Stochastic optimization to design climate resilient agricultural system

Climate change driven fluctuations in rainfall are expected to affect the agricultural sector. We have previously done work on developing an optimization model to decide the optimal cropping pattern to ensure that the local/regional water is not overexploited. The model considers the expected rainfall and recommends the preferred cropping patterns such that certain objectives (farmer profit, cereal production etc.) are met. However, the model currently is deterministic and considers only one representative rainfall pattern. 

The goal of this project will be to extend the model to consider uncertainties associated with the rainfall. Stochastic modeling will be done to capture rainfall fluctuations and stochastic optimization will be performed. The model will also be extended to consider multiple-year time horizons, resulting in a multi-period optimization problem. We are currently working on Maharashtra as the case study.

The work is completely computational in nature. The student working on this project will need to take appropriate coursework related to optimization theory.

Currently, no AI/ML component is envisioned for this project.