A hibridação interespecífica entre o caiaué (Elaeis oleífera (Kunth) Cortés) e o dendezeiro (E. guineensis Jacq.) tem sido explorada com o objetivo de desenvolver cultivares tão produtivas quanto as de dendezeiro, aliada à resistência a pragas e doenças, principalmente o amarelecimento fatal, elevada taxa de ácidos graxos insaturados e redução de porte características do caiaué. Por ser uma cultura perene com longo ciclo de produção, além dos altos custos para manutenção e avaliação dos experimentos de melhoramento genético, é necessário definir o período mínimo de avaliação para que a seleção dos híbridos seja realizada com eficiência e mínimo dispêndio de tempo e recursos. Este estudo teve como objetivo estimar os coeficientes de repetibilidade dos caracteres número de cachos, peso total de cachos e peso médio de cachos de híbridos interespecíficos e definir o número de anos consecutivos de avaliação necessário para seleção eficiente dos melhores cruzamentos e indivíduos. Os coeficientes de repetibilidade foram estimados pelos métodos da análise de variância, componentes principais com base na matriz de covariância (CPCV) e de correlações, e análise estrutural com base na matriz de correlações. O método dos CPCV demonstrou ser o mais adequado para o estudo da repetibilidade da produção de cachos, indicando quatro anos consecutivos de avaliação para selecionar progênies, representadas por dez plantas, com coeficientes de determinação (R²) superiores a 85%, e que para seleção individual de plantas são necessários pelo menos seis anos consecutivos de avaliação para atingir R² superior a 80%.

The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories to be completed efficiently, reducing labor and time. This is the first study to evaluate the effectiveness of classification of five eucalyptus species (E. camaldulensis, Corymbia citriodora, E. saligna, E. grandis, and E. urophyla) using hyperspectral images and machine learning. Spectral readings were taken from 200 leaves of each species and divided into three dataset sizes: one set containing 50 samples per species, a second with 100 samples per species, and a third set with 200 samples per species. The ML algorithms tested were multilayer perceptron artificial neural network (ANN), decision trees (J48 and REPTree algorithms), and random forest (RF). As a control, a conventional approach by logistic regression (LR) was used. Eucalyptus species were classified by ML algorithms using a randomized stratified cross-validation with 10 folds. After obtaining the percentage of correct classification (CC) and F-measure accuracy metrics, the means were grouped by the Scott–Knott test at 5% probability. Our findings revealed the existence of distinct spectral curves between the species, with the differences being more marked from the 700 nm range onwards. The most accurate ML algorithm for identifying eucalyptus species was ANN. There was no statistical difference for CC between the three dataset sizes. Therefore, it was determined that 50 leaves would be sufficient to accurately differentiate the eucalyptus species evaluated. Our study represents an important scientific advance for forest inventories and breeding programs with applications in both forest plantations and native forest areas as it proposes a fast, accurate, and large-scale species-level classification approach.

Euterpe precatoriais a palm tree belonging to the Arecaceae family, occurring in Western and Central Brazilian Amazonia. Its fruit, which is very appreciated in the Amazon region, produces pulp that is consumed in fresh form. Its production is carried out almost exclusively by extractive farmers. In order to establish adequate strategies to sustain this genetic resource, we need knowledge about the diversity and genetic structure in natural populations. This study aimed to evaluate the influence of geographic distance on genetic structure in the main extractive populations ofE. precatoriain the Brazilian Amazon. Leaves from 377 plants were collected in 19 populations located in 16 municipalities in the State of Amazonas and three in the State of Rondônia. Twelve microsatellite loci were used to genotype the plants. The diversity and genetic structure among populations were estimated. The average number of alleles per locus was 5.97. The observed heterozygosity means (H_O) were higher than expected (H_E) at the population level (H_O= 0.72,H_E= 0.66) and fixation index (f= -0.100) was negative. TheF_STvalue (0.1820) and the AMOVA results (Φ= 0.1796) showed population structure. The populations were clustered into three groups (K= 3) in the Bayesian analysis. The Discriminant Analysis of Principal Components (DAPC) confirmed eight clusters, with the populations close to those identified by the Bayesian analysis. The geographic differentiation was confirmed by the groupings obtained in the Structure analysis and the DACP function. Information related to phenotypic, genetic and environmental characterization of populations is important to guide conservation and management strategies and the formulation of public species management policies in Amazonia.

Euterpe precatoria (Açaí-do-Amazonas) produces fruits of which the fresh pulp is consumed. It is almost exclusively collected by extractivist farmers, because no selected genotypes are available for the establishment of plantations. For the domestication and breeding of the species, mating system studies are needed for strategy formulation. This study evaluated the mating system of a natural population of E. precatoria. Thirteen progenies were genotyped with 13 microsatellite loci by capillary electrophoresis in an automated DNA sequencer. Estimates of single-locus and multilocus outcrossing rates were 1.0, and paternity correlation was low ( ${\widehat{\mathrm{r}}}_{\mathrm{p}\left(\mathrm{m}\right)}$ >= 0.293). Euterpe precatoria families consist mainly of half-sibs and the reproductive strategy of the species is allogamy.

Studies using ecological niche models highlight the vulnerability of forest species to climate change. This work aimed to analyze the distribution of timber species Aspidosperma desmanthum, Cariniana micranta, Clarisia racemosa, Couratari oblongifolia, and Vouchysia guianensis, which are targets of deforestation, to predict the impacts of climate change and identify areas for their conservation in the Amazon. For this purpose, 37 environmental variables were used, including climatic and edaphic factors. The models were fitted using five algorithms, and their performance was evaluated by the metrics Area Under the Curve (AUC), True Skill Statistic, and Sorensen Index. The deforestation analysis was conducted using data accumulated over a period of 14 years. The study indicated that under the most pessimistic predictions, considering continued high emissions of greenhouse gases (GHGs) from the use of fossil fuels, SSP5–8.5, potential habitat loss for the studied species was more significant. Analyses of the species show that the Western Amazon has a greater climatic suitability area for the conservation of its genetic resources. Further study of the accumulated deforestation over 14 years showed a reduction in area for all species. Therefore, in situ conservation policies and deforestation reduction are recommended for the perpetuation of the analyzed forest species.

O objetivo deste trabalho foi avaliar genótipos de milho quanto à resistência ao acamamento e ao quebramento do colmo e apresentar metodologia para avaliar essas características. Os ensaios foram realizados em blocos ao acaso, com 85 genótipos tropicais de milho e quatro repetições, em cinco locais. Foram avaliadas: as forças necessárias para o arranquio da planta e para o quebramento do colmo e o ângulo no momento do quebramento. As medições foram realizadas com equipamentos desenvolvidos para essas finalidades. Foram realizadas análises de variância e teste de Scott-Knott quanto às características. Asforças de arranquio da planta, de quebramento do colmo e ângulo no momento do quebramento do colmo interagiram significativamente com a localidade. As médias variaram de 49,43 a 76,03 kgf para a força de arranquio da planta, de 1,07 kgf a 2,76 kgf para força de quebramento do colmo, e de 16,15 a 41,18º para o ângulo de quebramento do colmo. Existe variabilidade genética para seleção quanto à resistência ao acamamento e ao quebramento do colmo em genótipos tropicais de milho. A metodologia apresentada é eficiente para a avaliação e discriminação de genótipos.