In an effort to optimize soil management practices that can help mitigate terrestrial carbon emissions, biochar has been applied to a wide range of soil environments to examine its effect on soil greenhouse gas emissions. Such studies have shown that the soil methane (CH₄) flux response can vary widely leading to both increase and decrease in CH₄ flux upon biochar amendment. To address this discrepancy, multiple meta-analysis studies have been performed in recent years to determine the key factors that may control the direction of CH₄ flux upon biochar treatment. However, even comparing across conclusions from meta-analyses reveals disagreement upon which factors ultimately determine the change in direction and magnitude of CH₄ flux due to biochar addition. Furthermore, using multiple observations from a single study can lead to misinterpretation of the influence of a factor within a meta-analysis due to non-independence. In this study, we use a multivariate meta-regression approach that allows factor interactions to investigate which biochar, soil, and management practice factors in combination or individually best explain the CH₄ flux response in past biochar amendment studies. Our results show that the interaction of multiple soil factors (i.e., water saturation, soil texture, and soil organic carbon content) best explains the soil CH₄ flux response to biochar addition (minimum deviance information criterion (DIC) value along with lowest heterogeneity) as compared to all models utilizing individual factors alone. These findings provide insight into the specific soil factors that should be taken into account simultaneously when optimizing the CH₄ flux response to biochar amendments and building empirical models to quantitatively predict soil CH₄ flux.

The biomass type is one of the most important factors that affects biochar properties, but the relationship between the major constituents of different biomasses and the properties of the derived biochars, especially the pH, is still unclear. In this study, the cellulose, hemicellulose, lignin, and ash contents of 30 representative agro-forestry biomasses and the pH, surface acid functional groups, and carbonates of 150 resultant biochars prepared with different heat treatment temperatures (HTTs) were examined. The results showed that the lignin content of the biomass had a strong negative correlation with the biochar pH (r = −0.428, P < 0.01). When the HTT was 300 °C, a high lignin content in the biomass was found to have increased the acid functional groups in the biochar, which resulted in a relatively low pH. When the HTT was greater than or equal to 400 °C, the high lignin content in the biomass negatively correlated to the carbonates, which led to a low biochar pH. It was concluded that the lignin content of agro-forestry biomass affects the biochar pH negatively, and the acid functional groups and carbonates have different effects at different HTTs.

Background: Water shortages limit agricultural production in arid and semiarid regions around the world. The accurate estimation of reference evapotranspiration (ET₀) is of the utmost importance for computing crop water requirements, agricultural water management, and irrigation scheduling design. However, due to the combination of insufficient meteorological data and uncertain inputs, the accuracy and stability of the ET₀ prediction model were different to varying degrees. Methods: Six machine learning models were proposed in the current study for daily ET₀ estimation. Information on the weather, such as the maximum and minimum air temperatures, solar radiation, relative humidity, and wind speed, during the period 1960~2019 was obtained from eighteen stations in the northeast of Inner Mongolia, China. Three input combinations were utilized to train and test the proposed models and compared with the corresponding empirical equations, including two temperature-based, three radiation-based, and two humidity-based empirical equations. To evaluate the ET₀ estimation models, two strategies were used: (1) in each weather station, we trained and tested the proposed machine learning model, and then compared it with the empirical equations, and (2) using the K-means algorithm, all weather stations were sorted into three groups based on their average climatic features. Then, each station tested the machine learning model trained using the other stations within the group. Three statistical indicators, namely, determination coefficient (R²), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the performance of the models. Results: The results showed the following: (1) The temperature-based temporal convolutional neural network (TCN) model outperformed the empirical equations in the first strategy, as shown by the TCN’s R² values being 0.091, 0.050, and 0.061 higher than those of empirical equations; the RMSE of the TCN being significantly lower than that of empirical equations by 0.224, 0.135, and 0.159 mm/d; and the MAE of the TCN being significantly lower than that of empirical equations by 0.208, 0.151, and 0.097 mm/d, and compared with the temperature-based empirical equations, the TCN model markedly reduced RMSE and MAE while increasing R² in the second strategy. (2) In comparison to the radiation-based empirical equations, all machine learning models reduced RMSE and MAE, while significantly increasing R² in both strategies, particularly the TCN model. (3) In addition, in both strategies, all machine learning models, particularly the TCN model, enhanced R² and reduced RMSE and MAE significantly when compared to humidity-based empirical equations. Conclusions: When the radiation or humidity characteristics were added to the given temperature characteristics, all the proposed machine learning models could estimate ET₀, and its accuracy was higher than the calibrated empirical equations external to the training study area, which makes it possible to develop an ET₀ estimation model for cross-station data with similar meteorological characteristics to obtain a satisfactory ET₀ estimation for the target station.

Biochar as additive has been shown positive effect in animal production, which may be linked to the role of gastrointestinal microbial modulation. This study aimed to assess the effects of biochar on the gut microbial communities in terms of their structure and diversity. Illumina high-throughput technology was utilized to evaluate the cecal microbial community in Wistar rats received oral rice straw biochar (RSB) at 1120 mg/kg of body weight for 5 weeks. RSB improved the gut mucosal structure and epithelial integrity. More importantly, principal coordinate analysis of UniFrac distances based on a 97% operational taxonomic unit composition and abundance indicated that the bacterial community was ameliorated after RSB addition (P < 0.05). Firmicutes and Bacteroidetes were found to be the prevalent phyla accounting for approximately 90% of the sequences and their ratio of relative abundance was increased by RSB addition (P < 0.05). Improved bacterial proportion of unclassified Lachnospiraceae (P < 0.001), Oscillibacter (P = 0.02), and Clostridium IV (P = 0.02) and XIVa (P = 0.02) as well as decreased abundances of Prevotella (P < 0.001) and Bacteroides (P = 0.03) were also detected at genus level following RSB treatment. These results revealed that RSB altered and improved the cecal microbial community, which may contribute to the affected growth and gut status in rats.

In this paper, the carbonation characteristics of rice husk carbonised in the special laboratory carbonation furnace were studied. The biochar production rate and composition and content of gas under different conditions of temperature and carbonisation time were investigated. Meanwhile, reference coal gas calorific value calculation put forward and confirmed the biomass pyrolysis gas calorific value calculation formula. The results showed that temperature and carbonisation time both have different influence on the pyrolysis process. Among them, 450–500°C, ∼10 min was the best condition for biochar production; biochar yield was 40·28%. While under this condition, the combustible gas generated the highest value, it can reach 16 137·046 kJ m⁻³. It can provide the basis for further study of accuracy control and online monitoring of biochar production and improvement of the carbonisation furnace.