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首页> 外文期刊>Nature Communications >Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations
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Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations

机译：在全球作物产量模拟中，土壤数据的不确定性可能超过气候影响信号

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Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations.
机译：全球网格化作物模型（GGCM）越来越多地用于农业环境评估和气候变化对粮食生产影响的评估。最近，与土壤数据的影响不同，气候数据和天气变化对GGCM结果的影响已受到详细审查。在这里，我们将由GGCM模拟选择的土壤类型引起的产量变化与天气引起的产量变化进行比较。如果不施用化肥，与土壤类型相关的单产变异性通常会超过模拟的由于天气因素引起的单年度年度变异性。肥料和灌溉的增加使用可以减少这种变化，直到几乎可以忽略不计。重要的是，取决于所选的土壤类型，估计的气候变化对单产的影响可能是负面的也可能是正面的。因此，土壤有能力缓冲或扩大这些影响。我们的发现要求改进可用于作物建模的土壤数据，并在GGCM模拟中更明确地说明土壤变异性。

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