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Open-pollinated onion cultivars predominate in the southern region of Brazil, due to their higher adaptability to local climatic conditions, unlike commercial hybrids, which have shown a lower adaptability. Cytoplasmic male sterility (CMS) systems are employed to develop hybrid onion cultivars. Two molecular markers, 5’cob and orfA501, were used to differentiate the cytoplasm type, and the AcSKP1 marker to identify the nuclear male fertility-restoring locus (Ms). A total of 1,126 plants from the most common onion cultivars grown in southern Brazil, including Bola Precoce®, Crioula®, Valessul®, Mega®, Joia® and Princesa do Sul®, were analyzed using all the three markers. An extremely rare occurrence of the S cytoplasm was observed among the cultivars, being detected in only 1.8 % of the samples, while the T cytoplasm was the most prevalent, accounting for 56.3 % of the samples. Among the 1,126 plants analyzed, only three exhibited the S cytoplasm and were recessive for the Ms-locus (Smsms). Additionally, 49 plants with the N-cytoplasm (as per the Engelke’s classification) and recessive for the nuclear genotype (Nmsms) were identified, 45 of which were pollen producers. The male-fertility restoration occurred in 22.2 % of the crosses between Tmsms male-sterile plants and male-fertile N cytoplasmic-msms plants (as per the Engelke’s classification).

Abstract The objective of this work was to evaluate how heritability and the number of quantitative trait loci (QTL) controlling the trait can influence the prediction of genetic value by genomic selection methods. A prediction equation was established to estimate genetic correlation based on phenotypic correlation, using an F2 population with 1,000 individuals, simulated in different scenarios. Heritability (5, 20, 40, 60, 80, and 99%) and QTL number (60, 120, 180, and 240) varied in each scenario. The following four genomic selection methods were used in the analyses: ridge-regression best linear unbiased prediction (RR-BLUP), genomic BLUP (GBLUP), Bayesian estimation method B (Bayes B), and reproducing kernel Hilbert spaces regression (RKHS). The phenotypic and genotypic predictive abilities were calculated for each method, and Tukey’s test was used to compare means. The effect of heritability and of the number of QTL controlling the trait was evaluated by the regression analysis. Tukey’s test revealed differences between the methods, with Bayes B and RR-BLUP being superior to the others in almost all scenarios. Heritability presents a positive linear relationship with phenotypic predictive ability and a positive quadratic relationship with genotypic predictive ability. The number of QTL controlling the trait has no relationship with the phenotypic and genotypic predictive abilities.

Abstract The objective of this work was to compare the use of noninformative and informative priors in Bayesian models, as well as to evaluate the viability of including informative priors in the estimation of variance components and genetic values in soybean breeding programs. The used phenotypic data refer to the evaluation of 80 soybean genotypes in ten environments over three years. For each evaluated crop year, informative and noninformative priors were used, and the parameters were estimated using the Gibbs sampler algorithm. Parameter estimates from the previous crop year were used as prior information for the next evaluated crop year. The goodness-of-fit was calculated using the deviance information criterion (DIC). Selective accuracy showed the highest values for the models chosen through DIC for both crop years. However, the intervals of the highest posterior density are narrower for all models that adopted informative priors. Adding information into Bayesian inference does not always result in a better model fitting.

ABSTRACT Species within the genus Corymbia are regarded as potential alternatives to Eucalyptus. In addition to having superior wood quality, Corymbia spp. are tolerant to most pests, diseases, and abiotic stresses that affecting Eucalyptus plantations, including physiological disorders, water deficit, and wind damage. However, environmental stresses stimulate kino production, which decreases the quality of pulp and sawn wood. This study aimed to develop a method for evaluating kinoand estimate genetic parameters in Corymbia. For this, 16 Corymbia (C. citriodora × C. torelliana) hybrid clones and 5 clones of Eucalyptus were used. Two evaluation methods (M1 and M2) were tested for kino evaluation; M1 consisted of drilling the bark with Pilodyn and M2 consisted of drilling the heartwood with Pilodyn. The following kino parameters were evaluated: exudation incidence, exudate length which flowed over the stem, and exudate weight. Genetic parameters were estimated by a mixed model method (REML/BLUP). The significance of random effects of the statistical model was tested by the likelihood ratio test. Significant clone effects were obtained for all kino parameters, except for exudate length as assessed by M2. Kino parameters determined by M1 exhibited higher heritability and accuracy. Therefore, M1 should be preferred for kino evaluation in Corymbia.

ABSTRACT present study aimed to determine the effects of different light restriction levels (shading levels) on soybean genetic parameters using a Bayesian multi-trait model (MTM) and select high-yielding soybean cultivars. Eighteen commercial soybean cultivars bred in a soybean breeding program were evaluated over two agricultural seasons. Three shading levels were used over two agricultural crop seasons, giving six treatments (light restriction × crop season). The experiments were arranged in a randomized complete block design with six treatments replicated thrice. The genetic values and parameters were estimated using a Monte Carlo Markov Chain algorithm. Broad-sense heritability range from 0.2093 to 0.7153. The lowest genotypic variance estimate was observed at the 45 % photosynthetically active radiation level in the 2019/2020 crop season year compared with that of other shading levels. Furthermore, a 40 % selection intensity had the highest soybean yield under different shading levels. The Bayesian MTM combined with the factor analysis and genotype-ideotype distance method can be used to evaluate and select soybean genotypes considering different shading levels. The soybean cultivars 8579RSF, NS8338, NS7901, NS7667, RK8115, and 8473RSF had higher genetic potential than other cultivars under different shading levels.

ABSTRACT Objectives: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients. Methods: a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed. Results: among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%. Conclusions: the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.

Abstract Obtaining soybean genotypes that combine better nutrient uptake, higher oil and protein levels in the grains, and high grain yield is one of the major challenges for current breeding programs. To avoid the development of unpromising populations, selecting parents for crossbreeding is a crucial step in the breeding pipeline. Therefore, our objective was to estimate the combining ability of soybean cultivars based on the F2 generation, aiming to identify superior segregating parents and populations for agronomic, nutritional and industrial traits. Field experiments were carried out in two locations in the 2020/2021 crop season. Leaf contents of the following nutrients were evaluated: phosphorus, potassium, calcium, magnesium, sulfur, copper, iron, manganese, and zinc. Agronomic traits assessed were days to maturity (DM) and grain yield (GY), while the industrial traits protein, oil, fiber and ash contents were also measured in the populations studied. There was a significant genotype × environment (G × A) interaction for all nutritional traits, except for P content, DM and all industrial traits. The parent G3 and the segregating populations P20 and P27 can be used aiming to obtain higher nutritional efficiency in new soybean cultivars. The segregating populations P11 and P26 show higher potential for selecting soybean genotypes that combine earliness and higher grain yield. The parent G5 and segregant population P6 are promising for selection seeking improvement of industrial traits in soybean.

Multi-trait multi-environment (MTME) models were fitted to eucalyptus breeding trials data to assess residual variance structure, genetic stability and adaptability. To do so, 215 eucalyptus clones were evaluated in a randomized complete block design with 30 replicates and one plant per plot in four environments. At 36 months of age, tree diameter at breast height (DBH) and pilodyn penetration (PP) were measured. Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic, with the best MTME model selected using Bayesian information criterion. The harmonic mean of the relative performance of the genotypic values (HMRPGV) was used to determine stability and adaptability. Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy. In addition, genotype-by-environment interaction was complex, and there was low genetic correlation between DBH and PP. Rank correlation between the clones selected by the MTME models was high for DBH but low for PP. The HMRPGV facilitated clone selection through simultaneous evaluation of stability, adaptability, and productivity. Thus, our results suggest that heteroscedastic MTME model / HMRPGV can be efficiently applied in the genetic evaluation and selection of eucalyptus clones.

Abstract The objective of this work was to estimate the genetic parameters and to evaluate the genotypic diversity of 12 'Tahiti' acid lime (Citrus latifolia) genotypes grafted onto two rootstocks. The experiment was carried out from July 2017 to January 2019, in the municipality of São Mateus, in the state of Espírito Santo, Brazil. Vegetative (stem diameter, canopy projection diameter, and plant height), productive (yield and fruit number), and fruit quality (fruit diameter, soluble solids content, and juice yield) traits were determined. A mixed model was used to estimate heritability and repeatability coefficients, as well as to predict clonal values. Scion diversity was determined through the standardized mean difference of Euclidean distances, and genotypes were clustered by modified Tocher. Clustering quality and trait importance were evaluated using the principal component analysis (PCA). Genotypic variance was observed, which is indicative of the possibility of selection of superior genotypes. The Tocher method showed the presence of three clusters, which is in alignment with the PCA results. The multivariate analyses allows of the selection and the recommendation of superior 'Tahiti' acid lime genotypes.

Abstract Genome-wide selection (GWS) has been becoming an essential tool in the genetic breeding of long-life species, as it increases the gain per time unit. This study had a hypothesis that GWS is a tool that can decrease the breeding cycle in Jatropha. Our objective was to compare GWS with phenotypic selection in terms of accuracy and efficiency over three harvests. Models were developed throughout the harvests to evaluate their applicability in predicting genetic values in later harvests. For this purpose, 386 individuals of the breeding population obtained from crossings between 42 parents were evaluated. The population was evaluated in random block design, with six replicates over three harvests. The genetic effects of markers were predicted in the population using 811 SNP's markers with call rate = 95% and minor allele frequency (MAF) > 4%. GWS enables gains of 108 to 346% over the phenotypic selection, with a 50% reduction in the selection cycle. This technique has potential for the Jatropha breeding since it allows the accurate obtaining of GEBV and higher efficiency compared to the phenotypic selection by reducing the time necessary to complete the selection cycle. In order to apply GWS in the first harvests, a large number of individuals in the breeding population are needed. In the case of few individuals in the population, it is recommended to perform a larger number of harvests.

Abstract The objective of this work was to evaluate the genetic variability and associations of yield traits of pigeon pea, for indication of this crop for the breeding program in Mozambique. Eleven pigeon pea traits were evaluated in a experimental design with three randomized complete blocks, in the environments of Namapa (I) and Montepuez (II), in 2017/2018. To evaluate the traits, analyses of variance, genetic parameters, correlations, correlation network, and path analysis were used. In both environments, there is a high genetic and phenotypic variability for primary branches, secondary branches, pods per plant, number of seed per pod, pod width, pod length, and yield, as well as a high heritability for all traits except for plant height. The primary and secondary branches have a high correlation with yield and a positive direct effect on it. However, plant height and pods per plant show a high phenotypic variability and a positive correlation with yield in environment I. Pods per plant, weight of 100 seed, and primary branches show high direct positive effects in environment I, and secondary branches and number of seed per pod, in environment II. The primary and secondary branches show high variability and associations with yield in both environments and are indicated for the pigeon pea breeding program in Mozambique.

Dieback is a physiological disorder that has caused losses on eucalyptus plantations. Thinking that water stress is one of the triggers for the physiological disorder and aiming at the early identification of tolerant genotypes, we evaluated plantlets of four commercial clones with divergent behavior in field conditions. The plantlets were grown in a greenhouse where the drought conditions were provided by the application of polyethylene glycol 6000 solutions at 100 and 300 g L−1. After water deficit treatments, the morphological, physiological, nutritional and metabolic analyses were performed. SuzT maintained the carbon fixation and the instantaneous water use efficiency, even under water deficit, while sustaining growth. This resulted in higher leaf area and total dry mass in SuzT. Despite higher photosynthetic rate, SuzS exhibited reduced dry biomass accumulation, implying less efficient carbon use. SuzT revealed a higher level of calcium that supports cell structure and homeostasis and indicates higher capacity to manage specific resources and survival under water deficit. SuzT suffered reduction in some free amino acids; however, there was no significant variation for total amino acid content. The principal component and cluster analyses indicated that SuzMT and SuzTP genotypes behave similarly to SuzT under water deficit, while SuzS clustered in isolation. Our results support that there are common trends in water deficit responses for contrasting eucalypt genotypes. The existence of other strategies coping with water deficit resistance is not discarded and should be further evaluated.

In cotton crops, the cotton seed yield significantly contributes with the success of any cultivar. However, other traits are considered when an ideotype is pointed out in the selection, such as the fiber quality traits. The aim of this study was to applied genotype by yield*trait (GYT) biplot to a multi-environment trial data of cotton genotypes and selected the best genotypes. For this end, thirteen genotypes from nineteen trials were assessed. Seven traits were evaluated [cotton seed yield (SY), fiber percentage (FP), fiber length (FL), fiber uniformity (FU), short fiber index (SFI), fiber strength (FS), and elongation (EL)] and residual error variances structures [identity variance (IDV) and diagonal (Diag)] were tested by bayesian information criterion. After, the REML/BLUP approach was applied to predict the genetic values of each trait and the selective accuracy were measured from the prediction. Then, the GYT-biplot were applied to the data. For SP and SFI traits, the model with Diag residual variance was indicated, whereas for SY FL, FU, FS, and EL traits the model with IDV residual variance demonstrated the best fit to the data. Values of accuracy were higher than 0.9 for all traits analyzed. In the GYT-biplot acute angles were find for all traits relations, which means high correlation between the yield*traits combination. Besides that, the correlation still can be seen in the GYT-biplot, as shown by the magnitudes of the angles between the pairs Yield*FU-Yield*FS and Yield*FS-Yield*EL. Also, the GYT-biplot indicates the genotype G4 with the best performance for Yield*FS, Yield*SFI, Yield*FU, Yield*FL, and Yield*FP combined. The genotypes G4, G1, G13, G8, and G9 represent those genotypes with yield advantage over the other cultivars. Then, the genotype G4 combines all desirable characteristics and demonstrate have large potential in the cotton breeding. The GYT approach were valuable and were highly recommended in cotton breeding programs for selection purpose in a multivariate scenario.

ABSTRACT: Although the fruit yield has a core importance in Tahiti acid lime breeding programs, other traits stand out among the quality fruit and vegetative traits as ones that still need to be improved in selection of superior genotypes. Appling efficient tools aiming selection, such as the Bayesian inference, becomes an alternative in perennial crops. This study applied Bayesian inference in the genetic evaluation of Tahiti acid lime genotypes and estimated the interrelation between vegetative, productive and fruit quality traits. Twenty-four acid lime genotypes were evaluated for number of fruits, fruit yield, canopy volume, stem diameter, soluble solids content, shell thickness, and juice yield traits. The genotypic values were estimated through Bayesian inference and models with different residual structure were tested via deviance information criterion. Pearson’s correlation and the path analysis were estimated, removing the multicollinearity effect. The Bayesian inference estimates genotypic values with high selective accuracy. The correlations obtained between traits from different groups can be useful in selection strategies for improvement of Tahiti acid lime. The Bayesian inference demonstrated to be an important tool and should be considered in perennial breeding programs.

ABSTRACT: Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops.

Grain sorghum production has expanded during the off-season when rainfall oscillates and becomes insufficient. Aiming to obtain better adapted cultivars, breeding programs have sought new combinations of hybrids with earliness, high grain yield, and ideal plant height for harvesting. This study aimed to estimate de combining ability of grain sorghum lines, proposing a breeding strategy, to identify hybrids gathering high yield, earliness, and desired plant height. Thirty-six hybrids from crosses of 12 lines were evaluated at two sites in the Brazilian region known as Cerrado biome. The evaluated traits were: days to flowering, plant height, and grain yield. For the diallel analysis, Method 4 of Griffing adapted to partial diallel was adopted. By combining ability analysis, we identified promising lines to be used as parents to obtain more yielding, early, and ideal height hybrids. The findings allowed us to propose a breeding strategy, in which complex crosses should be performed to gather favorable alleles in new restorer and male-sterile lines. The hybrids 7, 9, 19, and 22 are the most suitable for growing in the evaluated sites. Highlights: Combining ability analysis allows the identification of promising parents to be used in grain sorghum breeding program. Favorable alleles for each trait are contained in different parents, which makes gene pyramiding a necessary strategy to simultaneously gathering earliness, plant height suitable for harvesting, and high yield in a single hybrid. To improve the R lines, the cross between M2 (good donor for shorter height) x M5 (good donor for earliness) should be performed, and the hybrid resulting from M2xM5 can be crossed with the M4 line (good donor for high grain yield). The hybrid generated by the cross F1-B x F4-B (high earliness) should be crossed with the hybrid derived from F6-B x F5-B (shorter height), and the hybrid resulting should be crossed with the hybrid generated by F2-B x F3-B (high grain yield).

Combining longitudinal data and statistical models from perennial crops enabled us to estimate the optimum number of measures (harvests), implying accurate discrimination of superior genotypes in those crops. Herein, the goal of this study was to determine the optimum number of harvests based on yield traits and recommend a superior graft/rootstock combination (GRC) for Citrus latifolia Tanaka. Twenty-four GRCs of 'Tahiti' acid lime were evaluated from July 2017 to August 2018 for fruit yield per plant (FYP), number of fruits per plant (NFP), and longitudinal (LFD) and transversal fruit diameter (TFD). The experimental design was a randomized complete block with 4 replications. The experimental unit consisted of three individuals, totalling 244 individuals. The GRCs were composed of (i) two hybrids that were used as rootstock, citrumelo ‘Swingle’ (Citrus paradisi x Poncirus trifoliata) and cintrandarin ‘Riverside’ (Citrus sunki x Poncirus trifoliata); and (ii) 12 different C. latifolia genotypes that were used as grafts: Bello Fruit, Eledio, Iconha, Itarana, Santa Rosa, Bearss lime, CNPMF 01, CNPMF 02, CNPMF 2001, CNPMF 5059, BRS Passos, and Persian 58. Mixed models were employed to estimate the variance components. The optimum number of harvests was determined based on selective efficiency values above 0.9. The estimated repeatability coefficients presented values of 0.14 (LFD), 0.16 (TFD), 0.36 (FYP), and 0.38 (NFD). Based on the results, four harvests were able to choose genotypes based on FYP and NFP, whereas LFD and TFD were considered inefficient traits for recommending superior GRCs.

This study aimed to estimate the predicted genetic gains with the simultaneous selection of yield traits and soluble solids content in cherry tomato hybrids. Twenty cherry tomato hybrids were evaluated in hydroponic cultivation in randomized block design with three replicates. The following traits were evaluated: number of clusters per plant, number of flowers per cluster, number of fruits per cluster, number of fruits per plant, fruit weight, fruit yield per plant, and total soluble solids content. The parameters of heritability, experimental cv, and genotypic cv were estimated. Subsequently, selection gains by direct selection and Mulamba and Mock index were estimated. Direct selection of cherry tomato hybrids for fruit yield and soluble solids content is inefficient because selection based on one of these traits will provide undesirable gains in the other. However, simultaneous selection for yield and taste quality is possible based on the Mulamba and Mock index because the methodology provided high selection gains for both yield and soluble solids content.

Popcorn is a food highly appreciated throughout the world, generating billions of dollars annually just in the North American market alone. Even in the face of the historical superiority of American hybrids, which occupy almost 100% of the grain production fields in Brazil, our researchers have been working to develop cultivars that combine important traits for the crop, such as tolerance to leaf diseases and high rates of grain yield and popping expansion. This work investigated the degree of genetic divergence among 40 diallel hybrids of popcorn, 16 parents and 6 elite controls using mixed models to discriminate superior materials to be adopted in the Brazilian agribusiness. Based on the individual Best Linear Unbiased Prediction (BLUP) of each of the 15 variables analyzed, the Unweighted Pair-Group Method using Arithmetic Averages (UPGMA) and Tocher clusters were performed, and the study of Pearson correlation was carried out. The results pointed out that there is genetic variability among the genotypes evaluated and that the best candidates for composing superior genotypes are in the combination between the P10×L77 parents and/or between the P7 and L88 lines. Linear correlations showed that earlier flowering and taller genotypes exhibited an association with materials more tolerant to Exserohilum turcicum intensity.

ABSTRACT: In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait.

This study assessed the efficiency of Genomic selection (GS) or genome-wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.

Spatial trends represent an obstacle to genetic evaluation in maize breeding. Spatial analyses can correct spatial trends, which allow for an increase in selective accuracy. The objective of this study was to compare the spatial (SPA) and non-spatial (NSPA) models in diallel multi-environment trial analyses in maize breeding. The trials consisted of 78 inter-populational maize hybrids, tested in four environments (E1, E2, E3, and E4), with three replications, under a randomized complete block design. The SPA models accounted for autocorrelation among rows and columns by the inclusion of first-order autoregressive matrices (AR1 ⊗ AR1). Then, the rows and columns factors were included in the fixed and random parts of the model. Based on the Bayesian information criteria, the SPA models were used to analyze trials E3 and E4, while the NSPA model was used for analyzing trials E1 and E2. In the joint analysis, the compound symmetry structure for the genotypic effects presented the best fit. The likelihood ratio test showed that some effects changed regarding significance when the SPA and NSPA models were used. In addition, the heritability, selective accuracy, and selection gain were higher when the SPA models were used. This indicates the power of the SPA model in dealing with spatial trends. The SPA model exhibits higher reliability values and is recommended to be incorporated in the standard procedure of genetic evaluation in maize breeding. The analyses bring the parents 2, 10 and 12, as potential parents in this microregion.

Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.

Extraction of inbred lines is a very important step in maize breeding since these lines will be used to obtain hybrids intended for the market. However, this expensive process, hinders genotype evaluation in contrasting environments, especially regarding nitrogen (N) content. This study aimed to evaluate the potential of maize populations for line extraction and select partially inbred lines under different soil nitrogen (N) availability. Five populations were evaluated regarding their potential for line extraction. Fifty-five S1 partially inbred lines were extracted from these populations. The top-cross method was used to evaluate lines by crossing them with two testers. Hybrids evaluation used 110 top-cross hybrids, five base populations, and six checks. Two trials were carried out in Dourados and Caarapó. One trial had adequate fertilization (high N) while the other was under stress condition (low N). Hybrid DKB 789 showed potential for extraction of partially inbred lines, aiming at selecting N use efficiency. Base population BP (07) was the most suitable for the extraction of partially inbred lines aiming at N use efficiency. Partially inbred lines BP (07) 13, BP (07) 14, and BP (07) 17 are the most suitable for the extraction of top-cross hybrids with high grain yield, efficiency, and responsiveness to N. Highlights Extraction of inbred lines is a very important step in maize breeding. Hybrid DKB 789 showed potential for extraction of partially inbred lines. BP (07) 13, BP (07) 14, and BP (07) 17 are the most suitable for the extraction of top-cross hybrids.

ABSTRACT: The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genetic selection. Thus, the present study aimed to estimate genetic parameters and to compare different selection strategies in the context of mixed models for soybean breeding. For this, data referring to the evaluation of 30 genotypes in 10 environments, regarding the grain yield trait, were used. The variance components were estimated through restricted maximum likelihood (REML) and genotypic values were predicted through best linear unbiased prediction (BLUP). Significant effects of genotypes and G×E interaction were detected by the likelihood ratio test (LRT). Low genotypic correlation was obtained across environments, indicating complex G×E interaction. The selective accuracy was very high, indicating high reliability. Our results showed that the most productive soybean genotypes have high adaptability and stability.

An efficient and informative statistical method to analyze genotype-by-environment interaction (GxE) is needed in maize breeding programs. Thus, the objective of this study was to compare the effectiveness of multiple-trait models (MTM), random regression models (RRM), and compound symmetry models (CSM) in the analysis of multi-environment trials (MET) in maize breeding. For this, a data set with 84 maize hybrids evaluated across four environments for the trait grain yield (GY) was used. Variance components were estimated by restricted maximum likelihood (REML), and genetic values were predicted by best linear unbiased prediction (BLUP). The best fit MTM, RRM, and CSM were identified by the Akaike information criterion (AIC), and the significance of the genetic effects were tested using the likelihood ratio test (LRT). Genetic gains were predicted considering four selection intensities (5, 10, 15, and 20 hybrids). The selected MTM, RRM, and CSM models fit heterogeneous residuals. Moreover, for RRM the genetic effects were modeled by Legendre polynomials of order two. Genetic variability between maize hybrids were assessed for GY. In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM. Thus, considering the criterion of parsimony and the possibility of predicting genetic values of hybrids for untested environments, RRM is a preferential approach for analyzing MET in maize breeding.

Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.

To increase yield, breeding programs must search for innovative and efficient methodologies for the development and selection of superior genotypes. Therefore, this study aimed to compare the single- and multiple-trait best linear unbiased prediction (BLUP) in genetic selection of parents and hybrids of grain sorghum. For this, an experiment conducted in alpha-lattice design with two replications was used. Flowering time (FT), plant height (PH), and grain yield (GY) were evaluated in 502 grain sorghum hybrids obtained by the cross of ten restorer lines and 54 male-sterile lines. Variance components were estimated via restricted maximum likelihood (REML). Significant effects of restorer lines, of male-sterile lines, and of specific combining ability were detected by the likelihood ratio test (LRT). The estimates of variance components, genetic parameters, and correlations were similar when obtained via single- and multiple trait- BLUP. Considering hybrids, the multiple-trait BLUP resulted in slightly higher predicted selection gain for the three evaluated traits and, therefore, can be efficiently applied in the genetic selection of grain sorghum.

The objectives of this study were to estimate the general and specific combining ability of the parents, and to verify the existence of maternal effect and inbreeding depression in Jatropha. The experiment was carried out from 2010 to 2015, in the municipality of Planaltina, Distrito Federal. The following traits were evaluated: plant height, stem diameter, canopy projection between the row, canopy projection on the row, number of branches, mass of one hundred grains, and grain yield. Cytoplasmic effects and effects of female parent nuclear genes were observed for all traits. Dominance effects were predominant in the genetic control of all traits. Genotypes 107 and 190 were the superior parents for the reduction of the size, and for the increase of grain yield. No inbreeding depression was observed for grain yield. The most promising crosses for the conduction of segregating populations and increment in grain yield were 190x107, 107x190 and 259x107.

Rbio is a free software for data processing. It is compatible and integrated with the free R software, which is globally accepted for statistical analysis. Thus, Rbio takes advantage of all processing potential of the R software. However, this new software allows the user to perform all the analyses without knowing the programming in R language. Rbio is available in Portuguese; it can be downloaded from the Internet (http://www.biometria.ufv.br), and requires the Windows operating system. It has a set of sample files, making it easy to be used. Currently, it supplies the user with all scripts internally used to process the analyses. Rbio can perform descriptive statistics, analysis of variance, estimation of genetic parameters and means tests, multivariate analysis, nonparametric tests, regressions, correlations, biometrics, bioinformatics, and simulation.

The objective of this study was to evaluate three systems of seedling production of interspecific hybrids of American oil palm and African oil palm (HIE), difference between 12 sib progenies of BRS Manicoré cultivar and the interaction between these two factors. The production systems were evaluated: (i) two- stage nursery with the initial shading (conventional system), (ii) two-stage nursery without shading and (iii) one-stage nursery without shading. The variables evaluated were plant height, number of leaves and stem diameter at 106, 191 and 393 days after sowing. In all variables and periods analyzed, the genotype and environmental factors were significant to F test, but the interaction genotype x environment was not significant. The best development of seedlings in the nursery production system occurred in one-step nursery without shading. The two stages nursery with shading in pre-nursery, which is currently the most widely adopted, resulted in lower seedling development.

Cowpea is a legume of great importance in the Brazilian nutrition, mainly in the Northeast region. Despite the low yield of Brazilian cowpea, the species presents a genetic potential to be explored. Thus, this work aimed to characterize the genetic diversity of cowpea genotypes by agronomic traits and select genotypes for possible crosses by multivariate analysis. Four value for cultivation and use tests were carried out with cowpea genotypes in 2005 and 2006, in the municipalities of Aquidauana, Chapadão do Sul, and Dourados, in the state of Mato Grosso do Sul. The experimental design was a complete randomized block with 20 genotypes and four replications. The evaluated traits were value for cultivation, plant lodging, pod length, grain weight of five pods, number of grains per pod, pod weight, severity of powdery mildew, and grain yield. To estimate the genetic diversity among the genotypes, the optimization methods of Tocher and UPGMA were used. The generalized distance of Mahalanobis was used as a dissimilarity measure. The clustering methods revealed genetic variability among the cowpea genotypes evaluated. The methods used formed a different number of groups for each environment. Genotypes TE97-309G-24, MNC99-542F-5, BRS Paraguaçu, BRS Paraguaçu, BR 17-Gurguéia, and CNC x 409-11F-P2 can be used to obtain promising combinations and high genetic variability.

The objective of this work was to estimate the general combining ability of partially inbred lines under high and low nitrogen (N) conditions and to select promising lines to obtain high yields for both conditions. Fifty-five partially inbred S1 progenies were evaluated using the top-cross method, where the lines were crossed with two testers. The 110 top-cross hybrids were evaluated for grain yield during the off-season of 2012 at two sites in the Brazilian Midwest. Partial diallel analysis was performed with the adjusted means of each of the individual analyses of the top crosses. The combining ability of partially inbred lines and testers as well as their interaction with N levels, was estimated for each site. The coefficient of concordance among 15 partially inbred lines selected by the highest general combining ability estimates for each N level was 0.60. The S1 lines 39, 11, 41, 55, 38, 37, 6, 8, and 14 were selected at both N levels. This indicates that these lines can be used to identify ideal hybrids for growing in the off-season in the Brazilian Midwest.

The goal of this work was to compare the effect of the accuracy and residual variance in genome wide selection using marker selection as well as using the effect of the indirect selection, using simulated and real data. In simulated data was used one sample with 200 individuals with 1,000 molecular markers in F2 population. The real data was obtained in maize with F2 population with 441 individuals and genotyping with 261 SSR markers. There was 11 traits evaluated (ear length, ear width, row number, kernels per row, 100-kernel weight, ear weight, grain yield, length of branch, number of branch, plant height and ear height). All data was analyzed using rrBLUP method and 10-fold cross-validation. In simulated and maize data the results were similar: the residual variance with few markers is lower than with the 1000 markers and the accuracy with few markers is bigger than with 1000 markers. For maize data multi trait selection, the accuracy increased when the correlation between traits is greater than 0.50 and residual variance decreased when the correlation is greater than 0.70. In this sense, these results showed that marker selection could be used as a first step in genome wide selection, improving the prediction and compute demand.

Environmental stratification studies are important for the plant breeding, since they allow to adequately plan the experimental network. The objective of this work was to identify similar environments for cotton cultivation in the Brazilian Cerrado regarding yield and fiber quality. Nineteen field studies were carried out in a randomized complete block design with twelve genotypes and four replicates. Agronomic (cotton seed yield and fiber percentage) and technological traits (length, micronaire, fiber strength) were evaluated. These results indicate that there are six environments (PVA3, MON, SHE1, SIN, PPA e TRIN) in which the cotton trials should be installed as a matter of priority owing to the phenotypic response pattern obtained for the evaluated traits. The remaining 13 environments are similar to each other for all traits and can be summarized in strategic locations depending on the ease of installation of the trials.

Photosynthetic efficiency has become the target of several breeding programs since the positive correlation between photosynthetic rate and yield in soybean suggests that the improvement of photosynthetic efficiency may be a promising target for new yield gains. However, studies on combining ability of soybean genotypes for physiological traits are still scarce in the literature. The objective of this study was to estimate the combining ability of soybean genotypes based on F2 generation aiming to identify superior parents and segregating populations for physiological traits. Twenty-eight F2 populations resulting from partial diallel crossings between eleven lines were evaluated in two crop seasons for the physiological traits: photosynthesis, stomatal conductance, internal CO2 concentration, and transpiration. General combining ability (GCA) of the parents and specific combining ability (SCA) of the F2 populations were estimated. Our findings reveal the predominance of additive effects in controlling the traits. The genotype TMG 7062 IPRO is the most promising parent for programs aiming at photosynthetic efficiency. We have also identified other promising parents and proposed cross-breeding with higher potential for obtaining superior lines for photosynthetic efficiency.

ABSTRACT: Forest breeding is a science that has been developing in Brazil since 1941 being the Eucalyptus a highlighted genus in this scenario. In a global scene, Brazil is displayed prominently in productivity of Eucalyptus planting, due to favorable environmental conditions to cultivation development, and the incentive in research for improvement of traits of interest t observed in it species and hybrids. This research included a historical review of Eucalyptus breeding over the years under genetic biometric perspective in Brazil, from reports describing the pioneer planting up to the current genome wide selection (GWS) that came as a complement of forest breeding success. This review showed some of the tracks performed by researchers aiming to improve the productive and quality of phenotypic traits from Eucalyptus genus.

Fiber length stands out among technological traits as one that still needs to be improved in cotton. Thus, the objectives of this work were to quantify the interrelations between agronomic and technological fiber traits and to identify traits that could be used for the indirect selection of cotton genotypes with longer fiber lengths. Agronomic and technological traits were evaluated in 36 elite lines of cotton that were cultivated in three environments. Canonical correlations were estimated between two groups of traits: agronomic and technological. Path analysis was performed that considered fiber length as the primary dependent variable. The genotypes presented variability in all the evaluated traits, and no significant genotype vs. environmental interaction was observed for any of them. The main technological traits of cotton crops can be improved by an indirect selection of agronomic traits. The selection of cotton genotypes with a smaller size and a higher mean boll weight can be used to increase fiber length and improve other technological traits. Selection indices containing plant height, mean boll weight, fiber strength, and fiber uniformity can be one of the main strategies for the selection of cotton genotypes with a high yield of cotton bolls and long fibers.

Diallel crosses in an unbalanced scheme were carried out in Jatropha to (i) evaluate the additive and non-additive genetic components; (ii) select parents through the general combining ability; (iii) estimate the specific combining ability used in the crosses; and (iv) verify the existence of the maternal effect and inbreeding depression. The experiment was carried out in a complete diallel scheme with four progenitors, unbalanced for the number of crosses. The experimental design consisted of a randomized block, with 5 replications and 3 plants per plot. The following characteristics were evaluated: stem diameter (SD), number of branches (NB), plant height (PH), canopy projection on the row (CPR), canopy projection between rows (CPB), mass of hundred grains (MHG) and grain yield (GY). Estimates of variances were obtained using the method of restricted maximum likelihood, while breeding values were estimated by the best linear unbiased prediction. It was concluded that the additive effect was predominant in the genetic control for SD, CPR, and CPB; the dominance effect was predominant for PH, NB, and GY; there was a cytoplasmic effect and nuclear genes of the female parent for all evaluated traits; parents 107 and 190 are promising for reducing the size and increasing the grain yield; there was inbreeding depression for SD and GY; and the favorable crosses for increasing GY were 190x107 and 190x190.

[This corrects the article DOI: 10.1371/journal.pone.0192189.].

Asian rust affects the physiology of soybean plants and causes losses in yield. Repeatability coefficients may help breeders to know how many measurements are needed to obtain a suitable reliability for a target trait. Therefore, the objectives of this study were to determine the repeatability coefficients of 14 traits in soybean plants inoculated with Phakopsora pachyrhizi and to establish the minimum number of measurements needed to predict the breeding value with high accuracy. Experiments were performed in a 3x2 factorial arrangement with three treatments and two inoculations in a random block design. Repeatability coefficients, coefficients of determination and number of measurements needed to obtain a certain reliability were estimated using ANOVA, principal component analysis based on the covariance matrix and the correlation matrix, structural analysis and mixed model. It was observed that the principal component analysis based on the covariance matrix out-performed other methods for almost all traits. Significant differences were observed for all traits except internal CO2 concentration for the treatment effects. For the measurement effects, all traits were significantly different. In addition, significant differences were found for all Treatment x Measurement interaction traits except coumestrol, chitinase and chlorophyll content. Six measurements were suitable to obtain a coefficient of determination higher than 0.7 for all traits based on principal component analysis. The information obtained from this research will help breeders and physiologists determine exactly how many measurements are needed to evaluate each trait in soybean plants infected by P. pachyrhizi with a desirable reliability.

Jatropha (Jatropha curcas) has become one of the most important species for producing biofuels. Currently, Genotype x Environment (GxE) interaction is the biggest challenge that breeders should solve to increase the section accuracy in the plant breeding. Therefore, the objectives in this study were to estimate the parameters in the 180 half-sib families in Jatropha evaluated for five production years, to verify the significance of the GxE interaction variance, to evaluate the adaptability and stability for each family based on three prediction methods, to select superior half-sib families based on the adaptability and stability analyses, and to predict the accuracy for the sixth production year. Jatropha half-sib families were classified and selected using the follow adaptability and stability methods: linear regression, bi-segmented linear regression and mixed models concepts called harmonic mean of the relative performance of genetic values (HMRPGV). The prediction accuracy was estimated by the Pearson correlation between the predicted genetic values by adaptability and stability methods and the phenotypic value in the sixth production year. In result, most half-sib families were classified as general adaptability and general stability for the evaluated traits. The selection gain obtained via HMRPGV was higher than other methods. The prediction accuracy for the sixth production year was 0.45. Therefore, HMRPGV is efficient to maximize the genetic gain, and it can be a useful strategy to select genotype with high adaptability and stability in Jatropha breeding as well as other species that should be evaluated for many years to take a suitable selection accuracy.

This study aimed to estimate the genetic diversity among soursop genotypes in terms of fruit yield evaluated in different crop years. Sixteen measurements for fruit yield in 71 soursop genotypes were carried out from 2000 to 2016. Based on ANOVA it was verified the existence of genetic variability among genotypes in the different measurements (harvests). The genotypes were clustered according to their respective fruit yield averages in the 16 years as well as from their means obtained in each measurement year by the Scott-Knott test (p

Developing strawberry cultivars that can be grown on a large scale, it is necessary to gather desirable characteristics such as: tolerance to Tetranychus urticae, high fruit yield and wide adaptability to several cropping managements. Therefore, our objective was to study the genetic diversity among 13 strawberry cultivars under different managements and to recommend promising crosses to obtain segreganting populations with high fruit yield and T. urticae tolerance. Trial was performed under field conditions at the Centro Regional de Desenvolvimento Rural Centro Serrano of the Instituto Capixaba for Technical Assistance and Rural Extension (Incaper), Domingos Martins-ES. We evaluated strawberry cultivars Albion, Aleluia, Aromas, Camarosa, Camino Real, Campinas, Diamante, Dover, Festival, Seascape, Toyonoka, Tudla, and Ventana, cultivated in three cropping managements: open field, low tunnel and high tunnel. Experimental design was randomized complete blocks with three replications. Variables evaluated were: number of two-spotted spider mite/cm2 on the leaf (NTSSM), total number of fruits (TNF), number of commercial fruits (NCF) and fruit yield (YIE, t/ha). We applied the generalized Mahalanobis distance and Tocher's optimization method to study the genetic diversity among cultivars in each management, and the relative contribution of traits to genetic diversity was evaluated according to the criterion described by Singh (1981). For the low tunnel and high tunnel environments, the crosses Aleluia x Camarosa, Aleluia x Aromas and Aleluia x Festival are the most promising to generate segregating populations with a higher possibility to appearance transgressive individuals, while for the open field cultivation system, we recommend the cross among Aleluia x Toynoka. The variables that most contributed for genetic dissimilarity were total number of fruits, fruit yield and number of commercial fruits for the environments open field, low tunnel and high tunnel, respectively.

Field experiment should be carried out with high accuracy since the discarding of a trial involves waste of time and resources, and can mislead breeders in selecting inferior genotypes. The simulation of a large number of experiments with great variability of traits is the main strategy to assist breeders on how to perform the experiment. Therefore, the objective of this study was to evaluate two criteria to infer the experimental quality: Coefficient of Variation (CV) and repeatability coefficient. Experimental data from evaluations of common bean lines carried out in 104 experiments were used in this study. The simulation of the scenarios used the following parameters: number of cultivars, number of blocks, genetic variance, mean yield, and coefficient of variation. Two thousand replications for each scenario were simulated. Genotypes means were arranged in an order, and the Spearman correlation was obtained between the estimated genotypes mean and the true genotypic values. Results revealed that CV is not a reliable parameter to evaluate the quality of experiment in the field. In conclusion, repeatability coefficient is the parameter that defines the discarding criteria of evaluation experiments and the recommendation of cultivars, as long as the values for each response variable are established.

The aim of this study was to evaluate the methodology of Artificial Neural Networks (ANN) in order to predict wood volume in eucalyptus and its impacts on the selection of superior families, and to compare artificial neural network with regression models. Data used were obtained in a random block design with 140 half-sib families with five replications at three years of age, and four replications at six years of age, both with five plants per plot. The volume was estimated using ANN and regression models. It was used 2000 and 1500 data to train ANN, and 1500 and 1300 to validate ANN for 3 and 6 years of age, respectively. It is concluded that ANN can help improving the accuracy to measure the volume in eucalyptus trees, and to automate the process of forestry inventory and were more accurate in predicting wood volume than almost all regression models.

The objective of this work was to select families with superior performance and large genetic variability for diallel crosses for potential use in the pre-breeding of oil palm. The experiment consisted of 42 full-sib families divided into three trials with 16 families and 3 witnesses in common in randomized blocks, with four blocks and 12 plants per block. The characteristics evaluated in the experiment were number of bunches per plant (NBP), bunch weight per plant (BWP) and bunch average weight per plant (BAW). The estimation of variance components showed that there was greater genetic variance within families than between families. The heritability for all traits was high, above 0.75, and the coefficient of environmental variation was low to moderate for all traits (between 4 and 13). Tocher’s method separated families into seven groups, whereas the UPGMA method identified six groups. To use the best families in diallel crosses, families 15, 14, 22, 21, 28, 37, 33 and 39 were selected based on individual performance as determined by the Scott-Knott test and genetic diversity by grouping.

O objetivo deste trabalho foi desenvolver e validar uma metodologia de análise da adaptabilidade e da estabilidade fenotípica baseada em regressão quantílica (RQ). Para tanto, foram simulados valores fenotípicos com distribuição simétrica e com distribuição assimétrica à direita e à esquerda, com ou sem a presença de "outliers". A metodologia proposta foi aplicada a um conjunto de dados provenientes de um experimento com 92 genótipos de alfafa (Medicago sativa), avaliados em 20 ambientes, e comparada às metodologias de Eberhart & Russell e de regressão não paramétrica. A metodologia da RQ proporcionou resultados iguais ou superiores aos obtidos com as metodologias alternativas avaliadas. No entanto, a ocorrência de resultados discordantes entre as metodologias evidencia a importância de se avaliar a simetria na distribuição dos valores fenotípicos. Para distribuições simétricas, na presença de "outliers", deve-se utilizar a RQ com valor de quantil estimado (τ) em 0,50; na ausência de "outliers", pode-se utilizar tanto a metodologia de Eberhart & Russell quanto a RQ (τ = 0,50). Para distribuições assimétricas, indica-se o uso da RQ com τ = 0,25, para assimetria à direita, e com τ = 0,75, para assimetria à esquerda, independentemente da presença de "outliers".

Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.