Relationships between milk mid-IR predicted gastro-enteric methane production and the technical and financial performance of commercial dairy herds. (22nd December 2017) - Delhez, P.; Wyzen, B.; Dalcq, A.-C.; Colinet, F. G.; Reding, E.; Vanlierde, A.; Dehareng, F.; Gengler, N.; Soyeurt, H.
Abstract : Considering economic and environmental issues is important in ensuring the sustainability of dairy farms. The objective of this study was to investigate univariate relationships between lactating dairy cow gastro-enteric methane (CH4 ) production predicted from milk mid-IR (MIR) spectra and technico-economic variables by the use of large scale and on-farm data. A total of 525 697 individual CH4 predictions from milk MIR spectra (MIR-CH4 (g/day)) of milk samples collected on 206 farms during the Walloon milk recording scheme were used to create a MIR-CH4 prediction for each herd and year (HYMIR-CH4 ). These predictions were merged with dairy herd accounting data. This allowed a simultaneous study of HYMIR-CH4 and 42 technical and economic variables for 1024 herd and year records from 2007 to 2014. Pearson correlation coefficients ( r ) were used to assess significant relationships ( P <0.05). Low HYMIR-CH4 was significantly associated with, amongst others, lower fat and protein corrected milk (FPCM) yield ( r =0.18), lower milk fat and protein content ( r =0.38 and 0.33, respectively), lower quantity of milk produced from forages ( r =0.12) and suboptimal reproduction and health performance (e.g. longer calving interval ( r =−0.21) and higher culling rate ( r =−0.15)). Concerning economic results, low HYMIR-CH4 was significantly associated with lower gross margin per cow ( r =0.19) and per litre FPCM ( r =0.09). To conclude, this study suggested that low lactating dairy cow gastro-enteric CH4 production tended to be associated with more extensive or suboptimal management practices, which could lead to lower profitability. The observed low correlations suggest complex interactions between variables due to the use of on-farm data with large variability in technical and management practices.