A Framework for Data Collection and Management to Support the Comprehensive Development of African Agriculture

Abstract

Agriculture is a key source of food and health for humanity. However, the hazardous way this activity is still cared for in some developing countries like the Republic of Benin, with poor quality farming data collection mechanisms, can guarantee neither quality and sustainability of production, nor comprehensiveness and efficiency of agricultural interventions. Such hortcomings ineluctably contribute to the limited success of the Comprehensive African Agriculture Development Program (CAADP) promoted by the African Union and its regional chapters, in most African countries. Based on these preliminary remarks, this paper suggests a systematic and automated monitoring, evaluation, learning, and adaptation (MEL) framework that will further be tested, consolidated, validated, and promoted, first in the Republic of Benin, and later in other African countries. This framework is made of local, regional, and national geographic information systems (GIS) which will further serve as databases for planning and interventions in the agricultural sector. The proposed GIS superposes lands’ details (sizes, soils’ characteristics, rainfall data, etc.), stakeholders’ details (demographics, crops, livestock, agricultural practices, etc.), and agricultural value chains’ details. Local, regional, and national agricultural value chains’ development platforms will bring stakeholders together, and serve as sources and co-users of the GIS data. Local, regional, and national MEL and GIS management skilled AEAS agents will facilitate systematic data collection and GIS database management. The data collection and treatment tools should allow the use of all kinds of data formats, including voice and image records, given the oral orientation of most agricultural stakeholders. This original idea emerged from observations during national and regional field experiences, and from the literature that revealed the efficiency of automated real-time and quality data for comprehensive and evidence-based decision-making in development interventions.