The Brazilian beef supply chain and food security: a productive inputs view




Production chain; Stages; Production inputs; Livestock; Sustainability.


The alignment of food production systems with the trends and demands of the world population plays an important global role. This study aims to discuss the convergence of trends related to the Brazilian beef cattle supply chain from a food security perspective. Therefore, it includes important reports on the future of this supply chain and its input production, taking on a qualitative approach to consider trends in animal health, genetics, nutrition, forage, and farm machinery in terms of the development of Brazilian agriculture and the future of food and agribusiness. From a managerial point of view, it was possible to provide information capable of leading to a sustainable understanding. Thus, a content analysis of the documents was carried out, coding them through the Sustainable Development Goals and categorizing them by taking into account the 2030 Agenda’s five Ps (people, planet, prosperity, peace, and partnerships). Along this line, the discussion highlights the themes of poverty and climate change, emphasizing them with regard to the categorization social aspects – the P of people). Future trends will require a workforce prepared to deal with the additional limitations that can arise with the use of new technologies as productivity increases.


Bardin, L. (2011). Análise de conteúdo. Edições 70.

Bickhart, D. et al. (2020). Advances in sequencing technology herald a new frontier in cattle genomics and genome-enabled selection. Journal of Dairy Science, 103(6), 5278-5290.

Brazilian Agricultural Research Corporation, Embrapa. (2020). O futuro da cadeia produtiva da carne bovina brasileira: uma visão para 2040. Embrapa Gado de Corte.

Brazilian Institute of Geography and Statistics, IBGE. (2019). Produção da pecuária municipal.

Centro de Estudos Avançados em Economia Aplicada, Cepea. (2021). PIB do agronegócio alcança participação de 26,6% no PIB brasileiro em 2020. 2021. Esalq/USP.

Cuthbertson, H., Tarr, G., & González, L. A. (2019). Methodology for data processing and analysis techniques of infrared video thermography used to measure cattle temperature in real time. Computers and Electronics in Agriculture, 167, 105019.

Dal Moro, L., & Brandli, L. L. (2020). Potentialities and challenges of family agriculture in a region of South Brazil. International Journal of Sustainable Development and World Ecology, 27, 129-139.

Embrapa’s Strategic Intelligence System, Agropensa. (2014). Visão 2014-2034: o futuro do desenvolvimento tecnológico da agricultura brasileira. Brasília: Embrapa.

Ferreira Neto, J. S. F. et al. (2016). Analysis of 15 years of the national program for the control and eradication of animal brucellosis and tuberculosis, Brazil. Semina: Ciências Agrárias, 37(5), 3385-3402.

Food and Agriculture Organization of the United Nations, FAO. (2017a). The future of food and agriculture: trends and challenges. Rome: Food and Agriculture Organization of the United Nations.

Food and Agriculture Organization of the United Nations, FAO. (2017b). The state of food security and nutrition in the world: building resilience for peace and food security. FAO, IFAD, UNICEF, WFP, WHO.

Galanakis, C. M. (2020). The food systems in the era of the coronavirus (Covid-19) pandemic crisis. Foods, 9(4), 523.

Gil, J. D. B., Garrett, R., & Berger, T. (2016). Determinants of crop-livestock integration in Brazil: evidence from the household and regional levels. Land Use Policy, 59, 557-568.

Keesing, F, Allan, B. F., Young, T. P., & Ostfeld, R. S. (2013). Effects of wildlife and cattle on tick abundance in central Kenya. Ecological Applications, 23, 1410-1418.

Kim, H., Min, Y., & Choi, B. (2019). Real-time temperature monitoring for the early detection of mastitis in dairy cattle: methods and case researches. Computers and Electronics in Agriculture, 162, 119-125.

Medialdea, J. et al. (2018). Potential of science to address the hunger issue: ecology, biotechnology, cattle breeding and the large pantry of the sea. Journal of Innovation & Knowledge, 3(2), 82-89.

Organization for Economic Cooperation and Development, OECD. (2019). OECD-FAO Agricultural outlook 2018-2027.

Pivoto, D., Waquil, P. D., Talamini, E., Finocchio, C. P. S., Dalla Corte, V. F., & Mores, G. V. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, 5(1), 21-32.

Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58-73.

United Nations, UN. (2021). The 17 Sustainable Development Goals.

Veloso, G. A. et al. (2020). Modelling gross primary productivity in tropical savanna pasturelands for livestock intensification in Brazil. Remote Sensing Applications: Society and Environment, 17, 100288.




How to Cite

CASAGRANDA, Y. G.; MORES, G. de V.; CASAROTTO, E. L.; MORO, L. D. .; ABRAHÃO, A. F. S.; MALAFAIA, G. C. . The Brazilian beef supply chain and food security: a productive inputs view . Research, Society and Development, [S. l.], v. 10, n. 13, p. e260101320895, 2021. DOI: 10.33448/rsd-v10i13.20895. Disponível em: Acesso em: 3 dec. 2021.



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