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[前沿资讯 ] New medium-fiber cotton varieties are being developed 进入全文
TURKMENISTAN Golden age
As in the whole country, in the Dashoguz velayat much attention is paid to scientific selection and the creation of new high-yielding varieties of cotton. Thus, last year, production tests of a new promising medium-fiber cotton variety “Dashoguz-150” were continued here. It was developed by the head of the Dashoguz velayat cotton breeding and seed production branch of the Research Institute of Cotton Growing of the Ministry of Agriculture of Turkmenistan Ch. Seyitmuradov. Two and a half hectares of land were sown with the new variety. According to the developer, the yield, on average, reached 35 centners per hectare. The new product, continuing the popular in the region line of varieties "Gubadag", in terms of its main indicators meets modern requirements for the crop and in various characteristics is not inferior to the basic varieties already tested in the region. The developer tried to "build" into the new product improved parameters for germination in local climatic conditions and increased disease resistance, high yield and strength of fiber, and others. Simultaneously with this variety, tests were also conducted on another new medium-fiber cotton variety, "Jeyhun-16", which also has good indicators for the main characteristics. Based on the results of the staged tests, conclusions will be made on the suitability of the new varieties for mass production. Further development of the cotton industry in the north of the country is planned to be carried out by increasing the yield with more efficient and rational use of land and water resources. Such a strategy requires, among other things, the creation of new varieties of the crop that have a set of economically useful characteristics and are adapted to growing conditions. Therefore, local breeders continue to consistently work on the creation of new varieties that fully satisfy the requirements of the domestic textile industry.
[学术文献 ] The impact of temperature on cotton yield and production in Xinjiang, China 进入全文
npj Sustainable Agriculture
Cotton production in Xinjiang is crucial to China’s economy, but the region’s cold climate poses challenges to cultivation. This study analyzes temperature data from 33 meteorological stations in Xinjiang (1981–2020) alongside cotton yield data to assess cold damage during key cotton growth stages. A comparison is made with cotton-producing counties in the U.S. southwest (Texas, Kansas, Oklahoma). Results show that Xinjiang has a shorter frost-free period (140–210 days) compared to the U.S. (235–300 days). The Pearson correlation coefficient indicates that spring cold damage (SpCD) during emergence stage significantly impacts yield. SpCD lasts 5–10 days in NXJ, 3–7 days in SXJ, and 3–4 days in the U.S. Severe cold damage, notably in 1996 and 2010, led to a 40% yield decline. To mitigate cold damage, breeding cold-tolerant cotton varieties and developing innovative cultivation technologies are critical for sustaining cotton production in Xinjiang.
[学术文献 ] Combining suitable brown lint color, fiber quality, and yield in F2 cotton hybrids 进入全文
INDUSTRIAL CROPS AND PRODUCTS
Naturally colored cotton is eco-friendly because its use in textiles does not require dyeing, and textiles made from colored cotton are the best for human health. Despite their many advantages, the low yields and unfavorable quality traits of colored cotton varieties limit extensive production. Therefore, F1 and F2 populations of inter- specific ( Gossypium hirsutum L. x Gossypium barbadense L.) and intraspecific ( Gossypium hirsutum L.) cotton hybrids were examined to develop brown-colored cotton varieties with superior traits. Gelincik and Nazilli Deve T & uuml;y & uuml; (NDT)-15 varieties ( Gossypium hirsutum L.) with brown-colored lint were used as female parents. The male parents included Giza-75, Bahar-82 ( Gossypium barbadense L.), Fiona, May-455, I(center dot)pek-607 and Claudia ( Gossypium hirsutum L.) varieties with white-colored lint. According to the line x tester mating design, 12 hybrid combinations were obtained. F1 populations had higher yield and fiber length values than their parents and F2 populations. F2 populations had more unfavorable color values (L: whiteness, a*: red-green, and b*: yellow-blue) than their colored parents but were superior to F1s. The ratio of general combination ability variance to specific combination ability variance was less than 1, indicating that yield, ginning out-turn, fiber quality, and color parameters were controlled by non-additive genes. In the biplot graph, color and fiber parameters are located in the opposite direction from yield, indicating that it is difficult to develop colored cotton with high yield and fiber quality. Among the 960 F2 single plants evaluated, ten plants were carefully selected according to the optimization of yield, fiber quality, and color values. Based on potential diallel crosses of the ten selected plants, it was decided to begin a recurrent selection with that population. Furthermore, it was determined that F2 seeds of Nazilli DT-15 x I(center dot)pek-607 and Gelincik x Giza-75 combinations may be successfully given to farmers.
[学术文献 ] Spatiotemporal transcriptome and metabolome landscapes of cotton fiber during initiation and early development 进入全文
NATURE COMMUNICATIONS
Cotton fibers are single cells that develop from the epidermal cells in the outer integument of developing seeds. The processes regulating fiber cell development have been extensively studied; however, the spatiotemporal transcriptome and metabolome profiles during the early stages of fiber development remain largely unknown. In this study, we profile the dynamics of transcriptome and metabolome during the early stages of cotton fiber cell development using a combination of spatial transcriptomic, single-cell transcriptomic, and spatial metabolomic analyses. We identify the key genes (e.g., DOX2, KCS19.4, BEE3, and HOS3.7) and metabolites (e.g., linoleic acid, spermine, spermidine, and alpha-linolenic acid) that may regulate the early development of fiber cells. Finally, knockdown and gain-of-function analyses identify the crucial role of GhBEE3/Gh_A09G062900 in cotton fiber initiation. We also construct a publicly accessible website (https://cotton.cricaas.com.cn/ovule/) for visualization of the spatiotemporal gene expression in cotton, providing a reference dataset for further studies on cotton fiber development.
[学术文献 ] Spatiotemporal transcriptome and metabolome landscapes of cotton somatic embryos 进入全文
NATURE COMMUNICATIONS
Somatic embryogenesis (SE) is a developmental process related to the regeneration of tissue-cultured plants, which serves as a useful technique for crop breeding and improvement. However, SE in cotton is difficult and elusive due to the lack of precise cellular level information on the reprogramming of gene expression patterns involved in somatic embryogenesis. Here, we investigate the spatial and single-cell expression profiles of key genes and the metabolic patterns of key metabolites by integrated single-cell RNA-sequencing (scRNA-seq), spatial transcriptomics (ST), and spatial metabolomics (SM). To evaluate the results of these analyses, we functionally characterized the potential roles of two representative marker genes, AATP1 and DOX2, in the regulation of cotton somatic embryo development. A publicly available web-based resource database (https://cotton.cricaas.com.cn/somaticembryo/) in this study provides convenience for future studies of the expression patterns of marker genes at specific developmental stages during the process of SE in cotton.
[学术文献 ] Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning 进入全文
THEORETICAL AND APPLIED GENETICS
Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic architecture of complex traits in other crops, its application in cotton has been limited. Here, we applied five machine learning models-AdaBoost, Gradient Boosting Regressor, LightGBM, Random Forest, and XGBoost-to identify loci associated with fiber quality and flowering days in cotton. We compared two SNP dataset down-sampling methods for model training and found that selecting SNPs with an Fscale value greater than 0 outperformed randomly selected SNPs in terms of model accuracy. We further performed machine learning quantitative trait loci (mlQTLs) analysis for 13 traits related to fiber quality and flowering days. These mlQTLs were then compared to those identified through genome-wide association studies (GWAS), revealing that the machine learning approach not only confirmed known loci but also identified novel QTLs. Additionally, we evaluated the effect of population size on model accuracy and found that larger population sizes resulted in better predictive performance. Finally, we proposed candidate genes for the identified mlQTLs, including two argonaute 5 proteins, Gh_A09G104100 and Gh_A09G104400, for the FL3/FS2 locus, as well as GhFLA17 and Syntaxin-121 (Gh_D09G143700) for the FSD09_2/FED09_2 locus. Our findings demonstrate the efficacy of machine learning in enhancing the identification of genetic loci in cotton, providing valuable insights for improving cotton breeding strategies.
