Biogas potential from forbs and grass-clover mixture with the application of near infrared spectroscopy

Field-scale differences in rhizosphere micro-characteristics of Cichorium intybus, Ixeris polycephala, sunflower, and Sedum alfredii in the phytoremediation of Cd-contaminated soil

Understanding the intricate interplay between Cd accumulation in plants and their rhizosphere micro-characteristics is important for the selection of plant species with profitable Cd phytoextraction and soil remediation efficiencies. This study investigated the differences in rhizosphere micro-ecological characteristics and Cd accumulation in chicory, Ixeris polycephala, sunflower, and Sedum alfredii in low-moderate Cd-contaminated soil. Data reveal that the dominant organic acids in rhizosphere soil that responded to Cd were oxalic and lactic acids in chicory and Ixeris polycephala, tartaric acid in sunflower, and succinic acid in Sedum alfredii. These unique organic acids could also influence the abundance of specific rhizobacterial communities in rhizosphere soil that were Sphingomonadaceae and Bradyrhizobiaceae in both Sedum alfredii (9.75 % and 2.56 %, respectively) and chicory (8.98 % and 2.82 %, respectively) rhizosphere soil, Xanthomonadaceae in both Sedum alfredii and Ixeris polycephala rhizosphere soil, and Gaiellaceae in chicory rhizosphere soil. In this case, the combined effects of the organic acids and unique rhizobacterial communities by plant species increased the bioavailable concentration of Cd in Sedum alfredii, Ixeris polycephala, and sunflower rhizosphere soil, while decreasing the Cd-DOM concentrations in chicory rhizosphere soil and the water-extractable Cd reduced by 88.02 % compared to the control. Though the capacity for Cd accumulation in the shoots of chicory was weaker than of Sedum alfredii but better than either Ixeris polycephala or sunflower, chicory presented better Cd translocation and harbored Cd mainly as the low toxic chemical form of pectates and proteins-bound Cd and Cd oxalate in its shoot. Generally, chicory, as an economic plant, is suitable for phytoremediation of low-moderate Cd-contaminated soil after Sedum alfredii.

Predicting methane yield by linear regression models: A validation study for grassland biomass

The objectives of this study were to assess and validate previously published prediction models with an independent dataset and to expose the power and limitations of linear regression models for predicting biomethane potential. Two datasets were used for the validation, one with all individual samples and one with the average values of each cultivar. The results revealed similar performances of all four models for the individual samples. The methane yields of the cultivars were predicted more accurately than the methane yields of the individual samples. The grassland specific model predicted the variation in the dataset with an R² of 0.84 and the slope of the regression line was equal to 1.0. Linear regression models are suitable to depict the variation in methane yield and for substrate ranking. However, the prediction error of the absolute values may be high since systematic external effects cannot be determined by a regression model.

Fast prediction of organic wastes methane potential by near infrared reflectance spectroscopy: A successful tool for farm-scale biogas plant monitoring

Currently, there is a growing worldwide interest for the treatment of wastes, and especially farm wastes, by anaerobic digestion. Biochemical methane potential is a key parameter for the design, optimisation and monitoring of the anaerobic digestion process, but it is also time consuming (4-7 weeks). Near infrared reflectance spectroscopy seems a promising method to predict the biochemical methane potential of a wide range of organic substrates. This study compares a 'global' predictive model mainly built with biogas plant feedstocks, and a more 'agricultural' specific one built with farm wastes only (e.g. manures and crop residues). The global model was calibrated with 245 samples and the specific one with 171 samples. In parallel, validation sets composed of 36 farm wastes and eight other wastes (sludge, fruit residues and vegetables) were used to evaluate and compare both models. Satisfying results were obtained on the validation sets considering, respectively for the global and the specific models, a root mean square error of prediction of 44 and 34 NL CH₄ kg^-1 volatile solid, a coefficient of determination of 0.76 and 0.83, and a ratio of performance to deviation of 2.0 and 2.4. In general rules, the specific model was better than the global one in the prediction of farm wastes methane potential. However, thanks to its larger sample variability, the global one was more robust, especially towards the 'other' wastes, which can be introduced punctually in agricultural biogas plant.

Influence of chemical composition on biochemical methane potential of fruit and vegetable waste

This study investigates the influence of chemical composition on the biochemical methane potential (BMP) of twelve different batches of fruit and vegetable waste (FVW) with different compositions collected over one year. BMP ranged from 288 to 516L_NCH₄kgVS^-1, with significant statistical differences between means, which was explained by variations in the chemical composition over time. BMP was most strongly correlated to lipid content and high calorific values. Multiple linear regression was performed to develop statistical models to more rapidly predict methane potential. Models were analysed that considered chemical compounds and that considered only high calorific value as a single parameter. The best BMP prediction was obtained using the statistical model that included lipid, protein, cellulose, lignin, and high calorific value (HCV), with R² of 92.5%; lignin was negatively correlated to methane production. Because HCV and lipids are strongly correlated, and because HCV can be determined more rapidly than overall chemical composition, HCV may be useful for predicting BMP.

Determination of hemicellulose, cellulose and lignin content using visible and near infrared spectroscopy in Miscanthus sinensis

Lignocellulosic components including hemicellulose, cellulose and lignin are the three major components of plant cell walls, and their proportions in biomass crops, such as Miscanthus sinensis, greatly impact feed stock conversion to liquid fuels or bio-products. In this study, the feasibility of using visible and near infrared (VIS/NIR) spectroscopy to rapidly quantify hemicellulose, cellulose and lignin in M. sinensis was investigated. Initially, prediction models were established using partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function neural network (RBF_NN) based on whole wavelengths. Subsequently, 23, 25 and 27 characteristic wavelengths for hemicellulose, cellulose and lignin, respectively, were found to show significant contribution to calibration models. Three determination models were eventually built by PLS, LS-SVM and ANN based on the characteristic wavelengths. Calibration models for lignocellulosic components were successfully developed, and can now be applied to assessment of lignocellulose contents in M. sinensis.

Forbs enhance productivity of unfertilised grass-clover leys and support low-carbon bioenergy

Intensively managed grasslands are dominated by highly productive grass-clover mixtures. Increasing crop diversity by inclusion of competitive forbs may enhance biomass production and sustainable biofuel production. Here we examined if one or all of three forbs (chicory, Cichorium intybus L.; caraway, Carum carvi L.; plantain, Plantago lanceolata L.) included in ryegrass-red clover mixtures enhanced above- and below-ground productivity, and assessed their biofuel potentials, based on a three-year experiment with and without fertilisation as cattle slurry. We determined herbage yield, standing root biomass, and estimated methane energy output and greenhouse gas (GHG) emissions per energy unit using life cycle assessment. Results showed that plantain-containing grass-clover mixtures significantly increased herbage yield, while chicory- or caraway-containing mixtures maintained similar yields to the grass-clover mixture. Standing root biomass of the grass-clover mixture was enhanced by inclusion of caraway and plantain, with that of plantain further enhanced by fertilisation. The highest methane energy output was achieved in plantain-containing grass-clover mixtures. All unfertilised mixtures achieved the 60% reduction in GHG emissions compared to fossil fuel, whereas all fertilised mixtures did not meet the 60% reduction target. These findings suggest that including competitive forbs such as plantain in grass-clover mixtures enhances productivity, supporting low-carbon footprint bioenergy production.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.