Data on the factors driving the decision of rural people to move into the city

The data set explores the driving factors of migration of rural people to the cities. Primary data were collected purposively from 172 farm households from three upazilas of kishoreganj district in Bangladesh. Among 172 households, 89 households had at least one migrant member and 83 households were without any migrant member. Probit model was used to analyze factors of migration decision at the household level. Data set reveals that various factors motivate the decision of the farm households for their member to move into the city. Among which household head age, number of active male member in the family and value of the household asset holding significantly influence migration decision. Beside econometric analysis, household's perception on different motivating factors of migration was also assessed. Most of the households perceived that too many family members, poor living condition, migrant's family influence and job availability in the city mostly motivate the people for migration into the city along with other driving factors.

The corporate social responsibility challenge on financial performance: Portuguese business situation

According to the definition that the European Commission (2001) offered us, corporate social responsibility is a set of measures that an organization can adapt, to improve the well-being of its stakeholders and promote the balance of economic, social, and environmental performance. The stakeholder theory clarifies that this decision-making must take into consideration all groups of people, being the only way of having a proportional benefit. Based on this premise, in this study, we intend to unravel the main determinants of corporate social responsibility in 63 Portuguese companies. To accomplish that, we used panel data, withdrawn from the SABI database, in the period comprehended between 2008 and 2018, applying the Probit model and the dynamic GMM for robustness check. The sample is composed of 42 companies that find themselves listed in the Portuguese stock exchange and 21 that do not. We can verify that the outcomes allow the analyses of nine variables, among them, only the audit, the asset rotation ratio, the debt ratio, the size, age, and financial autonomy show themselves as statistically positive in the implementation of corporate social responsibility practices. Even though the outcomes were not what we initially predicted, they revealed the positive impact of such practices. We consider them necessary and a reality that companies should pursue, even for the gains of capital that it might represent. We firmly believe that being socially responsible constitutes an essential practice from which all of us can benefit, contributing to life improvement in the community and for environmental sustainability.

A data fusion approach of police-hospital linked data to examine injury severity of motor vehicle crashes

Injury severity studies typically rely on police-reported crash data to examine risk factors associated with traffic injuries. The police crash database includes essential information on roadways, crashes and driver-vehicle characteristics but may not contain accurate and sufficient information on traffic injuries. Despite sizable efforts on injury severity modelling, very few studies have employed hospital records to classify injury severities accurately. As such, the inferences drawn from the police-recorded injury severity classifications may be questionable. This study investigates factors affecting road traffic injuries of motor vehicle crashes in two approaches (1) police-reported injury severity data and (2) a data fusion approach linking police and hospital records. Data from 2015 to 2019 were collected from the Abu Dhabi Traffic Police Department and linked with hospital records by the Department of Health, Abu Dhabi. A total of 6,333 casualty crashes were categorised into non-severe, severe, and fatal crashes following police-reported data and non-hospitalised, hospitalised and fatal crashes based on the police-hospital linked data. The state-of-the-art random thresholds random parameters hierarchical ordered Probit models were then employed to examine the differences in factors affecting crash-injury severities between police-reported and police-hospital linked data. While there are similarities between these two approaches, there are numerous notable differences in injury severity factors. For instance, head-on collisions are associated with high crash-injury severities in the model with police-hospital linked data, but they tend to show low injury severities in the model with police-reported data. In addition, the police-reported approach identifies that crashes occurred in remote areas and angle collisions are associated with low injury severities, which is not intuitive. These findings highlight that modelling the misclassified injury severity in police crash data may lead to wrong estimations and misleading inferences. Instead, the data fusion approach of police-hospital linked data provides critical and accurate insights into road traffic injuries and is a valuable approach for understanding traffic injuries.

How does urban violence impact choices of cultural participation? The case of the Maré favela complex in Rio de Janeiro

The impact of urban violence on society has been the subject of several studies, but the consequences of fear for habits of cultural consumption are missing in cultural economics research. This article investigates whether the fear of urban violence explains individuals' choice between different options of cultural participation with a particular focus on the activities of watching movies and listening to music. Based on individual data from a survey conducted in 2019 with 1211 residents from a conglomeration of sixteen favelas (slums) located in the Maré neighbourhood in Rio de Janeiro (Brazil), this study employs Simultaneous Bivariate Ordered Probit Models to verify the association between individuals' fear of violence and their choice of consuming culture in private or public spaces. Controlling for socioeconomic, demographic, and territorial variables, the findings indicate that consuming culture in private spaces is a substitute for public spaces when individuals are more afraid of violence. The results presented in this work provide evidence for the design and implementation of policies targeting territories impacted by high levels of violence.

Willingness to pay to enhance pandemic preparedness in Mauritius

OBJECTIVES

The objective of this study is to assess the determinants of willingness to pay to enhance pandemic preparedness in Mauritius.

STUDY DESIGN

A contingent valuation method is used to estimate willingness to pay to pay for enhancing pandemic preparedness using a sample of working people in Mauritius.

METHODS

A two-phase decision process analysis is carried out to model the willingness to pay to enhance pandemic preparedness. The first phase is to analyse the respondents' decision of whether or not to pay for enhancing pandemic preparedness using a Probit model. The second phase is to estimate the determinants of the amount of money respondents are willing to pay using a Tobit model.

RESULTS

Income earners are willing to pay an average of Rs. 1,900 (approximately USD 50) per annum to enhance pandemic preparedness. 'Perceived Response Efficacy', 'Awareness of the Need and Responsibility for Paying', 'Subjective Obligation to Pay' and the 'Theory of Planned Behaviour' are found to affect both stages of of the decision-making process. Knowledge on COVID-19 is found to have a positive impact on the decision to pay and health responsibility attitude is found to have a negative impact on the amount people are willing to pay.

CONCLUSIONS

On average, the government can potentially expect to mobilise an additional Rs. 1,047,470,000 (USD 27,565,000) from taxpayers to spend on enhancing pandemic preparedness in Mauritius. To increase willingness to pay for enhanced pandemic preparedness, the government can focus on improving knowledge on a pandemic, perceived response efficacy and awareness on need and responsibility of paying.

Synchronized agricultural credit and diversification adoption to catastrophic risk manage for wheat production in Punjab, Pakistan

In developing countries, particularly in agrarian economies like Pakistan, agricultural production is severely affected by successive incidences of adverse catastrophic risks. Inadequate and limited risk management measures from farming community to overcome such severe financial, production, and marketing risks not enough so need of adoption farm-based feasible risk management strategies. In addressing this research gap, this study investigated the potential association and implementing synchronized agricultural credit and diversification adoption to catastrophic risk manage for wheat production in Punjab, Pakistan. The study used the data of 480 wheat farmers' respondents and employed multinomial and bivariate probit regression models for empirical analysis. Empirical estimates illustrated the significant association in adoption of multiple risk management strategies as addressing the adoption single risk management strategy motivates farmers to adopt another strategy at same time. Furthermore, results also indicated as farmer schooling, age, family return, ownership of land, farm size, and farmer risk oppose character were highlighted the most influenced features for adopting various risk management strategies. Policy makers and state-based authorities can be assisted by these outcomes to evaluating plans of risk management and willingness of farmer in accept government supported regarding implementation of these catastrophic risk managing strategies.

The impacts of carbon emissions and voluntary carbon disclosure on firm value

Investors and other stakeholders are starting to pay attention to firms' carbon emissions and carbon disclosure. This study investigated the effects of voluntary carbon disclosure information and carbon emissions on firm value from listed companies in the Shanghai and Shenzhen 300 (CSI 300) Index. We also apply the Probit model to predict the probability of voluntary carbon disclosure information. The results indicate that the increase in carbon emissions has a negative impact on firm value. The action that companies select to disclose carbon emissions has a positive impact on firm value. The effect of leverage ratio on VCDI is increasing year by year. What is more, the probability of the average size firm carbon disclosure was 30.73% in 2020. Company management needs to pay attention to the risks caused by carbon emissions and ensure the quality of carbon disclosure information, especially the authenticity and reliability of the information.

Agricultural factor endowment differences and relative poverty nexus: an analysis of macroeconomic and social determinants

Poverty is a significant global ongoing issue that influences a substantial amount of people despite all efforts to eliminate or lessen it. Although poverty is an eminent problem because of its negative consequences, many people are unaware of the concept of poverty. Poverty not only degrades the human capacity but also undermines economic growth in developing regions. This research aims at exploring the impact and heterogeneity of agricultural factor endowment investment on rural relative poverty. By using the data obtained from China household tracking survey (CFPS-2018), probit model approach was applied to analyze the relationship between agricultural factor endowment investment and rural relative poverty. Finally, the intermediary effect of the selected model was validated through the robustness test. The study findings showed that the input of agricultural factor endowment can alleviate the relative poverty in rural areas of China. Our study findings also revealed that there is a significant heterogeneity in family size, endowment type of agricultural technology elements and the agricultural type. A positive association between the investment in agricultural technology and land factor endowment with relative poverty alleviation by increasing the total value of cash and deposits was revealed from the results. The overall findings of this study provide useful insights to facilitate government institutions to stabilize the agricultural labor force, accelerate the process of agricultural modernization and provide careful consideration to land property rights.

Action on Invasive Species: Control Strategies of Parthenium hysterophorus L. on Smallholder Farms in Kenya

Parthenium hysterophorus L. (Asteraceae) is an invasive alien weed with detrimental effects on agricultural production, biodiversity, human and animal health, threating rural livelihoods in Asia and Africa. The problem emerged recently in the Kenyan Rift Valley, where it began to affect the landholdings of both agro-pastoralists and crop farmers. These vulnerable smallholders depend heavily on natural resources for their livelihoods. In this study, we assessed the severity of parthenium invasion and farmers' management responses using a sample of 530 agro-pastoralists in Baringo County, Kenya, in 2019. We hypothesise that the implementation of existing management strategies depends on the state of parthenium invasion and household socio-economic characteristics. The prevalence and severity of parthenium invasion differed greatly among field plots. To control weeds, farmers resort to either hand weeding, the use of synthetic herbicides, or intensive tillage, sometimes in combination with mulching. A multivariate probit regression model shows that households' characteristics determine the type of control strategies used as well as their complementarity and substitutability. Hand weeding is the most common option, adopted by almost 40% of farmers. The use of agrochemicals or soil-based control strategies appears to be related to knowledge and information characteristics such as access to extension services, membership in organisations and the educational level of household heads. While hand weeding and the use of synthetic herbicides depict significant substitutability, the latter strategy is limited to a few larger farms with market-oriented production. As parthenium invasion continues, policies need to improve farmer awareness and access to knowledge to enable pro-poor and environmentally sustainable control of parthenium on smallholder farms.

EU-28's progress toward the 2020 renewable energy share: a club convergence analysis

This paper assesses the convergence of the EU-28 countries toward their common goal of 20% in the renewable energy share indicator by year 2020. The potential presence of clubs of convergence toward different steady-state equilibria is also analyzed from both the standpoints of global convergence to the 20% goal and specific convergence to the various targets assigned to Member States. Two clubs of convergence are detected in the former case, each corresponding to different renewable energy source targets. A probit model is also fitted with the aim of better understanding the determinants of club membership, which seemingly include real GDP per capita, expenditure on environmental protection, energy dependence, and nuclear capacity, with all of them having statistically significant effects. Finally, convergence is also analyzed separately for the transport, heating and cooling, and electricity sectors.

Traffic accident severity analysis in Barcelona using a binary probit and CHAID tree

Traffic accidents are still wide causation for fatalities around the globe. The set of alarm for this cause of deaths is still on, since the number of fatalities is still representing an enormous issue and a challenge for most governments. In Barcelona, similar to the rest of the world, traffic accidents are threatening lives and raising the need to lessen the number of both fatalities and severities. This study is conducted to grasp the correlations between different classification factors with accident severities and fatalities. A total of 47,153 traffic accident cases that occurred between 2016 and 2019 are utilized. Then, a binary probit model and Chi-square automatic interaction detector are exploited to grasp the impact of several risk factors. The results confirmed that males and 65 years and older injured persons are more exposed to severe or fatal injuries compared to other categories. Pedestrians and drivers are found to have higher probabilities compared to passengers in being involved in severe or fatal injuries. Weekends, afternoon, night timings all have higher odds of having severe or fatal traffic accidents. The findings of this study can help road authorities in targeting these risk factors to mitigate their impact to achieve Vision Zero.

Simultaneous adoption of diversification and agricultural credit to manage catastrophic risk for maize production in Bangladesh

Risk management strategies are made up of response variability that may lessen the likeliness of an unfavorable event occurring and/or decrease the damaging consequences if that event happens. To minimize the risk, farmers respond in three ways which are considered to be risk managing tools; these are production, marketing, and financial responses. This study aimed to look at the potential associations and effects of implementing multiple risk management techniques at the same time, as very limited number of research is available in this field. Stratified random sampling technique was used to collect 350 farmer's responses from four different agro-ecological districts of Bangladesh. This study applied bivariate and multinomial probit models to investigate the effects of social and farm features, farmers' views of disastrous risk, and their attitudes toward risk sources, as well as potential associations between diversification and agricultural credit as a risk management tool. The outcomes identified the correlation between adoptions of multiple risk management strategies and concluded that one risk management approach can convince farmers to follow another at the same time. Moreover, the outcomes observed that age, educational status, farm size, family returns, land ownership, and risk-opposed character of farmers were the most persuading aspects for adopting different risk management strategies. Besides, the results presented other interpretations and information which will clarify farmer's actions when it comes to handling various devastating risks and will also provide the policymakers with a platform to prepare for appropriate risk managing plans concerning farmers.

Factors influencing adoption of integrated soil fertility management technologies by smallholder farmers in Ghana

This study examines smallholder farmers’ adoption of both a full set of and multiple bundles of integrated soil fertility management technologies, and estimates the determinants of and assesses the relationship among adoption practices using the logistic model and multivariate probit model respectively. A cross-sectional survey was used to collect data from 300 smallholder farmers who benefitted from a sustainable food security and environmental health project in three districts in Ghana. Four ISFM technologies (zero or minimal tillage, inorganic fertilizer, leguminous crop, and crop rotation) serve as outcome variables. The result revealed that only 26.7% of the respondents adopted the full set of the ISFM technologies. Agroecological zone, a spatial variable has been found to significantly influence smallholder farmers adoption of the full ISFM technologies. One or more of the predictor variables, purpose of farming, land ownership, distance from house to the nearest input shop, access credit and agroecological zone, have been found to significantly influence the adoption of the multiple ISFM technologies. However, the computed correlation coefficients of the independent determinants show inconsistent significant values. The implication of this finding is that the adoption of multiple ISFM technologies cannot be estimated utilizing common determinants. Therefore, extension service in the region should focus on crucial factors that influence adoption of specific multiple ISFM technologies to maximize adoption options.

An evaluation of influencing factors and public attitudes for the adoption of biogas system in rural communities to overcome energy crisis: A case study of Pakistan

Energy is the backbone of a country's economy and development. The potential contribution of renewable energy technology to energy stability, climate change mitigation and economic growth is immense. Biogas, is a renewable energy resource and enriched with methane, produced through the breakdown of organic matters (anaerobically). The large quantity of livestock has significant contributions in Pakistan's economy, and also having a high potential for production of biogas. Therefore, this study was conducted in 6 southern districts of Khyber Pakhtunkhwa province of Pakistan, to identify the expected willingness of households for adopting any biogas system. A sum of 360 households (livestock-farmers) was selected for data collection by the procedure of equal allocation. The identification of the influencing factors on the household's willingness to adopt any biogas system was determined through probit analysis. It has been validated from the research outcomes of probit analysis that the selected household's qualification, electricity shortfall on daily basis and its impact on children's education and female work, awareness of the selected household's about the biogas utilization and its benefits as well as the availability of space have demonstrated their significance and relationship with the household's willingness to adopt biogas system. The overall model is statistically significant at 1% significance level and confirmed the impact of socioeconomic features as the prominent factors for the household's decision to adopt a biogas system. This study suggests the public awareness, which has more significant impact on identifying the household's adoption behavior. Similarly, adequate investments both at the private and public level, should be encouraged for promoting biogas technology. At the same time, the government's fiscal policy should be subsidized which will encourage the lower-income populations' participation in adopting and installing biogas plants.

The prevalence and determinants of unmet health care needs in Turkey

This study aims to examine the prevalence and determinants of unmet health care needs in Turkey. The nationally representative "Health Survey" data for the latest available year, 2016, is used in the analysis. The reasons for unmet health care needs are divided into three categories which are waiting time, affordability (cost), and the distance to health facilities. The probit methodology is employed to investigate the factors affecting unmet health care needs. Approximately 25% of participants (4392 of 17242) reported an unmet need for health care. Overall, females, individuals in lower income groups, those with chronic illness, those with lower education levels and those in younger age groups are more likely to report unmet health care needs. Estimation results point out that age and having a chronic illness have more influence on the reporting of unmet health care needs as compared to other variables included in the model. Therefore, it seems necessary to reorganize the health care system in Turkey to target the needs of individuals with chronic diseases.

COVID-19 implications on household income and food security in Kenya and Uganda: Findings from a rapid assessment

This study assessed implications of the Coronavirus Disease 19 (COVID-19) pandemic on household income and food security in two East African countries - Kenya and Uganda, using online survey data from 442 respondents. Results show that more than two-thirds of the respondents experienced income shocks due to the COVID-19 crisis. Food security and dietary quality worsened, as measured by the food insecurity experience scale and the frequency of consumption of nutritionally-rich foods. The proportion of food insecure respondents increased by 38% and 44% in Kenya and Uganda respectively, and in both countries, the regular consumption of fruits decreased by about 30% during the COVID-19 pandemic, compared to a normal period (before the pandemic). Results from probit regressions show that the income-poor households and those dependent on labour income were more vulnerable to income shock, and had poorer food consumption during the COVID-19 pandemic compared to other respondent categories. As such, they were more likely to employ food-based coping strategies compared to those pursuing alternative livelihoods, who generally relied on savings. Farmers were less likely to experience worsened food security compared to other respondent categories who depended to a great extent on market sources for food. In both countries, participation in national social security schemes was less likely to mitigate respondents' income shock during the COVID-19 period. Conversely, membership in savings and loan groups was correlated with less likelihood of suffering income shocks and reduction in food consumption. The results suggest that ongoing and future government responses should focus on structural changes in social security by developing responsive packages to cushion members pushed into poverty by such pandemics while building strong financial institutions to support the recovery of businesses in the medium term, and ensuring the resilience of food supply chains particularly those making available nutrient-dense foods.

Integration of Survival and Binary Data for Variable Selection and Prediction: A Bayesian Approach

We consider the problem where the data consist of a survival time and a binary outcome measurement for each individual, as well as corresponding predictors. The goal is to select the common set of predictors which affect both the responses, and not just only one of them. In addition, we develop a survival prediction model based on data integration. This article is motivated by the Cancer Genomic Atlas (TCGA) databank, which is currently the largest genomics and transcriptomics database. The data contain cancer survival information along with cancer stages for each patient. Furthermore, it contains Reverse-phase Protein Array (RPPA) measurements for each individual, which are the predictors associated with these responses. The biological motivation is to identify the major actionable proteins associated with both survival outcomes and cancer stages. We develop a Bayesian hierarchical model to jointly model the survival time and the classification of the cancer stages. Moreover, to deal with the high dimensionality of the RPPA measurements, we use a shrinkage prior to identify significant proteins. Simulations and TCGA data analysis show that the joint integrated modeling approach improves survival prediction.

Modelling two-vehicle crash severity by generalized estimating equations

The crash severity levels of two parties involved in a two-vehicle accident may differ markedly and may be correlated. Separately estimating the severity levels of two parties ignoring their potential correlation may lead to biased estimation; however, modelling their severity levels simultaneously by using a bivariate modelling approach requires a complex model setting. Thus, this study used generalized estimating equations (GEE) to accommodate potential correlations when estimating the crash severity levels of two parties. To investigate the performance of the GEE models, a case study on a total of 2493 crashes at 214 signalized intersections in Taipei City in 2013 is conducted. Univariate ordered probit model, bivariate ordered probit model, and GEE ordered probit model (GEE-OP) with different working matrices are respectively estimated and compared. The estimation results of GEE models showed that the GEE-OP with the exchangeable working matrix performs best and the most influential factor contributing to crash severity is vehicle type (motorcycle), followed by speeding, angle impact, and alcoholic use. Thus, to curtail motorcycle usage by increasing parking fee or reducing parking space of motorcycles, to crack down on speeding and alcoholic use, and to redesign the signal timings to avoid possible angle impact accidents are identified as key countermeasures.

Flood hazards and factors influencing household flood perception and mitigation strategies in Pakistan

Pakistan is ranked among the most climate change adversely affected countries of the world due to facing higher frequency of extreme natural hazards events as floods, drought, and earthquakes. Inadequate mitigation measures regarding severity and consecutive events of floods have badly affected millions of households' livelihoods. Pakistan has experienced five consecutive floods in the current decade from 2010 to 2015. These floods severely affected the mostly districts of Punjab province among these three most flood-affected districts Rahim Yar Khan, Muzaffargarh, and Rajanpur were selected for this research work. This study used the cross-sectional data of 840 flood-affected households from these three districts. Empirical estimates indicated the study area households major adaptation measures were foundation strengthen, elevated ground floor, precautionary saving, and construction of the house with reinforced material. Probit model estimates signified age, gender, location, and family size; monthly income, physical disability, education, and house ownership were major factors influencing mitigation strategies of households' level. Limited resources, inadequate planning for land use, lack of advanced and early warning system, and inadequate sound financial status were major obstacles regarding households' level adaptation of mitigation strategies. In addressing household level obstacles regarding advanced adaptation measures, development requires for local disaster policies such as ample spatial planning, codes of house building, practices of building infrastructure, propagation and forecasting updated flood information, and advanced and updated early warning system.

Evaluation of the bioaccumulation kinetics of toxic metals in fish (A. brasiliensis) and its application on monitoring of coastal ecosystems

This study proposes a pro-active approach for evaluations of methylmercury (MeHg), total mercury (THg), arsenic (As), cadmium (Cd) and lead (Pb) in situ bioaccumulation in fish (Atherinella brasiliensis) muscles, using specimens from the external sector of Guanabara Bay as a study case. This approach included an hierarchical sequence: analysis of the pollutants concentrations and their comparison to safety criteria; correlations between specimens concentrations vs length (as a proxy of exposure time); projections of concentrations in key lengths (sexual maturation, asymptotic, length limits for fishing and median of fish population) through polynomial regressions, dose-response analysis (Probit), decreasing curves and incorporation rates (using only three length intervals). The incorporation rates were ascending for MeHg and THg (continued bioaccumulation) and descending for As, Pb and Cd (possible biological dilution). The projections were satisfactory, evidencing their use for an improvement on the risks monitoring of fishing and fish consumption by humans in coastal environments.

Comparing six mathematical link function models of the antifeedant activity of lesser grain borer exposed to sub-lethal concentrations of some extracts from calotropis procera

In the present study, Probit, Cauchy Fractional and three types of Log methods, i.e., Logit, Log-log, and Complementary log-log were employed to model the feeding deterrence of the lesser grain borer, Rhyzopertha dominica (F) (Coleoptera: Bostrichidae), when fed latex protein, crude flavonoid fraction, 3-O-rutinosides of quercetin, kaempferol and isorhamnetin, isolated from Calotropis procera (Ait.) (Gentianales: Asclepiadaceae). A nutritional study with treated flour discs at sub-lethal concentrations indicated that the tested natural products negatively affected the feeding behavior of the lesser grain borer, causing high feeding deterrent indices. Our results assure that Probit, Logit and Clog-log model the feeding deterrent indices with high goodness of fit. The models aim to support the management of the test insect when fed grains treated with sub-lethal doses of the tested phytochemicals in order to develop a viable, precise and long-term strategy to minimize the excessive reliance on the chemical pesticides currently in use.

Seasonal differences in the subjective assessment of outdoor thermal conditions and the impact of analysis techniques on the obtained results

Wide research attention has been paid in the last two decades to the thermal comfort conditions of different outdoor and semi-outdoor urban spaces. Field studies were conducted in a wide range of geographical regions in order to investigate the relationship between the thermal sensation of people and thermal comfort indices. Researchers found that the original threshold values of these indices did not describe precisely the actual thermal sensation patterns of subjects, and they reported neutral temperatures that vary among nations and with time of the year. For that reason, thresholds of some objective indices were rescaled and new thermal comfort categories were defined. This research investigates the outdoor thermal perception patterns of Hungarians regarding the Physiologically Equivalent Temperature (PET) index, based on more than 5800 questionnaires. The surveys were conducted in the city of Szeged on 78 days in spring, summer, and autumn. Various, frequently applied analysis approaches (simple descriptive technique, regression analysis, and probit models) were adopted to reveal seasonal differences in the thermal assessment of people. Thermal sensitivity and neutral temperatures were found to be significantly different, especially between summer and the two transient seasons. Challenges of international comparison are also emphasized, since the results prove that neutral temperatures obtained through different analysis techniques may be considerably different. The outcomes of this study underline the importance of the development of standard measurement and analysis methodologies in order to make future studies comprehensible, hereby facilitating the broadening of the common scientific knowledge about outdoor thermal comfort.

Heritability of hypothyroidism in the Finnish Hovawart population

BACKGROUND

The Hovawart is a working and companion dog breed of German origin. A few hundred Hovawart dogs are registered annually in Finland. The most common disease with a proposed genetic background in Hovawarts is hypothyroidism. The disease is usually caused by lymphocytic thyroiditis, an autoimmune disorder which destroys the thyroid gland. Hypothyroidism can be treated medically with hormone replacement. Its overall incidence could also be reduced through selection, provided that the trait shows an adequate genetic basis. The aim of this study was to estimate the heritability of hypothyroidism in the Finnish Hovawart population.

RESULTS

The pedigree data for the study were provided by the Finnish Kennel Club and the hypothyroidism data by the Finnish Hovawart Club. The data included 4953 dogs born between 1990 and 2010, of which 107 had hypothyroidism and 4846 were unaffected. Prior to the estimation of heritability, we studied the effects of gender, birth year, birth month, and inbreeding on susceptibility to hypothyroidism. Heritability was estimated with the probit model both via restricted maximum likelihood (REML) and Gibbs sampling, using litter and sire of the dog as random effects. None of the studied systematic effects or level of inbreeding had a significant effect on susceptibility to hypothyroidism. The estimated heritability of hypothyroidism varied from 0.47 (SE = 0.18) using REML to 0.62 (SD = 0.21) using Gibbs sampling.

CONCLUSIONS

Based on our analysis, the heritability of hypothyroidism is moderate to high, suggesting that its prevalence could be decreased through selection. Thus, breeders should notify the breed association of any affected dogs, and their use for breeding should be avoided.