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Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.

Chickpea (Cicer arietinum L.) cultivation in Brazil has expanded as a winter or second-crop option, increasing demand for superior quality seeds. This study evaluated physiological performance, biochemical parameters and storability of BRS Aleppo seeds harvested at different maturation stages. Field production was performed at DAA/UFV from April to September 2020. Harvests were carried out at stages R11, R11.5, R12 and R12+7 (with 50%, 75% and 90% of pods with golden-yellow color and seven days after R12, respectively). The seeds were sealed in paper bags and stored at 23 °C ± 1.8 and 66% RH. At 0, 3, 6 and 9 months, samples were evaluated for seed moisture content, germination, accelerated aging, seedling emergence, tetrazolium test, activity of antioxidant enzymes (CAT and APX) and protein content. Chickpea seeds with higher physiological quality and storage potential were obtained when harvesting was carried out at the R11.5 and R12 stages, equivalent to 75% and 90% of the pods with golden-yellow color, respectively. Harvest delay (R12+7) reduced seed germination, vigor and storage potential, also reducing protein content and activity of APX and CAT enzymes. Storage under laboratory environment conditions for nine months reduces physiological quality of seeds, regardless of the harvest time.

In many cases, traditional analysis of breeding trials based on analysis of variance (ANOVA) do not allow a suitable genetic evaluation. Alternatively, mixed model-based approaches create the possibility of dealing with unbalanced data and modeling spatial trends. The aims of this study were to compare the goodness-of-fit of the model and the genotype ranking through different residual modeling approaches and to select the best performing tropical wheat genotypes based on the best-fitting model. A panel of tropical wheat genotypes was evaluated in three field trials conducted between 2020 and 2021 for grain yield. Linear mixed model analyses were used on the data to estimate the genetic parameters and to predict the genotypic values in analyses of single- and multi-environment trials. Accounting for spatial trends in the analyses of single-and multi-environment trials provides better outcomes than the compound symmetry model does.

We hypothesize that by simulating the natural priming in seeds of a species that forms transient seed banks it is possible to clarify molecular aspects of germination that lead to the recruitment of seedlings when the next rainy season begins. We used seeds of Solanum lycocarpum as a biological model. Our findings support the idea that the increment of seed germination kinetics when the rainy season returns is mainly based on the metabolism and embryonic growth, and that the hydropriming, at the end of seed dispersion, increases the germination window time of these seeds by mainly increasing the degradation of galactomannan of the cell wall. This can improve the energy supply (based on carbon metabolism) for seedling growth in post-germination, which improves the seedling's survival chances. From these findings, we promote a hypothetical model about how the priming at the end of the rainy season acts on mRNA synthesis in the germination of seeds from transient banks and the consequence of this priming at the beginning of the following rainy season. This model predicts that besides the Gibberellin and Abscisic Acid balance (content and sensitivity), Auxin would be a key component for the seed-seedling transition in Neotropical areas. Seed collection was performed under authorization number SISGEN AB0EB45.

The selection of combinations that include the best parents and the understanding of the genetic effects controlling the traits can be made using diallel analysis. This study selected parents with a high frequency of favorable alleles and segregating populations with the greatest potential to produce superior progenies, as well as to understand the genetic effects controlling the studied traits. For this end, 15 parents were divided into two groups and crossed in a 7×8 partial diallel scheme, resulting in a total of 56 hybrid combinations. Some of the F1 seeds were advanced to the F2 generation. The combinations in both generations (F1 and F2) were evaluated in an experimental field in a randomized complete block design, with two replicates, between June and October 2022, in Viçosa, Minas Gerais, Brazil. Days to heading, days to maturation, plant height, spike length, hundred-grain weight, and plot yield were assessed. A diallel analysis was performed using the Geraldi and Miranda-Filho model for partial diallel, adapted from Griffing’s method. Our results suggested a predominance of additive effects. The parents CD 1303, CD 151, BRS 254, ORSFEROZ, ORS Guardião and ORSSENNA exhibited favorable alleles for different traits. In total, 41 combinations were selected, 20 from F1 and 21 from F2 generations. Among these, seven populations were identified as having high genetic potential for producing superior progenies. RESUMO: A seleção de combinações que apresentem os melhores genitores e o conhecimento sobre os efeitos gênicos que controlam as características, são possibilitadas pelo uso da análise dialélica. O objetivo deste estudo, foi selecionar os genitores com alta frequência de alelos favoráveis e populações segregantes com maior potencial para produzir progênies superiores, e entender os efeitos genéticos no controle das características estudadas. Assim, 15 genitores foram divididos em dois grupos e cruzados em esquema de dialelo parcial 7×8, obtendo o total de 56 combinações híbridas. Parte das sementes F1 foram avançadas para geração F2. As combinações em duas gerações (F1 e F2) foram avaliadas em campo experimental, em delineamento em blocos ao acaso, com duas repetições, entre os meses de junho e outubro de 2022, em Viçosa, Minas Gerais, Brasil. As variáveis avaliadas foram espigamento, dias até a maturação (ciclo), altura da planta, comprimento da espiga, massa de cem grãos e produção de parcela. A análise dialélica foi realizada pelo modelo de Geraldi e Miranda-Filho, para dialelo parcial, adaptado do método de Griffing. O efeito aditivo predominou para o controle dos caracteres analisados. Os genitores CD 1303, CD 151, BRS 254, ORSFEROZ, ORS Guardião e ORSSENNA, apresentaram frequência de alelos favoráveis para diferentes características. No total, 41 combinações foram selecionadas, sendo, 20 em F1 e 21 em F2. Destas, foram identificadas sete populações de alto potencial genético para originar progênies.

The study of genotypic relationships between drought tolerance indices and agronomic traits of interest in wheat breeding is useful for designing selection strategies. The objective of this research was to investigate the cause-and-effect relationships between agronomic traits and drought tolerance indices through the analysis of canonical correlations. Two trials (control and stress) were conducted in winter 2020 in Viçosa, MG, Brazil. The traits evaluated were: (days for heading, plant height, mass and number of grains per spike, mass of one hundred grains, and grain yield). Grain yield data from the control and stress conditions were used to construct five drought tolerance indices. The data were subjected to mixed model analysis for estimation of genetic parameters and prediction of genotypic values (REML/BLUP), and then the genotypic values were used to calculate the correlation coefficients between the traits. Two groups of traits were established for the study of canonical correlations, the first group consisting of agronomic traits and the second by drought tolerance indices. There was a significant genotype effect for all evaluated traits. The canonical pairs were significant, which indicated the existence of dependence between the groups. Days to heading trait can be used in the indirect selection of wheat genotypes for drought tolerance. RESUMO: O estudo das relações genotípicas entre índices de tolerância à seca e caracteres agronômicos de interesse no melhoramento de trigo é útil para traçar estratégias de seleção. Objetivou-se com este trabalho investigar as relações de causa e efeito entre características agronômicas e índices de tolerância à seca via análise de correlações canônicas. Dois ensaios (controle e estresse) foram conduzidos no inverno de 2020 em Viçosa, MG, Brasil. Foram avaliados os caracteres (dias para o espigamento, altura da planta, massa e número de grãos por espiga, massa de cem grãos e rendimento de grãos). Os dados de produtividade dos ensaios de controle e estresse foram utilizados para construir cinco índices de tolerância à seca. Os dados foram submetidos à análise de modelos mistos para estimação dos parâmetros genéticos e predição dos valores genotípicos (REML/BLUP), em seguida, os valores genotípicos foram utilizados para calcular os coeficientes de correlação entre os caracteres. Dois grupos de caracteres foram estabelecidos para o estudo das correlações canônicas, sendo o primeiro grupo constituído pelas variáveis agronômicas e o segundo pelos índices de tolerância à seca. Houve efeito significativo de genótipo para todas as características avaliadas. Os pares canônicos foram significativos, o que indicou a existência de dependência entre os grupos. O caráter dias para o espigamento pode ser utilizado na seleção indireta de genótipos de trigo para tolerância à seca.

Seed physiology is related to functional and metabolic traits of the seed-seedling transition. In this sense, modeling the kinetics, uniformity and capacity of a seed sample plays a central role in designing strategies for trade, food, and environmental security. Thus, POMONA is presented as an easy-to-use multiplatform software designed to bring several logistic and linearized models into a single package, allowing for convenient and fast assessment of seed germination and or longevity, even if the data has a non-Normal distribution. POMONA is implemented in JavaScript using the Quasar framework and can run in the Microsoft Windows operating system, GNU/Linux, and Android-powered mobile hardware or on a web server as a service. The capabilities of POMONA are showcased through a series of examples with diaspores of corn and soybean, evidencing its robustness, accuracy, and performance. POMONA can be the first step for the creation of an automatic multiplatform that will benefit laboratory users, including those focused on image analysis.

Abstract Plant breeding programs often involve several segregating populations that must be selected for multiple traits. This study aimed to identify tropical wheat populations combining earliness and high grain yield (GY) using univariate and multivariate best linear unbiased prediction (BLUP)-based models within a stage-wise approach. Fifty-six F₂ and F₃ populations were evaluated in two environments for days to heading (DH) and GY. In the first stage, two modeling strategies were used: a univariate and multivariate model per generation. Genetic parameters and empirical genotypic values were estimated and used in the second stage for combined selection across generations. Both strategies yielded similar results in terms of genetic gains, genotype selection, and ranking, likely due to the low correlation between the traits. Populations 4H, 2F, 2D, 2A, 2E, 3E, 1G, 3A, 3B, 2G, 3F, 1D, and 1B were selected for earliness and yield and will be advanced to derive superior inbred lines.

The main of this research was to report an atypical foraging behavior in a colony of Trachymyrmex fuscus, situated in \"Cerrado\" (savanna ecoregion of Goiás, Brazil). The colony foraging activity was performed only by sexual caste. Comparison of the foraging rhythm of this colony with another of the same specie where foraging was performed only by worker caste, showed that working time hours were very similar. After observations on the foraging behavior, both colonies were excavated in order to characterize them (nest size, population composition and estimating of the symbiotic fungus volume). Besides the foraging activity performed only by sexual females, other important observations were highlighted in that colony: low number of workers, presence of worker larvae and pupae (45 and 43 respectively) and apparently normal growth of the symbiotic fungus. Our hypothesis is that sexual females were in charge of the entire colony maintenance. This could be a strategy of colony survival when the worker caste is reduced.

Sample size fluctuation and the restriction of measurements that demonstrate kinetics (typical of physiological processes) are two of the largest inferential constraints in studies on embryonic development in vitro. Thus, we hypothesize that a practical and robust way of aggregating knowledge on aspects of embryonic development in vitro is to use measurements based on the binary counting component. These are typically used to measure the germination process (intraeminal embryonal development). Our biological model was Dragon’s blood (Croton lechleri Müll Arg.), a species native to the Amazon with great socioeconomic impact. Matrices originating from two populations (one native and another cultivated) were the source of biological material. From this material, we studied five sampling densities (5, 25, 50, and 100 embryos), forming a 2 × 4 factorial ANOVA. Among the measurements studied, the coefficient of variation of time, uncertainty, and the synchronization index were the most sensitive to sample-size fluctuation. The synchronization index, however, also proved to be an interesting measurement to detect the parental effect related to the place of occurrence of the matrices. The embryonic development ability, mean development time, and mean development rate were not affected by fluctuations in the sample size or the origin of the material, demonstrating highly conserved traits of the species. Finally, in general, the measurements based on binary counting demonstrated robustness for modeling embryonic growth.