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Developing a predictive model for the energy content of goat milk as the basis for a functional unit formulation to be used in the life cycle assessment of dairy goat production systems

Published online by Cambridge University Press:  27 July 2017

P. P. Danieli  and
B. Ronchi
P. P. Danieli*
Affiliation:
Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia, Via S. C. de Lellis, snc, 01100, Viterbo, Italy
B. Ronchi
Affiliation:
Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia, Via S. C. de Lellis, snc, 01100, Viterbo, Italy
*
†E-mail: [email protected]
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Abstract

Recent reports on livestock environmental impact based on life cycle assessment (LCA) did not fully consider the case of the dairy goat. Assignment of an environmental impact (e.g. global warming potential) to a specific product needs to be related to the appropriate ‘unitary amount’ or functional unit (FU). For milk, the energy content may provide a common basis for a definition of the FU. To date, no ad hoc formulations for the FU of goat milk have been proposed. For these reasons, this study aimed to develop and test one or more predictive models (DPMs) for the gross energy (GE) content of goat milk, based on published compositional data, such as fat (F), protein, total solids (TS), solid non-fat matter (SNF), lactose (Lac) and ash. The DPMs were developed, selected and tested using a linear regression approach, as a meta-analysis (i.e. meta-regression) was not applicable. However, in the final stage, a control procedure for spurious findings was carried out using a Monte Carlo permutation test. Because several published predictive models (PPMs) for GE in cow milk and goat milk were found in the literature, they were tested on the same data set with which the DPMs were developed. The best-performing DPMs and PPMs were compared directly with a subset of the individual data retrieved from the literature. Overall, the paucity of direct measurements of the GE in goat milk was a limiting factor in collecting data from the literature; thus, only a small data set (n=26) was established, even though it was considered sufficiently representative of milks from different goat breeds. The three best PPMs based on F alone gave more biased estimates of the GE content of the goat milk than the three new DPMs based on F, F and SNF and F and TS, respectively. Accordingly, three different formulations of FU are proposed, depending on the availability of data including both F and TS (or F and SNF) or F alone. Even though several metrics can be used in defining the FU for milk to be used in LCAs of goat farming systems, the proposed FU formulations should be adopted in place of the similar energy-based ones developed for other dairy species.

Keywords

gross energyenvironmental sustainabilityregression modelMonte Carlo permutation testmodel selection
Type
Research Article
Information
animal , Volume 12 , Issue 2 , February 2018 , pp. 408 - 416
Copyright
© The Animal Consortium 2017 

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References

Ahamed, JU, Saidur, R, Masjuki, HH, Mekhilef, S, Ali, MB and Furqon, MH 2011. An application of energy and exergy analysis inagricultural sector of Malaysia. Energy Policy 39, 7922–7929.CrossRefGoogle Scholar
Brett, DJ, Corbett, JL and Inskip, MW 1972. Estimation of the energy value of ewe milk. Proceeding of the Australian Society of Animal Production 9, 286–291.Google Scholar
Cooper, JS 2003. Specifying Functional Units and reference flows for comparable alternatives. International Journal of Life Cycle Assessment 8, 337–349.CrossRefGoogle Scholar
de Vries, M and de Boer, IJM 2010. Comparing environmental impacts for livestock products: A review of life cycle assessments. Livestock Science 128, 1–11.CrossRefGoogle Scholar
Diez Roux, AV 2002. A glossary for multilevel analysis. Journal of Epidemiology and Community Health 56, 588–594.CrossRefGoogle ScholarPubMed
Finnvedena, G, Hauschild, MZ, Ekvall, T, Guinée, J, Heijungs, R, Hellweg, S, Koehler, A, Penningtonf, D and Suh, S 2009. Recent developments in Life Cycle Assessment. Journal of Environmental Management 91, 1–21.CrossRefGoogle Scholar
Fleischer, G and Schmidt, W-P 1996. Functional Unit for systems using natural raw materials. International Journal of Life Cycle Assessment 1, 23–27.CrossRefGoogle Scholar
Food and Agriculture Organization (FAO) 2010. Greenhouse gas emissions from the dairy sector. A life cycle assessment. Food and Agriculture Organization of the United Nations, Rome, Italy.Google Scholar
Food and Agriculture Organization Statistics Division 2016. Food and Agriculture Organization of the United Nations, Statistics Division. Retrieved on 28 May 2016 from http://faostat3.fao.org/compare/E Google Scholar
Gabas, AL, Cabral, RAF, de Oliveira, CAF and Telis-Romero, J 2012. Density and rheological parameters of goat milk. Food Science and Technology (Campinas) 32, 381–385.CrossRefGoogle Scholar
Gnan, SO, Erabti, HA and Rana, MS 1985. The composition of Libyan goat’s milk. Australian Journal of Dairy Technology 50, 163–165.Google Scholar
International Standard Organization (ISO) 2006a. ISO 14044:2006 Environmental management – life cycle assessment – requirements and guidelines. International Standard Organization, Geneva, Switzerland.Google Scholar
International Standard Organization (ISO) 2006b. ISO 14040:2006 Environmental management – life cycle assessment – principles and framework. International Standard Organization, Geneva, Switzerland.Google Scholar
Jenness, R 1980. Composition and characteristics of goat milk: review 1968-1979. Journal of Dairy Science 63, 1605–1630.CrossRefGoogle Scholar
Kanyarushoki, C, van der Werf, MGH and Fuchs, F 2010. Life cycle assessment of cow and goat milk chains in France. In Proceeding of 7th International Conference on Life Cycle Assessment in the Agri-Food Sector, 22–24 September 2010, Bari, Italy, 108-114.Google Scholar
Kleijnen, JPC, Bettonvil, B, Van Groenendal, W 1998. Validation of trace-driven simulation models: a novel regression test. Management Science 44, 812–819.CrossRefGoogle Scholar
Lesschen, JP, van den Berg, M, Westhoek, HJ, Witzke, HP and Oenema, O 2011. Greenhouse gas emission profiles of European livestock sectors. Animal Feed Science and Technology 166–167, 16–28.CrossRefGoogle Scholar
National Institute of Standards and Technology 2013. Handbook N. 44. Specifications, tolerances, and other technical requirements for weighing and measuring devices as adopted by the 97th National Conference on Weights and Measures, 2012. National Institute of Standards and Technology, Office of Weights and Measures, Gaithersburg, MD, USA.Google Scholar
Opio, C, Gerber, P, Mottet, A, Falcucci, A, Tempio, G, MacLeod, M, Vellinga, T, Henderson, B and Steinfeld, H 2013. Greenhouse gas emissions from ruminant supply chains – a global life cycle assessment. Food and Agriculture Organization of the United Nations, Rome, Italy.Google Scholar
Overman, OR and Gaines, WL 1933. Milk-energy formulas for various breeds of cattle. Journal of Agricultural Research 46, 1109–1120.Google Scholar
Park, YW, Juárez, M, Ramos, M and Haenlein, GFW 2007. Physico-chemical characteristics of goat and sheep milk. Small Ruminant Research 68, 88–113.CrossRefGoogle Scholar
Prasad, H, Tewary, HA and Sengar, OPS 2005. Milk yield and composition of the Beetal breed and their crosses with Jamunapari, Barbari and Black Bengal breeds of goat. Small Ruminant Research 58, 195–199.CrossRefGoogle Scholar
Pulina, G, Macciotta, NPP and Nudda, A 2004. Milk composition and feeding in the Italian dairy sheep. Italian Journal of Animal Science 4 (S1), 5–14.CrossRefGoogle Scholar
Rabasco, A, Serradilla, JM, Padilla, JA and Serrano, A 1993. Genetic and non-genetic sources of variation in yield and composition of milk in Verata goats. Small Ruminant Research 11, 151–161.CrossRefGoogle Scholar
Roy, P, Nei, D, Orikasa, T, Xu, Q, Okadome, H, Nobutaka, N and Shiina, T 2009. A review of life cycle assessment (LCA) on some food products. Journal of Food Engineering 90, 1–10.CrossRefGoogle Scholar
Sawaya, WN, Safi, WJ, Al-Shalhat, AF and Al-Mohammad, MM 1984. Chemical composition and nutritive value of goat milk. Journal of Dairy Science 67, 1655–1659.CrossRefGoogle Scholar
Schau, EM and Fet, AM 2008. LCA studies of food products as background for Environmental Product Declarations. International Journal of Life Cycle Assessment 13, 255–264.CrossRefGoogle Scholar
Schroeder, LA 1977. Caloric equivalents of some plant and animal material. The importance of acid corrections and comparison of precision between the Gentry-Wiegert Micro and the Parr Semi-Microbomb calorimeters. Oecologia 28, 261–267.CrossRefGoogle ScholarPubMed
Soryal, K, Beyene, FA, Zeng, S, Bah, B and Tesfai, K 2005. Effect of goat breed and milk composition on yield, sensory quality, fatty acid concentration of soft cheese during lactation. Small Ruminant Research 58, 275–281.CrossRefGoogle Scholar
Todaro, M, Scatassa, ML and Giaccone, P 2005. Multivariate factor analysis of Girgentana goat milk composition. Italian Journal of Animal Science 4, 403–410.CrossRefGoogle Scholar
Tziboula-Clarke, A 2003. Goat milk. In Encyclopedia of dairy sciences (ed. H Roguiski, J Fuquay and P Fox), pp. 1270–1279. Academic Press, Amsterdam, Netherlands.Google Scholar
Uusi-Rauva, E, Ali-Yrkko, S and Antila, M 1970. Composition of Finnish goats’ milk. Acta Chemica Fennica 43B, 178–182.Google Scholar
Weiss, F and Leip, A 2012. Greenhouse gas emissions from the EU livestock sector: a life cycle assessment carried out with the CAPRI model. Agriculture, Ecosystems and Environment 149, 124–134.CrossRefGoogle Scholar
Zeng, SS, Escobar, EN and Popham, T 1997. Daily variations in somatic cell count, composition, and production of Alpine goat milk. Small Ruminant Research 26, 253–260.CrossRefGoogle Scholar
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