How the data revolution could help design better agronomic investments?

A spatial ex ante framework for guiding agronomic investments in sub-Saharan Africa, Sebastian Palmas and Jordan Chamberlin, CIMMYT Kenya Brown Bag, 4 March 2019.

What fertilizer application will give me the best returns? What maize crop variety should I use? Each farmer faces farming constraints which include weather uncertainty, soil fertility management challenges, access to finance and markets for inputs and outputs. In order to improve African smallholder farmers’ yields and incomes, they need the most effective agronomic advices adapted to their own situation. Yet traditional agronomic research is not designed to fit with complex agroecological regions and farming systems. The challenge is even greater in sub-Saharan Africa where agricultural production landscapes are highly diverse. Research organisations have often limited resources to develop the necessary experiments to generate farm and site-specific agronomic advice at scale.

‘‘Agronomic research is traditionally not equipped to consider spatial or socio-economic diversity among the millions of farmers it targets.’’ says Sebastian Palmas, data scientist at the International Maize and Wheat Improvement Center (CIMMYT) in Nairobi, Kenya.

Palmas presented some of the learnings of the BMGF-funded Taking Maize Agronomy to Scale in Africa (TAMASA) initiative to use the data revolution to improve the way agronomic research and development is done, during a science seminar called ‘’A Spatial Ex Ante Framework for Guiding Agronomic Investments in sub-Saharan Africa’’ on March, 4, 2019.

TAMASA aims at addressing this challenge by using available geospatial and other (big) data resources, and new data science tools such as machine learning and Microsoft’s AI for Earth, to produce and package information that can help farmers, research or governments take better decisions on what agronomic practices and investments will give them the best returns.

By adapting the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model to the conditions of small farmers in TAMASA target countries (Ethiopia, Tanzania and Nigeria), using different layers of information, CIMMYT and its partners have developed a versatile geospatial tool for evaluating crop yield responses to fertilizer applications in different areas of a given country. Because calculations integrate spatial variation of fertilizer and grain prices, the tool evaluates for each location the profitability – a key factor influencing farmers’ fertilizer usage. The project team can generate maps that show, for instance, the estimated agronomic and economic returns to different fertilizer application scenarios. Such work could potentially help national fertilizer subsidy programmes be more targeted and impactful, like the ambitious Ethiopia’s Fertilizer Blending initiative which distributes up to 250,000 Mt fertilizer annually. Initial calculations showed that optimizing DAP (diammonium phosphate) and urea application, the profitability per hectare could improve by 14% in average compared to the current fertilizer recommendations.

Such an approach could generate farm-specific advice at scale and boost farmers’ incomes. It could also provide insights on many different issues, like estimating a market demand for a new fertilizer blend, or the estimated quantity of additional fertilizer required to bring about a targeted maize yield increase. Future extensions of the framework may incorporate varietal differences in nutrient management responses, and thus enable seed companies to use the framework to predict where a new maize hybrid would perform best. Similarly, crop breeders could adapt this ex ante assessment tool to weigh the pros and cons of a specific trait and the potential impact for farmers.

The TAMASA team plans to publish the code and user-friendly interface of this new geospatial assessment tool later this year.

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