This study aimed to elucidate the effects of different fertilization on the accumulation of soil organic carbon (SOC) and the community structure of carbon fixation bacteria in farmland, as well as their carbon sink mechanisms. Four field-based treatments were set up: no fertilizer (CK), organic fertilizer (OF), inorganic fertilizer (CF), and organic-inorganic compound fertilizer (OCF). Differences in soil cbbL bacterial communities, SOC and its component contents, and carbon pool management indices were analyzed using methods such as NMDS, Anosim, and metagenomeSeq tests. The aim was to identify the key differential species under different fertilization treatments, analyze the response relationship between cbbL bacteria communities and SOC and its component content, and clarify the important carbon fixation functional genes. The main findings were as follows: 1) CF and OCF significantly reduced soil cbbL bacterial diversity (p<0.05). Significant differences were observed among cbbL bacterial communities under different fertilization treatments (p=0.001). CK and OF treatments had higher numbers of unique OTUs and similar species composition. The six bacterial orders with significant differences among different fertilization treatments predominantly belonged to Pseudomonadota and Actinomycetota. 2) The CF treatment had the lowest content of SOC and its components, and carbon pool management indices. OF and OCF were beneficial to the improvement of SOC and its components, but there was no significant difference between the groups (p>0.05). Short-term fertilization differences had no significant effect on the carbon pool management index (p>0.05). 3) Compared with cbbL bacterial diversity, the differentially abundant species had a higher contribution to SOC accumulation (83.90%). Among them, Chromatiales significantly affected the active components of SOC and the carbon pool management index (p=0.04). Thiodictyon was a major functional genus under this order that had a significant positive effect on SOC (p=0.034), with the application of organic fertilizer exhibiting a targeted effect, promoting its abundance. The research results have revealed the key pathways for regulating microbial carbon fixation through fertilization, providing a novel theoretical basis and practical targets for carbon fixation and emission reduction in farmland ecosystems.

Soil salinization poses a major challenge to agricultural production, food security, and sustainability in arid and semi-arid regions worldwide. Effectively addressing this issue requires a thorough understanding of the spatiotemporal variations in soil salinity and its driving factors. This study investigates soil salinity in Xinjiang, China, using geostatistical methods to analyze its spatial distribution in cultivated lands across the region and its southern and northern sub-regions in 2021. Additionally, it examines the spatiotemporal changes in soil salinity from 2011 to 2021 in Bachu County (southern Xinjiang) and Nileke County (northern Xinjiang), which serve as representative areas. The results showed that in 2021, soil salinity across Xinjiang ranged from 0.1 to 27.7 g/kg, with an average of 2.8 g/kg and a coefficient of variation of 130.4%, indicating significant variability. Soil salinity levels were higher in southern Xinjiang (3.8 g/kg) compared to northern Xinjiang (2.0 g/kg), showing a spatial trend of “increasing salinity from north to south.” Key drivers of spatial variation included available potassium, mean annual precipitation, alkali-hydrolyzable nitrogen, elevation, and soil pH. Between 2011 and 2021, soil salinity in cultivated lands increased significantly by 2.0 g/kg in Bachu County, while it decreased by 4.2 g/kg in Nileke County, with these changes mainly influenced by climatic factors such as precipitation, evapotranspiration, and surface temperature. These findings provide critical insights and data support for monitoring and managing soil salinization in Xinjiang, offering valuable guidance for improving agricultural sustainability in the region.

Nitrogen (N) as a pivotal factor in influencing the growth, development, and yield of maize. Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture, based on unmanned aerial vehicle (UAV) remote sensing technology. In this study, the hyperspectral images were acquired by UAV and the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) were measured to estimate the N nutrition status of maize. 24 vegetation indices (VIs) were constructed using hyperspectral images, and four prediction models were used to estimate the LNC and LNA of maize. The models include a single linear regression model, multivariable linear regression (MLR) model, random forest regression (RFR) model, and support vector regression (SVR) model. Moreover, the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding fields. The results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll (NDchl) had the highest prediction accuracy for LNC (R², RMSE, and RE were 0.72, 0.21, and 12.19%, respectively) and LNA (R², RMSE, and RE were 0.77, 0.26, and 14.34%, respectively). And then, 24 VIs were divided into 13 important VIs and 11 unimportant VIs. Three prediction models for LNC and LNA were constructed using 13 important VIs, and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model, in which RFR model had the highest prediction accuracy for the validation dataset of LNC (R², RMSE, and RE were 0.78, 0.16, and 8.83%, respectively) and LNA (R², RMSE, and RE were 0.85, 0.19, and 9.88%, respectively). This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture.