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

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...

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Vydáno v:Hydrological sciences journal Ročník 68; číslo 2; s. 246 - 260
Hlavní autoři: Talukdar, Gaurav, Bhattacharjya, Rajib Kumar, Sarma, Arup Kumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 25.01.2023
Taylor & Francis Ltd
Témata:
Agricultural ecosystems
Agricultural production
agricultural productivity
agriculture
Aquatic ecosystems
artificial neural network
Artificial neural networks
climate
Crop production
Cropping systems
Decision making
Economic benefits
Ecosystem services
ecosystems
Fertility
flow forecasts
Forecasting
historical analogues
inexact multiobjective optimization
Linear programming
Neural networks
Optimization
Productivity
riparian areas
Seasonal variation
Seasonal variations
Stream discharge
Stream flow
Streamflow forecasting
Uncertainty
Urbanization
Water resources
ISSN:0262-6667, 2150-3435, 2150-3435
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Shrnutí: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.
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ISSN:0262-6667
2150-3435
2150-3435
DOI:10.1080/02626667.2022.2151914