Background Agroecological cropping systems are recognised as an alternative way to ensure the sustainability of cotton(Gossypium hirsutum L.) production in the context of climate change and degradation of soil fertili...Background Agroecological cropping systems are recognised as an alternative way to ensure the sustainability of cotton(Gossypium hirsutum L.) production in the context of climate change and degradation of soil fertility. A study was conducted in Benin from 2020 to 2023 to compare six different cotton cultivars in three agroecological cropping systems in two cotton-growing zones. Plough-based tillage plus incorporation of cover crop biomass(PTI), conservation agriculture with strip tillage(CA_ST), and conservation agriculture with no tillage(CA_NT) were compared with the reference plough-based tillage(PT). The objective was to identify morpho-physiological traits of cotton that increase yield in agroecological cropping systems. Our approach combined a field experiment and crop simulation model(CSM) of CROPGRO-Cotton to evaluate the effects of genotype(G) × environment(E) × management(M) interactions on seed cotton yield(SCY).Results Cultivars Tamcot_camde and Okp768 and simulated ideotypes performed best in CA systems. Increased seed mass, large and thick leaves, and later maturity were identified as beneficial for yield enhancement in CA systems. Cultivars and ideotypes that combine these traits also resulted in better nitrogen and water use efficiencies in CA systems. Under different climate scenarios up to 2050, ideotypes designed could increase SCY in Benin.Conclusion A set of morpho-physiological traits associated with vegetative vigour is required to ensure a good SCY in agroecological cropping systems. These results provide scientific evidence and useful knowledge for breeders and research programmes on cropping systems focused on the adaptation of cotton to climate change.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
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
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
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 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
Cotton is an essential agricultural commodity,but its global yield is greatly affected by climate change,which poses a serious threat to the agriculture sector.This review aims to provide an overview of the impact of ...Cotton is an essential agricultural commodity,but its global yield is greatly affected by climate change,which poses a serious threat to the agriculture sector.This review aims to provide an overview of the impact of climate change on cotton production and the use of genomic approaches to increase stress tolerance in cotton.This paper discusses the effects of rising temperatures,changing precipitation patterns,and extreme weather events on cotton yield.It then explores various genomic strategies,such as genomic selection and marker-assisted selection,which can be used to develop stress-tolerant cotton varieties.The review emphasizes the need for interdisciplinary research efforts and policy interventions to mitigate the adverse effects of climate change on cotton production.Furthermore,this paper presents advanced prospects,including genomic selection,gene editing,multi-omics integration,highthroughput phenotyping,genomic data sharing,climate-informed breeding,and phenomics-assisted genomic selection,for enhancing stress resilience in cotton.Those innovative approaches can assist cotton researchers and breeders in developing highly resilient cotton varieties capable of withstanding the challenges posed by climate change,ensuring the sustainable and prosperous future of cotton production.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
In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the e...In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the emergence of sucking pests(such as aphids,thrips,and whiteflies)poses a serious threat to cotton production,as they reduce lint yield by 40%–60%finally.Additionally,these pests also caused yield losses by spreading viral diseases.Promoting innovative and thorough control methods is necessary to counter the threat posed by these sucking pests.Such initiatives necessitate a multifaceted strategy that combines next-generation breeding technology and pest management techniques to produce novel cotton cultivars that are resistant to sucking pests.The discovery of novel genes and regulatory factors linked to cotton’s resistance to sucking pests will be possible by the combination of next-generation breeding technologies and omics approaches and employing those tools on special resistant donors.Continuous research aimed at understanding the genetic basis of insect resistance and improving integrated pest management(IPM)techniques is crucial to the sustainability and resilience of cotton cropping systems.To this end,a sustainable and viable strategy to protect cotton fields from sucking pests is outlined.展开更多
Background Cotton is an industrial crop renowned for its multifaceted applications in the textiles,pharmaceuticals,and biofuel industries.Plant regeneration through somatic embryogenesis(SE)plays a crucial role in the...Background Cotton is an industrial crop renowned for its multifaceted applications in the textiles,pharmaceuticals,and biofuel industries.Plant regeneration through somatic embryogenesis(SE)plays a crucial role in the genetic improvement of cotton.There is a strong correlation between SE and zygotic embryogenesis(ZE)in plants.Furthermore,the strategy of ectopic expression of cotton genes into the model plant Arabidopsis has been a widely accepted approach for functional study.Result Based on previous spatial transcriptomics of cotton somatic embryos,two genes,Gh HAT5 and Gh CRK29,were identified.They are highly expressed in cotyledon and epidermal cells of cotton cotyledonary embryos,respectively.In this study,Gh HAT5 and Gh CRK29 were ectopically expressed in Arabidopsis to investigate their functions.The result showed that in Arabidopsis zygotic embryos,the overexpression of Gh HAT5 promoted the development of apical embryonic upper-tier cells and embryonic cotyledon,while the overexpression of Gh CRK29 promoted the development of apical embryonic lower-tier cells and embryonic radicle.Given the similarities between somatic and zygotic embryogenesis,these findings suggest that Gh HAT5 and Gh CRK29 are involved in cotton SE.We also speculate that these genes may promote the expression of the Arabidopsis endogenous gene At SCR,which is crucial for embryonic development.Conclusion These results revealed that Gh HAT5 and Gh CRK29 regulate embryonic development and are essential in advancing our understanding of cotton SE and facilitating targeted genetic manipulation strategies to improve industrial crop traits and agricultural sustainability.展开更多
Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is inf...Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is influenced by genotype,explant type,and environmental conditions.To overcome these issues,this study uses different machine learning-based predictive models by employing multiple input factors.Cotyledonary node explants of two commercial cotton cultivars(STN-468 and GSN-12)were isolated from 7–8 days old seedlings,preconditioned with 5,10,and 20 mg·L^(-1) kinetin(KIN)for 10 days.Thereafter,explants were postconditioned on full Murashige and Skoog(MS),1/2MS,1/4MS,and full MS+0.05 mg·L^(-1) KIN,cultured in growth room enlightened with red and blue light-emitting diodes(LED)combination.Statistical analysis(analysis of variance,regression analysis)was employed to assess the impact of different treatments on shoot regeneration,with artificial intelligence(AI)models used for confirming the findings.Results GSN-12 exhibited superior shoot regeneration potential compared with STN-468,with an average of 4.99 shoots per explant versus 3.97.Optimal results were achieved with 5 mg·L^(-1) KIN preconditioning,1/4MS postconditioning,and 80%red LED,with maximum of 7.75 shoot count for GSN-12 under these conditions;while STN-468 reached 6.00 shoots under the conditions of 10 mg·L^(-1) KIN preconditioning,MS with 0.05 mg·L^(-1) KIN(postconditioning)and 75.0%red LED.Rooting was successfully achieved with naphthalene acetic acid and activated charcoal.Additionally,three different powerful AI-based models,namely,extreme gradient boost(XGBoost),random forest(RF),and the artificial neural network-based multilayer perceptron(MLP)regression models validated the findings.Conclusion GSN-12 outperformed STN-468 with optimal results from 5 mg·L^(-1) KIN+1/4MS+80%red LED.Application of machine learning-based prediction models to optimize cotton tissue culture protocols for shoot regeneration is helpful to improve cotton regeneration efficiency.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 Soil available phosphorus(AP)deficiency significantly limits cotton production,particularly in arid and saline-alkaline regions.Screening cotton cultivars for low phosphorus(P)tolerance is crucial for the s...Background Soil available phosphorus(AP)deficiency significantly limits cotton production,particularly in arid and saline-alkaline regions.Screening cotton cultivars for low phosphorus(P)tolerance is crucial for the sustainable development of cotton production.However,the effect of different growth media on the screening outcomes remains unclear.To address this,we evaluated the low P tolerance of 25 cotton cultivars through hydroponic culture at two P levels(0.01 and 0.5 mmol·L^(-1) KH_(2)PO_(4))in 2018 and field culture with two P rates(0 and 90 kg·hm^(-2),in P2O5)in 2019.Results In the hydroponic experiments,principal component analysis(PCA)showed that shoot dry weight(SDW)and P utilization efficiency in shoots(PUES)of cotton seedlings explained over 45%of the genetic variation in P nutri-tion.Cotton cultivars were subjected to comprehensive cluster analysis,utilizing agronomic traits(SDW and PUES)during the seedling stage(hydroponic)and yield and fiber quality traits during the mature stage(in field).These cultivars were grouped into four clusters:resistant,moderately resistant,moderately sensitive,and sensitive.In low P conditions(0.01 mmol·L^(-1) KH_(2)PO_(4) and 4.5 mg·kg^(-1) AP),the low-P-resistant cluster showed significantly smaller reduc-tions in SDW(54%),seed cotton yield(3%),lint yield(-2%),fiber length(-1)%),and fiber strength(-3%)compared with the low-P-sensitive cluster(75%,13%,17%,7%,and 9%,respectively).The increase in PUES(299%)in the resist-ant cluster was also significantly higher than in the sensitive cluster(131%).Four of the eight low-P-tolerant cotton cultivars identified in the field and six in the hydroponic screening overlapped in both screenings.Two cultivars overlapped in both screening in the low-P-sensitive cluster.Conclusion Based on the screenings from both field and hydroponic cultures,ZM-9131,CCRI-79,JM-958,and J-228 were identified as low-P-tolerant cotton cultivars,while JM-169,XM-33B,SCRC-28,and LNM-18 were identified as low P-sensitive cotton cultivars.The relationship between field and hydroponic screening results for low-P-tolerant cotton cultivars was strong,although field validation is still required.The low P tolerance of these cultivars was closely associ-ated with SDW and PUES.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
Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applicati...Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.展开更多
Background Cotton is a significant crop for fiber production;however,seed shape-related traits have been less investigated in comparison to fiber quality.Comprehending the genetic foundation of traits associated with ...Background Cotton is a significant crop for fiber production;however,seed shape-related traits have been less investigated in comparison to fiber quality.Comprehending the genetic foundation of traits associated with seed shape is crucial for improving the seed and fiber quality in cotton.Results A total of 238 cotton accessions were evaluated in four different environments over a period of two years.Traits including thousand grain weight(TGW),aspect ratio(AR),seed length,seed width,diameter,and roundness demonstrated high heritability and significant genetic variation,as indicated by phenotypic analysis.The association analysis involved 145 simple sequence repeats(SSR)markers and identified 50 loci significantly associated with six traits related to seed shape.The markers MON_DPL0504aa and BNL2535ba were identified as influencing multiple traits,including aspect ratio and thousand grain weight.Notably,markers such as HAU2588a and MUSS422aa had considerable influence on seed diameter and roundness.The identified markers represented an average phenotypic variance between 3.92%for seed length and 16.54%for TGW.Conclusions The research finds key loci for seed shape-related traits in cotton,providing significant potential for marker-assisted breeding.These findings establish a framework for breeding initiatives focused on enhancing seed quality,hence advancing the cotton production.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
Cotton,a crucial commercial fibre crop,depends heavily on seed-associated characteristics like germination rate,vigour,and resistance to post-harvest deterioration for both production and lint quality.Serious cellular...Cotton,a crucial commercial fibre crop,depends heavily on seed-associated characteristics like germination rate,vigour,and resistance to post-harvest deterioration for both production and lint quality.Serious cellular damage dur-ing post-harvest processes such as delinting,prolonged seedling emergence periods,decreased viability,increased susceptibility to infections,and lipid peroxidation during storage pose serious problems to seed quality.The perfor-mance of seeds and total crop productivity are adversely affected by these problems.Traditional methods of seed improvement,like physical scarification and seed priming,have demonstrated promise in raising cotton seed vigour and germination rates.Furthermore,modern approaches including plasma therapies,magnetic water treatments,and nanotechnology-based treatments have shown promise in improving seed quality and reducing environmen-tal stresses.By offering sustainable substitutes for conventional approaches,these cutting-edge procedures lessen the need for fungicides and other agrochemicals that pollute the environment.This review explores various con-ventional and emerging strategies to address the detrimental factors impacting cotton seed quality.It emphasizes the importance of integrating classical and advanced approaches to enhance germination,ensure robust crop estab-lishment,and achieve higher yields.In addition to promoting sustainable cotton production,this kind of integration helps preserve the ecosystem and create resilient farming methods.展开更多
基金supported by the Benin Cotton Research Institute (IRC)the Cotton Interprofessional Association (AIC)+1 种基金the French Agricultural Research Centre for International Development (CIRAD)the TAZCO_(2) project (Transition Agroécologique des Zones Cotonnières du Bénin),which is funded by the Republic of Benin and the French Development Agency (AFD)。
文摘Background Agroecological cropping systems are recognised as an alternative way to ensure the sustainability of cotton(Gossypium hirsutum L.) production in the context of climate change and degradation of soil fertility. A study was conducted in Benin from 2020 to 2023 to compare six different cotton cultivars in three agroecological cropping systems in two cotton-growing zones. Plough-based tillage plus incorporation of cover crop biomass(PTI), conservation agriculture with strip tillage(CA_ST), and conservation agriculture with no tillage(CA_NT) were compared with the reference plough-based tillage(PT). The objective was to identify morpho-physiological traits of cotton that increase yield in agroecological cropping systems. Our approach combined a field experiment and crop simulation model(CSM) of CROPGRO-Cotton to evaluate the effects of genotype(G) × environment(E) × management(M) interactions on seed cotton yield(SCY).Results Cultivars Tamcot_camde and Okp768 and simulated ideotypes performed best in CA systems. Increased seed mass, large and thick leaves, and later maturity were identified as beneficial for yield enhancement in CA systems. Cultivars and ideotypes that combine these traits also resulted in better nitrogen and water use efficiencies in CA systems. Under different climate scenarios up to 2050, ideotypes designed could increase SCY in Benin.Conclusion A set of morpho-physiological traits associated with vegetative vigour is required to ensure a good SCY in agroecological cropping systems. These results provide scientific evidence and useful knowledge for breeders and research programmes on cropping systems focused on the adaptation of cotton to climate change.
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.
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.
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.
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 major national R&D projects(No.2023ZD04040-01)National Natural Science Foundation of China(No.5201101621)National Key R&D Plan(No.2022YFD1200304).
文摘Cotton is an essential agricultural commodity,but its global yield is greatly affected by climate change,which poses a serious threat to the agriculture sector.This review aims to provide an overview of the impact of climate change on cotton production and the use of genomic approaches to increase stress tolerance in cotton.This paper discusses the effects of rising temperatures,changing precipitation patterns,and extreme weather events on cotton yield.It then explores various genomic strategies,such as genomic selection and marker-assisted selection,which can be used to develop stress-tolerant cotton varieties.The review emphasizes the need for interdisciplinary research efforts and policy interventions to mitigate the adverse effects of climate change on cotton production.Furthermore,this paper presents advanced prospects,including genomic selection,gene editing,multi-omics integration,highthroughput phenotyping,genomic data sharing,climate-informed breeding,and phenomics-assisted genomic selection,for enhancing stress resilience in cotton.Those innovative approaches can assist cotton researchers and breeders in developing highly resilient cotton varieties capable of withstanding the challenges posed by climate change,ensuring the sustainable and prosperous future of cotton production.
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M/s.RASI Seeds Pvt.Ltd.,Attur,Tamil Nadu,India for their generous financial assistance in setting up a MAS study in cotton for genetic improvement of sucking pest resistance.
文摘In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the emergence of sucking pests(such as aphids,thrips,and whiteflies)poses a serious threat to cotton production,as they reduce lint yield by 40%–60%finally.Additionally,these pests also caused yield losses by spreading viral diseases.Promoting innovative and thorough control methods is necessary to counter the threat posed by these sucking pests.Such initiatives necessitate a multifaceted strategy that combines next-generation breeding technology and pest management techniques to produce novel cotton cultivars that are resistant to sucking pests.The discovery of novel genes and regulatory factors linked to cotton’s resistance to sucking pests will be possible by the combination of next-generation breeding technologies and omics approaches and employing those tools on special resistant donors.Continuous research aimed at understanding the genetic basis of insect resistance and improving integrated pest management(IPM)techniques is crucial to the sustainability and resilience of cotton cropping systems.To this end,a sustainable and viable strategy to protect cotton fields from sucking pests is outlined.
基金supported by the National Key Research and Development Program of China(No.2022YFD1200300)。
文摘Background Cotton is an industrial crop renowned for its multifaceted applications in the textiles,pharmaceuticals,and biofuel industries.Plant regeneration through somatic embryogenesis(SE)plays a crucial role in the genetic improvement of cotton.There is a strong correlation between SE and zygotic embryogenesis(ZE)in plants.Furthermore,the strategy of ectopic expression of cotton genes into the model plant Arabidopsis has been a widely accepted approach for functional study.Result Based on previous spatial transcriptomics of cotton somatic embryos,two genes,Gh HAT5 and Gh CRK29,were identified.They are highly expressed in cotyledon and epidermal cells of cotton cotyledonary embryos,respectively.In this study,Gh HAT5 and Gh CRK29 were ectopically expressed in Arabidopsis to investigate their functions.The result showed that in Arabidopsis zygotic embryos,the overexpression of Gh HAT5 promoted the development of apical embryonic upper-tier cells and embryonic cotyledon,while the overexpression of Gh CRK29 promoted the development of apical embryonic lower-tier cells and embryonic radicle.Given the similarities between somatic and zygotic embryogenesis,these findings suggest that Gh HAT5 and Gh CRK29 are involved in cotton SE.We also speculate that these genes may promote the expression of the Arabidopsis endogenous gene At SCR,which is crucial for embryonic development.Conclusion These results revealed that Gh HAT5 and Gh CRK29 regulate embryonic development and are essential in advancing our understanding of cotton SE and facilitating targeted genetic manipulation strategies to improve industrial crop traits and agricultural sustainability.
文摘Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is influenced by genotype,explant type,and environmental conditions.To overcome these issues,this study uses different machine learning-based predictive models by employing multiple input factors.Cotyledonary node explants of two commercial cotton cultivars(STN-468 and GSN-12)were isolated from 7–8 days old seedlings,preconditioned with 5,10,and 20 mg·L^(-1) kinetin(KIN)for 10 days.Thereafter,explants were postconditioned on full Murashige and Skoog(MS),1/2MS,1/4MS,and full MS+0.05 mg·L^(-1) KIN,cultured in growth room enlightened with red and blue light-emitting diodes(LED)combination.Statistical analysis(analysis of variance,regression analysis)was employed to assess the impact of different treatments on shoot regeneration,with artificial intelligence(AI)models used for confirming the findings.Results GSN-12 exhibited superior shoot regeneration potential compared with STN-468,with an average of 4.99 shoots per explant versus 3.97.Optimal results were achieved with 5 mg·L^(-1) KIN preconditioning,1/4MS postconditioning,and 80%red LED,with maximum of 7.75 shoot count for GSN-12 under these conditions;while STN-468 reached 6.00 shoots under the conditions of 10 mg·L^(-1) KIN preconditioning,MS with 0.05 mg·L^(-1) KIN(postconditioning)and 75.0%red LED.Rooting was successfully achieved with naphthalene acetic acid and activated charcoal.Additionally,three different powerful AI-based models,namely,extreme gradient boost(XGBoost),random forest(RF),and the artificial neural network-based multilayer perceptron(MLP)regression models validated the findings.Conclusion GSN-12 outperformed STN-468 with optimal results from 5 mg·L^(-1) KIN+1/4MS+80%red LED.Application of machine learning-based prediction models to optimize cotton tissue culture protocols for shoot regeneration is helpful to improve cotton regeneration efficiency.
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the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2024D01A56)the National Key Research and Develop-ment Program of China(2017YFD0201906)+2 种基金the Central Research Institutes of Basic Research and the Public Service Special Foundation(1610162022044)the China Agriculture Research System(CARS-15-11)the Agricultural Sci-ence and Technology Innovation Program of Chinese Academy of Agricultural Sciences.
文摘Background Soil available phosphorus(AP)deficiency significantly limits cotton production,particularly in arid and saline-alkaline regions.Screening cotton cultivars for low phosphorus(P)tolerance is crucial for the sustainable development of cotton production.However,the effect of different growth media on the screening outcomes remains unclear.To address this,we evaluated the low P tolerance of 25 cotton cultivars through hydroponic culture at two P levels(0.01 and 0.5 mmol·L^(-1) KH_(2)PO_(4))in 2018 and field culture with two P rates(0 and 90 kg·hm^(-2),in P2O5)in 2019.Results In the hydroponic experiments,principal component analysis(PCA)showed that shoot dry weight(SDW)and P utilization efficiency in shoots(PUES)of cotton seedlings explained over 45%of the genetic variation in P nutri-tion.Cotton cultivars were subjected to comprehensive cluster analysis,utilizing agronomic traits(SDW and PUES)during the seedling stage(hydroponic)and yield and fiber quality traits during the mature stage(in field).These cultivars were grouped into four clusters:resistant,moderately resistant,moderately sensitive,and sensitive.In low P conditions(0.01 mmol·L^(-1) KH_(2)PO_(4) and 4.5 mg·kg^(-1) AP),the low-P-resistant cluster showed significantly smaller reduc-tions in SDW(54%),seed cotton yield(3%),lint yield(-2%),fiber length(-1)%),and fiber strength(-3%)compared with the low-P-sensitive cluster(75%,13%,17%,7%,and 9%,respectively).The increase in PUES(299%)in the resist-ant cluster was also significantly higher than in the sensitive cluster(131%).Four of the eight low-P-tolerant cotton cultivars identified in the field and six in the hydroponic screening overlapped in both screenings.Two cultivars overlapped in both screening in the low-P-sensitive cluster.Conclusion Based on the screenings from both field and hydroponic cultures,ZM-9131,CCRI-79,JM-958,and J-228 were identified as low-P-tolerant cotton cultivars,while JM-169,XM-33B,SCRC-28,and LNM-18 were identified as low P-sensitive cotton cultivars.The relationship between field and hydroponic screening results for low-P-tolerant cotton cultivars was strong,although field validation is still required.The low P tolerance of these cultivars was closely associ-ated with SDW and PUES.
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.
文摘Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.
基金supported by the Fund for BTNYGG(NYHXGG,2023AA102)the National Natural Science Foundation of China(32260510)+3 种基金the Key Project for Science,Technology Development of Shihezi city,Xinjiang Production and Construction Crops(2022NY01)Shihezi University high-level talent research project(RCZK202337)Science and Technology Major Project of the Department of Science and Technology of Xinjiang Uygur Autonomous region(2022A03004-1)the Key Programs for Science and Technology Development in Agricultural Field of Xinjiang Production and Construction Corps。
文摘Background Cotton is a significant crop for fiber production;however,seed shape-related traits have been less investigated in comparison to fiber quality.Comprehending the genetic foundation of traits associated with seed shape is crucial for improving the seed and fiber quality in cotton.Results A total of 238 cotton accessions were evaluated in four different environments over a period of two years.Traits including thousand grain weight(TGW),aspect ratio(AR),seed length,seed width,diameter,and roundness demonstrated high heritability and significant genetic variation,as indicated by phenotypic analysis.The association analysis involved 145 simple sequence repeats(SSR)markers and identified 50 loci significantly associated with six traits related to seed shape.The markers MON_DPL0504aa and BNL2535ba were identified as influencing multiple traits,including aspect ratio and thousand grain weight.Notably,markers such as HAU2588a and MUSS422aa had considerable influence on seed diameter and roundness.The identified markers represented an average phenotypic variance between 3.92%for seed length and 16.54%for TGW.Conclusions The research finds key loci for seed shape-related traits in cotton,providing significant potential for marker-assisted breeding.These findings establish a framework for breeding initiatives focused on enhancing seed quality,hence advancing the cotton production.
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.
基金the Indian Council of Agriculture Research-National Agriculture Higher Education Program(No.A4/003026/2023)to carry out this work during the international faculty training program at Nanyang Technological University,Singapore,under the Institution Development Plan.
文摘Cotton,a crucial commercial fibre crop,depends heavily on seed-associated characteristics like germination rate,vigour,and resistance to post-harvest deterioration for both production and lint quality.Serious cellular damage dur-ing post-harvest processes such as delinting,prolonged seedling emergence periods,decreased viability,increased susceptibility to infections,and lipid peroxidation during storage pose serious problems to seed quality.The perfor-mance of seeds and total crop productivity are adversely affected by these problems.Traditional methods of seed improvement,like physical scarification and seed priming,have demonstrated promise in raising cotton seed vigour and germination rates.Furthermore,modern approaches including plasma therapies,magnetic water treatments,and nanotechnology-based treatments have shown promise in improving seed quality and reducing environmen-tal stresses.By offering sustainable substitutes for conventional approaches,these cutting-edge procedures lessen the need for fungicides and other agrochemicals that pollute the environment.This review explores various con-ventional and emerging strategies to address the detrimental factors impacting cotton seed quality.It emphasizes the importance of integrating classical and advanced approaches to enhance germination,ensure robust crop estab-lishment,and achieve higher yields.In addition to promoting sustainable cotton production,this kind of integration helps preserve the ecosystem and create resilient farming methods.
