Data collection

Bacterial leaf blight severity was rated at maturity stage: for early maturity varieties from 80 days and for late maturity from 100 days after planting using the 1-9 scale (IRRI, 1996) to describe the symptoms. The rating was; 1 = 1-5% leaf area affected, 3=5-12% leaf area affected, 5=13-25% leaf area affected, 7=26-50 leaf area affected and 9=50> leaf area affected and thirty plants were randomly selected and identified for data collection. On each plant, data were collected on early vigour (scoring 1-9), days to early flowering (by counting number of days from cultivation to early flowering day), plant height (by measuring average of height from base to the tip of last leaf), panicle length (by measuring from the base (first node) to the tip of last spikelet of panicle), number of tillers per hill (by counting of tillers per hill).

82

Dead heart (scoring 1-9), lodging% (by recording % from 10%-100%) and days to maturity (the number of days from cultivation to maturity day at 80%) were also recorded. Data on dry straw weight (the total weight of straw after threshing per plot was measured in kg), number of spikelets per panicle, grain length (by measuring distance from the base of the lowest glume to the tip), grain width (measured as the distance across the fertile lemma and weighed in g), 1000-grain weight (one thousand seeds were counted and weighed (g)), harvest index (for total biological yield the entire plant above the ground lever was harvested, sun dried and weight at maturity, then harvest index was calculated by harvest index (%) = economical yield / biological yield x 100 and yield per plot (by weighing the total grains per plot) were also collected.

4.3.1 Data analysis

4.3.2 Phenotypic correlation analysis

Simple Pearson correlation coefficients were calculated using mean values for all traits from all locations using PROC CORR of SAS version 9.4 (SAS, 2014).

The phenotypic correlation was determined as follows, according to Know and Torrie (1964).



 

py px

pxy

r

p^ _

Where,

r

^p



phenotypic correlation,



^pxyphenotypic covariance of x and y characters,



^pxsquare root of phenotypic variance of x character,



^pysquare root of phenotypic variance of y character.

4.3.3 Path coefficient analysis

Correlation does not provide an exact picture of the relative importance of influence of each of the component characters, because it does not analyse the direct and indirect influence of characters on yield. The path coefficient analysis, a cause and effect relationship provides knowledge of relative importance of each of the component characters. Path coefficient analysis was done according to the procedure suggested by Dewey and Lu (1959).

If grain yield is the effect

y

and

x

¹is the cause, the path coefficient for the path from cause

x

¹to the effect

y

is





y

x1

Direct and indirect effects were worked out using phenotypic correlations as follows.

83 Direct effect of

x

^1on

^{y = p} x

¹

^y

Where,

px

₁is the path coefficient of

x

^{1 on}

^y

Similarly, direct effects of other attributes on grain yield were worked out.

Indirect effect of

x

^{1 via}

x

^{2 on}

^{y  p} x

²

^{y . r} x

¹

x

²

Where,

P x

²

y

is the path coefficient of the component character

x

^{2 on}

^y x

x

r

1 2 is the phenotypic correlation between

x

^{1 and}

x

^2.

The path coefficient scales suggested by Kiani, (2012), where 0.00-0.09 is negligible, 0.10- 0.19 low, 0.2 0-0.29 moderate, 0.30-0.99 high and >1.0 very high were used.

4.3.4 Genotype by trait model

From a genotype-by-environment-by-trait three-way table, genotype-by-trait tables across all environments or across a subset of the environments can be generated and visually studied using biplots. Biplot analysis of genotype by trait tables is a typical example of biplot analysis of multivariate data. The model for biplot analysis of genotype by trait data is SVD of trait- standardized two-way table, i.e., equation with sj being the standard deviation for trait j. A genotype by trait biplot can help understand the relationships among traits (breeding objectives) and help identify traits that are positively or negatively associated, traits that are redundantly measured, and traits that can be used in indirect selection for another trait. It also helps to visualize the trait profiles (strength and weakness) of genotypes, which is important for parent as well as variety selection (Bernal et al., 2013).

Adjusted mean values of the traits were used for the analysis of genotype by trait and trait associations. To display the genotype by trait two-way data in a biplot, the formula suggested by Yan & Rajcan, (2002) was used as follows:

sj ^{i j i j} ij

j

ij

T

T

^



1



1



1^



2



2



^2_

_

where,

T

^ij



is the average value of genotype

i

for traitj,

T

^jis the average value of traitj over all genotypes,sjis the standard deviation of trait j among the genotype averages;



_i1

and



_i2are the first principal component (PC1) and the second principal component (PC2)

84

scores, respectively, for genotype

i 

^j1and



^j2are the PC1 and PC2 scores, respectively, for traitj, andijis the residual of the model associated with the genotype

i

and traitj.

Equation is a principal component analysis of standardized data with two principal components. Because different traits use different units, the standardization is necessary to remove the units. PC1 and PC2 must be scaled so that the one value is symmetrically distributed between the genotype scores and the trait scores. A Genotype by trait biplot is constructed by plotting the PC1 scores against the PC2 scores for each genotype and each trait.

4.4 Results