Li et al. (2026) Dynamic Bayesian modeling and optimization of agricultural water–energy–food–carbon systems under resource constraints: Evidence from the North China Plain

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-09-01
• Authors: Hanbing Li, Rongqin Zhao, Liangang Xiao, Zhixiang Xie, Weiyi Xu, Wei Shan, Xinyuan Liang, Xiaobin Jin
• DOI: 10.1016/j.agwat.2026.110748

Research Groups

• School of human settlements, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China
• School of Public Policy and Management, China University of Mining and Technology, Xuzhou, 221116, China
• School of Management, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
• College of Public Administration, Nanjing Agricultural University, Nanjing, 210095, China
• School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China

Short Summary

This study developed a Dynamic Bayesian Network–Bayesian Neural Network (DBN–BNN) framework to assess the water–energy–food–carbon (WEFC) system across 492 counties from 2001 to 2020 in the North China Plain. The DBN represented temporal dependencies, while an approximate Bayesian neural network based on Monte Carlo dropout captured nonlinear responses and predictive uncertainty.

Objective

• Quantify the spatiotemporal evolution of county-level WEFC coordination across the North China Plain.
• Identify the dominant drivers and temporally directed dependencies associated with agricultural food production and carbon emissions.
• Develop spatially explicit optimization pathways through perturbation analysis under different water and energy constraints.

Study Configuration

• Spatial Scale: County-level administrative units (492 counties) in the North China Plain, China.
• Temporal Scale: 2001–2020.

Methodology and Data

• Models used: Dynamic Bayesian Network (DBN) and Bayesian Neural Network (BNN).
• Data sources:
  • Agricultural statistical yearbooks
  • Published gridded datasets
  • County-level activity data for carbon emissions calculation

Main Results

• The water- and energy-related indicators in the North China Plain showed distinct temporal trends between 2001 and 2020.
• Total cropland carbon emissions exhibited a rising-then-declining trend, increasing from 1.28 × 10⁹ kg CO2-eq in 2001 to a peak of 1.64 × 10⁹ kg CO2-eq in 2014, before declining to 1.45 × 10⁹ kg CO2-eq by 2020.
• The mean WEFC coordination score showed a rise–decline–recovery pattern, with the highest mean score occurring during 2006–2010 (1.01) and the lowest score recorded during 2002–2006 (0.97).

Contributions

• This study contributes to the WEFC nexus literature by introducing a hybrid probabilistic framework that captures nonlinear dynamics and uncertainty propagation.
• The findings provide fine-scale, policy-relevant insights into adaptive water–energy management.

Funding

• Projects: [Not specified]
• Programs: [Not specified]
• Reference codes: [Not specified]

Citation

@article{Li2026Dynamic,
  author = {Li, Hanbing and Zhao, Rongqin and Xiao, Liangang and Xie, Zhixiang and Xu, Weiyi and Shan, Wei and Liang, Xinyuan and Jin, Xiaobin},
  title = {Dynamic Bayesian modeling and optimization of agricultural water–energy–food–carbon systems under resource constraints: Evidence from the North China Plain},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110748},
  url = {https://doi.org/10.1016/j.agwat.2026.110748}
}