Lu et al. (2025) Deficit irrigation alleviates the increase in soil salinity content in saline-alkali regions of China and improves irrigation water productivity: A meta-analysis

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-10-06
• Authors: Dehao Lu, Liu Liu, Yanling Bai, Qiang An, Yongming Cheng, Guanhua Huang
• DOI: 10.1016/j.agwat.2025.109872

Research Groups

• State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University
• Center for Agricultural Water Research in China, China Agricultural University
• College of Water Resources and Civil Engineering, China Agricultural University

Short Summary

This meta-analysis investigated the effects of deficit irrigation (DEI) on soil salinity content, crop yield, and irrigation water productivity (IWP) in saline-alkali regions of China, finding that DEI significantly increased IWP by 29.77% while slightly increasing soil salinity by 2.52% and decreasing crop yield by 6.77%. The study identified optimal DEI strategies based on irrigation practices, climatic conditions, and soil properties using machine learning.

Objective

• To assess the impact of deficit irrigation (DEI) on soil salinity content, crop yield, and irrigation water productivity (IWP) in saline-alkali regions of China.
• To identify the key factors (irrigation practices, climatic conditions, soil conditions, and crop types) affecting soil salinity content, crop yield, and IWP under DEI.
• To explore the proportion of the impact of environmental factors using random forest models and correlation analysis to optimize DEI management measures.

Study Configuration

• Spatial Scale: Saline-alkali regions of China.
• Temporal Scale: Literature published from 1990 to 2024.

Methodology and Data

• Models used: Meta-analysis (random-effect model using MetaWin2.1), machine learning (Random Forest model using ‘RandomForestRegressor’ package in Python).
• Data sources: 1108 comparisons from 39 peer-reviewed articles collected from Web of Science, China National Knowledge Internet, Google Scholar, and Science Direct. Data included soil salinity content (648 pairs), crop yield (230 pairs), and irrigation water productivity (230 pairs).

Main Results

• Overall, deficit irrigation (DEI) increased soil salinity content by 2.52 %, decreased crop yield by 6.77 %, and significantly increased irrigation water productivity (IWP) by 29.77 % compared to full irrigation (FUI).
• Soil salinity content under DEI increased with increasing vertical distance from the emitter, showing increases of 5.34 % (0–20 cm), 7.80 % (20–40 cm), and 5.19 % (40–60 cm).
• An irrigation proportion (IP) of 0.85–0.9 crop evapotranspiration (ETc) resulted in the least impact on soil salinity content increase (1.45 %) and is more likely to achieve a trade-off between soil salinity, yield, and IWP.
• Salinity of irrigation water (SIW) between 0.7–2 dS/m significantly increased soil salinity content by 5.90 %, but maintaining SIW between 0.7–10 dS/m minimized the yield gap and ensured high IWP. SIW was identified as the most important factor affecting soil salinity content and IWP.
• Flood irrigation (FLI) and border irrigation (BI) effectively reduced soil salinity content by 38.18 % and 2.59 %, respectively. Shallow subsurface drip irrigation (SSDI) maximized IWP at 53.25 %.
• DEI is most appropriate for regions with mean annual precipitation (MAP) exceeding 200 mm and soil bulk density (BD) of 1.45–1.55 g/cm³.
• Wheat showed a non-significant reduction in soil salinity content (2.87 %) under DEI, while maize, cotton, and oil sunflower showed increases. Oil sunflower exhibited the highest IWP increase (67.35 %).

Contributions

• Provides a comprehensive quantitative analysis of DEI effects on soil salinity, crop yield, and IWP in saline-alkali regions of China, addressing a previous research gap.
• Identifies critical agronomic practices (irrigation proportion, salinity of irrigation water, irrigation method) and environmental factors (climatic and soil conditions) that influence the outcomes of DEI.
• Offers scientific recommendations for optimizing DEI strategies, such as specific irrigation proportions and water salinity levels, and suggests combining flood and drip irrigation for sustainable agriculture in saline-alkali regions.
• Utilizes machine learning (random forest model) to determine the relative importance of influencing factors, enhancing the robustness of the findings.

Funding

• National Natural Science Foundation of China (Grant number: 52379054 and 52079138)
• 2115 Talent Development Program of China Agricultural University (Grant Number: 00109019)

Citation

@article{Lu2025Deficit,
  author = {Lu, Dehao and Liu, Liu and Bai, Yanling and An, Qiang and Cheng, Yongming and Huang, Guanhua},
  title = {Deficit irrigation alleviates the increase in soil salinity content in saline-alkali regions of China and improves irrigation water productivity: A meta-analysis},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.109872},
  url = {https://doi.org/10.1016/j.agwat.2025.109872}
}