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General Model for Predicting Crop Response to Fertilizer
Author(s) -
Johnson G. V.
Publication year - 1991
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj1991.00021962008300020021x
Subject(s) - fertilizer , yield (engineering) , mathematics , quadratic model , crop yield , coefficient of determination , agronomy , soil science , statistics , environmental science , response surface methodology , metallurgy , biology , materials science
Predicting crop response to fertilizers is fundamental to determining fertilization profitability. This paper describes development of a general model to predict crop response to N, P, and K fertilizers. The Mitscherlich percent sufficiency concept and Bray mobility concept are the general underlying principles upon which the model is developed. Crop yields are projected within the limits of a maximum equal to the yield goal and a minimum calculated as a fraction of the yield goal based on percent sufficiency–calibrated P and K soil tests, and available mineral and organic N. Organic N contributions are estimated from algorithms developed to provide a means of indexing the degree to which crops in the past were grown in a N‐rich environment and easily mineralizable organic N accumulated. Output of the model generates N, P, and K response surfaces typical of those generally found in the literature. Evaluation of the quadratic P response components in the model indicate that a coefficient of 2 for the linear term and 1 for the squared term are good first approximations. Magnitude of P and K fertilizer responses is related directly to soil test value and amount of nutrient applied. Response to N fertilizer is large and nearly linear when the yield average is small compared to the yield goal. The response decreases and becomes more curvilinear as the yield average approaches the yield goal. The model is general enough to have broad application, yet allows for local specificity through use of the soil test and yield goal information.