Optimal cropping pattern based on short-term streamflow forecasts to improve agricultural economic benefits and crop productivity under uncertainty conditions

Abstract

ABSTRACT Accelerated urbanization has led to diminished land for agricultural activities. Riverine ecosystems play an important role in allocating fertile lands to support agricultural activities. A substantial component of the uncertainty in agricultural productivity comes from seasonal variations linked to inter-annual climate fluctuations. Therefore, understanding the complicated phenomena of streamflow in a riverine environment is important for agricultural and water resources decision making. The present work focuses on forecasting monthly to seasonal streamflow using persistence flow, historical analogues, and artificial neural network approaches. Based on these forecasts, decisions on cropping patterns were made by developing an optimization framework using the constrained linear programming and inexact multiobjective fuzzy linear programming approaches. The proposed fuzzy programming approach was found to be beneficial in producing fair and stable solutions under uncertainty. The findings reveal that integrating forecasting and optimization knowledge could aid in precisely evaluating ecosystem services and meeting rising food demand.