Soil microbial carbon utilization efficiency (CUE) is the efficiency with which microorganisms convert absorbed carbon (C) into their own biomass C, also referred to as microorganism growth efficiency. Soil microbial CUE is a critical physiological and ecological parameter in the ecosystem’s C cycle, influencing the processes of C retention, turnover, soil mineralization, and greenhouse gas emission. Understanding the variation of soil microbial CUE and its influence mechanism in the context of global environmental change is critical for a better understanding of the ecosystem’s C cycle process and its response to global changes. In this review, the definition of CUE and its measurement methods are reviewed, and the research progress of soil microbial CUE variation and influencing factors is primarily reviewed and analyzed. Soil microbial CUE is usually expressed as the ratio of microbial growth and absorption, which is divided into methods based on the microbial growth rate, microbial biomass, substrate absorption rate, and substrate concentration change, and varies from 0.2 to 0.8. Thermodynamics, ecological environmental factors, substrate nutrient quality and availability, stoichiometric balance, and microbial community composition all influence this variation. In the future, soil microbial CUE research should focus on quantitative analysis of trace metabolic components, analysis of the regulation mechanism of biological-environmental interactions, and optimization of the carbon cycle model of microorganisms’ dynamic physiological response process.

Dry ecosystems, despite their relative levels of aridity, are very diverse, and play a vital role in the livelihoods of many dryland inhabitants. It is therefore critical to investigate the relationship between land-use change and soil quality parameters to offer a scientific basis for optimizing land-use planning and improving soil quality status in dry ecosystems and ecologically vulnerable areas. This study, therefore, analyzed the physicochemical properties of soils in five different land-use types namely farmland, abandoned farmland, natural grassland, artificial lemon forest, and poplar woodland at YongDeng County. The soil quality status of the aforementioned land-use types was also evaluated through Principal component analysis. The results revealed that abandoned farmland and natural grassland recorded the highest average values of soil coarse particles of 24.0% and 23.4% respectively compared to the other land-use types. The highest average value (46.1%) of fine soil particles was recorded in poplar woodland followed by natural grassland (36.6%) and the average value of very fine soil particles was higher in farmland (40.8%) and artificial lemon woodland (38.3%) than in the other land-use types. The average value of clayey particles was highest in farmland (11.1%), followed by artificial lemon woodland (9.3%), and abandoned farmland (6.5%), then poplar woodland which recorded an average value of (4.2%). The average values of Soil water content, soil pH, soil electrical conductivity, and soil total nitrogen content were significantly higher in farmland compared to the other land-use types. Soil organic carbon content was significantly higher in abandoned farmland at (P < 0.03) and lemon woodland at (P < 0.01) than in farmlands, natural grasslands, and poplar stands. The soil quality indicators of the different land-use types were significantly correlated with each other. Among them, the correlation coefficient of each evaluation index was highest in poplar woodland, followed by natural grassland, lower in farmland and artificial lemon woodland, and lowest in abandoned farmland. The overall soil quality scores were in the following order: farmland > abandoned farmland > 0 > grassland > lemon woodland > poplar woodland. In the study area, the soil quality of farmland that has been finely managed and naturally restored to grassland following abandonment is superior, whereas the soil quality of natural grassland, artificial lemon woodland, and poplar forest land is substandard. The comprehensive analysis of soil quality demonstrates that conservation tillage and fine management of water-irrigated farmland, as well as the natural conversion of abandoned farmland to grassland, can significantly improve the soil quality of sandy soils, reduce water and soil loss, increase fertility, and gradually improve regional ecological environmental conditions.

The plant-specific SHI-related sequence (SRS) family of transcription factors plays a vital role in growth regulation, plant development, phytohormone biosynthesis, and stress response. However, the genome-wide identification and role in the abiotic stress-related functions of the SRS gene family were not reported in white sweet clover (Melilotus albus). In this study, nine M. albus SRS genes (named MaSRS01-MaSRS09) were identified via a genome-wide search method. All nine genes were located on six out of eight chromosomes in the genome of M. albus and duplication analysis indicated eight segmentally duplicated genes in the MaSRS family. These MaSRS genes were classified into six groups based on their phylogenetic relationships. The gene structure and motif composition results indicated that MaSRS members in the same group contained analogous intron/exon and motif organizations. Further, promoter region analysis of MaSRS genes uncovered various growth, development, and stress-responsive cis-acting elements. Protein interaction networks showed that each gene has both functions of interacting with other genes and members within the family. Moreover, real-time quantitative PCR was also performed to verify the expression patterns of nine MaSRS genes in the leaves of M. albus. The results showed that nine MaSRSs were up- and down-regulated at different time points after various stress treatments, such as salinity, low-temperature, salicylic acid (SA), and methyl jasmonate (MeJA). This is the first systematic study of the M. albus SRS gene family, and it can serve as a strong foundation for further elucidation of the stress response and physiological improvement of the growth functions in M. albus.

It is crucial to have precise and current maps of aboveground biomass (AGB) in boreal forests to accurately track global carbon levels and develop effective plans for addressing climate change. Remote sensing as a cost-effective tool offers the potential to update AGB maps for boreal forests in real time. This study evaluates different machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), for predicting AGB in boreal forests. Conducted in the Qilian Mountains, northwest China, the study integrated field measurements, space-borne LiDAR, optical remote sensing, and environmental data to develop a training dataset. Among 34 variables, 22 were selected for AGB estimation modeling. Our findings revealed that the LightGBM AGB model had the highest level of accuracy (R² = 0.84, RMSE = 15.32 Mg/ha), outperforming the XGBoost, RF, and SVR AGB models. Notably, the LightGBM AGB model effectively addressed issues of underestimation and overestimation. We also observed that the disparity in accuracy among the models widens with increasing altitude. Remarkably, the LightGBM AGB model consistently demonstrates optimal performance across all elevation gradients, with residuals generally below 25 Mg/ha for low-value overestimation and below −38 Mg/ha for high-value underestimation. The model developed in this study presents a viable and alternative approach for enhancing AGB estimation accuracy in boreal forests based on remote sensing technology.