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Measuring the Estimation Bias of Yield Response to N Using Combined On-Farm Experiment Data

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  • Du, Qianqian
  • Mieno, Taro
  • Bullock, David S.

Abstract

Accurately evaluating yield response to nitrogen can increase crop management profitability and sustainability. Many studies estimate yield response by fitting a regression model to data collected from different fields. But analysing such combined data requires that heterogeneity across fields be accounted for in the regression analysis along with the variation in input rates. This study uses data from 27 large-scale on farm experiments to test the potential danger of getting biased estimates of yield response functions. Models with and without field fixed effects are run. The yield response functions from the two models showed different slopes, which provides a visual representation of the bias resulting from the pooled estimation. Use of the Mundlak approach indicated that ignoring the endogeneity of regressors with respect to field effects leads to an unreliable estimation of yield response to N.

Suggested Citation

  • Du, Qianqian & Mieno, Taro & Bullock, David S., 2023. "Measuring the Estimation Bias of Yield Response to N Using Combined On-Farm Experiment Data," Land, Farm & Agribusiness Management Department 344222, Harper Adams University, Land, Farm & Agribusiness Management Department.
    • Handle: RePEc:ags:haaewp:344222
    DOI: 10.22004/ag.econ.344222
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    References listed on IDEAS

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    1. Wolfram Schlenker & Michael J. Roberts, 2006. "Nonlinear Effects of Weather on Corn Yields," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 28(3), pages 391-398.
    2. Wang, Ying & Shi, Wenjuan & Wen, Tianyang, 2023. "Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application," Agricultural Water Management, Elsevier, vol. 277(C).
    3. W. J. Spillman, 1923. "Application of the Law of Diminishing Returns to Some Fertilizer and Feed Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 5(1), pages 36-52.
    4. Tumusiime, Emmanuel & Brorsen, B. Wade & Mosali, Jagadeesh & Johnson, Jim & Locke, James & Biermacher, Jon T., 2011. "Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(4), pages 1-12, November.
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    Keywords

    Crop Production/Industries; Productivity Analysis; Risk and Uncertainty;
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