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Quantifying crop water footprint and spatial heterogeneity using data assimilation
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Jing XUE, Ting BAI, Yali YIN, Jiahui DONG, Jina ZHANG, Shikun SUN*
Transactions of the Chinese Society of Agricultural Engineering | 2026, 42(12) : 177 - 185
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Transactions of the Chinese Society of Agricultural Engineering | 2026, 42(12): 177-185
Soil and Water Engineering
Quantifying crop water footprint and spatial heterogeneity using data assimilation
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Jing XUE, Ting BAI, Yali YIN, Jiahui DONG, Jina ZHANG, Shikun SUN*
Affiliations
  • 1Ministry of Education Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Northwest A & F University, Yangling 712100, China
  • 2Institute of Water-saving Agriculture in Arid Areas, Northwest A & F University, Yangling 712100, China
  • 3College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling 712100, China
Published: 2026-06-30 doi: 10.11975/j.issn.1002-6819.202510141
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Water scarcity has long constrained agricultural sustainability in the Huang-Huai-Hai Plain, a vital grain production base in China. Regional water resources can also be regulated to improve water use efficiency in sustainable agriculture. It is often required to precisely assess agricultural water use efficiency. Crop production water footprint can be expected to measure the sustainability and efficiency of water resource utilization during the entire crop growth cycle. This study selected winter wheat as the research subject. Assimilated variables were utilized as remotely sensed leaf area index (LAI) and soil moisture (SM). A quantitative assessment was also developed for winter wheat water footprint, according to dual-variable assimilation of crop models and remote sensing data. Spatial dependency and clustering of winter wheat water footprint were then determined using spatial autocorrelation analysis. Furthermore, winter wheat yield–total water footprint quadrant classification, blue and green water resource dependency, and groundwater extraction proportion were integrated to clarify regional water source dependence and formulate differentiated water footprint management strategies. The results indicated that: 1) Data assimilation significantly improved the accuracy of the WOFOST model to simulate the winter wheat yield. There was strong consistency between the simulation and the statistical yield after data assimilation, with an R2 increased to 0.98 and an RMSE reduced to 67.68 kg/hm2. The accuracy significantly also improved after simulation, compared with an R2 of 0.42 and an RMSE of 566.78 kg/hm2; 2) The average green, blue, and total water footprint of winter wheat were 0.35, 0.30, and 0.65 m3/kg, respectively, after data assimilation. The green and the total water footprint exhibited a spatial distribution pattern higher in the south and lower in the north, while the blue water footprint showed a pattern higher in the north and lower in the south; 3) Spatial autocorrelation of winter wheat green and blue water footprint was stronger than that of the total water footprint. The blue, green, and total water footprint of winter wheat exhibited significant spatial clustering, primarily characterized by high-high and low-low clustering; 4) The northern region should prioritize stable production, water saving regulation, and reduction of groundwater extraction, whereas the southern region should focus on improving precipitation use efficiency. This finding can provide scientific support and decision-making basis for the refined and differentiated water resource strategies in typical water-scarce agricultural regions, such as the Huang-Huai-Hai Plain. A solid theoretical foundation and technical framework can help allocate agricultural water resources at the regional scale.

winter wheat  /  water footprint  /  data assimilation  /  spatial heterogeneity  /  Huang-Huai-Hai Plain
Jing XUE, Ting BAI, Yali YIN, Jiahui DONG, Jina ZHANG, Shikun SUN. Quantifying crop water footprint and spatial heterogeneity using data assimilation[J]. Transactions of the Chinese Society of Agricultural Engineering, 2026 , 42 (12) : 177 -185 . DOI: 10.11975/j.issn.1002-6819.202510141
Year 2026 volume 42 Issue 12
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doi: 10.11975/j.issn.1002-6819.202510141
  • Receive Date:2025-10-20
  • Online Date:2026-08-20
  • Published:2026-06-30
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History
  • Received:2025-10-20
  • Revised:2026-03-20
Affiliations
    1Ministry of Education Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Northwest A & F University, Yangling 712100, China
    2Institute of Water-saving Agriculture in Arid Areas, Northwest A & F University, Yangling 712100, China
    3College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling 712100, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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