Using Soil Attributes and GIS for Interpretation of Spatial Variability in Yield

Precision farming application requires better understanding of variability in yield patterns in order to determine the cause-effect relationships. This field study was conducted to investigate the relationship between soil attributes and corn (Zea mays L.)-soybean (Glycine max L.) yield variability using four years (1995-98) yield data from a 22-ha field located in central Iowa. Corn was grown in this field during 1995, 1996, and 1998, and soybean was grown in 1997. Yield data were collected on nine east-west transects, consisting of 25-yield blocks per transect. To compare yield variability among crops and years, yield data were normalized based on N-fertilizer treatments. The soil attributes of bulk density, cone index, organic matter, aggregate uniformity coefficient, and plasticity index were determined from data collected at 42 soil sampling sites in the field. Correlation and stepwise regression analyses over all soil types in the field revealed that Tilth Index, based upon soil attributes, did not show a significant relationship with the yield data for any year and may need modifications. The regression analysis showed a significant relationship of soil attributes to yield data for areas of the field with Harps and Ottosen soils. From a geographic information system (GIS) analysis performed with ARC/INFO, it was concluded that yield may be influenced partly by management practices and partly by topography for Okoboji and Ottosen soils. Map overlay analysis showed that areas of lower yield for corn, at higher elevation, in the vicinity of Ottosen and Okoboji soils were consistent from year to year; whereas, areas of higher yield were variable. From GIS and statistical analyses, it was concluded that interaction of soil type and topography influenced yield variability of this field. These results suggest that map overlay analysis of yield data and soil attributes over longer duration can be a useful approach to delineate subareas within a field for site specific agricultural inputs by defining the appropriate yield classes

Environmental Benefits and Management of Small Grain Cover Crops in Corn-Soybean Rotations

These slides offer research results on cover crops

Simulating long-term impacts of cover crops and climate change on crop production and environmental outcomes in the Midwestern United States

It is critical to evaluate conservation practices that protect soil and water resources from climate change in the Midwestern United States, a region that produces one-quarter of the world’s soybeans and one-third of the world’s maize. An over-winter cover crop in a maize–soybean rotation offers multiple potential benefits that can reduce the impacts of higher temperatures and more variable rainfall; some of the anticipated changes for the Midwest. In this experiment we used the Agricultural Production Systems sIMulator (APSIM) to understand how winter rye cover crops impact crop production and environmental outcomes, given future climate change. We first tested APSIM with data from a long-term maize–soybean rotation with and without winter rye cover crop field site. Our modeling work predicted that the winter rye cover crop has a neutral effect on maize and soybean yields over the 45 year simulation period but increases in minimum and maximum temperatures were associated with reduced yields of 1.6–2.7% by decade. Soil carbon decreased in both the cover crop and no cover crop simulations, although the cover crop is able to significantly offset (3% less loss over 45 years) this decline compared to the no cover crop simulation. Our predictions showed that the cover crop led to an 11–29% reduction in erosion and up to a 34% decrease in nitrous oxide emissions (N2O). However, the cover crop is unable to offset future predicted yield declines and does not increase the overall carbon balance relative to current soil conditions