HarvestStat Africa - Harmonized Subnational Crop Statistics for Sub-Saharan Africa

Abstract

Sub-Saharan Africa faces severe agricultural data scarcity amidst high food insecurity and a large agricultural yield gap, making crop production data crucial for understanding and enhancing food systems. To address this gap, HarvestStat Africa presents the largest compilation of open-access subnational crop statistics and time-series across Sub-Saharan Africa. Based on agricultural statistics collated by USAID's Famine Early Warning Systems Network, the subnational crop statistics are standardized and calibrated across changing administrative units to produce consistent and continuous time-series. The dataset includes 574,204 records, primarily spanning from 1980 to 2022, detailing quantity produced, harvested areas, and yields for 33 countries and 94 crop types, including key cereals in Sub-Saharan Africa such as wheat, maize, rice, sorghum, barley, millet, and fonio. This new dataset enhances our understanding of how climate variability and change influence agricultural production, supports subnational food system analysis, and aids in operational yield forecasting. As an open-source resource, it establishes a precedent for sharing subnational crop statistics to inform decision-making and modeling efforts.

Fig. 1

Flowchart illustrates the sequential workflow for data collection, processing, and output within the FEWS NET and HarvestStat Africa frameworks.

Fig. 2

Quality control flags for Dedza, Malawi flagging an outlier value for cotton (a) and low variance values for cassava (b).

Fig. 3

An illustrative example of changes in administrative boundaries in the provinces of Burkina Faso from pre-2001 (left panels; blue lines) to post-2001 (right panels; red lines). The background color represents a crop mask, with green-to-blue colors indicating cropland areas. Top panels (a and b) illustrate Case A, where a single district (E1) splits into two districts (E1 and E2), maintaining equivalent boundary areas. Bottom panels (c and d) illustrate Case B, where three districts (F1, F2, and F3) are reorganized into four districts (F1, F2, F3, and F4), resulting in changes to their boundary areas.

Fig. 4

(a) Administrative levels, (b) number of recorded years, and (c) first year covered by processed crop statistics in HarvestStat Africa v1.0. The data for (b,c) encompass all available crop types.

Fig. 5

(a) Number of years with data for records of quantity produced and (b) correlation coefficient of quantity produced at the national scale crop productions between the HarvestStat Africa v1.0 and FAOSTAT dataset for seven grain types. The record years do not necessarily represent consecutive years. The correlation was calculated when at least 5 years of data were available.

Fig. 6

Comparison of (a) the EarthStat dataset, (c) GDHY v1.3 dataset, and (e) HarvestStat Africa v1.0 dataset for maize yields around the year 2000 (1998–2002) (a,c,e) and in the change of maize yields from 2000 (1998–2002) to 2005 (2003–2007) for each dataset (b,d,f).