Intensive fertilization of vegetables can promote phosphorus (P) toxicity. However, it can be reversed using silicon (Si), although there is a lack of research clarifying its mechanisms of action. This research aims to study the damage caused by P toxicity to scarlet eggplant plants and whether Si can mitigate this toxicity. We evaluated the nutritional and physiological aspects of plants. Treatments were arranged in a 2 × 2 factorial design of two nutritional levels of adequate P (2 mmol L−1 of P) and toxic/excess P (8 to 13 mmol L−1 of P) combined with the absence or presence of nanosilica (2 mmol L−1 Si) in a nutrient solution. There were six replications. The excess P in the nutrient solution caused damage to scarlet eggplant growth due to nutritional losses and oxidative stress. We found that P toxicity can be mitigated by supplying Si, which decreases P uptake by 13%, improves C:N homeostasis, and increases iron (Fe), copper (Cu), and zinc (Zn) use efficiency by 21%, 10%, and 12%, respectively. At the same time, it decreases oxidative stress and electrolyte leakage by 18% and increases antioxidant compounds (phenols and ascorbic acid by 13% and 50%, respectively), and decreases photosynthetic efficiency and plant growth by 12% (by increasing 23% and 25% of shoot and root dry mass, respectively). These findings allow us to explain the different Si mechanisms used to reverse the damage caused by P toxicity to plants.

No presente trabalho buscou-se identificar possíveis associações entre internações hospitalares por pneumonia e variáveis meteorológicas no município de Santarém-PA. Para tanto foram realizadas análises da variabilidade de dados meteorológicos de temperatura do ar, precipitação e umidade relativa para o período de 2008 a 2021 e de possíveis associações com internações por pneumonia no mesmo período na área de estudo. Os dados meteorológicos foram obtidos no formato de médias mensais por meio do Banco de Dados meteorológicos para Ensino (BDMEP) e Pesquisa do Instituto Nacional de Meteorologia (INMET) e os dados de internação por meio do Departamento de Informática do Sistema Único de Saúde (DATASUS), listados por local de residência, e as técnicas estatísticas utilizadas foram, análise de regressão linear e regressão linear múltipla. Dentre os coeficientes de determinação obtidos na análise de regressão linear, destacaram-se, como os de maiores valores calculados, aqueles entre temperatura máxima e pneumonia com valores de   R² = 0,47 no mês de maio, R² = 0,53 em agosto e R² = 0,42 em setembro e aqueles entre umidade relativa e pneumonia, com valores de R² = 0,38 na média de abril e R² = 0,35 no mês de dezembro. Com auxílio da regressão múltipla o valor de R²=0,84 foi encontrado, demonstrando o efeito sinérgico das condições de tempo e a doença. A distribuição dessas associações ao longo desses diferentes meses aponta para o papel da sazonalidade típica dos períodos secos e chuvosos na região, e seus possíveis impactos em doenças respiratórias como pneumonia no município de Santarém.

The management of soil organic matter (SOM) is fundamental in agriculture for soil conservation and crop yields. However, in addition the soil being dynamics, it is heterogeneous, therefore, understanding the spatial variability of SOM is essential. The main objective of this study was to evaluate the spatial variability of SOM in a cassava cultivation under different management, seeking to classify its spatial dependence by geostatistics. A filed experiment was conducted out on soil classified as oxisol with four different management systems: irrigation (micro sprinkler, drip and no irrigation), spacing (1.0 × 0.8 m, 1.0 × 1.0 m and 1.0 × 1.5 m), weed control (manual control and no control) and acidity correction (limestone and withouth limestone), totaling 36 experimental plots. To determine the SOM, the wet oxidation method was used, and the semivariograms were generated by the GS+® software. The effect of the different management systems on the spatial variability of the SOM was evaluated at a depth of 0.0-0.2 m. The theoretical semivariogram model that best fitted the study was the Gaussian model, with a well-defined level, also expressing the condition of data stationarity. The spatial dependence was classified as strong and, through the thematic map generated from the kriging, it was possible to observe the variability in the SOM content for different management zones. The use of geostatistics techniques provided important information for understanding the spatial distribution of soil organic matter.

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