PurposeThis study aims to present a methodological framework to evaluate the relationship between social network centrality, individual competitiveness and network competitiveness.Design/methodology/approachTilapia fish farmers in the Canoas I hydroelectric dam (states of São Paulo and Paraná, Brazil) were provided with a roster of all actors in their network and interviewed to obtain information on relational and competitiveness variables. UCINET was used to calculate the degree centrality of each farmer. Seven competitiveness drivers were combined into a single indicator to determine the level of competitiveness. A four-quadrant matrix was constructed to investigate the relationship between degree centrality and level of competitiveness.FindingsA positive relationship was found between degree centrality and level of competitiveness.Research limitations/implicationsAgents upstream or downstream of fish farming were not interviewed, precluding an in-depth analysis of competitiveness in terms of market structure and market relations. The authors suggest that future studies should investigate the influence of upstream and downstream agents on the social network and competitiveness of fish farmers. It is also important to monitor changes in the level of competitiveness of fish farmers in the event of a national economic crisis.Originality/valueDevelopment of a novel methodological framework on the basis of two methodologies, social network analysis and competitiveness analysis.
A produção de leite nacional tem sido realizada tipicamente a partir de mão de obra familiar, cumprindo importante função econômica e social. Entretanto, tem se observado o esvaziamento no meio rural, decorrente da baixa sucessão familiar nas atividades agropecuárias. Diante deste problema, buscou-se neste trabalho analisar características sociais do produtor rural, estruturais e produtivas dos Sistemas Produtivos Leiteiros (SPL) em que há maior possibilidade de sucessão familiar. As análises foram feitas a partir da coleta de variáveis estruturais e produtivas em 184 sistemas leiteiros localizados no Paraná, bem como variáveis socioeconômicas de seus gestores – produtores rurais. Além dessas, foi coletada variável indicativa da possibilidade de sucessão familiar para a atividade leiteira. Esta variável foi utilizada para segregar os sistemas leiteiros em dois grupos, G1 – formado por Sistemas Produtivos Leiteiros em que seus gestores declararam que a sucessão familiar irá acontecer e G2 – formado por Sistemas Produtivos Leiteiros em que seus gestores declararam que a sucessão familiar não acontecerá. A maior propensão à sucessão familiar deverá ocorrer em Sistemas Produtivos Leiteiros com maior escala de produção e maior produtividade.
Aquaculture is one of the fastest-growing food sectors in Brazil. Cage fish farming has been widely practiced in the country, mainly in hydroelectric reservoirs. However, different regulatory, technical, and economic challenges may need to be overcome before the sector can achieve increased national and international competitiveness. This study aimed to analyze and compare the competitiveness of tilapia cage farms located on different sides of the Canoas I hydroelectric reservoir, which forms the border between the states of São Paulo and Paraná, Brazil. Structured questionnaires were administered to all fish farmers in the reservoir. Questions about seven competitiveness indicators were rated on a 5-point Likert scale. The results revealed that the major barriers to competitiveness are the institutional environment and environmental sustainability. Paraná has an active environmental inspection service, but São Paulo does not. As a result, tilapia fish farmers in Paraná gave more importance to environmental compliance than those located in São Paulo.
This study aimed to investigate factors associated with grain feeding and determine the typology of dairy farms that use high-grain diets. Twenty-two farm operators were interviewed in three municipalities located in the central-western region of Paraná state, Brazil. Information on reproductive and nutritional management practices, sociodemographic characteristics, and farm performance was collected. Data were analyzed using exploratory factor analysis, hierarchical cluster analysis, and multiple linear regression. Three factors (F1, F2, and F3) were extracted, which together explained 82.61% of the total variance. F1 comprised diet quality, technology, and breeding composition. F2 comprised labor and size. F3 comprised feed quality and schooling. Farms were classified into four groups and compared in terms of factor scores and performance parameters. Group 1 had the highest mean score on F1 (0.715), group 4 on F2 (1.642), and group 2 on F3 (1.116). Groups 4 and 1 had the highest milk productivity (2043.50 and 399.52 L day−1, respectively) and labor efficiency (418.16 and 148.63 L worker−1 day−1, respectively). Group 4 also had the highest mean number of cows per worker (25.52 cows worker−1). Regression analysis revealed that diet quality, technology, and breeding composition (F1) explained the variance in cow productivity. Labor and size, (F2) explained the variance in number of cows per worker. Daily productivity and labor efficiency were explained by both F1 and F2. Feed quality and farm operator’s level of schooling did not explain the variation in any of the variables. We found that roughage quality, breeding technology, and herd breed composition are the major factors associated with grain feeding. Farmers who feed cows high-quality roughage throughout the year and invest in genetic improvement and selective breeding strategies are more likely to adopt high-grain feeding and have high milk productivity.
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