Fertilizer and grain prices constrain food production in sub-Saharan Africa.

Crop yields across sub-Saharan Africa are much lower than what is attainable given the environmental conditions and available technologies. Closing this 'ecological yield gap' is considered an important food security and rural welfare goal. It is not clear, however, whether it is economically sensible for farmers to substantially increase crop yields. Here we estimate the local yield response of maize to fertilizer across sub-Saharan Africa with an empirical machine-learning model based on 12,081 trial observations and with a mechanistic model. We show that the average 'economic yield gap'-the difference between current yield and profit-maximizing yield-is about one-quarter of the ecological yield gap. Furthermore, although maize yields could be profitably doubled, the economic incentives to do so may be weak. Our findings suggest that agricultural intensification in sub-Saharan Africa could be supported by complementary agronomic approaches to improve soil fertility, lowering the fertilizer cost, and by spatial targeting of fertilizer recommendations.

Global drivers of food system (un)sustainability: A multi-country correlation analysis.

At present, our ability to comprehend the dynamics of food systems and the consequences of their rapid 'transformations' is limited. In this paper, we propose to address this gap by exploring the interactions between the sustainability of food systems and a set of key drivers at the global scale. For this we compile a metric of 12 key drivers of food system from a globally-representative set of low, middle, and high-income countries and analyze the relationships between these drivers and a composite index that integrates the four key dimensions of food system sustainability, namely: food security & nutrition, environment, social, and economic dimensions. The two metrics highlight the important data gap that characterizes national systems' statistics-in particular in relation to transformation, transport, retail and distribution. Spearman correlations and Principal Component Analysis are then used to explore associations between levels of sustainability and drivers. With the exception of one economic driver (trade flows in merchandise and services), the majority of the statistically significant correlations found between food system sustainability and drivers appear to be negative. The fact that most of these negative drivers are closely related to the global demographic transition that is currently affecting the world population highlights the magnitude of the challenges ahead. This analysis is the first one that provides quantitative evidence at the global scale about correlations between the four dimensions of sustainability of our food systems and specific drivers.

Spatial variation in fertilizer prices in Sub-Saharan Africa.

Low crop yields in Sub-Saharan Africa are associated with low fertilizer use. To better understand patterns of, and opportunities for, fertilizer use, location specific fertilizer price data may be relevant. We compiled local market price data for urea fertilizer, a source of inorganic nitrogen, in 1729 locations in eighteen countries in two regions (West and East Africa) from 2010-2018 to understand patterns in the spatial variation in fertilizer prices. The average national price was lowest in Ghana (0.80 USD kg-1), Kenya (0.97 USD kg-1), and Nigeria (0.99 USD kg-1). Urea was most expensive in three landlocked countries (Burundi: 1.51, Uganda: 1.49, and Burkina Faso: 1.49 USD kg-1). Our study uncovers considerable spatial variation in fertilizer prices within African countries. We show that in many countries this variation can be predicted for unsampled locations by fitting models of prices as a function of longitude, latitude, and additional predictor variables that capture aspects of market access, demand and environmental conditions. Predicted within-country urea price variation (as a fraction of the median price) was particularly high in Kenya (0.77-1.12), Nigeria (0.83-1.34), Senegal (0.73-1.40), Tanzania (0.90-1.29) and Uganda (0.93-1.30), but much lower in Burkina Faso (0.96-1.04), Burundi (0.95-1.05), and Togo (0.94-1.05). The correlation coefficient of the country level models was between 0.17 to 0.83 (mean 0.52) and the RMSE varies from 0.005 to 0.188 (mean 0.095). In 10 countries, predictions were at least 25% better than a null-model that assumes no spatial variation. Our work indicates new opportunities for incorporating spatial variation in prices into efforts to understand the profitability of agricultural technologies across rural areas in Sub-Saharan Africa.