Single or multiple weather station data were combined with soil textural data ranging from low to high detail, i.e., point data from a field station, the FAO Digital Soil Map of the World and a comprehensive data from national soil survey, as input to the Daisy model to simulate and upscale crop yields, drainage and nitrogen leaching for an agroecosystem in the North China Plain. Increasing the detail of the weather data increased the spatial variation of all simulated variables and decreased their regional median. Regional crop yields were simulated well with high-detail input data, though at a weak response to data detail. Simulated regional drainage and nitrogen leaching, and their spatial variability, however, responded well and increased two-to threefold, but their regional medians were similar for medium- and high-detail soil data. This work demonstrates the importance of explicit consideration of weather and soil variability for agro-environmental simulation studies at regional scale.

将单个或多个气象站数据与范围从低到高的范围内的土壤质地数据相结合，例如，来自野外站的点数据，粮农组织的《世界数字土壤图》和来自国家土壤调查的综合数据，作为雏菊的输入华北平原农业生态系统的模拟和高档作物产量，排水和氮淋失模型。增加天气数据的详细信息会增加所有模拟变量的空间变化，并降低其区域中位数。使用高详细输入数据可以很好地模拟区域作物的产量，尽管对数据细节的反应较弱。但是，模拟的区域排水和氮淋失及其空间变异性反应良好，并增加了2到3倍，但对于中高细节土壤数据，它们的区域中位数相似。这项工作证明了在区域规模的农业环境模拟研究中明确考虑天气和土壤变异性的重要性。