Irrigation plays a critical role in food production, especially in areas where rainfall alone cannot meet crop water needs. In the United States, farmers have increasingly moved away from traditional water-intensive methods toward more efficient systems such as center pivot (CP), low-energy precision application (LEPA), and subsurface drip irrigation (SDI). Each system has trade-offs in water savings, crop yields, and cost.

Our study combines over 50 years of research on these irrigation systems to compare their efficiency and performance. We found that SDI is generally the most water-efficient, though it is more expensive and complex to manage. LEPA performs nearly as well in the right conditions, while CP systems remain common for large, flat fields due to their affordability and ease of use.

To help farmers choose the best system for their fields, we developed an artificial intelligence tool using reinforcement learning. This tool analyzes local soil, climate, crop type, and economic data to suggest the most beneficial irrigation upgrade. The system continuously improves its recommendations over time as conditions change.

This approach can help farmers save water, increase yields, and make more informed decisions supporting more sustainable agriculture in a changing climate.