Estimates of repeatability coefficients and the number of the optimum measure to select superior genotypes in Annona muricata L.

The aim of this study was to evaluate repeated measures over the years to estimate repeatability coefficient and the number of the optimum measure to select superior genotypes in Annona muricata L. The fruit production was evaluated over 16 years in 71 genotypes without an experimental design. The estimation of variance components and the prediction of the permanent phenotypic value were performed using REML/BLUP proceedings. The coefficient of determination, accuracy, and selective efficiency increased when measures increased. The coefficient of determination of 80% was reached beyond 8 crop seasons with high accuracy and selective efficiency. Thus, the evaluation of 8 crop seasons can be suitable to select superior genotypes in the A. muricata L. breeding program. Predicted selection gain had a high magnitude for fruit production indicating that it is possible to take a progressive genetic advance for this trait over cycle breeding.

Diversity among elephant grass genotypes using Bayesian multi-trait model.

Elephant grass is a perennial tropical grass with great potential for energy generation from biomass. The objective of this study was to estimate the genetic diversity among elephant grass accessions based on morpho-agronomic and biomass quality traits and to identify promising genotypes for obtaining hybrids with high energetic biomass production capacity. The experiment was installed at experimental area of the State Agricultural College Antônio Sarlo, in Campos dos Goytacazes. Fifty-two elephant grass genotypes were evaluated in a randomized block design with two replicates. Components of variance and the genotypic means were obtained using a Bayesian multi-trait model. We considered 350,000 iterations in the Gibbs sampler algorithm for each parameter adopted, with a warm-up period (burn-in) of 50,000 Iterations. For obtaining an uncorrelated sample, we considered five iterations (thinning) as a spacing between sampled points, which resulted in a final sample size 60,000. Subsequently, the Mahalanobis distance between each pair of genotypes was estimated. Estimates of genotypic variance indicated a favorable condition for gains in all traits. Elephant grass accessions presented greater variability for biomass quality traits, for which three groups were formed, while for the agronomic traits, two groups were formed. Crosses between Mercker Pinda México x Mercker 86-México, Mercker Pinda México x Turrialba, and Mercker 86-México x Taiwan A-25 can be carried out for obtaining elephant grass hybrids for energy purposes.

Evaluation of genotype x environment interactions in cotton using the method proposed by Eberhart and Russell and reaction norm models.

Cotton produces one of the most important textile fibers of the world and has great relevance in the world economy. It is an economically important crop in Brazil, which is the world's fifth largest producer. However, studies evaluating the genotype x environment (G x E) interactions in cotton are scarce in this country. Therefore, the goal of this study was to evaluate the G x E interactions in two important traits in cotton (fiber yield and fiber length) using the method proposed by Eberhart and Russell (simple linear regression) and reaction norm models (random regression). Eight trials with sixteen upland cotton genotypes, conducted in a randomized block design, were used. It was possible to identify a genotype with wide adaptability and stability for both traits. Reaction norm models have excellent theoretical and practical properties and led to more informative and accurate results than the method proposed by Eberhart and Russell and should, therefore, be preferred. Curves of genotypic values as a function of the environmental gradient, which predict the behavior of the genotypes along the environmental gradient, were generated. These curves make possible the recommendation to untested environmental levels.

Minimum number of measurements for evaluating soursop (Annona muricata L.) yield.

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages.

Selecting sugarcane genotypes by the selection index reveals high gain for technological quality traits.

Sugarcane (Saccharum sp) is one of the most promising crops and researchers have sought for renewable alternative energy sources to reduce CO2 emission. The study of strategies, which allow breeders in the selection of superior genotypes for many traits simultaneously, is important. Therefore, the objectives of this study were: i) to apply path analysis to better understand the relationship between the lignocellulosic traits and technological quality traits with total recoverable sugars (TRS) and ii) to use several multivariate selection indexes to predict the genetic gain and to select superior genotypes in the sugarcane breeding. A total of 40 sugarcane genotypes were evaluated in an experimental design using incomplete blocks with two replicates. The follow traits were evaluated: dry matter (DM), total soluble solids (BRIX), apparent sucrose content in the juice (POL), apparent sucrose content in sugarcane (POLS), fiber content (FIB), purity (PUR), TRS, lignin content (LC), cellulose content (CC), hemicellulose content (HC), and ash content (AC). These traits were analyzed by analysis of variance, phenotypic correlation network, path analysis, and selection index. The highest direct effect on TRS was obtained by POLS (0.337), POL (0.299), BRIX (0.227), and FIB (-0.146). The estimates of phenotypic correlation between these characters and TRS were in the same direction, which demonstrated a cause-and-effect relationship. The highest indirect effect was of POL via POLS (0.331) followed by POLS via POL (0.294). BRIX presented high indirect effects via POLS (0.266) and via POL (0.246). On the other hand, FIB presented negative indirect effects via POLS (-0.169) and POL (-0.103). In conclusion, path analysis and index selection are useful strategies to help breeders in the selection of superior genotypes in sugarcane.

Selection of strawberry cultivars with tolerance to Tetranychus urticae (Acari: Tetranychidae) and high yield under different managements.

Tetranychus urticae Koch (Acari: Tetranychidae) is considered the main pest of strawberry. Several factors can favor its development, among them the genotype susceptibility and cropping system. The aims of this study were to evaluate the agronomic performance of strawberry cultivars under different managements and to identify strawberry cultivars that meet tolerance to T. urticae and high fruit yield. Thirteen cultivars of strawberry ('Albion', 'Aleluia', 'Aromas', 'Camarosa', 'Camino Real', 'Campinas', 'Diamante', 'Dover', 'Festival', 'Seascape', 'Toyonoka', 'Tudla', and 'Ventana') under three managements (open field, low tunnel, and high tunnel) were evaluated. The T. urticae attack to different cultivars was influenced by managements, being low tunnel the one that provided higher infestations in the most evaluated cultivars. 'Camarosa' was the cultivar with the lower incidence of pest and 'Dover' had the higher infestation. The genotype most suitable for growing under different managements is the 'Festival' genotype, since it meets tolerance to T. urticae, high fruit yield, and phenotypic stability.

Molecular analysis of genetic diversity among vine accessions using DNA markers.

Viticulture presents a number of economic and social advantages, such as increasing employment levels and fixing the labor force in rural areas. With the aim of initiating a program of genetic improvement in grapevine from the State University of the state of Rio de Janeiro North Darcy Ribeiro, genetic diversity between 40 genotypes (varieties, rootstock, and species of different subgenera) was evaluated using Random amplified polymorphic DNA (RAPD) molecular markers. We built a matrix of binary data, whereby the presence of a band was assigned as "1" and the absence of a band was assigned as "0." The genetic distance was calculated between pairs of genotypes based on the arithmetic complement from the Jaccard Index. The results revealed the presence of considerable variability in the collection. Analysis of the genetic dissimilarity matrix revealed that the most dissimilar genotypes were Rupestris du Lot and Vitis rotundifolia because they were the most genetically distant (0.5972). The most similar were genotypes 31 (unidentified) and Rupestris du lot, which showed zero distance, confirming the results of field observations. A duplicate was confirmed, consistent with field observations, and a short distance was found between the variety 'Italy' and its mutation, 'Ruby'. The grouping methods used were somewhat concordant.

Multivariate diallel analysis allows multiple gains in segregating populations for agronomic traits in Jatropha.

Jatropha is research target worldwide aimed at large-scale oil production for biodiesel and bio-kerosene. Its production potential is among 1200 and 1500 kg/ha of oil after the 4th year. This study aimed to estimate combining ability of Jatropha genotypes by multivariate diallel analysis to select parents and crosses that allow gains in important agronomic traits. We performed crosses in diallel complete genetic design (3 x 3) arranged in blocks with five replications and three plants per plot. The following traits were evaluated: plant height, stem diameter, canopy projection between rows, canopy projection on the line, number of branches, mass of hundred grains, and grain yield. Data were submitted to univariate and multivariate diallel analysis. Genotypes 107 and 190 can be used in crosses for establishing a base population of Jatropha, since it has favorable alleles for increasing the mass of hundred grains and grain yield and reducing the plant height. The cross 190 x 107 is the most promising to perform the selection of superior genotypes for the simultaneous breeding of these traits.

Path analysis and canonical correlations for indirect selection of Jatropha genotypes with higher oil yield.

Jatropha is a species with great potential for biodiesel production, and the knowledge on how the main agronomic traits are correlated will contribute to its improvement. Therefore, the objectives of this study were to estimate the genetic parameters of the traits: plant height at 12 and 40 months, canopy projection on the row at 12 and 40 months, canopy projection between the row at 12 and 40 months, number of branches at 40 months, grain yield, and oil yield; to verify the existence of phenotypic correlation between these traits; to verify the influence of the morphological traits on oil yield by means of path analysis; and to evaluate the relationship between the productive traits in Jatropha and the morphological traits measured at different ages. Sixty-seven half-sib families were evaluated using a completely randomized block design with two replications and five plants per plot. Analysis of variance was used to estimate the genetic value. Phenotypic correlations were given by the Pearson correlation between traits. For the canonical correlation analysis, two groups of traits were established: group I, consisting of traits of economic importance for the culture, and group II, consisting of morphological traits. Path analysis was carried out considering oil yield as the main dependent variable. Genetic variability was observed among Jatropha families. Productive traits can be indirectly selected via morphological traits due to the correlation between these two groups of traits. Therefore, canonical correlations and path analysis are two strategies that may be useful in Jatropha-breeding program when the objective is to select productive traits via morphological traits.

Comparison between different selection indices in energy cane breeding.

Sugarcane breeding programs have been adapting to a new market demand: aside from high sucrose yield per hectare, the sector needs new cultivars with higher fiber percentages. The selection of sugarcane clones based on phenotype alone is a complex task. The selected clones should display high performance in a series of yield- and quality-related traits. Selection indices can provide information about which clones can best combine the traits of agronomic interest. In this study, different selection indices were evaluated in a population of 220 clones. The following traits were evaluated: weight of 10 stalks with straw, weight of 10 stalks with no straw, tons of cane per hectare with straw, tons of cane per hectare with no straw, sucrose content, fiber percentage, and tons of fiber per hectare. The selection indices of Smith (1936) and Hazel (1943) and Mulamba and Mock (1978), the base index (Williams, 1962), and the index of Pesek and Baker (1969) were used. The selection index of Mulamba and Mock (1978) without economic weight estimates, the index of Mulamba and Mock with economic weights based on heritability, and the index of Pesek and Baker (1969) with the desired gains based on genetic standard deviations were efficient for the selection of energy cane clones with good fiber yield, sucrose content, and tons of cane per hectare.

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F2 populations by using genomic selection models.

Genomic selection is a useful technique to assist breeders in selecting the best genotypes accurately. Phenotypic selection in the F2 generation presents with low accuracy as each genotype is represented by one individual; thus, genomic selection can increase selection accuracy at this stage of the breeding program. This study aimed to establish the optimal number of individuals required to compose the training population and to establish the amount of markers necessary to obtain the maximum accuracy by genomic selection methods in F2 populations. F2 populations with 1000 individuals were simulated, and six traits were simulated with different heritability values (5, 20, 40, 60, 80 and 99%). Ridge regression best linear unbiased prediction was used in all analyses. Genomic selection models were set by varying the number of individuals in the training population (2 to 1000 individuals) and markers (2 to 3060 markers). Phenotypic accuracy, genotypic accuracy, genetic variance, residual variance, and heritability were evaluated. Greater the number of individuals in the training population, higher was the accuracy; the values of genotypic and residual variances and heritability were close to the optimum value. Higher the heritability of the trait, higher is the number of markers necessary to obtain maximum accuracy, ranging from 200 for the trait with 5% heritability to 900 for the trait with 99% heritability. Therefore, genomic selection models for prediction in F2 populations must consist of 200 to 900 markers of major effect on the trait and more than 600 individuals in the training population.

Selection of common bean genotypes for the Cerrado/Pantanal ecotone via mixed models and multivariate analysis.

The common bean, Phaseolus vulgaris, is predominantly grown on small farms and lacks accurate genotype recommendations for specific micro-regions in Brazil. This contributes to a low national average yield. The aim of this study was to use the methods of the harmonic mean of the relative performance of genetic values (HMRPGV) and the centroid, for selecting common bean genotypes with high yield, adaptability, and stability for the Cerrado/Pantanal ecotone region in Brazil. We evaluated 11 common bean genotypes in three trials carried out in the dry season in Aquidauana in 2013, 2014, and 2015. A likelihood ratio test detected a significant interaction between genotype x year, contributing 54% to the total phenotypic variation in grain yield. The three genotypes selected by the joint analysis of genotypic values in all years (Carioca Precoce, BRS Notável, and CNFC 15875) were the same as those recommended by the HMRPGV method. Using the centroid method, genotypes BRS Notável and CNFC 15875 were considered ideal genotypes based on their high stability to unfavorable environments and high responsiveness to environmental improvement. We identified a high association between the methods of adaptability and stability used in this study. However, the use of centroid method provided a more accurate and precise recommendation of the behavior of the evaluated genotypes.

Biplot analysis of strawberry genotypes recommended for the State of Espírito Santo.

Most strawberry genotypes grown commercially in Brazil originate from breeding programs in the United States, and are therefore not adapted to the various soil and climatic conditions found in Brazil. Thus, quantifying the magnitude of genotype x environment (GE) interactions serves as a primary means for increasing average Brazilian strawberry yields, and helps provide specific recommendations for farmers on which genotypes meet high yield and phenotypic stability thresholds. The aim of this study was to use AMMI (additive main effects and multiplicative interaction) and GGE biplot (genotype main effects + genotype x environment interaction) analyses to identify high-yield, stable strawberry genotypes grown at three locations in Espírito Santo for two agricultural years. We evaluated seven strawberry genotypes (Dover, Camino Real, Ventana, Camarosa, Seascape, Diamante, and Aromas) at three locations (Domingos Martins, Iúna, and Muniz Freire) in agricultural years 2006 and 2007, totaling six study environments. Joint analysis of variance was calculated using yield data (t/ha), and AMMI and GGE biplot analysis was conducted following the detection of a significant genotypes x agricultural years x locations (G x A x L) interaction. During the two agricultural years, evaluated locations were allocated to different regions on biplot graphics using both methods, indicating distinctions among them. Based on the results obtained from the two methods used in this study to investigate the G x A x L interaction, we recommend growing the Camarosa genotype for production at the three locations assessed due to the high frequency of favorable alleles, which were expressed in all localities evaluated regardless of the agricultural year.

Selection index using the graphical area applied to sugarcane breeding.

This study aimed to develop a multivariate selection index based on the graphical area of a polygon formed by standardized values, also known as radar chart. This methodology may be used to assist selection of superior genotypes in sugarcane breeding programs. Seven technological traits in 37 sugarcane genotypes were evaluated. An area index (AI) was constructed and the resulting polygon areas were used to rank genotypes under selection. In this study, we propose the use of restricted maximum likelihood to estimate genetic parameters and mixed model equations to predict genotypic and breeding values. The area of each polygon was calculated for phenotypic, genotypic, and estimated breeding values. Thereby, the genotypes with larger area can be selected based on a detailed a posteriori evaluation of the radar charts. The proposed AI can be adjusted based on the breeders' specific interests, it is perfectly useful in other crops, and may also be applied to studies on genotype-environment interactions. Moreover, AI is a powerful tool that can evaluate trait stability of genotypes based on slight differences in the area formed by each genotype. Hence, this method is easy to apply and shows great potential for use in sugarcane breeding programs as well as in other breeding programs.

Mixed models for selection of Jatropha progenies with high adaptability and yield stability in Brazilian regions.

The aim of this study was to estimate genetic parameters via mixed models and simultaneously to select Jatropha progenies grown in three regions of Brazil that meet high adaptability and stability. From a previous phenotypic selection, three progeny tests were installed in 2008 in the municipalities of Planaltina-DF (Midwest), Nova Porteirinha-MG (Southeast), and Pelotas-RS (South). We evaluated 18 families of half-sib in a randomized block design with three replications. Genetic parameters were estimated using restricted maximum likelihood/best linear unbiased prediction. Selection was based on the harmonic mean of the relative performance of genetic values method in three strategies considering: 1) performance in each environment (with interaction effect); 2) performance in each environment (with interaction effect); and 3) simultaneous selection for grain yield, stability and adaptability. Accuracy obtained (91%) reveals excellent experimental quality and consequently safety and credibility in the selection of superior progenies for grain yield. The gain with the selection of the best five progenies was more than 20%, regardless of the selection strategy. Thus, based on the three selection strategies used in this study, the progenies 4, 11, and 3 (selected in all environments and the mean environment and by adaptability and phenotypic stability methods) are the most suitable for growing in the three regions evaluated.

Selection in sugarcane based on inbreeding depression.

This study aimed to evaluate the gene action associated with yield-related traits, including mean stalk weight (MSW), tons of sugarcane per hectare (TCH), and fiber content (FIB) in sugarcane. Moreover, the viability of individual reciprocal recurrent selection (RRSI-S1) was verified, and the effect of inbreeding depression on progenies was checked. The results were also used to select promising genotypes in S1 progenies. Eight clones (RB925345, RB867515, RB739359, SP80-1816, RB928064, RB865230, RB855536, and RB943365) and their respective progenies, derived from selfing (S1), were evaluated. Several traits, including the number of stalks, MSW, soluble solids content determined in the field, stalk height, stalk diameter, TCH, soluble solids content determined in the laboratory, sucrose content, and FIB were evaluated in a randomized block design with hierarchical classification. The results showed that the traits with predominant gene action associated with the dominance variance of MSW and TCH were most affected by inbreeding depression. The FIB, with predominant additive control, was not affected by selfing of the clones, and the RB867515⊗, RB928064⊗, RB739359⊗ and RB925345⊗ progenies performed best. Therefore, the use of S1 progenies for RRSI-S1 in sugarcane breeding programs is promising, and it should be explored for the future breeding of clones with high FIB levels.

Superiority of artificial neural networks for a genetic classification procedure.

The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

Comparison of methods used to identify superior individuals in genomic selection in plant breeding.

The aim of this study was to evaluate different methods used in genomic selection, and to verify those that select a higher proportion of individuals with superior genotypes. Thus, F2 populations of different sizes were simulated (100, 200, 500, and 1000 individuals) with 10 replications each. These consisted of 10 linkage groups (LG) of 100 cM each, containing 100 equally spaced markers per linkage group, of which 200 controlled the characteristics, defined as the 20 initials of each LG. Genetic and phenotypic values were simulated assuming binomial distribution of effects for each LG, and the absence of dominance. For phenotypic values, heritabilities of 20, 50, and 80% were considered. To compare methodologies, the analysis processing time, coefficient of coincidence (selection of 5, 10, and 20% of superior individuals), and Spearman correlation between true genetic values, and the genomic values predicted by each methodology were determined. Considering the processing time, the three methodologies were statistically different, rrBLUP was the fastest, and Bayesian LASSO was the slowest. Spearman correlation revealed that the rrBLUP and GBLUP methodologies were equivalent, and Bayesian LASSO provided the lowest correlation values. Similar results were obtained in coincidence variables among the individuals selected, in which Bayesian LASSO differed statistically and presented a lower value than the other methodologies. Therefore, for the scenarios evaluated, rrBLUP is the best methodology for the selection of genetically superior individuals.

Artificial neural networks reveal efficiency in genetic value prediction.

The objective of this study was to evaluate the efficiency of artificial neural networks (ANNs) for predicting genetic value in experiments carried out in randomized blocks. Sixteen scenarios were simulated with different values of heritability (10, 20, 30, and 40%), coefficient of variation (5 and 10%), and the number of genotypes per block (150 and 200 for validation, and 5000 for neural network training). One hundred validation populations were used in each scenario. Accuracy of ANNs was evaluated by comparing the correlation of network value with genetic value, and of phenotypic value with genetic value. Neural networks were efficient in predicting genetic value with a 0.64 to 10.3% gain compared to the phenotypic value, regardless the simulated population size, heritability, or coefficient of variation. Thus, the artificial neural network is a promising technique for predicting genetic value in balanced experiments.

Selfing confirmation in sugarcane by using simple sequence repeat markers: an individual reciprocal recurrent selection scheme.

Superior inbred clones selected in S1 families can integrate an individual reciprocal recurrent selection program in sugarcane by eliminating the genetic load of the population and exploring superior hybrid combinations. Molecular markers can be used for reliable identification of the true selfing-derived clones in these S1 populations. The objective of this study was to confirm true self-fertilized individuals in sugarcane families using microsatellite markers aimed at the use of self-fertilized plants in an individual reciprocal recurrent selection strategy. Self-fertilized individuals from five cultivars were genotyped with eight simple sequence repeat (SSR) markers. The markers generated 62 polymorphic markers, with an average of seven polymorphic alleles across the cultivars tested. Three loci revealed highly informative bands and were used to assess the level of selfing in five S1 families. Selfing in these families ranged from 71.7 to 97.6%. The SSR loci provide a reliable and accurate method to identify S1 progenies in sugarcane crosses and can be used as a tool to assist selection strategies in sugarcane breeding programs.