Photo courtesy of Neil Palmer, CIAT

Where in sub-Saharan Africa is the difference between actual and potential yield of maize most striking? We can answer this question pixel by pixel, region by region, but for the purpose of this brief analysis, let’s talk countries.

Yield potential is defined as the yield of a crop cultivar when grown in environments to which it is adapted, with nutrients and water non-limiting, and pests, diseases, weeds, lodging, and other stresses effectively controlled (Evans, 1993). Africa’s maize yields have been lagging behind the world average for decades (van Dijk et al, 2012). The potential to increase yields, however, is high. But which countries are hot spots for intervention?

In this analysis, maize actual and rainfed potential yields at the pixel level (~100km2) were normalized across sub-Saharan Africa and aggregated by country. In the graph above, X and Y axes show where each country settles relative to other countries in terms of actual and potential maize yields. Each quadrant (Q) approximately characterizes a group of countries with varying levels of maize intensification. For example, South Africa’s (ZAF) position in Q4 (counting quadrants counter-clockwise) indicates a relatively high level of maize productivity despite a low level of inherent potential. This could be due to adequate investments in the maize sector so that farmers are able to implement best management practices and meet their potential. By contrast, Mozambique’s (MOZ) position in Q2 reveals a relatively large gap between actual and potential yields and a possible hot spot for maize-based interventions. This type of analysis may provide country-level insights as to where in sub-Saharan Africa you might expect a bigger return from investments in agriculture.

Method: Pixel-level actual yield was retrieved from HarvestChoice’s Spatial Production Allocation Model (SPAM) which provides spatially-disaggregated sub-national crop production statistics at 100km2 spatial resolution. Maize yield potential was estimated using CERES-Maize model of DSSAT Cropping System Model v4.5.1.023, configured to simulate nutrient-unlimited maize yield at the same spatial resolution as SPAM.

ISO3 codes and country names: AGO: Angola, BDI: Burundi, BEN: Benin, BFA: Burkina Faso, BWA: Botswana, CAF: Central African Republic, CIV: Cote d'Ivoire, CMR: Cameroon, COD: Congo, DRC, COG: Congo, DJI: Djibouti, ERI: Eritrea, ETH: Ethiopia, GAB: Gabon, GHA: Ghana, GIN: Guinea, GMB: The Gambia, GNB: Guinea-Bissau, KEN: Kenya, LBR: Liberia, LSO: Lesotho, MDG: Madagascar, MLI: Mali, MOZ: Mozambique, MRT: Mauritania, MWI: Malawi, NAM: Namibia, NER: Niger, NGA: Nigeria, RWA: Rwanda, SDN: Sudan, SEN: Senegal, SLE: Sierra Leone, SOM: Somalia, SWZ: Swaziland, TCD: Chad, TGO: Togo, TZA: Tanzania, UGA: Uganda, ZAF: South Africa, ZMB: Zambia, ZWE: Zimbabwe

Citation

HarvestChoice, 2013. "Minding the Yield Gap in Africa: A Country-Level Analysis." International Food Policy Research Institute, Washington, DC., and University of Minnesota, St. Paul, MN. Available online at http://harvestchoice.org/node/8781.

May 23, 2013