The global population is projected to reach 10 billion by 2050, requiring a 70% increase in agricultural production. Meeting this demand will significantly increase global water and energy demands, placing additional stress on natural resources and potentially increasing hunger. Current agricultural practices often fail to maximize production while minimizing water consumption, creating conflicts between food and water security. India, a major cereal producer, faces this challenge acutely in its Indo-Gangetic Plain (IGP), a critical food bowl for 400 million people, where the water-intensive rice-wheat cropping system is heavily reliant on groundwater. Rapidly declining groundwater levels in the IGP due to agricultural intensification, coupled with declining monsoon rainfall, threaten the sustainability of current practices. Solutions are needed to simultaneously improve food security while reducing water demand. Crop switching, replacing water-intensive crops with less demanding alternatives, has emerged as a potential solution. However, its impact on other sustainability dimensions, such as food supply and farmers' profits, needs thorough quantification. This study focuses on the IGP to determine the potential of crop switching to address its multidimensional sustainability challenges, providing a model applicable to other regions facing similar issues. The study area specifically focuses on Uttar Pradesh, Bihar, and West Bengal, which constitute a major portion of the IGP's agricultural production.

Existing literature highlights the unsustainable groundwater consumption in northern India, particularly in the IGP, attributed to declining monsoon rainfall and increased irrigation. Studies have shown the promise of optimized crop switching to improve water and food security at a global scale; however, policy-relevant analyses require local-to-regional scale assessments. Previous India-specific studies using single-objective optimizations demonstrated that replacing rice and wheat with less water-intensive crops such as millet and sorghum can increase food supply, reduce water and energy use, and improve environmental sustainability. However, these assessments lacked crucial considerations like farmers' profits, essential for real-world feasibility. The conflicting nature of social, economic, and environmental sustainability objectives necessitates a multi-objective framework to minimize trade-offs and optimize multiple outcomes. This research addresses this gap by focusing on cereal crops, which represent a significant portion of the total crop area and water consumption in the IGP, while also producing a major fraction of the calories consumed by the Indian population.

The study first quantified recent changes in water use, energy use, and food production due to agricultural intensification in the IGP, using data from various sources including the India Meteorological Department (IMD), agricultural census datasets, and the WaterGAP model. The IMD data provided rainfall information, while the agricultural census datasets provided information on crop production, harvested area, and irrigation. The WaterGAP model was used to estimate water consumption for domestic, industrial, and livestock sectors. A multi-objective optimization model was then developed to reallocate cropped areas between cereals, maximizing calorie production and farmers' profits while minimizing water consumption at the district level. The model considered three different scenarios: optimization with observed yield (OOY), optimization with simulated yield (OSY) using the FAO AquaCrop model, and optimization after meeting yield gap (OMYG) using the 90th percentile of observed yields within agroecological zones. The model also evaluated crop switching with and without yield gap closure in combination with a transition from flood to drip irrigation to assess the potential co-benefits of complementary interventions. The optimization model employed a max-min approach, using the Probabilistic Global Search Laussane (PGSL) algorithm, aiming to maximize the minimum satisfaction of all objectives. Groundwater modeling used the GEC-97 approach, accounting for recharge from rainfall infiltration and irrigation return flow to assess the impact of crop switching on net groundwater recharge. Energy consumption for groundwater pumping was also estimated. Finally, the study compared the simulated net groundwater recharge for three cases: (1) shifting from flood to drip irrigation without changing crop practices, (2) changing crop practices with conventional flood irrigation, and (3) changing both crop practices and irrigation method. The study also analyzed the nutritional value of the proposed crop switching scenario, comparing the production of protein, iron, and zinc between the original and the alternative crop mixes.

Analysis of historical data revealed decreasing rainfall trends and increasing agricultural water consumption trends in the IGP, leading to widespread groundwater depletion. The optimization model showed that replacing rice with millets (pearl millet) and sorghum in the Kharif season and wheat with sorghum in the Rabi season could significantly improve sustainability indicators. Specifically, the optimized crop switching scenario, using the 90th percentile observed yield values, resulted in a 55% reduction in water consumption in the Kharif season and a 9% reduction in the Rabi season, compared to current practices. Farmers' profits increased by 139% in the Kharif season and 152% in the Rabi season. Calorie production increased by 19% in the Kharif season and 38% in the Rabi season. Furthermore, the switch to millets and sorghum significantly increased the production of protein, iron, and zinc. Comparing crop switching with the transition from flood to drip irrigation revealed that crop switching alone leads to a 41% improvement in net groundwater recharge, while the change in irrigation practice alone improves net recharge by only 34%. Combining both interventions resulted in a 78% improvement in net groundwater recharge and a 36% improvement in energy savings. The sensitivity analysis showed that the key conclusions of the study remained largely unchanged even with a ±10% variation in the model's input parameters.

The findings address the research question by demonstrating the significant potential of crop switching to improve water and food security in the IGP while enhancing farmers' profits. The multi-objective optimization approach allowed for a holistic assessment of the impacts of crop switching, considering multiple and potentially conflicting objectives. The results highlight that the benefits of crop switching go beyond water savings, improving food security and economic viability for farmers simultaneously. The significant increase in the production of protein, iron, and zinc further strengthens the case for this approach, showcasing its nutritional co-benefits. The comparison with improved irrigation efficiency (drip irrigation) underscores the greater impact of crop switching in addressing groundwater depletion and energy consumption. The study's findings are relevant to other regions grappling with similar water scarcity and food security challenges. The model provides a practical framework for policymakers to design and implement targeted interventions for sustainable agricultural practices.

This study demonstrates the substantial co-benefits of crop switching in the Indo-Gangetic Plain, leading to significant improvements in water security, food security, and farmers' profits. Replacing water-intensive rice and wheat with millets and sorghum offers a more effective solution to groundwater depletion and energy consumption than improving irrigation efficiency alone. Future research should explore the challenges related to post-harvest processing, market access, and the need for granular-scale stakeholder engagement to ensure successful implementation. Further analysis should investigate the long-term sustainability of the proposed solution under various climate change scenarios and population projections.

The study's analysis is limited by the availability of data, restricting the spatial resolution to the district level. A more granular-scale analysis incorporating local crop suitability and detailed stakeholder engagement is needed for more precise recommendations. The optimization model relies on the assumption that changes in yield are independent of the cost of production, a factor that requires further investigation through field surveys. The sustainability of the proposed solution will also depend on future climate scenarios and changes in water and calorie demand.