The fate of nitrogen from different sources in a rice-wheat rotation system - A N labeling study.

High loss and low nitrogen (N) efficiency in agricultural production is severe. Also, ammonia volatilization and N leaching aggravated environmental pollution. The eutrophication of surface water and the emissions of NO increased, hence green fertilization management urgently needs to be rationalized. Coordinating N supply from different sources has been shown to reduce environmental pollution. Therefore, this study was dedicated to clarifying the transport of N sources in the rice-wheat rotation system. The stable isotope tracer technology was used to label fertilizer (F), soil (T), and straw (J) with N, respectively. The utilization of N by crops (the N ratio in organs), as well as the residual N in soil and loss status, were measured. According to the potential of response to N, all the wheat cultivars were divided into groups with high (HNV) and low efficiency (LNV). The N contribution ratio showed that 43.28%~45.70% of total N accumulation was from T, while 30.11%~41.73% and 13.82%~24.19% came from F and J. The trend in soil N residue (T > F > J) was consistent with the above, while it was the opposite in N loss (T< F< J). The seasonal effectiveness showed that T achieved the highest N utilization efficiency (31.83%~44.69%), followed by F (21.05%~39.18%) and J (11.02%~16.91%). The post-season sustainability showed that T decreased the most in soil N residue (2.08%~12.53%), and F decreased the most in N accumulation (9.64%~18.13%). However, J showed an increase in N recovery rate (2.87%~5.89%). N translocation and distribution showed that N from different sources in grains was significantly higher than that in stems, glumes, and leaves. The ratio of HNV (75.14%~79.62%) was higher than that of LNV (71.90%~74.59%) in grain, while it was the opposite in other organs. Plant N accumulation, soil N supply, and straw N transformation were determined jointly by the three N sources, thus reducing N loss and NO production. Therefore, the results will highlight the insights for constructing local N and emission reduction models.