Sixteen field pea (Pisum sativum L.) genotypes were evaluated using Randomized Complete Block Design (RCBD) with four
replications for evaluating agronomic performance, reaction to diseases (downy and powdery mildew and ascochyta blight),
genotype x environment interaction (GEI) and yield stability across 12 environments during 2004-2006 at highlands of Bale, South
eastern Ethiopia. In this study various statistical methods of analyzing agronomic performance, disease reaction and yield stability
were used. The analysis of variance (ANOVA) for agronomic characters revealed that there was highly significant (P<0.01)
variation were obtained between days to flowering, days to maturity, pod per plant, seed per pod and plant height across locations
and years. Similarly, the linear association of disease severity with grain yield and the stability parameters were analyzed indicating
that the variation of disease severity over different environments on a particular genotype/variety is one of the major factors that
contribute to seed yield stability. Furthermore, the stability parameters; Shukla’s stability variance ( i
2 σ ), Wricke’s ecovalence
(Wi), Eberhart & Russell’s deviation from regression (S2
di), and Additive Main effect and Multiplicative Interaction stability value
(ASV) had a highly significant correspondence over the studied environments. Furthermore, genotype IFPI-1523 and IFPI-2711
were better agronomic performance, reaction for diseases and the most stable genotypes across locations and years so that these
genotypes were recommended for large scale production. Generally, whenever new varieties are proposed for release information on
agronomic performances, reaction for disease and yield stability should be availed for end users. On the other hand, it can be
suggested more conclusive 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.