Heterosis of purple blotch (Alternaria porri (Ellis) Cif.) resistance, yield and earliness in tropical onions (Allium cepa L.)

Abstract

Five open pollinated onion cultivars Red Creole, Kaharda, Koumassa, Sokoto local and Ori were crossed in a complete diallel and their progenies were evaluated in replicated trials, at Sokoto and Talata Mafara, Nigeria, during the 2004/2005 and 2005/2006 seasons. The experiments were randomised as complete block design with three replications. 30 mls of 10⁻¹ cfu of spore suspension of Alternaria porri prepared by serial dilution was poured into the centre of each plot (sunken bed) immediately after irrigation at 2 weeks after transplanting. Combined analysis across seasons and locations indicated significant (P < 0.05) differences between the populations with respect to all the characters under study. Crosses Ori × Koumassa and Koumassa × Ori recorded highly significant (P < 0.01) positive high parent heterosis for bulb diameter. Crosses Ori × Sokoto local and Ori × Koumassa recorded highly significant (P < 0.01) negative high parent heterosis for days to maturity. Cross Sokoto local × Koumassa exhibited highly significant (P < 0.01) positive high parent heterosis for number of leaves/plant. Crosses Red Creole × Kaharda and Kaharda × Red Creeole recorded highly significant (P < 0.01) positive high parent heterosis for disease incidence. Red Creole × Kaharda and Kaharda × Red Creeole also recorded highly significant (P < 0.01) positive high parent heterosis for fresh bulb yield. Cross Kaharda × Ori recorded highly significant (P < 0.01) positive high parent heterosis for bulb weight. The cross Kaharda × Sokoto local recorded significant (P < 0.05) negative high parent heterosis for days to maturity while the cross Red Creole × Kaharda recorded highly significant (P < 0.01) positive high parent heterosis for number of leaves/plant. Bulb diameter and bulb weight recorded highly significant (P < 0.01) and significant (P < 0.05) environmental correlation with days to maturity.

Appendix

The formulae of Walton (1971) and Sinha and Khanna (1975),

$$ {\text{M}}{\text{.P}}{\text{.H}}{\text{. }}{\left( \% \right)} = \frac{{\ifmmode\expandafter\bar\else\expandafter\=\fi{F}_{1} - {\text{M}}{\text{.P}}{\text{.}}}} {{{\text{M}}{\text{.P}}{\text{.}}}} \times 100 $$

$$ {\text{H}}{\text{.P}}{\text{.H}}{\text{. }}{\left( \% \right)} = \frac{{\ifmmode\expandafter\bar\else\expandafter\=\fi{F}_{1} - {\text{H}}{\text{.P}}{\text{.}}}} {{{\text{H}}{\text{.P}}{\text{.}}}} \times 100 $$

where $\ifmmode\expandafter\bar\else\expandafter\=\fi{F}_{1} ,\;{\text{mean}}\,{\text{value}}\,{\text{of}}\,F_{1}$; H.P., high parent value of a particular cross combination; M.P., mid parent value of a particular cross combination and is given by

$$ \frac{{P_{1} + P_{2} }} {2} $$

Tests of significance computed using the standard error for individual and combined results using Paschal and Wilcox (1975) formular.

$$ {\text{Mid}}\,{\text{parent}}\,{\text{SD}} = t{\left( {{\sqrt {\frac{{3{\text{MSGL}}}} {{2r}}} }} \right)}$$

$$ {\text{High}}\,{\text{parent}}\,{\text{SD}} = t{\left( {{\sqrt {\frac{{2{\text{MSGL}}}} {r}} }} \right)} $$

where SD, differences between F ₁ and mid parent or high parent required for significance at 5% and 1% levels of probability; t = tabulated t value at 5% and 1% levels of probability and error degree of freedom; MSGL, mean square of genotype × location × season from analysis of variance; r, number of replications.

The estimates of components of covariance were used to determine phenotypic, genotypic and environmental correlations according to (Singh and Chaudhary 1985).

Mean square error (MS_E) = δ²e(xixi), similarly MS_G = δ²e(xixi) + rδ²g(xixi)

$$ \begin{aligned}{} {\text{Genotypic}}\,{\text{correlation}}\,r_{G} = & \frac{{{\text{Cov}}{\left( {x,y} \right)}}} {{{\sqrt {\text{var} {\left( x \right)}\text{var} {\left( y \right)}} }}} \\ = & \frac{{r\delta _{g} ^{2} {\left( {xy} \right)}}} {{{\sqrt {{\left( {r\delta _{g} ^{2} {\left( {xx} \right)}} \right)}{\left( {r\delta _{g} ^{2} {\left( {yy} \right)}} \right)}} }}} \\ \end{aligned} $$

$$ \begin{aligned}{} {\text{Phenotypic}}\,{\text{correlation}}\,r_{P} = & \frac{{{\text{Cov}}{\left( {x,y} \right)}}} {{{\sqrt {\text{var} {\left( x \right)}\text{var} {\left( y \right)}} }}} \\ = & \frac{{\delta _{e} ^{2} {\left( {xy} \right)} + r\delta _{g} ^{2} {\left( {xy} \right)}}} {{{\sqrt {{\left[ {\delta _{e} ^{2} {\left( {xx} \right)} + r\delta _{g} ^{2} {\left( {xx} \right)}} \right]}{\left[ {\delta _{e} ^{2} {\left( {yy} \right)} + r\delta _{g} ^{2} {\left( {yy} \right)}} \right]}} }}} \\ \end{aligned} $$

Environmental correlation = phenotypic correlation − genotypic correlation.