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Improving Agronomic Structure in Econometric Models of Climate Change Impacts


  • Ortiz-Bobea, Ariel


Economists are relying on agronomic concepts to construct weather or climate independent variables and improve the reliability and efficiency of econometric models of climate change impact on U.S. agriculture. The use of cumulative heat measures in agronomy (growing degree-days), has recently served as a basis for the introduction of plurimonthly calendar heat variables in these models. However, season-long weather conditions seem at odds with conventional agronomic wisdom that emphasizes crucial differences in crop stage sensitivity to environmental stress. In this paper I show that weather variables matched to key corn development stages provide an enhanced and more stable fit than their calendar counterparts. More importantly, the proposed season-disaggregated framework yields very different implications for adaptation than its calendar counterparts as it indicates that most of the projected yield damages are accounted during the flowering period, a relatively short period in the crop cycle. This should open the door to more advanced yield models that account for additional possibilities of adaptation and thus provide a more nuanced outlook on the potential impacts of climate change on crop yields.

Suggested Citation

  • Ortiz-Bobea, Ariel, 2011. "Improving Agronomic Structure in Econometric Models of Climate Change Impacts," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103656, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea11:103656

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    References listed on IDEAS

    1. Mendelsohn, Robert & Nordhaus, William D & Shaw, Daigee, 1994. "The Impact of Global Warming on Agriculture: A Ricardian Analysis," American Economic Review, American Economic Association, vol. 84(4), pages 753-771, September.
    2. Robert K. Kaufmann & Seth E. Snell, 1997. "A Biophysical Model of Corn Yield: Integrating Climatic and Social Determinants," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 178-190.
    3. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    4. Dixon, Bruce L. & Hollinger, Steven E. & Garcia, Philip & Tirupattur, Viswanath, 1994. "Estimating Corn Yield Response Models To Predict Impacts Of Climate Change," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 19(01), July.
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    Cited by:

    1. Sung, Jae-hoon & Miranowski, John A., 2015. "Adaptive Behavior of U.S. Farms to Climate and Risk," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205787, Agricultural and Applied Economics Association;Western Agricultural Economics Association.

    More about this item


    agriculture; climate change; corn; degree-days; phenology; proxy; yield; Production Economics; Research Methods/ Statistical Methods; Resource /Energy Economics and Policy; Q54; C23;

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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