Evaluation of the cubicle hood sampler for monitoring methane production of dairy cows under barn conditions

被引:0
作者
Levrault, Cecile M. [1 ]
Koerkamp, Peter W. G. Groot [1 ]
Peeters, Carel F. W. [2 ]
Ogink, Nico W. M. [1 ,3 ]
机构
[1] Wageningen Univ & Res, Agr Biosyst Engn, POB 16, NL-6700 AA Wageningen, Netherlands
[2] Wageningen Univ & Res, Math & Stat Methods Grp Biometris, POB 16, NL-6700 AA Wageningen, Netherlands
[3] Wageningen Res & Res, Wageningen Livestock Res, POB 338, NL-6700 AH Wageningen, Netherlands
关键词
Dairy cattle; Methane production rate; Modelling; Practical monitoring; Sensing; CARBON-DIOXIDE; SF6; TRACER; EMISSIONS; CATTLE; QUANTIFICATION; MITIGATION; MANAGEMENT; GREENFEED; BEEF;
D O I
10.1016/j.biosystemseng.2025.02.008
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
Monitoring methane production from individual cows is necessary to evaluate the success of greenhouse gas reduction strategies. However, monitoring methane production rates (MPR) under practical conditions remains challenging. In this paper, we investigate the performance of a potential solution to this challenge. The cubicle hood sampler (CHS) is an on-barn monitoring device placed in cubicles that collects the air exhaled by the animals while they lie down. The MPR of 28 dairy cows were measured by four CHS devices and compared to the levels measured by climate respiration chambers (CRC). A linear regression showed no strong correlation between the two sets of estimates (r = 0.24). The estimates made by the CHS appeared to be inaccurate due to a sampling bias (insufficient breath recovery), which could not be corrected for. Using Bayesian modelling, information was pooled across individuals to model complete methane production curves and potentially improve the accuracy of the MPR estimates. However, the model was unable to compensate for the biased observations used for fitting, and accuracy levels did not improve. An under-recovery of the breath samples by the hood is suspected. These issues must be resolved. Nevertheless, the CHS ranked cows satisfactorily, with Kendall W values of 0.625 (p = 0.201) in the original dataset, and 0.659 (p = 0.214) after using the model. Resolving the bias issue is expected to have a simultaneous positive effect on the agreement between the two MPR rankings. We recommend to keep using the model to convert discrete measurements into methane production curves.
引用
收藏
页码:115 / 125
页数:11
相关论文
共 46 条
  • [1] Alemu A.W., Vyas D., Manafiazar G., Basarab J.A., Beauchemin K.A., Enteric methane emissions from low– and high–residual feed intake beef heifers measured using GreenFeed and respiration chamber techniques1,2, Journal of Animal Science, 95, pp. 3727-3737, (2017)
  • [2] Alferink S.J.J., Heetkamp M.J.W., Gerrits W.J.J., Indirect calorimetry, chapter 14: Computing energy expenditure from indirect calorimetry data: A calculation exercise, (2015)
  • [3] Boadi D., Chiquette J., Masse, D, Benchaar C., Mitigation strategies to reduce enteric methane emissions from dairy cows, Update review, Canadian Journal of Animal Science, 84, pp. 319-335, (2004)
  • [4] Bolstad W.M., Understanding computational bayesian statistics, Journal of Statistical Software, 80, (2010)
  • [5] Burkner P.-C., Brms : An R package for bayesian multilevel models using stan, J Stat Softw, 80, (2017)
  • [6] Crompton L.A., Mills J.A.N., Reynolds C.K., France J., Fluctuations in methane emission in response to feeding pattern in lactating dairy cows, Modelling nutrient digestion and utilisation in farm animals, pp. 176-180, (2011)
  • [7] CVB Veevoedertabel 2018: Chemische samenstellingen en nutritionele waarden van voedermiddelen, nr 43, (2018)
  • [8] Eddelbuettel D., Francois R., Rcpp: Seamless R and C++ integration, Journal of Statistical Software, 40, pp. 1-18, (2011)
  • [9] Global anthropogenic non-CO2 greenhouse gas emissions, (2012)
  • [10] FAOSTAT analytical brief 25. Emissions from agriculture and forest land, (2021)