Adaptation response of agriculture to climate change

Scientists from the Ecosystems Services and Management (ESM) Program applied the GLOBIOM model to analyze a large number of climate change scenarios in order to investigate the extent to which producers facing climate change favored irreversible adaptation measures over low-cost field-scale adjustments.

© Kkronnui | Dreamstime

© Kkronnui | Dreamstime

Most assessments of the future impacts of climate change on the agricultural sector anticipate a high level of adaptation on the part of agricultural production systems. Such results emphasize how adaptable agriculture is. Adaptations can range from low-cost field-scale adjustments, for example by farmers adjusting sowing dates, to large-scale investments that are difficult to reverse, for example, relocation of production capacity or development of long-term water management infrastructures.

Scientists from ESM applied the GLOBIOM model to analyze a large number of climate change scenarios in order to investigate how much producers facing climate change might favor irreversible adaptation measures over low-cost field-scale adjustments. A crucial question was to determine whether the deployment of large-scale options would be equally attractive across all possible future climate scenarios. An analysis of the results showed that although opting for large-scale adaptation measures would be adequate in many cases, it would not be robustly adequate under all climate scenarios. Depending on risk preferences, the optimal responses to uncertainty would be either to delay these adaptation measures until better information is available or to actively implement them despite the significant risk of lock-in of production systems into a maladapted state.

The further development of the stochastic version of GLOBIOM also helped scientists to understand the performance of costly and sometimes irreversible ex ante strategic decisions compared to more operational decisions that were executed once additional information on uncertainties is revealed. Recent studies using stochastic GLOBIOM evaluated that robust storages and instantaneous adaptive market adjustments hedge production and consumption risks, stabilize trade, and fulfill food-feed-energy security requirements at lower costs both at the level of major countries and globally.

In 2014 the results of the two comparison exercises using stochastic GLOBIOM were published. The studies, which looked at how models react to different scenarios of climate change, also illustrate how they differ in terms of their adaptation mechanisms (management responses, trade adjustments, crop substitutions), and on other technical aspects, including food demand, land use change, and technical change [1][2][3][4][5][6][7][8][9].

Figure 1: Various adaptation means for three countries and nine climate change scenarios, with the red-shaded areas indicating adaptations of low reversibility and high cost.


References

[1] Leclère D, Havlík P, Fuss S, Schmid E, Mosnier A, Walsh B, Valin H, Herrero M, Khabarov N, Obersteiner M (2014). Climate change induced transformations of agricultural systems: insights from a global model Environ. Res. Lett. 9 124018  doi:10.1088/1748-9326/9/12/124018

[2] Ermoliev Y, Ermolieva T, Havlík P, Mosnier A, Fuss F, Leclere D, Obersteiner M., Kostyuchenko Y (2014). Estimating local-global dependencies of land use systems by downscaling from GLOBIOM model. In:Integrated management, Security and Robustness, Zagorodny AG, Ermoliev YM, Bogdanov VL (eds), published by the Committee for Systems Analysis and Presidium of National Academy of Sciences of Ukraine, Kyiv, 228-240, ISBN 978-966-02-7376-4

[3] Nelson GC, Valin H, Sands RD, Havlík P, Ahammad H, Deryng D, Elliott J, Fujimori S, Hasegawa T, Heyhoe E, Kyle P, von Lampe M, Lotze-Campen H, Mason D'Croz D, van Meijl H, van der Mensbrugghe D, Müller C, Popp A , Robertson R, Robinson S, Schmid E, Schmitz C, Tabeau A, Willenbockel D (2013). Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences.

[4] von Lampe M, Willenbockel D, Ahammad H, Blanc E, Cai Y, Calvin K, Fujimori S, Hasegawa T, Havlík P, Heyhoe E, Kyle P, Lotze-Campen H, Mason D'Croz D, Nelson GC, Sands RD, Schmitz C, Tabeau A, Valin H, van der Mensbrugghe D, van Meijl H (2014).Why do global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison. Agricultural Economics 45 (1) 3-20.

[5] Lotze-Campen H, von Lampe M, Kyle P, Fujimori S, Havlik P, van Meijl H, Hasegawa T, Popp A, Schmitz C, Tabeau A, Valin H, Willenbockel D, Wise M (2014). Impacts of increased bioenergy demand on global food markets: An AgMIP economic model intercomparison. Agricultural Economics 45(1) 103-16.

[6] Nelson GC, van der Mensbrugghe D, Ahammad H, Blanc E, Calvin K, Hasegawa T, Havlik P, Heyhoe E, Kyle P, Lotze-Campen H, von Lampe M, Mason d'Croz D, van Meijl H, Müller C, Reilly J, Robertson R, Sands RD, Schmitz C, Tabeau A, Takahashi K, Valin H, Willenbockel D (2014). Agriculture and climate change in global scenarios: why don't the models agree. Agricultural Economics 45 (1) 85--101.

[7] Robinson S, van Meijl H, Willenbockel D, Valin H, Fujimori S, Masui T, Sands R, Wise M, Calvin K, Havlik P, Mason d'Croz D, Tabeau A, Kavallari A, Schmitz C, Dietrich J P, von Lampe M (2014). Comparing supply-side specifications in models of global agriculture and the food system. Agricultural Economics 45 (1) 21-35.

[8] Schmitz C, van Meijl H, Kyle P, Nelson GC, Fujimori S, Gurgel A, Havlík P, Heyhoe E, D'Croz DM, Popp A, Sands RD, Tabeau A, van der Mensbrugghe D, von Lampe M, Wise M, Blanc E, Hasegawa T, Kavallari A, Valin H (2014). Land-use change trajectories up to 2050: Insights from a global agro-economic model comparison. Agricultural Economics 45 (1) 69-84.

[9] Valin H, Sands RD, van der Mensbrugghe D, Nelson GC, Ahammad H, Blanc E, Bodirsky B, Fujimori S, Hasegawa T, Havlík P, Heyhoe E, Kyle P, Manson-D'Croz D, Paltsev S, Rolinski S, Tabeau A, van Meijl H, von Lampe M, Willenbockel D (2014). The Future of Food Demand: Understanding Differences in Global Economic Models. Agricultural Economics 45 (1) 51-67.

Collaborators

BOKU University, Austria

CSIRO, Australia

International Food Policy Research Institute, USA

Organisation for Economic Co-operation and Development, France

Food and Agriculture Organisation, Italy

Postdam Institute for Climate, Germany

LEI-Wageningen University, Netherlands

US Department of Agriculture, USA

National Institute for Environmental Studies, Japan

Australian Bureau of Agricultural and Resource Economics, Australia

Pacific Northwest Laboratory, USA



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Last edited: 29 April 2015

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