The aim of this study was evaluate the economic and environmental benefits to meat packers based on OEE individual identification.Coordenador: Roberto Giolo de Almeida. II SIGEE

ALBERTINI, T. Z.

BARBOSA, M.

BARIONI, L. G.

BIASE, A. G.

CASTANHEIRA, M. A.

CERQUEIRA, A. G.

COUTINHO, M. A. da S.

FERNANDES, P. A. P.

FREUA, M. C.

GONÇALVES, H. C.

LANNA, D. P.

MEDEIROS, S. R. de

NEPOMUCENO, N. H. C.

OLTJEN, J. W.

TORRES JUNIOR, R. A. de A.

English

Repository Open Access to Scientific Information from Embrapa

191BeefTrader (part III): meat industry opportunity to improve its profitability reducing greenhouse gases emission and pollutants based on optimal economical endpoint identificationBeefTrader  (part  III):  meat  industry  opportunity  to  improve  its  profitability  reducing greenhouse gases emission and pollutants based on optimal economical endpoint identificationTiago Zanett ALBERTINI1*, Adriele Giaretta BIASE1, Marcio BARBOSA1, Pedro Affonso Pizelli FERNANDES1, Matheus Angeli CASTANHEIRA1, Marcelo Aranda da Silva COUTINHO1; Andressa Gonçalves CERQUEIRA2, Heitor Coelho GONÇALVES2, Dante Pazzanese LANNA2, Nelson Henrique Corrêa NEPOMUCENO3, Luis Gustavo BARIONI4, Sergio Raposo de MEDEIROS5, Rober-to Augusto de Almeida TORRES JUNIOR5, Mateus Castelani FREUA6, James W. OLTJEN71@Tech - Technological Information for Agriculture, 2Univervity of São Paulo - College of Agriculture (USP - ESALQ), 3Integra Software, 4Embrapa Informática Agropecuária, 5Embrapa Gado  de Corte, 6College of Animal Science and Food Engineering –  USP, 7University of California - Davis, 7Imaflo-ra. E-mail address of presenting author*: albertinitz@techagr.comIntroductionFeedlots rented or belonging to meat packers are increasing in Bra-zil and about half million cattle was produced on such feedlots in 2014. On the other side, the meat packers buy a significant quan-tity of cattle (~ 5 millions animals/year) from other feedlots. In-dependent if animals have being finished in livestock producers or meat packers feedlot, the individual monitoring based on the optimal economical endpoint (OEE) should improve the profitability decreasing greenhouse gases emission (GEE) and others pollutants for both sides of this market chain. However, the maximization of profitability is central for OEE, and the challenge is to solve it for meat packer, as the margi-nal value is calculated from carcass and non-carcass tissues deposition 192BeefTrader (part III): meat industry opportunity to improve its profitability reducing greenhouse gases emission and pollutants based on optimal economical endpoint identificationover time. Additionally, the marginal cost need to consider operational costs from industry, logistics (from farm-industry-final customer) and maintenance stocks (in or out of industry boundaries). The aim of this study was evaluate the economic and environmental benefits to meat packers based on OEE individual identification. The hypothesis of this study was: traditional slaughter endpoint (TSE) vs. OEE have di-fferences for marginal profit, GHG emission and manure production by kg of meat produced in the meat industry boundary. The current study is the third from three sequential abstracts based on BeefTrader DSS to maximize profitability of farmers and the meat industry.Material and MethodsIn the BeefTrader (part I) abstract two datasets (DS) were created. The DS1 was formed by experimental feedlot variables (diet chemical composition, intake, daily weight gain and body chemical composi-tion). The Davis Growth Model (DGM; Oltjen et al. 1986, 2000) reparametrized by Biase (2016) was used to predict the animal gro-wth and body chemical composition. Variables from DGM, exogenous information to predict OEE, GHG, water intake and manure production performed the DS2. The DS2 was used to simulate the carcass and non- carcass growing during the feedlot than DS3 was created. Main Brazilian meat cuts according MAPA (1988) and non-carcass commer-cial tissues (skin, liver, heart, kidney, rumen-reticulum, tong and internal fat) were modeled. The meat cuts:carcass and non-carcass:empty body weight ratios simulated in DGM were used to model these tissues deposition over time. The relative cuts and tissues weights were multi-plied by prices performed by industry to predict the marginal value daily. Nonparametric bootstrap (NB) resampling was used to create a synthetic population DS4 from DS1, DS2 and DS3. The NB performed 100,000 resampling nonparametric for each experimental unit and the confiden-ce of biased corrected percentile bootstrap was determined using the R software (Development Core Team, version 3.1.0, 2014). The mean, median, lower and upper limit were calculated from NB. Biased correc-ted percentile booststrap interval (95% confidence level) was used to 193BeefTrader (part III): meat industry opportunity to improve its profitability reducing greenhouse gases emission and pollutants based on optimal economical endpoint identificationcompare the methods from DS3 according its symmetric and asym-metric distributions probabilities (Efron, 1981).Results and ConclusionsThe OEE economic and environmental benefits for producers can also be observed for meat packers the same way as observed in the situations portrayed in the previous studies (BeefTrader part I and II). However, the simulation of this study shows that the impact on retail prices paid by the meat and internal tissues had a great influence on profitability. Logistics and stocks, national and international customers tendencies, business rules understanding (among farmer-industry--customer) are essential to be implemented in a more realistic model to maximize the profitability.ReferencesBIASE, A.G. Parameterization of Ordinary Differential Equations Systems in the growth of beef cattle and production of gases. Piracicaba, 2015. 146p. Thesis (Phd em Scien-ce) – “Luiz de Queiroz College of Agriculture”, University of São Paulo.EFRON, B. 1981. Nonparametric standard errors and confidence intervals. Canadian Jour-nal of Statistics. 9: 139-172.MAPA. Ministério da Agricultura, Pecuária e Abastecimento. Beef cattle cuts standardiza-tion. Portaria nº 5, nomember,8th 1988. Available in: <http://extranet.agricultura.gov.br/sislegis- consulta> Accessed: april 29, 2016.OLTJEN, J.W.; BYWATER, A.C.; BALDWIN R.L.; GARRETT W.N. 1986. Development of a dynamic model of beef cattle growth and composition. Journal of Animal Science 62: 86-97.OLTJEN, J.W.; PLEASANTS, A.B; SOBOLEVA, T.K. ODDY, V.H. Second-generation dynamic cattle growth and composition models. p. 197-209, 2000 In: Mcnamara, J.P.; France, J.; Beever, D.E. Modelling Nutrient Utilization in Farm Animals. CAB International, London, LDN, United Kingdom.194BeefTrader (part III): meat industry opportunity to improve its profitability reducing greenhouse gases emission and pollutants based on optimal economical endpoint identificationR Core Team (2014). R: A language and environment for statistical computing. R Foun-dation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/AcknowledgementsGrant #2015/07855-7[BeefTrader PIPE and scholarships (16/02347-6; 16/02451-8; 16/07154- 1;  16/05110-7;  16/03738-9)]  #2007/52028-5  and  2007/54086-2,  São  Paulo  Research Foundation (FAPESP). Grant #03.07.1.08.00.00 (Macroprograma 3; Embrapa Beef Cattle).

BeefTrader (part III): meat industry opportunity to improve its profitability reducing greenhouse gases emission and pollutants based on optimal economical endpoint identification.

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BeefTrader (part III): meat industry opportunity to improve its profitability reducing greenhouse gases emission and pollutants based on optimal economical endpoint identification.

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