The agreement between two next-generation laser methane detectors and respiration chamber facilities in recording methane concentrations in the spent air produced by dairy cows

Sorg, Diana; Mühlbach, Sarah; Rosner, Frank; Kuhla, Björn; Derno, M.; Meese, Susanne; Schwarm, Angela; Kreuzer, Michael; Swalve, Hermann H.

doi:10.1016/j.compag.2017.10.024

Cited by 19 publications

(24 citation statements)

References 19 publications

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“…These differences are likely due to the different experimental design adopted by Chagunda et al [12] who measured CH 4 in lactating cows. Methane emissions in the present study were lower even when compared with Sorg et al [10], and this difference is likely due to (i) the diverse conditions of measurements, (ii) different categories of animals, and (iii) the different feed administered to animals in the two studies. In the present study, measurements were carried out on Simmental heifers housed in an aerated barn, whereas Sorg et al [10] measured CH 4 exhaled from Holstein Friesian lactating cows in the spent air of the respiration chamber.…”

Section: Repeatability and Reproducibility Of Phenotypescontrasting

confidence: 84%

“…Methane emissions in the present study were lower even when compared with Sorg et al [10], and this difference is likely due to (i) the diverse conditions of measurements, (ii) different categories of animals, and (iii) the different feed administered to animals in the two studies. In the present study, measurements were carried out on Simmental heifers housed in an aerated barn, whereas Sorg et al [10] measured CH 4 exhaled from Holstein Friesian lactating cows in the spent air of the respiration chamber. Overall mean, mode, and median of CH 4 expressed as ppm × m were rather different from each other and thus, skewness and kurtosis were relatively far from zero (Table 1).…”

Section: Repeatability and Reproducibility Of Phenotypescontrasting

confidence: 84%

“…Laser methane detector (LMD) has been proposed as an alternative instrument to directly measure CH 4 emissions. The sensitivity and accuracy of LMD have been assessed in controlled conditions, by comparing data acquired through LMD with those measured through the respiration chamber (coefficient of determination, R 2 from 0.64 to 0.97) [9,10] and GreenFeed (R 2 = 0.64) [11]. However, it is still unclear if LMD may be appropriate in terms of repeatability and reproducibility to accurately measure CH 4 emissions in field conditions.…”

Section: Introductionmentioning

confidence: 99%

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Repeatability and Reproducibility of Measures of Bovine Methane Emissions Recorded using a Laser Detector

Niero

Cendron

Penasa

et al. 2020

Animals

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Methane (CH4) emissions represent a worldwide problem due to their direct involvement in atmospheric warming and climate change. Ruminants are among the major players in the global scenario of CH4 emissions, and CH4 emissions are a problem for feed efficiency since enteric CH4 is eructed to the detriment of milk and meat production. The collection of CH4 phenotypes at the population level is still hampered by costly and time-demanding techniques. In the present study, a laser methane detector was used to assess repeatability and reproducibility of CH4 phenotypes, including mean and aggregate of CH4 records, slope of the linear equation modelling the aggregate function, and mean and number of CH4 peak records. Five repeated measurements were performed in a commercial farm on three Simmental heifers, and the same protocol was repeated over a period of three days. Methane emission phenotypes expressed as parts per million per linear meter (ppm × m) were not normally distributed and, thus, they were log-transformed to reach normality. Repeatability and reproducibility were calculated as the relative standard deviation of five measurements within the same day and 15 measurements across three days, respectively. All phenotypes showed higher repeatability and reproducibility for log-transformed data compared with data expressed as ppm × m. The linear equation modelling the aggregate function highlighted a very high coefficient of determination (≥0.99), which suggests that daily CH4 emissions might be derived using this approach. The number of CH4 peaks resulted as particularly diverse across animals and therefore it is a potential candidate to discriminate between high and low emitting animals. Results of this study suggest that laser methane detector is a promising tool to measure bovine CH4 emissions in field conditions.

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Section: Repeatability and Reproducibility Of Phenotypescontrasting

confidence: 84%

Section: Repeatability and Reproducibility Of Phenotypescontrasting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

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Repeatability and Reproducibility of Measures of Bovine Methane Emissions Recorded using a Laser Detector

Niero

Cendron

Penasa

et al. 2020

Animals

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“…Novel methods to measure methane emissions from ruminants have been researched including open circuit Fourier-Transformed IR technology and micrometeorological techniques for groups of animals (McGinn et al, 2013), breath analysers (sniffers) mounted in an enclosure at the water point or feeders (Hegarty, 2013) or handheld lasers pointed at 1 m from the mouth and nostrils in housed conditions (Ricci et al, 2014;Sorg et al, 2017). Ricci et al (2014) used a handheld device to measure CH 4 concentration of exhaled breath from ewes and steers.…”

Section: Body Compositionmentioning

confidence: 99%

“…Some challenges have yet to be overcome to quantify daily methane production from these sensors, which include developing methods to measure the volume of gas exhaled from animals per unit of time, and methods to estimate the mixing ratio of exhaled breath with ambient air (which depends on wind speed) because tracer gas markers are not used. These laser methane detectors are becoming smaller in size and are connected to mobile phones (Sorg et al, 2017) and could, therefore, become of benefit to the livestock industries.…”

Section: Body Compositionmentioning

confidence: 99%

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

Gonzalez

Kyriazakis

Tedeschi

2018

Animal

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A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data ‘as is’, further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.

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

Sorg

Difford

Mühlbach

et al. 2018

Computers and Electronics in Agriculture

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The agreement between two next-generation laser methane detectors and respiration chamber facilities in recording methane concentrations in the spent air produced by dairy cows

Cited by 19 publications

References 19 publications

Repeatability and Reproducibility of Measures of Bovine Methane Emissions Recorded using a Laser Detector

Repeatability and Reproducibility of Measures of Bovine Methane Emissions Recorded using a Laser Detector

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

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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