Quantification and control of the greenhouse gas emissions from a dairy cow system

Baek, Chun-Youl; Lee, Kun-Mo; Park, Kyu Hyun

doi:10.1016/j.jclepro.2014.02.010

Cited by 25 publications

(20 citation statements)

References 17 publications

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“…Data from a dairy cow farm located in Korea were used in the case study to compare the two above-mentioned uncertainty analysis methods [25]. The number of heads in the dairy cow farm was used to normalize the collected data.…”

Section: Resultsmentioning

confidence: 99%

Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation

Lee

et al. 2017

Sustainability

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Uncertainty analysis of greenhouse gas (GHG) emissions is becoming increasingly necessary in order to obtain a more accurate estimation of their quantities. The Monte Carlo simulation (MCS) and non-parametric block bootstrap (BB) methods were tested to estimate the uncertainty of GHG emissions from the consumption of feedstuffs and energy by dairy cows. In addition, the contribution to variance (CTV) approach was used to identify significant input variables for the uncertainty analysis. The results demonstrated that the application of the non-parametric BB method to the uncertainty analysis, provides a narrower confidence interval (CI) width, with a smaller percentage uncertainty (U) value of the GHG emission model compared to the MCS method. The CTV approach can reduce the number of input variables needed to collect the expanded number of data points. Future studies can expand on these results by treating the emission factors (EFs) as random variables.

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Section: Resultsmentioning

confidence: 99%

Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation

Lee

et al. 2017

Sustainability

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“…Uniform distribution, triangular distribution, normal distribution, and log-normal distribution are frequently used in the Monte Carlo simulation. For selecting the probability distribution, at least 15-30 samples are required to represent the population, although the number can vary case by case [9,32]. However, it is difficult to collect a sufficient number of data samples to represent the population that can be applied to determine the probability distribution in the actual LCA.…”

Section: Step 3: Choice Of Probability Distribution and Monte Carlo Smentioning

confidence: 99%

“…This means that the livestock sector plays a significant role in global climate change, which makes it necessary to estimate the greenhouse gas (GHG) emissions within livestock products including enteric fermentation and manure treatment (on-farm) and feed production (pre-farm). Hence, life cycle assessments (LCA) have been widely used by many studies to assess the environmental impact of dairy cow systems [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…In the manure management: the total number of parameters were 27; they include the number of heads (five in the growth phase), daily volatile solid excreted in five growth phases, and others (see Baek et al [9] for further details).…”

mentioning

confidence: 99%

“…Thus, the significant parameters associated with the feeding and enteric fermentation activities for lactating cows were identified as: the feed intake of the lactating cow; the methane conversion factor (Y m ); and the EF (emissions factor) and GE (gross energy) of the six feedstuffs (alfalfa, silage maize, green barley, cotton seed meal, soybean cake, and maize). These parameters are the elements of the calculation method (see Baek et al [9] for further details) for the GHG emissions from the feeding and enteric fermentation activities of lactating cows.…”

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confidence: 99%

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Data Quality Assessment of the Uncertainty Analysis Applied to the Greenhouse Gas Emissions of a Dairy Cow System

et al. 2017

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Abstract:The results of an uncertainty analysis are achieved by the statistical information (standard error, type of probability distributions, and range of minimum and maximum) of the selected input parameters. However, there are limitations in identifying sufficient data samples for the selected input parameters for statistical information in the field of life cycle assessment (LCA). Therefore, there is a strong need for a consistent screening procedure to identify the input parameters for use in uncertainty analysis in the area of LCA. The conventional procedure for identifying input parameters for the uncertainty analysis method includes assessing the data quality using the pedigree method and the contribution analysis of the LCA results. This paper proposes a simplified procedure for ameliorating the existing data quality assessment method, which can lead to an efficient uncertainly analysis of LCA results. The proposed method has two salient features: (i) a simplified procedure based on contribution analysis followed by a data quality assessment for selecting the input parameters for the uncertainty analysis; and (ii) a quantitative data quality assessment method is proposed, based on the pedigree method, that adopts the analytic hierarchy process (AHP) method and quality function deployment (QFD). The effects of the uncertainty of the selected input parameters on the LCA results were assessed using the Monte Carlo simulation method. A case study of greenhouse gas (GHG) emissions from a dairy cow system was used to demonstrate the applicability of the proposed procedure.

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Statistical Concepts, Terminology and Notation

Heijungs

2024

Probability, Statistics and Life Cycle Assessment

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Quantification and control of the greenhouse gas emissions from a dairy cow system

Cited by 25 publications

References 17 publications

Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation

Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation

Data Quality Assessment of the Uncertainty Analysis Applied to the Greenhouse Gas Emissions of a Dairy Cow System

Statistical Concepts, Terminology and Notation

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