Wheat grain protein content (GPC), quantified as the mass proportion of protein relative to the total dry grain weight, is a key indicator for wheat quality evaluation. Based on space-air-ground collaborative observations, this study screened GPC-sensitive spectral parameters and multi-stage and multi-scale remote-sensing GPC estimation models. Results showed that the optimal feature set derived from unmanned aerial vehicle (UAV) imagery consisted of four vegetation indices (VIs) and three optimized texture indices (TIs). The Random Forest-based Soil-Plant Analysis Development (SPAD) model for field plots achieved the highest accuracy at the anthesis stage, with a coefficient of determination (R2) of 0.91 and a root mean square error (RMSE) of 1.77. Cross-year validation gave a correlation coefficient of 0.80 and RMSE of 3.83. Data analysis indicated that GPC of mature wheat had an extremely significant correlation with SPAD values across various growth stages, higher than leaf area index (LAI). We thus established a quantitative GPC estimation framework with UAV-derived SPAD as an intermediate variable, which kept stable R2 (0.60) and RMSE (1.1%) except during grain-filling. On this basis, UAV-retrieved GPC data were scaled up to 10 m. Eight optimal parameters were selected from Sentinel-2 satellite data, and the regional-scale GPC estimation model was developed via the partial least squares regression (PLSR) algorithm. Compared with the model established directly using ground measured data without scale conversion, the scale-up model increased R2 by 8.9% and reduced RMSE by 40%, improving the model inversion accuracy. The proposed regional-scale GPC remote-sensing estimation model effectively solves the scale mismatch between ground observation and satellite data, and provides technical support for field and regional wheat quality remote-sensing estimation.

Coastal salt-marsh wetlands sustain diverse native halophytes whose root-associated microbiomes mediate plant adaptation to saline-alkaline environments. Host filtering and niche differentiation between rhizosphere soil and root endosphere jointly shape bacterial community assembly, yet comparative information for co-existing native halophytes in the northern Yellow River Delta remains limited. Here, we collected 80 rhizosphere soil and root tissue samples from replicated plots in the Binzhou coastal salt-marsh wetland (northern Yellow River Delta), and applied Illumina MiSeq 16S rRNA sequencing to compare bacterial communities of four native pioneer halophytes (Phragmites australis, Suaeda salsa, Tamarix chinensis, Cynanchum chinense). Rhizosphere soils had markedly higher alpha diversity than root endospheres, with C. chinense rhizosphere reaching the highest diversity; Proteobacteria dominated roots, whereas Actinobacteriota, Bacteroidota and Chloroflexi accumulated in rhizosphere habitats. Root bacterial communities displayed greater interspecific divergence, and rhizospheres of S. salsa and P. australis possessed enhanced nitrogen metabolism and hydrocarbon degradation. LEfSe identified host-specific biomarkers, and Chloroflexi Subgroup_10 functioned as a core hub whereas no shared keystone genus existed across root endosphere networks, underscoring stronger host filtering in endophytic habitats. This study advances our understanding of host-driven bacterial assembly of native halophytes and provides baseline references for microbiome-assisted wetland restoration in the northern Yellow River Delta.

The distribution of 'Candidatus Phytoplasma prunorum', a phloem-limited pathogen, varies among plant tissues during the year, but knowledge about quantitative comparisons of its presence in roots and above-ground tissues across Prunus species remains limited. The variation in 'Ca. P. prunorum' in four Prunus species was monitored monthly during 2025 using quantitative PCR-based absolute quantification. Roots, one-year-old shoots and annual shoots were sampled monthly from 21 infected trees of Prunus armeniaca, Prunus domestica, Prunus persica and Prunus salicina. Phytoplasma titer varied with plant part and sampling month, and differences were also observed among the examined host groups. Because cultivar and rootstock were linked to species, these differences cannot be attributed to species alone. Roots provided the most consistent year-round detection, whereas above-ground tissues showed stronger monthly variation during the studied period. P. domestica displayed the most distinct pattern: roots remained frequently positive, while one-year-old shoots and particularly annual shoots had lower detection rates and lower phytoplasma titers. Multilocus genotyping detected ten multilocus profiles, but their uneven distribution among only 21 trees precluded a reliable evaluation of their relationship with phytoplasma titer or symptom expression.

The alpine mining area of the Qilian Mountains features a fragile ecosystem, severe soil degradation due to mining disturbances, and slow natural recovery. To clarify the ecological restoration potential of rhizosphere microorganisms associated with native dominant plants, this study investigated the community structure, diversity, and functional differentiation patterns of rhizosphere bacterial and fungal communities associated with seven native dominant plant species in the Tianzhu mining area. Using Illumina NovaSeq 6000 high-throughput sequencing, we amplified the bacterial 16S rRNA V3-V4 region and the fungal ITS1 region. A total of 24 sequencing libraries (seven plant species + bare soil, three replicates each) were analyzed. Bioinformatics analyses examined ASV distributions, alpha/beta diversity, species composition, and differentially enriched taxa, followed by functional predictions. Bacterial alpha diversity (Chao1: 623.39-1553.25; Shannon: 7.49-9.67) varied significantly among plant species, with Allium przewalskianum (AP) showing the highest bacterial richness and diversity (Chao1 = 1553.25 ± 35.89, Shannon = 9.67 ± 0.02). Fungal alpha diversity also showed significant variation (Chao1: 69.66-488.56; Shannon: 3.24-4.96), with Dasiphora fruticosa (DF) exhibiting the highest fungal diversity (Chao1 = 488.56 ± 18.71, Shannon = 7.32 ± 0.06). At the phylum level, Proteobacteria (32.22-50.32%) and Actinobacteriota (15.04-23.19%) were core bacterial groups, and Ascomycota (45.54-96.07%) dominated fungal communities. PERMANOVA confirmed significant differences in community composition among plant species (bacteria: R2 = 0.78, p < 0.001; fungi: R2 = 0.84, p < 0.001). Different plant species were associated with distinct predicted functional taxa, which may serve as candidate biomarkers for soil remediation. However, all functional interpretations are predictive and require experimental validation. In conclusion, rhizosphere microbial community composition and predicted functional profiles differed among native plant species, providing correlative evidence and candidate targets for future vegetation-microbe synergy studies in alpine mining area restoration.

Volatile organic compounds (VOCs) are emitted by plants and vary in quantity and composition depending on biotic and abiotic environmental conditions. Plant-emitted VOCs are altered by attacking herbivores and pathogens, and have been increasingly found to be fine-tuned by microbial associates and environmental pollutants. Here, we studied whether arbuscular mycorrhizal fungi (AMF) treatment, barley yellow dwarf virus (BYDV) infection, and soil pollution with glyphosate-based herbicide (GBH) residues affect the emission of volatile organic compounds by oat (Avena sativa) plants. Oat plants were grown in a common garden study in southwestern Finland, where seeds in half of the plots had received a dressing with AMF inoculant before being sown in plots with a history of regular glyphosate application and control plots without a history of glyphosate use. During the study, an average of 30% of the oat plants showed symptoms of infection by BYDV, which was then included as a treatment. We collected plant VOCs of eight plants per treatment via headspace sampling with subsequent analysis by gas chromatography coupled to mass spectrometry (GC-MS). The percentage of BYDV-infected plants did not differ between AMF-treated and untreated plants, nor between plants growing in glyphosate-polluted and glyphosate-free soil. The emission of VOCs displayed characteristic profiles depending on the soil GBH history, BYDV infection, and AMF seed treatment. In particular, AMF treatment caused an increase in the terpenoid (E)-β-ocimene, and the fatty acid derivatives 1-octen-3-ol and octanal. Virus infection reduced the emission of octanal and induced the emission of the terpenoids (E)-caryophyllene and β-farnesene. GBH-treated soil caused an increase in the emission of green leaf volatiles (Z)-3-hexen-1-ol, 1-hexanol, and (Z)-3-hexenyl-acetate compared to plants in control soil. Plant-emitted VOCs displayed a distinct pattern elicited by viral infection, fungal-root association, and herbicide pollution in soil, indicating the impact of biotic and abiotic factors on the plant's biology. While the biological mechanisms behind the distinct VOC profiles remain speculative, the results indicate that VOC emission patterns may be utilized to infer plant and soil health status in crop production.

The problem of acidification of yellow soil in the southwest of China is prominent. To rejuvenate the new quality productive forces of acidic yellow soil， five treatments were set up： no amendment （CK）， organic fertilizer （OF）， biochar （BC）， biochar + dolomite （BC+D）， and organic fertilizer + dolomite （OF+D）. The aim was to identify a comprehensive improvement plan suitable for high-quality and long-term acidified yellow soil. The results showed that： ① OF+D significantly reduced exchangeable hydrogen by 25.9% and exchangeable aluminum by 34.1%， increased base saturation by 45.8% and pH by 0.43 units， and could promptly buffer the input of acidic substances near the root layer， reducing the sharp fluctuations in pH caused by the hydrolysis of potential acid， achieving the best acid control and prevention. ② BC and BC+D promoted the uniform distribution of soil water and nitrogen， while OF and OF+D were more effective in increasing water and nitrogen reserves. Particularly， OF+D increased total nitrogen， organic carbon， and available phosphorus by 9.1%， 12.4%， and 551.6%， respectively， while BC+D showed antagonistic effects. ③ The dry matter accumulation of corn was in the order of OF+D>BC+D>OF>BC>CK. The significant advantages were shown before the jointing stage after the application of dolomite， and the accumulation of grain biomass and nitrogen was accelerated thereafter. The yield， nitrogen uptake efficiency， and partial factor productivity of nitrogen of OF+D increased by 10.3%，73.8%， and 10.3%， respectively， and the production efficiency was improved in a coordinated manner. Therefore， considering the comprehensive aspects of acid regulation and control， soil fertility improvement， and stable and increased production， the application of organic fertilizer combined with dolomite is the most ideal approach for the improvement of acidic yellow soil and the increase of corn yield.

The co-contamination of heavy metals and antibiotic resistance genes (ARGs) poses an increasing challenge to agroecosystems, yet its relationships with rhizosphere metabolism and microbial communities remain poorly understood. We integrated metagenomic sequencing, untargeted metabolomics, and absolute qPCR in a wheat pot experiment to characterize resistome patterns across unplanted bulk soil (O), root-zone soil (PS), and rhizosphere soil (PR) under three Cd treatments. At the booting stage, observed ARG subtype richness and total TPM-normalized ARG abundance were higher in root-associated compartments, whereas responses to cadmium within PR were heterogeneous and non-monotonic. Absolute qPCR broadly supported enrichment of selected resistance genes in root-associated soils. Community analyses identified Pseudomonadota and Actinomycetota as potential ARG-associated taxa. Metabolomic differentiation between PR and O was substantially greater than the differences among Cd treatments within PR, while metabolite-ARG associations partly reflected compartment-level covariation. ARG and metal resistance gene abundances showed strong covariation relationship, and contig-level analysis identified genetic contexts consistent with potential linkage among resistance determinants across both control and Cd-amended treatments. Overall, spatial differentiation among soil microdomains was more consistent than treatment-associated Cd patterns. These findings identify the wheat rhizosphere as an important zone of ARG enrichment under Cd exposure while highlighting the need for host-resolved and functional validation.

Rapid and non-destructive estimation of leaf flavonol content (Flav) is important for assessing crop physiological status and stress response. In this study, unmanned aerial vehicle (UAV)-based red, green, and blue (RGB) and multispectral imagery was used to estimate sunflower leaf Flav across the seedling, budding, flowering, and maturity stages. Data were collected at a single experimental site in the Yellow River Delta, China, during the 2025 growing season. UAV images were acquired at flight heights of 30 m, 50 m, and 80 m, and ground measurements of Flav were obtained within the same acquisition window using an MPM-100 plant multispectral pigment meter. A total of 320 plot-level observations were obtained from 80 sampling plots, which were divided at the plot level into 56 training plots and 24 testing plots, with all four growth-stage observations from the same plot assigned to the same subset. Feature selection and model development were conducted using the training data, which were further divided for hyperparameter optimization, whereas the testing set was used for comparative performance evaluation. A total of 44 UAV-derived features, including 22 vegetation indices, 16 texture features, and six color features, were extracted from sunflower canopy imagery. Pearson correlation coefficient (PCC) analysis and the Boruta algorithm were combined to identify compact feature subsets, and six regression models, including K-nearest neighbors, random forest, CatBoost, support vector regression, LightGBM, and Transformer regression, were evaluated. Genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO) were further used to tune the Transformer model at the intermediate flight height of 50 m. The PCC-Boruta strategy reduced feature dimensionality and produced favorable predictive performance on the testing set. Among the evaluated regression models, the Transformer model achieved the best overall performance. Using the optimal feature combination, the PCC-Boruta-Transformer model obtained test-set R[2] values of 0.932, 0.911, and 0.909 at 30 m, 50 m, and 80 m, respectively, with corresponding root mean square error (RMSE) values of 0.060, 0.069, and 0.070. Thus, the 30 m model provided the highest predictive accuracy among the three unoptimized flight-height models. The 50 m dataset, representing the intermediate level among the three evaluated flight heights, was used for subsequent hyperparameter optimization. Among the evaluated optimization configurations, the GWO-tuned Transformer achieved the best observed testing-set performance, with an R[2] of 0.947 and an RMSE of 0.053. These results demonstrate the feasibility of the evaluated workflow under the specific site, growing-season, sensor, and management conditions of this experiment.

Sugarcane yellow leaf disease, primarily caused by aphids-transmitted sugarcane yellow leaf virus (SCYLV), poses a major threat to sugarcane production. The complex interactions among sugarcane, SCYLV, and aphids render phenotype-based breeding inefficient for improving disease resistance. In this study, an F1 -population derived from a cross between the highly susceptible Saccharum spp. hybrids YT93-159 and the highly resistant XTT22 were evaluated for SCYLV resistance. Thirty resistant and 20 susceptible F1 -individuals were selected for bulked segregant RNA-seq (BSR-seq). A total of 276.53 Gb of clean data were generated, and 22 774 single nucleotide polymorphisms (SNPs) were identified. The top five sites with the highest ΔSNP-index values above the threshold were selected from the 108 candidate regions identified by linkage analysis of genotype and phenotype. Gene annotation of these associated regions yielded 1090 candidate genes, 17 of which encoded proteins containing multiple leucine-rich repeat (LRR) domains. One candidate gene encoding a protein containing 13 LRR domains was cloned and designed ScLRR. The ScLRR protein interacts with the SCYLV coat protein (SCYLV-CP) in the nucleus. Furthermore, we established a method for simultaneous ScLRR overexpression and SCYLV inoculation in sugarcane roots mediated by Agrobacterium rhizogenes strain K599. Overexpression of ScLRR significantly reduced SCYLV accumulation in both sugarcane and Nicotiana benthamiana. This study provides key genetic resources and theoretical foundations for understanding the molecular mechanisms and precision breeding of sugarcane resistance to SCYLV.

Climate warming is seasonally asymmetric, yet its impact on plant invasions is typically studied using uniform year-round warming, which may obscure season-specific mechanisms. In a pristine grassland, we used a four-year 2 × 2 factorial field-warming experiment coupled with a greenhouse bioassay to test how growing- and nongrowing-season warming influence soil nitrogen, fungal communities, and subsequent invasive plant performance. Growing-season warming increased soil NH4 [+]-N, NO3 [-]-N, and available nitrogen, and elevated the effective species number of pathogenic fungi. Nongrowing-season warming increased soil NO3 [-]-N and generated soil legacies that enhanced invasive plant biomass and root allocation. Conversely, year-round warming reduced soil NO3 [-]-N and suppressed invasive biomass relative to nongrowing-season warming. Moreover, invasive plant biomass was positively related to soil NO3 [-]-N but showed no correlation with the effective species number of pathogenic fungi. Our findings show that warming produces seasonally contingent soil legacies mediated by specific nitrogen pathways. Predicting invasion dynamics under climate change requires seasonally explicit frameworks, rather than relying on annual mean temperature increases alone.

Bacterial endophytes are increasingly recognized for their ability to enhance plant growth and productivity through multiple physiological and biochemical mechanisms. However, how these responses are coordinated across plant developmental stages and translate into final crop performance remains unclear. This study evaluated previously characterized bacterial endophytes belonging to Bacillus subtilis (BS-114, BS-120) and Pseudomonas wadenswilerensis (PPW-26) as plant growth-promoting agents in yellow bean under in vitro, greenhouse, and field conditions. Bacterial treatments enhanced early developmental responses relative to the non-inoculated control (NC), with BS-114 increasing germination (+19.1%) and seedling total root length (+71.3%), while PPW-26 increased root biomass (+61.7%). Under greenhouse conditions, BS-114 improved reproductive development, increasing bud count (+47.4%), immature pod count (+60.2%), and pod fresh mass (+42.1%). Cell-free extracts reproduced several of these beneficial effects, with the BS-114 cell-free extract (BS-114E) producing the highest cumulative pod fresh mass (+94.2%), suggesting the contribution of extracellular components to reproductive performance. Physiological and gene-expression analyses further indicated that treatment responses were not attributable to a single pathway. Among the measured gas-exchange parameters, photosynthetic rate increased (+175.8%) and stomatal conductance increased (+306.9%). Gene expression analyses showed treatment-specific upregulation of selected genes, with N-fixation-related expression (nifH) reaching +5.62 log2FC (49.31×) with the BS-114 live-cell treatment (BS-114L), while hormone-related responses were characterized by coordinated upregulation of auxin signaling-related expression +5.24 log2FC (37.89×) and cytokinin-related expression patterns consistent with increased biosynthesis and reduced degradation. Field validation showed that all treatments increased yield relative to NC, with the BS-114+BS-120 consortium achieving a +32.65% increase, which was comparable to NPK (+37.76%), despite limited shifts in soil microbial diversity and moderate variation in soil nutrients. Together, these findings indicate that the evaluated bacterial treatments improved plant performance through coordinated responses across multiple biological levels and plant developmental stages, supporting their application as plant growth-promoting agents under controlled and field conditions.

Calcium (Ca) functions as a secondary messenger and contributes to plant drought tolerance. This study examined how Ca supplementation influences photosynthetic performance and growth in Glycyrrhiza uralensis seedlings under drought stress. Drought significantly reduced net photosynthetic rate, stomatal conductance, and transpiration rate. Under severe drought, photosynthesis was primarily constrained by non-stomatal limitations, indicating damage to the photosynthetic apparatus. Ca supplementation partially restored gas exchange and alleviated drought-induced photoinhibition by mitigating the decline in maximum PSII efficiency. Drought also reduced growth and root yield (plant height, basal diameter, crown area, root length), whereas Ca supplementation promoted seedling growth and alleviated drought-related damage, thereby enhancing overall drought adaptability. These findings demonstrate that Ca plays a key role in maintaining photosynthetic function and alleviating photodamage under water-limited conditions. Appropriate Ca supplementation may therefore serve as a practical strategy to improve drought tolerance and support the sustainable cultivation of G. uralensis in arid and semi-arid regions.

The movement of virus-derived small interfering RNAs (vsiRNAs) between plant cells has prompted the widely held assumption that they help immunize as-yet-uninfected tissues. Ascertaining this role is challenging because it requires uncoupling vsiRNA mobility from viral movement while preserving viral virulence. Consequently, the contribution of non-cell-autonomous RNAi to plant antiviral immunity remains essentially unknown. Here, we investigate the naturally phloem-restricted infection of turnip yellows virus (TuYV) in the stereotypical Arabidopsis root system at cellular resolution. We uncover intertwined, complex mechanisms operating in both vsiRNA-emitting and -recipient cells, which drive reiterated short-range (∼3-4 cell) immunization events. We find that, while intracellular antiviral RNAi is essentially nullified by TuYV, non-cell-autonomous immunization by vsiRNAs accounts mainly-if not exclusively-for the containment of viral titers and tissue invasion at the whole-plant scale. Mobile vsiRNA-mediated immunization likewise spatially confines a TuYV-unrelated virus over ∼3-4 cells, uncovering a generic, essential, yet previously unrecognized component of plant antiviral defense.

The explanatory power of traditional topographic factors on the spatial variability of soil organic carbon (SOC) is limited due to the small topographic relief in flat areas, which restricts the accuracy in digital soil mapping. Based on data collected from 136 sampling points in Fengqiu County in Henan Province (located in the alluvial plain of the Yellow River) in 2023, we constructed random forest, random forest regression-kriging (RFRK), extreme gradient boosting, and other SOC digital models. Within those models, we introduced environmental variables such as climate and multi-temporal remote sensing image-derived phenological variables, including growth trend parameters such as the peak and mean values of normalized difference vegetation index (NDVI), enhanced vegetation index, soil-adjusted vegetation index, and normalized difference water index (NDWI) during different crop growth stages. We used the Boruta algorithm to screen environmental covariates. The simulation performance of different models and variable combinations was compared and analyzed using the coefficient of determination (R[2]), mean error, root mean square error, and consistency correlation coefficient (CCC) to determine the optimal digital model, and SOC was simulated for the years 2013 and 2023. The results showed that environmental factors related to water conditions (such as Euclidean distance), multi-temporal phenological variables derived from NDWI, and NDVI played dominant roles in SOC digital soil mapping. The RFRK model was the optimal model in terms of overall accuracy (R[2] of 0.45), consistency (CCC of 64.4%), and spatial continuity. The introduction of multi-temporal phenological variables could effectively improve SOC simulation results in flat areas (R[2] increased by 0.08, CCC increased by 14.0%). RFRK model simulation results showed that from 2013 to 2023, SOC exhibited an overall increasing trend. There were differences in the magnitude of change across different regions, with the largest increase in the northwest region, reaching 80.6%. In summary, introducing multi-temporal phenological variables in flat areas could improve the accuracy of SOC digital soil mapping and provide a methodological reference for refined mapping of SOC in flat areas.

Fine root functional diversity can help explain and predict ecosystem functioning by integrating multiple fine root traits. However, because different functional diversity metrics emphasize distinct aspects of trait variation, their explanatory and predictive power may vary. Under ongoing atmospheric nitrogen (N) deposition, the responses of fine root functional diversity to N inputs and its contribution to community-level fine root biomass remain insufficiently understood.