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Artificial Intelligence in Farm Management in Africa

Proper farm management is essential to maximize the socioeconomic welfare of farm families, including money income, subsistence food, crop-produced consumption goods and factors of production, and benefits such as education opportunities and healthcare. It encompasses the range of activities necessary to both maintain and optimize operations of the farm. Farm management activities involve business planning, government regulation adherence, crop insurance, employee management, and recordkeeping. As climate change worsens, tighter control over farming practices is basically a necessity to ensure adequate yields and substantial profits.

The most food-insecure areas of the world are expected to be most affected by the increased temperatures, variable precipitation and more intense and extreme weather events of climate change. African food security in particular faces unprecedented challenges in the face of the climate crisis. African agriculture will also be one of the hardest hit sectors with expected financial effects including local market destabilization, inhibited economic growth, and greater risk for investors. The majority of African countries depend on the agriculture sector as a large share of their GDP and 60% of Sub-Saharan Africa’s labor force is employed within it; thus, resilience to climate effects in the region is crucial. Precise and appropriate farm management tools and practices are imperative in building this resilience. The emergence of artificial intelligence in this field can assist in achieving this goal.


Farm management software utilizing AI technology can track precise data that optimizes farm activities. Machine learning and the general tracking of mass amounts of farm data will improve cost efficiency by using data points intelligently to increase productivity and quality of outputs. Microsoft and the International Crops Research Institute for Semi-Arid Tropics developed an AI application dedicated to identifying the best time for farmers to sow their crops. It was tested in Andhra Pradesh, India and resulted in up to a 30% increase in crop yields without an increase in capital expenditure.


Across the board, material such as pesticides and fertilizers will be used more efficiently, reducing environmental impact and increased financial cost due to overuse. Pests and diseases will be more readily and easily identified to ensure that crop success rates and yields. AI-interpreted data on soil, temperature, precipitation, and other climatic factors can provide farmers with better insights to make farm management decisions that are profit-optimizing.


Artificial intelligence continues to demonstrate increasing benefits in the African agricultural sector, across various fields. In our next blog post we’ll discuss the importance of AI in agricultural insurance and the future of technology in agricultural risk management.


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