Multi-objective Optimization for Improved Agricultural Water and Nitrogen Management in Selected Regions of Africa

African agriculture is one of the less productive owing to an inefficient use of available fertilizer and water resources using one-tenth of the world average. The largest increase in agricultural output will most likely come from the intensification of the production of existing agricultural land. This will require widespread adoption of sustainable land management practices and a more efficient use of irrigation and fertilization ( http://www.fao.org/docrep/013/i2050e/i2050e.pdf ). The largest increase in agricultural output will most likely come from the intensification of the production of existing agricultural land. Reaching an efficient management of natural resources often leads to a difficult and challenging multi-objective optimization problem, in which finding the trade-off strategy solutions is necessary, each solution being no better or worse than the other. Multi-objective optimization methods provide a systematic approach to search and compare trade-offs and to select alternatives that best satisfy the decision-maker’s criteria. In this context, we integrate a biophysical model with a multi-objective evolutionary algorithm (MOEA). The biophysical model adopted is EPIC, a model that allows to predict the effects of candidate modified agricultural management practices of different land use and land management scenarios. The integrated GIS-EPIC multi-objective analysis tool has been applied to identify optimum crop and land management patterns in different African countries, demonstrating the ability to provide trade-off Pareto solutions which simultaneously minimize total nitrate pollution through runoff and leaching, at the same time maximizing the exploitation benefits by choosing the adequate crop, fertilization, and irrigation management sequences. Knowledge of these sets helps the decision-makers to choose optimum alternative patterns of agricultural management specifically adapted to African countries characterized by multiple soil types and different climate and crops. The results prove how this optimization method is powerful and operational, an essential tool for taking management decisions. This methodology would be a valuable tool for policy makers and water managers, providing information about cost-effectiveness of different agricultural practices in African countries.