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. multitrait multi trait (MTM highyielding high yielding seasons season. . season) thrice algorithm Broadsense Broad sense 02093 0 2093 0.209 07153 7153 0.7153 4 20192020 2019 2020 2019/202 Furthermore genotypeideotype genotype ideotype 8579RSF RSF NS8338 NS NS7901 NS7667 RK8115 RK 0209 209 0.20 0715 715 0.715 2019202 201 202 2019/20 NS833 NS790 NS766 RK811 020 20 0.2 071 71 0.71 201920 2019/2 NS83 NS79 NS76 RK81 02 2 0. 07 7 0.7 20192 2019/ NS8 NS7 RK8

ABSTRACT This research aimed to compare statistical methods (random forest, RIDGE, LASSO, and elastic net regression) for the prediction of body weight in purebred and crossbred pigs reared in Brazil. This prediction was based on dorsal-view images obtained from video image processing. The study involved 69 animals belonging to breeds such as Large White, Piau, Duroc × Large White, and Piau × Large White. The data collection spanned 144 days, with measurements taken at approximately 20-day intervals, totaling eight measurements for each animal throughout their growth stages. Image acquisition was carried out in individual pens using an Intel RealSense Depth D435 digital camera. The features back area, back perimeter, back width, and body depth were extracted from the images. Pearson’s correlation analysis was conducted to assess the relationship between live weight and these features. The dataset was randomly divided into a training dataset (65%) and a test dataset (35%), and model training was performed by five-fold cross-validation balanced according to the growth stage, which was divided into three groups. This procedure was repeated 100 times, and the resulting metrics were taken as the average of the 100 repetitions. Although with a slight difference, the random forest method outperformed the others with the highest average R2 value (0.87), as well as the lowest average RMSE (14.32) and average MAE (10.13) values. Consequently, the random forest algorithm proved to be the most effective in predicting body weight. The back area, back width, and back perimeter were the most important variables in the model. RIDGE LASSO regression Brazil dorsalview dorsal view processing 6 White 14 days 20day day 20 intervals stages D D43 camera area width Pearsons Pearson s 65% 65 (65% 35%, 35 35% , (35%) fivefold five fold crossvalidation cross validation stage groups 10 times repetitions difference R 0.87, 087 0.87 0 87 (0.87) 14.32 1432 32 (14.32 10.13 1013 13 (10.13 values Consequently 1 2 D4 (65 3 (35% 08 0.8 8 (0.87 14.3 143 (14.3 10.1 101 (10.1 (6 (35 0. (0.8 14. (14. 10. (10. ( (3 (0. (14 (10 (0 (1

Abstract: The aim of this study was to obtain estimates of genetic parameters via meta-analysis and mixed models to evaluate the potential of the germplasm from Embrapa Pecuaria Sudeste for integration in breeding programs. Data on the dry matter yield of 77 accessions evaluated in 24 harvests were used. Descriptive analysis was performed for mixed model and meta-analytical estimates. The variances for each parameter were estimated. For the meta-analysis, variances were used as weighting measures, and the fixed and random models were tested. A diagonal structure was considered for the residual variance matrix. Mean dry matter yield ranged from 1019.5 to 2684 kg ha-1. The phenotypic, permanent environmental, and genotypic variances oscillated widely throughout the harvests, as well as heritability. Meta-analytical estimates proved to be an accurate and complementary approach to the mixed model analysis. Both mixed model and meta-analytical estimates indicated the breeding potential of the alfalfa germplasm.

Abstract The objective of this study was to investigate diversity, experimental conditions and dynamics of the Irrigated Rice Breeding Program of Minas Gerais - Brazil, by meta-analysis. The target traits were grain yield, plant height and days to flowering. Evaluations were based on estimates of 376 genotypes grown at three locations, in two final comparative trials, in 14 growing seasons. Stability of the overall averages of the traits plant height and days to flowering was observed, indicating an adequate plant height and medium cycle. High average yields (>5,000 kg ha-1), good experimental accuracy and genetic variability were recorded. However, the genetic variability of all traits decreased over time, indicating the need to increase genotypic diversity. The parameter estimates of the morphoagronomic traits studied in time meta-analysis indicated the dynamic nature and good accuracy of the Irrigated Rice Breeding Program of Minas Gerais.