scheduling site-specific irrigation for cotton in the southeastern coastal plain soils using linear-move irrigation system

The protocol for generating the site specific water application map were formulated as follows; 1 collecting and measuring the fields soil spatial parameters needed for irrigation schedu

SOUTHEASTERN COASTAL PLAIN SOILS USING LINEAR-MOVE IRRIGATION SYSTEM

A Dissertation Presented to the Graduate School of Clemson University

In Partial Fulfillment

of the Requirements for the Degree Doctor of Philosophy Biosystems Engineering

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To the Graduate School:

This dissertation entitled “Scheduling Site-specific Irrigation for Cotton in the

Southeastern Coastal Plain Soils Using Linear-move Irrigation System” and written by Burhan A M Niyazi is presented to the Graduate School of Clemson University I

recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy with a major in Biosystems Engingering

Dr Tom O Owino, Dissertation Advisor

We have reviewed this dissertation

and recommend its acceptance:

The main goal of this study was to determine the optimum irrigation scheduling method for cotton production in the southeastern coastal plain soils utilizing site-specific irrigation management Soils in southeastern coastal plains have high variability that affects productivity and efficient farming Advances in technology related to precision agriculture in the recent years have shown promise in site-specific irrigation

Specifically, this study developed a protocol for generating a field water application map that accounts for soils spatial variability The generated application maps were then remotely linked to a modified variable rate linear-move sprinkler irrigation to compare four irrigation scheduling methods The five scheduling methods compared in this study were; Time Domain Tansmissometry (TDT) soil moisture sensors (Gro-point sensors), tensiometers, reference Evapotrispiration model (Jensen-Haise), automated evaporation pan, and no-irrigation (control)

The protocol for generating the site specific water application map were formulated as follows; 1) collecting and measuring the fields soil spatial parameters needed for irrigation scheduling; 2) dividing the field into management zones based on the spatial variability 3) selecting a scheduling method for each management zone; 4) calibrating the irrigation system for applying site-specific irrigation; 5) calibrating sensors and equipments under field conditions; 6) calculating irrigation depths and create field application map; 7) managing the site-specific irrigation system to irrigate the field based on the application map

The soil electrical conductivity (SEC) and soil texture data were used successfully

to divide the test field into 5 management zones (classes) There were no significant differences in plant available water (PAW) among the five soil classes except for class III and V Based on the pressure plate tests, there were no significant differences in water holding capacity of the areas of the test field with higher clay contents compared to areas with less clay and high sand contents The average available soil moisture for the test field (Varina soil) was 0.10 cm/cm The depth to the Bt horizon varied between 25 to 47

cm in the test field There was a strong correlation between the depth to the Bt horizon and SEC The calibration equations for different types of TDT sensors and tensiometers were developed under actual field conditions Procedures for calculating the irrigation depth for each irrigation scheduling method were developed

There were no significant differences in seed cotton yield between the four irrigation scheduling treatments However, all irrigated plots yielded significantly higher then the no-irrigation plots There was a significant difference in depth of irrigation water applied between treatments The soil classes I, II, and III produced significantly higher seed cotton yield in comparison to classes IV and V There was a positive correlation between SEC and cotton yield

It was found that TDT soil moisture sensors and tensiometers can be used successfully for site-specific irrigation scheduling in production fields However, since

the evaporation pan and ET models provide irrigation depths independent of the soil

variations, it is not suitable for site-specific irrigation management

I dedicate this dissertation to the soul of my dad, to my mom, to my brothers for their endless support, to my sister, to my wife and to my kids for making me happy.

First of all, all thanks and praises due to ALLAH (GOD) for His blessings and

mercy

My sincere appreciation is given to my advisor Tom Owino for his friendship and

for his guidance and encouragement that allowed me to complete this work

A special give my special thanks and appreciation to Dr Ahmad Khalilian, the research advisor, for making all resources in Edisto Research and Education Center available for me to conduct this research I would thank him for his tireless efforts to get this work done

I would like to thank Dr Dale Linvill for what I learned from him in the past few years Also, I would like to thank Dr Young Han for his help and attitude in conducting this research Special thanks should go to Dr Virgil Quisenberry for what I learned from him since I came to Clemson and for his support

I also like to thanks Ms Vickie Byko for her kindness and help in all needed

paper work and office procedures I would like to thank all faculty and staff of the

Department of Agricultural and Biological Engineering at Clemson University, especially

Dr Allen and Dr Drapcho for their support and kindness

A special appreciation is held for all of my friends for their support and

encouragement

I would like to thank my mom for her prayers and patience I would thank my wife for her support and understanding and to my brothers and sister for their support A

special thank, love and appreciation goes to my kids who always understand why I spend most of my time in school and wait to hug me when I come home

Finally, appreciation should go to Cotton, Inc for supporting this research

Page

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CHAPTER II LITERATURE REVIEW 157

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Irrigation Schedulingg - cà tk HhgHH HH HH g0 HH tà HH 0à 18 Irrigation Scheduling DevelOpIm€I( ¿- 5 tt 3S 9294 1111 te 13 Soil Properties Related to Irrigation Scheduling . «+ Series 29 Soil Electric Conductivity as an Indicator of Soil Properfies .- -‹ +- 36 General Evaluation for Irrigation Scheduling Techn1ques .- - 37 Trenđs in Irrigation Scheduling - 6 Sàn HH hưng 39

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Page Irrigation Scheduling Methods - - ch HT TH HH KH KH ky 68 Seed Cotton Yield Statistical ÀAnaÌYS1S - nh nàn HH HH ng Hiệp 82 CHAPTER IV RESULTS AND DISCUSSIƠN Ăn, 86 Measuring SoIl PTOD€TtI€S Ác L1 HT HT TH TH Tư HH nh Hy 86 VR-LMSIS Uniformity Test and Speed and Application Depth Calibration

A-1 SoIl particle sizes for all plots, EREC, 2005 - - SĂ Si 126 B-1 Pressure plate data for 3 depth of 15 plots, EREC, 2005 128 C-1 Sand and hardpan layer thickness, EREC, 200S -S.Ă cà eses 131 D-1 Uniformity test data for lateral-move sprinkler irrigation system,

ANOVA block analysis for seed cotton yield, EREC, 2005 142 ANOVA statistical analysis for water use on seed cotton yield, EREC,

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Table Page 2.1 SoIl particle size ranges associated with textural groups — 31 4.1 Soil classes and SEC values ofthe test fieÏd - sàn HH Hiện 88 4.2 Average soil texture of 0-45 cm depth for all plots in cotton field 90 4.3 Plant available water with soil classes In the test field - + «cv sersevee 92 4.4 Thickness of sand and clay layers for each pÏOf sgk, 96 4.5 Soil classes and soil physical properties in the test fleld 2 leet eeeeeeseeeneeeeeees 98 4.6 Linear speed and travel time for each 10 points increment speed setting 101 4.7 Comparing manufacturer and calculated irrigation application depths 103 4.8 Application depths at different on/off combInafIOnS «se 96 4.9 Application efficiency of the lateral move 1rrigation system 1n ©rec 108

4.10 Cotton seed yield and irrigation application in the test field in EREC, 2005 124

Gro-poInt SOI] mOISTfUT€ S€TISOY 5c 2012113303913 1191110 17 111 1g ngư GPSMS in the field connected to a data logger and radio communication system With solar panel .cccceesecsssecesscesecsssceseeeesneseseeceseeceseecsseecsseecssaseaeesses Different sizes Of temSIOMEterS cccecsecsseesseseesssesscceseeeecsereneesesecseeennesserenesss tnsïss05)s 3ï 20 11070707 NOAA automatic weather sfafIOT\ cà 131 1111111111 px kệ

Part of the LMSIS applying VR irrigation next to cotton field ow eee

Schematic Illustration of linear-move irrigation system for variable-

Tate APPLICATION eeeeescsssceseeesscesscssecenecssecsseseecesesseeeeeseeseeseeesesenecesecenecsees Shallow soIl EC map for cotton field In Edisto REC, Blackville, SC Deep soil EC map for cotton field in Edisto REC, Blackville, SC Cotton field divided into 25 plots according to EC afa ca Plots classes and numbering In the test Íield - 5+ +5 << vssvesee

List of Figures (Continued)

The updated SS-type Gro-point soil moisture sensor calibration - 112

Comparing the estimate of VSMC between the old and updated GPSMS

calibration equations for the shallow sOIÌ ÌAW€T Ăn, 113

4.15

4.16

4.17

4.18

4.19

4.20

4.21

The updated MS-type Gro-point soil moisture sensor calibration - 114

Comparing the estimate of VSMC between the old and updated GPSMS calibration equations for the deep soll ÌaW€f c2 115 Tension versus volumetric soil moisture content 1n the A horizon 116

Tension versus volumetric soil moisture confent In the Bt hor1zon 116

Flow chart for irrigation scheduling using LMSIS for VR application 119

Spreadsheet for site-specific 1rrigation scheduling using VR-LMSTIS 121

Cotton irrigation application map for September 4, 2005 .- ‹-.«©- 122 PAW within the root zone according to JHE in cotton field in EREC,

INTRODUCTION

Justification Cotton (Gossypioum hirsutum L.) is one of major textile fiber in the world representing 40% of the total world fiber production The United States produces 20% of cotton fibers of world’s production and it is considered to be the leading exporter of cotton fibers Cotton industry accounts for more than 25 Billions dollars and provides jobs for more than 400,000 people More than 5,700,000 ha were planted in 2005 in the

US generating around 5.5 billion dollars (USDA-NASS, National Agricultural Statistical Services website) Southeastern region is one of the four major cotton production areas in the US that produces more than 16% of the total US cotton production (Moore, 2005) In

2005, around 108,000 ha of cotton were planted in South Carolina producing 420,000 bales of cotton (USDA-NASS)

Most of the cotton grown in South Carolina depends on rainfall while farmers are slowly adopting the idea of having supplementary irrigation systems for cotton (Mathur, 2003) Every year, South Carolina receives a sufficient amount of rain during the growing season for good crop production However, the distribution is far from ideal and dry spells exist from time to time during the growing season which could reduce cotton yield (Mathur, 2003) There is a probability that in one out of three years, a dry spell of

21 days might occur A 14-day dry spell can be expected to occur sometime in the

region is highly variable even within a farm scale

Using supplemental irrigation system for cotton production increased yield in coastal plain soils (Nunnery and Wilson, 1960) Camp et al (1997) and Khalilian et al (2000) reported that using subsurface drip irrigation system increased cotton yield between 16 and 103% over non-irrigated cotton Therefore, irrigation can significantly increase cotton yields in South Carolina, especially when a dry spell occurs during a critical stage

Water is the most important resource for human being to exist on this earth Fresh water resources become scarce more and more all around the world (Niyazi, 1997) In general, most fresh water is consumed by agricultural activities especially irrigation In agricultural production, water is considered as the most limiting resource Thus, agricultural production is the most cause of fresh water resources pollution since most of non-point source pollutants carried by water from agricultural fields in runoff, percolation

or seepage processes (Niyazi, 2001) Therefore, every effort should be made to conserve water use in agriculture to protect water resources

The quantity of water applied to crop is very important to achieve higher yield Under-irrigation causes lower yield and lower crop quality, which means lower crop return Over-irrigation causes loss of water as a valuable resource and causes leaching of chemical and nutrients from root zone leading to higher cropping cost Also, over- irrigation might cause soil erosion form agricultural fields, which reduces soil productivity by removing top soil and nutrients Therefore, it is very important for the

improve his crop profit and to reduce cropping impacts on the environment

The process of determining how much water needed by the crop and when to apply is called irrigation scheduling So farmer has to schedule the irrigation for crops to increase crops’ profitability The key factors of irrigation scheduling are soil water status and current crop water use The main objective of irrigation scheduling is to increase crop profitability by increasing the efficiency of water use in farming

Several irrigation scheduling approaches exist and used by irrigators Some irrigation scheduling approaches depend on soil water monitoring techniques while others depend on plant monitoring techniques Some other irrigation scheduling approaches uses water balance techniques by estimating water used by crop using weather information or measured evaporation amount to calculate water depleted from crops’ root zone using some crop specific factors Soil monitoring irrigation scheduling techniques and water balance techniques are the most commonly used by farmers

Most of the cotton production in South Carolina is in the coastal plain sandy soils, which are inherently low in fertility and water holding capacity Low productivity and high spatial variability of southeastern plains of the United States reduce cropping profits due to increases in production costs and decreases in yields because of improper management of variations within the field Dealing with the field as one homogeneous unit does not help farmer to get the most profit from his crop This is because some parts

of the field need more or less treatment than other parts due to variation in soil around the

field.

in cropping efficiency by reducing production cost This reduction in cropping cost and efficiency resulted in increase in gross return form crops and reduces environmental impact of cropping practices (National Research Council, 1997) Site-specific irrigation management was the water management part of precision farming to solve water availability variation within the field Water availability variation across the field is due

to soil variability or non-uniform water application Thus, site-specific irrigation management is introduced recently to conserve water and increase cropping efficiency and return (Miranda, 2003) Therefore, using supplemental irrigation system and applying proper technology needed to manage soil spatial variability has a potential to increase crop profitability, conserve water and energy and reducing environmental impact

of agricultural practices

There is no published information on optimum irrigation scheduling method in cotton production for site-specific irrigation management Nor there is a standard procedure to schedule irrigation based on the field’s spatial variability

Research Objectives The overall goal of this project was to determine the optimum irrigation scheduling method for cotton production in southeastern coastal plain soils utilizing site- specific irrigation managements

The specific objectives were to:

- Develop irrigation scheduling procedures for cotton using a site-specific linear- move irrigation system to apply water based on field’s spatial variability

effects on crop responses

Determine the best irrigation scheduling method suitable for fully automated site- specific sprinkler irrigation systems

LITERATURE REVIEW

Background The United States occupies a huge area of land containing several climatic and

geographic regions The southeastern coastal plains of the United States consist mainly of

low fertility sandy soil with high spatial variability within a small scale This spatial variability of farming lands and low productivity pose a need for special management practices so productivity and production cost will improve

Farmers in the southeastern region of the US are slow to accept the idea to irrigate their cotton as a means of increasing productivity because of the high cost of irrigation systems and the high rainfall averages in the region—even if that rain is not well- distributed in a timely manner (Mathur, 2003) Another reason that makes the idea of irrigation difficult for farmers is the need for calculation and knowledge about the amount and time of the irrigation necessary (Miranda, 2003)

The United States produces 20% of the world’s production of cotton fibers and is considered the world’s leading exporter of cotton The southeastern region, mentioned above, is one of the four major cotton production areas in the US that produces more than 16% of the total US cotton production (Moore, 2005) In 2005, around 108,000 ha were used to produce cotton in South Carolina producing 420,000 bales of cotton (USDA-

NASS).

practices of cropping to improve crop yield (Raju, 1984) Irrigation was considered as an art in many ancient civilizations Irrigation was and remains the soul of agricultural productions in most countries while agricultural production is the main economical sector

in many countries Many lands, especially in arid regions, are impossible to use for agricultural production without irrigation practices

Today, irrigation has become a science that allows many disciplines to come together to get the best results using different irrigation scheduling techniques and modern irrigation systems Plant physiology, engineering, crop production, economics, management, environmental science, and health science are among disciplines that actively participate in developing irrigation practices The main reason that irrigation became a multidiscipline science was the vast increase in water demands around the world due to an increase in world population The increase in world population increased the demand for freshwater for drinking and other municipal uses as well as for industrial and agricultural use In general, water consumed in agricultural activities is the major part

of water consumption all around the world (Raju, 1984)

Irrigation System Developments Because traditional irrigation methods were labor demanding and less efficient in applying water to plants as well as the inherent lack of homogeneity in distributing water

in agricultural fields, irrigation engineers over time have developed better irrigation systems Some irrigation techniques were developed to efficiently deliver water to plants Sprinkler and trickle (drip) irrigation systems are the most efficient irrigation systems in applying water to plants (Hansen et al., 1980) Sprinkler irrigation includes different

hose-reel, and continuous move laterals while drip irrigation includes drip tubes, emitters, porous tubes and subsurface irrigation tubes

Some researchers classify drip and small sprayer systems such as micro sprayers, spinners, and bubblers as micro irrigation systems because they apply water to plants at relatively small application rates (Fares & Alva, 2000) These developed irrigation systems vary in their efficiency in delivering water to the plant root zone and in their capability to precisely and homogenously deliver irrigation water to all plants in the field Also, each system has some characteristics that make it suitable for specific environments (Israeli, 1987; Allen, 1991)

In recent years, irrigation designers and consultants shifted from high pressure

large gun and impact sprinklers to low pressure sprinkler sprayers and drip systems This

is due to the lower operating pressure and higher efficiency (Arland & Howell, 1999) Low energy precision application (LEPA) sprinklers are the most recent sprinkler devices introduced to the irrigation sector LEPA can achieve 95-98% efficiency due to the little effect of wind drift and evaporation and can be operated at low pressure (Arland & Howell, 1999)

With the recent advances in precision farming technology, site-specific irrigation started as the irrigation aspect of precision farming The first development of irrigation systems to apply variable rate was for chemical application in precision farming (King et al., 1995) King et al (1995) modified an existing 3-span linear-move irrigation system to apply variable-rate irrigation and chemigation They concluded that chemical application uniformity using a variable-rate irrigation system is equal to the uniformity of the

using a linear-move irrigation system and concluded that the system performs well in applying variable-rate irrigation

According to Oliveira (2003), there are three approaches to applying variable-rate irrigation from nozzles on self-propelled sprinkler irrigation systems such as center pivot and linear move irrigation systems These three approaches are 1) pulsing on/off nozzles for portions of time, 2) using multiple manifolds with different-sized nozzles, and 3) altering the aperture of a nozzle using inserted pin and time cycling

In 1996, Heermann and Buchleiter used the pulsing system to control flow from a modified linear-move irrigation system to apply variable-rate irrigation using economical GPS units They found that the error of the economical global positioning system (GPS) unit can be up to 30 meters which is not acceptable to apply variable-rate irrigation This low precision in the economical GPS was due to not using the differential correction for GPS, which was expensive at that time Wall et al (1996) tested the technology applied

by King et al (1995) on a full-scale (392m) linear-move irrigation system to a commercial center-pivot irrigation system They found that the technology can be used successfully in full-scale center-pivot systems

Evans et al (1996) linked the center-pivot model and crop simulation model in a GIS platform for map-based variable-rate water and nutrient application They recommended extending this technology to other agricultural regions In 1997, Wall used

an angular position encoder to detect the center-pivot angle for site-specific irrigation application to determine the system’s position instead of the GPS system He concluded

that using the angular position encoder in variable-rate application systems is both reliable and flexible

Sadler and Camp (1998) modified two center-pivot irrigation systems by adding three additional manifolds to the truss to provide eight application rates in each of the 13 segments, 10-m long, along the lateral They used a programmable computer-controlled management system to use stored GIS data and real time soil and crop measurement updates Camp et al (1998) concluded that using the system developed by Sadler and Camp (1998) to apply water and nitrogen to a fixed boundary field was successful and obtained satisfactory application uniformity Eberlein et al (1999) evaluated a prototype

automated irrigation control system installed on a 3-span, 95 m long linear-move irrigation system They concluded that the automated irrigation control system performed

accurately in applying herbicide for site-specific management

Camp et al (1999) stated that using a modified center-pivot irrigation system to apply variable-rate irrigation has a good spatial accuracy in applying water with good distribution uniformity Millen et al (2000) stated that the use of modified continuous move irrigation systems to apply variable-rate irrigation is useful for farmers and researchers especially in the southeastern coastal plains of the United States and similar sandy areas Perry and Pocknee (2003) indicated that cycling nozzles (on/off) and changing pivot speed to control irrigation application rate did not alter the system’s uniformity They found that the calculated Christiansen coefficient of uniformity of the system was 89% for both 100% and 50% (of nozzles’ capacity) application rates King and Wall (2001) tested a site-specific chemical application, using on/off pulsing control, installed on a modified center-pivot irrigation system to apply variable rate irrigation and

found that the coefficient of uniformity for water application was 87% for 33% irrigation rate and 92% for 100% irrigation rate

King et al (2002) stated that using site-specific irrigation management in potatoes can increase the gross receipts by $165/ha Oliveira (2003) studied the benefits of managing field variability in grouping His theory was that there exist an optimum layout between the complete site-specific irrigation management and the uniform grouping that leads to maximum return He studied what level of grouping or classification of field variability for water management can maximize profit from the tomato He found that partial grouping of field variability maximized the net average return from the crop by 15.5% while fully site-specific management did not increase yield significantly but reduced the net average return by 0.3% He recommended a degree of grouping to the

field variability that captures most of the variability in a scale that took into consideration

the cost of unwarranted site-specific management

Miranda (2003) developed and tested a low cost, solar-powered feedback irrigation controller for distributed control of fixed irrigation systems He used controllers close to sensors and actuators in the field to avoid lightning strikes and to reduce wiring requirements He stated that using low-power controllers is the only way for economic benefit of such systems He concluded that the system was effective in maintaining soil water potential in the root zone in the desired range Also, he concluded that the system performed satisfactorily in the priority scheduling approach for water allocation

At Clemson University’s Edisto Research and Education Center (EREC) in Blackville, South Carolina, Moore et al (2005) developed a linear-move irrigation system to apply variable-rate irrigation to cotton The system was designed to test

different irrigation scheduling techniques on cotton and manage high soil spatial variability They used the nozzle-pulsing technique to control application rate and used a

GPS system to determine lateral position Also, they used a wireless radio system for

communication between data acquisition systems and the variable-rate irrigation system Additionally, they used a high-speed wireless internet connection to allow the system to download daily weather data from the National Climatic Data Center (NCDC) of the National Oceanic & Atmospheric Administration (NOAA) for evapotranspiration calculations They found that the error in water application was less than 2%

Testing Irrigation Systems for Application Uniformity With 75-90% efficiency, linear-move and center-pivot sprinkler irrigation systems

are among the most efficient sprinkler irrigation systems in delivering water to the plant

root zone Irrigation efficiency is one of the most important farming factors affecting crop return in irrigated crops (Solomon, 1988, Center for Irrigation Technology)

Having an efficient irrigation system is not enough to get a high production in an irrigated crop; the uniformity of an irrigation system is more important in that aspect Applying water to a cropped field can give the best yield if the water is applied adequately and uniformly This is because a non-uniform irrigation can result in dry or wet spots within the field Plant canopy can interfere with the system uniformity by intercepting water drops and redirecting them around the plant thereby reducing the uniformity of the system (Rogers & Sothers, Irrigation Management Series)

Nozzles’ spacing and wetting radius mainly determine the uniformity of the system Uniformity of any irrigation system can be altered if the operating pressure is not maintained constantly, and the system is not operated properly Wear and tear can also

affect the uniformity of the irrigation system, so with time the system’s uniformity is expected to change (Jordan et al., 1999)

Non-uniform irrigation application can cause water loss through runoff and surface evaporation as well as through deep percolation where the system applies more water than needed in some parts of the field Non-uniform irrigation can also mislead the irrigation managers when scheduling irrigation since they will schedule irrigation upon the false information that assumes the system will apply specific depth of water all over the field The uniformity of an irrigation system can be evaluated using a uniformity

measures include the uniformity coefficient of Christiansen or the distribution uniformity

The Uniformity Coefficient of Christiansen The uniformity coefficient (CU) is a measure used to evaluate the application uniformity of sprinkler irrigation systems The most known and frequently used method

to evaluate the CU of sprinkler irrigation systems is called the Christiansen coefficient of uniformity proposed by J.E Christiansen in 1942 (Solomon, 1988; Zoldoske & Solomon, 1988; Zoldoske, Solomon, & Norum, 1994, Center for Irrigation Technology) To determine the CU of any sprinkler irrigation system, a catch can test should be run and the Christiansen equation applied (ASAE Standard, 2003):

where CU, is the Christiansen Uniformity coefficient, NV is the number of collectors used

in data analysis, V; is the volume or depth of water collected in the ith collector, and V is the arithmetic average volume or depth caught by all collectors

The Distribution Uniformity Another measure is usually used to evaluate irrigation systems’ uniformity that considers the part of the field that receives less water than intended It called lower quarter distribution uniformity (DU) because it calculates the average of the lowest 25%

of the irrigation application depths of the field then relates it to the average of irrigation depth over the whole field (Burt et al., 2000) Equation 2.2 is usually used to calculate the

Where V,, is the arithmatic average of collected depths or volumes in the lowest 25% of

the catch cans and V is the arithmetic average of collected depth or volume of all catch

cans

Application Efficiency of the Irrigation System

An irrigation system’s efficiency is very important because it tells how much water the irrigation system can deliver to crops in the field compared to the amount that comes out of nozzles assuming there is no other possible loss of water within the system The efficiency depends on several factors including evaporation, wind drift, and possibly even runoff or erratic spraying patterns resulting from off target spraying Therefore, the efficiency of the irrigation system is the amount of water that reaches the root zone

compared to that emitted from nozzles Since it is hard to measure the exact amount of

water that reaches the root zone, a simple way of evaluating efficiency might be followed

to determine the system’s ability to deliver water to soil surface

Many factors can affect the system’s efficiency such as air temperature, wind speed, nozzles’ wetted radius, and water droplet size (Rogers & Sothers, Irrigation Management Series) Field conditions such as topography, land cover, soil type, soil moisture condition, soil physical properties, and field surroundings that affect the field microclimate have a major impact on the water delivery efficiency of sprinkler irrigation systems Therefore, a system’s efficiency can change from one season to another depending on those factors mentioned above If the irrigation system remains in good operating condition and operates under good management, the application efficiency is expected to be high Regardless of the wind factor, temperature has the most effect on evaporation rate from water droplets, which means summer days cause more evaporation from sprinkled water (Burt et al., 2000) Equation 2.3 is used to calculate the application efficiency for the sprinkler irrigation systems

SS

Cotton Irrigation According to Nunnery and Wilson (1960), using a supplemental irrigation system for cotton production increases yield Camp et al (1997) and Khalilian et al (2000) reported that using a subsurface drip irrigation system can increase cotton yield between

16 and 103% over non-irrigated cotton depending on the time when irrigation is applied

Hunsaker et al (1998) found that cotton lint yield increased by 15-20% in high frequency surface irrigation with a 55% allowable soil moisture depletion treatment

Aujla et al (2004) found that seed cotton yield increased by 32% in drip irrigation over the check-basin method when treated similarly with water and nitrogen Even when water application was reduced to 75% of the required irrigation depth in drip irrigation, seed cotton yield increased by 12% over the check-basin method Also, they found that decreasing nitrogen fertigation resulted in reduction in seed cotton yield in all water application levels but was the greatest under high water supply

Wanjura et al (2001) studied cotton lint yield response to irrigation in Texas They found that cotton lint yield responded by an average of 11.6 kg/ha for each centimeter of irrigation applied in the range between limits of 4 and 54 cm of water with lint yield ranging between 855 and 1630 kg/ha Howell et al (2004) indicated that cotton requires a special balance between water and water deficit control to achieve high yield They stated that cotton is an alternative crop for Texas’s high plains that responds better

to rain and irrigation Smith et al (2004) recommended the use of deficit irrigation in cotton to prevent deep drainage

Irrigation Scheduling The large increase in agricultural production around the world in recent years combined with the old concept of cultivation that says “ the more water you apply to the field the more yield you get ” causes many problems that threaten water resources

on the earth One of these problems is pollution in water resources especially freshwater

resources due to runoff or percolation from agricultural fields (Niyazi, 2001) Nutrients and chemicals as well as other pollutants from agricultural fields affect the quality of

freshwater and threaten the habitats and biological diversity of many ecosystems around the world Another problem caused by extensive agricultural production is the extensive withdrawal from nonrenewable freshwater resources (Niyazi, 1997)

The decrease in storage and quality of freshwater resources as well as the demand

to increase economical profitability from agricultural activities were the driving forces behind increased understanding of irrigation processes Thus, irrigation scheduling was the result of increasing studies and research activities in the irrigation sector (Fares & Alva, 2000)

Irrigation scheduling is a technique that involves determining how much water is needed and when to apply it to the field to meet crop demands The main purpose of scheduling irrigation is to increase the profitability of the crop by increasing the efficiency of using water and energy or by increasing crop productivity These objectives can be achieved by reducing water loss through runoff or deep percolation or by controlling the environment around the plant (to reduce water stress or over-irrigation) resulting in a reduction in fertilizer and chemical loss from the crop root zone

The key factors in irrigation scheduling are soil water status and current crop water use A good irrigation schedule is one that provides enough water at specific times

to meet the crop demands with minimum water loss through runoff and deep percolation

as well as the least water stress to the plant

By using a proper irrigation schedule, the farmer can maximize his profitability

by increasing crop production and by reducing the pumping cost This is because eliminating or reducing improper conditions in the field, such as water stress, water

logging, deep percolation, and runoff, that cause loss of minerals and nutrients from the

root zone reduces the amount of water applied and increases crop yield and quality

Irrigation Scheduling Developments The modern philosophy of irrigation planning is to efficiently utilize the irrigation

resources This has lead to more studies to improve water use efficiencies in irrigation

(Israeli, 1987) The development of irrigation scheduling during the last few decades is reviewed in this section

The irrigation process involves delivering water to the root zone of the plant This technique has been used for thousands of years around the world using conventional methods, which were based on flooding the field partially or completely by water such as basin, furrow, border, contour ditches, and water spreading

Calle (1994) described the oldest irrigation scheduling techniques that relied on the farmer’s experience in observing plant and soil characteristics Farmers used to determine irrigation time when the soil became dry by feeling the soil between their fingers or looking for signs of the plant wilt The amount of water to apply was determined by the farmer’s experience; so with time the farmer learned how much to irrigate But this amount depended on the irrigation supply system on his farm

This old-time method of irrigation scheduling is no longer accepted because available technology and knowledge have shown the negative impact of this scheduling method on crop profitability and on the environment When plants start to wilt, it means drought stress has already caused damage to the crop and reduced yield Also, the feel or look of the soil does not give an exact amount of moisture that is available in that soil because they vary from soil to soil

The crop can get less than its needs or be over-irrigated because there is no way to control the amount of water to apply since farmers do not know how much to irrigate exactly As mentioned previously, less irrigation can decrease yield while over-irrigation can cause many economical and environmental problems such as leaching of nutrients and chemicals from the root zone, deep-water percolation, polluting groundwater and surface water resources, and water-clogging problems, which finally decreases crop

of rainfall during the irrigation season (Israeli, 1987; Fox, 1997)

According to Raju (1984), the main objectives of most irrigation scheduling research are to maximize water use efficiency, maximize yields, and maximize net revenue of the crop Most irrigation scheduling problems are solved for single or multiple crops by different emphasis Those emphases include timing of irrigation, quantity of water to be irrigated, and irrigation planning at reservoir level (Raju, 1984)

Canpolat (1997) considered irrigation scheduling as an applied optimization problem that has nonlinear behavior restricted by some constraints, which are difficult to

compute Also, he considered it as a major component of crop production He stated that maximum net return is the main objective of irrigation management, but this objective is constrained by several conditions such as minimizing irrigation costs, maximizing yield, optimally distributing the water supply, minimizing groundwater pollution, or optimizing production from a limited irrigation system capacity

Braunworth (1987) classified irrigation scheduling methods into two categories; the water balance methods and crop stress related indices Hoffman et al (1990) classified irrigation scheduling methods quantitatively into three categories; monitoring the soil, monitoring the plant, and soil water balance methods

In 1987, Israeli classified irrigation scheduling into three categories based on the irrigation scheduling objectives These three irrigation scheduling classes included: scheduling irrigation to maximize yield, scheduling irrigation to maximize yield per unit

of water used, and scheduling irrigation to maximize the net return Also, he mentioned that there are two different concepts on how to schedule irrigation depending on soil water content The first one is to irrigate while soil water content is high so the crop will not be exposed to water stress leading to high yield The second one is to not irrigate until

soil water content reaches a specific lower level that leads to maximum yield per unit water used or the maximum return

Calle (1994) classified irrigation scheduling into three categories including soil based irrigation scheduling, meteorological-based irrigation scheduling and

physiological-based irrigation scheduling Also, he classified irrigation scheduling techniques into three categories including soil moisture measurements,

evapotranspiration models, direct and indirect measurements of plant-water status and

growth stage Additionally, he stated that there are three basic scientific principles used for scheduling irrigation including soil based indicators, meteorological or evapotranspiration models, and plant indicators

George et al (2000) classified quantitative irrigation scheduling method into three approaches including crop-monitoring techniques (leaf water potential, canopy temperature), soil moisture monitoring techniques (soil moisture content or soil tension), and water balance techniques using soil water budgeting over the root zone by predicting evapotranspiration and soil water content upon meteorological data for the specified area

Water Balance Techniques Irrigation scheduling methods that use the soil moisture budget approach are most common because of their simplicity, which makes them very practical They only use a single estimate of crop water use, irrigation efficiency, effective precipitation and drainage to calculate input and output from the soil profile Input in the soil profile is irrigation and precipitation while output is drainage and evapotranspiration Soil moisture content in the field can be measured or estimated Soil moisture content measurements are labor intensive and not representative of the whole field because of soil spatial

variability in the field Also, soil moisture estimation is inexact so it causes errors in

estimation of water content in the soil profile Thus, this method is used to take

measurements or estimation of soil water content for the whole field and apply a specific

amount of irrigation for the whole field assuming that the field is homogeneous (Allen, 1991)

Several methods are used to determine soil moisture content, directly or indirectly, including gravimetric techniques, tensiometers, neutron probes, gypsum

blocks, and soil moisture sensors The irrigation application should replenish consumed water from soil profile by crop To determine how much to apply, soil moisture content should be determined Also, field capacity of soil in the field should be known so over- irrigation will not occur (Calle, 1994) The water balance approach is a way to calculate the application depth

Irrigation scheduling can be described as an input and output system The basic input required to estimate soil water depletion in the soil water balance approach are evapotranspiration, deep percolation from the root zone, irrigation depth, effective precipitation, and flow to the root zone from nearby shallow aquifers Martin et al (1990) mentioned that scheduling irrigation is usually based on the percent depletion of available water in the root zone Therefore, crop root depth, soil available water, and the depletion allowed are required to be entered as additional input to the system He stated that the results from water balance irrigation scheduling vary depending on the input models or prediction methods and the interaction between these input variables The following equation is used to calculate soil moisture depletion in the root zone (Braunworth, 1987):

where d; is the soil water depletion in soil root zone through day i, d;.; is the depletion on day i-1, ET; is the estimate of evapotranspiration, P; is the effective precipitation, JR; is the irrigation, and DR; is the drainage into or out of the active root zone

The amount of water to apply in irrigation can be calculated using the following equation (Braunworth, 1987):

D; = CV (2.5)

where CY is a critical value (usually equals to 50% depletion of available water)

Another equation is used to calculate the soil water balance (Calle, 1994):

where M;, is the soil water content at field capacity, Myis the final soil water content, M; is the initial soil water content, Jy is the irrigation water applied, R, is the rainfall, R, is the runoff, P is the deep percolation, and ET is the crop evapotranspiration

Estimating Evapotranspiration (ET) Many methods have been used to estimate crop ET during the last few decades They vary in their simplicity, the environments in which they are applied, and variables they consider in calculating ET Some of them are highly empirical and need few weather variables for their input e.g the Blaney-Criddle and Jensen-Haise approaches Some of them are complex such as the Modified Penman equation that requires many climatic variables to predict ET (Calle, 1994) The Penman-Monteith method proved to be one of the best methods in determining ET values under different climatic conditions for a monthly, daily, or even hourly basis (Chiew et al., 1995; Hussien, 1999; & Todorovic, 1999), Jensen-Haise equation has several forms depending on the units used and climatic areas for which it is calibrated It uses monthly average of daily global solar radiation and temperature to calculate reference evapotranspiration

A checkbook irrigation scheduling method is used by many irrigators because it is simple and easy to use This method uses daily maximum temperature and growth stage

to determine daily water use for the specified week Tables of daily water use and sheets for water balance are maintained as a checkbook to determine the amount of water to

apply and the time of application to avoid excessive soil moisture depletion This method

has some practical problems such as the calculations needed to check and calculate soil moisture content and irrigation requirements (Yar, 1984)

Scheduling irrigation by computer using crop curves is another approach used by some researchers This method relies on determining the quantity of water used by the crop using crop coefficients and potential evapotranspiration determined by a modified Penman equation for the specified day Therefore, the farmer has to determine the amount and timing of each irrigation event Also, this approach requires some calculations such as calculating water depletion from soil profile and using crop coefficients that make this method not suitable for farmers (Yar, 1984)

Computer-assisted irrigation scheduling is another approach that assumes soil moisture content is at field capacity at the beginning of the season with no precipitation assumed during the summer because the study was done in an Arizona, which is a semiarid land (Yar, 1984)

Jensen-Haise Equation The JHE is considered as a radiation-based method in estimating reference evapotranspiration It uses the radiation as the basic climatic parameter (Niyazi, 1997) It was originally introduced by Jensen and Haise in 1963 upon observing 3000 measurements of evapotranspiration in the western part of the United States over 35 years period of time There are several versions of this equation specifically developed for different climates and different crops (Jensen, 1980) The standard universal form of JHE