Methane and carbon dioxide emissions and grazed forage intake from pregnant beef heifers previously classified for residual feed intake under drylot conditions

2020 ◽  
Author(s):  
Ghader Manafiazar ◽  
Thomas K. Flesch ◽  
Vern S. Baron ◽  
Lisa McKeown ◽  
Brittany Byron ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Feed Intake ◽  
Carbon Dioxide Emissions ◽  
Residual Feed Intake ◽  
Beef Heifers ◽  
Once Daily ◽  
Forage Intake ◽  
Carbon Dioxide Co2 ◽  
Fecal Sampling

Objectives were to quantify the effect of post-weaning residual feed intake (RFI) on subsequent grazed forage intake, methane (CH4) and carbon dioxide (CO2) emissions. Beef heifers classified for RFI adjusted for off-test backfat (RFIfat; 55 high and 56 low) at nine mo of age were monitored seven mo later for CH4 and CO2 emissions using the GreenFeed Emissions Monitoring system. Fifty-six of these heifers were also monitored as high and low RFIfat groups using Open Path Fourier-Transform Infrared Spectroscopy (OP-FTIR). Heifers were dosed with one kg of C32 labelled pellets once daily for 15 d, with twice daily fecal sampling the last eight d to determine individual grazed forage intake using the n-alkane method. Low RFIfat pregnant heifers consumed less forage (10.25 vs. 10.81 kg DM d-1; P < 0.001), and emitted less daily CH4 (238.7 vs. 250.7 g d-1; P = 0.009) and CO2 (7578 vs. 8041 g d-1; P < 0.001) compared with high RFIfat animals. Results from the OP-FTIR further confirmed that low RFIfat heifers emitted 6.3% less (g d-1; P = 0.006) CH4 compared to their high RFIfat cohorts. Thus, selection for low RFIfat will decrease daily CH4 and CO2 emissions from beef cattle.

Methane and carbon dioxide emissions from yearling beef heifers and mature cows classified for residual feed intake under drylot conditions

2020 ◽  
Vol 100 (3) ◽  
pp. 522-535 ◽  
Author(s):  
G. Manafiazar ◽  
V.S. Baron ◽  
L. McKeown ◽  
H. Block ◽  
K. Ominski ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Feed Intake ◽  
Carbon Dioxide Emissions ◽  
Body Weight Gain ◽  
Residual Feed Intake ◽  
Individual Performance ◽  
Feed Consumption ◽  
Beef Heifers ◽  
Air Temperatures ◽  
Rapid City

This study quantified methane (CH4) and carbon dioxide (CO2) production from beef heifers and cows classified for residual feed intake adjusted for off-test backfat thickness (RFIfat) and reared in drylot during cold winter temperatures. Individual performance, daily feed intake, and RFIfat were obtained for 1068 crossbred and purebred yearling heifers (eight trials) as well as 176 crossbred mature cows (six trials) during the winters of 2015–2017 at two locations. A portion of these heifers (147 high RFIfat; 167 low RFIfat) and cows (69 high RFIfat; 70 low RFIfat) was monitored for enteric CH4 and CO2 emissions using the GreenFeed Emissions Monitoring (GEM) system (C-Lock Inc., Rapid City, SD, USA). Low RFIfat cattle consumed less feed [heifers, 7.80 vs. 8.48 kg dry matter (DM) d−1; cows, 11.64 vs. 13.16 kg DM d−1] and emitted less daily CH4 (2.5% for heifers; 3.7% for cows) and CO2 (1.4% for heifers; 3.4% for cows) compared with high RFIfat cattle. However, low RFIfat heifers and cows had higher CH4 (6.2% for heifers; 9.9% for cows) and CO2 yield (7.3% for heifers; 9.8% for cows) per kilogram DM intake compared with their high RFIfat pen mates. The GEM system performed at air temperatures between +20 and −30 °C. Feed intake of heifers and mature cows was differently affected by ambient temperature reduction between +20 and −15 °C and similarly increased their feed intake at temperatures below −15 °C. In conclusion, low RFIfat animals emit less daily enteric CH4 and CO2, due mainly to lower feed consumption at equal body weight, gain, and fatness.

scholarly journals Estimation of Carbon Dioxide Emissions from a Traditional Nutrient-Rich Cambodian Diet Food Production System Using Life Cycle Assessment

2021 ◽  
Vol 13 (7) ◽  
pp. 3660
Author(s):  
Rathna Hor ◽  
Phanna Ly ◽  
Agusta Samodra Putra ◽  
Riaru Ishizaki ◽  
Tofael Ahamed ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Co2 Emissions ◽  
Production System ◽  
Agricultural Production ◽  
Carbon Dioxide Emissions ◽  
Food Production ◽  
Carbon Dioxide Co2 ◽  
Food Production System

Traditional Cambodian food has higher nutrient balances and is environmentally sustainable compared to conventional diets. However, there is a lack of knowledge and evidence on nutrient intake and the environmental greenness of traditional food at different age distributions. The relationship between nutritional intake and environmental impact can be evaluated using carbon dioxide (CO2) emissions from agricultural production based on life cycle assessment (LCA). The objective of this study was to estimate the CO2 equivalent (eq) emissions from the traditional Cambodian diet using LCA, starting at each agricultural production phase. A one-year food consumption scenario with the traditional diet was established. Five breakfast (BF1–5) and seven lunch and dinner (LD1–7) food sets were consumed at the same rate and compared using LCA. The results showed that BF1 and LD2 had the lowest and highest emissions (0.3 Mt CO2 eq/yr and 1.2 Mt CO2 eq/yr, respectively). The food calories, minerals, and vitamins met the recommended dietary allowance. The country’s existing food production system generates CO2 emissions of 9.7 Mt CO2 eq/yr, with the proposed system reducing these by 28.9% to 6.9 Mt CO2 eq/yr. The change in each food item could decrease emissions depending on the type and quantity of the food set, especially meat and milk consumption.

scholarly journals The travel-related carbon dioxide emissions of atmospheric researchers

2008 ◽  
Vol 8 (2) ◽  
pp. 7373-7389 ◽  
Author(s):  
A. Stohl
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Information Exchange ◽  
Carbon Dioxide Emissions ◽  
Radiative Forcing ◽  
Global Climate ◽  
Air Travel ◽  
Ozone Production ◽  
Per Capita ◽  
Carbon Dioxide Co2

Abstract. Most atmospheric scientists agree that greenhouse gas emissions have already caused significant changes to the global climate system and that these changes will accelerate in the near future. At the same time, atmospheric scientists who – like other scientists – rely on international collaboration and information exchange travel a lot and, thereby, cause substantial emissions of carbon dioxide (CO2). In this paper, the CO2 emissions of the employees working at an atmospheric research institute (the Norwegian Institute for Air Research, NILU) caused by all types of business travel (conference visits, workshops, field campaigns, instrument maintainance, etc.) were calculated for the years 2005–2007. It is estimated that more than 90% of the emissions were caused by air travel, 3% by ground travel and 5% by hotel usage. The travel-related annual emissions were between 1.9 and 2.4 t CO2 per employee or between 3.9 and 5.5 t CO2 per scientist. For comparison, the total annual per capita CO2 emissions are 4.5 t worldwide, 1.2 t for India, 3.8 t for China, 5.9 t for Sweden and 19.1 t for Norway. The travel-related CO2 emissions of a NILU scientist, occurring in 24 days of a year on average, exceed the global average annual per capita emission. Norway's per-capita CO2 emissions are among the highest in the world, mostly because of the emissions from the oil industry. If the emissions per NILU scientist derived in this paper are taken as representative for the average Norwegian researcher, travel by Norwegian scientists would nevertheless account for a substantial 0.2% of Norway's total CO2 emissions. Since most of the travel-related emissions are due to air travel, water vapor emissions, ozone production and contrail formation further increase the relative importance of NILU's travel in terms of radiative forcing.

scholarly journals Designing a Statistical Procedure for Monitoring Global Carbon Dioxide Emissions

2021 ◽  
Author(s):  
Mikkel Bennedsen
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Testing Procedure ◽  
Paris Agreement ◽  
Sequential Testing ◽  
Monitoring Procedure ◽  
Nationally Determined Contributions ◽  
Carbon Dioxide Co2 ◽  
Global Carbon

Abstract Following the Paris Agreement of 2015, most countries have agreed to reduce their carbon dioxide (CO2) emissions according to individually set Nationally Determined Contributions. However, national CO2 emissions are reported by individual countries and cannot be directly measured or verified by third parties. Inherent weaknesses in the reporting methodology may misrepresent, typically an under-reporting of, the total national emissions. This paper applies the theory of sequential testing to design a statistical monitoring procedure that can be used to detect systematic under-reportings of CO2 emissions. Using simulations, we investigate how the proposed sequential testing procedure can be expected to work in practice. We find that, if emissions are reported faithfully, the test is correctly sized, while, if emissions are under-reported, detection time can be sufficiently fast to help inform the 5 yearly global "stocktake" of the Paris Agreement. We recommend the monitoring procedure be applied going forward as part of a larger portfolio of methods designed to verify future global CO2 emissions.

scholarly journals Observing carbon dioxide emissions over China's cities and industrial areas with the Orbiting Carbon Observatory-2

2020 ◽  
Vol 20 (14) ◽  
pp. 8501-8510 ◽  
Author(s):  
Bo Zheng ◽  
Frédéric Chevallier ◽  
Philippe Ciais ◽  
Grégoire Broquet ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Power Plants ◽  
Global Climate ◽  
Large Cities ◽  
Independent Evaluation ◽  
Paris Climate Agreement ◽  
Carbon Dioxide Co2 ◽  
The Individual

Abstract. In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic. Here, we study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions with 5 years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around China. With the detailed information of emission source locations and the local wind, we successfully observe CO2 plumes from 46 cities and industrial regions over China and quantify their CO2 emissions from the OCO-2 observations, which add up to a total of 1.3 Gt CO2 yr−1 that accounts for approximately 13 % of mainland China's annual emissions. The number of cities whose emissions are constrained by OCO-2 here is 3 to 10 times larger than in previous studies that only focused on large cities and power plants in different locations around the world. Our satellite-based emission estimates are broadly consistent with the independent values from China's detailed emission inventory MEIC but are more different from those of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC), especially for the emission estimates for the individual cities. These results demonstrate some skill in the satellite-based emission quantification for isolated source clusters with the OCO-2, despite the sparse sampling of this instrument not designed for this purpose. This skill can be improved by future satellite missions that will have a denser spatial sampling of surface emitting areas, which will come soon in the early 2020s.

scholarly journals A Multivariate Grey Prediction Model Using Neural Networks with Application to Carbon Dioxide Emissions Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yu-Jing Chiu ◽  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Jingci Xie ◽  
Yen-Wei Ken
Keyword(s):  
Carbon Dioxide ◽  
Prediction Model ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Predicted Values ◽  
Carbon Dioxide Co2 ◽  
Grey Relational

The forecast of carbon dioxide (CO2) emissions has played a significant role in drawing up energy development policies for individual countries. Since data about CO2 emissions are often limited and do not conform to the usual statistical assumptions, this study attempts to develop a novel multivariate grey prediction model (MGPM) for CO2 emissions. Compared with other MGPMs, the proposed model has several distinctive features. First, both feature selection and residual modification are considered to improve prediction accuracy. For the former, grey relational analysis is used to filter out the irrelevant features that have weaker relevance with CO2 emissions. For the latter, predicted values obtained from the proposed MGPM are further adjusted by establishing a neural-network-based residual model. Prediction accuracies of the proposed MGPM were verified using real CO2 emission cases. Experimental results demonstrated that the proposed MGPM performed well compared with other MGPMs considered.

scholarly journals Observing carbon dioxide emissions over China's cities with the Orbiting Carbon Observatory-2

2020 ◽  
Author(s):  
Bo Zheng ◽  
Frederic Chevallier ◽  
Philippe Ciais ◽  
Gregoire Broquet ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Power Plants ◽  
Global Climate ◽  
Large Cities ◽  
Independent Evaluation ◽  
Paris Climate Agreement ◽  
Carbon Dioxide Co2 ◽  
The Individual

Abstract. In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic. Here, we study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions with five years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around China. With the detailed information of emission source locations and the local wind, we successfully observe CO2 plumes from 60 cities and industrial regions over China and quantify their CO2 emissions from the OCO-2 observations, which add up to a total of 1.6 Gt CO2 yr−1 that account for 17 % of mainland China's annual emissions. The number of cities whose emissions are constrained by OCO-2 here is three to ten times larger than previous studies that only focused on large cities and power plants in different locations around the world. Our satellite-based emission estimates are broadly consistent with the independent values from the detailed China's emission inventory MEIC, but are more different from those of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC), especially for the emission estimates for the individual cities. These results demonstrate some skill in the satellite-based emission quantification for isolated source clusters with the OCO-2, despite the sparse sampling of this instrument not designed for this purpose. This skill can be improved by future satellite missions that will have a denser spatial sampling of surface emitting areas, which will come soon in the early 2020s.

METHODS IN FORECASTING CARBON DIOXIDE EMISSIONS: A DECADE REVIEW

2015 ◽  
Vol 75 (1) ◽  
Author(s):  
Lazim Abdullah ◽  
Herrini Mohd Pauzi
Keyword(s):  
Artificial Intelligence ◽  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Journal Articles ◽  
Policy Makers ◽  
Healthy Environment ◽  
Artificial Intelligence Methods ◽  
Carbon Dioxide Co2

Analyses and forecasts of carbon dioxide (CO2) emissions is one of the key requirements to educate people about the issues of clean and healthy environment. Various methods have been proposed to forecast CO2 emissions. This paper reviews the literature of the methods for the forecasting as well as estimatingCO2 emissions. Related articles appearing in the international journals from 2003 to 2013 were gathered and analysed to find the answers for these two questions: (i) Which methods were prevalently applied? (ii) Which factors were regularly been investigated? Based on the overall observations on the journal articles some improvements and possible future works are recommended. This research not only provides evidence that the artificial intelligence methods are the most favour methods, but also aids the researchers and policy makers in applying the methods effectively. 

scholarly journals Soil carbon dioxide emissions controlled by an extracellular oxidative metabolism identifiable by its isotope signature

2016 ◽  
Vol 13 (22) ◽  
pp. 6353-6362 ◽  
Author(s):  
Benoit Kéraval ◽  
Anne Catherine Lehours ◽  
Jonathan Colombet ◽  
Christian Amblard ◽  
Gaël Alvarez ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Oxidative Metabolism ◽  
Carbon Dioxide Emissions ◽  
Abiotic Factors ◽  
Organic C ◽  
Cellular Origin ◽  
Heterotrophic Microorganisms ◽  
C Cycle ◽  
Carbon Dioxide Co2

Abstract. Soil heterotrophic respiration is a major determinant of the carbon (C) cycle and its interactions with climate. Given the complexity of the respiratory machinery, it is traditionally considered that oxidation of organic C into carbon dioxide (CO2) strictly results from intracellular metabolic processes. Here we show that C mineralization can operate in soils deprived of all observable cellular forms. Moreover, the process responsible for CO2 emissions in sterilized soils induced a strong C isotope fractionation (up to 50 ‰) incompatible with respiration of cellular origin. The supply of 13C glucose in sterilized soil led to the release of 13CO2 suggesting the presence of respiratory-like metabolism (glycolysis, decarboxylation reaction, chain of electron transfer) carried out by soil-stabilized enzymes, and by soil mineral and metal catalysts. These findings indicate that CO2 emissions from soils can have two origins: (1) from the well-known respiration of soil heterotrophic microorganisms and (2) from an extracellular oxidative metabolism (EXOMET) or, at least, catabolism. These two metabolisms should be considered separately when studying effects of environmental factors on the C cycle because the likelihood is that they do not obey the same laws and they respond differently to abiotic factors.

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