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Use of near infrared reflectance spectroscopy to predict nitrogen uptake by winter wheat within fields with high variability in organic matter

机译:利用近红外反射光谱法预测有机质变化较大的田间冬小麦对氮的吸收

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In this study, the ability to predict N-uptake in winter wheat crops using NIR-spectroscopy on soil samples was evaluated. Soil samples were taken from unfertilized plots in one winter wheat field for three years (1997–1999) and in another winter wheat field nearby for one year (2000). Soil samples were analyzed for organic C content and their NIR-spectra. N-uptake was measured as total N-content in aboveground plant materials at harvest. Models calibrated to predict N-uptake were internally cross-validated and validated across years and across fields. Cross-validated calibrations predicted N-uptake with an average error of 12.1 to 15.4 kg N ha−1. The standard deviation divided by this error (RPD) ranged between 1.9 and 2.5. In comparison, the corresponding calibrations based on organic C alone had an error from 11.7 to 28.2 kg N ha−1 and RPDs from 1.3 to 2.5. In three of four annual calibrations within a field, the NIR based calibrations worked better than the organic C based calibrations. The prediction of N-uptake across years, but within a field, worked slightly better with an organic C based calibration than with a NIR based one, RPD = 1.9 and 1.7, respectively. Across fields, the corresponding difference was large in favour of the NIR-calibration, RPD = 2.5 for the NIR-calibration and 1.5 for the organic C calibration. It was concluded that NIR-spectroscopy integrates information about organic C with other relevant soil components and therefore has a good potential to predict complex functions of soils such as N-mineralization. A relatively good agreement of spectral relationships to parameters related to the N-mineralization of datasets across the world suggests that more general models can be calibrated.
机译:在这项研究中,评估了使用近红外光谱对土壤样品预测冬小麦作物吸收氮的能力。在一个冬小麦田中三年(1997-1999年)和附近另一个冬麦田中一年(2000年)未施肥的土壤样品。分析土壤样品的有机碳含量及其近红外光谱。氮的吸收被测量为收获时地上植物材料中的总氮含量。经过校准以预测氮吸收的模型在内部进行了交叉验证,并在多年和跨领域进行了验证。交叉验证的校准预测氮的吸收,平均误差为12.1至15.4 kg N ha-1 。标准偏差除以该误差(RPD)介于1.9和2.5之间。相比之下,仅基于有机碳的相应标定值的误差为11.7至28.2 kg N ha-1 ,RPD值为1.3至2.5。在一个领域中的四个年度校准中的三个中,基于NIR的校准比基于有机C的校准效果更好。多年来,但在一个领域内,对氮吸收的预测与基于有机碳的校准相比,与基于NIR的校准(RPD = 1.9和1.7)略好。在整个场中,相应的差异很大,有利于NIR校准,RPD = 2.5(用于NIR校准)和1.5(用于有机C校准)。结论是,近红外光谱法将有机碳的信息与其他相关的土壤成分结合在一起,因此具有很好的潜力来预测土壤的复杂功能,例如氮矿化。光谱关系与与全球数据集的N矿化有关的参数的相对较好的一致性表明,可以校准更通用的模型。

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