Results 161 to 170 of about 1,875,668 (203)

Genetic improvement of Egyptian cotton (Gossypium barbadense L.) for high yield and fiber quality properties under semi arid conditions. [PDF]

Sci Rep
Lamlom SF, Yehia WMB, Kotb HMK, Abdelghany AM, Shah AN, Salama EAA, Abdelhamid MMA, Abdelsalam NR.   +7 more
europepmc   +1 more source

Adoption of a biologically-enhanced agricultural management (BEAM) approach in agroecosystems for regenerating soil fertility, improving farm profitability and achieving productive utilization of atmospheric CO₂. [PDF]

PeerJ
Johnson DC, Johnson HS.
europepmc   +1 more source

Seed treatment: an alternative and sustainable approach to cotton seed delinting. [PDF]

Front Bioeng Biotechnol
Zahra LT, Qadir F, Khan MN, Kamal H, Zahra N, Ali A, Zafar MM, Razzaq A, Jiang X.   +8 more
europepmc   +1 more source

PGPR and nutrient consortia promoted cotton growth, antioxidant enzymes, and mineral uptake by suppressing sooty mold in arid climate. [PDF]

Front Microbiol
Luqman M, Ahmad M, Dar A, Hussain A, Zulfiqar U, Mumtaz MZ, Mustafa A, Mustafa AEMA, Elshikh MS.   +8 more
europepmc   +1 more source

Special Regulation of <i>GhANT</i> in Ovules Increases the Size of Cotton Seeds. [PDF]

Genes (Basel)
Liu N, Chen Y, Guan Y, Guan G, Yang J, Nie F, Ming K, Bai W, Luo M, Yan X.   +9 more
europepmc   +1 more source

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Genotypic differences in sucrose metabolism with cotton bolls in relation to lint percentage

Field Crops Research, 2019
Lint percentage is an important cotton (Gossipium hirsutum L.) lint yield character which may have a close association with photoassimilates partitioning between fibers and seeds.
Haihua Luo, Gong Chen, Deyi Shao, Xin Gao, Changkai Yuan, Jinjian Peng, F. Tang   +6 more
semanticscholar   +4 more sources

Whole-genome resequencing of 240 Gossypium barbadense accessions reveals genetic variation and genes associated with fiber strength and lint percentage

Theoretical and Applied Genetics, 2021
Genetic variation in a G. barbadense population was revealed using resquencing. GWAS on G.barbadense population identified several candidate genes associated with fiber strength and lint percentage. Gossypium barbadense is the second-largest cultivated cotton species planted in the world, which is characterized by high fiber quality. Here, we described
Jingwen Yu, Yixuan Hui, Jinhong Chen, Hurong Yu, Xinpeng Gao, Zhaohui Zhang, Qin Li, Shuijin Zhu, Tianlun Zhao   +8 more
semanticscholar   +5 more sources

Quantitative trait loci mapping and genetic dissection for lint percentage in upland cotton (Gossypium hirsutum)

Journal of Genetics, 2014
Lint percentage is an important character of cotton yield components and it is also correlated with cotton fibre development. In this study, we used a high lint percentage variety, Baimian1, and a low lint percentage, TM-1 genetic standard for Gossypium hirsutum, as parents to construct a mapping populations in upland cotton (G. hirsutum).
Min Wang, Chengqi Li, Qinglian Wang
semanticscholar   +6 more sources

QTL mapping and candidate gene identification of lint percentage based on a recombinant inbred line population of upland cotton

Euphytica, 2021
Lint percentage is a major yield component in cotton breeding programmes. To identify quantitative trait loci (QTLs) and candidate genes related to lint percentage, we used an intraspecific recombinant inbred line population of 137 lines derived from Gossypium hirsutum cv. CCRI36 and G. hirsutum acc. G2005 for QTL mapping of lint percentage. Based on a
Hantao Wang, Xiao-lin Jia, Meng Kang, Wei Li, Xiaokang Fu, Liang Ma, Jianhua Lu, Hengling Wei, Shuxun Yu   +8 more
semanticscholar   +4 more sources

Microsatellite markers associated with lint percentage trait in cotton, Gossypium hirsutum

Euphytica, 2007
Molecular markers associated with fiber development traits have the potential to play a key role in understanding of cotton fiber development. Seventeen SSRs out of 304 markers tested from MGHES (EST-SSR), JESPR (genomic SSR), and TMB (BAC-derived SSR) collections showed significant linkage associations (using a Kurskal-Wallis non-parametric test) with
I. Abdurakhmonov, Z. Buriev, S. Saha, Alan E Pepper, J. Musaev, A. Almatov, S. Shermatov, F. Kushanov, G. T. Mavlonov, Umesh K. Reddy, John Z. Yu, J. N. Jenkins, R. Kohel, A. Abdukarimov   +13 more
semanticscholar   +4 more sources