CarbonSECO for Livestock: A Service Suite to Help in Carbon Emission

Decisions

Pedro Henrique Assis Silva

1 a

, Regina Braga

1 b

, Jos

e Maria David

1 c

Valdemar Vicente Graciano Neto

2 d

, Wagner Arbex

1,3 e

and Victor Stroele

1 f

Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil

Federal University of Goias, Goiania, GO, Brazil

Embrapa Dairy Cattle, Juiz de Fora, MG, Brazil

Keywords:

Software Ecosystem, Support Decision, Enteric Fermentation, Carbon Emission.

Abstract:

Global concerns about agriculture’s impact, mainly related to the livestock enteric fermentation producing

methane Greenhouse Gas (GHG) emissions, demand solutions to mitigate these impacts. The CarbonSECO

platform, tailored for carbon credit generation in Brazilian rural areas, responds to the popularity of carbon

credits to offset GHG emissions. However, additional solutions related to GHG need to be conceived. This

article extends the CarbonSECO platform, focusing on quantifying, monitoring, and controlling carbon emis-

sions from livestock enteric fermentation. Intelligent techniques, including ontologies and machine learning,

provide emissions management solutions for assessing and managing the environmental impact of livestock

farming. These techniques address the research question of mitigating carbon emissions from Brazilian dairy

farming. The article explores strategies, reviews related works, and proposes platform extensions. A feasibil-

ity study using data from an intelligent farm showcases the platform’s ability to predict and assess changes

for carbon emission reduction. As a result, this work enhances the CarbonSECO platform, offering emissions

management for dairy farming. Integrating ontologies and machine learning can promote standardization,

aiding property owners in better planning.

1 INTRODUCTION

Global warming and its possible consequences on life

on earth have gained more and more space and im-

portance in society, transforming the mitigation of

Greenhouse Gas (GHG) emissions into the main fo-

cus of environmental debates and policies. In the

Brazilian context, agriculture plays a signiﬁcant role

in greenhouse gas (GHG) emissions, representing

33.6% of the total, with 19% originating from enteric

fermentation (Embrapa, 2023b).

Enteric fermentation is a natural process in ru-

minant herbivorous animals, the result of convert-

ing vegetable carbohydrates into fatty acids used as

a source of energy by the animal that ingested the

food. During this conversion, gases such as methane

are produced, which is exhaled into the atmosphere

https://orcid.org/0009-0002-2793-958X

https://orcid.org/0000-0002-4888-0778

https://orcid.org/0000-0002-3378-015X

https://orcid.org/0000-0003-2190-5477

https://orcid.org/0000-0003-2005-4463

https://orcid.org/0000-0001-6296-8605

during the rumen. These gases are responsible for

worsening the greenhouse effect, resulting in acceler-

ated climate change (Siqueira and Caviglioni, 2021).

Livestock farming is responsible for 97% of methane

emissions, divided between 86% from beef cattle and

11% dairy cattle. Notably, methane from enteric fer-

mentation is one of the main sources of emissions in

milk production, contributing 40 to 58% of the total,

followed by waste management (Embrapa, 2023a).

Carbon credits represent a way to measure and

offset greenhouse gas (GHG) emissions associated

with speciﬁc activities, like dairy farming. A carbon

credit is generated for each ton of carbon that is no

longer emitted or captured from the atmosphere. Us-

ing selected and validated rules and methodologies,

whether projects are reducing emissions is analyzed.

To generate credits, actions must be taken to replace

an activity that would generate greenhouse gas emis-

sions with another solution that would reduce or elim-

inate these emissions (WayCarbon, 2022).

The ”baseline” (Vale Fund, 2022) is a key aspect

to be addressed by a carbon project. Every project

needs to determine what its emissions would have

been if the project had not been implemented. These

Silva, P., Braga, R., David, J., Neto, V., Arbex, W. and Stroele, V.

CarbonSECO for Livestock: A Service Suite to Help in Carbon Emission Decisions.

DOI: 10.5220/0012734300003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 2, pages 89-99

ISBN: 978-989-758-692-7; ISSN: 2184-4992

are called “baseline emissions” (Vale Fund, 2022).

The number of credits a project receives is then calcu-

lated by subtracting the project’s emissions from the

baseline emissions. The deﬁnition of the baseline will

depend on the project activity and the sector, to deter-

mine which elements and parameters should be con-

sidered for the project.

The Federal University of Juiz de Fora, in part-

nership with EMBRAPA (an acronym for Brazilian

Agricultural Research Corporation) and the Federal

University of Goi

as, develop projects in the domain

of dairy farming (Gomes et al., 2023; Ambr

osio et al.,

2021; Soares et al., 2021; Magaldi et al., 2017) to

seek computational solutions to support dairy pro-

duction activities. With this partnership, a software

ecosystem platform was proposed, called E-SECO

(Ambr

osio et al., 2021), an evolution of the collab-

orative E-Science project, to support the development

of solutions for agribusiness.

E-SECO encompasses several functionalities to

facilitate the development of this type of application,

considering the use of scientiﬁc workﬂows and the

process of scientiﬁc experimentation. In E-SECO,

data analysis is an important component (Gomes

et al., 2023). This platform offers a suitable envi-

ronment for carrying out various stages of experi-

ments involving data related to dairy farming, rang-

ing from problem investigation, prototyping, plan-

ning, conduction/analysis, packaging of results, and

preparation of reports (Ambr

osio et al., 2021).

Based on the evolution of the group’s research

(Gomes et al., 2023), we extended the E-SECO plat-

form to CarbonSECO (Santos et al., 2023). Carbon-

SECO aims to enable the development of applications

related to generating carbon credits on rural proper-

ties in Brazil. CarbonSECO prioritizes mechanisms

that offer analysis, reliability, and traceability of data

and processes in carbon certiﬁcation projects (Santos

et al., 2023).

Considering the importance of this topic today, the

relevance of Brazil in the global carbon emission sce-

nario, and speciﬁc decisions of COP 28

, this article

presents speciﬁc extensions made on CarbonSECO to

quantify, monitor, and control carbon emissions from

enteric fermentation. Speciﬁc services were devel-

oped, expanding the CarbonSECO support on car-

bon emissions in dairy farming, considering intelli-

gent techniques. These new services use ontologies

and ML techniques to provide intelligent solutions for

managing greenhouse gas emissions from livestock

farming. The main goal is to offer these services to

be used in developing applications for evaluation and

managing the environmental contributions of live-

https://www.un.org/en/climatechange/cop28

stock farming. Speciﬁcally, these services can assist

in developing decision-making applications, based on

predictions about speciﬁc features related to the pro-

duction process of milk and its derivatives. In this

regard, a feasibility study was conducted, with data

from the smart farm maintained by EMBRAPA- Gado

de Leite. It was possible to predict planned changes to

reduce carbon emissions using ontological processing

and ML.

This work’s Main Research Question (RQ) is

”How to help mitigate carbon emissions from dairy

farming, with a focus on Brazilian farms?”. To ad-

dress this speciﬁc topic, this article is organized into

the following sections, including this introduction.

Section 2 discusses Brazilian strategies for dealing

with issues related to carbon emissions. In section

3, related work is discussed. Section 4 presents the

CarbonSECO extension proposal. In section 5 a fea-

sibility study is detailed. In section 6, conclusions and

future work are presented.

2 BACKGROUND

The global reference for quantifying greenhouse gas

(GHG) emissions, is the IPCC Guidelines for Na-

tional Greenhouse Gas Inventories (IPCC, 2021). The

IPCC established a standard bottom-up predictive

model, which has undergone reﬁnements over time.

These models are stratiﬁed into different levels of

complexity. Tier 1 is the simplest, using standard

emission factors based on general literature. Tier

1 does not consider the characterization of regional

livestock systems, such as breed types, animal age,

and physiological issues (IPCC, 2021). Tier 2 in-

corporates detailed emission factors, adding speciﬁc

characterization of food and animals, using estimates

based on gross energy consumption (GEI) and CH

conversion factor (Ym, expressed as % of GEI con-

verted to CH

) (IPCC, 2021).

The Veriﬁed Carbon Standard (VCS) Program

stands out as a global leader in voluntary certiﬁcation

of carbon offsets (Greenﬁeld, 2023). With a back-

ground of more than a billion tons of carbon and

other greenhouse gas emissions reduced or removed

through VCS-certiﬁed projects, this program plays an

essential role in the ongoing global effort to protect

our shared environment (Verra, 2022).

Considering the VCS, the proposal presented in

this article is related to Tiers 1 and 2. Emissions in the

reference scenario are estimated as the sum of annual

emissions from enteric fermentation according to the

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

following equation:

Enteric

∑

j=1



Enteric

i, j

× GW P × 0.001



(1)

Where:

Enteric

= Total CH

emissions in the reference

scenario from enteric fermentation of cattle on the

farm i tCO

Enteric

i, j

= Enteric CH

emissions factor for an-

imal group j during the monitoring period kgCH

GW P = Global Warming Potential of Methane.

n = Total number of animal group on farm i.

To determine the enteric emission factor, it is pos-

sible to use two calculations, the choice depending on

the availability of data for each group of animals on

the farm. The ﬁrst option provides procedures to cal-

culate the enteric emission factor for each group of

animals, applying an IPCC Tier 2 method, using the

following equation:

Enteric

i, j

∑

j=1



GEI

× Y

× N

i, j

× Days

i, j

100 × EC



(2)

Where:

Enteric

i, j

= Enteric CH

emission factor for

group of animals j during the monitoring period

(kgCH

GEI

= Average gross energy consumption (GEI)

per animal in group j on farm i (MJhead

−1

= Conversion factor indicating the proportion

of GEI converted into enteric CH

energy by animal

group, CH

energy as a percentage of gross (dimen-

sionless) energy.

Days

i, j

= Number of days spent on farm i by each

animal in group j during the monitoring period (d).

i, j

= Average number of heads in group of ani-

mals j on farm i in the monitoring period (head).

EC = Energy content of methane (55.65MJkg

−1

)

Average Gross Energy Intake (GEI) is calculated

by multiplying Average Dry Matter Intake (DMI) by

the energy density of the food, using the following

equation:

GEI

= DMI

× ED (3)

Where:

DMI

= Average dry mass of food consumed by

the group of animals j on a given day (kg head

−1

ED = Average dry matter energy density (MJ

−1

). Standard values of 19.10 MJ kg

−1

for diets

that include oils with a fat content in the range of 4 to

6%, or 18.45 MJ kg

−1

for diets that include oils with

a fat content below 4%, can be utilized.

The second option is applicable in cases where

certain data required for the calculations of the ﬁrst

option are unavailable. In this scenario, Tier 1 enteric

fermentation emission factors for cattle and buffalo

are employed. The enteric emission factor for each

group of animals is computed using the formula:

Enteric

i, j

= EF

Production

i, j

× N

i, j

× Days

i, j

(4)

Where:

Enteric

i, j

= Enteric CH

emission factor for the

group of animals j during the monitoring period (kg

Production

i, j

= Average enteric C H

emission fac-

tor for the group of animals j during the monitoring

period (speciﬁc national or regional factors) (kg CH

head

−1

Days

i, j

= Number of days spent on farm i by each

animal in group j during the monitoring period (d).

i, j

= Average number of heads in the group of

animals j on farm i in the monitoring period (head).

In this article, we apply these calculations to quan-

tify emissions from enteric fermentation based on the

type of information provided by farms. Section 5

presents examples of speciﬁc calculations using the

proposed equations.

3 RELATED WORKS

The literature presents few works that deal with car-

bon emissions to mitigate and assist in decision-

making on emissions-related practices. Some works

deal with general context, but none effectively pro-

pose computational strategies, especially those in-

volving intelligent processing. In this context, we

seek to build an architecture with the objective of sup-

porting the development of a software ecosystem plat-

form, called CarbonSECO.

(Tedeschi et al., 2022) reviewed methods for

quantifying methane emissions from ruminants and

their manure. The study addresses various mea-

surement techniques from classical methodologies,

such as breathing chambers, to micrometeorological

approaches, and explores the challenges associated

with each method. The authors emphasize the need

for accurate quantiﬁcation of greenhouse gas emis-

sions, mainly methane, to ensure adequate reporting

in greenhouse gas inventories and to design effective

methane emissions mitigation strategies. The review

shows the diversity of approaches, including aircraft,

drones, and satellites and highlights the importance

of addressing knowledge gaps and research require-

ments in this ﬁeld. Among the methodologies covered

CarbonSECO for Livestock: A Service Suite to Help in Carbon Emission Decisions

are those for calculating methane emissions using the

Tier 1 and Tier 2 approaches proposed by IPCC.

(Mazzetto et al., 2022) investigated the carbon

footprint of bovine milk production in several coun-

tries, identifying critical factors such as allocation

methods and functional units that signiﬁcantly inﬂu-

ence results. The 21 studies analyzed revealed consid-

erable variations in carbon footprint estimates, high-

lighting the process’s complexity. Additionally, the

study highlighted mitigation strategies, focusing on

the importance of waste management, livestock feed

production, and fertilization methods to reduce emis-

sions. The ﬁndings provide practical insights for the

industry, indicating speciﬁc areas for interventions

aimed at reducing the environmental impact of bovine

milk production, with the caveat that strategies must

be adapted to the agricultural conditions of each coun-

try.

(Wang et al., 2023) conducted a review on the

impact of digital technologies, including the Internet

of Things, Big Data, cloud computing, blockchain

and AI, on sustainable supply chains. It highlights

the application of these technologies in speciﬁc prac-

tices, such as green procurement, environmentally

conscious production, sustainable consumption and

ecological logistics. The study emphasizes these

technologies’ interconnectedness and potential to re-

duce energy consumption, contributing to greener and

more efﬁcient supply chains. It also includes the need

to explore pricing on sharing platforms, the design of

integrated digital systems, and continued technologi-

cal innovations.

(O’Brien et al., 2014) investigated the impact of

methodological decisions in Life Cycle Evaluation

(LCE) on the carbon footprint of milk in dairy produc-

tion systems. The authors highlighted the inﬂuence

of greenhouse gas emissions allocation between co-

products and the lack of consistency in international

LCE standards. Issues related to carbon sequestration

and land use change emissions were addressed. The

study highlights the need for more speciﬁcity in the

LCA methodology for accurate comparisons between

different dairy production systems.

(Pirlo and Car

e, 2013) also address milk produc-

tion’s greenhouse gas (GHG) emissions. They pro-

posed LatteGHG, a tool to estimate the carbon foot-

print of cow’s milk produced under typical conditions

in Italy. The study shows the effectiveness of Lat-

teGHG in the environmental evaluation of production

systems, revealing the model’s sensitivity to variables

such as milk production and manure management.

Despite the simplicity of some emission factors, the

research highlights the continued need to improve the

accuracy of estimates of emissions of gases such as

methane and nitrous oxide, considering diet composi-

tion and animal performance.

(Vogel and Beber, 2022) examine the carbon foot-

print and mitigation strategies on heterogeneous dairy

farms in Paran

a, Brazil. Grouping farms into four

categories through statistical analysis and life cycle

evaluation, the study highlights striking differences in

carbon footprint between these groups. Farms with

the largest carbon footprint predominate in the region,

characterized by less specialized herds and less tech-

nical support. To address climate change in the sector,

the study highlights the need to promote sustainable

practices, integrating them into a broader approach

to environmental governance and regional socioeco-

nomic development.

(Desjardins et al., 2012)) analyzed the carbon

footprint of cattle in countries such as Canada, the

United States, the European Union, Australia, and

Brazil, highlighting variations from 8 to 22 kg of CO

per kg of live weight. Over the past few decades,

improvements in management practices have resulted

in signiﬁcant reductions in greenhouse gas emissions

per unit of product. The study addresses the inﬂu-

ence of different factors, such as breeding systems,

locations and management practices, and discusses

the competition for higher-quality land in beef pro-

duction. It also shows the importance of considering

changes in soil carbon due to land management. In

general, it considers that the carbon footprint is just

one aspect of beef production, and it is essential to

consider the impact on services and biodiversity for

a complete evaluation of the product’s environmental

sustainability.

(Singh et al., 2015) propose an integrated cloud-

based system to measure and reduce the beef supply

chain’s carbon footprint. Aimed especially at small

and medium-sized farms, the system allows access

to carbon calculators through a private cloud, provid-

ing crucial information to optimize emissions. Fur-

thermore, the collaborative approach aims to improve

stakeholder coordination, identify sustainable prac-

tices, and address the consumer demand for trans-

parency and traceability in beef production. The study

highlights the relevance of CCT in mitigating carbon

emissions, contributing to environmental efﬁciency

and government carbon reduction targets.

(Liu et al., 2023) investigated the impact of imple-

menting digital technology on carbon emission efﬁ-

ciency on dairy farms in China using empirical meth-

ods and farm data. The results highlighted a posi-

tive inﬂuence of digital technology on improving car-

bon emission efﬁciency, emphasizing precision feed-

ing technology as the biggest contributor. Farmers’

educational experience, technical training, and gov-

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

Table 1: Comparison of related works.

Authors Architecture Intelligent Enteric Methane Decision

and SECO Processing Emissions Support

(O’Brien et al., 2014) X

(Pirlo and Car

e, 2013) X

(Vogel and Beber, 2022) X X

(Desjardins et al., 2012) X

(Singh et al., 2015) X X X

(Liu et al., 2023) X X

(Berhe et al., 2020) X X

(Santos et al., 2023) X X X

(Gomes et al., 2023) X X X

ernment incentives contributed to carbon efﬁciency.

It was observed that the application of digital tech-

nology had a more pronounced effect on farms with

lower initial emission efﬁciency, highlighting the im-

portance of environmental regulation as a positive

moderator in this process. The ﬁndings provide in-

sights for strategies for transitioning to low-carbon,

digital dairy production systems.

(Berhe et al., 2020) analyzed the impacts of differ-

ent interventions on greenhouse gas (GHG) emissions

in livestock production systems in Ethiopia. A re-

duction in total GHG emissions was observed by im-

proving livestock feed, implementing improved ma-

nure management practices, and optimizing herd pro-

duction and reproductive parameters. Dietary supple-

mentation with corn grains resulted in a decrease in

enteric methane emissions. Furthermore, the joint im-

plementation of these interventions demonstrated an

overall reduction in emissions, highlighting the im-

portance of integrated management to promote envi-

ronmental efﬁciency in livestock production systems.

These results contribute to understanding the fac-

tors inﬂuencing GHG emissions in different produc-

tion systems, providing insights for environmentally

sustainable mitigation strategies. (Gomes et al., 2023)

present the evolution of the E-SECO platform to ad-

dress automation in livestock farming, highlighting

the e-Livestock architecture to improve animal pro-

duction. Two case studies at the Compost Barn sys-

tem demonstrate the platform’s effectiveness. The im-

portance of self-adaptation, AI, and IoT is highlighted

to face dynamic challenges in agriculture, promoting

efﬁciency and preventing losses in animal production.

The article points to the rise of Livestock IoT (LIoT)

as a new area of research, emphasizing the need to

study speciﬁc requirements and technologies for live-

stock farming with the widespread adoption of IoT.

(Santos et al., 2023) propose the CarbonSECO plat-

form for the Control of Carbon Credits, addressing

the growing concern about climate change. Focus-

ing on changing land use as Brazil’s main source of

emissions in 2021, the CarbonSECO platform is pre-

sented as support for developing applications related

to controlling emissions/carbon credits on rural prop-

erties. The aim is to generate knowledge to offer al-

ternatives for cultivating land and mitigating green-

house gas emissions. The platform incorporates in-

telligent data analysis, highlighting its potential pos-

itive impact on the Brazilian agricultural sector. Pre-

liminary results demonstrate the platform’s viability,

with an analysis of carbon stocks on a rural prop-

erty, highlighting its potential to provide insights into

sustainable agricultural practices and reducing green-

house gas emissions. The contribution of our work

in the group lies in introducing a service that deals

with managing carbon emissions while incorporating

intelligent data analysis to provide alternative solu-

tions in the livestock sector. Our approach seeks to

reduce emissions and promote sustainable livestock

practices.

Table 1 highlights the main features of each work

presented in this section. These studies reﬂect the

complexity and diversity of approaches needed to ad-

dress greenhouse gas emissions in livestock farming,

indicating the importance of quantiﬁcation methods,

mitigation strategies, and technological solutions. As

we can see, no works propose a suite of intelligent

services to support decision-making related to green-

house gas emissions using intelligent processing as

we do in our work. In this regard, our work presents

a proposal encompassing services for quantifying,

monitoring, and controlling emissions from enteric

fermentation while at the same time offering support

for evaluating management in livestock farming, ex-

tending the functionalities of the CarbonSECO plat-

form proposed in (Santos et al., 2023). The focus

CarbonSECO for Livestock: A Service Suite to Help in Carbon Emission Decisions

Figure 1: CarbonSECO Main Services with Enhanced Livestock Carbon Emissions and Control Services (Santos et al., 2023).

is to provide speciﬁc services that allow rural pro-

ducers to quantify methane emissions from ruminants

and make decisions based on strategic analyses pro-

vided by the platform, such as estimates of the impact

of a new diet strategy. These services reinforce the

support of the CarbonSECO platform in controlling

greenhouse gas emissions in agriculture in general,

adding speciﬁc services for evaluating and managing

environmental contributions in ruminant farming.

4 CARBONSECO FOR

LIVESTOCK

As stated before, the CarbonSECOfor Livestock is

an evolution of our previous platform CarbonSECO

(Santos et al., 2023) to provide a suite of services re-

lated to managing carbon emission/credits in the live-

stock domain. CarbonSECO emphasizes mechanisms

that bring intelligent analysis to Carbon certiﬁcation

projects. Figure 1 presents CarbonSECO’s main ser-

vices enhanced with new services (left of the ﬁgure)

related to livestock carbon emissions and control, the

article’s focus.

It is not our objective to detail the CarbonSECO

previous services. They are detailed in (Santos et al.,

2023). The new service components are discussed be-

low.

4.1 Smart Farm Dataset

In addition to the databases related to rural proper-

ties and crops, new data sources, primarily associated

with livestock, were added to CarbonSECO. The data

comes from various sources, whether data from sen-

sors attached to the animals, such as necklaces and

earrings, or platforms for weighing and feeders with

intake control. In addition, speciﬁc measurements of

milk production and animal registration data, includ-

ing their traceability, are part of the data from Smart

Datasets’ data sources. However, to guarantee the re-

liability of the carbon measurement process, it must

be based on international guidelines and methodolo-

gies, such as the IPCC, used by CarbonSECO (IPCC,

2021).

4.2 Ontology Processing

The EntericMeasureOnto ontology was developed to

identify semantic relationships between data, result-

ing in the discovery of speciﬁc features that must be

observed in the carbon measurement process. For ex-

ample, based on semantic relationships, we can iden-

tify a feeding protocol that explicitly does not present

an increase in carbon emissions but which, based on

the relationships identiﬁed in the ontology can be an

indirect source of increased carbon emissions. This

type of relationship can only be discovered by pro-

cessing inferences and semantic rules deﬁned in the

ontology. Consequently, optimizing livestock man-

agement on a speciﬁc rural property can improve the

carbon credits associated with that property.

For this, classes and different types of relation-

ships were deﬁned in EntericMeasureOnto Figure 2,

using the OWL language (World Wide Web Consor-

tium, 2004), SWRL rules (SWRL, 2004), and the Pel-

let reasoner (Sirin et al., 2007). The main ontolog-

ical classes are: RuralProperty, which represents a

rural property, BaselineEmissons, which represents

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

Figure 2: Main ontological classes.

Table 2: SWRL rules.

Tag Rule

S1 Cattle(?cattle) ˆ milkProduction(?cattle, ?production) -> DairyCattle(?cattle)

S2 Cattle(?cattle) ˆ DairyCattle(?cattle) ˆ emissionFactorTier1(?cattle, ?e) ˆ daysOnFarm(?cattle, ?d) ˆ

multiply(?result, ?e, ?d) ˆ -> individualEntericEmissionFactorTier1(?cattle, ?result)

S3 Cattle(?cattle) ˆ individualEntericEmissionFactorTier1(?cattle, ?eeft1) ˆ gwp(?cattle, ?gwp) ˆ

multiply(?result, ?eeft1, ?gwp, 0.001) ˆ -> individualEntericEmissionTier1(?cattle, ?ﬁnalResult)

S4 DairyCattle(?cattle) ˆ energyDensity(?cattle, ?e) ˆ dryMatterIntake(?cattle, ?d) ˆ multiply(?result,

?e, ?d) ˆ -> grossEnergyIntake(?cattle, ?result)

S5 Cattle(?cattle) ˆ grossEnergyIntake(?cattle, ?gei) ˆ daysOnFarm(?cattle, ?d) ˆ

emissionFactorTier2(?cattle, ?ef) ˆ multiply(?result, ?gei, ?d, ?ef, 0.01) ˆ divide(?ﬁnalResult,

?result, 55.65) ˆ -> individualEntericEmissionFactorTier2(?cattle, ?ﬁnalResult)

S6 Cattle(?cattle) ˆ individualEntericEmissionFactorTier2(?cattle, ?eeft2) ˆ gwp(?cattle, ?gwp) ˆ

multiply(?result, ?eeft2, ?gwp, 0.001) ˆ -> individualEntericEmissionTier2(?cattle, ?ﬁnalResult)

the baseline methane emissions from the enteric fer-

mentation of animals, BaselineEmissonsTier1 and

BaselineEmissonsTier2, which represents the base-

line methane emissions from the enteric fermentation

of animals from the Tier 1 and 2 calculation methods

respectively, Cattle, which represents the cows for

which enteric emissions are estimated, DairyCattle

and OtherCattle, which represent the cows that are

producing milk or not, respectively, Measurement-

Data, which represents the data measurements asso-

ciated with a Cattle instance. ObjectProperties were

also speciﬁed to implement the relationships and spe-

ciﬁc rules in SWRL (SWRL, 2004) to process carbon

emissions. As stated before, the rules were speciﬁed

according to Tier 1 and 2 calculations.

As a result, using the declared model (explicit

knowledge) with the addition of speciﬁc SWRL rules

and inference mechanism (Pellet reasoner), the ontol-

ogy infers, from the instantiated data, new relation-

ships under the actions on the rural property. As spe-

ciﬁc examples, in Table 2, Rule S1 infers that if an an-

imal has milk production, it is a DairyCattle instance.

Rule S2 infers the enteric C H

emission factor for the

animal during the monitoring period according to Tier

1. Rule S3 infers the enteric CO

emission for the an-

imal during the monitoring period according to Tier

1. Rule S4 infers the average gross energy consump-

tion for the animal. Rule S5 infers the enteric CH

emission factor for the animal during the monitoring

period according to Tier 2. Rule S6 infers the enteric

emission for the animal during the monitoring

period according to Tier 2.

4.3 Decision Support

Data analysis on a rural property can potentially dis-

cover insights that help manage and plan emissions

related to livestock practices. Furthermore, they pro-

vide support in the adoption of sustainable practices.

To assist in decision support related to carbon emis-

sions, in addition to the ontology, which allows the

discovery of new relationships between data, we use

ML techniques to predict speciﬁc actions to improve

practices related to carbon emissions. To do this, we

CarbonSECO for Livestock: A Service Suite to Help in Carbon Emission Decisions

Figure 3: Number of Dairy Cattle Over Time.

use linear regression to analyze and predict emissions

linked to practices carried out on the farm. A dataset

was used with data captured by sensors and speciﬁc

measurements on the farm, plus speciﬁc data gener-

ated from information delivered by experts, includ-

ing the average daily consumption of dry matter, the

energy density of the feed, and the average number

of cattle in a speciﬁc period. These variables were

used as training data for the linear regression model.

Throughout the training process, the algorithm ad-

justed the coefﬁcients to optimize the model’s ability

to predict emissions based on changes in the variables

considered.

In this context, the use of ML techniques aims

to assist in predictive analysis of the carbon emis-

sions of a rural property, taking into account expecta-

tions of changes in diets and the number of animals

over a speciﬁc period. This predictability helps in

planning farm activities and anticipating speciﬁc ac-

tions related to carbon emissions by the property, such

as sharing knowledge and allowing more informed

choices about the best conditions for using animal diet

inputs.

This data analysis is presented on a dashboard to

assist in decision-making. It uses graphs and spe-

ciﬁc variables, allowing an integrative view of car-

bon management on the property, offering insights

into the environmental performance of its practices,

and enabling strategic adjustments to achieve sustain-

able goals. For example, Figure 3 depicts a graph il-

lustrating the number of dairy cattle on the property

throughout 2021 in Embrapa’s Smart Farm.

5 FEASIBILITY STUDY

This section details a feasibility study to evaluate Car-

bonSECO livestock’s new services. This evaluation

considers using CarbonSECO to deal with quanti-

fying, monitoring, and controlling carbon emissions

from enteric fermentation. The scope of the study was

deﬁned as “Analyze the use of the CarbonSECO Live-

stock services from the point of view of Farmers in the

context of data extracted from Dairy Smart Farm”.

From this scope, we derived the RQ: “How to

help mitigate carbon emissions from dairy farming,

with a focus on Brazilian farms?”. The data extracted

can be reached at GitHub

. This data set encom-

passes 162 animals, including sensor data and pro-

duction system-related details, with 3,381 observa-

tions collected from January 2021 to December 2021,

encompassing monthly data collection that occurred

during three milking sessions. The dataset includes

key features such as Tag (serving as a unique iden-

tiﬁer), Weight (measured in kilograms), Milk Pro-

duction (indicating the quantity of milk produced),

and Date (recording the month of the measurement).

For this analysis, the monitored cow remained on the

property throughout the month. It’s important to note

that no measurements were recorded in June. To pro-

cess this data, we used the EntericMeasureOnto on-

tology.

To ﬁnd out existing ontologies that can be reused

in this evaluation, a search was conducted. Unfortu-

nately, no one that ﬁts our purposes was found, there-

fore, the domain ontology was speciﬁed, consider-

ing the steps speciﬁed in (Verra, 2020; Nicola and

Missikoff, 2016; Feilmayr and W

ob, 2016), and the

data extracted from the Smart Farm related to EM-

BRAPA’s research projects.

Using the CarbonSECO new services, we seek to

make predictions on calculating emissions in a future

scenario. These predictions can help choose strate-

https://github.com/PedroAssisDev/Measuring

Enteric Fermentation Emissions/blob/main/data/

pesoXleite.csv

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

Figure 4: Monthly Carbon Emissions using Tier 1 Methodology.

Figure 5: Projected Total Carbon Emissions using Carbon-

SECO Service.

gies for mitigating carbon emissions from livestock

farming. Therefore, based on the available data, it

was possible to carry out emission calculations using

the EntericMeasureOnto ontology, following the Tier

1 method. These calculations imply that the analysis

of carbon emissions in dairy farming beneﬁted from

the ontology’s structuring and organization, improv-

ing the results’ precision and reliability. Figure 4 il-

lustrates the carbon emissions trends over the months,

emphasizing the total emissions for the period.

To verify the calculation module utilizing the Tier

2 method, we added values into the dataset related

to the diet of animals. In this context, the Aver-

age Energy Density and Average Dry Matter Intake

were 25 kg per head daily and 18 MJ/kg, respectively.

The emissions were subsequently computed using the

EntericMeasureOnto ontology, following the Tier 2

method. Figure 6 presents the monthly progression

of carbon emissions, detailing both the individual

monthly values and the cumulative total for the spec-

iﬁed period.

To evaluate the feasibility of the forecasting mod-

ule, we processed a speciﬁc dataset with a ﬁxed dura-

tion of 335 days, providing a representative timeframe

for the analysis. Key input parameters included the

average daily dry matter intake (Average DMI), 21,

representing typical feeding conditions for cattle. The

energy density of the feed (Energy density of feed)

wwas 18, as a measure of the nutritional characteris-

tics of the diet. The average number of cattle (Aver-

age number of heads) in the dataset was 200.

Using the CarbonSECO service in processing this

dataset, it became feasible to forecast an estimated

emissions total. The results indicate a projected to-

tal of 598.48 tons of CO2, offering insights into the

anticipated environmental impact within the speciﬁc

context of the provided conditions and practices. This

projection is graphically illustrated in Figure 5 , pro-

viding an overview of the evolution of emissions and

the total impact over the speciﬁed period.

In summary, the feasibility study of CarbonSECO

Livestock’s new services yielded insights into carbon

emissions management in dairy farming. These re-

sults help farmers in decisions related to carbon emis-

sions in the farm. Therefore, the CarbonSeco helps in

decision support related to caron emissions. Utilizing

Tier 1 and Tier 2 methods, and incorporating speciﬁc

data, we could answer the RQ, “How to help mitigate

carbon emissions from dairy farming, with a focus on

Brazilian farms?”. The Tier 1 method, using actual

data from Dairy Smart Farm showcased the efﬁcacy

of the EntericMeasureOnto ontology in reﬁning car-

CarbonSECO for Livestock: A Service Suite to Help in Carbon Emission Decisions

Figure 6: Monthly Carbon Emissions using Tier 2 Methodology.

bon emission calculations, enhancing precision and

reliability. Illustrated trends in Figure 4 emphasized

monthly evolution and total impact, aiding informed

decision-making.

The Tier 2 method, employing speciﬁc values,

showed CarbonSECO’s calculation module capabil-

ity. The projection of 598.48 tons of CO2 over 335

days offers a predictive tool for farmers, providing

proactive mitigation strategies and fostering sustain-

able livestock management practices.

Therefore, CarbonSECO Livestock has the poten-

tial for carbon emission management in dairy farm-

ing, providing services for real-world and predic-

tive scenarios. The results and comparisons lay the

groundwork for further exploration, fostering envi-

ronmentally conscious decision-making in the agri-

cultural sector.

6 CONCLUSIONS AND FUTURE

WORK

This paper presented services for quantifying, mon-

itoring, and controlling emissions from enteric fer-

mentation that will be integrated into CarbonSECO.

This addition expands the platform’s capacity to cover

specialized services focused on carbon emissions and

supports the development of applications related to

generating carbon credits on Brazilian rural proper-

ties.

The utilization of ontologies and ML algorithms

contributed to the standardization and estimation of

future emissions, these intelligent technologies em-

power property owners with better planning capabili-

ties.

The integration of these services supports the Car-

bonSECO platform with new functionalities, pro-

vides the user with social and environmental consid-

erations, promoting sustainable production practices

with reduced carbon generation. This, in turn, pro-

vides valuable decision support for stakeholders deal-

ing with carbon emissions in the agricultural sector.

In future work, it is essential improve the pro-

posed service with different methods for calculating

fermentation emissions arising from enteric fermen-

tation. Additionally, there is a need for continuous

improvement of the machine learning module to en-

hance the system’s predictive accuracy. The adap-

tation and incorporation of new types of input data

to enrich the analytical capabilities of the platform

further. Lastly, the addition of a new service dedi-

cated to quantifying, monitoring, and controlling an-

imal waste emissions will provide a comprehensive

solution, ensuring a more embracing approach to en-

vironmental sustainability in livestock farming.

DATA AND CODE AVAILABILITY

The data used in this study and the code im-

plementation are available at https://github.com/

PedroAssisDev/Measuring Enteric Fermentation

Emissions

ACKNOWLEDGEMENTS

This work was partially funded by UFJF/Brazil,

CAPES/Brazil, CNPq/Brazil (grant: 307194/2022-

1), and FAPEMIG/Brazil (grant: APQ-02685-17),

(grant: APQ-02194-18).

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

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