Spatiotemporal patterns of crop water productivity under changing water–salinity conditions in an arid irrigation district of the upper Yellow River basin

Improving agricultural production under tightening water constraints requires a clear understanding of long-term changes in crop water productivity (CWP). This study integrated remote sensing, Mann-Kendall trend analysis, and GeoDetector to characterize the spatiotemporal patterns and trends of yield and CWP for wheat, maize, and sunflower and to examine factors associated with CWP variability in the Hetao Irrigation District (Hetao), the largest irrigation district in the upper Yellow River basin, during 2000–2021. Results showed that wheat yield remained generally stable, while maize and sunflower yields increased, with district-wide slopes of at least 0.1500 and 0.0153 t ha⁻ 1 yr⁻ 1 , respectively. The multi-year mean CWP values were 1.56, 2.36, and 0.97 kg m⁻ 3 for wheat, maize, and sunflower, respectively. Maize CWP increased markedly (slope ≥ 0.0383 kg m⁻ 3 yr⁻ 1 ), reaching 3.44 kg m⁻ 3 in 2021, while sunflower CWP increased from 0.76 to 1.08 kg m⁻ 3 and wheat CWP remained largely stable. GeoDetector analysis showed that annual maximum Normalized Difference Vegetation Index (NDVI max,a ), as an indicator of crop growth status, and Normalized Difference Salinity Index (NDSI), as an indicator of surface salinity conditions, had the highest q values for CWP. Their interaction showed the strongest enhancement effect among the selected factor combinations, with Δ q values of 0.232, 0.222, and 0.194 for wheat, maize, and sunflower, respectively. The explanatory power of NDSI increased significantly for wheat (slope = 0.0009, p < 0.05), whereas the q values of both NDVI max,a and NDSI both decreased significantly for maize and sunflower ( p < 0.05). Sub-district-scale analysis further revealed contrasting crop-specific trends, with wheat CWP decreasing most strongly in Jiefangzha and maize CWP increasing markedly in Wulate. These results reveal crop-specific and spatially heterogeneous responses of CWP to changing water–salinity conditions and support differentiated management of irrigation, salinity, and cropping structure in large arid irrigation districts.

Authors

Institutions

Publication Details

Journal
Agricultural Water Management
Published
2026-09-09
DOI
https://doi.org/10.1016/j.agwat.2026.110758
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatiotemporal patterns of crop water productivity under changing water–salinity conditions in an arid irrigation district of the upper Yellow River basin

Dongyang Ren, Quanzhong Huang, Xu Xu, Xinyi Li et al.
Agricultural Water Management
Remote Sensing in Agriculture
article

Spatiotemporal patterns of crop water productivity under changing water–salinity conditions in an arid irrigation district of the upper Yellow River basin

Dongyang Ren, Quanzhong Huang, Xu Xu, Xinyi Li, Chen Sun
article en

Abstract

Improving agricultural production under tightening water constraints requires a clear understanding of long-term changes in crop water productivity (CWP). This study integrated remote sensing, Mann-Kendall trend analysis, and GeoDetector to characterize the spatiotemporal patterns and trends of yield and CWP for wheat, maize, and sunflower and to examine factors associated with CWP variability in the Hetao Irrigation District (Hetao), the largest irrigation district in the upper Yellow River basin, during 2000–2021. Results showed that wheat yield remained generally stable, while maize and sunflower yields increased, with district-wide slopes of at least 0.1500 and 0.0153 t ha⁻ 1 yr⁻ 1 , respectively. The multi-year mean CWP values were 1.56, 2.36, and 0.97 kg m⁻ 3 for wheat, maize, and sunflower, respectively. Maize CWP increased markedly (slope ≥ 0.0383 kg m⁻ 3 yr⁻ 1 ), reaching 3.44 kg m⁻ 3 in 2021, while sunflower CWP increased from 0.76 to 1.08 kg m⁻ 3 and wheat CWP remained largely stable. GeoDetector analysis showed that annual maximum Normalized Difference Vegetation Index (NDVI max,a ), as an indicator of crop growth status, and Normalized Difference Salinity Index (NDSI), as an indicator of surface salinity conditions, had the highest q values for CWP. Their interaction showed the strongest enhancement effect among the selected factor combinations, with Δ q values of 0.232, 0.222, and 0.194 for wheat, maize, and sunflower, respectively. The explanatory power of NDSI increased significantly for wheat (slope = 0.0009, p < 0.05), whereas the q values of both NDVI max,a and NDSI both decreased significantly for maize and sunflower ( p < 0.05). Sub-district-scale analysis further revealed contrasting crop-specific trends, with wheat CWP decreasing most strongly in Jiefangzha and maize CWP increasing markedly in Wulate. These results reveal crop-specific and spatially heterogeneous responses of CWP to changing water–salinity conditions and support differentiated management of irrigation, salinity, and cropping structure in large arid irrigation districts.

Agricultural Water ManagementVol. 335
Southwest Forestry University (CN), Bay Institute (US), Chinese Academy of Agricultural Sciences (CN), Institute of Environment and Sustainable Development in Agriculture (CN), China Agricultural University (CN)
Zero hunger
Openalex Percentile: Top 10%
Remote Sensing in Agriculture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.