Additive Main Effects And Multiplicative Interaction Analysis Of Variance Research Articles

Deciphering the stability indices using additive main effect and multiplicative interaction model for high yielding registered variety of Lemongrass (Jor Lab L-8)

Cymbopogon flexuosus, commonly known as Lemongrass (Poaceae) is an industrially significant plant due to its fragrant compounds and ability to produce a large amount of essential oil. However, variations in chemical components, agronomical characters and essential oil output can be observed due to grown in different climatic conditions. Pertaining to the economic importance of the crop, the present study was designed to evaluate the stability indices of the developed and registered variety of lemongrass ‘Jor Lab L-8’. In this experiment, cv. Jor Lab L-8 was assessed along with two check varieties at eight different locations of NE India for two years (2021-2022, 2022-2023). The analysis of variance (ANOVA) by regression method revealed that Jor Lab L-8 had a significantly higher herbage yield, essential oil yield, citral content and total essential oil yield than the other two check varieties. Furthermore, AMMI ANOVA was used to confirm the consistent performance of the recorded agro-morphological and quality characters and Jor Lab L-8 was shown to be stable in plant height, herbage yield, essential oil yield, citral content and total essential oil yield. The GC-MS study of the essential oil identified 18 constituents, with citral emerging as the predominant constituent. Additionally, majority of the locations were pinpointed as ideal environments for cultivating Jor Lab L-8, underscoring its resilience across multiple locations and highlighting its long-term industrial promise.

Performance Evaluation of Potato Genotypes for Yield, Yield Related Traits in Bale High Lands South Eastern Ethiopia

Potato is an important food security crop in the world spacially for Africa, specifically Ethiopia which ranked 9^th in potato production in Africa. Bale Zone South Eastern Ethiopia is known for potato production both in irrigation and rainfed. Howevere, the productivity of the crop is low due to many factors among which lack of improved potato varieties which were high yeilder and tolerance to potato disease is major. This study was conducted to evaluate potato genotypes for high-yielding, stability and resistance to late blight. The experiment was laid out in a randomized complete block design with three replications at three locations (Sinana, Goba and Dinsho districts) for three years. The highest total tuber yield was recorded from genotypes CIP-392640-524 (45.75t/ha^-1) followed by CIP-395114.5 (40.01t/ha^-1) while the lowest total tuber yield was obtained from local checks (25.27t/ha^-1). Result from stability papameters indicated the stability of tested genotypes over the tested environment. The yield performance of genotypes (CIP-392640-524) across environments and yield advantage of genotype over standard checks (Moti) 11.45% with less disease (late blight reaction) and the result from AMMI analysis of variance and stability parameter also indicated the stability of genotype over tested environments. This suggested that the genotypes are promoted to a variety of verification trials for Bale highlands and similar agro-ecologies.

Comparison of Ammi, Parametric and Non-Parametric Models in Identifying High-Yielding and Stable Oilseed Rape Genotypes

One of the complex issue in the way of releasing new high-yielding and stable oilseed rape ‎cultivars is genotype by environment interaction (GEI) which reduce selection efficiency. In ‎the current study, parametric and non-parametric statistics as well as the AMMI model have ‎been compared to identify the best stability models to clarify GEI complexity. The ‎experiment has been conducted in the warm regions of Iran including; Gorgan, Sari, Zabol, ‎and Hajiabad during two cropping seasons (2016-2017 and 2017-2018) for 16 genotypes in a ‎randomized complete block design with three replications. The AMMI analysis of variance on ‎grain yield showed the significant effects of genotype, environment, and the interaction ‎effects of GEI on yield. Based on the AMMI ANOVA, the major contribution of GEI was ‎captured by the first and second interaction principal component axes (IPCA1 and IPCA2) ‎which explained 34.29% and 29.81% of GEI sum of the square, respectively. Additionally, ‎Different parametric and non-parametric stability methods including; bi, S2di, CVi, W2i, σ2i, Pi, ‎Si(1), Si(2), Si(3), Si(6), Npi(1), Npi(2), Npi(3), Npi(4), KR and TOP have also investigated. Based on ‎AMMI, parametric, and non-parametric stability statistics, genotypes G2 (SRL-95-7) and G9 ‎‎(SRL-95-16)‎‏ ‏were selected as the stable and high-yielding genotypes. Likewise, Principal ‎component analysis based on rank correlation matrix enabled us to distinguish high-yielding ‎genotypes to stable (high-yielding genotypes in various environments) and unstable (high-‎yielding genotypes in low-yielding environments) ones. Furthermore, a significant Spearman ‎correlation was observed between yield mean and GSI, Pi, Si(3), Si(6), Npi(3), Npi(4), and KR. ‎Therefore, different efficient strategies were identified in this study‏ ‏and since we looked up ‎high-yielding and stable genotypes, G2 (SRL-95-7) and G9 (SRL-95-16)‎‏ ‏were finally ‎selected.‎

Phenotypic Variability of Wheat and Environmental Share in Soil Salinity Stress [3S] Conditions

Through choosing bread wheat genotypes that can be cultivated in less productive areas, one can increase the economic worth of those lands, and increase the area under cultivation for this strategic crop. As a result, more food sources will be available for the growing global population. The phenotypic variation of ear mass and grain mass per ear, as well as the genotype × environment interaction, were studied in 11 wheat (Triticum aestivum L.) cultivars and 1 triticale (Triticosecale W.) cultivar grown under soil salinity stress (3S) during three vegetation seasons. The results of the experiment set on the control variant (solonetz) were compared to the results obtained from soil reclaimed by phosphogypsum in the amount of 25 t × ha−1 and 50 t × ha−1. Using the AMMI analysis of variance, there was found to be a statistically significant influence of additive and non-additive sources of variation on the phenotypic variation of the analyzed traits. Although the local landrace Banatka and the old variety Bankut 1205 did not have high enough genetic capacity to exhibit high values of ear mass, they were well-adapted to 3S. The highest average values of grain mass per ear and the lowest average values of the coefficient of variation were obtained in all test variants under microclimatic condition B. On soil reclaimed by 25 t × ha−1 and 50 t × ha−1 of phosphogypsum, in microclimate C, the genotypes showed the highest stability. The most stable genotypes were Rapsodija and Renesansa. Under 3S, genotype Simonida produced one of the most stable reactions for grain mass per ear.

Evaluation of Maize Grain Yield and Yield Stability Under Drought Stress by Ammi Analysis

Genotype-by-environment (G×E) interaction reduces the correlation between genotypic and phenotypic parameters and complicates progress of selection. Among several models proposed for evaluation of the G×E interaction, the Additive main effects and multiplicative interaction (AMMI) and GGE-biplot are the most informative models. The objective of this study was to estimate the G×E interaction in 55 maize genotypes to identify maize genotypes of stability and/or adaptability across two irrigation treatments at two locations using the AMMI and GGE-biplot models. A randomized complete block design was used in each environment with three replications. The AMMI analysis of variance indicated that the genotype (G), environment (E) and G×E interaction for Feddan Yielg data showed highly significant due to treatments, genotypes and environments this pointed out that all sources of variance are important in analysis, however genotypes contributed with (48.36%) in treatments variances, the environment contributed with (44.0%) in treatments variance also interaction principal component axis (IPCA) accounted for (53.52% and 39.44%) respectively, were found to be highly significant, the (IPCA1 and IPCA2) together with had a total (92.96%) variances of the interaction. GGE biplot analysis environments showed that the genotype (10, 20, 32 and 36) was found promising in normal irrigation treatment at Assuit location in descending order. The genotypes (22, 27 and 44) was promising in normal irrigation treatment at Minia location in descending order. The genotypes (14, 17, 25, 45 and 49) are suitable to stress irrigation treatment at Minia location. The genotypes (6 and 50) are suitable to drought irrigation treatment at Assuit location. The polygon reflects that G11, G33, G34, G53, and G56 are poor grain yielding and not suitable to either of the environments and relationships between genotypes and environments showed that the environments with a small angle between them are highly positively correlated, and they provide similar information on genotypes. Present investigations showed that N1 and N2 for grain yield. The two environments S1 and S2 are similar; they had small angle between them and they provide similar information on genotypes. In contrast, either N1 or S1 and N2 or S2 were dissimilar, since the angle was obtuse, and they provide different information on genotypes.

Comprehensive evaluation of sugar beet genotypes for yield and relative traits by multivariate analysis

Among few methods suggested for evaluation of the genotype – environment interactions (GE interaction), the AMMI method (Additive main Effect and Multiplicative Interaction) is the most instructive model. The objective of this experiment was to estimate the G×E interaction in sugar beet varieties of stability and adaptability through different environments using the AMMI model. As well as cluster analysis was automated to identify the interrelationships among the tested varieties, also Principal component analysis (PCA) was used to define the relationship between the studied traits. Twenty genotypes of sugar beet were evaluated at two locations (El- Dakahlia and El- Fayoum stations) in Egypt in two seasons (2018/19 and 2019/20). A randomized complete block design with three replications was used in each environment. Results showed that the AMMI analysis of variance was highly significant differences for the environment, variety and their interactions. Based on AMMI model, Glorius, Florima, Capel, Belatos, Classic and Toro genotypes in EN3 (Dakahlia 2019/2020), have the highest sugar yield, respectively. Results of cluster analysis demonstrated an extensive genetic diversity among the tested varieties, also the 1st sub cluster, (cluster I) was recorded high values of root weight, length, diameter and total weight. cluster II (Farida, TORO, Capel, Florima, Betamax, Glorius ) recorded the highest root and sugar yield, which led to increase of 106.66% and 112.91% higher than that compared with cluster V (Lilly, Heba, Mirador). According to PCA results, PCA1 accounted for 69.5% of the total phenotypic variation expressed and PCA2 accounted for 27.03% of the variation.

Genotype × Environment Interaction and Selection of High Yielding Wheat Genotypes for Different Wheat-growing Areas of Ethiopia

Evaluation of wheat genotypes under different environments is essential for testing stability of their performance and range of adaptations. Where, enhanced grain yield has been achieved in bread wheat (Tritium aestivum L.) through the development of adaptable, high-yielding, and rust-resistant genotypes by evaluating advanced genotypes for yield in multi-environment trials. The adaptability of a genotype over diverse environments is usually tested by the degree of its interaction with different environments under which it is grown. This study examines fifteen bread wheat genotypes for two consecutive years (2016 and 2017) across eight locations in Ethiopia. The experiment was laid out using a Randomized Complete Block design and replicated three times intending to determine high-yielding advanced genotype and release best performing genotypes as a variety for the end-user. Highly significant (P ≤ 0.001) effects of genotype, environment, and genotype by environment interaction were observed for grain yield. The AMMI analysis of variance indicated that environments accounted for 52.34% of the total sum of squares for grain yield (genotype yields ranged from 5.76 t/ha at E-11 to 1.31 t/ha at E-7). followed by interaction (22.95%) and genotypes (11.31%).The genotype ETBW8260 (G4) exhibited high mean grain yield and well performed to the tested environments. The ETBW8260 (G4) was selected as early maturing, high yielding, resistant to yellow rust and fitting for low to midland wheat growing areas and has a yield advantage over the standard Ogolcho and the local Kakaba. Both locations and genotypes are dispersed widely in all quadrants in the AMMI-1 biplot. As per the YSI Ogolcho (#15) ETBW8303 (G2), ETBW8454 (G12), ETBW8261 (G5), ETBW8406 (G10), ETBW8310 (G3), and ETBW8260 (G4), the genotypes of bread wheat were stable. Besides, the genotype ETBW8260 (G4 has highest mean yield with good stability. Therefore, after a one-year variety verification trial, ETBW8260 is released as a commercial variety in 2019 and a designated local name called “Balcha” and recommended for low-medium part of wheat production agroecology of the country.

Genotype environment interaction and stability for yield and its components in advanced breeding lines of red rice (Oryza sativa L.)

Effects of genotype, environment and their interaction for grain yield and yield attributing characters in 20 advanced breeding lines of rice across six environments was investigated. Yield stability and adaptability of yield performance were analyzed by Eberhart and Russel model and (GGE) bi-plot. The AMMI analysis of variance indicated that mean squares due to genotypes, location and genotype location contributed per cent 59.08, 5.79 and 21.63, respectively for total variability in grain yield per hectare. Estimates of GGE bi-plot revealed that the lines G1, G3, G11, G13, G15, G12, G16, G7 and G10 were positioned near GGL bi-plot origin indicating wider adaptation for the trait grain yield per hectare. Eberhart and Russel Model and GGE biplot model showed the advanced breeding lines viz., JB 1-11-7 (G1) and JA 6-2 (G15) exhibited wider adaptability across the tested environments for number of productive tillers per plant and yield per hectare.

Grain yield, stability and bacterial brown spot disease of dark red kidney dry bean (Phaseolus vulgaris L.) genotypes across six environments in South Africa

Dry bean (Phaseolus vulgaris L.) is grown under an extensive range of agro-climatic conditions and is an essential source of protein and income globally. This study aimed to evaluate yield performance, stability, and bacterial brown spot (BBS) disease resistance of fourteen dark red kidney genotypes across environments in South Africa namely Carolina, Clarens, Cedara, Middelburg, Potchefstroom, and Warden. Analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) and the genotype plus genotype by environment interaction (GGE-biplot) analysis were used to evaluate grain yield performance, stability, and BBS disease resistance. The AMMI ANOVA revealed that mean squares for grain yield and BBS severity for the environment, genotype, and genotype by environment interaction were highly significant (P<0.001). Four interaction principal components (IPCA1 - 4) for grain yield and IPCA1 for BBS severity were highly significant (P<0.001, P<0.01). Genotype G12 showed broad adaptation for both high grain yield and low BBS severity across the six environments, while genotypes G08, G06, G03, G02, G05, and G04 had specific adaption for high grain yield and low BBS severity. These genotypes recorded grain yield above the grand mean and the best check cultivar, both with 1.43 t ha-1 , and BBS severity below the grand mean (31.90%) and the best check (48.89%). The genotypes identified with either broad or specific adaptation can be released in the environments they are adapted to, or used as parents in breeding programmes aiming to improve grain yield and BBS disease resistance of dry bean for farmers in South Africa.

Adaptation of Grass Pea (Lathyrus sativus) to Mediterranean Environments

Grass pea (Lathyrus sativus) is an annual legume crop widely cultivated in South Asia and Sub-Saharan Africa, but in regression in Mediterranean region. Its rusticity and nutritious value is calling back attention for its reintroduction into Mediterranean rain-fed farming systems. We studied the adaptation of a range of breeding lines in multi-environment field testing in Spain and Egypt, showing wide variation for grain yield. Broomrape (Orobanche crenata) infection appeared as the major limiting factor in both countries. Level of broomrape infection was highly influenced by environmental conditions, being favored by moderate temperatures at crop flowering and rain and humidity after flowering. The additive main effects and multiplicative interaction (AMMI) analysis was applied to understand the interaction between genotype (G) and environment (E) on grain yield and on broomrape infection. AMMI analyses revealed significant G and E effects as well as G*E interaction with respect to both traits. The AMMI analysis of variance (ANOVA) revealed that both, yield and broomrape infection were dominated by the environment main effect. AMMI1 biplot for grain yield revealed Ls10 and Ls11 as the accession with highest yields, closely followed by Ls16, Ls18 and Ls19. However, these accessions showed also lower stability, being particularly adapted to Delta Nile conditions. On the contrary, accessions Ls12 and Ls14 were more adapted to rain fed Spanish conditions. Accessions Ls7, Ls1 and Ls3 were the most stable over environments for grain yield.

Performance evaluation and yield stability of upland rice (Oryza sativa L.) varieties in Ethiopia

Multi-environment evaluation of genotypes is important to identify the response of genotypes across environments and helps to know which genotypes would be appropriate in which production areas. Thirteen upland rice genotypes were evaluated in a randomized complete block design at Tepi, Assosa, Bako, and Bonga research centers in 2017, 2018, and 2019 cropping season with the objective of examining the agronomic performance and evaluating the yield stability of the varieties. The combined analysis of variance for yield and other agronomic characteristics revealed that main effects, genotypes, locations, years, and the three-way (G X L X Y) interaction effects revealed a significant difference for the measured traits. Moreover, AMMI analysis of variance also showed a highly significant difference for genotypes, environment, and genotype x environment (GE) interaction for grain yield. Environment effect (61.64%) was responsible for the greatest part of the variation, followed by genotype by environment interaction (20.86%) and genotype (6.97%) effects. The AMMI analysis explained 69.17% of the GxE variance, while GGE captured 72.76% of the GGE variance. Based on the GGE, AMMI analysis genotype variety Hidassie (G4), 4238.7 Kg ha−1 followed by NERICA-4 (G3), 4183.5 Kg ha−1 were the most stable and high yielding genotypes and recommended for production for the testing sites and similar upland rice-producing areas of Ethiopia.

Genotype by Environment Interaction for Grain Yield of Barley Mutant Lines

Abstract The aim of this study was to examine the genotype by environment interaction for grain yield and to identify high-yielding and stable mutant lines of 6-rowed winter barley under different growing seasons. The study was carried out during 7 growing seasons from 2010 – 2011 to 2016 – 2017 in the experimental field of the Institute of Agriculture – Karnobat, Southeastern Bulgaria. Fourteen advanced mutant lines and the check variety Vesletc were studied using a complete block design with 4 replications. The AMMI analysis of variance indicated that 20.54% of the variation for grain yield was explained by the effect of genotype and 37.34% and 42.12% were attributable to the environmental effects and genotype by environment interaction. The magnitude of the genotype by environment interaction was two times larger than that of genotypes, indicating that there was a substantial difference in genotype response across environments. The AMMI and GGE biplot analyses identified G9 as the highest yielding and stable genotype. This mutant line can be recommended for further evaluation for variety release. The mutant lines G6, G13 and G15 were suggested for inclusion in the breeding program of winter barley due to its high grain yield and intermediate stability.

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.

Screening for blast resistance in rice using AMMI models to understand G x E interaction in Guyana

In rice, totally one hundred and three genotypes were evaluated for resistance against blast disease in three hot spot locations of Guyana. The experiments were conducted in spring and autumn seasons of 2015, 2016 and 2017. The modified upland blast nursery (UBN) method was adopted for screening blast resistance. The Additive Main effects and Multiplicative Interaction (AMMI) analysis was applied to understand the interaction between genotype (G) and environment (E). The results of all seasons at three locations showed the consistent expression of high resistance by genotype FL 127 followed by FG12–08 and FG12–273. The AMMI analysis of variance (ANOVA) revealed that, resistance was dominated by the genotype main effect. The magnitude of genotypes by environment (G x E) sum of square (SS) was 3.64 times smaller than genotype SS, and 6.74 times larger than environment SS. The analysis has indicated that the differences of the genotypes across the environment were not substantial. All of the seven environment vectors appeared to be positively correlated. The screening at Onverwagt Back, autumn 2016 (E7) and Black Bush Polder, spring 2015 (E5) were observed to be the most representative environment. This was clearly revealed by smaller average - environment axis (AEA) angle. Likewise, E4 (Canje, autumn 2015) was observed closer to the “ideal test environment”, thus suggested as an ideal environment for blast resistant screening. Also, genotypes clustered near the origin have showed stable blast disease resistant status and contributed little to both G and G x E. These genotypes proposed as useful ones for breeding disease resistant varieties and hybrids in rice.

Stability Parameters and AMMI Analysis of Quinoa ( Chenopodium quinoa Willd.)

SCREENING for stable genotype entails estimating the genotype (G)×environment (E) interaction (GEI) in multi-environmental trials (MET). Quinoa is a nutritionally rich crop as a source of vitamins, minerals and essential amino acids. It has been introduced to many countries in diverse regions worldwide. We evaluated five genotypes of quinoa under ten environments including irrigated and rain-fed conditions across Egypt. We used several stability parameters as well as additive main effects and multiplicative interaction (AMMI) analysis to determine the best genotype for each environment/location across Egypt. Based on AMMI analysis of variance, the sum of squares (SS) of E, G, and GEI explained ≈ 78%, 14%, 8%, respectively, of the treatment sum of squares. The SS of interaction principal components analysis axis1 (IPCA1) and IPCA2 explained 75 and 18%, respectively. KVL-SRA3 was the most stable genotype according to ecovalence value (Wi), to deviation from regression coefficient value (S2di) of Eberhart and Russell and to IPCA1, IPCA2 and AMMI stability value (ASV). Regalona was the most unstable genotype based on the same parameters. These results were visualized using AMMI biplot analysis, which revealed that KVL-SRA3 was widely adapted to all environments unlike Regalona that was poorly adapted to most environments. The Spearman’s rank correlation among different stability parameters was significantly variable for both the five-quinoa genotypes and the ten investigated environments. Our results indicated that most stability parameters were consistent with AMMI parameters in identifying stable genotypes with some exceptions according to the concept of each of stability parameter (agronomic or biological). This study is an important step to open doors for the adoption of an extraordinary nutritional crop in Egypt.

THE USE OF AMMI MODEL FOR INTERPRETING GENOTYPE × ENVIRONMENT INTERACTION IN DURUM WHEAT

SUMMARYDurum wheat (Triticum durum) is one of the most important cereal crops in the Mediterranean region; however, its cultivation suffers from low yield due to environmental constrains. The main objectives of this study were to (i) assess genotype × environment (GE) interaction for grain yield in rainfed durum wheat and to (ii) analyse the relationships of GE interaction with genotypic/meteorological variables by the additive main effects and multiplicative interaction (AMMI) model. Grain yield and some related traits were evaluated in 25 durum wheat genotypes (landrace, breeding line, old and new varieties) in 12 rainfed environments differing in winter air temperature. The AMMI analysis of variance indicated that the environment had highest contribution (84.3% of total variation) to the variation in grain yield. The first interaction principal component axis (IPCA1) explained 77.5% of GE interaction sum of squares (SS), and its effect was 5.5 times greater than the genotype effect, indicating that the IPCA1 contributed remarkably to the total GE interaction. Large GE interaction for grain yield was detected, indicating specific adaptation of genotypes. While the postdictive success method indicated AMMI-4 as the best model, the predictive success one suggested AMMI-1. The AMMI biplot analysis confirmed a rank change interaction among the locations, indicating the presence of strong and unpredictable rank-change location-by-year interactions for locations. In contrast to landraces and old varieties, the breeding lines with high yield performance had high phenotypic plasticity under varying environmental conditions. Results indicated that the GE interaction was associated with the interaction of heading date, plant height, rainfall, air temperature and freezing days.

Genotype Stability Index for Root Yield and Tolerance to Sweetpotato Weevil Cylas puncticolis: A Tool for Identifying Climate Smart Varieties

Abstract Despite the ability of sweetpotato to grow in marginal areas, large differential genotypic responses have been reported under varying environmental conditions. Differences in pest and disease pressure contribute significantly to inconsistencies in performance of genotypes in various environments. Using a randomized complete block design, eight sweetpotato genotypes were evaluated in one location successively for five years (seasons) (2010-2015). Additive main effects and multiplicative interaction (AMMI) stability value (ASV) was used to identify best genotypes that combine stability with high resistance to sweetpotato weevil Cylas puncticolis and root yield across the five seasons (years). Stability of genotypes for weevil infestation and damage thereof, and fresh storage root yield were determined for each season. The data on each of these parameters were correlated with rainfall and temperature data for each and across five seasons. Results show variability in the ranking of genotypes’ stability for resistance to weevil infestation and associated damage. Significant negative correlation was recorded between total rainfall and sweetpotato weevil damage. However, AMMI analysis of variance indicates genotype main effects, environmental main effects and the interaction thereof were all significant for root yield and weevil damage. Genotype selection index assisted to identify at least three genotypes namely Kokota, Lunga, and Kalungwishi which combined stability for high root yield and tolerance to weevil damage.

Analysis of new experimental barley genotype performance for grain yield using AMMI biplot

Presence of GE interaction reduces the correlation between genotypic and phenotypic parameters and complicates progress of selection. Among several methods proposed for evaluation of the GE interaction, the AMMI model is one of the most commonly used. The objective of this study was to estimate the GE interaction in new barley lines using the AMMI model, and to identify winter barley genotypes with stable and high yield. Multi-location trials with 25 winter barley advanced lines were conducted at four locations. The AMMI model was used to estimate GE interaction. The AMMI analysis of variance indicated that the genotype, environment and GE interaction had significant influence (p<0,01) on barley grain yield. Based on AMMI method, G8 and G18 could be recommended for further testing due their high and stable yields.