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Near-infrared (nir) diffuse reflectance spectroscopy for the prediction of carbon and nitrogen in an oxisol

机译:近红外(nir)漫反射光谱法用于预测氧化物中的碳和氮

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摘要

The characterization of soil properties through laboratory analysis is an essential part of the diagnosis of the potential use of lands and their fertility. Conventional chemical analyzes are expensive and time consuming, hampering the adoption of crop management technologies, such as precision agriculture. The aim of the present paper was to evaluate the potential of near-infrared (NIR) diffuse reflectance spectroscopy for the prediction of the carbon and nitrogen of Typic Hapludox. In the A and B horizons, 1,240 samples were collected in order to determine the total carbon (TC) and nitrogen (TN) contents, obtain the NIR spectral curve, and build models using partial least squares regression. The use of diffuse reflectance spectroscopy and statistical techniques allowed for the quantification of the TC with adequate models of prediction based on a small number of samples, an residual prediction deviation RP D greater than 2.0, an R2 greater than 0.80 and a low root mean square error RMSE. For TN, models with a good level of prediction were not obtained. The results based on the NIR models were able to be integrated directly into the geostatistical evaluations, obtaining similar digital maps from the observed and predicted TC. The use of pedometric techniques showed promising results for these soils and constitutes a basis for the development of this area of research on soil science in Colombia.
机译:通过实验室分析表征土壤特性是诊断土地潜在用途及其肥力的重要部分。常规化学分析既昂贵又费时,从而妨碍了采用诸如精细农业之类的作物管理技术。本文的目的是评估近红外(NIR)漫反射光谱法在典型Typic Hapludox碳和氮预测中的潜力。在A和B层中,收集了1,240个样品,以确定总碳(TC)和氮(TN)含量,获得NIR光谱曲线,并使用偏最小二乘回归建立模型。使用漫反射光谱法和统计技术可对TC进行量化,并具有基于少量样本的适当预测模型,残余预测偏差RP D大于2.0,R2大于0.80和低均方根错误RMSE。对于TN,未获得具有良好预测水平的模型。基于NIR模型的结果能够直接整合到地统计评估中,并从观测到的和预测的TC获得类似的数字地图。计步技术的使用对这些土壤显示出了可喜的结果,并构成了哥伦比亚土壤科学研究领域发展的基础。

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