Analysis of irrigation scheduling technologies in maize - Martins, Rafael Gomes (2018)
The global demand for food and fiber, including sources for bio-energy and industrial purposes, is expected to increase by 70 percent during the middle of this century, with water being one of the most important components to ensure the plant production.However, agriculture is by far the largest user of water among the various competing uses. Consequently, the selection of the appropriate technique, that optimizes water use is key to avoid either non-productive water use, or stress conditions that may increase the cost of irrigation or cause yield losses. Improving irrigation management techniques has a significant impact on the sustainable use of water resources. The aim of this study was to evaluate the performance of four different methods of irrigation scheduling applied on Maize (Zea Mays L.), comparing the simulated values of soil water content with Neutron Probe measurements, under a standard irrigation regime to maximize the water productivity. The irrigations were triggered based on 40% maximum allowed depletion. The specific objectives were; (1) to test, evaluate and develop the integrated Crop water management model-driven decision support tool iCrop-beta version system to schedule irrigation; (2) test and evaluate the performance of the KanSched irrigation scheduling tool widely used in Kansas; (3) test and evaluate the New Acclima TDR 310-s soil sensor performance to measure the soil water content, and check their accuracy; (4) evaluate the use of the AquaCrop model for real time irrigation scheduling and evaluate the performance as an irrigation tool. The results showed that all the techniques when calibrated well were able to follow the soil water content. The iCrop beta version was able to serve as an irrigation management tool. KanSched is a good estimation method, principally when using onsite recorded rain. The Acclima TDR-310s was not reliable for agronomic management without site specific calibration. The AquaCrop model required accurate parameters to simulate the crop growth and development under limited conditions, however, exhibited excellent performance to track the soil water content.