Marker-Assisted Recurrent Selection for Pyramiding Leaf Rust and Coffee Berry Disease Resistance Alleles in Coffea arabica L.

In this study, marker-assisted recurrent selection was evaluated for pyramiding resistance gene alleles against coffee leaf rust (CLR) and coffee berry diseases (CBD) in Coffea arabica. A total of 144 genotypes corresponding to 12 hybrid populations from crosses between eight parent plants with desired morphological and agronomic traits were evaluated. Molecular data were used for cross-certification, diversity study and resistance allele marker-assisted selection (MAS) against the causal agent of coffee leaf rust (Hemileia vastatrix) and coffee berry disease (Colletotrichum kahawae). In addition, nine morphological and agronomic traits were evaluated to determine the components of variance, select superior hybrids, and estimate genetic gain. From the genotypes evaluated, 134 were confirmed as hybrids. The genetic diversity between and within populations was 75.5% and 24.5%, respectively, and the cluster analysis revealed three primary groups. Pyramiding of CLR and CBD resistance genes was conducted in 11 genotypes using MAS. A selection intensity of 30% resulted in a gain of over 50% compared to the original population. Selected hybrids with increased gain also showed greater genetic divergence in addition to the pyramided resistance alleles. The strategies used were, therefore, efficient to select superior coffee hybrids for recurrent selection programs and could be used as a source of resistance in various crosses.

Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice

Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs.

Genomic prediction through machine learning and neural networks for traits with epistasis.

Genomic wide selection (GWS) is one contributions of molecular genetics to breeding. Machine learning (ML) and artificial neural networks (ANN) methods are non-parameterized and can develop more accurate and parsimonious models for GWS analysis. Multivariate Adaptive Regression Splines (MARS) is considered one of the most flexible ML methods, automatically modeling nonlinearities and interactions of the predictor variables. This study aimed to evaluate and compare methods based on ANN, ML, including MARS, and G-BLUP through GWS. An F2 population formed by 1000 individuals and genotyped for 4010 SNP markers and twelve traits from a model considering epistatic effect, with QTL numbers ranging from eight to 480 and heritability ( h 2 ) of 0.3, 0.5 or 0.8 were simulated. Variation in heritability and number of QTL impacts the performance of methods. About quantitative traits (40, 80, 120, 240, and 480 QTLs) was observed highest R2 to Radial Base Network (RBF) and G-BLUP, followed by Random Forest (RF), Bagging (BA), and Boosting (BO). RF and BA also showed better results for traits to h 2 of 0.3 with R 2 values 16.51% and 16.30%, respectively, while MARS methods showed better results for oligogenic traits with R 2 values ranging from 39,12 % to 43,20 % in h 2 of 0.5 and from 59.92% to 78,56% in h 2 of 0.8. Non-additive MARS methods also showed high R2 for traits with high heritability and 240 QTLs or more. ANN and ML methods are powerful tools to predict genetic values in traits with epistatic effect, for different degrees of heritability and QTL numbers.

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice.

The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039-0.80, and 0.02-0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.

Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study.

The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F2 population represented by 500 individuals, obtained from a cross between contrasting homozygous parents, were simulated. Phenotypic traits were simulated based on previously established means and heritability estimates (30%, 50%, and 80%); traits were distributed in a genome with 10 linkage groups, considering two alleles per marker. Four different scenarios were considered. For the principal trait, heritability was 50%, and 40 control loci were distributed in five linkage groups. Another phenotypic control trait with the same complexity as the principal trait but without any genetic relationship with it and without pleiotropy or a factorial link between the control loci for both traits was simulated. These traits shared a large number of control loci with the principal trait, but could be distinguished by the differential action of the environment on them, as reflected in heritability estimates (30%, 50%, and 80%). The coefficient of determination were considered to evaluate the proposed methodologies. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the tested traits. Computational intelligence and machine learning were superior in extracting nonlinear information from model inputs and quantifying the relative contributions of phenotypic traits. The R2 values ranged from 44.0% - 83.0% and 79.0% - 94.0%, for computational intelligence and machine learning, respectively. In conclusion, the relative contributions of auxiliary traits in different scenarios in plant breeding programs can be efficiently predicted using computational intelligence and machine learning.

Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.

Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the characteristics of the coffee produced. The most efficient models choice to be applied should take into account the variety of information and the particularities of each biological material. This study was developed to evaluate statistical and machine learning models that would better discriminate environments through multi-traits of coffee genotypes and identify the main agronomic and beverage quality traits responsible for the variation of the environments. For that, 31 morpho-agronomic and post-harvest traits were evaluated, from field experiments installed in three municipalities in the Matas de Minas region, in the State of Minas Gerais, Brazil. Two types of post-harvest processing were evaluated: natural and pulped. The apparent error rate was estimated for each method. The Multilayer Perceptron and Radial Basis Function networks were able to discriminate the coffee samples in multi-environment more efficiently than the other methods, identifying differences in multi-traits responses according to the production sites and type of post-harvest processing. The local factors did not present specific traits that favored the severity of diseases and differentiated vegetative vigor. Sensory traits acidity and fragrance/aroma score also made little contribution to the discrimination process, indicating that acidity and fragrance/aroma are characteristic of coffee produced and all coffee samples evaluated are of the special type in the Mata of Minas region. The main traits responsible for the differentiation of production sites are plant height, fruit size, and bean production. The sensory trait "Body" is the main one to discriminate the form of post-harvest processing.

Half a century of studying adaptability and stability in maize and soybean in Brazil

The study of adaptability and stability underlies the cultivar recommendation process for all crops. There is a considerable number of statistical methods available for this purpose, but little is known about their actual adoption by the Brazilian scientific community. The objective of this study was to carry out a systematic review of the scientific literature on the adaptability and stability methods used in maize and soybean in Brazil from scientific articles published between 1970 and 2017 in Brazilian journals. Article searches were carried out in journals indexed through the SciELO database. The articles were classified according to the year of publication and the adaptability and stability methods used. We also evaluated the pattern of association between methods. We found 113 articles on adaptability and stability in maize and soybean, in which 21 methods were listed. The most commonly used method was the Eberhart and Russell methodology. The Cruz, Torres, and Vencovsky along with the AMMI methods were also widely used. The number of articles using most methods decreased in the current decade, except for the GGE Biplot, MHPRVG, and Centroid methods. In studies with more than one method, the methods were more likely to be used together with the Eberhart and Russell methodology. Adaptability and stability in maize and soybean have been widely studied over the last several decades in Brazil, although the number of publications on this subject has decreased over this time period.

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.

Chemical analyses of flowers and leaves for nutritional diagnoses of coffee trees / Diagnóstico nutricional do cafeeiro por meio de análise floral e foliar

The chemical analysis of flowers has been studied for some crops. In coffee trees, the flower tissue analysis could anticipate the nutritional diagnosis. This study aimed to: (i) compare the mineral composition of coffee flowers and leaves; and to (ii) generate reference values for nutritional diagnosis of coffee trees, based on flower and leaf analysis. Nutrient content of flowers and leaves and coffee productivity were evaluated in 26 commercial farms located in Manhuaçu, MG, Brazil throughout three years. The critical nutrient content range in flowers are respectively: 2.78 - 3.17, 0.23 - 0.28, 2.80 - 3.12, 0.30 - 0.37, 0.24 - 0.30, 0.15 - 0.18 dag kg-1 of N, P, K, Ca, Mg, and S; and 17 - 21, 12 - 18, 52 - 80, 26 - 43, and 28 - 48 mg kg-1 of Zn, Cu, Mn, Fe, and B. For leaves, the critical nutrient ranges are respectively: 2.63 - 2.86, 0.13 - 0.14, 2.13 - 2.33, 1.04 - 1.22, 0.27 - 0.33, 0.15 - 0.18 dag kg-1 of N, P, K, Ca, Mg, and S; and 9 - 14, 15 - 23, 80 - 115, 99 - 148, and 31 - 37 mg kg-1 of Zn, Cu, Mn, Fe, and B. The nutritional diagnosis of coffee trees for N, P, Ca, Fe, Cu, and Mn can be anticipated using flower analysis.(AU)

A análise química de flores tem sido estudada em algumas culturas. Para o cafeeiro, a análise do tecido floral possibilitaria a antecipação do diagnóstico nutricional das lavouras. O estudo objetivou (i) comparar a composição mineral de flores e de folhas de cafeeiros (ii) e gerar normas para diagnose nutricional do cafeeiro com base na análise de tecidos de flores e folhas das plantas. Para isso, foram avaliados os teores de nutrientes em flores e folhas e a produtividade de café em 26 lavouras comerciais na região de Manhuaçu, MG, durante três anos. As faixas críticas de nutrientes determinadas em flores são: 2,78 - 3,17; 0,23 - 0,28; 2,80 - 3,12; 0,30 - 0,37; 0,24 - 0,30; 0,15 - 0,18 dag kg-1 de N, P, K, Ca, Mg e S, e 17 - 21; 12 - 18; 52 - 80; 26 - 43 e 28 - 48 mg kg-1 para Zn, Cu, Mn, Fe e B, respectivamente. As faixas críticas de nutrientes em folhas foram: 2,63 - 2,86; 0,13 - 0,14; 2,13 - 2,33; 1,04 - 1,22; 0,27 - 0,33; 0,15 - 0,18 dag kg-1 de N, P, K, Ca, Mg e S, e 9 - 14; 15 - 23; 80 - 115; 99 - 148 e 31 - 37 mg kg-1 para Zn, Cu, Mn, Fe e B, respectivamente. A diagnose nutricional do cafeeiro, quanto aos nutrientes N, P, Ca, Fe, Cu e Mn, pode ser antecipada por meio da análise de flores.(AU)

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.(AU)

Identification of patterns related to linkage groups or disequilibrium by factor analysis / Identificação de padrões relacionados a grupos de ligação ou de desequilíbrio por análise de fatores

Empirical patterns of linkage disequilibrium (LD) can be used to increase the statistical power of genetic mapping. This study was carried out with the objective of verifying the efficacy of factor analysis (AF) applied to data sets of molecular markers of the SNP type, in order to identify linkage groups and haplotypes blocks. The SNPs data set used was derived from a simulation process of an F2 population, containing 2000 marks with information of 500 individuals. The estimation of the factorial loadings of FA was made in two ways, considering the matrix of distances between the markers (A) and considering the correlation matrix (R). The number of factors (k) to be used was established based on the graph scree-plot and based on the proportion of the total variance explained. Results indicated that matrices A and R lead to similar results. Based on the scree-plot we considered k equal to 10 and the factors interpreted as being representative of the bonding groups. The second criterion led to a number of factors equal to 50, and the factors interpreted as being representative of the haplotypes blocks. This showed the potential of the technique, making it possible to obtain results applicable to any type of population, helping or corroborating the interpretation of genomic studies. The study demonstrated that AF was able to identify patterns of association between markers, identifying subgroups of markers that reflect factor binding groups and also linkage disequilibrium groups.(AU)

Padrões empíricos de desequilíbrio de ligação (LD) podem ser utilizados para aumentar o poder estatístico do mapeamento genético. Este trabalho foi realizado com o objetivo de verificar a eficácia da análise de fatores (AF) aplicada a conjuntos de dados de marcadores moleculares do tipo SNP, visando identificar grupos de ligação e blocos de haplótipos. O conjunto de dados SNPs utilizado foi oriundo de um processo de simulação de uma população F2, contendo 2000 marcas com informações de 500 indivíduos. A estimação das cargas fatoriais (loadings) da AF foi feita de duas formas, considerando a matriz de distâncias entre os marcadores (A) e considerando a matriz de correlação (R). O número de fatores (k) a ser utilizado foi estabelecido com base no gráfico scree-plot e com base na proporção da variância total explicada. Os resultados indicam que as matrizes A e R conduzem a resultados similares. Com base no scree-plot considerou-se k igual a 10 e os fatores interpretados como sendo representativos dos grupos de ligação. O segundo critério conduziu a um número de fatores igual a 50, e os fatores interpretados como sendo representativos dos blocos de haplótipos. Isto mostra o potencial da técnica que permite obter resultados aplicáveis a qualquer tipo de população, corroborando a interpretação de estudos genômicos. O trabalho demonstrou que a AF foi capaz de identificar padrões de associação entre marcadores, identificando subgrupos de marcadores que refletem grupos de ligação fatorial e também grupos de desequilíbrio de ligação.(AU)

Half a century of studying adaptability and stability in maize and soybean in Brazil

The study of adaptability and stability underlies the cultivar recommendation process for all crops. There is a considerable number of statistical methods available for this purpose, but little is known about their actual adoption by the Brazilian scientific community. The objective of this study was to carry out a systematic review of the scientific literature on the adaptability and stability methods used in maize and soybean in Brazil from scientific articles published between 1970 and 2017 in Brazilian journals. Article searches were carried out in journals indexed through the SciELO database. The articles were classified according to the year of publication and the adaptability and stability methods used. We also evaluated the pattern of association between methods. We found 113 articles on adaptability and stability in maize and soybean, in which 21 methods were listed. The most commonly used method was the Eberhart and Russell methodology. The Cruz, Torres, and Vencovsky along with the AMMI methods were also widely used. The number of articles using most methods decreased in the current decade, except for the GGE Biplot, MHPRVG, and Centroid methods. In studies with more than one method, the methods were more likely to be used together with the Eberhart and Russell methodology. Adaptability and stability in maize and soybean have been widely studied over the last several decades in Brazil, although the number of publications on this subject has decreased over this time period.(AU)

Identification of patterns related to linkage groups or disequilibrium by factor analysis / Identificação de padrões relacionados a grupos de ligação ou de desequilíbrio por análise de fatores

ABSTRACT: Empirical patterns of linkage disequilibrium (LD) can be used to increase the statistical power of genetic mapping. This study was carried out with the objective of verifying the efficacy of factor analysis (AF) applied to data sets of molecular markers of the SNP type, in order to identify linkage groups and haplotypes blocks. The SNPs data set used was derived from a simulation process of an F2 population, containing 2000 marks with information of 500 individuals. The estimation of the factorial loadings of FA was made in two ways, considering the matrix of distances between the markers (A) and considering the correlation matrix (R). The number of factors (k) to be used was established based on the graph scree-plot and based on the proportion of the total variance explained. Results indicated that matrices A and R lead to similar results. Based on the scree-plot we considered k equal to 10 and the factors interpreted as being representative of the bonding groups. The second criterion led to a number of factors equal to 50, and the factors interpreted as being representative of the haplotypes blocks. This showed the potential of the technique, making it possible to obtain results applicable to any type of population, helping or corroborating the interpretation of genomic studies. The study demonstrated that AF was able to identify patterns of association between markers, identifying subgroups of markers that reflect factor binding groups and also linkage disequilibrium groups.

RESUMO: Padrões empíricos de desequilíbrio de ligação (LD) podem ser utilizados para aumentar o poder estatístico do mapeamento genético. Este trabalho foi realizado com o objetivo de verificar a eficácia da análise de fatores (AF) aplicada a conjuntos de dados de marcadores moleculares do tipo SNP, visando identificar grupos de ligação e blocos de haplótipos. O conjunto de dados SNPs utilizado foi oriundo de um processo de simulação de uma população F2, contendo 2000 marcas com informações de 500 indivíduos. A estimação das cargas fatoriais (loadings) da AF foi feita de duas formas, considerando a matriz de distâncias entre os marcadores (A) e considerando a matriz de correlação (R). O número de fatores (k) a ser utilizado foi estabelecido com base no gráfico scree-plot e com base na proporção da variância total explicada. Os resultados indicam que as matrizes A e R conduzem a resultados similares. Com base no scree-plot considerou-se k igual a 10 e os fatores interpretados como sendo representativos dos grupos de ligação. O segundo critério conduziu a um número de fatores igual a 50, e os fatores interpretados como sendo representativos dos blocos de haplótipos. Isto mostra o potencial da técnica que permite obter resultados aplicáveis ​​a qualquer tipo de população, corroborando a interpretação de estudos genômicos. O trabalho demonstrou que a AF foi capaz de identificar padrões de associação entre marcadores, identificando subgrupos de marcadores que refletem grupos de ligação fatorial e também grupos de desequilíbrio de ligação.

Chemical analyses of flowers and leaves for nutritional diagnoses of coffee trees / Diagnóstico nutricional do cafeeiro por meio de análise floral e foliar

ABSTRACT: The chemical analysis of flowers has been studied for some crops. In coffee trees, the flower tissue analysis could anticipate the nutritional diagnosis. This study aimed to: (i) compare the mineral composition of coffee flowers and leaves; and to (ii) generate reference values for nutritional diagnosis of coffee trees, based on flower and leaf analysis. Nutrient content of flowers and leaves and coffee productivity were evaluated in 26 commercial farms located in Manhuaçu, MG, Brazil throughout three years. The critical nutrient content range in flowers are respectively: 2.78 - 3.17, 0.23 - 0.28, 2.80 - 3.12, 0.30 - 0.37, 0.24 - 0.30, 0.15 - 0.18 dag kg-1 of N, P, K, Ca, Mg, and S; and 17 - 21, 12 - 18, 52 - 80, 26 - 43, and 28 - 48 mg kg-1 of Zn, Cu, Mn, Fe, and B. For leaves, the critical nutrient ranges are respectively: 2.63 - 2.86, 0.13 - 0.14, 2.13 - 2.33, 1.04 - 1.22, 0.27 - 0.33, 0.15 - 0.18 dag kg-1 of N, P, K, Ca, Mg, and S; and 9 - 14, 15 - 23, 80 - 115, 99 - 148, and 31 - 37 mg kg-1 of Zn, Cu, Mn, Fe, and B. The nutritional diagnosis of coffee trees for N, P, Ca, Fe, Cu, and Mn can be anticipated using flower analysis.

RESUMO: A análise química de flores tem sido estudada em algumas culturas. Para o cafeeiro, a análise do tecido floral possibilitaria a antecipação do diagnóstico nutricional das lavouras. O estudo objetivou (i) comparar a composição mineral de flores e de folhas de cafeeiros (ii) e gerar normas para diagnose nutricional do cafeeiro com base na análise de tecidos de flores e folhas das plantas. Para isso, foram avaliados os teores de nutrientes em flores e folhas e a produtividade de café em 26 lavouras comerciais na região de Manhuaçu, MG, durante três anos. As faixas críticas de nutrientes determinadas em flores são: 2,78 - 3,17; 0,23 - 0,28; 2,80 - 3,12; 0,30 - 0,37; 0,24 - 0,30; 0,15 - 0,18 dag kg-1 de N, P, K, Ca, Mg e S, e 17 - 21; 12 - 18; 52 - 80; 26 - 43 e 28 - 48 mg kg-1 para Zn, Cu, Mn, Fe e B, respectivamente. As faixas críticas de nutrientes em folhas foram: 2,63 - 2,86; 0,13 - 0,14; 2,13 - 2,33; 1,04 - 1,22; 0,27 - 0,33; 0,15 - 0,18 dag kg-1 de N, P, K, Ca, Mg e S, e 9 - 14; 15 - 23; 80 - 115; 99 - 148 e 31 - 37 mg kg-1 para Zn, Cu, Mn, Fe e B, respectivamente. A diagnose nutricional do cafeeiro, quanto aos nutrientes N, P, Ca, Fe, Cu e Mn, pode ser antecipada por meio da análise de flores.

Multivariate diallel analysis by factor analysis for establish mega-traits.

In plant breeding, the dialelic models univariate have aided the selection of parents for hybridization. Multivariate analyses allow combining and associating the multiple pieces of information of the genetic relationships between traits. Therefore, multivariate analyses might refine the discrimination and selection of the parents with greater potential to meet the goals of a plant breeding program. Here, we propose a method of multivariate analysis used for stablishing mega-traits (MTs) in diallel trials. The proposed model is applied in the evaluation of a multi-environment complete diallel trial with 90 F1's of simple maize hybrids. From a set of 14 traits, we demonstrated how establishing and interpreting MTs with agronomic implication. The diallel analyzes based on mega-traits present an important evolution in statistical procedures since the selection is based on several traits. We believe that the proposed method fills an important gap of plant breeding. In our example, three MTs were established. The first, formed by plant stature-related traits, the second by tassel size-related traits, and the third by grain yield-related traits. Individual and joint diallel analysis using the established MTs allowed identifying the best hybrid combinations for achieving F1's with lower plant stature, tassel size, and higher grain yield.

Impact of water deficit in the relationship among alfalfa traits / Impacto do déficit hídrico na relação entre as características da alfafa

This study aimed to evaluate the relationship among traits related to yield and nutritive value of alfalfa genotypes grown under deficit and full irrigation conditions. Seventy-seven alfalfa genotypes were evaluated in two different cuts, the first one with full irrigation, and the second, with water deficit. A randomized block design with three replications was used. The evaluated traits were vigor, plant height, dry matter biomass, stem-to-leaf ratio, dry matter percentage, leaf and stem protein contents, in vitro dry matter digestibility, neutral detergent fiber, acid detergent fiber, and lignin. Significant interaction between genotypes and environments was reported for vigor, plant height, and lignin. The correlation between traits and path analysis of dry matter biomass was performed for each cut, aiming to identify auxiliary traits for indirect selection. Water availability did not alter the phenotypic and genotypic correlations, only their magnitudes. Regardless of the environment, plant height is one of the most promising traits for the selection of alfalfa genotypes with higher dry matter biomass since it showed a high direct effect in the same sense of its phenotypic correlations. However, the coefficient of determination obtained by the model applied to full irrigation was higher than that of the water-deficit environment, indicating the importance of variables not included in this study in the determination of alfalfa dry matter biomass under dry conditions.

Este estudo objetivou avaliar a relação entre características relacionadas à produção e o valor nutritivo de genótipos de alfafa cultivados em condições de déficit e irrigação total. Setenta e sete genótipos de alfafa foram avaliados em dois cortes diferentes, o primeiro com irrigação total e o segundo com déficit hídrico. O delineamento experimental foi em blocos casualizados, com três repetições. As características avaliadas foram: vigor, altura de planta, biomassa de matéria seca, razão colmo-folha, porcentagem de matéria seca, teores foliar e foliar de proteína, digestibilidade in vitro da matéria seca, fibra em detergente neutro, fibra em detergente ácido e lignina. Houve interação significativa entre genótipos e ambientes para vigor, altura de planta e lignina. A correlação entre características e análise de trilha da biomassa da matéria seca foi realizada para cada corte, visando identificar características auxiliares para a seleção indireta. A disponibilidade de água não alterou as correlações fenotípicas e genotípicas, apenas suas magnitudes. Independentemente do ambiente, a altura das plantas é um dos caracteres mais promissores para a seleção de genótipos de alfafa com maior biomassa de matéria seca, uma vez que apresentou alto efeito direto no mesmo sentido de suas correlações fenotípicas. Entretanto, o coeficiente de determinação obtido pelo modelo aplicado à irrigação total foi superior àquele do ambiente com déficit hídrico, indicando a importância de variáveis não incluídas neste estudo na determinação da biomassa de matéria seca de alfafa em condições secas.

Nonlinear quantile regression to describe the dry matter accumulation of garlic plants / Regressão quantílica não linear para descrever o acúmulo de matéria seca de alho

The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.(AU)

Este trabalho teve como objetivo ajustar modelos de regressão quantílica não linear para o estudo do acúmulo de matéria seca total em plantas de alho ao longo do tempo, e compará-los com modelos ajustados pelo método dos mínimos quadrados. A matéria seca total de nove acessos de alho pertencentes ao Banco de Germoplasma de Hortaliças da Universidade Federal de Viçosa (BGH/UFV) foi avaliada em quatro períodos (60, 90, 120 e 150 dias após plantio), e estes valores foram utilizados para o ajuste de modelos de regressão - não linear - logística. Para cada acesso, foram ajustados um modelo de regressão quantílica (τ=0,5) e um modelo pela metodologia dos mínimos quadrados. Para avaliar a qualidade de ajuste dos modelos foi utilizado o Critério de Informação de Akaike. Os acessos foram agrupados pelo algoritmo UPGMA, utilizando as estimativas dos parâmetros com interpretação biológica como variáveis. A regressão quantílica não linear foi eficiente no ajuste de modelos para descrição do acúmulo de matéria seca ao longo do tempo. As estimativas de parâmetros foram mais uniformes e robustas na presença de dados assimétricos, variâncias heterogêneas e de valores discrepantes.(AU)

Nonlinear quantile regression to describe the dry matter accumulation of garlic plants / Regressão quantílica não linear para descrever o acúmulo de matéria seca de alho

ABSTRACT: The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.

RESUMO: Este trabalho teve como objetivo ajustar modelos de regressão quantílica não linear para o estudo do acúmulo de matéria seca total em plantas de alho ao longo do tempo, e compará-los com modelos ajustados pelo método dos mínimos quadrados. A matéria seca total de nove acessos de alho pertencentes ao Banco de Germoplasma de Hortaliças da Universidade Federal de Viçosa (BGH/UFV) foi avaliada em quatro períodos (60, 90, 120 e 150 dias após plantio), e estes valores foram utilizados para o ajuste de modelos de regressão - não linear - logística. Para cada acesso, foram ajustados um modelo de regressão quantílica (τ=0,5) e um modelo pela metodologia dos mínimos quadrados. Para avaliar a qualidade de ajuste dos modelos foi utilizado o Critério de Informação de Akaike. Os acessos foram agrupados pelo algoritmo UPGMA, utilizando as estimativas dos parâmetros com interpretação biológica como variáveis. A regressão quantílica não linear foi eficiente no ajuste de modelos para descrição do acúmulo de matéria seca ao longo do tempo. As estimativas de parâmetros foram mais uniformes e robustas na presença de dados assimétricos, variâncias heterogêneas e de valores discrepantes.

Multigenerational prediction of genetic values using genome-enabled prediction.

The identification of elite individuals is a critical component of most breeding programs. However, the achievement of this goal is limited by the high cost of phenotyping and experimental research. A significant benefit of genomic selection (GS) to plant breeding is the identification of elite individuals without the need for phenotyping. This study aimed to propose different calibration strategies using combinations between generations from different genetic backgrounds to improve the reliability of GS and to investigate the effects of LD in different types of mating systems: outcrossing (An) self-pollination (Sn) and hybridization (Hn). For this purpose, we simulated a genome with 10 linkage groups. In each group, two QTL were simulated. Subsequently, an F2 population was created, followed by four generations of inbreeding (S1 to S4, H1 to H 4, A1, to A4,). Quantitative traits were simulated in three scenarios considering three degrees of dominance (d/a = 0, 0.5 and 1) and two broad sense heritabilities (h2 = 0.30 and 0.70), totaling six genetic architectures. To evaluate prediction reliability, a model (RR-BLUP) was trained in one generation and used to predict the following generations of mating systems. For example, the marker effects estimated in the F2 population were used to estimate the expected genomic breeding value (GEBV) in populations S1 through A4. The squared correlation between the GEBV and the true genetic value were used to measure the reliability of the predictions. Independently of the population used to estimate the marker effect, reliability showed the lowest values in the scenario where d = 1. For any scenario, the use of the multigenerational prediction methodology improved the reliability of GS.

Genetic diversity among accessions of sacha inchi (Plukenetia volubilis) by phenotypic characteristics analysis / Diversidade genética entre acessos de sacha inchi (Plukenetia volubilis) por análise de características fenotípicas

Sacha inchi (Plukenetia volubilis) is native to the Amazon region and has a high seed content of mono and polyunsaturated fatty acids, making it interesting for the pharmaceutical and cosmetic industry. The purpose of this study was to analyze sacha inchi genetic diversity and describe accessions based on phenotypic characteristics. Fruits and seeds of 25 accessions from the sacha inchi genebank of Embrapa Amazônia Ocidental in Manaus, Amazonas state, were sampled and biometrically measured. The data were subjected to analysis of variance, Mahalanobis distance, canonical correlation, and genetic diversity among and within accessions by analysis of molecular variance (AMOVA). There were significant differences among the means of the analyzed traits, but no significant canonical correlation for the groups of traits. According to AMOVA, approximately 60% of the observed variation was within accessions. The results showed variability among accessions, and that the variation within accessions should be explored to obtain best results in breeding programs.(AU)

Sacha inchi (Plukenetia volubilis) é nativa da região amazônica e suas sementes tem um alto teor de ácidos graxos mono e poliinsaturados, tornando-a interessante para a indústria farmacêutica e cosmética. O objetivo deste estudo foi analisar a diversidade genética de sacha inchi e caracterizar os acessos com base em características fenotípicas. Foi realizada coleta e biometria de frutos e sementes de 25 acessos do banco de germoplasma de sacha inchi da Embrapa Amazônia Ocidental em Manaus-AM. Os dados foram submetidos a análise de variância, distância de Mahalanobis, correlação canônica e diversidade genética por análise de variância molecular (AMOVA). Houve diferenças significativas entre as médias das variáveis analisadas, contudo, não houve correlação canônica significativa para os grupos de variáveis. De acordo com AMOVA, aproximadamente 60% da variação observada esteve dentro de acessos. Os resultados mostram variabilidade entre acessos, sendo importante explorar a variação intra-acessos para obter melhores resultados em programas de melhoramento.(AU)