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Proceeding Paper

Accounting for Greenhouse Gas Emissions at Farm Level †

by
Vilma Naujokienė
*,
Kristina Lekavičienė
,
Rolandas Bleizgys
and
Dainius Savickas
Faculty of Engineering, Agriculture Academy, Vytautas Magnus University, Studentu Str. 15A, Akademija, LT-53362 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Presented at the 2nd International Electronic Conference on Processes: Process Engineering—Current State and Future Trends (ECP 2023), 17–31 May 2023; Available online: https://ecp2023.sciforum.net/.
Eng. Proc. 2023, 37(1), 120; https://doi.org/10.3390/ECP2023-14769
Published: 10 July 2023

Abstract

:
One of the main causes of climate change is greenhouse gases, which are dominated by an increased amount of CO2 in the atmosphere. The agricultural sector is one of the most important sources of greenhouse gas emissions. The goal is to prepare a calculation model system at farm level. When reducing GHG emissions, it is important to accurately determine gas emissions at farm level. While applying a GHG emissions accounting model, we aim to assess emission sources and apply effective measures to reduce gas emissions.

1. Introduction

Gases evaporate from manure, and mass exchange takes place between the liquid on the manure surface and the surrounding air flow. This evaporation process corresponds to the general structure of all evaporation processes, and the basis of its structure is convective mass exchange, wherein the gas flow varies depending on the convective mass transfer coefficient and the gas concentration gradient on the surface of the manure layer and on the surface of the manure [1]. When choosing methods for the study of GHG emissions, it is necessary to evaluate the technology and technical solutions of keeping animals in the barn. When modernizing animal husbandry technologies, it is very important to reduce their impact on environmental pollution. Gas emissions must be reduced at all stages of manure management: in barns, manure pits, and during transport and the incorporation of manure into soil [2,3,4]. In order to account for the modelling of greenhouse gas (GHG) emissions at farm level, it is necessary to define the main farm components from a farm-wide perspective [5].
In animal husbandry, the most GHG emission into the environment is CH4 gas, which accounts for as much as 91% of GHG emissions in animal husbandry; 79.0% evaporates from animal digestion processes, and 11.6% from manure management systems. Most methane evaporates from the digestive systems of cows (55.6%), from other cattle (39.2%), and from sheep (3.0%). In order to determine GHG emissions in animal husbandry, it is necessary to estimate emissions of the following gases: methane (CH4) and nitrous oxide (N2O). Understanding the carbon cycle is important for developing strategies to reduce CO2 (Figure 1).

2. Method

The accounting system for GHG emissions and CO2 absorptions at farm level is an IT tool created according to specially prepared GHG calculation methodologies and adapted formulas with selected variables and parameters. The prototype of the created accounting system is intended for use in the accounting of national greenhouse gas emissions and in “green” certification for providing consulting services. By applying the GHG emissions accounting system, the main aspects of the activities of mixed, animal husbandry, and crop farms that influence GHG can be evaluated.
It is mandatory to use the GHG accounting methodology of the Intergovernmental Panel on Climate Change [7]. According to the IPCC methodology, based on the experience of other countries, a spectrum of GHG emission sources has been determined at farm level, including criteria defining the sustainability of the farm, and a methodology and system for accounting for GHG emissions at farm level has been created. The developed model system for calculating GHG emissions is calculated in three stages. The animal population is divided into subgroups, and each of them is described. The emission coefficients of each subgroup in kilograms per animal per year and the number of animals in the subgroup are evaluated. Three methods and levels (Tier 1, Tier 2, Tier 3) of detail and complexity were used for calculation. The accounting system for GHG emissions at farm level was created according to specially prepared GHG calculation methodologies, and adapted formulas with selected variables and parameters. It calculates the main parameters: enteric fermentation, CH4, direct and indirect N2O emissions, recalculated CO2 emissions, and total emissions from manure management (Figure 2).
The calculation platform was tested using three scenarios: SC1—pasture 25%, solid manure management system 75%; SC2—pasture 0%, solid manure management system 100%; and SC3—aerobic recycling 100%.
Methane gas emissions are determined from animal digestion processes and manure management technologies, and direct and indirect emissions of nitrous oxide from manure. When calculating or experimentally determining the emission coefficients of methane and nitrous oxide gases, it is necessary to evaluate the conditions of keeping animals, the applied modern manure management technologies (manure removal from the barn, manure pits, manure incorporation into the soil), applied bio measures to optimize fermentation and microbiological processes, and temperature changes. A methodically based GHG accounting system that records more accurate data collection in specific farms would enable the state to identify problem areas to which measures aimed at reducing GHG emissions could be directed more appropriately, to carry out monitoring, and to analyze the benefits provided by the support.

3. Results and Discussion

After assessment of the calculation platform of different scenarios when simulating different manure management techniques, we identified effective measures for GHG reduction. It was evaluated that scenario SC1 (pasture 25%, solid manure management system 75%), with an average number of animals of 459 and an average animal weight of 500 kg, could reduce CO2 eq by 5% per year. Scenario SC2 (pasture 0%, solid manure management system 100%) was more effective, and could reduce CO2 eq by 15% per year. The most effective was scenario SC3 (aerobic recycling 100%), which could reduce CO2 eq by more than 19% per year (Figure 3).
Various researchers are searching and testing different methods and measurements to reduce GHG emissions from agriculture. Some scientific studies have determined the effectiveness of using bio-measures in reducing GHG emissions (Figure 4).
A CO2 reduction of 19 to 23% was achieved by measuring plowing fuel consumption with gas analyzers, after the use of biological preparations in spring, when winter wheat vegetation is restored [8].
Efforts are also being made to find methods and tools to calculate GHG emissions, and one such method is life cycle analysis. The maximum effectiveness of biopreparations for CO2 eq reduction during the LCA phase via fixed soil tillage was approximately 15% for the mixed biopreparation variant in first year, approximately 8% for the mixed biopreparation variant in the second year, and approximately 30% for the mixed biopreparation variant in the third year (Figure 5) [9]. Other researchers have also developed similar platforms for GHG calculation, but their basis was a questionnaire assessment, which is not always attractive and methodologically efficient; some examples of this methodology are the assessment of production-induced GHG pollution via survey [10], as well as software-based assessment of the specific GHG emissions of olive farms [11].

4. Conclusions

After analyzing all the factors that shape emissions at farm level and correctly reflecting sustainable farm actions that ensure the principles of circularity and sustainable resource use, the FarmGHG calculation tool will help determine the emission sources of technologies and tools applied on the farm, according to the IPCC methodology.
A methodically based GHG accounting system that records more accurate data collection on specific farms would enable the state to identify the problem areas to which support measures aimed at reducing GHG emissions could be directed more appropriately, to carry out monitoring, and to analyze the benefits provided by the support.
The FarmGHG assessment system is an effective tool for consultants providing consulting services, preparing farm sustainability plans, and monitoring the results of the implementation of measures. Additionally, more detailed farm-level data will allow farmers to make individual decisions related to reducing greenhouse gas emissions, optimizing their farms, and increasing productivity.

Author Contributions

Conceptualization, V.N. and R.B.; methodology, V.N.; software, D.S.; validation, V.N., R.B. and K.L.; formal analysis, K.L.; investigation, V.N. and D.S.; data curation, D.S. and V.N.; writing—original draft preparation, V.N.; writing—review and editing, K.L.; visualization, V.N. and D.S.; supervision, V.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data will be provided individually upon contacting the corresponding author. Link to the new tool created—FarmGHG—http://176.223.141.152/FarmGHG.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. Simulation of CO2 moving around the planet, from NASA Orbiting Carbon Observatory–2 Satellite’s grading spectrometer (NASA/JPL–Caltech), measuring CO2 levels with a precision of about 1 part per million. Interval of averaged CO2 concentration from 354.1 ppm–min value (marked by blue color) to 417.1 ppm–max value (marked by red color) [5,6].
Figure 1. Simulation of CO2 moving around the planet, from NASA Orbiting Carbon Observatory–2 Satellite’s grading spectrometer (NASA/JPL–Caltech), measuring CO2 levels with a precision of about 1 part per million. Interval of averaged CO2 concentration from 354.1 ppm–min value (marked by blue color) to 417.1 ppm–max value (marked by red color) [5,6].
Engproc 37 00120 g001
Figure 2. Visualization of the accounting system for GHG emissions at farm level (link to the new tool created—FarmGHG—http://176.223.141.152/FarmGHG). Translation: connection to the system —login name and password, and information about system creation methods.
Figure 2. Visualization of the accounting system for GHG emissions at farm level (link to the new tool created—FarmGHG—http://176.223.141.152/FarmGHG). Translation: connection to the system —login name and password, and information about system creation methods.
Engproc 37 00120 g002
Figure 3. The effect of different manure management scenarios on the total GHG of the farm (link to the new tool created—FarmGHG—http://176.223.141.152/FarmGHG).
Figure 3. The effect of different manure management scenarios on the total GHG of the farm (link to the new tool created—FarmGHG—http://176.223.141.152/FarmGHG).
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Figure 4. The effect of bio-measures on CO2 emission reduction in crop production [8].
Figure 4. The effect of bio-measures on CO2 emission reduction in crop production [8].
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Figure 5. CO2 eq reduction during the LCA phase [9].
Figure 5. CO2 eq reduction during the LCA phase [9].
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MDPI and ACS Style

Naujokienė, V.; Lekavičienė, K.; Bleizgys, R.; Savickas, D. Accounting for Greenhouse Gas Emissions at Farm Level. Eng. Proc. 2023, 37, 120. https://doi.org/10.3390/ECP2023-14769

AMA Style

Naujokienė V, Lekavičienė K, Bleizgys R, Savickas D. Accounting for Greenhouse Gas Emissions at Farm Level. Engineering Proceedings. 2023; 37(1):120. https://doi.org/10.3390/ECP2023-14769

Chicago/Turabian Style

Naujokienė, Vilma, Kristina Lekavičienė, Rolandas Bleizgys, and Dainius Savickas. 2023. "Accounting for Greenhouse Gas Emissions at Farm Level" Engineering Proceedings 37, no. 1: 120. https://doi.org/10.3390/ECP2023-14769

APA Style

Naujokienė, V., Lekavičienė, K., Bleizgys, R., & Savickas, D. (2023). Accounting for Greenhouse Gas Emissions at Farm Level. Engineering Proceedings, 37(1), 120. https://doi.org/10.3390/ECP2023-14769

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