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This study investigates potential changes in erosion rates in the Midwestern United States under climate change, including the adaptation of crop management to climate change. Previous studies of erosion under climate change have not taken into account farmer choices of crop rotations or planting dates, which will adjust to compensate for climate change. In this study, changes in management were assigned...
Backpropagation neural networks with five data coding schemes were used to predict maize yield at three scales in east-central Indiana of the Midwest USA, using 1901-1996 local crop-stage weather data and yield data from farm, county, and state levels. Input data included precipitation and air temperature during maize reproductive (R) stages R1 (silking) to R5 (denting of kernels), the year, and,...
The objective of this study was to determine the usefulness of on-farm precipitation measurement, through determining spatial and temporal precipitation variability and its effect on corn yield. CERES-Maize (DSSAT version 3.5) was used with three precipitation data sources, for an Indiana farm—an on-farm National Weather Service (NWS) station, the nearest non-urban NWS station with electronic reporting...
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