Modelos estadísticos para evaluar la vida útil de las cerdas reproductoras en una granja de cría comercial

dc.contributor.advisorLopera Gómez, Carlos Mariospa
dc.contributor.authorLópez Mesa, Juan Carlosspa
dc.date.accessioned2020-09-01T16:36:50Zspa
dc.date.available2020-09-01T16:36:50Zspa
dc.date.issued2020-08-24spa
dc.description.abstractlos partos dependen en gran medida los resultados económicos y técnicos de las granjas de cría de cerdos. Lograr un adecuado número de partos hará que el dinero invertido en la cerda reproductora sea amortizado en una mayor cantidad de cerdos que salen a mercado y que los partos ocurran dentro de los intervalos de tiempo esperados hará que se requiera menor inversión en sostenimiento de los animales en la granja. En la granja analizada, cerca del 45% de las cerdas no alcanzaron el objetivo de producción en cuanto al número de partos. Adicionalmente, las que si alcanzan el número de partos están tardando entre 31 y 41 días más de lo esperado. En este trabajo se analizó la recurrencia en el número de partos de las cerdas y se ajustaron diferentes modelos estadísticos para evaluar el efecto de variables medidas en hembras de reemplazo sobre la probabilidad de supervivencia o de descarte temprano o no deseado. Se espera que la implementación de estos ayude a optimizar la selección de hembras de reemplazo de tal modo que una mayor proporción de hembras logren los partos esperados y que lo hagan en menor tiempo. Se observó que la tasa de recurrencia en los partos es menor al potencial productivo de las cerdas. En los modelos ajustados de las variables medidas a hembras de reemplazo, el peso al nacimiento, el peso al destete y la edad a la pubertad son determinantes en la probabilidad de descarte por causas diferentes a la edad. Con la implementación de este trabajo es posible establecer criterios de selección de hembras de reemplazo basados en su peso al nacimiento, peso al destete y edad a la pubertad; se espera que las hembras seleccionadas a partir de estos nuevos criterios sean más longevas ya que tendrán una menor probabilidad de ser descartadas por casuas diferentes de la edad.spa
dc.description.abstractThe economic and technical results of breeding farms largely depend on the sows longevity and farrowings recurrence. Achieving an adequate sow parity will make invested money in the sow be amortized in a greater number of pigs that go to market and that the farrowings occur within the expected time intervals will require less investment in farm animals support. In the analyzed farm, about 45\% sows did not reach the parity goal. Additionally, those that do reach this parity are taking between 31 and 41 days longer than expected. In this work, the sows farrowing recurrence was analyzed and different statistical models were adjusted to evaluate the effect of variables measured in gilts on survival probability or early or unwanted culling. The implementation of these is expected to help optimize the gilts selection so that a higher sows proportion achieve the expected parity and do so in less time. It was observed that farrowing recurrence is less than the sows productive potential. In the adjusted models birth weight, weaning weight, and the puberty age are determining factors in the culling probability due to causes other than age. With the implementation of this work it is possible to establish gilts selection criteria based on their birth weight, weaning weight, and puberty age; sows selected with of these new criteria are expected to be more longevity since they will have a lower culling probability by causes other than age.spa
dc.description.degreelevelMaestríaspa
dc.format.extent103spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationLópez Mesa, Juan (2020). Modelos estadísticos para evaluar la vida útil de las cerdas reproductoras en una granja de cría comercial. Universidad Nacional de Colombia, Sede Medellín. Trabajo de Gradospa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/78353
dc.language.isospaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellínspa
dc.publisher.departmentEscuela de estadísticaspa
dc.publisher.programMedellín - Ciencias - Maestría en Ciencias - Estadísticaspa
dc.relation.referencesAbell, Caitlyn Elizabeth, R. L. Fernando, T. V. Serenius, Max F. Rothschild, K. A. Gray, y Kenneth J. Stalder (2016): “Genetic relationship between purebred and crossbred sow longevity”, Journal of Animal Science and Biotechnology, 7, 1, pp. 1-6, <https://doi.org/10.1186/s40104-016-0112-x>.spa
dc.relation.referencesAgresti, Alan (2013): Categorical Data Analysis, Third Ed. New Jersey, John Wiley & Sons, Inc.spa
dc.relation.referencesAmer, P. R., C. I. Ludemann, y S. Hermesch (2014): “Economic weights for maternal traits of sows, including sow longevity”, Journal of Animal Science, 92, 12, pp. 5345-5357, <https://doi.org/10.2527/jas2014-7943>.spa
dc.relation.referencesAndersen, Per Kragh, Ornulf Borgan, Richard Gill, y Niels Keiding (1993): Statistical Models Based on Counting Processes, First Ed. New York, NY, Springer New York.spa
dc.relation.referencesBorges, Rafael E. y Marianela Luzardo (2006): “Modelos de eventos recurrentes aplicados a la industria de producción de aluminio”, en XVI Simposio de Estadística, Bucaramanga, Colombia, Universidad Nacional de Colombia, pp. 1-10, <http://simposioestadistica.unal.edu.co/historico-de-memorias/>.spa
dc.relation.referencesBortolozzo, F. P., M. L. Bernardi, Rafael Kummer, y I. Wentz (2009): “Growth, Body State and Breeding Performance in Gilts and Primiparous Sows”, Society of Reproduction and Fertility supplement, 66, pp. 281-291.spa
dc.relation.referencesBreiman, Leo (2001): “Random Forest”, Machine Learning, 45, pp. 5-32, <https://doi.org/10.1023/A:1010933404324>.spa
dc.relation.referencesChantal Farmer (2015): The gestating and lactating sow, First. Chantal Farmer (ed.), Netherlands, Wageningen Academic Publishers.spa
dc.relation.referencesCleveland, William S. (1979): “Robust Locally Weighted Regression and Smoothing Scatterplots”, Journal of the American Statistical Association, 74, 368, pp. 829-836.spa
dc.relation.referencesCollet, David (1994): Modelling Survival Data in Medical Research, First Ed. Springer Science & Business Media, <https://doi.org/ISBN%201584883251>.spa
dc.relation.referencesCottney, Peter D., Elizabeth Magowan, M. Elizabeth E. Ball, y Alan Gordon (2012): “Effect of oestrus number of nulliparous sows at first service on first litter and lifetime performance”, Livestock Science, Elsevier B.V., 146, 1, pp. 5-12, <https://doi.org/10.1016/j.livsci.2012.02.013>.spa
dc.relation.referencesCox, David R. (1972): “Regression Models and Life-Tables”, Journal of the Royal Statistical Society, 34, 2, pp. 187-220, <https://doi.org/10.1007/978-1-4612-4380-9_37>.spa
dc.relation.referencesCrump, R. E. (2001): “A Genetic Analysis of Sow Longevity”, Proc. Association for the Advancement of Animal Breeding and Genetics, 14, pp. 223-226, <http://www.aaabg.org/livestocklibrary/2001/ab01053.pdf>.spa
dc.relation.referencesDagorn, J. y A. Aumaitre (1979): “Sow culling: Reasons for and effect on productivity”, Livestock Production Science, 6, 2, pp. 167-177, <https://doi.org/10.1016/0301-6226(79)90018-6>.spa
dc.relation.referencesD’Allaire, S., Allen D. A. D. Leman, y Richard Drolet (1992): “Optimizing Longevity in Sows and Boars”, Veterinary Clinics of North America: Food Animal Practice, Elsevier Masson SAS, 8, 3, pp. 545-557, <https://doi.org/10.1016/S0749-0720(15)30703-9>.spa
dc.relation.referencesD’Allaire, Sylvie, R. S. Morris, F. B. Martin, R. A. Robinson, y A. D. Leman (1989): “Management and environmental factors associated with annual sow culling rate: A path analysis”, Preventive Veterinary Medicine, 7, 4, pp. 255-265, <https://doi.org/10.1016/0167-5877(89)90010-X>.spa
dc.relation.referencesDeen, John (2003): “Sow longevity measurement”, en W. Christopher Scruton y Stephen Claas (eds.), 2003 Allen D. Leman Swine Conference, Minnesota, University of Minnesota, pp. 192-193, <http://conservancy.umn.edu/bitstream/handle/11299/146436/Deen%20%232.pdf?sequence=1&isAllowed=y>.spa
dc.relation.referencesDemaris, Alfred (1995): “A Tutorial in Logistic Regression”, Source Journal of Marriage and Family, 57, 4, pp. 956-968, <https://doi.org/10.2307/353415>.spa
dc.relation.referencesDe Vries, A. (1989): “A model to estimate economic values of traits in pig breeding”, Livestock Production Science, 21, 1, pp. 49-66, <https://doi.org/10.1016/0301-6226(89)90020-1>.spa
dc.relation.referencesDijkhuizen, A., R. M. M. Krabbenborg, y R. B. M. Huirne (1989): “Sow replacement: A comparison of farmers’ actual decisions and model recommendations”, Livestock Production Science, 23, 1-2, pp. 207-218, <https://doi.org/10.1016/0301-6226(89)90015-8>.spa
dc.relation.referencesDíaz, Carlos, María Rodríguez, Víctor Vera, Gloria Ramírez, Gloria Casas, y José Mogollón (2011): “Caracterización de los sistemas de producción porcina en las principales regiones porcicolas colombianas”, Revista Colombiana de Ciencias Pecuarias, 24, pp. 131-144.spa
dc.relation.referencesEngblom, Linda, Julia A. Calderón Díaz, M. T. Nikkilä, K. A. Gray, P. Harms, J. Fix, S. Tsuruta, John Mabry, y Kenneth J. Stalder (2016): “Genetic analysis of sow longevity and sow lifetime reproductive traits using censored data”, Journal of Animal Breeding and Genetics, 133, 2, pp. 138-144, <https://doi.org/10.1111/jbg.12177>.spa
dc.relation.referencesEngblom, Linda, Nils Lundeheim, Anne-Marie Marie Dalin, y Kjell Andersson (2007): “Sow removal in Swedish commercial herds”, Livestock Science, 106, 1, pp. 76-86,<https://doi.org/10.1016/j.livsci.2006.07.002>.spa
dc.relation.referencesEngel, J. (1988): “Polytomous logistic regression”, Statistica Neerlandica, 42, 4, pp. 233-252, <https://doi.org/10.1111/j.1467-9574.1988.tb01238.x>.spa
dc.relation.referencesFaust, M. A., W. Robison, y M. W. Tess (1993): “Genetic and Economic Analyses of Sow Replacement Rates in the Commercial Tier of a Hierarchical Swine Breeding Structure”, Journal of Animal Science, 71, 6, pp. 1400-1406.spa
dc.relation.referencesFaust, M. A., M. W. Tess, y W. Robison (1992): “A Bioeconomic Simulation Model for a Hierarchical Swine Breeding Structure”, Journal of Animal Science, 70, 70, pp. 1760-1774, <http://jas.fass.org/content/70/6/1760%0Awww.asas.org%20https://www.animalsciencepublications.org/publications/jas/pdfs/70/6/1760>.spa
dc.relation.referencesFowlkes, Edward B. (1987): “Some diagnostics for binary logistic regression via smoothing”, Biometrika, 74, 3, pp. 503-515, <https://doi.org/10.1093/biomet/74.3.503>.spa
dc.relation.referencesFox, John (2016): Applied Regression Analysis and Gereralized Linear Models, 3 edition. Los Angeles, Sage Publications, Inc.spa
dc.relation.referencesFox, John y Sanford Weisberg (2011): “Cox Proportional-Hazards Regression for Survival Data in R”, Second Ed. en An R Companion to Applied Regression, London, <https://socialsciences.mcmaster.ca/jfox/Books/Companion/appendix/Appendix-Cox-Regression.pdf>.spa
dc.relation.referencesFriendship, R. M., M. R. Wilson, G. W. Almond, I. McMillan, R. R. Hacker, R. Pieper, y S. S. Swaminathan (1986): “Sow wastage: reasons for and effect on productivity”, Canadian Journal of Veterinary Research, 50, 2, pp. 205-208.spa
dc.relation.referencesFu-Chang Hu (2017): Title Stepwise Variable Selection Procedures for Regression Analysis,spa
dc.relation.referencesGill, Pinder (2007): “Nutritional Management of the Gilt for Lifetime Productivity. Feeding for Fitness or Fatness?”, London Swine Conference – Today’s Challenges. Tomorrow’s Opportunities, Ontario, Canada„ April, pp. 83-99, <http://www.prairieswine.com/wp-content/uploads/2014/08/LSC2007_PGill.pdf>.spa
dc.relation.referencesGrambsch, Patricia y Terry M. Therneau (1994): “Proportional hazards tests and diagnostics based on weighted residuals”, Biometrika, 81, 3, pp. 515-526.spa
dc.relation.referencesGuo, Shenyang (2010): Survival Analisys. Pocket Guides to Social Work Research Methods, Oxford University Press, <https://doi.org/10.1017/CBO9781107415324.004>.spa
dc.relation.referencesHadaš, Z., M. Schild, y P. Nevrkla (2015): “Analysis of reason for culling of sows in production herd”, Research in Pig Breeding, 9, 2, <http://www.respigbreed.cz/2015/2/1.pdf>.spa
dc.relation.referencesHastie, Trevor, Robert Tibshirani, y Jerome Friedman (2009): The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Second Ed. New York, Springer Science & Business Media, <https://doi.org/10.1057/9780230355033.0018>.spa
dc.relation.referencesHilbe, J. M. (2009): Logistic regression models, First Edit. Boca Raton, Chapman; Hall/CRC.spa
dc.relation.referencesHilbe, J. M. (2015): Practical Guide to Logistic Regression, First Ed. Boca Raton, CRC Press, <https://www.crcpress.com/Practical-Guide-to-Logistic-Regression/Hilbe/9781498709576?utm_source=new_book_alerts&utm_medium=email&utm_campaign=CSL02>.spa
dc.relation.referencesHolder, R. B., W. R. Lamberson, R. O. Bates, y T. J. Safranski (1995): “Lifetime productivity in gilts previously selected for decreased age at puberty”, Animal Science, 61, 01, pp. 115-121, <https://doi.org/10.1017/S135772980001359X>spa
dc.relation.referencesHosmer, David W. y Stanley Lemeshow (2000): Applied Logistic Regression, Third. David Balding y Noel Cressie (eds.), Wiley Series in Probability and Statistics, New Jersey, John Wiley & Sons, Ltd, <https://doi.org/10.1002/0471722146>.spa
dc.relation.referencesHosmer, David W., Stanley Lemeshow, y Susanne May (2008): Applied Survival Analysis, Second Edi. New Jersey, John Wiley & Sons, Inc.spa
dc.relation.referencesHouben, E. H. P., J. G. M. Thelosen, R. B. M. Huirne, y A. Dijkhuizen (1990): “Economic comparison of insemination and culling policies in commercial sow herds, assessed by stochastic simulation”, Netherlands Journal of Agricultural Science, 38, 2, pp. 201-204.spa
dc.relation.referencesHoving, L. L., N. M. M. Soede, E. A.M. A. M. Graat, H. Feitsma, y B. Kemp (2011): “Reproductive performance of second parity sows: Relations with subsequent reproduction”, Livestock Science, Elsevier B.V., 140, 1-3, pp. 124-130, <https://doi.org/10.1016/j.livsci.2011.02.019>.spa
dc.relation.referencesHuirne, R. B. M., A. Dijkhuizen, A. Pijpers, J. H. M. Verheijden, y P. van Gulick (1991): “An economic expert system on the personal computer to support sow replacement decisions”, Preventive Veterinary Medicine, 11, 2, pp. 79-93, <https://doi.org/10.1016/S0167-5877(05)80030-3>.spa
dc.relation.referencesJungst, Steve B., Daryl L. Kuhlers, y Joe A. Little (1988): “Longevity and maternal productivity of F1 crossbred landrace sows managed in two different gestation systems”, Livestock Production Science, 19, 3-4, pp. 499-510, <https://doi.org/10.1016/0301-6226(88)90015-2>.spa
dc.relation.referencesKaplan, E. L. y Paul Meier (1958): “Nonparametric Estimation from Incomplete Observations”, Journal of the American Statistical Association, 53, 282, pp. 457- 481, <https://doi.org/10.1080/01621459.1958.10501452>.spa
dc.relation.referencesKartsonaki, Christiana (2016): “Survival Analysis”, Diagnostic Histopathology, Elsevier Ltd, 22, 7, pp. 263-270, <https://doi.org/10.1016/j.mpdhp.2016.06.005>.spa
dc.relation.referencesKleinbaum, David G. y Mitchel Klein (2010): Logistic Regression: A Self-learning Text, First. K. Dietz, Mitchell Gail, K. Krickeberg, Anastasios Tsiatis y Jonathan M. Samet (eds.), Statistics for Biology and Health Series, Springer, <https://doi.org/DOI%2010.1007/978-1-4419-1742-3_1>.spa
dc.relation.referencesKleinbaum, David G. y Mitchel Klein (2012): Survival Analysis. A Self Learning Text, Third. M. Gail, K. Krickeberg, J. M. Samet, A. Tsiatis y W. Wong (eds.), Statistics for Biology and Health, New York, Springer.spa
dc.relation.referencesKroes, Y. y J. P. Van Male (1979): “Reproductive lifetime of sows in relation to economy of production”, Livestock Production Science, 6, 2, pp. 179-183, <https://doi.org/10.1016/0301-6226(79)90019-8>.spa
dc.relation.referencesLantz, Brett (2013): Machine Learning with R, First Ed. Birmingham, Packt Publishing Ltd., <https://doi.org/10.1007/978-981-10-6808-9>.spa
dc.relation.referencesLawless, J. F. y C. Nadeau (1995): “Some simple robust methods for the analysis of recurrent events”, Technometrics, 37, 2, pp. 158-168, <https://doi.org/10.1080/00401706.1995.10484300>.spa
dc.relation.referencesLe, T. H., Elise Norberg, B. Nielsen, P. Madsen, Katja Nilsson, y N. Lundeheim (2015): “Genetic correlation between leg conformation in young pigs, sow reproduction and longevity in Danish pig populations”, Acta Agriculturae Scandinavica, Section A — Animal Science, 65, 3-4, pp. 132-138, <https://doi.org/10.1080/09064702.2016.1153709>.spa
dc.relation.referencesLesmeister, Cory (2015): Mastering Machine Learning with R, First. Birmingham, Packt Publishing Ltd.spa
dc.relation.referencesLi, Jialiang y Shuangge Ma (2013): Survival Analysis in Medicine and Genetics, Shein-Ching Chow (ed.), Boca Raton, Florida, Chapman; Hall/CRC, <https://doi.org/10.1201/b14978>.spa
dc.relation.referencesLiu, Xian (2012): Survival Analysis. Models and Applications, First. West Sussex, United Kingdom, John Wiley & Sons, Ltd, <https://doi.org/10.1002/9781119294016.ch5>.spa
dc.relation.referencesLopera-Gómez, CM y Eva Cristina Manotas (2011): “Aplicación del análisis de datos recurrentes sobre interruptores FL245 en interconexión eléctrica S.A”, Revista Colombiana de Estadistica, 34, SPEC. ISSUE 2, pp. 249-266.spa
dc.relation.referencesLucia, Thomaz, Gary D. Dial, y William E. Marsh (2000): “Lifetime reproductive performance in female pigs having distinct reasons for removal”, Livestock Production Science, 63, 3, pp. 213-222, <https://doi.org/10.1016/S0301-6226(99)00142-6>spa
dc.relation.referencesMasaka, Lawrence, Marvelous Sungirai, Casper Nyamukanza, y Chido Bhondai (2014): “Sow removal in a commercial pig herd in Zimbabwe”, Tropical Animal Health and Production, 46, 5, pp. 725-731, <https://doi.org/10.1007/s11250-014-0554-0>.spa
dc.relation.referencesMeeker, William Q. y Luis A. Escobar (1998): Statistical Methods for Reliability Data, First. V. Barnett, R. Bradley, C. Cressie, N. Fisher y L. Johnstone (eds.), United States of America, John Wiley & Sons, Inc., <https://doi.org/10.1017/CBO9781107415324.004>.spa
dc.relation.referencesMontgomery, Douglas, Elizabeth Peck, y Geoffrey Vining (2006): Introducción al Análisis de Regresión Lineal, First. Mexico, <https://doi.org/10.1017/CBO9781107415324.004>.spa
dc.relation.referencesMoore, Dirk F. (2016): Applied Survival Analysis Using R, First. Robert Gentleman, Kurt Hornik y Giovanni Parmigiani (eds.), Switzerland, Springer, <https://doi.org/10.1007/978-3-319-31245-3>.spa
dc.relation.referencesOnteru, S. K., J. W. Ross, y Max F. Rothschild (2009): “The role of gene discovery, QTL analyses and gene expression in reproductive traits in the pig”, First. en H. Rodriguez-Martinez, Jeffrey L. Vallet y A. J. Ziecik (eds.), Control of Pig Reproduction VIII, Nottingham, Nottingham University Press.spa
dc.relation.referencesPiñeros, R. J., José Mogollón, y M. A. Rincón (2007): “Causas de Mortalidad y Descarte de Cerdas en Dos Granjas de Producción Intensiva en Colombia”, Revista de la Facultad de Medicina Veterinaria y de Zootecnia, 54, I, pp. 17-24, <http://www.redalyc.org/articulo.oa?id=407642324005>.spa
dc.relation.referencesPorkColombia (2019): “Simulador Costos de Producción e Impacto Económico de Cranja Porcícola de Ciclo Completo o Cría”, <https://www.miporkcolombia.co/wp-content/uploads/2018/09/Simulador-de-costos-de-producci%7B/’%7Bo%7D%7Dn-v2.2.xlsm>.spa
dc.relation.referencesPrabhakaran, Selva (2016): Package Information. ValuePerformance Analysis and Companion Functions for Binary Classification Models, <https://github.com/selva86/InformationValue/issues>.spa
dc.relation.referencesQueensland Government (2010): “Pig Industry Terms and Definitions”, <https://www.daf.qld.gov.au/animal-industries/pigs/about-the-industry/terms-and-definitions>.spa
dc.relation.referencesR Core Team (2020): “R: A Language and Environment for Statistical Computing”, Vienna, Austria, R Foundation for Statistical Computing, <https://www.r-project.org/>.spa
dc.relation.referencesRipley, Brian y William Venables (2020): Package ’nnet’. Feed-Forward Neural Networks and Multinomial Log-Linear Modelsspa
dc.relation.referencesRoongsitthichai, A., P. Cheuchuchart, S. Chatwijitkul, O. Chantarothai, y P. Tummaruk (2013): “Influence of age at first estrus, body weight, and average daily gain of replacement gilts on their subsequent reproductive performance as sows”, Livestock Science, Elsevier, 151, 2-3, pp. 238-245, <https://doi.org/10.1016/j.livsci.2012.11.004>.spa
dc.relation.referencesRozeboom, D. W., J. E. Pettigrew, R. L. Moser, S. G. Cornelius, y S. M. El Kandelgy (1996): “Influence of Gilt Age and Body Composition at First Breeding on Sow Reproductive Performance and Longevity”, Journal of Animal Science, 74, 1, pp. 138-150, <https://doi.org/10.2527/1996.741138x>.spa
dc.relation.referencesSaito, Hikari, Y. Sasaki, y Yuzo Koketsu (2010): “Associations between Age of Gilts at First Mating and Lifetime Performance or Culling Risk in Commercial Herds”, Journal of Veterinary Medical Science, 187, 5, pp. 555-559, <https://doi.org/10.1292/jvms.10-0040>.spa
dc.relation.referencesSasaki, Y., Iain McTaggart, y Yuzo Koketsu (2012): “Assessment of Lifetime Economic Returns of Sows by Parity of Culled Sows in Commercial Breeding Herds”, Journal of Veterinary Epidemiology, 16, 1, pp. 37-45, <https://www.jstage.jst.go.jp/article/jve/16/1/16_37/_pdf>.spa
dc.relation.referencesSerenius, T., Kenneth J. Stalder, y M. Puonti (2006): “Impact of dominance effects on sow longevity”, Journal of Animal Breeding and Genetics, 123, 6, pp. 355-361,<https://doi.org/10.1111/j.1439-0388.2006.00614.x>.spa
dc.relation.referencesSoede, N. M., P. Langendijk, y B. Kemp (2011): “Reproductive cycles in pigs”, Animal Reproduction Science, Elsevier B.V., 124, 3-4, pp. 251-258, <https://doi.org/10.1016/j.anireprosci.2011.02.025>.spa
dc.relation.referencesSperandei, Sandro (2014): “Understanding logistic regression analysis”, Biochemia Medica, 24, 1, pp. 12-18, <https://doi.org/10.11613/BM.2014.003>.spa
dc.relation.referencesStalder, Kenneth J. (2017): “An economic analysis of sow retention in a United States breed-to-wean system”, Journal of Swine Health and Production, 25, 5, pp. 238-246, <https://www.researchgate.net/publication/319242171_An_economic_analysis_of_sow_retention_%20in_a_United_States_breed-to-wean_system>.spa
dc.relation.referencesStalder, Kenneth J., Mark Knauer, Tom J. Baas, Max F. Rothschild, y John W. Mabry (2004): “Sow longevity”, Pig News and Information, 25, 2, pp. 53-74, <https://www.researchgate.net/profile/Kenneth_Stalder%20https://www.researchgate.net/publication/230661632>.spa
dc.relation.referencesStalder, Kenneth J., R. Curt Lacy, Timothy L. Cross, Glenn E. Conatser, y C. S. Darroch (2000): “Net Present Value Analysis of Sow Longevity and the Economic Sensitivity of Net Present Value to Changes in Production, Market Price, Feed Cost, and Replacement Gilt Costs in a Farrow-to-Finish Operation”, The Professional Animal Scientist, Elsevier Masson SAS, 16, 1, pp. 33-40, <https://doi.org/10.15232/S1080-7446(15)31658-2>.spa
dc.relation.referencesStein, T. E., A. Dijkhuizen, Sylvie D’Allaire, y R. S. Morris (1990): “Sow culling and mortality in commercial swine breeding herds”, Preventive Veterinary Medicine, 9, 2, pp. 85-94, <https://doi.org/10.1016/0167-5877(90)90027-F>.spa
dc.relation.referencesTherneau, Terry M. (2020): Package ’survival’ Title Survival Analysis, <https://github.com/therneau/survival>.spa
dc.relation.referencesTherneau, Terry M. y Patricia Grambsch (2000): Modeling Survival Data. Extending the Cox Model, First Ed. New York, Springer Science & Business Media.spa
dc.relation.referencesWang, Wenjie, Haoda Fu, y Jun Yan (2019): Package ’reda’. Recurrent Event Data Analysis, <https://doi.org/10.1002/nav.3800260304>.spa
dc.relation.referencesWongsakajornkit, Nuttha y Nalinee Imboonta (2015): “Genetic Correlations among Average Daily Gain, Backfat Thickness and Sow Longevity in Landrace and Yorkshire Sows”, Thai Journal of Veterinary Medicine, 45, 2, pp. 221-227.spa
dc.relation.referencesWoodward, Mark (2014): Epidemiology Study Design and Data Analysis, Third Ed. Boca Raton, Chapman; Hall/CRC, <http://books.google.com/books?hl=en&lr=&id=OqjB1WBrjjEC&pgis=1>.spa
dc.relation.referencesYazdi, M. H. H., L. Rydhmer, E. Ringmar-Cederberg, N. Lundeheim, y K. Johansson (2000): “Genetic study of longevity in Swedish Landrace sows”, Livestock Production Science, 63, 3, pp. 255-264, <https://doi.org/10.1016/S0301-6226(99)00133-5>.spa
dc.relation.referencesYiu, Tony (2019): “Understanding Random Forest”, <https://towardsdatascience.com/understanding-random-forest-58381e0602d2>.spa
dc.relation.referencesZhao, Yunxiang, Xiaohong Liu, Delin Mo, Qingsen Chen, y Yaosheng Chen (2015): “Analysis of reasons for sow culling and seasonal effects on reproductive disorders in Southern China”, Animal Reproduction Science, Elsevier, 159, pp. 191-197, <https://doi.org/10.1016/j.anireprosci.2015.06.018>.spa
dc.rightsDerechos reservados - Universidad Nacional de Colombiaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial 4.0 Internacionalspa
dc.rights.spaAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/spa
dc.subject.ddc590 - Animalesspa
dc.subject.ddc510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasspa
dc.subject.proposalAnálisis de Supervivenciaspa
dc.subject.proposalSurvival Analysiseng
dc.subject.proposalMean Cumulative Functioneng
dc.subject.proposalFunción de Media Acumuladaspa
dc.subject.proposalAnálisis Recurrenciaspa
dc.subject.proposalRecurrence Analysiseng
dc.subject.proposalModelo de Coxspa
dc.subject.proposalCox Modeleng
dc.subject.proposalLogistic Regressioneng
dc.subject.proposalRegresión Logísticaspa
dc.subject.proposalRegresión Multinomialspa
dc.subject.proposalMultinomial Regressioneng
dc.subject.proposalBosques Aleatoriosspa
dc.subject.proposalRandom Forestseng
dc.subject.proposalCausas de Descarte de Cerdasspa
dc.subject.proposalSow cullingeng
dc.subject.proposalLongevidad de la Cerdaspa
dc.subject.proposalSow Longevityeng
dc.titleModelos estadísticos para evaluar la vida útil de las cerdas reproductoras en una granja de cría comercialspa
dc.title.alternativeStatistical models to evaluate sows longevity in a commercial breeding farmspa
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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