Climate change is significantly impacting cotton production in the Tarim River Basin.The study investigated the climate change characteristics from 2021 to 2100 using climate change datasets simulated per the coupled ...Climate change is significantly impacting cotton production in the Tarim River Basin.The study investigated the climate change characteristics from 2021 to 2100 using climate change datasets simulated per the coupled model inter-comparison project phase six(CMIP6)climatic patterns under the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5.The DSSAT-CROPGROCotton model,along with stepwise multiple regression analyses,was used to simulate changes in the potential yield of seed cotton due to climate change.The results show that while future temperatures in the Tarim River Basin will rise significantly,changes in precipitation and radiation during the cotton-growing season are minimal.Seed cotton yields are more sensitive to low temperatures than to precipitation and radiation.The potential yield of seed cotton under the SSP2-4.5 scenario would increase by 14.8%,23.7%,29.0%,and 29.4%in the 2030S,2050S,2070S,and 2090S,respectively.In contrast,under the SSP5-8.5 scenario,the potential yield of seed cotton would see increases of 17.5%,27.1%,30.1%,and 22.6%,respectively.Except for the 2090s under the SSP5-8.5 scenario,future seed cotton production can withstand a 10%to 20%deficit in irrigation.These findings will help develop climate change adaptation strategies for cotton cultivation.展开更多
Background Cotton crop is infested by numerous arthropod pests from sowing to harvesting,causing substantial direct and indirect yield losses.Knowledge of seasonal population trends and the relative occurrence of pest...Background Cotton crop is infested by numerous arthropod pests from sowing to harvesting,causing substantial direct and indirect yield losses.Knowledge of seasonal population trends and the relative occurrence of pests and their natural enemies is required to minimize the pest population and yield losses.In the current study,analysis of the seasonal population trend of pests and natural enemies and their relative occurrence on cultivars of three cotton species in Central India has been carried out.Results A higher number and diversity of sucking pests were observed during the vegetative cotton growth stage(60 days after sowing),declining as the crop matured.With the exception of cotton jassid(Amrasca biguttula biguttula Ishida),which caused significant crop damage mainly from August to September;populations of other sucking insects seldom reached economic threshold levels(ETL)throughout the studied period.The bollworm complex populations were minimal,except for the pink bollworm(Pectinophora gossypiella Saunders),which re-emerged as a menace to cotton crops during the cotton cropping season 2017–2018 due to resistance development against Bt-cotton.A reasonably good number of predatory arthropods,including coccinellids,lacewings,and spiders,were found actively preying on the arthropod pest complex of the cotton crop during the early vegetative growth stage.Linear regression indicates a significant relationship between green boll infestations and pink bollworm moths in pheromone traps.Multiple linear regression analyse showed mean weekly weather at one-or two-week lag periods had a significant impact on sucking pest population(cotton aphid,cotton jassid,cotton whitefly,and onion thrips)fluctuation.Gossypium hirsutum cultivars RCH 2 and DCH 32,and G.barbadense cultivar Suvin were found susceptible to cotton jassid and onion thrips.Phule Dhanvantary,an G.arboreum cotton cultivar,demonstrated the highest tolerance among all evaluated cultivars against all sucking pests.Conclusion These findings have important implications for pest management in cotton crops.Susceptible cultivars warrant more attention for plant protection measures,making them more input-intensive.The choice of appropriate cultivars can help minimize input costs,thereby increasing net returns for cotton farmers.展开更多
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textile...The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planti...Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.展开更多
The early-maturing cotton planting area in northern Xinjiang is a significant high-quality cotton production region in China.The screening and identification of early-maturing cotton germplasm resources are essential ...The early-maturing cotton planting area in northern Xinjiang is a significant high-quality cotton production region in China.The screening and identification of early-maturing cotton germplasm resources are essential for the selection and breeding of early-maturing machine-picked cotton varieties,thereby facilitating the development of high-quality early-maturing machine-picked cotton materials.In this study,19 self-fertilized early-maturing materials were screened and identified.Among these,the varieties G15 and G9 were selected based on their superior overall traits.Notably,the G9 variety exhibited exceptional early-maturing characteristics,with a reproductive period of 116 d.展开更多
Seed priming is an effective seed pretreatment technology that enhances germination and overall crop performance by optimizing seed hydration and metabolic processes before planting.Seed quality is a critical determin...Seed priming is an effective seed pretreatment technology that enhances germination and overall crop performance by optimizing seed hydration and metabolic processes before planting.Seed quality is a critical determinant of cotton(Gossypium hirsutum)crop performance,influencing germination,plant vigor,and yield.This study evaluates the effects of seed priming with potassium salts(1%and 2%KCl and K2SO4)on germination,morphological traits,and Cry1Ac gene expression in three Bt cotton cultivars(IUB-2013,NIAB-878B,FH-142)as Cry1Ac enhance the pest resistance in Bt cotton and reduce the plant’s dependence on chemical insecticides.Seeds were primed for six hours,air-dried,and sown in the field.Germination rates,plant height,number of bolls per plant,boll weight,seed cotton yield,and ginning outturn(GOT)were assessed at crop maturity.Cry1Ac gene expression was quantified to explore the influence of priming treatments on transgene activity.Results demonstrated that 1%K2SO4 priming significantly enhanced germination and yield-related traits,with Cry1Ac expression peaking in the IUB-2013 cultivar under 1%K2SO4 treatment.These findings suggest that potassium-based halopriming improves cotton seedling establishment and Bt gene expression.This study addresses the critical gaps in understanding the effects of seed halopriming on morphological traits,germination,and expression of the Cry1Ac gene in Bt cotton while providing a novel eco-friendly and cost-effective halopriming approach,offering the potential to improve cotton production.展开更多
One of the solutions to the global warming risk and other climate issues is to concentrate on research and development of utilizing biomass as a fossil fuel alternative.The current estimate of cotton residue waste in ...One of the solutions to the global warming risk and other climate issues is to concentrate on research and development of utilizing biomass as a fossil fuel alternative.The current estimate of cotton residue waste in the world is about 50 million tons.This massive volume of biomass waste should be turned into clean energy to avert burning the stalks in open fields after cotton harvesting.Therefore,harmful emissions such as CO_(2) will be reduced.This study aims to investigate the published literature to comprehend the bioenergy production from the thermal treatment of cotton stalks,including combustion,pyrolysis,carbonization,torrefaction,liquefaction,and gasification.Furthermore,the future outlook,utilization,and prospective challenges of agricultural biomass for biofuel production are discussed.According to the literature,biochar and bio-oil derived from cotton stalks have high heating values of about 27.5 and 37.2 MJ·kg~(–1),respectively.These values are double those of cotton stalk raw materials,which make it a good candidate for bioenergy production.This article offers valuable insight into cotton stalk utilization via thermochemical treatment and provides a solid reference for researchers,policymakers,and other stakeholders in this field.展开更多
Background The bromodomain(BRD) proteins play a pivotal role in regulating gene expression by recognizing acetylated lysine residues and acting as chromatin-associated post-translational modification-inducing proteins...Background The bromodomain(BRD) proteins play a pivotal role in regulating gene expression by recognizing acetylated lysine residues and acting as chromatin-associated post-translational modification-inducing proteins. Although BRD proteins have been extensively studied in mammals, they have also been characterized in plants like Arabidopsis thaliana and Oryza sativa, where they regulate stress-responsive genes related to drought, salinity, and cold. However, their roles in cotton species remain unexplored.Results In this genome-wide comparative analysis, 145 BRD genes were identified in the tetraploid species(Gossypium hirsutum and G. barbadense), compared with 82 BRD genes in their diploid progenitors(G. arboreum and G. raimondii), indicating that polyploidization significantly influenced BRD gene evolution. Gene duplication analysis revealed 78.85% of duplications were segmental and 21.15% were tandem among 104 in-paralogous gene pairs, contributing to BRD gene expansion. Gene structure, motif, and domain analyses demonstrated that most genes were intron-less and conserved throughout evolution. Syntenic analysis revealed a greater number of orthologous gene pairs in the Dt sub-genome than in the At sub-genome. The abundance of regulatory, hormonal, and defense-related cis-regulatory elements in the promoter region suggests that BRD genes play a role in both biotic and abiotic stress responses. Protein-protein interaction analysis indicated that global transcription factor group E(GTE) transcription factors regulate BRD genes. Expression analysis revealed that BRD genes are predominantly involved in ovule development, with some genes displaying specific expression patterns under heat, cold, and salt stress. Furthermore, qRT-PCR analysis demonstrated significant differential expression of BRD genes between the tolerant and sensitive genotype, underscoring their potential role in mediating drought and salinity stress responses.Conclusions This study provides valuable insights into the evolution of BRD genes across species and their roles in abiotic stress tolerance, highlighting their potential in breeding programs to develop drought and salinity tolerant cotton varieties.展开更多
Two cotton research institute(CRI)near-isogenic lines,CRI-12 glanded and CRI-12 glandless,were used to pinpoint potential genes and metabolic pathways linked to gossypol biosynthesis through transcriptome sequencing.W...Two cotton research institute(CRI)near-isogenic lines,CRI-12 glanded and CRI-12 glandless,were used to pinpoint potential genes and metabolic pathways linked to gossypol biosynthesis through transcriptome sequencing.We discovered more than 235 million clean reads and 1,184 differentially expressed genes(DEGs).Consecutively,we conducted a weighted gene co-expression network analysis and found a strong correlation between white and yellow modules containing GhTPS(GH_D09G0090)and GhCYP(GH_D05G2016)hub genes with the gossypol content.Importance of the GhTPS and GhCYP genes was demonstrated using RT-qPCR,virusinduced gene silencing(VIGS),and target metabolite analysis.Silencing these genes resulted in fewer glands on both leaves and stems two weeks after the infection compared to the wild type.In addition,152 metabolites were identified through targeted metabolite profiling.Differential metabolite screening revealed 12 and 18 significantly different metabolites in TRV:GhTPS and TRV:GhCYP plants vs.the control group,respectively,showing a reduction in the accumulation of metabolites compared to the control.Content of hemigossypol,the final product of gossypol biosynthesis,was also reduced,as revealed by target metabolite analysis,suggesting the role of these genes in the gossypol biosynthetic pathway.Furthermore,a highly significant difference in gossypol content between the glanded and glandless lines was recorded.Findings of this study reveal a strong link between the gossypol content and GhTPS and GhCYP hub genes,suggesting their role in the gossypol biosynthetic pathway to reduce the accumulation of hemigossypol,which may offer new comprehension into the regulatory checkpoints of the gossypol biosynthesis pathway in cotton.展开更多
Background 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...Background 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.Results 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%defoliation rate at 5 days after treatment(DAT),23.7 percentage points higher than TDZ alone,and comparable to the commercial TDZ·Diuron formulation with equivalent TDZ content.Field trials conducted in Beijing(Shangzhuang),Hebei(Hejian),and Xinjiang(Shihezi)confirmed that the combination of 0.6 mmol·L^(-1)Me-JA with 1.70 mmol·L^(-1)TDZ provided optimal defoliation performance.At 21 DAT,the defoliation rate increased by 13.5–16.3 percentage points compared with TDZ alone.Furthermore,boll opening rates improved by 5.7–12.7 percentage points relative to TDZ-only treatments.Phytohormonal analyses from the Shangzhuang site showed that the combined treatment significantly altered hormone levels in both leaves and petioles.Compared with TDZ alone,the mixture reduced concentrations of auxin(IAA),cytokinins(Z+ZR,iP+iPA,DHZ+DHZR),and gibberellic acid(GA3),while increasing levels of JA,abscisic acid(ABA),and brassinosteroids(BR).These hormonal shifts may underlie the enhanced defoliation observed with the combined treatment.Importantly,the TDZ-Me-JA combination did not adversely affect cotton yield,yield components,or fiber quality.Conclusion The combination of Me-JA and TDZ has a good defoliation effect without affecting crop yield or fiber quality.And it provides a promising foundation for the development of novel,environmentally friendly cotton defoliants.展开更多
Background Unravelling the relationship between trichome density and resistance to jassids in upland cotton,nine parental lines,viz.MCU 5,CO 14,CO 17,TCH 1828,KC 2,KC 3,GISV 323,GTHV 15–34,and RHC 1409 were obtained ...Background Unravelling the relationship between trichome density and resistance to jassids in upland cotton,nine parental lines,viz.MCU 5,CO 14,CO 17,TCH 1828,KC 2,KC 3,GISV 323,GTHV 15–34,and RHC 1409 were obtained from the Tamilnadu Agricultural University.These genotypes were subjected to molecular analysis using 27 primers,merely the JESPR 154 primer amplifying a 150-bp fragment in genotypes exhibiting the pubescence.Result This finding validated the association between pubescence and jassid resistance.Further analysis revealed that resistant genotypes(KC 3,GTHV 15–34,GISV 323,and RHC 1409)exhibited significantly higher trichome densities and length compared with susceptible genotypes.These results stalwartly support the hypothesis that trichomes play a pivotal role in conferring resistance to jassids in upland cotton.Conclusion By breeding cotton varieties with increased trichome density and length,it is possible to reduce jassid infestations,thereby decreasing the reliance on chemical pesticides and promoting a more sustainable agricultural environment.展开更多
Background Transgenic research in crops involves using genetic engineering techniques to introduce specific genes of interest from other organisms,or even entirely new genes into plant genomes to create crops with des...Background Transgenic research in crops involves using genetic engineering techniques to introduce specific genes of interest from other organisms,or even entirely new genes into plant genomes to create crops with desirable traits that wouldn’t be possible through conventional breeding methods.Transgenic crops have been developed for various traits globally.Whitefly,Bemisia tabaci(Gennadius)is one of the major sucking pests of cotton that cause significant damage to the cotton production.To combat whitefly infestations,researchers have developed four transgenic cotton lines expressing the fern protein.And those transgenic lines need to be evaluated for their performance against the target pest—whitefly.The evaluation was designed as controlled trials in polyhouse or muslin cloth cages under open-choice and no-choice conditions by comparing four transgenic cotton lines(A,B,C,and D)with three control groups,including untransformed cotton plants with a same genetic background of the transgenic line,conventionally bred whitefly-resistant cotton,and whitefly-susceptible cotton.In order to study the generational effect,the evaluation also involved studies on whitefly development in laboratory,muslin cloth cage,and polyhouse conditions.Results Both open-choice and no-choice experiments had shown that all the four transgenic cotton lines(A,B,C,and D)expressing the fern protein reduced adult whitefly numbers significantly compared with the control lines,except for the no-choice conditions in 2021,where the transgenic line C was non-significant different from the resistant control line.Notably,the nymphal population on the resistant control line was relatively low and nonsignificant different from the transgenic line C in 2021;and the transgenic lines A and C in 2022 under open-choice conditions.Under no-choice condition,the nymphal counts in the resistant control line was non-significant different from transgenic lines C and D in 2021;and transgenic line D in 2022.All transgenic lines showed significant decrease in egg hatching in 2021 and nymphal development in 2022,except for the transgenic line C which had no significant different in the nymphal development comparing with non-transgenic control lines in 2022.Adult emergence rates in both years of evaluation showed significant decrease in transgenic lines A and B comparing with the control lines.Additionally,the results showed a significant reduction in cotton leaf curl disease and sooty mold development in all the four transgenic lines compared with susceptible control under open-choice conditions,indicating potential benefits of transgenic lines beyond direct effect on whitefly control.Furthermore,the research explored the generational effects of the fern protein on whitefly which revealed the lowest fecundity in the transgenic line C across F0,F1 and F3 generations,lower egg hatching in F1 and F2 generations in transgenic lines A and B,shorter nymphal duration in F1 and F2 generations in transgenic line B,and the least total adult emergence in the transgenic line C in F0 and F3 generations.Conclusions These findings suggest that the transgenic cotton lines expressing fern protein disrupts whitefly populations and the life cycle to a certain extent.However,results are not consistent over generations and years of study,indicating these transgenic lines were not superior over control lines and need to be improved in future breeding.展开更多
Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explici...Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.展开更多
Alkaline stress in saline soil limits cotton production that may be improved by using emerging nanobiotechnology approaches.Here,we applied poly acrylic acid coated Mn3O4 nanoparticles(PMO)on cotton leaves which showe...Alkaline stress in saline soil limits cotton production that may be improved by using emerging nanobiotechnology approaches.Here,we applied poly acrylic acid coated Mn3O4 nanoparticles(PMO)on cotton leaves which showed higher chlorophyll content(up to 100.0%)and fresh weight(46.9%)and lower electrolyte leakage rate(up to 6.8%)and cell death rate(up to 84.8%)than controls.Further investigation showed that PMO can maintain reactive oxygen species(ROS)homeostasis,increase the stability of actin filament(AF),and reduce Na^(+)content.Confocal imaging and ROS content measurement showed that PMO foliar application effectively alleviated ROS over-accumulation(up to 16.4%decrease for H_(2)O_(2)and 45.3%decrease for O_(2)^(•-))in cotton leaves.Moreover,under alkaline stress,genes for AF depolymerization such as GhADF1/8 and GhADF6 and for AF polymerization such as GhADF5 were significantly down-regulated in PMO treated cotton lines relative to those in the control,consistent with the fluorescence intensities of AFs.Furthermore,our results showed that PMO mitigated Na^(+)toxicity under alkaline stress,as indicated by the reduced Na^(+)fluorescence intensity and Na^(+)content.Furthermore,relative to those of the control,PMO treatment increased seed yield and lint yield by 65.0%and 66.3%respectively.Together,our work demonstrates that ROS scavenging PMO alleviated alkaline stress by stabilizing actin filaments and reducing Na^(+)toxicity.展开更多
Premature senescence in Bacillus thuringiensis(Bt)cotton has emerged as a significant challenge to the formation and realization of fiber yield and quality since its commercialization in 1997.Initially,premature senes...Premature senescence in Bacillus thuringiensis(Bt)cotton has emerged as a significant challenge to the formation and realization of fiber yield and quality since its commercialization in 1997.Initially,premature senescence was thought to be an inherent trait associated with the Bt gene.However,subsequent research and practice have demonstrated that it is not directly linked to the Bt gene but rather results from a physiological imbalance between the sink and source,as well as between the root and shoot in Bt cotton.This short review provides an overview of the causes,mechanisms,and control measures for premature senescence in Bt cotton.It offers valuable insights for future research and the sustainable application of transgenic crops.展开更多
Background The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates t...Background The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates the potential of explainable machine learning for the geo-traceability of raw cotton.Results The findings indicate that principal component analysis(PCA) exhibits limited effectiveness in tracing cotton origins. In contrast, partial least squares discriminant analysis(PLS-DA) demonstrates superior classification performance, identifying seven discriminating variables: Na, Mn, Ba, Rb, Al, As, and Pb. The use of decision tree(DT), support vector machine(SVM), and random forest(RF) models for origin discrimination yielded accuracies of 90%, 87%, and 97%, respectively. Notably, the light gradient boosting machine(Light GBM) model achieved perfect performance metrics, with accuracy, precision, and recall rate all reaching 100% on the test set. The output of the Light GBM model was further evaluated using the SHapley Additive ex Planation(SHAP) technique, which highlighted differences in the elemental composition of raw cotton from various countries. Specifically, the elements Pb, Ni, Na, Al, As, Ba, and Rb significantly influenced the model's predictions.Conclusion These findings suggest that explainable machine learning techniques can provide insights into the complex relationships between geographic information and raw cotton. Consequently, these methodologies enhances the precision and reliability of geographic traceability for raw cotton.展开更多
基金supported by the Science and Technology Program of Xinjiang Construction Corps(No.2024AB064)the National Natural Science Foundation of China(Nos.41975044,42001314)。
文摘Climate change is significantly impacting cotton production in the Tarim River Basin.The study investigated the climate change characteristics from 2021 to 2100 using climate change datasets simulated per the coupled model inter-comparison project phase six(CMIP6)climatic patterns under the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5.The DSSAT-CROPGROCotton model,along with stepwise multiple regression analyses,was used to simulate changes in the potential yield of seed cotton due to climate change.The results show that while future temperatures in the Tarim River Basin will rise significantly,changes in precipitation and radiation during the cotton-growing season are minimal.Seed cotton yields are more sensitive to low temperatures than to precipitation and radiation.The potential yield of seed cotton under the SSP2-4.5 scenario would increase by 14.8%,23.7%,29.0%,and 29.4%in the 2030S,2050S,2070S,and 2090S,respectively.In contrast,under the SSP5-8.5 scenario,the potential yield of seed cotton would see increases of 17.5%,27.1%,30.1%,and 22.6%,respectively.Except for the 2090s under the SSP5-8.5 scenario,future seed cotton production can withstand a 10%to 20%deficit in irrigation.These findings will help develop climate change adaptation strategies for cotton cultivation.
基金Funding support for the Crop Pest Surveillance and Advisory Project(CROPSAP)。
文摘Background Cotton crop is infested by numerous arthropod pests from sowing to harvesting,causing substantial direct and indirect yield losses.Knowledge of seasonal population trends and the relative occurrence of pests and their natural enemies is required to minimize the pest population and yield losses.In the current study,analysis of the seasonal population trend of pests and natural enemies and their relative occurrence on cultivars of three cotton species in Central India has been carried out.Results A higher number and diversity of sucking pests were observed during the vegetative cotton growth stage(60 days after sowing),declining as the crop matured.With the exception of cotton jassid(Amrasca biguttula biguttula Ishida),which caused significant crop damage mainly from August to September;populations of other sucking insects seldom reached economic threshold levels(ETL)throughout the studied period.The bollworm complex populations were minimal,except for the pink bollworm(Pectinophora gossypiella Saunders),which re-emerged as a menace to cotton crops during the cotton cropping season 2017–2018 due to resistance development against Bt-cotton.A reasonably good number of predatory arthropods,including coccinellids,lacewings,and spiders,were found actively preying on the arthropod pest complex of the cotton crop during the early vegetative growth stage.Linear regression indicates a significant relationship between green boll infestations and pink bollworm moths in pheromone traps.Multiple linear regression analyse showed mean weekly weather at one-or two-week lag periods had a significant impact on sucking pest population(cotton aphid,cotton jassid,cotton whitefly,and onion thrips)fluctuation.Gossypium hirsutum cultivars RCH 2 and DCH 32,and G.barbadense cultivar Suvin were found susceptible to cotton jassid and onion thrips.Phule Dhanvantary,an G.arboreum cotton cultivar,demonstrated the highest tolerance among all evaluated cultivars against all sucking pests.Conclusion These findings have important implications for pest management in cotton crops.Susceptible cultivars warrant more attention for plant protection measures,making them more input-intensive.The choice of appropriate cultivars can help minimize input costs,thereby increasing net returns for cotton farmers.
基金supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107+1 种基金in part by the National Natural Science Foundation of China under Grant 31570714in part by the China Scholarship Council under Grant 202108320290。
文摘The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
文摘Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.
基金Supported by Major Project of Agricultural Biological Breeding(2024AB001)Germplasm Resource Innovation of Early-maturing Machine-picked Cotton in the Northern Xinjiang(2023RC04)New Germplasm Creation and Variety Selection and Application of Early-maturing and Anti-stress Machine-picked Cotton(2021NY01).
文摘The early-maturing cotton planting area in northern Xinjiang is a significant high-quality cotton production region in China.The screening and identification of early-maturing cotton germplasm resources are essential for the selection and breeding of early-maturing machine-picked cotton varieties,thereby facilitating the development of high-quality early-maturing machine-picked cotton materials.In this study,19 self-fertilized early-maturing materials were screened and identified.Among these,the varieties G15 and G9 were selected based on their superior overall traits.Notably,the G9 variety exhibited exceptional early-maturing characteristics,with a reproductive period of 116 d.
基金National Natural Science Foundation of China(3216045632360474+2 种基金32360486)grants from the Provincial Basic Research Program(Natural Science)([2020]1Z018)Provincial Key Technology R&D Program([2021]YiBan272).
文摘Seed priming is an effective seed pretreatment technology that enhances germination and overall crop performance by optimizing seed hydration and metabolic processes before planting.Seed quality is a critical determinant of cotton(Gossypium hirsutum)crop performance,influencing germination,plant vigor,and yield.This study evaluates the effects of seed priming with potassium salts(1%and 2%KCl and K2SO4)on germination,morphological traits,and Cry1Ac gene expression in three Bt cotton cultivars(IUB-2013,NIAB-878B,FH-142)as Cry1Ac enhance the pest resistance in Bt cotton and reduce the plant’s dependence on chemical insecticides.Seeds were primed for six hours,air-dried,and sown in the field.Germination rates,plant height,number of bolls per plant,boll weight,seed cotton yield,and ginning outturn(GOT)were assessed at crop maturity.Cry1Ac gene expression was quantified to explore the influence of priming treatments on transgene activity.Results demonstrated that 1%K2SO4 priming significantly enhanced germination and yield-related traits,with Cry1Ac expression peaking in the IUB-2013 cultivar under 1%K2SO4 treatment.These findings suggest that potassium-based halopriming improves cotton seedling establishment and Bt gene expression.This study addresses the critical gaps in understanding the effects of seed halopriming on morphological traits,germination,and expression of the Cry1Ac gene in Bt cotton while providing a novel eco-friendly and cost-effective halopriming approach,offering the potential to improve cotton production.
基金supported by the National Natural Science Foundation of China(Grants No.42177431 and No.U20A2086)the Beijing Natural Science Foundation(International Scientists Project,Grant No.IS24033)+3 种基金the joint research project between UniMAP and TIIAME NRU(Grant No.INTERES 9008-00064)the series training courses(Belt&Road Talent Exchange Program,Grant No.DL2021108001L)for academic writing guidance that conducted by Prof.Harold J.Annegarnthe support from China Scholarship CouncilTYSP program to the international students and scholars。
文摘One of the solutions to the global warming risk and other climate issues is to concentrate on research and development of utilizing biomass as a fossil fuel alternative.The current estimate of cotton residue waste in the world is about 50 million tons.This massive volume of biomass waste should be turned into clean energy to avert burning the stalks in open fields after cotton harvesting.Therefore,harmful emissions such as CO_(2) will be reduced.This study aims to investigate the published literature to comprehend the bioenergy production from the thermal treatment of cotton stalks,including combustion,pyrolysis,carbonization,torrefaction,liquefaction,and gasification.Furthermore,the future outlook,utilization,and prospective challenges of agricultural biomass for biofuel production are discussed.According to the literature,biochar and bio-oil derived from cotton stalks have high heating values of about 27.5 and 37.2 MJ·kg~(–1),respectively.These values are double those of cotton stalk raw materials,which make it a good candidate for bioenergy production.This article offers valuable insight into cotton stalk utilization via thermochemical treatment and provides a solid reference for researchers,policymakers,and other stakeholders in this field.
文摘Background The bromodomain(BRD) proteins play a pivotal role in regulating gene expression by recognizing acetylated lysine residues and acting as chromatin-associated post-translational modification-inducing proteins. Although BRD proteins have been extensively studied in mammals, they have also been characterized in plants like Arabidopsis thaliana and Oryza sativa, where they regulate stress-responsive genes related to drought, salinity, and cold. However, their roles in cotton species remain unexplored.Results In this genome-wide comparative analysis, 145 BRD genes were identified in the tetraploid species(Gossypium hirsutum and G. barbadense), compared with 82 BRD genes in their diploid progenitors(G. arboreum and G. raimondii), indicating that polyploidization significantly influenced BRD gene evolution. Gene duplication analysis revealed 78.85% of duplications were segmental and 21.15% were tandem among 104 in-paralogous gene pairs, contributing to BRD gene expansion. Gene structure, motif, and domain analyses demonstrated that most genes were intron-less and conserved throughout evolution. Syntenic analysis revealed a greater number of orthologous gene pairs in the Dt sub-genome than in the At sub-genome. The abundance of regulatory, hormonal, and defense-related cis-regulatory elements in the promoter region suggests that BRD genes play a role in both biotic and abiotic stress responses. Protein-protein interaction analysis indicated that global transcription factor group E(GTE) transcription factors regulate BRD genes. Expression analysis revealed that BRD genes are predominantly involved in ovule development, with some genes displaying specific expression patterns under heat, cold, and salt stress. Furthermore, qRT-PCR analysis demonstrated significant differential expression of BRD genes between the tolerant and sensitive genotype, underscoring their potential role in mediating drought and salinity stress responses.Conclusions This study provides valuable insights into the evolution of BRD genes across species and their roles in abiotic stress tolerance, highlighting their potential in breeding programs to develop drought and salinity tolerant cotton varieties.
基金financial help from the National Key R&D Program of China(2021YFE0101200)the Key Research and Development Project of Jiangsu Province,China(Modern Agriculture,BE2022364)+1 种基金the State Key Laboratory of Cotton Bio-breeding and Integrated Utilization Open Fund,China(CB2024A06)support of the Ministry of Science,Technological Development and Innovation of the Republic of Serbia(451-03-66/2024-03/200007)。
文摘Two cotton research institute(CRI)near-isogenic lines,CRI-12 glanded and CRI-12 glandless,were used to pinpoint potential genes and metabolic pathways linked to gossypol biosynthesis through transcriptome sequencing.We discovered more than 235 million clean reads and 1,184 differentially expressed genes(DEGs).Consecutively,we conducted a weighted gene co-expression network analysis and found a strong correlation between white and yellow modules containing GhTPS(GH_D09G0090)and GhCYP(GH_D05G2016)hub genes with the gossypol content.Importance of the GhTPS and GhCYP genes was demonstrated using RT-qPCR,virusinduced gene silencing(VIGS),and target metabolite analysis.Silencing these genes resulted in fewer glands on both leaves and stems two weeks after the infection compared to the wild type.In addition,152 metabolites were identified through targeted metabolite profiling.Differential metabolite screening revealed 12 and 18 significantly different metabolites in TRV:GhTPS and TRV:GhCYP plants vs.the control group,respectively,showing a reduction in the accumulation of metabolites compared to the control.Content of hemigossypol,the final product of gossypol biosynthesis,was also reduced,as revealed by target metabolite analysis,suggesting the role of these genes in the gossypol biosynthetic pathway.Furthermore,a highly significant difference in gossypol content between the glanded and glandless lines was recorded.Findings of this study reveal a strong link between the gossypol content and GhTPS and GhCYP hub genes,suggesting their role in the gossypol biosynthetic pathway to reduce the accumulation of hemigossypol,which may offer new comprehension into the regulatory checkpoints of the gossypol biosynthesis pathway in cotton.
基金funded by the China Agriculture Research System(CARS–15–16)。
文摘Background 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.Results 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%defoliation rate at 5 days after treatment(DAT),23.7 percentage points higher than TDZ alone,and comparable to the commercial TDZ·Diuron formulation with equivalent TDZ content.Field trials conducted in Beijing(Shangzhuang),Hebei(Hejian),and Xinjiang(Shihezi)confirmed that the combination of 0.6 mmol·L^(-1)Me-JA with 1.70 mmol·L^(-1)TDZ provided optimal defoliation performance.At 21 DAT,the defoliation rate increased by 13.5–16.3 percentage points compared with TDZ alone.Furthermore,boll opening rates improved by 5.7–12.7 percentage points relative to TDZ-only treatments.Phytohormonal analyses from the Shangzhuang site showed that the combined treatment significantly altered hormone levels in both leaves and petioles.Compared with TDZ alone,the mixture reduced concentrations of auxin(IAA),cytokinins(Z+ZR,iP+iPA,DHZ+DHZR),and gibberellic acid(GA3),while increasing levels of JA,abscisic acid(ABA),and brassinosteroids(BR).These hormonal shifts may underlie the enhanced defoliation observed with the combined treatment.Importantly,the TDZ-Me-JA combination did not adversely affect cotton yield,yield components,or fiber quality.Conclusion The combination of Me-JA and TDZ has a good defoliation effect without affecting crop yield or fiber quality.And it provides a promising foundation for the development of novel,environmentally friendly cotton defoliants.
文摘Background Unravelling the relationship between trichome density and resistance to jassids in upland cotton,nine parental lines,viz.MCU 5,CO 14,CO 17,TCH 1828,KC 2,KC 3,GISV 323,GTHV 15–34,and RHC 1409 were obtained from the Tamilnadu Agricultural University.These genotypes were subjected to molecular analysis using 27 primers,merely the JESPR 154 primer amplifying a 150-bp fragment in genotypes exhibiting the pubescence.Result This finding validated the association between pubescence and jassid resistance.Further analysis revealed that resistant genotypes(KC 3,GTHV 15–34,GISV 323,and RHC 1409)exhibited significantly higher trichome densities and length compared with susceptible genotypes.These results stalwartly support the hypothesis that trichomes play a pivotal role in conferring resistance to jassids in upland cotton.Conclusion By breeding cotton varieties with increased trichome density and length,it is possible to reduce jassid infestations,thereby decreasing the reliance on chemical pesticides and promoting a more sustainable agricultural environment.
文摘Background Transgenic research in crops involves using genetic engineering techniques to introduce specific genes of interest from other organisms,or even entirely new genes into plant genomes to create crops with desirable traits that wouldn’t be possible through conventional breeding methods.Transgenic crops have been developed for various traits globally.Whitefly,Bemisia tabaci(Gennadius)is one of the major sucking pests of cotton that cause significant damage to the cotton production.To combat whitefly infestations,researchers have developed four transgenic cotton lines expressing the fern protein.And those transgenic lines need to be evaluated for their performance against the target pest—whitefly.The evaluation was designed as controlled trials in polyhouse or muslin cloth cages under open-choice and no-choice conditions by comparing four transgenic cotton lines(A,B,C,and D)with three control groups,including untransformed cotton plants with a same genetic background of the transgenic line,conventionally bred whitefly-resistant cotton,and whitefly-susceptible cotton.In order to study the generational effect,the evaluation also involved studies on whitefly development in laboratory,muslin cloth cage,and polyhouse conditions.Results Both open-choice and no-choice experiments had shown that all the four transgenic cotton lines(A,B,C,and D)expressing the fern protein reduced adult whitefly numbers significantly compared with the control lines,except for the no-choice conditions in 2021,where the transgenic line C was non-significant different from the resistant control line.Notably,the nymphal population on the resistant control line was relatively low and nonsignificant different from the transgenic line C in 2021;and the transgenic lines A and C in 2022 under open-choice conditions.Under no-choice condition,the nymphal counts in the resistant control line was non-significant different from transgenic lines C and D in 2021;and transgenic line D in 2022.All transgenic lines showed significant decrease in egg hatching in 2021 and nymphal development in 2022,except for the transgenic line C which had no significant different in the nymphal development comparing with non-transgenic control lines in 2022.Adult emergence rates in both years of evaluation showed significant decrease in transgenic lines A and B comparing with the control lines.Additionally,the results showed a significant reduction in cotton leaf curl disease and sooty mold development in all the four transgenic lines compared with susceptible control under open-choice conditions,indicating potential benefits of transgenic lines beyond direct effect on whitefly control.Furthermore,the research explored the generational effects of the fern protein on whitefly which revealed the lowest fecundity in the transgenic line C across F0,F1 and F3 generations,lower egg hatching in F1 and F2 generations in transgenic lines A and B,shorter nymphal duration in F1 and F2 generations in transgenic line B,and the least total adult emergence in the transgenic line C in F0 and F3 generations.Conclusions These findings suggest that the transgenic cotton lines expressing fern protein disrupts whitefly populations and the life cycle to a certain extent.However,results are not consistent over generations and years of study,indicating these transgenic lines were not superior over control lines and need to be improved in future breeding.
基金supported by United States Department of Agriculture,Agricultural Research Service(No.58-8042-9-072)United States Department of Agriculture-National Institute of Food and Agriculture(No.2019-34263-30552)+1 种基金Management Information System(No.043050)United States Department of Agriculture-Agricultural Research Service-Non-Assistance Cooperative Agreement(No.58-6066-2-030).
文摘Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.
基金supported by the National Natural Science Foundation of China(32120103008)the National Key Research and Development Program of China(2023YFD1901700-3)+3 种基金Fundamental Research Funds for the Central Universities(2662024JC011,2662025HXPY005)the HZAU-AGIS Fund(SZYJY2021008)the Key Research and Development Projects of Henan province(231111113000)the Hubei Agricultural Science and Technology Innovation Center Program(2021-620-000-001-032).
文摘Alkaline stress in saline soil limits cotton production that may be improved by using emerging nanobiotechnology approaches.Here,we applied poly acrylic acid coated Mn3O4 nanoparticles(PMO)on cotton leaves which showed higher chlorophyll content(up to 100.0%)and fresh weight(46.9%)and lower electrolyte leakage rate(up to 6.8%)and cell death rate(up to 84.8%)than controls.Further investigation showed that PMO can maintain reactive oxygen species(ROS)homeostasis,increase the stability of actin filament(AF),and reduce Na^(+)content.Confocal imaging and ROS content measurement showed that PMO foliar application effectively alleviated ROS over-accumulation(up to 16.4%decrease for H_(2)O_(2)and 45.3%decrease for O_(2)^(•-))in cotton leaves.Moreover,under alkaline stress,genes for AF depolymerization such as GhADF1/8 and GhADF6 and for AF polymerization such as GhADF5 were significantly down-regulated in PMO treated cotton lines relative to those in the control,consistent with the fluorescence intensities of AFs.Furthermore,our results showed that PMO mitigated Na^(+)toxicity under alkaline stress,as indicated by the reduced Na^(+)fluorescence intensity and Na^(+)content.Furthermore,relative to those of the control,PMO treatment increased seed yield and lint yield by 65.0%and 66.3%respectively.Together,our work demonstrates that ROS scavenging PMO alleviated alkaline stress by stabilizing actin filaments and reducing Na^(+)toxicity.
基金supported by National Key Research and Development Program of China(2024YFD2300221)China Agricultural Research System(CARS-15–15)+1 种基金Agricultural Scientific and Technological Innovation Project of Shandong Academy of Agricultural Sciences(CXGC2024D03)Dong Hezhong Studio for Popularization of Science and Technology in Salt Tolerant Industrial Crops(202228297).
文摘Premature senescence in Bacillus thuringiensis(Bt)cotton has emerged as a significant challenge to the formation and realization of fiber yield and quality since its commercialization in 1997.Initially,premature senescence was thought to be an inherent trait associated with the Bt gene.However,subsequent research and practice have demonstrated that it is not directly linked to the Bt gene but rather results from a physiological imbalance between the sink and source,as well as between the root and shoot in Bt cotton.This short review provides an overview of the causes,mechanisms,and control measures for premature senescence in Bt cotton.It offers valuable insights for future research and the sustainable application of transgenic crops.
基金supported by Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Science。
文摘Background The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates the potential of explainable machine learning for the geo-traceability of raw cotton.Results The findings indicate that principal component analysis(PCA) exhibits limited effectiveness in tracing cotton origins. In contrast, partial least squares discriminant analysis(PLS-DA) demonstrates superior classification performance, identifying seven discriminating variables: Na, Mn, Ba, Rb, Al, As, and Pb. The use of decision tree(DT), support vector machine(SVM), and random forest(RF) models for origin discrimination yielded accuracies of 90%, 87%, and 97%, respectively. Notably, the light gradient boosting machine(Light GBM) model achieved perfect performance metrics, with accuracy, precision, and recall rate all reaching 100% on the test set. The output of the Light GBM model was further evaluated using the SHapley Additive ex Planation(SHAP) technique, which highlighted differences in the elemental composition of raw cotton from various countries. Specifically, the elements Pb, Ni, Na, Al, As, Ba, and Rb significantly influenced the model's predictions.Conclusion These findings suggest that explainable machine learning techniques can provide insights into the complex relationships between geographic information and raw cotton. Consequently, these methodologies enhances the precision and reliability of geographic traceability for raw cotton.
