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An Old Problem Revisited—and Solved: Upton and Dalton's Linear Production Response: A Note

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  • Garth Holloway

Abstract

This note presents a robust method for estimating response surfaces that consist of linear response regimes and a linear plateau. The linear response‐and‐plateau model has fascinated production scientists since von Liebig (1855) and, as Upton and Dalton indicated, some years ago in this Journal the response‐and‐plateau model seems to fit the data in many empirical studies. The estimation algorithm evolves from Bayesian implementation of a switching‐regression (finite mixtures) model and demonstrates routine application of Gibbs sampling and data augmentation—techniques that are now in widespread application in other disciplines.

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  • Garth Holloway, 2003. "An Old Problem Revisited—and Solved: Upton and Dalton's Linear Production Response: A Note," Journal of Agricultural Economics, Wiley Blackwell, vol. 54(3), pages 487-497, November.
    • Handle: RePEc:bla:jageco:v:54:y:2003:i:3:p:487-497
    DOI: 10.1111/j.1477-9552.2003.tb00074.x
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    Cited by:

    1. Humblot, Pierre & Jayet, Pierre-Alain & Petsakos, Athanasios, 2017. "Farm-level bio-economic modeling of water and nitrogen use: Calibrating yield response functions with limited data," Agricultural Systems, Elsevier, vol. 151(C), pages 47-60.
    2. Klaus Moeltner & A. Ford Ramsey & Clinton L. Neill, 2021. "Bayesian Kinked Regression with Unobserved Thresholds: An Application to the von Liebig Hypothesis," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(5), pages 1832-1856, October.
    3. Ajay Philip & Rahul R. Marathe, 2022. "A New Green Labeling Scheme for Agri-Food Supply Chains: Equilibrium and Information Sharing under Uncertainties," Sustainability, MDPI, vol. 14(23), pages 1-34, November.
    4. Brorsen, B. Wade, 2013. "Using Bayesian Estimation and Decision Theory to Determine the Optimal Level of Nitrogen in Cotton," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 142951, Southern Agricultural Economics Association.

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