Agronomy Journa l • Volume 106 , I s sue 4 • 2014 1179

Agronomy, Soils & Environmental Quality

Soil and Rainfall Factors Influencing Yields of a Dryland Cropping System in Colorado

Lucretia A. Sherrod,* Lajpat R. Ahuja, Neil C. Hansen, James C. Ascough, II, Dwayne G. Westfall, and Gary A. Peterson

Published in Agron. J. 106:1179–1192 (2014)doi:10.2134/agronj13.0520Copyright © 2014 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

ABSTRACTThe semiarid U.S. Great Plains experiences a large variation in crop yields due to variability in rainfall, soil, and other factors. We analyzed (24-yr) yields from a no-till rotation of winter wheat (Triticum aestivum L.)–corn (Zea mays L.) or sorghum [Sorghum bicolor (L.) Moench]–fallow at three sites, each with three soil types along a catena in Colorado. We investigated: (i) effects of soil organic carbon (SOC), length of slope (SL), and other soil properties on yields; and (ii) degree in which variability in annual yields are explained with water variables of rainfall during the fallow, vegetative and reproductive stages, and soil water at plant-ing. Wheat and corn/sorghum yields were strongly related to soil properties (R2 = 0.84; 0.97, respectively). Wheat yield regres-sion had only SOC as a significant variable, whereas corn/sorghum had SOC and SL. Water factors explained 37 to 73% of annual variability in wheat yields at the site by soil level, 19 to 63% when pooled over soils within sites, and 35 to 40% pooled over all. For corn, the corresponding percentages were 16 to 64%, 26 to 57%, and 40 to 45%, respectively. Fallow rain made the highest con-tribution to R2 in wheat whereas reproductive rain did for corn/sorghum. We conclude SOC is a stand-in for long-term effects of water availability on production, but other natural factors greatly influence the annual yield variability. Combining mean yields determined from soil properties with CV of pooled annual yields, we can estimate mean and standard deviation of yields.

L.A. Sherrod, L.R. Ahuja, J.C. Ascough, II, USDA-ARS, Agricultural Systems Research Unit, 2150 Centre Ave., Building D, Fort Collins, CO 80526; and N.C. Hansen, D.G. Westfall, and G.A. Peterson, Dep. of Soil and Crop Sciences, Colorado State Univ., Fort Collins, CO 80523. Received 4 Nov. 2013. *Corresponding author (lucretia.sherrod@ars.usda.gov).

Abbreviations: DAP, Dryland Agroecosystem Project; SL, slope length; SOC, soil organic carbon.

The semiarid West Central Great Plains of the United States has a large variation in crop yields under dryland conditions due to variability in rainfall, soil productivity, and other natural factors. The primary driving variables for crop yields in this region are generally considered to be the amount and timing of precipita-tion (Nielsen et al., 2005). The most critical rainfall periods are generally understood to be the stand establishment and repro-ductive phases but crop species respond differently to timing of rainfall (Nielsen et al., 2010; Ley, 1988; Brown, 1959). The precipi-tation pattern of a region influences the cropping sequence used which strives to maximize the use of rainfall received (Tanaka et al., 2005). An additional driving variable for crop yields, assuming adequate soil fertility (Hatfield et al., 2001), is a combination of soil properties that influence precipitation capture and soil produc-tivity. These properties include but are not limited to soil organic matter, porosity, water infiltration capacity, and slope position.

No-till management and retention of crop residues in the Cen-tral Great Plains have substantially increased precipitation storage and use efficiency (Farahani et al., 1998; Norwood, 1999; Nielsen and Vigil, 2010), which has allowed diversity in the crops grown

in this region beyond the traditional wheat–fallow (WF) crop-ping system. Spring planted crops such as corn and sorghum have been successfully incorporated into the fall planted winter wheat based cropping systems (Peterson et al., 1996; 2004; Farahani et al., 1998; Nielsen et al., 2005). In fact, the more intensive crop-ping systems like winter wheat–corn–fallow increase the amount of time when the fallow period is more efficient in capturing and storing rainfall and by having a plant in place to use water during the periods when fallow is not efficient (Farahani et al., 1998; Peterson and Westfall, 2004). In addition, crop residues in concert with no-till increase the SOC in the surface layer (Ortega et al., 2002; Sherrod et al., 2003, 2005) and improve soil physical properties (Shaver et al., 2002). However, in spite of these benefits, the high variability of rainfall from year-to-year causes a high variability in the yields of both summer crops in rotation, as well as winter wheat (Miner et al., 2013). It would be beneficial for scientists and managers to have a better quantitative understand-ing of the effects of soil properties on crop yields and the extent to which yearly variation in crop yields is caused by water factors such as variability in soil water storage in fallow periods, and rainfall during different vegetative and reproductive crop growth stages. This understanding would allow producers and managers to better match the cropping system to the precipitation pattern which could reduce risk associated with dryland cropping. Addition-ally, variability in rainfall within this region is likely to get more extreme with climate change, therefore it would be of interest to understand how much impact the timing of rainfall and stored soil water has on each crop within the cropping system.

Published May 30, 2014

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Multiple regression models have been extensively used to evaluate and quantify the effects of weather variables during the growing period, mainly rainfall and temperature, and changes in technology on the yields of corn and soybean in the U.S. Corn Belt (Davis and Harrell, 1941; Leeper et al., 1974a, 1974b; Thompson, 1988; Runge, 1968; Runge and Benci, 2008; Tannura et al., 2008). Kravchenko and Bullock (2000) found at a location in Illinois that soil properties, mainly soil organic matter content, explained about 30% (varying between 5 and 71% in different fields) of the corn yield variation; the topography explained about 20%. Majchrzak et al. (2001) developed a multiple regression model of wheat yield as a function of soil texture, bulk density, organic matter content, cation exchange capacity, Na content, and rooting depth on Illi-nois soils, explaining 78% of the yield variation. In the Mediter-ranean climate of southern France, soil available water capacity was identified as a good indicator of wheat yield (Wassenaar et al., 1999). In the study of Tannura et al. (2008), regression models using technology (i.e., methods of corn planting) and magnitude of precipitation in June and July and magnitude of temperatures in July and August explained 94% of the variation in corn yields and 89% of soybean yields from 1960 to 2006 in Illinois, Indiana, and Iowa. Olson and Olson (1985) related corn yields in the New York State to a RAINSTOR index, based on the rainfall and soil water storage capacity of the soils. Olson and Olson (1986) developed a larger scale multiple regression model for corn yield in New York, based on RAINSTOR as well as growing degree days, SOC, and adsorbed bases in the soil. This model explained only 66% of the yield variation, but was able to predict average yield over a 19-yr period within 3% of the observed value. In the Central Great Plains, Stone and Schlegel (2006) working near Tribune, KS, on a Ulysses silt loam (fine-silty, mixed, superactive, mesic Aridic Haplustoll) and Richfield silt loam (fine, smectitic, mesic Aridic Argiustoll) soils, showed wheat yields were correlated to available soil water at emergence and to in-season precipitation (15 Septem-ber to 14 June), explaining 70% of the variability. They also showed grain sorghum yields correlated well with available soil water at emergence and 15 June to 14 September in-season precipitation (R2 = 0.63). Nielsen et al. (2010) reported that at Akron, CO, corn yields were correlated primarily with precipitation from mid-July to the end of August, but this correlation was also influenced by soil water content at planting in May and the May rainfall. In Utah, Brown (1959) explained 54% of the variation in yield of winter wheat by using the sum of precipitation in September and October and May and June in a multiple linear regression. In Iran, Khashei-Siuki et al. (2011) showed that a statistical model based on maximum, minimum, and dew point temperatures explained 67% of the variation in yield of dryland wheat crop. In a 3-yr study in the Mt. Pleasant, NY, corn yields were related to topography and organic matter content and soil depth (Timlin et al., 1998) but the intra-annual differences in weather (temperature and precipita-tion) had the largest effect on grain yield.

Previous studies in the Great Plains Region have been limited by the number of crop years in the study and/or the number of loca-tions (soil types, variations of climate). There is little research that looks at the effect of both soil properties and water variables across a long enough time frame under similar management to quantify how they influence crop yields in semiarid environments. There-fore, to gain a quantitative understanding of how soil properties influence long-term yields and the extent to which rainfall and soil

water variables cause the yearly variation in yield, crop yield data over a 24-yr period (1986–2009) from a wheat–corn/sorghum–fal-low rotation at three catena sites in eastern Colorado (Peterson el al., 1993) were analyzed. Specific objectives were to quantify relationships between: (i) long-term average grain yields and SOC content, slope length potentially contributing to runoff (SL), poros-ity, and effective porosity and (ii) annual crop yields and soil profile water at planting, rainfall during the preceding fallow period, and rainfall during vegetative and reproductive growth stages.

MATERIALS AND METHODSStudy Sites, Landscape Positions,

and Soil PropertiesA long-term Dryland Agroecosystems Project (DAP) was initi-

ated in 1985 to evaluate the effect of cropping intensity on total biomass production, water-use efficiency, and selected soil chemical and physical properties in eastern Colorado (Peterson et al., 1993; Farahani et al., 1998). Major variables include: (i) climatic gradient (potential evapotranspiration, PET) across experimental sites; (ii) soil properties (e.g., plant available water) across landscape positions at each site; and (iii) crop rotations of different intensities under no-till management across landscape positions and sites. Climatic variability was captured by three experimental locations represent-ing different levels of annual PET from north to south: Sterling (low PET; 40.37° N, 103.13° W), Stratton (medium PET; 39.18° N, 102.26° W), and Walsh (high PET; 37.23° N, 102.17° W).

An automated weather station at each site measured daily pre-cipitation, air temperature (maximum, minimum), mean relative humidity, total solar radiation, wind direction, and mean wind speed. Long-term average annual precipitation/cropping season open pan evaporation amounts are 440/1600 mm for Sterling, 415/1725 mm for Stratton, and 395/1975 mm for Walsh (Sher-rod et al., 2005), indicating that precipitation differences across sites are small, but PET is greater at southern sites, especially at Walsh. This results in deficit moisture levels of 1160, 1310, and 1580 mm yr–1 at Sterling, Stratton and Walsh, respectively.

The topography at each site was divided into summit, sideslope, and toeslope landscape positions, with non-experimental transi-tion zones in between, and with variable slope along the length (Fig. 1). The Sterling site has a maximum elevation difference of 5.9 m along a 305 m north facing slope. Topography of the Stratton site is more complex, with a transitional wedge that has a steep (4% maximum) slope before the toeslope position representing a three-way “funnel effect” drainage area from east, south, and west slopes, for an elevation change of 3-m along a total slope length of 198 m. The Walsh site has a moderate elevation change of 1.1 m along the 183 m catenary sequence with a maximum slope of 5.3% on the west facing sideslope (Peterson et al., 1993).

The soil properties along with the estimated soil hydraulic proper-ties at each landscape position within each site were measured by the USDA National Soil Survey Laboratory in Lincoln, NE, in 1996 as part of a soil survey and classification. Selected properties are shown for just the first two soil horizons in Table 1. Soil properties used in this study (Table 2) included profile SOC analyzed by acid-dichromate digestion (USDA Soil Survey Laboratory method 6A1c, USDA-Natural Resources Conservation Service, National Soil Survey Center, 1996a), better known as modified Walkley–Black followed by FeSO4 titration, slope length (SL), percent porosity calculated from soil bulk density (USDA Soil Survey Laboratory

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method 4A1d, USDA-Natural Resources Conservation Service, National Soil Survey Center, 1996b) where % porosity = 100% × (1 – bulk density/particle density of 2.64 Mg m–3). Effective slope length was defined as the distance from top of summit to mid-point of the summit, sideslope, and toeslope positions, respectively. Effec-tive porosity was determined as porosity minus field capacity (Ahuja et al., 1989). Soil organic carbon was converted to kg ha–1 for each horizon and then summed for a total profile number. The equation used to convert SOC for a horizon is as follows:

SOC kg ha–1 = (horizon depth increment (cm)/10) × bulk density (ρb Mg m–3) × SOC mg kg–1 [1]

Soil porosity and volumetric field capacity data from the soil classification data was depth-weighted to obtain average values for each soil profile. Specifically, % porosity and % field capacity were multiplied by the horizon depth increment and then summed and divided by the total profile depth. Effective porosity was then calculated as the difference of profile % porosity minus profile % field capacity (Table 2).

Fig. 1. Catenary sequence represented of landscape positions for the (a) Sterling, (b) Stratton, and (c) Walsh from the Dryland Agroecosystem project locations in eastern Colorado (Ascough et al., 2010).

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Experimental Design and Cultural Practices

The DAP experiment is a split-split-block design. At each location two blocks were established with each phase of the crop rotations present. These rotations were randomized into strips within each block at the start of the experiment. These strips were laid out across the catena such that each phase of each rotation contained the com-plete catenary sequence. An experimental unit is therefore a crop rotation phase within a PET site and a slope position (landscape). All data were collected only within the three slope positions of summit, side, and toeslope. This study only considers the 3-yr rotation of

WCF for Sterling and Stratton locations and WSF for the Walsh location. Experimental units were 6.1 m wide and varied in length from 183 to 305 m long depending on the site with two replications within two strips along the three slope positions at each site, making a total of 18 plots for the above rotation.

All cropping systems were managed with no-till techniques to maximize water storage potential and eight cropping sequences were completed in 2009 (24th year of the experiment). Fertilizer N was applied to each experimental unit according to soil tests obtained from each soil within each rotation and targeted for the

Table 1. Soil physical and hydraulic properties for the top two soil horizons across landscape positions at the Dryland Agroecosystem Project experi-mental sites in eastern Colorado from classification data collected in 1992. (Adapted from Ascough et al., 2010.)

Soil horizon name

Soil horizon depth

Bulk density

(oven dry) Sand Clay

Total organic carbon

Total nitrogen

Field capacity (FC) water

content (33 KPa)

Wilting point (WP) water

content (1500 KPa)

Plant available water† Ksat*

cm g cm–3 –––––– % –––––– –––––– kg ha–1 –––––– ––––––––– m3 m–3 ––––––––– cm cm h–1

Sterling summit (Weld loam–fine, smectitic, mesic Aridic Argiustoll)Ap 0–8 1.52 45.0 20.8 11,674 900 0.205 0.095 1.21 1.14Bt1 8–20 1.6 33.4 30.8 14,592 1670 0.277 0.134 2.32 0.43Sterling sideslope (Satanta loam–fine-loamy, mixed, superactive, mesic Aridic Argiustoll)Ap1 0–11 1.58 53.9 20.7 15,642 1321 0.216 0.093 1.99 1.32Ap2 11–20 1.51 44.0 26.3 11,144 1060 0.249 0.121 1.54 0.71Sterling toeslope (Albinas loam–fine-loamy, mixed, superactive, mesic Pachic Argiustoll)Ap1 0–7 1.42 42.3 18.4 15,009 1223 0.223 0.095 1.17 1.40Ap2 7–18 1.57 46.8 19.6 12,210 1139 0.195 0.091 1.70 1.29Stratton summit (Norka clay loam–fine-silty, mixed, superactive, mesic Aridic Argiustoll)Ap 0–13 1.55 24.8 33.8 20,553 1874 0.268 0.167 1.77 0.33Bt 13–39 1.52 19.6 36.4 33,987 3517 0.306 0.223 2.72 0.30Stratton sideslope (Richfield loam–fine, smectitic, mesic Aridic Argiustoll)Ap1 0–10 1.47 41.4 20.2 14,994 1323 0.258 0.099 2.16 1.16Ap2 10–18 1.55 35.0 28.0 8,928 918 0.277 0.135 1.51 0.55Stratton toeslope (Kuma loam–fine-silty, mixed, superactive, mesic Pachic Argiustoll)Ap 0–15 1.44 25.1 25.9 41,040 3866 0.283 0.136 2.82 0.58Ab1 15–30 1.41 23.1 24.8 44,838 4738 0.302 0.126 3.33 0.58Walsh summit (undefined loamy sand-fine-loamy, mixed, mesic Aridic Ustochrept)Ap 0–18 1.58 65.4 14.3 9,954 1024 0.167 0.054 3.01 3.07Bk1 18–40 1.57 66.4 17.5 7,944 967 0.144 0.068 2.49 2.38Walsh sideslope (undefined sandy loam-fine, montmorillonitic, mesicUstollicHaplargid)Ap 0–10 1.68 71.5 10.4 6,552 588 0.150 0.048 1.58 4.90BAk 10–20 1.59 57.3 19.7 6,519 731 0.148 0.084 0.96 1.54Walsh toeslope (Nunn sandy clay loam-fine, smectitic, mesic Aridic Argiustoll)Ap 0–13 1.46 38.0 23.8 17,391 1569 0.280 0.113 2.87 0.78Ab 13–24 1.55 31.7 25.7 27,090 2752 0.269 0.120 2.20 0.63

† Plant available water calculated as (FC – WP) × BD × soil horizon depth.‡ Ksat, saturated hydraulic conductivity (estimated using Saxton and Rawls, 2006).

Table 2. Properties used for estimating average long-term crop yields within sites in the West Central Great Plains region.

Site Landscape Profile SOC† Effective slope length Effective profile porosity Profile porositykg ha–1 m –––––––––––––––––––––– % ––––––––––––––––––––––

Sterling Summit 75,300 23 6.1 41.1Side 72,300 145 15.9 35.1Toe 101,400 274 8.7 38.5

Stratton Summit 82,300 34 18.8 46.0Side 86,500 123 8.2 40.4Toe 176,800 179 3.6 40.7

Walsh Summit 55,400 15 15.8 43.0Side 87,800 26 9.0 42.3Toe 93,400 112 5.0 40.0

† SOC, soil organic carbon.

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crop present in a given year. The N fertilizer source, urea-NH4NO3 solution (32–0–0), was applied at planting by dribbling over the row behind the planter (Peterson et al., 1993). Soil residual NO3–N (at varying increments to a depth of 180 cm) was measured before planting and fertilizer was applied at rates recommended by Colo-rado State University extension fact sheets (Davis et al., 2009; Davis and Westfall, 2009). Phosphorus (10–34–0) was band-applied near the seed at planting at a rate of 20 kg ha–1 of P as P2O5.

Soil water contents were measured in 30-cm increments to a depth of 180 cm at planting and harvest by means of neutron attenu-ation. Grain yields were measured with a plot combine while total aboveground biomass was measured at harvest by hand sampling a small area (0.75 m2 area) in each experimental unit. The fallow rainfall period for wheat was defined as the 10-mo interval from November through August; for corn/sorghum the fallow rainfall period was the 10-mo interval from July through April. The vegeta-tive rainfall period for wheat was defined as the 8-mo interval from September through April; for corn/sorghum the vegetative rainfall period was May and June. The reproductive rainfall period for wheat was defined as May and June and for corn or sorghum as July and August. More detailed information on site and soil characteristics, weather data, and management practices for the DAP experiment is presented in Peterson et al. (1993) and Farahani et al. (1998).

Regression Analyses

Preliminary simple regression was used to identify soil proper-ties that showed a significant slope when regressed against mean crop yield over the 24-yr study period at each of the nine soils for objective 1. Properties selected were profile SOC, effective slope length, effective soil porosity, and soil porosity (Table 2). Effective porosity is a surrogate for saturated hydraulic conductivity (Ahuja et al., 1989) and hence infiltration capacity. These soil properties were shaped by the long-term effects of rainfall patterns, PET and other weather variables, along with native vegetation and cropping history. Multiple regressions were then performed with the mean 24-yr period yield of each crop at each landscape position of an experimental site as the independent variable and the above selected variables as predictors. Average long-term wheat multiple regression variables of SL, % porosity, and % effective porosity were not sta-tistically significant and were dropped from the regression leaving only SOC as a predictive variable. For corn and sorghum, multiple regression variables of % porosity and % effective porosity were not found to be statistically significant and thus were dropped from the multiple regression leaving SOC and SL as predictive variables.

For objective 2, multiple regression analyses of annual crop yields with soil water and rainfall driving variables (i.e., soil water at planting; and rainfall during fallow, vegetative and reproductive crop growth stages) were performed for each crop at three levels to quantify the effect of scale on: (i) annual yield data at each indi-vidual DAP experimental site and landscape position; (ii) annual yield data pooled across the three landscape positions (soils) at each DAP experimental site; and (iii) annual yield data pooled across all landscape positions and experimental sites. In addition, to remove the first-order interaction effects of soil type and runoff factors in the regressions for pooled data in (ii) above and for PET and crop variety differences among the sites in (iii) above, regression of relative annual crop yield (annual yield divided by the 24-yr period mean yield at a landscape position for a given experimental site) on soil water and rainfall driving variables was also performed.

The purpose of the above regression analyses was to see the relative contribution of each of the water-related variables, even if their partial contribution was not statistically significant, and not to develop prediction equations at this stage. Therefore, we kept all the variables in the regressions. We also explored the effect of yearly variation in seasonal heat units (growing degree days) on the variation in yields at one site (Sterling) using simple regression, but found no correlation and hence temperature as an independent variable in the fitted regressions are not reported here.

All regressions were implemented using the mixed procedure within SAS statistical software (SAS Institute, 2002–2003). The MIXED procedure was used to account for the auto-correlative nature of model errors among years by using the REPEATED statement and the choice of AR(1) for the covariance structure. The model used yield as the response variable and either soil or weather variables as predictors. The fixed effects in the mixed model in objective 1 for the prediction of mean wheat and corn yields were SOC and SOC and SL, respectively and the intercept with error being random. The fixed effects in the model in objec-tive 2 were soil profile water at planting, fallow, vegetative and reproductive rainfall, and the intercept with error being random. The root mean square error (RMSE) statistical evaluation criterion was used to evaluate the precision of regression model predictions and is defined as follows:

( )=

∑ 2

=1

-RMSE

n

i ii

P O

n

where Oi is the observed value, Pi is the multiple regression pre-dicted value, and n is the total number of observations.

RESULTS AND DISCUSSIONThe precipitation mean and standard deviation over 24 yr across

the complete 36-mo cycle of a winter wheat–corn–summer fal-low (Sterling and Stratton) or winter wheat–sorghum–summer fallow (Walsh) cropping system is shown in Fig. 2. The vegetative and reproductive stages of each crop and preceding fallow period between cropping periods for each of the three experimental sites is also shown. The majority of the precipitation was received dur-ing the reproductive period of wheat (May and June) and corn/sorghum (July and August). The largest precipitation mean and standard deviation over this period was in July for Sterling and Stratton sites and in August for Walsh.

Regression Analysis of Mean Yields with Soil Properties and Contributing Slope Variables

The regression relationship between the observed 24-yr period mean wheat grain yields and the regression estimated yields was very strong with an R2 value of 0.84, significant with a P = 0.0001 (Fig. 3, Table 3). The regression relationship between the observed 24-yr mean corn (sorghum at Walsh) grain yields and the regression estimated yields was also very strong with an R2 value of 0.97 with P = <0.0001 level (Fig. 3, Table 3). The profile SOC made the most significant contribution to the regression relation-ship (P = 0.0003). The highest yields were found on the toeslopes for both wheat and corn crops, with the Stratton site having the highest yields followed by Sterling and then Walsh (Fig. 3). Recall that the Stratton site has topography such that the toeslope has

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the potential to receive runoff water from three directions. The toeslope soil at Stratton also has approximately two times the profile SOC amounts found in the toeslopes at Sterling and Walsh (Table 2). The lowest yields for both wheat and corn/sorghum were found on the summit soil at Walsh followed by the summit soil at Sterling. The summit soil at Walsh has a convex topography which would tend to shed runoff water. The summit soil at Sterling has a shallow Ap horizon which transitions into a Bt1 horizon that has more than 30% clay resulting in slower infiltration rates (Table 1).

The 24-yr period mean yields for both wheat and corn/sorghum were strongly related to the profile SOC in all nine soils. A simple regression of only the SOC variable for the corn/sorghum average yields explained 79% of the variability in yields (intercept = 1130 and slope = 0.0178). This is not surprising in that organic matter levels are essentially a proxy for the long-term production that was achieved at the landscape level within a given precipitation and temperature range. In that sense, the result reflects the long-term effects of differences in water availability across landscape positions and sites. Similar results for soil organic matter content followed by topography was found to most influence the yields of corn and soybean in central Illinois and eastern Indiana (Kravchenko and Bullock, 2000). For the corn/sorghum regression for mean yields vs. soil properties, the effect of SL was also significant. The SL is related to runoff water received in the plot from upslope during the summer season when the majority of rainfall is received (Fig. 2). The nine soils, which cover a wide range of both micro- and macro-environmental conditions across the gradient of landscapes

and PET, are able to account for a wide range of variability in the west Central Great Plains, which results in a type of calibration curve for average yields versus the above soil properties.

Regression Analysis of Annual Wheat Yields with Soil Water and Rainfall Variables

The regression coefficient of determination (R2) was 0.52, 0.58, and 0.45 for the summit, sideslope, and toeslope landscape posi-tions at Sterling, respectively, with all regressions significant (Table 4). Fallow rain was the largest contributor to the regression at the sideslope and toeslope positions and soil water at planting was the largest contributor to the summit regression. This may indicate that fallow rain was the limiting factor for soil water storage on sideslope and toeslope soils, whereas water storage capacity was limiting at the summit soil. The R2 values for the regression estimated yields were slightly lower for the Stratton site, with R2 = 0.37, 0.48, and 0.49 for the summit, sideslope, and toeslope landscape positions, respectively (all significant at the P < 0.01 level). The highest contribution to the regression was made by soil water at planting for the summit and sideslope landscape positions and reproductive rain for the toeslope landscape position. The summit soil at this site has an eroded Ap horizon so we have a higher clay content at the near surface which might have impacted the significance of the soil water. The toeslope at Stratton was the only site by soil regression of wheat yields that showed statistical significance for the reproductive stage rainfall variable. This could be due to the topography of this location where water is able to run-on to this landscape position

Fig. 2. Precipitation mean and standard deviation over 24 yr (1985–2009) for the Dryland Agroecosystem project locations near (A) Sterling, (B) Stratton, and (C) Walsh, CO, over a 36-mo sequence used in the 3-yr rotation of wheat–corn or sorghum–fallow. Vegetative and reproductive periods for wheat and corn are noted in A but also include B and C, for wheat and corn or sorghum crops as well as the fallow period.

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from three directions. Additionally, this soil has montmorillonite clays which are able to retain a substantial amount of water. For the Walsh site, the R2 values for predicted yields were the highest with 0.73, 0.62, and 0.61 for the summit, sideslope, and toeslope land-scape positions, respectively (Table 4). Fallow period rainfall was significant for all landscapes positions at Walsh. Regression results for the summit and sideslope also showed that vegetative period rainfall demonstrated significant statistical impact within the mul-tiple regressions at Walsh (Table 4). Over the nine individual land-scape positions, fallow rainfall showed a significant contribution to the regression in seven out of nine comparisons (Table 4). This was followed by soil water with three P values out of nine at the 10% significant level. Walsh also had the only significant P values for vegetative rainfall with wheat yields at the site by soil level. This site has the largest water deficit to overcome which could explain the impact of vegetative rainfall period at this location. Interestingly, there were five of the nine regressions that had negative repro-ductive rainfall coefficients. This could be due to the fact that a significant percent of the rainfall occurring during this time period has hail with it. This trend was most evident during the 1990s when hail damage occurred nearly every other year to some degree at all sites, but especially at Walsh. It was difficult to estimate the amount of yield loss unless it impacted the decision to harvest or not, so this was not noted in our harvest records (personal communications with Plainsman Research Center Superintendent at Walsh). The magnitude of the R2 values for different soils (landscape positions) at the three sites did not show any trend with soil type, indicating that there was no interaction between the soil type or slope position and rainfall variables. Observed vs. multiple regression-estimated wheat grain yield for each DAP experimental site and landscape position are shown in Fig. 4. For all landscape positions at all sites, the regression model had little bias as the data points were evenly scattered above and below the 1:1 line. All experimental sites showed similar RMSE values (Fig. 4) within a landscape position but the largest values were found at the toeslope landscape posi-tion, which is also the highest yielding position across all sites. The multiple regressions reproduced the mean of all values closely to the actual but with a smaller standard deviation and hence smaller CV in all cases (Table 5).

The regression R2 for the observed wheat yield data pooled across the three landscape positions at Sterling was 0.46, as was the pooled relative yields R2 (Table 6). This indicated that there was no first-order interaction between the soil types or slope positions and the effect of rainfall variables. This R2 value is lower than the average of individual landscape position R2 values at Sterling (Table 4), which indicated that pooling added variability to the data, as would be expected. However, the CV of the pooled data

was actually lower than for the unpooled data in most cases (Table 5). The highest contribution to the regression relationships for the pooled actual and relative yield data was made by fallow period rain. For Stratton, the regression-predicted R2 for the pooled observed and relative yield data was 0.19 and 0.34 (P < 0.001), respectively (Table 6). The higher R2 value for the pooled relative

Fig. 3. (A) Wheat and (B) corn/sorghum grain 24-yr period mean yield for each slope and site as observed and as regression estimated with profile soil organic carbon (SOC) for wheat and multiple regression with SOC and contributing slope length for corn/sorghum with the nine soils in the Dryland Agroecosystem project.

Table 3. Multiple regression analysis of the 24-yr mean grain yields across each of three slope positions at each of the three experimental sites as a function of soil organic carbon and contributing slope length using the linear equation for wheat [Y kg ha–1 = β0 + (β1 × Soil organic C) and for corn [Y kg ha–1 = β0 + (β1 × Soil organic C) + (β2 × Slope length)].

Location/VariableWheat grain pooled over sites and slopes Corn/Sorghum grain pooled over sites and slopes

DF† Estimate P value DF† Estimate P value.

Intercept 1086 1212Soil organic carbon 4 0.0086 0.039 4 0.0126 0.0003Slope length 4 4 3.8154 0.001Coefficient of determination (R2) 0.84 0.0001‡ 0.97 <0.0001‡

† DF = degrees of freedom.‡ P value from Pearson correlation coefficients.

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Table 4. Multiple regression analysis over 24 yr of wheat grain yields as a function of soil water at planting, fallow, vegetative, and reproductive rainfall using the linear equation [Y kg ha–1 = β0 + (β1 × Soil Water) + (β2 × Fallow Rain) + (β3 × Vegetative Rain) + (β4 × Reproductive Rain)] by each experi-mental site and landscape slope position.

Location/Variable

Landscape positionSummit Sideslope Toeslope

DF† Estimate P value DF† Estimate P value DF† Estimate P valueSterling: Low PET‡ Intercept –812 –669 –507 Soil water 16 2.62 0.0195 15 1.25 0.1306 16 0.65 0.3239 Fallow rain 16 3.08 0.0639 15 5.12 0.0007 16 5.43 0.0037 Vegetative rain 16 2.25 0.5170 15 1.69 0.5635 16 3.94 0.3243 Reproductive rain 16 1.40 0.5488 15 0.12 0.9486 16 –0.90 0.7368 Coefficient of determination (R2) 0.52 0.0002§ 0.58 0.0001§ 0.45 0.0008§Stratton: Medium PET Intercept 1233 881 1520 Soil water 13 –1.12 0.0124 14 –0.91 0.0864 13 –0.27 0.4053 Fallow rain 13 4.90 0.0198 14 2.73 0.1499 13 3.53 0.2000 Vegetative rain 13 0.82 0.7445 14 1.93 0.5690 13 –6.25 0.1412 Reproductive rain 13 –4.45 0.1336 14 0.15 0.9628 13 8.42 0.0503 Coefficient of determination (R2) 0.37 0.0067§ 0.48 0.0010§ 0.49 0.0012§Walsh: High PET Intercept –582 –512 –419 Soil water 12 0.23 0.5401 12 0.40 0.2895 12 0.20 0.5774 Fallow rain 12 4.79 0.0060 12 4.47 0.0545 12 5.94 0.0312 Vegetative rain 12 5.59 0.0156 12 5.83 0.0485 12 5.38 0.1082 Reproductive rain 12 –3.52 0.1821 12 –2.95 0.4209 12 –5.02 0.2396 Coefficient of determination (R2) 0.73 <0.0001§ 0.62 0.0002§ 0.61 0.0002§

† DF = degrees of freedom.‡ PET = potential evapotranspiration.§ P value from Pearson correlation coefficients.

Table 5. Observed grain yield mean and regression estimated grain mean using multiple regression analysis of soil water at planting, fallow rainfall and rainfall during vegetative and reproductive growth stages using linear equation [Y = β0 + (β1 × Soil Water) + (β2 × Fallow Rain) + (β3 × Vegetative Rain) + (β4 × Reproductive Rain)], and coefficient of variation (CV) across landscape positions for winter wheat, corn and sorghum grain yields at three experimental sites in eastern Colorado over a 24-yr time period (1986–1997).

Location

Observed grain yield Predicted grain yieldSummit Sideslope Toeslope Summit Sideslope Toeslope

Mean CV Mean CV Mean CV Mean CV Mean CV Mean CVkg ha–1 % kg ha–1 % kg ha–1 % kg ha–1 % kg ha–1 % kg ha–1 %

Winter wheat grain yield Sterling 1700 48 1750 40 2200 39 1700 38 1750 33 2200 26 Pooled slopes 1900 43 1900 33 Stratton 2100 26 1750 43 2700 31 2100 23 1750 28 2800 22 Pooled slopes 2200 38 2200 30 Walsh 1550 47 1750 45 1950 47 1550 40 1750 36 1950 36 Pooled slopes 1750 46 1750 38 Pooled over all 1950 40 1950 35Corn grain yield Sterling 2100 64 2800 53 3550 52 2100 53 2800 37 3500 39 Pooled slopes 2800 59 2800 43 Stratton 2550 70 2750 48 4500 37 2350 47 2700 32 4400 28 Pooled slopes 3250 55 3150 34 Pooled over all 3000 57 2950 46

Sorghum grain yield Walsh 2100 50 2500 45 3100 53 2100 19 2450 21 3100 28 Pooled slopes 2550 52 2550 29

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Fig. 4. Annual wheat grain yield observed at Sterling, Stratton, and Walsh locations at summit, side, and toeslope soils vs. multiple regression estimated yield using soil water at planting, fallow, vegetative, and reproductive rainfall.

Table 6. Multiple regression analysis of 24 yr of pooled actual wheat grain yields and relative yields (yearly yield/mean yield) as a function of soil wa-ter at planting, fallow, vegetative, and reproductive rainfall using the linear equation [Y kg ha–1 = β0 + (β1 × Soil Water) + (β2 × Fallow Rain) + (β3 × Vegetative Rain) + (β4 × Reproductive Rain)] for each experimental site pooling data over the landscape.

Location/Variable DF†Actual yield Relative yield

Estimate P value Estimate P value

Sterling: Low PET‡

Intercept –662 –0.34

Soil water 57 1.35 0.0028 0.0006 0.0112

Fallow rain 57 4.66 <0.0001 0.0025 <0.0001

Vegetative rain 57 3.21 0.0996 0.0018 0.0805

Reproductive rain 57 –0.51 0.6987 –0.0002 0.7854

Coefficient of determination (R2) 0.46 <0.0001§ 0.46 <0.0001§

Stratton: Medium PET

Intercept 1004 0.50

Soil water 50 –0.41 0.1209 –0.0002 0.0393

Fallow rain 50 3.83 0.0216 0.0016 0.0071

Vegetative rain 50 –1.31 0.5711 0.0002 0.8173

Reproductive rain 50 1.65 0.4992 0.0006 0.4984

Coefficient of determination (R2) 0.19 0.0007§ 0.34 <0.0001§

Walsh: High PET

Intercept –574 –0.27

Soil water 46 0.38 0.0391 0.0001 0.3304

Fallow rain 46 4.92 <0.0001 0.0029 0.0071

Vegetative rain 46 5.96 <0.0001 0.0029 0.0005

Reproductive rain 46 –3.78 0.0468 –0.0017 0.0728

Coefficient of determination (R2) 0.63 <0.0001§ 0.64 <0.0001§† DF = degrees of freedom.‡ PET = potential evapotranspiration.§ P value from Pearson correlation coefficients.

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yield as compared to the pooled observed yield indicated that the soil type (landscape position) rainfall interaction was nega-tive. Similar to Sterling, the highest contribution to the regres-sion relationships for the pooled actual and relative yield data at Stratton was made by fallow rain. For Walsh, the regression R2 for the pooled actual and relative yield data was 0.63 and 0.64 (P < 0.0001), respectively. The rainfalls during the fallow and vegeta-tive growth period were the largest contributors to the regression relationship for pooled observed yield and relative yield at this high PET location. In contrast to the Sterling and Stratton sites, reproductive rain was also shown to have a statistically signifi-cant impact on the regression for both actual and relative yield at Walsh. Overall, the R2 values for both pooled actual and relative yield data were the highest at the Walsh site, perhaps because this site has the highest water deficit in terms of PET-rainfall ratio.

When the multiple regression analysis was run pooling across all sites and landscapes positions for wheat actual and relative grain yields as a function of the water variables, both fallow and vegeta-tive period rain showed a strong contribution to the regression (Table 7). The soil water and reproductive rainfall did not show statistical significance at this maximum pooling level. Overall, the R2 value was slightly higher in the pooled relative yield data (R2 = 0.40) than in the pooled actual yield data (R2 = 0.34), indicating that the regression of relative values took out some interaction effects of soil type, climate, and variety differences across soils and sites with rainfall variables. The observed wheat grain CV when pooled over all sites and slopes was 40% whereas the predicted grain CV was 35% (Table 5).

The above results indicated that in the west Central Great Plains regions, the water variables of soil water at planting, fallow, vegetative, and reproductive rainfall accounted for 34 to 40% of the variability of pooled average wheat yields across landscape positions and sites, for both wet and dry years, and 37 to 73% for unpooled data. A large amount of variability remains unaccounted for which shows the important role of unpredict-able effects from natural factors such as frost, hail damage, weed and insect outbreaks that influence crop yield, and factors other than soil water that affect germination and crop stand, and perhaps the finer timing of rainfall within growth stages. Regres-sion results were also evaluated for all pooled data run separately for wet and dry years within the 24-yr experimental period (wet years = 1985–1997; dry years = 1998–2009). The above water variables accounted for only 24% of the variability of wheat yields in wet years, and 58% in dry years (data not shown). This

suggests that for the winter wheat the water variables tested in this study are more important in drought years.

Rainfall during the preceding fallow period made the highest contribution to the regression relationships of wheat yields in most cases, although vegetative rain was also significant in some (two out of nine) cases. In addition, Walsh was the only site that showed all four variables within the regressions to be statistically signifi-cant for actual yield and all variables except soil water significant for relative yield, with fallow and vegetative rain showing the most impact (Table 6). Soil water at planting should be related to fallow rainfall, but it can include effects of other factors such as the soil water remaining from the previous crop, differences in infiltration due to differences in texture, porosity and soil hydraulic properties, and the differences in runoff water along the catena landscape.

For wheat yields at Stratton, the soil water contribution to the regression for the summit soil was statistically significant (P = 0.01) but had a negative coefficient (Table 4). This is indicative of co-linear relationships among the predictors. The multiple regressions with both wheat and corn produced some parameter estimates that were negative, but most were not significant. Only the wheat data at Stratton and Walsh had multiple regressions with parameter esti-mates that were significant and negative (5 cases out of 68 in total). We suspected that the negative parameter estimates for the soil water variable were due to correlation occurring between soil water and fallow rainfall in the wheat regressions. This was confirmed by performing the multiple regressions with the soil water variable without including the fallow rain variable or fallow rain without the soil water variable. These test regressions showed no significant negative parameter estimates when using soil water or fallow rain alone, except in one case of a negative parameter estimate signifi-cant at the 10% level. The wheat coefficient of determinations did not improve by using only fallow rain or soil water, in fact they were reduced. Therefore, we retained both the soil water content at plant-ing and fallow rain variables in the multiple regressions.

Regression Analysis of Annual Corn and Sorghum Yields with Soil Water and Rainfall Variables

Regression analysis results of corn (sorghum at Walsh) grain yields at the site by landscape position as a function of the water variables are presented in Table 8. For Sterling, the R2 values were 0.64, 0.47, and 0.56 for the summit, sideslope, and toeslope landscape positions, respectively. Reproductive growth stage rainfall had the highest contribution to the regression for all landscape positions, followed by vegetative growth period

Table 7. Multiple regression analysis over 24 yr of actual wheat grain yields and relative wheat grain yields as a function of soil water at planting, fallow, vegetative, and reproductive rainfall using the linear equation [Y kg ha–1 = β0 + (β1 × Soil Water) + (β2 × Fallow Rain) + (β3 × Vegetative Rain) + (β4 × Reproductive Rain)] pooling data over all sites and landscape positions.

Variable DF†

Wheat grain pooled over sites and slopesActual grain yield Relative grain yield

Estimate P value Estimate P value

Intercept 163 –32 0.2022Soil water 163 0.21 0.1637 –0.00004 0.6663Fallow rain 163 4.39 <0.0001 0.00174 <0.0001Vegetative rain 163 2.97 0.0056 0.00155 0.0017Reproductive rain 163 –0.84 0.4068 0.00004 0.9803Coefficient of determination (R2) 0.34 <0.0001‡ 0.40 <0.0001‡

†DF = degrees of freedom.‡ P value from Pearson correlation coefficients.

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rainfall for the summit and toeslope landscapes positions. For Stratton, the R2 values for observed yields were 0.47, 0.49, and 0.56 for the summit, sideslope, and toeslope landscape positions, respectively (Table 8). As was the case with Sterling, the highest contribution to the regression was made by the reproductive period rainfall at all landscape positions. Soil water at planting for the summit landscape position was also significant but at a lower level (P = 0.0836). Sorghum was grown only at the Walsh site and the R2 value for observed sorghum yields were low (R2 = 0.16) and not significant on the summit soil. However, a significant correlation at the 5 and 1% significance levels with R2 values of 0.25 and 0.44 were observed for the sideslope and toeslope landscape positions, respectively (Table 8). As in wheat, there was no consistent and strong trend of R2 values with soil type (landscape position) among the three sites, indicating no interaction of soil type with the water variables. The reproductive rainfall period had the highest contribution to regression (lowest P values), although not significant for all landscape positions. The graphs of observed vs. multiple regression estimated corn (sorghum at Walsh) grain yield for each site and landscape posi-tion were similar to those of wheat (Fig. 4) in that there was no bias with the data pattern being equally spread above and below the 1:1 line (Fig. 5). The multiple regressions reproduced the means very closely, but with smaller CV’s than for the observed data as was the case with wheat (Table 5).

When multiple regressions were analyzed by pooling landscape position soils data within the experimental site as a function of water variables, all coefficients of determination were strongly significant for both actual and relative yield (Table 9). For the Sterling site, both vegetative and reproductive period rainfall were significant for both

actual and relative yield with R2 of 0.45 and 0.54, respectively. The higher R2 for the relative yield indicates that the effect of landscape position on the regression for pooled actual yields was somewhat adverse. For Stratton, the R2 for the actual pooled data and relative yield data was 0.57 and 0.37 (P = <0.0001), respectively. The highest contribution to regression for the pooled actual and relative yield data at Stratton was also made by the reproductive period rainfall. Soil water at planting was also strongly impacted the regression (P = <0.0001) but this variable was not significant when looking at the relative yields. For Walsh, the R2 for the pooled actual and relative yield data was 0.28 and 0.26 (P < 0.001), respectively (Table 9). The highest contribution to regression for both the pooled actual and relative yield data was made by reproductive period rainfall as was the case at Sterling and Stratton for corn yields. Soil water at plant-ing was also significant for the actual yield regression and relative yield was significantly influenced by fallow period rain at the 5% level. As was the case for wheat and corn, the regressions reproduced the mean values very closely but with smaller CV’s for predicted grain yield data at all sites (Table 5).

When the data were scaled up by pooling over all PET sites and landscape position soils (corn sites only), the R2 for the multiple regressions with water variables was 0.40 for observed yields and 0.45 for relative yields, both strongly significant with P value = <0.0001 (Table 10). The highest contribution to the regression for both observed and relative yield was made by the reproductive period rainfall, followed by vegetative period rainfall. Again, the regressions reproduced the means very closely overall, but with smaller CV’s than those of the observed values for both actual and relative data (Table 5). Overall the relative yield data showed a higher R2 value (0.45) for corn, indicating that some interaction

Table 8. Multiple regression analysis over 24 yr of corn (sorghum at Walsh) grain yields as a function of soil water at planting, fallow, vegetative, and reproductive rainfall using the linear equation [Y kg ha–1 = β0 + (β1 × Soil Water) + (β2 × Fallow Rain) + (β3 × Vegetative Rain) + (β4 × Reproductive Rain)] by each experimental site and landscape slope position.

Location/Variable

Landscape positionSummit Sideslope Toeslope

DF† Estimate P value DF† Estimate P value DF† Estimate P valueSterling: Low PET‡ Intercept –885 –112 –734 Soil water 16 –0.29 0.7152 15 –1.47 0.3995 15 –0.13 0.9077 Fallow rain 16 –0.90 0.7744 15 1.97 0.6762 15 0.79 0.8853 Vegetative rain 16 7.15 0.0148 15 3.81 0.3760 15 9.24 0.0778 Reproductive rain 16 16.67 <0.0001 15 15.36 0.0050 15 19.77 0.0022 Coefficient of determination (R2) 0.64 <0.0001§ 0.47 0.0009§ 0.56 0.0002§Stratton: Medium PET Intercept –1355 246 –184 Soil water 14 7.37 0.0836 12 1.17 0.7816 12 4.47 0.2446 Fallow rain 14 –1.12 0.7547 12 0.96 0.8249 12 –6.37 0.1071 Vegetative rain 14 –4.88 0.5361 12 -0.47 0.9602 12 12.87 0.2595 Reproductive rain 14 12.26 0.0236 12 11.24 0.0226 12 14.87 0.0088 Coefficient of determination (R2) 0.47 0.0011§ 0.49 0.0016§ 0.56 0.0006§Walsh: High PET Intercept 982 799 –323 Soil water 12 –0.41 0.9137 13 –0.76 0.8731 13 1.70 0.6137 Fallow rain 12 3.02 0.3456 13 4.50 0.2788 13 2.94 0.4595 Vegetative rain 12 –2.22 0.7156 13 –0.18 0.7431 13 4.15 0.5446Reproductive rain 12 4.48 0.3230 13 4.72 0.2411 13 8.55 0.1333 Coefficient of determination (R2) 0.16 0.1124§ 0.25 0.0350§ 0.44 0.0027§

† DF = degrees of freedom.‡ PET = potential evapotranspiration.§ P value from Pearson correlation coefficients.

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effects of soil, climate, and varietal differences among sites and landscape positions with water variables were removed (Table 10).

The above results show that the water variables of soil water at planting, and fallow, vegetative, and reproductive rainfalls accounted for less than half of the variability of pooled average corn yields across landscape positions and sites for both wet and dry years, and between 47 and 64% of the variability for unpooled data. The water variables explained a higher percent of corn yield variability than that of wheat yield, perhaps because corn is much more sensitive to water stress. However for sorghum, the water variables explained a much lower percent of the yield variability than that of wheat, possibly because sorghum is more drought tolerant than corn and wheat. Again, this level of explained vari-ability still leaves more than 50% of variability unaccounted for which shows the important role of unpredictable effects of other natural factors. The regression results for all pooled corn data separately for wet years within the 24-yr accounted for 63% of the variability of corn yields, and only 23% in dry years. These results are the inverse of the wheat results (data not shown). This shows that for corn, drought increases the effect of natural factors other than the water variables tested. Additionally, the timing of when rainfall is received within the fallow, vegetative, and reproductive periods may be especially important for corn and other summer crops. For example, we defined the July and August months as the reproductive rainfall period for corn and sorghum. Nielsen et al. (2010) working in Akron, CO, defined the critical reproductive precipitation period for dryland corn as the 6-wk period between 16 July and 26 August and found two linear relationships that depended on whether the sum of available soil water at planting was less or greater than 250 mm. This level of finer detail of when rainfall is received was not used in this study as we used a monthly based growth-stage rainfall approach.

Our results from the inter-annual yield variability associated with water variables are most closely comparable to the results obtained by Stone and Schlegel (2006) working near Tribune, KS, with more than 30 yr of data. However, their results also included wheat and sorghum yields from various rotations and with and without irriga-tion as well as no-till, continuous, reduced, and conventional tillage. Our results for wheat yields showed similar RMSE values of 429 to 697 within our study and 658 to 711 kg ha–1 values reported by Stone and Schlegel (2006). Sorghum yields from Stone and Schlegel (2006) also had similar RMSE as our results from Walsh with our data ranging from 1100 to 1430 and the Tribune data reporting 1359 kg ha–1. Stone and Schlegel’s work also showed strong cor-relation for both crops between yield and soil water at emergence, whereas our results were very site and soil dependent. Our soil water measurements were taken with neutron probe readings, whereas the data from Tribune, KS, was derived from gravimetric moisture measurements which are much more accurate at the surface depth. The research performed by Stone and Schlegel (2006) explained 65% of the wheat grain variability and 59% of the sorghum grain variability. Our results were similar in most cases for wheat yields but our sorghum regressions only explained 28% of the variability at its best. This could be partially explained by the mean average precipita-tion at Tribune having 30 mm more average annual rainfall and less open pan evaporation demand.

SUMMARY AND CONCLUSIONSThe Dryland Agroecosystem project has allowed for unique

regression analyses over a large temporal scale that encompasses above normal and below normal annual rainfall across three catena sequences of varying topography. This provides us the ability to transfer results from field experiments to other loca-tions in the area which has been the challenge for agricultural

Fig. 5. Annual corn/sorghum grain yield observed at Sterling, Stratton, and Walsh locations at summit, side, and toeslope soils vs. multiple regression estimated using soil water at planting, fallow, vegetative, and reproductive rainfall.

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researchers, especially in this diverse dryland region. The produc-tivity gradients from a 3-yr cropping system of WCF at Sterling and Stratton and WSF at Walsh, provided useful quantitative estimates of the relative impact that the soil properties have on long-term average dryland crop yields. The most critical driver for both fall and spring planted crops was SOC. The organic C levels in this semiarid region essentially reflect the long-term effects of differences in water availability on production across landscape positions and sites. It is noteworthy that using only profile SOC we can estimate long-term average wheat yields explaining 84% of the variability and for corn and sorghum yields 79% of the vari-ability. Adding contributing slope length to the regression of aver-age corn and sorghum yields resulted in 97% of the variability in yields being accounted for. The results confirm why it is essential to conserve and whenever possible enhance soil organic matter levels with water conservation and management to maintain or enhance yields in this semiarid environment.

The water variables explained <50% of the observed annual variability in crop yields across both wet and dry years within this 24-yr period of analysis, when the data were aggregated over sites and landscape positions; a little higher percent of the variability was explained in the unpooled data and higher for corn than for wheat. Based on the coefficients of determination of regression relations for yields on individual soils and for pooled actual vs. relative yields, there were only small to no interactions between the soil types and water variables. There was a difference between crops for which water variables were most important in predicting annual yields. Wheat yields were most strongly related to fallow rain whereas corn and sorghum yields were most significantly impacted by the reproductive period rainfall. This knowledge is useful for manag-ers and producers in the Great Plains to add diversity to their crop rotations to take advantage of the regional rainfall distribution over time. This analysis explains why this 3-yr cropping system has become more popular in the West Central Great Plains region as

Table 9. Multiple regression analysis of 24 yr of actual corn grain yields (sorghum at Walsh) and relative yields (yearly yield/mean yield) as a function of soil water at planting, fallow, vegetative, and reproductive rainfall using the linear equation [Y kg ha–1 = β0 + (β1 × Soil Water) + (β2 × Fallow Rain) + (β3 × Vegetative Rain) + (β4 × Reproductive Rain)] for each experimental site pooling over landscape positions.

Location/Variable DF†Actual yield Relative yield

Estimate P value Estimate P valueSterling: Low PET‡ Intercept –610 –0.2534 Soil water 56 –0.55 0.3660 –0.0001 0.4904 Fallow rain 56 0.67 0.7808 0.0001 0.8287 Vegetative rain 56 6.38 0.0038 0.0024 0.0017 Reproductive rain 56 17.59 <0.0001 0.0063 <0.0001 Coefficient of determination (R2) 0.45 <0.0001§ 0.54 <0.0001§Stratton: Medium PET Intercept –818 0.0953 Soil water 48 6.63 <0.0001 0.0008 0.1424 Fallow rain 48 –2.90 0.1392 –0.0001 0.7920 Vegetative rain 48 0.76 0.8791 –0.0003 0.8616 Reproductive rain 48 11.87 <0.0001 0.0042 <0.0001 Coefficient of determination (R2) 0.57 <0.0001§ 0.37 <0.0001§Walsh: High PET Intercept 103 0.1396 Soil water 48 2.63 0.0979 0.0003 0.5908 Fallow rain 48 2.65 0.1407 0.0014 0.0422 Vegetative rain 48 –1.59 0.6058 –0.00004 0.9742 Reproductive rain 48 5.44 0.0357 0.0023 0.0177 Coefficient of determination (R2) 0.28 0.0003§ 0.26 <0.0001§

† DF = degrees of freedom.‡ PET = potential evapotranspiration.§ P value from Pearson correlation coefficients.

Table 10. Multiple regression analysis over 24 yr of actual corn yields and relative grain yields as a function of soil water at planting, fallow, vegetative, and reproductive period rainfall using the linear equation [Y kg ha–1 = β0 + (β1 × Soil Water) + (β2 × Fallow Rain) + (β3 × Vegetative Rain) + (β4 × Reproductive Rain)] pooling data over sites and landscapes.

Variable DF†Actual grain yield Relative grain yield

Estimate P value Estimate P value

Intercept 109 –61.87 –0.09251Soil water 109 0.4338 0.4821 0.000008 0.9686Fallow rain 109 –0.6701 0.6626 0.000035 0.9449Vegetative rain 109 5.5443 0.0107 0.001994 0.0069Reproductive rain 109 15.4309 <0.0001 0.005551 <0.0001Coefficient of determination (R2) 0.40 <0.0001‡ 0.45 <0.0001‡

† DF = degrees of freedom.‡ P value from Pearson correlation coefficients.

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it has two crops that have different critical rainfall periods. The regression relations developed here for water variables cannot be used for a reliable prediction of year to year yields at other locations in Colorado due to the large unaccounted for variability. However, the pooled CV’s by crop determined in this study can be used to estimate standard deviation of yearly yields in combination with the mean yield determined from soil properties’ equations.

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