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Statistical analyses of soil variability: effects of variability on level-basin irrigation of wheat
Agricultural Water Management, 21 ( 1992 ) 177-195
177
© 1992 Elsevier Science Publishers B.V. All rights reserved. 0378-3774/92/$05.00
Statistical analyses of soil variability: effects of variability on level-basin irrigation of wheat* D.J. Hunsaker a and D.A. Bucksb aUS Water Conservation Laboratory, Phoenix, AZ, USA and bUSDA-ARS, National Program Staff Beltsville, MD, USA (Accepted 24 February 1992)
ABSTRACT Hunsaker, D.J. and Bucks, D.A., 1991. Statistical analyses of soil variability: Effects of variability on level-basin irrigation of wheat. Agric. Water Manage., 21: 177-195. The transfer of plot-size water-use and yield information to field-size, surface irrigation conditions is generally inadequate due to nonuniform water applications and soil variability. In a two-year levelbasin irrigation study, water-use and yield (Y) relationships were characterized for wheat grown under three quantities of seasonal irrigation water on a 4.2-ha, sandy loam field site. The Mohall sandy loam had a sand particle fraction ranging from 46 to 79% with a mean of 61% at the 0 to 1.0-m soil depth. The sand particle fraction, soil water content and grain yield were highly correlated with one another. High sand content was associated with less evapotranspiration and lower yield within the driest irrigation treatment (two irrigations), as well as, higher rates of infiltrated water and lower yield in the wettest irrigation treatment (five irrigations ). The inclusion of sand content with seasonal infiltrated water depth improved estimates of yield as much as 42% for specific levels of irrigation management. Estimated yields for a given depth of seasonal infiltrated water varied 1250-1400 kg/ ha over the range of sand content within the field site. Knowing the sand content of the field, the farmer should be able to improve the scheduling of both irrigation and fertilizer application rates based on more realistic wheat yield goals.
INTRODUCTION
Level-basin irrigation usage is expanding in western regions of the United States and is estimated to be about 400,000 ha (Dedrick, 1990). Level-basin irrigation offers certain advantages over other surface irrigation methods including potentially higher water application uniformity, no runoff and reduced irrigation time (Erie and Dedrick, 1979). Because water is applied rapidly to level basins, differences in infiltration opportunity times are small. Correspondence to: Mr. Douglas J. Hunsaker, US Water Conservation Laboratory, 4331 East Broadway Road, Phoenix, Arizona 85040, USA. *Contribution of the US Department of Agriculture, Agriculture Research Service, Pacific West Area, USA.
178
D.J. HUNSAKER AND D.A. BUCKS
Therefore, the variability in water application will be primarily related to variations in soil infiltration properties, surface elevation irregularities and perhaps other factors (Clemmens, 1988). Information on level basin sizing and inflow design criteria for maintaining a desired water application uniformity has been presented by the U.S.D.A. (1974), and Clemmens and Dedrick ( 1982 ). Methods to estimate the effects of spatially variable soil infiltration on level basin application uniformity are available (Jaynes and Clemmens, 1986; Clemmens, 1988). Scientists and engineers have recognized for a long time soil and water application variability in irrigated fields and its effect on crop yields; however, this consideration is seldom included in surface irrigation water management decisions. Even a properly designed level-basin system will have some degree of nonuniformity in water application due to the inherent variability in soil properties of the system. Theoretical analyses have demonstrated that variable soil properties and nonuniform water applications can have significant effect on crop yields (Varlev, 1976; Warrick and Gardner, 1983; Letey, 1985 ). Solomon (1984) used hypothetical crops to demonstrate that common irrigation uniformity and efficiency measures can be related to expected yields from nonuniform irrigation practices. Seginer ( 1978 ) and Hart et al. (1980) presented analyses to compute optimum seasonal water application when water application is nonuniform. Feinerman et al. (1983) determined that the optimum seasonal water application for the nonuniform case will depend on the shape and form of the yield function under uniform irrigation. Sprinkler irrigation experiments, where the effects of variable soil properties of irrigation uniformities on yields were evaluated, have been presented by Bresler et al. (1981 ), Stern and Bresler (1983 ) and Ayars et al. (1990). Similar work has been presented by Russo (1984) and Russo ( 1986 ) for trickle irrigation systems. As pointed out by Russo ( 1986 ), the variability in crop yield due to the inherent spatial variability in soil properties within a field will likely reduce the average yield relative to that which could be attained were soil properties uniform within the field. Russo (1986 ) also suggested that the more the crop was subjected to stress conditions (water and salinity ), the greater the effect of the field spatial variability on crop yield. The objective of this paper is to evaluate the influence of soil and irrigation variabilities on water-use and yield for a wheat crop grown under field-size, level-basin irrigation systems. METHODS AND MATERIALS
Durum wheat (Triticum durum cv. 'Aldura') was surface irrigated in 1985 and 1986 in laser-leveled basins on a 4.2-ha field site ( 168 by 251 m) located on the University of Arizona, Maricopa Agricultural Center in central Ari-
STATISTICAL ANALYSES OF SOIL VARIABILITY
179
zona. Wheat was planted in 12 basins, each 14 m in width, on 3 January 1985 ( 1985 study) and on 10 December 1985 ( 1986 study). In the 1985 study, the 12 basins were separated into six, replicated treatments in a randomized block design. Treatments consisted of three seasonal irrigation quantities, designated Wet, M e d i u m and Dry; and two basin lengths, designated Long (251 m) and Short (190 m ) . In the 1986 study, the 12 basins were all the same length (251 m) and were laid out in a split-plot design having three seasonal irrigation quantities (Wet, Medium and Dry) and two irrigation water qualities, designated High salinity (electrical conductivity about 2.7 d S / m ) and Low salinity (electrical conductivity about 1.1 d S / m ) . All basins in the 1985 study were irrigated with the lower salinity water. In 1985, 15 and 11 neutron access tubes were installed in each long and short basin, respectively, at 15.2-m intervals down the basin length, equidistant from the basin boundaries. In 1986, 16 access tubes were installed in each basin with the same spacing as in 1985. Surface elevation measurements were made at each access tube site prior to planting in both years. Four neutron moisture gauges were calibrated at several locations within the field site and were used to measure volumetric soil water content in 0.2-m soil depth increments. Water content measurements were obtained over a 2.0-m soil depth at each access tube site beginning shortly after crop emergence ( 13 February 1985 and 8 January 1986). Subsequent water content measurements, which included measurements taken one day before and three days following each basin irrigation, were made throughout the growing season until crop harvest. Seasonal crop evapotranspiration (E) was estimated at each access tube site as the summation of the ( 1 ) measured soil water depletion during the growing season over a crop rooting depth of 1.6 m, (2) estimated evapotranspiration during the four day period following each irrigation and (3) seasonal rainfall. The major shortcoming of this method is the potential for unmeasured drainage water inclusion in the evapotranspiration estimate (ASCE, 1990). One of the shorter basins from the Medium irrigation treatment of 1985 was selected for more extensive water content measurement in a companion study (Jaynes and Hunsaker, 1989). In this basin a total of 44 access tubes were installed to a depth of 2.8 m, in a 4 by 11 array with a corresponding spacing of 3.0 by 15.2 m. Water contents were measured at these sites in 0.2m increments the day prior to and the day immediately following each of the four irrigations given. The variability of infiltrated water depth for each irrigation was calculated by subtracting the water content, integrated over the depth of the access tube, before irrigation from the value after irrigation. A linear calibration equation developed from the irrigations of this basin was used to estimate the variability of infiltrated water within the other basins in the field. A detailed description of the analysis is presented in an earlier paper (Hunsaker et al., 1991 ).
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D.J. HUNSAKER AND D.A. BUCKS
The soil at the site is a Mohall sandy loam (fine-loamy, mixed hyperthermic, Typic Haplargid). Soils samples were obtained with a coring device (0.042-m diameter) in 0.25 m increments, to a depth of 1.0 m near each access tube location prior to planting in both years. In 1985, soil samples from 85 of the locations were selected for particle size analysis by the hydrometer method (Gee and Bauder, 1986). In 1986, soil samples from 12 additional locations were selected for particle size analysis. Of the 388 soil samples taken at 97 locations within the site, 49% were classified as sandy loam, 25% as sandy clay loam, 8% as loam, and 17% as loamy sand. The irrigations applied to the Wet, Medium and Dry treatment basins were scheduled to replace 100, 80 and 50% of crop evapotranspiration, respectively. Irrigation scheduling followed the historical estimates of wheat evapotranspiration (Erie et al., 1982) in central Arizona, adjusted for planting date and atmospheric conditions. Meteorological data were recorded during the growing seasons by two portable electronic weather stations, one near the center of a Wet treatment basin and another in a nearby alfalfa field. Rectangular canal gates (0.6 by 0.7 m), installed at the head of each basin, controlled the water delivery from a concrete-lined open channel. The water delivery inflow rate was measured in the open channel during each irrigation with a broad-crested weir (Bos et al., 1984), located about 10 m upstream from the first basin turnout. An electronic logger was used with the weir to monitor the water level at the gauging point of the weir, and converted sensed water levels to flow rate and cumulative flow volume. The irrigation dates and the average depth of water applied per irrigation to the four basins within each irrigation treatment are presented in Table 1. In both years, the Wet, Medium and Dry irrigation treatment basins received a total of five, four and two seasonal irrigations, respectively. In the 1985 study, a pre-plant irrigation of 127 mm was given to all basins on 23 and 24 December 1984 for plant establishment. In the 1986 study, a 32-mm rainfall which occurred one day following planting provided adequate soil moisture for plant establishment. Rainfall during the growing season totaled 8 mm in 1985 and 51 mm in 1986. The field was top-dressed with 90 kg nitrogen/ha prior to planting in both years. A postplant application of nitrogen, in the form of urea-ammonium nitrate solution (32% nitrogen), was distributed to the wheat crops during irrigation at about the early heading stage. The postplant fertilizer was injected into the irrigation water at 90 and 100 kg nitrogen/ha in 1985 and 1986, respectively. Grain was harvested in early-June (1985) and mid-May (1986). Yield samples were measured in three, 1.3 by 6.7 m plots near each neutron access tube location. The three yield samples were averaged at each location. A total of 52 (1985) and 64 (1986) yield determinations were made within each irrigation treatment.
STATISTICAL ANALYSES OF SOIL VARIABILITY
181
TABLE 1 Irrigation schedules and average water application depths of five, four and two irrigations of the Wet, Medium and Dry treatments, respectively. Values given are the average of four basins in each treatment 1985
1986
Irrigation dates
Average water application (mm)
15 March 3 April 12 April 26 April 7 May
130 114 103 126 103
Total
576
21 March 9 April 18 April 7 May
140 114 101 104
Total
459
3 April 18 April
133 118
Total
251
Total
Pre-plant Irrigation (mm)
127
Pre-seasonal Rainfall (mm)
32 mm
8
Seasonal Rainfall (mm)
51 mm
Seasonal Rainfall (mm)
Irrigation dates
Wet irrigation treatment 24 January 11 March 1 April 17 April 30 April Total
Medium irrigation treatment 3 March 28 March 15 April 2 May Total
Dry irrigation treatment 25 March 23 April
Average water application (mm)
123 127 125 127 119 621
115 128 125 124 492
143 121 264
RESULTS AND DISCUSSION
Irrigation uniformity and crop evapotranspiration In both 1985 and 1986, level-basin irrigations were characterized by rapid water advance rates and small differences in infiltration opportunity times. The calculated irrigation water distribution uniformities (DU; where DU =average low-quarter infiltrated depth/average infiltrated depth) were relatively high, but varied from 75-87% in 1985 and from 73-92% in 1986
182
D.J. HUNSAKERAND D.A. BUCKS
over all treatments (Table 2 ). For this particular site, water distribution uniformity was found to be closely related to the variability in the soil water content at irrigation and soil surface elevation within the basin (Hunsaker et al., 1991 ). In 1986, basins of the Medium irrigation treatment were inadvertently under-watered during their first irrigation resulting in the lowest DUs for that year (73 and 77%). However, subsequent irrigations of the Medium treatment were more uniform (85-92%) so that the average seasonal distribution uniformities of the Medium treatment in 1986 were not statistically different from those of the Wet and Dry treatments of that year. In 1985, the average seasonal distribution uniformities were slightly lower in shorter than longer length basins, although not statistically different. Cumulative seasonal infiltrated water depth (I), where I is the summation TABLE 2 Irrigation water distribution uniformities (%) obtained for each of the five, four and two irrigations applied to the Wet, Medium and Dry treatments, respectively, in 1985 and 1986 1985 Irrigation treatment
Basin length treatment
Irrigation
Seasonal Average
1a
2
3
4
5
Wet
Long Sho~
85 82
81 78
80 80
87 79
81 78
Medium
Long Sho~
85 86
87 75
83 85
86 82
Dry
Long Sho~
81 81
83 79
83 79 85 82 82 80
1986 Irrigation treatment
Water Quality treatment
Irrigation 1a
2
3
4
5
Wet
High Low
80 82
82 83
85 84
86 82
83 84
Medium
High Low
77 73
92 88
91 88
89 85
Dry
High Low
86 89
86 87
Seasonal Average
83 83 87 84 86 88
~Numbers ( 1 - 5 ) refer to distribution uniformities obtained for the first, second etc., irrigations applied during the season to each treatment.
STATISTICALANALYSESOF SOIL VARIABILITY
183
of infiltrated water depths attained from all irrigations after crop emergence, was calculated for each of the 52 and 64 locations within the Wet, Medium and Dry irrigation treatments of 1985 and 1986, respectively. The I values reasonably fitted the normal distribution. For all three irrigation treatments in 1985 and 1986, the distributions of the seasonal infiltrated water depth could not be rejected at the 0.05 level of significance as being normally distributed by the Z 2 goodness of fit test (Snedecor and Cochran, 1980). The means and coefficients of variation (CV; where C V = standard deviation divided by the mean ) are presented in Table 3 for both the seasonal evapotranspiration (E) and infiltrated water depth (I), by irrigation treatment. Analysis of variance (Neter et al., 1985) established that irrigation treatment significantly affected seasonal E ( P < 0.01 ) in both 1985 and 1986, whereas basin length (1985) and irrigation water salinity (1986) did not affect E. Seasonal evapotranspiration increased with each increase in seasonal irrigation quantity at the 0.05 significance level in both years of study (Table 3 ). When plotted over all irrigation treatments, the overall relationship between seasonal E and infiltrated water depth was linear (Fig. 1 ). However, as shown in Fig. 1, E did not continue to increase within the Wet treatment at the highest levels of infiltrated water where the potential for deep percolation losses was greatest. Consequently, evapotranspiration was more uniform than infiltrated water depth within the Wet irrigation treatments and the CVs for E were only 0.069 and 0.060 in 1985 and 1986, respectively, or about one-half of those for I (0.152 and 0.117, respectively) within the Wet treatment. This would suggest that the variability in infiltrated water depth, while highly linked with the efficiency of irrigation, had relatively little impact on E variability TABLE 3 Irrigation treatment means and the linear correlation coefficients between seasonal evapotranspiration and infiltrated water depth; coefficients of variation are shown in parentheses Year
Irrigation treatment
Evapotranspiration, E (mm)
Infiltrated water depth, I (mm)
Linear correlation coefficient, r
19851
Wet Medium Dry
581 a,2 (0.069) 459 b (0.112 ) 321 ¢ (0.110)
576 (0.152 ) 459 (0.095) 251 (0.132)
0.378** 0.673*** 0.730***
19861
Wet Medium Dry
608 a (0.060) 490 b (0.089) 303 ¢ (0.085)
621 (0.117) 492 (0.064) 264 (0.093)
0.504*** 0.652*** 0.719***
~Number of samples for each irrigation treatment is 52 for 1985 and 64 for 1986. 2Evapotranspiration means within each year followed by different letters are significantly different at P<0.05. **and *** indicate significance at P < 0.01 and P < 0.001, respectively.
184
D.J. H U N S A K E R A N D D.A. BUCKS
BOO. - /~DFLTM oDl~r o
600
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° ***** 9.~e %o o ~ ~.o* ^ ~ o_~_~_~_~_~_~_~_~_~ o oO ~A
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400
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i
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1986
800
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o
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400E = 9 8 + 0.81"I
200-
r 2 = 0.92
00
200
400
600
800
INFILTRATED WATER DEPTH, I (ram)
Fig. 1. Relation of evapotranspiration (E) to infiltrated water depth (I) over all treatments in 1985 (n= 156) and 1986 (n= 192). for the frequently irrigated wheat crops with reasonable water distribution uniformities ( D U > 79%). Within the Medium and Dry treatments, the CVs of infiltrated water depth were somewhat lower than those attained within the Wet treatment. However, the variability of E in the Medium and Dry treatments was more strongly related to differences in infiltrated water as indicated by the highly significant (P < 0.001 ) linear correlation between E and I of those treatments (Table 3 ). Consequently, the CVs for E in the Medium (0.112 and 0.089) and Dry (0.110 and 0.085) treatments were greater than those of the Wet treatment (0.069 and 0.060) in both years.
Grain yield variability The means and CVs of grain yield (Y) are presented in Table 4, by irrigation treatment. Grain yield was affected by irrigation treatment ( P < 0.01 ), while basin length ( 1985 ) and water salinity ( 1986 ) did not affect Y significantly. Multiple comparison of irrigation treatment means showed that Y increased significantly from Dry to Wet treatments at P < 0.05 in both 1985 and 1986 (Table 4). The CVs for grain yield were observed to decrease from Dry (0.25) to Wet treatments (0.11-0.13 ). The CV for yield was about two times greater in the Dry than those of the Wet treatment in both years, and 1.3-1.5times greater than those of the Medium irrigation treatment.
STATISTICALANALYSESOF SOILVARIABILITY
18 5
TABLE 4 Grain yield irrigation treatment means and linear correlation coefficients with evapotranspiration and infiltrated water depth; coefficients of variation for grain yield are shown in parentheses Year
Irrigation treatment
Grain Yield, Y (kg/ha)
19851
Wet Medium Dry
5162 ~'2 (0.113) 3819 b (0.187) 1469~(0.246)
19861
Wet Medium Dry
6009~(0.130) 4998 b (0.169) 1892~(0.251)
Linear correlation coefficient, r Evapotranspiration, E
Infiltrated water depth, I
-0.051 0.583*** 0.371"*
-0.505*** 0.525*** 0.309*
0.114 0.326** 0.533***
-0.394** 0.458*** 0.397**
~Number of samples for each irrigation treatment is 52 for 1985 and 64 for 1986. 2Grain yield means within each year followed by different letters are significantly different at P < 0.05. *, ** and *** indicate significance at P < 0.05, P < 0.01 and P < 0.001, respectively.
8000 608oi l
-r 2 = 0.SB
f,ooot
. A
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o
42°°08oo°~m
198~
• Y = -1840 + 13.1*E r 2 = 0.8~
-
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,ooO. ooo.
~ ~ ~.¢0 ~_~
~,,,~'~*o-
o
AA~
0 206 400 600 EVAPOTRANSPIRAT]ON, E (ram)
800
Fig. 2. Relation of grain yield (Y) to evapotranspiration (E) over all treatments in 1985 ( n = 1 5 6 ) a n d 1986 ( n = 192).
Yield increased positively and significantly ( P < 0.05 ) with seasonal E and I i n both the Medium and Dry treatments, but was not correlated with E within the Wet treatments (Table 4 ). Thus, over the Wet treatment, yield variability could not be related to differences in seasonal evapotranspiration. However,
186
D.J. HUNSAKER
8000- -
~
AND
D.A. BUCKS
1085
r e g r e s s i o n fit
.Y = 3 3 7 6 - 29.1"I + 0 . 1 0 8 " I 2 - g . 0 . 1 0 - 5 , I 3 r 2 = 0.B4 6000-
e ° oo
oqb o
~"4000I a
2000
~m
A
i
Z
BOOO-
-regression fit Y ~ -5314 + 34.4"I - 0.026'I 2 ~ o o ~o r 2 = 0,85 ~eg
o
o
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o o ~
~o ft. o°o o
6000-
~o
o
4000+ ~
o
2000D 0
,
0
I
200
a I
400
I
600
I
800
INFILTRATED WATER DEPTH, I (ram)
Fig. 3. Grain yield (Y) as a curvilinear function of infiltrated water depth ( I ) over all treatm e n t s in 1985 ( n = 1 5 6 ) a n d 1986 ( n = 1 9 2 ) .
yields were negatively correlated with infiltrated water depth ( P < 0.01 ) within the Wet treatment reflecting reduced yield at higher levels of infiltrated water. This result is primarily attributed to the effects of deep percolation and associated nutrient leaching losses, although neither of these were measured directly in the field. When yield was regressed as a function of E over all treatments (Fig. 2), the overall relationship was linear (r2= 0.83 in both years). However, when yield was regressed with infiltrated water depth over all treatments (Fig. 3), the relationship was more adequately described by a thirdorder polynomial ( r 2 = 0 . 8 4 ) in 1985 and a second-order polynomial (r 2 = 0.85 ) in 1986. Clearly, Fig. 3 points out that yield variability was influenced by the quantity of infiltrated water. For the under-irrigated treatments (Medium and Dry), where E and I were closely related, yield response to infiltrated water was roughly linear. Under full irrigation (Wet treatment), the yield versus I relationship begins to curve over as deep percolation water losses and nutrient leaching are accelerated in some areas of the field. Soil water content variability The seasonal average CVs for volumetric soil water content (0) measured one day prior to irrigations (over an assumed crop root depth of 0-1.6 m) were somewhat lower for the Wet (0.14 (1985) and 0.12 ( 1 9 8 6 ) ) than the Medium (0.18 and 0.18) or the Dry (0.17 and 0.17) treatments. This was
STATISTICAL ANALYSES OF SOIL VARIABILITY
187
also the case for the seasonal average CVs for soil water content (0-1.6 m soil depth) measured three days following irrigations which were approximately 0.10, 0.13 and 0.13 for the Wet, Medium and Dry irrigation treatments in both years, respectively. In any treatment, however, the soil water contents measured on separate dates over the season were highly correlated ( P < 0.001 ) with one another. Locations that were wetter or drier prior to an irrigation tended to be wetter or drier after the irrigation and throughout the season. Figures 4 ( 1985 ) and 5 (1986) show the average soil water content from each o f the three irrigation treatments plotted with time. Linear correlation coefficients (r) obtained between grain yield and soil water content on the separate measurement dates are also shown in the figures. The significance of r varied from P < 0.05 to P < 0.001 over all correlation dates and treatments, with one or two exceptions. Note that grain yield was highly correlated ( P < 0 . 0 0 1 ) with the soil water content measured shortly after crop emergence (day 44 in 1985 and day 8 in 1986) in each treatment. Presumably, [] CORRELATIONCOEI~IC'I~ 1.00" • ,~ AVERAGES01LWAT~ C08'1'~V1' 0.50-
- 1.00
[ n~IGATIONDACE WET ~ T M ~ N T
0.80. []
1985
-0.50
~
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0.40-
~
- 0.40
~o.2o ~
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DRY TRE&T}~NT 1985
0.50.
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0.80
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Fig. 4. Average soil water content (0) with time, and correlation coefficient (r) between grain yield (Y) and soil water content with time for the Wet, Medium and Dry irrigation treatments ( n = 5 2 ) in 1985.
188
D.J. H U N S A K E R A N D D.A. BUCKS rl
1.00- ' •
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1988
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1986
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8
,
20
8'o
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Fig. 5. Average soil water content (0) with time, and correlation coefficient (r) between grain yield (Y) and soil water content with time for the Wet, Medium and Dry irrigation treatments (n=64) in 1986.
these correlations reflect yield variability associated with different water retention capabilities of the soil due to differences in texture (sand, silt and clay content) within the field. The coefficients of determination (r 2) for those dates indicate that between 0.27-0.36 in 1985 and between 0.19-0.44 in 1986 of the grain yield variability for a treatment was explained by the variability in soil water content measured prior to seasonal irrigation. Over time, as water was added and removed from the root zone, the distribution of 0 changed somewhat as did the correlation between grain yield and 0. In the Medium treatments, there was a marked increase in the values of r immediately after the first irrigation. In 1986, as noted earlier, the irrigation uniformities of the first irrigation were particularly poor in the Medium treatment which probably explains the sudden increase in the yield-water content correlation. In the 1985 Medium treatment, the stronger correlation following the first irrigation was more likely due to relatively large differences in the water application to the four basins of the treatment. In either year, however,
STATISTICALANALYSESOF SOIL VARIABILITY
189
the correlation values declined in the Medium treatment following the first irrigation as water was applied more uniformly in subsequent irrigations. Because the variability in the root zone water content was more consistent over time in the Wet and Dry treatments, the correlation between Y and 0 did not change appreciably over the growing season (although there was a gradual decline in r with time in the Wet treatments and a slight increase in r following the first irrigation of the Dry treatments). Over the two years, the variability in soil water contents explained as much as 0.44, 0.42 and 0.36 of the grain yield variability in the Dry, Medium and Wet treatments, respectively. Soil texture variability The sand, silt and clay particle contents within the 0-1.0 m soil depth, determined for the 97 sites within the field, averaged 61, 22 and 17%, respectively, with corresponding CVs of 0.12, 0.20 and 0.23 (Hunsaker et al., 1991 ). Sand content varied within the field from 46% to 79%, while the silt and clay content varied from 10 to 31% and 8 to 25%, respectively. Locations of higher sand or lower clay contents typically had the lowest soil water content in the field. Linear correlations indicated that the soil water content, measured shortly after crop emergence, was highly correlated (P<0.001 with both the sand (Table 5) and clay (Table 6) content (within the 0-1.0 m soil depth) over each irrigation treatment and over all treatments combined. Linear correlation coefficients between the sand (Table 5 ) or clay content (Table 6 ) with the seasonal infiltrated water depth and evapotranspiration are also presented. The significant, positive correlations obTABLE 5 Linear correlation coefficients between sand content (0-1.0 m soil depth) with soil water content (measured on day 44 in 1985 and day 8 in 1986), evapotranspiration and infiltrated water depth for the Wet, Medium and Dry irrigation treatments and all treatments combined Year
Irrigation treatment
No. of samples
Linear correlation coefficient, r Soil water content
Evapotranspiration
Infiltrated water depth
1985
Wet Medium Dry All
31 31 27 85
-0.858*** -0.725*** -0.747*** -0.760***
0.406* 0.028 -0.529** -0.043
0.638*** 0.001 -0.201 0.081
1986
Wet Medium Dry All
35 29 33 97
-0.778*** -0.814"** -0.732*** -0.779***
0.149 0.058 -0.294 0.032
0.390* -0.125 -0.211 0.074
*, ** and *** indicate significance at P<0.05, P<0.01 and P<0.001, respectively.
190
D.J. HUNSAKERAND D.A. BUCKS
TABLE 6 Linear correlation coefficients between clay content (0-1.0 m soil depth) with soil water content (measured on day 44 in 1985 and day 8 in 1986), evapotranspiration and infiltrated water depth for the Wet, Medium and Dry treatments and all treatments combined Year
Irrigation treatment
No. of samples
Linear correlation coefficient, r Soil water content
Evapotranspiration
Infiltrated water depth
1985
Wet Medium Dry All
31 27 27 85
0.715"** 0.606*** 0.648*** 0.650***
-0.323 0.012 0.632*** 0.043
-0.546** 0.023 0.028 0.075
1986
Wet Medium Dry All
35 29 33 97
0.720*** 0.770"** 0.616*** 0.705***
- 0.063 - 0.166 0.235 - 0.044
- 0.325 0.007 0.146 - 0.081
** and *** indicate significance at P < 0.01 and P < 0.001, respectively.
tained between infiltrated water depth and sand content in the Wet treatment of both years indicate that sandier locations infiltrated more water during the season under that irrigation regime. However, the seasonal infiltrated water depth was not related to sand content in either the Medium or Dry irrigation treatments. Seasonal evapotranspiration also increased with sand content in the Wet treatment, although not significantly so in 1986. Although seasonal E was not related to sand content within the Medium treatments in both years, there was a significant, negative correlation ( P < 0 . 0 1 ) between seasonal E and sand content within the Dry treatment of 1985. The decrease in E with sand content in the Dry treatment would most likely be related to differences in available soil water under the limited water supply of that treatment. Although the correlation between seasonal E and sand content was negative in 1986, it was not significant. There was also no indication of a relationship between sand content with either E or I when all treatments were combined. Linear correlation coefficients between seasonal I and E with clay content (Table 6 ) were similar to those with sand, although generally opposite in sign. Clay content was significantly correlated with the infiltrated water depth in the Wet treatment of 1985 ( P < 0 . 0 1 ) and with seasonal E in the Dry treatment of 1985 ( P < 0 . 0 0 1 ) . Table 7 gives the linear correlation coefficients obtained between grain yield and the soil separates over the individual irrigation treatments and over the treatments combined. The effect of higher sand content ultimately resulted in lower grain yields. Negative correlations of Y with sand content were significant at the 0.01 level over each treatment. Not surprising was the indication
STATISTICALANALYSESOF SOIL VARIABILITY
191
TABLE 7 Linear correlation coefficients between grain yield with sand, silt and clay contents within a 1.0 m soil depth for the Wet, Medium and Dry irrigation treatments and all treatments combined Year
Irrigation treatment
No. of samples
Linear correlation coefficient, r Sand
Silt
Clay
1985
Wet Medium Dry All
31 27 27 85
-0.654*** -0.635*** -0.716"** -0.261"
0.646*** 0.592** 0.621"** 0.279**
0.472** 0.545** 0.675*** 0.194
1986
Wet Medium Dry All
35 29 33 97
-0.444** -0.624*** -0.705*** -0.209*
0.433** 0.533** 0.657*** 0.210"
0.365* 0.603*** 0.579*** 0.147
*, ** and *** indicate significance at P < 0.05, P < 0.01 and P < 0.001, respectively.
that the a m o u n t of Y variability explained by sand content, the r:, was consistently highest in the Dry treatments ( ~ 0.50), followed by the Medium ( ~ 0.40) and Wet (0.20-0.43) treatments. This partially explains why the CVs for yield were larger in the Dry treatment than those in the Wet and Medium treatments. Grain yields were also significantly correlated ( P < 0.05 ) with sand content over the entire range of combined irrigation treatments. This would suggest that the soil texture variability had an overall effect on yield even when extremely different quantities of irrigation water were applied to the irrigation treatments. The effect of sand content on wheat yield variability is reduced less in the M e d i u m and Dry treatments than the Wet, when the combined effects of infiltrated water depth and sand content on Y are included in a linear multiple regression. This is shown in Table 8 which presents the r 2 when Y is first regressed by I and the resulting r 2 when sand content is added in multiple regression. The increase in r 2 can be interpreted as the marginal effect of sand content on yield variability after the effect of I has been taken into account. For the Dry treatments, sand content increased the r 2 by 0.40-0.42 and for the M e d i u m treatments by 0.32-0.40. However, for the Wet treatments, the increase in r 2 was only 0.08-0.10. This follows from the fact that infiltrated water depth was also significantly correlated with sand content in the Wet treatments. Another interpretation might be that the variability in sand content was responsible for differences in I which, in turn, influenced yield variability in the Wet treatment. For all treatments combined, the r 2 was increased by 0.07-0.11 when sand content was included in the linear multiple regression. Equations for grain yield as a function of infiltrated water depth and sand
192
D.J. HUNSAKER AND D.A. BUCKS
TABLE 8 Coefficients of determination (r 2) obtained when grain yield was regressed by infiltrated water depth, and when regressed by infiltrated water depth and sand content (0-1.0 m soil depth) for the Wet, Medium and Dry irrigation treatments Year
Irrigation treatment
No. of samples
Coefficient of determination, r 2 Infiltrated water depth
Infiltrated water depth and sand
1985
Wet Medium Dry
31 27 27
0.413"** 0.181" 0.165"
0.514"** 0.585*** 0.584***
1986
Wet Medium Dry
35 29 33
0.242** 0.263** 0.168"
0.317"* 0.582** 0.568***
*, ** and *** indicate significance at P < 0.05, P < 0.01 and P < 0.001, respectively.
TABLE 9 Multiple regression equations for grain yield (Y) as a function of infiltrated water depth (I) and sand content (S) in 1985 and 1986 Year
Regression Equation
r2
19851 19861
y2 = 5252-- 18.4•+ 0.07912- 6.75X10-513- 49.8S Y= - 1968 + 34.6•- 0.02712- 55.2S
0.88 0.87
tVariable units are kg/ha for grain yield (Y), m m for infiltrated water depth (I) and % for sand content (S). ~Number of samples is 85 for 1985 and 97 for 1986.
content were obtained by stepwise, multiple regression over the entire range of treatments (Table 9). The resultant regression equations expressed yield as a third- and second-order function of I in 1985 and 1986, respectively, and as a linear function of sand content. The regression coefficients were each significant at the 0.01 level in both years. The inclusion of the sand content variable with infiltrated water depth improved the r 2 by about 0.045 over that when only I was used in the model. By holding sand content constant, the regression equations were used to calculate crop production curves ( Y vs. I) for sand contents of 60% (near the field site sand content average of 61%), 50% and 75%. These are shown in Fig. 6 along with the fitted regression functions obtained when sand content
STATISTICAL
L
ANALYSES
8000-
--
6000-
.... 50% s a n d - - 6fl% s a n d "/5% s a n d
regression
193
OF SOIL VARIABILITY
fit without
sand
1985
content content content
4000
V2O00
Z
[
0
N 59006000-
,
I
'
]
regression fit without sand .... 50% sand content --
- - 60% s a n d 75% s a n d
content content
I 1958
" ............... "
4000.
2000
,
[ 205
,
i
|
i
400
655
800
INFILTRATED WATER DEPTH (ram)
Fig. 6. Calculated relationships between grain yield and infiltrated water depth as affected by sand content in 1985 and 1986.
was not included (reproduced from Fig. 3). As might be expected, the predicted curves for 60% sand content is nearly coincident with the fitted curve obtained without the sand content variable. The predicted curves for sand contents at 50 and 75% plot approximately 500-550 kg ha above and 750825 kg/ha below the 60% sand content curve, respectively, for a given infiltrated water depth. Thus, the preceding suggests that even if it were possible to achieve precisely the same I to all parts of the field, yields could be expected to vary in the order of 1250-1400 kg/ha due to the effects of sand content differences. One practical application would be that where sand content increases in the field, nitrogen application rates could decrease to obtain the same yield goal and possibly reduce nitrate leaching losses below the root zone. CONCLUSIONS
Strategies to economize irrigation water, minimize crop water-use variability and obtain high yields with level-basin irrigation must account for the effects of spatial variability under field-scale conditions. Variabilities in crop evapotranspiration and yield, evaluated in a 2-year wheat study, were related to the variability in seasonal infiltrated water depth, soil water content and soil particle size distribution. Expressing yield as a function of infiltrated water
194
D.J. HUNSAKER AND D.A, BUCKS
depth plus soil particle fraction may be a useful way to account for soil variability in a crop production model. In this study, sand content was used with infiltrated water depth to estimate expected yields. Sand composition can be obtained with relative ease and serve as a major input to improve yield prediction. The coefficients of variation for grain yield were about two times greater in a dry irrigation treatment (two irrigations) and from 1.3 to 1.7 times greater in a medium irrigation treatment (four irrigations) compared to those of a wet treatment (five irrigations). In the drier treatments, yield variability was manifested by the differential effects of water stress due to variations in both infiltrated water depth and soil water content. Under reduced irrigation, therefore, yield variability might be reduced somewhat by improving the distribution uniformities of infiltrated water (through more precise land leveling, changing basin flow rates etc. ). In the wet treatment, yields were reduced in locations of higher sand content which were expected to infiltrate more water during the growing season. Thus, under the wet irrigation conditions, yield variability was attributed to increased deep percolation rates and nutrient losses, as well as variability in soil water content. However, because of soil variability, minimizing deep percolation may be difficult to achieve with full irrigation. Knowing that the anticipated grain yields will decrease, a reduced amount of nitrogen fertilizer could be applied as the sand content increases in the field. The reduced amount of nitrogen would probably best be applied in multiple applications across the field during early stages of plant growth. At the same time, scheduling a maximum number of irrigations after crop establishment (at least five for wheat) with a minimum irrigation application amount of about 100 mm per irrigation would be advisable as the sand content increases and maximum grain yield becomes lower. ACKNOWLEDGEMENTS
The authors greatly acknowledge the contributions of W.L. Alexander, Agronomist, USDA-ARS and O.F. French, Irrigation Manager, University of Arizona for their assistance in this project.
REFERENCES ASCE, 1990. Evapotranspiration and irrigation water requirements. ASCE-Manuals and reports on engineering practice-No. 70, Irrig. and Drain. Div., American Society of Civil Engineers, New York, 322 pp. Ayars, J.E., Hutmacher, R.B., Hoffman, G.J., Letey, J., Ben-Asher, J. and Solomon, K.H., 1990. Response of sugar beet to non-uniform irrigation. Irrig. Sci., 11: 101-109.
STATISTICAL ANALYSES OF SOIL VARIABILITY
195
Bos, M.G., Replogle, J.A. and Clemmens, A.J. (Editors), 1984. Flow Measuring flumes for open channel systems. John Wiley and Sons, Inc., New York, 321 pp. Bresler, E., Dasbert, S., Russo, D. and Dagan, G., 1981. Spatial variability of crop yield as a stochastic process. Soil Sci. Soc. Am. J., 45: 600-605. Clemmens, A.J., 1988. Method for analyzing field scale surface irrigation uniformity. J. Irrig. Drain. Div. ASCE, 114: 74-88. Clemmens, A.J. and Dedrick, A.R., 1982. Limits for practical level basin design. J. Irrig. Drain. Div. ASCE, 108: 127-141. Dedrick, A.R., 1990. Level-basin irrigation - an update. Proceedings of the 3rd Natural Irrigation Symposium, Phoenix, AZ, USA, Oct. 28-Nov. I. Erie, L.J. and Dedrick, A.R., 1979. Level-basin irrigation: a method for conserving water and labor. US Dept. Agric., Farmers' Bull., 2261, Washington, DC, 23 pp. Erie, L.J., French, O.F., Bucks, D.A. and Harris, K., 1982. Consumptive use of water by major crops in the southwestern United States. US Dept. Agric., Conserv. Res. Rep. No. 29, Washington, DC, 42 pp. Feinerman, E., Letey, J. and Vaux, Jr., H.J., 1983. The economics of irrigation with nonuniform infiltration. Water Resour. Res., 19: 1410-1414. Gee, G.W. and Bauder, J.W., 1986. Particle-size analysis. In: A. Klute (Editor), Methods of soil analysis, 1. Am. Soc. Agron., Madison, WI, USA, pp. 383-411. Hart, W.E., Norum, D.I. and Peri, G., 1980. Optimal seasonal irrigation analysis. J. Irrig. Drain. Div. ASCE, 106: 221-235. Hunsaker, D.J., Bucks, D.A. and Jaynes, D.B., 1991. Irrigation uniformity of level basins as influenced by variations in soil water content and surface elevation. Agric. Water Manage., 19: 325-340. Jaynes, D.B. and Clemmens, A.J., 1986. Accounting for spatially variable infiltration in border irrigation models. Water Resour. Res., 22:1257-1262. Jaynes, D.B. and Hunsaker, D.J., 1989. Spatial and temporal variability of water content and infiltration on a flood irrigated field. Trans. ASAE, 32:1229-1238. Letey, J., 1985. Irrigation uniformity as related to optimum crop production - additional research is needed. Irrig. Sci., 6: 253-263. Neter, J., Wasserman, W. and Kutner, M.H. (Editors), 1985. Applied linear statistical models: regression, analysis of variance, and experimental designs. Richard D. Irwin, Inc., Homewood, IL, USA, 1127 pp. Russo, D., 1984. Statistical analysis of crop yield-soil water relationships in heterogeneous soil under trickle irrigation. Soil Sci. Soc. Am. J., 48: 1402-1410. Russo, D., 1986. A stochastic approach to the crop yield-irrigation relationships in heterogeneous soils: I. Analysis of the field spatial variability. Soil Sci. Soc. Am. J., 50: 736-745. Seginer, I., 1978. A note on the economic significance of uniform water application. Irrig. Sci., l: 19-25. Snedecor, G.W. and Cochran, W.G. (Editors), 1980. Statistical methods. Iowa State Univ. Press, Ames, Iowa, USA, 507 pp. Solomon, K.H., 1984. Yield related interpretations of irrigation uniformity and efficiency measures. Irrig. Sci., 5: 161-172. Stern, J. and Bresler, E., 1983. Nonuniform sprinkler irrigation and crop yield. Irrig. Sci., 4:1729.
USDA, 1974. Border irrigation. Chap. 4, Sect. 15 (Irrigation) Soil Conserv. Serv. Nat. Eng. Handbook, U.S. Department of Agriculture. Varlev, I., 1976. Evaluation of non-uniformity in irrigation and yield. J. Irrig. Drain. Div. ASCE, 102: 149-164. Warrick, A.W. and Gardner, W.R., 1983. Crop yield as affected by spatial variations of soil and irrigation. Water Resour. Res., 19: 181-186.
177
© 1992 Elsevier Science Publishers B.V. All rights reserved. 0378-3774/92/$05.00
Statistical analyses of soil variability: effects of variability on level-basin irrigation of wheat* D.J. Hunsaker a and D.A. Bucksb aUS Water Conservation Laboratory, Phoenix, AZ, USA and bUSDA-ARS, National Program Staff Beltsville, MD, USA (Accepted 24 February 1992)
ABSTRACT Hunsaker, D.J. and Bucks, D.A., 1991. Statistical analyses of soil variability: Effects of variability on level-basin irrigation of wheat. Agric. Water Manage., 21: 177-195. The transfer of plot-size water-use and yield information to field-size, surface irrigation conditions is generally inadequate due to nonuniform water applications and soil variability. In a two-year levelbasin irrigation study, water-use and yield (Y) relationships were characterized for wheat grown under three quantities of seasonal irrigation water on a 4.2-ha, sandy loam field site. The Mohall sandy loam had a sand particle fraction ranging from 46 to 79% with a mean of 61% at the 0 to 1.0-m soil depth. The sand particle fraction, soil water content and grain yield were highly correlated with one another. High sand content was associated with less evapotranspiration and lower yield within the driest irrigation treatment (two irrigations), as well as, higher rates of infiltrated water and lower yield in the wettest irrigation treatment (five irrigations ). The inclusion of sand content with seasonal infiltrated water depth improved estimates of yield as much as 42% for specific levels of irrigation management. Estimated yields for a given depth of seasonal infiltrated water varied 1250-1400 kg/ ha over the range of sand content within the field site. Knowing the sand content of the field, the farmer should be able to improve the scheduling of both irrigation and fertilizer application rates based on more realistic wheat yield goals.
INTRODUCTION
Level-basin irrigation usage is expanding in western regions of the United States and is estimated to be about 400,000 ha (Dedrick, 1990). Level-basin irrigation offers certain advantages over other surface irrigation methods including potentially higher water application uniformity, no runoff and reduced irrigation time (Erie and Dedrick, 1979). Because water is applied rapidly to level basins, differences in infiltration opportunity times are small. Correspondence to: Mr. Douglas J. Hunsaker, US Water Conservation Laboratory, 4331 East Broadway Road, Phoenix, Arizona 85040, USA. *Contribution of the US Department of Agriculture, Agriculture Research Service, Pacific West Area, USA.
178
D.J. HUNSAKER AND D.A. BUCKS
Therefore, the variability in water application will be primarily related to variations in soil infiltration properties, surface elevation irregularities and perhaps other factors (Clemmens, 1988). Information on level basin sizing and inflow design criteria for maintaining a desired water application uniformity has been presented by the U.S.D.A. (1974), and Clemmens and Dedrick ( 1982 ). Methods to estimate the effects of spatially variable soil infiltration on level basin application uniformity are available (Jaynes and Clemmens, 1986; Clemmens, 1988). Scientists and engineers have recognized for a long time soil and water application variability in irrigated fields and its effect on crop yields; however, this consideration is seldom included in surface irrigation water management decisions. Even a properly designed level-basin system will have some degree of nonuniformity in water application due to the inherent variability in soil properties of the system. Theoretical analyses have demonstrated that variable soil properties and nonuniform water applications can have significant effect on crop yields (Varlev, 1976; Warrick and Gardner, 1983; Letey, 1985 ). Solomon (1984) used hypothetical crops to demonstrate that common irrigation uniformity and efficiency measures can be related to expected yields from nonuniform irrigation practices. Seginer ( 1978 ) and Hart et al. (1980) presented analyses to compute optimum seasonal water application when water application is nonuniform. Feinerman et al. (1983) determined that the optimum seasonal water application for the nonuniform case will depend on the shape and form of the yield function under uniform irrigation. Sprinkler irrigation experiments, where the effects of variable soil properties of irrigation uniformities on yields were evaluated, have been presented by Bresler et al. (1981 ), Stern and Bresler (1983 ) and Ayars et al. (1990). Similar work has been presented by Russo (1984) and Russo ( 1986 ) for trickle irrigation systems. As pointed out by Russo ( 1986 ), the variability in crop yield due to the inherent spatial variability in soil properties within a field will likely reduce the average yield relative to that which could be attained were soil properties uniform within the field. Russo (1986 ) also suggested that the more the crop was subjected to stress conditions (water and salinity ), the greater the effect of the field spatial variability on crop yield. The objective of this paper is to evaluate the influence of soil and irrigation variabilities on water-use and yield for a wheat crop grown under field-size, level-basin irrigation systems. METHODS AND MATERIALS
Durum wheat (Triticum durum cv. 'Aldura') was surface irrigated in 1985 and 1986 in laser-leveled basins on a 4.2-ha field site ( 168 by 251 m) located on the University of Arizona, Maricopa Agricultural Center in central Ari-
STATISTICAL ANALYSES OF SOIL VARIABILITY
179
zona. Wheat was planted in 12 basins, each 14 m in width, on 3 January 1985 ( 1985 study) and on 10 December 1985 ( 1986 study). In the 1985 study, the 12 basins were separated into six, replicated treatments in a randomized block design. Treatments consisted of three seasonal irrigation quantities, designated Wet, M e d i u m and Dry; and two basin lengths, designated Long (251 m) and Short (190 m ) . In the 1986 study, the 12 basins were all the same length (251 m) and were laid out in a split-plot design having three seasonal irrigation quantities (Wet, Medium and Dry) and two irrigation water qualities, designated High salinity (electrical conductivity about 2.7 d S / m ) and Low salinity (electrical conductivity about 1.1 d S / m ) . All basins in the 1985 study were irrigated with the lower salinity water. In 1985, 15 and 11 neutron access tubes were installed in each long and short basin, respectively, at 15.2-m intervals down the basin length, equidistant from the basin boundaries. In 1986, 16 access tubes were installed in each basin with the same spacing as in 1985. Surface elevation measurements were made at each access tube site prior to planting in both years. Four neutron moisture gauges were calibrated at several locations within the field site and were used to measure volumetric soil water content in 0.2-m soil depth increments. Water content measurements were obtained over a 2.0-m soil depth at each access tube site beginning shortly after crop emergence ( 13 February 1985 and 8 January 1986). Subsequent water content measurements, which included measurements taken one day before and three days following each basin irrigation, were made throughout the growing season until crop harvest. Seasonal crop evapotranspiration (E) was estimated at each access tube site as the summation of the ( 1 ) measured soil water depletion during the growing season over a crop rooting depth of 1.6 m, (2) estimated evapotranspiration during the four day period following each irrigation and (3) seasonal rainfall. The major shortcoming of this method is the potential for unmeasured drainage water inclusion in the evapotranspiration estimate (ASCE, 1990). One of the shorter basins from the Medium irrigation treatment of 1985 was selected for more extensive water content measurement in a companion study (Jaynes and Hunsaker, 1989). In this basin a total of 44 access tubes were installed to a depth of 2.8 m, in a 4 by 11 array with a corresponding spacing of 3.0 by 15.2 m. Water contents were measured at these sites in 0.2m increments the day prior to and the day immediately following each of the four irrigations given. The variability of infiltrated water depth for each irrigation was calculated by subtracting the water content, integrated over the depth of the access tube, before irrigation from the value after irrigation. A linear calibration equation developed from the irrigations of this basin was used to estimate the variability of infiltrated water within the other basins in the field. A detailed description of the analysis is presented in an earlier paper (Hunsaker et al., 1991 ).
180
D.J. HUNSAKER AND D.A. BUCKS
The soil at the site is a Mohall sandy loam (fine-loamy, mixed hyperthermic, Typic Haplargid). Soils samples were obtained with a coring device (0.042-m diameter) in 0.25 m increments, to a depth of 1.0 m near each access tube location prior to planting in both years. In 1985, soil samples from 85 of the locations were selected for particle size analysis by the hydrometer method (Gee and Bauder, 1986). In 1986, soil samples from 12 additional locations were selected for particle size analysis. Of the 388 soil samples taken at 97 locations within the site, 49% were classified as sandy loam, 25% as sandy clay loam, 8% as loam, and 17% as loamy sand. The irrigations applied to the Wet, Medium and Dry treatment basins were scheduled to replace 100, 80 and 50% of crop evapotranspiration, respectively. Irrigation scheduling followed the historical estimates of wheat evapotranspiration (Erie et al., 1982) in central Arizona, adjusted for planting date and atmospheric conditions. Meteorological data were recorded during the growing seasons by two portable electronic weather stations, one near the center of a Wet treatment basin and another in a nearby alfalfa field. Rectangular canal gates (0.6 by 0.7 m), installed at the head of each basin, controlled the water delivery from a concrete-lined open channel. The water delivery inflow rate was measured in the open channel during each irrigation with a broad-crested weir (Bos et al., 1984), located about 10 m upstream from the first basin turnout. An electronic logger was used with the weir to monitor the water level at the gauging point of the weir, and converted sensed water levels to flow rate and cumulative flow volume. The irrigation dates and the average depth of water applied per irrigation to the four basins within each irrigation treatment are presented in Table 1. In both years, the Wet, Medium and Dry irrigation treatment basins received a total of five, four and two seasonal irrigations, respectively. In the 1985 study, a pre-plant irrigation of 127 mm was given to all basins on 23 and 24 December 1984 for plant establishment. In the 1986 study, a 32-mm rainfall which occurred one day following planting provided adequate soil moisture for plant establishment. Rainfall during the growing season totaled 8 mm in 1985 and 51 mm in 1986. The field was top-dressed with 90 kg nitrogen/ha prior to planting in both years. A postplant application of nitrogen, in the form of urea-ammonium nitrate solution (32% nitrogen), was distributed to the wheat crops during irrigation at about the early heading stage. The postplant fertilizer was injected into the irrigation water at 90 and 100 kg nitrogen/ha in 1985 and 1986, respectively. Grain was harvested in early-June (1985) and mid-May (1986). Yield samples were measured in three, 1.3 by 6.7 m plots near each neutron access tube location. The three yield samples were averaged at each location. A total of 52 (1985) and 64 (1986) yield determinations were made within each irrigation treatment.
STATISTICAL ANALYSES OF SOIL VARIABILITY
181
TABLE 1 Irrigation schedules and average water application depths of five, four and two irrigations of the Wet, Medium and Dry treatments, respectively. Values given are the average of four basins in each treatment 1985
1986
Irrigation dates
Average water application (mm)
15 March 3 April 12 April 26 April 7 May
130 114 103 126 103
Total
576
21 March 9 April 18 April 7 May
140 114 101 104
Total
459
3 April 18 April
133 118
Total
251
Total
Pre-plant Irrigation (mm)
127
Pre-seasonal Rainfall (mm)
32 mm
8
Seasonal Rainfall (mm)
51 mm
Seasonal Rainfall (mm)
Irrigation dates
Wet irrigation treatment 24 January 11 March 1 April 17 April 30 April Total
Medium irrigation treatment 3 March 28 March 15 April 2 May Total
Dry irrigation treatment 25 March 23 April
Average water application (mm)
123 127 125 127 119 621
115 128 125 124 492
143 121 264
RESULTS AND DISCUSSION
Irrigation uniformity and crop evapotranspiration In both 1985 and 1986, level-basin irrigations were characterized by rapid water advance rates and small differences in infiltration opportunity times. The calculated irrigation water distribution uniformities (DU; where DU =average low-quarter infiltrated depth/average infiltrated depth) were relatively high, but varied from 75-87% in 1985 and from 73-92% in 1986
182
D.J. HUNSAKERAND D.A. BUCKS
over all treatments (Table 2 ). For this particular site, water distribution uniformity was found to be closely related to the variability in the soil water content at irrigation and soil surface elevation within the basin (Hunsaker et al., 1991 ). In 1986, basins of the Medium irrigation treatment were inadvertently under-watered during their first irrigation resulting in the lowest DUs for that year (73 and 77%). However, subsequent irrigations of the Medium treatment were more uniform (85-92%) so that the average seasonal distribution uniformities of the Medium treatment in 1986 were not statistically different from those of the Wet and Dry treatments of that year. In 1985, the average seasonal distribution uniformities were slightly lower in shorter than longer length basins, although not statistically different. Cumulative seasonal infiltrated water depth (I), where I is the summation TABLE 2 Irrigation water distribution uniformities (%) obtained for each of the five, four and two irrigations applied to the Wet, Medium and Dry treatments, respectively, in 1985 and 1986 1985 Irrigation treatment
Basin length treatment
Irrigation
Seasonal Average
1a
2
3
4
5
Wet
Long Sho~
85 82
81 78
80 80
87 79
81 78
Medium
Long Sho~
85 86
87 75
83 85
86 82
Dry
Long Sho~
81 81
83 79
83 79 85 82 82 80
1986 Irrigation treatment
Water Quality treatment
Irrigation 1a
2
3
4
5
Wet
High Low
80 82
82 83
85 84
86 82
83 84
Medium
High Low
77 73
92 88
91 88
89 85
Dry
High Low
86 89
86 87
Seasonal Average
83 83 87 84 86 88
~Numbers ( 1 - 5 ) refer to distribution uniformities obtained for the first, second etc., irrigations applied during the season to each treatment.
STATISTICALANALYSESOF SOIL VARIABILITY
183
of infiltrated water depths attained from all irrigations after crop emergence, was calculated for each of the 52 and 64 locations within the Wet, Medium and Dry irrigation treatments of 1985 and 1986, respectively. The I values reasonably fitted the normal distribution. For all three irrigation treatments in 1985 and 1986, the distributions of the seasonal infiltrated water depth could not be rejected at the 0.05 level of significance as being normally distributed by the Z 2 goodness of fit test (Snedecor and Cochran, 1980). The means and coefficients of variation (CV; where C V = standard deviation divided by the mean ) are presented in Table 3 for both the seasonal evapotranspiration (E) and infiltrated water depth (I), by irrigation treatment. Analysis of variance (Neter et al., 1985) established that irrigation treatment significantly affected seasonal E ( P < 0.01 ) in both 1985 and 1986, whereas basin length (1985) and irrigation water salinity (1986) did not affect E. Seasonal evapotranspiration increased with each increase in seasonal irrigation quantity at the 0.05 significance level in both years of study (Table 3 ). When plotted over all irrigation treatments, the overall relationship between seasonal E and infiltrated water depth was linear (Fig. 1 ). However, as shown in Fig. 1, E did not continue to increase within the Wet treatment at the highest levels of infiltrated water where the potential for deep percolation losses was greatest. Consequently, evapotranspiration was more uniform than infiltrated water depth within the Wet irrigation treatments and the CVs for E were only 0.069 and 0.060 in 1985 and 1986, respectively, or about one-half of those for I (0.152 and 0.117, respectively) within the Wet treatment. This would suggest that the variability in infiltrated water depth, while highly linked with the efficiency of irrigation, had relatively little impact on E variability TABLE 3 Irrigation treatment means and the linear correlation coefficients between seasonal evapotranspiration and infiltrated water depth; coefficients of variation are shown in parentheses Year
Irrigation treatment
Evapotranspiration, E (mm)
Infiltrated water depth, I (mm)
Linear correlation coefficient, r
19851
Wet Medium Dry
581 a,2 (0.069) 459 b (0.112 ) 321 ¢ (0.110)
576 (0.152 ) 459 (0.095) 251 (0.132)
0.378** 0.673*** 0.730***
19861
Wet Medium Dry
608 a (0.060) 490 b (0.089) 303 ¢ (0.085)
621 (0.117) 492 (0.064) 264 (0.093)
0.504*** 0.652*** 0.719***
~Number of samples for each irrigation treatment is 52 for 1985 and 64 for 1986. 2Evapotranspiration means within each year followed by different letters are significantly different at P<0.05. **and *** indicate significance at P < 0.01 and P < 0.001, respectively.
184
D.J. H U N S A K E R A N D D.A. BUCKS
BOO. - /~DFLTM oDl~r o
600
J
I r
° ***** 9.~e %o o ~ ~.o* ^ ~ o_~_~_~_~_~_~_~_~_~ o oO ~A
A A
400
o
200
E = 146 + 0.72"I r 8 = 0.83
i
oj
1986
800
o
o 600-
o° % °o
o
o
400E = 9 8 + 0.81"I
200-
r 2 = 0.92
00
200
400
600
800
INFILTRATED WATER DEPTH, I (ram)
Fig. 1. Relation of evapotranspiration (E) to infiltrated water depth (I) over all treatments in 1985 (n= 156) and 1986 (n= 192). for the frequently irrigated wheat crops with reasonable water distribution uniformities ( D U > 79%). Within the Medium and Dry treatments, the CVs of infiltrated water depth were somewhat lower than those attained within the Wet treatment. However, the variability of E in the Medium and Dry treatments was more strongly related to differences in infiltrated water as indicated by the highly significant (P < 0.001 ) linear correlation between E and I of those treatments (Table 3 ). Consequently, the CVs for E in the Medium (0.112 and 0.089) and Dry (0.110 and 0.085) treatments were greater than those of the Wet treatment (0.069 and 0.060) in both years.
Grain yield variability The means and CVs of grain yield (Y) are presented in Table 4, by irrigation treatment. Grain yield was affected by irrigation treatment ( P < 0.01 ), while basin length ( 1985 ) and water salinity ( 1986 ) did not affect Y significantly. Multiple comparison of irrigation treatment means showed that Y increased significantly from Dry to Wet treatments at P < 0.05 in both 1985 and 1986 (Table 4). The CVs for grain yield were observed to decrease from Dry (0.25) to Wet treatments (0.11-0.13 ). The CV for yield was about two times greater in the Dry than those of the Wet treatment in both years, and 1.3-1.5times greater than those of the Medium irrigation treatment.
STATISTICALANALYSESOF SOILVARIABILITY
18 5
TABLE 4 Grain yield irrigation treatment means and linear correlation coefficients with evapotranspiration and infiltrated water depth; coefficients of variation for grain yield are shown in parentheses Year
Irrigation treatment
Grain Yield, Y (kg/ha)
19851
Wet Medium Dry
5162 ~'2 (0.113) 3819 b (0.187) 1469~(0.246)
19861
Wet Medium Dry
6009~(0.130) 4998 b (0.169) 1892~(0.251)
Linear correlation coefficient, r Evapotranspiration, E
Infiltrated water depth, I
-0.051 0.583*** 0.371"*
-0.505*** 0.525*** 0.309*
0.114 0.326** 0.533***
-0.394** 0.458*** 0.397**
~Number of samples for each irrigation treatment is 52 for 1985 and 64 for 1986. 2Grain yield means within each year followed by different letters are significantly different at P < 0.05. *, ** and *** indicate significance at P < 0.05, P < 0.01 and P < 0.001, respectively.
8000 608oi l
-r 2 = 0.SB
f,ooot
. A
: o A
A A
o
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198~
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-
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,ooO. ooo.
~ ~ ~.¢0 ~_~
~,,,~'~*o-
o
AA~
0 206 400 600 EVAPOTRANSPIRAT]ON, E (ram)
800
Fig. 2. Relation of grain yield (Y) to evapotranspiration (E) over all treatments in 1985 ( n = 1 5 6 ) a n d 1986 ( n = 192).
Yield increased positively and significantly ( P < 0.05 ) with seasonal E and I i n both the Medium and Dry treatments, but was not correlated with E within the Wet treatments (Table 4 ). Thus, over the Wet treatment, yield variability could not be related to differences in seasonal evapotranspiration. However,
186
D.J. HUNSAKER
8000- -
~
AND
D.A. BUCKS
1085
r e g r e s s i o n fit
.Y = 3 3 7 6 - 29.1"I + 0 . 1 0 8 " I 2 - g . 0 . 1 0 - 5 , I 3 r 2 = 0.B4 6000-
e ° oo
oqb o
~"4000I a
2000
~m
A
i
Z
BOOO-
-regression fit Y ~ -5314 + 34.4"I - 0.026'I 2 ~ o o ~o r 2 = 0,85 ~eg
o
o
1988
o o ~
~o ft. o°o o
6000-
~o
o
4000+ ~
o
2000D 0
,
0
I
200
a I
400
I
600
I
800
INFILTRATED WATER DEPTH, I (ram)
Fig. 3. Grain yield (Y) as a curvilinear function of infiltrated water depth ( I ) over all treatm e n t s in 1985 ( n = 1 5 6 ) a n d 1986 ( n = 1 9 2 ) .
yields were negatively correlated with infiltrated water depth ( P < 0.01 ) within the Wet treatment reflecting reduced yield at higher levels of infiltrated water. This result is primarily attributed to the effects of deep percolation and associated nutrient leaching losses, although neither of these were measured directly in the field. When yield was regressed as a function of E over all treatments (Fig. 2), the overall relationship was linear (r2= 0.83 in both years). However, when yield was regressed with infiltrated water depth over all treatments (Fig. 3), the relationship was more adequately described by a thirdorder polynomial ( r 2 = 0 . 8 4 ) in 1985 and a second-order polynomial (r 2 = 0.85 ) in 1986. Clearly, Fig. 3 points out that yield variability was influenced by the quantity of infiltrated water. For the under-irrigated treatments (Medium and Dry), where E and I were closely related, yield response to infiltrated water was roughly linear. Under full irrigation (Wet treatment), the yield versus I relationship begins to curve over as deep percolation water losses and nutrient leaching are accelerated in some areas of the field. Soil water content variability The seasonal average CVs for volumetric soil water content (0) measured one day prior to irrigations (over an assumed crop root depth of 0-1.6 m) were somewhat lower for the Wet (0.14 (1985) and 0.12 ( 1 9 8 6 ) ) than the Medium (0.18 and 0.18) or the Dry (0.17 and 0.17) treatments. This was
STATISTICAL ANALYSES OF SOIL VARIABILITY
187
also the case for the seasonal average CVs for soil water content (0-1.6 m soil depth) measured three days following irrigations which were approximately 0.10, 0.13 and 0.13 for the Wet, Medium and Dry irrigation treatments in both years, respectively. In any treatment, however, the soil water contents measured on separate dates over the season were highly correlated ( P < 0.001 ) with one another. Locations that were wetter or drier prior to an irrigation tended to be wetter or drier after the irrigation and throughout the season. Figures 4 ( 1985 ) and 5 (1986) show the average soil water content from each o f the three irrigation treatments plotted with time. Linear correlation coefficients (r) obtained between grain yield and soil water content on the separate measurement dates are also shown in the figures. The significance of r varied from P < 0.05 to P < 0.001 over all correlation dates and treatments, with one or two exceptions. Note that grain yield was highly correlated ( P < 0 . 0 0 1 ) with the soil water content measured shortly after crop emergence (day 44 in 1985 and day 8 in 1986) in each treatment. Presumably, [] CORRELATIONCOEI~IC'I~ 1.00" • ,~ AVERAGES01LWAT~ C08'1'~V1' 0.50-
- 1.00
[ n~IGATIONDACE WET ~ T M ~ N T
0.80. []
1985
-0.50
~
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0.50.
0.60- []
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~
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0.40
0.20- ~
0.20
0.00
40
o~o
,
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'
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Fig. 4. Average soil water content (0) with time, and correlation coefficient (r) between grain yield (Y) and soil water content with time for the Wet, Medium and Dry irrigation treatments ( n = 5 2 ) in 1985.
188
D.J. H U N S A K E R A N D D.A. BUCKS rl
1.00- ' •
COI~TION C O ~ C I ~
- 1.00
AVERAGE SOIL WATI~IcONTENT IRR/GATION DATE
0.80-
WET TREATMENT
1988
-0.80
0.80-
-0.60
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m 0.40
r I
io.~o
j~ 0 . 2 0 .
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1088
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0.20
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DRY TREATMENT
- 0.80
1986
0.60 -
-0.60
0.40-
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0.20.
-0.20 ,
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8
,
20
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,
I I I
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0
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=o'
,
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DAY OF YEAR
Fig. 5. Average soil water content (0) with time, and correlation coefficient (r) between grain yield (Y) and soil water content with time for the Wet, Medium and Dry irrigation treatments (n=64) in 1986.
these correlations reflect yield variability associated with different water retention capabilities of the soil due to differences in texture (sand, silt and clay content) within the field. The coefficients of determination (r 2) for those dates indicate that between 0.27-0.36 in 1985 and between 0.19-0.44 in 1986 of the grain yield variability for a treatment was explained by the variability in soil water content measured prior to seasonal irrigation. Over time, as water was added and removed from the root zone, the distribution of 0 changed somewhat as did the correlation between grain yield and 0. In the Medium treatments, there was a marked increase in the values of r immediately after the first irrigation. In 1986, as noted earlier, the irrigation uniformities of the first irrigation were particularly poor in the Medium treatment which probably explains the sudden increase in the yield-water content correlation. In the 1985 Medium treatment, the stronger correlation following the first irrigation was more likely due to relatively large differences in the water application to the four basins of the treatment. In either year, however,
STATISTICALANALYSESOF SOIL VARIABILITY
189
the correlation values declined in the Medium treatment following the first irrigation as water was applied more uniformly in subsequent irrigations. Because the variability in the root zone water content was more consistent over time in the Wet and Dry treatments, the correlation between Y and 0 did not change appreciably over the growing season (although there was a gradual decline in r with time in the Wet treatments and a slight increase in r following the first irrigation of the Dry treatments). Over the two years, the variability in soil water contents explained as much as 0.44, 0.42 and 0.36 of the grain yield variability in the Dry, Medium and Wet treatments, respectively. Soil texture variability The sand, silt and clay particle contents within the 0-1.0 m soil depth, determined for the 97 sites within the field, averaged 61, 22 and 17%, respectively, with corresponding CVs of 0.12, 0.20 and 0.23 (Hunsaker et al., 1991 ). Sand content varied within the field from 46% to 79%, while the silt and clay content varied from 10 to 31% and 8 to 25%, respectively. Locations of higher sand or lower clay contents typically had the lowest soil water content in the field. Linear correlations indicated that the soil water content, measured shortly after crop emergence, was highly correlated (P<0.001 with both the sand (Table 5) and clay (Table 6) content (within the 0-1.0 m soil depth) over each irrigation treatment and over all treatments combined. Linear correlation coefficients between the sand (Table 5 ) or clay content (Table 6 ) with the seasonal infiltrated water depth and evapotranspiration are also presented. The significant, positive correlations obTABLE 5 Linear correlation coefficients between sand content (0-1.0 m soil depth) with soil water content (measured on day 44 in 1985 and day 8 in 1986), evapotranspiration and infiltrated water depth for the Wet, Medium and Dry irrigation treatments and all treatments combined Year
Irrigation treatment
No. of samples
Linear correlation coefficient, r Soil water content
Evapotranspiration
Infiltrated water depth
1985
Wet Medium Dry All
31 31 27 85
-0.858*** -0.725*** -0.747*** -0.760***
0.406* 0.028 -0.529** -0.043
0.638*** 0.001 -0.201 0.081
1986
Wet Medium Dry All
35 29 33 97
-0.778*** -0.814"** -0.732*** -0.779***
0.149 0.058 -0.294 0.032
0.390* -0.125 -0.211 0.074
*, ** and *** indicate significance at P<0.05, P<0.01 and P<0.001, respectively.
190
D.J. HUNSAKERAND D.A. BUCKS
TABLE 6 Linear correlation coefficients between clay content (0-1.0 m soil depth) with soil water content (measured on day 44 in 1985 and day 8 in 1986), evapotranspiration and infiltrated water depth for the Wet, Medium and Dry treatments and all treatments combined Year
Irrigation treatment
No. of samples
Linear correlation coefficient, r Soil water content
Evapotranspiration
Infiltrated water depth
1985
Wet Medium Dry All
31 27 27 85
0.715"** 0.606*** 0.648*** 0.650***
-0.323 0.012 0.632*** 0.043
-0.546** 0.023 0.028 0.075
1986
Wet Medium Dry All
35 29 33 97
0.720*** 0.770"** 0.616*** 0.705***
- 0.063 - 0.166 0.235 - 0.044
- 0.325 0.007 0.146 - 0.081
** and *** indicate significance at P < 0.01 and P < 0.001, respectively.
tained between infiltrated water depth and sand content in the Wet treatment of both years indicate that sandier locations infiltrated more water during the season under that irrigation regime. However, the seasonal infiltrated water depth was not related to sand content in either the Medium or Dry irrigation treatments. Seasonal evapotranspiration also increased with sand content in the Wet treatment, although not significantly so in 1986. Although seasonal E was not related to sand content within the Medium treatments in both years, there was a significant, negative correlation ( P < 0 . 0 1 ) between seasonal E and sand content within the Dry treatment of 1985. The decrease in E with sand content in the Dry treatment would most likely be related to differences in available soil water under the limited water supply of that treatment. Although the correlation between seasonal E and sand content was negative in 1986, it was not significant. There was also no indication of a relationship between sand content with either E or I when all treatments were combined. Linear correlation coefficients between seasonal I and E with clay content (Table 6 ) were similar to those with sand, although generally opposite in sign. Clay content was significantly correlated with the infiltrated water depth in the Wet treatment of 1985 ( P < 0 . 0 1 ) and with seasonal E in the Dry treatment of 1985 ( P < 0 . 0 0 1 ) . Table 7 gives the linear correlation coefficients obtained between grain yield and the soil separates over the individual irrigation treatments and over the treatments combined. The effect of higher sand content ultimately resulted in lower grain yields. Negative correlations of Y with sand content were significant at the 0.01 level over each treatment. Not surprising was the indication
STATISTICALANALYSESOF SOIL VARIABILITY
191
TABLE 7 Linear correlation coefficients between grain yield with sand, silt and clay contents within a 1.0 m soil depth for the Wet, Medium and Dry irrigation treatments and all treatments combined Year
Irrigation treatment
No. of samples
Linear correlation coefficient, r Sand
Silt
Clay
1985
Wet Medium Dry All
31 27 27 85
-0.654*** -0.635*** -0.716"** -0.261"
0.646*** 0.592** 0.621"** 0.279**
0.472** 0.545** 0.675*** 0.194
1986
Wet Medium Dry All
35 29 33 97
-0.444** -0.624*** -0.705*** -0.209*
0.433** 0.533** 0.657*** 0.210"
0.365* 0.603*** 0.579*** 0.147
*, ** and *** indicate significance at P < 0.05, P < 0.01 and P < 0.001, respectively.
that the a m o u n t of Y variability explained by sand content, the r:, was consistently highest in the Dry treatments ( ~ 0.50), followed by the Medium ( ~ 0.40) and Wet (0.20-0.43) treatments. This partially explains why the CVs for yield were larger in the Dry treatment than those in the Wet and Medium treatments. Grain yields were also significantly correlated ( P < 0.05 ) with sand content over the entire range of combined irrigation treatments. This would suggest that the soil texture variability had an overall effect on yield even when extremely different quantities of irrigation water were applied to the irrigation treatments. The effect of sand content on wheat yield variability is reduced less in the M e d i u m and Dry treatments than the Wet, when the combined effects of infiltrated water depth and sand content on Y are included in a linear multiple regression. This is shown in Table 8 which presents the r 2 when Y is first regressed by I and the resulting r 2 when sand content is added in multiple regression. The increase in r 2 can be interpreted as the marginal effect of sand content on yield variability after the effect of I has been taken into account. For the Dry treatments, sand content increased the r 2 by 0.40-0.42 and for the M e d i u m treatments by 0.32-0.40. However, for the Wet treatments, the increase in r 2 was only 0.08-0.10. This follows from the fact that infiltrated water depth was also significantly correlated with sand content in the Wet treatments. Another interpretation might be that the variability in sand content was responsible for differences in I which, in turn, influenced yield variability in the Wet treatment. For all treatments combined, the r 2 was increased by 0.07-0.11 when sand content was included in the linear multiple regression. Equations for grain yield as a function of infiltrated water depth and sand
192
D.J. HUNSAKER AND D.A. BUCKS
TABLE 8 Coefficients of determination (r 2) obtained when grain yield was regressed by infiltrated water depth, and when regressed by infiltrated water depth and sand content (0-1.0 m soil depth) for the Wet, Medium and Dry irrigation treatments Year
Irrigation treatment
No. of samples
Coefficient of determination, r 2 Infiltrated water depth
Infiltrated water depth and sand
1985
Wet Medium Dry
31 27 27
0.413"** 0.181" 0.165"
0.514"** 0.585*** 0.584***
1986
Wet Medium Dry
35 29 33
0.242** 0.263** 0.168"
0.317"* 0.582** 0.568***
*, ** and *** indicate significance at P < 0.05, P < 0.01 and P < 0.001, respectively.
TABLE 9 Multiple regression equations for grain yield (Y) as a function of infiltrated water depth (I) and sand content (S) in 1985 and 1986 Year
Regression Equation
r2
19851 19861
y2 = 5252-- 18.4•+ 0.07912- 6.75X10-513- 49.8S Y= - 1968 + 34.6•- 0.02712- 55.2S
0.88 0.87
tVariable units are kg/ha for grain yield (Y), m m for infiltrated water depth (I) and % for sand content (S). ~Number of samples is 85 for 1985 and 97 for 1986.
content were obtained by stepwise, multiple regression over the entire range of treatments (Table 9). The resultant regression equations expressed yield as a third- and second-order function of I in 1985 and 1986, respectively, and as a linear function of sand content. The regression coefficients were each significant at the 0.01 level in both years. The inclusion of the sand content variable with infiltrated water depth improved the r 2 by about 0.045 over that when only I was used in the model. By holding sand content constant, the regression equations were used to calculate crop production curves ( Y vs. I) for sand contents of 60% (near the field site sand content average of 61%), 50% and 75%. These are shown in Fig. 6 along with the fitted regression functions obtained when sand content
STATISTICAL
L
ANALYSES
8000-
--
6000-
.... 50% s a n d - - 6fl% s a n d "/5% s a n d
regression
193
OF SOIL VARIABILITY
fit without
sand
1985
content content content
4000
V2O00
Z
[
0
N 59006000-
,
I
'
]
regression fit without sand .... 50% sand content --
- - 60% s a n d 75% s a n d
content content
I 1958
" ............... "
4000.
2000
,
[ 205
,
i
|
i
400
655
800
INFILTRATED WATER DEPTH (ram)
Fig. 6. Calculated relationships between grain yield and infiltrated water depth as affected by sand content in 1985 and 1986.
was not included (reproduced from Fig. 3). As might be expected, the predicted curves for 60% sand content is nearly coincident with the fitted curve obtained without the sand content variable. The predicted curves for sand contents at 50 and 75% plot approximately 500-550 kg ha above and 750825 kg/ha below the 60% sand content curve, respectively, for a given infiltrated water depth. Thus, the preceding suggests that even if it were possible to achieve precisely the same I to all parts of the field, yields could be expected to vary in the order of 1250-1400 kg/ha due to the effects of sand content differences. One practical application would be that where sand content increases in the field, nitrogen application rates could decrease to obtain the same yield goal and possibly reduce nitrate leaching losses below the root zone. CONCLUSIONS
Strategies to economize irrigation water, minimize crop water-use variability and obtain high yields with level-basin irrigation must account for the effects of spatial variability under field-scale conditions. Variabilities in crop evapotranspiration and yield, evaluated in a 2-year wheat study, were related to the variability in seasonal infiltrated water depth, soil water content and soil particle size distribution. Expressing yield as a function of infiltrated water
194
D.J. HUNSAKER AND D.A, BUCKS
depth plus soil particle fraction may be a useful way to account for soil variability in a crop production model. In this study, sand content was used with infiltrated water depth to estimate expected yields. Sand composition can be obtained with relative ease and serve as a major input to improve yield prediction. The coefficients of variation for grain yield were about two times greater in a dry irrigation treatment (two irrigations) and from 1.3 to 1.7 times greater in a medium irrigation treatment (four irrigations) compared to those of a wet treatment (five irrigations). In the drier treatments, yield variability was manifested by the differential effects of water stress due to variations in both infiltrated water depth and soil water content. Under reduced irrigation, therefore, yield variability might be reduced somewhat by improving the distribution uniformities of infiltrated water (through more precise land leveling, changing basin flow rates etc. ). In the wet treatment, yields were reduced in locations of higher sand content which were expected to infiltrate more water during the growing season. Thus, under the wet irrigation conditions, yield variability was attributed to increased deep percolation rates and nutrient losses, as well as variability in soil water content. However, because of soil variability, minimizing deep percolation may be difficult to achieve with full irrigation. Knowing that the anticipated grain yields will decrease, a reduced amount of nitrogen fertilizer could be applied as the sand content increases in the field. The reduced amount of nitrogen would probably best be applied in multiple applications across the field during early stages of plant growth. At the same time, scheduling a maximum number of irrigations after crop establishment (at least five for wheat) with a minimum irrigation application amount of about 100 mm per irrigation would be advisable as the sand content increases and maximum grain yield becomes lower. ACKNOWLEDGEMENTS
The authors greatly acknowledge the contributions of W.L. Alexander, Agronomist, USDA-ARS and O.F. French, Irrigation Manager, University of Arizona for their assistance in this project.
REFERENCES ASCE, 1990. Evapotranspiration and irrigation water requirements. ASCE-Manuals and reports on engineering practice-No. 70, Irrig. and Drain. Div., American Society of Civil Engineers, New York, 322 pp. Ayars, J.E., Hutmacher, R.B., Hoffman, G.J., Letey, J., Ben-Asher, J. and Solomon, K.H., 1990. Response of sugar beet to non-uniform irrigation. Irrig. Sci., 11: 101-109.
STATISTICAL ANALYSES OF SOIL VARIABILITY
195
Bos, M.G., Replogle, J.A. and Clemmens, A.J. (Editors), 1984. Flow Measuring flumes for open channel systems. John Wiley and Sons, Inc., New York, 321 pp. Bresler, E., Dasbert, S., Russo, D. and Dagan, G., 1981. Spatial variability of crop yield as a stochastic process. Soil Sci. Soc. Am. J., 45: 600-605. Clemmens, A.J., 1988. Method for analyzing field scale surface irrigation uniformity. J. Irrig. Drain. Div. ASCE, 114: 74-88. Clemmens, A.J. and Dedrick, A.R., 1982. Limits for practical level basin design. J. Irrig. Drain. Div. ASCE, 108: 127-141. Dedrick, A.R., 1990. Level-basin irrigation - an update. Proceedings of the 3rd Natural Irrigation Symposium, Phoenix, AZ, USA, Oct. 28-Nov. I. Erie, L.J. and Dedrick, A.R., 1979. Level-basin irrigation: a method for conserving water and labor. US Dept. Agric., Farmers' Bull., 2261, Washington, DC, 23 pp. Erie, L.J., French, O.F., Bucks, D.A. and Harris, K., 1982. Consumptive use of water by major crops in the southwestern United States. US Dept. Agric., Conserv. Res. Rep. No. 29, Washington, DC, 42 pp. Feinerman, E., Letey, J. and Vaux, Jr., H.J., 1983. The economics of irrigation with nonuniform infiltration. Water Resour. Res., 19: 1410-1414. Gee, G.W. and Bauder, J.W., 1986. Particle-size analysis. In: A. Klute (Editor), Methods of soil analysis, 1. Am. Soc. Agron., Madison, WI, USA, pp. 383-411. Hart, W.E., Norum, D.I. and Peri, G., 1980. Optimal seasonal irrigation analysis. J. Irrig. Drain. Div. ASCE, 106: 221-235. Hunsaker, D.J., Bucks, D.A. and Jaynes, D.B., 1991. Irrigation uniformity of level basins as influenced by variations in soil water content and surface elevation. Agric. Water Manage., 19: 325-340. Jaynes, D.B. and Clemmens, A.J., 1986. Accounting for spatially variable infiltration in border irrigation models. Water Resour. Res., 22:1257-1262. Jaynes, D.B. and Hunsaker, D.J., 1989. Spatial and temporal variability of water content and infiltration on a flood irrigated field. Trans. ASAE, 32:1229-1238. Letey, J., 1985. Irrigation uniformity as related to optimum crop production - additional research is needed. Irrig. Sci., 6: 253-263. Neter, J., Wasserman, W. and Kutner, M.H. (Editors), 1985. Applied linear statistical models: regression, analysis of variance, and experimental designs. Richard D. Irwin, Inc., Homewood, IL, USA, 1127 pp. Russo, D., 1984. Statistical analysis of crop yield-soil water relationships in heterogeneous soil under trickle irrigation. Soil Sci. Soc. Am. J., 48: 1402-1410. Russo, D., 1986. A stochastic approach to the crop yield-irrigation relationships in heterogeneous soils: I. Analysis of the field spatial variability. Soil Sci. Soc. Am. J., 50: 736-745. Seginer, I., 1978. A note on the economic significance of uniform water application. Irrig. Sci., l: 19-25. Snedecor, G.W. and Cochran, W.G. (Editors), 1980. Statistical methods. Iowa State Univ. Press, Ames, Iowa, USA, 507 pp. Solomon, K.H., 1984. Yield related interpretations of irrigation uniformity and efficiency measures. Irrig. Sci., 5: 161-172. Stern, J. and Bresler, E., 1983. Nonuniform sprinkler irrigation and crop yield. Irrig. Sci., 4:1729.
USDA, 1974. Border irrigation. Chap. 4, Sect. 15 (Irrigation) Soil Conserv. Serv. Nat. Eng. Handbook, U.S. Department of Agriculture. Varlev, I., 1976. Evaluation of non-uniformity in irrigation and yield. J. Irrig. Drain. Div. ASCE, 102: 149-164. Warrick, A.W. and Gardner, W.R., 1983. Crop yield as affected by spatial variations of soil and irrigation. Water Resour. Res., 19: 181-186.