Abstract
The text discusses the development and application of a decision support framework called "targetCSA" for targeting climate-smart agriculture at the national level. This framework integrates quantitative data on vulnerability indicators and CSA practices with stakeholder opinions to identify regions suitable for implementing CSA practices. By utilizing stakeholder preferences and spatially-explicit data, "targetCSA" helps in informed decision-making processes for planning agricultural adaptation and mitigation strategies.
Introduction
The introduction discusses the importance of addressing climate change in agriculture through planning for adaptation and mitigation, focusing on climate-smart agriculture (CSA) as a global development goal. It highlights the challenges of involving stakeholders, dealing with complexity and uncertainty in decision-making, and the need for reliable data to target CSA practices effectively at the national level. The text emphasizes the role of informed decision-making processes, stakeholder involvement, and the integration of comprehensive information in planning for agricultural adaptation and mitigation strategies.
Stage1
In Stage 1 of the decision-making process, stakeholders are identified and involved in developing a structured list of vulnerability indicators and Climate-Smart Agriculture (CSA) practices. Meetings with stakeholders from various sectors help create a comprehensive catalogue of context-specific information, which is essential for informed decision-making in implementing CSA practices. The process involves using tools like the Analytic Hierarchy Process (AHP) to decompose complex problems into pairs of criteria and gather stakeholder preferences through pair-wise comparisons to determine the importance of different factors in decision-making.
Stage2
In the second stage of the decision-making process, stakeholders' preferences are gathered and consensus is built using techniques like pair-wise comparisons and goal programming. Stakeholders provide their opinions through questionnaires or workshops, and these preferences are aggregated to determine overall priorities. The "targetCSA" framework employs a goal programming approach based on linear optimization to integrate and formalize stakeholder opinions for decision-making in climate-smart agriculture planning.
Stage3
In the context of the decision support framework "targetCSA", the third stage involves combining stakeholder preferences with spatial data on vulnerability and Climate-Smart Agriculture (CSA) practices. This stage utilizes a Weighed Linear Combination (WLC) method to integrate standardized spatial criteria with stakeholder preferences to derive vulnerability and CSA suitability scores. By masking irrelevant spatial constraints, the framework generates standardized indices for climate change vulnerability and CSA suitability, allowing for the identification of areas with high potential for specific CSA practices based on overlaying classes.
Application case in Korea
In the application example from Kenya, the text provides an overview of the country's agricultural sector, highlighting its importance for food production and the economy. It discusses the challenges faced by Kenya, such as erratic rainfall, droughts, and food insecurity, particularly in semi-arid regions. The vulnerability of the agricultural sector to climate change is emphasized, pointing out factors like exposure to harsh climate conditions and limited adaptive capacity due to socio-economic challenges.
Results
In the study, vulnerability indicators in Kenya, such as 'annual precipitation' and 'soil organic matter', show higher vulnerability in the Northern and Eastern regions. Social indicators like 'households with access to safe water sources' exhibit varying vulnerability patterns, while economic indicators like 'female participation in economic activities' and 'connectivity through transport infrastructure' highlight different vulnerability aspects across the country. These indicators help identify areas for implementing Climate-Smart Agriculture (CSA) practices tailored to address specific vulnerabilities in different regions of Kenya.
Conclusions
The conclusions of the text emphasize the importance of coordinated national planning processes for climate change adaptation and mitigation efforts, involving relevant stakeholders and informed by quantitative data on biophysical, social, and economic conditions. The "targetCSA" framework offers benefits such as problem structuring, spatially explicit indices based on stakeholder preferences, and the ability to explore different scenarios for more sustainable planning outcomes in the context of climate-smart agriculture. The framework's three-dimensional vulnerability concept allows for a demand-based assessment of Climate-Smart Agriculture potential, making it adaptable and transferable to different countries.