Su et al. (2025) Unravelling the hidden drivers of crop sensitivity to precipitation in the arid and semi-arid regions of Northwest China

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-10-10
• Authors: Zhan Su, Zhouchang Yu, Zhijia Gu, Zhao Pei, Jingjing Peng
• DOI: 10.1016/j.agwat.2025.109866

Research Groups

• Key Laboratory of Crop Harvesting Equipment Technology of Zhejiang Province, Jinhua Vocational and Technical University, Jinhua 321017, China
• Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed, College of Forestry, Jiangxi Agricultural University, Nanchang, 330045, China
• College of Engineering, Huazhong Agricultural University, Wuhan 430070, China

Short Summary

This study investigates the spatiotemporal sensitivity of maize and wheat to precipitation across the Loess Plateau from 2001 to 2023, revealing how aridity, soil texture, and elevated atmospheric CO₂ modulate crop responses and providing insights for adaptive agricultural management.

Objective

• To quantify how precipitation sensitivity varies across space and time during the wheat and maize growing seasons.
• To identify underlying climatic and physiological drivers shaping this sensitivity.
• To evaluate the extent to which anthropogenic factors, particularly elevated CO₂ concentrations, may have altered vegetation–precipitation coupling in agricultural landscapes.

Study Configuration

• Spatial Scale: Loess Plateau, north-central China (approximately 640,000 km²).
• Temporal Scale: 2001 to 2023 (23 years).

Methodology and Data

• Models used:
  • Dynamic Linear Models (DLM) (univariate and multivariate first-order autoregressive models)
  • MsTMIP terrestrial biosphere model ensemble (SG1, SG2, SG3, BG1 scenarios)
  • Conceptual hydrological model (simplified ecohydrological model by Porporato et al. (2001) modified by Good et al. (2017), modified Penman-Monteith equation)
• Data sources:
  • Remote sensing: Moderate Resolution Imaging Spectroradiometer (MODIS) for Normalized Difference Vegetation Index (NDVI) (16-day, 250 m spatial resolution).
  • Precipitation: Global Precipitation Measurement (GPM) mission (monthly, 0.1° (~10 km) spatial resolution).
  • Climate: Climatic Research Unit gridded Time Series dataset (CRU TS v4.09) for temperature and cloud cover (monthly, 0.5° spatial resolution). CRU-NCEP v6 for MsTMIP climate forcing.
  • Soil: Harmonized World Soil Database (HWSD) for soil texture, soil organic carbon, and total soil nitrogen (~1 km spatial resolution).
  • Atmospheric CO₂: National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory records. Enhanced GlobalView dataset for MsTMIP CO₂ concentration.
  • Land use: Hurtt et al. (2011) combined with SYNMAP for MsTMIP land-use history.
  • Nitrogen deposition: Enhanced Dentener dataset for MsTMIP.
  • Irrigation distribution: FAO AQUASTAT; Zhang et al. (2022); Zhang et al. (2024).

Main Results

• Maize exhibits a sharp unimodal sensitivity peak near an aridity index of 0.4, with maximum Normalized Difference Vegetation Index (NDVI) responses reaching 0.85 m⁻¹ H₂O, reflecting strong responsiveness to moderate moisture availability.
• Wheat displays a broader, less intense sensitivity peak shifted toward higher aridity (~0.7), with maximum responses of ~0.55 m⁻¹ H₂O, indicating greater adaptation to drier conditions.
• Soil texture further modulates these responses, with sandy soils amplifying Leaf Area Index (LAI) sensitivity due to their lower water retention capacity.
• Elevated atmospheric CO₂ increased water-use efficiency and enhanced LAI sensitivity (~0.15 for maize) under moderate aridity, though this effect weakened under higher aridity levels.
• Temporal analyses from 2001 to 2023 revealed declining trends in both NDVI and precipitation for both crops, with a sharper decline in wheat (NDVI: –0.401 per year; precipitation: –0.17 per year) compared to maize (NDVI: –0.35 per year; precipitation: –0.15 per year), highlighting wheat's greater vulnerability to water stress.
• Climate (temperature and radiation) positively influences sensitivity trends (maize: 0.7 % per year; wheat: 0.6 % per year), while CO₂ exerts a negative influence (maize: -0.4 % per year; wheat: -0.35 % per year), partially offsetting climate effects.

Contributions

• Provides a high-resolution, crop-season-specific assessment of precipitation sensitivity for wheat and maize on the Loess Plateau, addressing a gap in generalized annual-scale analyses.
• Integrates remote sensing data (NDVI) with dynamic linear modeling (DLM) to capture the dynamic spatiotemporal evolution of vegetation sensitivity, moving beyond static correlation frameworks.
• Mechanistically disentangles the combined influence of climatic drivers (aridity), soil properties (texture), and physiological responses (elevated CO₂) on crop sensitivity.
• Offers critical insights for adaptive management strategies, agricultural planning, and drought adaptation in arid and semi-arid regions under future climate change.

Funding

• National Natural Science Foundation of China (Grant No. 52275253)
• Key Laboratory of Crop Harvesting Equipment and Technology of Zhejiang Province (Grant 2021KY02)

Citation

@article{Su2025Unravelling,
  author = {Su, Zhan and Yu, Zhouchang and Gu, Zhijia and Pei, Zhao and Peng, Jingjing},
  title = {Unravelling the hidden drivers of crop sensitivity to precipitation in the arid and semi-arid regions of Northwest China},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.109866},
  url = {https://doi.org/10.1016/j.agwat.2025.109866}
}