The southeastern United States is a vital agricultural hub, with cotton, corn, sorghum, and soybeans among the top crops grown in the region. Many of these fields rely heavily on irrigation, a practice increasingly under scrutiny as the risks of drought and other erratic weather patterns rise. Accurate yield predictions for these crops are crucial for better management of regional water resources and informed decision-making by farmers. This is where a groundbreaking tool called RHEO (regional hydro-economic optimization) comes into play.
Developed by Dr. Hemant Kumar, now at the Indian Institute of Technology, along with Dr. Sankar Arumugam from North Carolina State University and Dr. Tingju Zhu of Zhejiang University, RHEO was introduced to Future Farming readers last year. The tool operates at the county level, integrating long-term and seasonal rainfall forecasts, groundwater level data from the US Geological Survey, and soil characteristics. By combining these elements with data on water consumption for each crop, irrigation costs, crop prices, and production budgets, RHEO offers a comprehensive analysis to regional water managers.
RHEO’s capabilities extend beyond mere yield prediction. The tool forecasts irrigation costs for various strategies and recommends the most profitable and environmentally sustainable crop and irrigation methods. As an open-source and free resource, it is immediately available for use by regional water planners in the Flint River Basin and surrounding areas. According to Dr. Kumar, the tool can be expanded to other crops based on community feedback, making it a versatile asset for water management.
The development of RHEO took approximately eight months, with the team facing significant challenges in merging datasets from different sources. These datasets varied in length and precision, complicating the integration process. For instance, while annual crop yields and harvested acreage were available for the entire 30-year study period, the percentage of acreage under irrigation was only recorded every five years. The team also had to reconcile different time scales between the crop yield simulation module, which runs daily, and the optimization module, which operates on a seasonal basis. A hierarchical model based on Bayesian inference was developed to link these modules, effectively reducing the runtime of the optimization process.
One of the key insights incorporated into RHEO is the phenomenon observed in crop watering during times of scarcity: the first increment of water supplied results in the highest gain in crop yield, with each subsequent unit of water producing diminishing returns. This understanding led the team to conclude that deficit irrigation—where water is supplied only when needed and in minimal amounts—is the most profitable and environmentally sustainable strategy. This approach is particularly relevant as climate change increases the risk of drought, prompting more farmers to adopt irrigation practices.
Tools like RHEO are crucial as they promote water conservation, enabling the same amount of water to irrigate more land. Dr. Kumar notes that RHEO can relieve pressure on over-exploited groundwater aquifers in two ways. First, by providing data to support more efficient irrigation strategies for existing crops, and second, by exploring the cultivation of less water-intensive crops in areas facing severe water scarcity.
As climate change continues to impact agricultural practices, tools like RHEO offer a beacon of hope for sustainable farming. By optimizing water use and promoting efficient irrigation strategies, RHEO not only supports farmers in making informed decisions but also contributes to the broader goal of water conservation.