Spatial variation in fertilizer prices in Sub-Saharan Africa

Abstract

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.

Genotypic variation in maize (Zea mays) influences rates of soil organic matter mineralization and gross nitrification

“…In southern Africa, specifically, maize (Zea mays L.) accounts for over 75% of the area under cereal production (FAO, 2021), with yields amongst the lowest in the world (Cairns & Prasanna, 2018) and current climate variability has had a significant impact on recent production (Ray et al, 2019). Restricted availability and use of fertiliser is also a key factor associated with this large yield gap (Cedrez et al, 2020). This gap is largest in female managed plots, with women applying less fertiliser to maize than male managed plots (Burke et al, 2018;Burke & Jayne, 2021).…”

“…This gap is largest in female managed plots, with women applying less fertiliser to maize than male managed plots (Burke et al, 2018;Burke & Jayne, 2021). Ultimately, increasing fertiliser use in southern Africa will require changes in policy, infrastructure and local manufacturing (Cedrez et al, 2020). Technologies such as maize varieties with tolerance to low nitrogen (N) conditions increase yields in this region, but unless higher levels of fertilizer are applied in the long term, they will further deplete soil inorganic N (Pasley et al, 2020), thereby further degrading the soil and threatening food security for future generations in southern Africa.…”

Seasonal, annual, and spatial variation in cereal prices in Sub-Saharan Africa

“…Our measure of annual precipitation (mm) is from WorldClim v2 ( Fick and Hijmans, 2017 ). We used the fitted model to predict maize prices across SSA, following a similar approach that was used to estimate fertilizer prices in SSA ( Bonilla Cedrez et al, 2020 ).…”

“…However, there has not been much empirical research on the broad spatio-temporal structure of market price variation with a view to predicting them at unobserved market locations and moments in time. An exception is Bonilla Cedrez et al (2020) who modeled spatial variation in fertilizer prices in SSA to predict local fertilizer prices at locations for which no empirical data was available.…”

Fertilizer profitability for smallholder maize farmers in Tanzania: A spatially-explicit ex ante analysis

“…Local farm-gate prices for maize in unsampled locations were estimated with a model that captures information on geographic location, as well as pixel-level meteorological and other environmental conditions, and market access characteristics, an approach similar to that described by [ 38 ]. Wholesale market prices for maize were obtained from the 4 th wave of the Tanzania LSMS National Panel Survey 2014–2015 [ 39 ].…”