Relevant bibliographies by topics / Genotype × environments

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Genotype × environments'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Genotype × environments"

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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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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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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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Dissertations / Theses on the topic "Genotype × environments"

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Mano, Ana Raquel de Oliveira. "Adaptabilidade e estabilidade fenotÃpica de cultivares de feijÃo de corda." Universidade Federal do CearÃ, 2009. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=3781.

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O feijÃo-de-corda (Vigna unguiculata (L.) Walp.), à uma espÃcie cultivada de grande importÃncia para a alimentaÃÃo das populaÃÃes rurais e urbanas das regiÃes tropicais e subtropicais do mundo. A produtividade dessa espÃcie varia muito, em virtude, principalmente, das variaÃÃes climÃticas e da utilizaÃÃo de materiais genÃticos pouco produtivos ou com caracterÃsticas indesejÃveis. A produtividade de grÃos à influenciada por efeitos genotÃpicos (G), efeitos ambientais (E) e das interaÃÃes genÃtipo x ambiente (G x E), que levam ao comportamento diferencial dos genÃtipos nos diversos ambientes. A
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Li, Haitao. "Genetic adaptation of aspen populations to spring risk environments a novel remote sensing approach /." Master's thesis, 2010. http://hdl.handle.net/10048/1118.

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Thesis (M.Sc.)--University of Alberta, 2010.<br>Title from PDF file main screen (viewed on July 15, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Forest Biology and Management, Department of Renewable Resources, University of Alberta. Includes bibliographical references.
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Books on the topic "Genotype × environments"

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Royal Society of Canada. Symposium. Challenging genetic determinism: New perspectives on the gene in its multiple environments. McGill-Queen's University Press, 2011.

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Book chapters on the topic "Genotype × environments"

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Álvarez-Castro, José M. "Discovering the Genotype." In Genes, Environments and Interactions. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41159-5_1.

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Bradshaw, John E. "Genotype x Environment Interactions and Selection Environments." In Plant Breeding: Past, Present and Future. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23285-0_7.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Bayesian and Classical Prediction Models for Categorical and Count Data." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_7.

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AbstractIn this chapter, we explain, under a Bayesian framework, the fundamentals and practical issues for implementing genomic prediction models for categorical and count traits. First, we derive the Bayesian ordinal model and exemplify it with plant breeding data. These examples were implemented in the library BGLR. We also derive the ordinal logistic regression. The fundamentals and practical issues of penalized multinomial logistic regression and penalized Poisson regression are given including several examples illustrating the use of the glmnet library. All the examples include main effec
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Linear Mixed Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_5.

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AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or
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Yusop, Mohd Rafii, Yusuff Oladosu, Abdul Rahim Harun, et al. "Application of mutation techniques and genotype × environment interaction for grain yield in ion beam induced mutant rice lines tested in multiple locations in Malaysia." In Mutation breeding, genetic diversity and crop adaptation to climate change. CABI, 2021. http://dx.doi.org/10.1079/9781789249095.0023.

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Abstract Genotype evaluation for stability and high yield in rice is an important factor for sustainable rice production and food security. These evaluations are essential, especially when the breeding objective is to release rice with high yields, adaptability and stability for commercial cultivation. To achieve this objective, this study was carried out to select high-yielding rice genotypes induced by ion beam irradiation. Seeds of the rice variety 'MR219' were subjected to different doses of 320 MeV carbon-ion beam irradiation to determine the optimum dose to produce high mutant frequency
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Bayesian Genomic Linear Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_6.

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AbstractThe Bayesian paradigm for parameter estimation is introduced and linked to the main problem of genomic-enabled prediction to predict the trait of interest of the non-phenotyped individuals from genotypic information, environment variables, or other information (covariates). In this situation, a convenient practice is to include the individuals to be predicted in the posterior distribution to be sampled. We explained how the Bayesian Ridge regression method is derived and exemplified with data from plant breeding genomic selection. Other Bayesian methods (Bayes A, Bayes B, Bayes C, and
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Functional Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_14.

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AbstractThis chapter deals with the main theoretical fundamentals and practical issues of using functional regression in the context of genomic prediction. We explain how to represent data in functions by means of basis functions and considered two basis functions: Fourier for periodic or near-periodic data and B-splines for nonperiodic data. We derived the functional regression with a smoothed coefficient function under a fixed model framework and some examples are also provided under this model. A Bayesian version of functional regression is outlined and explained and all details for its imp
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Reproducing Kernel Hilbert Spaces Regression and Classification Methods." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_8.

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AbstractThe fundamentals for Reproducing Kernel Hilbert Spaces (RKHS) regression methods are described in this chapter. We first point out the virtues of RKHS regression methods and why these methods are gaining a lot of acceptance in statistical machine learning. Key elements for the construction of RKHS regression methods are provided, the kernel trick is explained in some detail, and the main kernel functions for building kernels are provided. This chapter explains some loss functions under a fixed model framework with examples of Gaussian, binary, and categorical response variables. We ill
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Bustos-Korts, Daniela, Marcos Malosetti, Scott Chapman, and Fred van Eeuwijk. "Modelling of Genotype by Environment Interaction and Prediction of Complex Traits across Multiple Environments as a Synthesis of Crop Growth Modelling, Genetics and Statistics." In Crop Systems Biology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-20562-5_3.

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Bramel-Cox, P. "Breeding for Reliability of Performance Across Unpredictable Environments." In Genotype-by-Environment Interaction. CRC Press, 1996. http://dx.doi.org/10.1201/9781420049374.ch11.

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Conference papers on the topic "Genotype × environments"

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Fagan, David. "Genotype-phenotype mapping in dynamic environments with grammatical evolution." In the 13th annual conference companion. ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2002091.

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Huang, Chien-Feng, and Luis M. Rocha. "Tracking extrema in dynamic environments using a coevolutionary agent-based model of genotype edition." In the 2005 conference. ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068099.

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S. MADAB, Dawood, and Suaad M. HASSEN. "ESTIMATION GENOTYPIC ENVIRONMENTAL INTERACTION BY USING GGE BIPLOT ANALYSIS OF COTTON GENOTYPES (GOSSYPIUM HERSUTUM L.)." In VI.International Scientific Congress of Pure,Applied and Technological Sciences. Rimar Academy, 2022. http://dx.doi.org/10.47832/minarcongress6-38.

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Seven cotton genotypes were grown in a different environmental conditions (as a combination among plant densities: 15, 20, and 25 cm under salt and non salt stress irrigation) to estimate genetic behavior in different environments of cotton genotypes (Ceebro, W888, Pac-cot189, Lashata, Cocker310, Montana, and Ik259).Analysis of variance for the interaction environments with the genotypes according to Randomized Completely Block Design with three replications were used, furthermore GGE biplot analysis for the seed cotton yield. Results Showed : Environments affected high significant in seed cot
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Sholihin. "GGE and AMMI biplot for interpreting interaction of genotype X environments of cassava promising genotypes." In THE 2ND SCIENCE AND MATHEMATICS INTERNATIONAL CONFERENCE (SMIC 2020): Transforming Research and Education of Science and Mathematics in the Digital Age. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0041787.

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Davila, Jaime J. "Genotype coding, diversity, and dynamic environments: A study on an evolutionary neural network multi-agent system." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900593.

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Bratković, Kamenko, Kristina Luković, Vladimir Perišić, et al. "ANALYSIS OF GENOTYPE BY ENVIRONMENT INTERACTION FOR SPIKE TRAITS IN WINTER SIX-ROW BARLEY." In 1st International Symposium on Biotechnology. University of Kragujevac, Faculty of Agronomy, 2023. http://dx.doi.org/10.46793/sbt28.045b.

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This research was conducted with some spike traits of twenty winter six-row barley genotypes in six environments. The aim of this study was to determine the significance and take advantage useful genotype by environment interacton (GEI) by applying AMMI-1 model. High statistical significance GEI was determined. Wide adaptability genotypes were J-29, J-33, J-9 and J-21 for spike length (SL) as Grand and Ozren for grain number per spike (GNS). The winner genotypes in all environments were Ozren and Grand for SL as Ozren for GNS. All the examined environments can be considered as one megaenvironm
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"Maximizing wheat grain yield in irrigated mega-environments: Targeting optimal flowering period by selecting optimal sowing date and genotype with appropriate phenological development pattern." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.hu675.

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Bunjac, Nenad, Vladan Pešić, Vladimir Perišićv, Nenad Đurić, and Nidal Shaban. "INDUSTRIAL TOMATO CULTIVARS AND INBRED LINES YIELD STABILITY ANALYSIS." In 3rd International Symposium on Biotechnology. University of Kragujevac, Faculty of Agronomy in Čačak, 2025. https://doi.org/10.46793/sbt30.06nb.

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Phenotypic plasticity refers to the ability of genotypes to exhibit different phenotypes in response to environmental conditions. The AMMI model is often used to analyze this interaction in multienvironment trials. In a study involving six tomato genotypes grown in different environments, genotype L88 yielded the highest average at nearly 31 t/ha, while L27 and L53 had the lowest yields. The AMMI biplot indicated that genotypes L49 and L21 had strong interactions with the Pazardzhik environment, while L88 had a positive interaction with the Požega environment. The analysis concluded that AMMI
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Saşco, Elena, and S. Lyatamborg. "The behavior of some autumn tritical genotypes to biotic stress in vitro." In Scientific International Symposium “Advanced Biotechnologies - Achievements and Prospects” (VIth Edition). Institute of Genetics, Physiology and Plant Protection, 2022. http://dx.doi.org/10.53040/abap6.2022.74.

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The given research presents the response of some callus characters of mature embryos of the Costel, Ingen 54, L 161 and Haiduc triticale genotypes to the culture filtrates of Alternaria alternata, Drechslera sorokiniana and Fusarium solani, administered in the Murashige and Skoog nutrient medium. The reaction was differentiated by both triticale genotype and fungal strains. In relation to the witness variant, the variability of the investigated indices showed re-sistance (R), medium resistance (MR) and only medium sensitivity (MS) for the callus surface in the Costel genotype. Through the cluste
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Reports on the topic "Genotype × environments"

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Amzeri, Achmad, B. S. DARYONO, and M. SYAFII. GENOTYPE BY ENVIRONMENT AND STABILITY ANALYSES OF DRYLAND MAIZE HYBRIDS. SABRAO Journal of Breeding and Genetics, 2020. http://dx.doi.org/10.21107/amzeri.2020.2.

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The phenotypic analysis of new candidate varieties at multiple locations could provide information on the stability of their genotypes. We evaluated the stability of 11 maize hybrid candidates in five districts in East Java Province, Indonesia. Maize hybrids with high yield potential and early maturity traits derived from a diallel cross were planted in a randomized complete block design with two checks (Srikandi Kuning and BISI-2) as a single factor with four replicates. The observed traits were grain yield per hectare and harvest age. The effects of environment, genotype, and genotype × envi
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Hovav, Ran, Peggy Ozias-Akins, and Scott A. Jackson. The genetics of pod-filling in peanut under water-limiting conditions. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597923.bard.

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Pod-filling, an important yield-determining stage is strongly influenced by water stress. This is particularly true for peanut (Arachishypogaea), wherein pods are developed underground and are directly affected by the water condition. Pod-filling in peanut has a significant genetic component as well, since genotypes are considerably varied in their pod-fill (PF) and seed-fill (SF) potential. The goals of this research were to: Examine the effects of genotype, irrigation, and genotype X irrigation on PF and SF. Detect global changes in mRNA and metabolites levels that accompany PF and SF. Explo
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Hunter, Martha S., and Einat Zchori-Fein. Rickettsia in the whitefly Bemisia tabaci: Phenotypic variants and fitness effects. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7594394.bard.

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The sweet potato whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae) is a major pest of vegetables, field crops, and ornamentals worldwide. This species harbors a diverse assembly of facultative, “secondary” bacterial symbionts, the roles of which are largely unknown. We documented a spectacular sweep of one of these, Rickettsia, in the Southwestern United States in the B biotype (=MEAM1) of B. tabaci, from 1% to 97% over 6 years, as well as a dramatic fitness benefit associated with it in Arizona but not in Israel. Because it is critical to understand the circumstances in which a symbiont invas
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Eshel, Amram, Jonathan P. Lynch, and Kathleen M. Brown. Physiological Regulation of Root System Architecture: The Role of Ethylene and Phosphorus. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7585195.bard.

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Specific Objectives and Related Results: 1) Determine the effect of phosphorus availability on ethylene production by roots. Test the hypothesis that phosphorus availability regulates ethylene production Clear differences were found between the two plants that were studied. In beans ethylene production is affected by P nutrition, tissue type, and stage of development. There are genotypic differences in the rate of ethylene production by various root types and in the differential in ethylene production when P treatments are compared. The acceleration in ethylene production with P deficiency inc
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