Theabrownins (TBs) are the characteristic functional and quality components of dark teas such as Pu’er tea and Chin-brick tea.  TBs are a class of water-soluble brown polymers with multi-molecular weight distribution produced by the oxidative polymerisation of tea polyphenols during the fermentation process of dark tea, both enzymatically and non-enzymatically.  TBs have been extracted and purified from dark tea all the time, but the obtained TBs contain heterogeneous components such as polysaccharides and caffeine in the bound state, which are difficult to remove.  The isolation and purification process was tedious and required the use of organic solvents, which made it difficult to industrialise TBs.  In this study, epigallocatechin (EGC), epigallocatechin gallate (EGCG), epigallocatechin gallate (ECG), EGC/EGCG (mass ratio 1:1), EGCG/ECG (mass ratio 1:1), EGC/ECG (mass ratio 1:1) and EGC/EGCG/ECG (mass ratio 1:1:1) as substrates and catalyzed by polyphenol oxidase (PPO) and peroxidase (POD) in turn to produce TBs, named TBs-dE-1, TBs-dE-2, TBs-dE-3, TBs-dE-4, TBs-dE-5, TBs-dE-6 and TBs-dE-7.  The physicochemical properties and the antibacterial activity and mechanism of TBs-dE-1–7 were investigated.  Sensory and colour difference measurements showed that all seven tea browning samples showed varying degrees of brownish hue.  Zeta potential in aqueous solutions at pH 3.0–9.0 indicated that TBs-dE-1–7 was negatively charged and the potential increased with increasing pH.  The characteristic absorption peaks of TBs-dE-1–7 were observed at 208 and 274 nm by UV-visible (UV-vis) scanning spectroscopy.  Fourier transform infrared (FT-IR) spectra indicated that they were phenolic compounds.  TBs-dE-1–7 showed significant inhibition of Escherichia coli DH5α (E. coli DH5α).  TBs-dE-3 showed the strongest inhibitory effect with minimum inhibitory concentration (MIC) of 1.25 mg mL^–1 and MBC of 10 mg mL^–1, followed by TBs-dE-5 and TBs-dE-6.  These three TBs-dEs were selected to further investigate their inhibition mechanism.  The TBs-dE was found to damage the extracellular membrane of E. coli DH5α, causing leakage of contents, and increase intracellular reactive oxygen content, resulting in abnormal cell metabolism due to oxidative stress.  The results of the study provide a theoretical basis for the industrial preparation and product development of TBs.

The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.  Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data, it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions.  In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth.  This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China (Northeast China and the Huang–Huai region), covering 34 years.  Three effective machine learning algorithms (K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework.  The model’s generalizability was further improved through 5-fold cross-validation, and the model was optimized by principal component analysis and hyperparametric optimization.  The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness.  The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error (MAPE) was less than 5%.  The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.

Soil salinization is a critical environmental issue restricting agricultural production.  Deep return of straw to the soil as an interlayer (at 40 cm depth) has been a popular practice to alleviate salt stress.  However, the legacy effects of straw added as an interlayer at different rates on soil organic carbon (SOC) and total nitrogen (TN) in saline soils still remain inconclusive.  Therefore, a four-year (2015–2018) field experiment was conducted with four levels (i.e., 0, 6, 12 and 18 Mg ha^–1) of straw returned as an interlayer.  Compared with no straw interlayer (CK), straw addition increased SOC concentration by 14–32 and 11–57% in the 20–40 and 40–60 cm soil layers, respectively.  The increases in soil TN concentration (8–22 and 6–34% in the 20–40 and 40–60 cm soil layers, respectively) were lower than that for SOC concentration, which led to increased soil C:N ratio in the 20–60 cm soil depth.  Increases in SOC and TN concentrations in the 20–60 cm soil layer with straw addition led to a decrease in stratification ratios (0–20 cm:20–60 cm), which promoted uniform distributions of SOC and TN in the soil profile.  Increases in SOC and TN concentrations were associated with soil salinity and moisture regulation and improved sunflower yield.  Generally, compared with other treatments, the application of 12 Mg ha^–1 straw had higher SOC, TN and C:N ratio, and lower soil stratification ratio in the 2015–2017 period.  The results highlighted that legacy effects of straw application as an interlayer were maintained for at least four years, and demonstrated that deep soil straw application had a great potential for improving subsoil fertility in salt-affected soils.

Straw incorporation is a widespread practice to promote agricultural sustainability.  However, the potential effects of straw incorporation with the prolonged time on nitrogen (N) runoff loss from paddy fields are not well studied.  The current study addresses the knowledge gap by assessing the effects of straw incorporation on the processes influencing N runoff patterns and its impacts on crop yield, N uptake, total N (TN), and soil organic matter (SOM).  We conducted field experiments with rice (Oryza sativa L.)–wheat (Triticum aestivum L.) rotation, rice–tobacco (Nicotiana tabacum L.) rotation, and double-rice cropping in subtropical China from 2008 to 2012.  Each rotation had three N treatments: zero N fertilization (CK), chemical N fertilization (CF), and chemical N fertilization combined with straw incorporation (CFS).  The treatment effects were assessed on TN runoff loss, crop yield, N uptake, soil TN stock, and SOM.  Results showed that TN runoff was reduced by substituting part of the chemical N fertilizer with straw N in the double rice rotation, while crop N uptake was significantly (P<0.05) decreased due to the lower bioavailability of straw N.  In contrast, in both rice–wheat and rice–tobacco rotations, TN runoff in CFS was increased by 0.9–20.2% in the short term when straw N was applied in addition to chemical N, compared to CF.  However, TN runoff was reduced by 2.3–19.3% after three years of straw incorporation, suggesting the long-term benefits of straw incorporation on TN loss reduction.  Meanwhile, crop N uptake was increased by 0.8–37.3% in the CFS of both rotations.  This study demonstrates the challenges in reducing N runoff loss while improving soil fertility by straw incorporation over the short term but highlights the potential of long-term straw incorporation to reduce N loss and improve soil productivity.