Heat stress effect on dairy cattle production in Costa Rica

Authors

  • Juan Ismael Ruiz-Jaramillo Universidad Nacional / Costa Rica
  • Bernardo Vargas-Leitón Universidad Nacional/ Costa Rica https://orcid.org/0000-0002-1778-9672
  • Sergio Abarca-Monge Instituto Nacional de Innovación y Transferencia en Tecnología Agropecuaria (INTA
  • Hugo G. Hidalgo Universidad de Costa Rica

DOI:

https://doi.org/10.15517/am.v30i3.35984

Keywords:

milk production, heat tolerance, climate change, heat stress

Abstract

Introduction. Dairy production in tropical zones is characterized by a high dependence on the forage resource, which makes it sensitive to climatic variables of which there is limited information on their effect on the performance of specialized dairy breeds. Objective. The objective of this study was to evaluate the association between temperature and humidity index (THI) and daily milk production (kgl) of cows breeds Holstein, Jersey, and their crosses. Materials and methods. The study was conducted in the northern and central regions of Costa Rica, with data collected between 1990 and 2015. THI values were obtained using linear predictive models and spatial autocorrelation models, applied to 3,547 monthly records of maximum temperature and relative humidity from seventeen weather stations. 6,478,582 kgl records from 418 dairy herds were analyzed, using a generalized mixed linear model (GLMM), which considered the fixed effects of breed, year and month, birth number, lactation stage, breed × lactation stage, ENSO (Effect “El Niño”), ITH and breed×ITH, in addition to a random effect of the cow. Results. All factors had a highly significant effect (p<0.0001) on kgl. For the ITH range between 72-88, linear reductions of 0.41 (r2=0.94), 0.36 (r2=0.95) and 0.29 (r2=0.82) kg day-1 were estimated for Holstein, Holstein×Jersey and Jersey, respectively. No significant trends were observed for kgl when ITH <72. The economic losses attributable to the greater stress exposure in the north compared to the central region were estimated at $680, $587 and $477 per lactation and cow for Holstein, Holstein×Jersey and Jersey, respectively. Conclusion. There was an inverse relationship between temperature and humidity index and milk production in Holstein, Jersey and crossbreed cows for this tropical region.

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Author Biography

Bernardo Vargas-Leitón, Universidad Nacional/ Costa Rica

ESCUELA DE MEDICINA VETERINARIA

UNIVERSIDAD NACIONAL

ACADEMICO INVESTIGADOR

References

Armstrong, D. 1994. Heat stress interaction with shade and cooling. J. Dairy Sci. 77:2044-2050. doi:10.3168/jds.S0022-0302(94)77149-6

Bohmanova, J., I. Misztal, and J. Cole. 2007. Temperature-humidity indices as indicators of milk production losses due to heat stress. J. Dairy Sci. 90:1947-1956. doi:10.3168/jds.2006-513

Bolívar, D., J. Echevarry, L. Restrepo, y M. Muñoz. 2009. Productividad de vacas Jersey, Holstein y Jersey×Holstein en una zona de bosque húmedo montano bajo (Bh-MB). Livest. Res. Rural Dev. 21(6):80. http://www.lrrd.org/lrrd21/6/boli21080.htm (consultado 12 nov. 2016).

Bonilla, A. 2014. Patrones de sequía en Centroamérica. Su impacto en la producción de maíz y frijol y uso del Índice Normalizado de Precipitación para los Sistemas de Alerta Temprana. GWP Centroamérica, Tegucigalpa, HND. https://www.gwp.org/globalassets/global/gwp-cam_files/patrones-de-sequia_fin.pdf (consultado 11 jul. 2016).

Bouraoui, R., M. Lahmar, A. Majdoub, M. Djemali, and R. Belyea. 2002. The relationship of temperature-humidity index with milk production of dairy cows in a Mediterranean climate. Anim. Res. 51:479-491. doi:10.1051/animres:2002036

Bouroncle, C., P. Imbach, P. Läderach, B. Rodríguez, C. Medellín, E. Fung, M.R. Martínez, y C.I. Donatti. 2015. La agricultura de Costa Rica y el cambio climático: ¿Dónde están las prioridades para la adaptación? CGIAR, Montpellier, FRA. https://cgspace.cgiar.org/bitstream/handle/10568/45941/PB%20Costa%20Rica.pdf?sequence=7 (consultado 3 Jul. 2016).

Bryant, J.R., N. López-Villalobos, J.E. Pryce, C.W. Holmes, and D.L. Johnson. 2007. Quantifying the effect of thermal environment on production traits in three breeds of dairy cattle in New Zealand. N.Z.J. Agric. Res. 50:327-338. doi:10.1080/00288230709510301

Chilès, J.P., and N. Desassis. 2018. Fifty years of kriging. In: B.S. Daya et al., editors, Handbook of mathematical geosciences. Springer Nature, CHE. p. 589-612. doi:10.1007/978-3-319-78999-6_29

CPC (Climate Prediction Center). 2016. Cold & warm episodes by season. CPC, USA. http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml (accessed Aug. 3, 2016).

Dunn, R.J.H., N.E. Mead, K.M. Willet, and D.E. Parker. 2014. Analysis of heat stress in UK dairy cattle and impact on milk yields. Environ. Res. Lett. 9:064006. doi:10.1088/1748-9326/9/6/064006

Gantner, V., P. Mijić, K. Kuterovac, D. Solić, and R. Gantner. 2011. Temperature-humidity index values and their significance on the daily production of dairy cattle. Mljekarstvo 61(1):56-63.

Garcia, A., N. Angeli, L. Machado, F. de-Cardoso, and F. Gonzalez. 2015. Relationships between heat stress and metabolic and milk parameters in dairy cows in southern Brazil. Trop. Anim. Health Prod. 47:889-894. doi:10.1007/s11250-015-0804-9

González, J.M. 2014. El estrés calórico en bovinos. Universidad Especializada en Producción Animal (UDCA), ARG. http://www.produccion-animal.com.ar/etologia_y_bienestar/bienestar_en_bovinos/14-stres.pdf (consultado 13 nov. 2016).

Hammami, H., J. Bormann, N. M’hamdi, H. Montaldo, and N. Gengler. 2013. Evaluation of heat stress effects on production traits and somatic cell score of Holsteins in a temperate environment. J. Dairy Sci. 96:1844-1855. doi:10.3168/jds.2012-5947

IMN (Instituto Meteorológico Nacional). 2008. El clima, su variabilidad y cambio climático en Costa Rica. Segunda comunicación nacional. IMN, San José, CRI.

Iñamagua-Uyaguari, J.P., A. Jenet, L.G. Alarcón-Guerra, S.J. Vílchez-Mendoza, F. Casasola-Coto, y M.A. Wattiaux. 2016. Impactos económicos y ambientales de las estrategias de alimentación en lecherías de Costa Rica. Agron. Mesoam. 27:1-17. doi:10.15517/am.v27i1.21874

Kolovos, A. 2010. Everything in its place: efficient geostatistical analysis with SAS/STAT® spatial procedures. SAS Institute Inc., Cary, NC, USA. http://support.sas.com/resources/papers/proceedings10/337-2010.pdf (accessed Sep. 27, 2016).

NRC (National Research Council). 1971. A guide to environmental research on animals. National Academy of Sciences, WA, USA.

Ordaz, J., D. Ramírez, J. Mora, A. Acosta, y B. Serna. 2010. Costa Rica: efectos del cambio climático sobre la agricultura. CEPAL, México D.F., MEX.

Pragna, P., P. Archana, J. Aleena, V. Sejian, G. Krishnan, M. Bagath, A. Manimaran, V. Beena, E. Kurien, G. Varma, and R. Bhatta. 2017. Heat stress and dairy cow: Impact on both milk yield and composition. Int. J. Dairy Sci. 12:1-11. doi:10.3923/ijds.2017.1.11

Ravagnolo, O., and I. Misztal. 2000. Genetic component of heat stress in dairy cattle, parameter estimation. J. Dairy Sci. 83:2126-2130. doi:10.3168/jds.S0022-0302(00)75095-8

Ríos, N., y M. Ibrahim. 2008. Impactos del cambio climático sobre los recursos hídricos. Boletín Técnico No. 30. CATIE, Turrialba, CRI.

Romero, J., J. Rojas, y S. Estrada. 2011. El programa VAMPP Bovino, una herramienta para la toma de decisiones. Ventana Lechera 5:4-10.

Ruiz-Jaramillo, I. 2017. Variabilidad climática en las regiones Norte, Central y Caribe y su asociación con variables productivas en fincas lecheras costarricenses. Tesis M.Sc., Universidad Nacional, Heredia, CRI.

SAS Institute. 2013. SAS/STAT 9.4 User´s Guide. SAS Institute Inc., Cary, NC, USA.

St-Pierre, N.R., B. Cobanov, and G. Schnitkey. 2003. Economic losses from heat stress by US livestock industries. J. Dairy Sci. 86:E52-E77. doi:10.3168/jds.S0022-0302(03)74040-5

Vallejos, S., L. Esquivel, y M. Hidalgo. 2012. Histórico de desastres en Costa Rica (Febrero 1723 - Setiembre 2012). Biblioteca Virtual en Salud, San José, CRI. http://www.bvs.sa.cr/ambiente/4923.pdf (consultado 15 jul. 2016).

Vargas, B. 2016. Tendencias poblacionales por raza. Universidad Nacional, Heredia, CRI. http://www.medvet.una.ac.cr/posgrado/gen/tend/tendxraza1609.xlsx (consultado 15 dic. 2016).

Vargas, B., y G. Gamboa. 2008. Estimación de tendencias genéticas e interacción genotipo×ambiente en ganado lechero de Costa Rica. Téc. Pecu. Méx. 46:371-386.

Vargas, B., y J. Ulloa. 2008. Relación entre curvas de crecimiento y parámetros reproductivos en grupos raciales lecheros de distintas zonas agroecológicas de Costa Rica. Livest. Res. Rural Dev. 20(7):122. http://www.lrrd.org/lrrd20/7/varg20103.htm (consultado 18 oct. 2015).

Vargas-Leitón, B., Y. Marín-Marín, y J.J. Romero-Zúñiga. 2012. Comparación bioeconómica de grupos raciales Holstein, Jersey y Holstein×Jersey en Costa Rica. Agron. Mesoam. 23:329-342. doi:10.15517/am.v23i2.6533

Vargas-Leitón, B., O. Solís-Guzmán, F. Saénz-Segura, y H. León-Hidalgo. 2013. Caracterización y clasificación de hatos lecheros en Costa Rica mediante análisis multivariado. Agron. Mesoam. 24:257-275. doi:10.15517/am.v24i2.12525

West, J., B. Mullinix, and J. Bernard. 2003. Effects of hot, humid weather on milk temperature, dry matter intake, and milk yield of lactating dairy cows. J. Dairy Sci. 86:232-242. doi:10.3168/jds.S0022-0302(03)73602-9

Zewdu, W., B. Thombre, and D. Bainwad. 2014. Effect of macroclimatic factors on milk production and reproductive efficiency of Holstein Friesian×Deoni crossbred cows. J. Cell Anim. Biol. 8:51-60. doi:10.5897/JCAB2014.0408

Zimbelman, R., R. Rhoads, M. Rhoads, L. Baumgard, and R. Collier. 2009. A re-evaluation of the impact of temperature humidity index (THI) and black globe humidity index (BGHI) on milk production in high producing dairy cows. In: R.J. Collier, editor, Proc. 24th Annual Southwest Nutrition and Management Conference. University of Arizona, Tempe, AZ, USA. p. 158-169.

Published

2019-09-01

How to Cite

Ruiz-Jaramillo, J. I., Vargas-Leitón, B., Abarca-Monge, S., & Hidalgo, H. G. (2019). Heat stress effect on dairy cattle production in Costa Rica. Agronomía Mesoamericana, 30(3), 733–750. https://doi.org/10.15517/am.v30i3.35984

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