Awards

Kindie Tesfaye-Fantaye of the International Maize and Wheat Improvement Center in Mexico will develop a computational model that incorporates the variable characteristics of households and farms to better predict the outcomes of agricultural interventions in Ethiopia in order to inform policy choices. Agriculture is central to the Ethiopian economy; it accounts for almost 50% of the gross domestic product and 80% of total employment, yet the industry struggles with limited infrastructure and environmental challenges. Prioritization of agricultural policies has generally relied on analysis of past observations, which are static and tend to ignore variability. They will build and validate an agent-based model that uses current data to model future outcomes, and input biophysical (e.g., soil, climate) and socioeconomic (e.g., household characteristics, land use, access to market and financing) data. They will test their model by comparing five candidate policy options under consideration by the government in terms of impact, effectiveness, efficiency, and inclusiveness. Once established, the model will be scaled up for policy intervention in other Sub-Saharan countries including Tanzania and Nigeria.

William Martin of the International Food Policy Research Institute in the U.S. will combine existing, high-quality survey data collected from individual households in rural Ethiopia and Nigeria with agricultural information from the Global Agro-Ecological Zone (GAEZ) database to model the impact of specific investments on poor communities to better inform policy decisions. National Agricultural Investment Plans (NAIPS) shape policy development by outlining investments required to stimulate growth in Africa through transformation of the agricultural sector. However, they rarely consider the impact on individual households. To address this, they will use existing household survey data from the Living Standards Measurement Study – Integrated Services on Agriculture (LSMS-ISA), which was designed to dissect the relationship between agriculture and poverty. These data will be combined with the publicly available GAEZ database, which assesses agricultural resources and potential, to model the direct impact of policy decisions, such as investments in research and rural infrastructure (irrigation, electrification), on households. Results of the analysis will help prioritize future agricultural investments for maximum impact on the poorest families.

Christine Lamanna and Todd Rosenstock of the World Agroforestry Centre in Kenya will develop a strategy that combines local knowledge and a Bayesian network model to prioritize agricultural policy using Tanzania’s Agriculture and Food Security Investment Plan as a case study. Agriculture is responsible for nearly one third of Africa’s gross domestic product, yet productivity suffers from limited infrastructure and lack of access to markets and financing. Many policy options exist to stimulate agricultural transformation, however countries struggle to prioritize them and progress is limited. They will develop a Bayesian network to model the cost and risks of implementing specific agricultural policies as well as the economic, social and environmental benefits. Using the Tanzanian plan as a case study, they will develop a data-driven model for policy prioritization that incorporates risk (financial, climate, logistical, political) and reflects stakeholder perspectives to create a sense of ownership over the process. This strategy will allow for direct and transparent comparison of diverse policy options and provide decision-makers with clear prioritization information.

Benedict Mongula of the University of Dar es Salaam in Tanzania will analyze two apparently conflicting national agricultural policies centered on either large-scale agriculture or smallholder farmers and determine how to combine them to benefit all stakeholders for inclusive agricultural transformation. Agriculture is central to the Tanzanian economy, yet its impact is limited by a lack of infrastructure, education, and market access. Current agricultural policy is shaped by two conflicting approaches: large-scale agriculture under the Southern Agricultural Growth Corridor (SAGCOT), driven by wealthy foreign investors; and smallholder farming under the government-led Agricultural Sector Development Programme (ASDP). SAGCOT has been criticized for removing land from rural farmers and increasing poverty, while ASDP has many projects in place but has not demonstrated significant impact on the industry. They will investigate how to link the two approaches for inclusive agricultural transformation – improvements to the industry that protect all stakeholders, regardless of social class, culture, gender or age. Through consultation with participants, including government officials, investors and ordinary citizens, they will analyze the two approaches, identify which of the large-scale agricultural investments are most consistent with inclusive transformation, and determine to what extent both SAGCOT and ASDP address the needs of the diverse population. The new policy framework will be presented in the form of a journal article, stakeholder meetings, and workshops.