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The appearance of tea leaves is the most intuitive factor in assessing their quality and is an important indicator of the price. The study utilizes machine vision and hyperspectral image technology to develop a rapid method for assessing the appearance quality of seven grades of Dianhong Congou black tea samples based on shape, color, texture, and feature fusion. Using the shape metrics of length, width, perimeter, narrowness, area, and rectangularity, 88 texture values, and nine color values as features, the effectiveness of intelligent recognition algorithms such as support vector machine, random forest, and least squares support vector machine (LSSVM) in classifying single features (shape, color, and texture) and fused data models of the categories of Dianhong tea samples was explored. The results showed that the accuracy of the feature fusion-based appearance quality evaluation models was all higher than the modeling results of the corresponding single features, and the accuracy of the LSSVM model based on the fused data was 100%. This study showed that feature data fusion reflects the external information of tea leaves more comprehensively and that intelligent classification algorithms combined with image analysis techniques were an effective strategy for the identification of regional black tea. [ABSTRACT FROM AUTHOR]

[Display omitted] • Rapid detection of ash content in black tea using a homemade miniature NIR. • Optimizing features by the CARS, IRIV, VCPA-IRIV, and IVISSA. • The constructed IVISSA-SVR model obtains satisfactory prediction performance. Ash is a testing index with both health inspection value and quality decision value, and it is an essential detection item in the import and export trade of tea. To realize the rapid and effective quantitative analysis of ash content in tea, this study proposed the use of a homemade miniature near-infrared (NIR) spectroscopy combined with multivariate analysis for the rapid detection of ash content in black tea. First, NIR data of black tea samples from different countries were acquired and optimized by the spectral preprocessing method. Then, the optimized pre-processed spectral data were used as features, and four feature wavelength selection algorithms, such as competitive adaptive reweighted sampling, iteratively retaining informative variables (IRIV), variable combination population analysis (VCPA)-IRIV, and interval variable iterative space shrinkage approach (IVISSA), were utilized to optimize the feature spectra. Finally, the support vector machine regression (SVR) algorithm was employed to construct the quantitative models of ash content in black tea by combining the optimal wavelengths obtained from the four feature selection methods mentioned above. The experimental results showed that the IVISSA-SVR model had the best performance, with correlation coefficient (R p), root mean square errors of prediction (RMSEP), and relative prediction deviation (RPD) of 0.9546, 0.0192, and 5.59 for the prediction set, respectively. The results demonstrate that a miniature NIR sensing system combined with chemometrics as an effective analytical tool can realize the rapid detection of ash content in black tea. [ABSTRACT FROM AUTHOR]

To address the problem of the lack of rapid discrimination technology for comprehensive evaluation of product quality and grade in the field of tea quality control, this study uses near-infrared spectroscopy, electronic eye, electronic tongue, and electronic nose technology to sense the external and internal quality sensing characteristics of tea leaves, and realizes comprehensive evaluation of four quality factors such as "color, aroma, taste and shape" of tea leaves through cross-sensor multimodal quality characteristics data fusion. For the first time, a comprehensive method for assessing the grade quality of tea based on deep learning algorithms combining cross-sensor multimodal fusion features such as color, texture, shape, optical, electronic tongue, and electronic nose is established. The results showed that the constructed deep convolutional neural network (CNN) classification model had a misclassification rate of only 0.86% in the prediction set, which was better than the modeling results based on traditional classification methods. This study reveals that the CNN algorithm can effectively evaluate the quality grade of tea samples and realize the digital evaluation of multiple evaluation factors of tea sensory quality. [Display omitted] • Description of tea quality using deep learning and multi-sensor feature fusion. • A comprehensive evaluation for tea "color, aroma, taste and shape" is proposed. • The constructed CNN model has a misclassification rate of only 0.86%. [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]

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]

This study investigated the effects of different amounts of magnetite powder (i.e., 0 g, 1.5 g, 3 g, 4.5 g, 6 g) on the anaerobic co-digestion of pig manure (PM) and wheat straw (WS). The variations in pH, alkalinity, cellulase activity (CEA), dehydrogenase activity (DHA) and methane production, were analyzed by phases. Correlation of the activities of the two enzymes with methane production was also analyzed, and the Gompertz model was used to evaluate the efficiency of anaerobic digestion (AD) with the addition of magnetite powder. The results showed that magnetite powder had significant effects on the anaerobic co-digestion of PM and WS. The maximum total methane production with the addition of 3 g of magnetite powder was 195 mL/g total solids (TS), an increase of 72.1%. The CEA and DHA increased with magnetite powder in the ranges of 1.5–4.5 g, 1.5–6 g, respectively, while the methane production showed a better correlation with DHA than with CEA. Using the Gompertz model, the efficiency of AD was optimal when adding 3 g magnetite powder, with higher methane production potential (206 mL/g TS), shorter lag-phase time (14.9 d) and shorter AD period (44 d). [ABSTRACT FROM AUTHOR]

[Display omitted] • Digital diagnosis of Dianhong black tea quality using pocket-sized NIR. • Optimizing features by the PSO, ACO, SA, and GWO. • Recognition models for black tea categories were developed using ELM, PLS-DA, and SVM combined with the selected features. • The GWO-SVM model achieves the expected predictive performance. This paper investigates the feasibility of combining portable near-infrared (NIR) spectroscopy with chemometric algorithms to improve the quality prediction performance of Dianhong black tea. This study presented a pocket-sized NIR device (defined as NIR-S-R2) for the simultaneous identification of multiple categories of Dianhong black tea. Firstly, the spectral information of 700 multi-category Dianhong tea samples was sensed by the NIR-S-R2 system and high-quality analytical spectra of the samples were obtained by comparing multiple spectral pre-processing algorithms. Then, based on the best pre-processed spectral data, four characteristic wavelength evolution algorithms, namely particle swarm optimization, ant colony optimization, simulated annealing, and grey wolf optimization (GWO), were employed to obtain the characteristic spectral variables. Finally, extreme learning machine, partial least squares discriminant analysis, and support vector machine (SVM) methods were adopted to develop recognition models for seven categories of Dianhong black tea based on the selected features. The results showed that the GWO-SVM model had the best predictive capability, with a prediction accuracy of 91.42%. At suitable spectral wavelengths, the NIR-S-R2 system combined with chemometrics offers good discriminatory accuracy and potential for the application of extended detection. [ABSTRACT FROM AUTHOR]

Abstract: Near-infrared spectroscopy (NIRS) combined with chemometric tools was utilized as a rapid analysis method to assess quality and to differentiate geographical origins of black tea. A partial least squares (PLS) algorithm was employed for the calibration of models predicting the levels of caffeine, water extract, total polyphenols, and free amino acids, while a factorization method was proposed to trace black tea from different geographical origins. In the calibration set, the root mean squared error of cross validation (%) and the correlation coefficient (R) for caffeine, water extracts, total polyphenols and free amino acids were 0.102%, 0.654%, 0.552%, and 0.248% and 0.983, 0.977, 0.975, and 0.943, respectively. In the prediction set, the root mean squared error of prediction and R for the corresponding constituents were 0.160%, 0.685%, 0.594%, and 0.273% and 0.955, 0.962, 0.954, and 0.927, respectively. The identification accuracy for black tea from different geographical origins reached 94.3%. This study demonstrated that NIR spectroscopy can be successfully applied to rapidly determine the main chemical compositions and geographical origins of black tea. [Copyright &y& Elsevier]

• Assessing black tea quality by Vis-NIR spectra. • Selecting spectral features by the Si-PLS, CARS, iVISSA, and VISSA. • Chemometric models for black tea quality developed using SVM-based kernel methods. • The CARS-linear kernel SVM model shows promising results. Current evaluation systems for tea quality assessment are difficult to use and unstable. A rapid and cheap assessment method is required to distinguish the quality levels of tea. In this study, a visible-near-infrared (Vis–NIR) spectrometer and support vector machine (SVM)-based kernels were used for the qualitative categorization of black tea. First, a Vis–NIR system was used to acquire the spectral data of seven levels of tea samples. The obtained spectra were preprocessed using the Savitzky–Golay smoothing combined with the first derivative and standard normal variate transformation. Then, four characteristic wavelength selection algorithms, namely synergy interval partial least square, competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), and the interval VISSA, were used to obtain preprocessed spectral features. Finally, the SVM was used with four kernel functions, namely the linear, Gaussian, quadratic, and cubic functions, to develop models based on the variables obtained from the selected features for tea quality classification. The results revealed that the CARS–linear kernel SVM model exhibited the best results, with a correct identification rate of 91.85 % in the validation process. Our findings demonstrate that Vis-NIR spectroscopy can be a rapid, inexpensive, efficient, and alternative method for predicting the quality of black tea. [ABSTRACT FROM AUTHOR]

• Estimation of Congou black tea quality by an artificial lipid membrane taste sensor. • Optimizing taste features using ant colony optimization. • Multivariate analyses of quality categories and taste characteristics are performed. • The ACO-LS-SVM model achieves the desired predictive performance. The taste attribute is an important indicator to estimate the quality and rank of black tea. It is crucial to prevent fraud and avoid economic losses. This study creatively proposed the assessment of Congou black tea quality based on an electronic tongue system combined with the ant colony optimization (ACO) algorithm and stoichiometry. Firstly, the electronic tongue could effectively present the taste quality of black tea, capturing characteristic potential signals from 700 samples of seven different qualities and converting the signals into nine relative characteristic taste values. Then, the taste indicators from different artificial lipid membrane sensors were optimized using the ACO method. Finally, the discriminant models based on the extreme learning machine, support vector machine, partial least-squares discriminant analysis, and least squares-support vector machine (LS-SVM) were developed using the optimized taste characteristics for the assessment of black tea quality. Results indicated that the LS-SVM model, which was created using the five taste features, could obtain better predictive outcomes based on the generalization performance of the model. In the prediction set, the correct discriminant rate was 99.14%. The overall results demonstrate that the electronic tongue sensor array has potential application prospects for the assessment of Congou black tea products in the actual production process. [ABSTRACT FROM AUTHOR]

• Foliar spraying of sodium selenite significantly increased the apple quality. • Foliar spraying of sodium selenite effected the physicochemical indicators, and finally determined the apple quality. • Sucrose metabolism significantly correlated to the activities of key enzymes, SPS is a crucial enzyme. • Sodium selenite dose ranged from 100 and 150 mg/L could be appropriate. Selenium is an essential trace element that improves fruit quality and nutritional value. However, the effect of sodium selenite on apple quality and its relative sucrose metabolism activity remains unclear. In this study, we investigated the roles of selenite spraying, in improving Fuji apple quality and sucrose metabolism-related enzyme activity. Results showed that foliar spraying of sodium selenite significantly (P < 0.05) increased apple fruit yield and internal quality, but no significant effects on external quality. The apple yield, vitamin C content, sugar-acid ratio and total soluble sugar increased 4.4% to 11.7%, 4.68% to 20.86%, 3.07% to 31.57%, and 4.53% to 18.89%, respectively. Se content is 9.5-fold compared to the control. Significant correlations were observed between neutral invertase, sucrose synthase activity and sucrose phosphate synthase enzymes, and sucrose phosphate synthase enzyme was most crucial. Spraying sodium selenite of 100–150 mg/L could be appropriate for improving Fuji apple yield and quality. [ABSTRACT FROM AUTHOR]

The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors. Unlabelled Image • Rapid description of black tea quality based on multi-variable selection strategy • Feature information was selected by the GA, IGA and PSO. • Classification models for black tea categories developed using DT, PLS-DA and SVM • The SVM discriminator is implemented with four different kernel functions. • The IGA-PSO-SVM model obtained better predictive results based on four feature wavelengths. [ABSTRACT FROM AUTHOR]

• Evaluating tea quality using a lab-made computer vision and morphological features. • Identification models for tea quality are created using non-linear chemometrics. • The LS-SVM and SVM are implemented with three different kernels. • The polynomial kernel LS-SVM model yields satisfactory results with an accuracy of 100%. The feature of external shape in tea is a vital quality index that determines the rank quality of tea. The potential of a lab-made computer vision system (CVS) coupled with morphological features and chemometric tools is investigated for evaluating Congou black tea quality. First, Raw images of 700 tea samples from seven different quality grades are acquired using the CVS. The original images collected are processed by graying, binarization, and median de-noising. Then, six morphological parameters (viz. width, length, area, perimeter, length-width ratio, and rectangularity) from the samples are extracted by the shape segmentation of each tea leaf image, and the corresponding feature histogram is obtained. Finally, support vector machine (SVM) and least squares-support vector machine (LS-SVM) are utilized to build identification models based on the histogram distribution characteristic vectors. Three kernel methods (linear kernel, polynomial kernel, and radial basis function kernel) are compared for monitoring tea quality. The results show that the optimal LS-SVM model has a 12% higher correct discrimination rate (CDR) than the SVM model. The polynomial kernel LS-SVM model yields satisfactory classification results with the CDR of 100% based on selected six shape features in the calibration and prediction sets. This work demonstrates that it is feasible to discriminate Congou black tea quality using CVS technology along with morphological features and nonlinear chemometric methods. A new perspective on the sizes of morphological characteristics is proposed as an identifier of Congou black tea quality. [ABSTRACT FROM AUTHOR]

• Assessing tea rank by Vis-NIR spectra and a hybrid variable selection strategy. • Feature data are selected by the GA, IRIV, VCPA, VCPA-GA, and VCPA-IRIV. • Authentication models for tea ranks are created using LSSVM, RF, and BPNN. • The VCPA-IRIV-LSSVM model obtains superior performance. Tea is one of the most consumed and popular beverages around the world. Authenticating tea quality is essential to resist counterfeit goods and prevent fraud. This study innovatively aims to use a visible-near infrared (Vis-NIR) sensor combined with a variable combination population analysis (VCPA)-based hybrid variable selection algorithm to realize the authentication of tea quality. First, the spectra of 700 tea samples from seven different grades are acquired using a Vis-NIR sensor. Second, the raw spectra acquired are preprocessed by Savitzky-Golay convolution smoothing combined with first derivative (SG1), standard normal variate transformation (SNV), and multiplicative scatter correction (MSC), and the best pretreating technique is determined by support vector machine (SVM) algorithm. Third, the VCPA-based hybrid method optimizes the variable space of the optimal pretreated spectra data in the first step, and a genetic algorithm (GA) and iteratively retaining informative variables (IRIV) are utilized to execute further optimization and shrink the variable in the second step. Finally, authentication models of tea quality are constructed employing least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN) approaches based on the selected variables. The results show that the VCPA-IRIV-LSSVM model obtains the best predictive performance with an accuracy of 97.40% in the predictive process. Our findings demonstrate that it is feasible to apply the inexpensive vis-NIR sensor to assess quality attributes in tea. Also, the VCPA-based hybrid method can search the combination of the optimal wavelength and improve the identification accuracy and robustness of the model and can assist sensor manufacturers in developing a cheap and user-friendly smartphone-coupled micro spectroscopic sensor for authentication of tea quality. [ABSTRACT FROM AUTHOR]

The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of black tea. Unlabelled Image • 700 black tea samples encompassing seven classes are delineated by HSI. • Description models built using multiple decision tree methods. • NIR and image feature fusion to identify black tea tenderness and classes. • FT model employing fusion eigenvectors obtain the best predictive results. [ABSTRACT FROM AUTHOR]

From the perspective of combating fraud issues and examining keemun black tea properties, there was a contemporary urgent demand for a keemun black tea rankings identification system. Current rapid evaluation systems had been mainly developed for green tea grade evaluation, but there was space for improvement to establish a highly robust model. The present study proposed cognitive spectroscopy that combined near infrared spectroscopy (NIRS) with multivariate calibration and feature variable selection methods. We defined "cognitive spectroscopy" as a protocol that selects characteristic information from complex spectral data and showed optimal results without human intervention. 700 samples representing keemun black tea from seven quality levels were scanned applying an NIR sensor. To differentiate which wavelength variables of the acquired NIRS data carry key and feature information regarding keemun black tea grades, there were four different variables screening approaches, namely genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and shuffled frog leaping algorithm (SFLA), were compared in this study. Cognitive models were developed using least squares support vector machine (LSSVM), back propagation neural network (BPNN) and random forest (RF) methods combined with the optimized characteristic variables from the above variables selection algorithms for the identification of keemun black tea rank quality. Experimental results showed that all cognitive models utilizing the SFLA approach achieved steady predictive results based on eight latent variables and selected thirteen characteristic wavelength variables. The CARS-LSSVM model with the best predictive performance was proposed based on selecting ten characteristic latent variables, and the best performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0413, the correlation coefficients of prediction set (R p) was 0.9884, and the correct discriminant rate (CDR) was 99.01% in the validation process. This study demonstrated that cognitive spectroscopy represented a proper strategy for the highly identification of quality rankings of keemun black tea. Unlabelled Image • Highly identification of keemun black tea rank based on cognitive spectroscopy. • Feature information were selected by GA, SPA, CARS and SFLA. • Cognitive models based on keemun black tea grades were developed using LSSVM, BPNN and RF. • The SFLA approach achieved steady predictive results. • The CARS-LSSVM model with the best predictive performance was proposed. [ABSTRACT FROM AUTHOR]

• A productive equipment for sorting famous tea raw materials was designed. • The sorting scheme consists of classification, control and process scheme. • Higher grades of fresh tea leaves have a longer horizontal throwing trajectory. • The sorting effect of high-grade raw material is better under the optimal parameters. • Improving the classification performance of the model using genetic algorithms. Famous tea is a pillar of China's tea industry and must be processed using fresh leaves of consistent tenderness and size. However, equipment with high accuracy and efficiency are unavailable for sorting fresh tea leaves (FTLs). Therefore, in this study, we designed a machine-vision platform for sorting FTLs. This platform subjects FTLs to a horizontal tossing motion and change trajectory by blowing air, thereby achieving the sorting of different types of FTLs. The overall sorting scheme consists of classification scheme, control scheme, and process scheme. We found that different types of FTLs have different flight trajectories when subjected to horizontal tossing. And have different optimal classification features and sorting parameters. When sorting high-grade FTLs (low-grade FTLs), the classification feature used is area and mean saturation (equivalent diameter, major-axis length and G variance), the designed platform achieved a recognition rate, purity, selection rate, and integrity rate of 100% (98.5%), 94.32% (87.47%), 91.67% (90.67%), and 100% (100%), respectively. Under optimal parameters, the production efficiency of the platform reached 25 kg/h. Overall, the results of this study indicate that the designed platform can meet the small-scale production requirements for famous tea. [ABSTRACT FROM AUTHOR]

Abstract The use of crop residues as mulching materials or organic fertilizer, instead of burning, can help improve the soil organic carbon (SOC) and crop grain yield. An experiment was conducted for three consecutive seasons from 2013 to 2016, to study the effect of no straw (S0), S1/2 (700 kg/ha soybean straw, and 3000 kg/ha wheat straw), and S1 (1400 kg ha−1 soybean straw, and 6000 kg/ha wheat straw) treatments in wheat-soybean multi-crop system. The randomized complete block design was used with three repeats. Compared with the S0 regime, a significant increase of 0.5%–8.7% and 1.4%–27.7% in soil CO 2 emission was observed in the S1 regime during all growth stages of summer soybean and winter wheat, respectively. Soil temperature of S1/2 and S1 was 0.1–1.1 °C and 0.3–1.4 °C higher than that of S0 during the seeding stage and greening stage for wheat, during 2013 to 2016. During wheat season, soil moisture was higher in the S1 than in the S0 treatment. Likewise, wheat, soybean crop resulted the same results. Soil CO 2 emissions increase with the increasing in soil temperature, and 73.4–73.9% of the variation could be explained by seasonal variation in soil temperature in wheat season. Similarly, 69.5–74.7% of the variation in soil CO 2 emissions is recorded by seasonal variation in soil temperature in summer soybean season. Meanwhile (S1) increased the SOC and grain yield of wheat and soybean when compared to S0. Straw input increasing soil CO 2 , grain yield and SOC content, considering the benefits of straw inputs to crops yield and SOC content, It is concluded that the addition of straw improve agriculture production. However, the types of straw inputs in order to promote the sequestration of soil organic carbon with the decrease in greenhouse gas emission is the future research direction for agriculture development in Guanzhong region of China. Graphical abstract Unlabelled Image Highlights • Soil temperature was the main driven for soil CO 2 emission • Cumulative soil CO 2 emission increased by straw input • Straw input increased soil organic carbon • Straw returning and mulching increased soil CO 2 emission and crop yield [ABSTRACT FROM AUTHOR]

Highlights • Potato is sensitive to drought over the north single cropping zone in China. • The north single cropping zone of potato tended to become drier since the 1970s. • The study area as a whole will become drier in the potato mature stage. • The future drought trend is generally consistent with the current state. • Crop management strategies must be adapted for increased drought conditions. Abstract Knowledge about drought variability in the main potato producing areas can provide a scientific basis for management of potato crops. In this study, the variations and patterns of drought in the potato growing period (GP), seedling stage (SS), tuber-growth stage (TS), and mature stage (MS) identified by the Standardized Precipitation Evapotranspiration Index (SPEI) were investigated in the north single cropping zone (NSCZ) in China for the period of 1960–2016 based on the empirical orthogonal function analysis, Mann–Kendall statistical test, Mann–Kendall test, Sen's slope, Hurst exponent, and continuous wavelet transform. Additionally, grey theory was used to study the relationship between drought and potato production. The results of the study follow: 1) The spatiotemporal patterns of drought of the two leading patterns were identified. 2) Since the 1970s, the NSCZ has become drier in the GP and MS, has become wetter in the TS, but has always remained in an unstable state of alternating wet and dry conditions in the SS. 3) The changes in dryness/wetness conditions include significant periodic oscillation features. 4) The future drought trend is generally consistent with the current state. In addition, it is worth noting that the NSCZ as a whole will become drier in the MS. 5) The drought of SS and TS had a relatively large negative effect on potato production, whereas that of MS had a relatively small positive effect. Because the NSCZ is within the marginal region of the Asian monsoon and the agro-pastoral ecotone, it is an ecologically fragile region. Therefore, under the background of global climate change, studying changes in dryness/wetness conditions has important implications for the ecological security and potato production of the region. [ABSTRACT FROM AUTHOR]

Addition of organic material, such as crop straw mulch in most soils is considered a strategy for sustainable agricultural production. We conducted a two-year experiment in 2015 and 2016 to determine changes in soil biochemical properties and maize yield in response to treatment with wheat-straw mulch. The treatments consisted of the addition of different levels of wheat-straw mulch (S1: 0, S2: 2500, S3: 5000 kg ha −1 ). Soil samples from four depths (0.1, 0.2, 0.3, and 0.4 m) were collected and analyzed. Soil enzymes, such as invertase, phosphatase, urease, and catalase, were significantly higher in the S3 treatment than in the S1 treatment. Values were greater for the samples collected at 0.1 m soil depth than those collected from deeper soil layers. Regarding soil properties, soil organic carbon (SOC), available nitrogen (AN), available phosphorus (AP), total nitrogen (TN), total phosphorus (TP), and soil water content (SWC) were significantly higher in S3 at 0–0.1 m soil depth than in other treatments. Compared with the (S1), an average increase in SOC, AN, AP, TN, TP, and SWC in 0–0.4 m soil depth with full straw mulch (S3), were 32.4, 31.9, 32.0, 11.8, 16.7, and 18.5%, higher, respectively. On average, urease, phosphatase, invertase, and catalase increased by 15.1, 11.0, 88.4, and 24.0%, respectively in the S3 treatment compared with that in the S1 treatment at 0–0.1 m depth, and decreased with increasing soil depth. The S3 treatment had increased grain yield (7%), biomass yield (28%), and water use efficiency (8%), compared with the S1 treatment. Overall, our results suggested that the S3 straw mulch treatment (5000 kg ha −1 ) could be used to sustain maize productivity and promote a better relationship between soil enzymes and soil properties in the semi-arid conditions of the Guanzhong area. [ABSTRACT FROM AUTHOR]

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]

Straw retention and wheat-soybean rotation play critical role in maintaining soil quality. However, the correlation between bacterial diversity and community structure, and soil nutrients is unknown, and a systematic understanding of their responses to straw retention is lacking. In the field experiment, the straw retention treatments included no straw (NS), half straw (HS), and total straw (TS) retention during long-term wheat-soybean rotation. The mean contents of soil total nitrogen (TN), nitrate-N (NO3−-N), and microbial biomass nitrogen (MBN) increased by 15.06%, 21.10%, and 38.23%, respectively, with straw retention relative to NS, while that of ammonium-N (NH4+-N) reduced by 3.68%. The concentration of carbon components increased as straw retention increased. The levels of soil dissolved organic carbon (DOC), microbial biomass carbon (MBC), and soil organic carbon (SOC) increased by 4.34%, 7.63%, and 9.34%, respectively, with straw retention relative to NS. Soil bacterial alpha diversity was reduced with straw retention. Soil pH and nutrient content were identified as the main factors affecting the soil microbial diversity and structure at the phylum level. Accordingly, straw retention and soybean-wheat rotation enable sustainable agriculture in the dryland of northern China. [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]

Straw incorporation with half-plastic film mulch is a promising approach for enhancing soil quality in dryland farming. However, its effects on soil greenhouse gas (GHG) and NH 3 emissions remain poorly understood. This study aims to optimize the mulch practice to balance the plastic film input with comprehensive benefits and examine the temporal dynamics and influencing factors of soil GHG and NH 3 emissions. A 3-year field experiment was conducted in a wheat-maize cropping system in north-western China with a complete randomized design including three treatments replicated thrice: (a) no straw incorporation or half-plastic film mulch (CT), (b) straw incorporation without half-plastic film mulch (S), and (c) straw incorporation with half-plastic film mulch (SP). The study revealed that S treatment significantly enhanced crop yield and soil cumulative N 2 O, CO 2 , and NH 3 emissions during the maize season by 8.26%, 113.51%, 31.78%, and 64.92%, respectively, compared to CT treatment. SP treatment significantly decreased N 2 O and NH 3 emissions by 30.66% and 23.74%, respectively, and slightly stimulated CO 2 emissions by 8.73% relative to S treatment throughout the three maize seasons. The average maize yield gap between S and SP treatments ranged from 517 to 800 kg ha−1. N 2 O and NH 3 emissions were significantly associated with soil water content (SWC), soil N components, and enzyme activity (nitrate and nitrite reductase), whereas CO 2 emissions were significantly influenced by soil temperature. Compared with S treatment, half-plastic film mulch increased SWC (16.77%), reduced soil N content and inhibited activity of nitrate (12.15%) and nitrite reductase (8.40%), eventually mitigated straw incorporation-induced N 2 O and NH 3 emissions by 30.66% and 23.74%, respectively. Overall, we demonstrated that straw incorporation with half-plastic film mulch is an innovative practice for enhancing the sustainability of dryland farming by increasing maize yield and mitigating N 2 O and NH 3 emissions. [Display omitted] • Nitrogen cycle- related enzyme activities are key to regulating gas emissions. • S significantly increased N 2 O, CO 2 , and NH 3 emissions compared with CT. • SP significantly reduced N 2 O and NH 3 emissions and increased yield compared to S. • SP is an innovative practice for promoting the sustainability of dryland farming. [ABSTRACT FROM AUTHOR]

• Straw return and fertilization improve the soil nutrient status. • Straw return with nitrogen fertilizer reduced soil CO 2 emissions by 270.01%, compared with straw return. • Enzyme stoichiometry showed that soil microorganisms of winter wheat were carbon and nitrogen limited under straw-return conditions. • Microorganisms use their stoichiometric flexibility to regulate soil CO 2 emissions. Straw return is the key measure of green agricultural production. Nitrogen (N) and phosphorus (P) play pivotal roles in crop growth and carbon (C) cycling in agroecosystems. However, the mechanism of soil CO 2 emissions and enzyme stoichiometry regulation by straw return with N and P application remain unknown. Therefore, this study is aimed to use enzyme stoichiometry to investigate the effects of straw return with N and P fertilizer application on soil CO 2 emissions, enzyme activity, and microbial nutrient limitation through a two-year field experiment. A split-plot experimental design was used (straw as the main plot and fertilizer as the sub plot). The results indicated that the addition of N, P (SNP) increased CO 2 emissions by 25.54 %, and N application (SN) reduced CO 2 emissions by 27.01 %, compared with straw return (SW). Enzyme stoichiometry indicated that straw return with N and P application exhibited significant C and N limitations. In soil nutrients and nutrient stoichiometry, compared with the S 0 W treatment, straw return with fertilizer (SNP, SN) significantly increased microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), dissolved organic carbon (DOC) and dissolved organic nitrogen (DON); SNP treatment increased β-1,4- n −acetylaminoglucosidase (NAG) enzyme activity by 53.72 %, increased l −leucine aminopeptidase (LAP) enzyme activity by 58.11 %, and increased one P-acquiring enzyme alkaline monoalkaline phosphatase (AKP) enzyme activity by 39.49 % (P < 0.05). Structural equation modeling suggested that straw return and fertilization can influence microbial biomass phosphorus (MBP) and vector length (VL) by affecting MBN, which, in turn, affects CO 2 emissions. Soil microbes regulate CO 2 emissions using stoichiometric flexibility to maintain a stoichiometric balance of nutrients and microbes. Therefore, straw return with N and P fertilizers to winter wheat soils can meet the requirements of microbial stoichiometry, modulate microbial and extracellular enzyme activities, and effectively regulate CO 2 emissions and resource limitations. [ABSTRACT FROM AUTHOR]

Soil microbial nutrients and resource availability improve with mulching in agroecosystems, resulting in changes of soil microbial nutrient utilization strategies. However, the responses of soil microorganisms to nutrient utilization processes to regulate community assembly and thus maintain resource availability have not been clarified. Here, we performed a long-term field experiment with mulching measures, including no-mulching (NM), straw mulching (SM), plastic mulching (PM), and ridge mulching (RM), conducted on the Loess Plateau of China, to explore the community assembly process in response to microbial nutrient utilization. Microbial nutrient utilization strategies were analysed by measuring soil nutrients, microbial biomass, and extracellular enzyme activities. The results showed that PM and RM acquired C and N nutrients by secreting C- and N-extracellular enzymes in response to C:N- and C:P imbalances in soil chemistry, thereby reducing the nutrient limitation on microbial metabolism and improving microbial C utilization efficiency (CUE). Soil extracellular enzymatic stoichiometry increased and N utilization efficiency (NUE) decreased under mulching and drove an increase in beta-diversity and dominant species abundance in fungal communities. Mulching measures promoted the modular distribution of sensitive microorganisms and aggregation of fungal networks under PM and RM by reducing the stoichiometric imbalance and extracellular enzymatic N:P. At the same time, the microbial community assembly gradually changed from a stochastic to a deterministic process in response to the alleviation of soil C:P imbalance and changes in microbial nutrient utilization, in which the fungal community was dominant. In conclusion, soil microorganisms adaptively responded to changes in soil microbial nutrient utilization through the enhancement of aggregation of fungal co-occurrence networks and deterministic assembly processes. This study provides a better understanding of the impact of mulching on microbial nutrient utilization strategies from a community assembly perspective. • Microbial community composition change under mulching in loess soil was examined. • Absent mulching, community assembly was dominated by stochasticity. • Under mulching, change from stochastic to deterministic assembly was promoted. • Nutrient imbalance drove adaptive regulation of phylogenetic turnover. • Under nutrient imbalance, fungal community assembly was driven by N limitation. [ABSTRACT FROM AUTHOR]

• Compared impacts of 5 tillage systems on Calcic Cambisol erosion in Loess Plateau. • Measured soil physicochemistry, microbial diversity, and soybean yield over 9 y. • Ascomycota and Basidiomycota abundances correlated with soybean yield. • Ascomyta and Gemmatimonadetes correlated with available K and N, respectively. • BP neural network showed that ridge-furrow plastic mulching optimal for this region. Soil erosion has caused substantial nutrient losses in the hilly-gully area of the Loess Plateau in China. Consequently, traditional tillage provides little ecological benefit there, and conservation tillage is required instead. To identify a suitable conservation tillage mode for this region, we set up no-tillage (NT), straw mulching (SM), plastic mulching (PM), and ridge-furrow plastic mulching (RPM) systems, used conventional tillage (CT) as a control, and monitored all systems for nine years. We measured and compared relative soil physicochemical properties, fungal and bacterial community diversity and composition, and microbial responses to the various environmental factors. Based on the measured soil quality indicators, we used a back propagation (BP) artificial neural network to perform a comprehensive evaluation of the various treatments to identify the conservation tillage mode that provided the greatest ecological benefits. Long-term RPM was the most highly efficacious at preventing water erosion. Relative to CT, conservation tillage methods significantly increased soil available N and available K levels and, by extension, the soybean grain yields. Neither fungal nor bacterial alpha diversity indices were significantly different among the tillage treatments, but the relative abundances of the dominant bacterial and fungal phyla significantly differed among treatments, and these relative abundances were significantly correlated with both yield, soil available K, and available N contents. Based on the results of our other analyses, bulk density, water and soil conservation capacity, available N, available K, and yield were selected as the appropriate BP neural network model inputs. We then used the output, the comprehensive evaluation value of each cultivation mode, to generate comprehensive simulations. The regression coefficient of the expected and the actual output value reached 0.99. The model disclosed that all types of conservation tillage (except NT) were superior to traditional tillage and that RPM conferred the greatest comprehensive benefits. Based on BP neural network analysis, we established that ridge-furrow plastic mulching is the optimal conservation measure for the study region. The present study serves as a reference and provides guidance for selecting the ideal methods of cultivation for hillsides in the Loess Plateau of China. Our results are relevant for the mitigation of soil erosion in other similar agricultural regions worldwide. [ABSTRACT FROM AUTHOR]

Nitrogen fertilization significantly increases greenhouse gases (GHGs) emission, when applied from inorganic or organic sources. Minimizing GHGs from agroecosystems without compromising crop yield for stabilization of green production systems remains a challenge. Being an integral component of wheat production technology, the nitrogen (N) application deems to be indispensable. Thus, to reduce the application of N fertilizer and keep in view the minimization of GHGs emission, without compromising soil fertility and wheat production, field experiments were performed with treatments included maize straw mulch (S1: 0, S2: 4500, S3: 9000 kg ha−1) and nitrogen fertilizer (N1: 0, N2: 192 and N3: 240 kg ha−1) during 2015–17. Results showed that the cumulative CO 2 and N 2 O emission from 9000 kg ha−1 of maize straw mulch with 192 kg N ha−1 (S3N2) significantly decreased by 0.67% and 33.7%, respectively, averaged over two years compared with that of 9000 kg ha−1 of maize straw mulch with 240 kg N ha−1 (S3N3). Likewise, the average soil moisture content significantly increased by 10% and 10.6% for S3N2 and S3N3 treatments at 0–10 cm soil depth, respectively, compared to S1N1. Similarly, the S3N2 and S3N3 treatments had lowered the soil temperature by 0.2 and 0.1 °C, respectively, over S1N1 in wheat grown fields. The grain yield of wheat was increased by 45% and 45% under S3N3 and S3N2 treatments than S1N1, respectively. The S3N2 treatment was more economical than S3N3 for wheat crop. Therefore, maize straw mulch (S3) combined with 20% less N fertilizer (N2) from commercial source were considered as a viable production technology to improve crop yield, and reduce soil CO 2 and N 2 O emissions. Unlabelled Image • Cumulative emission of soil NO 2 decreased by 9000 kg ha−1 straw with 192 kg N ha−1. • Global warming potential was more in 9000 kg ha−1 straw (S3) with 240 kg N ha−1. • Wheat yield were significantly increased by S3 with 20% less N fertilizer. • 9000 kg ha−1 of straw with 192 kg N ha−1 is recommended an eco-friendly approach. [ABSTRACT FROM AUTHOR]

Mulching can effectively maintain and improve soil health and functionality. The mechanisms, however, have not been fully elucidated. Therefore, the effects of temperature on the biogeochemical properties of soil were investigated in the present study in relation to nitrogen management and soil functionality. The results of the 3-year field experiments showed that integrated straw mulch (S) and nitrogen fertilizer (N) treatments enhanced the activities of soil urease, invertase, alkaline phosphatase, and catalase by >1.8, 2.1, 2.0 and 1.4 fold, respectively, compared with the control treatment. Furthermore, these treatments increased soil available N by 28%, phosphorus by 45%, and potassium by 55%. In general, the soil organic carbon, dissolved carbon and labile organic carbon content in the treated plot were approximately 1.2–2.9 folds greater than in the control plot. These improvements in soil fertility and carbon indices increased the biomass and grain yield of soybean (67 and 75%, respectively) during the three-year study period. During 2015–2017, the straw mulch and nitrogen fertilizer treatment increased the soil moisture (23%) and decreased the soil temperature (8%) in comparison to the control in the 0–0.2 m soil depth and, therefore, improved soil enzyme activities, nutrient availability, and carbon stocks, and ultimately, soil functionality and sustainability, in the semiarid region. Unlabelled Image • Integrated use of straw mulch with nitrogen fertilizer improved soil carbon pool. • S + N regime improved soil nutrients and soil water content in 0–0.4 m soil layer. • S + N treatments significantly decreased the soil temperature. • Soil enzyme activities were positively correlated with nutrient dynamics. • Soybean yield was significantly enhanced by straw mulch with nitrogen fertilizer. [ABSTRACT FROM AUTHOR]

Straw retention is an effective method to conserve soil water content and improve soil carbon stocks. However, how soil carbon dynamics respond to different straw retention practices remains unclear. In this study, we investigated soil respiration and soil carbon sequestration at depths of 0–100 cm. We conducted a two-year field experiment with three crop rotation treatments and three straw retention treatments in northwest China. The straw retention treatments included no straw retention (NS), retention of half the straw (HS), and retention of the total amount of straw (TS). The crop rotations treatments included winter wheat plus summer soybean (WS), winter wheat plus summer maize (WM), and winter wheat plus summer fallow (WF). Mean soil respiration rates under WS, WM, and WF treatments were 5.14, 6.53, and 5.49 μmol·m-2·s-1; and 5.67, 5.47, and 6.03 μmol·m-2·s-1 under TS, HS, and NS treatments. The mean soil water content were 15.50%, 15.57%, and 15.74% under WS, WM, and WF rotations, and 15.81%, 15.41%, and 15.50% under TS, HS, and NS treatments. The soil organic carbon (SOC) concentration was higher with increased straw retention, and lower at deeper soil depths. Mean SOC concentrations under different rotations and straw treatments of TS, HS, and NS, respectively were as follows: WS: 6.91, 6.63, 6.39 g/kg; WM: 6.90, 6.72, 6.57 g/kg; and WF: 6.49, 6.52, 6.37 g/kg. Soil temperature was the main determinant of soil respiration rates. We conclude that WS rotation resulted in lower soil respiration, WM rotation resulted in a higher soil carbon sequestration potential, and WF rotation resulted in higher soil water content. However, continued, long-term monitoring is needed to confirm the effect of rotations and straw retention on soil respiration and carbon sequestration in dryland cropping systems in northern China. [ABSTRACT FROM AUTHOR]

Abstract The effects of vegetation restoration on plant characteristics and soil physicochemical properties have been widely documented; however, knowledge of the variation in soil ecoenzymatic activity and stoichiometry remains limited, particularly with respect to their relationship with plant and soil variables in restored ecosystems. Here, the vegetation and soil from one farmland and two restored land-use types (Robinia pseudoacacia plantations: RP and abandoned lands: AL) of four age classes (10, 18, 28, and 43-years) were investigated and sampled on the Loess Plateau, China. The activity of C, N, and P-acquiring enzymes (β-1,4-glucosidase, BG; β-1,4-N-acetylglucosaminidase, NAG; leucine aminopeptidase, LAP; and alkaline phosphatase, AP) and other major influencing factors (soil physical properties, soil nutrient contents, and microbial biomass) were determined. It was found that both restoration time and vegetation types significantly affect plant characteristics, soil physical properties, soil nutrient content, and microbial biomass. Soil BG, NAG + LAP, and AP enzyme activities were higher in RP and AL than in farmland, and increased with restoration time in AL. For RP, the activity of these enzymes increased from year 10 to year 28, then decreased from year 28 to year 43. Soil ecoenzymatic C:P and N:P activity ratios were ordered: farmland > AL > RP, and decreased with restoration time in AL and RP; thus, P limitation was stronger in RP than in AL and increased with restoration time in both AL and RP. Soil ecoenzymatic C:N:P acquisition ratios of farmland, AL, and RP deviated from the 1:1:1 ratio and depended on the availability of environmental nutrients and demand for microbial nutrients. The vegetation characteristics and soil physical properties were closely related to the nutrient acquisitions of microbes and, ultimately, contributed towards shaping soil ecoenzymatic activity and stoichiometry (particularly vegetation coverage, belowground biomass, soil water content, soil bulk density, and pH). Furthermore, variation in soil ecoenzymatic activity and stoichiometry was better accounted by dissolved nutrients in the soil (particularly C and N) and microbial biomass (particularly N and P) than by plant characteristics and soil properties. Overall, this study demonstrates that the C:N:P stoichiometry of soil microbes and ecoenzymatic activity is nutrient dependent, rather homeostatic, with the potential to influence nutrient cycling on the Loess Plateau. Graphical abstract Image 1 Highlights • Soil ecoenzymatic C:N:P acquisition ratios deviated from the 1:1:1. • Soil ecoenzymatic activity ratio was not in homeostasis but resource dependent. • P limitation of microbial metabolisms increased with restoration time. • Soil ecoenzymatic stoichiometry were influenced by both plant and soil properties. • Soil dissolved nutrients and microbial biomass have stronger effect on soil ecoenzymatic stoichiometry than plant and soil properties. [ABSTRACT FROM AUTHOR]

Abstract Agricultural non-point source (NPS) pollution in water source protection areas poses serious challenges for governments in developing countries. It is important to consider the environmental behavior of farmers when exploring the causes of NPS pollution as well as when establishing scientific controls and management measures. However, the poor understanding of factors influencing the environmental behavior of farmers and the lack of a suitable environmental socio-psychological model limit the application of the environmental behavior of farmers in NPS pollution management. In this study, we therefore integrated the theory of planned behavior (TPB) and the protection motivation theory (PMT) to identify the main determinants of the NPS pollution-related environmental behavior and intention of farmers in the Water Source Area of the Middle Route of the South-to-North Water Diversion Project in China. Results indicated that the integrated model provided a better understanding of the environmental behavior and intention of farmers than that provided by each component when used individually, and revealed that farmers perceived that the susceptibility and severity of threats caused by water deterioration influenced environmental intention through the mediating effects of subjective norm and attitude toward adopting pro-environmental behavior. At the same time, the perceived vulnerability of farmers was relatively high and their perceived severity was relatively low. Furthermore, the subjective norm, attitude, self-efficacy (i.e., the perceived behavioral control), and response efficacy positively and significantly influenced intention. However, response cost had a significantly negative effect on intention. Among them, subjective norm had the largest effect on intention. Intention was the key determinant for the actual environmental behavior of farmers, while self-efficacy also had a significantly positive effect on behavior. Managing and controlling agricultural NPS pollution requires a multi-disciplinary and comprehensive approach. Therefore, an integrated theoretical framework was developed in this study by integrating TPB and PMT to gain insight into the environmental behaviors and intention of farmers. The results provided a theoretical basis for NPS pollution control. Highlights • Integrated TPB and PMT to study farmers' environmental behavior. • Subjective norm had the greatest effect on farmers' environmental intention. • Attitude and subjective norm mediate the effects of threat appraisal on intention. • Pro-environmental behavior should be facilitated through comprehensive measures. [ABSTRACT FROM AUTHOR]

Ecological stoichiometry (C:N:P ratios) has been widely used to indicated the complicated microbial ecology system and microbial community, however, limited researches have been done in the anaerobic digestion process. In this study, different responses of microbial community to C:N:P stoichiometry were investigated. Results showed that bacterial diversity were higher than archaea; Beta diversity was decreased then remained stable and strongly correlated to C:N:P ratios, while it was not significant for alpha diversity. Furthermore, considering the archaeal dominant order, Methanomicrobiales showed no significant correlation with C:N:P stoichiometric ratios, but closely related to C, Methanosarcinales was positively influenced by C:N ratio, and Methanobacteriales was significantly and negatively impacted by N:P ratio. For bacteria, Clostridiales and Sphaerochaetales closely and positively related to N, Bacteroidales significantly and negatively related to P, but positively to C:N ratio, and Spirochaetales positively related to C. Moreover, changes of microbial taxa were significantly correlated with all considered process variables, which explained 94.1% and 96.6% of total variation in archaea and bacteria taxa, respectively. It is worthy note that these results could be used to predict the process performance and indicate the complicated microbial community by using the simple C:N, C:P and N:P ratios. • As digestion prolonged, C:N ratio decreased, while C:P and N:P increased. • Microbial diversity strongly related to process properties and C:N:P ratios. • Both archaeal and bacterial dominant order significantly responded to C:N:P ratios. • Process variables explained 94.1% (archaea) and 96.6% (bacteria) of total variation. [ABSTRACT FROM AUTHOR]

Abstract C:N:P stoichiometry in the plants and soils is an important indicator of biogeochemical cycles and functioning in ecosystems; however, the response of plant growth to ecological stoichiometry following afforestation remains unclear. To illustrate the C:N:P stoichiometry connections between the plants and soils and their effects on the growth of plants, soil and plant samples were collected from one farmland site and four Robinia pseudoacacia (RP) forests that were planted for 10, 15, 25, and 35 years. Plant community characteristics, growth of overstory trees and understory plants, physical and chemical properties of the soil, and C:N:P stoichiometry in plants and soil were measured. The results revealed that soil organic C, total N, total P, dissolved organic N, C:P and N:P ratios, and water content significantly increased, whereas soil bulk density, available P, and pH decreased with afforestation age. Leaf C:P and N:P ratios in RP were significantly higher than that in understory biomass because P concentrations gradually decreased in RP leaves and increased in understory biomass. The C, N, and P contents and stoichiometry in the soils and plants were significantly correlated, particularly for N:P ratio that can be used to reveal close coupling between plant and soil nutrients. The N:P ratio in RP leaf and understory biomass increased with afforestation age and varied from 14.44 to 20.81 and 7.59 to 10.96, respectively, suggesting that P limitation gradually increased in RP, and N limitation gradually declined in understory biomass. Furthermore, the responses of overstory trees and understory plant growth to N:P ratios were contradictory, because of their different nutrient acquisition patterns and nutrient-use efficiencies. Therefore, this finding provides evidence that the strong correlation between the plants and soils were tightly coupled to N and P concentrations and N:P ratios, and therefore, have the potential to influence the growth of forests on the Loess Plateau. Graphical abstract Unlabelled Image Highlights • Robinia pseudoacacia afforestation improved soil and plant community characteristics. • Leaf C:P and N:P ratios were higher for Robinia pseudoacacia than for understory plants. • N:P ratios indicate a tight coupling between soil and plant. • P limitation increased in Robinia pseudoacacia and N limitation declined in understory biomass. • N:P ratios negatively correlated with Robinia pseudoacacia growth, but positively correlated with understory biomass. [ABSTRACT FROM AUTHOR]

Abstract Afforestation greatly alters soil nitrogen (N) dynamics and soil microbial community, but the response of soil N dynamics to microbial activity, particularly to that of N-fixing microbes, remains unclear. In the present study, soil samples were collected from areas with a chronosequence of 17, 27 and 42 years of Robinia pseudoacacia L (RP17a, RP27a, and RP42a) and a sloped farmland (FL) as a control. The nifH gene was sequenced with the Illumina MiSeq platform to analyze soil microbial diversity and composition, and soil N fractions (TN, AN, NN, MBN, DON) were estimated. The soil N stocks, N fractions, and diversity of N-fixing microorganisms increased with the chronosequence of R. pseudoacacia. Beta diversity, analyzed using nonmetric multidimensional scaling, revealed that afforestation effectively improved soil microbial communities. The profiles of the N-fixing microorganism communities at sites 17a, 27a and 42a were clearly separated from the farmland, and soil N-fixing microbial community compositions generally changed in the farmland from Actinobacteria-dominated to Proteobacteria- dominated with stand ages. Redundancy analysis revealed that soil N fractions were positively correlated with the main species, particularly for AN, which had a 34.7% explanatory space in axis one and was significantly correlated with the Proteobacteria at the phylum level. Results implied the evolutionary tendency of the dominant microbial groups, and demonstrated the variation of the N fractions and some other physical-chemistry properties along the chronosequence of R. pseudoacacia, and that AN was the most sensitive N fraction to the N-fixing microorganism composition. Thus, our results provide evidence that the variation of soil compositions of N-fixing microbial communities is linked to the level of N fractions, especially the content of AN. Highlights • Soil diazotrophs abundance, diversity, and compositions changed along with the chronosequence. • With the increase in ages, the difference in species composition decreased. • Soil N fractions were significantly correlated with the major microbial species. • AN was the most sensitive N fraction to N-fixing microorganisms. [ABSTRACT FROM AUTHOR]

Abstract Although many studies have been done about anaerobic digestion of corn straw, the insight of process performance focusing on biogas production and systematic parameters still needs to be further investigated. This paper investigated biogas production, synergistic correlations of systematic parameters and their correlations based on the effects of total solid (TS) content and initial pH on the digestion performance of corn straw. Results demonstrated that biogas production was significantly correlated with systematic parameters, and the best digestion performance was found at TS content of 4% and initial pH 7.5. Furthermore, the ratio of inoculum to substrate (I/S) should be an indicator for forecasting process performance. The significant synergistic correlations of each kind parameter were also achieved. Moreover, three different synergistic effect pathways of I/S, pH, volatile fatty acid (VFA), hydrolysis rate (k), biogas production potential (P), the maximum biogas production rate (R max) and lag phase (λ) were concluded: (1) I/S→pH (R2 = 0.841, P < 0.01)→k (R2 = 0.831, P < 0.01)→R max (R2 = 0.818, P < 0.01), (2) I/S→pH (R2 = 0.841, P < 0.01)→P (R2 = 0.920, P < 0.01) and (3) I/S→VFA (R2 = −948, P < 0.01)→λ (R2 = −0.662, P < 0.05). Taken together, this report will be a basis for further study of industrial anaerobic digestion of corn straw. Highlights • TS content of 4% with initial pH 7.5 could be an appropriate adjusting measure. • I/S and VS were the main operating parameters influencing other parameters. • The significant synergistic correlations of each kind parameter were achieved. • Three different synergistic effect pathways of these parameters were concluded. [ABSTRACT FROM AUTHOR]

Highlights • Wheat straw mulch increases (7.4%) soil moisture and decreases (3%) soil temperature. • Increases in straw mulch and nitrogen significantly improved growth of soybean. • Straw mulch (6 tons ha−1) with 27 kg N ha−1 significantly increased yield of soybean. Abstract In North-west china, the soybean (Glycine max L. Merr.) is grown in June, when the climate is dry, and soil has limited moisture contents. Moisture deficiency limits the soybean biomass. We studied the effects of wheat straw mulching and nitrogen on soybean growth, physiology and soil properties in three-year field experiments. The treatments included three straw mulching i.e. S1 (0 kg ha−1), S2 (3000 kg ha−1) and S3 (6000 kg ha−1), and three nitrogen rates i.e. N1 (0 kg N ha−1), N2 (21.6 kg N ha−1), and N3 (27 kg N ha−1). Full mulching (S3) significantly increased moisture retention (7.4%) and decrease soil temperature (3.0%) in 0–20 cm soil depth, increased photosynthesis, SPAD-value, leaf area, leaf area index, growth, and soybean grain yield (20.8%) over no-mulching (S1). The S3 improved the roots mass, nodules number and weight than S1. The application of 27 kg N ha−1 or 100% N fertilizer (N3) had significantly increased photosynthesis, SPAD-value, growth, and biomass and seed yield of soybean over no-N application (N1). It was concluded that using straw mulching (6 Mg ha−1) can change the soil hydrothermal regime for provision of favorable condition for soybean growth when 27 kg N ha−1 was used in semi-arid condition of North-west China. [ABSTRACT FROM AUTHOR]

Vegetation restoration has been widely implemented to control soil degradation, reduce soil erosion, and improve soil quality. It is vital to understand the mechanisms affecting soil quality in soil restoration processes and to determine an appropriate recover pattern for soil restoration. Thus, a soil quality index was developed using integrated approach to assess soil quality after vegetation restoration in this study. Soil samples were collected from two restoration pathways (afforestation by Robinia pseudoacacia L. and natural recovery of abandoned farmland) with ages sequence of 0, 17,27 and 42 years old at two soil depths (0–10 and 10–20 cm) to measure soil physicochemical and biological properties on the Loess Hilly Region of China, China. The results showed that soil quality index (SQI) was developed based on microbial biomass carbon (MBC), fine particles (FP), and total phosphorus (TP). The MBC, which had the fastest increase rate than TP and FP, had the highest contribution to the final SQI and these contributions increased with recovery age. The MBC values were higher in Robinia pseudoacacia L. than in abandoned land sites at all recovery ages with greater increases along with restoration age. The SQI values significantly increased with increasing restoration age up to 27 years ( P < 0.05). After 27 years, SQI values for the AL sites remained stable, while SQI values for RP sites continually improved with increasing restoration age. In addition, SQI values were higher for RP sites than for AL sites for all restoration ages. [ABSTRACT FROM AUTHOR]

Straw incorporation is widely applied to improve crop productivity, and biochar amendment may be an effective countermeasure for mitigating greenhouse gas (GHG) emissions; however, the comprehensive evaluation of straw return and biochar amendment coupled with different N application rates on soil organic carbon (SOC) sequestration rate, GHG emissions, and reactive N losses have been inadequately recorded. Consequently, the field experiment had six treatments and three replications including two mineral N fertilization rates (conventional N fertilizer application (N 1) and 30% reduced N fertilizer application (N 0.7)) and three types of straw and biochar management variations (no straw incorporation (S 0), straw incorporation (S), and straw incorporation with biochar amendment (SB)) and the results were based on three years of soil SOC and GHGs emission data in wheat-maize agroecosystem. Our finding revealed that N 0.7 reduced the GHG emissions (CO 2 : 13.9%; N 2 O: 34.2%; CH 4 : 32.5%), greenhouse gas intensity (GHGI; 32.7%), carbon footprint (CF; 35.1%) and nitrogen footprint (NF; 24.5%), but had no significantly different crop yields compared with N 1 (p > 0.05). Straw incorporation had no significant impact on CF and yield-scaled CF but increased NF (29.7%) because the stimulated GHG emissions would be offset by SOC sequestration. Under S, the wheat and maize yields increased by 9.7% and 5.4%, respectively, whereas the GHGI increased by 59.6% compared with S 0. Biochar amendment reduced GHG emissions (CO 2 : 19.9%; N 2 O: 29.9%), GHGI (32.7%), CF (26.7%) and yield-scaled CF (29.2%), while improving crop yields (wheat: 5.8% (p < 0.05); maize: 1.5% (p > 0.05)) and SOC sequestration rate (74.5%) compared with that under S. Overall, N 0.7 SB exhibited enormous potential for enhancing wheat-maize agroecosystem productivity and drastically mitigating their CF and NF in the semi-arid conditions of the Guanzhong Plain. [Display omitted] • Soil quality, crop productivity, and environmental benefits of different field managements were evaluated. • Reduce N application mitigated GHG emissions, carbon and nitrogen footprints, but had no effect on crop yields. • Biochar inhibited the stimulation of GHG emissions by straw incorporation and increased the SOC sequestration. [ABSTRACT FROM AUTHOR]

The goal of this study was to determine the dynamics of physicochemical properties and biological activities of soil, and their relationship following afforestation. Soil samples were collected at 0–10 cm from five Pinus tabulaeformis plantation forests restored for 15, 25, 30, 45, and 70 years, as well as a millet ( Setaria italica ) farmland in the Damaiji catchment area. These five afforested lands were converted from similar farmlands. The activities of catalase (CAT), saccharase (SAC), urease (URE) and alkaline phosphatase (ALP), microbial biomass, soil water content, pH, soil bulk density, soil organic carbon (SOC), total nitrogen (TN), ammonium nitrogen, nitrate nitrogen, total phosphorus (TP), and available phosphorus (AP) were measured. The results revealed that the contents of SOC, TN, AP, microbial biomass C, N, and P, CAT, SAC, URE, and ALP in the P . tabulaeformis forest soil were significantly higher than those in the farmland by 64.97%–262.02%, 75.44%–254.28%, 46.63%–114.21%, 125.95%–554.83%, 101.35%–464.21%, 15.80%–167.35%, 22.57%–49.95%, 96.78%–145.73%, 6.98%–56.08% and 89.15%–177.89%, respectively. The soil properties, microbial biomass, enzymatic activities, and C:P and N:P ratios in soil and microbial biomass improved with increasing plantation chronosequence. Variations in C:N:P stoichiometry and higher C:P and N:P ratios in the soil and microbial biomass revealed the P limitation. Simultaneously, N:P ratios included more serviceable information that reflected the relationship between soil and microbes. Soil enzymatic activities had a high correlation with soil nutrient cycling and could be indicators of soil fertility status, particularly for ALP. The significant correlation between SOC, TN, enzymatic activities, and microbial biomass revealed that the substrate availability of carbon and nitrogen could influence the activity of soil enzymes and microorganisms. This study demonstrated that soil enzymatic activities and microorganisms respond to the process of afforestation and hence have the potential to affect nutrient balance and quality of soil. [ABSTRACT FROM AUTHOR]

Alt'itudinal gradients strongly affect plant biodiversity, but the effects on microbial patterns remain unclear, especially in the large scale. We therefore designed an altitudinal gradient experiment that covered three climate zones to monitor soil microbial community dynamics and to compare those with plant and soil characteristics. Illumina sequencing of the 16S rRNA gene and ITS gene was used to analyze soil microbial (bacterial and fungal) diversity and composition, and fumigation–extraction was used to determine microbial biomass; the plant community metrics (i.e., percent cover, Shannon-Wiener, grass biomass, and carbon/nitrogen in leaf and biomass) and soil properties (i.e., soil moisture, soil temperature, bulk density, organic carbon, total nitrogen, and available nitrogen) were determined. The results showed that carbon/nitrogen in microbial biomass was higher at medium altitude and was positively related to carbon and nitrogen in both soil and grass biomass along the altitudinal gradients. Soil bacterial alpha diversity was significantly higher at medium altitude but fungal alpha diversity did not affected by altitudinal gradients; the effect of altitudinal gradients on bacterial beta diversity was larger than that on fungal beta diversity, although both groups were significantly affected by altitudinal gradients. Moreover, Alpha - proteobacteria , Beta - proteobacteria , and Gemmatimonadetes were significantly more abundant in higher altitude than in lower altitude, both Acidobacteria and Actinobacteria significantly declined with increasing altitude; other bacterial taxa such as Chloroflexi , Nitrospirae , Gamma - proteobacteria , and Delta - proteobacteria were significantly higher at medium altitudes. For fungal taxa, Basidiomycota and Ascomycota were the dominant phyla and responded insignificantly to the altitudinal gradients. The responses of microbial alpha diversity were mostly associated with plant Shannon index, organic carbon, and total nitrogen, whereas microbial beta diversity and composition mainly depended on soil moisture and temperature. Overall, these results suggest that soil bacteria rather than fungi can reflect changes in plant and soil characteristics along altitudinal gradients. [ABSTRACT FROM AUTHOR]

Rainfall reductions influence ecosystem processes through impacts on the microbial community. However, the response of soil microbial community and their interactions with ecosystem processes remain unclear. In this study, we conducted a meta-analysis to synthesize the response of soil total microbial biomass and community composition to experimental rainfall reductions and the implications for soil carbon dynamics. The results showed that rainfall reductions significantly decreased soil total microbial biomass and bacterial abundance, but no significant effects on fungal abundance were observed. While, within bacterial and fungal groups, rainfall reductions only significantly influenced the relative abundance of Proteobacteria, Gemmatimonadetes, and Chloroflexi , but did not affect Gram-positive bacteria (GP) , Gram-negative bacteria (GN), Actinomycetes, Arbuscular mycorrhizal fungi, and other sequenced bacterial phyla. These contrasting responses suggested that rainfall reductions had major effects on total microbial biomass but minor effects on community compositions. Further analysis showed that the direction and magnitude of total microbial biomass responses were mainly explained by the size of rainfall reductions rather than the duration. Particularly, higher decreases in total microbial biomass were observed in sites where more rainfall was excluded. Reductions in total microbial biomass were larger in forests with higher aridity index (AI) than in grass/shrublands with lower AI, and stronger reductions in microbial biomass were observed at higher mean annual precipitation (MAP)/sites with higher AI. Moreover, both soil organic carbon (SOC) and soil respiration (SR) significantly declined under reduced rainfall experiments and had positive relationships with changes in total microbial biomass, especially in humid lands (AI≥ 0.65). The microbial responses to aridity levels indicated that soil carbon in humid lands may be highly susceptible to future drought scenarios. This meta-analysis highlighted the importance of considering the size of rainfall reductions and aridity levels when modeling and projecting soil carbon dynamics. [ABSTRACT FROM AUTHOR]

Altered precipitation regimes have a great impact on global climate change, with potentially important effects on below-ground carbon dynamics. Soil microbes and carbon (C)-degrading extracellular enzymes activities (EEAs) are considered as the rate-limiting step in C decomposition. However, the effect of altered precipitation on the microbial biomass, microbial EEAs, and mechanism for soil C dynamics has not been established. In current study, we synthesized the responses of microbial biomass, C-degrading EEAs, soil organic carbon (SOC), dissolved organic carbon (DOC), and soil respiration (SR) to altered precipitation from 70 published studies. The results showed that increased precipitation significantly enhanced soil microbial biomass and oxidative C-degrading EEAs (Ox-EEAs) by 16.18% and 6.58%, respectively, but had no effects on hydrolytic C-degrading EEAs (Hy-EEAs). Decreased precipitation led to a significant decline of soil microbial biomass and Ox-EEAs by 11.61% and 10.99%, respectively; however, Hy-EEAs increased by 25.79%. Furthermore, increased precipitation stimulated soil microbial biomass in shrub and grassland but had no effects in forest, while decreased precipitation repressed soil microbial biomass but increased Hy-EEAs in forest. The response ratios (RRs) of microbial biomass to altered precipitation were negatively correlated with the mean annual precipitation (MAP) and mean annual temperature (MAT); however, the RRs of Hy-EEAs were positively correlated with MAP. Particularly, in low MAP (≤600 mm), increased precipitation significantly increased soil microbial biomass by 21.40% but decreased precipitation did not affect soil microbial biomass; In contrast, in high MAP (>600 mm), decreased precipitation significantly declined soil microbial biomass by 15.37%, and significantly increased Hy-EEAs by 29.31% but increased precipitation did not affect both of them. Moreover, the RRs of microbial biomass and Ox-EEAs were significantly correlated with SOC, DOC, and SR; however, we only observed a negative relationship between RRs of Hy-EEAs and RRs of SOC, suggesting the concurrent responses of microbial biomass and C-degrading EEAs for below-ground C dynamics under simulated precipitation changes, and that a large amount of recalcitrant C in the soils would be highly susceptible to changing precipitation. [ABSTRACT FROM AUTHOR]

Kinetic characteristics play an important role in anaerobic digestion system for representing biogas production performance, however, its relationship with anaerobic digestion process parameters (i. e., volatile fatty acids, total ammonia nitrogen, pH and total alkalinity) and operational conditions remains poorly understood. To illustrate the linkage of kinetic parameters with process parameters and swine manure content and initial pH, and the effects of swine manure content and initial pH on anaerobic digestion performance, substrate properties, accumulative biogas production, specific biogas production rate and process parameters were analyzed under different treatments. Additionally, the effects of swine manure and initial pH on hydrolysis constant, lag phase, biogas production potential and the maximum biogas production rate were investigated. The results revealed that volatile solid and C:N ratio were significantly decreased with swine manure content increased which were considered as operational conditions with initial pH following the linkage illustration. Furthermore, accumulative biogas production, biogas production rate, final pH, total ammonia nitrogen and hydrolysis constant were increased with swine manure content and initial pH increased, while volatile fatty acids and lag phase showed the opposite trend. The results showed that these operational conditions significantly influenced process parameters and kinetic parameters, with close correlations were observed. Lag phase closely correlated with VFA which was closely correlated with C:N ratio. Therefore, C:N ratio impacted kinetic parameters via effecting VFA, while initial pH directly influenced kinetic parameters. Meanwhile, the correlation of kinetic parameters with C:N ratio was stronger than initial pH. Therefore, C:N ratio should be an indicator for estimating process performance, time the bacteria's acclimatization to the new environment and biogas production. Taken together, these findings provide a scientific theory for estimating anaerobic digestion performance. [ABSTRACT FROM AUTHOR]

Soil samples were collected in June and October from areas with three land-use types, i.e., Robinia pseudoacacia L. (RP), Caragana korshinskii Kom. (CK), and abandoned land (AL), of which the former two were afforested areas, whereas the latter was not. These areas were converted from similar farmlands 40 years prior. Illumina sequencing of 16S rRNA gene and fungal ITS gene was used to analyze soil bacterial and fungal diversity. Additionally, plant communities, soil properties, fine root biomass, and C, N, and P levels in fine root and microbial biomass were estimated. Compared to AL, the C:N:P stoichiometry in fine root and microbial biomass in the afforested lands was synchronously changed, especially the N:P ratio. Soil microbial diversities were affected by afforestation and were more related to N:P ratio than C:P and C:N ratios. Moreover, Alpha-proteobacteria, Gamma-proteobacteria, and Bacteroidetes were significantly more abundant in afforested soils than in the AL soil, and the abundances of Actinobacteria, Chloroflexi, Cyanobacteria, and Nitrospirae ranked as AL > RP or CK. For fungal taxa, Ascomycota abundance responded positively to afforestation, whereas Basidiomycota abundance responded negatively. Changes of soil microbial taxa were significantly correlated with the N:P ratio in fine root and microbial biomass, which explained 54.1 and 55% of the total variation in bacterial and fungal taxa, respectively. Thus, our results provide evidence that compositions of soil microbial communities are linked to the N:P ratio in the plant-soil system. [ABSTRACT FROM AUTHOR]

Temporal variation in soil enzyme activities has important significance for soil quality after afforestation. This study investigated the temporal variation in soil enzyme activities and their response to changes in soil properties following afforestation. Soil samples were collected during different periods of vegetation growth at sites in the Loess Plateau with different land use types: 40 year-old Robinia pseudoacacia L. (RP40a), Caragana Korshinskii Kom (CK40a), and abandoned land (AL40a), as well as millet ( Setaria italica ) farmland (FL). Activities of four enzymes (catalase, saccharase, urease and alkaline phosphatase) that are involved in C, N, P cycling and soil water content (SWC), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and available phosphorus (AP) were measured. The results revealed that temporal variation and land-use have significant effects on the patterns of SWC, DOC, and DON, although not on that of AP. In addition, the activities of soil enzymes were highest in June, indicating that there are differences in the temporal variation of soil enzyme activities within a given land-use type, especially for alkaline phosphatase. The consistent ranking of soil enzyme activities was also significantly increased by afforestation. Furthermore, nonmetric multidimensional scaling analysis showed that the influence degree of afforestation on activities of the four enzymes was higher than that of temporal variation. Significant correlations between soil enzyme activities and soil properties indicated that soil enzyme activities are closely related to soil nutrients dynamics, particularly with respect to DON. The present study accordingly demonstrates that soil enzyme activities respond to farmland-to-forest conversion and hence have the potential to affect soil qualities in the Loess Plateau. [ABSTRACT FROM AUTHOR]