Evaluation of the efficiency of biological and economic indicators in production systems on dairy farms
DOI:
https://doi.org/10.22267/rcia.20234003.213Keywords:
Dairy cow, economic, reproduction, social, sustainabilityAbstract
The efficiency of the livestock system is one of the factors of greatest social and economic interest in rural production areas. The project aimed to assess various biological and economic indicators to identify the most efficient dairy cows in grazing systems with supplementation within the Antioquia-Colombia dairy basin. We used retrospective data from 2009 to 2019 from farms in the municipalities of Entrerríos, San Pedro, and Belmira. The production systems in the northern region of Antioquia share common features typical of many dairy enterprises. They are primarily characterized by their utilization of grazing systems, each employing distinct supplementation regimes.The farms have their own records and official milk control from the cooperatives or associations from which the data was obtained. The productive categories of the evaluated cows were established according to the number of births, and within each subgroup, they were ordered by the total liters per lactation in ascending order. Productive, reproductive, health, and economic variables were recorded. Then cuts were made, thus forming three categories: low, medium, and high production. It was possible to determine the model that showed four groups of variables (breed, milk production per lactation, open days, and silage) with an important correlation of 97% and a greater contribution to the behavior of the cost per liter, obtaining an R2 of 0.91 (P <0.05) and a prediction error of US$ 0.0076 per liter of milk in the evaluated farms. It is concluded that, with few biological and economic predictive indicators, it was possible to identify the most efficient cows in grazing systems.
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Albertone, G.; Allen, S.; Redpath, A. (2020). Agriculture, forestry, and fishery statistics- Statistical books. 2020 ed. Luxembourg: Edward Cook. 256p.
ACHA-Asociación de Criadores de Holando Argentino. (2021). EL CONTROL LECHERO OFICIAL. https://www.acha.org.ar/index.php/control-lechero1
ADHI-Australian Dairy Herd Improvement Scheme. (2021). Welcome to Australian dairy herd improvement scheme. https://www.adhis.com.au/v2/sitev2.nsf/launch?open
Ahlman, T. (2010). Organic Dairy Production: Herd Characteristics and Genotype by Environment Interactions. Sweden: Dept. of Animal Breeding and Genetics, Swedish University of Agricultural Sciences. 60p https://res.slu.se/id/publ/30670
Aldaz-Álvarez, A. A. (2020). Factores de eficiencia reproductiva bovina en una granja lechera de la provincia de el oro. http://repositorio.utmachala.edu.ec/bitstream/48000/16112/1/ECUACA-2020-MV-DE00002.pdf
Alonso, A.C.; Iribán, C.A.; Benítez, M. (2018). “Typology of cattle farms in a peasant community from southwest of Holguín, Cuba”. Cuban Journal of Agricultural Science. 52(3). 263-270.
Alcaldías municipales de San Pedro Belmira y Entrerrios (2021). https://www.sanpedrodelosmilagros-antioquia.gov.co/
Arboleda Correa, M.P. (2020). Comparación de algunos parámetros productivos y reproductivos de vacas Holstein y sus cruces con Jersey y Gyr en un hato lechero en trópico alto colombiano. http://repository.unilasallista.edu.co/http://hdl.handle.net/10567/2712
Aprocal-Association pro-quality of milk and its derivatives. (2020). Driving reproductive of the dairy herd. http://www.aprocal.com.ar/boletines/manejo-reproductado-del-rodeo
Lozano, Guerrero N. (2021) Los seis grandes retos del sector lácteo colombiano. https://www.asoleche.org/revista-la-via-lactea/
Brickell, J.S.; McGowan, M.M.; Pfeiffer, D.U.; Wathes, D.C. (2009). "Mortality in Holstein-Friesian calves and replacement heifers in relation to body weight and IGF-I concentration, on 19 farms in England". Animal. 3(8): 1175–1182. https://doi.org/10.1017/S175173110900456X
Brickell, J.S.; Wathes, D.C. (2011). "A descriptive study of the survival of Holstein Friesian heifers through to third calving on English dairy farms. Journal of Dairy Science. 94(4): 1831–1838. https://doi.org/10.3168/jds.2010-3710
Borowski, P.; Pawlewicz, A.; Parzonko, A.; Harper, J.K.; Holden, L. (2020). Factors shaping cow’s milk production in the EU. Sustainability. 12(1): 420. https://doi.org/10.3390/su12010420
Boulton, A.C.; Rushton, J.; Wathes, D.C. (2017). An empirical analysis of the cost of rearing dairy heifers from birth to first calving and the time taken to repay these costs. Animal. 11 (8): 1372–1380. 10.1017/S1751731117000064
Cappellini, O.R. (2011). Dairy development in Argentina. http://www.fao.org/3/a-al744e.pdf
Carrasco, J.L.; Hernan, M.A. (2016). Estadística multivariante en las ciencias de la vida. Fundamentos, métodos y aplicación. España: Ciencia 3, D.L. 363p.
Carulla, J.; Ortega, E. (2016). Dairy production systems in Colombia: challenges and opportunities. Latin American Archives of Animal Production. 24(2): 83-87.
Cattaneo, L.; Baudracco, J.; Ortega, H.H.; Maciel, M.; Dick, A.; Lazzarini, B. (2012). Cost of open day in Holando Argentino dairy cows in continuous calving systems. Argentine Journal of Animal Production. 32(1): 21-79.
CEDAIT – Centro del Desarrollo Agrobiotecnológico de Inovacion e Integración Territorial. (2021). https://www.udea.edu.co/Cedait.%20Medell%C3%ADn%20Colombia
Comerón, E.; Romero, L.; Cuatrín, A.; Maciel, M. (2007). El efecto racial o genético. En: Taverna, M. Manual de referencias técnicas para el logro de leche de calidad. pp. 131-14. 3 ed. Buenos Aires: Edicones INTA. 180p.
Colanta - Cooperativa Lecheros de Antioquia. (2019). Dairy producer's booklet. Technical indicators. 9(1): 13-19.
De Luca, L. (2017). High producing dairy cows, metabolic adaptation during peripartal period. https://actualidadganadera.com/estrategias-nutricionales-durante-el-periodo-de-transicion-en-la-vaca-lechera-parte-i
De Vries, A. (2006). Economic value of pregnancy. J. Dairy Sci. 89: 3876–3885.
De Vries, A.; Van Leeuwen; J.; Thatcher, W.W. (2010). Economics of improved reproductive performance in dairy cattle. AN156 Ed. USA: University of Florida. 7p.
Dono, G.; Giraldo, L.; Nazzaro, E. (2013). Contribution of the calving interval to dairy farm profitability: results of a cluster analysis of FADN data for a major milk production area in southern Italy. Spanish Journal of Agricultural Research. 11(4): 857–868. https://doi.org/10.5424/sjar/2013114-3873
Dutour, E.J.; Melucci, I.M. (2010). Association between productive and reproductive parameters of dairy cows according to production systems. Association Latin American Animal Production. 18(3-4): 133-147.
Duque-López, N.P.; Casellas Vidal, J.; Quijano Bernal, J.H.; Casals Costa, R.; Martí Such, F.X. (2018). Fitting lactation curves in a Colombian Holstein herd using nonlinear models. Journal of the National School of Agronomy. 71(2): 8459–8468. https://doi.org/10.15446/rfna.v71n2.67424
FAO - Organización de las Naciones Unidas para la Agricultura y la Alimentación. (2019). El desarrollo del sector lechero. https://www.fao.org/dairy-production-products/socio-economics/dairy-development/es/
Fernández, R.P.M.; Biga, P.; Di Masso, J.R.; Marini P.R. (2020). Economic evaluation of productive and reproductive indicators in dairy cows with different ages at first calving, in grazing systems. Rev. Cuban Journal of Agricultural Science. 54(3):17-29.
Galvao, K.N.; Federico, P.; De Vries, A.; Scheunemann, G.M. (2013). Economic comparison of reproductive programs for dairy herds using estrus detection timed artificial insemination, or a combination. Journal of Dairy Science. 96(4): 2681-2693. https://doi.org/10.3168/jds.2012-5982
Giordano, J.O.; Kalantari, A.S.; Fricke, P.M.; Wiltbank, M.C.; Cabrera, V.E. (2012). A daily herd Markov-chain model to study the reproductive and economic impact of reproductive programs combining timed artificial insemination and estrus detection. Journal of Dairy Science. 95(9): 5442-5460. https://doi.org/10.3168/jds.2011-4972
Haile-Mariam, M.; Bowman, P.J.; Goddard, M.E. (2003). Genetic and environmental relationships among calving interval, survival, persistency of milk yield and somatic cell count in dairy cattle. Livest. Prod. Sci. 80:189-200.
Hailiang, Z.; Yachun, W.; Yao, Ch.; Hanpeng, L.; Brito, L.F.; Yixin, D.; Rui, S.; Yajing, W.; Ganghui, D.; Lin, L. (2019). Mortality-Culling Rates of Dairy Calves and Replacement Heifers and Its Risk Factors in Holstein Cattle. Animals. 9(10): 730. https://doi.org/10.3390/ani9100730
Heringstad, B.; Chang, Y.M.; Gianola, D.; Klemetsdal, G. (2016). Genetic analysis of longitudinal trajectory of clinical mastitis in first-lactation Norwegian Cattle. J. Dairy Sci. 86: 2676-2683.
Horn, M.; Knaus, W.; Kirner, L; Steinwidder, A. (2012). Economic Evaluation of Longevity in Organic Dairy Farming. Organic Agriculture. 2: 127–143. https://doi.org/10.1007/s13165-012-0027-6
Horvath, J.; Tóth, Z.; Miko, E. (2017a). The analysis of production and culling rate about the profitability in a dairy herd. Advanced Research in Life Sciences. 1(1): 48–52. https://doi.org/10.1515/arls-2017-0008
Horvath, J.; Tóth, Z.; Miko, E. (2017b). “The economic importance of productive lifetime in dairy cattle breeding”. Lucrări Stiințifice. 19(2): 73–78.
Hernández, A.; Ponce de León, R.E. (2018). Performance of dairy production, reproduction and longevity in Holstein and its crosses with Cebu. Cuban Journal of Agricultural Science. 52(3): 235-247.
Indrijani, H.; Anang, A.; Tasripin, D.; Salman, L.B. (2019). Milk Production Curves on Various. Test Day Patterns (Case in BBPTU-HPT Baturraden). https://doi.org/10.1088/1755-1315/334/1/012005
IICA - Instituto Interamericano de Cooperación para la Agricultura. (2019). IICA advocates for sustainable production systems in the dairies of America. https://apps.iica.int/SReunionesOG/Content/Documents/CE2020/6823caf3-a2fe-4545-92a9-c29752e4cd49_dt712_informe_anual_de_2019_del_iica.pdf
ICAR - International Committee for Animal Recording. (2021). Icar Guidelines. https://www.icar.org/index.php/icar-recording-guidelines/
Kadarmideen, H.N.; Thompson, R.; Coffey, M.P.; Kossaibati, M.A. (2013). Genetic parameters and evaluations from single- and multiple-trait analysis of dairy cow fertility and milk production. Livest. Prod. Sci. 81: 183-195.
Kong, L.; Li, J.; Li, R.; Zhao, X.; Ma, Y.; Sun, S.; Zhong, J. (2017). Estimation of 305-day milk yield from test-day records of Chinese Holstein cattle. Journal of Applied Animal Research. 46(1): 791–797. https://doi.org/10.1080/09712119.2017.1403918
Le Blanc, S. (2010). Assessing the association of the level of milk production with Reproductive performance in dairy cattle. J Reprod Dev. 56: 1–7.
Leon-Gomez; I.L.; Saray-Palacio, Y.T. (2020). Comparative analysis of the dairy sector Colombian versus the Pacific Alliance. http://repositorio.uniagustiniana.edu.co/handle/123456789/1477
Loaiza-Muñoz, E. (2020). Control lechero en el norte de Antioquia. http://repository.unilasallista.edu.co/dspace/handle/10567/2746
Lopes, M.A.; Cardoso, M.G.; Demeu, F.A. (2009a). Influence of different indices. Zootechnics in the Composition and Evolution of Leiteiros Bovine Herds. Ciência Animal Brasileira. 10(2): 446-453.
Lopes, M.A.; Demeu, F.A.; dos Santos, G.; Ghedini-Cardoso, M. (2009b). Impact economic of the calving interval in dairy cattle herds. Comunicação. Ciência e Agrotecnología. 33: 1908-1914. https://doi.org/10.1590/S1413-70542009000700036
Loor Cevallos, T.C.; Saltos Alcívar, S.P. (2021). Factibilidad económica financiera de la producción lechera en la unidad hato bovino de la ESPAM. Calceta: ESPAM MFL. 47p. http://repositorio.espam.edu.ec/handle/42000/1401
Mancuso, W.A. (2017). Evaluación y comparación de grupos genéticos lecheros en un sistema a pastoreo de la comarca lechera de Entre Ríos, Argentina. http://hdl.handle.net/10347/15513
Marini, P.R.; Oyarzabal, M.I. (2002). Production patterns in dairy cows. Description of the average cow and estimate of income according to categories of production. Rev. Arg. Prod. Anim. 22(1): 47-60.
Marini, P.R.; Biga, P.; Di-Masso, R.J. (2021). Multivariate characterization of efficiency productive-reproductive age and age at first calving in Holstein cows. Agronomy Mesoamerican. 32(1): 34-44.
Mayne, S. (2017). Selecting the correct dairy cow for grazing systems. Dairy Research Corporation Ltd. 45-49.
Montoya Zuluaga, J.J.; Munera Bedoya, O.D.; Ceron-Munoz M.F. (2017). Factors Associated with milk urea nitrogen in dairy cows. Livestock Research for Rural Development. 29(10): 9.
Murray, R. (2018). Artificial insemination: Troubleshooting to improve fertility. https://en.engormix.com/poultry-industry/forums/artificial-insemination-troubleshooting-improve-t44755/
Piccardi, M.G.; Romero, G.; Veneranda, E.; Castello, E.; Romero, D.; Balzarini, M.; Bó, G.A. (2016). Effect of puerperal metritis on reproductive and productive performance in dairy cows in Argentina. Theriogenology. 85(5): 887-893. 10.1016/j.theriogenology.2015.10.038
Reyes, F.; Chávez, J.; Condo, L.M.R. (2020) Association between milk production and reproductive parameters in Holstein biotypes with different productive potential. Cienc. Digit. 4(3): 6–23. https://doi.org/10.33262/cienciadigital.v4i3.1273
Rogers, G.W.; Van Arendonk, J.A.M.; McDaniel, B.T. (1988). Influence of involuntary culling on best culling rates and annualized net revenue. Journal of Dairy Science. 71: 3463-3469. https://doi.org/10.3168/jds.S0022-0302(88)79952-X
Shafer, W. (2006). Implementation of a stay ability EPD: American Simmental Association perspective. https://www.bifconference.com/bif2006/pdfs/Shaferstay.pdf
Sheskin, D.J. (2011). Handbook of parametric and nonparametric statistical procedures. https://dl.icdst.org/pdfs/files3/22a131fac452ed75639ed5b0680761ac.pdf
Tobón Roldán, J.F. (2022). Estudio de prefactibilidad en la construcción de un centro de ciencia, tecnología e innovación abierta en ganadería de leche para Colombia. http://hdl.handle.net/10784/31950
USDA - United States Department of Agriculture. (2020). Milk Production. https://www.nass.usda.gov/Charts_and_Maps/Milk_Production_and_Milk_Cows/index.php
Veerkamp, R.F.; Koenen, E.P.C; De Jong, G. (2015). Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models. J. Dairy Sci. 84: 2327–2335.
Villalobos, J.; Ching-Jones, R. (2019). Selección de vacas Jersey y Holstein durante la lactancia según características fenotípicas: producción y reproducción. Cuadernos de Investigación UNED. 11(3): 257-271. https://doi.org/10.22458/urj.v11i3.2579
Velázquez, R.V.; del Valle, W.J.; Landin, A.L. (2021). Efecto del uso combinado de Catosal, Vigantol y Tonofosfan sobre el comportamiento reproductivo de vacas Brahman. Roca: Revista Científico - Educaciones de la provincia de Granma. 17(1). 13.
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