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Refining spatial resolution and spillovers of a micro-econometric analysis of adapting portfolios to climate change using the global positioning system

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Abstract

This paper refines the spatial resolution and spillover effects of a micro-econometric analysis of adaptation of agricultural portfolios to climate change using the Global Positioning System (GPS). From the household surveys collected across South America by the World Bank, the GPS recordings of exact farm locations such as latitude, longitude, and altitude are matched with high resolution grid cell climate data from the Climate Research Unit as well as geographically referenced soils and geography data from the Food and Agriculture Organization. The choice of agricultural systems at the farm level is estimated using spatial Logit model and the conditional land value is estimated for each system of agriculture after correcting for selection bias and spatial spillovers. Future choices and land values are simulated using the fine resolution climate scenarios by the UKMO (United Kingdom Meteorological Office) and GISS (Goddard Institute for Space Studies). This paper finds that, under the UKMO HadGEM1 (Hadley Center Global Environmental Model 1) scenario by around 2060, the choices of the specialized systems are expected to fall, but the mixed system would increase. The land value of the crops-only falls by 29 %, but the mixed system land value falls only by 12 %. Under a milder GISS ER (ModelE-R) scenario, the land value of the mixed system increases by 6 %. With full adaptations of agricultural systems, the expected land value falls by 17 %. Without adaptations, the damage increases. This paper demonstrates that adaptation behaviors can be best studied by a fine resolution micro-econometric analysis of agricultural portfolios using the GPS reference.

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Notes

  1. Regressions were run for the land values reported in Argentina (including Uruguay) and Brazil owing to the high quality of the reported data in terms of land markets, land used for crops and livestock, and research networks.

  2. For A1 scenarios, refer to the previous study by Seo (2010b).

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Acknowledgments

I thank the World Bank for supporting the Climate Change and Rural Poverty in Latin America project from which the survey data were collected.

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  1. Faculty of Agriculture and Environment, University of Sydney, RD Watt 406, Sydney, NSW, 2006, Australia

    S. Niggol Seo

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Seo, S.N. Refining spatial resolution and spillovers of a micro-econometric analysis of adapting portfolios to climate change using the global positioning system. Mitig Adapt Strateg Glob Change 18, 1019–1034 (2013). https://doi.org/10.1007/s11027-012-9405-3

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