This thesis introduces a weather-driven bio-economic optimization model for agricultural planning and decision-making. The model integrates weather simulations—including precipitation, temperature, relative humidity, and reference evapotranspiration (ETo)—to estimate crop yields using the AquaCrop simulator. These yield estimates are then incorporated into a multi-objective optimization (MOO) model that aims to maximize gross profit and economic water productivity (ET), while minimizing land use. The MOO model's results provide insights into key agricultural planning questions, such as what, where, when, and how much to plant.The findings demonstrate the model's potential to enhance agricultural decision-making by offering optimized crop combinations that improve both economic returns and land use efficiency. This research contributes to the development of a dynamic agricultural planning model by integrating weather forecasting, crop simulation, and multi-objective optimization.
Author Keywords: AquaCrop, Artificial neural network, Markov chains, Multi-objective optimization, Reference evapotranspiration, Stochastic differential equation