A multi-year simulation study of optimal and sub-optimal irrigation of maize in Kansas

Increasing water demand, dwindling fresh water resources and extended periods of drought underscore the urgent need of improving water use efficiency in agriculture. The EU-funded project FIGARO aims to improve irrigation water use efficiency via the development and implementation of irrigation strategies that take into account in real-time soil water availability, local weather forecasts, crop physiological status and water needs. As part of this project we have developed two model-based procedures to compute optimal and sub-optimal irrigation schedules. The advantage of the sub-optimal approach is that it can be implemented in real-time to recalculate the irrigation schedule every few days based on the estimated soil water content, crop status and weather forecasts. In the present work we used 10 years of climatic data to simulate the performance of both approaches for a hypothetic maize crop in Kansas. In particular, we investigated the influence of the accuracy of short-term and long-term weather forecasts on the results. When perfect weather forecasts were assumed to be available for the whole season the sub-optimal approach produced results which were within a few percent of the optimal ones. Assuming that perfect weather forecasts were available only for the next five days did not change the results significantly on most years. Relying solely on historical weather led to poorer results, but the sub-optimal approach still achieved a yield which, on average, was less than 10% below the yield which could have been obtained with the same amount of irrigation water if perfect forecasts had been available.