Agriculture is an important economic driver for sustainable development in Africa and employs between 60 and 80 percent of the country’s rural population.
The sector also contributes to food security, foreign exchange earnings and provides raw materials for agro-based industries in the region. For instance, it accounts for about 30.2 percent of
the gross domestic product (GDP) in Kenya, 15.4 percent in Senegal, and 14.0 percent in Zimbabwe (World Bank 2014). Agriculture in most of African countries is largely rain fed, rendering productivity highly sensitive to climate variability and change.
There recently has been an increase in the frequency and intensity of extreme weather events, such as droughts, floods, strong winds and hailstorms, and an associated increase in pests and diseases, limiting the ability of the vulnerable households to produce food crops, especially in the marginal agricultural areas in most of the sub-Saharan Africa (SSA) countries.
Crop productivity in sub-Sahara Africa, including Kenya, Senegal and Zimbabwe is low and below global average levels (World Bank 2015). This is because production is dominated by smallholder farmers who are constrained by limited access to quality inputs and markets, limited access to credit, low use of appropriate production technologies and high food and energy costs. Despite the importance of crop production, and the associated challenges, Kenya, Senegal and Zimbabwe, similar to other countries in SSA, lack reliable and timely agricultural production forecasting systems to support decision-making at the national to household levels
This publication was prepared by Erick Khamala, Consultant for developing guidelines on utilizing the existing remote sensing tools, data, products, portals and methodologies to improve crop production forecasts at the Statistics Division (ESS) of the Food and Agriculture Organization of the United Nations (FAO).
Overall, this document is focused on developing an in-depth understanding of agricultural production systems in Kenya, Senegal and Zimbabwe as an example and provide guidelines on:
- Identifying and recommending reliable crop zones/masks for main staple crops; Characterizing key crop production systems and trends at the subnational level.
- Linking climate variability and change to crop production (acreage and yield) trends and risks;
- Undertaking statistical analysis of field based crop production (ministry of agriculture statistics) and remotely sensed products and crop model outputs with a view to determining the correlation between the freely available remote sensing data and crop production estimates;
- Assessing the strengths and weaknesses of various available independent data and products streams in multistage crop production assessments and forecasts; and
- Recommending best-practices in crop production forecasting based on remotely sensed products and well-targeted field assessments using ministry of agriculture extension officers.