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Determination of fibre and protein content in heterogeneous pastures using field spectroscopy and ultrasonic sward height measurements

机译:使用现场光谱法和超声草皮高度测量法测定异质牧场中的纤维和蛋白质含量

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Feeding of livestock on pastures requires constant monitoring of diet composition to ensure consistent levels of animal production. The widely used but conventional techniques to measure the components of feed are impractical to obtain in-field forage quality status for making real-time decisions. Assessment of forage quality parameters using proximal sensing is of particular interest. The present study aimed to demonstrate the potential of using a combination of ultrasonic and canopy reflectance data to predict forage quality variables of heterogeneous pastures. A field experiment with pastures continuously grazed by cows with three stocking density treatments (moderate, lenient and very lenient stocking) was used to calibrate ultrasonic and hyperspectral reflectance sensors. Hyperspectral analysis by a modified partial least square regression (MPLSR) resulted in maximum accuracy for the prediction of acid detergent fibre (ADF) and crude protein (CP) (R-calibration(2) = 0.63-0.85). Any reduction of hyperspectral data into vegetation indices based on few specific narrow wavebands or satellite broadbands reduced prediction accuracy significantly. However, prediction of ADF and CP was improved by a combined analysis of ultrasonic sward height and vegetation indices or satellite broadbands, so that most calibration models exceeded an RPD (ratio of standard deviation and standard error of prediction) value of 1.4, which is considered as an acceptable predicting capability for variable field condition. Our findings showed that combined sensing systems using reflectance and ultrasonic sensors may provide acceptable prediction accuracies for practical application even under extremely heterogeneous pasture conditions. (C) 2016 Elsevier B.V. All rights reserved.
机译:在牧场上饲养牲畜需要不断监控饮食组成,以确保动物生产水平稳定。广泛使用但常规的技术来测量饲料的成分对于获得现场草料质量状况以做出实时决策是不切实际的。使用近端感测对草料质量参数进行评估尤为重要。本研究旨在证明结合使用超声和冠层反射率数据来预测异质牧场草料质量变量的潜力。野外实验采用三种放养密度处理方法(中度,宽容和非常宽容放养)对母牛连续放牧的牧场进行校准,以校准超声波和高光谱反射率传感器。通过修正的偏最小二乘回归(MPLSR)进行的高光谱分析可最大程度地预测酸性洗涤剂纤维(ADF)和粗蛋白(CP)(R-calibration(2)= 0.63-0.85)。基于少数几个特定的​​窄波段或卫星宽带将高光谱数据减少为植被指数的任何方法都会大大降低预测的准确性。但是,通过对超声波草皮高度和植被指数或卫星宽带的组合分析,可以改善ADF和CP的预测,因此大多数校准模型都超过了1.4的RPD(标准偏差与预测标准误差之比)值,这被认为是作为可变现场条件的可接受的预测能力。我们的发现表明,即使在极端异构的牧场条件下,使用反射率和超声传感器的组合传感系统也可以为实际应用提供可接受的预测精度。 (C)2016 Elsevier B.V.保留所有权利。

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