Agroecosystem modelling has increasingly focused on the integration of soil biogeochemical processes and crop growth. However, few models are available that offer high computing efficiencies for region-scale simulations, integrated decision support tools, and a structure that allows for easy extension. This paper introduces a new modelling tool to fill this gap: the GDNDC (Gridded DNDC) system for gridded agro-biogeochemical simulations. Based on the established DeNitrification and DeComposition (DNDC) model version-95, its main advancements include (i) implementation of parallel computation to significantly reduce computation time across multiple scales; (ii) a built-in parameter optimization algorithm to improve the predictive accuracy, and (iii) several decision support tools. We demonstrate each of these for county-level maize growth simulations in Liaoning Province (China) and reveal the potential of this new modelling tool to guide both long-term policy decisions regarding optimal fertilizer application and near-term crop yield forecasting for reactive decisions required in times of drought.

农业生态系统建模日益集中于土壤生物地球化学过程和作物生长的整合。但是，很少有模型能够为区域规模的仿真，集成的决策支持工具以及易于扩展的结构提供高计算效率。本文介绍了一种填补这一空白的新建模工具：用于网格化农业生物地球化学模拟的GDNDC（网格化DNDC）系统。基于已建立的DeNitrification and DeComposition（DNDC）模型版本95，其主要改进包括：（i）实现并行计算，以显着减少跨多个规模的计算时间；（ii）内置参数优化算法以提高预测准确性，以及（iii）几种决策支持工具。