Comparison of a laser methane detector with the GreenFeed and two breath analysers for on-farm measurements of methane emissions from dairy cows

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  • Diana Sorg, University Halle-Wittenberg, German Environment Agency (Umweltbundesamt)
  • ,
  • Gareth F. Difford, Wageningen Animal Breeding and Genomics Centre, Wageningen University and Research Centre
  • ,
  • Sarah Mühlbach, University Halle-Wittenberg
  • ,
  • Björn Kuhla, Research Institute for the Biology of Farm Animals, Dummerstoft
  • ,
  • Hermann H. Swalve, University Halle-Wittenberg
  • ,
  • Jan Lassen
  • Tomasz Strabel, Poznan University of Life Sciences
  • ,
  • Marcin Pszczola, Poznan University of Life Sciences

To measure methane (CH4) emissions from cattle on-farm, a number of methods have been developed. Combining measurements made with different methods in one data set could lead to an increased power of further analyses. Before combining the measurements, their agreement must be evaluated. We analysed data obtained with a handheld laser methane detector (LMD) and the GreenFeed system (GF), as well as data obtained with LMD and Fourier Transformed Infrared (FTIR) and Non-dispersive Infrared (NDIR) breath analysers (sniffers) installed in the feed bin of automatic milking systems. These devices record short-term breath CH4 concentrations from cows and make it possible to estimate daily CH4 production in g/d which is used for national CH4 emission inventories and genetic studies. The CH4 is released by cows during eructation and breathing events, resulting in peaks of CH4 concentrations during a measurement which represent the respiratory cycle. For LMD, the average CH4 concentration of all peaks during the measurement (P_MEAN in ppm × meter) was compared with the average daily CH4 production (g/d) measured by GF on 11 cows. The comparison showed a low concordance correlation coefficient (CCC; 0.02) and coefficient of individual agreement (CIA; 0.06) between the methods. The repeated measures correlation (rp) of LMD and GF, which can be seen as a proxy for the genetic correlation, was, however, relatively strong (0.66). Next, based on GF, a prediction equation for estimating CH4 in g/d (LMD_cal) using LMD measurements was developed. LMD_cal showed an improved agreement with GF (CCC = 0.22, CIA = 0.99, rp = 0.74). This prediction equation was used to compare repeated LMD measurements (LMD_val in g/d) with CH4 (g/d) measured with FTIR (n = 34 cows; Data Set A) or NDIR (n = 39 cows; Data Set B) sniffer. A low CCC (A: 0.28; B: 0.17), high CIA (A: 0.91; B: 0.87) and strong rp (A: 0.57; B: 0.60) indicated that there was some agreement and a minimal re-ranking of the cows between sniffer and LMD. Possible sources of disagreement were cow activity (LMD: standing idle; sniffer: eating and being milked) and the larger influence of wind speed on LMD measurement. The LMD measurement was less repeatable (0.14–0.27) than the other techniques studied (0.47–0.77). Nevertheless, GF, LMD and the sniffers ranked the cows similarly. The LMD, due to its portability and flexibility, could be used to study CH4 emissions on herd or group level, as a validation tool, or to strengthen estimates of genetic relationships between small-scale research populations.

OriginalsprogEngelsk
TidsskriftComputers and Electronics in Agriculture
Vol/bind153
Sider (fra-til)285-294
Antal sider10
ISSN0168-1699
DOI
StatusUdgivet - okt. 2018

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