Work in agriculture is a significant area of research that highlights the problem of the integration of young people in the former, in particular, in the recent period. Work in agriculture is hard and not prestigious, and young people tend to leave rural areas in the search for alternative activities in urban areas. The study addresses the problem of how the youth should be integrated into agricultural workforce by focusing on identification of the reasons behind the (un)willingness to work in agriculture. The aim of the study is to assess the reasons behind the youth's (un)willingness of work in agriculture, using Lithuania as the case study. The data were collected by means of a questionnaire designed to investigate the perception and opinions toward work in agriculture. The Binary Logistic Regression was used to identify the factors affecting the youth's opinion about (un)willingness to work in agriculture. The study analyzed 430 young people 's responses to the questionnaires survey. The BLR has revealed that youth's unwillingness to work in agriculture is mostly affected by gender, area of residence and youths' beliefs that work in agriculture does not provide any opportunities for self-realization. In summary, this paper argues that the major motivation to work in agriculture is associated with having parents who are engaged in agricultural activities, love of animals and natural environment, and the availability of specialized training. The findings have confirmed the need to attract young people to work in agriculture. Its results are necessary for the scientific community, policy makers, farmers, and practitioners exploring the possibilities for integration of the youth into the agricultural workforce.
IntroductionAgriculture is one of the most hazardous industries in Europe, measured by work-related injuries, diseases, disabilities and deaths. Statistics and studies show great differences in national injury and occupational disease rates, as well as approaches and support for prevention of these adverse outcomes. This EU-COST action explores reasons why agriculture lags behind other sectors, and why some countries have been more successful than others in reducing agricultural injuries and diseases.MethodsEvaluation of health and safety programmes and approaches on the national levelidentification of knowledge, attitudes, behaviors and priorities among farmers regarding safety, health and risk managementidentification of effective measures for training and integrating vulnerable populations (including refugees) into the agricultural workforcedevelopment of indicators for monitoring progress and evaluating the impact of interventions on occupational injuries and diseases in agriculture.ResultsBenchmarked and evidence-based recommendations will be developed to inform and guide national initiatives and efforts. Results will be disseminated to stakeholders and the agricultural community.DiscussionThis action is in progress; previous Cooperation in Science and Technology (COST) Actions, like Monitoring Occupational Diseases and tracing New and Emerging Risks in a Network (MODERNET) show that such multidisciplinary, international focused exchange programmes are effective to trigger awareness raising and preventive actions.
This study is designed to develop the tool for risk assessment under the integrated approach. Analyzing risk several problems are encountered: the first one arises at the farm level – assessment of risk in the whole-farm context rather than in a partial context, i.e. an integrated risk assessment tool is necessary. The second problem is related to the dynamic aspect when determining how the risk changes over time and what the main drivers of these changes are. All these problems are solved in the presented research, creating an integrated risk assessment index (IRAI) and testing it in Lithuanian family farms. This index assesses four types of risk: economic, financial, production, and political. The research methodology is developed to make sure that the data collected on the IRAI behavior is as diverse as possible. A model of IRAI variation by farm size illustrating risk evolution at the Lithuanian farms and, at the same time, enabling visual diversification of the dependence of integrated risk on farm size is developed. Hierarchical cluster analysis is applied for identification of the integrated risk evolution models. Assessment of the interaction between the IRAIand output and input using nonparametric Kruskal-Wallis testis used to find out whether the type of integrated risk is based on differential logic. IRAI was tested using official statistical data of 1300 family farms collected in 2004–2013 for institutional purposes. The testing revealed that the designed IRAI allows identifying types of farms by their risk evolvement profiles and the key risk (s) acting on the farm in the historical period. Four meaningful clusters representing the changing pattern of the risk are identified during the testing of IRAI: increasing risk farms; reducing risk farms; relatively constant risk farms; varying risk farms. IRAI can be applied both for macro analysis (at a national, EU or other levels) and microanalysis (at the level of a single farm).
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