In the cotton zone of southern Mali, agroecological intensification (AEI) is a promising way to increase agricultural productivity and nutritious food production, while maintaining healthy ecosystems and equitably improving livelihoods. In the study sites for this project, the team diagnosed high risk, poor market linkages and limited access to seed, together with poor farmer capacity in farm planning and budgeting, as major bottlenecks to AEI. In the project’s first phase, the team developed an approach based on participatory on-farm trials and modelling to narrow a large range of options to more specific baskets of solutions that are relevant and promising for specific farm types and contexts. However, knowledge gaps around nutrition and environmental indicators and underdeveloped trade-off approaches still impede a thorough understanding of multi-scale impacts in the various AEI domains. Furthermore, effective policy-making and development efforts in the region are hampered by a lack of information on AEI pathways and the enabling policy and institutional conditions. A modelling framework for exploring the solution space of entire farm populations will help eliciting these AEI pathways in stakeholder consultations. In co-learning processes, effective communication among various stakeholders is essential, but appropriate tools and approaches are not well documented. With the farmer monitoring and evaluation networks, value chain interactions, stakeholder platforms and AEI pathway workshops, this project will be a useful laboratory for experimenting with communication products, tools and approaches, which will be refined based on feedback.
This project aims to strengthen AEI research as a priority theme for CCRP in West Africa. Farming systems analysis that is participatory, place-based and adaptive is well positioned to identify AEI options that are tailored to the multi-dimensional and multi-scale context in which farmers operate. Building on its first phase, this project aligns well with CCRP by (1) identifying and assessing solutions that improve the performance of agricultural systems and by (2) building the capacity of institutions and stakeholder groups to support farming communities to advance along AEI pathways. Through an adaptive co-learning process with a focus on gender inclusiveness, multi-directional information exchange, and joint decision making, the project aims at empowering smallholder farmers to drive the development process and benefit from contextualized agricultural research. This project intends to produce tailored AEI solutions, knowledge, tools, approaches and communication products. To achieve its objectives and outcomes, the project will use a farming systems analysis (FSA) approach, gradually scaling up to a food systems approach through local value chains and attention for nutrition-related activities. The research is embedded in stakeholder-researcher exchange networks, for which communication tools and approaches will be refined. The FSA relies on a combination of on-farm experiments, modelling and multiple stakeholder engagement to identify baskets of AEI solutions. Performance indicators such as agricultural productivity, income and food self-sufficiency will be complemented with information on nutrition, labor requirements, resilience and risk, which will all be gender-disaggregated.
Outputs and Outcomes:
Envisaged outputs can be categorized as tailored AEI solutions, knowledge, tools, approaches and communication products. Specifically, the project seeks to: Propose baskets of AEI options for different farm types, gender groups, agroecological and socio-economic contexts and improve the knowledge on their effects on various AEI criteria. Identify levers and refine approaches to understand and remove constraints for enhanced adoption of AEI options, through value chain interactions, risk assessment, farm planning and budgeting, and improved seed access. Elicit AEI pathways and their effects on agricultural productivity, income, environmental indicators, food security and nutrition at farm, community and regional levels. Develop and refine tools and methods to effectively communicate research findings to inform discussions between actors and support the decisions of various stakeholders. Consolidate and further energize the adaptive co-learning process and build the capacity of students and young researchers in farming systems analysis. By using these outputs in the co-learning process, the team aims to foster outcomes in terms of improvements in knowledge, skills and attitudes of various stakeholders. In the long term, these outcomes will contribute to positive impacts on:
In Mali, agriculture and livestock contribute about 23 % of export income and 40 % of the country’s gross domestic income. From a general development perspective, the livestock component of agriculture can therefore contribute substantially to improve farm productivity and poverty alleviation. It is with this in mind that this study was initiated. It aims […]
Source: Ousmane M. Sanogo, Salif Doumbia, Katrien Descheemaeker
Development actors, including the African Union, the Alliance for a Green Revolution in Africa and bilateral donors, promote a technology-driven sustainable intensification of agriculture as a way to feed a growing world population and reduce rural poverty. A broader view of smallholder agriculture in the context of rural livelihoods suggests that technological solutions alone are […]
The World Bank argued that West Africa’s Guinea Savannah zone forms part of “Africa’s Sleeping Giant,” where increases in agricultural production could be an engine of economic growth, through expansion of cultivated land in sparsely populated areas. The district of Bougouni, in southern Mali, falls within this zone. We used multiple data sources including a […]
Assessing how livelihoods in rural sub-Saharan Africa might change given future trends in socio-economic and biophysical conditions helps to identify and direct effective efforts towards poverty reduction. Based on existing literature, hypothetical changes in farmer practices and policy interventions were described and used to build five contrasting scenarios towards the year 2027.
Farm systems were re-designed together with farmers during three years (2013–2015) in Southern Mali with the aim to improve income without compromising food self-sufficiency. A cyclical learning model with three steps was used: Step 1 was the co-design of a set of crop/livestock technical options, Step 2 the on-farm testing and appraisal of these options […]