TY - JOUR
T1 - A data-driven approach to the processing of sniffer-based gas emissions data from dairy cattle
AU - Løvendahl, Peter
AU - Milkevych, Viktor
AU - Krogh Nielsen, Rikke
AU - Bjerring, Martin
AU - Manzanilla-Pech, Coralia
AU - Johansen, Kresten
AU - Difford, Gareth F.
AU - Villumsen, Trine M.
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/12
Y1 - 2024/12
N2 - “Sniffers” record methane (CH4) emissions from cows visiting milking robots, providing gas concentration data. These instruments have infrared carbon dioxide (CO2) and CH4 sensors, an air pump, and a data logger. In this study, a process for the synchronization of sniffer emissions data with cow identification (ID) data and records from automatic milking systems (AMSs) was developed. The process enables the extraction of gas phenotypes for genetic analysis. It involves the calculation of intermediate control variables to account for time drift in data loggers, sensor calibration drift, and background concentration fluctuations, and the condensation of data from each milking visit into a single datapoint. The process was developed and assessed with research station data from three groups of approximately 70 cows, each accessing one AMS unit over a 2-month period. Raw emissions data, including clock times, from CH4 and CO2 channels were recorded every second. They were synchronized with the AMS data using specific events occurring in the CH4 or CO2 channel at the beginning or end of each milking event. The synchronized data were divided into non-milking (baseline, ambient gas concentrations) and cow ID–linked milking (cow emissions) sets. The non-milking periods varied in duration from a few seconds to hours, and some were interrupted by unrecorded events. Baseline values were extracted after the filtering of non-milking period data against unrecorded events (e.g., washing, feed-only sessions) and the use of a small fractile as the baseline estimate. At the beginning of each milking event, 30–45 s were required for the CH4 and CO2 concentrations to reach stable high levels, and most events lasted at least 5 min. Accordingly, a restricted recording window of 30–300 s, which excluded the initial unstable period while retaining data from the majority of milking events, was established. Gas concentrations significantly exceeding the baseline were selected as responses to ensure that only data obtained when the cows’ heads were sufficiently close to the sniffer air inlets were included. The mean value of the selected records was used as the response phenotype for each milking event. The concentration phenotypes showed moderate to high repeatability, but the CH4:CO2 ratio had only moderate repeatability. The pipeline developed in this study enables the effective extraction of baseline-adjusted emissions phenotypes from sniffer data obtained in milking robots.
AB - “Sniffers” record methane (CH4) emissions from cows visiting milking robots, providing gas concentration data. These instruments have infrared carbon dioxide (CO2) and CH4 sensors, an air pump, and a data logger. In this study, a process for the synchronization of sniffer emissions data with cow identification (ID) data and records from automatic milking systems (AMSs) was developed. The process enables the extraction of gas phenotypes for genetic analysis. It involves the calculation of intermediate control variables to account for time drift in data loggers, sensor calibration drift, and background concentration fluctuations, and the condensation of data from each milking visit into a single datapoint. The process was developed and assessed with research station data from three groups of approximately 70 cows, each accessing one AMS unit over a 2-month period. Raw emissions data, including clock times, from CH4 and CO2 channels were recorded every second. They were synchronized with the AMS data using specific events occurring in the CH4 or CO2 channel at the beginning or end of each milking event. The synchronized data were divided into non-milking (baseline, ambient gas concentrations) and cow ID–linked milking (cow emissions) sets. The non-milking periods varied in duration from a few seconds to hours, and some were interrupted by unrecorded events. Baseline values were extracted after the filtering of non-milking period data against unrecorded events (e.g., washing, feed-only sessions) and the use of a small fractile as the baseline estimate. At the beginning of each milking event, 30–45 s were required for the CH4 and CO2 concentrations to reach stable high levels, and most events lasted at least 5 min. Accordingly, a restricted recording window of 30–300 s, which excluded the initial unstable period while retaining data from the majority of milking events, was established. Gas concentrations significantly exceeding the baseline were selected as responses to ensure that only data obtained when the cows’ heads were sufficiently close to the sniffer air inlets were included. The mean value of the selected records was used as the response phenotype for each milking event. The concentration phenotypes showed moderate to high repeatability, but the CH4:CO2 ratio had only moderate repeatability. The pipeline developed in this study enables the effective extraction of baseline-adjusted emissions phenotypes from sniffer data obtained in milking robots.
KW - Dairy cow
KW - Data processing
KW - Methane emission
KW - Phenotyping
KW - Pipeline
KW - Sniffer
UR - http://www.scopus.com/inward/record.url?scp=85206607992&partnerID=8YFLogxK
U2 - 10.1016/j.compag.2024.109559
DO - 10.1016/j.compag.2024.109559
M3 - Journal article
AN - SCOPUS:85206607992
SN - 0168-1699
VL - 227
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
IS - Part 1
M1 - 109559
ER -