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The increase in cultivated areas in tropical zones such as Colombia for avocado cv. Hass and the lack of knowledge on edaphoclimatic relationships with factors associated with quality led to the present research. The aim of this research was to establish the relationship of soil, climatic, spatial factors (plot location), and harvest seasonality (principal and transitory) with the multidimensional quality of avocado cv. Hass planted under tropical conditions. This research was carried out on eight farms located in three producing subregions. Soil, environmental and harvest data were recorded for three years (2015-2017) in each plot. Avocado fruit samples were used to determine the parameters of macronutrient, fatty acids, minerals, and vitamin E. Descriptive, inferential statistics, multivariate analysis, effect size, second-order exponential model, and causal relationships were used to determine variables associated with soil, climate, harvest seasonality, and spatial location, and to determine quality parameters. The results established a relationship between nutritional quality and the origin region. Similarly, it was possible to identify parameters associated with differential quality with a robust statistical methodology to propose origin as a differentiating factor for quality. This study provided useful information for the value chain that selected the best areas for avocado crops according to market expectations and nutritional quality criteria.

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The present study aimed to characterize the diet of Moenkhausia collettii and investigate possible changes due to environmental variations and its body size in streams in the eastern Amazon. The specimens were sampled monthly between April 2019 and March 2020. They were measured for standard length (SL) and total mass (Tm) and eviscerated for analysis of stomach contents. Food items were identified and grouped into categories. Dietary aspects such as food importance index (AI%), trophic niche width, and stomach repletion index (SRI%) were evaluated. Furthermore, generalized linear models (GLMs) were used to evaluate the relation between diet and the SL, as well as between diet and the environmental variables of streams. A total of 355 specimens with SL ranging from 11.06 to 46.03 mm and weight ranging from 0.020 to 2.373 g were evaluated. Out of the 355 stomachs analysed, 88 contained material in an advanced stage of decomposition and 12 were empty. The diet of M. collettii was considered omnivorous, with a tendency toward insectivory. Formicidae was the most important category in the diet of the species, followed by immature Diptera and plant material. The GLMs showed a relationship between the diet and a set of environmental variables such as dissolved oxygen, conductivity, flow, width, depth, wood, leaf bank, and SL. The trophic niche width and feeding intensity increased with the length of the species, as well as in the period of higher precipitation, reinforcing trophic opportunism for M. collettii. Therefore, new studies that combine the traditional method of stomach content analysis, the use of stable isotopes, as well as ecomorphological attributes, are crucial for a profound understanding of the trophic ecology of the ichthyofauna in the face of natural changes occurring in their environment.

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Urbanization in watersheds leads to the introduction of sources of microplastics and other pollutants in water bodies. However, the effect of urbanization on microplastic pollution and the relationship between microplastics and water quality are not well understood. We assessed the distribution of microplastics in tributaries urbanized, non-urbanized and in the receiving lagoon body of Conceição Lagoon watershed. The results show that urbanization significantly affects water quality but does not differentiate tributaries in terms of microplastic concentrations. Microplastic concentrations were lower in the receiving lagoon body compared with the tributaries, highlighting their importance in microplastic pollution in the studied lagoon. Microplastic concentration was correlated with low N:P ratios in the lagoon and associated with high levels of total phosphorus, which indicate the discharge of effluents. The correlations between microplastic concentration, water temperature, and dissolved oxygen in the lagoon were based on the temporal variations of these variables. Precipitation and wind velocity had influence on microplastic distribution in the watershed. Our findings underscore the importance of evaluating water quality parameters and meteorological variables to comprehend the microplastic distribution at small watersheds.

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BACKGROUND: Educational environments can have environmental conditions that are incompatible with the needs of students, compromising their well-being and affecting their performance. OBJECTIVE: To identify the environmental variables that influence the performance of university students and measure this influence through an experiment in indoor environments. METHODS: The study applied an experimental methodology for three consecutive days in seven educational environments located in different regions of Brazil, measuring the environ-mental conditions, the students' perception of the environment, and their cognitive performance. The impact of environmental variables and environmental perception on student performance was analyzed using Generalized Linear Models and a Structural Equation Model. RESULTS: Students who took the test at air temperatures between 22.4°C and 24.7°C had a 74.20% chance of performing better than those outside this range. Air temperatures between 26.2°C and 29°C were associated with an 86% chance of taking less time to complete the test. High illuminance levels increased the chance of taking longer to answer the test by 41.7%. CONCLUSIONS: Three environmental variables (relative humidity, lighting and air temperature) and two perceptual dimensions (light and thermal perception) directly influence student performance.

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In Mexico, few studies have explored how environmental conditions in tropical dry forests (TDF) influence bat fly load even though, according to climate change scenarios, this ecosystem will experience a drier and warmer climate. Such an extension of the dry season in these ecosystems could have dramatic consequences for biodiversity, particularly in regions with plains where animals do not have elevational climate shifts. The present study therefore evaluates the effect of prevailing environmental conditions during 2015-2019, as well as host body conditions, on the infestation and abundance of bat-specific ectoparasites and the composition and bat fly load in the dry season of a TDF in Yucatan. Since Yucatan has an essentially flat and low-lying topography, organisms cannot escape from the predicted extreme conditions with elevational shifts. This region is therefore an excellent location for assessment of the potential effects of warming. We collected 270 bat flies from 12 species. Three streblid species (Nycterophilia parnelli Wenzel, Trichobius johnsonae Wenzel, and Trichobius sparsus Kessel) are new records for Yucatan. Our overview of the dry season bat ectoparasite loads reveals low values of richness and prevalence, but high aggregation. Our models detected significant differences in ectoparasite infestation and abundance over the years, but the environmental and body host condition variables were unrelated to these. We report that pregnant females are parasitized to a greater extent by bat flies during the dry season, which generally represents the season of most significant nutritional stress.

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Rice is a widely consumed staple food for a large part of the world's human population. Approximately 90% of the world's rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.

O arroz é um alimento básico amplamente consumido por grande parte da população humana mundial. Aproximadamente 90% do arroz do mundo é cultivado no continente asiático e constitui um alimento básico para 2,7 bilhões de pessoas em todo o mundo. O crestamento bacteriano das folhas (BLB) causado por Xanthomonas oryzae pv. oryzae é uma das doenças devastadoras do arroz. Um experimento de campo foi realizado durante os anos de 2016 e 2017 para investigar a influência de diferentes parâmetros meteorológicos no desenvolvimento do BLB, bem como o cálculo de um modelo preditivo para prever a doença bem antes de seu aparecimento em campo. O conjunto de dados sazonais de incidência de doenças e fatores ambientais foi usado para avaliar cinco variedades/cultivares de arroz (Basmati-2000, KSK-434, KSK-133, Super Basmati e IRRI-9). O efeito acumulado de dados ambientais de dois anos; temperatura máxima e mínima, umidade relativa do ar, velocidade do vento e precipitação pluviométrica foram estudados e correlacionados com a incidência da doença. A temperatura média (máxima e mínima) apresentou correlação significativa negativa com a doença BLB e todas as outras variáveis; umidade relativa, precipitação e velocidade do vento tiveram uma correlação positiva com o desenvolvimento da doença BLB em variedades individuais. A análise de regressão stepwise foi realizada para indicar variáveis preditoras potencialmente úteis e para descartar parâmetros incompetentes. Os dados ambientais das safras de julho a outubro de 2016 e 2017 revelaram que, com exceção da temperatura mais baixa, todos os fatores ambientais contribuíram para o desenvolvimento da doença ao longo da safra. Um modelo de regressão múltipla de previsão de doença foi desenvolvido com base em dados de dois anos (Y = 214,3-3,691 Max T-0,508 Min T + 0,767 RH + 2,521 RF + 5,740 WS), que explicou 95% de variabilidade. Este modelo de previsão de doenças não só ajudará os agricultores na detecção precoce e gestão atempada da doença bacteriana das folhas do arroz, mas também pode ajudar a reduzir os custos de insumos e melhorar a qualidade e a quantidade do produto. O modelo será agricultor e ambientalmente amigável.

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Abstract Rice is a widely consumed staple food for a large part of the worlds human population. Approximately 90% of the worlds rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.

Resumo O arroz é um alimento básico amplamente consumido por grande parte da população humana mundial. Aproximadamente 90% do arroz do mundo é cultivado no continente asiático e constitui um alimento básico para 2,7 bilhões de pessoas em todo o mundo. O crestamento bacteriano das folhas (BLB) causado por Xanthomonas oryzae pv. oryzae é uma das doenças devastadoras do arroz. Um experimento de campo foi realizado durante os anos de 2016 e 2017 para investigar a influência de diferentes parâmetros meteorológicos no desenvolvimento do BLB, bem como o cálculo de um modelo preditivo para prever a doença bem antes de seu aparecimento em campo. O conjunto de dados sazonais de incidência de doenças e fatores ambientais foi usado para avaliar cinco variedades/cultivares de arroz (Basmati-2000, KSK-434, KSK-133, Super Basmati e IRRI-9). O efeito acumulado de dados ambientais de dois anos; temperatura máxima e mínima, umidade relativa do ar, velocidade do vento e precipitação pluviométrica foram estudados e correlacionados com a incidência da doença. A temperatura média (máxima e mínima) apresentou correlação significativa negativa com a doença BLB e todas as outras variáveis; umidade relativa, precipitação e velocidade do vento tiveram uma correlação positiva com o desenvolvimento da doença BLB em variedades individuais. A análise de regressão stepwise foi realizada para indicar variáveis preditoras potencialmente úteis e para descartar parâmetros incompetentes. Os dados ambientais das safras de julho a outubro de 2016 e 2017 revelaram que, com exceção da temperatura mais baixa, todos os fatores ambientais contribuíram para o desenvolvimento da doença ao longo da safra. Um modelo de regressão múltipla de previsão de doença foi desenvolvido com base em dados de dois anos (Y = 214,3-3,691 Max T-0,508 Min T + 0,767 RH + 2,521 RF + 5,740 WS), que explicou 95% de variabilidade. Este modelo de previsão de doenças não só ajudará os agricultores na detecção precoce e gestão atempada da doença bacteriana das folhas do arroz, mas também pode ajudar a reduzir os custos de insumos e melhorar a qualidade e a quantidade do produto. O modelo será agricultor e ambientalmente amigável.

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Aedes aegypti is the main vector of arboviruses in the world. This mosquito species is distributed from tropical to temperate regions. In Argentina, it has been reported in 20 out of 23 provinces and reaches its southernmost distribution in the world. Its distribution and persistence are affected by meteorological, demographic and environmental factors, such as temperature, precipitation, and population. The aim of this study was to update and model the occurrence of Aedes aegypti in its southern limit of distribution in Argentina. To this end, a total of 37 sites were inspected in La Pampa and Río Negro provinces. Generalized Linear Models were used to explain the occurrence of Aedes aegypti based on meteorological, environmental and demographic variables. Aedes aegypti was found in 11 cities of La Pampa province where it had not been previously reported, but was not found in any of the cities evaluated in Río Negro province. The averaged model explaining the occurrence of Aedes aegypti included the minimum temperature, precipitation and interactions between maximum temperature and precipitation as explanatory variables. Although precipitation was statistically significant, other factors such as minimum temperature are also important in modeling the occurrence of Aedes aegypti in its southernmost distribution limit.

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The measurement of performance within the water industry holds significant importance for policymakers, as it can help guide decision-making for future development and management initiatives. In this study, we apply data envelopment analysis (DEA) cross-efficiency techniques to evaluate the productivity change of the Chilean water industry during the years 2010-2018. Water leakage and unplanned interruptions are included in the analysis as quality of service variables. Moreover, we use cluster analysis and regression techniques to better understand what drives productivity change of water companies. The results indicate that the Chilean water industry is characterized by considerable high levels of inefficiency and low levels of productivity change. This is due to the existence of technical regress whereas gains in efficiency were small. Concessionary water companies were found to be more productive than full private and public water companies. Best and worst performers need to make efforts to reduce production costs and improve service quality. Other factors such as customer density and ownership type statistically affect productivity.

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Three years have passed since the outbreak of Coronavirus Disease 2019 (COVID-19) brought the world to standstill. In most countries, the restrictions have ended, and the immunity of the population has increased; however, the possibility of new dangerous variants emerging remains. Therefore, it is crucial to develop tools to study and forecast the dynamics of future pandemics. In this study, a generalized additive model (GAM) was developed to evaluate the impact of meteorological and environmental variables, along with pandemic-related restrictions, on the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Córdoba, Argentina. The results revealed that mean temperature and vegetation cover were the most significant predictors affecting SARS-CoV-2 cases, followed by government restriction phases, days of the week, and hours of sunlight. Although fine particulate matter (PM2.5) and NO2 were less related, they improved the model's predictive power, and a 1-day lag enhanced accuracy metrics. The models exhibited strong adjusted coefficients of determination (R2adj) but did not perform as well in terms of root-mean-square error (RMSE). This suggests that the number of cases may not be the primary variable for controlling the spread of the disease. Furthermore, the increase in positive cases related to policy interventions may indicate the presence of lockdown fatigue. This study highlights the potential of data science as a management tool for identifying crucial variables that influence epidemiological patterns and can be monitored to prevent an overload in the healthcare system.

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