Rapid determination of soil organic matter quality indicators using visible near infrared reflectance spectroscopy
Rapid determination of soil organic matter quality indicators using visible near infrared reflectance spectroscopy
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DOI：
10.1016/j.geoderma.2014.05.023
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发表时间：
2014-11
期刊：
影响因子：
6.1
通讯作者：
M. S. Luce;N. Ziadi;B. Zebarth;C. Grant;G. Tremblay;E. Gregorich
中科院分区：
文献类型：
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作者：
M. S. Luce;N. Ziadi;B. Zebarth;C. Grant;G. Tremblay;E. Gregorich
土壤有机质（SOM）质量的评估和监测对于确定和发展管理措施，以提高和保持农业土壤的生产力是非常重要的。这需要对多个土壤参数进行常规分析，这可能是耗时且昂贵的。研究表明，可见近红外反射光谱（VNIRS）可以作为一种快速和成本效益的工具，SOM质量评估。在这项研究中，VNIRS（400-2498 nm）首次被用来同时预测微生物生物量氮（MBN），水提取有机氮（WEON），轻组有机质氮（LFOMN），颗粒有机质氮（POMN），土壤全氮（TN），土壤有机碳（SOC）和土壤C/N比土壤有机质质量指标在加拿大西部的沼泽化土壤。2010年和2011年在6个地点进行的作物轮作试验中，在0-15 cm深度采集土壤样品（n= 200）。在去除通过主成分分析（PCA）识别的离群值（5个样品）后，随机选择75%的样品集用于校准（n= 146），剩余的用于验证（n= 49）。采用改进的偏最小二乘回归交叉验证建立预测模型。使用验证中的决定系数（R2 V）和验证集中参考数据的标准差与预测标准误差（RPDV）的比值评估模型的可靠性。VNIRS对LFOMN、POMN、TN和SOC（R2 V> 0.80，RPDV> 2.4）以及MBN（R2 V = 0.74，RPDV= 1.93）的预测被认为是可靠的，但对WEON（R2 V = 0.67，RPDV= 1.70）和土壤C/N比（R2 V = 0.54，RPDV= 1.45）的预测不太可靠。这项研究表明，VNIRS具有作为快速确定SOM质量指标的非破坏性且经济高效的工具的潜力。
Assessment and monitoring of soil organic matter (SOM) quality are important for determining and developing management practices that will enhance and maintain the productivity of agricultural soils. This requires routine analysis of multiple soil parameters, which can be time-consuming and expensive. Research has suggested that visible near infrared reflectance spectroscopy (VNIRS) may be used as a rapid and cost-efficient tool for SOM quality assessment. In this study, VNIRS (400–2498 nm) was used for the first time to simultaneously predict microbial biomass nitrogen (MBN), water-extractable organic N (WEON), light fraction organic matter N (LFOMN), particulate organic matter N (POMN), soil total N (TN), soil organic carbon (SOC) and soil C/N ratio as soil SOM quality indicators in Chernozemic soils of western Canada. The soil samples (n= 200) were collected at the 0–15 cm depth from a crop rotation experiment conducted at 6 sites in 2010 and 2011. After removal of outliers (five samples) identified by principal components analysis (PCA), 75% of the sample set was randomly selected for calibration (n= 146) and the remainder used for validation (n= 49). Modified partial least squares regression with cross-validation was used to develop prediction models. The reliability of the models was assessed using the coefficient of determination in validation (R2V) and the ratio of standard deviation of the reference data in the validation set to the standard error of prediction (RPDV). The VNIRS predictions were considered reliable for LFOMN, POMN, TN, and SOC (R2V> 0.80, RPDV> 2.4), as well as for MBN (R2V= 0.74, RPDV= 1.93), but less reliable for WEON (R2V= 0.67, RPDV= 1.70) and soil C/N ratio (R2V= 0.54, RPDV= 1.45). This study showed that VNIRS has the potential as a non-destructive and cost-efficient tool for rapid determination of SOM quality indicators.