Prioritizing investments for climate-smart agriculture: Lessons learned from Mali

Abstract

Agricultural productivity and growth in Mali are under threat from erratic rainfall, resulting in more frequent dry years. The national economy is vulnerable to climate change due to 50% of the gross domestic product coming from the agricultural sector and 75% of the population living in rural areas. The Climate-Smart Agriculture (CSA) concept arises from a need to provide innovative solutions towards the complex and integrated goals of increasing yields, improving resilience, and promoting a low emissions agricultural sector. A major challenge for policymakers to operationalize CSA is the identification, valuation (cost-benefit), and subsequent prioritization of climate-smart options and portfolios (groups of CSA options) for investment. This paper presents the process, results, and lessons learned from a yearlong pilot of the Climate-Smart Agriculture Prioritization Framework (CSA-PF) in Mali. Key national and international stakeholders participated in the co-development and prioritization of two CSA portfolios and related action plans for the Malian Sudanese zone. Initial steps towards outcomes of the process include inclusion of prioritized CSA practices in ongoing development projects and prompting discussion of modifications of future calls for agricultural development proposals by regional donors. (Résumé d'auteur

Identifying Climate-smart agriculture research needs

“…This context-specific approach takes into account the diversity of agricultural systems and stakeholders' priorities in order to support CSA implementation and broad adoption (Andrieu et al, 2017). It also explains why for many, CSA is simply a framework to address agriculture under climate change, thus leaving the door open to many interpretations.…”

“…There are innumerable websites mentioning CSA. Books and articles focused on CSA are flourishing as well (e.g., Campbell et al, 2014Harvey et al, 2014;Minang et al, 2015, Torquebiau, 2016, Andrieu et al, 2017. However, there have also been criticisms by some civil society organizations claiming that CSA opens "a new space for promoting agribusiness and industrial agriculture" (http://www.climatesmartagconcerns.info/rejection-letter.html Accessed 13/2/2018) and controversies over the meaning of CSA (Steenwerth et al, 2014).…”

Uptake of Climate-Smart Agricultural Technologies and Practices: Actual and Potential Adoption Rates in the Climate-Smart Village Site of Mali

“…The paper by Aggarwal et al [5] provides a comprehensive overview of the CCAFS CSV approach.In Mali, the CSV approach has since 2011 been used to test and validate many CSA technologies and practices (including drought tolerant crop varieties, micro-dosing, organic manure, intercropping, contour farming, farmer managed natural regeneration, agroforestry and climate information services) with the participation of farmers. Several authors have reported successful results of CSA technologies tested and their implications for local food security [6-9] and income [10]. This notwithstanding, there is limited information on the uptake and adoption of CSA practices and technologies.…”

Prioritizing Land and Water Interventions for Climate‐smart Villages

“…Multiple prioritization frameworks exist, consisting of a four‐phase prioritization framework proposed by Andrieu et al . (), consensus‐driven decision support framework ‘targetCSA’ by Brandt et al . () and climate‐smart agriculture rapid appraisal (CSA‐RA) by Mwongera et al .…”

“…To prioritize, it becomes important to understand the individual and combined impact, trade-offs and synergies of interventions on the three main objectives of CSA: productivity, adaptation and GHG mitigation. Multiple prioritization frameworks exist, consisting of a four-phase prioritization framework proposed by Andrieu et al (2017), consensus-driven decision support framework 'targetCSA' by Brandt et al (2017) and climate-smart agriculture rapid appraisal (CSA-RA) by Mwongera et al (2017). However, there are a limited number of studies that provide a tool/method to do quantitative impact analysis and the few that do exist are data intensive and involve subject matter expertise, including use of crop models and production functions (Shirsath et al, 2017;Webber et al, 2014).…”