The present study aimed to characterize, through descriptive statistics, data from scientific articles selected in a systematic integrative review that performed a microbiological diagnosis of Salmonella spp. in aquaculture. Data were obtained from research articles published in the BVS, Scielo, Science Direct, Scopus and Web of Science databases. The selected studies were published between 2000 and 2020 on samples of aquaculture animal production (fish, shrimp, bivalve mollusks, and other crustaceans) and environmental samples of aquaculture activity (farming water, soil, and sediments). After applying the exclusion criteria, 80 articles were selected. Data such as country of origin, categories of fish investigated, methods of microbiological diagnosis of Salmonella spp., sample units analyzed and most reported serovars were mined. A textual analysis of the word cloud and by similarity and descending hierarchical classification with the application of Reinert’s algorithm was performed using R^® and Iramuteq^® software. The results showed that a higher percentage of the selected articles came from Asian countries (38.75%). Fish was the most sampled category, and the units of analysis of the culture water, muscle and intestine were more positive. The culture isolation method is the most widespread, supported by more accurate techniques such as PCR. The most prevalent Salmonella serovars reported were S. Typhimurium, S. Weltevreden and S. Newport. The textual analysis showed a strong association of the terms “Salmonella”, “fish” and “water”, and the highest hierarchical class grouped 25.4% of the associated text segments, such as “aquaculture”, “food” and “public health”. The information produced characterizes the occurrence of Salmonella spp. in the aquaculture sector, providing an overview of recent years. Future research focusing on strategies for the control and prevention of Salmonella spp. in fish production are necessary and should be encouraged.

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R² = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R² = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R² of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0–10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management.

Canine sporotrichosis is a poorly described global disease and a spatial approach has not yet been used to assess the disease in dogs. Therefore, this study aimed to describe the occurrence of canine sporotrichosis in the state of Rio de Janeiro, Brazil, from 1998 to 2018 and its correlation with socioeconomic characteristics using exploratory spatial data analysis. A total of 295 cases of canine sporotrichosis were identified and 249 were georeferenced. There was a higher concentration of cases in the municipality of Rio de Janeiro, as well as along the border of the city and the adjacent municipalities in the great metropolitan area. The cases occurred in areas where most of the dwellings are houses. Moreover, no focus of disease density was found in the southern part of Rio de Janeiro city over the period studied, possibly due to better socioeconomic conditions. Areas with a high concentration of canine sporotrichosis cases coincided with regions that possessed a low proportion of households without paving, suggesting that the disease is not necessarily linked to extreme poverty. The mapping of areas with a greater density of cases is fundamental to formulate targeted and strategic plans in order to implement effective public health prevention and control measures.

The Brazilian Sac Brood is a disease that affects apiaries of Africanized bee hives in Brazil, thereby making them susceptible to high losses. This study investigated the pathogenicity of Africanized bee hives by the entomopathogenic fungi in a Brazilian Sac Brood endemic region. The degree of fungal contamination, presence of mycotoxins in beehive elements, and vulnerability of healthy beehives in environments subjected and not subjected to the disease were investigated. From the contaminating fungal load, species that are mycotoxin producers and pathogenic causing mortality in the bees have been isolated. The analysis of bee pollen and bee bread samples did not show the presence of the toxic pollen of Stryphnodendron (Fabaceae), which has been indicated as the causative agent of mortality in pre-pupal stage larvae. However, bee bread showed the highest correlation between substrate and fungal contamination.

Brazilian beekeeping has been developed from the africanization of the honeybees and its high performance launches Brazil as one of the world´s largest honey producer. The Southeastern region has an expressive position in this market (45%), but the state of Rio de Janeiro is the smallest producer, despite presenting large areas of wild vegetation for honey production. In order to analyze the honey productivity in the state of Rio de Janeiro, this research used classic and spatial regression approaches. The data used in this study comprised the responses regarding beekeeping from 1418 beekeepers distributed throughout 72 counties of this state. The best statistical fit was a semiparametric spatial model. The proposed model could be used to estimate the annual honey yield per hive in regions and to detect production factors more related to beekeeping. Honey productivity was associated with the number of hives, wild swarm collection and losses in the apiaries. This paper highlights that the beekeeping sector needs support and help to elucidate the problems plaguing beekeepers, and the inclusion of spatial effects in the regression models is a useful tool in geographical data.

Information on the altitudinal distribution of the hard ticks Amblyomma aureolatum and Amblyomma ovale in Brazil is scarce and mainly limited to occasional records. In this study we report our evaluation of records on the altitudinal distribution of A. aureolatum and A. ovale collected from dogs and humans and directly from the environment (host-questing ticks), based on active or passive procedures. The collections were conducted in rural areas of municipalities in the state of Rio de Janeiro, Brazil between 2013 and 2017. Active procedures consisted of dragging or flagging, visual examination of vegetation and removal of ticks present on the authors’ clothing or on infested dogs. Overall, 222 ticks were collected. The altitudes at the collection sites ranged from 98 to 1220 m a.s.l. We noted a significant difference in the altitudinal distribution of A. aureolatum and A. ovale (Mann–Whitney U-test, U = 518.5, P < 0.001). The overlap of these two species occurred at altitudes of between 650 and 900 m a.s.l. The results indicated that the higher the altitude, the greater the probability for the occurrence of A. aureolatum and, conversely, the lower the likelihood for the occurrence of A. ovale. The findings of this study improve currrent knowledge on the bioecology of these tick species and have implications for studies on the epidemiology of spotted fever in Brazil. Graphical Abstract