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Spatial Yield Risk Across Region, Crop and Aggregation Method

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  • Michael Popp
  • Margot Rudstrom
  • Patrick Manning

Abstract

"A researcher interested in crop yield risk analysis often has to contend with a lack of field- or farm-level data. While spatially aggregated yield data are often readily available from various agencies, aggregation distortions for farm-level analysis may exist. This paper addresses how much aggregation distortion might be expected and whether findings are robust across wheat, canola and flax grown in two central Canadian production regions, differing mainly by rainfall, frost-free growing days and soil type. Using Manitoba Crop Insurance Corporation data from 1980 to 1990, this research, regardless of crop or region analyzed, indicates that (i) spatial patterns in risk are absent; (ii) use of aggregate data overwhelmingly under-estimates field-level yield risk; and (iii) use of a relative risk measure compared to an absolute risk measure leads to slightly less aggregation distortion. Analysts interested in conducting farm-level analysis using aggregate data are offered a range of adjustment factors to adjust for potential bias." Copyright 2005 Canadian Agricultural Economics Society.

Suggested Citation

  • Michael Popp & Margot Rudstrom & Patrick Manning, 2005. "Spatial Yield Risk Across Region, Crop and Aggregation Method," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 53(2-3), pages 103-115, June.
  • Handle: RePEc:bla:canjag:v:53:y:2005:i:2-3:p:103-115
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    References listed on IDEAS

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    1. Harwood, Joy L. & Heifner, Richard G. & Coble, Keith H. & Perry, Janet E. & Somwaru, Agapi, 1999. "Managing Risk in Farming: Concepts, Research, and Analysis," Agricultural Economics Reports 34081, United States Department of Agriculture, Economic Research Service.
    2. Fulton, Joan R. & King, Robert P. & Fackler, Paul L., 1988. "Combining Farm And County Data To Construct Farm Level Yield Distributions," Staff Papers 13752, University of Minnesota, Department of Applied Economics.
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    Cited by:

    1. Jindřich Špička, 2009. "The Risk Analysis in the Agricultural Enterprises using Earnings at Risk Method," Ekonomika a Management, University of Economics, Prague, vol. 2009(3).
    2. Severini, Simone & Tantari, Antonella & Di Tommaso, Giuliano, 2016. "The instability of farm income. Empirical evidences on aggregation bias and heterogeneity among farm groups," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 5(1), April.
    3. Zhiwei Shen & Martin Odening, 2013. "Coping with systemic risk in index-based crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 44(1), pages 1-13, January.
    4. Finger, Robert, 2012. "Biases in Farm-Level Yield Risk Analysis due to Data Aggregation," Journal of International Agricultural Trade and Development, Journal of International Agricultural Trade and Development, vol. 61(1).
    5. Christopher N. Boyer & B. Wade Brorsen & Emmanuel Tumusiime, 2015. "Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 1-10, January.
    6. Li, Xiaofei & Tack, Jesse B. & Coble, Keith H. & Barnett, Barry J., 2016. "Can Crop Productivity Indices Improve Crop Insurance Rates?," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235750, Agricultural and Applied Economics Association.
    7. Joseph Cooper & Carl Zulauf & Michael Langemeier & Gary Schnitkey, 2012. "Implications of within county yield heterogeneity for modeling crop insurance premiums," Agricultural Finance Review, Emerald Group Publishing, vol. 72(1), pages 134-155, May.

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