An Innovative Approach for Modeling Crop Yield Response to Fertilizer Nutrients
Fertilizer recommendations seldom account for agro-climatic conditions, which are important factors that determine the response to fertilizer and the optimal rate of fertilizer. The nitrogen fertilizer response to open pollinated and hybrid canola types will also impact optimal nitrogen rates. This study used quantile regression to model canola yield response to nitrogen fertilizer. Quantile regression can apply different weights to the residuals, facilitating a response estimation where the agro-climatic conditions are not limiting and the yield response is due to the variable of interest. The economically optimal levels of fertilizers were calculated using the proposed and the conventional least squares procedures of the two canola types in western Canada. Results showed that the effects of nitrogen fertilizer on yield depended on the canola type and on the estimation procedure. Optimal levels of nitrogen for open-pollinated canola were estimated as 91, 115, and 134 kg ha-1 for severe, moderate and low levels of agro-climatic constraints. Hybrid had a higher yield potential, and also required more nitrogen fertilizer (137, 142, and 158 kg ha-1). Unlike conventional approach, proposed approach could benefit producer by recommending less (more) fertilizer when the crop response to fertilizer is expected to be low (high) due to agro-climatic conditions.
|Date of creation:||2006|
|Date of revision:|
|Contact details of provider:|| Web page: http://caes.ca/|
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- Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Babcock, Bruce A., 1992. "Effects of Uncertainty on Optimal Nitrogen Applications (The)," Staff General Research Papers Archive 10588, Iowa State University, Department of Economics.
- He X. & Hu F., 2002. "Markov Chain Marginal Bootstrap," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 783-795, September.
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