Crop yield simulation using the Denitrification–Decomposition (DNDC) model can help to understand key bottlenecks for improved nitrogen (N) use efficiency and estimate greenhouse gas (GHG) emissions in West African urban vegetable production. The DNDC model was successfully calibrated using high‐resolution weather records, information on management practices and soils, and measured biomass accumulation and N uptake by amaranth (Amaranthus L.), jute mallow (Corchorus olitorius L.), lettuce (Lactuca sativa L.), and roselle (Hibiscus sabdariffa L.) for different input intensities (May 2014–November 2015) in urban vegetable production of Tamale (N‐Ghana, West Africa). The root mean square error (RMSE) and relative error (E) values fell within the confidence interval (α 5%) of the measurements, and there was a high correlation (0.91 to 0.98) between measurements and predictions. However, the analysis of uncertainty and factor importance indicated that soil properties (pH, SOC, and clay content) and weather (precipitation) variability contributed highly to yield uncertainty of vegetable biomass.

中文翻译：

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土壤气候对DNDC模型不确定性的影响，在加纳塔马莱的Petroplinthic Cambisol模拟城市蔬菜生产下的生物量积累

使用反硝化分解（DNDC）模型进行的作物产量模拟可以帮助理解提高氮（N）利用率的关键瓶颈，并估算西非城市蔬菜生产中的温室气体（GHG）排放。DNDC模型已使用高分辨率天气记录，管理实践和土壤信息成功校准，并测量了a菜（Amaranthus L.），黄麻锦葵（Corchorus olitorius L.），生菜（Lactuca sativa L. ）的生物量积累和氮吸收。 ）和玫瑰茄（木槿sabdariffaL.）表示塔马莱（西非N-加纳）城市蔬菜生产中不同的投入强度（2014年5月至2015年11月）。均方根误差（RMSE）和相对误差（E）值落在测量值的置信区间（α5％）之内，并且测量值和预测值之间具有高度相关性（0.91至0.98）。但是，对不确定性和因子重要性的分析表明，土壤特性（pH，SOC和黏土含量）和天气（降水）变异性对蔬菜生物量的产量不确定性起了很大的作用。