2,091 research outputs found

    New methods to assess cotton varietal stability and indentify discriminating environments

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    Studies were conducted in 2001-2004 evaluating genotype by environment interactions in cotton (Gossypium hirsutum L.). Genotype by Environment interactions were characterized using GGE Biplot for conventional cotton cultivars and their transgenic derivatives. Significant interactions existed for several non-target traits. Transgenic cultivars were taller, had greater height to node ratios, larger seed, and lower lint percentages. Transgenic cultivars containing the Bollgard gene yielded more than their conventional parents and STV4691B was the highest yielding, most stable cultivar. In 2002-2004, GGE Biplot was used to identify two levels (high/low) of discriminating locations for three distinct selection criteria. Crosses were made with parents recommended by a least squares means analysis for each population criteria and F2 plants were planted in the high and low discriminating locations for each population. Gains by selection (h2) were calculated by regressing the F2:3 plants on their F2 parents. Genotypic variance was greater among F2:3 progeny in discriminating environments compared to non-discriminating environments, regardless of population. Heritability was greater in the population containing fiber traits compared to yield. In 2004, GGE Biplot was compared to other widely-accepted stability analysis tools. Correlation coefficients between GGE biplot (stability evaluation) and the Cultivar Superiority Measure, Shukla\u27s Stability Variance, the Eberhart-Russell regression model, Kang\u27s yield stability statistic, and AMMI were 0.54, 0.91, 0.86, 0.63, and 0.55, respectively. Correlation coefficients between GGE biplot (mean performance + stability evaluation) and the Cultivar Superiority Measure, the Eberhart-Russell regression model, Kang\u27s yield stability statistic, and AMMI were 0.95, 0.60, 0.85, and -0.33, respectively. Based on the results of this study and our experience using GGE Biplot, Model 3 with an entry-focused scaling is the most valuable analysis for breeders engaged in cultivar development. GGE Biplot was used with the 1993-2003 Louisiana Official Variety Trials to identify the most desirable (discriminating and representative) test locations in Louisiana for yield and fiber length. St. Joseph loam was ranked 1st for yield, Winnsboro irrigated was ranked 1st for fiber length, and St. Joseph loam was ranked 1st to simultaneously select for both traits. Winnsboro non-irrigated should not be used to select for yield or fiber length

    Genetic variability, stability and heritability for quality and yield characteristics in provitamin A cassava varieties

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    Open Access Article; Published online: 25 Jan 2020Cassava is widely consumed in many areas of Africa, including Ghana, and is a major part of most household diets. These areas are characterized by rampant malnutrition, because the tuberous roots are low in nutritional value. Provitamin A biofortified cassava varieties have been developed by the International Institute for Tropical Agriculture, but adoption of these varieties in Ghana will largely depend on their agronomic performance, including fresh root yield, dry matter content, resistance to major pests and diseases, mealiness, starch content and the stability of these traits. Eight provitamin A varieties with two white checks were planted in three environments for two seasons to determine stability and variability among the varieties for important traits. There were significant variations in performance between varieties and between environments for cassava mosaic disease, root number, fresh root yield and starch content. High broad-sense heritability and genetic advance were observed in all traits, except for storage root number, and could be exploited through improvement programs. This study identified the best performing enhanced provitamin A varieties for traits that are key drivers of variety adoption in Ghana. In view of this, some varieties can be recommended for varietal release after on-farm testing. The study also showed the possibility of tapping heterosis after careful selection of parents

    Cell mean versus best linear unbiased predictors in Biplot analysis of genotype Ă— environment interaction in Barley

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    In multi-environment trials, accurate estimation of yields in individual environments and astute choice of models to extract and display gronomically relevant signals enhance genotype evaluation and accelerate breeding progress. The objective of this study is to (i) compare patterns of genotype Ă— environment interaction (GE) using additive main effect and multiplicative interaction (AMMI) biplots arising from cell means versus best linear unbiased predictors (BLUPs), and (ii) examine some features of the genotype main effect plus GE interaction (GGE) in relation to AMMI in comprehending the GE patterns. A data set generated from 39 barley genotypes grown in 18 environments (three sowing dates and two crop protection treatments over three years) in the central highlands of Ethiopia was used. AMMI analysis of variance based on cell means depicted the first five principal components (PCs) to be significant. However, only the first two PCs were significant when BLUPs were used. Partitioning of the original GE sum of squares into signal and noise confirmed that only the first two AMMI PCs contained signals required to explain the real GE pattern. AMMI PC1 contained 76.5% and AMMI PC2 15.9% of the total GE variance. AMMI biplot based on BLUPs depicted patterns that were more in tandem with agronomic interpretations than biplot based on cell mean data. PC1 of GGE contained 66.9%, PC2 11.2% and PC3 14.5% of the total GE variance. AMMI2 explained as much GE variance as PC1, PC2 and PC3 of GGE put together. AMMI2 biplot depicted a GE pattern that was not obvious from GGE2. AMMI2 biplot was more similar to GGE PC1 versus PC3 biplot than GGE2 biplot. AMMI2 was more efficient than GGE2 for displaying patterns of GE interaction in this data set. However, GGE2 was quite elegant and simple for presenting G and GE combined in a biplot graph including the which-wonwhere pattern. BLUPs might improve yield estimation and pattern recognition, and that attempting both AMMI and GGE analysis might provide important insights on genotype performance and GE

    Analysis of Genotype by Environment Interaction on Cocoa Hybrids (Theobroma Cacao L.) Resistance to Phytophthora Pod Rot

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    Phenomenon of genotype by environment interaction was able to influence the stability performance of cocoa resistance to Phytophthora pod rot (PPR). This research had an objective to evaluate the effect of genotype by environment interaction on resistance of cocoa hybrids to PPR. The tested hybrids were F1 crosses between selected clones of TSH 858, Sulawesi 1, Sulawesi 2, NIC 7, ICS 13, KEE 2 and KW 165. There were 14 tested hybrids and an open pollinated hybrid of ICS 60 x Sca 12 was used as control in multilocation trials at four different agroclimatic locations, namely Jatirono Estate ((highland-wet climate), Kalitelepak Estate (lowland-wet climate), Kaliwining Experimental Station (low land-dry climate) and Sumber Asin Experimental Station (highland-dry climate). Trials were established in the randomized complete block design with four replications. Resistance to PPR were evaluated based on the percentage of infected pod for the years during wet climate of 2010 in Jatirono, Kalitelepak and Kaliwining followed in dry climate of 2011–2015 in Kaliwining and Sumber Asin. Variance of data were analyzed for detecting the effect of genotype by environment interaction (GxE) then visualized with a graph of genotype main effect and genotype by environment interaction (a graph of GGE) biplot. There was consistently no interaction effect between hybrid and location to PPR incidence which was affected by single factor of hybrid, year, location and interaction between year and location. The effect of year indicated yearly change of weather was more important to PPR incidence than location difference. A graph of GGE biplot indicated a stable performance of the tested hybrids among locations

    Stability Analysis of Nine Promising Clones of Tea (Camellia Sinensis)

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    There are many clones grown in Indonesia tea plantations. The latest clones were released around 1990. The new promising clones have been bred through crossing among selected clones in Pagilaran tea plantation. The experiment aimed to select high yield and stable clone. Nine clones were grown in Kayulandak (1300 m asl) and Andongsili (1100 m asl) in Randomized Completely Block Design with three replications. The data of fresh weight per plot in 2007, 2008, 2009, 2011, 2012 were recorded. Eberhart & Russell (1966) and GGE Biplot analysis method was applied for data analyzing. The result showed that all of clones were stable over years in each location except for PGL1 and PGL3 in Andongsili and PGL15 in Kayulandak based on Eberhart & Russell analysis. Significant regression coefficient (1.18) of PGL3 implied that PGL3 was high in yield and responsive. GGE biplot analysis indicated no ideal genotype for each location. PGL10, PGL3, PGL4 and PGL 12 were recommended for Kayulandak, while PGL3 and PGL12 clones were suitable for Andongsili. Both analysis of Eberhart & Russell and GGE biplot showed PGL3 and PGL12 as ideal clone, while PGL10, PGL4, and PGL 15 clone were desireable clones

    Identifying mega‑environments to enhance the use of superior rice genotypes in Panama

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    El objetivo de este trabajo fue evaluar tres métodos para identificar mega‑ambientes, para optimizar el uso del potencial genético de los cultivares de arroz, durante el proceso de selección, y para hacer recomendaciones sobre siembras comerciales en Panamá. Los datos experimentales fueron obtenidos de los ensayos de productividad de cultivares precoces realizados entre 2006 y 2008. Para lograr la estratificación de los ambientes y definir los mega‑ambientes, se utilizaron los métodos del genotipo vencedor mediante el modelo AMMI1, el modelo biplot GGE y el de conglomerado por el método de Ward, complementado con el biplot GGE. Los tres métodos utilizados identificaron dos mega‑ambientes, donde los cultivares sobresalientes fueron Fedearroz 473 e Idiap 145-05. Hubo una coincidencia de 100% en el agrupamiento del conglomerado x el biplot GGE, mientras que entre conglomerado x AMMI1 y biplot GGE x AMMI1 fue de 95,2%. El genotipo más estable, en ambos mega-ambientes, fue el cultivar Idiap 145-05, lo que indica capacidad de adaptación amplia y específica. La capacidad adaptativa de los genotipos superiores y no las condiciones agroclimáticas de las localidades evaluadas fue responsable de la definición de los mega‑ambientes.The objective of this work was to evaluate three methods to identify mega-environments, in order to optimize the use of the genetic potential of rice cultivars during the selection process and to make recommendations for commercial plantations in Panama. Experimental data were obtained from the test performance, between 2006 and 2008, for early maturing cultivars. To achieve the stratification of environments and define mega‑environments, the winner genotype method by the AMMI1 model, GGE biplot model and cluster by Ward’s method supplemented by GGE biplot were used. The three methods used identified two mega-environments, where the outstanding cultivars were Fedearroz 473 e Idiap 145-05. There was 100% coincidence in the grouping of the cluster x the GGE biplot, with 95.2% coincidence between the AMMI1 x cluster and GGE biplot x AMMI1. The most stable genotype, in both mega-environments, was the Idiap 145‑05 cultivar, which indicates its broad and specific adaptive capacity. The adaptive capacity of the superior genotypes and not the agroclimatic conditions of the assessed localities was responsible for defining the mega-environments.The objective of this work was to evaluate three methods to identify mega-environments, in order to optimize the use of the genetic potential of rice cultivars during the selection process and to make recommendations for commercial plantations in Panama. Experimental data were obtained from the test performance, between 2006 and 2008, for early maturing cultivars. To achieve the stratification of environments and define mega‑environments, the winner genotype method by the AMMI1 model, GGE biplot model and cluster by Ward’s method supplemented by GGE biplot were used. The three methods used identified two mega-environments, where the outstanding cultivars were Fedearroz 473 e Idiap 145-05. There was 100% coincidence in the grouping of the cluster x the GGE biplot, with 95.2% coincidence between the AMMI1 x cluster and GGE biplot x AMMI1. The most stable genotype, in both mega-environments, was the Idiap 145‑05 cultivar, which indicates its broad and specific adaptive capacity. The adaptive capacity of the superior genotypes and not the agroclimatic conditions of the assessed localities was responsible for defining the mega-environments

    Response study of canola (Brassica napus L.) cultivars to multi-environments using genotype plus genotype environment interaction (GGE) biplot method in Iran

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    To study the interaction of genotype and environment in canola crop, a study was carried out in 2010. Ten genotypes (Modena, Okapi, Hyola 401, Licord, Opera, Zarfam, RGS 003, SLM046, Sarigol, and Hyola 308) of canola were studied under normal conditions of irrigation in four locations (Karaj, Birjand, Shiraz, and Kashmar) using randomized complete block design with three replications. Using GGE biplot method, grain yield was investigated for each cultivar. According to analysis of variance, there was a very significant difference among the regions. According to the yield average and genotype stability, Licord, Hyola 308, Modena and Zarfam were the best cultivars. The graphs obtained from GGE biplot software indicated that Hyola 401, Opera, and Sarigol were better than the rest of the genotypes based on stability and yield performance. At location Shiraz, none of the genotype had appropriate stability or yield. Four locations were divided into three mega-environments including Karaj, Kashmar (first mega-environment), Birjand (second mega-environment), and Shiraz (third mega-environment). Moreover, Karaj was recognized as the best region of the classification and ranking of genotypes. The study indicated that the highest and lowest genotypic reaction rates belonged to Licord and SLM 046 cultivars, respectively.Keywords: Canola, genotype environment interaction, grain yield, GGE biplot

    Evaluation of Food Barley Genotypes for Grain Yield and Agronomic Traits in the Central Highlands of Ethiopia

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    The present study was undertaken to evaluate the performance of promising food barley genotypes for grain yield and yield related traits. The trial was conducted in 2017 and 2018 main cropping season using randomized complete block design with three replications. Variance analysis and GGE biplot were used to understand the nature of genotype × environment interaction (G × E) in a grain yield data collected from eighteen  barley genotypes grown in eight environments (Location and year combinations). The combined analysis of variance (ANOVA) showed significantly higher genotype, environment and genotype by environment interaction effects for all the traits studied. Accordingly, genotypes EH1493 X HB1307 (G10) and HB 1307 X ND25160 (G2) showed the highest mean grain yield of 4558 kg ha-1 and 4499 kgha-1, respectively. GGE biplot showed that G10 was the winner genotype at BK18, DB18 and HO18 environments and it has good grain yield stability across the testing environments. Therefore, G10 is a potential candidate variety to be included in the variety verification trial for possible release. Keywords: ANOVA, GGE biplot, grain yield, stability DOI: 10.7176/ALST/84-02 Publication date: December 31st 2020

    Yield Stability of Soybean Genotypes in Tropical Environments Based on Genotype and Genotype-by-Environment Biplot

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    Genotype × environment interaction is universal phenomenon when different genotypes are tested in a number of environments. The objective of this experiment was to determine the seed yield stability of soybean genotypes. Seven soybean genotypes and two check cultivars were evaluated at eight soybean production centers during the dry season 2015. Stability analysis on seed yield was based on the GGE biplot method. The combined analysis showed that yield and yield components were significantly affected by genotype (G), environments (E), and genotype × environment interaction (GEI), except for number of filled pods. The highest yield was G6 (3.07 ton ha-1), followed by G7 (2.93 ton ha-1). The “which-won-where” polygon mapping resulted two mega-environments. The best genotype for the first mega-environment was G1 (G511H/Anjasmoro//Anjasmoro-2-8) at E5 (Pasuruan2); and the second one was G6 (G511 H/Anj//Anj///Anj////Anjs-6-7) at E1 (Nganjuk), E2 (Mojokerto), E3 (Blitar), E4 (Pasuruan1), E6 (Jembrana), E7 (Tabanan), and E8 (Central Lombok). The G7 (G511 H/Anjasmoro-1-4-2) was closest to ideal genotype as indicated by relatively stable and produced high yield across environments. The analysis of multi-environment trials data using GGE is useful for determining mega-environment analysis and stability of genotype which focusing on overall performance to identify superior genotypes

    GGE biplot analysis of reactions of bread wheat pure lines selected from central anatolian landraces of Turkey to leaf rust disease (Puccinia triticina) in multiple location-years

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    The present study was conducted to determine the reactions of 88 bread wheat pure lines selected from landraces collected in Central Anatolian Region of Turkey against leaf rust (Puccinia triticina) under field conditions in 7 locations. GGE biplot analysis was used to determine the reactions of landrace genotypes against the disease. The GGE biplot explained 73.89% of total variation. Among the experimental locations, 6 (except for E3) were placed close to each other over the biplot graph, indicating two apparent mega-environments. The GGE biplot visually displayed the resistance and stability of the pure lines to leaf rust. The landrace genotypes L18, L19, L45, and L2 were identified as the most resistant/stable genotypes in all environments and L31 and L56 were the most susceptible/stable genotypes
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