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Days Suitable for Fieldwork in the US Corn Belt:Climate, Soils and Spatial Heterogeneity

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  • Gramig, Benjamin M.
  • Yun, Seong Do

Abstract

Days suitable for field work (DSFW) is an important piece of data for production agriculture and agricultural extension focused on practical decision making about investment in farm machinery and cropping systems management. It is, however, noteworthy that there has been limited attention paid to DSFW. To fill this gap, this study tries to answer two research questions: (1) what is the trend in DSFW during the planting and harvest period from 1980-2010? (2) what is the accuracy of a predictive econometric model of DSFW based on agro-environmental data? To tackle the economic dimensions of DSFW, we model DSFW consistent with two major approaches in climate change impacts on agriculture: the Ricardian approach and the panel estimation approach. We first specify the regression model of DSFW in panel model from two conceptual approaches: the response function and the factor demand function of cost minimization. Both approaches provide consistent regression specification of fixed and random effects models. We construct an unbalanced panel of weekly DSFW observations, historic weather data, and soil data in five Corn Belt States for 1980-2010 at the Crop Reporting District (CRD) level to implement out-of-sample and in-sample prediction analysis. The results show that the random effects model is the most suitable model to perform climate change response analysis for our data. This paper contributes to the literature in three ways. First, the analytical derivation of two econometric interpretations of DSFW and link them to econometric model specification strategies are easily extended to other agro-environmental analysis. Second, the estimation results for panel models empirically demonstrate that random effects model could be proper model specification taking into account soil effects. Lastly, we discuss that DSFW could be an important constraints for policy corresponding to climate change and its adaptation.

Suggested Citation

  • Gramig, Benjamin M. & Yun, Seong Do, 2016. "Days Suitable for Fieldwork in the US Corn Belt:Climate, Soils and Spatial Heterogeneity," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235726, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea16:235726
    DOI: 10.22004/ag.econ.235726
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    References listed on IDEAS

    as
    1. Melissa Dell & Benjamin F. Jones & Benjamin A. Olken, 2014. "What Do We Learn from the Weather? The New Climate-Economy Literature," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 740-798, September.
    2. repec:ags:joaaec:157405 is not listed on IDEAS
    3. Mesbah Motamed & Lihong McPhail & Ryan Williams, 2016. "Corn Area Response to Local Ethanol Markets in the United States: A Grid Cell Level Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(3), pages 726-743.
    4. 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.
    5. Griffin, Terry, 2009. "Acquiring and Applying Days Suitable for Fieldwork for your State," Journal of the ASFMRA, American Society of Farm Managers and Rural Appraisers, vol. 2009, pages 1-8.
    6. Dillon, Carl R. & Mjelde, James W. & McCarl, Bruce A., 1989. "Biophysical Simulation In Support Of Crop Production Decisions: A Case Study In The Blacklands Region Of Texas," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 21(1), pages 1-14, July.
    7. Olivier Deschênes & Michael Greenstone, 2007. "The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather," American Economic Review, American Economic Association, vol. 97(1), pages 354-385, March.
    8. Jesse Tack & Andrew Barkley & Lawton Lanier Nalley, 2015. "Estimating Yield Gaps With Limited Data: An Application to United States Wheat," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(5), pages 1464-1477.
    9. Boyer, Christopher N. & Larson, James A. & Roberts, Roland K. & McClure, Angela T. & Tyler, Donald D. & Zhou, Vivian, 2013. "Stochastic Corn Yield Response Functions to Nitrogen for Corn after Corn, Corn after Cotton, and Corn after Soybeans," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 45(4), pages 1-12, November.
    10. 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(1), pages 1-11, July.
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    Cited by:

    1. Yun, Seong Do & Gramig, Ben, 2017. "Crop Yield Response Function and Ex Post Economic Thresholds: The Impacts of Crop Growth Stage-specific Weather Conditions on Crop Yield," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258339, Agricultural and Applied Economics Association.
    2. Massey, Raymond E. & Gedikoglu, Haluk, 2021. "Manure application rules and environmental considerations," Agricultural Water Management, Elsevier, vol. 243(C).

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    Keywords

    Agricultural and Food Policy; Crop Production/Industries; Environmental Economics and Policy; Farm Management;
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