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首页> 外文期刊>Geoderma: An International Journal of Soil Science >Comparison of infrared spectroscopy and laser granulometry as alternative methods to estimate soil aggregate stability in Mediterranean badlands
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Comparison of infrared spectroscopy and laser granulometry as alternative methods to estimate soil aggregate stability in Mediterranean badlands

机译：红外光谱法和激光粒度分析法比较，作为估计地中海荒地土壤团聚体稳定性的替代方法

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Soil aggregate stability is a key indicator of soil resistance to erosion, but its measurement remains fastidious for large scale uses. Alternative time and cost-effective methods are thus needed. Our objective was to assess and compare the efficiency of laser granulometry (LG) and soil mid- and near-infrared spectroscopy (MIR/NIR) as alternative methods to assess soil aggregate stability in Mediterranean badland soils. A collection of 75 badland soil samples was used, showing wide variations in soil aggregate stability. Three different categories of measurements were performed: (i) the aggregate breakdown kinetics of the [1 mm] size fraction under stirring and sonication, tracked by repeated particle size distribution measurements, using LG, (ii) mid-(diffuse-MIR-DR and attenuate transmitted reflectance MIR-ATR) and near-(NIR-DR) infrared spectra of the fine soil fraction [2 mm] and (iii) the soil aggregate [3-5 mm] stability, using the standardized method (ISO/FDIS 10930, 2012). Partial least squares regression models were used to predict soil aggregate stability using LG data and infrared spectra. Results showed that NIR-DR and MIR-ATR data provided the best prediction model for soil aggregate stability values (RPD = 2.61 & 2.74; R-2 = 0.85 & 810.87), followed by MIR-DR data (RPD = 2.24; R-2 = 0.89) and finally LG data (RPD = 2.12; R-2 = 0.80). For a quantitative use of the models to assign soil samples to standardized soil aggregate stability classes (ISO/FDIS 10930, 2012), infrared spectra also provided the best accuracy, with a misclassification rate below 30% for NIR-DR and MIR-ATR models, while it reached 43% with the LG-based model. The combination of IR and LG data did not yield a better prediction model for soil aggregate stability values and classes, Infrared-based method also provided best results in terms of time-saving strategy, reducing the measurement time to 8 min only. To conclude, infrared spectra (NIR-DR and MIR-ATR) outperformed LG-data to predict soil aggregate stability. Further development of this technique would require calibrating a set of soil-type specific prediction models for a wide range of soil types. (C) 2016 Elsevier B.V. All rights reserved.
机译：土壤团聚体的稳定性是土壤抗侵蚀能力的关键指标，但对于大规模使用而言，其测量仍然很严格。因此需要替代的时间和成本有效的方法。我们的目标是评估和比较激光粒度分析（LG）和土壤中红外光谱和近红外光谱（MIR / NIR）的效率，作为评估地中海荒地土壤中土壤团聚体稳定性的替代方法。使用了75种荒地土壤样品，表明土壤聚集体稳定性差异很大。进行了三种不同类别的测量：（i）使用LG通过重复的粒度分布测量来跟踪[<1 mm]尺寸级分在搅拌和超声处理下的总分解动力学，（ii）中（扩散MIR-使用标准化方法（ISO）对细小土壤部分[<2 mm]和（iii）土壤骨料[3-5 mm]稳定性进行DR和衰减透射反射率MIR-ATR）和近（NIR-DR）红外光谱/ FDIS 10930，2012年）。偏最小二乘回归模型用于利用LG数据和红外光谱预测土壤团聚体的稳定性。结果表明，NIR-DR和MIR-ATR数据提供了土壤团聚体稳定性值的最佳预测模型（RPD = 2.61和2.74; R-2 = 0.85＆810.87），其次是MIR-DR数据（RPD = 2.24; R- 2 = 0.89），最后是LG数据（RPD = 2.12; R-2 = 0.80）。为了定量使用模型将土壤样品分配到标准化的土壤团聚体稳定性类别（ISO / FDIS 10930，2012），红外光谱也提供了最佳的准确性，对于NIR-DR和MIR-ATR模型，错误分类率低于30％ ，而基于LG的模型则达到了43％。 IR和LG数据的结合并不能提供更好的土壤团聚体稳定性值和类别的预测模型，基于红外的方法在省时策略方面也提供了最佳结果，将测量时间减少到仅8分钟。总而言之，红外光谱（NIR-DR和MIR-ATR）的表现优于LG数据，可预测土壤团聚体的稳定性。该技术的进一步发展将需要针对各种土壤类型校准一组特定于土壤类型的预测模型。 （C）2016 Elsevier B.V.保留所有权利。

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