Agricultural Water Management· 2026Q1
Modeling long-term and interannual crop area changes: Integrating socioeconomic and hydrometeorological factors in the Indus Basin
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- Q1SCImago
- 2026year
Short summary
A System Dynamics (SD) model accurately captured historical rice crop area (CA) changes in Pakistan's Punjab region (R 2 =0.95, NRMSE=9.8%), outperforming Multiple Linear Regression (MLR) by integrating farmer income feedback and water stress limitations.
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Key points
- A System Dynamics (SD) model accurately captured historical rice crop area changes in Punjab, Pakistan (R 2 =0.95, NRMSE=9.8%).
- The SD model integrates farmer income feedback and water stress limitations, outperforming MLR.
- Crop area changes are strongly influenced by lagged relationships with crop price, yield, and river water availability.
- Future projections indicate a 35-50% increase in crop area and a 45-60% rise in groundwater withdrawals by 2060.
AI-generated from the title and abstract; the full text is not read.
Abstract
Water management is critical for policymakers in agriculture-dominated regions, where water demand and withdrawals change in response to variations in crop area, such as in the Indus River Basin (IRB). However, the mechanisms driving long-term and interannual crop area changes, particularly farmers’ responses to socio-economic and hydro-meteorological factors, such as economic incentives and water availability, remain insufficiently explored. In this study, rice crop area (hereafter CA) dynamics in the IRB, with a focus on Punjab, Pakistan, are analyzed to identify drivers of both long-term trends and interannual variability and to incorporate these mechanisms into a modeling framework. Using observational socio-economic and hydro-meteorological data for 1950–2020, the analysis reveals that CA changes are strongly influenced by lagged relationships with crop price, yield, and river water availability. These insights guided the development of two modeling approaches, Multiple Linear Regression (MLR) and System Dynamics (SD). While MLR captured some CA variations, it failed to reproduce observed CA dynamics. In contrast, the SD model, integrating feedback of farmer income on CA expansion and the balancing feedback of water stress limiting CA growth, effectively captured historical CA changes (R 2 =0.95, NRMSE=9.8%) and significantly outperformed other models in capturing both long-term trends and interannual variability. Future projections indicate a 35–50% increase in CA and a 45–60% rise in groundwater withdrawals by 2060, highlighting an increasingly unsustainable reliance on groundwater. By incorporating interannual crop area variability into assessments of water withdrawals, the framework provides transferable insights for agricultural water management and groundwater sustainability in irrigation-dominated basins.
The authors' abstract, as published at the source. Agricultural Water Management, 2026 · DOI ↗
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Field: Ecology, Evolution, Behavior and Systematics
Ecology, Evolution, Behavior and SystematicsAgricultural and Biological Sciences