Introduction: Plastic film mulching has been widely used to enhance soil hydrothermal conditions and increase crop yields in cool, semi-arid areas. However, its impact on soil microbial carbon metabolic activity and functional diversity during plant growth remains unclear despite their important roles in nutrient cycling and soil quality evaluation.Methods: This study used the Biolog EcoPlate technique to investigate the dynamics and driving factors of soil microbial carbon metabolic activity and functional diversity at different maize growth stages following plastic film mulching.Results and discussion: The results revealed that film mulching significantly increased microbial carbon metabolic activities [represented by average well color development (AWCD)] by 300% at the seedling stage and by 26.8% at maturity but decreased it by 47.4% at the flowering stage compared to the control (without mulching). A similar trend was observed for the microbial functional diversity index. Redundancy analysis identified soil moisture (SM), soil temperature (ST), dissolved organic carbon (DOC), microbial biomass carbon (MBC), and bacteria amounts as the primary factors influencing changes in soil microbial carbon source utilization. The mulch treatment significantly increased SM at all growth stages, while its warming effect disappeared at the flowering stage. Soil DOC, MBC, and bacterial populations were notably higher under mulching at the seedling and maturity stages but lower at the flowering stage. Pearson correlation analysis showed that changes in SM, ST, DOC, MBC, and bacterial populations positively correlated with the utilization of all carbon source classes, AWCD, and functional diversity indexes after film mulching. Furthermore, maize grain yield and water use efficiency increased by 142 and 129%, respectively, following film mulching. In conclusion, plastic film mulching enhanced soil microbial carbon metabolic activity and functional diversity at the seedling and maturity stages, improving crop yields in cool, semi-arid areas. Furthermore, the decrease in soil carbon metabolic capacity at flowering stage highlights that supplementing soil carbon sources should be considered after continuous film mulching to sustain or enhance farmland productivity and soil quality.

Soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) are important indicators of soil fertility when undertaking a quality evaluation. Obtaining a high-precision spatial distribution map of soil nutrients is of great significance for the differentiated management of nutrient resources and reducing non-point source pollution. However, the spatial heterogeneity of soil nutrients lead to uncertainty in the modeling process. To determine the best interpolation method, terrain, climate, and vegetation factors were used as auxiliary variables to participate in the investigation of soil nutrient spatial modeling in the present study. We used the mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and accuracy (Acc) of a dataset to comprehensively compare the performance of four different geospatial techniques: ordinary kriging (OK), regression kriging (RK), geographically weighted regression kriging (GWRK), and multiscale geographically weighted regression kriging (MGWRK). The results showed that the hybrid methods (RK, GWRK, and MGWRK) could improve the prediction accuracy to a certain extent when the residuals were spatially correlated; however, this improvement was not significant. The new MGWRK model has certain advantages in reducing the overall residual level, but it failed to achieve the desired accuracy. Considering the cost of modeling, the OK method still provides an interpolation method with a relatively simple analysis process and relatively reliable results. Therefore, it may be more beneficial to design soil sampling rationally and obtain higher-quality auxiliary variable data than to seek complex statistical methods to improve spatial prediction accuracy. This research provides a reference for the spatial mapping of soil nutrients at the farmland scale.

Efficient monitoring of soil moisture is crucial for optimizing water usage and ensuring crop health in agricultural fields, especially under rainfed conditions. This study proposes a high-throughput soil moisture monitoring method that integrates LoRa-based wireless sensor networks with region-specific statistical models. Wireless sensors were deployed in the top 0–0.2 m soil layer to gather real-time moisture data, which were then combined with the Biswas model to estimate soil moisture distribution down to a depth of 2.0 m. The model was calibrated using field capacity and crop wilting coefficients. Results demonstrated a strong correlation between model predictions and actual measured soil moisture storage, with a coefficient of determination (R²) exceeding 0.94. Additionally, 83% of sample points had relative errors below 18.5%, and for depths of 0–1.2 m, 90% of sample points had relative errors under 15%. The system effectively tracked daily soil moisture dynamics during maize growth, with predicted evapotranspiration relative errors under 10.25%. This method provides a cost-effective and scalable tool for soil moisture monitoring, supporting irrigation optimization and improving water use efficiency in dryland agriculture.

Continuous tillage cultivation positioning trials can provide the basis for maintaining soil health, improving resource utilization efficiency and crop productivity, and achieving sustainable agricultural development. In this study, changes in soil stability and water–holding capacity characteristics were measured under different tillage cultivations from a multi–year microscopic perspective and analyzed to evaluate selected key indicators. Continuous monitoring of rainfall utilization efficiency and yield was carried out for five years. Here, we discuss the role of conservation tillage in buffering and stabilizing rainfall precipitation pattern on the fluctuation and uncertainty of soil water retention and water supply capacity and soil quality. The study was carried out on dryland areas of the Loess Plateau in northern China with eight tillage systems established in 2016: no–tillage (NT); no–tillage and straw (NTS); subsoiling (SU); subsoiling and straw (SUS); rotary tillage (RT); rotary tillage and straw (RTS); conventional tillage (CT); and conventional tillage and straw (CTS). All treatments were applied in conjunction with continuous cropping for five years. The evaluated soil parameters were mean weight diameter (MWD), geometric mean diameter (GMD), >0.25 mm aggregate content (R_0.25) of water–stable aggregates (WSAs), soil moisture characteristic curve (SMCC), specific soil water capacity (C_θ), soil organic matter (SOM), rainfall utilization efficiency (RUE), and maize yields for five consecutive years. The MWD, GMD, and R_0.25of SUS were 27.38%, 17.57%, and 7.68% more than CTS (control), respectively. Overall, SOM, average annual RUE, and average annual yields increased by 14.64%, 11.89%, and 9.59%, respectively, compared with 2016. Our results strongly suggest that conservation tillage can considerably improve these characterization indicators. SUS was more effective than CTS in the 0–40 cm soil layer at hedging against drought in the area, stabilizing crop production, and achieving sustainable agricultural development.

WUSCHEL-related homeobox (WOX) transcription factors are unique to plants and play pivotal roles in plant development and stress responses. In this investigation, we acquired protein sequences of foxtail millet WOX gene family members through homologous sequence alignment and a hidden Markov model (HMM) search. Utilizing conserved domain prediction, we identified 13 foxtail millet WOX genes, which were classified into ancient, intermediate, and modern clades. Multiple sequence alignment results revealed that all WOX proteins possess a homeodomain (HD). The SiWOX genes, clustered together in the phylogenetic tree, exhibited analogous protein spatial structures, gene structures, and conserved motifs. The foxtail millet WOX genes are distributed across 7 chromosomes, featuring 3 pairs of tandem repeats: SiWOX1 and SiWOX13, SiWOX4 and SiWOX5, and SiWOX11 and SiWOX12. Collinearity analysis demonstrated that WOX genes in foxtail millet exhibit the highest collinearity with green foxtail, followed by maize. The SiWOX genes primarily harbor two categories of cis-acting regulatory elements: Stress response and plant hormone response. Notably, prominent hormones triggering responses include methyl jasmonate, abscisic acid, gibberellin, auxin, and salicylic acid. Analysis of SiWOX expression patterns and hormone responses unveiled potential functional diversity among different SiWOX genes in foxtail millet. These findings lay a solid foundation for further elucidating the functions and evolution of SiWOX genes.

The region along the Great Wall is a typical dryland agricultural zone, serving as both a potential area for staple grain production and a key region for specialty crops like coarse grains and cool-climate vegetables. Studying the characteristics of drought during the spring sowing period is crucial for developing diversified planting strategies and ensuring food security. This study analyzes the drought conditions along the Great Wall from 1960 to 2023, revealing the spatial and temporal distribution of drought in the region and quantifying the impact of climate change on drought frequency and intensity. By doing so, it fills a gap in the existing drought research, which often lacks the long-term, multi-dimensional analysis of spring sowing drought characteristics. Using daily meteorological data from April 20 to May 20 during the spring sowing period between 1960 and 2023, the study employs the Meteorological Composite Drought Index (MCI) to quantitatively identify drought conditions and examine the spatial and temporal evolution of drought in the region. The results show that, on a daily scale, the frequency of mild and moderate droughts is 60.45% and 25.19%, respectively, with no occurrences of severe or extreme drought. On an annual scale, the intensity of drought and the ratio of affected stations show an increasing trend, with a decrease in mild drought frequency and an increase in moderate and severe drought occurrences. Additionally, the spatial distribution of drought frequency follows a pattern of “higher in the east than in the west” and “higher in the north than in the south”. The study also finds that the migration of drought frequency centers shows a clear temporal evolution, with the center shifting southwestward from the 1960s to the 2000s, and then moving northeastward from the 2000s to 2023. These findings provide critical data support for optimizing agricultural drought resistance strategies and offer new insights for future research on the relationship between drought and climate change. It is suggested that agricultural practices and water resource management policies should be adjusted according to the spatial migration of drought centers, with a particular focus on optimizing drought mitigation measures during the spring sowing period.