Estimating spatially variable soil properties for application of crop models in precision farming

Crop models have been useful for identifying underlying causes of yield variability and evaluating management prescriptions. However, estimating the spatial soil inputs required to calibrate crop models to historic yields has proven to be challenging and time consuming. Currently, calibration techniques require excessive computer time when applied over many grid points within a field, and procedures for estimating unknown inputs are not well defined. The objectives of this research were: (1) to develop an efficient procedure for estimating spatially variable soil properties for the CROPGRO-Soybean model, and (2) to demonstrate its use in diagnosing areas in the field where excess water or water stress reduce soybean yield. A study was conducted for a 12-ha field in Linn County, Iowa, using soybean data collected during two years (1996 and 1998). Yield, soil type, topography, and soil characterization data were used to estimate spatial variations in soil drainage factors (saturated hydraulic conductivity of an impeding layer and tile drainage spacing), water availability (SCS curve number and maximum rooting depth), and a soil fertility factor. A procedure was developed to create a database of predicted yields for combinations of coefficients, and to search the database using rules based on soil classification, drainage class, and topography to guide the parameter estimation process. When rules based on drainage class were used, the CROPGRO-Soybean model explained 45% to 70% of the yield variability for 1996 and 1998, respectively. When rules based on soil water availability, drainage characteristics, and topography were used, good predictions were obtained in both years (r2 = 0.70 for 1996 and 0.80 for 1998), and RMSE was 2.8% of grid level yields. The data base approach required less than half the time that simulated annealing required for the field with 48 grids.