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The influence of weather extremes on the spatial correlation of corn yields

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Abstract

Annual production shocks at the farm-level are driven by year-to-year weather variability. While identifying drivers of these shocks is important and well-researched, little attention has been paid to the extent to which these shocks aggregate up to the regional or national level. Here, we provide a method for simultaneously modeling the mean, variance, and spatial correlation of crop yields in the presence of evolving technology. Our approach allows one to condition spatial correlations on variables of interest—such as weather—in a straightforward manner. An application to state-level Iowa and Illinois corn yields provides evidence that spatial correlations roughly double in both good and bad weather years relative to normal years. Furthermore, we consider several functional forms for conditioning spatial correlations on weather and find that less flexible relationships generate misleading results as they vastly underestimate the degree of correlation in bad weather years. These findings have important implications for the climate change, food price volatility, crop insurance, and yield modeling literatures.

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Notes

  1. In the absence of ethanol-based demand for corn, Adjemian and Smith (2012) estimate this flexibility to be −1.51. The authors also provide a nice overview of the price flexibility literature, which started when Moore (1919) introduced the term flexibility of prices (Houck 1966). Examples of published price flexibilities include Gray et al. (1995) and Chua and Tomek (2010).

  2. A separate but related literature has evaluated the returns to agricultural research, with reviews provided in Evenson (2001), Huffman and Evenson (2006), Alston et al. (2010), and Villavicencio et al. (2013).

  3. The nine regions correspond to the “standard regions” as defined by Karl and Koss (1984). They include the Northeast; Upper Midwest (East North Central); Ohio Valley (Central); Southeast; Northern Rockies and Plains (West North Central); South; Southwest; Northwest; and West.

  4. The available RCEI’s are: Annual (Jan–Dec); Spring (Mar-May); Summer (Jun–Aug); Fall (Sep–Nov); Winter (Dec–Feb); Warm Season (Apr–Sep); Cold Season (Oct–Mar); and Hurricane Season (Jun–Nov).

  5. As noted on the CEI web page, mean maximum and minimum temperature stations were selected from the U.S. Historical Climatology Network (USHCN). Daily precipitation stations were extracted from the USHCN daily database and supplemented by Summary of the Day (TD3200) and pre-1948 (TD3206) daily precipitation stations. The NCDC climate division precipitation and temperature databases are used to calculate the PDSI (Karl 1986).

  6. The in-sample mean-squared-error for the cubic (local) model was 133.7 (141.8) for Iowa and 139.1 (144.9) for Illinois. A leave-one-out cross-validation exercise to assess out-of-sample performance produced the same pattern of results.

  7. A one-sample KS test for normality suggests rejection of the null at standard significance levels (p-value =0.003)

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Authors and Affiliations

  1. Department of Agricultural Economics, Mississippi State University, Mississippi State, MS, USA

    Jesse B. Tack

  2. Department of Economics, Finance and Legal Studies, The University of Alabama, Tuscaloosa, AL, USA

    Matthew T. Holt

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  1. Jesse B. Tack
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  2. Matthew T. Holt
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Correspondence to Jesse B. Tack.

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Tack, J.B., Holt, M.T. The influence of weather extremes on the spatial correlation of corn yields. Climatic Change 134, 299–309 (2016). https://doi.org/10.1007/s10584-015-1538-4

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  • DOI: https://doi.org/10.1007/s10584-015-1538-4

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