Comportamento, produção e qualidade do leite de vacas Holandês-Gir com climatização no curral

This study was conducted with the objective to evaluate the duration of exposure of animals to the evaporative cooling system in the waiting barn on the weather variables (temperature and relative humidity), animal behavior, production and quality aspects of milk from Girolando cows. Sixteen multiparous lactating cows were used having a genetic composition of 7/8 Holstein-Gir and mean milk production of 18 kg animal-1day-1. A 4 × 4 Latin square design was used. The experiment was conducted during the summer season (February-March 2009) and lasted 56 days. The treatments consisted of different periods of 10, 20 and 30 minutes of exposure to acclimatization and control. Time of 30 minutes of exposure to thermal conditioning system enabled the second milking (evening shift), providing an increase in milk production in the next milking of 0.640 kg animal-1. A higher percentage of time spent on food intake and rumination (17.31 and 30.19%) in 30 minutes treatment, compared to control (15.50 and 27.37%), respectively. There was no significant effect of treatments in the composition of milk.

Estimativa da radiação solar ultravioleta em Botucatu/SP Brasil utilizando técnicas de aprendizado de máquina

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Variabilidade do índice de vegetação por diferença normalizada (NDVI) em áreas reflorestamento: floresta estadual Edmundo Navarro de Andrade (FEENA)/Rio Claro (SP)

Pós-graduação em Geociências e Meio Ambiente - IGCE

Pediatric asthma incidence in Texas and associations with ambient ozone levels in an urban area an analysis using Medicaid claims data

Few recent estimates of childhood asthma incidence exist in the literature, although the importance of incidence surveillance for understanding asthma risk factors has been recognized. Asthma prevalence, morbidity and mortality reports have repeatedly shown that low-income children are disproportionately impacted by the disease. The aim of this study was to demonstrate the utility of Medicaid claims data for providing statewide estimates of asthma incidence. Medicaid Analytic Extract (MAX) data for Texas children ages 0-17 enrolled in Medicaid between 2004 and 2007 were used to estimate incidence overall and by age group, gender, race and county of residence. A 13+ month period of continuous enrollment was required in order to distinguish incident from prevalent cases identified in the claims data. Age-adjusted incidence of asthma was 4.26/100 person-years during 2005-2007, higher than reported in other populations. Incidence rates decreased with age, were higher for males than females, differed by race, and tended to be higher in rural than urban areas. With this study, we were able to demonstrate the utility of MAX data for estimating asthma incidence, and create a dataset of incident cases to use in further analysis. ^ In subsequent analyses, we investigated a possible association between ambient air pollutants and incident asthma among Medicaid-enrolled children in Harris County Texas between 2005 and 2007. This population is at high risk for asthma, and living in an area with historically poor air quality. We used a time-stratified case-crossover design and conditional logistic regression to calculate odds ratios, adjusted for weather variables and aeroallergens, to assess the effect of increases in ozone, NO2 and PM2.5 concentrations on risk of developing asthma. Our results show that a 10 ppb increase in ozone was significantly associated with asthma during the warm season (May-October), with the strongest effect seen when a 6-day cumulative lag period was used to compute the exposure metric (OR=1.05, 95% CI, 1.02–1.08). Similar results were seen for NO2 and PM 2.5 (OR=1.07, 95% CI, 1.03–1.11 and OR=1.12, 95% CI, 1.03–1.22, respectively). PM2.5 also had significant effects in the cold season (November-April), 5-day cumulative lag: OR=1.11, 95% CI, 1.00–1.22. When compared with children in the lowest quartile of O3 exposure, the risk for children in the highest quartile was 20% higher. This study indicates that these pollutants are associated with newly-diagnosed childhood asthma in this low-income urban population, particularly during the summer months. ^

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Temperature and Photoperiod Effects on Vicia faba Phenology Simulated by CROPGRO-Fababean

Models may be useful tools to design efficient crop management practices provided they are able to accurately simulate the effect of weather variables on crop performance. The objective of this work was to accurately simulate the effects of temperature and day length on the rate of vegetative node expression, time to flowering, time to first pod, and time to physiological maturity of faba bean (Vicia faba L.) using the CROPGRO-Fababean model. Field experiments with multiple sowing dates were conducted in northwest Spain during 3 yr (17 sowing dates: 12 used for calibration and five for validation). Observed daily minimum and maximum air temperatures were within the range of ?9.0 and 39.2°C and observed photoperiods within 10.1 to 16.6 h. Optimization of thermal models to predict leaf appearance raised the base temperature (Tb) from the commonly used value of 0.0 to 3.9°C. In addition, photothermal models detected a small accelerating effect of day length on the rate of leaf appearance. Accurate prediction of the flowering date required incorporating day length, but the solved Tb approached negative values, close to ?4°C. All the reproductive phases after flowering were affected only by temperature, but postanthesis Tb was also mayor que0°C and approached values close to 8°C for time to first pod set and 5.5°C for time from first pod to physiological maturity. Our data indicated that cardinal base temperatures are not the same across all phenological phases.

The rodent fauna of Long Pine Key, Everglades National Park: a comparison of habitat types

Rodents are often involved at several stages of trophic dynamics. Consequently they often play crucial roles in the structure and function of many complex ecological systems. This study sought to address the lack of baseline data concerning rodents in tropical areas, and south Florida in particular. Live trapping took place in the four major habitat types of the Long Pine Key area of Everglades National Park over the course of one year. I compared population structures and abundance of murid rodents in the four habitat types, and tested multiple weather variables for their effectiveness as predictors of rodent abundance. I found the Long Pine Key area to be depauperate in terms of species diversity. Each of the four species of rodent encountered favored a particular habitat type. The density of the understory vegetation and the avoidance of avian predators in particular appear to be the most important factors in the distribution and abundance of rodents in the Long Pine Key area of Everglades National Park.

Dynamique d’une population faunique en expansion sous différents scénarios climatiques : le dindon sauvage (Meleagris gallopavo)

Les changements climatiques récents ont mené à l’expansion de la répartition de plusieurs espèces méridionales, mais ont aussi causé l’extinction locale d’espèces se retrouvant à la limite de leur tolérance environnementale. Ces populations en expansion peuvent favoriser différentes stratégies d’histoire de vie en répondant à différents facteurs limitants. Dans cette thèse, je vise à déterminer et quantifier l’effet du climat et des évènements extrêmes sur le cycle de vie complet d’une espèce en expansion (le dindon sauvage) pour comprendre les changements au niveau populationnel ainsi que les mécanismes impliqués dans l’expansion de la distribution d’une espèce. J’ai défini les évènements extrêmes de pluie, d’épaisseur de neige au sol et de température, comme un évènement dont la fréquence est plus rare que le 10e et 90e percentile. En utilisant l’approche « Measure-Understand-Predict » (MUP), j’ai tout d’abord suivi trois populations le long d’un gradient latitudinal de sévérité hivernale pour mesurer l’effet de variables météorologiques sur la dynamique des populations. La survie des dindons sauvages diminuait drastiquement lorsque l’accumulation de neige au sol dépassait 30 cm pour une période de 10 jours et diminuait également avec la température. Au printemps, la persistance de la neige affectait négativement le taux d’initiation de la nidification et l’augmentation de la pluie diminuait la survie des nids. Dans une deuxième étape, j’ai examiné l’impact des évènements climatiques extrêmes et des processus démographiques impliqués dans l’expansion du dindon, liés à la théorie des histoires de vie pour comprendre la relation entre la dynamique de ces populations en expansions avec le climat. J’ai démontré que la fréquence des évènements extrêmes hivernaux et, d’une façon moins importante, les évènements extrêmes estivaux limitaient l’expansion nordique des dindons sauvages. J’ai appuyé, à l’aide de données empiriques et de modélisation, les hypothèses de la théorie classique des invasions biologiques en montrant que les populations en établissement priorisaient les paramètres reproducteurs tandis que la survie adulte était le paramètre démographique affectant le plus la dynamique des populations bien établies. De plus, les populations les plus au nord étaient composées d’individus plus jeunes ayant une espérance de vie plus faible, mais avaient un potentiel d’accroissement plus élevé que les populations établies, comme le suggère cette théorie. Finalement, j’ai projeté l’impact de la récolte sur la dynamique des populations de même que le taux de croissance de cette espèce en utilisant les conditions climatiques futures projetées par les modèles de l’IPCC. Les populations en établissement avaient un taux de récolte potentiel plus élevé, mais la proportion de mâles adultes, possédant des caractéristiques recherchées par les chasseurs, diminuait plus rapidement que dans les populations établies. Dans le futur, la fréquence des évènements extrêmes de pluie devrait augmenter tandis que la fréquence des évènements extrêmes de température hivernale et d’accumulation de neige au sol devraient diminuer après 2060, limitant probablement l’expansion nordique du dindon sauvage jusqu’en 2100. Cette thèse améliore notre compréhension des effets météorologiques et du climat sur l’expansion de la répartition des espèces ainsi que les mécanismes démographiques impliqués, et nous a permis de prédire la probabilité de l’expansion nordique de la répartition du dindon sauvage en réponse aux changements climatiques.

Evapotranspiration: Present and Future Challenges

The thesis first explored and evaluated some of the most used models that were developed to account for the effect of CO2 on evapotranspiration. This review depicts the complexity of the modeling procedure and underlines the advantages and shortcomings of each model. Then, the projected climate change in the near future (2021-2050) in different locations in Emilia-Romagna (Italy) was studied, with an emphasis on the opposite effect of an increase in both air temperature and CO2 levels on ETo. The case study used reanalysis data as a surrogate to historical weather stations measurements and an ensemble of regional climate models (RCMs) for the future projections. Results show that higher CO2 levels moderated the increase in ETo that accompanies an increase in air temperature, taking in consideration the change in other weather variables i.e. solar radiation, wind speed and dew point temperature. The outcomes of this study show that considering the CO2 fertilization effect when calculating reference evapotranspiration might give a more realistic estimation of water use efficiency and irrigation requirements in Emilia-Romagna and a better analysis of the future availability and distribution of water resources in the region. Finally, data from a model forecasting reference evapotranspiration (FRET) and the different variables involved in its calculation for the state of California (USA) were compared with similar data from the regional weather station network (CIMIS) to evaluate their accuracy and reliability. The evaluation was done in locations with different microclimates and included also sample irrigation schedules developed using FRET ETo. The obtained results demonstrate that FRET ETo forecasts are a viable alternative to traditional ETo measurements with some differences depending on the climatic condition of the location considered in this study. This implies that FRET could be replicated in other areas with similar climate settings.

HAND-FOOT-AND-MOUTH DISEASE EPIDEMIOLOGICAL STATUS AND RELATIONSHIP WITH METEOROLOGICAL VARIABLES IN GUANGZHOU, SOUTHERN CHINA, 2008-2012

Hand-foot-and-mouth disease (HFMD) is becoming one of the extremely common airborne and contact transmission diseases in Guangzhou, southern China, leading public health authorities to be concerned about its increased incidence. In this study, it was used an ecological study plus the negative binomial regression to identify the epidemic status of HFMD and its relationship with meteorological variables. During 2008-2012, a total of 173,524 HFMD confirmed cases were reported, 12 cases of death, yielding a fatality rate of 0.69 per 10,000. The annual incidence rates from 2008 to 2012 were 60.56, 132.44, 311.40, 402.76, and 468.59 (per 100,000), respectively, showing a rapid increasing trend. Each 1 °C rise in temperature corresponded to an increase of 9.47% (95% CI 9.36% to 9.58%) in the weekly number of HFMD cases, while a one hPa rise in atmospheric pressure corresponded to a decrease in the number of cases by 7.53% (95% CI -7.60% to -7.45%). Similarly, each one percent rise in relative humidity corresponded to an increase of 1.48% or 3.3%, and a one meter per hour rise in wind speed corresponded to an increase of 2.18% or 4.57%, in the weekly number of HFMD cases, depending on the variables considered in the model. These findings revealed that epidemic status of HFMD in Guangzhou is characterized by high morbidity but low fatality. Weather factors had a significant influence on the incidence of HFMD.

Interpretation of wind components as compositional variables

The classical statistical study of the wind speed in the atmospheric surface layer is madegenerally from the analysis of the three habitual components that perform the wind data,that is, the component W-E, the component S-N and the vertical component,considering these components independent.When the goal of the study of these data is the Aeolian energy, so is when wind isstudied from an energetic point of view and the squares of wind components can beconsidered as compositional variables. To do so, each component has to be divided bythe module of the corresponding vector.In this work the theoretical analysis of the components of the wind as compositionaldata is presented and also the conclusions that can be obtained from the point of view ofthe practical applications as well as those that can be derived from the application ofthis technique in different conditions of weather

The role of incidental variables of time in mood assessment

Determining what influences mood is important for theories of emotion and research onsubjective well-being. We consider three sets of factors: activities in which people areengaged; individual differences; and incidental variables that capture when mood ismeasured, e.g., time-of-day. These three factors were investigated simultaneously in a studyinvolving 168 part-time students who each responded 30 times in an experience samplingstudy conducted over 10 working days. Respondents assessed mood on a simple bipolarscale from 1 (very negative) to 10 (very positive). Activities had significant effects but,with the possible exception of variability in the expression of mood, no systematicindividual differences were detected. Diurnal effects, similar to those already reported inthe literature, were found as was an overall Friday effect. However, these effects weresmall. Lastly, the weather had little or no influence. We conclude that simple measures ofoverall mood are not greatly affected by incidental variables.

Proposal of three thermodynamic variables to discriminate between storms associated with hail and storms with intense rainfall in Catalonia (Proposta de tres variables termodinàmiques per discriminar entre tempestes amb pedregades i tempestes amb pluges intenses)

Between late spring and early fall, the development of storms is common in Catalonia. Despite the fact that they usually produce heavy showers of short duration, they can also involve severe weather with ice pellets or hail. While the latter usually affect inland regions, and there are numerous publications on these cases; the analysis of events affecting the coast and causing damage to public and private properties is not so well developed. The aim of this study is to provide additional thermodynamic indicators that help differentiate storms with hail from storms without hail, considering cases that have affected various regions of Catalonia, mainly coastal areas. The aim is to give more information to improve prognosis and the ability to detail information in these situations. The procedure developed involved the study of several episodes of heavy rainfall and hail that hit Catalonia during the 2003-2009 period, mainly in the province of Girona, and validated the proposal during the campaign of late summer and fall of 2009, as well as 2012. For each case, several variables related to temperature, humidity and wind were analyzed at different levels of the atmosphere, while the information provided by the radio sounding in Barcelona was also taken into account. From this study, it can be concluded that the temperature difference between 500 hPa and 850 hPa, the humidity in the lower layers of the atmosphere and the LI index are good indicators for the detection of storms with associated hail.

Interpretation of wind components as compositional variables

The classical statistical study of the wind speed in the atmospheric surface layer is made generally from the analysis of the three habitual components that perform the wind data, that is, the component W-E, the component S-N and the vertical component, considering these components independent. When the goal of the study of these data is the Aeolian energy, so is when wind is studied from an energetic point of view and the squares of wind components can be considered as compositional variables. To do so, each component has to be divided by the module of the corresponding vector. In this work the theoretical analysis of the components of the wind as compositional data is presented and also the conclusions that can be obtained from the point of view of the practical applications as well as those that can be derived from the application of this technique in different conditions of weather

Weather to disperse? Evidence that climatic conditions influence vertebrate dispersal

1. Dispersal is regarded as critical to the stability of existing populations and the spread of invading species, but empirical data on the effect of travelling conditions during the transfer phase are rare. We present evidence that both timing and distance of ex-natal dispersal in buzzards (Buteo buteo) are strongly affected by weather. 2. Dispersal was recorded more often when the wind changed to a more southerly direction from the more common westerly winds, and when minimum temperatures were lower. The effect of wind direction was greatest in the winter and minimum temperature was most important in the autumn. Poor weather did not appear to initiate dispersal. 3. Dispersal distance was most strongly correlated with maximum temperature during dispersal and wind direction in the following 5-day period. Combined with the sex of the buzzard these three variables accounted for 60% of the variation in dispersal distance. 4. These results are important for conservationists who manage species recovery programs and wildlife managers who model biological invasions.