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Journal articles on the topic 'Genotype × environments'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Laffont, Jean-Louis, Kevin Wright, and Mohamed Hanafi. "Genotype Plus Genotype × Block of Environments Biplots." Crop Science 53, no. 6 (2013): 2332–41. http://dx.doi.org/10.2135/cropsci2013.03.0178.

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Andiku, Charles, Geofrey Lubadde, Charles J. Aru, Michael A. Ugen, and Johnie Ebiyau. "Additive Main Effects and Multiplicative Interaction and Genotype Main Effect and Genotype by Environment Interaction Effects-Biplot Analysis of Sorghum Grain Yield in Uganda." Journal of Agricultural Science 12, no. 6 (2020): 98. http://dx.doi.org/10.5539/jas.v12n6p98.

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Genotype-by-environment interaction analysis is vital for cultivar release, and to identify suitable crop production sites. The current study aimed to determine sorghum grain yield stability and adaptability and to identify the most informative and representative environments for sorghum grain yield performance in Uganda. Sorghum grain yield data of eight (08) genotypes; ICSR 160, IS8193, IESV92043DL, IESV92172DL, GE17/1/2013A, GE35/1/2013A, SESO1, and SESO3 tested across eight (08) major sorghum production area in Uganda for two consecutive seasons of 2017 using randomised complete block desi

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3

KUMAR, AJAY, and T. S. DHILLON. "Stability of French bean (Phaseolus vulgaris) genotypes under diverse environments." Indian Journal of Agricultural Sciences 90, no. 1 (2020): 157–62. http://dx.doi.org/10.56093/ijas.v90i1.98663.

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The present investigation was undertaken to ascertain stable genotypes across six environments. Twenty five genotypes of French bean were evaluated for yield and quality traits in three different locations of Punjab during 2017–18. The stability of genotypes was worked out by using Eberhart and Russel (1966) and GGE biplot models. Highly significant mean squares for environments and genotype × environment interaction were recorded for all the traits except for protein content. The linear component of genotype × environment interaction was significant for all the characters under study. The hig

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4

Edugbo, Richmond Emuohwo, Godson Emeka Nwofia, and Lawrence Stephen Fayeun. "An Assessment of Soybean (Glycine max, L. Merrill) Grain Yield in Different Environments Using AMMI and GGE Biplot Models in Humidorest Fringes of Southeast Nigeria." Agricultura Tropica et Subtropica 48, no. 3-4 (2015): 82–90. http://dx.doi.org/10.1515/ats-2015-0012.

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Abstract The yield of four soybean (Glycine max, L. Merrill) genotypes under six planting dates in two years was assessed using the Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype and Genotype-by-Environment biplot models. The results of combined analysis of variance for grain yield of the four genotypes of soybean grown in 12 environments showed that soybean grain yield was significantly (P < 0.01) affected by environments (E), genotypes (G) and genotype by environment interactions (GE). Genotypes and environments accounted for about 6.56% and 47.66% of the variatio

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Savar Sofla, Sima. "Estimation of genetic correlation between milk production and fat yield in different climates of Iran." Proceedings of the British Society of Animal Science 2007 (April 2007): 70. http://dx.doi.org/10.1017/s1752756200019736.

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Performance of one genotype in similar climates is approximately the same but if this genotype is introduced into a different climate, its performance will be affected, based on Nizamani and Berger (1996). The function that relates phenotype to environment is unique for each genotype. Hence, the response to changes in environment may vary from one genotype to the other, based on Mulder et al. (2004). Different selection responses between environments are generally attributed to two types of genotype by environment interaction. The first type occurs when the genetic correlation between performa

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Sullivan, J. A., Weikai Yan, and J. P. Privé. "Genotype/Genotype × Environment Biplot Analysis for Cultivar Evaluation and Mega-environment Investigation in Primocane-fruiting Red Raspberry." Journal of the American Society for Horticultural Science 127, no. 5 (2002): 776–80. http://dx.doi.org/10.21273/jashs.127.5.776.

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Primocane-fruiting (PF) red raspberry (Rubus idaeus L.) cultivars are being grown in many regions as their popularity increases. However, testing of this perennial fruit crop is expensive and requires many years. Large genotype (G) × environment (E) interactions can make identification of superior genotypes difficult. The G/G × E (GGE) biplot can be used to measure cultivar performance and group locations into mega-environments. The GGE biplot was applied to yield trial data of three PF red raspberry cultivars Autumn Bliss, Heritage, and Redwing grown in 17 environments (year-location combinat

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Islam, Shams Shaila, Jakarat Anothai, Charassri Nualsri, and Watcharin Soonsuwon. "Analysis of genotype-environment interaction and yield stability of Thai upland rice (Oryza sativa L.) genotypes using AMMI model." February 2020, no. 14(02):2020 (February 20, 2020): 362–70. http://dx.doi.org/10.21475/ajcs.20.14.02.p1847.

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Genotype-environment interaction and stability analysis has been important for plant breeders and plays a vital role in identifying genotypes that are stable or unstable in a given environment. The experiments in this research were conducted to determine the effects of genotype, environment and genotype-environment interaction on grain yield using the AMMI statistical model, and to recognize the most stable rice genotypes among ten genotypes in southern Thailand’s provinces of environments in Songkhla, Satun and Phatthalung. Highly significant differences were shown from the combined analysis

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Reshma, O., P. Surendra, S. Bhaskar Reddy, and K. M. Shivaprasad. "Evaluation of Rice Genotypes for Yield Stability and Adaptability Across Multiple Environments Using AMMI and GGE Biplot Analysis." Journal of Advances in Biology & Biotechnology 27, no. 8 (2024): 1164–76. http://dx.doi.org/10.9734/jabb/2024/v27i81239.

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The study aimed to identify elite rice genotypes with the highest yield response and broad adaptability, as well as those with specific adaptability to unique or groups of environments. Three different environments were selected for the experiment with 23 rice genotypes in Dharwad, Malagi, and Sirsi, Karnataka, during the year 2020 (Kharif season). The ANOVA revealed that environments contributed the highest (33.5%) to the total sum of squares, followed by genotypes × environments (21.7%), indicating a major role played by environments and their interactions in realizing final yield. The AMMI

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Ukalski, Krzysztof, and Marcin Klisz. "Application of GGE biplot graphs in multi-environment trials on selection of forest trees." Folia Forestalia Polonica 58, no. 4 (2016): 228–39. http://dx.doi.org/10.1515/ffp-2016-0026.

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Abstract In the studies on selection and population genetics of forest trees that include the analysis of genotype × environment interaction (GE), the use of biplot graphs is relatively rare. This article describes the models and analytic methods useful in the biplot graphs, which enable the analyses of mega-environments, selection of the testing environment, as well as the evaluation of genotype stability. The main method presented in the paper is the GGE biplot method (G - genotype effect, GE -genotype × environment interaction effect). At the same time, other methods have also been referred

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Krzysztof, Ukalski, and Klisz Marcin. "Application of GGE biplot graphs in multi-environment trials on selection of forest trees." FOLIA FORESTALIA POLONICA, SERIES A – FORESTRY 58, no. 4 (2016): 228–39. https://doi.org/10.1515/ffp-2016-0026.

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In the studies on selection and population genetics of forest trees that include the analysis of genotype × environment interaction (GE), the use of biplot graphs is relatively rare. This article describes the models and analytic methods useful in the biplot graphs, which enable the analyses of mega-environments, selection of the testing environment, as well as the evaluation of genotype stability. The main method presented in the paper is the GGE biplot method (G – genotype effect, GE – genotype × environment interaction effect). At the same time, other methods have al

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P, MANIVEL, and JAVAD HUSSAIN H.S. "Genotype x environment interaction in castor." Madras Agricultural Journal 87, september (2000): 394–97. http://dx.doi.org/10.29321/maj.10.a00481.

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The phenotype stability of 79 genotypes of castor (60 hybrids and 19 parents) grown over four environments was studied for oil content, seed yield and other related traits. Variance due to genotypes, environments, G x E (liner) components was highly significant for all the traits. However, the liner component was more than the non-liner component except for capsules per plant and 100 seed weight. In general hybrids showed greatest stability for all the traits. The traits like number of nodes up to primary raceme, 100 seed weight and oil content were less affected by the changing environments o

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Farshadfar, E. "Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique." Acta Agronomica Hungarica 61, no. 3 (2013): 185–94. http://dx.doi.org/10.1556/aagr.61.2013.3.2.

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GGE biplot analysis is an effective method, based on principal component analysis (PCA), to fully explore multi-environment trials (METs). It allows visual examination of the relationships among the test environments, genotypes and the genotype-by-environment interactions (G×E interaction). The objective of this study was to explore the effect of genotype (G) and the genotype × environment interaction (GEI) on the grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008–2011). The yield data were analysed using the GGE

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M, KANDASWAMI. "PHENOTYPIC STABILITY FOR SEED YIELD IN GREEN GRAM (Vigna radiata) IN SODIC SOIL." Madras Agricultural Journal 85, April (1998): 211–13. http://dx.doi.org/10.29321/maj.10.a00719.

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A set of five improved advanced, genotypes of green gram were evaluated in summer '92, rabi '92 and Summer '93 seasons in saline/sodic soil conditions (soil pH 8.6, 8.7 and 8.5). Pooled- analysis of variances indicated significant differences among the genotypes and the environments. Moreover, the genotype environment interaction was highly significant indicating .differential performance of the genotype under varied environmental conditions. The genotype SSRC 9 showed higher yield and stability of yield performance. The genotype SSRC 7 and CO.5 had high yield stability, better adapted to rich

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M, KANDASWAMI. "PHENOTYPIC STABILITY FOR SEED YIELD IN GREEN GRAM (Vigna radiata) IN SODIC SOIL." Madras Agricultural Journal 82, March (1995): 211–13. http://dx.doi.org/10.29321/maj.10.a01169.

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A set of five improved advanced, genotypes of green gram were evaluated in summer '92, rabi '92 and Summer '93 seasons in saline/sodic soil conditions (soil pH 8.6, 8.7 and 8.5). Pooled analysis of variances indicated significant differences among the genotypes and the environments. Moreover, the genotype environment interaction was highly significant indicating .differential performance of the genotype under varied environmental conditions. The genotype SSRC 9 showed higher yield and stability of yield performance. The genotype SSRC 7 and CO.5 had high yield stability, better adapted to rich

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15

Muhammad Ejaz, Faiza Aziz, Muhammad Nadeem Sadiq, Abdullah Baloch, and Muhammad Hamzeh. "Effect of genotype × environment interaction on grain yield factors in durum wheat." International Journal of Frontiers in Science and Technology Research 5, no. 1 (2023): 019–28. http://dx.doi.org/10.53294/ijfstr.2023.5.1.0074.

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Identifying environment-specific and widely adapted genotypes is necessary through understanding of environmental interaction (GEI). To estimate the enormousness of genotype (G), environment (E) and GEI results on yield and yield components and it is necessary to conduct multi-locations trials of durum wheat. During the year 2019/20 cropping season eleven (11) durum wheat genotypes were appraised under three locations within Balochistan, Pakistan. Almost all traits exhibit significant results for combined analyses of variance for genotypes (G) and Genotype X Environmental Interaction (GEI), th

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16

K, Jhansi Rani, Sai Kumar R, and Sudarshan M R. "Identification of Stable Maize Hybrids Over Environments." Madras Agricultural Journal 99, September (2012): 435–37. http://dx.doi.org/10.29321/maj.10.100107.

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The objective of the present study was to analyse the pattern of genotype x environment interaction for grain yield in experimental maize hybrids. The Additive Main Effects and Multiplicative Interaction(AMMI) model was applied to yield data obtained from a zonal trial conducted over five locations, involving fourteen experimental and three commercial hybrid checks. AMMI analysis indicated genotype, environment and the genotype x environment interaction were significant. The first two principal components were significant and jointly accounted for 74.5% of total interaction. Based on PCA value

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17

Yonas, Wondimu, Abush Tesfaye, and Sentayehu Alamere. "Evaluation of yield performance of early maturing soybean (Glycine max L. Merill) genotypes in Ethiopia by using GGE Biplot model." International Journal of Agricultural Research, Innovation and Technology 12, no. 2 (2023): 101–10. http://dx.doi.org/10.3329/ijarit.v12i2.64094.

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Genotype main effect and genotype by environment interaction biplot analysis is the best fit model for which-won-where pattern analysis, genotype, and test environment evaluation. Hence, the aim of this study was to identify stable and high-yielding soybean genotypes for production in diverse environments by using the genotype main effect and genotype by environment biplot stability model. Eighteen soybean genotypes were evaluated across six environments during the 2019 cropping season by using a randomized complete block design with four replications. Among evaluated environments and genotype

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Wondimu, Yonas, Tesfaye Abush, and Alamere Sentayehu. "Evaluation of yield performance of early maturing soybean (Glycine max L. Merill) genotypes in Ethiopia by using GGE Biplot model." International Journal of Agricultural Research, Innovation and Technology 12, no. 2 (2022): 101–10. https://doi.org/10.3329/ijarit.v12i2.64094.

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Genotype main effect and genotype by environment interaction biplot analysis is the best fit model for which-won-where pattern analysis, genotype, and test environment evaluation. Hence, the aim of this study was to identify stable and high-yielding soybean genotypes for production in diverse environments by using the genotype main effect and genotype by environment biplot stability model. Eighteen soybean genotypes were evaluated across six environments during the 2019 cropping season by using a randomized complete block design with four replications. Among evaluated environments and genotype

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19

Mohammadi, Reza, and Ahmed Amri. "Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods." Canadian Journal of Plant Science 92, no. 4 (2012): 757–70. http://dx.doi.org/10.4141/cjps2011-133.

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Mohammadi, R. and Amri, A. 2012. Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods. Can. J. Plant Sci. 92: 757–770. Multi-environment trials (MET) are conducted annually throughout the world in order to use the information contained in MET data for genotype evaluation and mega-environment identification. In this study, grain yield data of 13 durum and one bread wheat genotypes grown in 16 diversified environments (differing in winter temperatures and water regimes) were used to analyze genotype by environment (GE) interac

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20

Nleya, T. M., G. C. Arganosa, A. Vandenberg, and R. T. Tyler. "Genotype and environment effect on canning quality of kabuli chickpea." Canadian Journal of Plant Science 82, no. 2 (2002): 267–72. http://dx.doi.org/10.4141/p01-082.

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Chickpea (Cicer arietinum) has become an important pulse crop in Saskatchewan where both the large-seeded (kabuli) and small-seeded (desi) market classes are grown. In North America, kabuli chickpea is mainly used for canning and in salad bars. Both the genotype and the environment affect the canning quality. This study was conducted to determine the effect of genotype, environment and genotype × environment interactions on canning quality traits of three kabuli chickpea cultivars. The three cultivars were grown in 17 environments in Saskatchewan and Alberta during 1996, 1997 and 1998. The gen

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21

Tahir, Izzat S. A., Elfadil M. E. Elbashier, Hala M. Mustafa, et al. "Durum Wheat Field Performance and Stability in the Irrigated, Dry and Heat-Prone Environments of Sudan." Agronomy 13, no. 6 (2023): 1598. http://dx.doi.org/10.3390/agronomy13061598.

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Developing climate-resilient crop varieties with better performance under variable environments is essential to ensure food security in a changing climate. This process is significantly influenced, among other factors, by genotype × environment (G × E) interactions. With the objective of identifying high-yielding and stable genotypes, 20 elite durum wheat lines were evaluated in 24 environments (location–season combination) during 5 crop seasons (2010/11–2014/15). The REML (residual maximum likelihood)-predicted means of grain yield of 16 genotypes that were common across all environments rang

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Medina, José López, Patrick P. Moore, Carl H. Shanks, Fernando Flores Gil, and Craig K. Chandler. "Genotype × Environment Interaction for Resistance to Spider Mites in Fragaria." Journal of the American Society for Horticultural Science 124, no. 4 (1999): 353–57. http://dx.doi.org/10.21273/jashs.124.4.353.

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Genotype × environment interaction for resistance to the twospotted spider mite (Tetranychus urticae Koch) of eleven clones of Fragaria L. sp. (strawberries) grown in six environments throughout the United States was examined using two multivariate analysis techniques, principal coordinate analysis (PCA) and additive main effect and multiplicative interaction (AMMI). Both techniques provided useful and interesting ways of investigating genotype × environment interaction. PCA analysis indicated that clones X-11 and E-15 were stable across both low and high environments for the number of spider

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Shojaei, Seyed Habib, Khodadad Mostafavi, Seyed Hamed Ghasemi, et al. "Sustainability on Different Canola (Brassica napus L.) Cultivars by GGE Biplot Graphical Technique in Multi-Environment." Sustainability 15, no. 11 (2023): 8945. http://dx.doi.org/10.3390/su15118945.

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Knowledge about the extent of genotype in environment interaction is helpful for farmers and plant breeders. This is because it helps them choose the proper strategies for agricultural management and breeding new cultivars. The main contribution of this paper is to investigate genotype on environmental interaction using the GGE biplot method (Genotype and the Genotype-by-Environment) in ten canola cultivars. The experimental design was a randomized complete block design (RCBD) with three replications to assess the stability of grain yield of ten canola cultivars in five regions of Iran, includ

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Kumar, Ramesh, Yashmeet Kaur, Abhijit K. Das, et al. "Stability of maize hybrids under drought, rainfed and optimum field conditions revealed through GGE analysis." Indian Journal of Genetics and Plant Breeding (The) 83, no. 04 (2023): 499–507. http://dx.doi.org/10.31742/isgpb.83.4.6.

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Identification of high-yielding and stable cultivars across different environments through multi-location trials are very important inmaize breeding. A study was conducted to evaluate 30 maize hybrids in three diverse environments, viz., drought, rainfed and optimalconditions during the years, 2016 and 2017. Environments, genotypes and Genotype × Environment interactions (G × E) were foundto be highly significant in both the years. The biplot explained 69.49% of total variation which was partitioned into 53.61 and 15.88%relative to genotype and genotype by environment interaction. Genotype, ZH

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Mahmud, F., M. Z. Ullah, and K. M. K. Huda. "GENOTYPE-ENVIRONMENT INTERACTION FOR SEED YIELD AND YIELD CONTRIBUTING CHARACTERS IN CHICKPEA (Cicer arietinum L.)." Bangladesh Journal of Plant Breeding and Genetics 20, no. 1 (2007): 09–12. http://dx.doi.org/10.3329/bjpbg.v20i1.17012.

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Genotype-environment interaction was studied in seven genotypes of chickpea under four different cultural environments. Significant variation for genotype (G), environment (E) and G × E interactions were found for the characters days to maturity, plant height, pods/plant, seeds/plant, 100-seed weight and seed yield/plant. On the basis of stability parameters the genotypes Barichola-2, Barichola-3, Barichola-4, Barichola-7 and Barichola-8 could be considered stable for seed yield but suitable only under poor environments where no fertilizers were used. The genotype Barichola-1 was highly respon

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Kaloki, Peter, Richard Trethowan, and Daniel K. Y. Tan. "Effect of genotype × environment × management interactions on chickpea phenotypic stability." Crop and Pasture Science 70, no. 5 (2019): 453. http://dx.doi.org/10.1071/cp18547.

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Crop varieties interact with the environment, which affects their performance. It is imperative to know how the environment affects these crop varieties in order to choose carefully the optimal environment for growth. Chickpea (Cicer arietinum L.) is grown in varying environmental conditions including conventional and no-tillage under both irrigated and rainfed farming systems. Hence, genotype × environment × management interactions can affect yield stability. An experiment was conducted in north-western New South Wales, Australia, to investigate these interactions and to determine possible en

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Hasan, MJ, MM Hossain, Z. Akond, and MM Rahman. "Identification of stable and adaptable hybrid Rice genotypes." SAARC Journal of Agriculture 12, no. 2 (2015): 1–15. http://dx.doi.org/10.3329/sja.v12i2.21912.

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Development of varieties with high yield potential coupled with wide adaptability is an important plant breeding objective. Presence of genotype and environment (G×E) interaction plays a crucial role in determining the performance of genetic materials, tested in different locations in different years. This study was under taken to assess yield performance, stability and adaptability of seventeen hybrid rice genotypes evaluated over 12 environments. The analysis of variance for growth duration and grain yield (t ha-1) for genotype, environment year, environment × genotype, year × environment, y

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Wodebo, Kibreab Yosefe, Taye Tolemariam, Solomon Demeke, et al. "AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments." Plants 12, no. 17 (2023): 3064. http://dx.doi.org/10.3390/plants12173064.

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This paper reports an evaluation of eleven oat genotypes in four environments for two consecutive years to identify high-biomass-yielding, stable, and broadly adapted genotypes in selected parts of Ethiopia. Genotypes were planted and evaluated with a randomized complete block design, which was repeated three times. The additive main effect and multiplicative interaction analysis of variances revealed that the environment, genotype, and genotype–environment interaction had a significant (p ≤ 0.001) influence on the biomass yield in the dry matter base (t ha−1). The interaction of the first and

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MOHAMMADI, R., A. AMRI, R. HAGHPARAST, D. SADEGHZADEH, M. ARMION, and M. M. AHMADI. "Pattern analysis of genotype-by-environment interaction for grain yield in durum wheat." Journal of Agricultural Science 147, no. 5 (2009): 537–45. http://dx.doi.org/10.1017/s0021859609008831.

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SUMMARYPattern analysis, cluster and ordination techniques, was applied to grain yield data of 20 durum wheat genotypes grown in 19 diversified environments during 2005–07 to identify patterns of genotype (G), environment (E) and genotype-by-environment (G×E) interaction in durum multi-environment trials (METs). Main effects due to E, G and G×E interaction were highly significant, and 0·85 of the total sum of squares (SS) was accounted for by E. Of the remaining SS, the G×E interaction was almost 12 times the contribution of G alone. The knowledge of environmental and genotype classification h

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Madhavilatha, L., M. ShanthiPriya, N. Anuradha, C. V. Chandra Mohan Reddy, P. Soumya, and M. Hemanth Kumar. "Stability Analyses for Yield and Its Components in Little Millet (Panicum sumatrense L.)." Journal of Scientific Research and Reports 30, no. 5 (2024): 99–107. http://dx.doi.org/10.9734/jsrr/2024/v30i51926.

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Linear regression model of Eberhart and Russell is used to identify high yielding stable little millet genotypes suitable across environments. Pooled analysis of variance revealed significant genetic variability among the little millet genotypes for yield and yield attributing traits. Significant variability among environments confirms the heterogeneity in the locations for the traits. Significant genotype x environment interaction for all the traits indicated differential response of the genotypes for the traits in different locations. Among genotypes BL-6, LMNDL-4, LMNDL-3, OLM 203, VS 13, V

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A, Nirmalakumari, Revathi S, Ulaganathan V, Priyadharshini C, and Balasubramanian T. "Stability Analysis in Barnyardmillet (Echinochloa frumentacea (Roxb.) Link.) Genotypes." Madras Agricultural Journal 98, December (2011): 305–7. http://dx.doi.org/10.29321/maj.10.001203.

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Five barnyardmillet genotypes viz., Sadai kudiraivali, Pullu kudiraivali, CO (KV) 2, VL 29 and VL 172 were evaluated over five different environments to study the stability parameters viz., regression coefficient (bi) and mean square deviations (S 2 d i ). Variances due to genotype, environment, genotype x environment, environment (linear) and pooled deviation were significant for days to 50 per cent flowering, days to maturity, plant height and grain yield. Based on the stability analysis, the genotype CO (KV) 2 was found to be stable across five different environments for days to maturity, p

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Popoola, Bosede Olufunke, Patrick Obia Ongom, Saba B. Mohammed, et al. "Assessing the Impact of Genotype-by-Environment Interactions on Agronomic Traits in Elite Cowpea Lines across Agro-Ecologies in Nigeria." Agronomy 14, no. 2 (2024): 263. http://dx.doi.org/10.3390/agronomy14020263.

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The yield of cowpea varieties is affected by environmental variability. Hence, candidate varieties must be tested for yield stability before release. This study assessed the impacts of genotypes, environments, and their interaction on the performance of elite cowpea lines for key adaptive, grain yield, and associated traits across different locations. A total of 42 elite genotypes were evaluated in five Nigerian environments, representing various savanna ecologies, during the 2021 growing season. The experimental design employed was an alpha lattice arrangement, with each genotype replicated t

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Krisnawati, Ayda, and And Mochammad Muchlish Adie. "Yield Stability of Soybean Genotypes in Tropical Environments based on Genotype and Genotype-by-Environment Biplot." Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) 46, no. 3 (2019): 231–39. http://dx.doi.org/10.24831/jai.v46i3.18333.

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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 nu

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Mohammadi, Mohtasham, Peyman Sharifi, and Rahmatollah Karimizadeh. "Stability Analysis of Durum Wheat Genotypes by Regression Parameteres in Dryland Conditions." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 62, no. 5 (2014): 1049–56. http://dx.doi.org/10.11118/actaun201462051049.

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The objectives of this study were to estimate genotype × environment (GE) interaction effects and to determine the stable durum wheat (Triticum turgidum var. durum Desf.) genotypes for grain yield in warm winter areas of Iran. Twenty durum wheat genotypes, including 18 experimental lines and two local checks were evaluated during three cropping seasons (2004–2006) at five research sites. The combined analysis of variance indicated that the main effects of location and genotype and interaction effects of genotype × year, genotype × location and genotype × year × location were highly significant

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Abid, Saleem, and Saleem Zahid. "Stability of Maize Hybrids across Environments Using Gge Biplot and Ammi Analysis." Asian Journal of Agriculture and Rural Development 8, no. 2 (2019): 188–94. http://dx.doi.org/10.18488/journal.1005/2018.8.2/1005.2.188.194.

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Twenty six yellow maize hybrids on the basis of stability analysis were evaluated in National Uniform Maize Hybrid Yield Trials conducted across eight diversified environments of Pakistan. Combined analysis of variance based AMMI analysis shown highly significant differences for environments, genotypes and their interactions. The environments explained about 78 percent of the total yield variation followed by genotype by environment interaction. Environment was the main aspect that influences the performance of maize yield in study area. The first two interaction principal component axes (IPCA

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Akinyosoye, Solomon Tayo, Opeyemi Adeola Agbeleye, Johnson Adedayo Adetumbi, Paul Chiedozie Ukachukwu, and Oluwafemi Daniel Amusa. "Genotype – genotype × environment (GGE) biplot analysis of winged bean for grain yield." Acta Horticulturae et Regiotecturae 26, no. 1 (2023): 53–63. http://dx.doi.org/10.2478/ahr-2023-0009.

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Abstract The winged bean is an underutilized legume that is adapted to the tropics. It has good prospects as a significant multi-purpose food crop including human nutrition, cattle feed, and environmental protection. However, little research attention has been given to the crop to address the identified constraints, especially low yield in Nigeria. To improve its yield potential, GGE biplot analysis was used to identify high-yielding and stable winged bean genotypes, previously collected from the continent of Asia, and Nigeria for yield improvement. Twenty winged bean genotypes were being eval

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Merga, Wakuma, Wosene Gebreselassie, and Weyessa Garedew. "Genotype × Environment Interaction Studies of Promising Teppi Coffee (Coffea arabica L.) Genotypes in Southwestern Ethiopia." International Journal of Agronomy 2021 (May 11, 2021): 1–9. http://dx.doi.org/10.1155/2021/5519467.

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Coffee is the stimulant crop plant that belongs to the genus Coffea, in the family Rubiaceae. The yield variation and pattern of the crop varied within short geographic distance and thus attributed to low productivity and unpredictable production. The objective of this study was to determine the extent of genotype × environment interaction on yields of promising Teppi coffee genotype. In this study, seventeen Arabica coffee genotypes, representing coffee growing areas of Teppi, southwestern Ethiopia were evaluated at six environments. This study was conducted by using completely randomized blo

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Wardofa, Gadisa A., Hussein Mohammed, Dawit Asnake, and Tesfahun Alemu. "Genotype X Environment Interaction and Yield Stability of Bread Wheat Genotypes in central Ethiopia." Journal of Plant Breeding and Genetics 7, no. 2 (2019): 87–94. http://dx.doi.org/10.33687/pbg.007.02.2847.

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The present study was conducted to interpret Genotype main effect and GEI obtained by AMMI analysis and group the genotype having similar response pattern over all environments. Fifteen bread wheat genotypes were evaluated by RCBD using four replications at six locations in Ethiopia. The main effect differences among genotypes, environments, and the interaction effects were highly significant (P ≤ 0.001) for the total variance of grain yield. Results of AMMI analysis of mean grain yield for the six locations showed significant differences (P0.001) among the genotypes, environments and GEI. The

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Wardofa, Gadisa A., Dawit Asnake, and Hussein Mohammed. "GGE Biplot Analysis of Genotype by Environment Interaction and Grain Yield Stability of Bread Wheat Genotypes in Central Ethiopia." Journal of Plant Breeding and Genetics 7, no. 2 (2019): 75–85. http://dx.doi.org/10.33687/pbg.007.02.2846.

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GGE biplot is an effective method based on principal component analysis to fully explore mega-environments trials data. The study conducted was to identify the best performing, high yielding stable advanced bread wheat genotype for selection environments, the identification of mega-environments and analysis of the ideal genotype and environment by GGE biplot method. Fifteen bread wheat genotypes were evaluated using RCBD with four replications at six locations in Ethiopia. The results of combined analysis of variance for grain yield of fifteen bread wheat genotypes indicated that genotype, env

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Bilate Daemo, Berhanu, Derbew Belew Yohannes, Tewodros Mulualem Beyene, and Wosene Gebreselassie Abtew. "AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments." International Journal of Agronomy 2023 (April 4, 2023): 1–13. http://dx.doi.org/10.1155/2023/6759698.

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Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation.

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Sarker, A., M. Singh, F. El-Ashkar, W. Erskine, and E. De-Pauw. "Approaches to rationalising selection of test environments for on-farm lentil variety trials in Mediterranean rainfed cropping systems." Australian Journal of Agricultural Research 58, no. 4 (2007): 335. http://dx.doi.org/10.1071/ar05418.

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This study focused on various approaches to rationalising the selection of test environments using on-farm trial data from 5 lentil (Lens culiniaris Medikus subsp. culinaris) genotypes. It was conducted over 3 years in 30 environments across 16 locations in Syria. There was maximum discrimination in the ratio of between-cluster to within-cluster variances, based on genotype yield responses to the environments. Four clusters represented the test locations, reflecting a gradient in the levels of yield and seasonal rainfall. We observed significant genotypic differences and genotype × environment

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Kumar, M. V. Nagesh, V. Ramya, C. V. Sameer Kumar, et al. "Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses." Indian Journal of Genetics and Plant Breeding (The) 81, no. 01 (2021): 63–73. http://dx.doi.org/10.31742/ijgpb.81.1.7.

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Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important pulse crop grown under Indian rainfed agriculture. Twenty eight pigeonpea genotypes were tested for stability and adaptability across ten rainfed locations in the States of Telangana and Karnataka, India using AMMI (additive main effects and multiplicative interaction) model and GGE (genotype and genotype by environment) biplot method. The grain yields were significantly affected by environment (56.8%) followed by genotype × environment interaction (27.6%) and genotype (18.6%) variances. Two mega environments were identified with severa

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Srivastava, Ashutosh, Puja Srivastava, R. S. Sarlach, and Mayank Anand Gururani. "BIPLOT ANALYSIS FOR IDENTIFICATION OF SUPERIOR GENOTYPES IN A RECOMBINANT INBRED POPULATION OF WHEAT UNDER RAINFED CONDITIONS." Journal of Experimental Biology and Agricultural Sciences 9, no. 5 (2021): 598–609. http://dx.doi.org/10.18006/2021.9(5).598.609.

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Physiological traits of wheat genotypes and their trait relation to drought conditions are important to identify the genotype in target environments. Thus, genotype selection should be based on multiple physiological traits in variable environments within the target region. This study was conducted at Punjab Agricultural University during rabi crop seasons 2012-13 and 2013-14 to study the recombinant inbred lines (RILs) of wheat genotypes derived from traditional landraces and modern cultivars (C518/2*PBW343) based on various morpho-physiological traits. A total of 175 RILs were selected for t

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Adham, Azmi, Mohamad Bahagia Ab Ghaffar, Asmuni Mohd Ikmal, and Noraziyah Abd Aziz Shamsudin. "Genotype × Environment Interaction and Stability Analysis of Commercial Hybrid Grain Corn Genotypes in Different Environments." Life 12, no. 11 (2022): 1773. http://dx.doi.org/10.3390/life12111773.

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The introduction of superior grain corn genotypes with high and stable yield (YLD) in most environments is important to increase local production and reduce dependency on imported grain corn. In this study, days to tasseling (DT), plant height, and YLD of 11 grain corn genotypes were observed in 10 environments to evaluate the effects of genotype (G), environment (E), and genotype by environment interactions (GEI) using GGE analysis and the stability of genotypes using stability parameters. In each location, grain corn genotypes were arranged in three replications using a randomized complete b

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Johnson, G. R., and C. Cartwright. "Genotype × shade effects for western hemlock." Canadian Journal of Forest Research 35, no. 6 (2005): 1496–501. http://dx.doi.org/10.1139/x05-061.

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Western hemlock (Tsuga heterophylla (Raf.) Sarg.) families were grown under different levels of shade for 2 or 3 years at two nursery sites to determine whether families performed differently relative to one another in the different shade environments. Differences were found both for levels of shade and families, but no family × shade interaction was found. Results suggest that families selected in full-sun environments (clearcuts or farm fields) may be well suited for use in silvicultural systems where seedlings are planted in understory conditions.

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Kumba, Yannah Karim, and Emmanuel Norman Prince. "Genotype × Environment Interaction and Stability Analysis for Selected Agronomic Traits in Cassava (Manihot esculenta)." International Journal of Environmental & Agriculture Research 7, no. 8 (2021): 17–28. https://doi.org/10.5281/zenodo.5336002.

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Cassava (Manihot esculenta Crantz) is an important root and tuber crop worldwide. The crop is highly influenced by variations in production environments. A significant Genotype × Environment Interaction (GEI) presents challenges in the selection of superior genotypes. This study determined the magnitude of GEI and stability performances of 26 cassava genotypes for key agronomic traits across three multi-environments. The trial was laid out in a randomized complete block design during 2016/2017 cropping season. Genotype TR0288 had the highest starch content at Pendembu and Kambia, while T

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Chobe, Adane C., and Abebe D. Ararsa. "AMMI and GGE Biplot Analysis of Linseed (Linum usitatissimum L) Genotypes in Central and South-Eastern Highlands of Ethiopia." Journal of Plant Breeding and Genetics 6, no. 3 (2018): 117–27. http://dx.doi.org/10.33687/pbg.006.03.2785.

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Twelve linseed genotypes were evaluated in 13 environments during the main cropping season in central highlands of Ethiopia. The objective of the study was to determine the magnitude and pattern of G × E interaction and yield stability in linseed genotypes. The study was conducted using randomized complete block design with 3 replications. Genotype × environment interaction and yield stability were estimated using the additive main effects and multiplicative interaction and site regression genotype plus genotype × environment interaction biplot. Pooled analysis of variance for seed yield showe

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Bakare, Moshood A., Siraj Ismail Kayondo, Cynthia I. Aghogho, et al. "Exploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods." PLOS ONE 17, no. 7 (2022): e0268189. http://dx.doi.org/10.1371/journal.pone.0268189.

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Variety advancement decisions for root quality and yield-related traits in cassava are complex due to the variable patterns of genotype-by-environment interactions (GEI). Therefore, studies focused on the dissection of the existing patterns of GEI using linear-bilinear models such as Finlay-Wilkinson (FW), additive main effect and multiplicative interaction (AMMI), and genotype and genotype-by-environment (GGE) interaction models are critical in defining the target population of environments (TPEs) for future testing, selection, and advancement. This study assessed 36 elite cassava clones in 1

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Adjebeng-Danquah, Joseph, Isaac Kwadwo Asante, Joseph Manu-Aduening, Richard Yaw Agyare, Vernon Edward Gracen, and Samuel Kwame Offei. "Genotypic Variability in Some Morpho-Physiological Traits in Different Environments and Their Relationship with Cassava (Manihot esculenta Crantz) Root Yield." International Journal of Agronomy 2020 (July 26, 2020): 1–19. http://dx.doi.org/10.1155/2020/5871351.

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Cassava root yield under diverse environments is influenced by morpho-physiological traits that are in turn influenced by genotype, environment, and genotype × environment interaction (GEI). Most GEI analyses in cassava have been limited to root yield with less emphasis on stability of other yield-related traits. This study was carried out to assess the effect of GEI on some morpho-physiological traits in cassava and key traits that are useful for selection in different environments. The study utilized 20 cassava genotypes evaluated in six environments, namely, Fumesua 2013 and 2014, Nyankpala

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Chaudhary, Eishaina, Surakshya Sharma, Pratik Gautam, et al. "AMMI GGE biplot analysis of wheat genotypes under heat stress and heat drought environment." Archives of Agriculture and Environmental Science 8, no. 4 (2023): 484–89. http://dx.doi.org/10.26832/24566632.2023.080404.

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Wheat is the third most important cereal crop of Nepal. Climatic changes have been a major threat on overall production and productivity of wheat in Nepal. With the aim of evaluating twenty elite wheat genotypes under heat stress and heat drought environments, a field experiment was conducted using alpha lattice design at Bhairahawa, Rupandehi, Nepal. The analysis of variance (ANOVA) revealed significant differences in the yield across wheat growing environments (p<0.001). Environment explained 75.11% of the total variation in grain yield. NL 1404 was the most stable followed by NL 1368, an

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