Techniques for computerized irrigation management

Computers and Electronics in Agriculture, 3 11989) 189-208 Elsevier Science Publishers B.V., Amsterdam - - Printed in The Netherlands

18!)

Techniques for Computerized Irrigation Management C.J. PHENE Water Management Research Laboratory, USDA Agricultural Research Service, 202I S. Peach Avenue, Fresno, CA 93727 (U.S.A.) (Accepted 18 November 1988)

ABSTRACT Phene, C.J., 1989. Techniques for computerized irrigation management. Comput. Electron. Agric, 3: 189-208. Water use efficiency can be improved by using irrigation systems which apply water uniformly at high application efficiency and/or by improving the schedule of irrigation. Irrigation scheduling is based on two fundamental decisions: (1) when to irrigate and (2) how much to irrigate. Computers can be used to make these decisions effectively, provided that timely and accurate inputs into the computer are available. In addition to decision making, computers can also be used to implement the decisions, monitor the performance of the irrigation system, adjust the irrigation applications as climatic or other conditions change during the irrigation, process feedback data to evaluate the irrigation process, and keep a complete accounting of seasonal and current irrigation or other activities. Some of the inputs needed for making decisions on irrigation are: soil and plant water status, plant growth and phenological stages, climatic conditions, water availability, water quality, and operational status of the irrigation system. Instrumentation and techniques to provide these inputs for the computer have been used in research and, in some cases, some of these techniques have been implemented commercially. Examples of instrumentation and techniques for computerized irrigation such as: automated weather station networks, soil and plant measurement systems, and dynamic simulation models of soil-plant systems will be presented and discussed.

INTRODUCTION P r o b l e m s a r i s e w h e n i r r i g a t i o n is p r a c t i c e d w i t h o u t r e s t r a i n t o r k n o w l e d g e o f b a s i c f a c t o r s g o v e r n i n g soil, i r r i g a t i o n w a t e r r e q u i r e m e n t s , s a l i n i t y , a n d p l a n t - w a t e r u s e . A g o o d e x a m p l e o f t h i s is p r e s e n t l y o c c u r r i n g i n t h e S a n J o a quin Valley of California where excessive agricultural drainage waters have no n a t u r a l o u t l e t s a n d t e n d t o a c c u m u l a t e a n d c o n c e n t r a t e s a l t s a n d t r a c e elements in soils and/or evaporation reservoirs beyond tolerable levels. Irrigation water requirements rate dependent upon precipitation before and

190

during the growing season, soil water-holding characteristics, water quality, rooting depth of the crop, and incoming energy intercepted by the plant and soil which causes the evaporative demand. Many other factors may interact and affect irrigation water requirements indirectly (Table 1 ) but they are difficult to quantify. As competition for the limited fresh water supply continues to increase, the choice of the appropriate best irrigation system for the conditions and objectives to be achieved in conjunction with irrigation water management will play a major role in assuring that the plant obtains the desired water quantity and/or quality with minimum non-beneficial water losses occurring in the process. The goal of water management should be for irrigation systems which allow the application of water at frequencies and amounts necessary to maintain high water-use efficiency levels. The development of irrigation methods capable of operating frequently, such as center pivots, lateral move, mini-sprinkler, trickle, and subsurface irrigation offer the means to maintain soil water at nearly constant levels and, thus, minimize or impose plant water stress at the desire of the irrigator (Phene, 1986a). However, with frequent irrigations, control of the soil-water-root enTABLE 1 Factors affecting irrigation water requirement (Phene, 1986a)

Water factors

Soil factors

water availability (amount and time ) water quality

soil structure soil texture soil depth mechanical impedance infiltration rate drainage rate soil aeration water retention characteristics hydraulic conductivity water table soil salinity soil fertility soil temperature soil borne organisms

Climatic~weather factors ambient temperature (day/night ) solar radiation wind speed rainfall humidity day length length of growing season

Plant factors crop variety rooting characteristic drought tolerance growth stage harvestable constituent yield and quality length of growing season salt tolerance nutrient requirement stomatal mechanism canopy architecture

Management factors dates of planting/harvesting plant population irrigation system critical growth stages fertilization crop protection cultivation

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vironment system is critically dependent on the irrigation manager whether h u m a n or computer. Any disruption or disturbance to the irrigation schedule will quickly create detrimental water or oxygen stress on the crop. Therefore,, control of high-frequency drip irrigation must be automatic, redundant, and capable of responding to small and rapid changes in soil water, plant water, or evapotranspiration. The objectives of this paper are to discuss: irrigation water requirements and water use efficiency, irrigation scheduling requirements and how the computer can be used to automate this process, input measurements needed for the computer to perform irrigation scheduling functions, and several examples of new technology which can be used for scheduling irrigation automatically. IRRIGATION WATER R E Q U I R E M E N T S

Many factors which affect water requirements must be considered when planning and operating irrigation systems: crop evapotranspiration (E~¢,), deep percolation (Dp), and runoff (Ro), precipitation (P), leakage losses (L) changes in soil water storage (A0), and an unit efficiency factor (E~) to counter the inability of the system to apply water precisely as needed to fulfill the irrigation water requirement (Doorenbos and Pruitt, 1977; Pruitt and Doorenbos, 1977; B u r m a n et al., 1983). B u r m a n et al. (1980) have defined the unit efficiency factor as the ratio of the depth (or volume) of irrigation water required for beneficial use (dbu) in the specified irrigated area to the depth (or volume ) of' water delivered to this area (d~): Ei-

dbu

(1~

di

The relationship between the factors affecting irrigation water requirement can be expressed mathematically as:

di=Et~,+Dp+R,,+L-P-AO-

Etc - P - AO Ei

(2}

where dl represents the total depth of water delivered to the area and required for irrigation. All units are in mm, except Ei which is a dimensionless fraction. Other water requirements include: seed germination, chemical applications, frost protection, climate modification, soil temperature and dust suppression (Bouyoucos and Mick, 1947). Irrigation systems and measurement methods which minimize these measurements are desirable. For instance, Dp in excess of leaching requirement of salinity control (Lr), Ro and L waste water and usually require additional management. Those systems which are not dependent on soil to transport the water across the field (sprinkler, center pivot, lateral move and drip systems ) can be managed so that Ro and L are minimized

192

or eliminated and Dp can be reduced to L r. Under nonsaline conditions L~ can be ignored. High-frequence irrigation applications can maintain 0 constant and under saline conditions leaching can be performed continuously during the irrigation season by applying the Lr plus Et~ or by applying one large amount for leaching before or after the growing season so that during the growing season:

di-

Etc -t-Lr - P

(3)

Ei

Thus, under well-managed high frequency irrigation regimes, Etc, El, Lr and P are the principal factors in determining irrigation water requirements. Precipitation and Lr can be measured and Ei can be measured or estimated based on design or from previous year's experience. Evapotranspiration is the most difficult factor to quantify and utilizing lysimeters is the only direct method for quantifying Etc. Indirect methods include: measurement of soil water, the combination method (energy balance and mass transport) for calculating reference evapotranspiration, evaporation pan, and dynamic plant simulation models. WATER USE EFFICIENCY Water use efficiency (WUE), yield per unit of water evapotranspired, has important implications when considering irrigation requirement, irrigation scheduling, soil and water conservation, productivity, and sustainability of irrigated agriculture Figure 1 shows the hypothetical relative yield of a crop and water losses by drainage and runoffas a function of water applied. The maximum relative yield

100

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Fig. 1. Hypothetical crop-water production function and water losses as affected by water applied.

19;3

(100%) corresponds to the m a x i m u m cumulative crop evapotranspiration ( E t ..... ). Once water applied exceeds E t . . . . . no further yield increase usually occurs and the excess water will be lost as drainage or runoff. In some cases, excess applications of water will cause yields to decrease, losses of soluble nutrients, and deterioration of soil physical properties. Unfortunately, the matter is complicated because these water losses (or deficiencies) occur every time irrigation water is applied so that the drainage and runoff losses shown in Fig. 1 may really occur simultaneously as evapotranspiration. Increasing WUE will depend greatly on the capacity of the irrigation system to apply water uniformly in time and space over the entire field to maximize yield. Irrigation amount and timing and uniformity of water application are the most important irrigation factors to be considered when yields are to be maximized and water losses are to be minimized. Irrigation timing and amount are the objectives of irrigation scheduling. IRRIGATION SCHEDULING

Irrigation scheduling involves two decisions: how much water to apply (volume or depth), and how often to apply water (frequency). Irrigation timing is usually based on either soil water measurements (Phene et al., 1973; Phene and Howell, 1984), plant measurements (Hiler and Clark, 1971; Stegman et al., 1976; Jackson, 1982 ), soil water accounting (Jensen et al., 1971 ), or various combinations of these methods. Irrigation quantity is usually based on the type of irrigation system, plant response to water deficit, plant growth stage, soil infiltration, salinity control, and soil water deficit. These decisions are critical to the management of any irrigation system but even more critical with drip irrigation since the primary objective of drip irrigation is to maintain a small portion of the soil profile at optimal soil water potential with relatively small and frequent applications of water. Therefore, the concept implies that irrigation frequency should increase as the soil texture becomes coarser and the soil becomes shallower. Weather influences m a n y of the factors required to make optimum irrigation scheduling decisions. Therefore, meteorological measurements are often used to make irrigation decision (Howell et al., 1984 ). Similarly, one needs to know precisely how much water is applied to the field and how this water is distributed in the soil profile throughout the field. Hence flowmeters and soil water sensing instrumentation (Phene, 1986b) are also necessary to make the best irrigation decisions. All these decision making processes are complex and interactive and are best suited for computers. Software can now be easily developed to manage multiple input measurements (soil water, plant water stress, plant growth, weather variables, water availability, water applied, etc.), to manipulate these measurements within the decision making system and to decide how much and when to irrigate automatically based on real time information. Although corn-

194

puters have been used increasingly in agriculture and particularly in Israel to control irrigation, they have not been in complete control, in that manual inputs and interventions are still made by the operators. In this paper, four computerized management techniques which do not require daily human intervention will be discussed. The first two of these methods have been used commercially in many countries. The last two methods have been used mostly in research applications. These methods include: (1) Irrigation scheduling based on evapotranspiration calculation (weather station ) (Howell et al., 1983, 1984 ) and measurement (lysimeter) (Phene et al., 1985b, 1986; Phene, 1986b). (2) Irrigation scheduling based on soil water measurements (Phene et al., 1978, 1979; Phene and Howell, 1984; Phene, 1986b). (3) Irrigation scheduling based on leaf water potential inferred from stem diameter changes (Klepper et al., 1971; Huck and Klepper, 1977; Parsons et al., 1979; Phene, 1986a). (4) Dynamic simulation models (Jensen et al., (1970). 1. Irrigation scheduling based on evaporation calculation and/or measurements

Scheduling frequent irrigation can be performed with automatic feedback systems based on evapotranspiration either measured directly by a lysimeter (Phene et al., 1986) or calculated from data collected by a computerized weather station (Howell et al., 1984). Figure 2 shows the block diagram for a lysimeter system operated as an irrigation controller for several years. Similarly, a weather station or a reference lysimeter (planted to grass or alfalfa) can be used to DISTURBANCE

Etc LYSIMETER IRRIGATION TANK REFILL VALVE OUTS DE WATER

I

LYSIMETER IRRIGATION VALVE I

LYSIMETER IRRIGATION "~'~ (~)~'-~'1 LYSIMETER TANK

]

WATER APPLIED

---- Etc

I

WEIGHING I LYSIMETER SCALE NO LEVELTANK

FEEDBACK I SYSTEM HP 85•3497

Fig. 2. Block diagram for close loop, feedback irrigation control system based on real time Etc from the lysimeter which was irrigated every time one mm of Etc occurred (adapted from Phene et al. (1986).

195

calculate crop evapotranspiration (Etc) based on calculated or measured ref-erence evapotranspiration (Etr) and a crop coefficient for each crop (Phene et al., 1985a). Figure 3 shows the layout of a system which uses weighing lysimeters a n d / o r a weather station to schedule irrigation automatically on sev.eral crops with remote i n p u t - o u t p u t transmission via telephone line. In one of our applications, a water tank was attached to the lysimeter so that the weight of the daily irrigation was included in the weight of the lysimeter. Every hour, the data acquisition system measured the scale weight of the lysimeter and calculated the weight change from the previous hour. Whenever ] mm of Etc had occurred during the hour, the lysimeter was automatically irri-gated with a i mm irrigation by a subsurface trickle irrigation system to main-rain the soil water content steady without altering the weight of the lysimeter. The water tank was refilled automatically daily at midnight to a constant level. An external water column gave visual verification that the water level in the tank was maintained and that each irrigation pulse applied exactly 1 mm of water (4 kg). Thus, the net daily increase in lysimeter weight represented the increased weight of the crop while weight decrease during the day represented Etc which was replaced at midnight when the tank was refilled to the same level. Figure 4 shows the flow chart of the algorithm used to measure lysimeter

FRESNO WATER M A N A G E M E N T RESEARCH LAB C E N T R A L COMPUTER /

LYSIMETER &lot WEATHER STATION

~ , ~

INSTRUMENTS (1) (2) (3) (4)

NET & SOLAR RAOIATION TEMPERATURE & HUMIDITY WIND SPEEO S DIRECTION RAINFALL & PAN EVAPORATION

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MICROLOGGER I CONTROLLER

f

FIELD MICRO COMPUTER DATA LOGGER/CONTROLLER

(t) (2) (3) (4) (5) (6)

CALCULATES Etr LOOK UP CROP COEFFICIENT K c CALCULATE Etc = Kc x Err SWITCH IRRIGATION VALVES ON/OFF UPDATES RECORDS/STORES DATA PRINT DATA SUMMARIES J

FIELD VALVES PUMP FILTERS " ~ FERTILIZER INJECTORS PRESSURE SOIL MOISTURE SENSORS I PLANT SENSORS I ETC...

Fig, 3. Weighing lysimeter and computerized weather station systems used for remote automatic irrigation scheduling by the USDA-ARS Water Management Research Laboratory.

196

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1

I Fig. 4. Logic flow chart for lysimeter load cell weight measurements and irrigation control sequenee for irrigating crop at three irrigation control levels (after Phene, 1986a).

weight, schedule irrigations in the crop lysimeter and in the surrounding field at two different irrigation frequencies, refill the lysimeter water tank daily at midnight, and calculate the evapotranspiration from the lysimeter. An experiment was conducted with processing tomatoes to verify that the lysimeter-controller system could adequately schedule irrigations in the field by a similar subsurface drip irrigation system (SSD) and by two slightly different surface drip irrigation systems, one operated at high frequency (HFSD) and the other operated at low frequency (LFSD).

197

A basic criteria for the lysimeter-controller to be successful is that the crop in the field surrounding the lysimeter be similar to that in the lysimeter. W h e n and if this is the case. Etc can be assumed to be the same in the field and in the lysimeter and the lysimeter-controller should schedule irrigations properly for the surrounding field based o n Etc from the lysimeter. A soil water balance was conducted using neutron probe measurements. Figure 5 shows the mean of eight volumetric soil water content profiles at the beginning (day 60) and at the end of the 198 experiment (day 213) for the tomato plots irrigated by the SSD system. The differences between the two lines represent the change in volumetric water content (A0) at each depth during the entire growing season. W h e n these differences are summed over the entire 3-m soil profile, the sum is 24 mm of water added to the soil profile over a period of 153 days. Because of the precision of the management of irrigation, we assumed that there was no deep drainage in the field. The time courses of the mean volumetric water content for the three soil profiles summed over a soil depth o:~' three m are presented in Fig. 6. The mean volumetric soil water content of the SSD t r e a t m e n t was 0.321 + 0.008 m3/m ~ from day 60 to day 213. The nine other measurements taken during the season are either within or very close to the profiles for day 60 and 213. The components of the water balance for the SSD, H F S D , and L F S D treatments are shown in Table 2. The Etc'S of the field-grown tomatoes estimated for the SSD (751 mm), the H F S D (744 ram) and the L F S D (724 mm) treatments are close to the Etc measured directly by the lysimeter (783 mm) which 0

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( m3 / m3

Fig. 5. Mean volumetric soil water content profile at the beginning (day 60 ) and at the end of the test (day 213 ) for the tomato irrigated by the high frequency subsurface drip irrigation system in 1985 {after Phene et al., 1986).

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140

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180

Irrigation Terminate(

t•

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200

220

Time ( Day Of The Year )

Fig. 6. Changes in the mean volumetric soil water content (A0) of the 3 m soil profiles for the plots irrigated respectively by the high and low frequency surface drip, and the subsurface drip irrigation systems in 1985 (after Phene et al., 1986). TABLE 2 Crop evapotranspiration (Etc) estimated from the soil water balance and measured directly with the lysimeter Treatments

SSD HFSD LFSD Lysimeter

Drainage, D (mm)

Change in soil water content, A0 (mm)

Precipitation, P (mm)

Irrigation, I Sprinkler (mm)

Drip (mm)

0 0 0 28

+24 +15 +29 0

19 19 19 19

107 107 107 107

649 633 627 685

Evapotranspiration, Etc (mm) 751 b 744 b 724 b 783 a

~'no irrigation cutoff. ~'irrigation cutback from day 189 and no irrigation from day 212 to day 216.

include 33 mm of irrigation water which was added to the lysimeter during the irrigation cutoff period in the field (day 212 to 216). Similar irrigation scheduling has been performed successfully on cotton using the weather station or a reference lysimeter planted to grass and a cotton crop coefficient developed for drip irrigation. 2. Irrigation scheduling based on direct soil water measurements

Scheduling frequent irrigations can also be accomplished with automatic feedback control based on soil matric potential. With drip irrigation, the storage capacity of soil is de-emphasized and water is applied to supply the water

199

potential continuum and match the evapotranspiration rate; thus, there is less; margin for error but timeliness is very important. irrigations based on soil water potential are among the oldest irrigation scheduling techniques used. Tensiometers (Richards and Gardner, 1936), thermal methods (Shaw and Baver, 1939 ), gypsum blocks (Bouyocos and Mick. 1947), and thermocouple psychorometers (Richards and Ogata, 1958), have; all been used to manually schedule irrigation successfully. Recently, Cary and Fisher (1983) used microprocessors and soil water sensors to simplify irrigation decisions. In one system, a micro-processor-based circuit coupled to a pro.grammable calculator provides an on-site estimate of the allowed time until the next irrigation, based on field data and an operator-supplied parameter. iX thermal method which measures soil matric potential independent of soil texture, temperature or salinity, is based on frequent measurements of the ability of a porous ceramic sensor to dissipate a small amount of applied hew: (Phene et al., 1971, 1973; Phene and Howell, 1984). With proper calibration, the sensor has been used to monitor soil matric potential and control irrigation automatically. In addition to water availability, soil physical properties such as oxygen diffusion rate ( O D R ) (aeration) and soil mechanical strength (impedance) are used to define the range of soil matric potentials (q/,~) optimal for root growth and activity. Figure 7 shows an example of the optimal ~y,, for a Hanford fine sandy loam. Within this range, a soil matric potential i,~ defined at which irrigations are to be started (threshold). The optimal [J,1 should be about - 2 5 to - 3 5 J / k g and has a range extending between a b o m 10 and - 60 J/kg. The optimal ~'m has a range which increases as soil texture becomes finer but is extremely narrow in compacted coarse-textured soils. Theretore, physical characterization of sandy soils can be approximated. For close loop-feedback automated irrigation, the soil sensor should be placed near the center of the root zone. In this location the majority of the root zone is :never allowed to dry below the soil matric potential threshold before the -

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200

Et

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/[SO,L MATR,JC ~ [ POTENTIAL

FIELD &

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VALVE SIGNAL CONDITIONING & TRANSMISSION FEEDBACK ELEMENTS 1 AND IRRIGATION

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CONTROL SYSTEM

Fig. 8. Close loop, feedback irrigation control system using soil matric potential (~J~) as the control variable. The time variable is used to adjust the amount of water being applied. Here ~m is dependent on evapotranspiration (Et) and the irrigation depth of applied water (d~).

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Fig. 9. Soil matric potentials at three soil depths in the root zone of a melon crop, irrigated by a high-frequency subsurface drip irrigation system controlled by a lysimeter, next to the emitter (top) and halfway between the emitters {bottom).

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I SET KEYBOARD PROGRAM INTERRUPTS I

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ENSOR CALIBRATION PARAMETERS

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LOAD iRRIGATION PARAMETERS FROM TAPE

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t Fig. 10. Flow chart showing the basic logic system for an automatic irrigation controller based on measurement of ~m.

202

sensor detects the drying trend and triggers another irrigation. Figure 8 depicts the close-loop automatic system used to control high-frequency irrigation systems (Phene, 1986a). In this system, the external variable evapotranspiration (E t) causes water extraction from the soil which disturbs the soil matric potential, measured by the sensor. Electrical signals are transmitted to the computer which analyzes the soil matric potential information with respect to the threshold level and either calls for irrigation or if not needed, prints out the status and repeat the operation with the next field. Typical soil matric potentials at three depths in the root zone of a high frequency subsurface drip irrigated melon crop are shown in Fig. 9. These data represent the hourly measurements obtained by the electronic soil matric potential sensors (Phene, 1986b) and a small portable micrologger (Campbell Scientific, Model 21X). Monitoring soil matric potential and controlling an irrigation system automatically requires equipment to: sample automatically several sensors sequentially, compare each sensor output to the threshold value, and have computer outputs capable of controlling and monitoring the irrigation system. Desktop computers and microprocessors have been successfully applied (Phene and Howell, 1984 ). Commercial equipment is also available to measure soil matric potential and control the irrigation system automatically (Phene, 1986b). Typical basic program logic is shown in Fig. 10 (Phene, 1986a).

3. Irrigation scheduling based on direct plant water measurements

Most of the plant growth processes are affected by plant water deficit; but cell enlargement (growth), photosynthesis, pollination, and fruit setting are particularly affected by cumulative low levels of plant water stress and can result in reduced yield and crop quality. Probably the plant process most sensitive to water deficit is growth by cell enlargement (Hsaio, 1973 ). When subjected to water deficit, the water content of the cells decreases and as the positive pressure potential (~,p) (also referred to as turgor) approaches zero, cell enlargement stops even though all other necessary chemical and physical requirements are met. Several methods are available to estimate plant water status. These include determination of relative water content, leaf diffusive conductance, leaf water potential (Hoffman and Rawlins, 1972), and plant temperature (Jackson, 1982). However, plant temperature and leaf diffusive conductance may not have the resolution needed at high leaf water potential (~'L). Leaf water potential from direct or indirect measurements is probably the best indicator of plant water stress. Measurement of ~L by the pressure chamber is performed routinely in research and production agriculture but does not lend itself readily

20,7

to automation. Automatic feedback control of irrigation systems can be achieved by using ~L estimated indirectly from stem diameter measurements (Parsons et al., 1979). Stem diameter and ~L are closely relatedto each other (Klepper et al., 1971 ). Thus, stem diameter measurements could be used to monitor continuously long-term stem growth and plant water status. Two methods are available in using stem diameter to predict the diurnal variation of xylem water potential (Huck and Klepper, 1977). The first and simplest procedure, called the 'Shrinkage Modulus Method', determines an arbitrarily calibrated shrinkage modulus and relates a measured change in stem diameter to a corresponding difference in leaf water potential. The second method, called the 'Dynamic Flux Method', simulates water flow between xylem and associated phloem parenchymal tissues, resulting from changes in plant water potential. Water potential differences between the xylem and surrounding tissues are assumed to induce a radial flux of water across the cambial boundary layer, causing swelling or shrinking of the stem. Stem diameter changes (AS) of continuously drying cotton plants was measured with a linear variable differential transformer (Parsons et al., 1979 ). The reference stem diameter, for computation of stem diameter change, was measured before sunrise throughout the experiment. Stem diameter stress was integrated numerically using the equation: tl

{4)

l s s = I A S ( t ) dt

~'~ -1000 y =-5013.2x - 220 r 2 -- 0 . 9 6

-1500 Z

W -2000 I

W

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g

COTTONII

-2500 w

3000 020

|

0.30 STEM

0.40

0.50

I

0.60

DIAMETER CHANGE ( r n m )

Fig. 11. Linear regression of minimum observed leaf water potential versus maximum stem dia m e t e r change from the reference stem diameter. Broken lines represent 90% confidence intervals based on the regression analysis (adapted from Parson et al., 1979).

204 where ISS is the integrated stem stress (mm day), to is the pre-sunrise time (h), tl is the post-sunrise time (h), and AS(t) is the stem diameter change, from the non-stress stem diameter (mm) at time t. Leaf water potentials were measured at sunrise and periodically each day to insure that maximum and minimum values were obtained. The relationship between the observed stem diameter change and the minimum observed ~L is presented in Fig. 11. This measurement technique could be used for feedback control of automatic drip irrigation systems. Periodic calibration of stem diameter changes versus ~L should be obtained at least for each phenological stage of the plants used for measurements. For cotton, irrigation threshold can be set at ~L ranging from -- 1500 to -- 1800 J / k g based on feedback calibrated stem diameter measurements. From crop phenological stages and known water requirements of cotton, ~L threshold value scan be adjusted as necessary. Simultaneous measurements of soil water and or Etc should be used as a feedback and to gain confidence in the method.

4. Dynamic simulation models Irrigation scheduling models based on evapotranspiration have been widely used worldwide (Jensen et al., 1970, 1971 ). Essential crop evapotranspiration (Etc) information required for these models and the irrigation decision criteria include: (1) a climatically estimated reference evapotranspiration (Etr), (2) an index for relating 'expected' crop water use to Etr (crop coefficient curve Kc), (3) an index for estimating the additional soil water evaporation from a wet soil surface, (4) an index for estimating the effect of soil water depletion on the actual Etc rates, (5) an estimation of extractable soil water volumes by specific crops from specific soils, and (6) a relationship between 'expected' crop yield and crop water use. Many of the input variables needed to operate the model are still not well defined and need to be estimated. These models can predict irrigation requirements accurately for low frequencies, but recent progresses in instrumentation and data communication make the method feasible for scheduling high-frequency irrigation as well. An Etc model which employs an hourly version of the Modified Penman equation (Pruitt and Doorenbos, 1977) has been used in California for 3 years on a state-wide network of approximately 45 computerized weather stations. The program, California Irrigation Management Information System (CIMIS), provides reference crop (grass) evapotranspiration (Etr) data to growers on hourly and daily bases. A phone modem, keyboard, and terminal are needed by the users to communicate with a computer located in Sacramento, California, through a menu-driven software package. Daily, at midnight, the computer automatically calls each weather station in the system, transfers all the weather data into its storage medium, and calculates Etr for each hour of the past 24 h.

205

The grower uses the previous day's or several day's Err with the proper crop coefficient, and makes the essential irrigation decisions of when and how much to irrigate. Normally a well trained irrigations advisor, employed either fulltime or hired on a consulting basis is used to schedule irrigation on a large farm but full automation could also be achieved if necessary. The feasibility of building simulation models of plant growth and yield (which can include the evapotranspiration and irrigation processes) has been demonstrated, and models of cotton, corn, alfalfa, soybeans, peanuts, sugar beets.~ wheat, and sorghum are now available. Such models have been developed at research locations in Israel, the U.S.A., England, Australia, and The Netherlands. They have in common the fact that they are dynamic material balance~, (Baker et al., 1978; Lambert et al., 1978). The model which I am most familiar with is GOSSYM (Baker et al., 1978) which incorporates a soil-water-root sub-model called RHIZOS (Lambert et al., 1978). RHIZOS provides the GOSSYM with three parameters; an effective soil water potential which is used tc, calculate plant water potential, an estimate of metabolite sink strength in the roots, and a nitrogen uptake rate. Irrigation decisions can be made by using a soil water potential threshold and irrigation accordingly. GOSSYM is arranged

Fig. 12. The dynamic simulation model of cotton, GOSSYM, showing its subroutine structure. The RHIZOS subroutines are included and called by CLYMAT AND SOIL (after Baker et al., 1978).

206

by subroutine as shown in Fig. 12. The RHIZOS subroutines are in the upper right of the figure called by, and including CLYMAT and SOIL. More recently, an update of GOSSYM call COMAX has been implemented for personal computers in the US by Baker et al. (Crop Simulation Unit, Starkville, MS, personal communication, 1987). One of these applications was used with several growers in California for irrigation scheduling. Results are not yet available, but it is only a matter of time until this method can be used in a full automated mode to manage and irrigate cotton and other crops. OTHER INSTRUMENTATION

Automated irrigation control systems should use feedback sensors to monitor on a real-time basis important functions such as: water quantity, flow rate, and water pressure. Continuous monitoring and control of system performance with flowmeters, pressure transducers, solenoid valves, and pressure regulators at strategic locations will enable irrigation operation at maximum efficiency (Phene, 1986a). Data or control functions can be transmitted by electrical wires, hydraulic lines, radio frequency signals, microwave, laser or infrared devices. The interest in automation of trickle irrigation systems has resulted in increased research and development in the field of instrumentation and hardware needed to accomplish the task. A large variety of instrumentation and hardware with varying characteristics are available commercially. These can be subdivided into six major categories: controller, valve, flowmeter, filter, WEATHER S TAT,ON

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207

chemical injector, and environmental sensor. Details of installation, function and maintenance are not the objective of this paper but many of these are discussed by Phene ( 1986a ). A schematic of a fully automated trickle irrigation system showing various input-output functions is shown in Fig. 13. SUMMARY

The concepts of irrigation water requirement, water use efficiency, and irrigation scheduling have been reviewed. Computers can be used to implement irrigation decisions, monitor the performance of the irrigation system, adjust the irrigation applications as climatic or other conditions change during the irrigation, process feedback data to evaluate the irrigation process, and keep a complete accounting of seasonal and current irrigation or other activities. Some of the inputs needed for making irrigation decisions and examples of instrumentation and techniques for computerized irrigation such as: automated weather station networks, soil and plant measurement systems, and dynamic simulation models of soil-plant systems were presented and discussed.

REFERENCES Baker, D.N., Lambert, J.R., Phene, C.J. and McKinion, J.M., 1978. GOSSYM: a simulator of cotton crop dynamics. In: Proc. U.S.-U.S.S.R. Sem. Agricultural Industrial Complexes, 1 5 October 1976. Scientific Research Institute of Planning, GOSPLAN, Riga, pp. 100-133. Bouyoucos, G.N. and Mick, A.H., 1947. Improvements in the plaster of Paris absorption block electrical resistance method for measuring soil moisture under field conditions. Soil Sci., 63: 455-465. Burman, R.D., Nixon, P.R., Wright, J.L. and Pruitt, W.O., 1980. Water requirements. In: M.E. Jensen (Editor), Design and Operation of Farm Irrigation Systems. ASAE Monogr., 3:189 232. Burman, R.D., Cuenca, R.H. and Weiss, A., 1983. Techniques for estimating irrigation water requirements. In: D. Hillel (Editor), Advances in Irrigation, 2. Academic Press, New York, pp. 335-394. Cary, J.W. and Fisher, H.D., 1983. Irrigation decisions simplified with electronics and soil water sensors. Soil Sci. Soc. Am. J., 47: 1219-1223. Doorenbos, J. and Pruitt, W.O., 1977. Crop water requirements. FAO Irrig. Drain Pap. 24, Food and Agriculture Organization, Rome, 193 pp. Hiler, E.A. and Clark, R.N., 1971. Stress day index to characterize effects of water stress on crop yields. Trans. ASAE, 14: 757-761. Hoffman, G.J. and Rawlins, S.L., 1972. Silver-foil psychrometer for measuring leaf water potential in situ. Science, 177: 802-804. Howell, T.A., Miller, R.J., Phene, C.J. and Meek, D.W., 1983. Evaporation from screened Class A evaporation pans in a semi-arid climate. Agric. Meteorol., 29:111 124. Howell, T.A., Meek, D.W., Phene, C.J., Davis, K.R. and McCormick, R.L., 1984. Automated weather data collection for research on irrigation scheduling. Trans. ASAE, 27: 386-391,396. Hsaio, T.C., 1973. Plant responses to water stress. Annu. Rev. Plant Physiol., 24:519 570.

208 Huck, M.G. and Klepper, B., 1977. Water relations of cotton. II. Continuous estimates of plant water potential from stem diameter measurements. Agron. J., 69: 593-597. Jackson, R.D., 1982. Canopy temperature and crop water stress. Adv. Irrig., 1: 43-85. Jensen, M.E., Robb, D.C.N. and Franzoy, C.E., 1970. Scheduling irrigations using climate-cropsoil data. J. Irrig. Drain. Div. ASCE, 6 (IR1): 25-38. Jensen, M.E., Wright, J.L. and Pratt, B.J., 1971. Estimating soil moisture depletion from climate, crop, and soil data. Trans. ASAE, 14: 954-959. Klepper, B., Browning, V.D. and Taylor, H.M., 1971. Stem diameter in relation to plant water status. Trans. ASAE, 48: 683-683. Lambert, J.R., Baker, D.M. and Phene, C.J., 1978. Dynamic simulation of processes in the soil under growing row crops: Rhizos. In: Proc. U.S.-U.S.S.R. Sem. Management of Large-scale Agricultural Enterprises, 1-5 October 1976. Institute of Planning, GOSPLAN, Riga. Parsons, J.E., Phene, C.J., Baker, D.N., Lambert, J.R. and McKinion, J.M., 1979. Soil water stress and photosynthesis in cotton. Physiol. Plant., 47: 185-189. Phene, C.J., 1986a. Automation. In: F.S. Nakayama and D.A. Bucks (Editors), Trickle Irrigation for Crop Production. Development in Agricultural Engineering, 9. Elsevier, Amsterdam, pp. 188-215. Phene, C.J., 1986b. Measurement of soil water by the thermal diffusion method. In: Proc. California Plant and Soil Conf., 28-30 January 1986, Sacramento, CA, pp. 40-47. Phene, C.J. and Howell, T.A., 1984. Soil sensor control of high-frequency irrigation. Trans. ASAE, 27: 392-396. Phene, C.J., Hoffman, G.J. and Rawlins, S.L., 1971. Measuring soil matric potential in situ by sensing heat dissipation within a porous body: I. Theory and sensor construction. Soil Sci. Soc. Am. Proc., 35: 27-33. Phene, C.J., Hoffman, G.J. and Austin, R.S., 1973. Controlling automated irrigation with a soil matric potential sensor. Trans. ASAE, 16: 773-776. Phene, C.J., Fouss, J.L. and Sanders, DC., 1979. Water-nutrient-herbicide management of potatoes with trickle irrigation. Am. Potato J., 56: 51-59. Phene, C.J., McCormick, R.L., Miyamoto, J.M., Meek, D.W. and Davis, K.R., 1985a. Evapotranspiraton and crop coefficient of trickle-irrigasted tomatoes In: Proc. 3rd Int. Drip/Trickle Irrigation Conf., 18-21 November 1985, Fresno, CA. ASAE Publ., 10-85: 823-831. Phene, C.J., Meek, D.W., Davis, K.R., McCormick, R.L. and Hutmacher, R.B., 1985b. Real time crop evapotranspiration and determination of crop coefficients. In: Proc. Nat. Conf. Advances in Evapotranspiration, Chicago, IL, December 1985. ASAE Publ., 15-85: 122-129. Phene, C.J., Miyamoto, J.M., Davis, K.R., McCormick, R.L., and Liu, R.C., 1986. Automated feedback irrigation scheduling and control with a weighing lysimeter. In: Proc. 2nd Int. Conference and Exposition on Automation in Agriculture, 3-5 March 1986, Chicago, IL. American Society of Agricultural Engineers, St. Joseph, MI, pp. 135-147. Pruitt, W.O. and Doorenbos, J., 1977. Empirical calibration, a requisite for evapotranspiration formulae based on daily or longer mean climatic data. In: Conf. Proc. Evapotranspiration, 2628 May 1977, Hungarian National Committee, ICID, Budapest. Richards, L.A. and Gardner, W., 1936. Tensiometers for measuring the capillary tension of soil water J. Am. Soc. Agron., 28: 352-358. Richards, L.A. and Ogata, G., 1958. Thermocouple for vapor pressure measurement in biological and soil systems at high humidity. Science, 128: 1089-1090. Shaw, B. and Baver, L.D., 1939. An electrothermal method for following moisture changes of the soil in situ. Soil Sci. Soc. Am. Proc., 4: 78-83. Stegman, E.C., Schiele, L.H. and Bauer, A., 1976. Plant water stress criteria for irrigation scheduling. Trans. ASAE, 19: 850-855.