Sebastião Martins Filho

Background and Aims: Seed germination analysis often ignores temporal dynamics and non-germinating fractions, limiting biological inference. Conventional methods assume all seeds germinate, which is unrealistic for heterogeneous forest seed lots. Long-term survival (cure fraction) models offer a framework to address this limitation. This study uses seed germination of native Brazilian forest species to demonstrate the applicability and advantages of long-term modeling. Methods: Five independent datasets from native Brazilian forest species were analyzed under varying environmental conditions, including temperature, osmotic stress, light, and seed traits. Germination was modelled as a time-to-event process using parametric (log-normal, Weibull, log-logistic) and semiparametric (Cox) approaches with long-term survival models. Kaplan–Meier estimators were used for initial exploration and model comparison. Model selection was based on graphical fit and Cox–Snell residuals. Key Results: All datasets showed a fraction of non-germinated seeds, with germination curves stabilizing below 100%, which corroborates the use of cure models. Long-term models consistently outperformed traditional survival approaches, providing better fit and biological interpretability. Environmental factors differentially affected incidence and latency components: extreme temperatures and water stress significantly reduced the probability of germination, while having smaller effects on germination speed. The probability of germination varied widely between species and conditions, from 10% to 95%. The models also revealed complex interactions between environmental and seed characteristics that influence germination dynamics. Conclusions: This study demonstrates that long-term survival models provide a robust, biologically realistic statistical framework for analyzing germination data from forest seeds. Partitioning the germination process into incidence (probability of germination) and latency (time to germination) components enabled key advances. First, it allowed precise quantification of the fractions of germinating and non-germinating seeds. Second, it provided more accurate and informative estimates than traditional survival methods. Finally, it enabled the isolation of the effects of environmental factors on germination capacity and speed.

This study aimed to evaluate the germination of faba bean (Vicia faba L.) and lentil (Lens culinaris Medikus) seeds after disinfection with sodium hypochlorite and acetic acid, using parametric time-to-event analysis. The germination was evaluated under the effect of four concentrations (0.005%, 0.05%, 0.1%, and 0.5%) of sodium hypochlorite and acetic acid in addition to the control treatment (0%). Seed germination was counted daily for seven days. Seeds that did not germinate by the end of this period were considered censored. The main parametric models used in the time-to-event analysis, Exponential, Weibull, Log-normal, Log-logistic, Logistic, and Gaussian, were adjusted for each product and concentration, plus the control. The model selected was the Log-logistic, and the choice was based on Akaike and weighted Akaike information criteria. The adequacy of the selected model was verified by Cox-Snell residuals. The germination curve estimated by the Log-logistic model demonstrated an inverse relationship between the increase in concentrations and the percentage of germination. It is concluded that to maintain germination above 80% for both crops, the application of sodium hypochlorite should not exceed 0.1%. In the case of acetic acid, concentrations should not exceed 0.05% for Vicia faba and 0.005% for Lens culinaris. The parametric survival model effectively estimated the seed germination.

A Capsicum annuum L. é a espécie mais cultivada e economicamente importante do gênero e, por tal motivo, estudos em divergência genética são indispensáveis. Sendo assim, os objetivos foram avaliar os genótipos mais promissores para a divergência genética, utilizando métodos de agrupamento hierárquicos e verificar quais os caracteres mais importantes no estudo dos genótipos de C. annuum L. O experimento foi conduzido em casa de vegetação do Departamento de Agronomia da Universidade Federal de Viçosa, no delineamento inteiramente casualizado com quatro repetições, sendo avaliados nove genótipos de C. annuum L., baseados em cinco caracteres quantitativos. Os dados foram submetidos à análise de agrupamento, por meio dos métodos UPGMA e Ward, com base na distância generalizada de Mahalanobis e utilizado o critério de Singh para avaliar a importância relativa de cada caráter para a divergência genética. Portanto, os resultados indicaram que ambos os métodos apresentaram dois grupos, que ao serem validados pelo coeficiente de correlação cofenética, foram significativos e possuíram boa consistência de agrupamento. Pelo critério de Singh, os caracteres peso total de frutos por plantas e comprimento do fruto maduro foram os principais responsáveis pelo estudo de divergência genética. Além disso, os genótipos Pimentão quadrado e Pimenta Doce Italiana apresentaram os resultados mais satisfatórios para os objetivos em questão. Logo, pode-se concluir que são genótipos que contribuem para obter ganhos significativos em um programa de melhoramento e orienta cruzamentos que possuem alta variabilidade genética.

The purpose of this work is to implement and compare three types of Skew-Normal distributions together with the Normal submodel for the experimental error through a Bayesian nonlinear modeling of pepper phenotype growth. The posterior means of the parameters were shown to be invariant to the experimental error. The Sahu Skew-Normal error showed evidence of null asymmetry for all growth models. In general, all Skew-Normal distributions presented the highest accuracy ( ) in relation to the Normal error. The problematic points stand out for the computational cost in the millions of MCMC iterations and the limitations of the BUGS language. The Skew-Normal distribution of Azzalini and Fernandéz & Steel modeled the asymmetry of the data and provided the best goodness of fit (DIC) and the best precision ( ) for the Gompertz and Von Bertalanffy model in relation to the Normal error.

Mabea fistulifera Mart. (Euphorbiaceae) is a native tree recommended for the restoration of degraded areas. The objective was to evaluate the quality of M. fistulifera seed lots through germination and vigor tests. The germination test was conducted in a factorial design (3 x 2 x 2), involving three seed colors (red, orange, and brown), with and without elaiosome, and two substrates (paper roll - PR and top of paper - TP). The conductivity test was arranged in a factorial design (3 x 2), with three seed colors and with and without elaiosome. Survival analysis was performed, along with log-rank and Wilcoxon tests, to compare germination curves. Brown seeds exhibited the highest conductivity and lowest germination, while red seeds showed the lowest conductivity and highest germination. On the PR substrate and without elaiosome, orange seeds had higher initial germination; brown seeds germinated more, and red and orange seeds showed greater deterioration. Red and orange seeds exhibit better physiological quality. Seed color is a reliable indicator of vigor and quality in M. fistulifera seeds. Paper is an effective substrate for germination. The absence of elaiosome may influence water absorption, but its effect on germination is not evident.

This study assessed distributions associated with Bayesian nonlinear modeling error in the description of total plant dry matteraccumulation (TDMA) of Allium sativumas a function of days after planting (DAP). According to the DIC criterion, Logistic and Gompertzmodels that use student’s t distribution error exhibited the highest DIC with logistic error distribution. In general, the difference of DIC in all the scenarios was not more than 5.The Bayes factor (BF) criterion showed no difference in the Logistic and Gompertzmodel fit when four distributions are used for the errors, where BF values do not exceed 2. Posterior distributions and the usual estimators of Logistic and Gompertz model parameters were similar even forvaried error distribution. In summary, there was no difference in the use of 4 distributions associated with the modeling error of garlic plant growth by the Bayes factor, whereby the results showed that alternating between error distributions significantly changes the number of Markov Chain Monte Carlo (MCMC) iterations.

In statistical analyses of seed germination experiments, it is essential to ensure that the assumptions of the chosen methods are not violated to obtain valid and reliable results. When these assumptions are violated, alternative methods should be considered, one of which is survival analysis. This study aimed to evaluate the application of this methodology, using a nonparametric approach, to assess the germination of Cecropia pachystachya and Jacaranda caroba. In the first experiment, alternating temperatures of 30/20 °C and 25/15 °C were applied under two light conditions (light and darkness). In the second, germination was assessed at constant temperatures ranging from 15 to 35 °C. Germination reached or exceeded 50% in both species under alternating temperatures and light conditions. In continuous darkness, J. caroba exhibited a higher germination speed and percentages, whereas C. pachystachya was unaffected by light conditions. The recommended alternating temperature for both species was 30/20 °C. The optimal constant temperatures were 25 °C for J. caroba and 30 °C for C. pachystachya. It is concluded that the nonparametric survival analysis technique effectively discriminates seed germination of the studied species.

The use of silicates in plant tissue culture has resulted in positive effects regarding the morphological, anatomical and physiological characteristics of in vitro plants. However, biological responses to this mineral are dose and genotype dependent. The objective of the present study was to evaluate the influence of silicon supplementation on the somatic embryogenesis of oil palm (Elaeis guineensis Jacq.). The study was carried out in three stages of the cloning process, namely: Regeneration (i) and germination (ii) of somatic embryos and during ex vitro acclimatization (iii) of regenerated plantlets. The culture media were supplemented with four doses (0.0, 0.5, 1.0 and 1.5 g L^-1) of calcium silicate (CaSiO₃). The resulting plantlets were transplanted and acclimatized in a greenhouse. The regression analysis showed that the dose of 0.63g L^-1 of CaSiO₃ induced a higher rate of regeneration, which would allow a 58% increase in the number of somatic embryos formed. However, the germination of the plantlets, as well as their morphometric characters, were not affected by the different doses of CaSiO₃, although the presence of this compound promoted an increase in the thickness of the epidermis and a reduction in the amount of epicuticular wax. Overall, silicon does contribute to the cloning of E. guineensis.

The objective of this work aimed to evaluate to effect of nitrogen fertilization on growth and quality of amendoim-bravo (Pterogyne nitens Tull) seedlings. Were used six doses of nitrogen (0, 60, 120, 180, 240 and 300 mg.dm ^-³) also parceled out and applied at 0, 21^th, 42^th and 63^th days after the first thinning. The experimental design was a randomized block design, using as substrate in red yellow latosol, with four repetitions. After 120 days after sowing collected data of height, collar diameter, aerial part dry weight and root, and the ration between height and collar diameter, height aerial part and dry weight, aerial part dry weight and dry weight root and Dickson quality index (IQD). The nitrogen fertilization were positive and significant for all traits except for collar diameter and ration dry weight aerial part and dry weight root. Recommended for seedlings of amendoim-bravo application of 200 mg.dm^-³ of ammonium sulfate.

The increasing use of genomic selection (GS) in plant and animal breeding programs has led to the development of software that fits models based on unique scenarios. Accordingly, several R packages have been developed for GS. The lmekin function from the coxme R package was one of the first functions implemented in R to fit models with random family effects using the pedigree–based relationship matrix. The function allows the user to provide the covariance structures for the random effects; thus, the GBLUP model can be fitted. This fitting process consists of replacing, in the traditional BLUP model, the additive relationship matrix derived from a pedigree by the additive relationship matrix derived from markers. Thus, the objective of this study was to employ the lmekin function in the context of genomic prediction by comparing the results of this function with those obtained using five R packages for GS: rrBLUP, BGLR, sommer, lme4qtl, and lme4GS. The comparisons were performed considering the computational times and predicted values for a wheat dataset and simulated big data. In addition, we implemented a 5-fold cross-validation scheme through considering the values predicted by the lmekin function for the wheat dataset. The results indicated that the lmekin function was effective in predicting genomic breeding values considering multiple random effects and relatively small sample sizes. The rrBLUP package processed the fastest for the scenario with only one genetic random effect, and the high temporal efficiency of the sommer package was confirmed for the scenario with more than one genetic random effect. Differences in computational times occurred because of the different algorithms implemented in the packages to estimate the variance components.