CGIAR and partners need to assess ”which innovations might work, where, and for whom”. The Mixed Farming Systems Initiative brought together seven CGIAR Initiatives to develop and apply a common framework for quantitative Farming Systems Analysis (FSA) at different levels (e.g., the field, farm, village/landscape scales, and beyond) based on increased availability and interconnectivity of data. Through this multi-scale FSA framework, CGIAR and partners can prioritize, target, and tailor specific R&D interventions in different contexts, taking into account the complexity and diversity of mixed farming systems.
In September 2022, representatives from seven CGIAR Initiatives: Mixed Farming Systems, Livestock and Climate, Nature Positive Solutions, Sustainable Animal Productivity, and Diversification in East and Southern Africa, Climate Resilience, and Excellence in Agronomy, came together and held a joint workshop and develop a common quantitative FSA framework. The need for coherent systems analysis at different scales was priority for all these initiatives aware of the diversity of complexity of small-scale farming systems. The development of this framework is based on the fact that: (i) data availability has increased drastically over the past 10 years; and (ii) new tools to analyze data, assess impact, and quantify trade-offs within farming systems have been developed and applied, as have approaches that allow for communication between datasets and tools. An example of this is the Agriculture Adaptation Atlas where data and analytics come together in new innovative ways to map climate risks and find targeted solutions.
“We convened this meeting considering that the One CGIAR Initiatives offer different kinds of expertise and data sets that are essential for a comprehensive farming systems analysis. This is therefore an effort in the spirit of One CGIAR for greater impact.” — Santiago López-Ridaura, Co-Lead of the Mixed Farming Systems Initiative, CIMMYT
The multi-level quantitative FSA framework encompasses the diagnosis, intervention identification and assessment, definition of baskets of alternatives for different types of MFS, and delineation of scaling-out and up pathways. The FSA framework is aligned with needs of main users and decision-makers identified at different integration levels, while the constraints and opportunities consider the wider set of actors with interests, influence, and/or stakes around the different CGIAR impact areas. This framework starts from the higher integration levels (what are the big questions, problems, or constraints?) toward the finer more micro-level scales to see how these factors work out at the local scale. Based on these findings, the assessment of a wide range of options and their potential impact is assessed to different types of farming systems, which can be aggregated across scales to prioritize and target specific interventions. Methods and datasets at different aggregation levels are then articulated at different levels and for different purposes, thereby generating a flexible and fit-for-purpose framework that can be used in different settings and to address different development issues.
Two use cases have been selected for the application of the multi-scale FSA framework in East and Southern Africa (ESA). ESA contains a mosaic of natural resources, human settlements, and institutions which shape the major farming systems, with a characteristic mixture of trees, crops, livestock, fish, and livelihoods — and particular development pathways. In the first use case, “Prioritization and targeting of agronomy-related sustainable intensification options in Malawi”, the team is compiling newly available micro and macro level data to diagnose investment opportunities for agronomy-related technologies and scale them up while in the second use case, “Financial instruments for climate risk management in in Kenya”, the team is making better use of existing micro-level data to develop climate vulnerability assessments and better evaluate the investment in climate risk insurance to improve climate risk management by smallholder farmers. In both cases, a thorough data landscape analysis being performed taking advantage of vast datasets publicly available and legacies of previous investments. Gridded data (e.g. MapSpaM-IFPRI, Data For Better Lives-World Bank, Gridded Livestock of the World (GLW3)-FAO), and point household level data (e.g. LSMS-ISA World Bank, RHoMIS, baseline and panel household surveys from bi-lateral projects such as Africa RISING and SIMLESA) are being compiled and aggregated to different levels, based on decision-makers’ needed scales and resolutions. The FSA framework enables interconnectivity of datasets, methods, and tools for systems analysis, which is allowing the regional and thematic CGIAR initiatives to interact, and search together systemic options to improve the livelihoods of small-scale farmers and influence decision-making of different actors at different aggregation levels.
Header image: A farmer in Malawi examines her maize crops in the field. Photo by Neil Palmer/CIAT
