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A system’s approach to assess the exposure of agricultural production to climate change and variability

Author

Listed:
  • Aavudai Anandhi

    (Florida Agricultural and Mechanical University
    Grazinglands Research Laboratory)

  • Jean L. Steiner

    (Grazinglands Research Laboratory)

  • Nathaniel Bailey

    (Florida Agricultural and Mechanical University)

Abstract

Estimating the exposure of agriculture to climate variability and change can help us understand key vulnerabilities and improve adaptive capacity, which is vital to secure and increase world food production to feed its growing population. A number of indices to estimate exposure are available in literature. However, testing or validating them is difficult and reveals a considerable variability, and no systematic methodology has been developed to guide users in selecting indices for particular applications. This need is addressed in this paper by developing a flowchart from a conceptual model that uses a system’s approach. Also, we compare five approaches to estimate exposure indices (EIs) to study the exposure of agriculture to climate variability and change: single stressor-mean climate, single stressor-extreme climate, multiple stressor-mean climate, multiple stressor-extreme climate; and combinations of the above approaches. The developed flowchart requires gathering information on the region of study, including its agriculture, stressor(s), climate factor(s) (CF), period of interest and the method of aggregation. The flowchart was applied to a case study in Kansas to better understand the five approaches to estimate EIs and the implications of the choices made in each step on the estimated the exposure. The flowchart provides options that guide EI estimation by selecting the most appropriate stressor(s), associated CF(s), and aggregation methods when a detailed methodological analysis is possible, or proposes a default method when data or resources do not allow a detailed analysis. Climate adaptation involves integration of a multitude of factors across complex systems. A more standardized approach to assessing exposure can promote information sharing across different locations and systems as this rapidly evolving area of study moves forward.

Suggested Citation

  • Aavudai Anandhi & Jean L. Steiner & Nathaniel Bailey, 2016. "A system’s approach to assess the exposure of agricultural production to climate change and variability," Climatic Change, Springer, vol. 136(3), pages 647-659, June.
    • Handle: RePEc:spr:climat:v:136:y:2016:i:3:d:10.1007_s10584-016-1636-y
    DOI: 10.1007/s10584-016-1636-y
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    References listed on IDEAS

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    Cited by:

    1. Pagliacci, Francesco & Salpina, Dana, 2022. "Territorial hotspots of exposure to climate disaster risk. The case of agri-food geographical indications in the Veneto Region," Land Use Policy, Elsevier, vol. 123(C).
    2. Jean L. Steiner & David D. Briske & David P. Brown & Caitlin M. Rottler, 2018. "Vulnerability of Southern Plains agriculture to climate change," Climatic Change, Springer, vol. 146(1), pages 201-218, January.
    3. Thi Tam Duong & Tom Brewer & Jo Luck & Kerstin Zander, 2019. "A Global Review of Farmers’ Perceptions of Agricultural Risks and Risk Management Strategies," Agriculture, MDPI, vol. 9(1), pages 1-16, January.
    4. Ran Wang & Yao Jiang & Peng Su & Jing’ai Wang, 2019. "Global Spatial Distributions of and Trends in Rice Exposure to High Temperature," Sustainability, MDPI, vol. 11(22), pages 1-53, November.
    5. Bentley, Chance & Anandhi, Aavudai, 2020. "Representing driver-response complexity in ecosystems using an improved conceptual model," Ecological Modelling, Elsevier, vol. 437(C).

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