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

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journal contribution
posted on 24.11.2022, 11:40 authored by Gaurav Talukdar, Rajib Kumar Bhattacharjya, Arup Kumar Sarma

Accelerated urbanization has led to diminished mainland for agricultural activities. Riverine ecosystem plays 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 plays a significant role in 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, the decision on cropping pattern was 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 study’s findings revealed that integrating forecasting and optimization knowledge could aid in precisely evaluating ecosystem service and fulfilling rising food demand.


The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.