1. Thesis Introduction
7.4 Discussion
7.4.3 Evaluation of adaptation potential of introgression lines across test environments 143
When AMMI1 provides a good description of the data, the main effects and IPCA1 scores for genotypes and environments are plotted on the same diagram, facilitating inference about the specific interactions of individual genotypes and environments by using the sign and magnitude of IPCA1 values (Fox et al., 1997). These authors explain that any genotype with IPCA1 values close to zero shows general or wide adaptation to the tested environments. Its response pattern across the environment parallels the mean of all the genotypes in the trial. A large genotypic
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IPCA1 score reflects more narrow or specific adaptation to environments with IPCA1 scores of the same sign.
In this study a number of genotypes exhibited IPCA scores close to zero and mean performance greater than the general mean and hence good general or wide adaptation to all the three environments. Genotypes observed in this category included G6 (Intsinzi x Yunertian), G2 (Rumbuka x Yunertian) and G8 (Gakire x Yunertian) for disease severity. Another cluster comprised of genotypes such as G7 (Gakire x Yunyine), G1 (Rumbuka x Yunyine) and G3 (Buryohe x Yunyine) recorded negative values of IPCA1 and mean lesion size less than the average. These genotypes were considered as more resistant to sheath rot but with narrow or specific adaption to environments. Genotype G5 (Intsinzi x Yunertian) recorded less mean lesion size than the grand mean, but positive IPCA1 values close to zero. This is also a genotype with good general adaptation for sheath resistance. For yield and its related traits a number of genotypes showed a good general adaptation to environments. These include G4 (Buryohe x Yunertian) and G6 (Intsinzi X Yunertian) for grain yield, G6 (Intsinzi X Yunertian) and G2 (Rumbuka x Yunertian) for tillering ability and G6 (Intsinzi X Yunertian) for number of grains per panicle. The performance of other genotypes for all the four traits was considerably influenced by test environments as they were distantly scattered all around the quadrants of the biplot. These genotypes were categorized as genotypes exhibiting higher means than general means coupled with large values of IPCA for all the traits. This situation reflects site specific or narrow adaptation of the genotypes to test environments. The results of this study provide good and considerable information for further breeding efforts for sheath rot resistance. This is because in breeding for wide adaptation, the aim is to obtain a variety which performs well in nearly all environments whereas in breeding for specific adaptation, the aim is to obtain a variety which performs well in a definite subset of environments within a target region (Annicchiarico, 2002). Consequently, with further few backcrosses, genotypes will be recommended in specific or a wide range of environments.
7.4.4 Stability analysis of introgression lines across the test environments
Breeding for wide adaptation and for high yield stability have sometimes been considered one and the same, in so far as the latter two terms indicate a consistently good yield response
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across environments. However, besides wide and specific adaptation concept of genotypes, yield stability and its related traits are also a matter of concern. In these regards, stability of our introgression lines was assessed through types of analysis i.e AMMI stability value, dynamic stability and static stability.
Being analogous to the biological concept of homeostasis; a stable genotype tends to maintain a constant yield across environments; the static stability is more useful than dynamic stability which rather implies for a stable genotype a yield response in each environment that is always parallel to the mean response of the tested genotypes, i.e. zero GE interaction (Annicchiarico, 2002). The measure of dynamic stability depends on the specific set of tested genotypes, unlike the measure of static stability (Lin et al., 1986).
On the other hand, the AMMI stability value (ASV) proposed by Purchase et al. (2000) was used to quantify and rank genotypes according to the yield stability. Though there are other statistical methods widely used to measure stability, the ASV statistic is the most suitable for AMMI analysis (Farshadfar, 2008). The present study revealed large stability values whatever estimation method was used. This is an indication of large differences among generated introgression lines. These differences may have been due to large genetic diversity observed (chapter 4) within the parental materials that were used to generate these introgression lines.
Based on the three stability analysis G7 (Gakire x Yunyine), G4 (Buryohe x Yunertian) and G6 (Intsinzi X Yunertian) can be recommended having high yielding potential for a variety of environments. Yield stability was high for G6 (Intsinzi X Yunertian), G4 (Buryohe Yunertian) and G7 (Gakire x Yunyine). The results of ASV and SSC were almost the same with minor differences on the order of ranks. The dynamic stability comes with another quite different ranking with G3 (Buryohe x Yunyine), G2 (Rumbuka x Yunertian) and G4 (Buryohe x Yunertian) as best genotypes. This is obvious as the measure of dynamic stability depends on the specific set of tested genotypes, unlike the measure of static stability (Lin et al., 1986) whereas ASV parameter also follows the static stability concept. The results of yield stability more or less corroborate with those of lesion size where ASV ranked the first three genotypes as G5 (Instinzi x Yunyine), G6 (Intsinzi X Yunertian) and G2 while SSC ranked them as G1 (Rumbuka x
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Yunyine), G5 (Intsizni x Yunyine) and G6 (Intsinzi x Yunertian). SUP ranked them differently.
This is an indication of an already close relationship between sheath rot of rice and yield potential.
However, from a farmer’s point of view, yield consistency in space also deserves consideration in the presence of sizeable genotype – location (GxL) interaction, since a selected or recommended genotype should be stable-yielding both across years and across locations in its area of adaptation or recommendation (Piepho, 1998). This is particularly so when there is a prospect of wide adaptation or recommendation, because in the context of a specific adaptation or recommendation the GL effects are minimized by the division of the target region into sub regions.
In these regards, a number of genotypes deserve much attention in further backcrossing generation for maximum recovery of the recurrent parents genomes. These include genotypes G6 (Intsinzi X Yunertian), G4 (Buryohe x Yunertian), and G2 (Rumbuka x Yunertian) which showed not only a good wide adaption potential but also yield stability across environments.
However, genotypes with good wide adaptation and good yield stability across environment are not necessarily the high yielding ones. G2 (Rumbuka x Yunertian) and G3 (Buryohe x Yunyine) were the high yielding genotypes across the environments but revealed specific adaptation and low yield stability across environments. This is normal because according to (Annicchiarico, 2002) high yield stability usually refers to a genotype’s ability to perform consistently, whether at high or low yield levels, across a wide range of environments. These results are in close corroboration with a number of other rice breeders. Bose et al. 2014 obtained genotypes with high yielding potential, wide adaptation and good yield stability on one hand and low yielding and good stable genotypes on the other hand. Nearly same results were obtained by Krishnamurthy et al. (2016).
As farmers always seek high yield, further selection programme should focus on high yielding genotypes disregarding stability across environments and specific adaptation in targeted environments would be exploited.
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