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Review

Revamping of Cotton Breeding Programs for Efficient Use of Genetic Resources under Changing Climate

by
Muhammad Salman Mubarik
1,
Chenhui Ma
2,
Sajid Majeed
3,
Xiongming Du
2,* and
Muhammad Tehseen Azhar
3,4,*
1
Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture, Faisalabad 38040, Pakistan
2
State Key Laboratory of Cotton Biology, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang 455000, China
3
Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad 38040, Pakistan
4
School of Agriculture Sciences, Zhengzhou University, Zhengzhou 450001, China
*
Authors to whom correspondence should be addressed.
Agronomy 2020, 10(8), 1190; https://doi.org/10.3390/agronomy10081190
Submission received: 10 July 2020 / Revised: 1 August 2020 / Accepted: 10 August 2020 / Published: 13 August 2020
(This article belongs to the Special Issue Sustainable Agriculture Based on Crop Ecophysiological Functioning)
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Abstract

:
Empirical cotton breeding was based upon the concept of selecting single and best high-yielding progeny from the segregating populations to develop a cultivar. It helped in releasing of high yielding varieties with superior fibre quality through conventional breeding. Though the production of cotton has been increased manifold in the last decades, but it has also seen enormous ebb and flow of yield uncertainties during the past several years. Therefore, the development of climatic resilient cotton ideotypes through the introduction of wild alleles and using contemporary genetic markers have become inevitable. Emerging genome engineering technologies such as CRISPR/Cas9 system can also potentially exploited to edit the disease susceptible and negative regulators of yield related genes in cotton. In the present review we concentrate on accomplishments and forthcoming of plant breeding and biotechnology to facelift the cotton breeding programs.

1. Introduction

Cotton is a resourceful crop being grown to provide raw material to the textile industry. It is cultivated on approximately 3% of the total world arable land (ICAC https://icac.org/). The leading cotton producing countries are India, China, USA, Brazil, and Pakistan (Figure 1). To date, the improvement in cotton crop have been achieved through releasing improved cultivars with better yield potential, higher fibre qualities and capacity to tolerate changing climatic conditions along with early maturity, high fertilizers and water use efficiency [1]. Most of these varieties were developed by selecting superior plants from narrow germplasm. Moreover, the efficiency of selection also depends upon numerous factors, i.e., heritability, genetic advance, and the environment [2]. Since most of the economically important traits are polygenic in nature and is being highly influenced by the environment. Therefore, marker assisted breeding and whole-genome sequencing technologies have led to the identification of genes and quantitative trait loci (QTLs) linked with traits of interest [3]. Genetically modified (GM) cotton is considered to be one of the earlier transgenic crops commercially released during 1996 [4]. Transgenic cotton has provided the resistance against bollworm complex and minimize the use of pesticides and herbicides [5]. The success of transgenic cotton diverted the attention of seed and biotechnological enterprises for the development of improved GM cotton with novel resistance genes [6]. Research and field assessment of abiotic stress-tolerant transgenic cotton is also under process and anticipating commercializing in a coming years. Functional genomic databases facilitate the mining of valuable genes and markers linked to those for the identification of important metabolic pathways involved in lint quality, oil and protein contents [7]. Here we discussed the breeding strategies, genomic selection and biotechnological approaches to improve cotton breeding programs. We advocated for revamping of cotton breeding programs in tandem with whole genome sequencing databases and gene editing technologies to develop next-generation genetically modified cotton cultivars, collectively setting a stage for sustainable production under yield uncertainties, ever-increasing effects of climate change and socio-economic needs (Figure 2).

2. Concept of Sustainable Cotton Production

Cotton fibre yield is the outcome of collected bolls, their weight and lint percentage from a certain plant population over a single (determinate type) or multiple (indeterminate type) picking events depends upon growth habit of cultivar [8] However, the yield is also dependent upon various other traits and sustainable production can only be achieved by modifications and improvement in such attributes. Production can be sustained by preserving biodiversity, minimizing the discharge of soil nutrients along with improving adaptability, quality, input use efficiencies and resilience to environmental calamities [9]. Moreover, precision agriculture technologies are now also being implemented for the optimum use of farm resources [10]. If a new technology works out to improve crops without affecting the environment, then it is appropriate to adapt to enhance the farming profit. Therefore, a combination of empirical breeding, biotechnological tools along with various better agronomic practices and adaptation to mechanized technologies is requisite for the sustainable farming of cotton.

3. Cotton Genetic Resources

The genus Gossypium is comprised of more than 50 species, out of which 46 species are wild in nature while four are cultivated species [11]. The narrow genetic base of cultivated cottons is the evidence of bottleneck events in the course of evolution, domestication of land-races, and intensive breeding via artificial selections [12]. The cultivated allotetraploid cotton (G. hirsutum) is evolved from two divergent diploid species where the donor of A genome is still under investigation while D genome is contributed from G. raimondii [13]. Cotton have eight highly divergent genomes, i.e., A genome is native to Africa and Asia, B genome from Africa and Cape Verde Islands, C and D genome evolved in Australia and Mexico, respectively. E genome is native to Arabian Penisula, Northeast Africa, Southwest Asia and F genome evolved in East Africa. G genome is native to Australia while the K genome is native to NW Australia and Cobourg Peninsula [14]. Limited information is available about the nature of regulatory evolution which follows crop domestication for polyploid crops such as cotton. Regulatory variation is strongly correlated with genome evolution, patterns of parental expression inheritance, properties of the co-expression gene network, and the genomic loci responsible for domesticated traits [15]. Although G. hirsutum was grown continuously after the polyploidization event and is better suited to grating environmental conditions and produce a more lint yield than G. barbadense [16]. But the domestication of upland cotton also leads to several genetic consequences including genetic erosion [17].
Several phenotypic and molecular data have also confirmed the occurrence of low genetic diversity in cotton germplasm [18,19]. But comprehensive evidence for low molecular genetic diversity of cotton is still under investigation [16]. So, there is a dire need to broaden the genetic base of existing cotton germplasm through wide hybridization and mutation [20]. The knowledge of geographic distribution and origin of genomes can potentially be exploited to broaden the distinctiveness and adaptability of cultivated cotton [21,22]. The wild cotton germplasm provide novel genes related to stress tolerance, adaptability, fibre quality, oil content, and cytoplasmic male sterility to induce in cultivated lines [23].
In past, synthetic cultivars were rebuilt from G. raimondii and G. herbaceum species, but linkage-drag was the major drawback to transfer valuable genes from those experimental lines into the cultivars. Due to recombination, linkage’s disequilibrium breaks up creates different allelic combinations, affects natural crop selection, and plays a significant part in crop domestication and improvement [24]. Linkage-drag reduced the yield, lint quality, disturbed several gene regulatory systems, created chromosomal abnormalities, hybrid run-out and genome assortments [25]. To break such linkage-drag, a set of chromosomal segment introgression lines (CSILs) has been developed by interspecific hybridization between Egyptian and American cotton [26]. Several other CSILs are also under development. These CSILs can be used for fine QTLs mapping [27].
Due to the limited genetic diversity in cotton compared to many other crop plants, and the associated genetic complications, the latest state of the art genomics technologies significantly assist fundamental and advanced cotton research [16]. High-quality genome assembly of Gossypium species is a daunting challenge because of their large and complex genomes. Exploring the genetic and molecular basis of the origin of Gossypium species, diversification, speciation, and advancing genomic-assisted breeding requires thorough analysis and a deep understanding of the genome organization [12]. The lack of accurate genomic data has been a significant drawback for developing improved cotton cultivars.
Advancement in sequencing technologies has strengthened the field of genomics and supported the dissection and manipulation of useful agronomic traits. It also demonstrated the genomic evolution of polyploidy crops [28]. The presence of a large amount of cotton sequence data set flickered the scientist to have an integrated cotton functional genomics resource. Currently existing large set of cotton databases includes cotton database resource developed by International Cotton Genome Initiative (ICGI) (https://www.cottongen.org/), Cotton Functional Genomics Database (https://cottonfgd.org/) [7]. and the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/genome?term=Gossypium+hirsutum%5Borgn%5D&cmd=DetailsSearch). These databases facilitate simple, rapid, and convenient access to the required information related to Gossypium species. Thus, the genomic information facilitates the breeding by design for robust approaches including fine QTL mapping to find the allelic variation of mapped loci and use of selected loci in cotton breeding programs [29]. Recent advances in functional genomics methods led the development of fast trait mapping and cloning methods like BSR-Seq [30], genome-wide association [31], MutMap [32], QTLseq [33], Indel-seq [34], and map-based cloning [35]. Therefore, a combination of these methods along with multi-omics and reverse genetic approaches accomplish the identification and cloning of agronomically important genes [12]. One such example is the identification of GhHOX3 transcription factor, which regulates the elongation of cotton fibres. Overexpression of GhHOX3 leads to longer fibre, while its silencing reduces the fibre length by more than 80% [36].
The mapping of RNA sequencing data set is also an additional remarkable application of cotton genomics to identify the differentially expressed genes under various circumstances [37]. Furthermore, the accessibility of reference cotton genome sequences provide insights into the fiber quality attributes such as length and fineness. Although several innovations are reported due to the use of genome assembly methods but haplotypes are not developed so far due to heterozygosity, large genome size and repetitive sequences in the cotton genome. To overcome these impediments, use of PacBio and Oxford Nanopore technologies is a way forward.

4. Classical Cotton Breeding Concept

Before the era of genomics and molecular markers, plant breeding was a “number game”. The greater number of crosses, the more chances to discover superior recombinants. This fundamental strategy was based upon the idea of “cross best with the best likewise select the best and expect the best” [38]. Such breeding approaches were efficiently used for the development of cultivars with higher yield and superior fibre quality attributes in cotton [39]. In the contrary, those breeding plans also gave rise to homogeneity and drained genetic diversity from the germplasm. Production of hybrid cotton seed for commercial cultivation lifted the yield barriers to a greater extent but attained limited attention among seed companies due to the high cost of production and poor seed setting in interspecific crosses [40]. Making large number of random mutant populations either through physical or chemical mutagens before the selection of desirable plants had played a remarkable role in improving cotton yield [41]. Some impressive achievements were reflected after the release of NIAB-78, Lumian No.1, and MA-9 cotton cultivars for general cultivation in Pakistan, China and India, respectively [42]. However, mutation breeding still had limited scope due to less frequency of desirable alleles, large population screening and pleiotropic effects. Although the introduction diversity by various means like site-specific, mutation, and wide hybridization is requisite [43,44]. The breeding challenges are also emerging than before, the use of modern day biotechnological and genomics tools has become indispensable. So, it is imperative to use cotton genome sequence information and deploy biotechnological tools in breeding programs to further augment the lint yield and quality in a short time frame.

5. Tolerance to Environmental Stresses

Global cotton production has been intimidated by various biotic and abiotic stresses among that insects/pest infestation, bacterial/viral/fungal diseases, heat, salinity, and drought stresses are the major impediments in high lint yield. Biotic stresses in cotton caused up to 30% losses worldwide annually [45]. Among various biotic stresses in cotton, whitefly transmitted cotton leaf curl disease (CLCuD) is considered to be one of the major diseases of cotton crop due to its rapid spread and virus genome plasticity. Afterward, the infestation of cotton bollworms is, found to be a major pest of cotton crop and now has been aptly controlled by the introduction of Bt cotton [46]. In addition, some new problems have arisen as a result of the ongoing cultivation of Bt cotton that leads to the development of insects/pest resistance to Bt toxins [47]. For instance, pink bollworm resistance breakdown has been spotted against Bt cotton in India [48]. Thus, to counter the insect resistance and disease management, different strategies were also utilized include; proteinase inhibitors, engineering of other toxins, proteases, and double-stranded RNA technology [49,50,51,52]. Protease inhibitors (PIs) have been identified as a key plant defense player against insect herbivores. Transgenic tobacco overexpressing the tomato and potato receptor II PIs greatly delayed insect growth compared with non-transgenic plants [53]. Many other toxins with insecticidal activity may contribute to the arsenal of engineered Bt toxins. For example, an α -amylase inhibitor gives resistance to pea weevil in Pisum sativum and mannose-binding lectins, especially Galanthusnivalis agglutinin (GNA), are toxic to various insects in monocot plants [54,55]. Studies have discovered considerable strides over the last three decades in discovering and unraveling many facets of the dsRNA mediated gene silencing. The engineered dsRNA as an active ingredient for formulations of biopesticides is considered a raw material which can be applied to different purposes such as the pest management. A proof of concept research revealed that a dsRNA construct in a genetically manipulated maize plant may induce larval mortality in Diabrotica virgifera, the western corn rootworm (WCR) [56].
Among abiotic stresses, drought and heat are the major threats to cotton growth and development. The rise in the CO2 level has changed the annual day and night temperature. It can reduce the plant’s ability to retain the bolls and caused 40% cotton bolls abscission [57]. Additionally, high temperature also had adverse effect on viability of pollen, antioxidant activities, plant physiology, plant morphology, yield, and quality of fibre [58,59]. Heat stress also boosted the evapotranspiration losses which increase the crop water requirement. Several candidate genes were previously mined within the QTL region related to chlorophyll content, cell membrane stability, and saturated leaf weight. Numerous miRNAs, such as miR164, are associated with NAC and MYB genes, which have important role in improving drought tolerance in cotton [60]. Such findings provide an opportunity to use these QTLs/genes in breeding programs to developing climate-resilient cotton cultivars.
Screening of domesticated and wild cotton germplasm for various agronomic traits drive the cotton breeding programs by hybridizing these attributes. Also, fine QTL mapping of these attributes associated with heat and drought stress tolerance can speed-up the hybridization process and decrease the linkage-drags in the advanced breeding lines [60,61]. As there were many other agronomically important genes found using forward and reverse genetic approaches. The integration of these genes has brought no yield gaps because they are not negatively associated with yield related traits. Previously, the breeders had made considerable efforts to develop the heat and drought-tolerant cotton cultivars. The efforts of the cotton breeders were usually meant to increase the yield of cotton under the growing climatic changes. Table 1 offers a few possible ideotypes for cotton plants under various challenging conditions.

6. New Emerging Technologies

Transgenic crops are now being grown over an area of 191.7 million hectares after more than two decades of commercialization [69]. The majority of the genes introduced to develop transgenic crops were related to herbicide and insect resistance. Globally, transgenic cotton is cultivated on about 70% (189.9 M ha) of the total cotton area [70]. More than 50 distinctive events carrying insect and herbicide resistance genes have been commercially integrated into cotton and cultivated in various regions of the world. After soybean, transgenic cotton is the foremost commercially successful biotech crops. In 1996, Monsanto with Delta and Pineland Co. (D&PL) commercially released two transgenic cotton varieties (NuCOTN33 and NuCOTN35) with a trademark Bollgard™ for general cultivation [71]. Introduction of Bt cotton nearly doubled the lint yield by minimizing production losses due to the infestation of bollworm complex. Cry toxin minimized the use of pesticides by 40%, reduced yield losses by 20%, and enhanced the profitability of farmers by 70% [72]. Currently grown transgenic cotton cultivars constitutively express transgenes resistant to insects/pest attacks. Controlling the spatiotemporal expression of transgenes to the specific plant tissues is a limitation to improve transgenic performance and to decrease its environmental effects. Therefore, use of tissue specific promoter’s native to cotton would restrict the transgene expression to certain tissues where it is required, e.g., leaves and stems only. Thus, the identification and utilization of tissue-specific promoters will provide an essential shift in the production of next-generation transgenic cultivars. However, genetically modified crops are setting under intense regulatory formalities which may delay the lab research to come into the market.
In contrast to early genetic engineering techniques that randomly insert a gene of interest into a host genome, genome-editing works with site-specific genetic manipulation in a predefined manner. The genome editing tools have become more accessible for agriculture production over the last decade. The first-generation gene editing tools namely, zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and CRISPR)/Cas system are being deployed to induce site-specific double-strand breaks (DSBs) in plant genomes. CRISPR/Cas9 is one of the emerging tools to mutate the detrimental genes in a pre-defined manner [73]. After the access to G. hirsutum genome sequence [74]. CRISPR tools may now be used to achieve predefined DNA modifications. In 2017, Janga et al. reported first successful targeted gene editing study in cotton using CRISPR/Cas9 system [75]. They demonstrated the simultaneous editing of three homeologous genes (GhPDS, GhCLA1, and GhEF1) in the cotton genome. Subsequently, a number of cotton genes have been targeted using CRISPR/Cas9 system like Cloroplastos alterados 1 (GhCLA1), vacuolar H+-pyrophosphatase (GhVP) [76], cotton fiber development controlling GhMYB25 homoeologous (GhMYB25-like A and GhMYB25-like D) [77], and Gh14-3-3d [78] etc. In addition to reported resistance to Verticillium dahliae in cotton [78], CRISPR/Cas9 system has also been used to confer resistance against CLCuD, a member of family begomoviruses [79,80,81]. Additionally, already identified several key cotton gene (s) related to seed germination (GhHSP24) [82], flowering (GhAAI66) [83], fiber development (GhARF2, GhARF18, GhXLIM6) [84,85], gossypol contents (GhCAD1-A) [86], resistance to biotic (GhCRR1, GbSOBIR1) [87,88] and abiotic stresses (GhABF2, GhDsPTP3a, GhERF38) [89,90,91] can be manipulated.
The delivery of CRISPR reagents into the cotton genome is an imperative step, but one of the most efficient methods of delivering CRISPR/Cas9 into the cotton genome is possible with the shooting apexes as receptors via an Agrobacterium-mediated method and DNA-free delivery of CRISPR reagents [92]. Therefore, rational use of CRISPR/Cas9 system in cotton gene editing experiments would be effective for cotton resistance breeding programs against biotic and abiotic stresses. Furthermore, applying this incredible tool to increase lint yield, quality, and climate resilience would have everlasting benefits.

7. Outlook

Revamping of cotton breeding programs by integrating classical breeding approaches, knowledge on functional genomics, and gene editing technologies would open new avenues for selection, characterization, and incorporation of desirable traits in elite cotton cultivars. Tunable and tissue-specific transgene expression and gene stacking can alleviate the potential risks that accompany the resistance against cry genes. In nutshell, the use of CRISPR/Cas system in engineering cotton plants can also be helpful in reducing environmental stresses and disease attacks affecting overall cotton yield and lint quality.

Author Contributions

Conceptualization, M.T.A., S.M. and M.S.M.; writing and draft preparation, M.S.M., S.M. and C.M.; writing—review and editing, M.T.A., S.M., M.S.M., C.M. and X.D.; supervision, X.D. and M.T.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

Authors are thankful to Higher Education Commission, Pakistan for provision of research grant under NRPU project “Development of salt tolerance in cotton”.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ulloa, M.; De Santiago, L.M.; Hulse-Kemp, A.M.; Stelly, D.M.; Burke, J.J. Enhancing Upland cotton for drought resilience, productivity, and fiber quality: Comparative evaluation and genetic dissection. Mol. Genet. Genom. 2020, 295, 155–176. [Google Scholar] [CrossRef] [PubMed]
  2. Lu, H.J.; Myers, G.O. Combining abilities and inheritance of yield components in influential upland cotton varieties. Aust. J. Crop Sci. 2011, 5, 384–390. [Google Scholar]
  3. Thyssen, G.N.; Jenkins, J.N.; McCarty, J.C.; Zeng, L.H.; Campbell, B.T.; Delhom, C.D.; Islam, M.S.; Li, P.; Jones, D.C.; Condon, B.D.; et al. Whole genome sequencing of a MAGIC population identified genomic loci and candidate genes for major fiber quality traits in upland cotton (Gossypium hirsutum L.). Theor. Appl. Genet. 2019, 132, 989–999. [Google Scholar] [CrossRef] [PubMed]
  4. Raman, R. The impact of Genetically Modified (GM) crops in modern agriculture: A review. GM Crop. Food Biotechnol. Agric. Food Chain 2017, 8, 195–208. [Google Scholar] [CrossRef]
  5. Srikanth, P.; Maxton, A.; Masih, S. Bt Cotton: A Boon against Insect Resistance. J. Pharm. Phys. 2019, 8, 202–205. [Google Scholar]
  6. Fangbin, Q. Fifteen Years of Bt Cotton in China: The Economic Impact and its Dynamics. World Dev. 2015, 70, 177–185. [Google Scholar]
  7. Zhu, T.; Liang, C.Z.; Meng, Z.G.; Sun, G.Q.; Meng, Z.H.; Guo, S.D.; Zhang, R. CottonFGD: An integrated functional genomics database for cotton. BMC Plant Biol. 2017, 17. [Google Scholar] [CrossRef] [Green Version]
  8. Constable, G.A.; Bange, M.P. The yield potential of cotton (Gossypium hirsutum L.). Field Crop. Res. 2015, 182, 98–106. [Google Scholar] [CrossRef]
  9. Gruda, N.; Bisbis, M.; Tanny, J. Influence of climate change on protected cultivation: Impacts and sustainable adaptation strategies—A review. J. Clean. Prod. 2019, 225, 481–495. [Google Scholar] [CrossRef]
  10. Abobatta, W. Precision Agriculture Age. Open Acc. J. Agric. Res. 2020, 2, 1–5. [Google Scholar]
  11. Fryxell, P.; Craven, L.; Stewart, J. A Revision of Gossypium Sect. Grandicalyx (Malvaceae), Including the Description of Six New Species. Syst. Bot. 1992, 17, 91. [Google Scholar] [CrossRef]
  12. Yang, Z.R.; Qanmber, G.; Wang, Z.; Yang, Z.E.; Li, F.G. Gossypium Genomics: Trends, Scope, and Utilization for Cotton Improvement. Trends Plant Sci. 2020, 25, 488–500. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Huang, G.; Wu, Z.G.; Percy, R.G.; Bai, M.Z.; Li, Y.; Frelichowski, J.E.; Hu, J.; Wang, K.; Yu, J.Z.; Zhu, Y.X. Genome sequence of Gossypium herbaceum and genome updates of Gossypium arboreum and Gossypium hirsutum provide insights into cotton A-genome evolution. Nat. Genet. 2020, 52, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Wendel, J.F.; Brubaker, C.; Alvarez, I.; Cronn, R.; Stewart, J.M. Evolution and Natural History of the Cotton Genus. In Genetics and Genomics of Cotton; Paterson, A.H., Ed.; Springer: New York, NY, USA, 2009; pp. 3–22. [Google Scholar] [CrossRef]
  15. Bao, Y.; Hu, G.J.; Grover, C.E.; Conover, J.; Yuan, D.J.; Wendel, J.F. Unraveling cis and trans regulatory evolution during cotton domestication. Nat. Commun. 2019, 10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Hu, Y.; Chen, J.; Fang, L.; Zhang, Z.; Ma, W.; Niu, Y.; Ju, L.; Deng, J.; Zhao, T.; Lian, J.; et al. Gossypium Barb. Gossypium barbadense and Gossypium hirsutum genomes provide insights into the origin and evolution of allotetraploid cotton. Nat. Genet. 2019, 51, 739–748. [Google Scholar] [CrossRef] [Green Version]
  17. Lubbers, E.L.; Chee, P.W. The Worldwide Gene Pool of G. hirsutum and its Improvement. In Genetics and Genomics of Cotton; Paterson, A.H., Ed.; Springer: New York, NY, USA, 2009; pp. 23–52. [Google Scholar] [CrossRef]
  18. Ai, X.T.; Liang, Y.J.; Wang, J.D.; Zheng, J.Y.; Gong, Z.L.; Guo, J.P.; Li, X.Y.; Qu, Y.Y. Genetic diversity and structure of elite cotton germplasm (Gossypium hirsutum L.) Using Genome-Wide SNP Data. Genetica 2017, 145, 409–416. [Google Scholar] [CrossRef]
  19. Hinze, L.L.; Hulse-Kemp, A.M.; Wilson, I.W.; Zhu, Q.H.; Llewellyn, D.J.; Taylor, J.M.; Spriggs, A.; Fang, D.D.; Ulloa, M.; Burke, J.J.; et al. Diversity analysis of cotton (Gossypium hirsutum L.) Germplasm Using CottonSNP63K Array. BMC Plant Biol. 2017, 17. [Google Scholar] [CrossRef] [Green Version]
  20. Shaheen, T.; Tabbasam, N.; Atif, M.; Muhammad, I.; Yusuf, A. Cotton genetic resources. A review. Agron. Sustain. Dev. 2012, 32, 419–432. [Google Scholar]
  21. Fang, L.; Guan, X.Y.; Zhang, T.Z. Asymmetric evolution and domestication in allotetraploid cotton (Gossypium hirsutum L.). Crop J. 2017, 5, 159–165. [Google Scholar] [CrossRef] [Green Version]
  22. Wang, M.J.; Tu, L.L.; Lin, M.; Lin, Z.X.; Wang, P.C.; Yang, Q.Y.; Ye, Z.X.; Shen, C.; Li, J.Y.; Zhang, L.; et al. Asymmetric subgenome selection and cis-regulatory divergence during cotton domestication. Nat. Genet. 2017, 49, 579–587. [Google Scholar] [CrossRef] [Green Version]
  23. Shim, J.; Mangat, P.K.; Angeles-Shim, R.B. Natural variation in wild Gossypium species as a tool to broaden the genetic base of cultivated cotton. J. Plant Res. 2018, 2, 005. [Google Scholar]
  24. Shen, C.; Wang, N.; Huang, C.; Wang, M.J.; Zhang, X.L.; Lin, Z.X. Population genomics reveals a fine-scale recombination landscape for genetic improvement of cotton. Plant J. 2019, 99, 494–505. [Google Scholar] [CrossRef] [PubMed]
  25. Yu, J.; Zhang, K.; Li, S.; Yu, S.; Zhang, J. Mapping quantitative trait loci for lint yield and fiber quality across environments in a Gossypium hirsutum × Gossypium barbadense backcross inbred line population. Theor. Appl. Genet. 2013, 126, 275–287. [Google Scholar] [CrossRef] [PubMed]
  26. Wang, F.R.; Gong, Y.C.; Zhang, C.Y.; Liu, G.D.; Wang, L.M.; Xu, Z.Z.; Zhang, J. Genetic effects of introgression genomic components from Sea Island cotton (Gossypium barbadense L.) on fiber related traits in upland cotton (G. hirsutum L.). Euphytica 2011, 181, 41–53. [Google Scholar] [CrossRef]
  27. Zhai, H.C.; Gong, W.K.; Tan, Y.N.; Liu, A.Y.; Song, W.W.; Li, J.W.; Deng, Z.Y.; Kong, L.L.; Gong, J.W.; Shang, H.H.; et al. IIdentification of Chromosome Segment Substitution Lines of Gossypium barbadense Introgressed in G. hirsutum and Quantitative Trait Locus Mapping for Fiber Quality 316 and Yield Traits. PLoS ONE 2016, 11, e0159101. [Google Scholar] [CrossRef]
  28. Gaur, R.; Jyoti, A.; Kaushik, S.; Srivastava, V.K. Sequencing Technologies: Introduction and Applications. Int. J. Hum. Genet. 2019, 19, 123–133. [Google Scholar] [CrossRef]
  29. Zaidi, S.S.E.A.; Mansoor, S.; Paterson, A. The Rise of Cotton Genomics. Trends Plant Sci. 2018, 23, 953–955. [Google Scholar] [CrossRef]
  30. Liu, S.Z.; Yeh, C.T.; Tang, H.M.; Nettleton, D.; Schnable, P.S. Gene Mapping via Bulked Segregant RNA-Seq (BSR-Seq). PLoS ONE 2012, 7, e36406. [Google Scholar] [CrossRef] [Green Version]
  31. Sun, H.; Meng, M.H.; Yan, Z.H.; Lin, Z.X.; Nie, X.H.; Yang, X.Y. Genome-wide association mapping of stress-tolerance traits in cotton. Crop J. 2019, 7, 77–88. [Google Scholar] [CrossRef]
  32. Zhu, J.K.; Chen, J.D.; Gao, F.K.; Xu, C.Y.; Wu, H.T.; Chen, K.; Si, Z.F.; Yan, H.; Zhang, T.Z. Rapid mapping and cloning of the virescent-1 gene in cotton by bulked segregant analysis-next generation sequencing and virus-induced gene silencing strategies. J. Exp. Bot. 2017, 68, 4125–4135. [Google Scholar] [CrossRef] [Green Version]
  33. Du, X.; Huang, G.; He, S.; Yang, Z.; Sun, G.; Ma, X.; Li, N.; Zhang, X.; Sun, J.; Liu, M. Resequencing of 243 diploid cotton accessions based on an updated A genome identifies the genetic basis of key agronomic traits. Nat. Genet. 2018, 20, 796–802. [Google Scholar] [CrossRef] [PubMed]
  34. Wu, J.Y.; Zhang, M.; Zhang, X.X.; Guo, L.P.; Qi, T.X.; Wang, H.L.; Tang, H.N.; Zhang, J.F.; Xing, C.Z. Development of InDel markers for the restorer gene and Rf1 assessment of their utility for marker-assisted selection in cotton. Euphytica 2017, 213, 251. [Google Scholar] [CrossRef]
  35. Ahmed, M.M.; Huang, C.; Shen, C.; Khan, A.Q.; Lin, Z.X. Map-based cloning of qBWT-c12 discovered brassinosteroid-mediated control of organ size in cotton. Plant Sci. 2020, 291. [Google Scholar] [CrossRef] [PubMed]
  36. Shan, C.M.; Shangguan, X.X.; Zhao, B.; Zhang, X.F.; Chao, L.M.; Yang, C.Q.; Wang, L.J.; Zhu, H.Y.; Zeng, Y.D.; Guo, W.Z.; et al. Control of cotton fibre elongation by a homeodomain transcription factor GhHOX3. Nat. Commun. 2014, 5. [Google Scholar] [CrossRef] [PubMed]
  37. Liu, X.; Zhao, B.; Zheng, H.J.; Hu, Y.; Lu, G.; Yang, C.Q.; Chen, J.D.; Chen, J.J.; Chen, D.Y.; Zhang, L.; et al. Gossypium barbadense genome sequence provides insight into the evolution of extra-long staple fiber and specialized metabolites. Sci. Rep. 2015, 5, 14139. [Google Scholar] [CrossRef]
  38. Van Ginkel, M.; Ortiz, R. Cross the Best with the Best, and Select the Best: HELP in Breeding Selfing Crops. Crop Sci. 2018, 58, 17–30. [Google Scholar] [CrossRef] [Green Version]
  39. Bourland, F.; Myers, G. Conventional Cotton Breeding. Cotton 2015, 57, 205–228. [Google Scholar]
  40. McKinney, K. “Hybrid cottonseed production is children’s work”: Making sense of migration and wage labor in western India. ACME 2014, 13, 404–423. [Google Scholar]
  41. Shamsuzzaman, K.M.; Hamid, M.A.; Azad, M.A.K.; Hussain, M.; Majid, M.A. Varietal improvement of cotton (Gossypium hirsutum) through mutation breeding. In Improvement of New and Traditional Industrial Crops by Induced Mutations and Related Biotechnology; International Atomic Energy Agency: Vienna, Austria, 2003; pp. 81–94. [Google Scholar]
  42. Ahloowalia, B.S.; Maluszynski, M.; Nichterlein, K. Global impact of mutation-derived varieties. Euphytica 2004, 135, 187–204. [Google Scholar] [CrossRef]
  43. Dhakal, C.; Lange, K.; Parajulee, M.; Segarra, E. Dynamic Optimization of Nitrogen in Plateau Cotton Yield Functions with Nitrogen Carryover Considerations. J. Agric. Appl. Econ. 2019, 1–17. [Google Scholar] [CrossRef] [Green Version]
  44. Meredith, W.R., Jr. Cotton yield progress-why has it reached a plateau. Better Crop. 2000, 84, 6–9. [Google Scholar]
  45. Tarazi, R. Biotechnological solutions for major cotton (Gossypium hirsutum) pathogens and pests. Biotechnol. Res. Innov. 2020, 3, 19–26. [Google Scholar] [CrossRef]
  46. Morrison, N.I.; Simmons, G.S.; Fu, G.L.; O’Connell, S.; Walker, A.S.; Dafa’alla, T.; Walters, M.; Claus, J.; Tang, G.L.; Jin, L.; et al. Engineered Repressible Lethality for Controlling the Pink Bollworm, a Lepidopteran Pest of Cotton. PLoS ONE 2012, 7, e50922. [Google Scholar] [CrossRef] [PubMed]
  47. Trapero, C.; Wilson, I.W.; Stiller, W.N.; Wilson, L.J. Enhancing Integrated Pest Management in GM Cotton Systems Using Host Plant Resistance. Front. Plant Sci. 2016, 7. [Google Scholar] [CrossRef] [Green Version]
  48. Tabashnik, B.E.; Carriere, Y. Global Patterns of Resistance to Bt Crops Highlighting Pink Bollworm in the United States, China, and India. J. Econ. Entomol. 2019, 112, 2513–2523. [Google Scholar] [CrossRef]
  49. Haq, S.K.; Atif, S.M.; Khan, R.H. Protein proteinase inhibitor genes in combat against insects, pests, and pathogens: Natural and engineered phytoprotection. Arch. Biochem. Biophys. 2004, 431, 145–159. [Google Scholar] [CrossRef]
  50. Harrison, R.L.; Bonning, B.C. Proteases as Insecticidal Agents. Toxins 2010, 2, 935–953. [Google Scholar] [CrossRef] [Green Version]
  51. Kim, J.Y.; Park, S.C.; Hwang, I.; Cheong, H.; Nah, J.W.; Hahm, K.S.; Park, Y. Protease Inhibitors from Plants with Antimicrobial Activity. Int. J. Mol. Sci. 2009, 10, 2860–2872. [Google Scholar] [CrossRef] [Green Version]
  52. Mao, Y.B.; Tao, X.Y.; Xue, X.Y.; Wang, L.J.; Chen, X.Y. Cotton plants expressing CYP6AE14 double-stranded RNA show enhanced resistance to bollworms. Transgenic Res. 2011, 20, 665–673. [Google Scholar] [CrossRef] [Green Version]
  53. Singh, S.; Singh, A.; Kumar, S.; Mittal, P.; Singh, I.K. Protease inhibitors: Recent advancement in its usage as a potential biocontrol agent for insect pest management. Insect Sci. 2020, 27, 186–201. [Google Scholar] [CrossRef] [Green Version]
  54. Morton, R.L.; Schroeder, H.E.; Bateman, K.S.; Chrispeels, M.J.; Armstrong, E.; Higgins, T.J.V. Bean alpha-amylase inhibitor 1 in transgenic peas (Pisum Sativum) Provid. Complet. Prot. Pea Weevil (Bruchus Pisorum) Field Cond. Proc. Natl. Acad. Sci. USA 2000, 97, 3820–3825. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Chougule, N.P.; Bonning, B.C. Toxins for Transgenic Resistance to Hemipteran Pests. Toxins 2012, 4, 405–429. [Google Scholar] [CrossRef] [PubMed]
  56. Baum, J.A. AGRO 121-Control of coleopteran insect pests through RNA interference. Abstr. Pap. Am. Chem. Soc. 2008, 236, S2. [Google Scholar]
  57. Singh, R.P.; Prasad, P.V.V.; Sunita, K.; Giri, S.N.; Reddy, K.R. Influence of high temperature and breeding for heat tolerance in cotton: A review. Adv. Agron. 2007, 93, 313–385. [Google Scholar] [CrossRef]
  58. Majeed, S.; Malik, T.A.; Rana, I.A.; Azhar, M.T. Antioxidant and Physiological Responses of Upland Cotton Accessions Grown Under High-Temperature Regimes. Iran. J. Sci. Technol. Trans. A Sci. 2019, 43, 2759–2768. [Google Scholar] [CrossRef]
  59. Pettigrew, W.T. The effect of higher temperatures on cotton lint yield production and fiber quality. Crop Sci. 2008, 48, 278–285. [Google Scholar] [CrossRef] [Green Version]
  60. Magwanga, R.; Pu, L.; Kirungu, J.; Cai, X.; Zhou, Z.; Agong, G.; Gaya, S.; Wang, K.; Wang, Y. Identification of QTLs and candidate genes for physiological traits associated with drought tolerance in cotton. J. Cotton Res. 2020, 3, 3. [Google Scholar] [CrossRef] [Green Version]
  61. Majeed, S.; Ahmad, I.; Atif, R.M.; Azhar, M.T. Role of SNPs in determining QTLs for major traits in cotton. J. Cotton Res. 2019, 2. [Google Scholar] [CrossRef] [Green Version]
  62. Azhar, M.T.; Wani, S.; Chaudhary, M.; Jameel, T.; Kaur, P.; Du, X. Heat Tolerance in Cotton: Morphological, Physiological, and Genetic Perspectives; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2020; Volume 1, pp. 1–22. [Google Scholar] [CrossRef]
  63. Blum, A. Towards a conceptual ABA ideotype in plant breeding for water limited environments. Funct. Plant Biol. 2015, 42, 502–513. [Google Scholar] [CrossRef]
  64. Arora, R.; Kataria, S.K.; Singh, P. Breeding for Insect Resistance in Cotton: Advances and Future Perspectives. In Breeding insect Resistant Crops for Sustainable Agriculture; Paterson, A.H., Ed.; Springer: New York, NY, USA, 2017; pp. 265–288. [Google Scholar]
  65. Rathore, K.S.; Pandeya, D.; Campbell, L.M.; Wedegaertner, T.C.; Puckhaber, L.; Stipanovic, R.D.; Thenell, J.S.; Hague, S.; Hake, K. Ultra-Low Gossypol Cottonseed: Selective Gene Silencing Opens Up a Vast Resource of Plant-Based Protein to Improve Human Nutrition. Crit. Rev. Plant Sci. 2020, 39, 1–29. [Google Scholar] [CrossRef] [Green Version]
  66. Bowman, D.T.; Bourland, F.; Kuraparthy, V. Measuring maturity in cotton cultivar trials. J. Cotton Sci. 2016, 20, 40–45. [Google Scholar]
  67. Zhang, X.; Rui, Q.Z.; Li, Y.; Chen, Y.; Zhang, X.L.; Chen, D.H.; Song, M.Z. Architecture of stem and branch affects yield formation in short season cotton. J. Integr. Agric. 2020, 19, 680–689. [Google Scholar] [CrossRef]
  68. Yue, H.; Sun, C. Analysis on the Key Problems of Chinese Cotton Full Mechanization Based on System Engineering Theory. IOP Conf. Ser. Mater. Sci. Eng. 2019, 688, 055077. [Google Scholar] [CrossRef]
  69. Kumar, P.; Kirti, S.; Rana, V.A. Glance on genetically engineered (GE) crops. AGRIALLIS 2020, 2, 3. [Google Scholar]
  70. Bhatti, K.; Bardak, A.; Parlak, D.; Ashraf, F.; Imran, H.; Haq, H.; Mian, M.; Mehmood, Z.; Akhtar, M. Biotechnology for Cotton Improvement. Cotton Prod. Uses 2020, 509–525. [Google Scholar] [CrossRef]
  71. Traxler, G.; Godoy-Avila, S.; Falck-Zepeda, J.; Dejesús, J. Transgenic Cotton in Mexico: Economic and Environmental Impacts; The International Consortium on Applied Bioeconomy Reasearch: Buenos Aires, Argentina, 2001; p. 1. [Google Scholar]
  72. Rauf, S.; Shehzad, M.; Al-Khayri, J.M.; Imran, H.M.; Noorka, I.R. Cotton (Gossypium hirsutum L.) Breeding Strategies In Advances in Plant Breeding Strategies: Industrial and Food Crops; Springer Nature: Cham, Switzerland, 2019; pp. 28–59. [Google Scholar] [CrossRef]
  73. Gupta, S.; Shukla, P. Gene editing for cell engineering: Trends and applications. Crit. Rev. Biotechnol. 2016, 37, 1–13. [Google Scholar] [CrossRef]
  74. Li, F.G.; Fan, G.Y.; Lu, C.R.; Xiao, G.H.; Zou, C.S.; Kohel, R.J.; Ma, Z.Y.; Shang, H.H.; Ma, X.F.; Wu, J.Y.; et al. Genome sequence of cultivated Upland cotton (Gossypium hirsutum TM-1) provides insights into genome evolution. Nat. Biotechnol. 2015, 33, 242–524. [Google Scholar] [CrossRef] [Green Version]
  75. Janga, M.; Campbell, L.; Rathore, K. CRISPR/Cas9-mediated targeted mutagenesis in upland cotton (Gossypium hirsutum L.). Plant Mol. Biol. 2017, 94. [Google Scholar] [CrossRef]
  76. Chen, X.G.; Lu, X.K.; Shu, N.; Wang, S.; Wang, J.J.; Wang, D.L.; Guo, L.X.; Ye, W.W. Targeted mutagenesis in cotton (Gossypium hirsutum L.) using CRISPR/Cas9 System. Sci. Rep. 2017, 7, 44304. [Google Scholar] [CrossRef] [Green Version]
  77. Li, C.; Unver, T.; Zhang, B.H. A high-efficiency CRISPR/Cas9 system for targeted mutagenesis in Cotton (Gossypium hirsutum L.). Sci. Rep. 2017, 7, 43902. [Google Scholar] [CrossRef] [Green Version]
  78. Zhang, Z.; Ge, X.; Luo, X.; Wang, P.; Fan, Q.; Hu, G.; Xiao, J.; Li, F.; Jiahe, W. Simultaneous Editing of Two Copies of Gh14-3-3d Confers Enhanced Transgene-Clean Plant Defense Against Verticillium Dahliae Allotetraploid Upl. Cotton. Front. Plant Sci. 2018, 9. [Google Scholar] [CrossRef]
  79. Ali, Z.; Ali, S.; Tashkandi, M.; Zaidi, S.S.E.A.; Mahfouz, M. CRISPR/Cas9-Mediated Immunity to Geminiviruses: Differential Interference and Evasion. Sci. Rep. 2016, 6, 26912. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Khan, Z.; Khan, S.H.; Ahmad, A.; Aslam, S.; Mubarik, M.S.; Khan, S. CRISPR/dCas9-Mediated Inhibition of Replication of Begomoviruses. Int. J. Agric. Biol. 2019, 21, 711–718. [Google Scholar] [CrossRef]
  81. Mubarik, M.; Khan, S.; Sadia, B.; Ahmad, A. CRISPR-Cas9 based suppression of cotton leaf curl virus in Nicotiana Benthamina. Int. J. Agric. Biol. 2019, 22, 517–522. [Google Scholar] [CrossRef]
  82. Ma, W.; Guan, X.; Li, J.; Pan, R.; Wang, L.; Liu, F.; Ma, H.; Zhu, S.; Hu, J.; Ruan, Y.L.; et al. Mitochondrial small heat shock protein mediates seed germination via thermal sensing. Proc. Natl. Acad. Sci. USA 2019, 116, 4716–4721. [Google Scholar] [CrossRef] [Green Version]
  83. Qanmber, G.; Lu, L.L.; Liu, Z.; Yu, D.Q.; Zhou, K.H.; Huo, P.; Li, F.G.; Yang, Z.R. Genome-wide identification of GhAAI Genes Reveal. That GhAAI66 Triggers A Phase Transit. Induce Early Flowering. J. Exp. Bot. 2019, 70, 4721–4735. [Google Scholar] [CrossRef] [Green Version]
  84. Xiao, G.H.; He, P.; Zhao, P.; Liu, H.; Zhang, L.; Pang, C.Y.; Yu, J.N. Genome-wide identification of the GhARF Gene Fam. Reveal. That GhARF2 GhARF18 Are Involv. Cotton Fibre Cell Initiat. J. Exp. Bot. 2018, 69, 4323–4337. [Google Scholar] [CrossRef]
  85. Li, Y.; Wang, N.N.; Wang, Y.; Liu, D.; Gao, Y.; Li, L.; Li, X.B. The cotton XLIM protein (GhXLIM6) is required for fiber development via maintaining dynamic F-actin cytoskeleton and modulating cellulose biosynthesis. Plant J. 2018, 96, 1269–1282. [Google Scholar] [CrossRef] [Green Version]
  86. Tian, X.; Ruan, J.X.; Huang, J.Q.; Yang, C.Q.; Fang, X.; Chen, Z.W.; Hong, H.; Wang, L.J.; Mao, Y.B.; Lu, S.; et al. Characterization of gossypol biosynthetic pathway. Proc. Natl. Acad. Sci. USA 2018, 115, E5410–E5418. [Google Scholar] [CrossRef] [Green Version]
  87. Han, L.B.; Li, Y.B.; Wang, F.X.; Wang, W.Y.; Liu, J.; Wu, J.H.; Zhong, N.Q.; Wu, S.J.; Jiao, G.L.; Wang, H.Y.; et al. The Cotton Apoplastic Protein CRR1 Stabilizes Chitinase 28 to Facilitate Defense against the Fungal Pathogen Verticillium dahliae. Plant Cell 2019, 31, 520–536. [Google Scholar] [CrossRef]
  88. Zhou, Y.; Sun, L.; Wassan, G.M.; He, X.; Shaban, M.; Zhang, L.; Zhu, L.; Zhang, X. GbSOBIR1 Confers Verticillium Wilt Resist. Phosphorylating Transcr. Factor GbbHLH171 Gossypium Barb. Plant Biotechnol. J. 2019, 17, 152–163. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Liang, C.; Meng, Z.; Meng, Z.; Malik, W.; Yan, R.; Lwin, K.M.; Lin, F.; Wang, Y.; Sun, G.; Zhou, T.; et al. GhABF2, a bZIP transcription factor, confers drought and salinity tolerance in cotton (Gossypium hirsutum L.). Sci. Rep. 2016, 6, 35040. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  90. Mu, C.; Zhou, L.; Shan, L.B.; Li, F.J.; Li, Z.H. Phosphatase GhDsPTP3a interacts with annexin protein GhANN8b to reversely regulate salt tolerance in cotton (Gossypium spp.). New Phytol. 2019, 223, 1856–1872. [Google Scholar] [CrossRef] [PubMed]
  91. Ma, L.F.; Hu, L.X.; Fan, J.B.; Amombo, E.; Khaldun, A.B.M.; Zheng, Y.; Chen, L. Cotton GhERF38 gene is involved in plant response to salt/drought and ABA. Ecotoxicology 2017, 26, 841–854. [Google Scholar] [CrossRef]
  92. Ran, Y.D.; Liang, Z.; Gao, C.X. Current and future editing reagent delivery systems for plant genome editing. Sci. China Life Sci. 2017, 60, 490–505. [Google Scholar] [CrossRef]
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Figure 1. World cotton production status in last two decades. The data is retrieved from OECD/FAO (2019), “OECD-FAO Agricultural Outlook”, OECD Agriculture statistics database.
Figure 1. World cotton production status in last two decades. The data is retrieved from OECD/FAO (2019), “OECD-FAO Agricultural Outlook”, OECD Agriculture statistics database.
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Figure 2. Proposed revamped cotton breeding program. Fine-tune the already present traits and add new traits to cultivated cotton varieties through plant breeding, genetic engineering, and genome editing tools. Pyramiding of useful traits by crossing between genetically altered and elite cultivars to develop climate resilient cotton cultivars.
Figure 2. Proposed revamped cotton breeding program. Fine-tune the already present traits and add new traits to cultivated cotton varieties through plant breeding, genetic engineering, and genome editing tools. Pyramiding of useful traits by crossing between genetically altered and elite cultivars to develop climate resilient cotton cultivars.
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Table
Table 1. Ideotypes of cotton for various breeding objectives.
Table 1. Ideotypes of cotton for various breeding objectives.
Breeding TraitIndeotypesReferences
Heat toleranceEfficient canopy architecture, capable to hold flower and retain bolls at lower nodes, leaf angle and thickness.[62]
Drought toleranceEarly maturity, deep root system, small leaf area, improved stem reserve mobilization, high cuticular waxes.[63]
Insect resistantSmall and okra type, and hairy leaves, long frego bract, red colored leaves and stem, extra glands in leaves.[64]
Ultra-low gossypol in seedUltra-low gossypol contents in seed, tissue specific suppression of genes involved in gossypol synthesis in seed.[65]
Early maturitySwift vegetative growth phase, efficient and quick flowering, plant height to node ratio, boll retention at first fruiting branch.[66]
High yield potentialCompact canopy type, extended reproductive growth, high photosynthetic rate, increase number of bolls per plant, effective boll opening, earlier anthesis.[67]
Mechanized farmingSmall to medium compact canopy type, early reproductive growth, early maturing, uniform flowering and fluffy boll opening.[68]

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Mubarik, M.S.; Ma, C.; Majeed, S.; Du, X.; Azhar, M.T. Revamping of Cotton Breeding Programs for Efficient Use of Genetic Resources under Changing Climate. Agronomy 2020, 10, 1190. https://doi.org/10.3390/agronomy10081190

AMA Style

Mubarik MS, Ma C, Majeed S, Du X, Azhar MT. Revamping of Cotton Breeding Programs for Efficient Use of Genetic Resources under Changing Climate. Agronomy. 2020; 10(8):1190. https://doi.org/10.3390/agronomy10081190

Chicago/Turabian Style

Mubarik, Muhammad Salman, Chenhui Ma, Sajid Majeed, Xiongming Du, and Muhammad Tehseen Azhar. 2020. "Revamping of Cotton Breeding Programs for Efficient Use of Genetic Resources under Changing Climate" Agronomy 10, no. 8: 1190. https://doi.org/10.3390/agronomy10081190

APA Style

Mubarik, M. S., Ma, C., Majeed, S., Du, X., & Azhar, M. T. (2020). Revamping of Cotton Breeding Programs for Efficient Use of Genetic Resources under Changing Climate. Agronomy, 10(8), 1190. https://doi.org/10.3390/agronomy10081190

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