Irrigation and fertilization management to optimize rice yield, water productivity and nitrogen recovery efficiency

Increasing water scarcity and environmental contamination with excess chemical nitrogen fertilizer use necessitate the development of water-nitrogen conservation technology in rice production. Therefore, a 2-year field experiment (2017–2018) was conducted with three water regimes, namely (1) continuous flooding irrigation, CF; (2) safe alternate wetting and drying irrigation, AWD safe ; and (3) severe alternate wetting and drying irrigation, AWD severe , and four nitrogen application (N app ) rates, namely 0 (N 0 ), 90 (N 1 ), 180 (N 2 ), and 270 (N 3 ) kg N ha −1 , to determine the effects of water regimes and N app rates on rice yield, total water productivity (WP i + r ) and nitrogen recovery efficiency (NRE). The results demonstrated that the water regime, N app rate and their interaction showed significant effects on rice yield, WP i + r and NRE and similar variations were observed in 2017 and 2018. The rice grain yield and WP i + r (or the water productivity of irrigation, WP i ) significantly increased from N 0 to N 2 treatments but varied little between N 2 and N 3 treatments. The rice yield under AWD safe was higher than that under AWD severe , whereas their WP i + r and WP i values showed the opposite trends. The WP i values in 2018 were substantially higher than those in 2017 due to the lower irrigation amount in 2018. The highest rice NRE occurred with the combination of N 2 with the CF and AWD safe conditions, and it was significantly higher than that under AWD severe . The dualistic and quadric regression equations of water and N app rate showed that rice yield, WP i + r and NRE could not be maximized simultaneously. Based on the maximum likelihood method, it was demonstrated that maintaining the water quantity and N app rate at 11,000 m 3 ha −1 and 160 kg N ha −1 can serve as a suitable strategy to achieve maximal comprehensive benefits for rice grain yield, WP i + r and NRE in certain regions with water shortage. The optimization model can save approximately 17.0% of water input and 11.1% of N app rate, respectively, compared to the traditional strategy. However, further research should validate and adapt these technologies in larger-scale fields.