Cao et al. (2026) A multi-objective optimization for coordinating water-land resources considering crop suitability and drought effects

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-01-12
• Authors: Kaihua Cao, Xiao Liu, Yijia Wang, Zhaoqiang Zhou, Mo Li
• DOI: 10.1016/j.agwat.2026.110146

Research Groups

• School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, China
• National Key Laboratory of Smart Farm Technology and System, Harbin, Heilongjiang, China
• Heilongjiang Province Key Laboratory of Smart Water Network, Northeast Agricultural University, Harbin, Heilongjiang, China
• International Cooperation Joint Laboratory of Health in Cold Region Black Soil Habitat of the Ministry of Education, Harbin, Heilongjiang, China
• Key Laboratory of Effective Utilization of Agricultural Water Resources of Ministry of Agriculture, Northeast Agricultural University, Harbin, Heilongjiang, China
• China Institute of Water Resources and Hydropower Research, Beijing, China

Short Summary

This study proposes a spatial multi-objective optimization method for land and water resources, integrating regional crop suitability and drought effects to balance economic benefits and blue water utilization. Applied in the Sanjiang Plain, the method optimized 4.5 million 100 m x 100 m grid cells, increasing water productivity for key crops and reducing economic risk from drought by 14% compared to traditional models.

Objective

• To quantitatively characterize the yield reduction effects of drought on crops at the regional grid scale and integrate them into water and land resource optimization models.
• To couple crop suitability assessment with cellular automata models to achieve synergistic optimization of crop spatial layout and irrigation water allocation.
• To evaluate the effectiveness of the proposed spatially refined water and land resource optimization method in enhancing water use efficiency and reducing agricultural economic risks under drought scenarios.

Study Configuration

• Spatial Scale: Regional scale (Sanjiang Plain, China), with optimization performed on approximately 4.5 million grid cells, each 100 meters x 100 meters.
• Temporal Scale: Single growing season (April to September) within a single year for optimization. Drought scenarios were developed using precipitation data from 1964 to 2020, with 2007 selected as a representative dry year.

Methodology and Data

• Models used:
  • Multi-objective collaborative optimization method
  • Multi-Scale Drought Evaluation Index (MSEDI)
  • Cellular Automaton (CA) model
  • Regional grid-based multi-objective planning model
  • Analytic Hierarchy Process (AHP)
  • Entropy Weight Method (EWM)
  • Minimum Deviation Method based on Principal Component Analysis (PCA)
  • Restrictive Factor Method (for crop suitability evaluation)
  • Jensen model (for crop yield calculation)
  • FAO-56 method (for crop evapotranspiration)
  • Genetic Algorithm (for model solution)
• Data sources:
  • Meteorological data: China Meteorological Administration Center
  • Soil data: China Soil Data Center
  • Digital Elevation Model (DEM) data: Geospatial Data Cloud
  • Socio-economic information, crop yields, water supply relationships, and water resource availability (2015–2020): Heilongjiang Statistical Yearbook, Jiamusi Statistical Yearbook, and field surveys.
  • Precipitation data (1964–2020) for drought classification.

Main Results

• A Multi-Scale Drought Evaluation Index (MSEDI) was developed, showing a strong relationship with crop yield reduction (R² = 0.8). The SMCI (Soil Moisture Condition Index) had the highest weight in the MSEDI (0.316), indicating its critical role.
• Crop suitability evaluation classified the Sanjiang Plain into: Highly Suitable (23%), Suitable (42%), Moderately Suitable (29%), and Not Suitable (4%).
• The optimized planting structure resulted in a reduction of rice area by 73,437 hectares (1.63%) and corn area by 63,873 hectares (1.42%), while soybean area increased by 137,313 hectares (3.05%). Approximately 65% of the crop spatial structure was adjusted.
• Water productivity increased by 18.3% for rice, 16.9% for corn, and 8.8% for soybeans.
• The optimized irrigation strategy followed a "low in the early stage, high in the mid-stage, and reduced in the late stage" pattern, aligning with crop water demand.
• The ratio of surface water to groundwater usage improved from 63%:37% to 72%:28%, reducing groundwater over-extraction.
• Under drought scenarios, the model increased irrigation inputs during critical growth stages (e.g., rice jointing by 5.2%, corn grain-filling by 14.4%).
• The model, when considering drought-induced yield reduction, increased economic benefits by 29.32 billion yuan and reduced economic risks from drought-related yield loss by 14% compared to traditional models.

Contributions

• First-ever integration of grid-scale crop suitability assessment, multi-scale drought-induced yield reduction quantification, and cellular automata spatial evolution processes into a multi-objective collaborative optimization framework for water and land resources.
• Explicitly accounts for drought risk to achieve refined synergistic optimization of crop spatial layout adjustment and irrigation water allocation.
• Provides a scientific methodology and decision-making support for refined management and sustainable allocation of agricultural water and land resources in drought-prone regions.

Funding

• National Key Research and Development Program of China (2024YFD1502000)
• National Natural Science Foundation of China (52479035, 52222902)

Citation

@article{Cao2026multiobjective,
  author = {Cao, Kaihua and Liu, Xiao and Wang, Yijia and Zhou, Zhaoqiang and Li, Mo},
  title = {A multi-objective optimization for coordinating water-land resources considering crop suitability and drought effects},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110146},
  url = {https://doi.org/10.1016/j.agwat.2026.110146}
}