Multi-Environments Evaluation of Elite Mutant Lines of Improved Ginger for Yield Stability and Selection

Abstract

A multi-location evaluation of 15 improved elite ginger mutant lines was conducted in the 2023 and 2024 cropping seasons across 10 states in Nigeria. The states were Abia, Bauchi, Benue, Borno, Cross River, Kaduna, Nasarawa, Osun, Plateau and FCT. The objective of the work was to evaluate the newly developed  improved ginger lines across the major producing areas in Nigeria to ascertain their yield potential, yield stability,adaptability and suitability for selection. The experiment was laid out in a Randomized Complete block Design with 3 replications. Data were collected on  yield-  related traits including plant height, number of tillers per plant, number of rhizome fingers, rhizome length and rhizome yield.The combined analysis of variance showed that Genotype, Environment, Genotype by Environment Interaction(GEI) and Years were significant at P< 0.01 and P< 0.05 for all yield related traits but no significant difference in G xY interaction in rhizome yield. Multivariate analysis using AMMI and GGE biplots was performed to identify stable and high- yielding ginger mutant lines. The AMMI analysis of variance showed a high level of environmental effect, accounting for a larger percentage of 66.5% (plant height) , 52.43% (rhizome fingers), 46.26% ( numbers of tillers) ,40.95% (rhizome length) and 49.10% (rhizome yield) of the total variation across all yield components.  The interaction sum of squares was partitioned into IPCA 1 and IPCA 2 which joinly accounted  for  the total variations with IPCA 1 being significant in plant height (10.85%) ,Tillers (9.0%), rhizome fingers (8.05%) , rhizome length (5.53%) and rhizome yield(8.06%) .The GGE biplot analysis was used to visualize the relationship between tester environment and the ginger mutant lines to determine the Which-Won-Where portion which reveals the stability of genotypes. The GGE biplot explained 88.2% of the total variation in rhizome yield attributable to genotype and GE interactions. The E4 (Cross River) and E10 (Abia) were the most discriminatory environments for rhizome yield. The polygon view of the GGE biplot for rhizome yield identified the best genotypes for the environment and the result showed G13(UG2-5-49) as winner in the mega environment of E4(Cross River) and E10(Abia). Two environments E1 (Bauchi) ,E7 (FCT) favors UG2-5-04, UG2-5-52, UG2-9-01, UG1-13-02) while the environments E2 (Benue) ,E3 (Borno), E5(Kaduna), E8,(Osun) and E9 (Plateau) favors UG1-11-07, UG1-5-31 and UG2-5-48. From multiple environment testing, five best- performing genotypes with relative stability across all yield attributes were identified and selected. These genotypes are UG1-11-03, UG1-13-02, UG2-5-04, UG2-5-49 and UG2-9-01. These genotypes will be recommended for registration and release to farmers.