Search Results (6)

Land surface temperature (LST) is one of the most important variables in the physical processes of surface energy and water balance. The temporal behavior of LST was analyzed between the latitudes 32°00′ S and 34°24′ S (Valparaíso and Metropolitana regions of Chile) for three summer months (December, January, and February) in the 2000–2017 period, using the Terra MODIS image information and applying the Mann–Kendall test. The results show an increase in LST in the study area, particularly in the Andes mountain range in January (5240 km²), which mainly comprises areas devoid of vegetation and eternal snow and glaciers, and are zones that act as water reserves for the capital city of Santiago. Similarly, vegetated areas such as forests, grasslands, and shrublands also show increasing trends in LST but over smaller surfaces. Because this study is regional, it is recommended to improve the spatial and temporal resolutions of the images to obtain conclusions on more local scales. Full article

Backyard production systems (BPS) are distributed worldwide, rearing animals recognized as reservoirs of Salmonella enterica and Shiga toxin-producing Escherichia coli (STEC), both zoonotic pathogens. The aim of this study was to characterize isolates of both pathogens obtained from animals raised in BPS from two central Chile regions. The presence of pathogens was determined by bacterial culture and confirmatory PCR for each sampled BPS, calculating positivity rates. Multivariate logistic regression was used to determine risk factors. Additionally, phenotypic antimicrobial resistance was determined. A positivity rate of 2.88% for S. enterica and 14.39% for STEC was determined for the complete study region (Valparaíso and Metropolitana regions). Risk factor analysis suggests that the presence of ruminants (OR = 1.03; 95% CI = 1.002–1.075) increases the risk of STEC-positive BPS, and the presence of ruminants (OR = 1.05; 95% CI = 1.002–1.075) and the animal handlers being exclusively women (OR = 3.54; 95% CI = 1.029–12.193) increase the risk for S. enterica/STEC positivity. Eighty percent of S. enterica isolates were multidrug resistant, and all STEC were resistant to Cephalexin. This study evidences the circulation of multidrug-resistant zoonotic bacterial strains in animals kept in BPS and the presence of factors that modify the risk of BPS positivity for both pathogens. Full article

In the Metropolitana region of Chile there are 3836 backyard production systems (BPS), characterized as small-scale systems. They act as a source of zoonotic pathogens, such as Salmonella enterica and Shiga toxin-producing Escherichia coli (STEC), whose prevalence in BPS has not been fully described. The objective of this study was to determine the positivity for both agents in BPS and to establish the risk factors related to their presence. In each BPS, an epidemiological survey was undertaken, and stool samples were collected to detect these pathogens via bacteriological culture and conventional PCR techniques. Subsequently, multivariable logistic regression models were applied to establish the risk factors associated with their presence. BPS positivity rates of 11.76% for STEC and 4.7% for S. enterica were observed. The systems showed poor welfare standards and a lack of biosecurity measures. The risk factor analysis concluded that the Gini–Simpson index (p = 0.030; OR = 1.717) and the presence of neighboring intensive poultry or swine production systems (p = 0.019; OR = 20.645) act as factors that increased the risk of positivity with respect to STEC. In the case of S. enterica, exchanging embryonated eggs (p = 0.021; OR = 39) and the presence of debeaked chickens (p = 0.001; OR = 156) were determined as factors that increased the risk of positivity for this agent. For positivity with respect to both pathogens, the Gini–Simpson index (p = 0.030; OR = 1.544) and being INDAP/PRODESAL users (p = 0.023; OR = 15.026) were determined as factors that increased the risk, whereas the type of confinement (p = 0.002; OR = 0.019) decreased it. Epidemiological surveillance of these neglected populations is lacking, highlighting the fact that STEC and S. enterica maintenance on BPS represents a potential threat to public health. Full article

Castor bean is found in Chile between the Arica and Parinacota and Maule regions and is one the most important industrial crops worldwide because of the presence of ricinoleic acid in its oil. However, there is little information about it in our country. In this study, we analyzed the oil content and fatty acid profiles of 17 castor bean Chilean accessions from the Metropolitana and Valparaíso regions. The seed-oil was extracted using the Soxhlet extraction process, and the fatty acid profiles were determined using the GC-FID method. The oil content in castor bean Chilean accessions ranged between 45.7% and 54.0%. Among the 17 accessions analyzed, H-15 had the highest oil content (54.0%; p < 0.05), whereas the H-10 and H-08 accessions had the lowest oil content, (45.7% and 45.9%, respectively; p < 0.05). Ricinoleic acid was the most abundant fatty acid (between 87.64% and 89.83%) in the seed-oil. The highest level of ricinoleic acid was found in the H-08 accession. This was only statistically higher (p < 0.05) for three accessions, whereas the H-04 accession had the lowest ricinoleic acid content. Although the H-08 accession registered one of the least abundant oil contents, it had the highest amount of ricinoleic acid. No significant correlation was found between oil content and ricinoleic acid. Our study suggests that oil content does not influence the castor oil fatty profile. The high oil content and ricinoleic acid level registered in castor bean Chilean accessions justify their production in Chile and their utilization for developing bio-based products. Furthermore, Chilean castor bean could grow in semi-arid lands. However, further field studies are needed to identify the cultivars best suited for Mediterranean conditions. Full article

Over the last years, the detection of pesticide residues in the official food surveillance programs of Chile has been increased, mainly in fresh vegetables such as tomatoes and lettuces. The Metropolitana Region of Chile presents the highest detections in the country. The lack of evaluations of toxicological risks in human health have increased uncertainty of the potential effects of pesticides exposures in the Chilean population. This research aims to determinate health risks assessment of pesticide residues associated to tomatoes and lettuces produced in Metropolitana Region. The findings of this study reveal that tomatoes and lettuces cultivated in the MR show more than 50% of samples with one or multiple pesticides residues. From the total samples, 16% were over the Chilean Maximum Residue Levels (MRLs). The main pesticides detected in tomatoes and lettuces were methamidophos, methomyl, difenoconazole, cyprodinil and boscalid. The results obtained using the official data of the Ministry of Health of Chile (MINSAL) compared to the World Health Organization (WHO), describe relevant risks through the Estimated Daily Intakes (EDI), Hazard Quotients (HQ) and Hazard Index (HI) for the Chilean population due to high concentrations of methamidophos, methomyl and cyprodinil. More restrictions for the use of methamidophos, methomyl, difenoconazole, cyprodinil and boscalid and effective control programs should be implemented in order to mitigate the impacts on the Chilean population. Full article

The intensification of the hydrological cycle because of global warming raises concerns about future floods and their impact on large cities where exposure to these events has also increased. The development of adequate adaptation solutions such as early warning systems is crucial. Here, we used deep learning (DL) for weather-runoff forecasting in región Metropolitana of Chile, a large urban area in a valley at the foot of the Andes Mountains, with more than 7 million inhabitants. The final goal of this research is to develop an effective forecasting system to provide timely information and support in real-time decision making. For this purpose, we implemented a coupled model of a near-future global meteorological forecast with a short-range runoff forecasting system. Starting from a traditional hydrological conceptual model, we defined the hydro-meteorological and geomorphological variables that were used in the data-driven weather-runoff forecast models. The meteorological variables were obtained through statistical scaling of the Global Forecast System (GFS), thus enabling near-future prediction, and two data-driven approaches were implemented for predicting the entire hourly flow time-series in the near future (3 days), a simple Artificial Neural Networks (ANN) and a Deep Learning (DL) approach based on Long-Short Term Memory (LSTM) cells. We show that the coupling between meteorological forecasts and data-driven weather-runoff forecast models are able to satisfy two basic requirements that any early warning system should have: The forecast should be given in advance, and it should be accurate and reliable. In this context, DL significantly improves runoff forecast when compared with a traditional data-driven approach such as ANN, being accurate in predicting time-evolution of output variables, with an error of 5% for DL, measured in terms of the root mean square error (RMSE) for predicting the peak flow, compared to 15.5% error for ANN, which is adequate to warn communities at risk and initiate disaster response operations. Full article