Varietal Screening of Cotton (Gossypium Hirsutum L.) Against Cotton Leaf Curl Virus and its Association with Whitefly (Bemisia Tabaci L.)
DOI:
https://doi.org/10.55627/agribiol.003.01.1258Keywords:
Cotton, Whitefly, Cotton Leaf Curl Virus, Yield, Germplasm screeningAbstract
The disease Cotton Leaf Curl Virus (CLCuV) caused by the whitefly (Bemisia tabaci) is a serious threat to the production of the global economic commodity Cotton (Gossypium hirsutum L.), which represents an important step for fiber production of the world. The goal of this study was to determine the level of the severity of the disease and response of yield properties in ten different genotypes of cotton. As a Randomized Complete Block Design (RCBD), three treatment groups representing increasing levels of whitefly infestation were used (1, 2, and 3), as well as control. The phytochemical parameters i. e. plant height (PH), number of sympodial branches (SB), number of bolls (NOB), boll weight (BW), ginning outturn (GOT) and seed cotton yield (SCY) were recorded in the experiment along with the severity of the disease (PADI). A higher infestation of whiteflies resulted in significant increases in PDI, which resulted in significant decreases of all yield-related traits. With lower PDI values and improved yield stability in high infestation conditions, genotypes FH-333 (G1) and CKC-3 (G10) were considered the most tolerated genotypes to CLCuV. On the other hand, the mutants MNH-1016 (G8) and CIM-615 (G3) demonstrated the greatest losses in yield and PDI. While SCY was positively related to BW, GOT, SB and NOB, ANOVA and correlation analysis demonstrated a highly significant negative association between PDI and yield-related traits. Selecting CLCuV-resistant genotypes for breeding programs was suggested. The results showed that integrated pest management (IPM) techniques should be used for controlling whitefly populations and reducing the effects of disease.
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Copyright (c) 2025 Saima Naseer, Arbaz Hassan, Muhammad Atif Shabir, Mehwish Naz, Majid Ali, Zia Ullah Ashraf, Ali Ahmed, Saba saeed, Sajawal, Anisa Umer (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
