Manganese promoted wheat straw decomposition by regulating microbial communities and enzyme activities

AIMS

This study investigated the dose-effect of manganese (Mn) addition on wheat straw (WS) decomposition, and explored the potential mechanisms of Mn involved in the acceleration of WS decomposition in regards to the soil microbial communities and enzyme activities.

METHODS AND RESULTS

A 180-day incubation experiment was performed to examine the decomposition of WS under four Mn levels, that is, 0, 0.25, 1 and 2 mg g^-1 . The effects of microbial communities and enzyme activities were evaluated using control (0 mg g^-1 ) and Mn (0.25 mg g^-1 ) treatments. Our results revealed that Mn (0.25 mg g^-1 ) addition significantly increased WS decomposition, and enhanced the release of carbon and nitrogen. Optimal Mn addition (0.25 mg g^-1 ) also caused significant increases in the activity of neutral xylanase (NEX), laccase (Lac), manganese peroxidase (MnP) and lignin peroxidase (LiP) within the incubation period. Mn (0.25 mg g^-1 ) addition also enriched some operational taxonomic units (OTUs) that, in turn, had the potential ability to decompose crop straw, such as secreting lignocellulolytic enzymes.

CONCLUSIONS

Mn (0.25 mg g^-1 ) could promote WS decomposition through enrichment of the microbial species involved in biomass decomposition, which enhanced the lignocellulose-degrading enzyme activity.

SIGNIFICANCE AND IMPACT OF THE STUDY

This study provides evidence for Mn to promote WS biodegradation after Mn application, opening new windows to improve the utilization efficiency of crop residues.

Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics

BACKGROUND

Rapid and accurate diagnosis of nitrogen (N) status in field crops is of great significance for site-specific N fertilizer management. This study aimed to evaluate the potential of hyperspectral imaging coupled with chemometrics for the qualitative and quantitative diagnosis of N status in tea plants under field conditions.

RESULTS

Hyperspectral data from mature leaves of tea plants with different N application rates were preprocessed by standard normal variate (SNV). Partial least squares discriminative analysis (PLS-DA) and least squares-support vector machines (LS-SVM) were used for the classification of different N status. Furthermore, partial least squares regression (PLSR) was used for the prediction of N content. The results showed that the LS-SVM model yielded better performance with correct classification rates of 82% and 92% in prediction sets for the diagnosis of different N application rates and N status, respectively. The PLSR model for leaf N content (LNC) showed excellent performance, with correlation coefficients of 0.924, root mean square error of 0.209, and residual predictive deviation of 2.686 in the prediction set. In addition, the important wavebands of the PLSR model were interpreted based on regression coefficients.

CONCLUSION

Overall, our results suggest that the hyperspectral imaging technique can be an effective and accurate tool for qualitative and quantitative diagnosis of N status in tea plants. © 2019 Society of Chemical Industry.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics

BACKGROUND

Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds.

RESULTS

Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples.

CONCLUSION

The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.