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Predictive Model Improves Irrigation Efficiency

Engineers at Cornell University have developed a predictive model for irrigation that combines plant physiology, real-time soil conditions, and weather forecasts. They predict that using this information to decide how and when to irrigate could save 40% of the water consumed by traditional irrigation practices. In addition, this “smart irrigation” could help improve the quality of specialty crops such as grapes, by ensuring that they receive the right amount of water. Researchers conducted a case study using this model for irrigation of grass crops in Iowa, and they are preparing to test it on apples in New York. The research includes an assessment of the costs and benefits of switching from a model based on human decisions to an automated one.