Agro-morphological variation in “Jhinuwa” rice landraces (Oryza sativa L.) of Nepal
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Date
2013-05-25
Date Issued
ISI Journal
Impact factor: 1.524 (Year: 2013)
Citation
Mahendra Tripathi, Sthapit Bhuwon, Lakshmi Subedi, Srawan Sah, Sanjaya Gyawali. (25/5/2013). Agro-morphological variation in “Jhinuwa” rice landraces (Oryza sativa L. ) of Nepal. Genetic Resources and Crop Evolution, 60 (8), pp. 2261-2271.
Abstract
Jhinuwa is an aromatic rice (Oryza sativa L.) landrace from the Pokhara Valley of Nepal. A total of 210 accessions comprising seven types of Jhinuwa rice landraces were randomly collected from the rice fields to evaluate inter-and intra-population variability based on agro-morphological traits. The experiment was conducted in factorial randomized complete block design with three replicates in 2005. The first six principal components (PCs) accounted for 76.6 % variation for agro-morphological traits. Major traits that accounted for the variation by six PCs includes days to heading, days to maturity, total grain panicle(-1), fertile grain panicle(-1), culm length, panicle length, milling recovery, head rice recovery, aroma, 1,000 grain weight, sterile grain panicle(-1), grain sterility %, and leaf characteristics. Both principal coordinate analysis and cluster analyses revealed four phenotypic groups, two of which represent Bayarni, Jhinuwa, and Biramphul while the other two account for Tunde and Pakho Tunde. Tunde, Pakho Tunde, Kalo Bayarni, and Seto Bayarni showed higher intraas well as inter-population variation compared to other populations. The phenotypic and genotypic coefficients of variation, broad sense heritability (h(2)B) and genetic advance (GA) as a percent of the mean assessed for 210 accessions revealed high h(2)B and GA estimates for leaf width, leaf length breadth ratio, ligule length, sterile grain panicle(-1), grain sterility % and 1,000 grain weight. The current study demonstrates that improvement in Jhinuwa rice landrace is possible by selecting superior accessions from existing natural populations while selection should be focussed to market traits with higher h(2)B and GA estimates.