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Characterising effects of management practices, snow cover, and soil texture on soil temperature: Model development in DNDC

机译:管理实践,雪覆盖和土壤质地对土壤温度的特征:DNDC模型开发

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Agro-ecosystem models, such as the DNDC (DeNitrification and DeComposition) model are useful tools when assessing the sustainability of agricultural management. Accuracy in soil temperature estimations is important as it regulates many important soil biogeochemical processes that lead to greenhouse gas emissions (GHG). The objective of this study was to account for the effects of snow cover in terms of the measured snow depth (mm of water), soil texture and crop management in temperate latitudes in order to improve the surface soil temperature mechanism in DNDC and thereby improve GHG predictions. The estimation of soil temperature driven by the thermal conductivity and heat capacity of the soil was improved by considering the soil texture under frozen and unfrozen conditions along with the effects of crop canopy and snow depth. Calibration of the developed model mechanisms was conducted using data from Alfred, ON under two contrasting soil textures (sandy loam vs. clay). Independent validation assessments were conducted using soil temperatures at different depths for contrasting managements for two field sites located in Canada (Guelph, ON and Glenlea, MB). The validation results indicated high model accuracy (R-2 & 0.90, EF &= 0.90, RMSE & 3.00 degrees C) in capturing the effects of management on soil temperature. These developments in soil heat transfer mechanism improved the performance of the model in estimating N2O emissions during spring thaw and provide a foundation for future studies aimed at improving simulations in DNDC for better representations of other biogeochemical processes. Crown Copyright (C) 2017 Published by Elsevier Ltd on behalf of IAgrE. All rights reserved.
机译:农业生态系统模型,如DNDC(反硝化和分解)模型在评估农业管理可持续性时是有用的工具。土壤温度估计的准确性很重要,因为它调节了导致温室气体排放(GHG)的许多重要土壤生物地球化学过程。本研究的目的是考虑到雪覆盖在温带纬度的测量雪深(水),土壤质地和作物管理方面的影响,以改善DNDC的表面土壤温度机制,从而改善温室气体预测。通过考虑冷冻和未冷却条件下的土壤质地以及作物冠层和雪深度的影响,改善了土壤导热率和热容量驱动的土壤温度估计。使用来自Alfred的数据进行开发模型机制的校准,在两个对比土壤纹理(桑迪壤土与粘土)下进行。使用不同深度的土壤温度进行独立的验证评估,以便在加拿大的两个田地网站(Guelph,On和Glenlea,MB)对比管理。验证结果表明了高模型精度(R-2& GT; 0.90,EF& GT; = 0.90,RMSE& LT; 3.00摄氏度)在捕获对土壤温度的影响时。土壤传热机制的这些发展改善了模型在春季解冻期间估计N2O排放的性能,为未来研究提供了旨在改善DNDC模拟的基础,以便更好地表示其他生物地球化学过程。皇冠版权(c)2017由elsevier有限公司代表IAGRE发布。版权所有。

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