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Estimation of von Liebig Response Functions

Author

Listed:
  • Quirino Paris
  • Keith Knapp

Abstract

Two distinct but related approaches for the estimation of von Liebig response functions are presented. The first approach is based upon a two-phase, ordinary least squares procedure combined with bootstrapping for computing the standard errors of the estimates. The second approach generates maximum likelihood estimates of the parameters and can be implemented according to two different parameterizations of the model. Application of the procedures to a sample of experimental data suggests a satisfactory degree of conformity of the results. A test of normality of the disturbance term based upon the estimated residuals failed to reject the null hypothesis, further supporting the maximum likelihood approach.

Suggested Citation

  • Quirino Paris & Keith Knapp, 1989. "Estimation of von Liebig Response Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(1), pages 178-186.
  • Handle: RePEc:oup:ajagec:v:71:y:1989:i:1:p:178-186.
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    File URL: http://hdl.handle.net/10.2307/1241786
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    1. Banerji, A. & Meenakshi, J.V. & Khanna, Gauri, 2012. "Social contracts, markets and efficiency: Groundwater irrigation in North India," Journal of Development Economics, Elsevier, vol. 98(2), pages 228-237.
    2. Wu, JunJie & Zilberman, David & Babcock, Bruce A., 2001. "Environmental and Distributional Impacts of Conservation Targeting Strategies," Journal of Environmental Economics and Management, Elsevier, vol. 41(3), pages 333-350, May.
    3. Peter Berck & Jacqueline Geoghegan & Stephen Stohs, 2000. "A Strong Test of the von Liebig Hypothesis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(4), pages 948-955.
    4. Buaha, Gabriel Toichoa & Apland, Jeffrey & Hicks, Dale, 1995. "A Regression Analysis Of The Effects Of Planting Date And Variety On Corn Yields In Minnesota," Staff Papers 13872, University of Minnesota, Department of Applied Economics.
    5. Babcock, Bruce A. & Blackmer, Alfred M., 1992. "The Value Of Reducing Temporal Input Nonuniformities," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 17(2), pages 1-13, December.
    6. Kampas, Athanasios & Petsakos, Athanasios & Rozakis, Stelios, 2012. "Price induced irrigation water saving: Unraveling conflicts and synergies between European agricultural and water policies for a Greek Water District," Agricultural Systems, Elsevier, vol. 113(C), pages 28-38.
    7. Kampas, Athanasios & Melfou, Katerina & Aftab, Ashar, 2013. "Designing Regulatory Policies for Complex Externalities: The Case of Agricultural Pollution," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 14(2), pages 1-14.
    8. Rodriguez, Divina Gracia P. & Bullock, David S., 2015. "Testing the Validity of Stanford's 1.2 Rule for N Fertilizer Recommendation," 2015 Conference, August 9-14, 2015, Milan, Italy 212289, International Association of Agricultural Economists.
    9. Rodriguez, Divina Gracia P. & Bullock, David S., 2015. "An Empirical Investigation of the Stanford’s “1.2 Rule” for Nitrogen Fertilizer Recommendation," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205314, Agricultural and Applied Economics Association.

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