GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes

Abstract

The experiments were conducted for three consecutive years across 14 locations using nine non-waxy proso millet genotypes and 16 locations using seven waxy proso millet genotypes in China. The objectives of this study were to analyze yield stability and adaptability of proso millets and to evaluate the discrimination and representativeness of locations by Analysis of variance (ANOVA) and GGE biplot methods. Grain yields of proso millet genotypes were significantly influenced by environment (E), genotype (G), and their interaction (G×E) (P<0.1%). G×E interaction effect was six times higher than G effect in non-waxy group and seven times in waxy group. N04-339 in non-waxy and NM6 in waxy showed higher grain yields and stability compared with other genotypes. Also, NM9 a non-waxy showed higher adaptability in six locations and 90322-2-3 a waxy in eleven locations. For non-waxy, Dalat, Inner Mongolia (E2) and Wuzhai, Shanxi (E5) were the best sites among all the locations for maximizing the variance among candidate cultivars, and Yanchi, Ningxia (E10) had the best representativeness. Wuzhai, Shanxi (e9) and Yanchi, Ningxia (e14) were the best representative locations, and Baicheng, Jilin (e2) was best discriminating locations than others for waxy genotypes. Based on our results, E10 and e14 have enhanced efficiency and accuracy for non-waxy genotypes and waxy genotypes selection, respectively in national regional test of proso millet varieties.

Screening Oat Genotypes for Tolerance to Salinity and Alkalinity

“…A biplot can be used to visualize three types of patterns: (1) relationships among the testers (treatments, traits, or their combination), (2) similarities or differences among genotypes, and (3) the discriminating power of the treatment or testing location. Crop breeders have used GGE to evaluate the performance of cultivars, and the discrimination and representativeness of locations ( Zhang et al, 2016 ). For example, based on yield data from 13 durum wheat genotypes tested at four locations in northwestern Ethiopia, Abate et al (2015) identified that the test location of Debretabor was the most discriminating environment for maximizing the variance among candidate cultivars compared to the locations of Adet and Simada.…”

Evaluation of genotype x environment interaction using GGE-biplot on dry beans(Phaseolus vulgaris L.) in Limpopo province of South Africa

“…The environments within the same sector share the same winning genotype and environments in different sectors share different winning genotypes. The identification of best cultivar for each location using "Which-won-where" was previously reported in proso millet (Zhang et al, 2016) and cotton (Xu et al, 2014).…”

Multi-environment evaluation of foxtail millet advanced lines for forage yield stability

“…In order to deal with G×E interaction in breeding programs and separating yield potential and yield stability in multi-environment trials, numerous stability analysis methods including variation coefficient, linear regression, Wrick's equivalence (Wi), stability variance of Shukla ( 2 i  ), additive main effect and multiplicative interaction (AMMI) and genotype and genotype by environment interaction (GGE) biplot have been proposed (GAUCH and ZOBEL, 1988;ZHANG and KONG, 2002). In the recent decade, AMMI and GGE statistical analysis have been widely used in different studies (RAKSHIT et al, 2012;BOSE et al, 2014;HONGYU et al, 2014;TEMESGEN et al, 2015;ZHANG et al, 2016). Although AMMI analysis provides information on main and interaction effects, interaction effects have been only taken in to account in AMMI analysis and genotype effects have been ignored (ZHANG et al, 2016).…”

“…In the recent decade, AMMI and GGE statistical analysis have been widely used in different studies (RAKSHIT et al, 2012;BOSE et al, 2014;HONGYU et al, 2014;TEMESGEN et al, 2015;ZHANG et al, 2016). Although AMMI analysis provides information on main and interaction effects, interaction effects have been only taken in to account in AMMI analysis and genotype effects have been ignored (ZHANG et al, 2016). In addition, AMMI analysis requires a greater number of genotypes and several years for evaluation in comparison to GGE Biplot, moreover, it makes misleading in identifying whichwon-where (YAN et al, 2007;FASAHAT et al, 2015).…”