Patterns of production and sustainability of cattle ranching in the state of Pará - Brazilian Amazon

The aim of this study was to analyze the production patterns present in rural properties producing cattle in the micro-regions that make up the state of Pará. Exploratory Factor Analysis (EFA) was applied to identify the patterns, and these data are used to evaluate correlation between the heterogeneity of rural properties and the environmental impact on the identified patterns. The theoretical contribution is based on discussions on global impacts of food production and environmental sustainability and the impacts of livestock production systems in Brazil and the Amazon. Survey data were taken from the 2017 Agricultural Census, available for the 144 municipalities in the state, and pooled into 22 micro-regions. Three patterns of rural properties were identified: the first related to conservation management practices and called transition management; the second highlights aspects associated with information technology and communication (ICT) and productivity called technical productive efficiency; the third indicates the importance of social organization and access to information called social participation. With these patterns, it was possible to develop the Traditional Performance Indicator (TPI), in which the micro-regions of São Félix do Xingu, Itaituba and Conceição do Araguaia were those with the highest values of this indicator, water protection practices are present in the properties, however, in precarious conditions, and conservation practices are rarely used. The correlation between heterogeneity, measured by the size of pasture area in each microregion, and the TPI is positive, strong and significant. In this transition context, public policies are essential to provide access to infrastructure, credit and good animal health and biotechnology practices

The Key Factors of the Industrial Revolution 4.0 in the Malaysian Smart Manufacturing Context

IR 4.0 is a new phase for the current trend of automation and data exchange in manufacturing industry that focuses on cloud computing, interconnectivity, the Internet of Things, machine learning, cyber physical learning and creating smart factory. The purpose of this article was to unveil the key factors of the IR 4.0 in Malaysian smart manufacturing context. Two key data collection methods were used: (1) primary data from the face-to-face interview (2) secondary data from the previous study. Significantly, five key factors of IR 4.0 consider for this study. Autonomous production lines, smart manufacturing practices, data challenge, process flexibility, and security. As a result, IR 4.0 for quality management practices might get high impact for the best performance assessment, which addressed in various ways; there are few studies in this area have been conducted in Malaysian manufacturing sector, and to recommend the best practices implemented from the managers’ perspectives. For scholars, this enhances their understanding and highlight opportunities for further research.