Abstract
Sixteen field pea (Pisum sativum L.) genotypes were evaluated using Randomized Complete Block Design (RCBD) with four replications for evaluating genotype x environment interaction (GEI) and yield stability across 12 environments during 2004 to 2006 at south eastern Ethiopia. The objectives were to compare various statistical methods of analyzing yield stability and to determine the most suitable parametric procedure to evaluate and describe yield stability of field pea (Pisum sativum L.) genotypes performance under south eastern Ethiopian conditions. Several statistical analyses were conducted: coefficient of variation (CVi); mean (); Shukla’s stability variance (); Wricke’s ecovalence (Wi); regression coefficient (bi); deviation from regression (Sdi2); cultivar superiority measure (Pi); deviation from the linear response (λi) coefficient of determination (r2i) and additive main effect and multiplicative interaction. IFPI-1523 and IFPI -2711 were the most stable genotypes according to 8 out 10 stability statistics used in the study. Spearman’s rank correlation coefficient between the stability parameters indicated that Shukla’s stability variance (σi2), Wricke’s ecovalence (Wi), Eberhart and Russell’s deviation from regression (S2di), and additive main effect and multiplicative interaction stability value (ASV) had a highly significant correspondence over the three years of study. No significant rank correlation between Lin and Binns’s superiority measure (Pi) and Finlay and Wilkinson’s procedure (bi) with the other procedures were found. Thus, these two procedures are not recommended for use on their own as a measurement of yield stability. Conclusively, it is suggested that the use of appropriate biometrics techniques is necessary for identifying the most adapted, responsive and stable genotypes in the final phases of the plant breeding program, where the high cost and the time spent in assays are powerful justifications to search for improved methods. Key words: Field pea, Pisum sativum L., Genotype x Environment interactions, yield stability.
Highlights
In the past, plant breeding programs mostly focused on developing high yielding cultivars
The result of combined analysis of variance for grain yield of 16 field pea genotypes tested across 12 environments showed that 79.68% of the total sum of squares was attributed to environmental effects, whereas genotypic and genotype environment interactions (GEI) effects explained 4.53 and 5.70%, respectively
Compression of biometrical methods to describe stability analysis for grain yield of field pea genotypes revealed that genotypes IFPI-1523 and IFPI -2711 were stable in yield and such stable performance is a desirable attribute of cultivars, for countries such as Ethiopia, where environmental variations are very high and unpredictable
Summary
Plant breeding programs mostly focused on developing high yielding cultivars. Recent development comprises a multiplicative interaction model, which was first introduced in social science (Crossa, 1990), that was later adapted to the agricultural context as additive main effect and multiplicative interaction (AMMI) (Piepho, 1996) This model was considered appropriate if one is interested in predicting genotypic yields in specific environments (Annicchiarico, 1997). Field pea (Pisum sativum L.) is one of the major pulses grown in the highlands (1800-3000 m a.s.l.) of Ethiopia, where the need for chilling temperature is satisfied This crop is very much important in the south eastern Ethiopia; since it fetches cash for the farming community and serves as rotational crop which plays great role in controlling disease epidemics in areas where cereal monocropping is abundant. The present study were carried out (1) To compare various statistical methods of analyzing yield stability and to determine the most suitable procedure to evaluate and describe yield stability of field pea (P. sativum L.) genotypes performance under South eastern Ethiopian conditions (2) to measure the genotype-environment interaction in field pea genotypes, giving emphasis to grain yield and (3) To estimate rank correlations between stability statistics
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