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Quantifying the uncertainty in nitrogen application and groundwater nitrate leaching in manure based cropping systems

机译:基于粪便种植系统中氮应用和地下水硝酸盐浸出的不确定性

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Uncertainty in application rates of nitrogen (N) limits the effectiveness of nutrient management plans that aim to realize yield goals while minimizing environmental N losses. Measuring N application rates accurately is especially difficult in manure dependent cropping systems. California dairies use both solid manure and process wastewater (PWW) to fertilize forage crop fields. California dairy farmers measure the ratio of N applied to N removed from each crop field, the N Ratio, based on many uncertain measurements of the quantity and concentration of applied N sources. In this study, a Monte Carlo based model was used to propagate the uncertainty of each application measurement to the overall N application rate. Then the Monte Carlo model was used to stochastically vary the N application rate around farmer measured values for two forage fields (A: silty clay soil, B: sandy loam soil) over a 5 year period. The N Ratios and groundwater nitrate concentrations were simulated for 1000 stochastic N application scenarios using the HYDRUS-1D water and solute transport model and the results were compared to monitoring well measurements. Results showed that uncertainty in measurements of N applied in PWW were the dominant source of uncertainty in total N application rates, contributing 64 to 94% of overall uncertainty. N Ratio uncertainty varied widely between harvests. The average upper and lower limits of the 95% confidence interval deviated from the median simulated N Ratio by 0.25 (range: 0.13 to 0.48) and 0.27 (range: 0.12 to 0.55), respectively, among all harvested crops. Simulated groundwater nitrate concentrations were lower than monitoring well measurements by an average of 20 and 2 mg L-1 for Fields A and B, respectively. All simulated and observed nitrate concentrations in groundwater recharge from both fields were three to eight times the federal maximum contaminant level for nitrate as N of 10 mg L-1. Determining and reducing uncertainty in measurements of N applied in PWW is likely the most impactful way to improve accuracy of overall N application rates. Within current measurement and management systems, measurements of N application in manure dependent cropping systems are not accurate enough to identify application rates that will both support yield goals and reduce environmental N losses.
机译:氮气施用率的不确定性限制了营养管理计划的有效性,旨在实现产量目标,同时最大限度地减少环境N损失。在粪便依赖性裁剪系统中,准确测量N施用速率特别困难。加州乳房使用固体粪便和工艺废水(PWW)施肥饲料田野。加州乳制品农民根据应用N来源的数量和浓度的许多不确定测量,测量从每个作物场中取出的n的n施加的n的比率。在这项研究中,基于蒙特卡罗的模型用于将每个应用测量的不确定性传播到整体N施用率。然后,Monte Carlo模型用于随机改变5年内的两种牧草(A:粉质粘土土壤,B:桑迪土壤)的农民测量值的N申请率。使用氢气-1d水和溶质运输模型模拟100个比率和地下水硝酸盐浓度,并将结果与​​监测井测量进行比较。结果表明,PWW中施用N的测量中的不确定性是总不确定性的主要不确定性来源,有助于总体不确定性的64%至94%。 N比率不确定性在收集之间变化广泛。在所有收获的作物中,95%置信区间的95%置信区间的平均上限和下限偏离中值模拟N比率0.25(范围:0.13至0.48)和0.27(范围:0.12至0.55)。模拟地下水硝酸盐浓度分别低于监测井测量的平均值A和B的平均值20和2mg L-1。所有模拟和观察到从两个场的地下水补给的硝酸盐浓度均为硝酸N的联邦最大污染物水平的三倍至八倍,硝酸N为10mg l-1。在PWW中施加的N的测量中确定和减少不确定性可能是提高整体N应用率准确性的最有影响力的方法。在当前测量和管理系统中,粪肥依赖性裁剪系统中N应用的测量不足以足以识别支持产量目标并降低环境N损失的申请率。

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