EVALUATION OF GENETIC VARIABILITY AND STABILITYOF SELECTED PEARL MILLET GENOTYPES (PENNISETUM GLAUCUM (L.BR.) IN ARID AND SEMI-ARID LANDS OF NORTH RIFT, KEN

Abstract

Thirty-six genotypes of pearl millet (Pennisetum glaucum) differing in their performance from local varieties (OPV) were evaluated in two environments (Marigat and Koibatek, Kenya) to examine their yield and stability across the two diverse environment. The main objective is to improve pearl millet production in arid and semi-arid lands of Kenya. Improvements of hybrids have been made in the past and yet no yield ceilings have been reached however germplasm provide little genetic variability for yield and therefore there is need to improve pearl millet in arid and semi-arid lands of Kenya. The genotypes were laid out in randomized complete block design in two replication and data collected on yield and yield components. Data analyses was done using Genstat 12 edition. Data on grain yield was further subjected to GGE biplot analysis to determine stability and genotype by environment interaction of the genotypes. The genotypes were grouped into eleven clusters. The clustering pattern gave indication of classification of genotypes according to the yield and yield components. The test environments provided improved yields levels from low (600kg ha-1 ) at Koibatek to high (6200kg ha-1 ) at Marigat hence there was high significant variation at P ≤ 0.001 observed among the grain yield in both sites. Performance of the hybrids showed various pattern of stability to test environments. More genotypes performed better in Koibatek than Marigat even though Marigat showed good yield performance. Cluster nine showed good yield performance across the location because of its characteristic which might have influence the response to the test environments. Local variety Kat pm 2 appeared to be among the stable genotypes but among the poor performance than the hybrids. Results indicated that best performing genotypes were not best adapted in the test environments hence stable hybrids can be identified through evaluation over diverse environments.