[Spatial Differentiation and Driving Analysis of Nitrogen in Rice Rotation Based on Regional DNDC: Case Study of...

Cycling dynamics of nitrogen in paddy rotation areas have a practical significance for ensuring food supply and realizing sustainable development of the regional ecology in the Min delta urban agglomeration. However, with rapid urbanization, the negative externalities of paddy rotation areas have been gradually increased because of unreasonable utilization behavior, and the environmental costs are increasing. Therefore, the spatial differentiation of nitrogen indicators and its driving factors were analyzed, which provides a macro-decision making basis for the implementation of farmland management measures. In this study, the paddy rotation area in Jinjiang River watershed was selected as the research object. The denitrification decomposition (DNDC) model was used to simulate the nitrogen cycle in the paddy rotation area. The hot spot analysis and geographical weight regression (GWR) model were used to analyze the spatial otherness characteristics and driving attribution of various nitrogen indices. The main results showed that: ① The DNDC model was validated by parameters, and the results showed preferably regional adaptability. ② Based on the comparison of different rotation patterns, the rice-vegetable rotation pattern not only established the maximum input of nitrogen fertilizer but also revealed the highest nitrogen absorption efficiency and the maximum values of nitrogen loss, followed by the rice-rice rotation pattern and rice-fallow rotation pattern. ③ In the spatial distribution of nitrogen indicators, except for the crop nitrogen absorption, the NH₃ emission, N₂O emission and nitrogen leaching showed a spatial clustering distribution, and the main trend line based on the standard deviation ellipse was mainly "Gande-Changkeng" township.④ According to the analysis of spatial influence factors for various nitrogen indices, soil attribute factors had the strongest effect; the SOC_max was the strongest influential factor for both NH₃ and N₂O emissions, and the spatial distribution was "west high, east low". The pH_min was the strongest influential factor in nitrogen leaching, and the spatial distribution was "north and south high, east and west low".