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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Rangeland Resources & Systems Research » Research » Publications at this Location » Publication #347884
Title: A model-based real-time decision support system for irrigation scheduling to improve water productivity

Author
item CHEN, X - Chinese Academy Of Sciences
item QI, Z - McGill University - Canada
item GUI, D - Chinese Academy Of Sciences
item GU, Z - Jiangsu University
item Ma, Liwang
item ZENG, F - Chinese Academy Of Sciences
item LI, L - Chinese Academy Of Sciences

Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/25/2019
Publication Date: 10/27/2019
Citation: Chen, X., Qi, Z., Gui, D., Gu, Z., Ma, L., Zeng, F., Li, L. 2019. A Decision Support System for Irrigation Scheduling (DSSIS) based on model predicted water stress index and forecast weather data. Agronomy. 9:686. https://doi.org/10.3390/agronomy9110686.
DOI: https://doi.org/10.3390/agronomy9110686

Interpretive Summary: Development of methods and equipment to determine the timing and quantity of irrigation is important for arid farmland. This study reports a newly developed Decision Support System for Irrigation Scheduling (DSSIS) based on the Root Zone Water Quality Model (RZWQM) predicted crop water stress index and forecast 4-days weather data. The DSSIS was constructed and compared with soil moisture sensor based and with conventional experience-based irrigation methods in Xinjiang province, China. The results showed that, compared to other methods, the DSSIS performed the best with a significant reduction in irrigation amount and a significant increase in water use efficiency (WUE); Deficit irrigation with DSSIS saved the largest amount of water and increased yield by 16.9% and WUE by 121%. This study concludes that the DSSIS is a promising tool for irrigation scheduling and deficit irrigation with DSSIS serves as the best choice for irrigation scheduling in the study area.

Technical Abstract: Development of methods and equipment to determine the timing and quantity of irrigation for automatic implementation is vitally important for arid farmland. This study reports a newly developed Decision Support System for Irrigation Scheduling (DSSIS) based on the Root Zone Water Quality Model (RZWQM) predicted crop water stress index and forecast 4-days weather data. The DSSIS was constructed and compared with soil moisture sensor-based and with conventional experience-based irrigation methods in Xinjiang province, China. The results showed that, compared to other methods, the DSSIS performed the best with a significant reduction in irrigation depth and a significant increase in water use efficiency (WUE) (p<0.05); Deficit irrigation with DSSIS saved the largest amount of water and increased yield (16.9%) and WUE (121%). This study concludes that the DSSIS is a promising tool for irrigation scheduling and deficit irrigation with DSSIS serves as the best choice for irrigation scheduling in the study area.