Simultaneous selection of peach rootstocks by mixed models

The term adaptability refers to the ability of a genotype to respond favorably to environmental spur, while stability is the predictability of genotypic behavior. Therefore, the objective was to select Prunus rootstock cultivars with greater adaptability and genotypic stability for subtropical environmental conditions using the HMPRVG method. The experiment was conducted in Chapecó, Santa Catarina State, Brazil. Twenty-one rootstock genotypes were evaluated under the ‘BRS-Libra’ canopy cultivar and one genotype from self-rooted seedlings. The 22 genotypes were evaluated for canopy volume, yield, fruit diameter and fruit set in the growing seasons 2015/16, 2016/17, 2017/18 and 2018/19. Adaptability and stability were measured by means of the harmonic mean relative performance of genotypic values (HMRPGV). In addition, genetic parameters for heritability and ratio test were measured. According to the results, the self-rooted, ‘De Guia’, ‘I-67-52-4’, ‘Mexico Row 1’ and ‘Rosaflor’ genotypes coincided most frequently in the ranking of the three most adaptable and stable genotypes. On the other hand, the ‘P. mandshurica’, ‘Rigitano’ and ‘Santa Rosa’ genotypes corresponded to the lowest adaptability and stability values, thus constituting low quality genetic materials for cultivation. It can be concluded that under the tested conditions the HMPRVG method is efficient for the Prunus rootstock selection cultivars and the ‘BRS-Libra’ grafted on ‘Mexico Row 1’, ‘Rosaflor’ rootstocks and trees from self-rooted seedlings have greater adaptability and phenotypic stability under the subtropical cultivation conditions.

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Identification of the Optimum Environments for the High Yield and Quality Traits of Lentil Genotypes Evaluated in Multi-Location Trials

Sustainability ◽

10.3390/su13158247 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8247

Author(s):

Dimitrios N. Vlachostergios ◽

Christos Noulas ◽

Anastasia Kargiotidou ◽

Dimitrios Baxevanos ◽

Evangelia Tigka ◽

...

Keyword(s):

Crude Protein ◽

Agronomic Traits ◽

High Yield ◽

Breeding Programs ◽

Growing Seasons ◽

Yield And Quality ◽

Food And Feed ◽

Superior Genotypes ◽

Selection Of ◽

The Ideal

Lentil is a versatile and profitable pulse crop with high nutritional food and feed values. The objectives of the study were to determine suitable locations for high yield and quality in terms of production and/or breeding, and to identify promising genotypes. For this reason, five lentil genotypes were evaluated in a multi-location network consisting of ten diverse sites for two consecutive growing seasons, for seed yield (SY), other agronomic traits, crude protein (CP), cooking time (CT) and crude protein yield (CPY). A significant diversification and specialization of the locations was identified with regards to SY, CP, CT and CPY. Different locations showed optimal values for each trait. Locations E4 and E3, followed by E10, were “ideal” for SY; locations E1, E3 and E7 were ideal for high CP; and the “ideal” locations for CT were E3 and E5, followed by E2. Therefore, the scope of the cultivation determined the optimum locations for lentil cultivation. The GGE-biplot analysis revealed different discriminating abilities and representativeness among the locations for the identification of the most productive and stable genotypes. Location E3 (Orestiada, Region of Thrace) was recognized as being optimal for lentil breeding, as it was the “ideal” or close to “ideal” for the selection of superior genotypes for SY, CP, CT and CPY. Adaptable genotypes (cv. Dimitra, Samos) showed a high SY along with excellent values for CP, CT and CPY, and are suggested either for cultivation in many regions or to be exploited in breeding programs.

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Gradient boosting for linear mixed models

The International Journal of Biostatistics ◽

10.1515/ijb-2020-0136 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Colin Griesbach ◽

Benjamin Säfken ◽

Elisabeth Waldmann

Keyword(s):

Random Effects ◽

Mixed Models ◽

Selection Procedure ◽

Classification Theory ◽

Gradient Boosting ◽

Random Structure ◽

Boosting Algorithm ◽

The One ◽

Biased Estimates ◽

Selection Of

Abstract Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approaches also offer methods accounting for random effects and thus enable prediction of mixed models for longitudinal and clustered data. However, these approaches include several flaws resulting in unbalanced effect selection with falsely induced shrinkage and a low convergence rate on the one hand and biased estimates of the random effects on the other hand. We therefore propose a new boosting algorithm which explicitly accounts for the random structure by excluding it from the selection procedure, properly correcting the random effects estimates and in addition providing likelihood-based estimation of the random effects variance structure. The new algorithm offers an organic and unbiased fitting approach, which is shown via simulations and data examples.

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Selection strategies of segregant soybean populations for resistance to Asian rust

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2009001100012 ◽

2009 ◽

Vol 44 (11) ◽

pp. 1452-1459 ◽

Cited By ~ 8

Author(s):

Aliny Simony Ribeiro ◽

José Francisco Ferraz de Toledo ◽

Magno Antonio Patto Ramalho

Keyword(s):

Genetic Variance ◽

Additive Genetic Variance ◽

Infected Leaf ◽

Good Resistance ◽

Narrow Sense Heritability ◽

Growing Seasons ◽

Selection Strategies ◽

Line Selection ◽

Soybean Resistance ◽

Selection Of

The objective of this work was to identify the best selection strategies for the more promising parental combinations to obtain lines with good resistance to soybean Asian rust (Phakopsora pachyrhizi). Two experiments were carried out in the field during the 2006/2007 and 2007/2008 growing seasons, to determine the percentage of infected leaf area of individual plants of five parents and their segregant F2 and F3 populations. The data obtained indicates that additive genetic variance predominates in the control of soybean resistance to Asian rust, and that the year and time of assessment do not significantly influence the estimates of the genetic parameters obtained. The narrow-sense heritability (h²r) ranged from 23.12 to 55.83%, and indicates the possibility of successful selection of resistant individuals in the early generations of the breeding program. All the procedures used to select the most promising populations to generate superior inbred lines for resistance to P. pachyrhizi presented similar results and identified the BR01-18437 x BRS 232 population as the best for inbred line selection.

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ECOLOGICAL-GEOGRAPHIC APPROACHES TO THE STUDY OF GENETIC DIVERSITY OF BARLEY AND OAT FROM THE VIR COLLECTION

Ecological genetics ◽

10.17816/ecogen16128 ◽

2020 ◽

Cited By ~ 1

Author(s):

Igor Loskutov ◽

Lubov Novikova ◽

Olga Kovaleva ◽

Nadezhda Ivanova ◽

Elena Blinova ◽

...

Keyword(s):

Environmental Conditions ◽

Growing Season ◽

Regression Coefficients ◽

Cultivation Conditions ◽

High Productivity ◽

Sum Of Effective Temperatures ◽

Economically Valuable Traits ◽

Effective Temperatures ◽

The Stability ◽

Selection Of

Under conditions of climate change, the assessment of the stability of genotypes is of particular importance. To conduct directed selection of genotypes with a narrow or broad reaction rate, it is necessary to assess their stability already in the early stages of breeding. The aim of the study was to study the stability of breeding significant traits of oat and barley samples in contrasting ecological and geographical conditions. 25 oat samples and 25 barley samples were studied over 3 years under contrasting conditions in St. Petersburg and the Tambov region. Varieties are characterized by average values of economically valuable traits and genotype regression coefficients on the influence of the bi environment according to Eberhart and Russell. The most sensitive to a change in the ecological and geographical situation were the durations of the germination-heading, germination-harvest periods and grain yield. These characters varied to a greater extent depending on the cultivation conditions than on the genotype. According to regression coefficients for environmental conditions, significant differences in genotypes were only in yield. Contrasting groups of varieties were distinguished by regression coefficients on environmental conditions, genotypes with high productivity. The durations of germination-heading, germination-harvest, the plant height reacted to the change in the environment the same in different varieties. The duration of the growing season was determined by the sum of effective temperatures above 15C. The reduction of the growing season in both crops was 3 days with an increase in the sum of effective temperatures above 15C by 100C.

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On estimating and testing associations between random coefficients from multivariate generalized linear mixed models of longitudinal outcomes

Statistical Methods in Medical Research ◽

10.1177/0962280214568522 ◽

2015 ◽

Vol 26 (3) ◽

pp. 1130-1145 ◽

Cited By ~ 3

Author(s):

Susan K Mikulich-Gilbertson ◽

Brandie D Wagner ◽

Paula D Riggs ◽

Gary O Zerbe

Keyword(s):

Likelihood Ratio ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Mathematical Proof ◽

Linear Mixed Models ◽

Delta Method ◽

Standard Errors ◽

Ratio Test ◽

Type I ◽

Time Lags

Different types of outcomes (e.g. binary, count, continuous) can be simultaneously modeled with multivariate generalized linear mixed models by assuming: (1) same or different link functions, (2) same or different conditional distributions, and (3) conditional independence given random subject effects. Others have used this approach for determining simple associations between subject-specific parameters (e.g. correlations between slopes). We demonstrate how more complex associations (e.g. partial regression coefficients between slopes adjusting for intercepts, time lags of maximum correlation) can be estimated. Reparameterizing the model to directly estimate coefficients allows us to compare standard errors based on the inverse of the Hessian matrix with more usual standard errors approximated by the delta method; a mathematical proof demonstrates their equivalence when the gradient vector approaches zero. Reparameterization also allows us to evaluate significance of coefficients with likelihood ratio tests and to compare this approach with more usual Wald-type t-tests and Fisher’s z transformations. Simulations indicate that the delta method and inverse Hessian standard errors are nearly equivalent and consistently overestimate the true standard error. Only the likelihood ratio test based on the reparameterized model has an acceptable type I error rate and is therefore recommended for testing associations between stochastic parameters. Online supplementary materials include our medical data example, annotated code, and simulation details.

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Estimation of genetic parameters and selection of elephant-grass ( Pennisetum purpureum Schumach.) for forage production using mixed models

Chilean journal of agricultural research ◽

10.4067/s0718-58392018000200198 ◽

2018 ◽

Vol 78 (2) ◽

pp. 198-204 ◽

Cited By ~ 1

Author(s):

Wanessa F. Stida ◽

Rogério F. Daher ◽

Alexandre P. Viana ◽

Ana Kesia F. Vidal ◽

Rafael S. Freitas ◽

...

Keyword(s):

Mixed Models ◽

Genetic Parameters ◽

Pennisetum Purpureum ◽

Elephant Grass ◽

Forage Production ◽

Estimation Of Genetic Parameters ◽

Selection Of

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Estimate of genetic parameters and predicted gains with early selection of physic nut families

Ciência e Agrotecnologia ◽

10.1590/s1413-70542012000200004 ◽

2012 ◽

Vol 36 (2) ◽

pp. 163-170 ◽

Cited By ~ 13

Author(s):

Bruno Galvêas Laviola ◽

Alexandre Alonso Alves ◽

Fábio de Lima Gurgel ◽

Tatiana Barbosa Rosado ◽

Rhayanne Dias Costa ◽

...

Keyword(s):

Grain Yield ◽

Genetic Parameters ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Biodiesel Production ◽

Early Selection ◽

Physic Nut ◽

Genetic Gains ◽

Canopy Volume ◽

Selection Of

An initial evaluation of early selection of physic nut genotypes based on phenotypic data is presented. In order to predict the genetic gains with early selection, genetic parameters, e.g. additive genetic variance, were first obtained for grain yield along with other numerous traits. The results demonstrated that additive genetic variance exists not only for grain yield, which is considered to be the most important trait for oil and biodiesel production, but also for numerous other traits. The predicted genetic gains for grain yield, considering the selection of the 30, 20, 10 and 5 best families in the second crop year are respectively, 40.47, 48.43, 61.78 and 70.28%. With the selection of highly yielding physic nut genotypes indirectly genotypes with enhanced volume would be also selected, because yield exhibits moderate to high genetic correlations with height e canopy volume. The results here presented demonstrate the potential of the population gathered in the Brazilian physic nut germplasm bank for genetic breeding purposes and that superior physic nut families can be selected with high accuracy based on the evaluation of its second crop.

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Salicornia as a crop plant in temperate regions: selection of genetically characterized ecotypes and optimization of their cultivation conditions

AoB Plants ◽

10.1093/aobpla/plu071 ◽

2014 ◽

Vol 6 ◽

Cited By ~ 17

Author(s):

Devesh Singh ◽

Anne K. Buhmann ◽

Tim J. Flowers ◽

Charlotte E. Seal ◽

Jutta Papenbrock

Keyword(s):

Crop Plant ◽

Cultivation Conditions ◽

Selection Of

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Correlation among agronomic, physiological and phytochemical traits in Artemisia annua L. varieties

Revista Brasileira de Plantas Medicinais ◽

10.1590/s1516-05722013000100019 ◽

2013 ◽

Vol 15 (1) ◽

pp. 135-141

Author(s):

C.O. Bolina ◽

J.A. Marchese ◽

D. Capelin ◽

D.J. Bertoncelli ◽

C. Pinnow ◽

...

Keyword(s):

Essential Oil ◽

Artemisia Annua ◽

Significant Negative Correlation ◽

Oil Yield ◽

Significance Level ◽

Canopy Volume ◽

The Relationship ◽

Essential Oil Yield ◽

Artemisinin Content ◽

Selection Of

The aim of this study was to estimate the correlations among agronomic, physiological and phytochemical traits of two varieties of Artemisia annua and to identify correlations that can be used in selection and breeding processes for this species. The design was completely randomized and the treatments were for Artemisia annua varieties 2/39x5x3M and 2/39x1V, which were subjected to agronomic, physiological and phytochemical evaluations. The relationship among the studied traits was estimated with Pearson's correlation coefficient, and the significance level of correlations was established by the t-test using Genes software. A significant positive correlation was found between the traits canopy volume and essential oil yield for 2/39x5x3M variety, showing that the selection of plants with a larger canopy volume would facilitate indirect selection of the trait essential oil yield. This correlation was not found for the 2/39x1V variety. In both varieties, there was a significant negative correlation between canopy volume and artemisinin content, which suggested that the selection of plants with a large canopy volume and a large number of branches should be avoided if the goal is to increase artemisinin content.

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