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Explaining The Climate-Dependent Distribution Of Crops In Space –The Example Of Corn And Corn-Cob-Mix In Baden-Württemberg

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  • Lippert, Christian
  • Chatzopoulos, Thomas
  • Schmidtner, Eva
  • Aurbacher, Joachim
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    Abstract

    This article analyses the current climate-dependent spatial distribution of corn and corn-cob-mix in Baden-Württemberg using 2007 data at the county and community level. We use OLS and spatial econometric models to estimate the effects of different climate and non-climate variables on the share of grain maize in UAA. Whereas the temperature effect is missed by means of OLS regression, the adequate spatial error model at the county level yields a highly significant positive effect of mean annual temperature. Additionally, it displays a temperature cut-off point after which corn share is less likely to rise due to temperature increase. These effects are supported by a non-spatial multinomial logit model at the community level. The latter further indicates that soil quality also plays a role. The positive effect of annual precipitation remains ambiguous.

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    Bibliographic Info

    Paper provided by German Association of Agricultural Economists (GEWISOLA) in its series 51st Annual Conference, Halle, Germany, September 28-30, 2011 with number 114504.

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    Date of creation: Sep 2011
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    Handle: RePEc:ags:gewi11:114504

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    Keywords: Spatial distribution of corn; spatial econometrics; multinomial logit; climate change; Agribusiness; Crop Production/Industries;

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    1. P. Wilner Jeanty, 2010. "SPMLREG: Stata module to estimate the spatial lag, the spatial error, the spatial durbin, and the general spatial models by maximum likelihood," Statistical Software Components S457135, Boston College Department of Economics, revised 25 Dec 2013.
    2. P. Wilner Jeanty, 2010. "SPLAGVAR: Stata module to generate spatially lagged variables, construct the Moran Scatter plot, and calculate Moran's I statistics," Statistical Software Components S457112, Boston College Department of Economics, revised 09 Aug 2012.
    3. D. McFadden & J. Hausman, 1981. "Specification Tests for the Multinominal Logit Model," Working papers 292, Massachusetts Institute of Technology (MIT), Department of Economics.
    4. Seo, S. Niggol & Mendelsohn, Robert, 2008. "An analysis of crop choice: Adapting to climate change in South American farms," Ecological Economics, Elsevier, vol. 67(1), pages 109-116, August.
    5. P. Wilner Jeanty, 2010. "ANKETEST: Stata module to perform diagnostic tests for spatial autocorrelation in the residuals of OLS, SAR, IV, and IV-SAR models," Statistical Software Components S457113, Boston College Department of Economics, revised 11 Mar 2010.
    6. P. Wilner Jeanty, 2010. "SPWMATRIX: Stata module to generate, import, and export spatial weights," Statistical Software Components S457111, Boston College Department of Economics, revised 15 Mar 2014.
    7. 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 753-71, September.
    8. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    9. Gunnar Breustedt & Hendrik Habermann, 2011. "The Incidence of EU Per‐Hectare Payments on Farmland Rental Rates: A Spatial Econometric Analysis of German Farm‐Level Data," Journal of Agricultural Economics, Wiley Blackwell, vol. 62(1), pages 225-243, 02.
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