The limited available water resources and competition among different water use sectors have become the main constraints of food security and sustainability. Faced with the inability to expand the area of cultivated land due to urbanization and population growth, one of the biggest challenges and risks for developing countries is to ensure the supply of food quantity and quality under extremely limited water resources. To achieve water-saving and improve calorie supply by adjusting crop production allocations, three objectives—of minimum blue water footprint, maximum calorie production, and each crop production no less than the reference level of nine main crops in China—were achieved using a non-dominated sorting genetic algorithm II. The results display that compared with the reference year, model Maize+ (maize production increased) had significant blue water saving (~32%), the blue water footprint of crop production in all provinces reduced, and its calorie production increased by 4%. This solution is not realistic for China because wheat and rice production need to be reduced by 82 and 80%, respectively. However, model Citrus– (citrus production decreased) reduced the blue water footprint of crop production (~16%), and increased calorie production (~12%). Compared with other solutions, it is a sustainable crop production structure that is easier to realize because it is better at meeting the production of each crop. Therefore, China can appropriately increase the planting area of maize and reduce the planting of citrus and other crops that consume more blue water and produce fewer calories to ensure the security and sustainability of food supplies. However, the improvement of water saving-technology, rationalization of agricultural water resources management, crop production allocations mentioned in this study, and other efforts are necessary to achieve this goal.

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The gray water footprint (GWF) can quantitatively evaluate the effect of non-point pollution on water quality in the context of water quantity. It is crucial to explore the driving forces behind the GWF to solve water quality problems. This study quantified the unit GWFs of grain crops and oil crops at the municipal scale in six provinces of western China over 2001–2018, then jointly applied the extended STIRPAT model and path analysis methods to analyze the climatic and socioeconomic driving forces of the GWF. Results show that the key driving forces affecting the GWF obtained by the two methods were consistent. Planting structure and population were the main factors increasing the total GWF, while crop yield was the largest factor inhibiting the unit GWF and demonstrates regional differences. However, when the indirect influence of the driving factor through other factors was large, some driving forces obtained by different methods were reversed. For example, the indirect impact of per capita cultivated land area on the total GWF in Inner Mongolia was large, resulting in a significant positive impact in path analysis and a slight negative impact in the STIRPAT model. To draw more comprehensive and referential conclusions, we suggest using multiple methods together to verify the driving forces and account for the regional differences.