Context: Mepiquat chloride (N, N-dimethyl piperidinium chloride, MC) is a widely used plant growth regulator that inhibits excessive vegetative growth and promotes reproductive development in cotton. Variable-rate application (VRA) refers to delivering MC tailored to site-specific crop requirements. This approach reduces canopy variability while enhancing yield-related traits compared with uniform spraying. However, research in this field remains relatively limited. Objective: This study aims first to identify cotton phenotypes sensitive to MC, subsequently establish their estimation models to generate prescription maps of MC for VRA, and ultimately assess the performance of this technique across two environmental conditions. Methods: A field experiment with four MC application rates (0, 6, 12, and 18 g center dot ha-1) was carried out in Hejian City, Hebei Province, in 2023. An unmanned aerial vehicle (UAV) equipped with five monochrome sensors (blue, green, red, red edge, and near-infrared) was used to capture multispectral images for phenotypic estimation. Then, the VRA of MC based on the retrieved phenotypes was evaluated in Hejian City, Hebei Province, in 2024, as well as in Huanggang City, Hubei Province, in 2025. Results and conclusions: Statistical analyses identified the daily increment in plant height (PHI) and the length of the top five internodes (TOP5) as MC-responsive phenotypes. Direct extraction of PHI from differential digital surface model images performed slightly better (mean R2 = 0.79) than indirect calculation based on plant height (PH) measurements at two adjacent time points (mean R2 = 0.77). For TOP5 estimation, the accuracy achieved using features derived from multispectral images, particularly vegetation indices, was superior (R2 = 0.82) to that using point cloud-derived features (R2 = 0.73). Among the six machine learning models tested, the Gaussian process regression model demonstrated the best performance for TOP5 estimation, with an R2 of 0.87, an RMSE of 1.46 cm, and an NRMSE of 8.25%. In the field validation plots, VRA of MC based on prescription maps generated from TOP5 estimation not only effectively reduced the variability of PH, PHI, and TOP5 compared with uniform spraying, but also increased seed cotton yield (5.3% in 2024; 21.9% in 2025) and economic benefits (118.9 and 745.3 USD center dot ha-1 in 2024 and 2025, respectively).

Boll-opening concentration is critical for mechanical cotton harvesting, yet it is still assessed mainly by manual records and single time-point indicators that miss temporal dynamics. To bridge the lack of a unified workflow linking vision foundation models, multi-temporal boll-opening monitoring, and harvest decision-making, we developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX. DINO-BollGX couples a DINO v3 backbone, a RetinaNet detection head, and an adaptive refinement-and-suppression module for robust open-boll detection under complex field conditions. Using multi-temporal UAV imagery collected over two years for 383 cultivars, we reconstructed plot-scale time series of open-boll counts, derived dynamic features describing progression and intensity changes, and proposed a Cotton Boll-Opening Temporal Stability Index (CTSI) to quantify boll-opening rhythm and concentration; CTSI was further integrated with a time-based risk function to generate harvest decision curves. Under unified data and training settings, DINO-BollGX achieved precision = 0.91, F1 = 0.88, and AP@0.50 = 0.80, and provided accurate boll-count estimation (R2 = 0.98; MAE = 3.10), outperforming YOLOv11, YOLOv12, YOLOv13, and RT-DETR. On an independent cross-year dataset acquired at 5 m altitude, it obtained precision = 0.98 and F1 = 0.87. An internal consistency analysis showed that CTSI had the expected negative association with Window_days (r = -0.83) and positive associations with Max_count (r = 0.78) and the boll-opening efficiency index (r = 0.92), reflecting the co-occurrence of temporal compactness and main-phase opening intensity in the cultivar population. CTSI ranged from -2.72 to 4.69 across cultivars, enabling identification of highly synchronized boll-opening. Harvest decision curves indicated that the relative net income index peaked at day 67 after the first observation and a compact optimal harvest window near the end of monitoring; on a fixed harvest date, Kuche 130,292 (CTSI = 4.69) produced 486 open bolls versus 182 for Xinluzao 36 (CTSI = 0.53) and 90 for Andizhan-60 (CTSI = -2.72). Overall, the framework integrates dynamic boll-opening phenotyping with harvest timing optimization, supporting scalable cultivar screening and mechanization-ready deployment, with potential extension to harvest decision scenarios in other crops.

Timely monitoring of cotton defoliation progress is crucial for optimizing the quality of mechanical harvesting. To accurately assess the defoliation status prior to mechanical picking, a field experiment was conducted in Hejian, Hebei Province, China, in 2022. Using a DJI P4M multispectral drone, canopy images of cotton were collected before and after defoliation at three flight altitudes: 25 m, 50 m, and 100 m. The study employed machine learning algorithms including linear regression, Support Vector Machine (SVM), Generalized Additive Model (GAM), and Random Forest (RF) to invert the Leaf Area Index (LAI). Additionally, SVM-based supervised classification was introduced to eliminate background interference from soil and open cotton bolls, while the XGBoost model and SHAP method were used to analyze the main factors influencing LAI inversion. Key findings include the following: The univariate linear relationship between EVI and LAI proved to be the most robust, with the model constructed from 100 m flight altitude data performing best (validation set: R2 = 0.921, RMSE = 0.284). The rate of LAI change showed a strong positive correlation with field-measured defoliation rate (r = 0.83–0.88), confirming its reliability as a proxy indicator for defoliation progress. Soil and open cotton bolls were identified as major negative factors affecting LAI inversion accuracy. The optimal machine learning prediction model varied with days after spraying, demonstrating significant temporal variability. This study demonstrates that high-throughput LAI inversion based on drone-derived multispectral EVI enables precise and dynamic monitoring of cotton defoliation. The approach provides farmers and field managers with an efficient, non-destructive monitoring tool. By delivering real-time insight into defoliation progress, it plays a pivotal role in enabling precision defoliation management, reducing excessive chemical use, optimizing the scheduling of mechanical operations, and ultimately enhancing both the sustainability and profitability of cotton production.

Context: Harvest aids are widely used in mechanical cotton harvesting to improve defoliation and yield. However, the effects of environmental factors, cotton canopy structure, and management practices on these outcomes have not been systematically quantified. Methodology: We conducted a meta-analysis of 51 studies, using data from over 1130 observations of defoliation and 405 yield measurements and a two-year sink-source field experiment to assess the effects on cotton defoliation and yield. Additionally, XGBoost and SHAP analysis were applied to identify key factors influencing cotton defoliation and yield. Results: Compared to the control (only water), harvest aids increased defoliation and cotton yield by 88.6 % and 3.7 %, respectively, suggesting that the critical threshold for determining the optimal mechanical harvesting window is at a defoliation rate of 88.6 %. Thidiazuron-ethephon showed the highest performance, particularly at application rates of 2500-3000 mL/ha. Collectively, a pre-spray sink-source ratio of 0.30-0.55, boll opening rate > 55 %, post-spray rainfall <= 10 mm, and average humidity > 50 % significantly improved cotton defoliation and yield. Partial least square path modeling indicated that the cotton canopy structure before spraying (sink-source ratio and boll opening rate) was significantly positively correlated with defoliation (0.39) and yield (0.16), whereas rainfall was significantly negatively correlated with defoliation (-0.24) and yield (-0.36). Meta-regression and SHAP analysis revealed that the weather and cotton canopy structure before spraying jointly affect cotton defoliation and yield after defoliant application. XGBoost had good predictive ability for defoliation (R-2 > 0.8, RMSE < 15 %) and yield (R-2 > 0.6, RMSE < 10 %). Conclusion: Appropriate harvest aid selection under reasonable cotton strain configurations and post-application environmental conditions is a win-win for defoliation and yield and provides insights for reasonably adjusting harvest aid application for sustainable agriculture.

Chemical defoliation is essential for mechanized cotton harvesting, but excessive thidiazuron (TDZ), frequently induces a "leaf sticking" phenotype characterized by wilting without abscission, which severely compromises harvest efficiency. We integrated field and greenhouse experiments with anatomical, physiological, and transcriptomic analyses to investigate this abnormal response. TDZ (0.45 and 2.27 mM) was rapidly absorbed by leaf blades within 3-6 h, and accumulation increased with concentration. Early cellular damage (3-24 h) featured membrane integrity loss, and cell death, occurring prior to substantial ROS accumulation or senescence/PCD-related gene activation, suggesting direct membrane perturbation rather than ROS- or transcription-mediated injury. At later stages (48-96 h), excessive ROS and upregulated senescence/PCD transcripts further aggravated cellular damage. The optimal concentration (0.45 mM) promoted abscission zone (AZ) cell separation and complete abscission by 96 h, an excessive concentration (2.27 mM) initiated AZ separation as early as 24 h but halted its progression, causing persistent adhesion and leaf sticking. Localized application revealed that TDZ application to the blade alone was sufficient to induce petiole wilting, confirming that leaves retain the ability to generate signals even under severe injury. Direct AZ application neither induced abscission nor visibly damaged AZ cells. These findings indicate that abscission failure under excessive TDZ is not due to impaired blade-to-AZ signaling or direct killing of AZ cells, but rather to disrupted coordination of blade-derived abscission cues. Collectively, excessive TDZ uncouples leaf damage from AZ differentiation by impairing signal coordination, providing a mechanistic framework for understanding defoliation failure and optimizing TDZ application strategies in cotton production.

The sink-source relationship significantly influences chemical defoliation in cotton. However, studies on the effects of sink-source manipulations at different phenological stages remain limited. To address this, we designed four treatments with varying sink-source ratios at the initial flowering stage (IFS), flowering and boll stage (FABS), and full boll stage (FBS): conventional plant type (CK), 50% leaves of the fruit branches removal (1/2 L), 50% boll removal (1/2B) and 100% boll removal (0B). The results demonstrated that the sink-source ratio affects defoliation outcomes, with distinct effects depending on the treatment stage. At IFS and FABS, defoliation rates increased with higher sink-source ratios following defoliant application, with the 1/2 L treatment exhibiting the highest defoliation rate and the 0B treatment the lowest. Conversely, at FBS, the CK treatment showed the highest defoliation rate, while the 1/2 L treatment had the lowest. This discrepancy may be attributed to compensatory mechanisms at FBS, where leaf removal enhanced the physiological activity of leaves (e.g., higher IAA content, lower ABA content, and increased net photosynthetic rate (Pn)), reducing sensitivity to defoliants. In contrast, at IFS and FABS, the compensatory effects of leaf removal likely subsided by the time of defoliation, and the high boll load accelerated leaf senescence (e.g., lower IAA content, higher ABA content, and reduced Pn), enhancing defoliant sensitivity. Consequently, the 1/2 L treatment not only raised the defoliation rate by 0.5-6.2% and 0.9-1.5% at IFS and FABS, but more importantly, it substantially reduced residual leaves by 33.3-65.9% and 28.2-39.0%, respectively. Additionally, there was no significant difference in yield between the CK and the 1/2 L treatment during these two periods. Thus, moderately increasing the sink-source ratio during early and middle FABS optimizes defoliation efficiency. This study provides theoretical insights and practical guidance for optimizing sink-source dynamics to improve chemical defoliation in cotton.

High crop yields depend on sufficient nutrient availability; however, potassium (K+) uptake mechanisms remain less well characterized than those for nitrogen and phosphorus. Although the KT/HAK/KUP transporter family mediates high-affinity K+ uptake in plants, its transcriptional regulation in crops remains largely unexplored. Herein, we identified GhKUP3aD as a pivotal high-affinity K+ transporter in cotton and demonstrated its essential role in maintaining K+ homeostasis under low-K+ stress. Mechanistically, we found that the C2H2-type zinc-finger transcription factor GhZAT10 directly binds to the GhKUP3aD promoter and functions as its transcriptional activator. Furthermore, GhMYC2s and GhEIN3dD, downstream components of jasmonate (JA) and ethylene signaling pathways, respectively, enhance GhZAT10 expression by directly binding to its promoter. Crucially, we discovered that GhMYC2s physically interact with GhEIN3dD, forming a composite transcriptional complex that synergistically amplifies GhZAT10 activation beyond their individual effects. Under K+-sufficient conditions, JA signaling repressors GhJAZ2/10 sequester GhMYC2s and GhZAT10, thereby preventing unnecessary metabolic expenditure. Notably, field experiments further confirmed that exogenous application of methyl jasmonate and 1-aminocyclopropane-1-carboxylic acid (an ethylene precursor) exerted substantial additive effects on photosynthetic performance and leaf K+ concentration, resulting in a combined 11.6% increase in seed cotton yield under moderate-to-severe soil K+ deficiency with available K+ levels below 60 mg kg-1. Taken together, our findings reveal that JA and ethylene signaling converge through a GhMYC2s-GhEIN3dD transcriptional complex to synergistically regulate the GhZAT10-GhKUP3aD module. These findings provide a mechanistic basis for hormonal crosstalk in cotton K+ uptake and suggest a potential mechanistic framework for enhancing K+ use efficiency in agricultural systems.

Seed priming with engineered nanoparticles can promote seed germination. Herein, we investigated how priming seeds with antioxidant poly(acrylic acid)-coated cerium oxide nanoparticles (PNC, 0.05 mM) impacts seed germination in cotton (Gossypium hirsutum L.). Seed priming with PNC significantly increased cotton hypocotyl elongation by 13 %-37 %, promoting seed germination in pot experiment. Meanwhile, the emergence rate increased by 15 %-16 % with 0.05 mM PNC-seed priming in the field. Transcriptome analysis identified PNC-induced differentially expressed genes (DEGs) related to the phytohormone, auxin (IAA), and brassinosteroid (BR) biosynthesis (e.g. GhTAA1, GhYUCCA, GhALDH, GhGH3, GhCYPs) and signal transduction (e.g. GhSAUR, GhBZR1). Consistently, PNC priming increased the accumulation of IAA (10 %-25 %) and BR (86 %-100 %) in cotton hypocotyls. In addition, PNC enhanced the expression of the xyloglucan endotransglucosylase/hydrolase (XTHs) genes, regulated by SAUR and BZR1 through IAA and BR signaling pathway and critical for cell elongation. Also, the cell lengths of the epidermis, endodermis, xylem, and pith in cotton hypocotyl increased by 21 %, 17 %, 31 %, and 21 %, respectively upon seed priming with 0.05 mM PNC. The results provide insights into the molecular mechanisms of nanoparticles-seed priming enhancement of plant seed gemination.

(1) Background: Nitric oxide (NO) serves as a crucial signaling molecule in plant abiotic stress responses. Although its role in enhancing drought resistance in cotton has been recognized, the specific mechanisms underlying this physiological and molecular regulation remain largely unexplored. This study aims to elucidate the multi-layered mechanisms by which NO modulates drought resistance in cotton; (2) Methods: Cotton seedlings were subjected to drought stress with the application of the NO donor sodium nitroprusside (SNP). A combination of confocal laser scanning microscopy, transcriptional expression analysis, biochemical assay of enzyme activity, virus-induced gene silencing (VIGS), and in vitro protein modification assays was applied to characterize the effects of NO on the drought stress response in cotton; (3) Results: Exogenous NO significantly reinforced drought resistance in cotton seedlings by improving leaf water retention capacity and photosynthetic efficiency, eliminating excessive drought-induced reactive oxygen species (ROS), upregulating the transcription and enzymatic activity of antioxidant enzymes, and promoting stomatal closure. Mechanistically, NO triggered S-nitrosylation of the plasma membrane H+-ATPase isoform GhHA2, thereby enhancing its protein stability; (4) Conclusions: These findings reveal that exogenous NO orchestrates cotton drought tolerance via multiple interconnected physiological and molecular pathways, in which the activation of the antioxidant defense system and the modulation of stomatal closure serve as central regulatory mechanisms.

Context: Dual-planting in cotton, with two seedlings per hill established at the same final plant density as singleplanting, represents a practical within-density planting configuration that can maintain yield without increasing total biomass. Our previous work explained this response mainly from an aboveground perspective, showing that canopy restructuring, altered light signaling, and improved assimilate partitioning suppress vegetative branching and favor reproductive output. However, it remains unclear whether belowground reorganization is aligned with the late-season source persistence required for this yield-stability pattern. Methods: A two-year field experiment compared two within-density planting configurations, single-planting (1S) and dual-planting (2S), at the same final plant density. Root vertical distribution, rhizosphere bacterial and fungal communities, leaf physiological traits, phytohormone-related indices, and cross-tissue transcriptomic associations were integrated to evaluate whether belowground changes under 2S were aligned with delayed functional leaf senescence and stable yield formation. Treatment effects were interpreted at the plantingconfiguration level rather than as isolated effects of hill density or seedlings per hill. Results: Compared with 1S, 2S maintained similar final seed-cotton yield but showed stronger late-season compensation, with second-harvest boll weight and seed-cotton yield increasing by 19.5% and 18.3% in 2024 and by 22.2% and 11.4% in 2025, respectively. Dual-planting redistributed roots from the surface layer to deeper soil, increasing root density traits in the 20-40 cm layer by about 30-50%. At the same time, 2S was associated with rhizosphere community reassembly, including shifts in bacterial and fungal composition, enrichment of taxa putative nutrient-cycling or plant-growth-promoting relevance, and altered microbial co-variation structure. These belowground changes coincided with higher late-season photosynthetic rate and chlorophyll retention and lower oxidative damage, with Pn increasing by 17.1% and 26.2%, Chl by 7.3% and 26.7%, and MDA decreasing by 29.1% and 27.8% at boll-setting and boll-opening, respectively. Cross-tissue integration further showed that root hormone-related transcriptional indices were positively associated with leaf hormone-responsive enrichment patterns, consistent with coordinated belowground-aboveground adjustment under 2S. Conclusion: Taken together with our previous aboveground study, the present results indicate that yield stability in dual-planting cotton is achieved not by increasing total biomass or final yield potential, but by reorganizing how yield is realized over time. In this framework, canopy regulation suppresses low-efficiency vegetative sinks, whereas belowground reorganization is associated with late-season source persistence required for continued boll filling. These findings provide a more complete crop physiological explanation for 2S yield stability under the same final plant density, while the specific causal roles of rhizosphere microorganisms, root-derived signals, and root exudate-mediated recruitment require further validation

Chemical defoliation is essential for the mechanical harvesting of cotton, and defoliation rate serves as a critical indicator for monitoring defoliation progress and determining optimal harvest timing. Traditional methods for assessing defoliation rate are labor-intensive and inefficient. This study developed a defoliation rate retrieval model using the rate of vegetation index change (VIc) derived from UAV-based multispectral data. When compared to the validation set of the defoliation rate retrieval model based solely on vegetation indices (VI), the model constructed with VIc exhibited significant improvements: the coefficient of determination (R2) increased by 0.046, the root mean square error (RMSE) decreased by 1.372%, and the relative root mean square error (RRMSE) was reduced by 2.758%. Subsequently, in the independent dataset acquired in 2021, the testing was conducted: the advantages of the defoliation rate inversion model based on VIc were more evident, with R2 increasing by 0.445, RMSE decreasing by 7.446%, and RRMSE decreasing by 11.312%. The model achieved validation set RMSE values below 12% and RRMSE values below 24% across different planting densities. Overall, predicting the defoliation rate-itself a measure of change-using the "rate of change" in vegetation indices is both statistically superior and conceptually more coherent than using static VI values. This approach enables rapid, non-destructive monitoring of defoliation, supporting precision management decisions such as secondary defoliant application or harvest timing.

BackgroundCotton lodging has become increasingly prevalent due to extreme environmental conditions and agronomic practices, severely compromising yield, fiber quality, and mechanical harvesting efficiency. However, research on cotton lodging remains limited, with most studies focusing on individual or isolated indices rather than a comprehensive system. This study systematically compared four lodging-resistant varieties (LR-1, LR-2, LR-3, LR-4) and four lodging varieties (L-1, L-2, L-3, L-4) across multiple indices: morphological traits, boll distribution, internode filling degree, stem density, mechanical strength, anatomical structure, and chemical composition.ResultsThe results showed that at the boll-opening stage, lodging-resistant varieties exhibited higher density in the first (increased by 11.6%) and third (increased by 23.5%) basal internodes compared with lodging varieties and significantly greater filling degree in the first (increased by 22.6%), second (increased by 23.1%), and third (increased by 26.1%) basal internodes; significantly higher stem puncture strength (increased by 41.2%) and stem bending resistance (increased by 38.2%); and a significantly lower stem lodging coefficient (19.0% lower in lodging-resistant varieties). Additionally, lodging-resistant varieties showed significantly enhanced anatomical structures, including greater cortex thickness, more mechanical tissue layers, and larger pith cavity, xylem, and phloem areas. Conversely, no significant differences were observed in morphological traits, boll distribution, or chemical composition between the lodging-resistant and lodging types.ConclusionLodging-resistant varieties exhibited thicker cortical tissue and mechanical tissue layers, along with larger xylem area and phloem area in basal internodes. These structural characteristics provide superior support for the filling degree and density of basal internodes, thereby enhancing stem puncture strength and bending resistance, and ultimately improving lodging resistance in cotton. These findings provide a theoretical basis for reducing the occurrence of cotton lodging.

Chemical defoliation and ripening are a prerequisite for mechanical harvesting of cotton, and the boll opening rate is a critical determinant of timing and rate of defoliates and ripening agents as well as harvest. Given the low efficiency and poor timeliness of manual determination of boll opening rates, we have developed a rapid method based on digital images. Field images were collected 7 days before and 7, 14, and 21 days after the application of harvest aids, with the boll opening rates (BOR) varying from 25 to 95%. We set four shooting heights, five shooting angles and two shooting directions, and a total of 912 original images (each 5,184×3,456 pixels) were obtained. Actual ground boll opening rates were monitored simultaneously. Each single image was segmented into 500×500 pixels sub-images. The four deep learning networks were used to identify opened and unopened cotton bolls, and YOLOv5 performed best in balancing recognition time and accuracy. To address the issue of boundary boll recognition caused by image segmentation, the original images were segmented into 10 different sizes (100, 200, 300, 400, 500, 600, 700, 800, 900, and 1,000 pixels), and YOLOv5 model was then used to identify bolls in each size of the sub-images. The bounding boxes marking cotton bolls at the same position of two different sizes of sub-images, were combined to obtain new corrected bounding boxes in merged image. Based on the true values of BOR, the best combination of sub-images is 400×400 pixels with 700×700 pixels. This combination was used to examine the recognition results of various shooting parameters, and we found that the optimal shooting height for the digital camera was 20-30 cm above the canopy, with a downward angle of 0-30° (BOR higher than 40%) and 15-30° (BOR lower than 40%) from the horizontal and shooting direction parallel to the planting rows. The method established in this study can enable a less-destructive and rapid detection of BOR in the range of 25 to 95% boll opening rate, with a model R2; value >92% and a relative root mean square error <10%, suggesting its high precision and stability for field application.

Thidiazuron (TDZ) is a widely used chemical defoliant in commercial cotton production and is often combined with the herbicide Diuron to form the commercial defoliant mixture known as TDZ·Diuron (T·D, 540 g·L-1 suspension). However, due to increasing concerns about the environmental and biological risks posed by Diuron, there is an urgent need to develop safer and more effective alternatives. Jasmonic acid (JA) and its derivatives are key phytohormones in organ senescence and abscission. Greenhouse experiments at the seedling stage revealed that Me-JA (0.8 mmol·L-1) alone did not induce defoliation. However, its co-application with TDZ (0.45 mmol·L-1) at concentrations of 0.6, 0.8, and 1.0 mmol·L-1 significantly enhanced defoliation efficacy. The most effective combination—TDZ with 0.8 mmol·L-1 Me-JA—achieved a 100

The sink-source balance critically regulates leaf abscission dynamics in cotton (Gossypium hirsutum L.), yet its targeted manipulation to optimize defoliation efficiency remains unexplored. Here, we conducted a two-year field experiment with cultivars ‘Xinluzao 78’ (XLZ78) and ‘Yuanmian 11’ (YM11) under four sink-source treatments: no cutting source and sink treatment (CK), cutting 1/2 leaves per plant (1/2L), cutting 1/2 bolls per plant (1/2B) and cutting all bolls per plant. Concurrently, XLZ78 received differential irrigation regimes (deficit: 2970 m3; ha-1; conventional: 3,420 m3; ha-1; supplementary: 4,095 m3; ha-1) to probe boll retention effects. Key findings revealed that sink reduction (1/2B, 0B) delayed defoliation by elevating leaf relative water content (LRWC) and phytohormones (IAA and ZR), while suppressing ABA, and canopy temperature, compared to CK. This ultimately reduces the activity of cellulase (CEL) and polygalacturonase (PG) in the abscission zone. Specifically, 1/2B and 0B reduced defoliation rates by 7.3 and 13.4% (XLZ78) and 0.8 and 9.2% (YM11), over the two-year average. The 1/2L treatment had a similar effect to the cutting boll treatment. Conversely, supplementary irrigation (I4095) enhanced boll retention, sink-source ratios and canopy temperature during the defoliant application period, which increased the leaf abscission rate by 14.0% over deficit irrigation (I2970). Mechanistically, under sink limitation conditions, the combination of lower canopy temperature and hormonal changes suppresses the activation of abscission zones. These results demonstrate that managing sink-source equilibrium by minimizing leaf damage while optimizing boll load fine-tunes defoliation efficiency through microenvironment-hormone crosstalk. Our work advances the physiological framework for precision defoliation strategies in high-density cotton systems, reconciling mechanical harvesting efficiency with yield preservation.

Under refined management, high-yield cotton fields are approaching their maximum output. However, how to break this yield upper limit, specifically the source–sink relationship is still inadequately researched. This experiment was conducted to explore the interaction mechanism between yield formation and source–sink parameters (photosynthesis, nitrogen content, canopy structure and dry matter accumulation and distribution). The treatments consisted of a no cutting source and sink treatment (CK), cutting 1/2 leaves per plant (1/2L) and cutting 1/2 bolls per plant (1/2B) at the initial flowering stage (IFS), the flower and boll stage (FABS), and the full boll stage (FBS). The results showed that 1/2L treatment minimized yield losses to 2.3–5.9% by enhancing photosynthetic compensation, with FBS-1/2L showing the smallest reduction (2.3–2.9%) due to higher leaf N content and SPAD values, whereas, the 1/2B treatments resulted in significant yield losses attributable to fewer bolls, especially the FBS-1/2B treatments, which reduced yields by 35.7–41.9%, with a compensatory rate of only 8.1–14.3%. It is noteworthy that the compensation rates of IFS-1/2B and FABS-1/2B could reach 26.7–32.3% and 18.7–23.8% of their yields due to the higher leaf N content. In a word, the source damage can be buffered by physiological compensation, while the sink loss leads to yield collapse due to the irreversibility of reproductive development. Thus, the core regulator of high-yield cotton fields was sink strength. Accordingly, optimizing the sink quality was performed through moderate boll thinning at the IFS, enhancing water and fertilizer supply at the FABS and strengthening sink organ protection at the FBS in order to realize a breakthrough in yield limit.

The post-translational phosphorylation modification of stress-related proteins regulated by kinases and phosphatases is one of the crucial regulatory mechanisms for plants in response to salt stress. However, the paired kinases and phosphatases of the same substrate that participate in response to salt tolerance in crops, especially in cotton, remain to be elucidated. Here, we identified GhTOPP4aD as a negative regulator of salt-stress response in cotton. GhTOPP4aD interacted with Raf-like kinase 36 (GhRAF36) and ABA Insensitive 1 (GhABI1) respectively, thereby inhibiting the phosphorylation activity of GhRAF36 and directly dephosphorylating GhABI1 to counteract GhRAF36 regulation. The phosphatase activity of GhABI1 was inhibited by GhRAF36-mediated phosphorylation at two unique residues Thr124 and Ser357 in cotton, whereas it was compromised by GhTOPP4aD. GhTOPP4aD thereby limited ABA signal transduction and orchestrated ABA-responsive gene expression. Together, modulation of the phosphorylation dynamics of GhABI1 by GhRAF36 kinase and GhTOPP4aD phosphatase constitutes an essential mechanism for ABA response and salt tolerance in cotton.

Context: Dual-planting systems, characterized by retaining two seedlings per hole, offer a labor-efficient strategy for cotton cultivation by suppressing vegetative branching (VB) without compromising yield. However, the mechanisms underlying VB inhibition and yield stability remain poorly resolved. Method: This study integrates ecological, physiological, and molecular approaches to unravel how light-hormone crosstalk modulates branching plasticity in dual-planting cotton. Field trials comparing single- (1S) and dualplanting (2S) systems were conducted over two seasons, coupled with canopy microclimate analysis, stable isotope (13C) tracing, transcriptomics, and hormonal profiling. Results: Results demonstrated that dual-planting reduced VB-sourced boll density by 56.3 % while increasing fruiting branch (FB)-sourced yield by 12.9 %, maintaining total seed cotton yield parity with 1S. Canopy restructuring under 2S lowered photosynthetically active radiation (PAR) and red/far-red (R/FR) ratios at VB positions by 45.5-55.6 % and 38.4 %, respectively, intensifying light competition. This activated the phyB-PIFsBRC1 signaling axis, triggering hormonal reconfiguration: suppressed auxin (IAA; 22.1 %) and cytokinin (CTKs; 24.3-52.2 %) levels alongside elevated jasmonate (JA; 49.7 %) and abscisic acid (ABA; 27.8 %). VB biomass correlated positively with PAR and growth-promoting hormones (IAA, CTKs) but negatively with ABA. Transcriptomic analysis revealed downregulation of photosynthesis-related genes (GhLHCB, GhPHYB) and growthpromoting pathways (GhYUC8, GhIPT1), alongside upregulation of stress-responsive genes (GhLOX1, GhPYL9). Concurrently, 13C tracing showed preferential photoassimilate allocation to FBs, enhancing fiber quality (7.3 % longer, 12.4 % stronger fibers) without yield loss. Conclusion: These findings establish a tripartite regulatory framework linking canopy ecology, hormonal dynamics, and light signaling to optimize resource partitioning. By elucidating the molecular basis of branching plasticity, this work provides actionable insights into breeding shade-resilient cultivars and refining high-density planting systems, advancing sustainable cotton production under labor-constrained scenarios.

Gynostemma pentaphyllum (Thunb.) Makino (GP) (Cucurbitaceae), commonly considered as a folk remedy, has a long history of use in many parts of Asia. Polysaccharides are one of the main components of GP and possess antioxidant, antitumor, anti-inflammatory, immunomodulatory, and cardioprotective properties. In recent decades, Gynostemma pentaphyllum polysaccharides (GPP) have been widely studied for their various health benefits and potential economic value. However, there are relatively few reviews documenting these GPP. Therefore, this paper reviews the research progress of GPP in terms of their sources, isolation and purification, structural characterization, bioactivity, conformational relationships, and applications. It is anticipated that these theoretical foundations will provide a useful reference for the future development and application of natural polysaccharides in functional foods and pharmaceuticals.