Alyce Carla Rodrigues Moitinho

This study aimed to identify promising soybean lines by combining genetic parameter estimates, simultaneous selection for grain yield and oil content, and a complementary multivariate classification of already phenotyped materials. Four soybean populations were evaluated in Jaboticabal, São Paulo, Brazil, across three agricultural years corresponding to the F5:6, F5:7, and F5:8 generations. The breeding populations comprised 142 advanced lines, whereas the augmented-block field experiment contained 281 plot-level experimental records in each agricultural year because the complete layout also included the repeated check plots required by the design. Population-based analyses were conducted using REML/BLUP, likelihood-ratio tests, and a standardized Z selection index for grain yield and oil content. The Z index identified the highest-ranked lines within each population, including genotypes 19, 42, 38, and 27 in populations A, B, C, and D, respectively. A complementary Random Forest analysis was used as a decision-support classification procedure applied to observed phenotypic information, rather than as a model for forecasting future performance or estimating unknown oil or protein values. The procedure organized the phenotypic profiles into oil-based, protein-based, and combined oil-plus-protein groups. For the combined scenario, the classification criterion rewarded high standardized values for both seed-composition traits while penalizing imbalance between them. Out-of-fold classification results showed high agreement with the predefined operational groups, with overall accuracy rates of 0.99, 1.00, and 0.98 for the oil, protein, and combined scenarios, respectively. Principal component analysis described the multivariate structure of the same phenotypic dataset. Because generation and crop year coincide, changes in genetic parameter estimates across successive evaluations cannot be attributed exclusively to selection. Overall, the results support the use of mixed models and the Z index for line selection, while Random Forest provides a complementary framework for organizing already measured materials according to contrasting and combined seed-composition objectives.

Soybean is the main oilseed crop cultivated worldwide and is important for human and animal nutrition. The duration of flowering, the reproductive period, and maturity is determined by genetic factors and influenced by environmental conditions, including temperature, photoperiod, precipitation, soil type, and crop management, which affect yield and regional adaptation. However, important gaps remain in understanding how soybean lines from different relative maturity groups respond to these conditions, particularly regarding the effects of temperature and precipitation on phenological development across growing seasons. Therefore, this study aimed to characterize the behavior of soybean populations and the environmental effect on the phenological cycle of the crop. Two populations were developed from biparental crosses with contrasting RMG values among the parental cultivars. The E2 population, comprising 150 lines, originated from the cross BMX Potência RR (RMG 6.7) × BMX Energia RR (RMG 5.3), whereas the E3 population, comprising 80 lines, originated from BRS 245 RR (RMG 7.3) × BRS 278 RR (RMG 9.4). The populations were evaluated over four growing seasons using an augmented block design, and the traits were analyzed using mixed models with environmental covariates. REML/BLUP methodology was used to estimate genetic parameters and predict genotypic values, enabling the selection of dual-purpose lines combining earliness and high yield. Higher temperatures and lower precipitation were associated with shorter phenological periods, whereas higher precipitation and lower temperatures were associated with longer periods. Population E2 showed more consistent associations with temperature and precipitation, whereas Population E3 showed weaker or nonsignificant associations for some traits, particularly NDM. These population-specific responses may be relevant for the selection of soybean lines in breeding programs.

Soybean is the main oilseed crop cultivated worldwide and is important for human and animal nutrition. The duration of flowering, reproductive period, and maturity is determined by genetic factors and influenced by environmental variables such as temperature, photoperiod, precipitation, soil type, and crop management, affecting yield and regional adaptation. However, gaps remain regarding the interaction between relative maturity groups (RMG) and the environment. Therefore, this study aimed to characterize the behavior of soybean populations and the environmental effect on the phenological cycle of the crop. Two populations were developed from biparental crosses with a narrow range in RMG among the parental cultivars. The E2 population originated from BMX Potência RR (RMG 6.7) × BMX Energia RR (RMG 5.3), and E3 from BRS 245 RR (RMG 7.3) × BRS 278 RR (RMG 9.4). The populations were evaluated over four growing seasons in an augmented block design, and traits were analyzed using mixed models with environmental covariates. Higher temperatures and lower precipitation were associated with shorter phenological periods, whereas higher precipitation and lower temperatures were associated with longer periods. Earlier lines were more environmentally sensitive, whereas later lines showed greater phenological stability.

Charcoal rot, a destructive disease of soybean roots, has limited control options, and the development of resistant cultivars is the most effective, economical, and sustainable strategy. However, tropical cultivars are generally susceptible, while exotic resistant accessions are poorly adapted to tropical conditions. This study represents the initial step toward developing tropical soybean cultivars combining resistance and agronomic performance. We evaluated the agronomic performance of progenies derived from crosses between exotic lines partially resistant to Macrophomina phaseolina (PI 594302 and PI 567562A) and three commercial tropical cultivars with high yield potential (relative maturity groups (RMGs) 5.5, 6.2, and 6.9). Populations were advanced to the F₆ generation and assessed for yield, plant height, first pod insertion, and agronomic value using a partial diallel design. Grain yield ranged from 18.5 to 33.2 g·plant⁻¹ across generations. The combining ability analysis revealed predominance of additive genetic effects, highlighting PI 594302 and VMAX RR (RMG 6.2) as key parents for plant height, agronomic value, and grain yield and exhibited the greatest potential for superior progeny across generations. These results identify promising lines with high agronomic performance and adaptation, which will undergo resistance confirmation in subsequent studies, supporting the long-term goal of developing tropical soybean cultivars that combine productivity, sustainability, and tolerance to charcoal rot.

One of the challenges for genetic and plant breeding is the selection of genotypes that are increasingly adapted, productive, and stable to the cultivation environments, without losing the desired agronomic traits. These evaluations are important for determining the ideal genotypes for each environment. Without these evaluations, the behavior of cultivars or genotypes cannot be predicted, affecting performance, and causing losses for farmers. This work aimed to characterize, by different methodologies, the agronomic performance, adaptability, and stability of soybean genotypes in a preliminary trial, to select the superior genotypes, aiming at the Registration and Protection of Cultivars. The experiment was conducted in the field in a randomized block design with two replications, where 50 soybean genotypes were evaluated, including 3 checks, during two agricultural, two different environments. Agronomic traits were evaluated: number of days to maturity, number of days to flowering, plant height at maturity, the weight of a thousand grains, lodging, oil content, and grain yield. To determine the adaptability and productive stability of the genotypes, GGE biplot multivariate prediction methodologies and REML/BLUP mixed models were used and compared, using the main agronomic traits of production and oil content, which are of industrial importance in human and animal nutrition. The methods of the analysis showed differences regarding the ordering of adaptability and stability, however, they were unanimous in considering the genotypes as superior: 24; 29; 30; 32; 34, and 42 for yield and 4; 17; 37, and 44 for oil content. The predominant pedigree of the selected genotypes consists of bi-parental crosses, except genotype 24, which comes from a quadruple cross.

The objective of this study was to analyze the genetic variability generated from two-way, four-way, and eight-way hybridizations in soybean, derived from parental lines contrasting in resistance to cyst nematode and powdery mildew. We assessed F2 populations using Simple Sequence Repeat (SSR) molecular markers located within the 50 cM region surrounding the Rmd (powdery mildew) and Rhg1 (cyst nematode) resistance genes. All markers were nonsignificant by chi-square test (P > 0.05), indicating that observed values align with the expected genotypic inheritance ratio in F2 populations (1:2:1). Lower polymorphism observed among parents explained the reduced recombination frequency in these populations. A higher mean crossover rate was observed in G4 (4.00) within linkage group G, and J8 (2.91) within linkage group J. In terms of the number of generations needed to form each population, crossover rates of 2.02 and 0.97 were found for G2 and J8, respectively. Recombination between alleles was present in some populations. Crosses involving a greater number of parents exhibited a higher frequency of crossovers, which positively impacts genetic variability. Therefore, the inclusion of more than two parents in hybridizations is recommended to enhance crossover frequency and genetic diversity. These findings provide valuable insights for breeding programs aiming to improve resistance in soybean, indicating that multi-parental crosses can be a strategic approach to increase genetic diversity and resilience against pathogens.

The primary objective of soybean-breeding programs is to develop cultivars that offer both high grain yield and a maturity cycle tailored to the specific soil and climatic conditions of their cultivation. Therefore, predicting the genetic value is essential for selecting and advancing promising genotypes. Among the various analytical approaches available, deep machine learning emerges as a promising choice due to its capability to predict the genetic component of phenotypes assessed under field conditions, thereby enhancing the precision of breeding decisions. This study aimed to determine the efficiency of artificial neural networks (ANNs) in predicting the genetic values of soybean genotypes belonging to populations derived from crosses between parents of different relative maturity groups (RMGs). We characterized populations with broad and restricted genetic bases for RMG traits. Data from three soybean populations, evaluated over three different agricultural years, were used. Genetic values were predicted using the multilayer perceptron (MLP) artificial neural network and compared to those obtained using the best unbiased linear prediction from variance components using restricted maximum likelihood (RR-BLUP). The MLP neural network efficiently predicted genetic values for the relative maturity group trait for genotypes belonging to populations of broad and restricted crosses, with an R² of 0.999 and root-mean-square error (RMSE) of 0.241, and for grain yield, there was an R² of 0.999 and an RMSE of 0.076. While the percentage of coincident superior genotypes remained relatively consistent, a significant difference was observed in their ranking order. The genetic gain with selection estimated using MLP was higher by 30–110% compared to RR-BLUP for the relative maturity group trait and 90–500% for grain yield. Artificial neural networks (ANNs) showed higher efficiency than RR-BLUP in predicting the genetic values of the soybean population. Local selection at intermediate latitudes is conducive to developing lines adaptable for regions at higher and lower latitudes.

As correlações e os parâmetros genéticos são estimativas utilizadas para otimizar os trabalhos de melhoramento de plantas. O objetivo desse trabalho foi quantificar tais parâmetros para famílias de meios-irmãos de mamoeiro da cultivar Rubi Incaper 511, aos 8 meses pós semeadura. Foram avaliadas dezoito famílias de meios-irmãos e a cultivar Rubi Incaper 511, em delineamento de blocos ao acaso com cinco repetições e nove plantas por parcela. Foram estimados as variâncias genotípica, fenotípica e ambiental; o coeficiente de determinação genotípico e as correlações fenotípicas, genotípicas e ambientais entre os caracteres: altura da planta (AP), altura de inserção dos primeiros frutos (AF) e diâmetro de caule (DC). As correlações genotípicas foram maiores que a fenotípica, exceto para AP x DC e houve correlação fenotípica significativa entre as características AP x AF e AP x DC. A variância genotípica foi maior que a ambiental em todas as características avaliadas. Os coeficientes de determinação genotípico indicaram que é possível ter sucesso na seleção das variáveis analisadas.

A cultura de mamoeiro (Carica papaya) necessita de novas cultivares, mais produtivas, adaptadas e que atendam às exigências dos mercados externo e interno. Nesse trabalho foram avaliados caracteres morfoagronômicos em famílias de meios-irmãos da cultivar Rubi Incaper 511, durante a fase vegetativa. O experimento foi conduzido em delineamento de blocos ao acaso, com cinco repetições e nove plantas por parcela. Foram avaliados dezoito famílias de meios-irmãos provenientes da cultivar Rubi Incaper 511 e a própria cultivar, como testemunha. Foi medida a altura de planta, a altura de inserção dos primeiros frutos e diâmetro de caule. Foi calculada a análise de variância, o coeficiente de variação e o coeficiente de determinação genotípico. As médias foram agrupadas pelo método Scott-Knott. Houve diferenças significativas entre os genótipos para todas as variáveis analisadas. Foi possível identificar famílias com menor AP e AF e maior DF, características que atendam ao interesse de produtores de mamoeiro.