Gerald W. Buchleiter

Soil Electrical Conductivity and Topography Related to Yield for Three Contrasting Soil–Crop Systems

Along with yield mapping, producers have expressed increased interest in characterizing soil and topographic Many producers who map yield want to know how soil and landvariability (Wiebold et al., 1998). Numerous properties scape information can be used to help account for yield variability influence the suitability of soil as a medium for crop and provide insight into improving production. This study was conducted to investigate the relationship of profile apparent soil electrical root growth and yield. These include soil water-holding conductivity (ECa) and topographic measures to grain yield for three capacity, water infiltration rate, texture, structure, bulk contrasting soil–crop systems. Yield data were collected with combine density, organic matter, pH, fertility, soil depth, topograyield-monitoring systems on three fields [Colorado (Ustic Haplarphy features (i.e., slope, aspect, etc.), the presence of gids), Kansas (Cumuic Haplustoll), and Missouri (Aeric Vertic Epiarestrictive soil layers, and the quantity and distribution qualfs)] during 1997–1999. Crops included four site-years of corn (Zea of crop residues. These properties are complex and vary mays L.), three site-years of soybean (Glycine max L.), and one sitespatially (and with some, temporally) within fields. No year each of grain sorghum [Sorghum bicolor (L.) Moench] and winter single measurement adequately describes the influence wheat (Triticum aestivum L.). Apparent soil electrical conductivity of the soil environment on rooting and crop growth and was obtained using a Veris model 3100 sensor cart system. Elevation, obtained by either conventional surveying techniques or real-time yield. Georeferenced soil sampling for fertility status, kinematic global positioning system, was used to determine slope, typically from the surface layer from 0 to 20 cm, is often curvature, and aspect. Four analysis procedures were employed to used by producers in developing recommendation maps investigate the relationship of these variables to yield: correlation, for variable-rate fertilizer application. Information obforward stepwise regression, nonlinear neural networks (NNs), and tained from these samples [including fertility, organic boundary-line analysis. Correlation results, while often statistically matter, cation exchange capacity (CEC), and texture] significant, were generally not very useful in explaining yield. Using has also been used in some research to evaluate yield either regression or NN analysis, ECa alone explained yield variability variation (Kravchenko and Bullock, 2000; Nolin et al., (averaged over sites and years R2 0.21) better than topographic 2001; Ward and Cox, 2001), but usually little or no variables (averaged over sites and years R2 0.17). In six of the nine site-years, the model R2 was better with ECa than with topography. significance has been found. Combining ECa and topography measures together usually improved Inexpensive and accurate methods for measuring model R2 values (averaged over sites and years R2 0.32). Boundary within-field soil variation would have the potential to lines generally showed yield decreasing with increasing ECa for Kansas greatly improve site-specific crop management. Sensors and Missouri fields. Results of this study can benefit farmers and are ideal for mapping soil properties because they can consultants by helping them understand the degree to which sensorprovide data without the need to collect and analyze based soil and topography information can be related to yield variation samples and can be linked to global positioning systems for planning site-specific management. (GPS) and computers for on-the-go spatial data collection. Sensors that measure soil properties could play an important role in helping to characterize yield variation. Y monitoring and mapping have given producOne sensor-based measurement that has shown ers a direct method for measuring spatial variability promise is ECa, which is a measure of the ability to in crop yield (Lark and Stafford, 1996; Pierce and Noconduct electrical current through the soil profile. Sevwak, 1999). Yield maps have shown high-yielding areas eral authors have reported on relating ECa to variation to be as much as 150% higher than low-yielding areas in crop production caused by soil differences (Jaynes et (Kitchen et al., 1999) and have revolutionized the way al., 1995; Kitchen et al., 1999; Luchiari et al., 2001; Zhang producers view yield as they seek to learn how they and Taylor, 2001). Rapid spatial measurement of ECa might improve production. However, yield maps are can be accomplished using noncontact electromagnetic confounded by many potential causes of yield variability induction sensors (McNeil, 1992; Jaynes et al., 1993; (Pierce et al., 1997) as well as potential error sources Sudduth et al., 2001) or with direct-contact sensors such from combine yield sensors (Lamb et al., 1995; Blackas rolling coulters that measure electrical resistance dimore and Marshall, 1996). When other georeferenced rectly (Lund et al., 1999; Sudduth et al., 1999). In geninformation is available, producers naturally want to eral, ECa can be affected by a number of different soil know if and how these various layers of data can be properties, including clay content, soil water content analyzed to help explain yield variability and provide (Kachanoski et al., 1990; Morgan et al., 2001), varying insight into improving production practices. depths of conductive soil layers, temperature, salinity, N.R. Kitchen, S.T. Drummond, and K.A. Sudduth, USDA-ARS, Abbreviations: CEC, cation exchange capacity; DEM, digital elevaCropping Syst. and Water Qual. Res. Unit, Columbia, MO 65211; tion model; ECa, apparent soil electrical conductivity; ECa-dp, deep E.D Lund, Veris Technol., 601 N. Broadway, Salina, KS 67401; and (100 cm) apparent soil electrical conductivity; ECa-sh, shallow (30 cm) G.W. Buchleiter, USDA-ARS, Water Manage. Unit, Ft. Collins, apparent soil electrical conductivity; GPS, global positioning system; CO 80523. Received 1 June 2001. *Corresponding author (kitchenn@ MLR, multiple linear regression; MQR, multiple quadratic regression; missouri.edu). MQR Int, multiple quadratic regression including two-way linear interactions; NN, neural network. Published in Agron. J. 95:483–495 (2003).

WATER PRODUCTION FUNCTIONS FOR CENTRAL PLAINS CROPS

Sustaining irrigated agriculture with limited water supplies requires maximizing productivity per unit of water. Relationships between crop production and water consumed are basic information required to maximize productivity. This information can be used to determine if deficit irrigation is economically desirable and how to best manage limited water supplies. Field trials of corn, sunflower, dry bean, and wheat production with six levels of water application were used to develop water production functions based on consumptive use and to better understand water timing effects and crop responses to stress. Initial results indicate linear relationships between yield and crop ET and transpiration. The field data are being used to improve and validate crop models so they can be used to generalize the field results for other climate and soil characteristics.

Predicting and Mapping Herbicide-Soil Partition Coefficients for EPTC, Metribuzin, and Metolachlor on Three Colorado Fields

Abstract Understanding the spatial variability of herbicide sorption to soil is important in determining the bioavailability as well as leaching potential of the chemical across a field. Multiple methods have been used to estimate herbicide sorption variability at the macroscale, but it has been difficult to measure soil heterogeneity or herbicide sorption at the individual field level. One method to determine soil heterogeneity is to create zones within a field based on maps of the apparent bulk soil electrical conductivity (ECa). These zones can be used to direct soil sampling to determine the fraction of organic carbon (foc) of each zone. The foc, in turn, can be used to predict the variability of herbicide binding among zones. Surface (0 to 30 cm) bulk-soil electrical conductivity (ECs) maps were made for three sandy fields in eastern Colorado, and soil samples were taken from the ECs zones within each field. The foc, and the soil–water partition coefficient (Kd) for EPTC, metribuzin, and metolachlor were determined for each sample. There were significant correlations between ECs and foc (R = 0.75) and between foc and Kd for EPTC, metribuzin, and metolachlor (R = 0.66, 0.61, and 0.71, respectively) across all three fields. Additional soil samples taken from the ECs zones located in previously unsampled areas of the three fields showed that one could reasonably predict Kd values for metribuzin, metolachlor, and possibly, EPTC based on the foc zones derived from ECs maps. Nomenclature: EPTC; metolachlor; metribuzin.

Water Production Functions for High Plains Crops

Increasing demands on limited water supplies will require maximizing crop production per unit water. Field studies are being carried out near Greeley, Colorado to develop water production functions for crops grown in the Great Plains. These yield per unit water relationships can be used to determine if deficit irrigation is economically desirable and how to best manage limited water supplies. A field facility, the Limited Irrigation Research Farm, was developed specifically to carry out limited irrigation research. Irrigation water is applied through drip irrigation systems; precipitation and reference evapotranspiration (ET) is measured with a weather station; soil water content is measured with time-domain reflectometry (TDR) and neutron probes; canopy temperatures are monitored; and growth, ground cover, biomass, and yields are measured. Yields are related to irrigation applications, crop ET, and crop transpiration. Initial results with corn, sunflower, wheat, and dry beans show linear relationships between yield and crop ET and transpiration.

Evapotranspiration Estimates for Deficit Irrigated Corn

Water deficits must be imposed on crops during non-critical growth periods to maximize net economic output per unit of water consumed by the plant. The reference ET-crop coefficient procedure widely used for managing fully irrigated crops would be easiest to implement for irrigation management of deficit irrigated crops provided the water stress coefficient (Ks) used in the procedure was more responsive to plant water stress. The objective of this paper was to further investigate the use of a canopy temperature (Tc) ratio calculated as a quotient of Tc measured over a fully irrigated crop divided by Tc measured over the water stressed crop as a substitute for Ks presently used in the reference ET-crop coefficient irrigation scheduling procedure. Four irrigation levels were imposed on corn (Zea mays L.) ranging from fully irrigated to 55% of seasonal ET applied at critical growth stages. Canopy temperature was continuously monitored in the four treatment levels and the Tc ratio was computed for the warmest period of the day (1300 to 1500 h MST). Crop ET was calculated solely as reference ET multiplied by the basal crop coefficient times the Tc ratio to estimate soil water deficit (SWD) with a water balance. This SWD estimate was compared to the SWD estimated by the traditional reference ET-crop coefficient-Ks procedure and measured SWD. The estimated SWD via the Tc ratio technique compared favorably with measured SWD for the 55% ET treatment. Advantages of this technique is that it does not require knowledge of soil water properties such as field capacity and total available water as well as the crop root depth.