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Yield trend estimation in the presence of non-constant technological change and weather effects

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  • Conradt, Sarah
  • Bokusheva, Raushan
  • Finger, Robert
  • Kussaiynov, Talgat

Abstract

The application of yield time series in risk analysis prerequisites the estimation of technological trend which might be present in the data. In this paper, we show that in presence of highly volatile yield time series and non-constant technology, the consideration of the weather effect in the trend equation can seriously improve trend estimation results. We used ordinary least squares (OLS) and MM, a robust estimator. Our empirical analysis is based on weather data as well as farm-level and county-level yield data for a sample of grain-producing farms in Kazakhstan.

Suggested Citation

  • Conradt, Sarah & Bokusheva, Raushan & Finger, Robert & Kussaiynov, Talgat, 2012. "Yield trend estimation in the presence of non-constant technological change and weather effects," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122541, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaa123:122541
    DOI: 10.22004/ag.econ.122541
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    References listed on IDEAS

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    3. Finger, Robert, 2012. "Biases in Farm-Level Yield Risk Analysis due to Data Aggregation," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 61(01), pages 1-14, February.
    4. Tannura, Michael A. & Irwin, Scott H. & Good, Darrel L., 2008. "Weather, Technology, and Corn and Soybean Yields in the U.S. Corn Belt," Marketing and Outlook Research Reports 37501, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    5. Finger, Robert, 2010. "Evidence of slowing yield growth - The example of Swiss cereal yields," Food Policy, Elsevier, vol. 35(2), pages 175-182, April.
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    8. Robert Finger, 2010. "Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 205-211.
    9. Scott M. Swinton & Robert P. King, 1991. "Evaluating Robust Regression Techniques for Detrending Crop Yield Data with Nonnormal Errors," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(2), pages 446-451.
    10. Antoine Leblois & Philippe Quirion, 2013. "Agricultural insurances based on meteorological indices: realizations, methods and research challenges," Post-Print hal-00656778, HAL.
    11. Chen, Shu-Ling & Miranda, Mario J., 2006. "Modeling Yield Distribution In High Risk Counties: Application To Texas Upland Cotton," 2006 Annual meeting, July 23-26, Long Beach, CA 21392, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
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    Cited by:

    1. Bokusheva, Raushan & Conradt, Sarah, 2012. "Catastrophic crop insurance effectiveness: does it make a difference how yield losses are conditioned?," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122443, European Association of Agricultural Economists.
    2. Finger, Robert, 2012. "How strong is the “natural hedge”? The effects of crop acreage and aggregation levels," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122538, European Association of Agricultural Economists.
    3. Pavlova, Vera N. & Varcheva, Svetlana E. & Bokusheva, Raushan & Calanca, Pierluigi, 2014. "Modelling the effects of climate variability on spring wheat productivity in the steppe zone of Russia and Kazakhstan," Ecological Modelling, Elsevier, vol. 277(C), pages 57-67.

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