Edaphoclimatic variation and harvest seasonality as determining factors of multidimensional quality in avocado cv. hass grown in the tropics.

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.

Feeding ecology of Moenkhausia collettii (Steindachner, 1882) (Characiformes: Characidae) in streams in the eastern Amazon: Environmental factors and body size.

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.

Effect of urbanization and water quality on microplastic distribution in Conceição Lagoon watershed, Brazil.

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.

A Five-Year Study on Infestation and Abundance of Bat Flies (Hippoboscoidea: Streblidae) Under Severe Dry Season Conditions in the Tropical Dry Forest of Yucatan, Mexico.

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.

Influence of environmental variables on students' cognitive performance in indoor higher education environments.

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.

Modeling and updating the occurrence of Aedes aegypti in its southern limit of distribution in South America.

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.

A disease predictive model based on epidemiological factors for the management of bacterial leaf blight of rice / Um modelo preditivo de doenças baseado em fatores epidemiológicos para o manejo da queima bacteriana das folhas do arroz

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.

A disease predictive model based on epidemiological factors for the management of bacterial leaf blight of rice

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.

Assessing the dynamic performance of water companies through the lens of service quality.

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.

Environmental, meteorological and pandemic restriction-related variables affecting SARS-CoV-2 cases.

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.

Unraveling the effect of land use on the bacterioplankton community composition from highly impacted shallow lakes at a regional scale.

Bacterioplankton communities play a crucial role in global biogeochemical processes and are highly sensitive to changes induced by natural and anthropogenic stressors in aquatic ecosystems. We assessed the influence of Land Use Land Cover (LULC), environmental, and geographic changes on the bacterioplankton structure in highly connected and impacted shallow lakes within the Salado River basin, Buenos Aires, Argentina. Additionally, we investigated how changes in LULC affected the limnological characteristics of these lakes at a regional scale. Our analysis revealed that the lakes were ordinated by sub-basins (upper and lower) depending on their LULC characteristics and limnological properties. In coincidence, the same ordination was observed when considering the Bacterioplankton Community Composition (BCC). Spatial and environmental predictors significantly explained the variation in BCC, although when combined with LULC the effect was also important. While the pure LULC effect did not explain a significant percentage of BCC variation, the presence of atrazine in water, an anthropogenic variable linked to LULC, directly influenced both the BCC and some Amplicon Sequence Variants (ASVs) in particular. Our regional-scale approach contributes to understanding the complexity of factors driving bacterioplankton structure and how LULC pervasively affect these communities in highly impacted shallow lake ecosystems from the understudied Southern Hemisphere.

Secondary production of Acartia tonsa Dana, 1849 and A. lilljeborgii Giesbrecht, 1889 in an Amazonian estuary (Brazil).

The dynamics of the copepods A. tonsa and A. lilljeborgii were described for the first time in the Taperaçu Estuary. The acartiids were collected using plankton nets (200 µm) in June 2012, March 2013 (rainy season) and September 2012 and 2013 (dry season). The oscillations in rainfall and the fluctuations in hydrological variables influenced the abundance, biomass, and production of both A. tonsa (17 ± 23 to 8501 ± 13,248 ind.m-3; 16,385.29 mg.C.m-3; 0.09 ± 0.21 to 355.17 ± 590.84 mg.C.m-3.d-1) and A. lilljeborgii (14 ± 11 to 1470 ± 1591 ind.m-3; 22,398.40 mg.C.m-3; 177.99 ± 263.13 mg.C.m-3.d-1) with clear monthly, seasonal, and spatial patterns. The high levels of production observed may be related to the presence of waters rich in particulate organic material derived from the adjacent mangrove forests. This material is consumed by a number of copepod species, in particular A. tonsa and A. lilljeborgii, favoring the development and reproduction of both species which are characterized by high rates of productivity in the study estuary. The present results indicate that the biomass and productivity in equatorial mangrove estuaries may be relatively high in comparison with the levels observed in other coastal systems around the world and that earlier stages of both species have a great relevance for biomass and production in Amazonian estuaries.

The skull variation of the olive field mouse Abrothrix olivacea (Cricetidae: Abrotrichini) is localized and correlated to the ecogeographic features of its geographic distribution.

The relationship between phenotypic variation and landscape heterogeneity has been extensively studied to understand how the environment influences patterns of morphological variation and differentiation of populations. Several studies had partially addressed intraspecific variation in the sigmodontine rodent Abrothrix olivacea, focusing on the characterization of physiological aspects and cranial variation. However, these had been conducted based on geographically restricted populational samples, and in most cases, the aspects characterized were not explicitly contextualized with the environmental configurations in which the populations occurred. Here, the cranial variation of A. olivacea was characterized by recording twenty cranial measurements in 235 individuals from 64 localities in Argentina and Chile, which widely cover the geographic and environmental distribution of this species. The morphological variation was analyzed and ecogeographically contextualized using multivariate statistical analyses, which also included climatic and ecological variation at the localities where the individuals were sampled. Results indicate that the cranial variation of this species is mostly clustered in localized patterns associated to the types of environments, and that the levels of cranial differentiation are higher among the populations from arid and treeless zones. Additionally, the ecogeographical association of cranial size variation indicate that this species does not follow Bergmann's rule and that island populations exhibit larger cranial sizes compared to their continental counterparts distributed at the same latitudes. These results suggest that cranial differentiation among the populations of this species is not homogeneous throughout its geographic distribution, and that the patterns of morphological differentiation are also not completely consistent with the patterns of genetic structuring that have been described recently. Finally, the analyses performed to ponder morphological differentiation among populations suggest that the contribution of genetic drift in the formation of these patterns can be ruled out among Patagonian populations, and that the selective effect imposed by the environment could better explain them.

Environmental Sensing in High-Altitude Mountain Ecosystems Powered by Sedimentary Microbial Fuel Cells.

The increasing need for fresh water in a climate change scenario requires remote monitoring of water bodies in high-altitude mountain areas. This study aimed to explore the feasibility of SMFC operation in the presence of low dissolved oxygen concentrations for remote, on-site monitoring of physical environmental parameters in high-altitude mountainous areas. The implemented power management system (PMS) uses a reference SMFC (SMFCRef) to implement a quasi-maximum power point tracking (quasi-MPPT) algorithm to harvest energy stably. As a result, while transmitting in a point-to-point wireless sensor network topology, the system achieves an overall efficiency of 59.6%. Furthermore, the control mechanisms prevent energy waste and maintain a stable voltage despite the microbial fuel cell (MFC)'s high impedance, low time response, and low energy production. Moreover, our system enables a fundamental understanding of environmental systems and their resilience of adaptation strategies by being a low-cost, ecological, and environmentally friendly alternative to power-distributed and dynamic environmental sensing networks in high-altitude mountain ecosystems with anoxic environmental conditions.

Factors influencing eco-efficiency of municipal solid waste management in Chile: A double-bootstrap approach.

Improving eco-efficiency in the provision of municipal solid waste plays an important role for a sustainable economy. Eco-efficiency of municipal solid waste service providers (MSWSPs) has been generally assessed using the conventional data envelopment analysis (DEA) method. However, this approach is sensitive to data noise and has no statistical properties. To overcome these limitations, in this paper, we adopt the double-bootstrap DEA model to derive robust eco-efficiency scores. This nonparametric method allows conducting statistical inference to explore environmental factors affecting the eco-efficiency of MSWSPs. The empirical approach focused on a sample of 298 MSWSPs in Chile, a middle-income country whose policies for promoting waste recycling are incipient. The results indicated that based on the bias-corrected eco-efficiency scores, the potential saving in costs and unsorted waste could be up to 37.8% on average to generate the same level of output (recycled waste). The findings showed that dealing with data noise and uncertainly is of great importance when conducting benchmarking analysis. The region where the municipality is located, tourism, population density and waste per capita are environmental variables that significantly influenced eco-efficiency of Chilean MSWSPs. Several policy implications are discussed based on the findings of this study.

The eco-efficiency of municipalities in the recycling of solid waste: A stochastic semi-parametric envelopment of data approach.

Eco-efficiency assessment of municipal solid waste (MSW) suppliers is a useful tool in the transition to a circular economy. Furthermore, it provides evidence of the economic and environmental performance of municipalities that can be used for decision-making and/or elaboration of regulatory policies. In this study, eco-efficiency scores were computed for a sample of 140 Chilean municipalities in the provision of MSW services. In doing so, the stochastic semi-parametric envelopment of data method was applied. It is a novel technique which overcomes the limitations of parametric (stochastic frontier analysis) and non-parametric (data envelopment analysis) methods previously employed to evaluate the eco-efficiency of MSW services. The average eco-efficiency of the 140 assessed municipalities was 0.332 which indicates that they could save 66.8% of their operational costs and recycling the same amount of waste. Moreover, 61.4% of the evaluated municipalities presented an eco-efficiency score which was lower than 0.4, whereas the other municipalities (38.6% of the sample) exhibited an eco-efficiency which raged between 0.4 and 0.80. Hence, none of the municipalities assessed was identified as eco-efficient which, implies that there is room for all municipalities to reduce operational costs in the management of MSW. Population density, tourism and location of the municipality were identified as factors influencing the eco-efficiency of the municipalities in MSW management.

Spatial analysis of factors influencing bacterial leaf blight in rice production / Análise espacial dos fatores que influenciam a praga bacteriana foliar na produção de arroz

Xanthomonas oryzae pv. oryzae (Xoo) causes bacterial leaf blight that is a major threat to rice production. Crop losses in extreme situations can reach up to75%, and millions of hectares of rice are affected each year. Management of the disease required information about the spatial distribution of BLB incidence, severity, and prevalence. In this study, major rice-growing areas of Pakistan were surveyed during 2018-2019 for disease occurrence, and thematic maps were developed using geographic information system (GIS). Results showed that Narowal district had highest percentage of disease incidence (54-69%), severity (42-44%), and prevalence (72-90%) meanwhile Jhung district had the lowest incidence (21-23%), severity (18-22%), and prevalence (45-54%). To understand the environmental factors contributing to this major rice disease, the research analyze, the spatial relationships between BLB prevalence and environmental variables. Those variables include relative humidity (RH), atmospheric pressure (A.P), minimum temperature, soil organic carbon, soil pH, and elevation, which were evaluated by using GIS-based Ordinary Least Square (OLS) spatial model. The fitted model had a coefficient of determination (R2) of 65 percent explanatory power of disease development. All environmental variables showed a general trend of positive correlation between BLB prevalence and environmental variables. The results show the potential for disease management and prediction using environmental variable and assessment.

Xanthomonas oryzae pv. oryzae (Xoo) causa o crestamento bacteriano das folhas, que é uma grande ameaça à produção de arroz. As perdas de safra em situações extremas podem chegar a 75% e a milhões de hectares de arroz são afetados a cada ano. O manejo da doença exigia informações sobre a distribuição espacial da incidência, gravidade e prevalência de BLB. Neste estudo, as principais áreas de cultivo de arroz do Paquistão foram pesquisadas durante 2018 e 2019 para ocorrência de doenças, e mapas temáticos foram desenvolvidos usando o sistema de informações geográficas (GIS). Os resultados mostraram que o distrito de Narowal teve a maior porcentagem de incidência da doença (54-69%), gravidade (42-44%) e prevalência (72-90%), enquanto o distrito de Jhung teve a menor incidência (21-23%), gravidade (18-22%) e prevalência (45-54%). Para compreender os fatores ambientais que contribuem para esta importante doença do arroz, a pesquisa analisa as relações espaciais entre a prevalência de BLB e as variáveis ​​ambientais. Essas variáveis ​​incluem umidade relativa (UR), pressão atmosférica (PA), temperatura mínima, carbono orgânico do solo, pH do solo e altitude, que sendo avaliadas a partir do modelo espacial Ordinary Least Square (OLS) baseado em GIS. O modelo ajustado teve um coeficiente de determinação (R2) de 65 por cento de poder explicativo do desenvolvimento da doença. Todas as variáveis ​​ambientais apresentaram tendência geral de correlação positiva entre prevalência de BLB e variáveis ​​ambientais. Os resultados mostram o potencial de manejo e predição de doenças usando variáveis ​​e avaliações ambientais.

Chytrid fungus in amphibians from the lowland Brazilian Amazon.

Infectious diseases are one of the main threats to biodiversity. The fungus Batrachochytrium dendrobatidis (Bd) is associated with several amphibian losses around the globe, and environmental conditions may dictate the success of pathogen spread. The Brazilian Amazon has been considered climatically unsuitable for chytrid fungus, but additional information on Bd dynamics in this ecoregion is still lacking. We sampled 462 amphibians (449 anurans, 4 caudatans and 9 caecilians), representing 57 species from the Brazilian Amazon, and quantified Bd infections using qPCR. We tested whether abiotic variables predicted the risk of Bd infections, and tested for relationships between biotic variables and Bd. Finally, we experimentally tested the effects of Bd strains CLFT 156 and CLFT 102 (from the southern and northern Atlantic Forest, respectively) on Atelopus manauensis. We detected higher Bd prevalence than those previously reported for the Brazilian Amazon, and positive individuals in all 3 orders of amphibians sampled. Both biotic and abiotic predictors were related to prevalence, and no variable explained infection load. Moreover, we detected higher Bd prevalence in forested than open areas, while the host's reproductive biology was not a factor. We detected higher mortality in the experimental group infected with CLFT 156, probably because this strain was isolated from a region characterized by discrepant climatic conditions (latitudinally more distant) when compared with the host's sampling site in Amazon. The lowland Brazilian Amazon is still underexplored and future studies targeting all amphibian orders are essential to better understand Bd infection dynamics in this region.

Mapping the global distribution of Strongyloides stercoralis and hookworms by ecological niche modeling.

BACKGROUND: The WHO has established a control strategy for Strongyloides stercoralis in school-aged children as well as targets and to maintain control programs for Ascaris lumbricoides, Trichuris trichiura and hookworms. For an efficient development of control programs, it is necessary to know the target countries around the world, as well as the areas within each country where efforts should be focused. Therefore, maps that provide information on the areas at risk for soil-transmitted helminth (STH) infections on a national and sub-national scale would allow for a better allocation of resources. METHODS: We used the ecological niche models MaxEnt and Kuenm R library to estimate the global distribution of S. stercoralis and hookworms. We used occurrence points of both species extracted from surveys of two literature reviews and from the Global Atlas of Helminth Infection database, together with 14 raster maps of environmental variables. RESULTS: We obtained two raster maps with the presence probability of S. stercoralis and hookworm infections at a global level and then estimated the global population at risk to be 2.6 and 3.4 billion, respectively. The population at risk was also estimated at the country level using estimations for areas as small as 25 km2. A relationship was found between the probability of the presence of S. stercoralis and its prevalence, and a raster map was generated. Annual precipitation, annual temperature, soil carbon content and land cover were the main associated environmental variables. The ecological niches of Strongyloides stercoralis and hookworms had an overlap of 68%. CONCLUSIONS: Here we provide information that can be used for developing more efficient and integrated control strategies for S. stercoralis and hookworm infections. This information can be annexed to the study of other risk factors or even other diseases to assess the health status of a community. GRAPHICAL ABSTARCT.

Design and implementation of 3-D printed radiation shields for environmental sensors.

The measurement of outdoor environmental and climatic variables is needed for many applications such as precision agriculture, environmental pollution monitoring, and the study of ecosystems. Some sensors deployed for these purposes such as temperature, relative humidity, atmospheric pressure, and carbon dioxide sensors require protection from climate factors to avoid bias. Radiation shields hold and protect sensors to avoid this bias, but commercial systems are limited, often expensive, and difficult to implement in low-cost contexts or large deployments for collaborative sensing. To overcome these challenges, this work presents an open source, easily adapted and customized design of a radiation shield. The device can be fabricated with inexpensive off-the-shelf parts and 3-D printed components and can be adapted to protect and isolate different types of sensors. Two material approaches are tested here: polylactic acid (PLA), the most common 3-D printing filament, and acrylonitrile styrene acrylate (ASA), which is known to offer better resistance against UV radiation, greater hardness, and generally higher resistance to degradation. To validate the designs, the two prototypes were installed on a custom outdoor meteorological system and temperature and humidity measurements were made in several locations for one month and compared against a proprietary system and a system with no shield. The 3-D printed materials were also both tested multiple times for one month for UV stability of their mechanical properties, their optical transmission and deformation under outdoor high-heat conditions. The results showed that ASA is the preferred material for this design and that the open source radiation shield could match the performance of proprietary systems. The open source system can be constructed for about nine US dollars, which enables mass development of flexible weather stations for monitoring needed in smart agriculture.