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Weedy rice: An insight into genetic diversity and population structure for effective weed management Cover

Weedy rice: An insight into genetic diversity and population structure for effective weed management

Open Access
|Jun 2023

Abstract

Weedy rice (Oryza sativa f. spontanea) is a conspecific weed that has invaded cultivated rice fields, effectively adapted to modern management practices, leading to substantial yield losses and reduced harvest quality. The understanding of weedy rice genetic diversity and population structure is critical in developing effective control measures. Twenty weedy rice populations were collected around the island and evaluated using 33 SSR markers. Our findings indicated that the genetic diversity (He) in a population of weedy rice was reasonably high (0.305 - 0.560). A high level of within-population (79%) and a low level of among-population (21%) genetic variation were found by analysis of molecular variance (AMOVA). There is no significant correlation across genetic and geographical distances (P>0.05). The unweighted pair group method with arithmetic mean (UPGMA) demonstrated that 20 populations were structured into two well-separated groups. Remarkably, many admixed individuals were evident according to the STRUCTURE. Weedy rice management strategies should focus on micromorphological monitoring to detect, group, and eliminate weedy rice in paddy fields, restricting the exchange of saved paddy seeds among geographical regions, increasing the production and distribution of certified seeds, the varietal recommendation for rice-growing regions to minimize mixing of varieties with different age classes, raising awareness are the recommendations to reduce the emergence of future weeds.

Language: English
Page range: 135 - 150
Published on: Jun 28, 2023
Published by: Faculty of Agriculture, University of Ruhuna
In partnership with: Paradigm Publishing Services

© 2023 A. Tennakoon, S. Sandamal, S. Ge, B. Marambe, D. Ratnasekera, published by Faculty of Agriculture, University of Ruhuna
This work is licensed under the Creative Commons Attribution 4.0 License.