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Agronomy Journal 92:821-832 (2000)
© 2000 American Society of Agronomy

SPARSE CANOPY SYMPOSIUM INTRODUCTION

A General System to Measure and Calculate Daily Crop Water Use

Robert J. Lascano

Texas A&M Univ. Res. and Ext. Center, Rt. 3, Box 219, Lubbock, TX 79403-9757 and USDA-ARS, 3810 4th St., Lubbock, TX 79415 USA

r-lascano{at}tamu.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 Materials and methods
 Results and discussion
 Summary and conclusions
 REFERENCES
 
There is a need for an accurate method to calculate and to measure crop water use on real-time. We implemented a system that combines knowledge of crop water use and available technology to control the timely application of water. Our objective was to test the system and compare it to the empirical engineering approach that uses a crop coefficient to relate crop water use to a reference evapotranspiration. Technologies involved are the measurement of plant water use with stem flow gauges, of soil water with time domain reflectometry, and weather variables. Measurements are coupled with calculated values of crop water use obtained with the model ENWATBAL. A single computer controls all functions, for example, measurements, model execution, activation of water delivery system. The system was tested for a 2-yr period with cotton (Gossypium hirsutum L.) in Lubbock, TX, using surface drip irrigation. Field experiments were conducted on an Olton clay loam (fine, mixed, superactive, thermic Aridic Paleustolls). Comparison of measured and calculated values of crop transpiration and soil water evaporation were in close agreement. Simulated results indicated that for a 3-d frequency irrigation with small quantities of water the engineering approach lacks the resolution to accurately calculate daily requirements of cotton under the semiarid conditions of the Texas High Plains (THP). This is particularly true early in the growing season when predominant evaporative losses are from the soil and not from the crop. We conclude that the proposed system is general and can be applied to schedule irrigations based on accurate estimates of water loss.

Abbreviations: DOY, day of year • ENWATBAL, energy and water balance model • LAI, leaf area index (m2 m-2) • LEPA, low energy precision application • THP, Texas High Plains • TDR, time domain reflectometry • ET, evapotranspiration (mm) • ETo, reference ET (mm) • ETp, potential ET (mm) • Ec, crop transpiration (mm) • Es, soil water evaporation (mm) • and, Kc, crop coefficient


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
Materials and methods
 Results and discussion
 Summary and conclusions
 REFERENCES
 
THE THP REGION covers a large land area subject to different rainfall regimes and soil types resulting in several cropping systems. In general, rain in this semiarid region decreases from north to south and from east to west with an average long-term (1947–1996) annual rain of 504 ± 126 mm in Amarillo and long-term (1931–1997) mean of 458 ± 146 mm in Lubbock (NCDC, 1999). Most of the rain falls during the growing season; however, the monthly and annual pattern is highly variable. The topography of this region is relatively flat, with an average elevation of 1.1 km, and the majority of the soils in cultivation are classified in three dominant series: Olton, Pullman, and Amarillo (Unger and Pringle, 1981, 1998; Baumhardt et al., 1995). Soils tend to be sandier in the southern region, with a predominant sandy clay loam texture and finer-textured soils more common in the northern region. Every year approximately 4 x 106 ha are under crop cultivation with around 60% dryland and 40% irrigated with water from the Ogalalla aquifer (TWDB, 1996). This unique combination of different soils and rain pattern result in very diverse production systems across the THP. For example, the northern region is predominantly a grain-based system with winter wheat (Triticum aestivum L.), corn (Zea mays L.), and grain sorghum [Sorghum bicolor (L.) Moench] as the main crops; whereas, in the southern region cotton is the principal crop.

Water management for irrigation of cotton and other crops in the southern THP should consider three factors: (i) rain distribution and amount; (ii) well-capacity and application system used, for example, furrow, sprinkler, low energy precision application (LEPA) (Lyle and Bordovsky, 1981), and drip; and (iii) scheduling according to the soil water balance, and the crop's needs and physiological response to water. Rain distribution and amount is important before, during, and after the growing season. For example, late fall and early spring rains can adequately fill the soil profile and provide the cotton crop with adequate water during the growing season. However, much of this water will be stored in portions of the soil profile that will only be available when the crop is well established with a deep-rooted system. During the growing season rain is extremely variable in both amount and frequency and in most years inadequate to supply the crop water needs (Fig. 1) . For example, in Lubbock, TX, during the normal cotton-growing season from May to October average potential evapotranspiration (ETp) exceeds average rain by 1213 mm, and on an annual basis average ETp is 2431 mm, that is, five times > than rain (Dugas, 1983). In addition, the monthly CV for rain is >70%. Due to frequent droughts and the water demands of crops during the growing season irrigation was introduced to the THP in the 1950s and is now a common and well-established practice.



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  		Fig. 1 Mean monthly rain (1931–1997) and potential evapotranspiration (ETp) for Lubbock, TX. The bars indicate mean monthly rain ± 1 SD

 
Long-term irrigation trends in the THP were evaluated by Musick et al. (1990) indicating that from 1974 to 1989 the irrigated area was reduced by 28% with a corresponding decline of 44% in the groundwater use during this 15-yr period. However, during the last decade this trend has reversed and the irrigated area is 1.8 x 106 ha (TWDB, 1996). In the southern THP the decline of groundwater is manifested as an increase to the water table depth and/or a reduction in well-capacity. The depletion of the underground water source from the Ogallala aquifer has forced producers to adopt more efficient application systems, such as LEPA and drip, instead of sprinkler and furrow irrigation. In the southern THP, irrigation well-capacity ranges from a very limited (2.5 mm d-1) to adequate (>7 mm d-1) supply to provide the daily water requirements of cotton. An irrigation well producing 2.5 mm d-1 is the minimum amount of water recommended for LEPA irrigation of cotton in this area (Bordovsky and Lyle, 1996). In many cases, well capacity dictate the amount of water that can be applied regardless of environmental demand and needs of the crop.

In the THP, LEPA irrigation strategies for cotton and other crops are designed to make use of seasonal rain and to use irrigation water efficiently (Lyle and Bordovsky, 1983, 1995; Bordovsky et al., 1992; Howell et al., 1995; Yazar et al., 1999). Cotton is a perennial plant grown as an annual well adapted to arid and semiarid climates that responds well to frequent (<=3 d) deficit irrigation under the often short growing season of the THP (Bordovsky and Lyle, 1996). Deficit irrigation refers to the practice of applying less water than the water demand of the crop, which can be calculated by multiplying a reference evapotranspiration (ETo) by a crop coefficient (e.g., Allen et al., 1998). This method, referred to as the engineering approach, was first suggested by Jensen (1968) is now the standard and recommended procedure for irrigation of crops worldwide (Allen et al., 1998).

In the engineering approach, the standard procedure is to calculate ETo with a Penman-Monteith equation and then multiply ETo by either a single crop coefficient, Kc or a dual crop coefficient (Kc = Kcb + Ke), where Kcb is a basal crop coefficient and Ke is a coefficient for soil water evaporation (e.g., Allen et al., 1998; Ventura et al., 1999). This empirical approach is an approximation to estimate the daily water requirements of a crop and is probably best suited for infrequent (15–20 d) furrow irrigation practices designed to deliver a minimum of 75 to 100 mm of water per irrigation event. However, high frequency LEPA irrigation requires accurate calculation of irrigation quantities. These requirements are more prevalent under the semiarid conditions of the THP where the leaf area index (LAI) of a cotton crop seldom exceeds 3 and throughout the growing season a large portion of the soil surface, mostly between rows, is not covered by the plant canopy. Thus to describe and calculate the ET of a sparse canopy a mechanistic approach is necessary and can be used (e.g., Lascano et al., 1987). In this approach the simulation model ENWATBAL can be used to calculate the water and energy balance of the soil surface and plant canopy. The model ENWATBAL is a numerical model for calculating the disposition of energy and of soil water by a crop, given meteorological events, and soil and crop characteristics (e.g., Evett and Lascano, 1993; Lascano et al., 1994; Lascano and Baumhardt, 1996). In first analysis, the purpose of irrigation is to make up the transpiration losses of the crop without waste and this objective can be achieved using a model that calculates the water balance of the crop. Current emphasis on precision and conservation of water resources and on the delivery of water to the root system has given rise to control techniques that are physically sensitive to the rate and amount of transpiration (Lascano et al., 1996; van Bavel et al., 1996).

Our primary objective was to describe an accurate method to measure the crop water use in real-time and to control the timely application of water in the correct amount. A secondary objective was to compare calculated daily ET values obtained with the proposed method to values derived using the engineering approach. In our approach, measurements were coupled with the mechanistic ENWATBAL model (Lascano et al., 1987; Evett and Lascano, 1993). The proposed system was tested and evaluated using cotton as part of a long-term project to evaluate LEPA irrigation systems for cotton production in the THP.


   Materials and methods
TOP
 ABSTRACT
 INTRODUCTION
 Materials and methods
Results and discussion
 Summary and conclusions
 REFERENCES
 
Central Control System
A description of an integrated real-time measurement and calculation of a crop water use system has been given by Lascano et al. (1996). The central control system consists of five components: (i) measurement, (ii) calculation (simulation model), (iii) a feedback loop that verifies calculated values of crop water use, (iv) water delivery, and (v) a central processing and control unit. A brief description of each component follows.

Measurement
Soil water and temperature, soil heat flux, crop transpiration, and weather parameters were measured with off-the-shelf equipment (Fig. 2) . Profiles of soil water content are measured with time-domain reflectometry (TDR) equipment (Topp et al., 1980) using a Tektronix cable tester (Model 1502C, Beaverton, OR) and an automated multiplexed system (Vadose Equipment Co., Dynamax Inc., Houston, TX) described by Evett (1998a). Soil temperature profiles are measured with type-T thermocouples (Lascano et al., 1987) and soil surface heat flux is measured with heat flux transducers (Model HFT3-L, REBS, Seattle, WA). Crop transpiration is measured with the stem heat balance method (Baker and van Bavel, 1987) using commercial gauges (Dynamax Inc., Houston, TX). A weather station (Campbell Scientific, Logan, UT) measures air temperature and humidity (Model HMP-45C-L Vaisala Inc., Woburn, MA), wind speed (Model 05103-L, R. M. Young, Traverse City, MI), global (Model LI200X, LI-COR Inc., Lincoln, NE) and net (Model Q7.1-L, REBS, Seattle, WA) irradiance, and rain (Model TE525WS-L, Texas Electronics Inc., Dallas, TX). All sensors were connected to three dataloggers all linked to a central computer used to download raw data.



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  		Fig. 2 Connectivity of data acquisition systems to a computer used to measure weather, soil temperature, soil heat flux, crop transpiration, and soil moisture. Also shown is the type of asynchronous connection between the central computer and sensors and synchronous serial from the TDR to the computer (From Lascano et al., 1996)

 
Calculation
The ENWATBAL model (Lascano et al., 1987; Evett and Lascano, 1993) was used to calculate soil water and temperature profiles, soil surface heat flux, and water evaporation from the soil surface and from the plant canopy. These values were then compared to measured ones. In addition, terms of the water and energy balance for both the soil surface and plant canopy are calculated. The model was executed daily after midnight using as input measured half-hourly weather data from the previous day.

Feedback
This system was designed so that measured crop transpiration and profiles of soil water content and temperature could be compared to calculated values obtained with ENWAT BAL. All measurements and data collection are automated and processed via a central computer that also executes the model. Comparison of measured to calculated values can be done for instantaneous as well as for integrated values.

Water Delivery
The amount of water to be applied to the root zone via an irrigation system can be calculated from the simulated values obtained with ENWATBAL and verified by the measurements of crop transpiration and soil water content. Once the amount of water is calculated the water delivery system is activated and monitored to apply the correct amount of water. The delivery of water is controlled and monitored by the central computer.

Central Control
A single common computer links and controls all system components. The main computer functions to download raw data from all sensors and to process, reduce and format input data to execute ENWATBAL. Software written in Visual Basic v. 6.0 (Microsoft Corp., Redmond, WA) compared measured and calculated values of crop water use, and activated the water delivery system. In addition, a graphical interface provided the user with the opportunity to view and plot instantaneous, hourly and daily values of all measured variables and calculated values obtained with the model.

Data Acquisition Systems
Hardware used for data acquisition were selected based on three criteria, (i) must be commercially available, (ii) must be portable and rugged for field use, and operate with DC current for remote use, and (iii) the system must be of modular design. Advantages of a modular design are that the user can customize applications and not all sensors are connected to a single datalogger. This configuration avoids unnecessary downtime in case of failure on the single datalogger. Data backup is enhanced because data are not only stored in individual dataloggers, but also in a common computer. A diagram showing how field equipment was connected to dataloggers and to a central computer is shown in Fig. 2 (Lascano et al., 1996).

With the exception of the TDR system, one CR7X and two CR10X dataloggers (Campbell Scientific, Inc., Logan, UT) were used to measure all variables (Fig. 2). The CR7X was used to measure weather variables, soil temperature with thermocouples and soil surface heat flux with transducers. Frequency of sampling was 10 s and 30-min averages were calculated and stored. The CR10X's were used to measure crop transpiration each handling up to 32-flow stem gauges (Flow-32 System, Dynamax, Inc., Houston, TX). Frequency of sampling was 15 s and 30-min averages were processed and stored. Output from the three dataloggers were first converted from an asynchronous RS-232C to an asynchronous RS-484 signal (Signal Converters, Black Box Corp., Pittsburgh, PA) and then reconverted to a asynchronous RS-232C that was input to a 8 serial port multiplexer (Code Operated Switch II, Black Box Corp., Pittsburgh, PA) connected to a laptop PC computer. These conversions were necessary to maintain the integrity of signals from the dataloggers to the main computer located in a portable shed (2.4 x 3.0 m) next to the field.

Soil water content was measured by TDR using a cable tester (Model 1502C, Tektronix, Beaverton, OR). A total of 90 three conductor wave-guides, each 0.20-m long, were multiplexed (Vadose Zone Equip. Co., Dynamax, Houston, TX). Each multiplexer can handle up to 16 signals from TDR wave-guides connected to a 50 ohm coaxial (unbalanced) cable to one output (Evett, 1998a). Output from the TDR cable tester was directly connected to the laptop computer, that is, synchronous RS-232C signal. Frequency of sampling was 30 min.

The laptop computer, RS-232C multiplexer, cellular phone, and irrigation control were all located in the portable shed. The cellular phone transmitted raw and unprocessed data to a computer server, for network access, and to a desktop computer located at the facilities of the Texas Agric. Exp. Stn. in Lubbock. This desktop computer was also used to run the ENWATBAL model.

Software
Whenever possible public domain and/or commercially available software was used to activate and control measurement sensors, and for data retrieval and processing. To monitor weather data we used PC-208, GraphTerm v 2.0 (Campbell Scientific Inc., Logan, UT), with the stem flow gauges we used FLOW-32 v 2.1 (Dynamax Inc., Houston, TX) and for TDR data acquisition and control we used TACQ.EXE (Vadose Zone Equip. Co, Dynamax Inc., Houston, TX) described by Evett (1998b).

An interface shell to provide the user with an instrument control panel was written in Visual Basic (Fig. 3) and we also used Norton pcAnywhere (Symantec Corp., Eugene, OR) for remote monitoring. The control panel named Remote Instrument Monitoring and Control Software (RIMACS) allows the user to select functions, irrigation controls, soil moisture (TDR), and FLOW-32. The user function allows copying and downloading data from any of the field dataloggers, monitoring the Flow-32 and weather station input locations. The RIMACS shell interacts with all data acquisition systems and gives the user instantaneous access to any field sensor.



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  		Fig. 3 Remote instrument monitoring and control software (RIMACS) that provides the user with user functions, irrigation control, soil moisture (TDR) and Flow 32. The User Functions options are shown

 
Energy and Water Balance Model
For the calculation of the soil water balance and the separate calculation of soil water evaporation (Es) from crop transpiration (Ec) for a sparse cotton canopy we used the ENWATBAL model (Lascano et al., 1987). This model is based on three different models that are CONSERVB (Lascano and van Bavel, 1983 and 1986), WATBAL (van Bavel et al., 1984), and MICROWEATHER (Goudriaan, 1977; Chen, 1984). The ENWATBAL model has been applied to calculate the ET of cotton (Lascano et al., 1987; 1994), sorghum (Krieg and Lascano, 1990; Lascano, 1991; Zaongo, 1993; Qiu et al., 1999), and corn (Evett et al., 1991). In addition, ENWATBAL has been used to calculate the effects of N on water use of irrigated and dryland sorghum (Lascano, 1991). The effects of crop residue on the water balance (Lascano et al., 1994) and energy balance (Lascano and Baumhardt, 1996) of a cotton system have also been evaluated with ENWATBAL.

Calculated values of Es and Ec, and their sum ET, have been experimentally verified and tested for a wide variety of soil and environmental conditions. These tests have shown that ENWATBAL correctly calculates ET and its two components. The ENWATBAL model is a one-dimensional numerical model coded in the BASIC language (Evett and Lascano, 1993) and runs on personal computers. The solution method consists of the simultaneous solution of the flow for both water and heat in the soil and the equations that define the energy of the soil surface and plant canopy. For each energy balance the surface temperature that satisfies each balance is solved implicitly by the method given by Press et al. (1986) and described by Evett and Lascano (1993). The integration time-step used was a variable time step algorithm independent of the integration function that in this case was rectangular. In addition, the user has the option to specify a lower and an upper time step. Runtimes for a 100-d simulation on a 300 MHz, Pentium-II based PC typically take <2 h. The theory of the model can be found in Lascano et al. (1987), and Lascano and Baumhardt (1996).

Inputs to ENWATBAL are grouped in three categories: soil, plant, and weather-related parameters. Soil inputs are the hydraulic properties of each soil horizon, and the number and thickness of each soil horizon that define the geometry of the soil system being modeled. Plant inputs are the relation between leaf conductance and leaf water potential, leaf conductance and incident short-wave irradiance, root distribution as a function of depth and time, and crop LAI as a function of time. Weather inputs are air temperature and humidity, short-wave global irradiance, windspeed, and rain and irrigation as a function of time. Weather input data may be either half-hourly or daily. Additional inputs are the screen height for meteorological measurements and initial profiles, that is, onset of simulation, of soil water content and soil temperature. Additional information on input data for ENWATBAL is given in Evett and Lascano (1993).

In our simulations we used the hydraulic properties given by Lascano and van Bavel (1983), Lascano et al. (1987) and Baumhardt et al. (1995). Plant inputs used are given in Lascano et al. (1987) and Lascano and Baumhardt (1996). We used half-hourly weather-input data measured with the weather station located in the center of the experimental field.

Field Experiments
The system described was used to measure and calculate crop water use in real-time of cotton during the 1994 and 1995 growing season in Lubbock, TX. In 1994 the cotton was furrow irrigated and in 1995 the crop was irrigated with a surface drip system used to simulate a LEPA irrigation system (e.g., Bordovsky et al., 1992). The field was 80 x 210 m located at the Texas Agric. Exp. Stn., Lubbock on an Olton clay loam. Field performance and reliability of the system was tested in 1994 and irrigation strategies recommended for cotton (Bordovsky et al., 1992) in the THP were tested in 1995. In addition, during 1996 to 1999, different irrigation levels with alternate and every furrow irrigation were evaluated. Furthermore, in these tests different termination dates of irrigation and the effect of a growth regulator, that is, mepiquat chloride, on cotton lint yield were also included.

In 1994, the experimental field was uniformly treated as no treatments were used. In 1995 to 1999, experimental design was a randomized, split by four irrigation levels, split by alternate and every furrow irrigation, split by three termination dates, and split by two growth regulator rates. Each irrigation level plot was 14 m wide and 100 m long and replicated four times. Irrigation was applied with a surface drip used to simulate LEPA on a 3-d frequency. Irrigation levels were dryland, low (2.5 mm d-1), medium (5.0 mm d-1), and high (7.5 mm d-1), which represent the range of well capacity across the southern THP. The three termination dates were 900, 1000, and 1120 cumulative heat units since emergence using a 15.5°C base temperature. The growth regulator mepiquat chloride was applied at the pinhead and 50% bloom growth stages at a concentration of 0.28 kg ha-1 using a broadcast applicator with a ground spray rig.

Each year the experimental field was fertilized with 110 kg ha-1 of N and 45 kg ha-1 of P applied before planting the crop, based on recommendations from the local soil testing laboratory. Cotton variety planted, planting and emergence dates between 1994 and 1999 are given in Table 1 . In all years, cotton was planted in bedded rows, 1.0 m apart along an E-W orientation at a density of 120000 plants ha-1. Both pre-emergence (Trifluralin and Prometryn) and post-emergence (glyphosate directed spot treatment with shield) herbicides were applied using recommended rates for cotton on an Olton soil.


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  		Table 1 Cotton variety planted, and planting and emergence dates at Lubbock, TX, in 1994–1999

 
Measurements
Due to the large number of treatments only the high and low irrigation levels with every row irrigation were instrumented to measure Ec and soil water content with TDR. Soil water content and temperature, Ec, soil surface heat flux and weather variables were measured using sensors and methods previously described. The net radiometer used was calibrated by comparing its output to readings from a calibrated net radiometer (J.M. Baker, personal communication, 1995) In addition, in 1994 Es was measured for 12 d after an irrigation using mycrolysimeters (Lascano and van Bavel, 1986; Lascano et al., 1987). Every year, the LAI was measured as described by Hicks and Lascano (1995). Soil heat flux at 0.05-m depth on a bed and furrow was measured with a set of three transducers connected in series. Soil temperature was measured on the bed and furrow with type-T thermocouples at depths of 0.05, 0.10, 0.20, 0.30, 0.50, 1.0, and 2.0 m from the top of the bed and furrow. These measurements were replicated three times. Soil water content was measured weekly with a neutron meter and every 30-min with a TDR system. Three aluminum neutron access tubes were installed in each irrigation plot to a depth of 3.0 m and measurements were made every 0.25-m starting 0.25 m from the soil surface. Access tubes were placed in beds along the planted row. The neutron probe was calibrated for this soil using the procedure given by Lascano et al. (1986). The TDR system had 6 multiplexers connected to waveguides at six locations in different irrigation plots. Duplicate 0.20-m long waveguides were inserted into the bed and parallel to the soil surface at depths of 0.05, 0.10, 0.20, and 0.30 m. Other waveguides were installed vertically in 0.2-m increments to 1.2 m starting at 0.4 m. Measurement of Ec with stem flow gauges started in mid-July after stem diameter >9 mm. Stem flow data were converted to crop transpiration per unit leaf area using biweekly measured LAI and using the procedure given by Ham et al. (1991). Typically, a minimum of six stem flow gauges was used to characterize the Ec of any experimental block, that is, irrigation level.


   Results and discussion
TOP
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
Summary and conclusions
 REFERENCES
 
System Performance
Examples of soil temperature, soil water content, soil heat flux, and stem flow measurements obtained with the automated system in 1994 for 2 d are shown in Fig. 4 . The cotton was well watered during these 2 d as it was furrow irrigated with 100 mm on day of year (DOY) 209 starting at 1600 h and a rain of 25 mm fell on DOY 210 at 0400 h. The measured LAI was 2.0 m2 m-2 on DOY 209. Soil temperature (Fig. 4A) shows the diurnal variation at three depths from the top of the bed. In this example, prior to irrigation (DOY 209) at noontime the soil temperature at 0.01 m is about 5°C warmer that at 0.05 m; however, due to soil wetting this difference diminished the following day. As expected, the diurnal amplitude at 0.3 m is less than at the surface 0.01- and 0.05-m depth. These values are typical for cotton in the THP (Lascano and Baumhardt, 1996). Soil water content measured with the TDR at three depths is shown in Fig. 4B. Prior to irrigation on DOY 209 the water content at 0.05 and 0.10 m was <3% and was 15% at 0.30 m. After irrigation, the water content rapidly increased to 30% near the surface and to 40% at 0.30 m. This example illustrates the rapid response of the TDR in detecting a water front moving downward. In addition, the wetting of a 25-mm rain on DOY 210 at 0400 h was also detected by the TDR sensors as the water content increased by 5% near the soil surface. The soil heat flux measured 0.05 m from the top of the bed and furrow is shown in Fig. 4C. These results are typical for conventional cotton with an LAI = 2 m2 m-2 (Lascano and Baumhardt, 1996). Prior to irrigation at nighttime when the soil is dry, heat flux from the bed is negative (< -25 W m-2) and increases to +25 W m-2 during the day. The same pattern is observed for the bed after irrigation on DOY 210. However, the diurnal pattern for the heat flux at the bottom of the furrow is different prior to and after irrigation. During the middle of the day, before irrigation the furrow heat flux is twice as large when compared to the bed heat flux and about three times larger after irrigation on DOY 210. Hourly mean crop transpiration measured with 9-stem flow gauges, before and after irrigation is given in Fig. 4D. Prior to irrigation on DOY 209, mean peak transpiration rate was 50 g h-1 and this value increased to 80 g h-1 after irrigation on DOY 210. Hourly fluctuations of transpiration, particularly in the afternoon hours of DOY 209 and 210, were due to cloud cover reducing energy driving latent heat flux and to stomatal closure (e.g., Kanemasu and Tanner, 1969).



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  		Fig. 4 Mean hourly soil (bed) temperature (A) and water content (B) measured at three depths, soil heat flux (C) measured on top of a bed and furrow, and mean stem flow gauge. The crop was irrigated on DOY 209 at 1600 h and a rain fell on DOY 210 at 0400 h

 
The results shown in Fig. 4 are an example of the type and quality of data that can be measured with the system in real-time. These measurements can be combined with instantaneous as well as integrated values of the terms that define the energy and water balance of the soil surface and crop canopy calculated with the ENWATBAL model. These capabilities give user the option of evaluating in real-time the effect of a plant growth regulator on, for example, crop transpiration. We are currently doing these evaluations along with irrigation strategies for cotton in the THP.

Partitioning of Evapotranspiration
The daily partitioning of ET into its two components, Es and Ec, of the cotton crop for 12 d in 1994 is illustrated in Fig. 5 . During this 12-d period, from DOY 211 to 222, the LAI increased from 2.1 to 2.5 m2 m-2. The crop was irrigated with 100 mm on DOY 209. Daily values of Es, Ec, ET and ETp are shown in Fig. 5A, and corresponding cumulative values are given in Fig. 5B. Crop transpiration was measured with stem flow gauges, Es was measured with mycrolysimeters, and ETp was calculated with a Penman-Monteith equation as given by Allen et al. (1998) using hourly weather data as input. In this example, ETo is assumed to be equal to ETp. These results clearly show that ET continued at the potential rate immediately following an irrigation and slightly declined to <ETp after DOY 217. The decline of evaporation on DOY 214 to <1 mm d-1 was due to cloudy and cool weather. Daily solar irradiance for this day was 6 MJ m-2 and daily average air temperature was 20.6°C. In contrast, daily ET on DOY 217 to 219 was >8 mm d-1, and on these days daily irradiance was >28 MJ m-2 and daily average air temperature was >25°C. These results are similar to those reported for conventional cotton by Lascano et al. (1987).



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  		Fig. 5 Measured daily (A) and cumulative (B) soil water evaporation (Es), crop evaporation (Ec) and their sum evapotranspiration (Ec + Es), and calculated potential evapotranspiration (ETp). The crop was irrigated on DOY 208

 
The dynamics of Es and Ec are indicated by the change in their losses with respect to ET. For example, the ratio Ec/ET increased linearly from 0.60 on DOY 211 to 0.98 on DOY 222; whereas, Es/ET for the same time period, decreased linearly from 0.45 to <0.05. These results show how this system can be used to evaluate different irrigation strategies using the engineering approach first proposed by Jensen (1968) to calculate the crop coefficient (Kc = ET/ETo).

Cumulative values of Es, Ec, ET and ETp for the 12-d period are shown in Fig. 5B. After 12 d, measured cumulative Es was 19 mm and measured cumulative Ec was 61 mm, that is, Es was 24% of total ET. Cumulative ET was 80 mm and cumulative calculated ETp was 83 mm within 4%. The mean CV for measured Es was 26 and 10% for measured Ec.

A comparison of daily average measured and calculated values of Es and Ec for the 12-d period between DOY 211 and 222 is shown in Fig. 6 . These results indicated that calculated values of Es and Ec were similar to measured ones. In all cases calculated values were within one SD of the mean value. The agreement between calculated and measured values is also indicated by linear regression analysis (Fig. 7) . In this analysis the slope of the line was not significantly different than 1 and the intercept was not significantly different than zero. These results are similar to those reported by Lascano et al. (1987) and (1994), and confirm that ENWATBAL correctly calculates the ET and its partitioning to Es and Ec of a cotton crop under the semiarid conditions of the THP.



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  		Fig. 6 Measured and calculated values of soil water evaporation (A) and plant evaporation (B) for a period of 12 d. The measured values are the mean ± SD

 


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  		Fig. 7 Calculated daily values of Es and Ec as a function of measured ones. Shown is the linear regression equation and coefficient of simple determination

 
Model Simulations
To illustrate model performance and utility we simulated the energy and water balance for the 3-d frequency and high irrigation level (7.5 mm d-1) with every furrow irrigation for cotton during the 1995 and 1996 growing season. Seasonal calculated daily values of Es, Ec, and ET for a 64 d period from DOY 191 to 254, 1995 are shown in Fig. 8A and corresponding cumulative values are given in Fig. 8B. In this year, seasonal Es was 125 mm and seasonal Ec was 225 mm for a seasonal ET of 350 mm. Measured mean seasonal ET was 351 ± 15 mm, in close agreement with the calculated values. During the simulation period, 84 mm of rain fell and 278 mm of irrigation was applied. Early in the season when LAI <1 m2 m-2 the magnitude of daily Es and Ec are similar, but as the crop develops and when LAI >2.6 m2 m-2, the dominant evaporation is from the crop (Fig. 8A). Until DOY 225, cumulative Es was > than cumulative Ec and this trend was reversed in the latter parts of the growing season (Fig. 8B). This evaporation pattern is typical for cotton in the THP where even under high irrigation the LAI seldom is > than 3.0 m2 m-2.



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  		Fig. 8 Calculated daily (A) and calculated cumulative (B) values of soil water and crop, and soil + crop evaporation

 
A comparison of hourly values of measured and calculated crop transpiration for DOY 221, 1996 is given in Fig. 9 . When Ec is expressed as mass of water loss per unit time (Fig. 9A) the peak transpiration of six gauges ranged 300% from 40 to 120 g h-1; however, when these values are expressed on a per unit leaf area (Fig. 9B) as suggested by Ham et al. (1991) peak transpiration ranged 40% from 0.8 to 1.2 mm h-1. The mean LAI of crop was 2.0 m2 m-2 on DOY 221. A comparison of calculated and measured hourly values of Ec for DOY 221 is shown in Fig. 9B, indicating close agreement between both values. Similar results were obtained for other days and low irrigation levels (data not shown). These results again confirm what was indicated earlier (Fig. 6B), that ENWATBAL correctly calculates the measured Ec of a cotton crop in the semiarid climate of the THP.



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  		Fig. 9 Hourly measured values of crop transpiration (DOY 221, 1996) measured with six stem gauges. Transpiration is expressed in g h-1 (A), per unit leaf area in mm h-1 (B), and calculated values are compared to mean measured ones in mm h-1 (C)

 
Cotton in the southern THP is often grown under conditions of limited rain and irrigation. Under these conditions cotton responds well to frequent irrigations, for example, 3 d (Bordovsky et al., 1992) and scheduling is normally done using the engineering approach (Jensen, 1968). However, under conditions of limited water the Ec rate is controlled by a set of interacting factors that cannot be expressed by a simple formula or rule. Rather, it is necessary to maintain a continuously updated, calculated water balance that considers the unfolding pattern of weather, crop growth and the soil hydraulic properties in the root zone. In addition, for sparse canopies the separation of ET into Ec and Es is essential to correctly calculate the water transpired by a crop. This can be accomplished with a mechanistic model such as ENWATBAL to correctly calculate the crop's water requirement and manage irrigation water.

Cotton Crop Coefficients
In 1995, cotton was irrigated on a 3-d frequency applying a maximum of 7.5 mm d-1 based on reference ETo and a cotton crop coefficient (W.M. Lyle, personal communication, 1995) taking into consideration rain. The pattern of rain and irrigation is shown in Fig. 10 . During DOY 191 to 254, the crop received 84 mm of rain and 278 mm of irrigation. Daily values of ET shown in Fig. 8 were used to calculate daily crop coefficients using calculated daily values of ETo obtained with a Penman-Monteith equation. Reference ET was calculated with hourly weather input from DOY 191 to 254. The resulting calculated Kc values, that is, Kc = ET/ETo, are shown in Fig. 11 . The daily, and 3 and 8 d moving average Kc values tend to increase with time. Early in the season, DOY 191 to 210, when the leaf area is low and a large portion of the soil surface is exposed daily values of Kc ranged from 0.1 to 0.9 and did not follow any particular pattern. In the middle of the growing season, DOY 211 to 230, daily Kc ranged from 0.5 to 1.0 and remained relatively constant afterwards. Notice that using a 3 and/or a 8 d moving average tends to remove the day-to-day variability and in general conforms to the general shape of a crop coefficient for cotton (e.g., Grimes and El-Zik, 1990). However, removing this variability is to ignore the dynamic nature of the evaporative losses of water from the soil and from the canopy, particularly under low values of LAI. Under these circumstances, the use of the engineering approach is at best an approximation and will more than likely result in applying more water than needed. For example, applying water due only to crop transpiration and on a 3-d schedule would have resulted in a reduction of 20% of the irrigation water applied. These results were obtained with the ENWATBAL model showing how this model can be used to schedule irrigations. This amount of water savings is significant in the THP were water shortages prevail most of the growing season. With current irrigation technology, for example, LEPA, it is possible to irrigate crops on a frequent basis with small quantities of water. However, the smaller the quantity of water that can be applied the more accurate the method of calculating the water requirement must be. The engineering approach lacks the adequate time resolution for frequent irrigation events and is better suited for infrequent irrigation with large quantities of water, that is, >50 mm.



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  		Fig. 10 Daily rain and irrigation as a function of day of year, 1995. Irrigation was applied on a 3-d frequency

 


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  		Fig. 11 Daily, and 3 and 8 d moving average crop coefficients as a function of day of year, 1995

 
Current LEPA irrigation practices of cotton in the THP are based on the pioneering work by Lyle and Bordovsky (1983) and by Bordovsky et al. (1992). Irrigation scheduling is based on the engineering approach of using cotton Kc. However, in the THP and in most cases a single Kc function is applied regardless of row spacing, cotton variety, irrigation system, and water stress condition. The engineering approach is practical and simple, and in some cases can produce satisfactory results as Kc can be adjusted over the length of the growing season to reflect a wet soil surface or for water stress conditions (Allen et al., 1998). However, as our results show the use of a Kc value to irrigate a cotton crop with a low LAI on a frequent basis and with small quantities of water lacks accuracy. Furthermore, current technology to measure Ec with stem flow gauges and the ability to mechanistically calculate the evaporative losses from a crop on real-time gives us an alternative method that is accurate and commercially available (e.g., van Bavel et al., 1996). However, these automated systems have certain disadvantages, such as, the cost and maintenance of the equipment, and availability of soil, plant and weather input required for model execution. In the future, three factors will contribute to the adaptation of mechanistic over empirical approaches to manage irrigation. First, is the low cost and increasing speed of personal computers and control technology. Second, are the proliferation of weather networks and data accessibility through the World Wide Web, and thirdly, the use of remote sensing techniques to estimate crop LAI (e.g., Maas, 1998).


   Summary and conclusions
TOP
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
 Summary and conclusions
REFERENCES
 
We described an automated system designed to measure and calculate the water requirements of a crop. Measurements include profiles of soil water content and temperature, crop transpiration, soil heat flux, and weather variables. The mechanistic ENWATBAL model was used to calculate the energy and water balance for both the soil surface and plant canopy. This combination of measurement and simulation gives the user a tool than can be used to schedule irrigations. The proposed system was built with commercially available instruments and data acquisition systems, and specific software was written to remotely monitor and control instruments. The system was tested for 2 yr with cotton under the semiarid climate of the Texas High Plains. The system can be used to schedule irrigations, to determine the water requirement of different crops, and as a research tool.

The engineering approach, first suggested by Jensen (1968) introduced the concept of a crop coefficient which when considering the potential ET of the crop is supposed to describe the pattern of crop water use throughout the growing season. This method is widely used and because of its practicality has become the standard and accepted way to calculate the daily water requirement of a crop. However, we show that for cotton on the THP where the LAI of the crop seldom reaches 3.0 m2 m-2, this method fails to adequately describe the daily ET of the crop as it lacks the necessary sensitivity to capture the dynamic nature of evaporation from the soil surface and crop canopy. High frequency irrigation of cotton with limited water requires accurate control of irrigation quantities particularly early in the growing season when the LAI of the crop is low. Current irrigation technology with LEPA systems has evolved for use in areas where irrigation water is limited and has the necessary control to apply small quantities of water. In this case, the engineering approach lacks the necessary resolution to calculate daily ET. Rather a mechanistic approach is needed that considers the daily weather pattern, continuously updates the soil water balance, and the growth of the crop.Texas Water Development Board. 1996


   ACKNOWLEDGMENTS
 
This research was possible from grants from the National Science Foundation (Project BIR-9419446) and from the Texas Higher Education Coordinating Board (Project 999902-081). In addition the help and advice received from R. Louis Baumhardt, Steve R. Evett, James L. Heilman, Stan K. Hicks, Don (Buddy) R. Salisbury, and C.H.M. van Bavel is appreciated and acknowledged.

Received for publication September 15, 1999.
   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
 Summary and conclusions
 REFERENCES
 




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