Spikelet fertility (%)

3.3.4 Recording of data

3.3.4.11 Spikelet fertility (%)

Spikelet fertility (%) of the panicle was estimated from randomly selected 5 panicle in each plot following formula:

., a nfl

Total no of filled grain panicle1

Spikelet fertility (%) = 100

Total no. of filled grain panicle + Total no. of unfilled grain panicle' 3.3.4.12 Paniele weight

Randomly selected 5 particles were weighted by electric balance.

3.3.4.13 1000-grain weight

Average dry weight (g) of 1000-grain was recorded at 14% moisture content.

3.3.4.14 Yield planf'

Average grain yield (g) from 5 plants was recorded at 14% moisture content.

W(lO0-M) Grain yield at 14% MC = ---

(100-14) Where, W = Weight of sun dried grain

M = % moisture of sun dried grain

3.3.5 Statistical analysis 3.3.5.1 Combining ability

The mean values recorded for hybrids and parents were subjected to line x tester analysis and mean sum of squares (MSS) along with variance of CiCA of the parents and SC.A of the hybrids following standard procedure developed by Kempthorne (1957). The combining ability effect and their significance were estimated following standard statistical tools (Singh and Chaudhury. 1985).

Partitioning of treatments SS

Partitioning of treatments SS were perfonned according to following formulae:

ac2

^{ij+P2 ii}

Treatments SS = CF (over all)

r

(Grand total) 2

C.F. (over all) = [Total number of treatment x number of replication]

C2 ij

S.S. (crosses) = - C.F. (Crosses) r

Where, Cij = observation for i x crosses r = Number of replication

[Grand total (Crosses)] 2 C.F. (Crosses) =

[Total number of crosses x number of replication]

flP2

^ij

S.S. (Parent) = --- - C.F. (parents) Where, pii = observation for i parent

r = Number of replication

[Grand total (patents)] 2 C.F. (Crosses) =

[Total number of parents x number of replication]

SS (Parent vs Cross) = Treatments SS- S.S. (crosses) - S.S. (parent) or

= C.F. (Crosses) - C.F. (parents) - CF (over all) Line x Tester analysis

flL2 u

S.S. (Lines) = --- - CS. (Crosses) rxt

Where, yyl,2 I] = Sum of squares of line total r = Replication number

t = Tester number

a?r

2 ij

S.S. (Tester) = ---- - -- - C.F. (Crosses) rxl

40

Where, YJT2 ij = Sum of squares of tester total r = Replication number

I = Line number

S.S due to Line x Tester = S.S. (Crosses) - S.S. (Lines) - S.S. (Testers)

Estimation of GCA effects

GCA effects for lines and testers will be calculated by the following formula:

GCA for lines:

xr.. x..

---- tr ltr GCA for testers:

x._j. X..

Ir hr

Estimation of SCA effects

SCA effects for crosses will be calculated by following formula:

xi.. x.. x...

Sij= ---- - --- - --- + --- - ---

r tr ft Itt

Where,

= Individual cross value Xi.. = Line total

X= Tester total

r = Replication number

= Line number t= Tester number

Estimation of Standard Error (S.E.) for combining ability effects

S.E. of GCA for line, GCA for tester and SCA effects will be calculated by the following formula:

S.E. (GCA for line) = (M/(r x

0} U2

S.E. (GCA for tester) = (Mj(r x ^{l)) 1/2} S.E. (SCA for effects) = {Mjr} ^I'?

S.E. (gi-gj) for line = {2M/ (r x t)} ² S.E. (gi-gj) for tester = {2M/(rxD} ^I/I S.E. (sij-kl) = (2Mjr) ^l'2 Where,

MC = Error mean sum square

gi-gj = Difference of GCA for any line or tester pair

Estimation of genetic components of variation

Variance of GCA and SCA will be calculated by the following formula:

M1-M (I ' I)

Coy H.S. (lines) =

(r x t) M1-M (I N L)

Coy H.S. (tester) =

(r x I) 1

Coy 11.5. (average) = ---[

r (21t-I-t)

(I- I)

NO + (

^{t- 1)}

NO

(1 0-2

I + F

a2gca = Ccv H.S. (average) = [---

I

^{a2 A} 4

1-F

a2sca - J 2 &D

r

42

Where.

= Mean sum of square of line

= Mean sum of square tester

M j,, = Mean sum of square line x tester Coy 1-1.5. = Covariance of half sib progeny

F = Inbreeding coefficient (for self pollinated crop F = 1 and cross pollinated, F0)

& A = Additive genetic variance

& D = Dominance genetic variance

Estimation of proportional contribution of line, tester and line tester interaction to total variance

Contribution of lines (%) = (SS1 I SScross) x 100 Contribution of testers (%) = (SS/ ^SScrosc) x 100

Contribution of lines x testers (%) = (SS1 / SS .... ) x 100 Where,

SSI= Sum of square for lines SS1 = Sum of square for testers

SS1 = Sum of square for lines x testers

= Sum of square for crosses

3.3.5.2 Heterosis

1-leterosis was calculated as percentage increase or decrease of ^F1 mean performance over the mean performance of line in question. The overall niean value for each parent or hybrid in all the three replications for each character was taken for the estimation of' heterosis. I leterosis was calculated as percent deviation of the ^F1 hybrid from the mid parental value between two corresponding parents.

The magnitude of heterosis was expressed as heterosis over mid-parent (MPH) over better parent (BPFI) and over standard check variety (SH) for 14

agronomic Iraits. Standard heterosis (Si-I) over the check varieties were computed over I3RRI dhan33 and BRRI dhan39. The following tbrmulac were used for estimation of heterosis.

Heterosis over mid parent (MPH)

%MPH=(F1-MP)IMPX 100 SE ='3Me/2r

'1' value = (F1-MP)/SE x MPH Heterosis over better parent (BPI-()

%BPH =(F1-BP)/BPx 100

SE =Vieir

't' value = (F1-BP)/SE x BPH

Heterosis over check variety (SI-I)

%S1l =(F1-CV)/CVX 100 SE = 42Me/r

't value = (Fr CV)/SE x SF!

Where, Me = Error mean sum of squares from RCBD ANOVA F1 = Mean ofri

BP = Mean of better parent CV = Mean of check variety MP = Mean of mid parent

44

3.3.5.3 Character associations

Simple correlation coefficients between various character pairs were worked out separately for the selected parents, hybrids, GCA effects. SCA effects and

heterosis.

Estimation of correlation coefficients

Correlation between mean performances of character pairs was calculated by following formula;

>N (xr ' y 2

) -

n

---

I {Lx- ---}{

Y' ---

^)]112

n ¹¹

Significance test for correlation coefficients

Paired student't' test was used to test the significance of coefficient between mean of pairs of characters by using following Ibrmula:

t = ---with (n-2) dl (1 _r2)t2

\Vherc, ii = no. of pair of observations r = correlation coefficient

3.3.5.4 1)cterrninatiOfl of high-low status of combining ability effects

The relationship between combining ability and heterosis were examined as per the following procedure:

Fhe procedure, as detailed in Arunachalam and I3andyopadhay (1979) was followed to determine the specific combining ability (SCA) status over all characters studied as high (II) or low (L). The procedure consisted in brief of the following:

As in the case of heterosis, the desirable direction of improvement of each character was considered in the case of GCA also.

The SCA effects were tested whether they were significantly different from zero on either side by two-tailed 1.-test at 5% and as well as 1% level of significant.

k, the mean-value of all significant SCA effects, was calculatcd.

k was used as the norm. Signiflcant SCA effects whose values were greater than or equal to k, receive a score + I; those significant effects which were less than k received a score -I; all

non-significant effects received a zero score.

V. A final SCA score was obtained for each cross by addition of the individual scores for each character. The mean across the crosses was calculated. A cross whose final score was greater than or equal to this mean was allotted a Fligh (ii) overall SCA status and one whose final score was less than this mean, a Low (14 overall SCA status.

The crosses were grouped into the classes H or L based on their overall SCA status. Combining ability status for OCA was also determined as per thcse procedures.

CHAPTER IV