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 managementprescriptions. However, estimating the spatial soil inputs required to calibrate crop models to historic yields has proven tobe challenging and time consuming. Currently, calibration techniques require excessive computer time when applied overmany grid points within a field, and procedures for estimating unknown inputs are not well defined. The objectives of thisresearch were: (1) to develop an efficient procedure for estimating spatially variable soil properties for theCROPGROSoybean model, and (2) to demonstrate its use in diagnosing areas in the field where excess water or water stressreduce soybean yield.A study was conducted for a 12ha field in Linn County, Iowa, using soybean data collected during two years (1996 and1998). Yield, soil type, topography, and soil characterization data were used to estimate spatial variations in soil drainagefactors (saturated hydraulic conductivity of an impeding layer and tile drainage spacing), water availability (SCS curvenumber and maximum rooting depth), and a soil fertility factor. A procedure was developed to create a database of predictedyields 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, theCROPGROSoybean model explained 45% to 70% of the yield variability for 1996 and 1998, respectively. When rules basedon 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 thanhalf the time that simulated annealing required for the field with 48 grids.