Greenhouse gas emissions from naturally ventilated freestall dairy barns

Joo, Hung-Soo; Ndegwa, Pius M.; Heber, Albert J.; Ni, Ji‐Qin; Bogan, Bill W.; Ramirez-Dorronsoro, J.C.; Cortus, Erin L.

doi:10.1016/j.atmosenv.2014.11.067

Cited by 34 publications

(22 citation statements)

References 39 publications

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“…This error value of 20% is closely linked to the TAE, which focuses only on the aggregated emission value. With regard to an annual emission value the scenarios 12, 22, 24, and 25 showed the best performance, all of which were scenarios where no winter measurements, but many measurements in the transition time, were included (such as, for example, in the studies of Joo et al in 2015 or Ngwabie et al in 2014 [15,16]).…”

Section: Sound Selection Of Model Evaluation Criteriamentioning

confidence: 99%

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How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning

et al. 2020

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Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days.

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Section: Sound Selection Of Model Evaluation Criteriamentioning

confidence: 99%

“…On the other hand, in past literature, researchers used very different approaches for temporal sampling. For example, Wu et al and Joo et al both measured in the summer and transition seasons, with 7-27 days per season [10,15]. Ngwabie et al measured only the transition seasons, with around 60 days in spring and 60 days in fall [16].…”

Section: Introductionmentioning

confidence: 99%

How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning

et al. 2020

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“…Quantifying gaseous emissions from naturally ventilated barns, however, has additional challenges primarily because of the complexity of air exchange rates (AER) determination (Kiwan et al, 2012;Ogink et al, 2013). Natural ventilation (NV) systems in dairy 63 barns are commonly used in regions with mild climate due to low capital and low energy demand (Andonov et al, 2003;Joo et al, 2014;Joo et al, 2015b). However, AER in NV barns are 65 directly dependent on atmospheric conditions (Snell et al, 2003;Ngwabie et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Indirect method versus direct method for measuring ventilation rates in naturally ventilated dairy houses

Wang

Ndegwa

Joo

et al. 2016

Biosystems Engineering

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Indirect methods are widely used for determining air exchange rates (AER) in naturally ventilated barns because they are relatively easier and cheaper than direct methods, which measure actual airflow in and out of the barns. The main goal of this study was to evaluate a common indirect method (CO 2 mass balance) against a direct method, and identify factors influencing this indirect method. The mean AER based on 24-h averaging, irrespective of method, ranged from 13 to 39 h −1 during the study-periods. The CO 2-balance method tended to overestimate barn AER. The cows' CO 2 production rate, in the current study, was estimated at 0.178 m 3 h −1 hpu −1 based on 24-h averaging. The CO 2-balance method with 24-h data averaging yielded more reliable barn AER than with shorter averaging times (i.e., 1, 2, and 12 h). The 1-h averaging, however, was chosen to analyze the effects of other pertinent factors on the CO 2balance method to capture diurnal variations of AER. Both wind speed and wind direction had significant effects on barn AER as well as the difference between the CO 2-balance method and direct method. Barn AER, in general, increased with wind speed. The CO 2-balance method was unreliable during milking times, and when indoor-outdoor CO 2 concentration and temperature differences were less than 70 ppm and 0°C, respectively.

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“…The dilution of emitted methane from the exhalation point can be affected by several factors: the airflow pattern around the cow, the distance between exhalation and sampling point, and the cow's head movement in the feeder. Existing variable airflow in dairy barns (Joo et al, 2015;Wu et al, 2016) mixes with and dilutes the cow's emitted methane in breath. Huhtanen et al (2015) observed a significant decrease and variation of measured concentrations by the BMC method in the laboratory with a model cow head when sampling distance changed from 0 to 30 cm, moving the head, or introducing wind through the feed bin.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty assessment of the breath methane concentration method to determine methane production of dairy cows

Koerkamp

Ogink

2018

Journal of Dairy Science

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The breath methane concentration method uses the methane concentrations in the cow's breath during feed bin visits as a proxy for the methane production rate. The objective of this study was to assess the uncertainty of a breath methane concentration method in a feeder and its capability to measure and rank cows' methane production. A range of controlled methane fluxes from a so-called artificial reference cow were dosed in a feed bin, and its exhaled air was sampled by a tube inside the feeder and analyzed. The artificial reference cow simulates the lungs, respiratory tract, and rumen of a cow and releases a variable methane flux to generate a concentration pattern in the exhaled breath that closely resembles a real cow's pattern. The strength of the relation between the controlled methane release rates of the artificial reference cow and the measured methane concentrations was analyzed by linear regression, using the coefficient of determination (R 2 ) and the residual standard error as performance indicators. The effect of error sources (source-sampling distance, air turbulence, and cow's head movement) on this relation was experimentally investigated, both under laboratory and barn conditions. From the laboratory to the dairy barn at the 30-cm sampling distance, the R 2 -value decreased from 0.97 to 0.37 and the residual standard error increased from 75 to 86 ppm as a result of barn air turbulence, the latter increasing to a theoretical 94 ppm if modeled variability due to cow's head movement was accounted for as well. In practice, the effect of these random errors can be compensated by sampling strategies including repeated measurements on each cow over time, thus increasing the distinctive power between cows. However, systematic errors that may disturb the relation between concentration and production rate, such as cow variation in air exhalation rate and air flow patterns around sampling locations that differ between barns, cannot be compensated by repeated measurements. As a result, the methane concentrations of breath air will vary between cows with the same methane production. We conclude that the capability of the breath concentration measurement method to adequately measure and rank methane production rates among cows is highly uncertain and requires further investigation into variation sources with a systematic nature.

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Greenhouse gas emissions from naturally ventilated freestall dairy barns

Cited by 34 publications

References 39 publications

How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning

How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning

Indirect method versus direct method for measuring ventilation rates in naturally ventilated dairy houses

Uncertainty assessment of the breath methane concentration method to determine methane production of dairy cows

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