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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #309382

Research Project: ADAPTING SOIL AND WATER CONSERVATION TO MEET THE CHALLENGES OF A CHANGING CLIMATE

Location: Agroclimate and Natural Resources Research

Title: A system's approach to assess the exposure of agricultural production to climate change and variability

Author
item Anandhi, Aavudai - Florida A & M University
item Steiner, Jean
item Bailey, Nathaniel - Florida A & M University

Submitted to: Climatic Change
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/21/2016
Publication Date: 4/23/2016
Citation: Anandhi, A., Steiner, J.L., Bailey, N. 2016. A system's approach to assess the exposure of agricultural production to climate change and variability. Climatic Change. 136(3):647-659.

Interpretive Summary: Estimating the exposure of agriculture to climate variability and change can help us to understand the key vulnerability as well as improve the adaptive capacity which is important for increasing food production to feed the world’s increasing population. A number of indices are available in literature to estimate agricultural exposure to climate. However, no systematic methodology has been developed that will guide the user in selecting appropriate indices for particular applications. In this paper, we contrasted five conventional approaches to estimate exposure indices (EIs) for agriculture: single stressor – mean climate, single stressor – extreme climate, multiple stressor – mean climate, multiple stressor – extreme climate and combinations of the above approaches. A flowchart was developed to indicate the most relevant steps, processes and information that should be taken into account to estimate the exposure of agriculture to climate using an index based approach. To apply this method for estimating EIs, requires information on region of study, its agriculture, stressor(s), climate factor (CF), and the method of aggregation. The flowchart was applied to a case study in Kansas to better illustrate the five approaches to estimate EIs and the implications of the choices made in each step on the estimated exposure. Here, stressors refer to climate variables (e.g., temperature), variables estimated from climate variables (e.g., evapotranspiration) and natural hazards (e.g., landslides) that stress agriculture. Climate factors (CF) refers to estimated variables (e.g., drought) and statistics (e.g., change in rainfall) used to represent one or more stressors. The flowchart provides two options that guide EI estimation: (1) selecting the most appropriate stressor(s), associated CF(s), and aggregation methods when a detailed methodological analysis is possible, or (2) using a default method suggested in this study.

Technical Abstract: Estimating the exposure of agriculture to climate variability and change can help us to understand the key vulnerability as well as improve the adaptive capacity which is important for increasing food production to feed the world’s increasing population. A number of indices are available in literature to estimate agricultural exposure to climate. However, no systematic methodology has been developed that will guide the user in selecting appropriate indices for particular applications. In this paper, we contrasted five conventional approaches to estimate exposure indices (EIs) for agriculture: single stressor – mean climate, single stressor – extreme climate, multiple stressor – mean climate, multiple stressor – extreme climate and combinations of the above approaches. A flowchart was developed to indicate the most relevant steps, processes and information that should be taken into account to estimate the exposure of agriculture to climate using an index based approach. To apply this method for estimating EIs, requires information on region of study, its agriculture, stressor(s), climate factor (CF), and the method of aggregation. The flowchart was applied to a case study in Kansas to better illustrate the five approaches to estimate EIs and the implications of the choices made in each step on the estimated exposure. Here, stressors refer to climate variables (e.g., temperature), variables estimated from climate variables (e.g., evapotranspiration) and natural hazards (e.g., landslides) that stress agriculture. Climate factors (CF) refers to estimated variables (e.g., drought) and statistics (e.g., change in rainfall) used to represent one or more stressors. The flowchart provides two options that guide EI estimation: (1) selecting the most appropriate stressor(s), associated CF(s), and aggregation methods when a detailed methodological analysis is possible, or (2) using a default method suggested in this study.