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The Bayesian choice of crop variety and fertilizer dose

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  • Chris M Theobald
  • Mike Talbot

Abstract

Recent contributions to the theory of optimizing fertilizer doses in agricultural crop production have introduced Bayesian ideas to incorporate information on crop yield from several environments and on soil nutrients from a soil test, but they have not used a fully Bayesian formulation. We present such a formulation and demonstrate how the resulting Bayes decision procedure can be evaluated in practice by using Markov chain Monte Carlo methods. The approach incorporates expert knowledge of the crop and of regional and local soil conditions and allows a choice of crop variety as well as of fertilizer level. Alternative dose–response functions are expressed in terms of a common interpretable set of parameters to facilitate model comparisons and the specification of prior distributions. The approach is illustrated with a set of yield data from spring barley nitrogen–response trials and is found to be robust to changes in the dose–response function and the prior distribution for indigenous soil nitrogen.

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  • Chris M Theobald & Mike Talbot, 2002. "The Bayesian choice of crop variety and fertilizer dose," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(1), pages 23-36, January.
    • Handle: RePEc:bla:jorssc:v:51:y:2002:i:1:p:23-36
    DOI: 10.1111/1467-9876.04863
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    Cited by:

    1. Abraham, Christophe, 2005. "Asymptotics in Bayesian decision theory with applications to global robustness," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 50-65, July.
    2. Theobald, Chris M. & Talbot, Mike, 2004. "Bayesian selection of fertilizer level when crop price depends on quality," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 867-880, November.

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