Abstract:
CONTEXT AND BACKGROUND: Contract farming has been promoted as a more 'inclusive business model' in which local smallholder farmers can participate in and benefit from the wider benefits of investments in rural areas such as
infrastructure development (power supply, roads, water supply), spillovers from increased incomes and, in some cases, mandatory development of education and health facilities. Contract farming models could have a positive impact on agricultural development and innovation in developing countries. Contract farming creates a system that links smallholder farmers with domestic and
international buyers. Contract farming could secure existing local land rights of smallholder farmers by continuing farming on their land, promoting investments by investors and fostering the commercialization of smallholder farmers. Contract farming could enhance local food security. However, contract farming models do not always have a positive impact. Sometimes contractors
make a profit without supporting or, sometimes, exploiting contracted smallholders.
GOAL AND OBJECTIVES: The primary focus of this paper is to analyze the impact of contract farming on household food security. The paper will address the following research questions: What are the determinant factors that affect participation in contract farming? and What is the impact of contract farming on household food security in Kenya and Madagascar?
METHODOLOGY: This study used three internationally recognized food security indicators to measure the food security status of the household: household dietary diversity score (HDDS), food consumption score (FCS) and the months of adequate household food provisioning (MAHFP). This study used an endogenous switching regression (ESR) model to estimate the impact of contract farming on
household food security. The research is purely empirical research is based on observation and measurement of phenomena, as directly experienced by the researcher
RESULTS: AI can be effectively applied by Informal Cross-Border Traders (ICBT) to enhance their businesses and enhance competitiveness. There are several AI applications accessible to ICBT within their operational context. Although the adoption and utilization of AI in Africa are still in their infancy, there is considerable promise for the future. Africans must address the challenges hindering the adoption and utilization of AI, as technology is advancing rapidly, and opportunities await those who embrace it.
