Weather variables

En el SE de Santa Cruz, se desarrolló un trabajo con el objetivo de evaluar las pérdidas de corderas de reposición respecto al campo natural y la evolución del peso vivo desde enero a mayo durante dos años (2015-2016 y 2017-2018) en una pastura mezcla de pasto ovillo (Dactylis glomerata) y agropiro intermedio (Agropyron intermedium (Host) Beauvois. Tres lotes de corderas señaladas provenientes de tres campos fueron elegidas al azar y evaluadas. En los animales identificados se determinó en tres fechas (enero, febrero-marzo y abril-mayo) el peso vivo (PV), ganancias y rangos de PV. La pastura se muestreo en tres fechas (diciembre, marzo y mayo) para determinar la biomasa aérea disponible, el material vivo (MV) y muerto, la proteína bruta y digestibilidad de la materia seca. El análisis estadístico relaciono el PV de los animales con los días mediante regresiones y el PV de las corderas con el MV y las variables nutricionales mediante correlaciones. El trabajo incluyó registros de lluvias y temperaturas. En ambos años, se redujeron las pérdidas de animales en la pastura (< al 5%) respecto al campo natural (18%). La perfomance animal incluyó pérdidas de PV y mantenimiento del rango de PV inicial y final (1° año) y mejoras en ambos parámetros (2° año). Se concluye que el manejo aplicado genera un beneficio global y una mayor eficiencia en el sistema productivo aunque el desempeño animal depende del estado madurativo de la pastura influenciado por las condiciones hídricas ambientales.

The focus of the paper is liquidity analysis, which is an essential tool of the corporate financial management process. Converting assets, especially working capital, into cash is the primary way for a company to obtain the resources it needs to pay its current liabilities. The aim of the research is to look at the movement of current, quick and cash liquidity indicators, their comparative analysis, as well as differences in the level of liquidity for the period 2011-2020 on the example of certain oil companies in the Republic of Serbia. Ratio analysis based on their official financial statements was used to measure the liquidity of selected companies (NIS, OMV and LUKOIL), while the one-way ANOVA test was used to test hypotheses. The research results show that there is a statistically significant difference in the level of current liquidity between NIS and LUKOIL and OMV and LUKOIL, while there is no significant difference between NIS and OMV. There is a statistically significant difference in terms of quick ratios between NIS and LUKOIL, while there is no significant difference between NIS and OMV and OMV and LUKOIL. Observing the cash liquidity indicator, there is no significant difference between these indicators in the companies that are the subject of the research.

Small cardamom is an economically important spice crop. However, cardamom is susceptible to several diseases that significantly reduce yield. Proactive prevention of these diseases based on advance warning can enhance the efficiency of disease control and reduce environmental load of pesticides. Many of these diseases are governed by weather variables (for example, through control of fungal growth). This work presents a disease (capsule rot of cardamom) forecast model based on a set of meteorological variables. While no single weather variable provides successful simulation, an optimal combination of weather variables provides sufficient skill for advance warning of the disease

Background Based on several previous studies (regional and national), Tijuana, Baja-California, Mexico (across the border from San Diego, California, USA), has shown the highest rate of Meningococcal Meningitis (MeM) in the country, however, the reason for this has not yet been known. In the “African Meningitis Belt”, the Harmattan seasons are associated with MeM outbreaks. The Santa Ana winds seasons (SAWs) are hot and dry winds (similar to Harmattan seasons) that occur seasonally in southwestern California, USA, and North of Baja-California, Mexico. Objectives Our aim was to demonstrate, as a short communication, a potential association of SAWs with MeM in Tijuana, Baja-California, Mexico, which in turn, may partially explain the high rate of this disease in the region. Methods Based on own previously published data obtained from thirteen years of active surveillance of MeM, and a 65 years review showing the occurrence of SAWs, we estimated the risk ratio (RR) of total cases numbe...

La cantidad de humedad al sur de la Patagonia (argentino-chilena), en parte condicionada por la presencia de la cordillera, determina cambios en los atributos de las comunidades vegetales. Los índices de vegetación obtenidos por sensores remotos están asociado a la cantidad de material fotosintéticamente activo y Productividad Primaria Neta Aérea (PPNA), entre otras características de la vegetación. El índice de vegetación más utilizado es el índice Verde Diferencia Normalizada (NDVI). Particularmente la I-NDVI (Integral del índice de vegetación) está asociada a PPNA. El objetivo de este trabajo es describir el comportamiento de la vegetación en dos sitios del sur de la Patagonia (Río Gallegos y Punta Arenas) a través del NDVI y su relación con variables meteorológicas. Datos de precipitación y temperatura fueron obtenidos de estaciones meteorológicas y los datos espectrales (NDVI- de imágenes MOD13Q1, 250m, 16 días) utilizando la plataforma Google Earth Engine. Se calcularon índice...

The specific adaptation of 15 rubber tree (Hevea brasiliensis) clones was assessed by analyzing yield during a normal year (1997-98) and a year (1998-99) in which the yield was exceptional. Differences in yield in response to changes in weather conditions over the years were evident with clones RRII 203, RRIM 703, PB 5/51 and PB 235 which all exhibited a negative trend with increasing wind velocity during 1997-98, these clones also exhibited a negative correlation with minimum temperature during 1998-99. The prominent yield differences across the years made selection based on both yield and stability inevitable through computing weather variables and environmental index as covariant. To determine the contribution of variable(s) to genotype-environment (GE) interactions, the GE interaction was partitioned into heterogeneity and residual GE interaction. Heterogeneity only for environmental index was highly significant (p = 0.01), meaning that stability or instability of clones was due to a linear effect of the environmental index. The non-significant values of heterogeneity for the weather variables revealed that none of these factors individually was sufficient to explain heterogeneity. A QBASIC computer program called STABLE was used to select simultaneously for yield and stability. Clones PB 235, RRII 118, RRII 203, RRIM 703 and RRIM 600 were stable over the years investigated.

Se estudió el efecto de los factores ambientales sobre la población de Monalonion velezangeli Carvalho & Costa en aguacate cv. Hass, plantado en los Andes colombianos.Se obtuvieron datos de clima correspondientes al período 2010-2011, usando seis estaciones meteorológicas automáticas. La actividad del insecto fue monitoreada mediante el registro de individuos de M. velezangeli y la presencia de daño fresco en doce ramas marcadas en cada árbol, ubicadas en los cuatro puntos cardinales, en los estratos bajo, medio y alto. Se establecieron relaciones entre los índices de población obtenidos con valores de temperatura, humedad relativa y velocidad del viento, mediante representaciones Biplot y análisis de correlación. Estos análisis se hicieron no solo con los registros tomados el mismo día, sino que incluían también los valores retroacumulados de los factores climáticos, esto es, la suma durante un período de días antes de la evaluación.Los resultados sugieren que la temperatura y la h...

Artículo de investigación Aplicación de una metodología de análisis de datos obtenidos por percepción remota orientados a la estimación de la productividad de caña para panela al cuantificar el NDVI (índice de vegetación de diferencia normalizada) Application of a method of analysis of remote sensing data obtained by targeting the estimated productivity in cane for quantifying panela NDVI (normalized difference vegetation index)

The effects of meteorological conditions around silking (Si) and physiological maturity (PM) on production of fumonisins (FB=FB1 + FB2) in maize kernels were analyzed. Kernel FB contents were determined on kernel samples collected from an experimental susceptible hybrid (n = 35) and a Bt commercial hybrid (n = 23) planted in several growing seasons/sites from the Argentinean Pampas. Considering the effect of genetic divergence of the two maize hybrids, total kernel FB concentrations (n = 52) binary coded, were predicted appropriately by weather-based logistic regression models but underestimated in some samples severely contaminated with FB. After removing these misclassified cases that registered maximum values of a drought-heat stress index (DI) calculated over 30 days around Si, and weather conditions (assessed by weather interactive components) conducive to infection/production of FB in PM, new logistic models were fitted. These new models improved their predictive ability indices. It was remarkable a model including the discrete genetic variable and the weather variable associated with the simultaneous occurrence of precipitation, temperature between 19.5-33°C and relative humidity >70%, required for fungal infection in Si. Models that also included weather variables calculated in PM and associated with the kernel drying rate and fungal infection, did not result in better prediction outcomes. Opposite to the general trend, the occurrence of both severe heat-drought stress around Si and favorable weather around PM led to high kernel FB contents. We hypothesize that husk shortening by stress at silking might expose ears, promoting Fusarium verticillioides colonization/FB synthesis in late stages of kernel development and maturity, whenever favorable environmental conditions for both processes prevail.

Peasant farming had been seen for many years as a means of fighting rural poverty in the Sub-Saharan zone of Africa. With the passing of time, new researches had shown that the notion is fading off since studies have established that only a small percentage of those farms have seen expansion and intensification leaving the rest stagnated with lowing productivity, decrease in size and low output. Alternative sources of rural income are therefore important to determine for specific locations. This paper looks at the effects of shea picking on rural household income in four (4) communities in the Sumbrungu area of the Bolgatanga Municipality of the Upper East Region of Ghana. Regression analysis performed indicated that shea picking activity in the selected communities is a signification source of income as compared to the official minimum dairy wage in Ghana. The main factors that influence income of shea-picker household are the age of the picker and the quantity of shea fruits picked per day and an interaction between shea quantity and location of community with respect to Bokom community. Income distribution among interested households under this work appeared to be well distributed which indication that a common taughtfully designed poverty reduction strategy could be rolled out in all the communities in the area and among all shea-picker households.

Introduction: Bacterial meningitis is a disease of major public health importance especially for countries such as Ghana; whose northern part lies within the meningitis belt. The Northern region of Ghana has been recording cases with outbreaks over the years. In order to generate evidence to improve surveillance, we described the epidemiology of bacterial meningitis using surveillance data of the northern region. Methods: Bacterial meningitis datasets from January 2010 to December 2015 for all the 26 districts of the Northern region were retrieved from line lists. Data were analyzed in terms of person, place, time, and identity of causative organisms using descriptive statistics. The results were presented as proportions, rates, tables and graphs. Results: A total of 1,176 cases were reported. Of these, 53.5% (629/1,176) were males. The proportion of cases aged 0 to 29 years was 77.4%. The Overall Case Fatality Rate (CFR) was 9.7% (114/1,176). About 65% of all cases were recorded from January to April. Only 23.7% (279/1,176) of cases were laboratory-confirmed. Neisseria meningitides and Streptococcus pneumonia accounted for 91.4% of confirmed cases. Over the period, the incidence reduced from 9.0/100,000 population to 3.8/100,000 population and CFR reduced from 16.6% to 5.7%. Conclusion: Most cases of bacterial meningitis were recorded in the dry season and in persons younger than 30 years. Less than a quarter of cases were laboratory confirmed, and no new bacteria species were identified. Both morbidity and mortality rates were on the decline. There is the need to consolidate these gains by intensifying meningitis surveillance and improving on the rate of laboratory case confirmation.

The current study discourses the impact of variation in PM 2.5 concentration on the ambient air quality of Delhi. The 24-hourly PM 2.5 concentration dataset was obtained from air quality measurement site (Anand Vihar) of Delhi Pollution Control Committee (DPCC) for the duration of April 2015 to December 2018. The annual and seasonal variability in the trend of ambient PM 2.5 along with cumulative impact of meteorological parameters have been analyzed. The overall percentage increase in annual PM 2.5 concentration, compared to National Ambient Air Quality Standards (NAAQS) guidelines, is observed to be 286.09%. The maximum concentration of Bne particulate matter was recorded to be 788.6 lg/m 3 during post-monsoon season and it was found to be associated with lower ambient temperature of 21.34°C and wind speed of 0.33 m/sec. Further, PM 2.5 concentration was found to be correlated with CO (R = 0.6515) and NH 3 (R = 0.6396) indicating similar sources of emission. Further, backward trajectory analysis revealed contribution in PM 2.5 concentration from the states of Punjab and Haryana. The results indicated that particulate pollution is likely to occur in urban atmospheric environments with low temperatures and low wind speeds.

The objective of this study was to explore the influences of the health and socio-economic factors associated with the poverty level of households in Bangladesh, through an analysis of data from the Household Income and Expenditure Survey (HIES) 2010 conducted by Bangladesh Bureau of Statistics (BBS). A total of all 12,240 households was considered in this study. CBN method was applied for estimating poverty of the household. A logistic regression analysis was used to identify the main factors that influence the household’s poverty. The results showed that the probability of the household being poor was higher when the household’s head suffered from various chronic diseases like chronic fever, injuries/disability, eczema, leprosy, and asthma/breathing trouble as compared to the household whose head didn’t suffer from any chronic diseases. From the analysis, it was also found that when a large number within household suffered from any chronic disease, the probability of the household...

Teniendo en cuenta los reportes emitidos por los modelos de pronósticos de largo plazo de los grandes centros internacionales sobre la región del Comahue y Patagonia, que coinciden en proyectar para el período 2080–2100 una importante disminución de las precipitaciones invernales en la misma, en este trabajo se verifica si el comportamiento actual de sus principales ríos es compatible con dichos pronósticos. Sin embargo, una de las mayores dificultades que presenta el estudio del cambio climático en dichas regiones, es la escasez de series climatológicas e hidrológicas lo suficientemente largas en el período instrumental como para aplicarles test estadísticos de detección de tendencias y saltos en la media. Siendo el río Limay el más representativo de la primera región y el Chubut de la segunda, dado que cuentan con mediciones regulares de caudales incompletas y/o con datos faltantes, se los seleccionó para extender y optimizar sus series temporales obteniendo valores estimados de p...

Despite the constant development of the system of forecasting unpredicted events and risks, there are many economic sectors where revenues depend directly on the wheather variables. Risk management is a continuous process whose goals are to predict and stabilize cash flows and maximize profit of a company. Weather derivatives are special form of financial risk transfer instruments and they are associated with specific weather events or conditions that directly or indirectly affect the third party outcomes. Unlike traditional insurance contracts, weather derivatives are realized when the agreed time variable exceeds a predefined value for a specific period. Therefore, financial derivatives make more probable the uncertainty of future operations in terms of present actions. Market for the weather derivatives is the youngest and also the fastest growing market for financial derivatives. Insurance against weather risk primarily was applied in the energy sector. Market development and th...

There is a continuous addition of unwanted particles in the environment due to an increase in industrialization and urbanization in metro cities. These unwanted particles contaminate the natural resources and hence lead to environmental pollution. As per WHO 2018, Delhi is the 6th most polluted city in the world. Major sources of pollution in Delhi are various industries and heavy vehicular density. In the present study, an effort has been made to predict PM 10 concentration for different hotspot sites. Four Artificial Neural Networks, NARX, RNN, FFBP, ELMAN were used to predict the air pollutant concentration at these hotspots. The results depicted that the NARX network provides better results among the other neural networks. The R and MSE values obtained for the study were 0.94795 and 0.0088474 respectively for PM 10. This study will facilitate better prediction models for accurate PM 10 concentration.

Cerebrospinal Meningitis (CSM) is one of the climate-sensitive diseases affected by global climate change. Its causal agent, Neisseria meningitidis bacterial, thrives well in warm environments. CSM epidemics are likely to be exacerbated by the warming globe emanating from climate change. Yet studies have rarely examined the association between climat’e change and CSM. Moreover, studies drawing on quantitative and qualitative data to understand the pathways of climate change domains and CSM relationships as well as community perspectives of CSM are rare. This study deployed mixed-method research to analyse community perceptions of and the relationship between climate change and CSM. We collected 96 months of data on changes in metrological weather parameters (temperatures, sunshine, relative and absolute humidity, and rainfall) and CSM cases and mortalities recorded between 2012 and 2019. Community-level data were elicited using a semi-structured questionnaire. The results showed a s...

La cantidad de humedad al sur de la Patagonia (argentino-chilena), en parte condicionada por la presencia de la cordillera, determina cambios en los atributos de las comunidades vegetales. Los índices de vegetación obtenidos por sensores remotos están asociado a la cantidad de material fotosintéticamente activo y Productividad Primaria Neta Aérea (PPNA), entre otras características de la vegetación. El índice de vegetación más utilizado es el índice Verde Diferencia Normalizada (NDVI). Particularmente la I-NDVI (Integral del índice de vegetación) está asociada a PPNA. El objetivo de este trabajo es describir el comportamiento de la vegetación en dos sitios del sur de la Patagonia (Río Gallegos y Punta Arenas) a través del NDVI y su relación con variables meteorológicas. Datos de precipitación y temperatura fueron obtenidos de estaciones meteorológicas y los datos espectrales (NDVI- de imágenes MOD13Q1, 250m, 16 días) utilizando la plataforma Google Earth Engine. Se calcularon índice...

Precision agriculture represents a promising technological trend in which governments and local authorities are increasingly investing. In particular, optimising the use of pesticides and having localised models of plant disease are the most important goals for the farmers of the future. The Trentino province in Italy is known as a strong national producer of apples. Apple production has to face many issues, however, among which is apple scab. This disease depends mainly on leaf wetness data typically acquired by fixed sensors. Based on the exploitation of artificial neural networks, this work aims to spatially extend the measurements of such sensors across uncovered areas (areas deprived of sensors). Achieved results have been validated comparing the apple scab risk of the same zone using either real leaf wetness data and estimated data. Thanks to the proposed method, it is possible to get the most relevant parameter of apple scab risk in places where no leaf wetness sensor is available. Moreover, our method permits having a specific risk evaluation of apple scab infection for each orchard, leading to an optimization of the use of chemical pesticides.

Fire seasonality, an important characteristic of fire regimes, commonly is delineated using seasons based on single weather variables (rainfall or temperature). We used nonparametric cluster analyses of a 17-year (1993-2009) data set of weather variables that influence likelihoods and spread of fires (relative humidity, air temperature, solar radiation, wind speed, soil moisture) to explore seasonality of fire in pine savanna-grassland landscapes at the Avon Park Air Force Range in southern Florida. A four-variable, three-season model explained more variation within fire weather variables than models with more seasons. The three-season model also delineated intra-annual timing of fire more accurately than a conventional rainfall-based two-season model. Two seasons coincided roughly with dry and wet seasons based on rainfall. The third season, which we labeled the fire season, occurred between dry and wet seasons and was characterized by fire-promoting conditions present annually: drought, intense solar radiation, low humidity, and warm air temperatures. Fine fuels consisting of variable combinations of pyrogenic pine needles, abundant C4 grasses, and flammable shrubs, coupled with low soil moisture, and lightning ignitions early in the fire season facilitate natural landscape-scale wildfires that burn uplands and across wetlands. We related our three season model to fires with different ignition sources (lightning, military missions, and prescribed fires) over a 13-year period with fire records (1997-2009). Largest wildfires originate from lightning and military ignitions that occur within the early fire season substantially prior to the peak of lightning strikes in the wet season. Prescribed ignitions, in contrast, largely occur outside the fire season. Our delineation of a pronounced fire season provides insight into the extent to which different human-derived fire regimes mimic lightning fire regimes. Delineation of a fire season associated with timing of natural lightning ignitions should be useful as a basis for ecological fire management of humid savanna-grassland landscapes worldwide.

The aim behind doing this research is to analyse the impact of odd-even policy andlockdown implementation on the air quality index of Delhi by doing the case study on the fourregions Ashok Vihar, Anand Vihar, Dwarka and R.K. Puram. The data is been collected fromDPCC and the main parameters we looked for are PM10 and PM2.5. In which we find out that.highest levels of the pollutants PM10 and PM2.5 been observed during the time of odd-evenpolicy implementation for the year 2019 (04 November 2019- 15 November 2019) whereasduring the lockdown period (23 March 2020-31st August 2020) a great decline in pollutantlevels is been detected. This we further try to correlate with the spatial variations of Delhiregion and able to discern that meteorological parameters (Ambient Temperature, RelativeHumidity, Wind Speed and Solar Radiations) in respect with seasonal variations have a majorinfluence on PM 10 and PM 2.5 levels. During the winter season both the parameters PM10& PM2.5 are touching the...

Kenya has taken numerous steps in ensuring universal access to water among all households by 2030. However, the country may not achieve this by 2030 due to challenges related to the implementation of objectives including inadequate data on the indicators to allow for better policy formulation. The study aimed at finding out the effect of household head characteristics on access to water. The study employed multinomial logistic regression modeling using 2015/2016 Kenya Integrated Household Budget Survey data. Arising from the study findings, an increase in the income of the household head led to an increase in the household's access to clean water. Education levels (primary, secondary, and tertiary) of household heads compared to no education increased the probability of household heads selecting clean water sources. Being employed as well as being male increased the probability of accessing clean water. Further, residing in a rural area by a household head reduced the probability of using clean water compared to residing in an urban area. Based on the findings, the study suggests that there is a need to develop a policy around the key and significant household head characteristics to improve access to clean water in Kenya.

The relationship between access to formal finance and poverty reduction lies at the heart of the development literature and policy discourse, particularly in developing countries, where access to financial services is often argued to have poverty-alleviating potential. Most of the stylised theoretical literature and empirical evidence, however, focus their efforts on the poverty-alleviating potential of access to finance at a given point in time, which ignores the dynamic and multidimensional nature of poverty. Using a nationally representative panel data set of households, this paper explores the effect of access to formal finance on household welfare dynamics in Nigeria between 2010–11 and 2012–13. Applying a bivariate probit model, which addresses the endogenous selection associated with households’ initial welfare status, our estimates indicate that controlling for the exogeneity of initial household status is relevant when exploring the implications of access to finance for wel...

Sufficient production of maize in Kenya is synonymous to food security and a source of income. Majority of the households in the country grow maize as the main staple food and forms the diet of over 85 percent of the population. Climate change potentially compromises maize production as 98 percent of agriculture is rainfed, threatening food security and rural livelihoods. This study sought to understand the effects of the changing temperature and rainfall patterns in Kenya on maize output. The study adopted Autoregressive distributed lag econometric modeling approach using data for the period between 1970 and 2014. The findings shows mixed response of maize output to rainfall and temperature changes depending on the period, with temperature variability having negative effects. In absence of climate change adaptation and mitigation, Kenya will become more food insecure. There is need to formulate all-inclusive policies paramount in building adaptation and mitigation mechanisms.

The aim behind doing this research is to analyse the impact of odd-even policy and lockdown implementation on the air quality index of Delhi by doing the case study on the four regions Ashok Vihar, Anand Vihar, Dwarka and R.K. Puram. The data is been collected from DPCC and the main parameters we looked for are PM10 and PM2.5. In which we find out that highest levels of the pollutants PM10 and PM2.5 been observed during the time of odd-even policy implementation for the year 2019 (04 November 2019-15 November 2019) whereas during the lockdown period (23 March 2020-31st August 2020) a great decline in pollutant levels is been detected. This we further try to correlate with the spatial variations of Delhi region and able to discern that meteorological parameters (Ambient Temperature, Relative Humidity, Wind Speed and Solar Radiations) in respect with seasonal variations have a major influence on PM 10 and PM 2.5 levels. During the winter season both the parameters PM10 & PM2.5 are touching the peak because of the impact of three major meteorological parameters Ambient Temperature, Wind Speed and Solar Radiation and during the monsoon season air quality conditions are quite favourable because of Ambient Temperature and Wind Speed parameters. In the end we use the ensembled machine learning algorithms like Random Forest and Extra trees regressor to have an accurate estimation of PM2.5 levels for all the four regions of Delhi and perceived that these ensembled learning techniques are better than other machine learning algorithms like Neural Networks, Linear regression and SVMs. The Random Forest and Extra trees regressor models give the R 2 value 0.75 and 0.78 respectively for estimation of PM2.5; R 2 value is a statistical measurement which explains the variance of dependent variable based on the independent variables of a regression model.

It is evidence that for several years rural communities in Northern Ghana have been confronted with single rainfall season which is also unpredictable. The poor weather condition has cumulatively contributed to the high incidence of poverty among households. In the midst of this, the indigenes have stayed and persevered through other diversified means of survival with little or no external support. The study therefore seeks to identify traditional and diversified livelihoods coping mechanisms that these rural families have adopted to at least secure them, minimal food and other socioeconomic security/assets. Participatory Rapid Appraisal (PRA) techniques were mostly used to collect data. Some of the techniques used were group discussions, wealth ranking, structured and unstructured questionnaires, and observation. The data analysis employed both qualitative and quantitative approaches to examine the key issues identified. The study reveals that, most communities are living in difficult condition but are resilient to make ends meet. They have done this through diversification of their livelihood portfolios which include diversified crop cultivation, livestock rearing, dry season gardening, petty trading and especially outmigrant remittances. This strategy adopted also enable them cope with other competing demands in terms of food, healthcare, education, farming and shelter among others. The study observed that farmers/households will do better if they are incentivized (inputs and extension service) to boost subsistence farming, diversify more into income generating activities (animal rearing).

Background: Globally meningitis mortality ranges between 2% and 30% and that of in Ghana it is estimated in range between 36% to 50%. The East Mamprusi district in Ghana reports cases due to its location in the meningitis belt. This study assessed the distribution of cases in the district by person, place and time from 2011-2015. Methods: Meningitis surveillance data in the East Mamprusi district in northern Ghana from 2011-2015 was analyzed. Case based forms, laboratory tests, results and weekly line lists were reviewed. MS excel was used to analyze the data. We calculated the incidence of the disease and assessed the trend using the alert and epidemic thresholds. We determined the distribution of cases by persons history. Case fatality rates and prevalence of etiological agents were estimated. Results: The Northern Region recorded a total of 961 suspected meningitis cases from 2011-2015. Numerous subjects are identified, amongst them 205 were confirmed of various organisms with 77 deaths. Between 2011 and 2015, 293 suspected and 83 confirmed meningitis cases were reported from East Mamprusi district. Males are representing 57.5% (216/376) of the total population. About

This study aimed at characterising the spatiotemporal pattern of meningitis in Nigeria, in order to detect vulnerable areas. Stratified sampling was deployed in research inquiry based on the following variables: geographic location; population density; poverty status; and adult literacy level in accordance to diseases incidence rate. Population-based ratios were then computed for each stratum for the identification of high risk areas. Global Moran's Index spatial autocorrelation technique was additionally used to investigate the extent to which neighbouring values of incidence rate are correlated and to determine meningitis demographic risk factors. A significant spatial clustering of meningitis incidence rate has been found in northern Nigeria, with less or no clustering in the southern part of the country. A significant correlation of disease, with geographical location and poverty was perceived. The study commends that improving the socioeconomic status of the affected population may reduce the incidents of meningitis in the high risk area in Nigeria.

This study aimed at characterising the spatiotemporal pattern of meningitis in Nigeria, in order to detect vulnerable areas. Stratified sampling was deployed in research inquiry based on the following variables: geographic location; population density; poverty status; and adult literacy level in accordance to diseases incidence rate. Population-based ratios were then computed for each stratum for the identification of high risk areas. Global Moran's Index spatial autocorrelation technique was additionally used to investigate the extent to which neighbouring values of incidence rate are correlated and to determine meningitis demographic risk factors. A significant spatial clustering of meningitis incidence rate has been found in northern Nigeria, with less or no clustering in the southern part of the country. A significant correlation of disease, with geographical location and poverty was perceived. The study commends that improving the socioeconomic status of the affected population may reduce the incidents of meningitis in the high risk area in Nigeria.

The advances in computer systems and resources make it possible to develop models to simulate the behavior of the fluids in greenhouses. However, the prediction of the gradients of mass and energy in the greenhouses with the crop and natural ventilation is difficult due to the stochastic nature of the wind and the relationships of dependence among temperature, CO 2 and relative humidity. There are heuristic techniques, such as the Bayesian Networks, which help to know the relationships among the variables that cannot be determined with statistical tools. The objective of the present study was to determine temperature, CO 2 concentration and relative humidity with respect to crop height, in a greenhouse with natural ventilation, by means of Bayesian Networks applied to a model of Computational Fluid Dynamic. The Bayesian Network made it possible to determine the spaces of the greenhouse with adverse environmental conditions for the crop development and the most probable climatic states, from the relationships among the variables studied.

There exist different methods and definition how to measure poverty. It can be measured by income or consumption, objectively or as a perception of individuals for their socioeconomic situation.The objective and subjective poverty are highly correlated and both are influenced by socioeconomic factors. Till now, it is the objective poverty mostly considered while the subjective one has been analyzed only as a part of it. Assessments of the subjective poverty are more scarce, especially in the case of transition countries. Different studies proposed advantages and disadvantages of using each method. The significance of different factors shows the characteristics and the trend of monetary or subjective poverty. The national poverty is Albania is calculated through monetary poverty. There are a set of influenced factors related with household composition, geographic division, education and other socioeconomic indicators. In this paper we analyze the perception of individuals for the pov...

This study aimed at characterising the spatiotemporal pattern of meningitis in Nigeria, in order to detect vulnerable areas. Stratified sampling was deployed in research inquiry based on the following variables: geographic location; population density; poverty status; and adult literacy level in accordance to diseases incidence rate. Population-based ratios were then computed for each stratum for the identification of high risk areas. Global Moran's Index spatial autocorrelation technique was additionally used to investigate the extent to which neighbouring values of incidence rate are correlated and to determine meningitis demographic risk factors. A significant spatial clustering of meningitis incidence rate has been found in northern Nigeria, with less or no clustering in the southern part of the country. A significant correlation of disease, with geographical location and poverty was perceived. The study commends that improving the socioeconomic status of the affected population may reduce the incidents of meningitis in the high risk area in Nigeria.

Background: Descriptive analysis of meningitis outbreak in Jaman North districts of Brong Ahafo Region.Design: Descriptive secondary data analysisData Source: records of meningitis cases were extracted from case-based forms and line list.Main outcome measure: The source and pattern of outbreakResults: A total of 367 suspected cases with 44 confirmed were recorded from Jaman North during the period of January to March 2016. The mean age of those affected was 58 ± 13years. The case fatality rate was 0.82% and the proportion of males to females was 1:1.3 (160/207). The age group most affected was 15-29 years (54.7%) and the least was 45-49 years (3.0%). Streptococcus pneumoniae formed 77.3% of confirmed cases whilst Neisseria meningitides was 20.5%. Cases with Neisseria meningitides came from a border town in La Côte d’Ivoire.Conclusion: A protracted propagated meningitis outbreak occurred; and the predominant bacteria strain among confirmed cases was Streptococcus pneumoniae. Cases we...

Sufficient production of maize in Kenya is synonymous to food security and a source of income. Majority of the households in the country grow maize as the main staple food and forms the diet of over 85 percent of the population. Climate change potentially compromises maize production as 98 percent of agriculture is rainfed, threatening food security and rural livelihoods. This study sought to understand the effects of the changing temperature and rainfall patterns in Kenya on maize output. The study adopted Autoregressive distributed lag econometric modeling approach using data for the period between 1970 and 2014. The findings shows mixed response of maize output to rainfall and temperature changes depending on the period, with temperature variability having negative effects. In absence of climate change adaptation and mitigation, Kenya will become more food insecure. There is need to formulate all-inclusive policies paramount in building adaptation and mitigation mechanisms.

Farmers face gender differentiated unique social and economic circumstances which may in part determine their time allocation behavior. Using primary survey data from Murang'a County, Kenya, this study employs the double hurdle and Tobit models to investigate gendered patterns of labor allocation in avocado production under contract and non-contract scenarios, non-farm activities as well as the intensity of time use. Results show that while avocado commercialization through contract farming has to some extent altered traditional gender roles in farming, there is still limited participation of women in avocado marketing under contract farming. Hence, interventions aimed at enhancing smallholder avocado production should incorporate mechanisms that will enable women participate at all levels of the avocado value chain.

Poverty remains one of the key development goals for developing countries. Achieving this goals may be far-fetched given the increasing vagaries from climate change. This paper sought to estimate the effect of climate and weather variability on chronic poverty using household panel data for Kenya. Using Chamberlain random effects probit model with control function, the paper found that weather variability reduces the likelihood of a household falling into chronic poverty. Household heads with post-secondary education were less likely to fall into chronic poverty and those who earned off-farm income and had access to credit facilities had lower probability of falling into poverty as compared to those who did not. The findings suggests the need to devise policies on climate mitigation in order to cushion households from devastating effects of weather variability and to create awareness through dissemination of climate information in easy and widely accessible formats.

There exist different methods and definition how to measure poverty. It can be measured by income or consumption, objectively or as a perception of individuals for their socioeconomic situation.The objective and subjective poverty are highly correlated and both are influenced by socioeconomic factors. Till now, it is the objective poverty mostly considered while the subjective one has been analyzed only as a part of it. Assessments of the subjective poverty are more scarce, especially in the case of transition countries. Different studies proposed advantages and disadvantages of using each method. The significance of different factors shows the characteristics and the trend of monetary or subjective poverty. The national poverty is Albania is calculated through monetary poverty. There are a set of influenced factors related with household composition, geographic division, education and other socioeconomic indicators. In this paper we analyze the perception of individuals for the poverty, the relationship of this perception with the objective poverty, and the socio-demographic factors that influence the probability of being poor. There are used the data from the Albanian Living Standard Measurement Survey (LSMS).

Background: Given the escalating morbidity and mortality recorded annually due to meningitis outbreak in Nigeria, it is fast becoming a disease of public health concern. Objectives: This study described and discussed preventive strategies based on the disease pattern to generate information on the concept of prompt, proactive, and preventive integrated strategies (PPPIS). Methods: The study was a narrative overview of relevant literatures obtained through hand search, databases, and authoritative materials. Results: The highest suspected cases of meningitis 15733 was recorded in 1996 with 2411 (15.3%) deaths, and in 2009 when 54766 suspected cases and 2449 (4.5%) deaths were recorded. Meningitis was reported in five states of Northern Nigeria with 1407-suspected cases and 211 deaths between January 2016 and March 2017. This figure rose to 13, 420 suspected cases and 1, 069 deaths in 23 states with the five initial states inclusive. The fatality ratio was 8% in May 2017. Conclusion: Meningitis has become an increasing contributor to disease burden with high mortality, morbidity, and economic consequences. Adoption of prompt, proactive, and preventive strategies among other integrated approaches could stem and reverse the trend.

La ventilación natural en invernaderos produce altas tasas de intercambio de aire; sin embargo, este comportamiento se producen cerca de las ventanas, lo que ocaciona un bajo intercambio de aire en la zona central del invernadero, debido a un efecto de estancamiento que reduce la distribución del viento en todo el invernadero. La predicción de los gradientes en un invernadero con ventilación natural es difícil de lograr, debido a la naturaleza inherentemente estocástica del flujo de aire. Las Redes bayesianas son técnicas numéricas de incertidumbre que se pueden utilizar para estudiar este problema. Se obtuvo un conjunto de datos experimentales: temperatura del aire, humedad del aire, velocidad del viento, y concentración de CO 2 a uno y tres metros sobre el suelo, en el espacio de cultivo. El conjunto de datos fue discretizado y utilizado para desarrollar un modelo de Red Bayesiana que describe las relaciones entre las variables estudiadas. El modelo muestra las diferencias que nos permitan identificar el grado de dependencia de las variables, así como cuantificar su inferencia.

The following work describes the control method developed on a greenhouse destined to the cultivation of 4 different varieties of chrysanthemums. There is also included a detail report from the control method applied to each one of the weather variables, such as temperature, relative humidity, luminosity and the measurement of CO 2 , all of which affect the growing process of the flowers inside the greenhouse.

The following work describes the control method developed on a greenhouse destined to the cultivation of 4 different varieties of chrysanthemums. There is also included a detail report from the control method applied to each one of the weather variables, such as temperature, relative humidity, luminosity and the measurement of CO 2 , all of which affect the growing process of the flowers inside the greenhouse.

Meningococcal infection has been recognized as a serious problem for almost 200 years. Cerebrospinal or Meningococcal meningitis (MCM) can occur as a sporadic case, institution based focal outbreak, or a large epidemic year. The distribution of serogroups causing meningococcal disease (A, B, C, Y, W-135) varies over time and by geographic location and spatial distribution of the disease indicate close linkage of it and weather climatologies. The highest burden is in sub-Saharan Africa, where it occurs in a cyclic mode. The paper while drawing facts on the disease history, African Meningitis Belt, causative agents, disease statistics, risk factors, and overall etiology, compares the information on cases and deaths of MCM from various literature reports, noted the linkage between regional climate variability/ seasonal variation and MCM. Efficient management of the risks of cerebrospinal meningitis requires a multi-sectoral approach, which brings information about how climate interact with the various elements of society, environment and economy. Strong partnerships and high-quality data are needed from all sectors involved. Climate services, that include climate advisories, predictions, warnings and alerts, could also help societies take advantage of the opportunities associated with climate events.