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Forecasting World Crop Yields as Probability Distributions

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  • Ferris, John N.

Abstract

Traditionally, agricultural forecasts, whether for the coming year or several years into the future, have been based on assumptions of normal weather and trend crop yields. That weather is seldom normal and that yields seldom fit trends are well recognized. However, relatively little attention has been given to projecting crop yields stochastically even though computer capacity and software programs are available to do so. One reason is that the task is more challenging than to assign standard deviations to various crop yields and simulate normal distributions using random number generators. For one, deviations of crop yields from trends may be correlated especially if the locations of the crops overlap such as is the case with US corn and soybeans. To model US agriculture, those correlations must be taken into account. Secondly, deviations of crop yields from trends may not be normal. Typically, crop yield deviations are skewed to the low side, with yields lower in poor crop years than higher in favorable crop years. This paper demonstrates how computer software programs can be used to generate probability distributions of yields taking into consideration correlations among crops and non-normality in distributions. Included are thirteen crops and crop aggregates with global coverage of coarse grains, wheat and oilseeds. Probability forecasts are made for 2006 and illustrated for US corn, soybeans and wheat.

Suggested Citation

  • Ferris, John N., 2006. "Forecasting World Crop Yields as Probability Distributions," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25649, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae06:25649
    DOI: 10.22004/ag.econ.25649
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    References listed on IDEAS

    as
    1. Richardson, James W. & Klose, Steven L. & Gray, Allan W., 2000. "An Applied Procedure For Estimating And Simulating Multivariate Empirical (Mve) Probability Distributions In Farm-Level Risk Assessment And Policy Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 32(2), pages 1-17, August.
    2. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    3. repec:ags:joaaec:v:30:y:1998:i:1:p:21-33 is not listed on IDEAS
    4. repec:ags:joaaec:v:32:y:2000:i:2:p:299-315 is not listed on IDEAS
    5. Teigen, Lloyd D. & Bell, Thomas M., 1978. "Confidence Intervals For Corn Price And Utilization Forecasts," Journal of Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, vol. 30(1), pages 1-7, January.
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    Cited by:

    1. Mario Cunha, 2010. "Modelling the Cyclical Behaviour of Wine Production in the Douro Region Using a Time-Varying Parameters Approach," Working Papers 2010.1, International Network for Economic Research - INFER.

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    More about this item

    Keywords

    Crop Production/Industries;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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