4.7 Article

The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis-NIR spectroscopy and comparative methods

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PLANT AND SOIL
卷 246, 期 2, 页码 139-149

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SPRINGER
DOI: 10.1023/A:1020612319014

关键词

carbon content; carbon mineralisation; grassland soils; NIR spectroscopy; nitrogen content; nitrogen mineralisation

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The development of a rapid, accurate and cost-effective method for the prediction of constituents related to soil nitrogen (N) supply is considered important. The potential of using visible (Vis) and near infrared reflectance (NIR) spectroscopy (400-2500 nm) as such a method was investigated. Vis-NIR calibrations were performed for organic carbon (C-org) and total N (N-tot) content and their potential mineralisation using 80 grassland soil samples of rather heterogeneous origin. Prediction accuracy was tested using a 'take-out-four' validation strategy (48 samples). Within investigated variables a ratio of standard deviation of reference data to standard error of bias corrected prediction (RPD) within 1.7 (r(2) = 0.65 and 2.7 (r(2) = 0.87) were achieved. Apparent differences in Vis-NIR prediction accuracy among the variables were partly due to errors in the reference values. Thawed moist samples tend to be more accurately predicted than dried samples, and no benefit was derived from the grinding of sieved (4 mm) and dried samples. Prediction accuracy did not differ using two different systems for sample presentation to the Vis-NIR analyses. Comparative predictions of C-org and N-tot and their potential mineralisations were performed using the take-out-four validation strategy and simple linear regression to loss on ignition (LOI) values and hot KCl extracted NH4-N (N-hotKCl) values as predictors. Likewise, the reference values of C-org and N-tot were also used as predictors for each other and for the potential C and N mineralisation constituents. Accuracy obtained for the Vis-NIR predictions of investigated constituents was in general equal or better than prediction accuracy obtained by these comparative methods. The Vis-NIR method provided promising predictions of variables important for the soil N supply.

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