Genetic Variability and Association of Yield Attributing Characters with Grain Yield in Deepwater Rice

L. K. Bose,S. K. Pradhan,A. Mohanty,M. Nagaraju 韓國作物學會 2005 Korean journal of crop science Vol.50 No.4

A study on genetic variability and association of yield attributing characters with grain yield was carried out using 35 deepwater rice genotypes. High genotypic co-efficient of variation (GCV) was observed for plot yield, EBT/m2 , plant height and days to 50~% flowering (DFF). For all the traits, estimates of the phenotypic co-efficient of variation (PCV) were higher than GCV, indicating presence of environmental influence. High heritability and genetic advance was observed for plot yield, EBT/m2 and plant height. Plot yield had significant positive association with test weight, EBT/m2 and DFF. However, test weight had the maximum direct effect on grain yield

Stability Analysis for Grain Yield of Lowland Rice for the Largest Rice-Growing Region of Eastern india

Bose, L.K.,Mohanty, A.,Kar, M.K.,Nagaraju, M. The Korean Society of Crop Science 2004 Korean journal of crop science Vol.49 No.2

Twenty-one lowland rice genotypes were evaluated for their stability parameters with respect to grain yield in a multi locational trial at five different sites of Eastern India viz. CRRI, Cuttack (Orissa); OUAT, Bhubaneswar (Orissa); CRS, Masodha (UP); RAU, Pusa (Bihar) and RARS, North Lakhimpur (Assam). Pooled analysis of variance reflects existence of genotype x environment interactions and contribution of both linear and nonlinear components to genotype (G) x environment (E) interactions. Through stability parameter analysis it was found that Rayda $\textrm{B}_3$, CR 778-95 and CR 661-236 were suitable for over all environments where as Sabita, OR 1334-16 and OR 1358-RGA-4 were suitable for rich environments. PSR 1209-2-3-2, CR 780-1937, Ambika, OR 877-ST-4-2 and CR 662-2211 were identified for poor environments.

Genetic Variability and Association of Yield Attributing Characters with Grain Yield in Deepwater Rice

Bose L. K.,Pradhan S. K.,Mohanty A.,Nagaraju M. The Korean Society of Crop Science 2005 Korean journal of crop science Vol.50 No.4

A study on genetic variability and association of yield attributing characters with grain yield was carried out using 35 deepwater rice genotypes. High genotypic co-efficient of variation (GCV) was observed for plot yield, $EBT/m^2$, plant height and days to $50\%$ flowering (DFF). For all the traits, estimates of the phenotypic co-efficient of variation (PCV) were higher than GCV, indicating presence of environmental influence. High heritability and genetic advance was observed for plot yield, $EBT/m^2$ and plant height. Plot yield had significant positive association with test weight, $EBT/m^2$ and DFF. However, test weight had the maximum direct effect on grain yield

Stability Analysis for Grain Yield of Lowland Rice for the Largest Rice-Growing Region of Eastern india

L. K. Bose,A. Mohanty,M. K. Kar,M. Nagaraju 韓國作物學會 2004 Korean journal of crop science Vol.49 No.2

Twenty-one lowland rice genotypes were evaluated for their stability parameters with respect to grain yield in a multi locational trial at five different sites of Eastern India viz. CRRI, Cuttack (Orissa); OUAT, Bhubaneswar (Orissa); CRS, Masodha (UP); RAU, Pusa (Bihar) and RARS, North Lakhimpur (Assam). Pooled analysis of variance reflects existence of genotype x environment interactions and contribution of both linear and nonlinear components to genotype (G) x environment (E) interactions. Through stability parameter analysis it was found that Rayda ~textrmB3 , CR 778-95 and CR 661-236 were suitable for over all environments where as Sabita, OR 1334-16 and OR 1358-RGA-4 were suitable for rich environments. PSR 1209-2-3-2, CR 780-1937, Ambika, OR 877-ST-4-2 and CR 662-2211 were identified for poor environments.

Stability Analysis for Grain Yield of Lowland Rice for the Largest Rice-Growing Region of Eastern india

L. K. Bose,A. Mohanty,M. K. Kar,M. Nagaraju 한국작물학회 2004 Korean journal of crop science Vol.49 No.3

Twenty-one lowland rice genotypes were evaluated for their stability parameters with respect to grain yield in a multi locational trial at five different sites of Eastern India viz. CRRI, Cuttack (Orissa); OUAT, Bhubaneswar (Orissa); CRS, Masodha (UP); RAU, Pusa (Bihar) and RARS, North Lakhimpur (Assam). Pooled analysis of variance reflects existence of genotype x environment interactions and contribution of both linear and non-linear components to genotype x environment interactions. Through stability parameter analysis (Eberhart and Russell, 1966) it was found that Rayda B3, CR 778-95 and CR 661-236 were suitable for over all environments where as Sabita, OR 1334-16 and OR 1358-RGA-4 were suitable for rich environments. PSR 1209-2-3-2, CR 780-1937, Ambika, OR 877-ST-4-2 and CR 662-2211 were identified for poor environments.

Additive Main Effects and Multiplicative Interaction Analysis of Host-Pathogen Relationship in Rice-Bacterial Blight Pathosystem

Nayak, D.,Bose, L.K.,Singh, S.,Nayak, P. The Korean Society of Plant Pathology 2008 Plant Pathology Journal Vol.24 No.3

Host-pathogen interaction in rice bacterial blight pathosystem was analyzed for a better understanding of their relationship and recognition of stable pathogenicity among the populations of Xanthomonas oryzae pv. oryzae. A total number of 52 bacterial strains isolated from diseased leaf samples collected from 12 rice growing states and one Union Territory of India, were inoculated on 16 rice varieties, each possessing known genes for resistance. Analysis of variance revealed that the host genotypes(G) accounted for largest(78.4%) proportion of the total sum of squares(SS), followed by 16.5% due to the pathogen isolates(I) and 5.1% due to the $I{\times}G$ interactions. Application of the Additive Main effects and Multiplicative Interaction(AMMI) model revealed that the first two interaction principal component axes(IPCA) accounted for 66.8% and 21.5% of the interaction SS, respectively. The biplot generated using the isolate and genotypic scores of the first two IPCAs revealed groups of host genotypes and pathogen isolates falling into four sectors. A group of five isolates with high virulence, high absolute IPCA-1 scores, moderate IPCA-2 scores, low AMMI stability index '$D_i$' values and minimal deviations from additive main effects displayed in AMMI biplot as well as response plot, were identified as possessing stable pathogenicity across 16 host genotypes. The largest group of 27 isolates with low virulence, small IPCA-1 as well as IPCA-2 scores, low $D_i$ values and minimal deviations from additive main effect predictions, possessed stable pathogenicity for low virulence. The AMMI analysis and biplot display facilitated in a better understanding of the host-pathogen interaction, adaptability of pathogen isolates to specific host genotypes, identification of isolates showing stable pathogenicity and most discriminating host genotypes, which could be useful in location specific breeding programs aiming at deployment of resistant host genotypes in bacterial blight disease control strategies.

Additive Main Effects and Multiplicative Interaction Analysis of Host-Pathogen Relationship in Rice-Bacterial Blight Pathosystem

D. Nayak,L. K. Bose,S. Singh,P. Nayak 한국식물병리학회 2008 Plant Pathology Journal Vol.24 No.3

Host-pathogen interaction in rice bacterial blight pathosystem was analyzed for a better understanding of their relationship and recognition of stable pathogenicity among the populations of Xanthomonas oryzae pv. oryzae. A total number of 52 bacterial strains isolated from diseased leaf samples collected from 12 rice growing states and one Union Territory of India, were inoculated on 16 rice varieties, each possessing known genes for resistance. Analysis of variance revealed that the host genotypes (G) accounted for largest (78.4%) proportion of the total sum of squares (SS), followed by 16.5% due to the pathogen isolates (I) and 5.1% due to the I x G interactions. Application of the Additive Main effects and Multiplicative Interaction (AMMI) model revealed that the first two interaction principal component axes (IPCA) accounted for 66.8% and 21.5% of the interaction SS, respectively. The biplot generated using the isolate and genotypic scores of the first two IPCAs revealed groups of host genotypes and pathogen isolates falling into four sectors. A group of five isolates with high virulence, high absolute IPCA-1 scores, moderate IPCA-2 scores, low AMMI stability index ‘Di’ values and minimal deviations from additive main effects displayed in AMMI biplot as well as response plot, were identified as possessing stable pathogenicity across 16 host genotypes. The largest group of 27 isolates with low virulence, small IPCA-1 as well as IPCA- 2 scores, low Di values and minimal deviations from additive main effect predictions, possessed stable pathogenicity for low virulence. The AMMI analysis and biplot display facilitated in a better understanding of the host-pathogen interaction, adaptability of pathogen isolates to specific host genotypes, identification of isolates showing stable pathogenicity and most discriminating host genotypes, which could be useful in location specific breeding programs aiming at deployment of resistant host genotypes in bacterial blight disease control strategies.

A Linear-Time Algorithm for the Geodesic Center of a Simple Polygon

Ahn, H. K.,Barba, L.,Bose, P.,Carufel, J. L.,Korman, M.,Oh, E. Springer Science + Business Media 2016 Discrete & Computational Geometry Vol.56 No.4

<P>Let P be a closed simple polygon with n vertices. For any two points in P, the geodesic distance between them is the length of the shortest path that connects them among all paths contained in P. The geodesic center of P is the unique point in P that minimizes the largest geodesic distance to all other points of P. In 1989, Pollack et al. (Discrete Comput Geom 4(1): 611-626, 1989) showed an -time algorithm that computes the geodesic center of P. Since then, a longstanding question has been whether this running time can be improved. In this paper we affirmatively answer this question and present a deterministic linear-time algorithm to solve this problem.</P>