Vertical farming systems (VFs) offer high production efficiency in controlled environments (CEA), but their energy requirement and associated carbon footprint are strongly constrained by the high energy demand of artificial lighting is strongly constrained by the energy demand of artificial lighting. This study assessed whether different combinations of photoperiod and photosynthetic photon flux density (PPFD; 16 L:8 D at 250 µmol m⁻² s⁻¹, 12 L:12 D at 340 µmol m⁻² s⁻¹, and continuous 24 L:0 D at 170 µmol m⁻² s⁻¹) affect growth, physiology, and energy performance of two crisphead lettuce cultivars [( Lactuca sativa L. var. crispa - ‘Falstaff’ (green) and ‘Copacabana’ (red)] when the daily light integral (DLI) is maintained constant (14.4 mol m⁻² day⁻¹). Yield, morphological traits, chlorophyll fluorescence, and gas exchange parameters did not differ among lighting treatments, indicating comparable photosynthetic functioning under all photoperiod–PPFD combinations. However, continuous lighting (24 L:0 D) improved energy use efficiency (EUE) and light use efficiency (LUE), while reducing lighting costs per unit of produced biomass and demonstrating a clear benefit in terms of resource utilization. Cultivar-related differences were more pronounced than treatment effects, with red lettuce showing higher levels of phenolic compounds, carotenoids, anthocyanins, and antioxidant capacity, while maintaining similar morphological responses. Overall, the results show that under a constant DLI, photoperiod manipulation obtained by adjusting PPFD has a limited impact on plant physiology but can substantially influence yield and energy efficiency. Continuous moderate-intensity lighting thus emerges as an effective strategy to enhance the economic and environmental sustainability of VFs without compromising crop performance.

Fluctuating irradiance forces leaves to balance energy conversion with protection against reactive oxygen species (ROS) produced when light harvesting exceeds metabolic demand. In chloroplasts, this balance is strongly governed by the thylakoid proton motive force (pmf, ΔμH + ) and by its partitioning between a pH gradient (ΔpH) and an electric field (Δψ). A proton-circuit framework in which proton deposition by linear and cyclic electron flow builds pmf, chloroplast ATP synthase spends pmf as ATP with an effective proton conductivity g(H + ), and counter-ion fluxes reshape ΔpH:Δψ on seconds-to-minutes timescales. Δψ-relieving anion pathways (VCCN1, CLCe) promote rapid ΔpH expression during light increases, enabling timely engagement of PsbS-dependent qE and ΔpH-dependent photosynthetic control at cytochrome b 6 f, whereas the K + /H + antiporter KEA3 accelerates ΔpH relaxation after transitions to lower light to speed recovery. These dynamics link to stromal metabolism by describing how stromal alkalinization and Mg² + /thioredoxin regulation activate Calvin–Benson–Bassham enzymes, how CEF pathways (PGR5/PGRL1 and NDH) increase pmf without net NADPH production, and how phosphate recycling and triose-phosphate utilization constrain ATP synthase flux. This review examines how thylakoid architecture could generate spatial heterogeneity in proton dynamics and highlight what remains inferred versus directly measured. Finally, we present an operating-regime map and a minimal diagnostic toolkit—multiwavelength ECS (pmf, ΔpH/Δψ, g(H + )) combined with NPQ, P700, and gas exchange—to translate mechanism into testable predictions and improve cross-study comparability. The unifying design principle is timing: rapid ΔpH formation to protect PSI during upshifts, followed by timely relaxation to minimize unnecessary quenching and sustain CO 2 assimilation.

Introduction Cyathula capitata (Wall.) Moq., a widely used medicinal herb in Yi medicine, is often combined with other herbs to treat traumatic injuries, rheumatism, and rheumatoid arthritis. This study aims to characterize the chloroplast genomes, assess variation levels, and elucidate the phylogenetic relationships among Cyathula capitata , Cyathula officinalis , and their hybrid ( Cyathula officinalis × Cyathula capitata ). The findings provide valuable references for species identification, genetic background analysis, and quality control of Cyathula medicinal materials. Methods We employed second-generation high-throughput sequencing technology to sequence the chloroplast genomes of Cyathula capitata , Cyathula officinalis , and their hybrid ( Cyathula officinalis × Cyathula capitata ). Comparative genomic analyses were conducted to examine their genomic structures, simple sequence repeats (SSRs), codon usage preferences, and inverted repeat (IR) regions. Additionally, a phylogenetic tree based on chloroplast genome sequences was constructed to clarify their evolutionary relationships. Results The chloroplast genomes of all three taxa displayed the typical quadripartite structure, comprising a large single-copy region, a small single-copy region, and two IR regions (IRa and IRb). The Cyathula capitata genomes ranged from 151,428 to 151,436 bp, showing notable intraspecific diversity likely influenced by geographic factors. However, two C. capitata samples shared an identical genome length of 151,518 bp and exhibited consistent genotypes across locations, indicating relative genomic conservation. Repeat sequence analysis identified hexanucleotide SSRs, a unique 16-bp single nucleotide insertion in the rpl22 gene, and a 30–40 bp forward repeat within the rps16_1-trnQ-UUG intergenic region as distinguishing markers for Cyathula officinalis and its hybrids. Codon usage analysis revealed no strong bias at the third codon position among the three species, although codons ending with thymine (T) were used more frequently. IR boundary analysis showed variation only among isolated C. capitata samples. Comparative genomics highlighted psbI-trnS-GCU and rps16_1-trnQ-UUG as highly variable regions. Phylogenetic analysis indicated that the hybrid (Z4) grouped within C. capitata , consistent with the maternal inheritance pattern of chloroplast genomes. Given the potential for variations in maternal parents among different hybrid batches, the positions of these elements in the phylogenetic tree may accordingly vary. Conclusion This study elucidated the chloroplast genome features and phylogenetic relationships of C. capitata, C. officinalis , and their hybrid. The findings offer significant molecular insights that facilitate species identification, genetic analysis, and quality assessment of Cyathula medicinal resources. These insights support the sustainable utilization and conservation of these resources.

Malania oleifera Chun & S.K. Lee is a rare and endangered tree species endemic to the karst forests of southwestern China. Its seeds are rich in nervonic acid, a compound of significant ecological and economic value. However, habitat fragmentation, overharvesting, and climate change have imposed severe survival pressures on this species, leading to a risk of genetic diversity loss. In this study, we employed genotyping-by-sequencing (GBS) to investigate the genome-wide genetic diversity and population structure of 89 individuals from 16 natural populations. A total of 332,551 high-quality single nucleotide polymorphisms (SNPs) were obtained. The results showed moderate genetic diversity, with populations in Guangxi exhibiting significantly higher nucleotide diversity than those in Yunnan. Population structure analyses identified six genetic clusters that corresponded closely to their geographic distribution, indicating that geographic isolation is the main driver of genetic differentiation. Mantel tests revealed a highly significant positive correlation between genetic and geographic distances but no correlation with environmental distance, representing a typical isolation-by-distance (IBD) pattern. Redundancy analysis (RDA) identified 4,361 SNPs significantly associated with environmental variables suggesting potential local adaptation signals. Demographic reconstruction revealed that M. oleifera began a sharp and continuous decline in effective population size approximately 30 kya, likely triggered by climatic fluctuations during the Last Glacial Maximum. These findings provide valuable insights for the conservation, restoration, and regional management of this ecologically and economically important species.

To explore the relationship between carbon storage and environmental factors in Populus plantations of different stand ages, and to reveal the carbon sequestration mechanisms of Populus plantations across different age classes, this study employed field surveys and laboratory analysis to investigate the distribution patterns and influencing factors of carbon storage in trunk-branch-leaf-root-soil systems of Populus plantations with different stand ages (10 y, 30 y, 40 y, 50 y) in the Luxi Yellow River floodplain. The results showed that the carbon storage in trunks, branches, and roots increased gradually with increasing stand age, while the carbon storage in leaves reached a maximum of 7.52 t·hm 2 at 40 y, followed by a gradual decrease. Soil carbon storage increased consistently with stand age. Overall, the total carbon storage of Populus plantations across different age classes exhibited a linear increasing trend with advancing standage. Correlation analysis, principal component analysis, and structural equation modeling indicated that diameter at breast height (DBH), tree height (H), tree age (AGE), and stand density (SD) were the key factors affecting carbon storage in Populus plantations. The findings of this study can provide theoretical basis and technical support for enhancing carbon sequestration and sink capacity, as well as ecological restoration of Populus plantations in the Luxi Yellow River floodplain.

Introduction Wildfires are becoming an increasingly prevalent phenomenon in sub-Mediterranean regions, including areas where the vegetation is not historically adapted to fire. However, post-fire successional dynamics in these regions remain poorly documented. Methods Vegetation was monitored annually for three years (2023–2025) following a major wildfire on the Kras Plateau (SW Slovenia) in 2022. Monitoring was conducted in 50 permanent plots assigned to five fire-severity classes, including unburned control plots (class 0) and four burned classes (classes 1, 2a, 2b, and 3). We analyzed species composition, vegetation structure, ecological indicator values, species origin and habitat preference, and plant functional traits using ordination and trait-based approaches. Results Post-fire succession followed an initial floristic composition model and an enhanced autosuccessional pathway across all fire-severity classes. The early dominance of ephemeral and ruderal species declined rapidly, while the abundance of perennial grasses, shrubs, and resprouting woody species increased. Functional traits shifted along the C–R axis of Grime’s CSR strategy framework: from ruderal towards competitive and stress-tolerant, and successional trajectories consistently converged towards zonal thermophilous deciduous forest communities. High amounts of precipitation facilitated rapid structural recovery, with shrubland developing within three years Conclusion Sub-Mediterranean forest vegetation on the Kras Plateau exhibits high resilience to wildfire, despite limited historical adaptation to fire. Enhanced autosuccession, combined with favorable post-fire moisture conditions, enables rapid recovery and reduces the likelihood of long-term degradation or the establishment of persistent post-fire shrublands.

Drought is a major problem to mungbean ( Vigna radiata L.) productivity, necessitating the identification of tolerant genotypes and the exploration of their adaptive mechanisms. This study evaluated seven mungbean genotypes ‘BARI Mung-8’, ‘BMX-010015’, ‘K851’, ‘L-92’, ‘BARI Mung-1’, ‘FH-18’, and ‘PDM-139’ under control and drought treatments to characterize their physiological, biochemical, and molecular responses. Physiological traits, including chlorophyll content, photosynthesis rate (Pn), cell membrane stability (CMS), and relative water content (RWC), varied significantly (p≤ 0.05). Under drought, ‘BARI Mung-8’, ‘BMX-010015’, and ‘K851’ maintained chl content of 1.85–2.10 mg g -1 FW and Pn of 138–145 μmol m -2 s -1 , compared to 1.25 mg g -1 FW and 78 μmol m -2 s -1 in ‘BARI Mung-1’. These tolerant lines also retained high RWC (89–92%) and CMS (84–86%). Biochemically, they accumulated greater osmolytes, proline (38.7–42.1 µg g -1 FW) and glycine betaine (118–132 µg g -1 FW), and depicted enhanced antioxidant enzyme activities, including SOD (39.8–41.2 U mg -1 protein) and CAT (14.5–15.2 U mg -1 protein). Principal component analysis (PCA) and heatmap clustering grouped tolerant genotypes with these key adaptive traits, illustrating combined stress-response processes. Gene expression profiling showed significant upregulation (2.5–4.8 fold) of osmotic adjustment genes ( VrP5CS1 , VrBADH ), antioxidant defense genes ( VrSOD1 , VrCAT1 , VrPOD1 ), water transport gene ( VrPIP2-1 ), and stress signaling genes ( VrDREB2A , VrLEA3 ). The aquaporin gene VrPIP2–1 was associated with higher RWC, while VrCHLH stability supported chl retention. Integration of physiological, biochemical, and molecular data proved that drought tolerance in mungbean is regulated by coordinated cellular hydration, osmotic regulation, ROS detoxification, and transcriptional activation. “BARI Mung-8’, ‘BMX-010015’, ‘K851’, and ‘L-92’ emerged as eminent candidates for breeding programs targeting drought-prone environments, and the identified genes provide potential markers for selection of genotypes in climate-resilient legume improvement.

Pseudomonas syringae functions as a model phytopathogen causing numerous crop diseases, resulting in substantial economic losses in global agriculture. Presently, management of P. syringae predominantly depends on chemical pesticides; however, their prolonged application has contributed to escalating resistance and environmental contamination, highlighting urgent requirement for sustainable biological control approaches. In this review, we examine recent advances in the utilization and mechanistic understanding of natural products derived from plants, animals, and microorganisms for the control of P. syringae. Plant-derived compounds—including flavonoids, terpenoids, and alkaloids—inhibit P. syringae infection by targeting the bacterial type III secretion system (T3SS), disrupting cell membrane integrity, promoting reactive oxygen species (ROS) accumulation, and activating plant immune signaling pathways such as salicylic acid (SA) and jasmonic acid (JA) cascades. Animal-derived substances, such as chitosan, propolis, and antimicrobial peptides, primarily exert antibacterial effects through membrane disruption and immune system stimulation. Microbial-derived natural products contribute to synergistic disease suppression by modulating host immunity and interfering with the pathogen’s quorum sensing mechanisms. Evidence indicates that these natural products possess multi-target antimicrobial properties, offering a rich repository of candidate molecules, such as baicalein, lignans, and carvacrol, for the development of eco-friendly antibacterial agents. Future investigations should focus on detailed characterization of these bioactive compounds and their specific disease targets, optimization of extraction methodologies to improve stability and bioavailability, and comprehensive assessment of environmental safety to advance the industrial implementation of sustainable biocontrol strategies

Low selenium bioavailability in selenium-rich soils represents a key constraint limiting selenium biofortification in agriculture. This study evaluated soil conditioning strategies to enhance selenium bioavailability, providing a theoretical foundation for efficient utilization of selenium-enriched soil resources. A pot experiment tested four soil conditioners: organic fertilizer, potassium humate, lime, and biochar, across three consecutive maize plantings. Soil conditioners effectively modified soil physicochemical properties: lime significantly increased pH and available phosphorus, while organic fertilizer increased available sulfur. These amendments markedly affected soluble and exchangeable selenium fractions. All treatments progressively increased the proportion of soluble selenium, with lime and biochar showing the most substantial gains in batches two and three (0.59%, 0.23%, 0.67%, and 0.30%, respectively). Organic fertilizer, lime, and biochar consistently elevated root selenium concentrations across all three batches by 7.79–11.01%, 33.31–135.41%, and 28.84–40.81%, respectively. Potassium humate increased root selenium by 9.04–26.42% in batches two and three. Notably, only lime consistently enhanced shoot selenium by 40.13–87.38% across all batches, while biochar increased shoot selenium by 5.60% in batch three. Plant selenium translocation analysis revealed that only lime treatment in batch three significantly increased the selenium transfer coefficient. Correlation analysis demonstrated a highly significant positive relationship between shoot selenium content and soil pH, whereas root selenium showed no significant correlation with soluble or exchangeable selenium fractions. In selenium-rich dryland soils, conditioner application increases soil pH, thereby enhancing selenium availability and root absorption. Lime proved most effective for increasing crop selenium content, while biochar also substantially improved soil selenium availability.

In agricultural automation, precise cotton segmentation is a key step for tasks such as intelligent harvesting and yield estimation. However, in complex field environments, factors such as background interference and irregular target shapes severely affect segmentation accuracy. Existing deep learning methods offer certain advantages but still generally suffer from limitations including insufficient accuracy, over-segmentation, and misidentification. To address these challenges, this study proposes a novel dual-branch cotton segmentation network, Cotton-aware Mamba-enhanced UNet (CMNet), which optimizes the ParaTransCNN architecture by incorporating the 2D Selective Scan (SS2D) module to replace the original Transformer branch, effectively balancing the extraction of local details and global semantic information while reducing computational burden. To enhance the model’s perception of irregularly shaped cotton, a Deformable Convolutional Networks v1 (DCNv1) module is integrated into the Vision Mamba (VMamba) branch, further improving the delineation of target boundaries. Additionally, an Atrous Spatial Pyramid Pooling (ASPP) module is introduced at the end of the Convolutional Neural Network (CNN) branch to strengthen multi-scale feature representation. To optimize the fusion of channel and spatial information, the Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanism replaces the original module, enhancing feature modeling capability. Experimental results on an in-field cotton image dataset demonstrate that CMNet outperforms existing mainstream methods, achieving Dice, mIoU, and Accuracy of 91.06%, 84.18%, and 98.10%, respectively, while reducing parameter count and computational complexity, thus exhibiting excellent performance. Furthermore, generalization experiments on multiple other plant datasets also achieved outstanding results, validating the model’s adaptability and potential for broader applications in multi-crop segmentation tasks, providing valuable insights for smart agriculture segmentation research. The source code and dataset of this work are publicly available at https://github.com/halidanmu/CMNet.git .