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Copyright of Agronomy Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Anaerobic digestion is a technology that converts complex organic matter into another form of matter, from which energy can be more easily extracted. The stability and digestion of anaerobic co‐digestion with multiple raw materials as substrates is better than the performance of anaerobic mono‐digestion. In the past two decades, there has been a large amount of literature on how to improve the performance of methane production in the anaerobic co‐digestion process, but few systematic reviews have been conducted. This paper summarizes the factors affecting the development of anaerobic co‐digestion. The process parameters, substrate optimization strategy, microbial community, and model application for improving anaerobic co‐digestion performance were systematically analyzed. Methane purification technology and policies to support digestion projects are also discussed. At present, the technological process is basically mature, but the interaction between various parameters can be studied in depth. Methane purification technology expands the market for the biogas industry and increases its economic benefits. Policy is the key to promoting process technology and the methane market. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR]

Aim: Fresh carbon (C) inputs to the soil can have important consequences for the decomposition rates of soil organic matter (priming effect), thereby impacting the delicate global C balance at the soil–atmosphere interface. Yet, the environmental factors that control soil priming effect intensity remain poorly understood at a global scale. Location: Global. Time period: 1980–2020. Major taxa studied: Soil priming effect intensity. Methods: We conducted a global dataset of CO2 effluxes in 711 pairwise soils with 13C or 14C simple C sources inputs and without C inputs from incubation experiments in which isotope‐labelled C was used to quantify fresh C‐induced rather than exudate‐induced priming. Results: Soil priming effect intensity is predominantly positive. Soil texture and C content were identified as the most important factors associated with priming effects, with sandy soils from tropical and mid‐latitudes supporting the highest soil priming effect intensity, and soils with greater C content and fine textures from high latitudes maintaining the lowest soil priming effects. The negative association between C content and soil priming effect intensity was also indirectly driven by changing mean annual temperature, net primary productivity, and fungi : bacteria ratio. Using this information, we generated a global map of soil priming effect intensity, and found that the priming was lower at high latitudes and higher at lower latitudes. Main conclusions: Global patterns of soil priming effect intensity can be predicted using environmental data, with soil texture and C content playing a predominant role in explaining in priming effects. These effects were also indirectly driven by climate, vegetation and soil microbial properties. We present the first global atlas of soil priming effect intensity and advance our knowledge on the potential mechanisms underlying soil priming effect intensity, which are integral to improving the climate change and soil C dynamics components of Earth System models. [ABSTRACT FROM AUTHOR]

Free‐living diazotrophs play a significant role in the process of soil biological nitrogen fixation. Long‐term field management has a cumulative effect on soil microbial communities. However, after long‐term mulching measures in erosion areas, the stability difference in diazotrophs in bulk‐rhizosphere soil and the temporal dynamic changes remain unclear. In this study, we analysed the dynamic variability of soil diazotrophic community structures using high‐throughput sequencing. The result of Chao1 diversity index showed that mulching effectively increased the richness of soil diazotrophs. Combining the community composition, it was found that the diversity level of the soil diazotrophic community under mulching decreased more drastically along the development stages compared with no‐tillage, and Bradyrhizobium played a major role in nitrogen fixation. In rhizosphere soil, mulching promoted the relative abundances of Azohydromonas, Bradyrhizobium, and Skermanella. Co‐occurrence network analysis indicated that mulching measures strengthened the connection between the dominant components to improve the aggregation of the network. The partial least squares‐path model showed that the richness of diazotrophs in rhizosphere soil had an indirect effect on mulching through the available carbon and nitrogen nutrients of bulk soil, thereby improving the response rate to environmental variables. Overall, our findings showed that under long‐term mulching, the richness and aggregation of dominant components of the soil diazotrophic community increased with the phenological period and distance from roots, revealing the importance of mulching in eroded areas to improve farmland soil nitrogen fixation capacity. This study provides a theoretical reference for microecology to effectively improve soil fertility in similar areas. [ABSTRACT FROM AUTHOR]

Summary: Food fraud causes significant economic losses for the industry and generates distrust between the consumers and traders. Tea is one of the most valued beverages throughout the world, being vulnerable to economically motivated cheat. The objective of the study was to develop the potential of hyperspectral imaging (HSI) allying multivariate analysis and data fusion to identify the authenticity of Keemun black tea quality categories. Data fusion that integrated of texture characteristics based on grey level co‐occurrence matrix and visible and near‐infrared spectral features via competitive adaptive reweighted sampling (CARS) was as the target data for modelling. Support vector machine (SVM) and random forest (RF) were utilised for the classification of tea samples of seven grades. The RF model using fused data gave the best performance with the correct discriminant rate of 92.70% for the prediction set. This study demonstrated that HSI coupled with RF was effective in identifying tea sample rank. [ABSTRACT FROM AUTHOR]

BACKGROUND Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS: Two matrix statistical algorithms encompassing a gray‐level co‐occurrence matrix (GLCM) and a gradient co‐occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near‐infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION: This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Biochar has been shown to be potentially beneficial for enhancing yields and soil properties, and diminishing nitrogen (N) losses. However, it remains unclear how biochar regulates soil carbon (C) and N to mitigate N losses induced by straw mixing with N fertilizer in dryland soils. Therefore, we investigated the effects of straw mixing (S1), S1 with biochar (SB) and no straw inputs (S0), and routine urea application rates (N1) and 70% of routine rates (N0.7) on yields and N losses, and identify the relationship between N losses and soil C and N compounds. Results showed that N0.7 and N1 were suitable for the maize and wheat seasons, respectively, contributing to mitigating N losses without reducing crop yields. Moreover, in the maize season, N0.7‐SB significantly mitigated the straw‐induced NH3‐N and N2O‐N emissions by 106% and 81%, respectively. In the wheat season, N1‐SB reduced the straw‐induced NH3‐N and N2O‐N emissions by 35% and 66%, respectively. In addition, N0.7‐SB sharply reduced soil inorganic N (SIN) storage in the maize season. Furthermore, the NH3‐N and N2O‐N emission rates were negatively correlated with dissolved organic carbon/SIN content (0–20 cm) (DOC/SIN0‐20). N losses (N2O‐N and NH3‐N emissions and SIN storage) were positively correlated with SIN0‐20, but negatively correlated with soil organic carbon / SIN0‐20 (SOC/ SIN0‐20). This study provides further evidence that biochar with an appropriate N application rate decreased SIN0‐20 and increased DOC/SIN0‐20, thus reducing SIN storage and the straw‐induced gaseous N emissions without decreasing crop yields. [ABSTRACT FROM AUTHOR]

BACKGROUND Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy. RESULTS: Cognitive spectroscopy was proposed to combine near‐infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K‐nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO–SVM model showed the best predictive performance with the correlation coefficients of prediction set (Rp) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin. CONCLUSION: The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least‐squares discriminant analysis (PLS‐DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near‐infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA‐SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades. [ABSTRACT FROM AUTHOR]

BACKGROUND: Preprocessing technologies of fresh tea leaves have a great influence on tea quality. A multifunctional preprocessing device for tea raw materials has been designed and utilized as a novel item of equipment to synchronously meet the process needs of spreading of green tea, withering of black tea and shaking of oolong tea. RESULTS: The preprocessing parameters of fresh tea leaves for spreading of green tea, withering of black tea and shaking of oolong tea were optimized by orthogonal experiments. Sensory assessment combined with statistical tools was employed as an analytical method to evaluate the pretreatment effect of processing different sorts of tea. The range analysis and variance analysis of tea sensory evaluation combined with chemical components (total polyphenols, free amino acids and soluble sugar) showed that A3B2C3 (70%, 25 °C, 8 h), A1B3C1 (60%, 28 °C, 18 h) and A1B1C3 (5 min, 20 °C, discontinuous leaf turning c) were considered to be the optimum schemes for the best pretreatment conditions of the above three major processing types of tea, respectively. The verification experiment of the proposed schemes was performed with satisfactory performance. CONCLUSION: This study demonstrated that a multifunctional preprocessing device for fresh tea leaves can be successfully applied to simultaneously estimate spreading of green tea, withering of black tea and shaking of oolong tea. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Field experiments were conducted to study soil properties, soil enzymes activities, water use efficiency (WUE) and crop productivity after six years of soya bean straw mulching in the semi‐arid conditions of China. The experiment included four treatments: CK (Control), N (240 kg N ha‐1), H (soya bean straw mulching at half rate 700 kg ha‐1 with 240 kg N ha‐1) and F (soya bean straw mulching at full rate 1,400 kg ha‐1 with 240 kg N ha‐1). Soil organic carbon (SOC), soil labile organic carbon (LOC), soil available N (AN), available P (AP) and enzyme activities were analysed after wheat harvesting in 2016 and 2017. Results show that straw amounts had positive effects on the soil fertility indices being higher for treatment F. The SOC, LOC, AN, AP and enzyme activities (i.e. saccharase, urease and alkaline phosphatase) were in the order of F > H > N > CK. High wheat grain yield and WUE were observed for F treatment. A total of six years mulching along with 240 kg ha‐1 nitrogen fertilizer application is sufficient for wheat yield stability and improving soil properties except urease activities in the semi‐arid condition of China. However, the straw mulching amount should be further studied with minimum nitrogen fertilizer for an environment‐friendly and effective approach for improving the soil biological properties with adequate crop production on a sustainable basis in the semi‐arid region of China. [ABSTRACT FROM AUTHOR]

Organic carbon (OC) sequestration through soil aggregation is an important aspect of land use change/conversion (LUCC) influencing the terrestrial ecosystem C cycle, although little is known on the changes in aggregate dynamics and their contributions to OC accumulation after LUCC in regions with serious soil erosion. Therefore, bulk soil samples under four land uses (farmland and three vegetated soils converted from farmland 42 years ago: Robinia pseudoacacia [RP42yr], Caragana korshinskii [CK42yr], and abandoned land [AL42yr]) in the Loess Plateau, China, was collected, separated into seven aggregate size fractions, and examined for OC content. Farmland conversion into AL42yr, CK42yr, and RP42yr increased macroaggregate (>2 mm) and mesoaggregate (2–0.25 mm) proportions, mean weight diameter, and geometric mean diameter but decreased microaggregates (0.25–0.053 mm) amount. Bulk soil and aggregates OC content and stock varied with soil depth and land use types but were usually highest in RP42yr. Mesoaggregates contained higher OC content and stock than other aggregates at 0‐ to 20‐cm depth under all land uses. Increases in the OC stocks of mesoaggregates accounted for 46% and 85% of the increase in bulk soil OC stocks at 0‐ to 20‐ and 20‐ to 40‐cm depth, respectively. Thus, soil OC accumulation after LUCC is mainly due to increased OC stock within mesoaggregates, which is further attributed to increased mesoaggregate proportions. Overall, vegetation restoration promotes the physical protection of OC by increasing soil aggregation, being a management option to enhance the C sequestration potential in ecological fragile regions. [ABSTRACT FROM AUTHOR]