Chromatin remodelers are essential for regulating plant growth, development, and responses to environmental stresses. HIT4 (HEAT-INTOLERANT 4) is a novel stress-induced chromatin remodeling factor that has been less studied in abiotic stress and stress resistance, particularly in cotton. In this study, we conducted a comprehensive analysis of the members of the HIT4 gene family in Gossypium hirsutum using bioinformatics methods, including phylogenetic relationships, gene organization, transcription profiles, phylogenetic connections, selection pressure, and stress response. A total of 18 HIT4 genes were identified in four cotton species, with six HIT4 gene members in upland cotton. Based on the evolutionary relationships shown in the phylogenetic tree, the 18 HIT4 protein sequences were classified into four distinct subgroups. Furthermore, we conducted chromosome mapping to determine the genomic locations of these genes and visually represented the structural characteristics of HIT4 in G. hirsutum. In addition, we predicted the regulatory elements in HIT4 in G. hirsutum and conducted an analysis of repetitive sequences and gene collinearity among HIT4 in four cotton species. Moreover, we calculated the Ka/Ks ratio for homologous genes to assess the selection pressure acting on HIT4. Using RNA-seq, we explored the expression patterns of HIT4 genes in G. hirsutum and Gossypium barbadense. Through weighted gene co-expression network analysis (WGCNA), we found that GHHIT4_4 belonged to the MEblue module, which was mainly enriched in pathways such as DNA replication, phagosome, pentose and glucuronate interconversions, steroid biosynthesis, and starch and sucrose metabolism. This module may regulate the mechanism of upland cotton resistance to Verticillium wilt through DNA replication, phagosome, and various metabolic pathways. In addition, we performed heterologous overexpression of GH_D11G0591 (GHHIT4_4) in tobacco, and the results showed a significant reduction in disease index compared to the wild type, with higher expression levels of disease resistance genes in the transgenic tobacco. After conducting a VIGS (virus-induced gene silencing) experiment in cotton, the results indicated that silencing GHHIT4_4 had a significant impact, the resistance to Verticillium wilt weakened, and the internode length of the plants significantly decreased by 30.7% while the number of true leaves increased by 41.5%. qRT-PCR analysis indicated that GHHIT4_4 mainly enhanced cotton resistance to Verticillium wilt by indirectly regulating the PAL, 4CL, and CHI genes. The subcellular localization results revealed that GHHIT4_4 was predominantly distributed in the mitochondria and nucleus. This study offers preliminary evidence for the involvement of the GHHIT4_4 in cotton resistance to Verticillium wilt and lays the foundation for further research on the disease resistance mechanism of this gene in cotton.

Introduction: Nitrogen plays a pivotal role in determining cotton yield and fiber quality. Nevertheless, because high-dimensional remote-sensing data are inherently complex and redundant, accurately estimating cotton plant nitrogen concentration (PNC) from unmanned aerial vehicle (UAV) imagery remains problematic, which in turn constrains both model precision and transferability.Methods: Accordingly, this study introduces a hierarchical feature-selection scheme combining Elastic Net and Boruta–SHAP to eliminate redundant remote-sensing variables and evaluates six machine-learning algorithms to pinpoint the optimal method for estimating cotton nitrogen status.Results: Our findings reveal that five critical features (Mean_B, Mean_R, NDRE_GOSAVI, NDVI, GRVI) markedly enhanced model performance. Among the tested algorithms, random forest achieved superior performance (R² = 0.97–0.98; RMSE = 0.05–0.08), exceeding all alternatives. Both in-field observations and model outputs demonstrate that cotton PNC consistently decreases throughout development, but optimal conditions of 450 mm irrigation and 300 kg N ha⁻¹ sustain relatively elevated nitrogen levels.Discussion: Collectively, the study provides robust guidance for precision nitrogen management in cotton production within arid regions.

The accurate assessment of the rise in nitrous oxide (N₂O) under global changes in grasslands has been hindered because of inadequate annual observations. To measure the seasonal response of N₂O emissions to increased water and nitrogen (N) deposition, one year round N₂O emissions were investigated by chamber weekly in the growing season and every two weeks in the non-growing season in semi-arid temperate grasslands northern China. The results showed the temperate semi-arid grassland to be a source of N₂O with greater variability and contribution during the non-growing season. The individual effects of water or N addition increased N₂O emissions during the growing season, while the effects of water or N addition depended on the N application rates during the non-growing season. Soil properties, particularly soil temperature and water-filled pore space (WFPS), played key roles in regulating N₂O emissions. Structural equation modeling revealed that these factors explained 71% and 35% of the variation in N₂O fluxes during the growing and non-growing season, respectively. This study suggested that without observations during the non-growing season it is possible to misestimate the annual N₂O emissions and the risk of N₂O emissions increasing under global change. This would provide insights for future management strategies for mitigating greenhouse gas emissions.

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios.

Arid regions, while providing essential ecosystem services, are among the most ecologically vulnerable worldwide. Understanding and monitoring their long-term vegetation dynamics is essential for accurate environmental assessment and climate adaptation strategies. This study examined the spatiotemporal variations and driving forces of the vegetation dynamics in arid Northwestern China during 2000 to 2020, using the annual peak fractional vegetation cover (FVC) as the primary indicator. The Sen’s slope estimator with the Mann–Kendall test and the coefficient of variation were employed to assess the spatiotemporal variations in FVC, while the Pearson correlation, geographic detector model and random forest model were applied to identify the dominant driving factors for FVC. The results indicated that (1) overall vegetation cover was low (averaged peak FVC = 0.191), showing a spatial pattern of higher values in the northwest and lower values in the southeast; high FVC values were primarily observed in mountainous areas and river corridors; (2) the annual peak FVC increased significantly at a rate of 0.0508 yr⁻¹, with 33.72% of the region showing significant improvements and 5.49% degradation; (3) the spatial pattern of FVC was shaped by the distribution of land use types (59.46%), while the temporal dynamics of FVC were driven by land use changes (16.37%) and the land use intensity (37.56%); (4) both the spatial pattern and the temporal dynamics were limited by the environmental conditions. These findings highlight the critical role of anthropogenic activities in shaping the spatiotemporal variations in FVC, particularly emphasizing the distinct contributions of changes in land use types and land use intensity. This study could provide a scientific basis for sustainable land management and restoration strategies in arid regions facing global changes.

As an important strategic material in China, cotton policy has positive significance in stabilizing the safety of domestic cotton, ensuring cotton income, and stabilizing cotton output. At the same time, affected by the future market price, the cotton industry supply chain cost management is particularly important. Based on the data of China’s cotton price, consumption, yield, and wheeling-in and wheeling-out from 1978 to 2016, the simultaneous equation model is used to empirically analyze the transmission effect of the rotation mechanism of reserve cotton (RMRC) on cotton supply and demand. Studies have shown that China’s cotton reserve policy and the RMRC play a positive role in balancing the supply and demand of the domestic cotton market and preventing excessive fluctuations in cotton prices. The transmission effect of the RMRC on domestic cotton yield and consumption is significant and there are certain differences. The transmission effect of domestic cotton yield is stronger than the transmission effect on domestic cotton consumption. The difference is owing to the transfer path of the RMRC being different. We should improve the regulation and control effect of the RMRC on cotton demand, realize the normalization of the RMRC, and clarify the policy objectives of the RMRC.