Fan Ru-qin, Yang Xue-ming, Zhang Xiao-ping, Shen Yan, Liang Ai-zhen, Shi Xiu-huan, Wei Shou-cai, Chen Xue-wen
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2012 Feb;32(2):349-53.
The soil organic carbon (SOC) associated with different soil fractions varies in the composition and dynamics. The present work is aimed to evaluate the potential of near infrared spectroscopy (NIRS) to predict SOC content in different soil fractions of black soils. SOC contents of 136 black soil samples in China were analyzed and the NIR spectra were collected using a VECTOR/22 (Fourier transform infrared spectroscopy). Partial least squares (PLS) regression with cross validation was used to develop calibrations between reference data and NIRS spectra (n = 100) which were validated using an independent set of samples (n = 36). Predictions for water-sieved aggregate associated organic carbon were generally good with R2 (coefficient of determination) ranging from 0.69 to 0.82 and the RPD (residual prediction deviation) from 1.2 to 1.8. NIRS well predicted the SOC in < 53 microm mineral fraction (R2 = 0.97, RPD = 5.4), but the prediction for SOC in 250-2 000 microm or in 53-250 microm particulate matter fractions was poor. However, the prediction for the SOC in 53-2 000 microm fraction was good (R2 = 0.79, RPD = 2.2). In addition, NIRS very well predicted the SOC in fine particle fraction (< 20 microm) (R2 = 0.93, RPD = 3.8). Accordingly, NIRS showed a good potential to predict SOC in some soil fractions and could reduce tedious laboratory analysis.
与不同土壤组分相关的土壤有机碳(SOC)在组成和动态方面存在差异。本研究旨在评估近红外光谱(NIRS)预测黑土不同土壤组分中SOC含量的潜力。分析了中国136个黑土样品的SOC含量,并使用VECTOR/22(傅里叶变换红外光谱仪)收集近红外光谱。采用具有交叉验证的偏最小二乘法(PLS)回归,在参考数据和近红外光谱(n = 100)之间建立校准模型,并使用独立样本集(n = 36)进行验证。对水筛团聚体相关有机碳的预测总体良好,决定系数(R2)范围为0.69至0.82,剩余预测偏差(RPD)为1.2至1.8。近红外光谱能很好地预测<53微米矿物组分中的SOC(R2 = 0.97,RPD = 5.4),但对250 - 2000微米或53 - 250微米颗粒物质组分中SOC的预测较差。然而,对53 - 2000微米组分中SOC的预测良好(R2 = 0.79,RPD = 2.2)。此外,近红外光谱能很好地预测细颗粒组分(<20微米)中的SOC(R2 = 0.93,RPD = 3.8)。因此,近红外光谱在预测某些土壤组分中的SOC方面具有良好潜力,可减少繁琐的实验室分析。