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Use of the SWB-Sci model for nitrogen management in sludge-amended land

机译:使用SWB-SCI模型在污泥修正的土地中的氮气管理模型

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Process-based computer simulation models are often used as reasoning support tools to integrate the complex processes involved in the soil-plant-atmosphere system. The objectives of this study were to evaluate the performance of the SWB-Sci model as a reasoning support tool for sludge management in agricultural lands, and use the validated model to assess the long-term agronomic and environmental implications of water availability and crop intensity on sludge-amended land. The model was calibrated for the test crops, maize (Zea mays Pan6966) and oats (Avena sativa L.), using data collected during the 2004/2005 growing season from irrigated plots at the East Rand Water Care Works, Gauteng, South Africa. Model validation was performed using independent data sets collected during the 2004/2005 to 2007/2008 growing seasons. The model was successfully calibrated for maize and oats as all the statistical parameters were within the prescribed ranges [index of agreement (d) >0.8; relative mean absolute error (MAE%) <20%; coefficient of determination (R-2) >0.8]. The results indicate that SWB-Sci simulated aboveground biomass (TOM) and grain yield (GY) of maize and oats with high accuracy (d>0.85, MAE% <= 20%, and R-2>0.91) but with a slight overestimation by 0.2-4 Mg ha(-1). The model predicted nitrate leaching and crop N uptake reasonably well (d >0.85, MAE% <= 14%, and R-2 >0.8), with slight overestimation of TDM and GY N uptake by 11-57 and 4-48 kg ha(-1), respectively. Long-term model simulations indicate that fixed sludge application rate recommendations generated from laboratory incubation studies may in the long-term result in spontaneous excessive nitrate leaching below the active root zone during high rainfall events, if recommendations do not consider N contribution from soil organic matter. Modelling also showed that leaving room for rain during each irrigation event may minimize the risk of nitrate leaching. (C) 2015 Elsevier B.V. All rights reserved.
机译:基于过程的计算机仿真模型通常用作推理支持工具,以整合土壤 - 植物气氛系统中涉及的复杂过程。本研究的目标是评估SWB-SCI模型作为农业土地污泥管理的推理支持工具,并使用经过验证的模型来评估水可用性和作物强度的长期农艺和环境影响污泥修正的土地。该模型被校准,用于测试作物,玉米(Zea Mays Pan6966)和燕麦(Avena Sativa L.),使用2004/2005年生长季节从东方兰德水护理作品,南非豪登腾,南非的灌溉季节收集的数据。使用2004/2005年至2007/2008年生长季节收集的独立数据集进行模型验证。该模型已成功校准玉米和燕麦,因为所有统计参数都在规定范围内[协议指数(d)> 0.8;相对平均绝对误差(MAE%)<20%;测定系数(R-2)> 0.8]。结果表明,SWB-SCI在高精度(D> 0.85,MAE%<= 20%和R-2> 0.91)但具有轻微高估0.2-4 mg ha(-1)。该模型预测硝酸盐浸出和作物N采用合理良好(D> 0.85,MAE%<= 14%和R-2> 0.8),略微高估TDM和GY n吸收率11-57和4-48 kg HA (-1)分别。长期模型模拟表明,从实验室孵化研究中产生的固定污泥申请率建议可以在高雨事件期间长期导致在活性根区下方的自发过量硝酸盐浸出,如果建议不考虑土壤有机物质的贡献。建模还表明,在每个灌溉事件期间留下雨的空间可能会使硝酸盐浸出的风险最小化。 (c)2015 Elsevier B.v.保留所有权利。

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