The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039–0.80, and 0.02–0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.
The objective of this work was to estimate the best approach for prediction and establish a network with better predictive power in white oat using methodologies based on regression, artificial intelligence, and machine learning. Seventy-eight white oat genotypes were evaluated in 2008 and 2009. Were evaluated without and with fungicide, established prediction models in four experimental sets. The characteristics evaluated were grain yield, which was used as a response variable, and ten others as explanatory variables. Assessing the importance of variables through the impact of destructuring or disturbing the information of a given input on the estimation of R2. This importance was estimated by exchanging information or making the phenotypic value of each characteristic constant and checking for changes in the estimates of R2. When the values of a feature are disturbed, the value of R2 decreases, indicating that the feature is important over the others for prediction purposes. The importance of variables using the radial basis function network was estimated according to the MLP. For machine learning, decision trees, bagging, random forest, and boosting were used. The quality of the predictive model was adjusted based on R2 was used to quantify the importance of the phenotypic trait. The characters indicated to assist in decision-making are plant height, leaf rust severity, and lodging percentage. The R2 ranged from 30.14% − 96.45% and 10.57% − 94.61%, for computational intelligence and machine learning, respectively. The bagging technique showed a high estimate of the coefficient of determination more elevated than the others.
The presence of non-informative markers in Genome Wide Selection (GWS) needs to be evaluated so that the genomic prediction is more efficient in a breeding program. This study proposes to evaluate the efficiency of RR-BLUP after reducing the dimensionality of SNP's markers in the presence of different levels of dominance, heritability, and epistatic interactions in order to demonstrate that the results obtained with reduced information improve prediction and preserve the same biological conclusions when using a larger data set. 10 F2 populations of a diploid species (2n = 2x = 20) with an effective size of 1000 individuals were simulated, involved the random combination of 2000 gametes generated from contrasting homozygous parents. 10 linkage groups (LG) with a size of 100 cM each and comprised 2010 bi-allelic SNP´s distributed equally and equidistant form. Nine traits were simulated, formed by different degrees of dominance, heritability, and epistatic interactions. The dimensionality reduction was performed randomly in the simulated population and then the efficiency of RR-BLUP was tested in two different studies. The parameters square of correlation (r2), root mean squares error (RMSE), and the Akaike Information Criterion (AIC) was used to evaluate the efficiency of the model used in the RR-BLUP. The results obtained from the reduced information predicted by the RR-BLUP were able to improve the prediction and preserve the same biological conclusions when using a larger data set. Non-informational or small effect markers can be removed from the original data set. The inclusion of dominance effects was an efficient strategy to improve predictive capacity.
Machine learning and computational intelligence are rapidly emerging in plant breeding, allowing the exploration of big data concepts and predicting the importance of predictors. In this context, the main challenges are how to analyze datasets and extract new knowledge at all levels of research. Predicting the importance of variables in genetic improvement programs allows for faster progress, carrying out an extensive phenotypic evaluation of the germplasm, and selecting and predicting traits that present low heritability and/or measurement difficulties. Although, simultaneous evaluation of traits provides a wide variety of information, identifying which predictor variable is most important is a challenge for the breeder. The traditional approach to variable selection is based on multiple linear regression. It evaluates the relationship between a response variable and two or more independent variables. However, this approach has limitations regarding its ability to analyze high-dimensional data and not capture complex and multivariate relationships between traits. In summary, machine learning and computational intelligence approaches allow inferences about complex interactions in plant breeding. Given this, a systematic review to disentangle machine learning and computational intelligence approaches is relevant to breeders and was considered in this review. We present the main steps for developing each strategy (from data selection to evaluating classification/prediction models and quantifying the best predictor).
The biggest challenge in the alfalfa breeding program is to obtain cultivars with high persistence, high productivity, and adaptability. Therefore, studies about selection methods are necessary for the success of alfalfa breeding programs. This study aimed to evaluate dry matter yield and persistence in alfalfa for selecting genotypes, using appropriate statistical models for experiments with repeated measures. The experiment was conducted at Embrapa Southeast Livestock, in São Carlos, state of São Paulo, Brazil in a randomized blocks design, in plots subdivided in time, with three replicates. Eight genotypes were evaluated, and the agronomic trait evaluated was dry matter yield. The experiments in split-plots were used with two and three errors and generalized linear models with the following correlation structures: composite symmetry (CS), heterogeneous composite symmetry (HCS), auto regressive (AR), heterogeneous auto regressive (HAR), and variance components (VC). The best model was selected according to the lowest value of the Akaike Information Criterion (AIC), and three methodologies were used to identify the genotype with greater productivity and persistence: Average test for multiple comparisons, adaptability, and stability by multi-information, and similarity between genotype and ideotype. The interaction between genotypes and cuts was significant, demonstrating the existence of the different behavior of the alfalfa genotypes over the cuts. Different methodologies allowed to measure the average yield of the alfalfa genotype and the persistence over the cuts. PSB 4 genotype demonstrated promissory behavior in terms of productivity and persistence throughout the production cycle of alfalfa.
One of the major goals of modern agriculture is to achieve increased crop yield using less water. Despite the significant advances in genomics, a phenotypic characterization efficient is essential for the success of a modern breeding program, which wants to speed up the genetic gains by deploying selection in the early stages. Thus, this study aimed to identify which traits are most important to discriminate the maize genotypes to support early selection under contrasting water availability conditions. For this, we used a public diversity panel consisting of 360 tropical maize inbred lines, involving two conditions, well-watered (WW) and water-stress (WS), in eight trials. Evaluations were carried out in the phenological stage V6 for shoot and root traits. There was a significant variation in the panel performance, mainly for root traits under WS conditions, composing six clusters. However, the traits showed a similar pattern of clustering evidenced by principal components in WW and WS conditions. Moreover, a strong relationship was found among the roots' length, surface area, and volume. Based on this, we suggest discarding the most error-prone ones. Our results showed via Redundancy Analysis (RDA) that plant height, stalk diameter, and lateral roots length are traits more sensitive to WS and, therefore, may be considered in early selection in breeding programs aiming for water use efficiency.
The objectives of this study were to use a bayesian multi-trait model, estimate genetic parameters, and select ood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-ve rice genotypes belonging to the ood-irrigated rice improvement program were evaluated. The grain yields, grain length, width and thickness, grain length, and grain width and weight of 100-grains in the agricultural year 2016/2017. The experimental design used in all experiments was a randomized block design with three replications. The Monte Carlo Markov Chain algorithm estimated genetic parameters and genetic values. The grain thickness trait was considered highly heritable, with a credibility interval ranging from: h 2 : 0.9480; 0.9440; 0.8610, in environments 1, 2, and 3, respectively. The grain yields showed a low correlation estimate between grain thickness and 100-grain weight, in all environments, with a credibility interval ranging from ρ= 0.5477; 0.5762; 0.5618 and 0.5973; 0.5247; 0.5632, grain thickness and 100-grain weight, in environments 1, 2, and 3, respectively). The Bayesian multi-trait model proved to be an adequate strategy for the genetic improvement of ood-irrigated.
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