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An Analytical Technique for Estimating Weather Indexes from Meteorological Measurements

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  • John P. Doll

Abstract

A model is developed to estimate crop yield response to weather and time trend. Weather is defined as those meteorological variables directly affecting crop yields, and direct measures of meteorological variables are included in the model. The weather response function is used to determine an index which indicates the effects of weather on crop yields in each year. The index does not depend directly upon the method of trend estimation and all data are readily available from secondary sources. Estimates are obtained through use of a nonlinear regression routine. The model is applied to corn yields and rainfall measurements for the state of Missouri, 1930–1963.

Suggested Citation

  • John P. Doll, 1967. "An Analytical Technique for Estimating Weather Indexes from Meteorological Measurements," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 49(1_Part_I), pages 79-88.
    • Handle: RePEc:oup:ajagec:v:49:y:1967:i:1_part_i:p:79-88.
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    File URL: http://hdl.handle.net/10.2307/1237069
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    Citations

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    Cited by:

    1. Kirtti Ranjan Paltasingh & Phanindra Goyari, 2018. "Statistical Modeling of Crop-Weather Relationship in India: A Survey on Evolutionary Trend of Methodologies," Asian Journal of Agriculture and Development, Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), vol. 15(1), pages 42-60, June.
    2. Toland, Gerald Jr & Schmiesing, Brian H. & Black, J. Roy, 1989. "Analysis and Comparison of Alternative Estimations of Crop Yield Probability Distributions," WAEA/ WFEA Conference Archive (1929-1995) 244976, Western Agricultural Economics Association.
    3. Irwin, Scott H. & Good, Darrel L. & Tannura, Mike, 2009. "Early Prospects for 2009 Soybean Yields in Illinois, Indiana, and Iowa," Marketing and Outlook Briefs 183513, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    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. Tomek, William G. & Robinson, Kenneth L., 1977. "PART V. Agricultural Price Analysis and Outlook," AAEA Monographs, Agricultural and Applied Economics Association, number 337217, january.
    6. Podbury, Troy & Sheales, Terry & Hussain, Intizar & Fisher, Brian S., 1998. "Use of El Nino climate forecasts in Australia," 1998 Annual meeting, August 2-5, Salt Lake City, UT 269839, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. Mjelde, James W. & Hollinger, Steven E., 1987. "Development of Climate Indices for Application in Empirical Crop Production Studies," Staff Paper Series 257982, Texas A&M University, Department of Agricultural Economics.
    8. Paltasingh, Kirtti Ranjan & Goyari, Phanindra & Mishra, R.K., 2012. "Measuring Weather Impact on Crop Yield Using Aridity Index: Evidence from Odisha," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 25(2).
    9. Schlenker, Wolfram & Hanemann, W Michael & Fisher, Anthony C, 2007. "Water Availability, Degree Days, and the Potential Impact of Climate Change on Irrigated Agriculture in California," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt8q8309qn, Department of Agricultural & Resource Economics, UC Berkeley.
    10. Geigel, Joanne M. & Sundquist, W. Burt, 1984. "A Review And Evaluation Of Weather-Crop Yield Models," Staff Papers 13699, University of Minnesota, Department of Applied Economics.

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