Analysis Of Multi-environment Trials Research Articles

Cross-validation to select the optimum rank for a reduced-rank approximation to multivariate data

ABSTRACT In this paper we consider the Gabriel form of cross-validation (CV) and we investigate how to estimate the optimum rank for lower rank approximations of any dataset that can be written in matrix form, with particular application in multivariate analysis and in the analysis of multienvironment trials. The literature related to the method suggests that it can produce overfitting and poor-quality predictions, characteristics that result in overestimation of the rank. Because of this, it is proposed to change the rank selection criterion, testing thirteen statistics both in the original method and in four proposed extensions that seek to solve the above problems. A comparison is made with two gold standard methods for CV through a simulation study and through the analysis of seventeen real datasets, two of which are general multivariate and fifteen are from experiments with genotype-by-environment interaction. It is concluded that from a predictive point of view, the highest accuracy in estimating the rank is obtained by using a regularized singular value decomposition.

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp.

Application of factor analytic and spatial mixed models for the analysis of multi-environment trials in common bean (Phaseolus vulgaris L.) in Ethiopia.

Common bean (Phaseolus vulgaris L.) is one of the most important grain legumes consumed globally, especially in Ethiopia, for its edible seeds, cash crops, and supply of protein for farmers. Efficient statistical methods must be employed for the evaluation of common bean varieties to accurately select superior varieties that contribute to agricultural productivity. The objective of this study was to identify promising large mottled bean varieties through analysis of multi-environment trials (MET) data using multiplicative spatial mixed models. In this study, 16-18 large mottled common bean varieties, including one check, were sown across nine growing environments in Ethiopia using lattice and alpha lattice designs, with three replications laid out in a square or rectangular (row by column) array of plots, respectively during the main cropping season from 2015 to 2018. We present a linear mixed model analysis that integrates spatial and factor analytic (FA) models, and the heritability measure was used to evaluate the efficiency of these models with the conventional analysis. The analysis of the spatial model, and more significantly, the spatial+FA model, revealed a notable enhancement in heritability. With the exception of a trial conducted at Kobo, a genotype DAP 292, found to be good performing for days to flowering and maturity, but for yield only across four clusters of trials, C2, C3, C5 and C7, formed with trials of relatively high genetic variance. Across these clusters, the yield advantage of this variety over the check ranged from 10-32%. This genotype also has a yield that is somewhat comparable to the check across the remaining clusters. Overall, both the spatial and factor analytic models proved to be effective approaches for analyzing the data in this study. The analysis of multi-environment trial data through the use of more efficient statistical models can provide a more robust platform for evaluating common bean varieties with greater confidence in selecting superior varieties across a range of environments. Hence, scaling up the use of this efficient analysis method is indispensable for enhancing the selection of superior varieties.

Genetic Diversity and Association of Yield-Related Traits in Taro (Colocasia esculenta (L.) Schott) Sourced from Different Agroecological Origins of Nigeria

Taro’s production is characterized by low productivity due to a lack of sufficient improved cultivars suited to the different growing areas coupled with biotic and abiotic stresses. The first step in plant breeding program is to examine and quantify variations for traits of interest in a given set of genotypes so those variations can be exploited in breeding programmes. One hundred taro accessions were evaluated for 16 quantitative traits in a simple lattice design with the objective of estimating variability and determine and quantify association of characters with yield. Multienvironment trial analysis combined over seasons showed highly significant differences among taro accessions for several traits. Yield per plant showed a strong, positive, and highly significant genotypic correlation with plant height (0.99), leaf area (0.82), corm weight (0.99), and cormel weight (0.99). Genotypic path coefficient analysis showed plant height (1.49), leaf area (0.97), corm weight (0.76), and cormel weight (0.21) exerted a high positive direct effect on yield per plant indicating true relationship between the characters. Accessions EBNFC054, EBNFC045, EBNFC100, EBNFC046, EBNFC032, EBNFC075, EBNFC084, EBNFC057, and EBNFC037 were superior and could be promoted to field evaluation. This assessment of variability and associations can assist breeders to tap the potential of the genotypes for certain traits and identify major traits that could be used as a basis for the selection of superior taro genotypes.

Fully efficient, two-stage analysis of multi-environment trials with directional dominance and multi-trait genomic selection

Key messageR/StageWise enables fully efficient, two-stage analysis of multi-environment, multi-trait datasets for genomic selection, including support for dominance heterosis and polyploidy.Plant breeders interested in genomic selection often face challenges to fully utilizing multi-trait, multi-environment datasets. R package StageWise was developed to go beyond the capabilities of most specialized software for genomic prediction, without requiring the programming skills needed for more general-purpose software for mixed models. As the name suggests, one of the core features is a fully efficient, two-stage analysis for multiple environments, in which the full variance–covariance matrix of the Stage 1 genotype means is used in Stage 2. Another feature is directional dominance, including for polyploids, to account for inbreeding depression in outbred crops. StageWise enables selection with multi-trait indices, including restricted indices with one or more traits constrained to have zero response. For a potato dataset with 943 genotypes evaluated over 6 years, including the Stage 1 errors in Stage 2 reduced the Akaike Information Criterion (AIC) by 29, 67, and 104 for maturity, yield, and fry color, respectively. The proportion of variation explained by heterosis was largest for yield but still only 0.03, likely because of limited variation for the genomic inbreeding coefficient. Due to the large additive genetic correlation (0.57) between yield and maturity, naïve selection on an index combining yield and fry color led to an undesirable response for later maturity. The restricted index coefficients to maximize genetic merit without delaying maturity were identified. The software and three vignettes are available at https://github.com/jendelman/StageWise.

Handling outliers in multi-environment trial data analysis: in the direction of robust SREG model

ABSTRACT Site regression model (SREG) is utilized by plant breeders for the analysis of multi-environment trials (MET) to examine the relationships among test environments and genotypes (G) and genotype-by-environment interaction (GE). In its regular form, singular-value decomposition (SVD) is applied on residual matrix from one-way analysis of variance (ANOVA) to partition G plus GE effects. However, ANOVA and SVD are sensitive to atypical observations, which are common in MET. To overcome this problem, three robust models are proposed to obtain valid results even in the presence of outliers. Their efficacy was evaluated by simulation and compared with standard SREG. Different scenarios were considered to identify the appropriate strategies to deal with outliers in real situations. Two real datasets are also presented to highlight the usefulness of the proposed methods in agricultural data. Our results indicate that the use of the proposed alternatives enables to effectively analyze MET data in the presence of outliers and maintain good performance without them as well.

Unravelling genetic variability for moisture stress tolerance in Indian mustard and identification of high breeding value donor lines

Aim: Identification of high breeding value donor lines harbouring tolerance to moisture stress from diversity stock of 443 genotypes of Brassica juncea. Methodology: Germplasm stock of 443 Indian mustard (Brassica juncea L.) genotypes, comprising introgression lines, land races, old cultivars and advance breeding lines, were evaluated under two environments viz., irrigated and rainfed conditions to check the variability for yield and related traits. Statistical software META-Rver 6.0 (Multi Environment Trial Analysis using R) was used for computation of Best Linear Unbiased Predictions, Analysis of Variance (ANOVA), Genetic Correlations and Heritability. Drought susceptibility index and per cent seed yield reduction under rainfed conditions were used as indicators to determine moisture stress tolerance in genotypes. Results: The variability for moisture stress tolerance has been unravelled in a diverse genetic stock of Brassica juncea under two environments: irrigated and rainfed. For high prediction accuracy, the ranking of genotypes was done based on Best Linear Unbiased Predictions for seed yield and its component traits. PBR-378, RGN-329, RB-73, RB-50 and PBR-422 and Giriraj were identified as moisture stress tolerant genotypes. One land race Sahib 36 and three introgression lines viz., MCP 12-211, PTJ-3-69 and MSC-3 have also been identified as potential genetic resources for moisture stress tolerance. Principal component analysis based on biplots depicted specific distribution of variables for each environment. Interpretation: This study led to the identification of potential donors for moisture stress tolerance with high predictive accuracy. Low drought susceptibility index and high breeding value in a land race and three introgression lines derived from Erucastrum cardaminoides, B. tournefortii and B. carinata emphasized their utilization as potential genetic resources to breed for moisture stress tolerance in B. juncea.

A Multi-Environment Trial Analysis of Frost Susceptibility in Wheat and Barley Under Australian Frost-Prone Field Conditions.

Low temperatures during the flowering period of cereals can lead to floret sterility, yield reduction, and economic losses in Australian crops. In order to breed for improved frost susceptibility, selection methods are urgently required to identify novel sources of frost tolerant germplasm. However, the presence of genotype by environment interactions (i.e. variety responses to a change in environment) is a major constraint to select the most appropriate varieties in any given target environment. An advanced method of analysis for multi-environment trials that includes factor analytic selection tools to summarize overall performance and stability to a specific trait across the environments could deliver useful information to guide growers and plant breeding programs in providing the most appropriate decision making-strategy. In this study, the updated selection tools approached in this multi-environment trials (MET) analysis have allowed variety comparisons with similar frost susceptibility but which have a different response to changes in the environment or vice versa. This MET analysis included a wide range of sowing dates grown at multiple locations from 2010 to 2019, respectively. These results, as far as we are aware, show for the first-time genotypic differences to frost damage through a MET analysis by phenotyping a vast number of accurate empirical measurements that reached in excess of 557,000 spikes. This has resulted in a substantial number of experimental units (10,317 and 5,563 in wheat and barley, respectively) across a wide range of sowing times grown at multiple locations from 2010 to 2019. Varieties with low frost overall performance (OP) and low frost stability (root mean square deviation -RMSD) were less frost susceptible, with performance more consistent across all environments, while varieties with low OP and high RMSD were adapted to specific environmental conditions.

Identification of drought tolerant Chickpea genotypes through multi trait stability index.

Drought is a major and constantly increasing abiotic stress factor, thus limiting chickpea production. Like other crops, Kabuli Chickpea genotypes are screened for drought stress through Multi-environment trials (METs). Although, METs analysis is generally executed taking into account only one trait, which provides less significant reliability for the recommendation of genotypes as compared to multi trait-based analysis. Multi trait-based analysis could be used to recommend genotypes across diverse environments. Hence, current research was conducted for selection of superior genotypes through multi-trait stability index (MTSI) by using mixed and fixed effect models under six diverse environments. The genotypic stability was computed for all traits individually using the weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype vs environment interaction (GEI) effects produced by a linear mixed-effect model index. A superiority index, WAASBY was measured to reflect the MPS (Mean performance and stability). The selection differential for the WAASBY index was 11.2%, 18.49% and 23.30% for grain yield (GY), primary branches per plant (PBP) and Stomatal Conductance (STOMA) respectively. Positive selection differential (0.80% ≤ selection differential ≤ 13.00%) were examined for traits averaged desired to be increased and negative (-0.57% ≤ selection differential ≤ -0.23%) for those traits desired to be reduced. The MTSI may be valuable to the plant breeders for the selection of genotypes based on many characters as being strong and simple selection process. Analysis of MTSI for multiple environments revealed that, the genotypes G20, G86, G31, G28, G116, G12, G105, G45, G50, G10, G30, G117, G81, G48, G85, G17, G32, G4, and G37 were the most stable and high yielding out of 120 chickpea genotypes, probably due to high MPS of selected traits under various environments. It is concluded that identified traits can be utilized as genitors in hybridization programs for the development of drought tolerant Kabuli Chickpea breeding material.

Genome-wide association study of seed coat color in sesame (Sesamum indicum L.).

Sesame (Sesamum indicum L.) is an important and ancient oilseed crop. Sesame seed coat color is related to biochemical functions involved in protein and oil metabolism, and antioxidant content. Because of its complication, the genetic basis of sesame seed coat color remains poorly understood. To elucidate the factors affecting the genetic architecture of seed coat color, 366 sesame germplasm lines were evaluated for seed coat color in 12 environments. The genome-wide association studies (GWAS) for three seed coat color space values, best linear unbiased prediction (BLUP) values from a multi-environment trial analysis and principal component scores (PCs) of three seed coat color space values were conducted. GWAS for three seed coat color space values identified a total of 224 significant single nucleotide polymorphisms (SNPs, P < 2.34×10−7), with phenotypic variation explained (PVE) ranging from 1.01% to 22.10%, and 35 significant SNPs were detected in more than 6 environments. Based on BLUP values, 119 significant SNPs were identified, with PVE ranging from 8.83 to 31.98%. Comparing the results of the GWAS using phenotypic data from different environments and the BLUP values, all significant SNPs detected in more than 6 environments were also detected using the BLUP values. GWAS for PCs identified 197 significant SNPs, and 30 were detected in more than 6 environments. GWAS results for PCs were consistent with those for three color space values. Out of 224 significant SNPs, 22 were located in the confidence intervals of previous reported quantitative trait loci (QTLs). Finally, 92 candidate genes were identified in the vicinity of the 4 SNPs that were most significantly associated with sesame seed coat color. The results in this paper will provide new insights into the genetic basis of sesame seed coat color, and should be useful for molecular breeding in sesame.

Multiple-trait, random regression, and compound symmetry models for analyzing multi-environment trials in maize breeding.

An efficient and informative statistical method to analyze genotype-by-environment interaction (GxE) is needed in maize breeding programs. Thus, the objective of this study was to compare the effectiveness of multiple-trait models (MTM), random regression models (RRM), and compound symmetry models (CSM) in the analysis of multi-environment trials (MET) in maize breeding. For this, a data set with 84 maize hybrids evaluated across four environments for the trait grain yield (GY) was used. Variance components were estimated by restricted maximum likelihood (REML), and genetic values were predicted by best linear unbiased prediction (BLUP). The best fit MTM, RRM, and CSM were identified by the Akaike information criterion (AIC), and the significance of the genetic effects were tested using the likelihood ratio test (LRT). Genetic gains were predicted considering four selection intensities (5, 10, 15, and 20 hybrids). The selected MTM, RRM, and CSM models fit heterogeneous residuals. Moreover, for RRM the genetic effects were modeled by Legendre polynomials of order two. Genetic variability between maize hybrids were assessed for GY. In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM. Thus, considering the criterion of parsimony and the possibility of predicting genetic values of hybrids for untested environments, RRM is a preferential approach for analyzing MET in maize breeding.

BLUP and stability analysis of multi-environment trials of potato varieties in sub-tropical Indian conditions

Potato is an important crop in India with area spread across Himalayan hills in the North to hot tropical conditions in South, albeit major area in sub-tropical Indo-Gangetic plains. The first common requirement in all regions is that the variety should have high performance for tuber yield along with essential agronomic traits. The present study was carried out to identify an ideal variety with wide adaptability for tuber yield and dry matter. Six varieties were evaluated in 9, 11 and 10 locations in the years 2014–15, 2015–16 and 2016–17, respectively for TY, MY and DM. The data were analysed with ANOVA, mixed models, BLUPs and GGE biplot as well as univariate stability statistics. Combined analysis of variance showed significant genotype, environment and genotype × environment interactions. The relative magnitudes of G, E and G×E variances accounted for 6.76–8.91, 51.85–76.65 and 12.41–23.19 per cent for TY and 2.86–4.66, 65.87–72.85 and 13.74–20.04 per cent for DM. Although the genotypes contributed significantly, major part of the variation was explained by environments for all the three traits. Mean across locations and years, and BLUP values of varieties for all the three traits showed similar results with Kufri Khyati as the best variety for TY and MY, whereas Kufri Jyoti and Kufri Garima were best for DM. Based on GGE biplot and univariate stability statistics, Kufri Khyati was the ideal high yielding wide adaptable variety in all the three years and Kufri Jyoti was the ideal variety based on mean dry matter and stability. The environments were very diverse and their clustering suggested three groups, which can be used as three separate zones for varietal evaluation and regional deployment of varieties.

META-R: A software to analyze data from multi-environment plant breeding trials

META-R (multi-environment trial analysis in R) is a suite of R scripts linked by a graphical user interface (GUI) designed in Java language. The objective of META-R is to accurately analyze multi-environment plant breeding trials (METs) by fitting mixed and fixed linear models from experimental designs such as the randomized complete block design (RCBD) and the alpha-lattice/lattice designs. META-R simultaneously estimates the best linear and unbiased estimators (BLUEs) and the best linear and unbiased predictors (BLUPs). Additionally, it computes the variance-covariance parameters, as well as some statistical and genetic parameters such as the least significant difference (LSD) at 5% significance, the coefficient of variation in percentage (CV), the genetic variance, and the broad-sense heritability. These parameters are very important in the selection of top performing genotypes in plant breeding. META-R also computes the phenotypic and genetic correlations among environments and between traits, as well as their statistical significance. The genetic correlations between environments or traits can be visualized in a biplot graph or a tree diagram (dendrogram). Genetic correlations are very important for identifying environments with similar behavior or making indirect selection and identifying the most highly associated traits. META-R performs multi-environment analyses by using the residual maximum likelihood (REML) method; these analyses can be done by environment, across environments by grouping factors (stress conditions, nitrogen content, etc.) and across environments; the analyses across environments can be done with a pre-defined degree of heritability.

Optimization of Eucalyptus breeding through random regression models allowing for reaction norms in response to environmental gradients

Reaction norms fitted through random regression models are a powerful tool to identify and quantify the genotype × environment (G × E) interaction and they represent a promising alternative in forest tree breeding for analysis of multi-environment trials. Thus, the objective of this study was to compare random regression models with the compound symmetry model in Eucalyptus breeding for analysis of multi-environment trials. To this end, a data set with 215 Eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The random regression models provided a better fit for both traits. Results showed that there was genotypic variability among Eucalyptus clones and that the reaction norms over the environmental gradients identified the G × E interaction. The compound symmetry model and the random regression models are highly correlated in terms of genotype ranking for both traits. The main advantage of random regression models over the compound symmetry model is the ability to predict genotypic performance in environments where a genotype has not been evaluated. Thus, our results suggest that reaction norms fitted through random regression models can be successfully used in forest tree breeding for analysis of multi-environment trials.

Variance component estimations and mega-environments for sweetpotato breeding in West Africa.

The current study was aimed at identifying mega‐environments in Ghana and evaluating adaptability of superior sweetpotato [Ipomoea batatas (L.) Lam.] genotypes from a targeted breeding effort. Three sets of genotypes were evaluated in multi‐environment trials (MET). Twelve sweetpotato varieties were evaluated across nine environments representing the main agro‐ecological zones in Ghana. MET analysis was conducted using a stage‐wise approach with the genotype × environment (G × E) table of means used as a starting point to model the G × E interaction for sweetpotato yield. Emphasis was given to the genetic correlation matrix used in a second‐order factor analytic model that accommodates heterogeneity of genetic variances across environments. A genotype main effect and G × E interaction of storage root yield explained 82% of the variation in the first principal component, and visualized the genetic variances and discriminating power of each environment and the genetic correlation between the environments. Two mega‐environments, corresponding to northern and southern trial sites, were delineated. Six breeding lines selected from the south and eight breeding lines selected from the north were tested and compared to two common check clones at five locations in Ghana. A Finlay–Wilkinson stability analysis resulted in stable performances within the target mega‐environment from which the genotypes were selected, but predominantly without adaptation to the other region. Our results provide a strong rationale for running separate programs to allow for faster genetic progress in each of these two major West African mega‐environments by selecting for specific and broad adaptation.

Mean Performance and Stability in Multi‐Environment Trials II: Selection Based on Multiple Traits

Modeling the genotype × environment interaction (GEI) and quantifying genotypic stability are crucial steps for selecting/recommending genotypes in multi‐environment trials (METs). The efficiency in selection/recommendation could be greater if based on multiple traits, but identifying genotypes that combine high performance and stability across many traits has been a difficult task. In this study, we propose a multi‐trait stability index (MTSI) for simultaneous selection considering mean performance and stability (MPE) in the analysis of METs using both fixed and mixed‐effect models. Data from an MET where 14 traits were assessed in 22 genotypes of Avena sativa L. were used to illustrate the application of the method. The genotypic stability was quantified for each trait using the weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the GEI effects generated by an linear mixed‐effect model (WAASB) index (lower is better). A superiority index, WAASBY (higher is better) was calculated to consider the MPE. The selection differential for the WAASBY index ranged from 9.7 to 44.6%. Positive selection differential (1.75% ≤ selection differential ≤ 17.8%) were observed for trait means that wanted to increase and negative (–11.7%) for one variable that wanted to reduce. The negative selection differential observed for WAASB (–63% ≤ selection differential ≤ −12%) suggested that the selected genotypes were more stable. The MTSI should be useful for breeders and agronomists who desire a selection for MPE based on multiple traits because it provides a robust and easy‐to‐handle selection process, accounting for the correlation structure of the traits. The application of the MTSI in future studies is facilitated by a step‐by‐step guide and an R package containing useful functions.Core Ideas The genotypic stability was quantified in multi‐environment trials (MET) using mixed models. A superiority index that allows weighting between mean performance and stability was used. A multi‐trait stability index (MTSI) for identifying superior genotypes in MET was proposed. Using a real dataset from a MET, stable and high‐performance genotypes were identified. The MTSI should facilitate the genotype selection in a multi‐trait framework.

Comparison of Weighted and Unweighted Stage‐Wise Analysis for Genome‐Wide Association Studies and Genomic Selection

ABSTRACTBoth genome‐wide association studies (GWAS) and genomic selection (GS) are done using phenotypic and genomic data. The phenotypic data are usually based on multi‐environment trials (MET). For both GWAS and GS the analysis can be conducted using a single‐stage or a stage‐wise approach. Single‐stage analysis is most efficient but it can also be computationally demanding. The computational demand increases compared to purely phenotypic analysis when marker information is added for doing the GWAS or the GS. Application of stage‐wise analysis is a common alternative procedure to alleviate the computational burden in MET analysis, and it can also be used for GWAS and/or GS. If done properly, it can closely mimic single‐stage analysis. The aim of this study is to compare weighted stage‐wise analysis versus unweighted stage‐wise analysis for GWAS and GS using phenotypic and genotypic maize data. For weighting we use a fully efficient and a diagonal method. Our result show that weighting is to be preferred over unweighted analysis and that there is a modest advantage in using the fully efficient weighting method over other weighting methods for GS. For GWAS the diagonal weighting method performs better, however, its difference from the fully efficient weighting is very small.

Spatial analysis in multi-environment trials of malt barley in Ethiopia

Selection of superior genotypes and measuring heritability are some of the basic objectives of plant breeding. For this purpose, plant breeders grow crops across environments. Understanding the pattern of response across environments is an integral component of selection of superior and stable genotypes. The objective of this study was to improve selection strategies in barley breeding of Ethiopia through modeling spatial field trend. A set of multi-environment trials (MET) data from the national variety trial series conducted over four years, was taken from the Ethiopian Barley Breeding Programme, spanning stages from early generation to national variety trial testing for yield, was used in this study. The trials were analysed in a linear mixed model framework. Then, fitting a one-stage model for MET data, including a correlated spatial process for field trend within each trial, and combining a factor analytic (FA) model for genotype by environment interaction was conducted. The genetic correlations from this MET analysis were then used to cluster the environments based on their similarity. Performance of genotypes across these environmental clusters indicate broad (Bekoji-2005 and Bekoji-2004) and specific adaptation (Sgonder-2007 and Sgonder-2006) of genotype to certain types of environments. In addition, analysis of this historical MET data shed light on how breeding programme design can be improved to capture responses across the target population of environments, as it can inform the adequacy of the current number of barley grown areas in Ethiopia and the improvement in measuring heritability. Key words: Barley MET, heritability, linear mixed model

The provision of grower and breeder information on the frost susceptibility of wheat in Australia

AbstractThe frost susceptibility of Australian commercial cereal crops, in particular wheat and barley, has become an economically devastating issue for growers. The relative risk to frost damage of the currently available varieties is obtained through testing varieties in a series of field experiments at locations susceptible to frost events (FEs). The experimental design, measurement protocols and resultant data from these frost expression experiments (FEEs) are complex due to the unpredictability of the timing and severity of FEs, and the maturity of the plants at the time of the events. Design and protocol complexities include the use of multiple sowing dates and the recording of plant maturity. Data difficulties include a high degree of unbalance, and in the instance of multiple frosts in a FEE, there is a longitudinal aspect. A linear mixed model analysis was adopted to accommodate these characteristics of individual FEEs and the multi-environment trial analysis of 17 FEEs. Finally, an approach is demonstrated for dissemination of results that are of use to both growers and breeders.

Multi-Environment Trial Analysis of Food Barley in Ethiopia Using AMMI and GGE Biplot Methods

Food barley released varieties were tested in 2012 for performance across major environments in Ethiopia consisting of 12 varieties Diribe, Tilla, Abbay, Biftu, Defo, Dinsho, Mulu, Setegn, Misiratch, Basso, Mezezo and local checks over six locations Gergera, Estayish, Shambu, Arjo, Robe and Sinana. The objective was to determine genotype by environment interaction using AMMI and GGE biplot, compare the two models for identifying the adaptable and stable genotypes. Sinana was identified as the high yielding environment and MULU the high yielding variety with mean yields of 3466.31 and 3137.67 kg/ha, respectively. The mean yield at Estayish was lower (1535 kg/ha) than other environments whereas lower yield (2212.16 kg/ha) was also obtained from the variety DINSHO. The AMMI analysis of Variance indicated that 47% of the total sum of squares is attributed to the Environmental effect, 8% to the genotypic effect and 25% to the interaction. The first three principal components of the GEI explained 81% of the variation. Genotypes Basso, Biftu and Setegn were the most stable whereas Diribe was unstable. Variety Mulu was identified as the winner genotype by AMMI model whereas Diribe was identified as the winner by the GGE model. GGE model better explains the which-won-where scenario and hence preferred to AMMI model. The discriminating and representative view of the GGE biplot depicted that Sinana and Shambu are discriminating environments whereas Sinana, Estayish and Gergera are representative environments. Therefore, Sinana is the ideal environment for discriminating genotypes and representing other environments for selecting ideal genotypes.