Genotype-environment interactions and phenotypic stability analyses of linseed in Ethiopia

Seed yield of 10 linseed genotypes, tested in a randomized block design with four replications across 18 environments of Ethiopia was analysed using different stability models. The objectives were to assess genotype‐environment (G‐E) interactions, determine stable genotypes, and to compare the stability parameters. Year by location and location variability were the dominant source of interactions. The stability analyses identified ‘R12‐N10D’, ‘Chilalo’ and ‘P13611’ב10314D’ as more stable genotypes, while ‘R11‐N1266’, ‘R10‐N27G’ and ‘R12‐D24C’ were specifically adapted to some environments. The highly significant rank correlations found among the deviations from regression, additive main effects multiplicative interaction, stability values, coefficients of determination, and stability variances indicated their close similarity and effectiveness in detecting stable genotypes over a range of Ethiopian environments.     

Assessing the agronomic potential of linseed genotypes by multivariate analyses and association mapping of agronomic traits

High prices of fish oil make linseed attractive for aquaculture and animal feed. To ensure a constant supply of linseed, the development of stable cultivars is of strategic importance. In this study, 35 linseed genotypes were evaluated in five Chilean environments (E) from 2009 to 2012. The additive main effect and multiplicative interaction analysis (AMMI), genotype (G) plus genotype by environment (GE) interaction (GGE) biplot analysis and three stability parameters were tested with the aim of identifying adapted genotypes for the development of linseed cultivars. An association mapping (AM) analysis was also conducted for four agronomic traits and the stability of the associated markers was evaluated using the QQE (QTL main effect and QTL by environment interaction) approach. Combined analysis of variance for yield, seeds per boll (SPB), plant height (PH) and days to flowering (DTF) were significant for G, E and GE (P < 0.001). The combined stability analysis identified some Canadian, Argentinean and Chilean accessions to be the best adapted and highest yielding genotypes. Coancestry analysis indicated that crossing Canadian and Chilean genotypes could maximize transgressive segregation for yield. Significant associations for DTF, PH and SPB explained up to 59 % of the phenotypic variation for these traits. The QQE and AM analyses were consistent in identifying marker LGM27B as the most stable and significant across all environments with the largest effect in reducing DTF. The combined application of the stability, AM and QQE analyses could accelerate the development of marketable linseed cultivars adapted to Southern Chile.     

Nonparametric phenotypic stability analysis in advanced barley (<Emphasis Type="Italic">Hordeum vulgare</Emphasis> L.) genotypes

The development of genotypes with adaptation to a wide range of environments is one of the most important goals of plant breeding programs. In order to compare nonparametric stability measures and to identify promising high-yield and stable barley (Hordeum vulgare L.), 20 barley genotypes selected from the Iran/ICARDA joint project and grown in nine environments during 2009-11 in Iran. Four nonparametric statistical tests of significance for genotype × environment (GE) interaction and 10 nonparametric measures of stability were used to identify stable genotypes in nine environments. Results of nonparametric tests of G×E interaction (Kubinger, Hildebrand, and Kroon/ Laan) and a combined ANOVA across environments, indicated the presence of both crossover and non-crossover interactions. Also, only TOP and rank-sum values were positively associated with high yield. Thus, in the simultaneous selection for high yield and stability, only the rank-sum and TOP methods were useful in terms of the principal component analysis results, and correlation analysis of nonparametric stability statistics and yield. According to these stability parameters (TOP and rank-sum), three genotypes (G13, G12, and G17) were the most stable for grain yield. The results also revealed that based on nonparametric test results, stability could be classified into three groups, according to agronomic and biological concepts of stability.     

GGE Biplot Analysis of Genotype by Environment Interaction in Field Pea (<Emphasis Type="Italic">Pisum sativum</Emphasis> L.) Genotypes in Northwestern Ethiopia

Ten field pea genotypes were evaluated in randomized complete block design with four replications for three consecutive years (2010-2012) main cropping seasons at four locations in each year. The objectives were to determine magnitude of genotype by environment interaction and to identify stable field pea genotype with high grain yield to be released as a cultivar to producer for Northwestern Ethiopia. The GGE [genotype main effect (G) and genotype by environment interaction (GE)] biplot graphical tool was used to analyze yield data. The combined analysis of variance revealed a significant difference (P<0.01) among genotypes, environments and genotype-by-environment interaction for grain yield. The average environment coordinate biplot revealed that EH99005-7 (G2) was the most stable and the highest yielding genotype. Polygon view of GGE-biplot showed the presence of three mega-environments. The first section includes the test environments E1 (Adet 2010), E3 (Debretabor 2010), E5 (Adet 2011), E6 (Motta 2011), E7 (Debretabor 2011), E8 (Dabat 2011), E9 (Adet 2012) and E12 (Dabat 2012) which had the variety G1 (EH99009-1) as the winner; the second section contains the environments E4 (Dabat 2010), E10 (Motta 2012) and E11 (Debretabor 2012) with G2 as the best grain yielder and the third section contains the E2 (Motta 2010) with G4 (Tegegnech X EH90026-1-3-1) as the best grain yielder. The comparison GGE- biplot of field pea genotypes with the ideal genotype showed that G2 was the closest genotype for the ideal cultivar. Among the twelve environments, E2, E6 and E10 were more discriminating and E3, E9 and E12 were less discriminating. Genotype EH99005-7 was the most stable and the highest yielding genotype. As a result it is released officially for Northwestern Ethiopia. Therefore, it is recommended to use genotype EH99005-7 for wider cultivation in Northwestern Ethiopia and similar areas.     

Non-parametric measures of phenotypic stability in chickpea genotypes (<Emphasis Type="Italic">Cicer arietinum</Emphasis> L.)

Multi-environment trials (MET) play an important role in selecting the best cultivars and/or agronomic practices to be used in future years at different locations by assessing a cultivar's stability across environments before its commercial release. Objective of this study is to identify chickpea (Cicer arietinum L.) genotypes that have high yield and stable performance across different locations. The genotypes were developed by various breeders at different research institutes/stations of Iran and the International Center for Agricultural Research in Dray Areas (ICARDA), Syria. Several statistical methods were used to evaluate phenotypic stability of these test chickpea genotypes. The 17 genotypes were tested at six different research stations for two years in Iran. Three non-parametric statistical test of significance for genotype × environment (GE) interaction and ten nonparametric measures of stability analyses were used to identify stable genotypes across the 16 environments. The non-parametric measures (Kubinger, Hildebrand and Kroon/Van der) for G × E interaction were highly significant (P < 0.01), suggesting differential responses of the genotypes to the test environments. Based on high values of nonparametric superiority measure (Fox et al. 1990) and low values of Kang's (1988) rank-sum stability parameters, Flip 94-123C was identified as the most stable genotype. These non parametric parameters were observed to be associated with high mean yield. However, the other nonparametric methods were not positively correlated with mean yield and were therefore not used in classifying the genotypes. Simple correlation coefficients using Spearman’s rank correlation, calculated using ranks was used to measure the relationship between the stability parameters. To understand the nature of relationships among the nonparametric methods, a hierarchical cluster analysis based on non weighted values of genotypes, was performed. The 10 stability parameters fell into three groups.     

Regression analysis of genotype x environment interaction in sweet potato

Summary Two experiments, each involving a set of 10 sweet potato clones, were conducted for three years at 4 sites (Ekona, Ebolowa, Nkolbisson, and Bambui Plain) in Cameroon. Data obtained were subjected to analysis of variance to determine the presence of genotype x environment (G x E) interactions, and to regression analysis to assess the performance of clones across anvironments. Environments were assessed in two ways: (i) the mean response of all clones (dependent assessment), and (ii) the average performance of a different set of clones (independent assessment).The first experiment (Expt 1) produced higher yields but had fewer stable clones than the second (Expt 2).The analysis of variance revealed that the clones interacted significantly with environments for all traits.The study has identified high yielding and stable sweet potato clones for distribution to growers in the major areas of cultivation in the tountry. Despite slight differences in numbers of clones judged stable by the various regression indices in the two methods of environmental assessments, the rankings of clones on the basis of their linear regression coefficients were similar. In a developing country like Cameroon, with limited resources and where sophisticated equipment for obtaining physical or biological measures of the environment may be lacking, the mean performance of genotypes may still be the most reliable measure of environment in evaluating the stability of performance of crop cultivars.     

Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments

Twenty parametric and non-parametric measures derived from grain yield of 15 advanced durum genotypes evaluated across 12 variable environments during the 2004–2006 growing seasons were used to assess performance stability and adaptability of the genotypes and to study interrelationship among these measures. The combined ANOVA and the non-parametric tests of Genotype × environment interaction indicated the presence of significant crossover and non-crossover interactions, and of significant differences among genotypes. Principal component analysis based on the rank correlation matrix indicated that most non-parametric measures were significantly inter-correlated with parametric measures and therefore can be used as alternatives. The results also revealed that stability measures can be classified into three groups based on static and dynamic concepts of stability. The group related to the dynamic concept and strongly correlated with mean grain yield of stability included the parameters of TOP (proportion of environments in which a genotype ranked in the top third), superiority index (P _i) and geometric adaptability index. The second group reflecting the concept of static stability included, Wricke’s ecovalence, the variance in regression deviation (S ² _di), AMMI stability value, the Huehn’s parameters [S _i⁽¹⁾, S _i⁽²⁾], Tennarasua’s parameter [NP_i⁽¹⁾], Kang’s parameter (RS) and yield reliability index (I _i) which were not correlated with mean grain yield. The third group influenced simultaneously by grain yield and stability included the measures S _i⁽³⁾, S _i⁽⁶⁾, NP_i⁽²⁾, NP_i⁽³⁾, environmental variance (S ² _xi), coefficient of variability and coefficient of regression (b _i). Based on the concept of dynamic stability, genotypes G6, G4, and G3 were found to be the most adapted to favorable environments, whereas genotypes G8, G9, and G12 were more stable and are related to the concept of static stability.     

Interpreting genotype × environment interactions for durum wheat grain yields using nonparametric methods

The objective of this study was to compare nonparametric stability procedures and apply different nonparametric tests for genotype × environment (G × E) interactions on grain yields of 15 durum wheat genotypes selected from Iran/ICARDA joint project grown in 12 environments during 2004–2006 in Iran. Results of nonparametric tests of G × E interaction and a combined ANOVA across environments indicated the presence of both crossover and noncrossover interactions, and genotypes varied significantly for grain yield. In this study, high values of TOP (proportion of environments in which a genotype ranked in the top third) and low values of sum of ranks of mean grain yield and Shukla’s stability variance (rank-sum) were associated with high mean yield. The other nonparametric stability methods were not positively correlated with mean yield but they characterized a static concept of stability. The results of correlation analysis indicated that only TOP and rank-sum methods would be useful for simultaneous selection for high yield and stability. These two methods identified lines Mrb3/Mna-1, Syrian-4 and Mna-1/Rfm-7 as genotypes with dynamic stability and wide adaptation. According to static stability parameters, the genotypes 12A-Mar8081 and 19A-Mar8081 with lowest grain yield were selected as genotypes with the highest stability.     

Genotype × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran

Evaluation of genotype × environment interaction (GEI) is an important component of the variety selection process in multi-environment trials. The objectives of this study were first to analyze GEI on seed yield of 18 spine safflower genotypes grown for three consecutive seasons (2008–2011) at three locations, representative of rainfed winter safflower growing areas of Iran, by the additive main effects and multiplicative interaction (AMMI) model, and second to compare AMMI-derived stability statistics with several stability different methods, and two stability analysis approaches the yield-stability (Ysi) and the GGE (genotype + genotype × environment) biplot that are widely used to identify high-yielding and stable genotypes. The results of the AMMI analysis showed that main effects due to genotype, environment, and GEI as well as first six interaction principle component axes (IPCA1 to 6) were significant (P < 0.01). According to most stability statistics of AMMI analyses, genotypes G5 and G14 were the most stable genotypes across environments. According to the adjusted stability variance (s²), the high-yielding genotype, G2, was unstable due to the heterogeneity caused by environmental index. Based on the definition of stable genotypes by regression method (b = 1, S _d ²  = 0), genotypes G11, G9, G14, G3, G12 and G13 had average stability for seed yield. Stability parameters of Tai indicated that genotype G5 had specific adaptability to unfavorable environments. The GGE biplot and the Ysi statistic gave similar results in identifying genotype G2 (PI-209295) as the best one to release for rainfed conditions of Iran. The factor analysis was used for grouping all stability parameters. The first factor separated static and dynamic concepts of stability, in which the Ysi and GGED (i.e., the distance from the markers of individual genotypes to the ideal genotype) parameters had a dynamic concept of stability, and the other remaining parameters had static concept of stability.     

Performance Evaluation of Malt Barley: from Malting Quality and Breeding Perspective

Lack of suitable malt barley varieties that exhibit high yielding, stable performance, and good malting quality is the major factor among several production constraints contributing to low productivity of malt barley in the North Gondar Zone. The present study was done to evaluate and recommend the best performing varieties in the major potential areas of North Gondar. The experiment was conducted at three locations for two consecutive years (2015 and 2016) during the main cropping season using twelve improved varieties. The design was randomized complete block design with three replications. Analysis of variance and GGE [genotype main effect (G) and genotype-by-environment interaction (GE)] biplot analysis were conducted following their respective procedures. Combined analysis of variance revealed a highly significant difference (P < 0.01) among genotypes, environments, and genotype-by-environment interaction for grain yield, most agronomic and malt quality traits. All the varieties had acceptable malt quality traits. The variety IBON-174/03 was found to be the highest yielding and the most stable variety across environments. According to the polygon view of biplot analysis, the varieties were spread across four sections and the test environments spread across two sections. Among the six test environments, D and C were more discriminating and F and B were less discriminating. Test environments F, E, and A were found to be more representative of the mega-environment than D. Considering early maturity, malt quality, grain yield, and stability performance; it is recommended to use the variety IBON-174/03 for production in the study areas and in similar areas.     

Analysis of genotype-by-environment interaction for grain yield of rainfed durum wheat genotypes in warm winter areas of Iran

This study was performed for pattern analysis of genotype-by-environment (GE) interaction on 20 durum wheat genotypes grown in 15 testing environments during 2004–06 in Iran. Combined analysis of variance showed significant genotypes (G), environments (E), and GE interactions (P < 0.01), with environmental main effects being the predominant source of variation, followed by GE interaction. The results showed various patterns of genotype responses to different environment groups and assisted in structuring the durum wheat testing locations with identification of two major-environment groups with high genotype discrimination ability. The locations (Gachsaran and Ilam) corresponding to warm and semi-arid aresa were similar in genotype discrimination and showed no association with the other testing locations (Gonbad, Moghan, and Khoramabad) representing the Mediterranean area, indicating they differ in rankings of genotypes. The top-yielding genotypes, G13, G14 and G9, were highly adapted to warm and semi-arid environments, but those corresponding to the Mediterranean area had a high ability to discriminate the genotypes G16, G11, and Saimareh. The stability and adaptability of specific genotypes were assessed by plotting their nominal grain yields at specific environments in an ordination biplot, which aided in the identification of environment groups. Appropriate check genotypes for all environments or for specific environments were also identified. Pattern analysis allowed a sensible and useful summarization of GE interaction data set and helped to facilitate selecting superior genotypes for target-growing sites.     

Adaptation and environmental sensitivity of mungbean genotypes evaluated in the international mungbean nursery

Summary Yield data from the 5th–12th international mungbean nursery (IMN) trials conducted at 23 sites in 15 countries were analyzed by conventional stability analysis—regression of genotype mean on the environmental index, and by segmented regression analysis—fitting separate linear regressions in low yielding and high yielding environments. The gene pool base concept allows comparison of genotypes from different IMN trials grown in different years and sites. A very high positive linear relationship was observed between the regression coefficient and the average yield of cultivars, indicating that high yielding cultivars were less stable across environments. When data points of the regression of genotype mean and site mean for VC 1973A, a high yielding and widely adapted cultivar, were examined, the relationship appeared not to be linear. The segmented regression analysis improved the coefficient of determination (r²) and the genotypes were grouped based on regression coefficients in high yielding and low yielding environments. Different categories of genotypes suitable for high input environments, widely adaptable genotypes, and highly stable genotypes were identified.Texas Agricultural Experiment Station Technical Article 23208.     

Effects of Early and Late Harvest on Agronomic Performance and Stability of Late Blight Resistant (R-gene Free) Potato Genotypes

Late blight is an important constraint to potato production and genotype resistance is an effective disease control mesure. Ten late blight resistant potato genotypes (R-gene free) were assessed for yield performance and stability at early (90 days) and late harvest (120 days) at two locations in Kenya during two years. Significant differences (P ≤ 0.05) in area under disease progress curves (AUDPC) were detected among potato genotypes. Resistant genotypes free of R-genes had significantly (P ≤ 0.05) higher yield at late than early harvest, perhaps due to increased tuber bulking period. The rank of genotypes for AUDPC, late blight resistance, and tuber yield varied across seasons and locations (environment). Additive main effects and multiplicative interaction (AMMI) analysis of tuber yield and late blight resistance resulted in significant (P ≤ 0.05) effects of genotypes (G) and environments (E). The proportion of genotypic variance was larger than the environmental variance and the G × E interactions. For tuber yield, the G, E, and G × E interactions accounted for 42.9, 39.6 and 17.5%; and 53.4, 29.7, and 16.9% at early and late harvests, respectively. For AUDPC, G, E, and G × E accounted for 80.2, 5.0, and 14.8%; as well as 82.3, 4.6, and 13% for early and late harvests, respectively. The resistance of potato genotypes without R-genes varied. Selective deployment of resistant genotypes can improve potato tuber yield.     

马铃薯产量组分的基因型与环境互作及稳定性

本研究主要探究基因型和基因型与环境互作(genotype+genotypes and environment interactions, GGE)双标图在马铃薯育种中的应用。综合评价马铃薯品系产量性状在不同环境中的丰产性、稳定性和适应性,筛选出适应不同生态环境的产量性状优良品系。同时评价各试点的区分力和代表性,为试点的选择提供依据。2015年和2016年在甘肃安定区鲁家沟镇、安定区内官镇、渭源县五竹镇3个试点种植国际马铃薯中心引进的101份高代品系和对照青薯9号。收获后记录小区产量、小区大薯产量、小区小薯产量、单株产量、单株大薯产量、单株小薯产量、单株结薯数、单株大薯数、单株小薯数;采用联合方差和GGE双标图对产量性状进行基因型与环境互作分析。方差分析表明,除小区小薯产量在基因型与环境互作效应中无显著差异外,其他产量组分在基因型效应、环境效应和互作效应中均呈现极显著差异(P<0.01)。小区产量、小区大薯产量、小区小薯产量、单株产量、单株大薯产量、单株结薯数环境效应平方和占总方差平方和最大;单株小薯产量、单株大薯数和单株小薯数的基因型与环境互作效应平方和占总方差平方和最大。GGE...     

Nonparametric measures of phenotypic stability. Part 1: Theory

Summary For an estimation of phenotypic stability of genotypes grown in different environments three stability parameters have been proposed which are based upon the ranks of the genotypes in each environment: In a two-way table with K rows (genotypes) and N columns (environments) the original data x_ij (=phenotypic value of the i th genotype in the j th environment (i=1,2,...,K;j=1,2,...,N)) are transformed into ranks for each of the N environments separately. We denote: r_ij=rank of genotype i in environment j. Then, a genotype i may be considered to be stable over environments if its ranks are similar over environments (maximum stability = equal ranks over environments). Each statistic for the similarity of the ranks in each row = genotype may be used as a stability parameter. Three different measures are proposed and discussed.One of these nonparametric measures is defined as a ratio between variability of the r_ij's and mean of the r_ij's and, therefore, it represents a confounding and simultaneous consideration of stability and yield.Differences among genotypes have an effect on the stability measures and may lead to differences in stability among genotypes when in fact there is no genotype-environment interaction. To avoid this ambiguity one may correct the x_ij values for the genotypic effects and the nonparametric measures may be computed using the ranks based on the corrected values x_ij ^*=x_ij–(\-x_i.–\-x..)where \-x_i.=marginal mean of genotype i and \-x_\2=overall mean.Finally, approximate tests of significance based on the normal distribution are discussed for the two nonparametric measures mean absolute rank difference and variance of the ranks for 1) testing the stability of a certain genotype and 2) comparing the stabilities of different genotypes.     

Genotype × environment interactions for tea yields

Several methods were used to evaluate phenotypic stability in 20 tea (Camellia sinensis) genotypes, many of which are cultivated widely in East Africa. The genotypes were evaluated for annual yields at two sites over a six year period. Data obtained were used to compare methods of analysis of G × E interactions and yield stability in tea. A standard multi-factor analysis of variance test revealed that all first order interactions (genotypes × sites; genotypes × years; sites × years) as well as second order interactions (sites × genotype × years) were significant. Regression analysis was used to assess genotype response to environments. Regression coefficients (b_i) obtained ranged from 0.78 to 1.25. Deviations from regression (S²d) were significant (p < 0.05) from 0.0 for all the test genotypes. Analysis for sensitivity to environment change (SE² _i) revealed that the test genotypes differed in their level of sensitivity. The hierarchical cluster analysis method was used to assemble the test genotypes into groups with similar regression coefficients (b_i) and mean yield, which proved useful for the identification of high yielding genotypes for breeding purposes as well as for commercial exploitation. Rank correlation between yield and some stability parameters were significant. Mean yield was significantly correlated to b_i (r = 0.80^***) and SE² _i(0.74^***) which is an indication that selection for increased yield in tea would change yield stability by increasing b_i and SE² _i leading to development of genotypes that are specifically adapted to environments with optimal growing conditions. Genotypes differed in response to years and sites. As stand age increased, genotype yields generally increased though annual yield fluctuations were more pronounced in some genotypes than others. This response was not consistent across the sites for all genotypes indicating the need to test clones at multiple sites over longer periods of time. This revised version was published online in August 2006 with corrections to the Cover Date.     

Genotype by environment interaction of tomato blossom-end scar size

Summary Large blossom-end scar is a disorder in tomato fruit which reduces its marketability. The disorder is affected by genotype and by several environmental factors and therefore the genotype by environment interaction was studied by stability analysis. Blossom-end scar size was recorded for 4 tomato cultivars grown in 6 fields. The blossom-end scar size (BSC) was affected by the genotype, the field and their interaction. Stability analysis revealed that most of the interaction resulted from different stability of the cultivars. Heterogeneity of the slopes was significant (P<0.0013). The stability slopes were 0.29, 0.74, 1.11 and 1.85 for BR-214, FA-38, Hayslip and Suncoast, respectively. The stability slopes seemed to associate with the means of the cultivars over all environments, which were 1.57, 2.92, 3.84 and 5.43, respectively. Analysis of a blossom-end scar index (BSI), which also takes fruit size into account, revealed stability similar to BSC. It was concluded, that selection for small BSC under most conditions would yield cultivars with small and stable BSC under most growing environments, however differences between genotypes in non-inducing environments are expected to be small.     

Grain yield response and stability for oat lines with low-, medium-, and high-yielding ability

Summary Relationships that exist among grain yielding ability and response and stability of grain yields when tested over variable environments were examined. Two sets of oats lines were tested over many environments that had wide ranges in productivities. The lines in each set were divided into high-, medium-, and low-yielding groups on the basis of means across all environments, and variance components for genotype × environment interactions and means of regression responses and coefficients of determination were computed for the three yield categories in each set.Mean grain yields for the high-, medium-, and low-yielding groups across both sets of oats lines were 2.7, 2.3, and 1.9 Mg ha^-1, respectively. Coefficients of variability for the genotype × environment interaction were 18%, 16%, and 12% for the high-, medium-, and low-yielding categories, respectively. Means for regression responses were 1.22 for the high group, 0.99 for the medium, and 0.78 for the low. Most responses for the high and low groups were significantly different from 1.0. Means for coefficients of contingency were 0.63, 0.56, and 0.51 for the high-, medium-, and lowyielding groups, respectively.There was a positive relationship between mean grain yield and response of grain yield to improving environments. Thus, high yielding lines are also the responsive lines. Our study gave conflicting results about stability of production for the three yield groups. Coefficients of variation for genotype × environment interaction indicated that the high-yielding group was more interactive with environments than were the medium- and low-yielding ones: However, the means for coefficients of contingency indicated that the high yielding group was the most stable.Journal Paper No. J-12128 of the Iowa Agric. and Home Econ. Exp. Stn., Ames, IA 50011. Project 2447.     

AMMI analysis of rainfed lowland rice (Oryza sativa) trials in Nigeria

The additive main effects and multiplicative interaction (AMMI) model is used to analyse the grain yield data of 13 rice genotypes grown in 12 rainfed lowland rice environments. The trials were organized by the International Network for Genetic Evaluation of Rice in Africa (INGER-Africa) and conducted in Nigeria. Main effects due to environments (E), genotypes (G) and G × E interaction were found to be significant (P = 0.001). Cross validation analysis suggested that an AMMI model with one interaction principal component axis (IPCA) was most useful predictively, whereas Gollobs’ test declared two components, IPCA1 and IPCA2, statistically significant (P = 0.01). The IPCAl, however, accounted for most (47.8%) of the G × E sum of squares. Correlation and regression analysis, and relative scatter of genotype and environment points on the AMMI biplot suggest that the interaction partitioned in IPCA1 resulted from differences in the days to flowering among the genotypes. The paper discusses these in relation to the occurrence of Fe toxicity at the test sites and varietal tolerance to the stress.