Study of the fluctuations of Methane (CH4) and Carbon Dioxide (CO2), in bovine production bars for milk from Paraguay, using “IoT” technology

• Oscar Roberto Martínez-López Centro Multidisciplinario de Investigaciones Tecnológicas, Universidad Nacional de Asunción. San Lorenzo, Paraguay. Facultad de Ciencias Veterinarias, Universidad Nacional de Asunción. San Lorenzo, Paraguay.
• María Inés Rodríguez-Acosta Facultad de Ciencias Veterinarias, Universidad Nacional de Asunción. San Lorenzo, Paraguay

DOI: https://doi.org/10.52973/rcfcv-luz313.art4

Keywords: Livestock, milky, technology, polluting, integrated

Abstract

The work was carried out to record fluctuations in Carbone Dioxide (CO₂) and Methane (CH₄) in traditional Paraguayan dairy models, including two “Systems” (intensive vs semi-intensive). The objective was to generate the first real database in the Country, with which, to begin to really size it and categorize it. It was emphasized that bovine farming is a substantially important socio-economic area of the Country, with it, the dairy sector is extremely relevant to cover national consumption and exports. Likewise, it was sought to discriminate by production “System”, its inferred in the fluctuation of CO₂ and CH₄. Also, fragmenting the day into four time bands (Early Morning, Day, Afternoon and Night), if they verified important differences in the emanation of these greenhouse gases GHGs. For the purpose, the “IoT” (internet of things) technology was used, by means of a Smart Environment Libelilum equipment, which generated in real time, a reading of gases mentioned every 6 to 7 minutes and transmitted to a digital platform, forming the basis of data. More than 8,500 data were analyzed for each gas and parallel to temperature, humidity and atmospheric pressure. Statistical software R was implemented for the analysis of the results. Overall, the highest average parts per million (ppm) CO₂ by time zone was found in the morning (06:00 to 12:00). Regarding Systems, the highest mean CO₂ was evidenced in the Intensive. The fluctuating CH₄ (% LEL) levels in both bovine milk production sheds, regardless of category, remained below the smart sensor uptake level (70 ppm). A moderate positive correlation  was detected between levels of CO₂ and temperature (ºC). Negative correlation between CO₂ and humidity. The fluctuating levels of CO₂ (ppm) in both dairy systems, in Paraguay, regardless of time bands or systems, can be considered low.

References

ALBERTON, B. A. V.; NICHOLS, T. E.; GAMBA, H. R.; WINKLER, A. M. Multiple testing correction over contrasts for brain imaging. NeuroImage. 216: 1-14. 2020.

ANDERSON, M. J.; MILLAR, R. B. Spatial variation and effects of habitat on temperate reef fish assemblages in northeastern New Zealand. J. Exper. Marine Biol. Ecol. 305(2): 191-221. 2004.

ANDERSON, M. J. Permutation tests for univariate or multivariate analysis of variance and regression. Canad. J. Fish. Aquatic Sci. 58(3): 626-639. 2001.

AUBRY, A.; YAN, T. Meta-analysis of calorimeter data to establish relationships between methane and carbon dioxide emissions or oxygen consumption for dairy cattle. Anim. Nutr.1: 128-134. 2015.

AVELLO-MARTÍNEZ, R.; SEISDEDO-LOSA, A. El procesamiento estadístico con R en la investigación científica. MediSur.15(5): 583-586. 2017.

BULOT, F. M. J.; JOHNSTON, S. J.; BASFORD, P. J.; EASTON, N. H. C.; APETROAIE-CRISTEA, M.; FOSTER, G. L.; MORRIS, A. K. R.; COX, S. J.; LOXHAM, M. Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment. Sci. Rep. 9(1): 1–13. 2019.

COMMISSION INTERNATIONALE DU GENIE RURAL (CIGR). Climatization of Animal Houses, Report of working group on climatisation of animal houses. Report of working group. 1984. Aberdeen, Scotland. On Line: https://bit.ly/3hNcyJ8.01-09-20.

COLE, N. A.; MEYER, B. E.; PARKER, D. B.; NEEL, J.; TURNER, K. E.; NORTHUP, B. K.; JENNINGS, T.; JENNINGS, J. S. Effects of diet quality on energy metabolism and methane production by beef steers fed a warm-season grass-based hay diet*. Appl. Anim. Sci. 36: 652-667. 2020

FEDDES, J. J. R.; LEONARD, J. J.; MCQUITTY, J. B. Carbon Dioxide Concentration as a Measure of Air Exchange in Animal Housing. Can. Agric. Eng. 26: 53-56. 1984.

FERNÁNDEZ-LIZANA, M. I. Ventajas de R como herramienta para el Análisis y Visualización de datos en Ciencias Sociales. Rev. Científ. UCSA. 7(2): 97-111. 2020.

HARPER, L. A.; DENMEAD, O. T.; FRENEY, J. R.; BYERS, F. M. Direct measurement of methane emissions from grazing and feedlot cattle. J. Anim. Sci. 77: 1392-1401. 1999.

JUNGBLUTH, T.; HARTUNG, E.; BROSE, G. Greenhouse gas emissions from animal houses and manure stores. Nutr. Cycl. Agroecosyst. 60: 133-145. 2001.

KINSMAN, R.; SAUER, F. D.; JACKSON, H. A.; WOLYNETZ, M. S. Methane and carbon dioxide emissions from dairy cows in full lactation monitored over a six-month period. J. Dairy Sci. 78(12): 2760-2766. 1995.

KIRCHGESSNER, M.; WINDISH, W.; MÜLLER, H. L.; KREUZER, M. Release of stocking methane and of carbon dioxide by dairy cattle. Agribiol. Res. 44: 91-102. 1991.

LIBELIUM. Libelium World. 2021. Smart Environment. On Line: https://www.libelium.com/. 22-05-2021.

MADSEN, J.; BJERG, B. S.; HVELPLUND, T.; WEISBJERG, M. R.; LUND, P. Methane and carbon dioxide ratio in excreted air for quantification of the methane production from ruminants. Livest. Sci. 129: 223-227. 2010.

MARTÍNEZ-LÓPEZ, R. Contrastes de normalidad. En: Métodos estadísticos aplicados en Zootecnia. 1a Ed. Etigraf, Asunción. 292pp. 2017.

PÉREZ, R.; NARVAJAS, S.; TERRY, E. IoT en ALC 2019: Tomando el pulso al Internet de las Cosas en América Latina y el Caribe. 2019. Banco Interamericano de Desarrollo (BID). En linea: https://doi.org/gmtr. 28-09-20.

R CORE TEAM. R: A language and environment for statistical computing. 2020. R Foundation for Statistical Computing, Vienna, Austria. On Line: https://www.r-project.org/.01-09-20

ROBERTSON, L. J.; WAGHORN, G. C. Dairy industry perspectives on methane emissions and production from cattle fed pasture or total mixed rations in New Zealand. Proc. N. Z. Soc. Anim. Prod. 62: 213–218. 2002.

RODRÍGUEZ, J. C.; PAZ-PELLAT, F.; WATTS, C.; LIZARRAGACELAYA, C.;YÉPEZ-GONZÁLEZ, E.; JIMÉNEZ-FERRER, G.; CASTELLANOS-VILLEGAS, A.; HINOJO-HINOJO, C.; MACÍAS-VÁZQUEZ, C. E. Methane and carbon dioxide measurements using the eddie covariance technique in semi-stabled dairy cattle in Sonora, México. Terra LatinAme. 37(1): 69-80. 2019.

SIEGEL, S.; CASTELLAN, N. J. Medidas de Asociación no paramétricas. En: Estadística no paramétrica: aplicada a las ciencias de la conducta. 4a Ed. Trillas, México. 437pp. 1995.

TEYE, F. K.; ALKKIOMAKI, E.; SIMOJOKI, A.; PASTELL, M.; AHOKAS, J. Instrumentation, measurement and performance of three air quality measurement systems for dairy buildings. Appl. Eng. Agric. 25: 247–256. 2009.

TEYE, K. F.; HAUTALA, M.; PASTELL, M.; PRAKS, J.; VEERMÄE, I.; POIKALAINEN, V.; PAJUMÄGI, V.; KIVINEN, T.; AHOKAS, J. Microclimate and ventilation in Estonian and Finnish dairy buildings. Energy Build. 40(7): 1194-1201. 2007.

UNITED NATIONS FRAMEWORK CONVENTION ON CLIMATE CHANGE (UNFCCC). Acuerdo Internacional de París. 2015. Framework Convention on Climate Change. United Nations. On Line: https://bit.ly/3hQU4ra. 28-09-20