Author Information

S. Aref
D. G. Bullock

Abstract

Large detailed yield databases incorporating GPS makes it possible to predict yield on a small scale. The objective of this study was to determine how closely yield could be predicted in grids of 60-ft2 units. Com and soybean yields were averaged to the 60-ft2 grid. The yields were modeled on previous yields, soil fertility, soil type, and terrain variables. Soil fertility variables were kriged from a I-acre grid to the 60-ft2 grid. Terrain data and soil type data were available at the same scale. Multiple regression models and models with spatial correlation determined from yield semivariograms differed some. Previous yields and wetness were the most significant variables. Soil variables alone were not good predictors.

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Apr 30th, 9:15 AM

YIELD PREDICTION IN 60ft2 GRIDS

Large detailed yield databases incorporating GPS makes it possible to predict yield on a small scale. The objective of this study was to determine how closely yield could be predicted in grids of 60-ft2 units. Com and soybean yields were averaged to the 60-ft2 grid. The yields were modeled on previous yields, soil fertility, soil type, and terrain variables. Soil fertility variables were kriged from a I-acre grid to the 60-ft2 grid. Terrain data and soil type data were available at the same scale. Multiple regression models and models with spatial correlation determined from yield semivariograms differed some. Previous yields and wetness were the most significant variables. Soil variables alone were not good predictors.