09 0.18 0.21 0.25 0.18 0.15 0.09 0.17 0.23230 1600 0.18 0.14 0.09 0.17 0.21 0.25 0.18 0.15 0.09 0.16 0.23 0.03230 1800 0.17 0.14 0.09 0.18 0.21 0.25 0.18 0.15 0.09 0.16 0.23 0.02230 2000 0.17 0.14 0.09 0.18 0.21 0.25 0.18 0.15 0.09 0.16 0.23 0.02253 1200 0.15 0.16 0.15 0.23 0.27 0.34 0.15 0.15 0.07 0.22 0.28 0.03

101

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

253 1600 0.15 0.16 0.15 0.23 0.27 0.34 0.15 0.15 0.06 0.23 0.28 0.03253 1800 0.15 0.16 0.15 0.23 0.27 0.33 0.15 0.14 0.15 0.23 0.28 0.05253 2000 0.15 0.16 0.15 0.23 0.27 0.33 0.15 0.15 0.08 0.22 0.28 0.02253 2200 0.15 0.16 0.15 0.23 0.27 0.33 0.14 0.14 0.27 0.22 0.28 0.04254 0 0.15 0.16 0.15 0.23 0.27 0.33 0.15 0.14 0.07 0.22 0.28 0.03254 200 0.15 0.16 0.15 0.23 0.27 0.33 0.15 0.14 0.04 0.22 0.28 0.02254 400 0.15 0.16 0.15 0.23 0.27 0.33 0.14 0.14 0.09 0.22 0.28 0.02254 600 0.15 0.16 0.15 0.23 0.27 0.33 0.14 0.14 0.08 0.22 0.28254 800 0.15 0.16 0.15 0.23 0.27 0.33 0.14 0.14 0.07 0.22 0.28 0.03254 1000 0.15 0.16 0.15 0.23 0.27 0.33 0.14 0.14 0.12 0.22 0.28254 1200 0.15 0.16 0.15 0.23 0.26 0.32 0.14 0.14 0.46 0.22 0.28 0.02254 1400 0.15 0.15 0.15 0.23 0.26 0.32 0.14 0.14 0.07 0.22 0.28 0.04254 1600 0.15 0.15 0.15 0.23 0.26 0.32 0.14 0.14 0.08 0.22 0.28 0.04254 1800 0.15 0.15 0.15 0.23 0.26 0.32 0.14 0.14 0.05 0.22 0.28 0.03254 2000 0.15 0.15 0.15 0.23 0.26 0.32 0.14 0.14 0.07 0.22 0.28 0.02254 2200 0.15 0.15 0.15 0.23 0.26 0.32 0.14 0.14 0.05 0.22 0.28 0.03255 0 0.15 0.16 0.15 0.23 0.26 0.32 0.14 0.14 0.08 0.22 0.28255 200 0.15 0.15 0.15 0.23 0.26 0.32 0.14 0.13 0.36 0.21 0.27255 400 0.15 0.16 0.15 0.23 0.26 0.32 0.14 0.13 0.13 0.21 0.28 0.02255 600 0.15 0.16 0.15 0.23 0.26 0.32 0.14 0.14 0.45 0.22 0.28 0.01255 800 0.15 0.15 0.15 0.22 0.26 0.32 0.14 0.14 0.09 0.22 0.27255 1000 0.15 0.15 0.15 0.23 0.26 0.32 0.14 0.14 0.08 0.21 0.27 0.03255 1200 0.14 0.15 0.15 0.23 0.26 0.32 0.14 0.14 0.07 0.22 0.27 0.02255 1400 0.14 0.15 0.14 0.23 0.26 0.32 0.14 0.14 0.08 0.22 0.27 0.10255 1600 0.14 0.15 0.15 0.23 0.26 0.32 0.14 0.14 0.08 0.22 0.27 0.01255 1800 0.14 0.15 0.14 0.23 0.26 0.32 0.14 0.13 0.06 0.22 0.27 0.03255 2000 0.14 0.15 0.15 0.23 0.26 0.31 0.14 0.13 0.08 0.21 0.27 0.07255 2200 0.14 0.15 0.15 0.22 0.26 0.31 0.13 0.13 0.07 0.21 0.27256 0 0.14 0.15 0.15 0.22 0.26 0.32 0.13 0.13 0.06 0.21 0.27256 200 0.14 0.15 0.15 0.22 0.26 0.31 0.13 0.13 0.07 0.21 0.27 0.03256 400 0.14 0.15 0.15 0.22 0.26 0.31 0.13 0.13 0.12 0.21 0.27256 600 0.14 0.15 0.15 0.23 0.26 0.31 0.13 0.13 0.09 0.21 0.27 0.03256 800 0.14 0.15 0.15 0.22 0.26 0.31 0.13 0.13 0.05 0.21 0.27 0.05256 1000 0.14 0.15 0.15 0.22 0.26 0.31 0.13 0.13 0.07 0.21 0.27 0.00256 1200 0.14 0.15 0.15 0.23 0.25 0.31 0.13 0.13 0.07 0.21 0.27 0.04256 1400 0.14 0.15 0.14 0.22 0.25 0.31 0.13 0.13 0.07 0.21 0.27

102

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

256 1600 0.14 0.14 0.14 0.22 0.25 0.31 0.13 0.13 0.08 0.21 0.27256 1800 0.13 0.14 0.14 0.22 0.25 0.31 0.13 0.13 0.09 0.21 0.27 0.01256 2000 0.14 0.14 0.14 0.22 0.25 0.31 0.13 0.13 0.07 0.21 0.27256 2200 0.13 0.14 0.14 0.22 0.25 0.31 0.13 0.12 0.05 0.21 0.27 0.03257 0 0.13 0.14 0.14 0.22 0.25 0.31 0.12 0.13 0.05 0.21 0.27257 200 0.13 0.14 0.14 0.22 0.25 0.31 0.12 0.12 0.08 0.20 0.27 0.03257 400 0.13 0.14 0.14 0.22 0.25 0.31 0.12 0.13 0.08 0.20 0.26 0.01257 600 0.13 0.14 0.14 0.22 0.25 0.31 0.12 0.12 0.08 0.20 0.26257 800 0.13 0.14 0.14 0.22 0.25 0.31 0.12 0.13 0.12 0.20 0.26 0.02257 1000 0.13 0.14 0.14 0.22 0.25 0.31 0.12 0.13 0.07 0.21 0.26257 1200 0.13 0.14 0.14 0.22 0.25 0.31 0.12 0.13 0.10 0.21 0.26257 1400 0.13 0.14 0.13 0.22 0.24 0.30 0.12 0.13 0.10 0.20 0.26257 1600 0.13 0.13 0.13 0.22 0.24 0.30 0.12 0.13 0.05 0.20 0.26 0.02257 1800 0.13 0.13 0.13 0.21 0.24 0.30 0.12 0.12 0.05 0.20 0.26257 2200 0.13 0.13 0.13 0.21 0.24 0.30 0.12 0.12 0.07 0.20 0.26 0.02258 400 0.13 0.13 0.13 0.21 0.24 0.30 0.12 0.12 0.08 0.20 0.26 0.01258 600 0.13 0.13 0.13 0.21 0.24 0.30 0.12 0.12 0.09 0.20 0.26258 800 0.13 0.13 0.14 0.21 0.24 0.30 0.12 0.12 0.08 0.20 0.26 0.00258 1000 0.13 0.13 0.13 0.21 0.24 0.30 0.12 0.12 0.12 0.20 0.26 0.01258 1200 0.13 0.13 0.13 0.21 0.24 0.29 0.12 0.12 0.07 0.20 0.26258 1400 0.12 0.13 0.13 0.21 0.24 0.29 0.12 0.12 0.08 0.20 0.26 0.02258 1600 0.12 0.13 0.12 0.21 0.24 0.29 0.12 0.12 0.07 0.20 0.26 0.01258 1800 0.12 0.13 0.12 0.21 0.24 0.29 0.11 0.12 0.08 0.20 0.26 0.02258 2000 0.12 0.13 0.12 0.21 0.24 0.29 0.11 0.12 0.06 0.20 0.26 0.01258 2200 0.12 0.13 0.13 0.21 0.24 0.29 0.11 0.12 0.07 0.19 0.26259 0 0.12 0.13 0.13 0.21 0.24 0.29 0.11 0.11 0.42 0.20 0.26 0.04259 200 0.12 0.13 0.13 0.21 0.24 0.29 0.11 0.12 0.07 0.19 0.26 0.01259 400 0.12 0.13 0.13 0.21 0.24 0.29 0.11 0.11 0.06 0.19 0.25 0.01259 800 0.12 0.13 0.13 0.21 0.24 0.29 0.11 0.12 0.26 0.20 0.25 0.03259 1000 0.12 0.13 0.13 0.21 0.24 0.29 0.11 0.11 0.06 0.19 0.26 0.01259 1200 0.12 0.13 0.13 0.21 0.24 0.29 0.11 0.12 0.11 0.20 0.26 0.01259 1400 0.12 0.13 0.12 0.21 0.24 0.29 0.11 0.12 0.05 0.19 0.25 0.03259 2200 0.12 0.12 0.12 0.21 0.24 0.29 0.10 0.11 0.10 0.19 0.25 0.00260 0 0.11 0.12 0.12 0.21 0.24 0.29 0.10 0.11 0.05 0.19 0.25260 200 0.12 0.12 0.12 0.21 0.24 0.29 0.10 0.11 0.07 0.19 0.25 0.02260 400 0.12 0.12 0.12 0.21 0.24 0.29 0.10 0.11 0.12 0.19 0.25

103

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

260 800 0.11 0.12 0.13 0.21 0.24 0.29 0.10 0.11 0.08 0.19 0.26260 1000 0.12 0.12 0.13 0.21 0.24 0.29 0.10 0.11 0.06 0.19 0.25 0.02260 1200 0.11 0.12 0.12 0.21 0.24 0.28 0.10 0.11 0.11 0.19 0.25260 1400 0.13 0.12 0.12 0.21 0.24 0.29 0.11 0.11 0.09 0.19 0.25 0.02260 1600 0.13 0.12 0.13 0.21 0.24 0.29 0.11 0.11 0.05 0.19 0.25260 1800 0.13 0.12 0.12 0.21 0.24 0.28 0.11 0.11 0.05 0.19 0.25260 2000 0.13 0.12 0.13 0.21 0.24 0.29 0.11 0.11 0.04 0.19 0.25 0.04260 2200 0.12 0.12 0.12 0.21 0.24 0.29 0.11 0.11 0.09 0.19 0.25 0.00261 0 0.12 0.12 0.13 0.21 0.24 0.29 0.11 0.11 0.07 0.19 0.25261 200 0.12 0.12 0.13 0.21 0.24 0.29 0.11 0.11 0.06 0.19 0.25 0.02261 400 0.12 0.12 0.13 0.21 0.24 0.29 0.11 0.10 0.06 0.19 0.25 0.03261 600 0.12 0.12 0.13 0.21 0.24 0.28 0.11 0.11 0.07 0.19 0.25 0.02261 800 0.12 0.12 0.12 0.21 0.24 0.28 0.11 0.11 0.30 0.19 0.25261 1000 0.12 0.12 0.13 0.21 0.24 0.28 0.11 0.11 0.07 0.19 0.25 0.02261 1200 0.12 0.12 0.13 0.21 0.24 0.29 0.11 0.11 0.08 0.19 0.25261 1400 0.12 0.12 0.13 0.21 0.23 0.28 0.11 0.11 0.06 0.19 0.25261 1600 0.12 0.12 0.13 0.21 0.24 0.28 0.11 0.11 0.10 0.19 0.25 0.02261 1800 0.12 0.12 0.13 0.21 0.23 0.28 0.11 0.11 0.05 0.19 0.25 0.03261 2000 0.12 0.12 0.12 0.21 0.23 0.28 0.11 0.11 0.45 0.19 0.25 0.02261 2200 0.12 0.12 0.13 0.21 0.24 0.28 0.11 0.11 0.42 0.19 0.25 0.04262 0 0.12 0.12 0.12 0.21 0.23 0.28 0.11 0.10 0.08 0.19 0.25 0.02262 200 0.12 0.12 0.13 0.21 0.23 0.28 0.11 0.11 0.06 0.19 0.25 0.04262 400 0.12 0.12 0.13 0.21 0.23 0.28 0.11 0.11 0.39 0.19 0.25262 600 0.12 0.12 0.13 0.21 0.23 0.28 0.11 0.10 0.07 0.19 0.25262 800 0.12 0.12 0.13 0.21 0.23 0.28 0.11 0.11 0.15 0.19 0.25 0.02262 1000 0.12 0.12 0.13 0.21 0.24 0.28 0.11 0.10 0.10 0.19 0.25 0.04262 1200 0.12 0.12 0.13 0.21 0.23 0.28 0.11 0.10 0.08 0.19 0.25 0.03262 1400 0.12 0.12 0.12 0.21 0.23 0.28 0.11 0.10 0.23 0.19 0.25 0.02262 1600 0.12 0.12 0.12 0.21 0.23 0.28 0.11 0.11 0.06 0.19 0.25 0.04262 1800 0.12 0.12 0.12 0.21 0.23 0.28 0.11 0.11 0.09 0.19 0.25262 2000 0.12 0.12 0.12 0.21 0.23 0.28 0.11 0.11 0.04 0.19 0.25 0.03262 2200 0.12 0.12 0.12 0.21 0.23 0.28 0.11 0.10 0.08 0.19 0.25 0.03263 0 0.12 0.11 0.12 0.20 0.23 0.28 0.11 0.10 0.07 0.18 0.25 0.03263 200 0.12 0.12 0.12 0.20 0.23 0.28 0.11 0.10 0.08 0.18 0.25263 400 0.11 0.12 0.12 0.20 0.23 0.28 0.11 0.10 0.11 0.18 0.25 0.03263 600 0.12 0.12 0.13 0.20 0.23 0.28 0.11 0.10 0.08 0.19 0.25 0.01

104

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

263 800 0.11 0.12 0.13 0.20 0.23 0.28 0.11 0.10 0.06 0.18 0.25 0.03263 1000 0.12 0.12 0.13 0.21 0.23 0.28 0.11 0.10 0.08 0.19 0.25263 1200 0.12 0.11 0.12 0.21 0.23 0.28 0.11 0.10 0.10 0.19 0.25 0.04263 1400 0.12 0.11 0.12 0.20 0.23 0.28 0.11 0.10 0.06 0.18 0.25 0.02263 1600 0.11 0.12 0.12 0.20 0.23 0.28 0.11 0.10 0.17 0.18 0.25263 1800 0.11 0.12 0.12 0.21 0.23 0.28 0.11 0.10 0.05 0.18 0.25 0.01263 2000 0.12 0.12 0.12 0.20 0.23 0.28 0.11 0.10 0.07 0.18 0.25 0.02263 2200 0.11 0.11 0.12 0.20 0.23 0.28 0.11 0.10 0.06 0.18 0.25 0.05264 0 0.12 0.12 0.12 0.20 0.23 0.28 0.11 0.10 0.07 0.18 0.24 0.04264 200 0.11 0.12 0.12 0.20 0.23 0.28 0.10 0.10 0.06 0.18 0.24264 400 0.11 0.12 0.12 0.20 0.23 0.28 0.11 0.10 0.18 0.18 0.24 0.11264 600 0.12 0.12 0.12 0.20 0.23 0.28 0.10 0.10 0.46 0.18 0.25 0.05264 800 0.11 0.12 0.13 0.20 0.23 0.28 0.10 0.10 0.05 0.18 0.25 0.03264 1000 0.12 0.11 0.12 0.20 0.23 0.28 0.11 0.10 0.12 0.18 0.25264 1200 0.11 0.11 0.12 0.20 0.23 0.28 0.11 0.10 0.10 0.18 0.24264 1400 0.12 0.11 0.12 0.20 0.23 0.27 0.11 0.10 0.06 0.18 0.24 0.05264 1600 0.13 0.11 0.12 0.20 0.23 0.27 0.12 0.10 0.08 0.18 0.24 0.02264 1800 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.06 0.18 0.24 0.03264 2000 0.15 0.11 0.12 0.20 0.23 0.28 0.15 0.09 0.06 0.18 0.24 0.03264 2200 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.06 0.18 0.24265 0 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.04 0.18 0.24 0.00265 200 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.06 0.18 0.25 0.03265 400 0.15 0.12 0.12 0.20 0.23 0.27 0.15 0.10 0.29 0.18 0.24265 600 0.15 0.12 0.12 0.20 0.23 0.27 0.15 0.10 0.10 0.18 0.24 0.03265 800 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.08 0.18 0.24265 1000 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.03 0.19 0.24 0.04265 1200 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.39 0.18 0.24265 1400 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.07 0.18 0.24 0.03265 1600 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.05 0.18 0.24 0.02265 1800 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.08 0.18 0.24 0.02265 2000 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.27 0.18 0.24265 2200 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.04 0.18 0.24266 0 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.05 0.18 0.24 0.03266 200 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.05 0.18 0.24 0.03266 400 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.49 0.18 0.24266 600 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.13 0.18 0.24 0.03

105

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

266 800 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.06 0.18 0.24266 1000 0.15 0.11 0.12 0.20 0.23 0.27 0.16 0.10 0.06 0.18 0.24266 1200 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.07 0.18 0.24 0.03266 1400 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.03 0.18 0.24 0.01266 1600 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.08 0.18 0.24 0.05266 1800 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.06 0.18 0.24 0.02266 2200 0.15 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.10 0.18 0.24 0.04267 400 0.14 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.14 0.18 0.24267 1000 0.14 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.08 0.18 0.24 0.02267 1200 0.14 0.11 0.12 0.20 0.23 0.27 0.15 0.10 0.10 0.18 0.24 0.04268 2000 0.14 0.11 0.12 0.20 0.22 0.27 0.14 0.10 0.08 0.18 0.24 0.02270 2000 0.13 0.10 0.12 0.20 0.22 0.26 0.13 0.10 0.27 0.17 0.23 0.03270 2200 0.13 0.10 0.12 0.20 0.22 0.26 0.13 0.10 0.06 0.17 0.23 0.00271 0 0.13 0.10 0.12 0.20 0.22 0.26 0.13 0.10 0.10 0.17 0.24 0.02271 200 0.13 0.10 0.12 0.20 0.22 0.26 0.13 0.10 0.06 0.17 0.24 0.04271 400 0.13 0.10 0.12 0.20 0.22 0.27 0.13 0.10 0.09 0.17 0.24 0.04271 600 0.13 0.11 0.12 0.20 0.22 0.26 0.13 0.10 0.44 0.17 0.24 0.04271 800 0.13 0.10 0.12 0.20 0.22 0.27 0.13 0.10 0.08 0.17 0.24 0.02271 1000 0.13 0.10 0.12 0.19 0.22 0.27 0.13 0.09 0.06 0.17 0.23271 1200 0.13 0.10 0.12 0.20 0.22 0.26 0.13 0.10 0.06 0.18 0.23 0.03271 1400 0.13 0.10 0.12 0.20 0.22 0.26 0.13 0.10 0.09 0.17 0.23 0.03271 1600 0.13 0.10 0.12 0.20 0.22 0.26 0.13 0.10 0.10 0.17 0.23 0.03271 1800 0.13 0.10 0.12 0.20 0.22 0.26 0.13 0.10 0.05 0.17 0.23271 2000 0.15 0.10 0.12 0.20 0.22 0.26 0.13 0.10 0.08 0.17 0.23271 2200 0.20 0.10 0.12 0.19 0.22 0.26 0.18 0.11 0.06 0.17 0.23272 0 0.22 0.10 0.12 0.20 0.22 0.27 0.18 0.14 0.34 0.17 0.23 0.04272 200 0.23 0.12 0.12 0.20 0.22 0.26 0.22 0.15 0.03 0.17 0.23272 400 0.23 0.15 0.12 0.20 0.22 0.26 0.23 0.16 0.05 0.17 0.23272 600 0.23 0.16 0.12 0.20 0.22 0.26 0.23 0.17 0.07 0.17 0.23272 800 0.23 0.16 0.12 0.20 0.22 0.26 0.23 0.17 0.07 0.17 0.23 0.02272 1000 0.23 0.17 0.12 0.20 0.22 0.26 0.23 0.17 0.31 0.17 0.23272 1200 0.23 0.17 0.12 0.20 0.22 0.26 0.23 0.17 0.05 0.17 0.23 0.01272 1400 0.25 0.30 0.35 0.27 0.35 0.26 0.28 0.30 0.33 0.23 0.01272 1600 0.25 0.30 0.34 0.34 0.35 0.37 0.28 0.30 0.33 0.35 0.03

106

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

272 1800 0.25 0.30 0.29 0.33 0.35 0.37 0.27 0.30 0.33 0.35 0.04272 2000 0.25 0.30 0.26 0.31 0.35 0.37 0.27 0.29 0.31 0.35 0.03272 2200 0.25 0.26 0.25 0.31 0.35 0.37 0.26 0.26 0.31 0.35 0.00273 0 0.25 0.25 0.24 0.30 0.35 0.36 0.25 0.24 0.30 0.35 0.04273 200 0.25 0.29 0.24 0.29 0.34 0.37 0.26 0.27 0.30 0.35 0.03273 400 0.26 0.30 0.34 0.32 0.35 0.37 0.27 0.29 0.33 0.35 0.03273 600 0.25 0.30 0.29 0.34 0.35 0.37 0.27 0.28 0.34 0.35 0.02273 800 0.25 0.27 0.28 0.34 0.35 0.37 0.26 0.26 0.34 0.35 0.02273 1000 0.25 0.26 0.28 0.34 0.35 0.37 0.26 0.25 0.34 0.35 0.04273 1200 0.25 0.26 0.29 0.34 0.35 0.38 0.25 0.25 0.34 0.36 0.04273 1400 0.25 0.27 0.30 0.34 0.35 0.38 0.26 0.26 0.34 0.36 0.05273 1600 0.25 0.31 0.34 0.34 0.35 0.38 0.26 0.29 0.34 0.36 0.04273 1800 0.25 0.29 0.35 0.35 0.35 0.38 0.26 0.28 0.34 0.36 0.04273 2000 0.25 0.28 0.35 0.35 0.35 0.38 0.26 0.27 0.34 0.36 0.04273 2200 0.25 0.28 0.35 0.35 0.35 0.38 0.26 0.27 0.34 0.36 0.03274 0 0.25 0.28 0.35 0.35 0.35 0.38 0.26 0.27 0.34 0.36 0.02274 200 0.25 0.28 0.35 0.35 0.35 0.38 0.26 0.27 0.34 0.36 0.04274 400 0.25 0.28 0.35 0.35 0.35 0.38 0.26 0.26 0.34 0.36 0.04274 600 0.25 0.27 0.35 0.35 0.35 0.38 0.26 0.26 0.34 0.36 0.04274 800 0.25 0.27 0.35 0.35 0.35 0.38 0.26 0.26 0.34 0.36 0.04274 1000 0.25 0.27 0.35 0.35 0.35 0.38 0.26 0.26 0.34 0.36 0.05274 1200 0.24 0.27 0.35 0.35 0.36 0.38 0.26 0.26 0.34 0.36 0.03274 1400 0.24 0.27 0.35 0.35 0.36 0.38 0.26 0.26 0.34 0.36 0.04274 1600 0.24 0.27 0.35 0.35 0.36 0.38 0.25 0.26 0.34 0.36 0.03274 1800 0.24 0.27 0.35 0.35 0.36 0.38 0.25 0.25 0.34 0.36 0.04274 2000 0.24 0.27 0.35 0.35 0.36 0.38 0.25 0.25 0.34 0.36 0.02274 2200 0.24 0.26 0.35 0.35 0.36 0.38 0.25 0.25 0.34 0.36 0.02275 0 0.24 0.26 0.35 0.35 0.35 0.38 0.25 0.24 0.34 0.36 0.04275 200 0.24 0.26 0.35 0.35 0.36 0.38 0.25 0.25 0.34 0.36 0.05275 400 0.24 0.26 0.34 0.35 0.36 0.38 0.25 0.25 0.34 0.36 0.01275 600 0.24 0.26 0.34 0.35 0.36 0.38 0.25 0.24 0.34 0.36 0.06275 800 0.24 0.26 0.33 0.35 0.36 0.38 0.25 0.24 0.34 0.36275 1000 0.24 0.26 0.33 0.35 0.36 0.38 0.25 0.24 0.34 0.36 0.04

107

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-

6060-90

90-120

120-150

150-180

275 1200 0.23 0.26 0.33 0.35 0.36 0.38 0.24 0.24 0.34 0.36 0.05

275 1400 0.23 0.26 0.33 0.35 0.36 0.38 0.24 0.24 0.34 0.36 0.05Note: Blank cells indicate incorrect readings.

108

Table B-2. Time domain reflectometry data for non-irrigated zone.Non-irrigated Zone, TDR1 Non-irrigated Zone, TDR2

Layer (cm) Layer (cm)

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

174 1600 0.04 0.10 0.10 0.15 0.19 0.16 0.05 0.08 0.12 0.17 0.21 0.22174 2000 0.04 0.09 0.10 0.15 0.19 0.16 0.04 0.07 0.13 0.16 0.20 0.22175 0 0.04 0.09 0.09 0.15 0.19 0.16 0.05 0.08 0.12 0.16 0.21 0.22175 400 0.04 0.10 0.10 0.15 0.19 0.16 0.05 0.08 0.12 0.17 0.21 0.22175 800 0.04 0.09 0.10 0.15 0.19 0.16 0.05 0.07 0.12 0.16 0.21 0.22175 1200 0.04 0.09 0.10 0.15 0.20 0.16 0.05 0.07 0.13 0.16 0.21 0.22175 1600 0.04 0.09 0.10 0.15 0.19 0.16 0.05 0.08 0.13 0.17 0.20 0.22175 2000 0.04 0.09 0.10 0.15 0.19 0.16 0.05 0.07 0.13 0.16 0.21 0.22176 0 0.04 0.09 0.09 0.15 0.19 0.16 0.05 0.08 0.12 0.16 0.21 0.22176 400 0.04 0.09 0.09 0.15 0.19 0.16 0.05 0.08 0.12 0.16 0.21 0.22176 800 0.04 0.09 0.09 0.15 0.19 0.16 0.05 0.08 0.12 0.16 0.20 0.22176 1200 0.04 0.10 0.10 0.15 0.20 0.16 0.05 0.08 0.13 0.16 0.21 0.22176 1600 0.04 0.09 0.10 0.15 0.19 0.16 0.04 0.08 0.13 0.17 0.21 0.22176 2000 0.10 0.09 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.21 0.22177 0 0.12 0.09 0.10 0.15 0.19 0.16 0.12 0.08 0.12 0.16 0.20 0.22177 400 0.11 0.09 0.10 0.15 0.19 0.16 0.12 0.08 0.12 0.16 0.21 0.22177 800 0.11 0.10 0.10 0.15 0.19 0.16 0.11 0.08 0.12 0.16 0.20 0.22177 1200 0.10 0.09 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.21 0.22177 1600 0.09 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.17 0.20 0.22177 2000 0.08 0.09 0.10 0.15 0.19 0.16 0.09 0.08 0.13 0.16 0.21 0.22178 0 0.09 0.09 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.17 0.21 0.22178 400 0.09 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.12 0.16 0.20 0.22178 800 0.09 0.09 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.21 0.22178 1200 0.08 0.09 0.09 0.15 0.19 0.16 0.09 0.08 0.13 0.17 0.21 0.22178 1600 0.08 0.09 0.10 0.15 0.20 0.16 0.08 0.08 0.12 0.16 0.21 0.22178 2000 0.08 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.21 0.22179 0 0.08 0.09 0.10 0.15 0.19 0.16 0.09 0.08 0.13 0.16 0.21 0.22179 400 0.08 0.10 0.10 0.15 0.20 0.16 0.09 0.08 0.12 0.16 0.21 0.22179 800 0.08 0.10 0.09 0.15 0.20 0.16 0.08 0.08 0.12 0.16 0.21 0.22179 1200 0.07 0.09 0.10 0.15 0.20 0.16 0.08 0.08 0.13 0.17 0.21 0.22179 1600 0.07 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.22179 2000 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22180 0 0.07 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.21 0.22180 400 0.07 0.10 0.09 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.22

109

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

180 800 0.07 0.10 0.09 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.22180 1200 0.07 0.10 0.10 0.15 0.20 0.16 0.07 0.08 0.13 0.17 0.21 0.22180 1600 0.06 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22180 2000 0.06 0.10 0.10 0.15 0.20 0.16 0.07 0.08 0.13 0.16 0.20 0.22181 0 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22181 400 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.21181 800 0.07 0.10 0.09 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22181 1200 0.06 0.09 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22181 1600 0.06 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.23 0.22181 2000 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.22182 0 0.06 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22182 400 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.20 0.22182 800 0.06 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22182 1200 0.06 0.10 0.10 0.15 0.20 0.16 0.06 0.08 0.13 0.16 0.21 0.22182 1400 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.22182 1600 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.20 0.22182 1800 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.21182 2000 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.22182 2200 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.20 0.22183 0 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.22183 200 0.06 0.10 0.09 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.20 0.22183 400 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.21183 600 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.24 0.22183 800 0.06 0.09 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.20 0.21183 1000 0.06 0.09 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.20 0.22183 1200 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.22183 1400 0.06 0.10 0.10 0.15 0.20 0.16 0.06 0.08 0.13 0.16 0.21 0.22183 1600 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.21183 1800 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.22183 2000 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21183 2200 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.21184 0 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.21184 200 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.22184 400 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.20 0.21184 600 0.05 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.20 0.21184 800 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.20 0.21

110

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

184 1000 0.06 0.10 0.10 0.15 0.19 0.16 0.06 0.08 0.13 0.16 0.21 0.22184 1200 0.05 0.10 0.10 0.15 0.20 0.16 0.06 0.08 0.13 0.17 0.21 0.21184 1400 0.12 0.10 0.10 0.15 0.19 0.16 0.12 0.08 0.13 0.16 0.21 0.21184 1600 0.11 0.10 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.22184 1800 0.11 0.10 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.21 0.22184 2000 0.10 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.20 0.22184 2200 0.10 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.21 0.21185 0 0.10 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.21 0.21185 200 0.10 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.20 0.21185 400 0.11 0.09 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.20 0.22185 600 0.10 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.12 0.16 0.20 0.21185 800 0.10 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.20 0.21185 1000 0.10 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.21 0.22185 1200 0.09 0.10 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.21 0.22185 1400 0.08 0.10 0.10 0.15 0.19 0.16 0.09 0.08 0.13 0.17 0.20 0.21185 1600 0.08 0.10 0.10 0.15 0.19 0.16 0.09 0.08 0.13 0.16 0.22 0.22185 1800 0.08 0.10 0.10 0.15 0.19 0.16 0.09 0.08 0.13 0.16 0.21 0.22185 2000 0.08 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.21 0.22185 2200 0.08 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.21186 0 0.08 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.21 0.22186 200 0.08 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.22 0.22186 400 0.08 0.10 0.10 0.15 0.19 0.16 0.09 0.08 0.13 0.16 0.20 0.21186 600 0.09 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.22186 800 0.09 0.10 0.10 0.15 0.19 0.16 0.09 0.08 0.13 0.16 0.22 0.22186 1000 0.08 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.22186 1200 0.08 0.10 0.09 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.21 0.22186 1400 0.08 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.21 0.21186 1600 0.07 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.25 0.22186 2000 0.07 0.10 0.10 0.15 0.19 0.16 0.08 0.09 0.13 0.16 0.22 0.22186 2200 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.21187 0 0.07 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.21 0.21187 200 0.07 0.10 0.09 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.21187 400 0.07 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.21187 600 0.08 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.21187 800 0.07 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.21

111

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

187 1000 0.07 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.21 0.21187 1200 0.07 0.09 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.22187 1400 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22187 1600 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.17 0.21 0.22187 1800 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22187 2000 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.19 0.21187 2200 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.21188 0 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22188 200 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.21188 400 0.07 0.10 0.09 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.20 0.21188 600 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.20 0.21188 800 0.07 0.10 0.10 0.15 0.19 0.16 0.08 0.08 0.13 0.16 0.20 0.22188 1000 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.21188 1200 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.20 0.21188 1400 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.09 0.13 0.16 0.20 0.21188 1600 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.21188 1800 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.20 0.22188 2000 0.06 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.23 0.21188 2200 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.21189 0 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.20 0.21189 200 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22189 400 0.07 0.10 0.09 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.20 0.22189 600 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.21189 800 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.20 0.21189 1000 0.07 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.21189 1200 0.06 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22189 1400 0.06 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.21 0.22189 1600 0.06 0.10 0.10 0.15 0.19 0.16 0.07 0.08 0.13 0.16 0.20 0.21189 1800 0.21 0.10 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.20 0.21189 2000 0.21 0.10 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.20 0.21189 2200 0.20 0.10 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.21190 0 0.20 0.10 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.21190 200 0.19 0.10 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.21190 400 0.19 0.10 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.22190 600 0.19 0.10 0.09 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.22

112

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

190 800 0.18 0.10 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.22190 1000 0.18 0.10 0.09 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.21190 1200 0.18 0.11 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.22190 1400 0.17 0.11 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.21 0.22190 1600 0.17 0.11 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.21 0.22190 1800 0.16 0.11 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.21 0.21190 2000 0.17 0.11 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.22190 2200 0.19 0.11 0.09 0.15 0.19 0.16 0.16 0.08 0.13 0.16 0.20 0.22191 0 0.19 0.11 0.09 0.15 0.19 0.16 0.16 0.08 0.13 0.16 0.20 0.21191 200 0.18 0.12 0.10 0.15 0.19 0.16 0.16 0.08 0.13 0.16 0.20 0.22191 400 0.18 0.12 0.10 0.15 0.19 0.16 0.16 0.08 0.13 0.16 0.20 0.22191 600 0.18 0.12 0.10 0.15 0.19 0.16 0.16 0.08 0.12 0.16 0.20 0.22191 800 0.18 0.12 0.10 0.15 0.19 0.16 0.16 0.08 0.13 0.16 0.20 0.22191 1000 0.18 0.12 0.09 0.15 0.19 0.16 0.16 0.08 0.13 0.16 0.20 0.21191 1200 0.17 0.12 0.09 0.15 0.19 0.16 0.15 0.08 0.13 0.16 0.20 0.21191 1400 0.17 0.13 0.10 0.15 0.19 0.16 0.15 0.08 0.13 0.16 0.20 0.21191 1600 0.16 0.13 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.21 0.21191 1800 0.15 0.13 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.21191 2000 0.15 0.13 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.21191 2200 0.15 0.13 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.21 0.21192 0 0.15 0.13 0.09 0.15 0.19 0.16 0.14 0.08 0.12 0.16 0.20 0.22192 200 0.15 0.13 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.21192 400 0.15 0.13 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.22192 600 0.15 0.13 0.10 0.15 0.19 0.16 0.14 0.08 0.12 0.16 0.20 0.22192 800 0.15 0.13 0.10 0.15 0.19 0.16 0.14 0.08 0.13 0.16 0.20 0.21192 1000 0.15 0.13 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.20 0.21192 1200 0.14 0.13 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.21 0.21192 1400 0.13 0.13 0.10 0.15 0.19 0.16 0.12 0.08 0.13 0.16 0.21 0.22192 1600 0.13 0.13 0.10 0.15 0.19 0.16 0.12 0.08 0.13 0.16 0.21 0.22192 1800 0.13 0.13 0.10 0.15 0.19 0.16 0.12 0.08 0.13 0.16 0.21 0.22192 2000 0.13 0.13 0.10 0.15 0.19 0.16 0.12 0.08 0.13 0.16 0.21 0.21192 2200 0.13 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.22 0.21193 0 0.13 0.13 0.10 0.15 0.19 0.16 0.12 0.08 0.13 0.16 0.20 0.21193 200 0.13 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.21193 400 0.13 0.13 0.10 0.15 0.19 0.16 0.12 0.08 0.13 0.16 0.20 0.21

113

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

193 600 0.13 0.13 0.10 0.15 0.19 0.16 0.12 0.08 0.13 0.16 0.20 0.21193 800 0.13 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.21193 1000 0.13 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.22193 1200 0.13 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.21193 1400 0.12 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.21193 1600 0.12 0.13 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.20 0.22193 1800 0.12 0.13 0.10 0.15 0.19 0.16 0.10 0.08 0.13 0.16 0.20 0.21193 2000 0.12 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.21193 2200 0.12 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.21194 0 0.12 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.21194 200 0.13 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.12 0.16 0.20 0.21194 400 0.13 0.13 0.10 0.15 0.19 0.16 0.11 0.08 0.13 0.16 0.20 0.21194 600 0.15 0.13 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.20 0.21194 800 0.14 0.13 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.20 0.21194 1000 0.15 0.13 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.20 0.22194 1200 0.15 0.13 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.20 0.21194 1400 0.14 0.13 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.20 0.22194 1600 0.15 0.13 0.10 0.15 0.19 0.16 0.13 0.08 0.13 0.16 0.20 0.21194 1800 0.15 0.13 0.10 0.15 0.19 0.16 0.13 0.08 0.12 0.16 0.20 0.22194 2000 0.17 0.13 0.10 0.15 0.19 0.16 0.16 0.08 0.13 0.16 0.20 0.21194 2200 0.17 0.13 0.10 0.15 0.19 0.16 0.16 0.08 0.13 0.16 0.21 0.21195 0 0.17 0.13 0.10 0.15 0.19 0.16 0.16 0.09 0.13 0.16 0.20 0.22195 200 0.17 0.13 0.10 0.15 0.19 0.16 0.16 0.08 0.12 0.16 0.20 0.22195 400 0.17 0.13 0.10 0.15 0.19 0.16 0.16 0.09 0.12 0.16 0.20 0.21195 600 0.17 0.13 0.10 0.15 0.19 0.16 0.16 0.08 0.12 0.16 0.20 0.21195 800 0.17 0.13 0.10 0.15 0.19 0.16 0.16 0.09 0.12 0.16 0.20 0.22195 1000 0.17 0.13 0.10 0.15 0.19 0.16 0.15 0.08 0.13 0.16 0.21 0.21195 1200 0.16 0.13 0.10 0.15 0.19 0.16 0.15 0.08 0.13 0.16 0.20 0.21195 1400 0.16 0.13 0.10 0.15 0.19 0.16 0.15 0.08 0.13 0.16 0.20 0.21195 1600 0.15 0.14 0.10 0.15 0.19 0.16 0.15 0.08 0.13 0.16 0.21 0.21195 1800 0.16 0.14 0.10 0.15 0.19 0.16 0.16 0.08 0.13 0.16 0.20 0.21195 2000 0.20 0.13 0.10 0.15 0.19 0.16 0.19 0.08 0.13 0.16 0.20 0.21195 2200 0.20 0.14 0.10 0.15 0.19 0.16 0.19 0.09 0.13 0.16 0.20 0.21196 0 0.19 0.14 0.10 0.15 0.19 0.16 0.19 0.09 0.13 0.16 0.20 0.22196 200 0.19 0.15 0.10 0.15 0.19 0.16 0.18 0.09 0.13 0.16 0.20 0.21196 400 0.19 0.15 0.10 0.15 0.19 0.16 0.18 0.10 0.13 0.16 0.20 0.22

114

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

196 600 0.19 0.15 0.10 0.15 0.19 0.16 0.18 0.10 0.13 0.16 0.20 0.21196 800 0.19 0.15 0.10 0.15 0.19 0.16 0.18 0.10 0.12 0.16 0.20 0.21196 1000 0.18 0.15 0.10 0.15 0.19 0.16 0.17 0.10 0.13 0.16 0.20 0.21196 1200 0.17 0.15 0.10 0.15 0.19 0.16 0.17 0.10 0.13 0.16 0.20 0.21196 1400 0.17 0.16 0.11 0.15 0.19 0.16 0.17 0.10 0.13 0.16 0.20 0.21196 1600 0.16 0.16 0.11 0.15 0.19 0.16 0.16 0.10 0.13 0.16 0.21 0.21196 1800 0.15 0.16 0.11 0.15 0.19 0.16 0.16 0.10 0.13 0.16 0.22 0.21196 2000 0.15 0.15 0.11 0.15 0.19 0.16 0.15 0.10 0.13 0.16 0.20 0.21196 2200 0.15 0.16 0.11 0.15 0.19 0.16 0.15 0.10 0.13 0.16 0.20 0.22197 0 0.15 0.16 0.11 0.15 0.19 0.16 0.15 0.10 0.13 0.16 0.20 0.21197 200 0.15 0.15 0.11 0.15 0.19 0.16 0.15 0.10 0.13 0.16 0.20 0.22197 400 0.15 0.15 0.11 0.15 0.19 0.16 0.15 0.10 0.13 0.16 0.20 0.21197 600 0.15 0.15 0.11 0.15 0.19 0.16 0.15 0.10 0.12 0.16 0.20 0.21197 800 0.15 0.15 0.11 0.15 0.19 0.16 0.15 0.10 0.13 0.16 0.20 0.21197 1000 0.14 0.15 0.11 0.15 0.19 0.16 0.15 0.10 0.13 0.16 0.20 0.21197 1200 0.14 0.15 0.11 0.15 0.19 0.16 0.15 0.11 0.13 0.16 0.21 0.21197 1400 0.17 0.15 0.11 0.15 0.19 0.16 0.16 0.10 0.13 0.16 0.20 0.21197 1600 0.18 0.15 0.11 0.15 0.19 0.16 0.17 0.11 0.13 0.16 0.20 0.21197 1800 0.27 0.17 0.11 0.15 0.19 0.16 0.20 0.11 0.13 0.16 0.20 0.22197 2000 0.23 0.20 0.11 0.15 0.19 0.16 0.20 0.12 0.13 0.16 0.20 0.21197 2200 0.21 0.21 0.12 0.15 0.19 0.16 0.20 0.13 0.13 0.16 0.20 0.21198 0 0.20 0.20 0.12 0.15 0.19 0.16 0.19 0.14 0.12 0.16 0.20 0.21198 200 0.19 0.20 0.13 0.16 0.19 0.16 0.19 0.14 0.13 0.16 0.20 0.21198 400 0.19 0.20 0.13 0.16 0.19 0.16 0.18 0.15 0.13 0.16 0.20 0.21198 600 0.19 0.20 0.14 0.16 0.19 0.16 0.18 0.15 0.13 0.16 0.20 0.21198 800 0.19 0.20 0.14 0.16 0.19 0.16 0.18 0.15 0.13 0.16 0.20 0.21198 1000 0.18 0.19 0.15 0.16 0.19 0.16 0.17 0.15 0.13 0.16 0.20 0.21198 1200 0.18 0.19 0.15 0.17 0.19 0.16 0.17 0.15 0.13 0.16 0.20 0.21198 1400 0.17 0.19 0.15 0.17 0.19 0.16 0.16 0.15 0.13 0.16 0.21 0.21198 1600 0.16 0.19 0.15 0.17 0.19 0.16 0.16 0.16 0.13 0.16 0.21 0.21198 1800 0.16 0.19 0.15 0.17 0.19 0.16 0.16 0.15 0.14 0.16 0.20 0.21198 2000 0.16 0.19 0.15 0.18 0.19 0.16 0.16 0.15 0.14 0.16 0.20 0.21198 2200 0.16 0.18 0.15 0.18 0.19 0.16 0.15 0.15 0.14 0.16 0.20 0.21199 0 0.16 0.18 0.15 0.18 0.19 0.16 0.16 0.15 0.13 0.16 0.20 0.21199 200 0.16 0.18 0.15 0.18 0.19 0.16 0.16 0.15 0.14 0.16 0.20 0.21199 400 0.16 0.18 0.14 0.18 0.19 0.16 0.16 0.15 0.14 0.16 0.20 0.21199 600 0.16 0.18 0.14 0.18 0.19 0.15 0.15 0.15 0.14 0.16 0.20 0.21199 800 0.15 0.18 0.14 0.18 0.19 0.15 0.16 0.15 0.14 0.16 0.20 0.21

115

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

199 1000 0.15 0.18 0.15 0.18 0.19 0.16 0.15 0.15 0.14 0.17 0.20 0.21199 1200 0.14 0.18 0.14 0.18 0.20 0.16 0.15 0.15 0.14 0.16 0.21 0.21199 1400 0.14 0.18 0.15 0.19 0.20 0.16 0.14 0.15 0.14 0.17 0.21 0.21199 1600 0.14 0.17 0.15 0.18 0.20 0.16 0.14 0.15 0.14 0.17 0.21 0.21199 1800 0.13 0.17 0.15 0.19 0.20 0.16 0.13 0.15 0.14 0.17 0.20 0.21199 2000 0.13 0.17 0.15 0.18 0.20 0.16 0.13 0.15 0.14 0.17 0.21 0.21199 2200 0.13 0.17 0.14 0.18 0.20 0.16 0.13 0.15 0.14 0.17 0.20 0.21200 0 0.13 0.17 0.14 0.18 0.20 0.16 0.13 0.15 0.14 0.17 0.20 0.21200 200 0.13 0.17 0.14 0.18 0.20 0.16 0.13 0.15 0.14 0.17 0.21 0.21200 400 0.13 0.17 0.14 0.18 0.20 0.16 0.13 0.15 0.14 0.17 0.21 0.21200 600 0.13 0.16 0.14 0.18 0.20 0.16 0.13 0.15 0.14 0.17 0.21 0.21200 800 0.13 0.16 0.14 0.18 0.20 0.16 0.13 0.14 0.14 0.17 0.20 0.22200 1000 0.13 0.16 0.14 0.18 0.20 0.16 0.13 0.15 0.14 0.17 0.21 0.21200 1200 0.13 0.16 0.14 0.18 0.20 0.16 0.13 0.14 0.14 0.17 0.21 0.21200 1400 0.12 0.16 0.14 0.18 0.20 0.16 0.12 0.14 0.15 0.17 0.21 0.21200 1600 0.12 0.16 0.14 0.18 0.20 0.16 0.11 0.14 0.14 0.17 0.20 0.21200 1800 0.12 0.16 0.14 0.18 0.20 0.16 0.11 0.14 0.14 0.17 0.21 0.21200 2000 0.12 0.16 0.14 0.18 0.20 0.16 0.11 0.14 0.15 0.17 0.21 0.21200 2200 0.12 0.16 0.14 0.18 0.20 0.16 0.11 0.14 0.14 0.17 0.21 0.22201 0 0.12 0.16 0.14 0.18 0.20 0.16 0.11 0.14 0.14 0.17 0.21 0.21201 200 0.12 0.16 0.14 0.18 0.20 0.17 0.11 0.14 0.15 0.17 0.21 0.21201 400 0.12 0.16 0.14 0.18 0.20 0.16 0.11 0.14 0.14 0.17 0.20 0.22201 600 0.12 0.16 0.14 0.18 0.20 0.16 0.11 0.14 0.14 0.17 0.21 0.22201 800 0.12 0.16 0.14 0.18 0.20 0.16 0.11 0.14 0.14 0.17 0.21 0.22201 1000 0.12 0.16 0.14 0.18 0.20 0.16 0.11 0.14 0.14 0.17 0.21 0.21201 1200 0.11 0.15 0.14 0.18 0.20 0.16 0.11 0.14 0.14 0.17 0.21 0.21201 1400 0.11 0.16 0.14 0.18 0.20 0.17 0.10 0.14 0.15 0.17 0.21 0.21201 1600 0.12 0.16 0.14 0.18 0.20 0.17 0.11 0.14 0.15 0.17 0.21 0.21201 1800 0.12 0.15 0.14 0.18 0.20 0.16 0.10 0.13 0.15 0.17 0.21 0.22201 2000 0.11 0.15 0.14 0.18 0.20 0.17 0.10 0.13 0.15 0.17 0.21 0.22201 2200 0.11 0.15 0.13 0.18 0.20 0.16 0.10 0.13 0.14 0.17 0.21 0.22202 0 0.11 0.15 0.14 0.18 0.20 0.17 0.10 0.13 0.14 0.17 0.21 0.22202 200 0.11 0.15 0.14 0.18 0.20 0.17 0.10 0.14 0.15 0.17 0.21 0.22202 400 0.11 0.15 0.13 0.18 0.20 0.17 0.10 0.13 0.14 0.17 0.21 0.22202 600 0.11 0.15 0.13 0.18 0.20 0.17 0.10 0.13 0.15 0.17 0.21 0.22202 800 0.11 0.15 0.13 0.18 0.20 0.17 0.10 0.13 0.14 0.17 0.21 0.22

116

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

202 1000 0.11 0.15 0.13 0.18 0.20 0.17 0.10 0.13 0.15 0.17 0.21 0.22202 1200 0.11 0.15 0.13 0.18 0.20 0.17 0.09 0.13 0.14 0.17 0.21 0.22202 1400 0.10 0.15 0.13 0.18 0.20 0.17 0.09 0.13 0.15 0.17 0.21 0.22202 1600 0.10 0.15 0.13 0.18 0.20 0.17 0.08 0.13 0.15 0.17 0.21 0.22202 1800 0.10 0.15 0.13 0.18 0.20 0.17 0.09 0.13 0.14 0.17 0.23 0.22202 2000 0.10 0.15 0.13 0.18 0.20 0.17 0.08 0.13 0.15 0.17 0.21 0.22202 2200 0.10 0.15 0.13 0.18 0.20 0.17 0.08 0.13 0.15 0.17 0.21 0.22203 0 0.10 0.15 0.13 0.18 0.20 0.17 0.09 0.13 0.14 0.17 0.21 0.22203 200 0.10 0.14 0.13 0.18 0.20 0.17 0.08 0.13 0.15 0.17 0.21 0.22203 400 0.10 0.15 0.13 0.18 0.20 0.17 0.08 0.13 0.14 0.17 0.21 0.22203 600 0.10 0.15 0.13 0.18 0.20 0.17 0.08 0.13 0.14 0.17 0.21 0.22203 800 0.10 0.14 0.13 0.18 0.20 0.17 0.08 0.13 0.14 0.17 0.21 0.22203 1000 0.10 0.14 0.13 0.18 0.20 0.17 0.08 0.13 0.15 0.17 0.21 0.22203 1200 0.10 0.14 0.13 0.18 0.20 0.17 0.08 0.13 0.14 0.17 0.21 0.22203 1400 0.09 0.14 0.13 0.18 0.20 0.17 0.08 0.13 0.14 0.17 0.22 0.22203 1600 0.09 0.14 0.13 0.18 0.20 0.17 0.07 0.13 0.14 0.17 0.21 0.22203 1800 0.09 0.14 0.13 0.18 0.20 0.17 0.07 0.13 0.15 0.17 0.21 0.22203 2000 0.09 0.14 0.13 0.18 0.20 0.17 0.07 0.12 0.14 0.17 0.21 0.22203 2200 0.09 0.14 0.13 0.18 0.20 0.17 0.07 0.12 0.14 0.17 0.21 0.22204 0 0.09 0.14 0.13 0.18 0.20 0.17 0.07 0.12 0.15 0.17 0.21 0.22204 200 0.09 0.14 0.13 0.18 0.20 0.17 0.07 0.12 0.14 0.17 0.21 0.22204 400 0.09 0.14 0.13 0.18 0.20 0.17 0.07 0.12 0.14 0.17 0.21 0.22204 600 0.09 0.14 0.13 0.18 0.20 0.16 0.07 0.12 0.14 0.17 0.21 0.22204 800 0.09 0.14 0.13 0.18 0.20 0.17 0.07 0.12 0.14 0.17 0.21 0.22204 1000 0.09 0.14 0.13 0.18 0.20 0.17 0.07 0.12 0.14 0.17 0.21 0.22204 1200 0.08 0.14 0.13 0.18 0.20 0.17 0.07 0.12 0.14 0.17 0.21 0.22204 1400 0.08 0.14 0.13 0.18 0.20 0.17 0.06 0.12 0.14 0.17 0.21 0.22204 1600 0.08 0.14 0.13 0.18 0.20 0.17 0.06 0.12 0.15 0.17 0.21 0.22204 1800 0.08 0.13 0.13 0.18 0.20 0.17 0.06 0.12 0.14 0.17 0.21 0.22204 2000 0.08 0.14 0.13 0.17 0.20 0.17 0.06 0.12 0.14 0.17 0.21 0.22204 2200 0.08 0.14 0.13 0.18 0.20 0.17 0.06 0.12 0.14 0.17 0.21 0.22205 0 0.08 0.14 0.13 0.17 0.20 0.17 0.06 0.12 0.14 0.17 0.21 0.22205 200 0.08 0.14 0.13 0.18 0.20 0.17 0.06 0.12 0.14 0.17 0.21 0.22205 400 0.08 0.14 0.13 0.18 0.20 0.16 0.06 0.12 0.14 0.17 0.21 0.22205 600 0.08 0.14 0.13 0.17 0.20 0.17 0.06 0.12 0.14 0.17 0.21 0.22205 800 0.08 0.14 0.13 0.17 0.20 0.17 0.06 0.12 0.14 0.17 0.21 0.22

117

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

205 1000 0.08 0.13 0.13 0.18 0.20 0.17 0.06 0.12 0.14 0.17 0.21 0.22205 1200 0.07 0.14 0.13 0.17 0.20 0.17 0.05 0.12 0.14 0.17 0.21 0.22205 1400 0.07 0.13 0.13 0.18 0.20 0.17 0.05 0.12 0.14 0.17 0.21 0.22205 1600 0.07 0.13 0.13 0.18 0.20 0.17 0.05 0.11 0.14 0.17 0.21 0.22205 1800 0.06 0.13 0.13 0.18 0.20 0.17 0.05 0.11 0.14 0.17 0.21 0.22205 2000 0.06 0.13 0.13 0.17 0.20 0.17 0.05 0.11 0.14 0.17 0.21 0.22205 2200 0.06 0.13 0.13 0.17 0.20 0.17 0.01 0.11 0.14 0.17 0.21 0.22206 0 0.04 0.13 0.12 0.17 0.20 0.17 0.05 0.11 0.14 0.17 0.21 0.22206 200 0.04 0.13 0.13 0.17 0.20 0.17 0.05 0.11 0.14 0.17 0.21 0.22206 400 0.04 0.13 0.13 0.17 0.20 0.17 0.04 0.11 0.14 0.17 0.21 0.22206 600 0.04 0.13 0.13 0.17 0.20 0.17 0.05 0.11 0.14 0.17 0.21 0.22206 800 0.06 0.13 0.12 0.17 0.20 0.17 0.05 0.11 0.14 0.17 0.21 0.22206 1000 0.03 0.13 0.13 0.17 0.20 0.17 0.02 0.11 0.14 0.17 0.21 0.22206 1200 0.06 0.13 0.13 0.17 0.20 0.17 0.04 0.11 0.14 0.17 0.21 0.22206 1400 0.04 0.13 0.12 0.17 0.20 0.17 0.04 0.11 0.14 0.17 0.21 0.22206 1600 0.05 0.13 0.12 0.17 0.20 0.17 0.04 0.11 0.14 0.17 0.21 0.22206 1800 0.03 0.13 0.13 0.17 0.20 0.17 0.04 0.11 0.14 0.17 0.21 0.22206 2000 0.03 0.12 0.13 0.17 0.20 0.17 0.04 0.11 0.14 0.17 0.21 0.22206 2200 0.03 0.12 0.12 0.17 0.20 0.17 0.04 0.11 0.14 0.17 0.21 0.22207 0 0.03 0.13 0.12 0.17 0.20 0.17 0.01 0.11 0.14 0.17 0.21 0.22207 200 0.03 0.12 0.12 0.17 0.20 0.17 0.04 0.10 0.14 0.17 0.21 0.22207 400 0.02 0.13 0.12 0.17 0.20 0.17 0.02 0.10 0.14 0.17 0.21 0.22207 600 0.02 0.13 0.12 0.17 0.20 0.17 0.04 0.10 0.14 0.17 0.21 0.22207 800 0.02 0.12 0.12 0.17 0.20 0.17 0.04 0.10 0.14 0.17 0.21 0.22207 1000 0.02 0.12 0.12 0.17 0.20 0.17 0.04 0.11 0.14 0.17 0.21 0.22207 1200 0.02 0.12 0.12 0.17 0.20 0.17 0.04 0.10 0.14 0.17 0.21 0.22207 1400 0.02 0.12 0.12 0.17 0.20 0.17 0.04 0.10 0.14 0.17 0.21 0.22207 1600 0.02 0.12 0.12 0.17 0.20 0.17 0.04 0.10 0.14 0.17 0.21 0.22207 1800 0.01 0.12 0.12 0.17 0.20 0.17 0.03 0.09 0.14 0.17 0.21 0.22207 2000 0.01 0.11 0.12 0.17 0.20 0.17 0.03 0.09 0.14 0.17 0.21 0.22207 2200 0.11 0.12 0.17 0.20 0.17 0.03 0.09 0.14 0.17 0.21 0.22208 0 0.12 0.12 0.17 0.20 0.17 0.03 0.09 0.14 0.17 0.21 0.22208 200 0.12 0.12 0.17 0.20 0.17 0.03 0.09 0.14 0.17 0.21 0.22208 400 0.00 0.12 0.12 0.17 0.20 0.17 0.03 0.09 0.14 0.17 0.21 0.22208 600 0.12 0.12 0.17 0.20 0.17 0.03 0.09 0.14 0.17 0.21 0.22

118

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

208 800 0.00 0.12 0.12 0.17 0.20 0.17 0.03 0.09 0.14 0.17 0.21 0.22208 1000 0.12 0.12 0.17 0.20 0.17 0.03 0.10 0.14 0.17 0.21 0.22208 1200 0.00 0.12 0.12 0.17 0.20 0.17 0.03 0.09 0.14 0.17 0.21 0.22208 1400 0.00 0.11 0.12 0.17 0.20 0.17 0.03 0.08 0.14 0.17 0.21 0.22208 1600 0.08 0.11 0.11 0.17 0.20 0.17 0.07 0.08 0.14 0.17 0.21 0.22208 1800 0.08 0.11 0.12 0.17 0.20 0.17 0.07 0.09 0.14 0.17 0.21 0.22208 2000 0.11 0.11 0.12 0.17 0.20 0.17 0.11 0.09 0.14 0.17 0.21 0.22208 2200 0.11 0.11 0.12 0.17 0.20 0.17 0.11 0.09 0.14 0.17 0.21 0.22209 0 0.11 0.12 0.12 0.17 0.20 0.17 0.11 0.09 0.14 0.17 0.21 0.22209 200 0.11 0.12 0.12 0.17 0.20 0.17 0.10 0.09 0.14 0.17 0.21 0.22209 400 0.11 0.12 0.11 0.17 0.20 0.17 0.10 0.09 0.14 0.17 0.21 0.22209 600 0.11 0.12 0.12 0.17 0.20 0.17 0.10 0.09 0.14 0.17 0.21 0.22209 800 0.11 0.12 0.12 0.17 0.20 0.17 0.10 0.09 0.14 0.17 0.21 0.22209 1000 0.11 0.12 0.11 0.17 0.20 0.17 0.10 0.09 0.14 0.17 0.21 0.22209 1200 0.10 0.11 0.11 0.17 0.20 0.17 0.10 0.09 0.14 0.17 0.21 0.22209 1400 0.10 0.11 0.12 0.17 0.20 0.17 0.09 0.09 0.14 0.17 0.21 0.22209 1600 0.09 0.11 0.12 0.17 0.20 0.17 0.09 0.09 0.14 0.17 0.22 0.22209 1800 0.15 0.11 0.12 0.17 0.20 0.17 0.13 0.09 0.14 0.17 0.21 0.22209 2000 0.23 0.15 0.12 0.17 0.20 0.17 0.13 0.14 0.17 0.21 0.22209 2200 0.21 0.16 0.13 0.18 0.20 0.17 0.13 0.11 0.14 0.17 0.21 0.22210 0 0.20 0.17 0.13 0.19 0.21 0.17 0.14 0.11 0.14 0.17 0.21 0.22210 200 0.20 0.17 0.14 0.19 0.21 0.17 0.14 0.11 0.14 0.17 0.21 0.22210 400 0.19 0.17 0.14 0.20 0.21 0.17 0.14 0.11 0.14 0.17 0.21 0.22210 600 0.19 0.17 0.14 0.20 0.21 0.18 0.14 0.11 0.14 0.17 0.21 0.22210 800 0.18 0.17 0.14 0.20 0.21 0.17 0.14 0.12 0.14 0.17 0.21 0.22210 1000 0.19 0.17 0.14 0.20 0.21 0.17 0.15 0.12 0.14 0.17 0.21 0.22210 1200 0.18 0.17 0.14 0.20 0.22 0.18 0.14 0.11 0.14 0.17 0.21 0.22210 1400 0.17 0.17 0.14 0.20 0.22 0.18 0.14 0.11 0.14 0.17 0.21 0.22210 1600 0.17 0.17 0.14 0.20 0.22 0.17 0.14 0.12 0.14 0.17 0.22 0.22210 1800 0.24 0.28 0.23 0.23 0.22 0.18 0.19 0.14 0.14 0.17 0.21 0.22210 2000 0.22 0.26 0.22 0.25 0.25 0.18 0.19 0.16 0.14 0.17 0.21 0.22210 2200 0.21 0.25 0.21 0.24 0.26 0.18 0.19 0.17 0.14 0.17 0.21 0.22211 0 0.20 0.24 0.21 0.24 0.26 0.18 0.18 0.17 0.14 0.17 0.21 0.22211 200 0.19 0.24 0.21 0.24 0.26 0.18 0.18 0.17 0.14 0.17 0.21 0.22211 400 0.19 0.23 0.20 0.24 0.26 0.19 0.18 0.17 0.14 0.17 0.21 0.22

119

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

211 600 0.18 0.22 0.20 0.24 0.26 0.19 0.17 0.18 0.14 0.17 0.21 0.22211 800 0.18 0.22 0.20 0.23 0.25 0.20 0.17 0.17 0.14 0.17 0.21 0.22211 1000 0.18 0.22 0.19 0.23 0.25 0.21 0.17 0.17 0.14 0.17 0.21 0.22211 1200 0.17 0.21 0.19 0.23 0.25 0.21 0.17 0.17 0.14 0.17 0.21 0.22211 1400 0.17 0.21 0.19 0.23 0.25 0.21 0.16 0.17 0.14 0.17 0.21 0.22211 1600 0.16 0.21 0.19 0.23 0.25 0.22 0.16 0.17 0.14 0.17 0.23 0.22211 1800 0.16 0.21 0.19 0.23 0.25 0.22 0.16 0.17 0.14 0.17 0.21 0.22211 2000 0.16 0.21 0.18 0.23 0.25 0.22 0.16 0.17 0.14 0.17 0.21 0.22211 2200 0.16 0.20 0.18 0.22 0.25 0.22 0.15 0.17 0.14 0.17 0.21 0.22212 0 0.16 0.20 0.18 0.22 0.25 0.22 0.15 0.17 0.15 0.17 0.21 0.22212 200 0.16 0.20 0.18 0.22 0.25 0.22 0.15 0.17 0.15 0.17 0.21 0.22212 400 0.16 0.20 0.18 0.22 0.25 0.22 0.15 0.16 0.15 0.17 0.21 0.22212 600 0.16 0.20 0.18 0.22 0.24 0.22 0.15 0.16 0.15 0.17 0.21 0.22212 800 0.16 0.19 0.18 0.21 0.24 0.22 0.15 0.16 0.14 0.18 0.21 0.22212 1000 0.16 0.19 0.17 0.22 0.24 0.22 0.15 0.16 0.15 0.17 0.21 0.22212 1200 0.15 0.19 0.18 0.22 0.24 0.22 0.15 0.16 0.15 0.17 0.23 0.22212 1400 0.14 0.17 0.22 0.24 0.22 0.14 0.16 0.15 0.18 0.21 0.22212 1600 0.13 0.19 0.17 0.21 0.24 0.22 0.14 0.16 0.15 0.18 0.21 0.22212 1800 0.13 0.18 0.17 0.21 0.24 0.22 0.14 0.16 0.15 0.18 0.22212 2000 0.13 0.18 0.17 0.21 0.24 0.22 0.14 0.16 0.15 0.18 0.21 0.22212 2200 0.13 0.18 0.17 0.21 0.24 0.22 0.14 0.16 0.15 0.18 0.21 0.22213 0 0.13 0.18 0.17 0.21 0.24 0.22 0.14 0.15 0.15 0.18 0.21 0.22213 200 0.14 0.18 0.16 0.21 0.24 0.22 0.14 0.15 0.15 0.18 0.21 0.22213 400 0.25 0.26 0.18 0.21 0.24 0.22 0.20 0.18 0.15 0.18 0.21 0.22213 600 0.23 0.25 0.20 0.24 0.24 0.22 0.19 0.20 0.15 0.18 0.21 0.22213 800 0.24 0.25 0.20 0.24 0.26 0.22 0.21 0.21 0.15 0.18 0.21 0.22213 1000 0.22 0.25 0.20 0.24 0.26 0.22 0.20 0.22 0.15 0.18 0.21 0.22213 1200 0.21 0.25 0.20 0.24 0.26 0.23 0.19 0.21 0.15 0.18 0.21 0.22213 1400 0.20 0.24 0.20 0.24 0.26 0.23 0.19 0.21 0.15 0.18 0.21 0.22213 1600 0.19 0.23 0.20 0.24 0.26 0.24 0.18 0.20 0.16 0.18 0.21 0.22213 1800 0.19 0.23 0.19 0.24 0.26 0.24 0.18 0.20 0.16 0.18 0.21 0.22213 2000 0.18 0.22 0.19 0.24 0.26 0.24 0.17 0.20 0.16 0.18 0.21 0.22213 2200 0.18 0.22 0.19 0.23 0.26 0.24 0.17 0.19 0.16 0.18 0.21 0.22214 0 0.18 0.21 0.19 0.23 0.26 0.24 0.17 0.19 0.16 0.18 0.21 0.22214 200 0.18 0.21 0.19 0.23 0.26 0.24 0.17 0.19 0.17 0.19 0.21 0.22

120

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

214 400 0.18 0.21 0.18 0.23 0.25 0.24 0.17 0.19 0.17 0.19 0.21 0.22214 600 0.17 0.21 0.18 0.23 0.25 0.24 0.17 0.19 0.17 0.19 0.21 0.23214 800 0.17 0.21 0.18 0.22 0.25 0.24 0.16 0.18 0.17 0.19 0.22 0.23214 1000 0.17 0.20 0.18 0.22 0.25 0.24 0.16 0.18 0.17 0.19 0.22 0.23214 1200 0.17 0.20 0.18 0.22 0.25 0.24 0.16 0.18 0.17 0.19 0.22 0.23214 1400 0.16 0.20 0.18 0.22 0.25 0.24 0.15 0.18 0.17 0.19 0.22 0.23214 1600 0.15 0.20 0.18 0.22 0.25 0.24 0.15 0.18 0.17 0.19 0.22 0.23214 1800 0.15 0.20 0.17 0.22 0.25 0.24 0.15 0.18 0.17 0.19 0.22 0.23224 1600 0.10 0.12 0.12 0.18 0.22 0.21 0.09 0.11 0.15 0.19 0.23 0.25224 1800 0.09 0.12 0.12 0.18 0.22 0.21 0.09 0.11 0.15 0.19 0.23 0.25224 2000 0.09 0.11 0.12 0.18 0.22 0.22 0.09 0.11 0.15 0.19 0.23 0.25224 2200 0.09 0.12 0.12 0.18 0.22 0.22 0.09 0.11 0.15 0.19 0.23 0.25225 0 0.11 0.13 0.18 0.22 0.21 0.09 0.11 0.15 0.19 0.23 0.25225 200 0.09 0.11 0.13 0.17 0.22 0.22 0.09 0.10225 400 0.09 0.12 0.12 0.18 0.22 0.21 0.09 0.11 0.15 0.19 0.23 0.25225 600 0.09 0.12 0.12 0.18 0.22 0.21 0.09 0.11 0.15 0.19 0.23 0.25225 800 0.09 0.12 0.12 0.18 0.22 0.21 0.09 0.11 0.15 0.19 0.23 0.25225 1000 0.09 0.11 0.12 0.18 0.22 0.21 0.09 0.11 0.15 0.19 0.22 0.25225 1200 0.09 0.11 0.12 0.18 0.22 0.09 0.11 0.15 0.23 0.25225 1400 0.09 0.12 0.12 0.18 0.22 0.21 0.09 0.11 0.15 0.19 0.23 0.24225 1600 0.08 0.12 0.12 0.17 0.22 0.21 0.08 0.10 0.15 0.19 0.23 0.24225 1800 0.08 0.12 0.12 0.17 0.22 0.21 0.08 0.11 0.15 0.19 0.23 0.24225 2000 0.08 0.11 0.12 0.17 0.22 0.21 0.08 0.11 0.15 0.19 0.23 0.24225 2200 0.11 0.12 0.17 0.22 0.21 0.11 0.15 0.19 0.23 0.24226 0 0.08 0.12 0.12 0.17 0.22 0.21 0.08226 200 0.08 0.11 0.12 0.18 0.22 0.21 0.08 0.11 0.15 0.19 0.23 0.25226 400 0.08 0.12 0.12 0.18 0.21 0.08 0.11 0.15 0.19 0.23 0.25226 600 0.12 0.12 0.18 0.21 0.11 0.15 0.19 0.23 0.25226 800 0.08 0.11 0.12 0.18 0.22 0.21 0.08 0.11 0.14 0.19 0.23 0.24226 1000 0.08 0.11 0.12 0.18 0.22 0.21 0.08 0.10 0.15 0.23 0.25226 1200 0.08 0.10 0.15 0.23 0.25226 1400 0.07 0.11 0.12 0.17 0.22 0.21 0.07 0.10 0.15 0.19 0.23 0.24226 1600 0.07 0.11 0.12 0.17 0.22 0.21 0.07 0.10 0.15 0.19 0.23 0.25226 1800 0.07 0.11 0.12 0.17 0.22 0.21 0.10 0.15 0.19 0.23 0.25226 2000 0.07 0.11 0.12 0.17 0.22 0.21 0.07 0.10 0.14 0.19 0.23 0.24

121

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

226 2200 0.07 0.12 0.12 0.17 0.22 0.21 0.07 0.10 0.15 0.19 0.23 0.24227 0 0.07 0.11 0.12 0.17 0.22 0.21 0.07 0.10 0.14 0.19 0.22 0.25227 200 0.07 0.11 0.12 0.17 0.22 0.21 0.07 0.10 0.14 0.19 0.22 0.24227 400 0.07 0.11 0.12 0.17 0.22 0.21 0.07 0.11 0.15 0.18 0.23 0.24227 600 0.11 0.12 0.18 0.22 0.21 0.07 0.10 0.14 0.18 0.22 0.24227 800 0.07 0.12 0.12 0.17 0.22 0.21 0.07 0.10 0.15 0.19 0.22 0.24227 1000 0.11 0.12 0.18 0.22 0.21 0.07 0.10 0.15 0.18 0.23 0.24227 1200 0.06 0.12 0.12 0.17 0.22 0.21 0.06 0.10 0.15 0.19 0.23 0.25227 1400 0.06 0.11 0.12 0.17 0.22 0.21 0.06 0.10 0.15 0.19 0.23 0.24227 1600 0.06 0.11 0.12 0.17 0.22 0.21 0.06 0.10 0.15 0.18 0.23 0.24227 1800 0.23 0.11 0.12 0.17 0.22 0.21 0.14 0.10 0.16 0.18 0.23 0.24227 2000 0.21 0.17 0.12 0.17 0.22 0.21 0.13 0.12 0.17 0.18 0.23 0.24227 2200 0.20 0.18 0.13 0.17 0.22 0.21 0.14 0.12 0.17 0.18 0.22 0.24228 0 0.20 0.19 0.14 0.18 0.22 0.21 0.14 0.12 0.16 0.19 0.22 0.24228 200 0.19 0.19 0.15 0.18 0.21 0.14 0.12 0.16 0.18 0.23 0.24228 400 0.19 0.19 0.15 0.19 0.22 0.21 0.14 0.12 0.17 0.18 0.22 0.24228 600 0.18 0.19 0.15 0.19 0.22 0.21 0.14 0.12 0.17 0.18 0.22 0.24228 800 0.18 0.19 0.15 0.19 0.22 0.21 0.14 0.12 0.16 0.18 0.23 0.24228 1000 0.18 0.19 0.16 0.20 0.22 0.21 0.14 0.12 0.16 0.19 0.22 0.24228 1200 0.17 0.20 0.22 0.21 0.16 0.18228 1400 0.16 0.20 0.22 0.21 0.16 0.24228 1600 0.18 0.16 0.20 0.22 0.21 0.13 0.16 0.18 0.23 0.24228 1800 0.18 0.16 0.20 0.22 0.21 0.13 0.16 0.18 0.23 0.24228 2000 0.20 0.21 0.16 0.20 0.23 0.21 0.18 0.16 0.16 0.18 0.22 0.24228 2200 0.20 0.21 0.16 0.20 0.23 0.21 0.16 0.16 0.18 0.22 0.24229 0 0.19 0.20 0.16 0.20 0.23 0.21 0.18 0.16 0.16 0.18 0.22 0.24229 200 0.19 0.20 0.16 0.20 0.23 0.21 0.18 0.16 0.17 0.18 0.22 0.24229 400 0.19 0.20 0.16 0.20 0.23 0.21 0.17 0.17 0.17 0.18 0.23 0.24229 600 0.18 0.20 0.16 0.21 0.23 0.21 0.17 0.17 0.16 0.18 0.22 0.24229 800 0.18 0.20 0.16 0.21 0.23 0.21 0.17 0.17 0.16 0.18 0.22 0.24229 1000 0.18 0.20 0.16 0.21 0.23 0.21 0.17 0.17 0.16 0.18 0.22 0.24229 1200 0.18 0.20 0.16 0.21 0.23 0.21 0.17 0.17 0.17 0.22 0.24229 1400 0.17 0.19 0.16 0.21 0.23 0.21 0.16 0.17 0.16 0.18 0.22 0.24229 1600 0.17 0.19 0.16 0.20 0.23 0.21 0.16 0.17 0.17 0.19 0.22 0.24229 1800 0.17 0.19 0.16 0.21 0.23 0.21 0.16 0.16 0.16 0.18 0.22 0.24229 2000 0.17 0.19 0.16 0.21 0.23 0.21 0.16 0.16 0.16 0.18 0.22 0.24

122

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

229 2200 0.17 0.20 0.23 0.21 0.16 0.18 0.22 0.24230 0 0.17 0.19 0.16 0.20 0.23 0.21 0.16 0.16 0.16 0.18 0.22230 200 0.17 0.19 0.16 0.16 0.16 0.19 0.22 0.24230 400 0.17 0.19 0.16 0.20 0.23 0.21 0.16 0.16 0.16 0.18 0.22 0.24230 600 0.17 0.19 0.15 0.20 0.23 0.21 0.15 0.16 0.16 0.18 0.22 0.24230 800 0.17 0.19 0.15 0.20 0.23 0.21 0.16 0.16 0.18 0.22 0.24230 1000 0.17 0.19 0.15 0.20 0.23 0.21 0.16 0.16 0.18 0.22 0.24230 1200 0.16 0.18 0.16 0.20 0.23 0.21 0.15 0.16 0.16 0.19 0.23 0.24230 1400 0.15 0.18 0.16 0.15 0.16 0.16 0.19 0.23 0.24230 1600 0.15 0.18 0.16 0.20 0.23 0.22 0.15 0.16 0.16 0.19 0.23 0.24230 1800 0.15 0.18 0.15 0.20 0.23 0.21 0.14 0.16 0.16 0.18 0.22 0.24230 2000 0.15 0.18 0.15 0.20 0.23 0.21 0.14 0.16 0.16 0.18 0.22 0.24253 1200 0.10 0.15 0.19 0.22 0.28 0.34253 1600 0.10 0.15 0.19 0.22 0.28 0.34253 1800 0.10 0.15 0.18 0.22 0.28 0.34253 2000 0.10 0.15 0.19 0.22 0.28 0.34253 2200 0.10 0.15 0.18 0.22 0.28 0.34254 0 0.10 0.15 0.18 0.22 0.28 0.34254 200 0.10 0.15 0.18 0.22 0.28 0.34254 400 0.10 0.15 0.18 0.22 0.28 0.34254 600 0.10 0.15 0.18 0.22 0.28 0.34254 800 0.09 0.15 0.18 0.22 0.28 0.34254 1000 0.10 0.15 0.18 0.22 0.28 0.34254 1200 0.09 0.15 0.18 0.22 0.28 0.33254 1400 0.09 0.14 0.18 0.22 0.28 0.33254 1600 0.09 0.14 0.18 0.22 0.28 0.33254 1800 0.09 0.15 0.18 0.22 0.28 0.33254 2000 0.09 0.14 0.18 0.22 0.27 0.33254 2200 0.09 0.14 0.18 0.22 0.27 0.33255 0 0.09 0.14 0.18 0.22 0.28 0.33255 200 0.09 0.14 0.18 0.22 0.27 0.33255 400 0.09 0.14 0.17 0.22 0.27 0.33255 600 0.09 0.14 0.17 0.22 0.27 0.33255 800 0.09 0.14 0.18 0.22 0.27 0.33255 1000 0.09 0.14 0.18 0.22 0.27 0.33

123

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

255 1200 0.09 0.14 0.18 0.22 0.27 0.32255 1400 0.09 0.14 0.18 0.22 0.27 0.32255 1600 0.09 0.14 0.18 0.22 0.27 0.32255 1800 0.08 0.14 0.18 0.22 0.27 0.32255 2000 0.09 0.14 0.18 0.22 0.27 0.32255 2200 0.08 0.14 0.18 0.22 0.27 0.32256 0 0.09 0.14 0.17 0.22 0.27 0.32256 200 0.09 0.14 0.18 0.21 0.27 0.32256 400 0.08 0.14 0.17 0.21 0.27 0.32256 600 0.08 0.14 0.18 0.21 0.27 0.32256 800 0.08 0.14 0.18 0.21 0.27 0.32256 1000 0.08 0.14 0.17 0.21 0.27 0.32256 1200 0.09 0.14 0.18 0.22 0.27 0.32256 1400 0.08 0.14 0.17 0.21 0.27 0.32256 1600 0.08 0.14 0.17 0.21 0.27 0.31256 1800 0.08 0.14 0.17 0.21 0.27 0.32256 2000 0.08 0.14 0.18 0.21 0.27 0.31256 2200 0.08 0.14 0.17 0.21 0.27 0.31257 0 0.08 0.14 0.17 0.21 0.27 0.31257 200 0.08 0.14 0.18 0.21 0.27 0.31257 400 0.08 0.14 0.18 0.21 0.27 0.31257 600 0.08 0.14 0.17 0.20 0.27 0.31257 800 0.08 0.14 0.17 0.20 0.27 0.31257 1000 0.08 0.14 0.17 0.21 0.27 0.31257 1200 0.08 0.14 0.17 0.21 0.31257 1400 0.08 0.14 0.17 0.21 0.31257 1600 0.07 0.13 0.17 0.21 0.27 0.31257 1800 0.07 0.13 0.17 0.21 0.27 0.31257 2200 0.07 0.13 0.17 0.21 0.31258 400 0.07 0.13 0.17 0.21 0.26 0.31258 600 0.07 0.13 0.17 0.21 0.30258 800 0.07 0.13 0.17 0.21 0.26 0.30258 1000 0.07 0.13 0.17 0.21 0.30258 1200 0.07 0.13 0.17 0.21 0.26 0.30258 1400 0.07 0.13 0.17 0.21 0.26 0.30258 1600 0.07 0.13 0.17 0.21 0.26 0.30

124

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

258 1800 0.07 0.13 0.17 0.21 0.30258 2000 0.07 0.13 0.17 0.21 0.30258 2200 0.07 0.13 0.17 0.21 0.30259 0 0.07 0.13 0.17 0.21 0.23 0.30259 200 0.07 0.13 0.17 0.21 0.26 0.30259 400 0.07 0.13 0.17 0.20 0.30259 800 0.07 0.13 0.17 0.21 0.26 0.30259 1000 0.07 0.13 0.17 0.21 0.30259 1200 0.07 0.13 0.17 0.20 0.26 0.30259 1400 0.07 0.13 0.17 0.20 0.26 0.30259 2200 0.07 0.13 0.17 0.20 0.26 0.30260 0 0.07 0.13 0.17 0.20 0.26 0.30260 200 0.07 0.13 0.17 0.20 0.26 0.30260 400 0.07 0.13 0.16 0.20 0.26 0.30260 800 0.07 0.13 0.16 0.20 0.30260 1000 0.07 0.13 0.16 0.20 0.23 0.30260 1200 0.07 0.13 0.17 0.20 0.26 0.30260 1400 0.08 0.13 0.16 0.20 0.26 0.30260 1600 0.08 0.13 0.17 0.20 0.26 0.29260 1800 0.08 0.13 0.16 0.20 0.30260 2000 0.08 0.13 0.17 0.20 0.26 0.29260 2200 0.08 0.13 0.17 0.20 0.29261 0 0.08 0.13 0.16 0.20 0.27 0.29261 200 0.08 0.13 0.16 0.20 0.25 0.29261 400 0.08 0.13 0.16 0.20 0.29261 600 0.08 0.13 0.16 0.20 0.22261 800 0.08 0.13 0.16 0.20261 1000 0.08 0.13 0.16 0.20 0.25261 1200 0.08 0.13 0.16 0.20 0.25261 1400 0.08 0.13 0.16 0.20 0.25261 1600 0.08 0.13 0.16 0.20 0.25261 1800 0.08 0.13 0.16 0.20 0.25261 2000 0.08 0.13 0.16 0.20 0.25261 2200 0.08 0.13 0.16 0.20 0.25262 0 0.08 0.13 0.16 0.20 0.25

125

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

262 200 0.08 0.13 0.16 0.20 0.25262 400 0.08 0.13 0.16 0.20 0.25262 600 0.08 0.13 0.16 0.20 0.25262 800 0.08 0.13 0.16 0.20 0.25262 1000 0.08 0.13 0.16 0.20 0.25262 1200 0.08 0.13 0.16 0.20 0.25262 1400 0.08 0.12 0.16 0.20 0.25262 1600 0.07 0.12 0.16 0.20 0.25262 1800 0.07 0.12 0.16 0.20262 2000 0.07 0.12 0.16 0.20 0.26262 2200 0.07 0.13 0.16 0.20 0.25263 0 0.07 0.12 0.16 0.20 0.25263 200 0.07 0.12 0.16 0.20 0.25263 400 0.08 0.12 0.16 0.20263 600 0.07 0.12 0.16 0.20 0.25263 800 0.08 0.12 0.16 0.20 0.25263 1000 0.07 0.12 0.16 0.20263 1200 0.07 0.12 0.16 0.20 0.25263 1400 0.07 0.12 0.16 0.20 0.21263 1600 0.07 0.12 0.16 0.20 0.25263 1800 0.07 0.12 0.16 0.20 0.25263 2000 0.07 0.12 0.16 0.20 0.25263 2200 0.07 0.12 0.16 0.20 0.24264 0 0.07 0.12 0.16 0.20 0.25264 200 0.07 0.12 0.16 0.20 0.25264 400 0.07 0.12 0.16 0.20 0.25264 600 0.07 0.12 0.16 0.20 0.25264 800 0.07 0.12 0.16 0.20 0.25264 1000 0.07 0.12 0.16 0.20 0.24264 1200 0.07 0.12 0.16 0.20 0.25264 1400 0.08 0.12 0.16 0.20 0.25264 1600 0.10 0.12 0.16 0.20 0.24264 1800 0.12 0.12 0.16 0.20 0.25264 2000 0.13 0.12 0.16 0.19 0.25264 2200 0.13 0.12 0.16 0.19 0.24265 0 0.12 0.12 0.16 0.20 0.25

126

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

265 200 0.12 0.12 0.16 0.19 0.24265 400 0.12 0.12 0.16 0.19 0.25265 600 0.12 0.12 0.16 0.20 0.25265 800 0.12 0.12 0.16 0.20 0.25265 1000 0.12 0.12 0.16 0.20 0.24265 1200 0.13 0.12 0.16 0.19 0.25265 1400 0.13 0.12 0.16 0.19 0.25265 1600 0.13 0.12 0.16 0.19265 1800 0.13 0.12 0.16 0.19 0.25265 2000 0.13 0.12 0.16 0.19265 2200 0.13 0.12 0.16 0.19 0.25266 0 0.13 0.12 0.16 0.19 0.24266 200 0.13 0.12 0.16 0.19 0.24266 400 0.13 0.12 0.16 0.19 0.25266 600 0.13 0.12 0.16 0.19 0.25266 800 0.13 0.12 0.16 0.19 0.25266 1000 0.13 0.12 0.15 0.19 0.25266 1200 0.12 0.12 0.16 0.19266 1400 0.12 0.12 0.16 0.19 0.25266 1600 0.12 0.12 0.16 0.20266 1800 0.12 0.12 0.16 0.19 0.22266 2200 0.12 0.12 0.16 0.19 0.24267 400 0.12 0.12 0.16 0.19 0.21267 1000 0.12 0.12 0.16 0.19267 1200 0.12 0.12 0.16 0.19 0.24268 2000 0.11 0.12 0.16 0.19 0.24 0.07270 2000 0.10 0.12 0.16 0.19 0.24 0.04270 2200 0.10 0.12 0.15 0.19 0.04271 0 0.10 0.12 0.16 0.19 0.21 0.08271 200 0.10 0.12 0.16 0.19 0.24 0.05271 400 0.10 0.12 0.15 0.19 0.05271 600 0.10 0.11 0.15 0.19 0.24 0.06271 800 0.10 0.11 0.15 0.19 0.05271 1000 0.10 0.12 0.15 0.19 0.24 0.08271 1200 0.10 0.12 0.16 0.19 0.24 0.06271 1400 0.10 0.12 0.16 0.19 0.05

127

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

271 1600 0.09 0.12 0.16 0.19 0.24 0.05271 1800 0.09 0.12 0.16 0.19 0.24 0.26271 2000 0.11 0.11 0.15 0.19 0.24 0.04271 2200 0.18 0.11 0.15 0.19 0.24 0.05272 0 0.19 0.12 0.15 0.19 0.24 0.06272 200 0.22 0.14 0.15 0.19 0.24 0.05272 400 0.22 0.15 0.15 0.19 0.24 0.05272 600 0.21 0.17 0.15 0.19 0.24 0.06272 800 0.21 0.17 0.16 0.19 0.24 0.05272 1000 0.20 0.18 0.16 0.19 0.24 0.06272 1200 0.21 0.18 0.16 0.19 0.24 0.08272 1400 0.29 0.30 0.31 0.29 0.34 0.04272 1600 0.28 0.30 0.29 0.30 0.34272 1800 0.27 0.28 0.28 0.28 0.35272 2000 0.27 0.27 0.27 0.27 0.34272 2200 0.25 0.26 0.27 0.27 0.35273 0 0.23 0.25 0.27 0.28 0.35273 200 0.26 0.26 0.27 0.29 0.35273 400 0.28 0.30 0.30 0.35 0.35273 600 0.26 0.30 0.34 0.35 0.35273 800 0.26 0.31 0.34 0.36 0.35273 1000 0.26 0.32 0.34 0.35 0.35273 1200 0.26 0.32 0.34 0.36 0.35273 1400 0.26 0.33 0.34 0.36 0.35273 1600 0.28 0.33 0.34 0.36 0.35273 1800 0.28 0.33 0.34 0.36 0.35273 2000 0.29 0.33 0.34 0.36 0.35273 2200 0.29 0.33 0.34 0.36 0.35274 0 0.28 0.33 0.34 0.36 0.35274 200 0.28 0.33 0.34 0.36 0.35274 400 0.28 0.33 0.34 0.36 0.35274 600 0.28 0.32 0.33 0.35 0.35274 800 0.27 0.32 0.33 0.36 0.35274 1000 0.27 0.32 0.33 0.36 0.35274 1200 0.27 0.32 0.33 0.36 0.35274 1400 0.26 0.32 0.33 0.36 0.36

128

Day Time 0-30 30-60

60-90

90-120

120-150

150-180 0-30 30-60 60-90 90-

120120-150

150-180

274 1600 0.26 0.32 0.33 0.36 0.35274 1800 0.26 0.32 0.33 0.36 0.35274 2000 0.25 0.31 0.34 0.36 0.35274 2200 0.25 0.31 0.34 0.36 0.36275 0 0.25 0.31 0.34 0.36 0.35275 200 0.24 0.31 0.33 0.36 0.35275 400 0.24 0.31 0.33 0.35 0.35275 600 0.24 0.30 0.33 0.36 0.35275 800 0.23 0.30 0.34 0.36 0.36275 1000 0.23 0.30 0.34 0.36 0.35275 1200 0.23 0.30 0.34 0.36 0.35275 1400 0.22 0.30 0.33 0.36 0.36Note: Blank cells indicate incorrect readings. Data collection in TDR-1 in the non-irrigated zone was discontinued after DOY 253.

APPENDIX CCOMPARISON OF SIMULATED SWC VALUES FROM RAWLS INPUT

PARAMETERS, TDR MEASUREMENTS, AND OBSERVED SWC IN THEIRRIGATED ZONE 1

0.00

0.05

0.10

0.15

0.20

0.25

0.30

150 180 210 240 270 300

Day of Year

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, cm

3 cm

-3

Rawls Simulations Using Gravimetric Initial SWCGravimetric MeasurementsTDR Measurements

Figure C-1. Simulated volumetric SWC using Rawls soil input parameters versus observed volumetric SWC values from gravimetricmeasurements and irrigated TDR1 in the 30-60 cm Layer.

130

131

0.05

0.10

0.15

0.20

0.25

150 180 210 240 270 300

Day of Year

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3 cm

-3


Figure C-2. Simulated volumetric SWC using Rawls soil input parameters versus observed volumetric SWC values from gravimetricmeasurements and TDR in the 60-90 cm Layer.

132

0.05

0.10

0.15

0.20

0.25

150 180 210 240 270 300

Day of Year

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Figure C-3. Simulated volumetric SWC using Rawls soil input parameters versus observed volumetric SWC values from gravimetricmeasurements and TDR in the 90-120 cm Layer.

APPENDIX DIRRIGATION AND RAINFALL DATA FROM RAIN GAGES AND THE WEATHER

STATION

134

Table D-1. Irrigation and rainfall data (mm) collected from rain gages (RG) and aweather station from the Georgia Automated Environmental Monitoring Network(http://www.griffin.peachnet.edu/bae/)

Date Day ofYear RG 2 RG 3 RG 5 RG 6 RG 1B Weather Station

6/5/98 156 10.9 10.9 8.4 8.6 9.7 8.96/6/98 157 11.4 8.6 7.9 8.4 9.1 7.96/11/98 162 3.0 4.8 3.8 5.1 4.2 0.06/14/98 165 2.5 6.1 4.8 10.4 6.0 1.06/15/98 166 3.3 7.4 5.8 5.3 5.5 0.86/16/98 167 5.3 10.2 7.9 8.1 7.9 0.06/17/98 168 5.3 14.0 7.4 0.0 6.7 0.06/19/98 170 14.2 18.8 15.7 17.0 16.4 8.66/20/98 171 0.3 0.3 0.0 0.3 0.2 0.36/25/98 176 19.3 20.8 16.3 17.0 18.4 16.56/27/98 178 0.5 0.5 0.5 0.3 0.4 0.36/30/98 181 4.8 9.9 7.9 11.4 8.5 0.07/1/98 182 4.6 10.4 0.0 7.9 5.7 0.07/3/98 184 13.7 11.2 11.4 11.4 9.9 9.47/6/98 187 7.6 16.5 9.1 9.4 11.7 0.07/7/98 188 7.9 18.0 18.3 13.5 11.4 0.07/8/98 189 23.6 25.1 20.1 19.8 25.1 18.87/9/98 190 7.6 9.7 7.4 7.4 6.9 6.97/13/98 194 17.3 18.3 15.5 16.5 16.5 16.87/14/98 195 14.0 15.5 11.9 10.9 13.7 11.47/16/98 197 29.0 33.3 24.9 24.9 28.4 27.27/17/98 198 0.0 0.8 0.8 0.3 0.3 0.37/20/98 201 0.5 1.5 1.3 0.8 1.3 1.07/22/98 203 0.0 0.0 0.0 0.8 0.0 0.07/25/98 206 7.6 0.0 16.0 0.0 13.2 0.07/26/98 207 8.4 22.4 16.0 14.7 9.9 0.07/27/98 208 17.5 23.4 16.5 19.3 20.3 16.87/28/98 209 0.0 35.8 25.4 28.4 0.0 24.67/29/98 210 37.6 44.7 33.3 33.5 35.1 30.27/30/98 211 0.0 0.3 0.0 0.0 0.0 0.38/1/98 213 44.2 50.0 39.1 35.8 41.4 36.38/7/98 219 4.3 3.0 3.0 2.8 3.6 3.0

Note: Shaded Values Indicate Averages of RG 2, 3, 5, and 6.

http://www.griffin.peachnet.edu/bae/

135

Table D-1 Continued.

Date Day ofYear RG 2 RG 3 RG 5 RG 6 RG 1B Weather Station

8/9/98 221 3.6 3.6 4.1 3.0 3.0 3.38/10/98 222 0.3 0.3 0.3 0.3 0.3 0.38/11/98 223 15.0 13.7 14.5 11.4 15.5 13.58/15/98 227 31.8 28.7 26.2 27.4 30.0 25.78/16/98 228 17.0 18.8 16.0 16.3 18.5 15.78/18/98 230 41.7 48.3 39.1 40.1 43.2 34.58/28/98 240 0.0 20.6 5.8 15.0 0.0 0.08/29/98 241 6.9 18.8 8.6 14.7 13.5 0.08/31/98 243 29.5 0.0 12.4 0.0 10.9 0.09/2/98 245 45.7 49.8 39.4 39.9 44.2 51.69/3/98 246 117.9 132.3 100.3 103.9 109.2 88.69/17/98 260 2.8 1.5 2.3 2.8 2.3 0.09/21/98 264 11.9 15.5 12.4 11.2 14.2 11.79/22/98 265 5.1 1.8 3.3 3.0 3.0 2.59/25/98 268 1.0 1.3 1.0 0.8 0.8 0.89/28/98 271 33.5 33.8 28.4 31.5 28.2 35.89/29/98 272 118.4 127.3 102.9 113.5 121.4 95.59/30/98 273 57.4 57.7 51.6 49.5 57.4 42.710/7/98 280 22.1 0.0 19.1 19.6 17.5 17.0

TOTAL 887.7 1025.7 844.0 853.9 880.4 686.2

136

Table D-2. Irrigation amounts (mm) used in the 1998 yield simulations.Day of Year Zone2 Zone3 Zone4 Zone5 Zone6

162 3.0 4.8 4.8 3.8 5.1165 1.5 5.1 5.1 3.8 9.4166 2.5 6.6 6.6 5.1 4.6167 5.3 10.2 10.2 7.9 8.1168 5.3 14.0 14.0 7.4 0.0170 5.6 10.2 10.2 7.1 8.4181 4.8 9.9 9.9 7.9 11.4182 4.6 10.4 10.4 0.0 7.9187 7.6 16.5 16.5 9.1 9.4188 7.9 18.0 18.0 18.3 13.5206 7.6 0.0 0.0 16.0 0.0207 8.4 22.4 22.4 16.0 14.7240 0.0 20.6 20.6 5.8 15.0241 6.9 18.8 18.8 8.6 14.7243 29.5 0.0 0.0 12.4 0.0260 2.8 1.5 1.5 2.3 2.8

Total 103.4 168.9 168.9 131.6 125.0

APPENDIX ECOMPARISON OF SIMULATED SOIL WATER CONTENT VALUES FROM

RAWLS INPUT PARAMETERS AND OBSERVED SWC VALUES

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

150

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, cm

3 cm

-3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

150

Volu

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3 cm

-3

Figure E-1. Zoncm (a) and 30-6

a

d

a

180 210 240 270 300

Day of Year

Simulated

Actual

e0

180 210 240 270 300Day of Year

3 c

138

: Simulated and observed soil water content in the soil profile: 0-30m (b).

139

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

Volu

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Con

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, cm

3 cm

-3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

150

Volu

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ater

Con

tent

, cm

3 cm

-3

Figure D-1 Co

c

d

Simulated

Actual

180 210 240 270 300

Day of Year

ntinued.

140

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

Volu

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ater

Con

tent

, cm

3 cm

-3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

150

Volu

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tent

, cm

3 cm

-3

Figure D-2. Zone cm (a) and 30-60

a

Simulated

Actual

180 210 240 270 300

Day of Year

4: Simulated and observed soil water content in the soil profile: 0-30cm (b).

141

0.000.020.040.060.080.100.120.140.160.180.200.220.240.260.280.300.320.340.360.38

Volu

met

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ater

Con

tent

, cm

3 cm

-3

0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

0.32

0.36

0.40

0.44

150

Volu

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Con

tent

, cm

3 cm

-3

Figure D-2 Conti

c

Simulated

Actual

180 210 240 270 300

Day of Year

nued.

142

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

Volu

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, cm

3 cm

-3

Simulated

Actual

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32

150 180 210 240 270 300Day of Year

Volu

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3 cm

-3

Figure D-3. Zone 5: Simulated and observed soil water content in the soil profile: 0-30cm (a) and 30-60 cm (b).

143

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32

Volu

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ater

Con

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, cm

3 cm

-3

0.000.020.040.060.080.100.120.140.160.180.200.220.240.260.280.300.320.340.36

150

Volu

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ater

Con

tent

, cm

3 cm

-3

Figure D-3 Co

c

d

Simulated

Actual

180 210 240 270 300Day of Year

ntinued.

144

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

Volu

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, cm

3 cm

-3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32

150

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, cm

3 cm

-3

Figure D-4. Zonecm (a) and 30-60

a

b

Simulated

Actual

180 210 240 270 300Day of Year

6: Simulated and observed soil water content in the soil profile: 0-30 cm (b).

145

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32

Volu

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ater

Con

tent

, cm

3 cm

-3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32

150

Volu

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ater

Con

tent

, cm

3 cm

-3

Figure D-4 Con

c

d

Simulated

Actual

180 210 240 270 300Day of Year

tinued.

APPENDIX FMANAGEMENT AND SOIL INPUT PARAMETERS FOR 1998 SIMULATIONS

147

Table F-1. Management and soil information for the 1998 simulation.Cultivar: Hartz 5566-RRMaturity group: 7Initial Plant Populations: 29.9 Plants/m2

Plant Row Spacing: 45 cmPlanting Depth: 4.0 cm

Initial Soil Water Content Conditions (cm3/cm-3)

Day of year 156Depth (cm) Zone2 Zone3 Zone4 Zone5 Zone6

0-30 0.079 0.127 0.080 0.156 0.18730-60 0.062 0.136 0.197 0.126 0.16460-90 0.066 0.180 0.273 0.190 0.19090-120 0.151 0.164 0.308 0.272 0.229

Observed Plant Population (Plants/m2)

Day of year 289Sample 1 12 9 7 9 11Sample 2 6 9 6 6 24

Other Soil Input Parameters

For all zonesAlbedo (fraction) 0.18Evaporation Limit (cm) 8.0Drainage Rate (fraction day-1) 0.40Runoff Curve Number (Soil Conservation Service) 73.0Mineralization factor (0 to 1 scale) 1.00Photosynthesis factor (0 to 1 scale) 0.92

Saturated Upper Limit (calculated with Rawls method) RootGrowthFactor

Depth (cm) Zone2 Zone3 Zone4 Zone5 Zone6 All Zones(0.0 to 1.0)

0-30 0.382 0.379 0.391 0.328 0.350 1.00030-60 0.377 0.315 0.326 0.372 0.328 0.49860-90 0.398 0.361 0.368 0.366 0.372 0.29490-120 0.355 0.333 0.358 0.350 0.357 0.133

148

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BIOGRAPHICAL SKETCH

Ravic Nijbroek was born on March 26, 1973, in San Juan, Puerto Rico. At the age

of one he moved to Paramaribo, Suriname. After receiving his high school diploma in

1991 from the Mr. Dr. J.C. de Miranda College, he moved to the United States to

continue his education. Ravic first spent two years at Santa Fe Community College in

Gainesville, Florida, studying general engineering principles. He transferred to the

University of Florida in 1994 and received his Bachelor of Science in Environmental

Engineering degree in 1997. Soon after, he joined the Crop Systems Modeling

Laboratory in the Agricultural and Biological Engineering Department in Gainesville.

Ravic received his Master of Engineering degree from this department with a

specialization in precision agriculture in December 1999.

Documents

UNDERSTANDING AND SIMULATING SPATIAL SOIL WATER AND …ufdcimages.uflib.ufl.edu/UF/00/10/07/07/00001/nijbroek.pdf · TDR1 plot. 0-30 cm layer. Arrows indicate the point when the drained