scholarly journals Multi-information analysis for recommendation of flooded-irrigated rice for adaptability and phenotypic stability

2021 ◽  
Vol 8 ◽  
pp. 1-15
Author(s):  
Antônio Carlos Siva Júnior ◽  
Michele Jorge Silva ◽  
Weverton Gomes Costa ◽  
Ithalo Coelho Sousa ◽  
Cosme Damião Cruz ◽  
...  
Keyword(s):  
Information Criterion ◽  
Information Criteria ◽  
Efficient Tool ◽  
Breeding Programs ◽  
Irrigated Rice ◽  
Gxe Interaction ◽  
Aggregation Of Information ◽  
Environmental Variations ◽  
Rice Genotypes ◽  
Selection Phase

The GxE interaction is one of the major difficulties of plant breeding programs, both in the selection phase and in the recommendation of cultivars. To assess adaptability and stability, various statistical methods are used. The simultaneous use of some methodologies, using multi-information criteria for cultivar’s recommendation, can extract information that cannot be observed using each methodology separately. The aim of this work was to perform a large description of the behavior of flooded-irrigated rice genotypes, responding to environmental variations, using methods already established in the literature, but exploring the particularities of each methodology that together establish an information criterion for cultivar recommendation. To this end, 18 rice genotypes belonging to flood-irrigated rice breeding program were evaluated over four agricultural years, 2012/2013 to 2015/2016, totaling 12 environments (3 sites × 4 years). Multi-information estimates were performed to adaptability and stability analysis. There was no sign for the effect of the genotypes, and there was the significance of the effects of environment and GxE interaction. The aggregation of information and the large description of the behavior of the flooded rice genotypes demonstrated to be an efficient tool for studies of adaptability and stability.

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

scholarly journals Using Model Selection Criteria to Choose the Number of Principal Components

2021 ◽  
Vol 20 (3) ◽  
pp. 450-461
Author(s):  
Stanley L. Sclove
Keyword(s):  
Model Selection ◽  
Principal Components ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Information Criteria ◽  
Akaike's Information Criterion ◽  
Model Selection Criteria ◽  
Adequate Number ◽  
Number Of Principal Components

AbstractThe use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.

scholarly journals Sensitivity and specificity of information criteria

2019 ◽  
Vol 21 (2) ◽  
pp. 553-565 ◽  
Author(s):  
John J Dziak ◽  
Donna L Coffman ◽  
Stephanie T Lanza ◽  
Runze Li ◽  
Lars S Jermiin
Keyword(s):  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Information Criteria ◽  
The Other ◽  
Biological Research ◽  
Ratio Test ◽  
Relative Importance ◽  
Fields Of Study ◽  
Alternative Perspective ◽  
Practical Implications

Abstract Information criteria (ICs) based on penalized likelihood, such as Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. They may not realize that the criteria may disagree. Others try to compare models using multiple criteria but encounter ambiguity when different criteria lead to substantively different answers, leading to questions about which criterion is best. In this paper we present an alternative perspective on these criteria that can help in interpreting their practical implications. Specifically, in some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC. This perspective may lead to insights about how to interpret the ICs in more complex situations. For example, AIC or BIC could be preferable, depending on the relative importance one assigns to sensitivity versus specificity. Understanding the differences and similarities among the ICs can make it easier to compare their results and to use them to make informed decisions.

Genotypic and environmental variations in root morphology in rice genotypes under upland field conditions

2003 ◽  
Vol 255 (1) ◽  
pp. 189-200 ◽  
Author(s):  
M. Kondo ◽  
P.P. Pablico ◽  
D.V. Aragones ◽  
R. Agbisit ◽  
J. Abe ◽  
...  
Keyword(s):  
Root Morphology ◽  
Field Conditions ◽  
Environmental Variations ◽  
Rice Genotypes ◽  
Upland Field

A penalized regression approach for DNA copy number study using the sequencing data

2019 ◽  
Vol 18 (4) ◽  
Author(s):  
Jaeeun Lee ◽  
Jie Chen
Keyword(s):  
Copy Number ◽  
Information Criterion ◽  
Penalized Regression ◽  
Parameter Selection ◽  
Information Criteria ◽  
Tuning Parameter ◽  
Dimensional Structure ◽  
Sequencing Data ◽  
Dna Copy Number ◽  
Regression Approach

Abstract Modeling the high-throughput next generation sequencing (NGS) data, resulting from experiments with the goal of profiling tumor and control samples for the study of DNA copy number variants (CNVs), remains to be a challenge in various ways. In this application work, we provide an efficient method for detecting multiple CNVs using NGS reads ratio data. This method is based on a multiple statistical change-points model with the penalized regression approach, 1d fused LASSO, that is designed for ordered data in a one-dimensional structure. In addition, since the path algorithm traces the solution as a function of a tuning parameter, the number and locations of potential CNV region boundaries can be estimated simultaneously in an efficient way. For tuning parameter selection, we then propose a new modified Bayesian information criterion, called JMIC, and compare the proposed JMIC with three different Bayes information criteria used in the literature. Simulation results have shown the better performance of JMIC for tuning parameter selection, in comparison with the other three criterion. We applied our approach to the sequencing data of reads ratio between the breast tumor cell lines HCC1954 and its matched normal cell line BL 1954 and the results are in-line with those discovered in the literature.

Resistance of rice genotypes to Tibraca limbativentris (Hemiptera: Pentatomidae)

2019 ◽  
Author(s):  
André Cirilo De S Almeida ◽  
Cássio A Dierings ◽  
Celio Borella Júnior ◽  
Flávio G Jesus ◽  
Jose A F Barrigossi
Keyword(s):  
Molecular Biology ◽  
Adverse Effects ◽  
Rice Plant ◽  
Crop Damage ◽  
Stink Bug ◽  
Asian Origin ◽  
Breeding Programs ◽  
Rice Farmers ◽  
Rice Genotypes ◽  
Antibiosis Resistance

Abstract The Tibraca limbativentris is a pest that causes rice crop damage and may lead to grain yield reductions of up to 90%. The most commonly used tactic for T. limbativentris control is chemical, which causes adverse effects on the environment. This study was conducted to identify sources of antixenosis and antibiosis resistance to T. limbativentris mediated by the hardness and diameter of the stem of the rice plant. Antibiosis and antixenosis tests were carried out with 22 rice genotypes of Brazilian and Asian origin. The Canela de Ferro, BRS Esmeralda, and Desconhecido Branco genotypes had the lowest proportions of stems showing symptoms of the damage caused by T. limbativentris. Canela de Ferro, Primavera, and IR 22 genotypes had the lowest nymphs survivorship and the genotypes Marabá Branco, Marabá, Skirivimankoti, Pepita, BR IRGA 409, Curinga, IR 40, Bonança, Desconhecido Branco, Bico Ganga, Primavera, and BRS Esmeralda were the least attractive to the insects. The genotypes BRS Esmeralda, Primavera, Desconhecido Branco, and Canela de Ferro were resistant to the rice stalk stink bug by antibiosis and/or antixenosis. These genotypes can be used directly by rice farmers for T. limbativentris management. Molecular biology studies should be conducted to identify and characterize resistance genes in these genotypes so that they can be used in breeding programs.

Tolerance and Phenotypic Analysis of Irrigated Rice Genotypes under Iron Toxicity

2019 ◽  
pp. 1-11
Author(s):  
Eduardo Anibele Streck ◽  
Gabriel Almeida Aguiar ◽  
Paulo Henrique Karling Facchinello ◽  
Lais Perin ◽  
Pedro Ujacov da Silva ◽  
...  
Keyword(s):  
Negative Correlation ◽  
Evaluation System ◽  
Dry Matter ◽  
Block Design ◽  
Iron Toxicity ◽  
Breeding Program ◽  
Temperate Climate ◽  
Irrigated Rice ◽  
Excess Iron ◽  
Rice Genotypes

Introduction: The development of tolerant rice genotypes is the main tool to minimize the problem caused by the excess of iron in the soil. Aims: The study aims to evaluate the response of genotypes of the Embrapa breeding program of irrigated rice against iron stress, as well as to envision the relationship of the effect of this disorder on leaf mass production in different phenological phases of the plant. Study Design: The trial was established in a randomized block design with additional controls, with the plots consisting of four rows 3 m long with 0.20 m spacing between rows. Place and Duration of Study: The experiment was conducted in the experimental field of Embrapa Temperate Climate Lowlands Station, in a period of nine years, consisting of the harvest of 2006/2007 until 2014/2015. Methodology: It was evaluated 255 lines along with 4 additional control cultivars with known tolerance levels. The symptoms were evaluated at 40, 70 and 100 days after plant emergence through a visual assessment in the field, based on the standard evaluation system for rice. In addition to the determination of the average toxicity levels of genotypes, the mass was collected, constituted by the dry matter of shoot, for the detection of interrelationships through their correlations. Results: It was found that 58.82% of the developed irrigated rice lines show good tolerance to toxicity by excess iron. The association of the content of dry matter of shoots and the levels of indirect toxicity of iron showed a significant negative correlation (-0.6848), being that the highest magnitude of negative correlation was at 70 DAE (-0.6161). Conclusion: There is variability for tolerance to indirect iron toxicity between the irrigated rice genotypes assessed. The breeding program of irrigated rice of Embrapa has been effective in developing genotypes with tolerance by excess iron in the soil over nine years. There is a negative association between the content of dry matter of shoots and the levels of indirect iron toxicity.

scholarly journals Genetic diversity assessment of Rice (Oryza sativa L.) germplasm using ssr markers

2015 ◽  
Vol 1 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Ahasanul Hoque ◽  
Shamsun Nahar Begum ◽  
Lutful Hassan
Keyword(s):  
Genetic Diversity ◽  
Ssr Markers ◽  
Oryza Sativa L ◽  
Growth Duration ◽  
Breeding Programs ◽  
Diversity Assessment ◽  
Days To Heading ◽  
Early Maturing ◽  
Rice Genotypes ◽  
Selection Of

Diversity at molecular level among thirty rice genotypes, selected based on earliness and morphometric diversity was evaluated through five SSR markers associated with days to heading. Three primers viz., RM147, RM167 and RM215 showed polymorphism for growth duration related traits. A total of 17 alleles were detected among the 30 rice genotypes with an average of 5.66 alleles per locus. Polymorphism Information Content (PIC) ranged from 0.356 to 0.798 with an average of 0.543. A dendrogram based on total microsatellite polymorphism grouped 30 genotypes into four major clusters at 0.39 similarity coefficient differentiating early maturing genotypes from others. This information about the genetic diversity will be very useful for proper identification and selection of appropriate parents for future breeding programs, including gene mapping. The results also showed that microsatellite markers associated to genes or QTLs controlling growth duration properties are suitable tools for marker assisted selection (MAS) to select rice lines with short growth duration. DOI: http://dx.doi.org/10.3329/ralf.v1i1.22354 Res. Agric., Livest. Fish.1(1): 37-46, Dec 2014

Genetic diversity in sunflower (Helianthus annuus L.) as revealed by random amplified polymorphic DNA analysis

1994 ◽  
Vol 45 (7) ◽  
pp. 1319 ◽  
Author(s):  
WR Lawson ◽  
RJ Henry ◽  
JK Kochman ◽  
GA Kong
Keyword(s):  
Genetic Diversity ◽  
Ribosomal Rna ◽  
Dna Analysis ◽  
Random Amplified Polymorphic Dna ◽  
Efficient Tool ◽  
Breeding Programs ◽  
Breeding Lines ◽  
Sunflower Genome ◽  
Pair Wise Comparison ◽  
Selection Of

A cross-section of sunflower genotypes grown in Australia including commercial cultivars (Suncross 40R, Hysun 33, Hysun 45CQ, Advance, DK3873), breeding lines (Sunfola, S37- 388, PhRR3, HA-R2, MC29, MC69, S37-388RR), wild sunflower varieties (H. annuus, H. argophyllus), a distantly related species (Tithonia diversifolia), and a hexaploid/diploid cross (H. tuberosus L.x H. annuus L.) were assessed for genetic diversity using RAPD (Random Amplified Polymorphic DNA) analysis. A considerable amount of polymorphism was revealed. Of the total of 158 markers amplified, 133 were polymorphic for at least one pair-wise comparison within the 16 genotypes. Overall, 33% dissimilarity was detected, with an average of 27% dissimilarity revealed among the hybrids and breeding lines, which exhibited 38% dissimilarity to the wild varieties H. annuus and H. tuberosus, and 51% dissimilarity to Tithonia and H. tuberosus x H, annuus. PCR of the 5S ribosomal RNA gene spacer region did not reveal any polymorphisms among the cultivated and breeding lines, but did distinguish between H. tuberosus and the other wild species. This survey of a selection of sunflower genotypes indicates that the genetic base of domesticated sunflower may be quite wide. These results suggest that RAPD methodology will provide an efficient tool for the analysis of the sunflower genome, in particular in breeding programs.

scholarly journals Anpassung eines Fixed Regression Modells für die tägliche Zunahme von Fleckviehbullen mit Hilfe von Informationskriterien

2007 ◽  
Vol 50 (1) ◽  
pp. 47-58
Author(s):  
N. Mielenz ◽  
H. Krejčová ◽  
J. Přibyl ◽  
L. Schüler
Keyword(s):  
Fixed Effects ◽  
Covariance Structure ◽  
Information Criterion ◽  
Complex Model ◽  
Second Order ◽  
Information Criteria ◽  
Covariance Structures ◽  
Expected Gain ◽  
Bayesian Criteria ◽  
Daily Gain

Abstract. Title of the paper: Fitting a fixed regression model for daily gain of bulls using information criterion In this study the model choice is demonstrated exemplarily on data of 6405 Czech Simmental bulls using information criterion. Per bull up to 8 observations were available for the trait daily gain. Because the animals showed different age on control day, the expected gain curves were described in the population and within the herd*year*season-classes by second, third or fourth order Legendre polynomials of age. For optimization of the fixed effects and to choice the covariance structure of the repeated records the information criteria of Akaike (AIC), the Bayesian criteria (BIC) and the ICOMP-criteria, developed mainly from Bozdogan, were used. Within and over all covariance structures AIC selected generally the most complex model. On the other hand, BIC and ICOMP favoured a model with second order polynomials of age nested within the head*year*seasonclasses. All criterion selected models with nested second order polynomials within the herd*year*season-classes in comparison to models with non-nested polynomials of age.

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