Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils

Soil organic carbon (SOC) originates from a complex mixture of organic materials, and to better understand its role in soil functions, one must characterise its chemical composition. However, current methods, such as solid-state 13C nuclear magnetic resonance (NMR) spectroscopy, are time-consuming and expensive. Diffuse reflectance spectroscopy in the visible and infrared regions (vis–NIR: 350–2500 nm; mid-IR: 4000–400 cm-1) can also be used to characterise SOC chemistry; however, it is difficult to know the frequencies where the information occurs. Thus, we correlated the C functional groups from the 13C NMR to the frequencies in the vis–NIR and mid-IR spectra using two methods: 1) 2-dimensional correlations of 13C NMR spectra and the diffuse reflectance spectra, and 2) modelling the NMR functional C groups with the reflectance spectra using support vector machines (validated using 5 times repeated 10-fold cross-validation). For the study, we used 99 mineral soils from the agricultural regions of Sweden. The results show clear correlations between organic functional C groups measured with NMR and specific frequencies in the vis–NIR and mid-IR spectra. While the 2D correlations showed general relationships (mainly related to the total SOC content), analysing the importance of the wavelengths in the SVM models revealed more detail. Generally, models using mid-IR spectra produced slightly better estimates than the vis–NIR. The best estimates were for the alkyl C group (R2 = 0.83 and 0.85, vis–NIR and mid-IR, respectively), and the O/N-alkyl C group was the most difficult to estimate (R2 = 0.34 and 0.38, vis–NIR and mid-IR, respectively). Combining 13CNMR with the cost effective diffuse reflectance methods could potentially increase the number of measured samples and improve the spatial and temporal characterisation of SOC. However, more studies with a wider range of soil types and land management systems are needed to further evaluate the conditions under which these methods could be used

Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy?

Near infrared (NIR) spectroscopy was used to predict the soil organic carbon (SOC) contents at local scale in eleven target sites. For that, eight spectral libraries of different sizes (ranging from 3482 to 36 samples) were used to construct national, provincial and local scale models. Inaccurate predictions were obtained except when the largest national library was used to construct the model. We also obtained SOC predictions once the models were adapted to target sites characteristics. For the models' adaptation, we used a two-step approach consisting on spiking (as first step) and extra-weighting (as second step). The effect of spiking was small in larger-sized models and high in smaller-sized models, whereas the effect of extra-weighting was small in smaller-sized models and large in larger-sized models. The very high accuracy obtained after models' adaptation (R2>0.95; RPIQ>5.48), regardless of the size of the spectral library, suggests that large spectral libraries are not needed for local scale SOC assessment. These results have important implications regarding the way that NIR spectroscopy can result highly effective for land management and how users can focus and organize the analytical efforts

Assessment of soil organic carbon at local scale with spiked NIR calibrations: Effects of selection and extra-weighting on the spiking subset

Spiking is a useful approach to improve the accuracy of regional or national calibrations when they are used to predict at local scales. To do this, a small subset of local samples (spiking subset) is added to recalibrate the initial calibration. If the spiking subset is small in comparison with the size of the initial calibration set, then it could have little noticeable effect and a small improvement can be expected. For these reasons, we hypothesized that the accuracy of the spiked calibrations can be improved when the spiking subset is extra-weighted. We also hypothesized that the spiking subset selection and the initial calibration size could affect the accuracy of the recalibrated models. To test these hypotheses, we evaluated different strategies to select the best spiking subset, with and without extra-weighting, to spike three different-sized initial calibrations. These calibrations were used to predict the soil organic carbon (SOC) content in samples from four target sites. Our results confirmed that spiking improved the prediction accuracy of the initial calibrations, with any differences depending on the spiking subset used. The best results were obtained when the spiking subset contained local samples evenly distributed in the spectral space, regardless of the initial calibration's characteristics. The accuracy was improved significantly when the spiking subset was extra-weighted. For medium- and large-sized initial calibrations, the improvement from extra-weighting was larger than that caused by the increase in spiking subset size. Similar accuracies were obtained using small- and large-sized calibrations, suggesting that incipient spectral libraries could be useful if the spiking subset is properly selected and extra-weighted. When small-sized spiking subsets were used, the predictions were more accurate than those obtained with 'geographically-local' models. Overall, our results indicate that we can minimize the efforts needed to use near-infrared (NIR) spectroscopy effectively for SOC assessment at local scales. © 2014 British Society of Soil Science

Estimation of Soil Carbon Input in France: An Inverse Modelling Approach

Development of a quantitative understanding of soil organic carbon (SOC) dynamics is vital for management of soil to sequester carbon (C) and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component (i.e., C input) constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network (the RMQS database). Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values (P < 0.05) for 14 out of 17 crop types in the range from 1.84 ± 0.69 t C ha-1 year-1 (silage corn) to 5.15 ± 0.12 t C ha-1 year-1 (grassland/pasture). Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction. © 2013 Soil Science Society of China

Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring

The soil science community is facing a growing demand of regional, continental, and worldwide databases in order to monitor the status of the soil. However, the availability of such data is very scarce. Cost-effective tools to measure soil properties for large areas (e.g., Europe) are required. Soil spectroscopy has shown to be a fast, cost-effective, environmental- friendly, nondestructive, reproducible, and repeatable analytical technique. The main aim of this paper is to describe the state of the art of soil spectroscopy as well as its potential to facilitating soil monitoring. The factors constraining the application of soil spectroscopy as an alternative to traditional laboratory analyses, together with the limits of the technique, are addressed. The paper also highlights that the widespread use of spectroscopy to monitor the status of the soil should be encouraged by (1) the creation of a standard for the collection of laboratory soil spectra, to promote the sharing of spectral libraries, and (2) the scanning of existing soil archives, reducing the need for costly sampling campaigns. Finally, routine soil analysis using soil spectroscopy would be beneficial for the end users by a reduction in analytical costs, and an increased comparability of results between laboratories. This ambitious project will materialize only through (1) the establishment of local and regional partnerships among existent institutions able to generate the necessary technical competence, and (2) the support of international organizations. The Food and Agriculture Organization (FAO) of United Nations and the Joint Research Centre of the European Commission are well placed to promote the use of laboratory and field spectrometers for monitoring the state of soils.JRC.H.5-Land Resources Managemen