Joseph A. Schumacher

Modeling spatial variation in productivity due to tillage and water erosion

The advent of precision farming practices has heightened interest in managing field variability to optimize profitability. The large variation in yields across many producer fields demonstrated by yield‐monitor‐equipped combines has generated concern about management-induced causes of spatial variation in soil productivity. Soil translocation from erosion processes may result in variation in soil properties across field landscape positions that produce long-term changes in soil productivity. The objective of this study was to examine the relationships between soil redistribution caused by tillage and water erosion and the resulting spatial variability of soil productivity in a soil catena in eastern South Dakota. An empirical model developed to estimate tillage erosion was used to evaluate changes expected in the soil profile over a 50-year period on a typical toposequence found in eastern South Dakota and western Minnesota. Changes in the soil profile due to water erosion over a 50-year period were evaluated using the WEPP hillslope model. The tillage erosion model and the WEPP hillslope model were run concurrently for a 50-year period to evaluate the combined effect of the two processes. The resulting changes in soil properties of the root zone were evaluated for changes in productivity using a productivity index model. Tillage erosion resulted in soil loss in the shoulder position, while soil loss from water erosion occurred primarily in the mid to lower backslope position. The decline in soil productivity was greater when both processes were combined compared to either process acting alone. Water erosion contributed to nearly all the decline in soil productivity in the backslope position when both tillage and water erosion processes were combined. The net effect of soil translocation from the combined effects of tillage and water erosion is an increase in spatial variability of crop yields and a likely decline in overall soil productivity. # 1999 Elsevier Science B.V. All rights reserved.

Herbicide Sorption Coefficients in Relation to Soil Properties and Terrain Attributes on a Cultivated Prairie

The sorption of 2,4-D and glyphosate herbicides in soil was quantified for 287 surface soils (0-15 cm) collected in a 10 x 10 m grid across a heavily eroded, undulating, calcareous prairie landscape. Other variables that were determined included soil carbonate content, soil pH, soil organic carbon content (SOC), soil texture, soil loss or gain by tillage and water erosion, and selected terrain attributes and landform segments. The 2,4-D sorption coefficient (Kd) was significantly associated with soil carbonate content (-0.66; P < 0.001), soil pH (-0.63; P < 0.001), and SOC (0.47; P < 0.001). Upper slopes were strongly eroded and thus had a significantly greater soil carbonate content and less SOC compared with lower slopes that were in soil accumulation zones. The 2,4-D Kd was almost twice as small in upper slopes than in lower slopes. The 2,4-D Kd was also significantly associated with nine terrain attributes, particularly with compounded topographic index (0.59; P < 0.001), gradient (-0.48; P < 0.001), mean curvature (-0.43; P < 0.001), and plan curvature (-0.42 P < 0.001). Regression equations were generated to estimate herbicide sorption in soils. The predicted power of these equations increased for 2,4-D when selected terrain attributes were combined with soil properties. In contrast, the variation of glyphosate sorption across the field was much less dependent on our measured soil properties and calculated terrain attributes. We conclude that the integration of terrain attributes or landform segments in pesticide fate modeling is more advantageous for herbicides such as 2,4-D, whose sorption to soil is weak and influenced by subtle changes in soil properties, than for herbicides such as glyphosate that are strongly bound to soil regardless of soil properties.

Results and Advantages of a Spatially-Variable Technology for Crop Yield

The Field Grid Sense system was adapted to locate and record crop yield in the field. Accurate measurement of yield in spatially variable fields is needed to achieve maximum profitability in crop production. The harvester system is composed of a laptop computer, data acquisition system, fifth wheel, and an ultrasonic level sensor assembled on a combine. The fifth wheel, in combination with travel lanes, is used to identify yield with position where yield is determined by the change of grain volume in the combine hopper. Yield maps can be generated from the collected data and then used as a production management tool in conjunction with other field characteristic information.