Abstract:
The economy of many African countries is heavily based on agriculture and, as it is also one of the most vulnerable sectors to climate change. Considering that it provides a main source of income for many households in rural areas in Africa, it is undeniable that negative impacts of climate change will increase their vulnerability. Their vulnerable position is due to their high dependence on natural resources and minimal financial and technical means to cope with the changes. This is also the case for rural communities in Tanzania, a country that - despite experiencing an economic growth of 6-7% over the recent years - is still struggling with a high rural poverty rate. The vast majority thereby depends on rain-fed subsistence agriculture, which is characterized by the lack of modern farming technologies and low productivity. While these factors put a large part of the labor force at risk, especially in the due to the inability to adapt to climate change, it also prevents the country from reaching its true agricultural potential. Increasing the farmer’s capacities could change Tanzania's output and more importantly, resolve Tanzania’s poverty and malnutrition in rural areas and securing its future. Farmer-level transformation should therefore aim to increase yields and decrease post-harvest failures through promoting cost-effective, productivity-enhancing technologies. A computational agent-based model (MP-MAS) is therefore applied to simulate the agricultural system in the Iringa and Njombe region of Tanzania and analyzes the effect of the adoption of agricultural innovations such as improved maize varieties and fertilizers as a means to increase the food security and prosperity in these rural areas.
