Recommendations on benchmarks for the DeNitrification–DeComposition model application in China: Insights from literature analysis

Abstract

This study addresses the lack of standardized evaluation criteria for the DeNitrification–DeComposition (DNDC) model, widely used to assess greenhouse gas emissions in agricultural systems. Based on a comprehensive analysis of literature data, we propose a set of benchmarks to improve the model's reliability, focusing on crop yield, soil organic carbon (SOC), nitrous oxide (N₂O), and methane (CH₄) emissions within the context of Chinese agriculture. Key performance indicators, including correlation coefficient (R), normalized root mean square error (nRMSE), and index of agreement (IOA), are defined to enhance model calibration and validation. The proposed benchmarks aim to provide a consistent reference for DNDC applications, facilitating accurate assessments of greenhouse gas emissions and supporting sustainable agricultural practices. By synthesizing existing research, this study contributes to improving model accuracy and enhancing agricultural management strategies, with implications for climate change mitigation.