scholarly journals Analysis of Inputs Parameters Used to Estimate Enteric Methane Emission Factors Applying a Tier 2 Model: Case Study of Native Cattle in Senegal

2021 ◽  
Author(s):  
Séga Ndao
Keyword(s):  
Sensitivity Analysis ◽  
Methane Emission ◽  
Input Parameter ◽  
Emission Factors ◽  
Future Research ◽  
Tier 2 ◽  
Lactating Cows ◽  
Enteric Methane ◽  
Input Parameters ◽  
Native Cattle

In the context of the Paris Agreement, and considering the importance of methane emissions from cattle in West Africa, application of a Tier 2 method to estimate enteric methane emission factors is clearly pertinent. The current study has two purposes. Firstly, it aims to detect how much each input parameter contributes to the overall uncertainty of enteric methane emission factors for cattle. Secondly, it aims to identify which input parameters require additional research efforts for strengthening the evidence base, thus reducing the uncertainty of methane enteric emission factors. Uncertainty and sensitivity analysis methodologies were applied to input parameters in the calculation of enteric methane emission factors for lactating cows and adult male Senegalese native cattle using the IPCC Tier 2 model. The results show that the IPCC default input parameters, such as the coefficient for calculating net energy for maintenance (Cfi), digestible energy (DE) and the methane conversion rate (Ym) are the first, second and third most important input parameters, respectively, in terms of their contribution to uncertainty of the enteric methane emission factor. Sensitivity analysis demonstrated that future research in Senegal should prioritize the development of Ym, Cfi and DE in order to estimate enteric methane emission factors more accurately and to reduce the uncertainty of the national agricultural greenhouse gas inventory.

scholarly journals Methane emission factors for enteric fermentation in beef cattle using IPCC Tier-2 method in Indonesia

2016 ◽  
Vol 21 (2) ◽  
pp. 101 ◽  
Author(s):  
Yeni Widiawati ◽  
M.N. Rofiq ◽  
B. Tiesnamurti
Keyword(s):  
Beef Cattle ◽  
Methane Emission ◽  
Energy Content ◽  
Emission Factors ◽  
Tier 2 ◽  
Cattle Population ◽  
Enteric Fermentation ◽  
Agriculture Sector ◽  
Enteric Methane ◽  
Country Specific

<p class="abstrak2">Methane emission from enteric is a sub-category considered under the Agriculture sector greenhouse gas emissions by UNFCCC, thus Indonesia developed calculation on enteric CH<sub>4</sub> EF for ruminant using Tier-2 method as country-specific emission factors (EF). Indonesia has huge amount of beef cattle population, which contributes significant amount to national enteric methane emission. The aim of this study was to estimate enteric methane EF for beef cattle in Indonesia using IPCC Tier-2 method.  The EF generated from this study is then used to estimate the methane emitted from beef cattle. Data on beef cattle population was obtained from BPS, data on energy content of feed, feed intake and digestibility were compiled from laboratory analysis and published paper. Equations were adopted and followed the instruction of IPCC 2006. Local cattle has different CH<sub>4</sub> EF among each sub-category, which are  ranging from 18.18 to 55.89 Kg head-1 yr-1, with the average of 36.75  head-1 yr-1. Imported beef cattle has lower  CH<sub>4</sub> EF (25.49 kg head-1 yr-1) than the average for local beef cattle. Overall, the national CH<sub>4</sub> EF of beef cattle calculated by using IPCC Tier-2 method in Indonesia is 33.14 head-1 yr-1. The value is lower than default EF from IPCC for Asia country (47 kg head-1 yr-1). The conclusion is enteric CH<sub>4</sub> EF for beef cattle in Indonesia calculated using Tier-2 method shows the real livestock system in Indonesia condition. Further research needed to be addressed are calculation of EFs for various breeds and feeding systems, since large variations of breeds and types of feed among provinces in Indonesia.</p>

scholarly journals Enteric Methane Emission Estimates for the Zimbabwean Sanga Cattle Breeds of Tuli and Mashona

2021 ◽  
Author(s):  
Showman Gwatibaya ◽  
Chrispen Murungweni ◽  
Irvine Mpofu ◽  
Raphael Jingura ◽  
Accadius Tinarwo Tigere ◽  
...  
Keyword(s):  
Methane Emission ◽  
Methane Emissions ◽  
Emission Factors ◽  
Tier 2 ◽  
Cattle Breeds ◽  
Tier 1 ◽  
Enteric Methane ◽  
Gross Energy ◽  
Sanga Cattle ◽  
Enteric Methane Emissions

Abstract The effectiveness of methane mitigation in ruminant livestock production systems depends on the accuracy of estimating methane emission factors and providing accurate emission inventories. Following the Paris Climate agreement, it is recommended that countries adopt the Tier-2 approach for estimating enteric methane emissions from ruminants instead of the Tier-1 approach currently used by most countries. This study sought to provide base line enteric methane emission estimates for the Tuli and Mashona Sanga cattle breeds in Zimbabwe using the IPCC Tier-2 model. Using animal characterization data collected from 412 cattle from Grasslands Research Institute and 406 cattle from Makoholi Research Institute, net energy requirements were estimated. From this and the estimate for digestibility, gross energy intake and dry matter intake were estimated. Gross energy intakes and the estimated methane conversion factor were used to estimate enteric methane emissions. Mean emission factors for Tuli were 45.1, 56, 28.5, 28.4, 20.6kg CH4/head/year for cows, bulls, heifers, steers and calves respectively. For Mashona, they were 47.8, 51.9, 29, 29.1 and 20.7kgCH4/head/year for cows, bulls, heifers, steers and calves respectively. Generally, estimated Tier-2 emission factors were significantly different from the IPCC Tier-1 default emission factors. This study concluded that enteric methane emission factors estimated using the IPCC Tier-2 model offer insights into the controversial use of the default IPCC Tier-1 emission factors.

Seismic hazard sensitivity analysis: A multi-parameter approach

1991 ◽  
Vol 81 (3) ◽  
pp. 796-817
Author(s):  
Nitzan Rabinowitz ◽  
David M. Steinberg
Keyword(s):  
Sensitivity Analysis ◽  
Seismic Hazard ◽  
Subjective Probability ◽  
Probability Distributions ◽  
Input Parameter ◽  
The Other ◽  
Probabilistic Assessment ◽  
Input Parameters ◽  
Parameter Approach

Abstract We propose a novel multi-parameter approach for conducting seismic hazard sensitivity analysis. This approach allows one to assess the importance of each input parameter at a variety of settings of the other input parameters and thus provides a much richer picture than standard analyses, which assess each input parameter only at the default settings of the other parameters. We illustrate our method with a sensitivity analysis of seismic hazard for Jerusalem. In this example, we find several input parameters whose importance depends critically on the settings of other input parameters. This phenomenon, which cannot be detected by a standard sensitivity analysis, is easily diagnosed by our method. The multi-parameter approach can also be used in the context of a probabilistic assessment of seismic hazard that incorporates subjective probability distributions for the input parameters.

scholarly journals Pyrolysis modeling, sensitivity analysis, and optimization techniques for combustible materials: A review

2019 ◽  
Vol 37 (4-6) ◽  
pp. 377-433
Author(s):  
Tatenda Nyazika ◽  
Maude Jimenez ◽  
Fabienne Samyn ◽  
Serge Bourbigot
Keyword(s):  
Sensitivity Analysis ◽  
Material Properties ◽  
Input Parameter ◽  
Fire Behavior ◽  
Sensitivity Study ◽  
Optimization Techniques ◽  
Decomposition Reactions ◽  
Input Parameters ◽  
Gradient Based ◽  
Complete Set

Over the past years, pyrolysis models have moved from thermal models to comprehensive models with great flexibility including multi-step decomposition reactions. However, the downside is the need for a complete set of input data such as the material properties and the parameters related to the decomposition kinetics. Some of the parameters are not directly measurable or are difficult to determine and they carry a certain degree of uncertainty at high temperatures especially for materials that can melt, shrink, or swell. One can obtain input parameters by searching through the literature; however, certain materials may have the same nomenclature but the material properties may vary depending on the manufacturer, thereby inducing uncertainties in the model. Modelers have resorted to the use of optimization techniques such as gradient-based and direct search methods to estimate input parameters from experimental bench-scale data. As an integral part of the model, a sensitivity study allows to identify the role of each input parameter on the outputs. This work presents an overview of pyrolysis modeling, sensitivity analysis, and optimization techniques used to predict the fire behavior of combustible solids when exposed to an external heat flux.

Impact of feeding balanced rations on milk production, methane emission, metabolites and feed conversion efficiency in lactating cows

2016 ◽  
Author(s):  
P. L. Sherasia ◽  
B. T. Phondba ◽  
S. A. Hossain ◽  
B. P. Patel ◽  
M. R. Garg
Keyword(s):  
Milk Production ◽  
Conversion Efficiency ◽  
Methane Emission ◽  
Feed Conversion ◽  
Feed Conversion Efficiency ◽  
Lactating Cows ◽  
Microbial Nitrogen ◽  
Enteric Methane ◽  
Effect Of Feeding ◽  
Phosphorus Intake

A field study on early lactating crossbred cows (n=35) was conducted to evaluate the effect of feeding balanced rations on milk production, enteric methane emission, metabolites and feed conversion efficiency (FCE). In comparison to requirements, the dietary intake of protein and energy were higher by 25.0 and 12.7% whereas, calcium and phosphorus intake were lower by 30.0 and 27.0%, respectively. Balanced feeding improved daily 4% FCM yield by 0.7 kg/cow (P<0.05) and intestinal flow of microbial nitrogen (N) by 37.0% (P<0.01), whereas, reduced (P<0.01) feeding cost by 17.0% and enteric methane emission (g/d/cow and g/kg milk yield) by 14.6 and 18.1%, respectively. Level of IgG, IgA, IgM and uric acid content increased significantly, whereas BUN level reduced (P<0.01) from 18.2 to 15.0 mg/dl. FCE improved (P<0.01) from 0.8 to 1.0 and efficiency of microbial protein synthesis also improved (P<0.01) by 63.6% owing to feeding of balanced rations indicating better performance of cows. Present study indicates that feeding nutritionally balanced rations improved milk production, feed conversion efficiency and reduced methane emission in lactating cows under field conditions.

Contextualized re-calculation of enteric methane emission factors for small ruminants in sub-humid Western Africa is far lower than previous estimates

2018 ◽  
Vol 51 (4) ◽  
pp. 919-928 ◽  
Author(s):  
Séga Ndao ◽  
Charles-Henri Moulin ◽  
El Hadji Traoré ◽  
Mamadou Diop ◽  
François Bocquier
Keyword(s):  
Methane Emission ◽  
Small Ruminants ◽  
Emission Factors ◽  
Western Africa ◽  
Enteric Methane

Uncertainty Quantification in Support of Severe Accident Analysis Code User Confidence Using MELCOR-DAKOTA

2021 ◽  
Author(s):  
Emmanuel Boafo ◽  
Emmanuel Numapau Gyamfi
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Hydrogen Generation ◽  
Input Parameter ◽  
Distribution Functions ◽  
Physical Models ◽  
Severe Accident ◽  
Accident Analysis ◽  
Melt Flow Rate ◽  
Input Parameters

Abstract Uncertainty and Sensitivity analysis methods are often used in severe accident analysis for validating the complex physical models employed in the system codes that simulate such scenarios. This is necessitated by the large uncertainties associated with the physical models and boundary conditions employed to simulate severe accident scenarios. The input parameters are sampled within defined ranges based on assigned probability distribution functions (PDFs) for the required number of code runs/realizations using stochastic sampling techniques. Input parameter selection is based on their importance to the key FOM, which is determined by the parameter identification and ranking table (PIRT). Sensitivity analysis investigates the contribution of each uncertain input parameter to the uncertainty of the selected FOM. In this study, the integrated severe accident analysis code MELCOR was coupled with DAKOTA, an optimization and uncertainty quantification tool in order to investigate the effect of input parameter uncertainty on hydrogen generation. The methodology developed was applied to the Fukushima Daiichi unit 1 NPP accident scenario, which was modelled in another study. The results show that there is approximately 22.46% uncertainty in the amount of hydrogen generated as estimated by a single MELCOR run given uncertainty in selected input parameters. The sensitivity analysis results also reveal that MELCOR input parameters; COR_SC 1141(Melt flow rate per unit width at breakthrough candling) , COR_ZP (Porosity of fuel debris beds) and COR_EDR (Characteristic debris size in core region) contributed most significantly to the uncertainty in hydrogen generation.

Development of a dynamic mathematical model for investigating mammary gland metabolism in lactating cows

2009 ◽  
Vol 148 (1) ◽  
pp. 31-54 ◽  
Author(s):  
V. VOLPE ◽  
J. P. CANT ◽  
R. C. BOSTON ◽  
P. SUSMEL ◽  
P. MOATE
Keyword(s):  
Mathematical Model ◽  
Sensitivity Analysis ◽  
Mammary Gland ◽  
Milk Protein ◽  
Energy Charge ◽  
Atp Synthesis ◽  
Protein Yield ◽  
Future Research ◽  
Lactating Cows ◽  
Dynamic Mathematical Model

SUMMARYA dynamic mathematical model of a closed mammary system in lactating cows was developed to incorporate the setpoint concept of tissue activity, using equations where nutrient supply and absorption are locally regulated so as to maintain a given rate of milk protein yield. The model consists of 12 differential equations, 11 of which are concerned with intracellular biochemical compartments and one describes the volume of tissue actively perfused by blood (AP). The intracellular compartments are: amino acids (AAs), acetate, fatty acids (FAs), β-hydroxybutyrate, glucose-6-phosphate, fructose-6-phosphate, phospho-glyceraldehyde, pyruvate, mitochondrial acetyl-CoA, adenosine triphosphate (ATP) and adenosine diphosphate (ADP). The model simulates mechanisms which are aimed at reproducing and, thereby, explain variations in mammary plasma flow (MPF) observed experimentally. The AP changes according to variations in the metabolic status or in the metabolic requirements of the gland. Should the tissue energy charge (i.e. ATP/ADP ratio) exceed a baseline ratio, then AP decreases and consequently MPF declines. Conversely, when milk protein yield increases, AP increases and MPF rises. In the present model, AA uptake by the mammary gland is inhibited by intracellular AAs. It is also assumed that, when milk protein yield diminishes, the respiratory chain and ATP synthesis become uncoupled and consequently ATP yield is reduced. Model evaluation included behavioural analysis and sensitivity analysis. Behaviour analysis was conducted to test whether the model mechanisms reproduced the scenarios from which the model hypotheses were developed, and took into consideration: an increase in arterial glucose concentration (HIGLC), increases in arterial concentrations of non-esterified FAs, triacylglycerol and β-hydroxybutyrate (HIFAT), a 50% reduction of arterial histidine concentration (LOHIS), and a hyperinsulinaemic euglycaemic clamp (HIINS). Both HIGLC and HIFAT resulted in a decrease in MPF and in milk protein yield; moreover, the scenario HIGLC also produced a notable decrease in the extraction of glucose. The scenario LOHIS resulted in increased MPF and extraction of His from plasma. However these responses were not sufficiently large to prevent a severe reduction of milk protein yield which was accompanied by a reduction in the extraction of other essential AAs. The scenario HIINS resulted in an increase of MPF and of milk protein yield, in the extraction of His and of other essential AAs. Model sensitivity analysis focused on variation of both affinity and inhibition constants of some of the Michaelis–Menten equations. Improvements in model structure and directions for future research suggested by the modelling analysis are discussed.

scholarly journals Sensitivity Analysis of Common Input Parameters in Tools for Modeling Energy in Homes

2016 ◽  
Vol 13 (2) ◽  
Author(s):  
Sheikh Tijan Tabban ◽  
Nelson Fumo
Keyword(s):  
Sensitivity Analysis ◽  
Energy Consumption ◽  
Reference Model ◽  
Residential Buildings ◽  
Input Parameter ◽  
Heating Equipment ◽  
Water Heating ◽  
Common Input ◽  
Heating And Cooling ◽  
Input Parameters

Energy models of buildings can be developed and used for analysis of energy consumption. A model offers the opportunity to simulate a building under specific conditions for analysis of energy efficiency measures or optimum design. Due to the great amount of information needed to develop an energy model of a building, the number of inputs can be reduced by making variable the most relevant input parameters and making the others to take common or standard values. In this study, an analysis of input parameters required by computational tools to estimate energy consumption in homes was done in two stages. In the first stage, common input parameters were identified for three software and three webtools based on the criteria that the input parameter should be common for at least two software and at least one webtool. In the second stage, a sensitivity analysis was performed on the inputs identified in the first stage. The software BEopt, developed by the National Renewable Energy Laboratory, was used as the source of typical input parameters to be compared, and to perform the simulations for the sensitivity analysis. The base or reference model to perform simulations for the sensitivity analysis corresponds to a model developed with information from a research house located on the campus of the University of Texas at Tyler and default inputs for the BEopt B-10 reference benchmark. Results show that besides the location, and consequently the weather, common parameters are building orientation, air leakage, space conditioning settings, space conditioning schedule, water heating equipment, and terrain. Among these parameters, the sensitivity analysis identified the largest variations in energy consumption for variations on space conditioning schedule (heating and cooling setpoints), followed by the type of water heating equipment. KEYWORDS: Residential Buildings; Energy Consumption; Energy Analysis; Input Parameters; Building Simulation; Source Energy

Methane emissions from enteric fermentation in Alberta’s beef cattle population

2005 ◽  
Vol 85 (4) ◽  
pp. 501-512 ◽  
Author(s):  
J. A. Basarab ◽  
E. K. Okine ◽  
V. S. Baron ◽  
T. Marx ◽  
P. Ramsey ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Methane Emission ◽  
Ghg Emissions ◽  
Beef Cows ◽  
Methane Emissions ◽  
Emission Factors ◽  
Tier 2 ◽  
Cattle Population ◽  
Enteric Fermentation ◽  
Canadian Research

This study determined methane emissions from enteric fermentation in Alberta’s beef cattle population by using three methodologies: (1) Intergovernmental Panel on Climate Change (IPCC), Tier 2 guidelines for cattle, (2) actual methane emission factors, expressed as a percentage of gross energy intake, from Canadian research trials and; (3) CowBytes© plus the basic equation developed by Blaxter and Clapperton (1965). Methane emissions, in carbon dioxide equivalents (CO2-E), from Alberta’s beef cattle were determined for 1990, 1996 and 2001. Census of Agriculture numbers for Alberta (Statistics Canada; www.statcan.com) were used and beef cattle were subdivided into 31 distinct categories based on animal type, physiological status, gender, weight, growth rate, activity level and age. Emission of greenhouse gases (GHG) from Alberta ’s beef cattle population, based on IPCC Tier 2 guidelines, were 4.93, 6.57 and 7.01 Mt CO2-E yr-1 in 1990, 1996 and 2001, respectively. Emissions based on methane emission factors from Canadian research trials were 6.23, 8.26 and 8.77 Mt CO2-E yr-1 in 1990, 1996 and 2001, respectively. Estimated methane emissions based on CowBytes© and Blaxter and Clapperton’s (1965) equation were 6.24, 8.35 and 8.94 Mt CO2-E yr-1 in 1990, 1996 and 2001, respectively. The IPCC Tier 2 values were 25.2–26.5% lower than the GHG emissions calculated using emission factors from western Canadian research and 26.7–27.6% lower than GHG emissions calculated from CowBytes© and Blaxter and Clapperton’s equation. IPCC Tier 1 values, which were calculated by multiplying total beef cattle in Alberta by four single value emission factors (beef cows = 72 kg CH4 yr-1; bulls = 75 kg CH4 yr-1; replacement heifers = 56 kg CH4 yr-1; calves, steer and heifer calves for slaughter = 47 kg CH4 yr-1), were 4.83, 6.40 and 6.83 Mt CO2-E in 1990, 1996 and 2001, respectively. Thus, IPCC Tier 1 GHG emissions from enteric fermentation in beef cattle were 2.0–2.7, 28.6–29.1 and 29.2–31.0% lower than those calculated from IPCC Tier 2, western Canadian research trials, and CowBytes© plus Blaxter and Clapperton’s equation, respectively. These results reflect the uncertainty associated with estimating methane emissions from enteric fermentation in cattle and suggest that further research is required to improve the accuracy of methane emissions, particularly for beef cows in their second and third trimester of pregnancy and fed in confinement. They also indicate that a more robust methodology may be to combine CowBytes© predicted dry matter intake with regional specific methane emission factors, where methane loss is expressed as a percentage of gross energy intake. Key words: Cattle, enteric fermentation, greenhouse gas, methane

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