Correlation and path analysis of morpho-physiological and yield traits in &lt;italic&gt;boro&lt;/italic&gt; rice (&lt;italic&gt;Oryza sativa&lt;/italic&gt; L.)

Das, S. K.; Sarma, Debojit

doi:10.18805/asd.v35i4.6855

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…The length/ breadth ratio of the grain showed a significantly positive correlation with an average length of the grain (0.30), plant height at vegetative stage (0.28), and negative correlation with the weight of 1000 grain (-.48) and total yield/plot (-0.23). Similar outcomes were also stated by different researchers SUNYA ET AL 911 (Akhter et al, 2004;Chhangte et al, 2019 andDas et al, 2015).…”

Section: Correlation Between Yield and Plant Characterssupporting

confidence: 84%

“…Similar results have been reported by Zhang et al (2017). Das et al (2015) examined 30 rice germplasm of exotic and local origin to ascertain the phenotypic as well as genotypic correlation among twenty one morpho-physiological and yield parameters and their indirect/direct contribution to the grain yield under boro season. The result obtained clearly indicated that the grain yield/plant had a significant correlation (positive) with the biological yield, harvest index, days to flowering, panicle length, grain/panicle, and the weight of the 1000 grain at both phenotypic as well as genotypic levels.…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of improved varieties of rice (Oryza sativa L.) under environmental condition of Namsai, Arunachal Pradesh, India

Sunya¹,

Sharma²,

Manpoong³

et al. 2022

EEC

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An experiment was performed for evaluation of improved varieties of rice under environmental conditions occurring at Namsai district of Arunachal Pradesh in Research Farm of Arunachal University. Studies were conducted during Kharif season for identifying the promising variety. Thirteen varieties, namely Deepthi; NPT Cul-1; Red Matta Triveni; Jyothi; Uma; Akhaya; Sapmpada; Aathiva; JS-7; Khamti Lahi; Thailand Lahi; MTU and Bora were assessed in replicated randomized block design with 2 replication. The results revealed that the varieties showed significant variations on growth and yield parameters viz; Plant height at the vegetative stage, Number of productive tillers, Number of days for panicle emergence, Crop duration, Number of grains per panicle, Length of panicle, Length of grain, 1000 grain weight, Yield per plot and Length/Breadth ratio of grains. From the above findings, it is observed that, considering the growth and yield parameters, improved rice varieties Uma and JS-7, exotic variety Thailand Lahi, indigenous variety Bora and Khamti Lahi were found to be promising under the Namsai condition of Arunachal Pradesh.

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Section: Correlation Between Yield and Plant Characterssupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Evaluation of improved varieties of rice (Oryza sativa L.) under environmental condition of Namsai, Arunachal Pradesh, India

Sunya¹,

Sharma²,

Manpoong³

et al. 2022

EEC

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“…Rice breeders also often face the problem of negative correlations among different yield traits (Sandhu et al, 2013 ; Ranawake and Amarasinghe, 2014 ), such as grain number per panicle (GNP), panicle number per plant (PN), kilo-grain weight (KGW), and rate of seed setting (Sakamoto and Matsuoka, 2008 ; Zhao et al, 2016 ). Many published works suggest that there is a disadvantageous positive correlation between heading date (HD) and GNP, but negative correlations among yield traits (Das and Sarma, 2015 ; Kharb et al, 2016 ). Most high-yield varieties have long growth duration in regions with adequate light and energy resources because longer growth duration provides more metabolic activity and nutrition for grain filling (Okada et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Genetic Basis Underlying Correlations Among Growth Duration and Yield Traits Revealed by GWAS in Rice (Oryza sativa L.)

Xie

Zhu

et al. 2018

Front. Plant Sci.

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Avoidance of disadvantageous genetic correlations among growth duration and yield traits is critical in developing crop varieties that efficiently use light and energy resources and produce high yields. To understand the genetic basis underlying the correlations among heading date and three major yield traits in rice, we investigated the four traits in a diverse and representative core collection of 266 cultivated rice accessions in both long-day and short-day environments, and conducted the genome-wide association study using 4.6 million single nucleotide polymorphisms (SNPs). There were clear positive correlation between heading date and grain number per panicle, and negative correlation between grain number per panicle and panicle number, as well as different degrees of correlations among other traits in different subspecies and environments. We detected 47 pleiotropic genes in 15 pleiotropic quantitative trait loci (pQTLs), 18 pleiotropic genes containing 37 pleiotropic SNPs in 8 pQTLs, 27 pQTLs with r2 of linkage disequilibrium higher than 0.2, and 39 pairs of interactive genes from 8 metabolic pathways that may contribute to the above phenotypic correlations, but these genetic bases were different for correlations among different traits. Distributions of haplotypes revealed that selection for pleiotropic genes or interactive genes controlling different traits focused on genotypes with weak effect or on those balancing two traits that maximized production but sometimes their utilization strategies depend on the traits and environment. Detection of pQTLs and interactive genes and associated molecular markers will provide an ability to overcome disadvantageous correlations and to utilize the advantageous correlations among traits through marker-assisted selection in breeding.

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“…The consequences of trait correlations are the most important issues to be considered in breeding, such as the negative correlation between grain number per panicle and panicle number, and between grain number per panicle and kilo-grain weight 25 , positive correlation between heading date and grain yield per plant, and between heading date and grain number per panicle 25 , 65 and positive correlation between root weight and shoot weight in this study. Pleiotropy and LD are two considerable factors responsible for trait correlations, and it is hard to distinguish them in a short physical interval.…”

Section: Discussionmentioning

confidence: 82%

Genetic basis and network underlying synergistic roots and shoots biomass accumulation revealed by genome-wide association studies in rice

Zhao

Zhen

Wang

et al. 2021

Sci Rep

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Genetic basis and network studies underlying synergistic biomass accumulation of roots and shoots (SBA) are conducive for rational design of high-biomass rice breeding. In this study, association signals for root weight, shoot weight, and the ratio of root-to-shoot mass (R/S) were identified using 666 rice accessions by genome-wide association study, together with their sub-traits, root length, root thickness and shoot length. Most association signals for root weight and shoot weight did not show association with their sub-traits. Based on the results, we proposed a top-to-bottom model for SBA, i.e. root weight, shoot weight and R/S were determined by their highest priority in contributing to biomass in the regulatory pathway, followed by a lower priority pathway for their sub-traits. Owing to 37 enriched clusters with more than two association signals identified, the relationship among the six traits could be also involved in linkage and pleiotropy. Furthermore, a discrimination of pleiotropy and LD at sequencing level using the known gene OsPTR9 for root weight, R/S and root length was provided. The results of given moderate correlation between traits and their corresponding sub-traits, and moderate additive effects between a trait and the accumulation of excellent alleles corresponding to its sub-traits supported a bottom-to-top regulation model for SBA. This model depicted each lowest-order trait (root length, root thickness and shoot length) was determined by its own regulation loci, and competition among different traits, as well as the pleiotropy and LD. All above ensure the coordinated development of each trait and the accumulation of the total biomass, although the predominant genetic basis of SBA is still indistinguishable. The presentation of the above two models and evidence of this study shed light on dissecting the genetic architecture of SBA.

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Correlation and path analysis of morpho-physiological and yield traits in <italic>boro</italic> rice (<italic>Oryza sativa</italic> L.)

Cited by 5 publications

References 4 publications

Evaluation of improved varieties of rice (Oryza sativa L.) under environmental condition of Namsai, Arunachal Pradesh, India

Evaluation of improved varieties of rice (Oryza sativa L.) under environmental condition of Namsai, Arunachal Pradesh, India

Genetic Basis Underlying Correlations Among Growth Duration and Yield Traits Revealed by GWAS in Rice (Oryza sativa L.)

Genetic basis and network underlying synergistic roots and shoots biomass accumulation revealed by genome-wide association studies in rice

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