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Within plant fiber variability has long contributed to discrepancy and inconsistency in fiber quality and yarn quality. Fiber quality uniformity is a primary plant breeding objective related to cotton commodity economic value. The physiological impact of source and sink relationships render stress on the upper sympodial branches of the cotton plants thereby leading to reduced genetic potential for fiber growth. Objectives of this study were to quantify variability within the top and bottom halves of the plants and evaluate various boll sampling protocols for efficient fiber quality estimation. This research was conducted in College Station, Texas for three years in 2009, 2010 and 2011 at the Texas A&M AgriLife Research farm. Ten fiber samples were collected from each plot varying in boll numbers and regions within the plant and compared against a machine harvested sample. Fiber quality testing was done using High Volume Instrument (HVI) and Advanced Fiber Information System (AFIS). Statistical analyses inclusive of Analysis of Variance (ANOVA) and Best Linear Unbiased Prediction (BLUP) and correlations among fiber properties were performed. It was concluded that the best boll sampling method for accurate fiber quality estimation included a mixed sample of at least 25 bolls picked from all regions of the plant. A statistic to identify the least variable genotype was calculated using the difference between top and bottom bolls. It was concluded that the extent of variability within a plant for fiber length and length uniformity are genotype dependent. Correlation analyses revealed strong relationships among fiber length uniformity, strength, UHML, maturity ratio, and fineness. Strong negative relationships of fiber length measurements with short fiber content and immature fiber content were observed. Correlation analysis within the bottom and top bolls of the plants revealed opposite trends in fiber relationships. [ABSTRACT FROM AUTHOR]

Sticky cotton causes severe processing problems at textile mills and can be caused by plant sugars deposited directly from the plant to the lint in the absence of insects. A partial diallel design was used to investigate the combining ability of six upland cotton, Gossypium hirsutum L., genotypes in Lubbock, Texas. Sucrose and glucose on fibers were measured as well as more mechanical fiber properties including stickiness ratings. Variance component analysis indicated significant differences among genotypes for all fiber traits and for sugar content. The greatest differentiation occurred in samples taken from more immature fruiting forms. Moreover, significant GCA and SCA differences were determined among parental lines for fiber traits including stickiness ratings. Findings suggest that breeding efforts could be directed toward lines with a low likelihood of producing sticky cotton. [ABSTRACT FROM AUTHOR]

Limited genetic diversity could be contributing to the limited genetic gains in upland cotton, Gossypium hirsutum L., fiber lengths and strengths. Smith and Coyle (1997) reported negative associations between many fiber quality traits and the number of fibers per unit surface area of seed (FSSA), the most basic lint yield component. The objective of this study was to determine if mutagenesis could be used to modify the associations of within-boll yield components and fiber quality parameters. TAM 94L-25 (Smith, 2003) (PI 631440) seeds were treated with 3% v/v ethyl methanesulfonate (EMS). Twelve TAM 94L-25 M4:6 mutant (M) lines, 'FiberMax FM 832' (Constable et al., 2001) (PVP 9800258; PI 603955) and 'Phytogen PSC 355' (PI 612974), and the unmutated parent TAM 94L- 25 were grown in performance trials in 2008 and 2009. The data suggests EMS-based mutagenesis was successful in producing variation for within-boll yield characteristics as well as fiber quality. Whether or not linkages or pleiotropic effects were disrupted is equivocal, with some correlation values significantly different between the control cultivars and the M lines. Mutant lines M2, M3, M50, and M51 were identified with improved FSSA. Ethyl methanesulfonate-based mutagenesis could be a valuable additional tool to create selectable variation in cotton for basic lint yield components and to break linkages of basic yield components and fiber quality. [ABSTRACT FROM AUTHOR]

It is well documented that cotton fiber variability exists across and within cultivars, across environments, soil quality, biotic and abiotic stresses. Having uniform fiber quality is a primary goal for cotton breeders to enhance profitability and sustainability. The objective of this research was to identify the within-plant variability in fiber quality of six diverse genotypes of Gossypium hirsutum and Gossypium barbasense. This research was conducted in College Station, TX, for 3 yr in 2009, 2010, and 2011 at the Texas A&M AgriLife Research farm. Fiber samples were collected from individual sympodial branches of the plants in three replications and ginned on a tabletop saw gin. Fiber-quality testing was done using the Advanced Fiber Information System. Statistical analyses inclusive of ANOVA and best linear unbiased predictors and fiber correlations were performed. A distinct pattern of within-plant fiber variability was observed, with a trend showing that beneficial traits lie in the bottom sympodial fruiting positions while the poor-quality bolls are located at the higher sympodial positions within the plant. This pattern was seen across all genotypes in all years. Percentage of difference in fiber length among the longest and shortest fibers within the plant showed that 'FM 832' had the maximal within-plant fiber length variability while 'DP HTO Pima' had the least within-plant length variability. Correlation analyses revealed a strong negative relationship between fiber length and standard fineness and a positive relationship between length and fiber maturity. A positive correlation was observed between short fiber content, immature fiber content and fiber neps. [ABSTRACT FROM AUTHOR]

The environmental impact of genetically modified crops has been extensively investigated. However, few reports on the influence of transgenic traits on genetic structure have been reported in the literature. It is unknown how or if transgenic cultivars have affected genotypic variation in upland cotton (Gossypium hirsutum L.) since its rise to dominance in cotton production. In this study, the genotypic variance components, g, of lint yield (LY) and fiber quality were compared among transgenic and nontransgenic cotton in the USDA Regional High Quality (RHQ) tests from 2002 through 2018. The popular transgenic and nontransgenic cultivars/lines developed by the major private and public cotton breeding programs in the United States during this period were included. Testing cycles within the RHQ protocol consist of standardized control cultivars plus experimental entries. Variance components were dissected in each testing year within six such testing cycles. Lint yield of the transgenic cotton was generally higher than nontransgenic cotton. Fiber quality of the nontransgenic cotton was generally higher than the transgenic cotton. For LY, the proportion of g to the total variance was lower in the transgenic cotton than in the nontransgenic cotton, but the difference diminished in the recent two cycles. The proportion of g was lower in the transgenic cotton compared with the nontransgenic cotton for fiber length, fiber strength, fiber uniformity, and micronaire. The discrepancy between the two types of cotton in the RHQ tests reflects the influences of differential breeding schemes in the private and public breeding programs on means and genotypic variance of LY and fiber quality. Core Ideas: Transgenic cotton affected genotypic variation in cotton cultivars.Differences in breeding schemes in public and private programs influenced genotypic variations.There has been an increase of genotypic variation for yield of transgenic cultivars in recent years.The yield gap between transgenic cotton and nontransgenic cotton has narrowed in recent years. [ABSTRACT FROM AUTHOR]

In this research a machine learning framework was developed for cotton yield estimation using multi-temporal remote sensing data collected from unmanned aircraft system (UAS). The proposed machine learning model was based on an artificial neural network (ANN) and used three types of crop features derived from UAS data to predict the yield, namely; multi-temporal features including canopy cover, canopy height, canopy volume, normalized difference vegetation index (NDVI), excessive greenness index (ExG); non-temporal features including cotton boll count, boll size and boll volume, and irrigation status as a qualitative feature. The model provided low residual values with predicted yield values close to the observed yield values (R2 ~ 0.9). ANN model performance was compared with support vector regression (SVR) and random forest regression (RFR). Comparison results revealed that ANN model outperforms SVR and RFR. Additionally, redundant features were removed using correlation analysis, and an optimal subset of features was obtained that included canopy volume, ExG, boll count, boll volume and irrigation status. Moreover, the relative significance of each feature in the optimal input feature subset was determined using sensitivity analysis. It was found that canopy volume and ExG contributed around 50% towards the corresponding yield. Finally, further analysis was performed to investigate how early in the growing season the model can accurately predict yield. It was observed that even at 70 days after planting the model predicted yield with reasonable accuracy (R2 of 0.72 over test set). This study revealed that UAS derived multi-temporal data along with non-temporal and qualitative data can be combined within a machine learning framework to provide a reliable estimation of crop yield and provide effective understanding for crop management. [ABSTRACT FROM AUTHOR]

The global output of cottonseed contains ∼10.8 trillion grams of protein that can meet the basic protein requirements of ∼590 million people at a rate of 50 g/day. However, gossypol, a toxic terpenoid present in seed glands, renders cottonseed unfit as food for human consumption or as feed for nonruminants. RNAi, under the control of a seed-specific promoter, was used to selectively silence δ-cadinene synthase gene to reduce gossypol levels in the seed by 97% without affecting the levels of gossypol and related terpenoids in rest of the plant where they are needed for defense against insects and diseases. The resulting Ultra-low Gossypol Cottonseed (ULGCS) is considered safe as food or as feed for more efficient (both, in terms of feed conversion ratio and protein conversion ratio) monogastric animals. Field trials conducted over multiple years in multiple states confirm the stability and heritability of the trait with no diminution of fiber/seed yield, quality or agronomic performance. A ULGCS event, TAM66274, was deregulated by USDA-APHIS in October 2018 and FDA concluded its food safety consultation in September 2019 and concurred with our determination of its safety. Global adoption of TAM66274, with more efficient and expanded usage of its protein, has the potential to significantly improve nutrition security and boost farmers' income without requiring additional inputs or acreage under cultivation, thus making cotton farming more sustainable. [ABSTRACT FROM AUTHOR]

Highlights • Divergent selection (for fiber elongation) was used to develop breeding lines. • Yarn quality of these breeding lines were evaluated for two years, two locations. • It is possible to improve fiber elongation through breeding. • Fiber elongation was found to have an effect on multiple fiber and yarn properties. • Improving fiber elongation can improve yarn quality. Abstract Fiber quality is important to the textile industry because it directly relates to processing performance, productivity and yarn quality. For marketing purposes, the fiber quality of U.S. cotton is determined by the United States Department of Agriculture (USDA) classing offices using High Volume Instrument (HVI). HVI measures fiber length, length uniformity, strength, micronaire, color, leaf and extraneous matter. Recent research shows other fiber properties not included in the USDA classification should be considered when developing new cultivars to improve spinning performance, yarn quality, and market value. Fiber properties of interest include but are not limited to fiber maturity, short fiber content, fiber elongation, and work-to-break characteristics. Breeding for higher fiber elongation is expected to result in new cultivars that can withstand processing with less fiber breakage, perform better in spinning, and produce better yarn quality. The purpose of this research was to evaluate spinning performance and yarn quality of cotton lines developed to place emphasis on fiber elongation during the breeding process. Fifteen F2 populations were followed through a series of selections to the stage of "end product" by conducting spinning tests on F5 progeny lines. Divergent selection for fiber elongation was conducted in the F2 and F3 generations with nine lines being selecting in the F4 generation for spinning. Yarn properties and fiber properties of each line were evaluated together for correlations and potential trends. Yarn quality of experimental lines was also compared to yarn quality of a commercial check cultivar. Fiber and yarn data were evaluated to determine if and clarify how fiber elongation influenced spinning performance and yarn quality. It was determined that it is possible to improve fiber elongation through breeding and that by placing an emphasis on fiber elongation improvements can be made to overall fiber and yarn quality. Results also demonstrate the potential impact of disregarding fiber elongation or selecting genotypes with low fiber elongation; there were negative effects on both fiber and yarn. There is opportunity to indirectly address multiple fiber quality objectives and improve the end-product by placing selection pressure on this specific fiber characteristic during the breeding process. [ABSTRACT FROM AUTHOR]

Background: Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits. Results: The SNP markers distinctly separated G. hirsutum from other Gossypium species and distinguished the wild from cultivated types of G. hirsutum. The markers also efficiently discerned differences among cultivars, which was the primary goal when designing the CottonSNP63K array. Population structure within the genus compared favorably with previous results obtained using SSR markers, and an association study identified loci linked to factors that affect cottonseed protein content. Conclusions: Our results provide a large genome-wide variation data set for primarily cultivated cotton. Thousands of SNPs in representative cotton genotypes provide an opportunity to finely discriminate among cultivated cotton from around the world. The SNPs will be relevant as dense markers of genome variation for association mapping approaches aimed at correlating molecular polymorphisms with variation in phenotypic traits, as well as for molecular breeding approaches in cotton. [ABSTRACT FROM AUTHOR]

Crop yield data is critical for precision agriculture, breeding programs, and other activities, but collecting this data at fine scales is labor-intensive. Unmanned aerial systems (UAS) allow collection of imagery with unprecedented temporal, spatial, and spectral resolutions and could be better leveraged to estimate or predict yield while limiting labor requirements. Therefore, the objectives of this study were to develop a relatively simple pixel-based multispectral image classification technique for cotton (Gossypium hirsutum L.) yield estimation, termed "Boll Area Index" or BAI, which is collected after defoliation, and to identify in-season co-predictors derived from multispectral and thermal imagery to improve estimate accuracy. A field study was conducted over four growing seasons (2017–2020) at College Station, TX. The experimental treatments included three irrigation rates (0%, 40%, and 80% ET c replacement) and eight commercial cotton cultivars each year. Multispectral and thermal infrared imagery were captured biweekly. In addition to BAI, three vegetation indices (Normalized Difference Vegetation Index or NDVI, Normalized Difference Red Edge or NDRE, and Optimized Soil Adjusted Vegetation Index or OSAVI) and canopy temperature were derived from orthomosaics and analyzed. There were positive linear relationships between BAI and seed cotton yield each year (R2 = 0.61–0.79). Multiple linear regression including BAI, vegetation indices, and/or canopy temperature from two flight dates produced better yield estimates (R2 = 0.79–0.89) than BAI alone. Cameras or payloads with both optical and thermal sensors are ideal for strictly in-season yield estimation endeavors, but thermal was not necessary when BAI was included in the models because canopy temperature provided minimal improvement as a third predictor. Multiple regressions involving NDVI and BAI already had quite strong relationships with yield (R2 = 0.7–0.87) without including canopy temperature. Cross validation of multiple linear regression models derived from BAI and NDVI, using data from two years to predict yield in a third, had R2 values that varied from 0.51 to 0.88 and RMSE varied from 273 to 508 kg ha−1. This is a level of error that may be acceptable for some purposes, such as screening lines in early stages of cotton breeding selection, but may be unacceptable for screening of advanced lines when greater accuracy is crucial. Overall, the results indicate that derivatives from just two or three UAS flights presents a detailed dataset for cotton yield prediction, while limiting labor and required computational resources. • BAI had linear relationships with seed cotton yield (R2 = 0.61–0.79). • Multiple regression using in-season and BAI data produced better yield estimates. • Cross validated yield predictions had RMSE of 273–508 kg ha−1. • Error may be acceptable for screening lines in early stages of cotton breeding. [ABSTRACT FROM AUTHOR]

Numerous DNA markers associated with quantitative trait loci (QTL) for cotton (Gossypiumspp.) fiber quality traits have been identified in the literature, but there are still significant challenges regarding the use of these QTL in marker-assisted selection. While one of the primary limitations to the application of marker-assisted selection for fiber quality traits has been the inconsistency of marker-trait associations, more recent studies have reported numerous marker-trait associations and colocating of QTL in different genetic backgrounds and environments. The objectives of this study were to assess the published microsatellite markers linked to upper-half mean fiber length (UHML) and fiber bundle strength (Str) QTL in different genetic backgrounds and to characterize the utility of stable marker-trait associations in selection for improved fiber quality within the context of an applied breeding program. Using the results of 32 published QTL mapping studies, six stable marker-trait associations each for UHML and Str were detected. For each trait, the mean of F3:4 progeny rows that were grouped based on the six marker genotypes was compared with the mean of F3:4 progeny rows that were grouped based on phenotype. In all but one case, the mean UHML and Str of F3:4 progeny rows with the majority of alleles (i.e., four to six alleles) in the desirable state was similar to the mean of F3:4 progeny rows derived from F3 plants in the top 20% for UHML and Str. Our results indicate that, after proper validation, published QTL for UHML and Str could be utilized in selection for improved fiber quality. [ABSTRACT FROM AUTHOR]

Determination of environmental influence on seed traits is critical for genetic improvement of seed quality in upland cotton ( Gossypium hirsutum L.). The objective of this study was to analyze the relative contribution of environment and genotype (G) for seed oil, nitrogen (N), and gossypol contents using historical data from the regional high quality (RHQ) tests conducted from 1996 through 2013. The 18-year tests of RHQ were divided into six 3-year cycles with an average of about 20 genotypes and 7-10 testing locations (loc) in each cycle. Variance components of oil, N, and gossypol contents were estimated in each cycle and expressed as percentages of the total variance. Highly significant G × loc effects were identified for all seed quality traits in each cycle. For oil content, variance estimates of G to the total variance ranged from 20 to 57 % in different cycles. For N content, loc was the main source of variance with variance estimates of loc to the total variance ranging from 44 to 73 % in different cycles. In most cycles, loc and G were the main source of variance for free-gossypol content. For most seed quality traits, there was not a clear trend of changes among testing cycles for the variance estimates of G to the total variance. Broad-sense heritability for oil content, N content, and free-gossypol ranged from 0.79 to 0.96, 0.65 to 0.86, and 0.28 to 0.93, respectively. Highly significant G × loc interactions indicate that multiple location trials for testing seed quality traits are necessary. However, heritability estimates for these seed traits indicate stability across environments as well as the potential for genetic improvement. Significant reduction in seed index was observed in half of the testing cycles with a range of 10.4-9.52 within cycles. Correlation between seed index and oil content was positive with r values ranging from 0.23 to 0.77 in different cycles. [ABSTRACT FROM AUTHOR]

High-throughput genotyping arrays provide a standardized resource for plant breeding communities that are useful for a breadth of applications including high-density genetic mapping, genome-wide association studies (GWAS), genomic selection (GS), complex trait dissection, and studying patterns of genomic diversity among cultivars and wild accessions. We have developed the CottonSNP63K, an Illumina Infinium array containing assays for 45,104 putative intraspecific single nucleotide polymorphism (SNP) markers for use within the cultivated cotton species Gossypium hirsutum L. and 17,954 putative interspecific SNP markers for use with crosses of other cotton species with G. hirsutum. The SNPs on the array were developed from 13 different discovery sets that represent a diverse range of G. hirsutumgermplasmand five other species: G. barbadense L., G. tomentosum Nuttal · Seemann, G. mustelinum Miers · Watt, G. armourianum Kearny, and G. longicalyx J.B. Hutchinson and Lee. The array was validated with 1,156 samples to generate cluster positions to facilitate automated analysis of 38,822 polymorphic markers. Two high-density genetic maps containing a total of 22,829 SNPs were generated for two F2 mapping populations, one intraspecific and one interspecific, and 3,533 SNP markers were co-occurring in both maps. The produced intraspecific genetic map is the first saturated map that associates into 26 linkage groups corresponding to the number of cotton chromosomes for a cross between two G. hirsutum lines. The linkage maps were shown to have high levels of collinearity to the JGI G. raimondii Ulbrich reference genome sequence. The CottonSNP63K array, cluster file and associated marker sequences constitute a major new resource for the global cotton research community. [ABSTRACT FROM AUTHOR]

Texas A&M AgriLife Research released several upland cotton (Gossypium hirsutum L.) germplasm lines that exhibit near extra long and extra long staple (ELS) upper half mean length (UHML) fibers, similar to those produced by pima cotton (Gossypium barbadense L.) and significantly longer than those of upland cultivars currently grown in the United States. While present-day global marketing systems do not reward the production of such upland cotton fibers, future competitiveness of upland cotton may depend on availability of such variability. Thirty-six improved upland cotton cultivar accessions from the USDA-ARS Cotton Collection were crossed to two testers to determine if any contained alleles for additional improvement in fiber length or strength parameters of germplasm developed by Texas A&M AgriLife Research. 'Tamcot CAMD-E' was chosen as a standard fiber quality tester and TAM B182-33 ELS was the elite fiber quality tester. The lines, testers, and F1s were grown in Tecoman, Colima, Mexico, and at College Station, TX, in 2010 and at College Station only in 2011. Fiber data were subjected to line × tester analysis. Funtua FT-5 from northern Africa and Stoneville 474 from the United States may harbor additional UHML and length uniformity alleles for improving TAM B182-33 ELS. Phytogen 72 (United States) and BJA 592 (northern Africa) may contain additional alleles to improve fiber bundle strength. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium spp.) fibers are produced primarily by G. hirsutum L, upland, and G barbadense L, pima, or Extra Long Staple (ELS). Two ELS upland lines, developed by Texas AgriLife Research, along with 'FiberMax 832LU (FM 832) and 'DPL Pima HTO' (Pima HTO), were grown in a randomized complete block design at College Station, TX, during 2007 and 2008. Subplots of spindle-harvested seedcotton were ginned on a saw gin or roller gin with appropriate lint cleaning. High-volume instrument (HVI) and Advanced Fiber Information System (AFIS) fiber parameters were determined. Combed 50-Ne (English yarn count) and 80-Ne yarns were produced. Upland ELS lines exhibited longer (p < 0.01) fibers than FM 832, but only TAM B182-33 ELS produced fibers longer or equal (p < 0.01) to Pima HTO. Upland ELS lines produced stronger 50-Ne ring-spun yarns than FM 832 but not Pima HTO, which also produced stronger 80-Ne ring-spun yarn than the upland ELS lines. While roller ginning resulted in significantly higher micronaire, longer upper half mean length, more uniform fiber lengths, and longer upper-quartile length than saw ginning among these genotypes, yarn parameters reported were not affected. All genotypes responded the same to ginning platform, suggesting that further development of the upland ELS trait will not dictate a change in ginning equipment for upland cotton. The upland ELS trait contributes to significantly improved upland yarn quality. [ABSTRACT FROM AUTHOR]