Exploring the Sensitivity of Crop Water Footprints to Scale and Estimation Methods in Sustainable Agriculture

Agriculture accounts for the majority of global freshwater consumption, underscoring the importance of accurate crop water footprint (WF) estimation to ensure sustainable water use and secure food supplies. Given agriculture’s extensive reliance on freshwater resources, precise WF assessments are crucial for effective resource management and policy-making. Variations in crop WF estimates arise across fields, basins, regions, and countries due to differing methodologies, agro-climatic conditions, regional agricultural practices, and data availability. Most studies have focused on estimating only green and blue WF, often overlooking the water quality dimension (grey WF) and the distinctions among the individual footprints (green, blue, and grey). These variations are critical for understanding diverse water uses and their impacts on both water quantity and quality. This study assessed the variation in all three WFs and measures to address this variability under different WF estimation approaches and scales. Five significant WF estimation approaches were considered: field crop water requirement (FCWR), field soil water balance (FSWB), regional water balance (RWB), remote sensing (RS), and field measured water balance (FMWB). The WF variation for wheat, rice, maize, potato, and sugarcane was assessed from 2002 to 2023. The analysis suggests that the FSWB approach has less variability in WF estimation than the FCWR approach, with the coefficient of variation (CV) for rice, wheat, and maize under the FSWB approach being 45.25%, 61.16%, and 86.21%, respectively. RWB and RS approaches show higher accuracy and feasibility at regional, basin, and country scales. The FMWB approach is the most accurate at the field scale and exhibits the lowest variation in WF estimate, with CVs of 32.23% for rice and 24.49% for wheat. Additionally, the FMWB approach can be used to calibrate and validate other large-scale approaches due to its limitation of upscaling and advantage of higher accuracy. A case study was also performed to estimate WF using the FCWR approach for sugarcane and wheat crops in the Hindon River basin in India to assess the variability in  WF estimation. The total WF of sugarcane and wheat was 266.93 m³/t and 1506 m³/t, respectively, showing variation as these values are nearly equal to, more than, or less than many national and international studies. This variation could be due to scale, data availability, methodology, agro-climatic conditions, and regional agricultural practices. It is recommended that the effects of scale, data accuracy, and suitability of WF estimation approaches for specific crops be considered when making regional water policies based on WF estimation.

Keywords: Water Footprint, Methodology, Scale variability, Crop, Sustainable Agriculture