Aluminum (Al) toxicity is one of the main aspects restricting the development of fabaceous plants grown in soils with spontaneous vegetation prevalence in temperate climate zones. Salicylic acid (SA) minimizes the effects of stress on plants. Therefore, the aim of the current study was to evaluate the ability of SA seed priming to mitigate the effects of Al on seed germination and seedling performance in two Trifolium species. Trifolium vesiculosum (annual) and Trifolium repens (perennial) seeds were primed in solution added, or not, with SA (25 μM) and placed on germination paper moistened with aluminum sulfate (Al 2 (SO 4) 3) solutions at three different doses: 0 mM (control), 0.25 mM (moderate dose), and 1.25 mM (high dose). Seed priming with SA has mitigated the global toxicity effects of Al on T. vesiculosum and T. repens seedlings. Inferior damages were observed in T. vesiculosum root length and dry mass and in T. repens shoot dry mass, after SA pretreatment. T. vesiculosum seed priming with SA in the presence of Al has significantly reduced the osmotic potential of seedling sap. Salicylic acid (SA) has also enabled increased antioxidant activity of enzymes such as superoxide dismutase (SOD) in the two investigated plant species and ascorbate peroxidase (APX) in T. repens. In addition to the increased antioxidant activity, SA-primed seeds reduced the malondialdehyde content in T. vesiculosum seedlings exposed to Al. Overall, seed priming with SA mitigates oxidative effects of Al and improves T. vesiculosum and T. repens seedling performance in the presence of this element.
RESUMO As palmeiras são importantes componentes das florestas na Amazônia porque apresentam grande variedade de formas de crescimento e são encontradas em todos os estratos florestais, tipos de solos e níveis topográficos. São também abundantes nos ecossistemas onde ocorrem e apresentam grande diversidade de usos e importância sociocultural e econômica, pois possuem frutos comestíveis, estipes, raízes, folhas e outras partes passíveis de algum tipo de aproveitamento. Este estudo avaliou parâmetros fitossociológicos, distribuição, composição, diversidade (Shannon-Wiener) e a similaridade florística (Jaccard) de comunidades de palmeiras em três parcelas medindo 20 m x 100 m (2.000 m²) instaladas em diferentes gradientes topográficos em área de floresta da Reserva Florestal Humaitá, no Município de Porto Acre, Acre. Foram encontrados 596 indivíduos pertencentes a 12 gêneros e 17 espécies de palmeiras. A diversidade encontrada foi de 1,74, a densidade total 596 ind. ha-1 e a área basal 1,684 m2. ha-1. Phytelephas macrocarpa apresentou maior densidade e frequência relativa (38,26% e 8,33%) e Euterpe precatoria maior valor de importância (VI=20,37%). A maioria das espécies (70,59%) apresentou padrão de agregação uniforme. As palmeiras da área de platô apresentaram estrutura etária tendendo para o 'J' invertido. A maior similaridade florística foi observada entre as áreas de platô e encosta (70%), e a menor entre o baixio e a encosta (63,36%). A maioria das espécies era desprovida de espinho e apresentou estipe com hábito solitário não escandente. Conclui-se que as palmeiras são mais abundante nos baixios e mais diversa na área de platô.
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The seed sector faces several challenges when it comes to ensuring a quick and accurate decision making when working with large amounts of data on physiological quality of seed lots, which makes the process time-consuming and inefficient. Thus, artificial intelligence (AI) emerges as a new technological option in the seed sector to solve database problems in the post-harvest stages. This study aims to use machine learning to classify maize seed lots. Data were obtained from eight maize seed crops from a private company. These data were mined using the following classifiers: J48 (DecisionTree), RandomForest, CVR (ClassificationViaRegression), lBk (lazy.IBK), MLP (MultiLayerPercepton), and NäiveBayes. Cross-validation was used for data measurement, with the data set, including training and testing data, being divided into 10 subsets. The described steps were performed using the Weka software. It is concluded that results obtained allow the classification of maize seed lots with high accuracy and precision, and these algorithms can better classify the maize seed lot through vigor attributes, thus enabling more accurate decision making based on vigor tests on a reduced evaluation time.
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