Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models
AbstractThe parameters of yield response functions can vary by year. Past studies usually assume yield functions are nstochastic â€˜â€˜limitedâ€™â€™ stochastic. In this study, we estimate ryeâ€“ ryegrass yield functions in which all parameters are random. The three functional forms considered are the linear response plateau, the quadratic, and the Spillman-Mitscherlich. Nonstochastic yield models are rejected in favor of stochastic parameter models. Quadratic functional forms fit the data poorly. Optimal nitrogen application recommendations are calculated for the linear response plateau and Spillman-Mitscherlich. The stochastic models lead to smaller recommended levels of nitrogen, but the economic benefits of using fully stochastic crop yield functions are small because expected profit functions are relatively flat for the stochastic yield functions. Stochastic crop yield functions provide a way of incorporating production, uncertainty into input decisions.
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Bibliographic InfoArticle provided by Southern Agricultural Economics Association in its journal Journal of Agricultural and Applied Economics.
Volume (Year): 43 (2011)
Issue (Month): 04 (November)
cereal ryeâ€“ryegrass; Monte Carlo; nitrogen; random parameters; stochastic plateau; Production Economics; Q10; C12; D24;
Other versions of this item:
- Tumusiime, Emmanuel & Brorsen, B. Wade & Biermacher, Jon T. & Mosali, Jagadeesh & Johnson, Jim & Locke, James, 2010. "Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models," 2010 Annual Meeting, February 6-9, 2010, Orlando, Florida 56514, Southern Agricultural Economics Association.
- Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
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