Improving crop water footprint modelling and mapping towards comprehensive sustainability assessment for agricultural systems

Crop production is the biggest water user and key contributor to anthropogenic greenhouse gas emissions, and environmental degradations globally. Detailed, timely and multi-sourced modelling and mapping of water footprint, i.e., water consumption, for crop production are important precondition for wise and sustainable agricultural water allocations towards the aiming just and safe globe. However, the system boundary, calculation principle, accuracy criteria, and practical implementations of crop water footprint assessment are still in debate.We developed three types of improved crop water footprint modelling approaches for faster updates of gridded datasets for different purposes across river basin, national and global scales. (i) Distinguishing impacts of irrigation techniques on green (soil water) and blue (irrigation) water consumption in large scale (Wang et al., 2023; Yue et al., 2025). (ii) Machine learning modelling of green and blue water footprints for fast scenario analysis considering effects from intensive human activities (Li et al., 2025). (iii) Robust long-term global crop water footprint dataset generation with fewer inputs and shorter time (Liu et al., 2025). In addition, we investigated the feasibility of extending system boundary for crop water footprint estimation in line with the other environmental footprints, capable for monitoring the status of crop production systems in terms of synergies and trade-offs among resources appropriation and environmental impacts (Feng et al., 2022; Sun et al., unpublished).ReferencesFeng, B., Zhuo, L., Mekonnen, M.M., Marston, L., Yang, X., Xu, Z., Liu, Y., Wang, W., Li, Z., Ji, X., Wu, P. (2022) Inputs for staple crop production in China drive burden shifting of water and carbon footprints transgressing part of provincial planetary boundaries, Water Research, 2022, 221: 118803.Li, Z., Sahotra, H., Ahmad, S., Wang, W., Yang, Z., Wu, P., Khan, E., Zhuo, L. (2025). A distributed machine learning model for blue and green water resources with transferable applications in similar climatic zones. Water Resources Research, 61: e2024WR039169.Liu, Y., Zhuo, L., Ji, X., Tian, P., Gao, R., Wu, P. (2025). Accounting and evolution of global spatial explicit blue and green water footprint of maize production with fewer inputs. Water Resources Research, 61: e2024WR037184.Wang, W., Zhuo, L., Ji, X., Yue, Z., Li, Z., Li,M., Zhang, H., Gao, R., Yan, C., Zhang, P., Wu, P. (2023) A gridded dataset of consumptive water footprints, evaporation, transpiration, and associated benchmarks related to crop production in China during 2000–2018. Earth Syst. Sci. Data, 15, 4803–4827.Yue, Z., Zhuo, L., Ji, X., Tian, P., Gao, J., Wang, W., Sun, F., Duan, Y., Wu, P. (2025) Water-saving irrigated area expansion hardly enhances crop yield while saving water under climate scenarios in China. Communications Earth & Environment, 6:295.