Modeling denitrification nitrogen losses in China's rice fields based on multiscale field‐experiment constraints

Denitrification plays a critical role in soil nitrogen (N) cycling, affecting N availability in agroecosystems. However, the challenges in direct measurement of denitrification products (NO, N2O, and N2) hinder our understanding of denitrification N losses patterns across the spatial scale. To address this gap, we constructed a data‐model fusion method to map the county‐scale denitrification N losses from China's rice fields over the past decade. The estimated denitrification N losses as a percentage of N application from 2009 to 2018 were 11.8 ± 4.0% for single rice, 12.4 ± 3.7% for early rice, and 11.6 ± 3.1% for late rice. The model results showed that the spatial heterogeneity of denitrification N losses is primarily driven by edaphic and climatic factors rather than by management practices. In particular, diffusion and production rates emerged as key contributors to the variation of denitrification N losses. These findings humanize a 38.9 ± 4.8 kg N ha−1 N loss by denitrification and challenge the common hypothesis that substrate availability drives the pattern of N losses by denitrification in rice fields. The quantification of denitrification N losses has been challenging and limiting our understanding of denitrification N losses patterns in spatial scales. To address this gap, we constructed a “double constraint method” based on multiscale observations to optimize the DNDC model. We quantified the magnitude of denitrification N losses in Chinese rice fields and found that production rate and diffusion rate were more important than substrate concentration in determining the spatial variability of differentiation N losses. This insight provides an improved approach for simulating denitrification process.