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SOIL & WATER

Estimating Plant-Available Water Using the Simple Inverse Yield Model for Claypan Landscapes

^a Dep. of Environmental Sci., 2323 Geology Bldg., Univ. of California, Riverside, CA 92521
^b 269 Agric. Eng. Bldg., USDA-ARS, Cropping Systems and Water Quality Research Unit, Columbia, MO 65211
^c 302 ABNR Bldg., Dep. of Soil, Environmental and Atmospheric Sci., Univ. of Missouri, Columbia, MO 65211

^* Corresponding author (pingping.jiang{at}ucr.edu).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Plant-available water (PAW) is one of the fundamental soil factors affecting crop yield, yet quantitative determination of plant-available water capacity (PAW_c) at a field scale has been challenging. A simple inverse yield model (SIYM) has been devised and shown to be successful in estimating PAW_c at a field scale for well-drained soils by matching simulated corn (Zea mays L.) yield with measured yield. For other soils, however, SIYM is yet to be tested. Our objective was to evaluate SIYM performance in estimating PAW_c for poorly-drained claypan-soil landscapes. Soil PAW_c to a depth of 1.2 m (PAW_1.2) was measured at 19 and 18 sampling locations for two claypan-soil fields, Fields 1 and 2, respectively. Corn yield maps of the two fields (nine site-years between 1993 and 2003) were used with the model to estimate PAW_c. Yield reduction associated with low precipitation and high vapor-pressure deficit during corn reproductive stages, and large yield variation in dry years were indications available water was the main yield-limiting factor. The regression r² values between SIYM-estimated PAW_c and the measured PAW_1.2 were 0.43 for Field 1 and 0.31 for Field 2 with estimating errors of 18 and 50 mm, respectively. In Field 2, SIYM estimated markedly lower PAW_c compared with the PAW_1.2 at the most-eroded backslope areas, where claypan characteristics were most prevalent. The SIYM-PAW_c estimates would be more informative in assessing soil productivity because they are based on crop-water relations and not solely on soil texture.

Abbreviations: EC_a, bulk soil apparent electrical conductivity • PAW, plant available water • PAW_c, plant available water capacity • PAW_1.2, plant available water capacity for a 1.2 m soil profile • VPD, vapor pressure deficit • SIYM, simple inverse yield model

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
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.

Received for publication June 19, 2007.

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
PLANT-AVAILABLE WATER is one of the fundamental soil factors affecting crop yield. However, quantitative determination of PAW_c is not an easy task. Challenges in measurement include labor intensive activities such as permanent installation of soil moisture devices, repeated monitoring of water content, destructive sampling, and water extraction from soil samples. These difficulties prevent extensive assessment of the spatial variability of PAW_c at a field scale, information that would be useful for site-specific management and for crop growth modeling at high spatial resolution.

Several approaches have been proposed to estimate PAW_c for fields. One approach is to use terrain analysis and soil-landscape modeling (Moore et al., 1993). A range of soil properties related to PAW have been found to be correlated with topographic variables derived from terrain analysis. These soil properties include water storage (Tomer and Anderson, 1995), water retention at –33 kPa (Pachepsky et al., 2001), organic matter content, A horizon thickness (Moore et al., 1993; Gessler et al., 2000), and soil texture (Pachepsky et al., 2001). Even though these properties are related to PAW, the information they provide is indirect. A second approach is to use apparent soil electrical conductivity (EC_a). Following upon the relationships between soil water content and soil EC_a (Kachanoski et al., 1988, 1990; Sudduth et al., 2001; Reedy and Scanlon, 2003), some researchers also investigated relationships of EC_a with PAW (Morgan et al., 2000; Wong et al., 2006; Jiang et al., 2007b). Even though somewhat empirical, the EC_a approach provided a quick and reasonably accurate method to generate PAW information for a field. One shortcoming of the soil EC_a approach was that the root depth for PAW calculation was variable and arbitrary. Thus, the approach was not based on the real rooting depths in the field and hence was less germane to soil productivity.

Recent studies have presented a biophysical approach for estimating PAW for a field (Timlin et al., 2001a, 2001b; Morgan et al., 2003). In a SIYM devised by Morgan et al. (2003), profile PAW can be obtained by two model steps. The first step (forward step) is a corn yield simulating step, which uses a daily water-budget algorithm, and weather and a range of given PAW values typically encountered in the field as inputs. Thus, the daily amount of water taken up by a crop and stored in the soil can be evaluated. The baseline relationship for simulating yield is the transpiration efficiency equation given as follows:

[1]

where Y is the total plant biomass, T is the cumulative transpiration throughout the growing season, VPD is the mean daytime vapor-pressure deficit of the air for the growing season, and k is the transpiration efficiency constant. The crop water budget algorithm used in yield simulation calculates soil evaporation and transpiration separately. The outcome of this first step simulates corn yield as a function of input PAW values. Then in the second step (inverse step) of the SIYM, the measured yield data, usually from a combine equipped with a yield monitoring system, are matched with the simulated yield, and when the closest match is found, PAW for a given location can be estimated from looking up the input PAW values. During the process, the SIYM is run for each individual year over a range of years whenever weather and yield data are available. Thus, a PAW value is estimated for each individual year (PAW_year) for each single yield value. Finally, PAW_c for a location can be estimated as an average of selected PAW_year over a range of years. The selected PAW_year were usually from water-stressed years when corn yield was highly reliant on stored profile PAW.

The SIYM successfully evaluated PAW_c for well-drained loam-based Alfisols and Mollisols in Wisconsin, where the model was first developed (Morgan et al., 2003). The main assumption of the SIYM is that PAW is the primary yield limiting factor. This assumption also lends rationality to the SIYM for not having a water routing routine, because runoff water is considered to be captured at lower slope positions, and this addition to the profile water storage would be reflected through a higher yield.

For claypan soils, the topsoil thickness above the claypan layer is highly related to PAW (Jiang et al., 2007b) and crop yield (Gantzer and McCarty, 1987; Kitchen et al., 1999). Prior experience indicated that low and unpredictable precipitation in July and August is mainly responsible for year-by-year yield variations (Hu and Buyanovsky, 2003). In addition, claypan characteristics such as low hydraulic conductivity, slow recharge, and poor drainage can affect plant–water relations (Jamison and Kroth, 1958; Thompson et al., 1991, 1992; Blanco-Canqui et al., 2002; Jiang et al., 2007a). If proven useful for claypan soils, the SIYM approach could provide a quick and economical way to map PAW at high resolution for a field, as yield monitor data have become increasingly commonplace. Further, PAW_c estimates obtained from SIYM were expected to be more relevant for assessing potential soil productivity, compared with other approaches, as the model used actual crop yield and was based on crop–water relationships.

The specific objective of this study was to evaluate SIYM performance in estimating PAW_c for poorly-drained claypan-soil landscapes.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Study Sites
Study sites were two claypan-soil fields within a distance of 2 km from each other, near Centralia in central Missouri. Field 1 (39^o38' N, 92^o20' W) was 36 ha and Field 2 (39^o38' N, 92^o25' W) was 13 ha in size. Elevation ranged from 262 to 266 m in Field 1 and from 256 to 266 m in Field 2. The primary soil series found in the study fields include Mexico (fine, smectitic, mesic Vertic Epiaqualfs), Adco (fine, smectitic, mesic Vertic Albaqualfs), both with 1 to 5% slope, and Leonard (fine, smectitic, mesic, Vertic Epiaqualfs) with 2 to14% slope. All these soil series were somewhat-poorly or poorly drained. They were typical claypan soils characterized by an abrupt claypan horizon at varying depths, depending generally on slope position. The typical texture for topsoil was silt loam, and silty clay to clay for the claypan layer.

Both fields were managed in a corn–soybean [Glycine max (L.) Merr] rotation with mulch tillage for at least 10 yr prior to this study. For the years used in this research, Field 1 was managed in mulch tillage and Field 2 was managed in no-tillage. Both fields were under intensive management with a high yield goal for this region. Only in localized small areas in some years did we note plant growth negatively affected by soil compaction, weed pressure, insects, and disease. The mean annual temperature in the area was 12^oC, and the mean annual precipitation was 96.9 cm (National Climate Data Center, 2002).

Yield Data
Five years (1993, 1997, 1999, 2001, and 2003) of corn yield data from Field 1 and 4 yr (1997, 2000, 2002, and 2005) from Field 2 were available for analysis. These yield data were collected using commercial yield monitors mounted on combine harvesters. During harvest, the combine usually traveled at approximately 5 to 8 km h^–1, and yield data were recorded every second. Thus, depending on swath width, a single yield data point represented an average yield for an area approximately 6 to 10 m². An automatic yield data processing program—Yield Editor (Sudduth and Drummond, 2007)—was used to remove questionable and unrealistic yield data points caused by operating errors such as abrupt changes of speed, partial swath, and combine stops and starts. Then, yield data were aggregated to a 10 by 10 m cell resolution using ArcGIS neighborhood analysis (ESRI, 2006). The yield averaged in a single cell typically included two harvest transects and two to three data points in each transect.

Simple Inverse Yield Model Inputs and Estimation of Plant-Available Water
The "forward step" of the SIYM was run for each available year to produce a corn yield vs. PAW relationship curve. Required weather inputs (i.e., mean maximum and minimum daily air temperature in ^oC, daily precipitation in millimeters, mean-season day-time hourly vapor pressure deficit (VPD) in kPa, and total daily radiation in MJ m^–2 d^–1) were obtained from a weather station located adjacent to Field 1. The k value in Eq. [1] used to convert cumulative transpiration to yield was chosen to be 0.008 kPa for all years. This k value fulfilled the recommendation of Morgan et al. (2003) that the k value should be such that SIYM simulates 95% of the highest yield. Physiological inputs such as tasseling and maturity dates were also required but were not observed. Hence these dates were computed by a SIYM subroutine based on cumulative degree days required to reach each of the two dates. Several corn varieties were planted. For varieties whose cumulative degree days to maturity were not available, a value of 750 (10^oC base), considered common in Missouri, was used. In the "inverse step" of the SIYM, the simulated yield values were matched with measured yield data for a given year to obtain the PAW_year for each cell.

To verify the effect of PAW on yield variation over the study period, average VPD values, as an indicator for water deficit, calculated for three periods of each season (before tasselling, after tasselling, and season-long) were correlated with yield.

Field Measurements for Plant-Available Water
Profile samples were taken at 19 locations in Field 1 and 18 locations in Field 2 in October 2005 using a hydraulic soil coring probe (38.1 mm diam.). The sampling sites were distributed throughout the fields such that major land features were represented. Horizonation was determined during the sampling. Depth of each horizon was recorded, and then soil profiles were separated by horizon and each horizon sample was collected and sealed in a plastic bag. These horizon samples were left to air-dry for 2 wk before an air-dry weight was obtained. A subsample of about 50 g was oven-dried to determine water content for the air-dry horizon samples. Thus, bulk density for each horizon was calculated using air-dry soil mass, water content of the oven-dried subsample, and sample volume. Bulk density was used to convert gravimetric water content to volumetric water content.

Sample material passed through a 2-mm sieve was used to determine water retention at –1500 kPa, which was used as the lower limit. Profile samples were taken again at the same locations on 29 Mar. 2006, following wintertime profile recharge, to determine field capacity. Volumetric water content was determined for each horizon sample using the gravimetric method and bulk density. Plant-available water was determined by the difference between the field capacity and –1500 kPa water content. Profile PAW_c was then determined by summing horizon PAW to a 1.2-m depth (PAW_1.2).

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Yield Variation, Weather, and Plant-Available Water
In Central Missouri, highly variable weather patterns during the growing season gave rise to large year-by-year variation in yield. As presented in Table 1 , during the study period, average corn yield ranged from 2.1 Mg ha^–1 (2003) to 7.5 Mg ha^–1 (1993) for Field 1 and from 3.2 Mg ha^–1 (2002) to 9.0 Mg ha^–1 (2000) for Field 2. The cumulative daily precipitation of the growing season (Fig. 1 ) indicated severe water deficit during the critical development stages (usually during the period from July to mid-August) in 1999, 2002, 2003, and 2005, and resulted in serious yield loss in 4 yr, similarly to that documented by Hu and Buyanovsky (2003) for claypan soils. The large yield coefficients of variation (CV) for 3 of these 4 yr (2002, 2003, and 2005) was indicative of the role of topsoil in supplying PAW to corn plants under dry conditions. Measured topsoil thickness ranged from 11 to 120 cm with an average of 34.8 cm for Field 1 and from 0 to 120 cm with an average of 40.1 cm for Field 2 (Jiang et al., 2007b). For a 1.2 m soil profile, the amount of PAW stored could change from 276 mm, assuming all topsoil with silt loam texture, to 144 mm, assuming the claypan occurred at the surface. In water-stressed years, areas with greater topsoil depth, hence a greater amount of profile PAW, supported high yield; while areas with shallow topsoil depth, especially highly-eroded backslopes, only produced very low, sometimes nil, grain yield.

View larger version (24K): [in this window] [in a new window]   Fig. 1. Cumulative daily precipitation from April to September, along with 30-yr cumulative daily averages (1975–2004) obtained near the study site. The x-axis labels are day of year.

View larger version (19K): [in this window] [in a new window]   Fig. 2. Corn grain yield as a function of mean day-time vapor pressure deficit (VPD) before tasseling, after tasseling, and for the whole growing season, during the study period.

Estimating Plant-Available Water Using Simple Inverse Yield Model
Simulated corn grain yield as a function of input PAW values from the "forward-step" of the SIYM are shown in Fig. 3 . Yield was most responsive to PAW in the range from about 125 to 250 mm in water-stressed years. At PAW values smaller than 125 mm, the yield increase with PAW was minor and at PAW values >250 mm, yield began to level off. In years when rainfall was not limiting (i.e., 1993 and 2000), the modeled yield started high at low PAW, but quickly leveled off after a rapid increase over a short range of PAW, also suggesting yield was not limited by PAW in those years.

View larger version (22K): [in this window] [in a new window]   Fig. 3. Simulated corn grain yield using the simple inverse yield model (SIYM) as a function of profile plant-available water (PAW) for each individual year.

View larger version (21K): [in this window] [in a new window]   Fig. 4. Estimated plant-available water (PAW) for each individual year using the simple inverse yield model (SIYM PAWyear) vs. measured profile plant-available water for a 1.2-m soil profile (PAW1.2).

Relationships between SIYM PAW_c and the measured PAW_1.2 are plotted in Fig. 5 , along with a 1:1 reference line. Compared with the value of 0.78 reported in Morgan et al. (2003), the r² values between SIYM PAW_c and the measured PAW_1.2 for our dataset were 0.43 and 0.31 for Fields 1 and 2, respectively. The root mean square errors (RMSE) were 18 and 50 mm for the two fields. In Field 2, SIYM estimated markedly lower PAW values compared to the measured PAW_1.2 for a group of four locations (circled in Fig. 5). This occurred because observed grain yields were consistently lower than values simulated by SIYM based on the measured PAW_1.2 at these sites. For example, at the level of the measured PAW_1.2, SIYM simulated an average of 3.0 Mg ha^–1 for these four sites over the two driest years of 2002 and 2005; the actual average yield, however, was only about 1.5 Mg ha^–1. These four sites were located in the most-eroded and lowest-yielding backslope areas of Field 2, where the topsoil depth was the shallowest or the claypan was mixed into the surface soil. Crop production in such areas was especially prone to water stress and yield loss because of the following claypan characteristics: (i) the likely not-well-recharged soil profile before the growing season; and (ii) the high soil resistance (i.e., low conductivity) to water movement to roots during the growing season. Under such conditions, the crop could be "hydraulic property limited" and experience irreversible stunting before profile PAW was completely depleted, as water was not available for uptake in a timely manner. These results suggested that claypan soil characteristics caused additional yield variability, which cannot be readily explained by profile PAW measured using conventional methods.

View larger version (27K): [in this window] [in a new window]   Fig. 5. Plant available water capacity estimated by the simple inverse yield model (SIYM PAWc) vs. measured plant available water to a 1.2-m depth (PAW1.2). The SIYM PAWc for Field 1 was obtained by averaging SIYM PAW estimates for 1997, 1999, and 2003. The SIYM PAWc for Field 2 was obtained by averaging SIYM PAW estimates for 1997, 2002, and 2005. The dashed circle indicates the data points with the largest underestimation errors.

	CONCLUSIONS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

Besides the precipitation deficit in July and August, the large CV of corn yield in dry years can be explained by the unique physical and hydraulic properties of the claypan, such as low hydraulic conductivity, slow recharge, poor drainage, and high soil resistance for water movement to roots. These properties further reduced yield potential where topsoil thickness was shallow; while with greater topsoil thickness, a relatively high yield was maintained. For this reason, the correlation between yield and the measured PAW_1.2 was lower for our study field than for well-drained soils because the measured PAW_1.2 did not account for the additional yield variability.

The SIYM-PAW_c estimates showed that the largest disagreement with the measured PAW_1.2 occurred in areas where topsoil thickness was shallow and the claypan characteristics were most prevalent close to the soil surface. At these positions, yield was consistently lower than the SIYM-simulated yield based on the level of the measured PAW_1.2, therefore SIYM–PAW_c estimates were considerably lower than the measured PAW_1.2. Using the conventionally-measured PAW_1.2 as the benchmark, SIYM–PAW_c estimates did not agree with the measured PAW_1.2 values as well as for well-drained soils. However, for claypan soils, it is questionable whether the conventionally-measured PAW represents the "true" amount of soil water that can be used by the crop. On the other hand, the SIYM estimates would be more useful in assessing soil productivity and making site-specific management decisions because SIYM is based on yield measurements and crop water use, and less strongly on soil and conventional measurement techniques (e.g., pressure chamber or soil moisture probe sensors), which do not take crop–soil–water interactions into account. This may be more important for claypan soils because recharge is difficult and conventionally-measured PAW may be less representative of the amount of stored water that can be taken up by plants for these soils.

	ACKNOWLEDGMENTS

 
We thank Dr. Cristine Morgan for her consistent support in all stages of the preparation of this manuscript.

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.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

• Blanco-Canqui, H., C.J. Gantzer, S.H. Anderson, E.E. Alberts, and F. Ghidey. 2002. Saturated hydraulic conductivity and its impact on simulated runoff for claypan soils. Soil Sci. Soc. Am. J. 66:1596–1602.[Abstract/Free Full Text]
• ESRI. 2006. ArcGIS 9.2 Desktop help. ESRI, Redlands, CA.
• Gantzer, C.J., and T.R. McCarty. 1987. Predicting corn yields on a claypan soil: A soil productivity index. Trans. ASAE 30:1347–1352.[ISI]
• Gessler, P.E., O.A. Chadwick, F. Chamran, L. Althouse, and K. Holmes. 2000. Modeling soil-landscape and ecosystem properties using terrain attributes. Soil Sci. Soc. Am. J. 64:2046–2056.[Abstract/Free Full Text]
• Grecu, S.J., M.B. Kirkham, E.T. Kanemasu, D.W. Sweeney, L.R. Stone, and G.A. Milliken. 1988. Root growth in a claypan with a perennial-annual rotation. Soil Sci. Soc. Am. J. 52:488–494.[Abstract/Free Full Text]
• Hu, Q., and G. Buyanovsky. 2003. Climate effect on corn yield in Missouri. J. Appl. Meteorol. 42:1626–1635.
• Jamison, V.C., and E.M. Kroth. 1958. Available moisture storage capacity in relation to textural composition and organic matter content of several Missouri soils. Soil Sci. Soc. Am. Proc. 22:189–192.
• Jiang, P., S.H. Anderson, N.R. Kitchen, E.J. Sadler, and K.A. Sudduth. 2007a. Landscape and conservation management effects on hydraulic properties of a claypan-soil toposequence. Soil Sci. Soc. Am. J. 71:803–811.[Abstract/Free Full Text]
• Jiang, P., S.H. Anderson, N.R. Kitchen, K.A. Sudduth, and E.J. Sadler. 2007b. Estimating plant-available water capacity for claypan landscapes using apparent electrical conductivity. Soil Sci. Soc. Am. J. 71:1902–1908.[Abstract/Free Full Text]
• Kachanoski, R.G., E. de Jong, and I.J. Van Wesenbeeck. 1990. Field scale patterns of soil water storage from non-contacting measurements of bulk electromagnetic conductivity. Can. J. Soil Sci. 70:537–542.
• Kachanoski, R.G., E.G. Gregorich, and I.J. Van Wesenbeeck. 1988. Estimating spatial variations of soil water content using non-contacting electromagnetic inductive methods. Can. J. Soil Sci. 68:715–722.
• Kitchen, N.R., K.A. Sudduth, and S.T. Drummond. 1999. Soil electrical conductivity as a crop productivity measure for claypan soils. J. Prod. Agric. 12:607–617.
• Morgan, C.L.S., J.M. Norman, and B. Lowery. 2003. Estimating plant-available water across a field with an inverse yield model. Soil Sci. Soc. Am. J. 67:620–629.[Abstract/Free Full Text]
• Morgan, C.L.S., J.M. Norman, R. Wolkowski, B. Lowery, G. Morgan, and R. Shuler. 2000. Two approaches to mapping plant-available water: EM-38 measurements and inverse yield modeling. In Proc. Precis. Agric. 5th Int. Conf., Minneapolis, MN. [CD-ROM] ASA, CSSA, and SSSA, Madison, WI.
• Moore, I.D., P.E. Gessler, G.A. Nielsen, and G.A. Peterson. 1993. Soil attribute prediction using terrain analysis. Soil Sci. Soc. Am. J. 57:443–452.[Abstract/Free Full Text]
• Myers, D.B., N.R. Kitchen, K.A. Sudduth, R.J. Miles, and R.E. Sharp. 2007. Soybean root distribution related to claypan soil properties and apparent soil electrical conductivity. Crop Sci. 47:1498–1509.[Abstract/Free Full Text]
• National Climate Data Center. 2002. Monthly normals of temperature, precipitation, and heating and cooling degree days 1971–2000. Climatography of the United States No. 81. (Missouri). NCDC, Asheville, NC.
• Pachepsky, Ya.A., D.J. Timlin, and W.J. Rawls. 2001. Soil water retention as related to topographic variables. Soil Sci. Soc. Am. J. 65:1787–1795.[Abstract/Free Full Text]
• Reedy, R.C., and B.R. Scanlon. 2003. Soil water content monitoring using electromagnetic induction. J. Geotech. Geoenviron. Eng. 129:1028–1039.[CrossRef]
• Sudduth, K.A., and S.T. Drummond. 2007. Yield editor: Software for removing errors from crop yield map. Agron. J. 99:1471–1482.[Abstract/Free Full Text]
• Sudduth, K.A., S.T. Drummond, and N.R. Kitchen. 2001. Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture. Comput. Electron. Agric. 31:239–264.[CrossRef]
• Thompson, A.L., C.J. Gantzer, and S.H. Anderson. 1991. Topsoil depth, fertility, water management, and weather influences on yield. Soil Sci. Soc. Am. J. 55:1085–1091.[Abstract/Free Full Text]
• Thompson, A.L., C.J. Gantzer, and R.D. Hammer. 1992. Productivity of a claypan soil under rain-fed and irrigated conditions. J. Soil Water Conserv. 47:405–410.
• Timlin, D.J., Y. Pachepsky, V.A. Snyder, and R.B. Bryant. 1998. Spatial and temporal variability of corn grain yield on a hillslope. Soil Sci. Soc. Am. J. 62:764–773.[Abstract/Free Full Text]
• Timlin, D.J., Y. Pachepsky, V.A. Snyder, and R.B. Bryant. 2001a. Water budget approach to quantify corn grain yields under variable rooting depths. Soil Sci. Soc. Am. J. 65:1219–1226.[Abstract/Free Full Text]
• Timlin, D.J., Y. Pachepsky, C. Walthall, and S. Loechel. 2001b. The use of a water budget model and yield maps to characterize water availability in a landscape. Soil Tillage Res. 58:219–231.[CrossRef]
• Tomer, M.D., and J.L. Anderson. 1995. Variation of soil water storage across a sand plain hillslope. Soil Sci. Soc. Am. J. 59:1091–1100.[Abstract/Free Full Text]
• Wong, M.T.F., S. Asseng, and H. Zhang. 2006. A flexible approach to managing variability in grain yield and nitrate leaching at within-field to farm scales. Precis. Agric. 7:405–417.

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Jiang, P. Articles by Sudduth, K. A. PubMed Articles by Jiang, P. Articles by Sudduth, K. A. Agricola Articles by Jiang, P. Articles by Sudduth, K. A. Related Collections Water Content Water Use