corn yield and evapotranspiration under simulated drought conditions

Irrig Sci (1981) 2:193-204 Irrigation

clence © Springer-Verlag 1981

Corn Yield and Evapotranspiration Under Simulated Drought Conditions *

W. I. Wenda and R. J. Hanks 1

Department of Soil Science and Biometeorology, Utah State University, Logan, UT 84322, USA

Received November 8, 1980

Summary. Measurements of corn yield and evapotranspiration (ET) were made under a wide variety of limited irrigations simulating drought conditions. Three locations were studied in two seasons. There was a strong linear relation between relative yield and ET (R2=0.95 for dry matter yield and R2=0.87 for grain yield) where variable irrigation was applied throughout the season as well as where irrigation was applied only at the early part of the season. Yield predictions using the model PLANTGRO (Hanks, 1974) were made from soil, crop, and climatic data. Agreement between prediction and measurements was better for relative dry matter yield (R 2 ranged from 0.91 to 0.99) than a relative grain yield (R 2 ranged from 0.93 to 0.97). The method for predicting grain yields could be improved but a relation involving seasonal estimates of relative transpiration gave good first-order predictions.

Shortages of water for irrigation are increasingly becoming a problem in irrigated areas of the world. During years of drought, water supplies are frequently cut back. The need for food and fiber by a growing population, urban encroachment onto fer- tile farmlands, and the subsequent residential and industrial requirements have con- tributed to these shortages.

These problems have caused legislators and planners to devise water allocation programs which, for the agricultural sector, are based on a crude relationship between crop yield and irrigation. Planners often feel handicapped because the com- plexity of the problems of water allocation is escalating faster than our understand- ing of how crop yield is influenced by irrigation rate and timing.

Stewart etal., (1977) demonstrated a strong linear correlation between evapotranspiration (ET) and corn (Zea rnays L.) dry matter and grain yield for all growth stages. The concept of a linear correlation between ET and crop yield is not

* Contribution from Utah State University Agricultural Experiment Station Journal Paper No 2506 1 Research Assistant and Professor, Utah State University, Logan

0342-7188/81/0002/0193/$ 02.40

194 W.I. Wenda, R. J. Hanks

new. DeWi t (1958), Arkley (1963), and Hanks et al., (1969) are a few of the investi- gators who have descr ibed exper iments showing the relationship. Doorenbos and Kassam (1979) deve loped a h a n d b o o k describing crop water requirements based on this correlat ion. In general , support ive da ta have only been collected in semi-ar id cli- mates where solar rad ia t ion does not l imit growth, and where water stress conditions are frequent.

The object o f this s tudy was to collect yield da ta on corn subjected to various irr igation reductions pr ior to crop maturi ty. A secondary objective was to test the sensitivity of P L A N T G R O , a simple water ba lance mode l (Hanks, 1974), in simulating corn yield as influenced by these i rr igat ion reductions. Steward et al., (1977) tested three water balance models, including P L A N T G R O , under some water stress conditions. It was shown that the P L A N T G R O mode l closely s imulated corn yield da ta collected from four states (Arizona, California, Colorado, and Utah). However, P L A N T G R O was not tested under condit ions whereby irr igat ion was discont inued at different stages o f physiological deve lopment before crop maturi ty.

Materials and Methods

A range of irrigation treatments were applied to corn planted at two sites in Northern Utah in 1977 and one site in 1978. Descriptions of the sites, selected for different soil and climatic conditions, are summarized in Table 1.

Available water was determined by the difference in volumetric water contents (measured with a neutron probe) to 150 cm rooting depth between field capacity (spring of each year) and wilting of corn (measured in a non-irrigated portion of each site in the fall).

Precipitation at the Greenville site occurred in 34 events during the 1977 growing season totaling 24 cm. One-third of this fell on 18 August. Precipitation of 24 cm also occurred at the Farmington site (1977) in 27 events. The largest storm was 3.5 cm on 18 May in addition to a total of 6.5 cm recorded during the four days prior to this date. Only 13 cm of rain occurred at the Evans site (1978) in 25 events. The largest daily amount of precipitation was 2.8 cm on 18 September.

The experimental plot design centered around a recently developed sprinkler system re- ferred to as the line source (Hanks et al., 1976). Imposing the water stress treatments at right angles to the sprinkler line resulted in a continuous variable design. Bauder et al., (1975) and

Table 1. Site descriptions

Location, Year Soil Great Available Altitude, soil group soil water Growing season

Farmington, 1977 Kidman * 9% 1326 m 30 km north of fine sandy loam 1 May - 12 Oct Salt Lake City

Greenville, 1977 Millville ** 12% 1460 m 1 km north of silt loam 29 May - 25 Sept Logan

Evans, 1978 Nibley *** 8% 1460 m 2 km south of silty clay loam 29 May - 25 Sept Logan

* Coarse loamy mixed mesic Calcic Haploxeroll; ** Coarse silty carbonatic mesic Typic Haploxeroll; *** Fine mixed mesic Aquic Argiestoll

Corn Yield and Evapotranspiration under Simulated Drought Conditions 195

Irrig level

11213i41516

P • •

Fig. 1. General plot layout of Greenville, • • • Farmiugton, and Evans experimental sites. Sprinklers are designated by (O) and neu- | tron probe access holes by (e) J _ k_. 36 m

P.

• E

Hanks et al., (1980) have discussed the statistical implications of such a design. The character- istic feature of the system is the application of water at a maximum rate nearest the line source and an approximate linear decrease in application rate to zero, at about 15 meters laterally from the pipeline. Figure 1 illustrates that the width of the experimental plots was limited by the wetted diameter of the sprinklers, since they were positioned along one irrigation fine. Each side of the line source was divided into six segments of"irrigation levels" which received a nearly identical water application to their symmetrical counterparts on the other side of the line source. The amount applied at each irrigation was measured for every level.

Irrigation amounts were varied in two ways. First due to the characteristics of the line source, areas parallel to and equidistant from the pipeline were irrigated with a fraction of the maximum irrigation requirement trough the growing season. This fraction depended on the lateral distance from the irrigation line. Second, sprinklers on either end of the line source were turned off progressively at weekly intervals. Thus the subplots midway between the two ends of the pipeline received irrigation for a longer period of time in the season. Progressively

196 W.I . Wenda, R. J. Hanks

shutting off sprinklers along the pipeline was intended to simulate irrigation cutbacks as might be imposed upon irrigators of arid lands during a drought year. It was thus possible to apply the same total amount of water for the season at two locations on the overall plot, but the time at which the irrigation was applied and the number of events required to apply that of water differed.

Actual evapotranspiration (ET) of corn was estimated by the widely known water balance method:

E T = Irr + R n + SWD - D r - R (1)

where Irr is irrigation Rn is precipitation occurring during the growing season, SWD is soil water depletion from the beginning to the end of the season, Dr is drainage below the root zone, and R is runoff from the field. Efforts were made in this experiment to eliminate runoff and minimize drainage. Just prior to each irrigation, neutron probe moisture measurements were taken to 150 cm depth at several locations of the overall plot. The amount of water needed to fill the soil profile was determined and only enough irrigation was applied to refill the profile in the highest irrigation level (IL6). Hence any drainage amount which may have occurred was small compared to the total seasonal ET. However, a large amount of drainage (10 cm) was estimated to have occurred at the Farmington site due to an early season storm on 18 May, 1977.

Soil water depletion was computed from initial and final soil water contents in the root zone. All initial readings were taken before any water uptake by plants occured and the final readings were taken at harvest.

Harvest was completed at each site between 130 and 140 days after planting. Fresh weight measurements were made on all subplots, and moisture samples of grain and dry matter were collected. The field weight of grain was corrected to 15.5% moisture content. The dry matter samples were dried in a forced air oven at 50 °C until a constant weight was obtained.

The corn variety, Northrup King PX 20 (2200 growing degree days, GDD), was grown in 1977 and 1978. Three other Northrup King varieties were added in 1978:PX448 (2150 GDD), PX 11 (2050 GDD) and PX414 (1950 GDD). No significant yield differences due to variety were noticed, thus all varieties were averaged together in 1978. Optimal soil fertility was main- tained by application of fertilizer prior to planting at rates indicated by preplant soil tests. Weeds and pests were controlled as necessary.

The Model

The model PLANTGRO (Hanks, 1974) has a feature to account for the differential effects of water stress on corn grain yield, since it has been established that a water stress does not influence grain yield uniformly at all stages of growth (Miller and Duley, 1925). The model for simulating dry matter assumes that the total seasonal production is directly proportional to the seasonal transpiration. In relative terms this may be expressed as:

Y/Yp --- T/Tp (2)

in which T is transpiration, Tp is transpiration when water is not limiting growth, Y is yield, and Yp is yield when T = Tp. PLANTGRO is unique among the models that Stewart et al., (1977) have tested because it separates ET into its components, evaporation and transpiration.

The model for grain yield simulation assumes that the seasonal grain production is proportional to the product of the relative transpiration ratios (T/Tp) obtained at individual growth stages. An exponent or weighting factor is then assigned to each stage of growth, de- pending on how much the water stress is considered to influence the final seasonal yield. Five stages in corn were chosen: 1. planting to beginning of rapid growth (about 10 days), 2. early vegetative to tassel, 3. tassel to milk, 4. milk to dent, and 5. dent to maturity or harvest. The relationship used for relative grain yield is:

Y/Yp = (T/Tp) ° (T/Tp).4 (T/Tp).4 (T/Tp).4 (T/Tp)0 (3)

The weighting factors were developed empirically from previous experiments (Steward et al., 1977). In all the studies reported herein, the initial soil water at planting was at "field capacity" so stress did not occur in the first stage of growth.. The model can be used to predict absolute

Corn Yield and Evapotranspiration under Simulated Drought Conditions

Table 2. Some inputs and outputs of model PLANTGRO

197

Variable Description

Inputs DAYS AWFAC

RTDMAX BGSM

DDST

R ET

GIRR FIRR

Ou~u~ DAY CVAP TRANS SOLEV CETACT IRRIG RAIN DRAIN CWADD SMi KC

Number of days between planting and harvest Fraction of available soil water below which transpiration will be less than potential (0.5 is used) Number of days to maximum rooting depth Array containing values of extractable water at each soii layer at the beginning of the computation Array containing the duration of each stage of growth in days or energy units Array containing the day and amount of rain Array containing the day and amount ofET (ET computed from Penman, pan evaporation, etc.) Array containing the day and amount of irrigation Factor to decrease or increase amount of irrigation

Day of the season Cumulative potential evapotranspiration Cumulative actual transpiration, T Cumulative soil evaporation, E Cumulative actual evapotranspiration Cumulative irrigation Cumulative rain Cumulative drainage Cumulative water added Total available water in each layer (T + E)/ET potential

dry matter and grain yields ifa maximum yield (Yp) is known from experience for a given site (soil type, climate, water quality, etc.) Some of the inputs and outputs of the PLANTGRO model are listed in Table 2.

Results and Discussion

A summary of the water budget and yields of corn dry matter and grain are present- ed in Tables 3 - 5 for the three sites. The data shown are averages of several plots with similar treatments. The top set of data o f Tables 3 - 5 are for the ends o f the plots where irrigation was cut off early in the year. The bot tom set o f data shows the part of the plot where irrigation was continued all season, and the middle set o f data shows an intermediate part o f the plot where irrigation w a s cut off later in the season than indicated at the top set but earlier than at the bottom set.

The weather influenced fields most where irrigation was limited, particularly in 1977 when about twice as much more rain was recorded than in 1978. This caused many of the lightly irrigated subplots, especially at the Greenville site (Table 3), to produce relatively higher yields than at the 1978 Evans experimental site (Table 5). Dry matter yields in the nonirrigated subplots at the Greenville site were not below


Table 3. Irrigation scheme, water budget and yield of corn for the Greenville site, 1977 (drainage was estimated using the PLANTGRO model)

Irrig Irrig No. of Last lrrig Rain Profile Drain- ET Dry mat- Grain Level Irrig Date Depl age ter Yield Yield

(mm) (mm) (mm) (mm) (mm) (T/ha) (T/ha)

2 8 4 Jul 20 242 225 25 442 7.48 2.17 3 59 4 Jul 20 242 225 25 501 8.92 3.90 4 104 5 Jul 28 242 225 25 546 9.38 4.43 5 138 5 Jul 28 242 225 25 580 9.78 6.38 6 165 5 Jul 28 242 225 25 607 10.48 6.58

2 15 7 Aug 4 242 225 25 457 7.21 3.11 3 86 7 Aug 4 242 214 25 517 9.04 4.82 4 146 9 Aug 11 242 203 25 566 10.02 5.85 5 195 9 Aug 11 242 197 25 609 12.51 6.81 6 234 9 Aug 11 242 190 25 641 12.24 7.80


Table4. Irrigation scheme, water budget and yield of corn for the Farmington site, 1977 (drainage was estimated using the PLANTGRO model)

Irrig Irrig No. of Last lrrig Rain Profile Drain- ET Dry mat- Grain Level Irrig Date Depl age ter Yield Yield

(mm) (mm) (mm) (ram) (mm) (T/ha) (T/ha)

2 20 6 Jul 22 235 128 100 263 4.15 1.47 3 57 6 Jul 22 235 139 100 331 5.38 2.62 4 96 6 Jul 22 235 143 100 374 7.18 3.66 5 140 7 Jul 29 235 115 100 390 8.48 4.84 6 187 7 Jul 29 235 86 100 409 8.97 5.84




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GREENVILLE 1977 o O

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Fig. 2. Yield of corn dry matter (left) and grain (right) as influenced by ET at the Greenville site (shown with the best fit regression fine)

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Fig. 3. Yield of corn dry matter (left) and grain (right) as influenced by ET at the Farmington site (shown with the best fit regression fine)


Table 5. Irrigation scheme, water budget and yield of corn for the Evans site, 1978 (drainage was estimated using the PLANTGRO model)

Irrig Irrig No. of Last Irrig Rain Profile Drain- ET Dry mat- Grain Level Irrig Date Depl age ter Yield Yield

(mm) (mm) (mm) (mm) (mm) (T/ha) (T/ha)

1 0 0 - 127 116 0 243 2.33 0.85 2 42 7 Jul 19 127 116 0 285 3.34 0.79 3 76 7 Jul 19 127 116 0 319 4.03 1.14 4 109 8 Jul 27 127 116 0 352 4.78 1.71 5 150 8 Jul 27 127 105 0 382 5.68 2.37 6 171 8 Jul 27 127 83 0 381 7.38 2.60

1 0 0 - 127 101 0 228 3.20 1.11 2 76 9 Aug 3 127 103 0 306 4.04 1.35 3 133 9 Aug 3 127 101 0 361 6.11 2.42 4 201 9 Aug 3 127 83 0 411 7.65 3.57 5 270 12 Aug 23 127 75 0 472 10.30 5.04 6 320 12 Aug 23 127 64 0 511 11.90 5.81

1 0 0 - 127 113 0 240 2.93 1.33 2 109 12 Aug 23 127 111 0 347 5.18 2.21 3 186 12 Aug 23 127 110 0 423 8.29 4.32 4 285 12 Aug 23 127 98 0 510 11.03 5.14 5 386 12 Aug 23 127 87 0 600 13.54 6.39 6 464 12 Aug 23 127 68 30 629 15.00 6.59

EVANS 1978 Q

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Fig. 4. Yield of corn dry matter (left) and grain (right) as influenced by ET at the Evans site (shown with the best fit regression line)


50% of the maximum yields obtained under optimum irrigation conditions. At the Framington site (Table 4), yields of dry matter were only 35% of the maximum, while at the Evans site, yields were less than 20% of the maximum, measured at each site. Grain yields followed a similar pattern.

There was no difference in the relation of yield to ET for plots where irrigation was cut off early in the year compared to those plots receiving the normal irrigation throughout the season. There was a slight indication for the Greenville data of Table 3, that the yield for a given ET was greater where irrigation was cut off early in the year than when irrigation was continued longer. This effect was not notice- able for the other two sites.

Figures 2, 3 and 4 illustrate how yields of corn dry matter and grain were related to ET, determined by the water balance method at the three sites. The coefficient of determination (R 2) between ET and yield at the Greenville site (Fig. 2) was 0.88 for dry matter and 0.95 for grain. A more linear relationship occurred at the Far- mington site (Fig. 3) where R 2 was 0.94 for dry matter yield vs. ET and 0.95 for grain yields vs. ET. The correlation between dry matter yield and ET at the Evans site (Fig. 4) was the highest of the three sites with an R 2 of 0.97. The R 2 for grain yields vs. ET was 0.93.

As can be seen from Figures 2 - 4, the slope and intercept of the regression line are different for the different sites. However, as indicated by Stewart et al., (1977) these differences can be minimized, if relative yields are plotted against relative ET, resulting in a more general relation than if absolute yields were to be calculated in terms of absolute amounts of ET at each site.

The relation of relative yields, Y/Yp, and relative evapotranspiration, ET/ETp, is shown in Fig. 5. Relative yields were expressed as ratios of the highest yield, Yp, at each site. Values of ET were developed similarly.

The regression equation for relative dry matter yield is

Y -1 .33(EE@p)-0.35 (4) Yp

with an R 2 = 0.95. The regression equation for relative grain yield is

Y = 1.46 (E~p) -0.51 (5) Yp

with an R 2 = 0.87

The slope of regression equations 4 and 5 is slightly higher than found in 1974 and 1975 for corn in a different experiment (Stewart et al., 1977) being about 1.2 for dry matter and 1.3 for grain. Thus, these equations give a good first order approxi- mation of yield as related to ET even though the coefficients are somewhat site specific.

Equations (4) and (5) were developed from data obtained in northern Utah, and are therefore siie specific. In order to develop a more general method of yield prediction, one can rely on a more complex form of simulating the actual physical soil- plant processes which influence yield. In this study the model PLANTGRO was tested for such a purpose. The physical parameters (soil and plant characteristics, weather, etc.) and the irrigation treatments for each site were used as inputs to the model to measure its sensitivity in simulating water extraction and yields.


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Fig. 5. Relative yield of corn matter (left) and grain (right) as influenced by relative ET, at the Greenville (©), Farmingtou (Zk), and Evans (+ ) sites (shown with the best fit regression line)

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Fig. 6. Comparison between simulated and measured relative yields of dry matter (left) and grain (right) using equation [2] to compute yields for Greenville (©), Farmington (/k), and Evans (+ ) soil and climatic data


Table 6. Regression equations and comparison of simulated (sim) and measured (meas) corn yields, indicating the model which was used (equation (2) or (3)). Relative measured yields wede based on the maximum yield obtained at each site

Site Dry matter Grain Grain Year eq (2) eq (2) eq (3)

Greenville 1977 Meas = 0.95 (sire) Meas = 1.20 (sim) Meas -- 0.73 (sim) +0.03 -0.23 +0.18

avg. deviation 0.04 0.08 0.07 R 2 0.91 0.94 0.93

Farmington 1977 Meas = 1.06 (sim) Meas = 1.40 (sim) Meas = 0.90 (sire) - 0.10 - 0.47 +0.15

avg. deviation 0.06 0.17 0.09 R 2 0.95 0.97 0.93

Evans 1978 Meas = 1.07 (sim) Meas = 1.22 (sim) Meas = 0.90 (sim) -0.12 -0.21 +0.15

avg. deviation 0.08 0.11 0.12 R 2 0.99 0.97 0.95

The comparisons between measured and s imulated yields using the P L A N T - G R O model for the three exper imental sites are i l lustrated in Figs. 6 and 7 and Table 6. Genera l ly the correlat ion between the measured and s imulated yields was high (Table 6). Values o f R 2 ranged from 0.91 to 0.99 with an overall average devi- at ion of 9%. The relative measured corn yields were based on the m a x i m u m yield obta ined by each site.

The predict ion of dry mat ter yields was bet ter than that of grain yields (Fig. 6). The use o f equat ion (2) to simulate grain yields general ly overpredicted the measured results at every site. This indicates that the grain yields were more sensitive to

Fig. 7. Comparison between simulated and me a- sured relative grain yields using equation [3] to compute yields for Greenville (O), Farmington (A), and Evans (+ ) soil and climatic data

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water stress than the dry matter yields. The use of equat ion (3) for simulating grain yields resulted in under-predictions at low ET levels and over-predictions at high ET levels (Fig. 7). Since the R 2 between measured and simulated grain yields using equation (2), was higher than for equat ion (3) (Table 6), and the average deviation between measured and simulated yields were similar, a modification of equat ion (2) for predicting grain yields is suggested:

Y / Y p -- a + b (T/Tp)season (6)

The regression equations shown in Table 6 indicate that the value of "a" is about -0 .2 and the value of "b" is about 1.2 for the Greenville and Evans sites, and about -0 .5 (a) and 1.4 (b) for the Farmington.site. An "a" o f - 0 . 3 and "b" of 1.25 fits the data from all sites quite well and is suggested for making estimations at other locations.

References

Arkley RJ (1963) Relation between plant growth and transpiration. Hilgardia 34:559 Bauder JW, Hanks RJ, James DW (1975) Crop production function determinations as in-

fluenced by irrigation and nitrogen fertility using a continuous variable design. Soil Sci Soc Am Proc 39:1187

DeWit CT (1958) Transpiration and crop yield. Institute of Biological and Chemical Research on Field Crops and Herbage, Wageningenl The Netherlands, Verse-Landbouwk, onder Z., No. 64.6S, Gravenhage

Doorenbos J, Kassam AK (1979) Yield response to water. FAO Irrigation and Drainage Paper 33, Food and Agriculture Organization of the United Nations, Rome

Hanks RJ (1974) Model for predicting plant growth as influenced by evapotranspiration and soil water. Agron J 66:660

Hanks RJ, Gardner HR, Florian RL (1969) Plant growth evapotranspiration relations for several crops in the central Great Plains. Agron J 61:30

Hanks RJ, Keller J, Rasmussen VP, Wilson GD (1976) Line source sprinkler for continuous variable irrigation-crop production studies. Soil Sci Soc Am Proc 391:1187

Hanks RJ, Hurst RL, Sisson DV, Sorensen VM (1980) Statistical analysis of results from irrigation experiments using the line source sprinkler system. Soil Sci Soc Am J 44:886

Miller MF, Duley FL (1925) The effect of varying moisture supply upon the development and composition of the maize plant at different periods of growth. Missouri Agriculture Exper- iment Station Research Bulletin 76

Retta A, Hanks RJ (1980) Corn and Alfalfa Production as influenced by limited irrigation. Irrig Sci 1:135

Steward JI, hagan RM, Pruitt WO, Danielson RE, Franklin WT, Hanks RJ, Riley JP, Jack- son EB (1977) Optimizing crop production through control of water and salinity levels in the soil. Utah Water Research Laboratory. PRWG 151-1. College of Engineering, Utah State University, Logan, Utah

corn yield and evapotranspiration under simulated drought conditions

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