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DIVISION S-8—NUTRIENT MANAGEMENT & SOIL & PLANT ANALYSIS

Yield Models Implied by Traditional Fertilizer Recommendations and a Framework for Including Nontraditional Information

^a 304F Waters Hall, Dep. of Agricultural Economics, Kansas State University, Manhattan, KS 66506
^b 2004 Throckmorton Plant Sciences Center, Dep. of Agronomy, Kansas State University, Manhattan, KS 66506
^c 306B Waters Hall, Dep. of Agricultural Economics, Kansas State University, Manhattan, KS 66506

* Corresponding author (tkastens{at}agecon.ksu.edu)

Recently emerged technologies permitting precision farming provide opportunities to collect and evaluate vast amounts of data; yet, a formal method for incorporating additional yield-affecting information into fertilizer-yield response models is still largely unavailable to site-specific managers. This research develops an integrated yield model that combines laboratory-based fertilizer recommendations with data from multiple independent variables. Kansas wheat (Triticum aestivum L. sp. aestivum) was the target crop, and simulated soil test and yield data were characteristic of data from the literature and this geographic area. Independent variables in the plateau-type yield model included fertilizer N and P, soil test N (STN) and soil test P (STP), and Z (representing all other causal factors). Consideration of the potential benefits of managing STP is an explicit outcome of this research. To expand the modeling procedures to include actual farm information, data from a northwest Kansas farm were used to replace the simulated Z variable. Additionally, maximum entropy (ME) was examined as a statistical technique for using the farm's N and P data to modify the predicted N- and P-response of laboratory-based models. Based on out-of-sample yield prediction accuracy, the ME models were generally the most accurate, improving root mean squared prediction error by as much as 14% relative to a model estimated with only farm data. Because the ME models were the most accurate and provided fertilizer recommendations similar to those from soil testing laboratories, this approach should provide a reliable framework for developing site-specific fertilizer recommendations that depend on factors other than STN and STP.

Abbreviations: CSU, Colorado State University • CV, coefficient of variation • H, high • KSU, Kansas State University • L, low • M, medium • MAPE, mean absolute percentage error • ME, maximizing entropy • OAL, Olsen's Agricultural Laboratory • om, soil organic matter content • RMSE, root mean squared error • SSE, sum of squared errors • STN, soil test N • STP, soil test P • UNL, University of Nebraska-Lincoln • VL, very low • VH, very high • VRA, variable rate application