首页> 外文期刊>Canadian Journal of Soil Science >Predicting soil organic carbon and total nitrogen using mid- and near-infrared spectra for Brookston clay loam soil in Southwestern Ontario, Canada
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Predicting soil organic carbon and total nitrogen using mid- and near-infrared spectra for Brookston clay loam soil in Southwestern Ontario, Canada

机译:使用中红外光谱和近红外光谱预测加拿大西南安大略省布鲁克斯顿黏土壤土的土壤有机碳和总氮

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摘要

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy of soils have been tested to estimate soil organic carbon (SOC) and total N (TN) concentrations at local, regional and national scales. However, these methods have rarely been used to assess SOC and TN concentrations of the same soil under different management practices. The objective of this study was to determine if models developed from infrared spectra of Brookston clay loam soils under different management practices could be used to estimate SOC, and TN concentrations and the C:N ratio. Soils used for model calibration included 217 samples from a long-term fertilization and crop rotation study and a long-term compost study, whereas 78 soil samples from a long-term tillage study on the same soil type were used for model validation. Soil organic carbon and TN concentrations of all samples were also analyzed using dry combustion techniques. Soil samples were scanned from 4000 to 400 cm(-1) (2500-25 000 nm) for MIR spectra and from 8000 to 4000 cm(-1) (1250-2500 nm) for NI R spectra. Partial least squares regression (PLSR) analysis was used for the calibration dataset to build prediction models for SOC:, TN and C:N ratio. The SOC and TN concentrations determined using dry combustion techniques were compared with the prediction from the models using the calibration datasets. The predictions of SOC and TN concentrations by the PLSR method using infrared spectra were statistically sound, with high coefficient of determination with the calibration dataset (R-cal(2), SOCMIR=0.99 and SOCNIR=0.97, TNMIR=0.98 and TNNIR=0.97) and the validation dataset (R-val(2), SOCMIR=0.96 and SOCNIR=0.95, TNMIR=0.96 and TNNIR=0.95) and low root mean square error (RMSEPcal, SOCMIR=0.93 and SOCNIR=1.60, TNMIR=0.08 and TNNIR=0.12; RMSEPval SOCMIR=1.40 and SOCNIR=1.75, TNMIR=0.11 and TNNIR=0.12). The predictions of SOC and TN concentrations in the 5 to 30 cm depth were better than the predictions for either the surface (0 to 5 cm) soils or for soils from lower depths (> 30 cm). The models could be used as an alternative method for determining SOC and TN concentrations of Brookston clay loam soils; however, larger sample populations and improved model algorithms could further improve predictions.
机译:已对土壤的中红外(MIR)和近红外(NIR)光谱进行了测试,以估算地方,区域和国家规模的土壤有机碳(SOC)和总氮(TN)浓度。但是,在不同的管理方法下,很少使用这些方法来评估同一土壤的SOC和TN浓度。这项研究的目的是确定在不同管理方法下从布鲁克斯顿壤土的红外光谱中建立的模型是否可用于估算SOC,TN浓度和C:N比。用于模型校准的土壤包括来自长期施肥和轮作研究以及长期堆肥研究的217个样品,而来自长期耕作研究的相同土壤类型的78个土壤样品用于模型验证。还使用干式燃烧技术分析了所有样品的土壤有机碳和总氮浓度。从MIR光谱从4000到400 cm(-1)(2500-25 000 nm)扫描土壤样品,从NI R光谱从8000到4000 cm(-1)(1250-2500 nm)扫描土壤样品。偏最小二乘回归(PLSR)分析用于校准数据集,以建立SOC:,TN和C:N比的预测模型。将使用干式燃烧技术确定的SOC和TN浓度与使用校准数据集的模型预测值进行比较。通过PLSR方法使用红外光谱对SOC和TN浓度的预测在统计上是合理的,并且具有校准数据集(R-cal(2),SOCMIR = 0.99和SOCNIR = 0.97,TNMIR = 0.98和TNNIR = 0.97的测定系数) )和验证数据集(R-val(2),SOCMIR = 0.96和SOCNIR = 0.95,TNMIR = 0.96和TNNIR = 0.95)和低均方根误差(RMSEPcal,SOCMIR = 0.93和SOCNIR = 1.60,TNMIR = 0.08和TNNIR = 0.12; RMSEPval SOCMIR = 1.40和SOCNIR = 1.75,TNMIR = 0.11和TNNIR = 0.12)。 5至30厘米深度的SOC和TN浓度的预测要好于表层(0至5厘米)土壤或较低深度(> 30厘米)的土壤。该模型可用作确定布鲁克斯顿黏土壤土中有机碳和总氮浓度的替代方法。但是,更大的样本数量和改进的模型算法可以进一步改善预测。

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