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Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal


Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

Fax: +86 29 82668723 Email:

Sustainable Milk Production: Application of the Hierarchical Analytical Process Towards a Regional Strategic Planning

Journal of Environmental Accounting and Management 4(4) (2016) 385--398 | DOI:10.5890/JEAM.2016.12.003

Max W. Olveira$^{1}$; Feni Agostinho$^{2}$; Cecília M.V.B. Almeida$^{2}$; Biagio F. Giannetti$^{2}$

$^{1}$ Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais, Praça Tiradentes 416, CEP 37576-000, Centro, Inconfidentes, Minas Gerais, Brasil

$^{2}$ Universidade Paulista, Programa de Pós-Graduação em Engenharia de Produção, Laboratório de Produção e Meio Ambiente, Rua Dr. Bacelar, 1212, CEP 04026-002, São Paulo, Brasil

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Milk corresponds to 20% of the Brazilian agribusiness GDP and its production is concentrated at Minas Gerais State. Different milk production systems co-exist in this region, which results in different sustainability performance and call for a multi-criteria analysis to subsidize decisions towards a regional milk production planning. This work uses the Analytical Hierarchical Process (AHP) to hierarchize milk production systems as for their sustainability. A cluster analysis considering 92 milk producers identified five distinct production systems: G1, G2, G3, G4, and G5. Social, economic, and environmental indices are calculated and used in the AHP to prioritize sustainability according to the decision-maker’s egalitarian, or hierarchical, or individualist profiles. Results indicate that system G1 features higher priority degree (higher sustainability) for all three profiles compared to other systems assessed, reaching 27% for the egalitarian, 31% for the hierarchical, and 31% for the individualist profiles. The worst performances were fared by system G4 in the egalitarian (12%), and hierarchical (13%), and by system G3 in the individualist profile (10%). Numbers could be considered by decision makers in order to subsidize a strategic planning towards a sustainable regional milk production, whereby a given system could be politically, technologically, and economically prioritized in detriment of another. Notwithstanding, the objective approach discussed in defining criteria and their weights could be useful in future studies related to a hierarchization of sustainability of any production system in different regions.


The authors wish to thank the Vice-Reitoria de Pós-graduação da Universidade Paulista (UNIP), the Federal Institute of Education, Science, and Technology of South of Minas Gerais (IFSULDEMINAS), the Coordination for the Improvement of Higher Education Personnel (CAPES; PROSUP PhD scholarship granted to the first author), and to CNPq Brazil (proc. no. 307422/2015-1) for the fellowship provided to second author. Thanks, also, to the cattle breeders who provided the authors with information and data that were essential to the development of this work, the Technical Assistance and Rural Outreach Corporation (EMATER) and the Minas Gerais State Institute of Agriculture (IMA) technicians for recommendations on which properties to be visited and assessed, and for validating the obtained results.


  1. [1]  Agostinho, F. and Siche, R. (2014), Hidden costs of a typical embodied energy analysis: Brazilian sugarcane ethanol as case study, Biomass and Bioenergy 71, 69-83.
  2. [2]  Abudeif, A.M., Abdel Moneim, A.A. and Farrag, A.F. (2015), Multicriteria decision analysis based on analytic hierarchy process in GIS environment for siting nuclear power plant in Egypt, Annals of Nuclear Energy 75, 682-692.
  3. [3]  Assis, A.G., Stock, L.A., Campos, O.F., Gomes, A.T., Zoccal, R. and Silva, M.R. (2005), Sistemas de produção de leite no Brasil. Circular Técnica 85. Embrapa Gado de Leite, Juiz de Fora, MG. (Milk production systems in Brazil; Technical Information no.85) Available at Accessed on 15th February 2016.
  4. [4]  Bang, W. and Chang, B. (2013), Quality factor analysis of metalworking process with AHP, International Journal of Production Research 51, 5741-5756.
  5. [5]  BRASIL (2002), Instrução Normativa no 51. Regulamentos Técnicos de Produção, Identidade e Qualidade do Leite tipo A, do Leite tipo B, do Leite tipo C, do Leite Pasteurizado, e do Leite Cru Refrigerado, e o Regulamento Técnico da Coleta de Leite Cru Refrigerado e seu Transporte a Granel. (Brazilian regulatory Instruction n.51 regarding milk production and its quality) Available at 000001051.PDF. Accessed on 22nd January 2016.
  6. [6]  Boukherroub, T., Ruiz, A., Guinet, A. and Fondrevelle, J. (2015), An integrated approach for sustainable supply chain planning, Computers & Operations Research 54, 180-194.
  7. [7]  Brown, M.T. and Ulgiati, S. (2002), Emergy evaluations and environmental loading of electricity production systems, Journal of Cleaner Production 10, 321-334.
  8. [8]  Brown, M.T. and Ulgiati, S. (2004), Emergy analysis and environmental accounting, Encyclopedia of Energy 2, 329-354.
  9. [9]  ESI (2005), Environmental Sustainability Index, 2005. Benchmarking National Environmental Stewardship. Appendix A, Methodology. Available at methodology.pdf. Accessed on 4th December 2015.
  10. [10]  CEPEA (2014), Centro de Estudos Avançados em Economia Aplicada (Center for Advanced Studies in Applied Economics), ESALQ/USP. Available at Accessed on 5th September 2015.
  11. [11]  Chavez, M.D., Berentsen, P.B.M. and Oude Lansink, A.G.J.M. (2012), Assessment of criteria and farming activities for tobacco diversification using the analytical hierarchical process (AHP) technique, Agricultural Systems 111, 53-62.
  12. [12]  Goedkoop, M. and Spriensma, R. (2000), The eco-indictor 99: a damage oriented method for life cycle impact assessment. Methodology Report, Second Edition, Pré-Consultants Netherlands. Available at Accessed on 4th December 2015.
  13. [13]  IBGE (2014), Instituto Brasileiro de Geografia e Estatística (Brazilian Institute of Geography and Statistics). Produção da Pecuária Municipal. Available at Accessed on 12th July 2015.
  14. [14]  Jaklic, T., Juvancic, L., Kavcic, S. and Debeljak, M. (2014), Complementarity of socio-economic and emergy evaluation of agricultural production systems: The case of Slovenian dairy sector, Ecological Economics 107, 469-481.
  15. [15]  Karami, E. (2006), Appropriateness of farmer’s adoption of irrigation methods: the application of the AHP model, Agricultural Systems 87, 101-119.
  16. [16]  Liberatore, M.J. and Nydick, R.L. (2008), The analytic hierarchy process in medical and health care decision making: a literature review, European Journal of Operational Research 189, 194-207.
  17. [17]  Macharis, C. and Bernardini, A. (2015), Reviewing the use of Multi-Criteria Decision Analysis for the evaluation of transport projects: Time for a multi-actor approach, Transport Policy 37, 177-186.
  18. [18]  Nascif, C. (2008), Indicadores técnicos e econêmicos em sistemas de produção de leite de quatro mesorregiões do estado de Minas Gerais (Technical and economic indicators for milk production in Minas Gerais State, Brazil). M.Sc. dissertation, Pos-graduation Program in Animal Sciences, Federal University of Viçosa, Brazil.
  19. [19]  Odum, H.T. (1996), Environmental Accounting: Emergy and Environmental Decision Making. John Wiley and Sons, New York.
  20. [20]  Oliveira, M. and Agostinho, F. (2015), Assessing alternative developments for milk production in the southern region of Minas Gerais State, Brazil: an emergy perspective. In: Brown, M.T. et al. (eds). Emergy Synthesis 8: Theory and Applications of the Emergy Methodology. Proceedings of the 8th Biennial Emergy Conference. Center for Environmental Policy, University of Florida, Gainesville.
  21. [21]  Paracchini, M.L., Bulgheroni, C., Borreani, G., Tabacco, E., Banterle, A., Bertoni, D., Rossi, G., Parolo, G., Origgi, R. and De Paola, C. (2015), A diagnostic system to assess sustainability at a farm level: the SOSTARE model, Agricultural Systems 133, 35-53.
  22. [22]  Reed, B., Chan-Halbrendt, C., Tamang, B.B. and Chaudhary, N. (2014), Analysis of conservation agriculture preferences for researchers, extension agents, and tribal farmers in Nepal using Analytic Hierarchy Process, Agricultural Systems 127, 90-96.
  23. [23]  Russo, R.F.S. and Camanho, R. (2015), Criteria in AHP: a systematic review of literature, Procedia Computer Science 55, 1123-1132.
  24. [24]  Saaty, T.L. (1991), Método de análise hierárquica (Method for hierarchical analysis). São Paulo: McGraw-Hill, 367p.
  25. [25]  Saaty, T.L. (1994), How to Make a Decision: The Analytic Hierarchy Process, Interfaces 24, 19-43.
  26. [26]  Saaty, T.L. (2008), Decision making with the analytic hierarchy process, Services Sciences 1, 83-98.
  27. [27]  Shrestha, R.K., Alavalapati, J.R.R. and Kalmbacher, R.S. (2004), Exploring the potential for silvopasture adoption in south-central Florida: an application of SWOT-AHP method, Agricultural Systems 81, 185-199.
  28. [28]  Tsoutsos, T., Tsitoura, I., Kokologos, D. and Kalaitzakis, K. (2015), Sustainable siting process in large wind farms case study in Crete, Renewable Energy 75, 474-480.
  29. [29]  Tsyganok, V.V., Kadenko, S,V. and Andriichuk, O.V. (2012), Significance of expert competence consideration in group decision making using AHP, International Journal of Production Research 50, 4785-4792.
  30. [30]  Veisi, H., Liaghati, H. and Alipour, A. (2016), Developing an ethics-based approach to indicators of sustainable agriculture using analytic hierarchy process (AHP), Ecological Indicators 60, 644-654.
  31. [31]  Vigne, M., Peyraud, J.L., Lecomte, P., Corson, M.S. and Wilfart, A. (2013), Emergy evaluation of contrasting dairy systems at multiple levels, Journal of Environmental Management 129, 44-53.
  32. [32]  Vinholis, M.M.B., Tupy, O., Souza, G.B., Nogueira, A.R.A. and Primavesi, O. (2006), Avaliação dos impactos econômicos, sociais e ambientais de tecnologias da Embrapa Pecuária Sudeste (Evaluation of economic, social and environmental aspects of technologies promoted by Brazilian Agricultural Research Coorporation). Embrapa, ISSN 1980-6841, São Carlos, SP. Available at Accessed on 11st March 2016.
  33. [33]  Zhang, J., Su, Y., Wu, J. and Liang, H. (2015), GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China, Computers and Electronics in Agriculture 114, 202-211.