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Why we need to know what and where cows are urinating - a urine sensor to improve nitrogen models

机译:为什么我们需要了解奶牛的内容和何处尿 - 一种改善氮模型的尿路传感器

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Urine of grazing livestock is the greatest contributor to leached nitrogen (N) in our environment. While many N cycling models have used average urine excretions as input data, concentration and volume of individual urination events can vary greatly.Stock camps receive large amounts of urine and pose a high risk of N leaching, especially when animals are set stocked over several days. Anew urine sensor is described, along with the variation in urine characteristics of break-grazed cows. N leaching was modelled based on sensor data collection, and paddock-scale estimates were compared to those based on average urine data. There was a 10% difference in estimated N leached by two pumice soils, but both leached 10% less N when varying urine values wereused compared to average urine values. The new data showed a frequency distribution pattern of urinary N concentration in urination events that differed to that estimated using earlier data. Thus, frequency distribution patterns have a large effect on modelled N leaching loss and need to be based on extensive data collection to increase the confidence in improving estimates of N leaching. Campsites, which occupy 5-15% of a hill country paddock, account for about half of all excreted urine; their locations can be predicted for the targeting of N-loss mitigation strategies using a simple topographic map of the farm.
机译:放牧牲畜的尿液是我们环境中浸出的氮气(n)的最佳贡献者。虽然许多N循环模型使用平均尿液排泄作为输入数据,但个人排尿事件的浓度和体积可以大大变化。营地接受大量的尿液,并且造成高风险的N次浸出,特别是当动物在几天内储存时。还描述了一种尿液传感器,以及破碎的奶牛的尿量特征的变化。 N浸出基于传感器数据收集建模,并将围场估计与基于平均尿液数据的估计进行了比较。估计的N次数有10%的差异,两个浮石土壤浸出,但与平均尿液值相比,当尿液值不同的尿液值时,均浸出10%。新数据显示了使用早期数据的估计不同的排尿事件中尿N浓度的频率分布模式。因此,频率分布模式对模型的N浸出损失具有很大的影响,并且需要基于广泛的数据收集来增加改善N浸出估计的置信度。露营地占山地围场的5-15%,占所有排泄尿的一半;他们的位置可以预测使用农场的简单地形图的N-Loss缓解策略的定位。

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