Using a subsurface drip irrigation system to measure crop water use

Transcription

1 Irrig Sci (2004) 23: DOI /s ORIGINAL PAPER W. R. DeTar Using a subsurface drip irrigation system to measure crop water use Received: 14 January 2004 / Accepted: 14 May 2004 / Published online: 6 July 2004 Ó Springer-Verlag 2004 Abstract Procedures are presented for determining crop water use and crop coefficients for a row crop, using a neutron scattering probe with an efficient subsurface drip irrigation system. One procedure is called the slopeprojection method, and the other is called a covariance procedure. Field tests were conducted with full-season, narrow-row cotton (Gossypium hirsutum L.) on a welldrained, sandy soil in a semiarid environment over a 5-year period. The goal was to improve automated irrigation scheduling, by relating evapotranspiration (ET) to growing degree days (GDD). The result, using a Penman Monteith reference ET, was an average midseason crop coefficient of 1.11, with a standard error of With class A pan evaporation as the reference ET, the average midseason crop coefficient was 0.877, with a standard error of A fifth-order polynomial for the pan-based crop coefficient as a function of GDD was programmed into a controller and used successfully to irrigate a field automatically for one season. Introduction Carefully developed crop coefficients, along with their corresponding reference evapotranspirations (ET o ), can help determine when and how much water to apply. Judicious scheduling is especially important when water supplies are limited. The procedures for computing crop water requirements are thoroughly covered in Allen et al. (1998), hereafter called FAO-56. Weighing lysimeters can be used to get very accurate measurements of crop water use but, as pointed out in Allen and Fisher (1990), it takes extreme care to get good results. A soil Communicated by A. Kassam W. R. DeTar USDA-ARS, Shafter, CA 93263, USA wrdetar@pw.ars.usda.gov water balance (SWB) procedure can be used to estimate crop water use and is described in FAO-56. A field example of the use of the SWB for surface-irrigated, short-season cotton on a 1-m row spacing is given in Hunsaker (1999). Phene et al. (1989) used the SWB procedure with a neutron scattering probe on subsurface drip irrigation (SDI) of tomatoes on a fine-textured soil, and found good agreement with a weighing lysimeter. Payne et al. (1995) conducted a SWB on a sandy soil, using a neutron scattering probe and SDI, and avoided the problem of bulbs of wet soil by closely spacing the emitters. The goal here is to present simple, but accurate, new procedures for obtaining estimated crop coefficients for a row crop on a sandy soil, with an unusual approach, involving the non-uniformity of soil moisture. Materials and methods The site for this field experiment was the Shafter Research and Extension Center of the University of California, which is located near the southern end of the San Joaquin Valley, at N, W, and 109 m above sea level. Annual average precipitation is 167 mm with little rainfall from May to September. All the soils on the station are mapped as Wasco sandy loam (coarseloamy, mixed, nonacid, thermic Typic Torriothents), but the 0.8-ha area used in this study is in a sandy phase, which is closer to a loamy sand in texture. More soil data are available in Grimes and Dickens (1974), Grimes and Hughes (1981), and Rechel et al. (1991). This experiment with cotton (Gossypium hirsutum L.) was started in 1996, and the field was in continuous cotton for five seasons thereafter, with applications of metam sodium between seasons to limit the build-up of nematodes and pathogens. Prior to this experiment, the field had been laserleveled to a zero percent grade in all directions and then used for potato production for 3 years. The field had a history of medium-high productivity. The cotton was grown in north south rows, 100 m long, with a 0.76-m spacing between rows. Prevailing winds at the site are

2 112 from the NNW and traverse an orchard of mature almond trees and a 12-m wide, unpaved road before reaching the north edge of the planted area. About 2 weeks before planting each season, the field was irrigated with sufficient water, using a subsurface drip irrigation system (SDI), so that in combination with winter rains, the soil was wet to field capacity to a depth of 1.5 m. A few days before planting, the seedbed was prepared and herbicides (Treflan and Caparol) were incorporated. Planting was generally done in April with a target population of 12.4 plants/m 2. For weed control, the field was usually cultivated twice mechanically and twice manually. Insect control involved spraying at least once per season for lygus bugs, and in some seasons, additional applications were required for spider mites and aphids. Thrips were somewhat of a problem every year, especially early in the season, but were not treated because they provide a natural control of the spider mite population. A liquid nitrogen fertilizer (urea) was applied continuously through the drip system at a rate of 44 mg of nitrogen per liter of water for all irrigations applied from first square to about a week after cutout (termination of main-stem growth). The resulting total nitrogen application was kg/ha (Table 1). Nitrogen was the only fertilizer applied. Plant growth was monitored weekly and included plant height and canopy width. Dripper lines were buried 0.26 m below ground under every plant row, a depth which was shallow enough to permit germination of the seed; the lines ran the full length of the field. To keep friction loss to a minimum, the dripper lines had a diameter of 22 mm, one size larger than normal. They were of the tape type (T-Tape TSX ), with 10-mm wall thickness, and high-flow outlets every 0.30 m. The average operating pressure was 60 kpa, and the average discharge rate was 19 ml/min. Pressures across the field did not vary more than 4 kpa. During the first 2 years, the system was set up to apply water at six different rates, so that there were six treatments, with two replications each, and eight-row plots, as shown in the plot plan in Fig. 1. Four-row buffer strips of cotton were planted on both the west side and the east side of the test area. The main irrigation treatments ranged from about half of normal water application to nearly twice normal, the goal being to find the water application level required for optimum yield. At the control center there were six circuits, each supplying water to 16 rows, and each with its own control valve, flowmeter, and pressure regulator. Water was applied daily starting at 1300 hours, and was controlled by clocks which were adjusted manually twice a week. The cotton variety used was Acala Maxxa for all 5 years, but for the first 2 years the plots were split between Acala Maxxa and Pima S-7. One-meter-wide walkways were cleared of plants across the field, east-to-west, to permit personnel access for sampling and measurements. Two-meter-long by 50-mm diameter access tubes for neutron probe readings were installed at a location 2 m to the south of the south edge of the walkway, one to each eight-row plot. During the first 2 years, two walkways were used, one across the middle of each subplot, as shown in Fig. 1, with a total of 24 access tubes, and in the other 3 years, when all plots received the same amount of water, there was only one walkway across the middle of the field, with a total Fig. 1 Plot plan Table 1 Final seasonal measurements Year Final plant height Final node count Lint yield Total water use Water use efficiency Nitrogen applied Kern County yields (m) (kg/ha) (mm) (kg/m 3 ) (kg/ha) (kg/ha) , , , , , , ,611

3 113 of 12 access tubes. The access tubes were installed vertically at a lateral distance of 0.13 m from the dripper line, and 15-s neutron probe (Troxler, Raleigh, N.C.; Model no. 3226) readings were taken once a week to a depth of 1.5 m, at 0.3 m intervals, between 0900 and 1000 hours. The neutron probe was calibrated using a combination of gravimetric sampling and a two-probe soil density meter. The irrigation water, originating from snow melt from the nearby Sierra Nevada mountains, arrived by canal and was stored in a holding pond near the field. It contained a very low level of total dissolved solids (TDS), with an electrical conductivity of 0.02 ds/m but, being open to the environment, it contained debris such as leaves and, at times, algae. The water was treated by injecting liquid sodium hypochlorite, enough to maintain 1.5 mg/l free chlorine at the supply end of the field, and enough sulfuric acid was injected to keep the ph near 6.7. The water then passed through a set of sandmedia filters. All water volumes and flow rates leaving the control center were recorded with an electronic, paddle-wheel type flowmeter, which was calibrated by temporarily directing all the flow to a section of gated pipe with 12 openings, and measuring the discharge with a stop watch and a 2-liter graduated cylinder. The flow rate was checked periodically by comparing it with flow through other meters, such as a propeller-type flow meter, or several household-type water meters. Changes in pond volume were monitored closely with a calibrated depth pole. Flow from the canal was also metered into the pond. Reference evapotranspiration (ET o ) was calculated using data taken from the Research Center s weather station, which is located 0.9 km to the north-northeast of the test field. The weather station is one of a network of about 100 stations in the California Irrigation Management Information System (CIMIS), which is described in Snyder and Pruitt (1992). Weather data are available from this weather station dating back to 1922, and includes class A pan evaporation. Procedure I: Covariance In 1996, the application rate for the six different irrigation treatments consisted of the product of the C p values shown in the legend of Fig. 2, the ground cover, and the pan evaporation. Treatments were numbered 1 through 6, also as shown in Fig. 2. Ground cover was determined by measuring the average width of the plant canopy and dividing by the row spacing. The duration of irrigations was set on the time clocks by estimating the pan evaporation for the next 3 or 4 days, using the 21-year normal pan evaporation. Ground cover was plotted versus time and extrapolated forward over the same time period. A basic assumption behind the covariance procedure is that as long as too much water is being applied to the soil it will get wetter and wetter until reaching nearsaturation. Likewise, if too little water is applied the soil will get drier and drier until leveling off at a point near the wilting point. Somewhere in between is an equilibrium application rate, where the soil moisture remains Fig. 2 Soil moisture, as indicated by neutron probe, during 1996, for six application rates

4 114 constant. Figure 2 shows these changes in soil moisture, as indicated by neutron probe readings, for each treatment in The weekly change in indicated total moisture in the 1.5-m profile at each of the 24 access tubes was plotted against the average daily water application for that treatment, as shown in Fig. 3, which is an example for the week of June For this year, Acala Maxxa was grown in the northeast and southwest quarters of the field, and is labeled in Fig. 3 as Maxxa-I and Maxxa-II, respectively. Pima S-7 was grown in the southeast and northwest quarters, and labeled Pima-I and Pima-II, respectively. Using covariance analysis, which permits statistical pooling of all the data available into one analysis, parallel regression lines were determined for the four quarters of the field. The application rate corresponding to the point where the regression line crossed the zero-change level for total soil moisture, was the equilibrium application rate for that quarter, i.e., it was the rate at which the water being applied to the field was the same as that being depleted from the soil. As there were no consistent differences between the four quarters, and no significant difference between Pima S-7 and Acala Maxxa, the equilibrium application rates for all four quarters were pooled to determine one average value for the entire field each week. From the measurements and analysis in the section below entitled System efficiency and soil water balance, deep percolation losses and soil surface evaporation losses were found to be minimal, and therefore it can be assumed that within the limits of normal spatial variability, that this equilibrium application rate was the same as the crop water use. The equilibrium application rate was Fig. 3 Example of covariance procedure. Change in the weekly indicated soil moisture vs average daily application, for each quarter of the field, June 1996 then divided by the average daily value for three different reference ET terms to produce crop coefficients. The reference ET for the pan evaporation is designated as ET op. For the CIMIS modified Penman ET (Snyder and Pruitt 1992), it is ET oc, and for the Penman Monteith reference ET (Allen et al. 1998), using an Eppley pyranometer for solar radiation and daily time steps, it is ET om. Similarly, the resulting crop coefficients were designated K cp, K cc, and K cm respectively. These crop coefficients were then plotted versus growing degree days (GDD) above 15.6 C, accumulated from the time of planting, without an upper threshold, using the triangle method of Zalom et al. (1983). In 1997 the covariance procedure was repeated. To reduce the effect of soil variability, the Pima S-7 subplots were interchanged with the Acala Maxxa subplots. Because the wettest treatment in 1996 had created some extremely wet conditions in the field, making it difficult to cultivate with a tractor, it was replaced in 1997 with a treatment consisting of an application level of 0.75 times the product of ground cover and pan evaporation. Procedure II: Slope-projection A different approach was used on the same field in 1998, 1999, and 2000 using only Acala Maxxa cotton. With some minor exceptions, all the plots received the same water application. The scheduling target was to apply about 95% of the moisture required to obtain constant soil moisture, but the actual application varied considerably. Basically, the irrigation duration set on the time clocks was the time required to apply the product of the ground cover and the normal pan evaporation until 85% ground cover occurred, after which about 81% of normal pan evaporation was applied. Because of a highly efficient irrigation system and a slightly deficit irrigation (see the section System efficiency and soil water balance ), it can be assumed again that, at equilibrium, the amount of water applied was the same as the amount used by the plants. The procedure is shown graphically starting with the scatter diagram in Fig. 4, which shows an example from Each of the data points shown is the result of 120 neutron probe readings. The average daily change in the indicated moisture in the soil profile for each 1-week period is plotted versus the average value for A/ET op, the ratio of water application to the pan evaporation for that week. It should be noted that the results for the entire irrigation season can be displayed on just one graph. There is a transition period during the first 3 weeks, when the root distribution pattern and the bulb of wet soil must adjust to each other and stabilize. Point number 3 in Fig. 4 is an example of this adjustment, in that the bulb of wet soil got larger because the roots had not yet fully developed in that zone. Logically, after root-bulb stabilization, if insufficient water was applied to maintain equilibrium, the soil would lose moisture; if too much was applied, it would gain moisture; and

5 115 Fig. 4 Scatter diagram for average daily change in indicated soil moisture vs value of A/ET op for each week of 1998 season. Labels are week numbers somewhere in between, it would remain just about constant. An example can be shown starting with point number 7 in Fig. 4. If a little more water had been applied that week, the point would have moved to the right due to the larger A, and it would have moved upward due to the smaller loss. By passing a line through each of the points in Fig. 4 with the proper positive slope, i.e., projecting it up to the right in the case of insufficient application, its intersection with the line representing zero-change in soil moisture would indicated that value of A/ET op that would produce equilibrium soil moisture. This A/ET op at equilibrium is a crop coefficient. The procedure above can be summarized with the equation K cp ¼ A/ET op dw /S ð1þ where K cp = the crop coefficient for use with pan evaporation, ET op = pan evaporation (mm/day), A = depth of water applied to the field (mm/day), S = slope of the projection line (mm/day), and dw = apparent change in depth of water in 1.5 m of soil (mm/day), as indicated by neutron probe readings. So the crux of this procedure then lies in choosing the proper slope, S, for the projection line. If the soil moisture were uniform, the slope would be the same as the reference ET. But, as is shown in the section below entitled Slope considerations and non-uniformity of soil moisture, the slope S can be two to three times the reference ET. From the results of the covariance procedure in 1996 and 1997 it was noted that the crop coefficient tends to remain fairly constant for the midseason range of 540<GDD<900 degree-days. It was assumed that the projected equilibrium value for all the points during that part of the season should be the same, with allowance for normal variability. The change in Fig. 5 Average daily change in indicated soil moisture vs average A/ET op during midseason for 5 years indicated soil moisture versus A/ET op for that range was plotted in Fig. 5 for all 5 years, and shows that a very definite relationship exists, and that the slope is very steep. The regression slope depends on which years are included; the exact slope is not critical, as will be discussed later. The data for 1996 and 1997 came from treatment 3, which is a near-equilibrium application; based on only four replications; it has a little more scatter than data from the subsequent 3 years. The slope of the regression line shown is 19.7 mm/day, which came from the data for , and is the slope for use with the pan reference, ET op. The corresponding slopes for the other reference ET values were 16.8 mm/day for use with ET oc, and 16.5 mm/day for use with ET om. Figure 6 shows all the points projected to their equilibrium value for the year The same procedure was repeated in 1999, and 2000, using ET op as the reference ET. The procedure was then repeated for the same 3 years using ET oc, and again for ET om. Results and discussion All the resulting crop coefficients for the two procedures and five seasons are plotted versus GDD in Figs. 7, 8, and 9. Fifth-order polynomial equations were fitted to the data by regression analysis, using the form Y ¼ C 0 þ C 1 *X þ C 2 *X 2 þ C 3 *X 3 þ C 4 *X 4 þ C 5 *X 5 ð2þ where X is GDD/1,000, and Y is one of the three K c terms. The appropriate coefficients for each K c are shown in Table 2 along with the r 2 for each. The K cp* shown is for the first 4 years of data, and this was

6 116 Fig. 6 Slope projection procedure applied to 1998 scatter diagram. Labels are week numbers Fig. 8 Fifth-order polynomial fit to crop coefficient for use with CIMIS ET o, as a function of growing degree days Fig. 7 Fifth-order polynomial fit to crop coefficient for use with pan evaporation, as a function of growing degree days Fig. 9 Fifth-order polynomial fit to crop coefficient for use with Penman Monteith ET o, as a function of growing degree days Table 2 Coefficients for polynomial functions used in Figs. 6, 7, and 8.Y=C 0 +C 1 *X+C 2 *X 2 +C 3 *X 3 +C 4 *X 4 +C 5 *X 5 Y X C 0 C 1 C 2 C 3 C 4 C 5 Y rs R 2 K cp* GDD/1, K cp GDD/1, K cc GDD/1, K cm GDD/1,

7 117 programmed into the datalogger for the automated operation in the year In comparing Figs. 7, 8, and 9, one notes considerably less scatter of data points and a higher r 2 for K cp than with K cc and K cm. The midseason averages for the crop coefficients are shown in Table 3, along with the standard error among the data points, and the coefficient of variability, CV. At 3.3%, the CV for K cp is about half that of the other two. From Table 4, at 75% ground cover, the average value for GDD was 493 degree-days, at which point the crop coefficients are not yet at their midseason value. FAO-56 shows that the midseason value for the crop coefficient should start when ground cover is 70 80%, consistent with the data in this study where it occurred at an average of 81% ground cover. FAO-56 gives the midseason value of the basal crop coefficient (for use with the Penman Monteith ET o ) for cotton as ranging from 1.10 to 1.15 for subhumid conditions. When this is adjusted to our semiarid conditions, using the minimum relative humidity and wind given in Table 5 and the plant height given in Table 1, the resulting midseason K cm becomes By comparison, our value of K cm = in Table 3 is close to, but slightly below, the FAO-56 values. To facilitate a comparison of our crop coefficients to the more traditional day-of-year (DOY) relationship Table 3 Average and variability of mid-season crop coefficients Crop coefficient Average Standard error K cp K cc K cm Coefficient of variability (%) used by others, Fig. 10 was developed using the polynomial regressions shown in Figs. 7, 8, and 9 and Table 2 in combination with long-term normal (27 years) for daily values for degree-days, and a planting date of 15 April. For cotton in the San Joaquin Valley, Ayars and Hutmacher (1994) found the peak value for K cc to be 1.10, similar to that of in Table 3, but with the timing of their peak about a month later than found in this study. Hanson et al. (1999) suggest a range of for the midseason K cc, starting 16 June to 1 July, varying somewhat with date of planting. Figure 11 was developed in a manner similar to that of Fig. 10. Using normal GDD values for each DOY for a 15 April planting, adjacent columns were set up in a spreadsheet to calculate daily values of K cc, using the 5-year, fifth-order polynomial from Table 2. Eleven-year average daily CIMIS ET o values, ET oc, were put in the next column. Finally, normal daily values for crop water use, ET c, were calculated by multiplying K cc by ET oc, and the results were plotted against DOY. This process was then repeated for plantings on 1 and 29 April. In the same spreadsheet, weekly averages for daily crop water use were computed for all three dates of planting, and these are shown in Table 6 along with normal monthly and seasonal totals in Table 7. Actual planting dates are shown in Table 8. Grimes and El-Zik (1982) found the typical peak water use to be 8.1 mm/day starting in early July, which is about 8% higher than shown in Table 6, but the timing for the peak is about the same as in this study. Their value for total normal seasonal water use was 719 mm compared to 660 mm in this study for a planting date of 15 April. Using drip-irrigation, Davis et al. (1980) reported that as little as 670 mm of water could be used without Table 4 Stages of plant development by growing degree days (GDD) Year Planting Emergence First square 20% ground cover First bloom 75% ground cover Vegetative cutout First boll opening Start irrigation cutback First defoliation Harvest ,130 1,459 1, ,137 1,425 1, ,014 1,291 1, ,164 1, ,226 1,338 Avg ,032 1,313 1,400 Table 5 Weather data Actual ET oc (mm/day) Normals ET oc Min RH Wind Average air temperature (mm/day) (%) (m/s) ( C) May June July Aug Sept

8 118 Table 6 Normal water use (mm/day) for three planting dates (DOP) Week ending DOP 1 April 15 April 29 April Fig. 10 Crop coefficients as a function of DOY for normal growing degree days with a DOP of 15 April 10 May May May May June June June June July July July July August August August August August September September September September October Table 7 Normal water use (mm/month) and season total (mm) Month DOP 1 April 15 April 29 April May June July August September October Season Fig. 11 Normal daily crop water use based on normal CIMIS ET o, Kcc, and DOP of 15 April reducing cotton yields. State of California (1993) gives the average daily water use for each week, based on 15 years of data, with the peak (week ending 19 July) at 8.23 mm/day and the average of the highest 3 weeks at 7.98 mm/day. The normal seasonal total water use is given as 801 mm. The peak use in this study is 7.46 mm/ day and the top 3-weeks average 7.34 mm/day. These comparisons show that the crop coefficients and crop water use resulting from the new procedures in this study Table 8 Stages of plant development by day of year (DOY) Year Planting Emergence First square 20% ground cover First bloom 75% ground cover Vegetative cutout First boll opening Start irrigation cutback First defoliation Harvest

9 119 are quite consistent with the results of previous studies for cotton in this region. System efficiency and soil water balance A study of the system efficiency showed that the water losses were small. After four seasons of irrigation with the drip system, a distribution uniformity (DU) test was conducted in February With the entire field irrigated as one set, the emitter discharge rate was measured at 24 different points in the field. The results are shown in Fig. 12. The DU, which is the ratio of the average discharge from the lowest 25% of the emitters to the average of all the emitters, was Any DU over 0.93 is considered very high (Burt and Styles 1999). The coefficient of variability was , producing a statistical uniformity of 0.963, which is considered excellent in the Standard EP458 by ASAE (2000). In 1998, 1999, and 2000, the irrigation system was operated aiming at a 5% deficit; that is, applying only about 95% of the application rate needed to maintain constant soil moisture at the average point in the field. Assuming a linear distribution of the discharge rates and a 5% deficit, both ASAE (2000) and Wu (1995) indicate that the irrigation application efficiency could approach 100%. Wu s approach indicates that deep seepage can be eliminated, or at least minimized, by deficit micro irrigation. In 2000, a pair of tensiometers was installed in each of six plots to check the deep seepage losses. One of the pair was set at the depth of 1.2 m and the other at 1.5 m, in the plant row and in the zone just below the depth of root activity. The unsaturated hydraulic conductivity had been previously measured at this research center by Rechel et al. (1991), using the instantaneous profile Fig. 12 Results of distribution uniformity test method (Watson 1966). The sandier phases of this soil fit the equation K ¼ 2500 ððh 0:05Þ= ð0:377 0:05ÞÞ 0:5 0:333 2 ð3þ 1 1 ððh 0:05Þ= ð0:377 0:05ÞÞ 3 where K is the unsaturated hydraulic conductivity (mm/ day), and h is the moisture content of the soil in m 3 /m 3. The form for Eq. 3 is from van Genuchten (1980), with the saturated hydraulic conductivity of the soil, 2,500 mm/day, coming from Saxton et al. (1986). For typical values of moisture contents of 0.075, 0.100, and m 3 /m 3, Eq. 3 produces K values of , , and mm/day, respectively, with these latter three values being the seepage losses for a downward unit gradient. The soil moisture in this zone remained fairly constant at the higher level above until mid-july, producing a downward unit-gradient and an estimated seepage loss of mm/day. The soil moisture then declined steadily for the remainder of the season. Also at mid-july, the gradient actually changed from downward flow to upward flow. The middle moisture level above occurred at the end of July, when there was a near-unit gradient in an upward direction. This can be considered a negative seepage loss or capillary rise; the magnitude of the upward flow was very small, affecting only the fourth significant digit in the typical daily loss or gain in moisture from the entire soil profile, so that it can easily be ignored. By the end of August 2000, the upward gradient increased to three times unity, but since the moisture content decreased to the lower value indicated above, the resulting movement was even smaller than that at the end of July. The soil in between the plant rows, most of which is outside the bulb of wet soil, was drier than the in-row soil, so had less flux. An error analysis of the data for the first 9 weeks of irrigation for the year 2000 showed that if the seepage loss factor were removed from the change in soil moisture, the resulting crop coefficient would be reduced by an average of 0.001, which is an error of 0.7% for the first week and a 0.1% error for each of the last 5 weeks of that time period. These errors are small compared with the 3% error that can occur between pairs of identical lysimeters (Allen and Fisher 1990). It seems that Wu was indeed correct about possible minimal deep seepage loss under regulated deficit micro irrigation. There was almost no precipitation, no runoff, no runon (runoff to this field from adjacent areas), no subsurface lateral flows, and no leaks in the irrigation system. The only other important factor in the soil water balance is evaporation from the soil surface. From the over-watering done in some of the treatments of the covariance procedure in 1996 and 1997, it was found that a great deal of water could come to the surface from a subsurface drip system. However, under the slight deficit application used in slope-projection tests of 1998, 1999, and 2000, almost no wet spots were observed on

10 120 the soil surface. The effect of surface evaporation from dry soil is minimal and usually included in the basal crop coefficient (Allen et al. 1998). There is a possibility that plant size could affect water use, but in DeTar et al. (1998), it was shown that there was little difference in plant size among cotton plants normally irrigated, 10% underirrigated, and 20% overirrigated, until after vegetative cut-out (termination of main stem node development). As seen in Table 8, vegetative cut-out normally occurs well after 75% ground cover is reached. The midseason water use starts at this point and the crop coefficient at this stage is generally considered to be independent of plant size. Considering all of the above, the depth of water used by the crop was essentially the same as that which was applied. Slope considerations and non-uniformity of soil moisture The moisture in the soil around an emitter is not uniform. Bresler (1977) discussed movement of the wetting front and wetting patterns from a drip source, indicating that in sandy soil the wetting may not extend much past 0.3 m laterally, even at high discharge rates, for several hours. Mmolawa and Or (2000), in a thorough review of the subject, discussed the uneven uptake by roots. The wetted volume of soil under and around an emitter has been referred to as being onion-shaped (Burt and Styles 1999). In the sandy soil for the field in this study, lateral movement of moisture seldom exceeded 0.30 m from the source. Due to the non-uniformity problem, the indicated changes in the moisture content of the soil in and near the wetted bulb can be much greater than the changes in the average moisture content of the entire field. For example, to get the desired midseason weekly loss of 0.8 mm/day from the soil profile, as indicated by the neutron probe, required an application of just 0.29 mm/ day less than the equilibrium application rate. This relationship is exemplified by the steep slope (19.7 mm/ day) of the regression line shown in Fig. 5. From the geometry shown in Figs. 5 and 6, it can be shown that if the soil moisture were uniform, i.e., the change in soil moisture would be the same as the difference in the application, then this slope would have been the same as the reference ET, which averages only 7 9 mm/day. This relationship was especially notable near the end of the season, a few weeks after the last irrigation, when the soil moisture was more or less uniformly low. In this case, it is assumed that if the depth of application were the same as what disappeared from the soil, i.e. A ¼ dw ð4þ then the soil moisture would have remained constant, and the slope would have been S ¼ dw = A/ET op : ð5þ Combining Eqs. 4 and 5 produces S ¼ ET op ð6þ showing that the slope S is the same as the reference ET for uniform soil moisture. At the beginning of the season before the start of irrigation, the soil moisture is fairly uniform, and the appropriate slope to use in slope-projection procedure is the reference ET. A sensitivity analysis on slope selection shows that if daily soil moisture gains or losses are limited to 1.5 mm/ day during the main part of the irrigation season, an error of 10% in choosing the slope causes only about a 1% error in the resulting crop coefficient. Thus the choice of slope is not highly critical. During transition periods, such as during the first 2 weeks of irrigation, and during irrigation cutback (the 2 weeks before shutoff) a slope halfway between the midseason value and the reference ET was used. After the water was completely turned off, the slope used was the reference ET. Some refinements are possible in future work on slope selection. At the end of the season, the equilibrium value of A/ET o should decline steadily, with a nearexponential decay rate. Thus one guiding principle in the choice of slope during that period is that the projection lines (Fig. 6) should not cross before reaching the zerochange line for soil moisture. Another suggestion is the use a continuous function for the slope, such as this one which is a sigmoidal form of a Richards function (Richards 1959) S ¼ ET o ð1 þ 1:8 ð1 þ e^ ð5:6 11 K c /K m ÞÞ^ 2Þ ð7þ where ET o is the reference ET, K c is the crop coefficient at any time of the season, and K m is the average midseason value of K c. The slope, S, which varies from ET o to 2.8 times ET o in Eq. 7, is a measure of the non-uniformity of soil moisture. The automated system To verify the procedures above, the scheduling for the irrigation system was completely automated in the year 2000, using the regression polynomial for all the K cp -vs- GDD data from the previous 4 years, and using a modification of the equipment and program described in Phene et al. (1992). A thermocouple was mounted in a radiation shield to measure air temperature 2 m above ground level at the control center, and the datalogger was reprogrammed to calculate GDD. The K cp was calculated on a daily basis using this GDD in the polynomial, with the required water application for any one day being calculated as the product of the K cp and the pan evaporation from the previous day. Similar to the procedure in Phene et al. (1992), an adjustable offset was provided for the value of GDD, to account for unusual lateness or earliness in plant development and

11 121 to also account for slight differences in the temperatures between the control center and the weather station. An adjustable multiplier accounted for the difference in pan evaporation in the field and that at the weather station, and also provided for the desired level of deficit irrigation. The system operated very smoothly all season, with very little change needed in the two adjustable factors. The resulting soil moisture measurements for the 2000 season are shown in Fig. 13 along with the line showing the desired levels for a 5% deficit irrigation, which approaches the threshold level of soil moisture at the start of the irrigation cutback. The soil moistures for 1999 are also included in Fig. 13 as an example of the results using manually-adjusted time clocks. The total seasonal water use in the year 2000 was 610 mm, one of the lowest of the five years, and the lint yield was the highest at 1,577 kg/ha. The resulting water-use efficiency was kg of lint per m 3 of water. As seen in Table 1, the yield for this experiment in year 2000 compared favorably with the average yield for this area, Kern County, and is an indication that the automated system was operating properly. The high yield for Kern County in 2000 was partly due to the lack of pressure from insects, and also due to cool weather in July, which was conducive to good fruit set. It should be pointed out that this experiment was conducted under ideal circumstances. Using SDI on flat land with almost no summer rainfall and very high quality water, made it possible to run this experiment for 5 years without complications. The average daily application of water, A, can easily be adjusted to include the effective depth of any incidental rainfall, but because of the danger of salt build-up in the root zone, the longterm use of deficit irrigation would not have been possible without the use of the low-tds water. The installation could be considered a challenge to weighing lysimeters for accuracy in measuring crop coefficients. Summary and conclusions Two new procedures are presented to estimate crop water use by a row crop. Both require an efficient subsurface drip irrigation system and use of the neutron scattering probe. One of the methods presented, the slope-projection procedure, starts with a daily application which is a rough estimate of the proper amount of water to the field. The resulting error, which is the change in soil moisture, is used to fine-tune the crop coefficient polynomial. The other method, the covariance procedure, involves the application of different irrigation rates to the field, and interpolating statistically to find the level at which no change occurs in the soil moisture. The resulting crop coefficients agree with other published values for cotton, with the average midseason value for K c, for use with the Penman Monteith reference ET, equal to The resulting polynomial relationship between the pan crop coefficient and GDD was used in an automated system, producing good cotton yields, with relatively low total water use. Soil moisture was well controlled. The regulated deficit irrigation (about 95% of normal) along with a distribution uniformity of 0.95, and minimal losses to surface evaporation or to deep percolation led to a high system efficiency and justification for the assumption that the depth of water applied was the crop ET. The procedure is simple, accurate, and inexpensive. References Fig. 13 Soil moisture control, year 2000 compared with 1999, also showing desired rate of moisture decline Allen RG, Fisher DK (1990) Low-cost electronic weighing lysimeters. Trans ASAE 33: Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration guidelines for computing crop water requirements. (Irrigation and drainage paper 56) Food and Agriculture Organization of the United Nations, Rome ASAE (2000) Field evaluation of micro irrigation systems: standard ASAE EP458 DEC99. ASAE Standards 2000: Ayars JE, Hutmacher RB (1994) Crop coefficients for irrigating cotton in the presence of groundwater. Irrig Sci 15:45 52 Bresler E (1977) Trickle-drip irrigation: principles and application to soil water management. Adv Agron 29: Burt CM, Styles SW (1999) Drip and macro irrigation for trees, vines, and row crops. 2nd edn. ITRC, BioResource and Agricultural Engineering Department, California Polytechnic State University, San Luis Obispo, Calif., USA Davis KR, Nightingale HI, Phene CJ (1980) Consumptive water requirements of trickle irrigated cotton. I. Water use and plant response. (ASAE paper no ) ASAE, St Joseph, Mich., USA DeTar WR, Mass SJ, McLaughlin JR (1998) Cotton irrigation using subsurface drip: growth, cutout and yields depend on the amount of water applied. In: Proceedings of the 1998 Beltwide Cotton Conference, vol 1. National Cotton Council, Memphis, Tenn., USA, pp

12 122 Genuchten MT van (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44: Grimes DW, Dickens WL (1974) Dating termination of cotton irrigation from soil water-retention characteristics. Agron J 66: Grimes DW, El-Zik KM (1982) Water management for cotton. (University of California Division of Agricultural Science Bulletin 1904) University of California, Berkeley, Calif., USA Grimes DW, Hughes S (1981) Water management evaluation for cotton in the San Joaquin Valley. (Progress report to California plant cotton seed distribution) University of California, Davis, Calif., USA Hanson BR, Schwankl LJ, Fulton AE (1999) Scheduling irrigations: when and how much water to apply. (Water management series publication no 3394) University of California Irrigation Program, Davis, Calif., USA Hunsaker DJ (1999) Basal crop coefficients for use for early maturity cotton. Trans ASAE 42: Mmolawa K, Or D (2000) Water and solute dynamics under a dripirrigated crop: experiments and analytical model. Trans ASAE 43: Payne WA, Gerard B, Klaig MC (1995) Subsurface drip irrigation to evaluate transpiration ratios of pearl millet. In: Lamm F (ed.) Micro irrigation for a changing world. Proceedings of the 5th International Microirrigation Congress. ASAE St Joseph, Mich., USA Phene CJ, McCormick RL, Davis KR, Pierro JD, Meek DW (1989) A lysimeter feedback irrigation controller system for evapotranspiration measurements and real time irrigation scheduling. Trans ASAE 32: Phene CJ, DeTar WR, Clark DA (1992) Real-time irrigation scheduling of cotton with an automated pan evaporation system. Appl Eng Agric 8: Rechel EA, DeTar WR, Meek BD, Carter LM (1991) Alfalfa (Medicago sativa L.) water-use efficiency as affected by harvest traffic and soil compaction in a sandy loam soil. Irrig Sci 12:61 65 Richards FJ (1959) A flexible growth function for empirical use. J Exp Bot 10: Saxton KE, Rawls WJ, Roemberger JC, Papendick RI (1986) Estimating generalized soil water characteristics from texture. Soil Sci Soc Am J 50: Snyder RL, Pruitt WO (1992) Evapotranspiration data management in California. In: Proceedings of Water Forum 1992, ASCE, August 2 6, Baltimore, Md., USA State of California (1993) Crop use in California: a guide for scheduling irrigations in the southern San Joaquin Valley, California State Department of Water Resources, Sacramento, Calif., USA Watson KK (1966) An instantaneous profile method for determining the hydraulic conductivity of unsaturated porous materials. Water Resour Res 2: Wu IP (1995) Optimal scheduling and minimizing deep seepage in micro irrigation. Trans ASAE 38: Zalom FG, Goodell PB, Wilson LT, Barnett WW, Bently WJ (1983) Degree days: the calculation and use of heat units in pest management. University of California Leaflet 21373