Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges

Parisa Niloofar; Deena P. Francis; Sanja Lazarova-Molnar; Alexandru Vulpe; Marius Constantin Vochin; George Suciu; Mihaela Balanescu; Vasileios Anestis; Thomas Bartzanas

doi:10.1016/j.compag.2021.106406

Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges

Parisa Niloofar^*, Deena P. Francis, Sanja Lazarova-Molnar, Alexandru Vulpe, Marius Constantin Vochin, George Suciu, Mihaela Balanescu, Vasileios Anestis, Thomas Bartzanas

^*Corresponding author for this work

Research output: Contribution to journal › Review › peer-review

Abstract

Precision Livestock Farming (PLF) is a concept that allows real-time monitoring of animals, by equipping them with sensors that surge livestock-related data to be further utilized by farmers. PLF comes with many benefits and ensures maximum use of farm resources, thus, enabling control of health status of animals, while potentially mitigating Greenhouse Gas (GHG) emissions. Due to the complexity of the decision making processes in the livestock industries, data-driven decision support systems based on not only real-time data but also expert knowledge, help farmers to take actions in support of animal health and better product yield. These decision support systems are typically based on machine learning, statistical analysis, and modeling and simulation tools. Combining expert knowledge with data obtained from sensors minimizes the risk of making poor decisions and helps to assess the impact of different strategies before applying them in reality. In this paper, we highlight the role of data-driven decision support tools in PLF, and provide an extensive overview and categorization of the different data-driven approaches with respect to the relevant livestock farming goals. We, furthermore, discuss the challenges associated with reduction of GHG emissions using PLF.

Original language	English
Article number	106406
Journal	Computers and Electronics in Agriculture
Volume	190
Number of pages	16
ISSN	0168-1699
DOIs	https://doi.org/10.1016/j.compag.2021.106406
Publication status	Published - Nov 2021

Bibliographical note

Funding Information:
We would like to express our gratitude to the FarmSustainaBl project, which is administered through the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 618123 [ICT-AGRI 2]. The project has received funding from the General Secretariat for Research and Technology (Greece), the Ministry of Environment and Food (Denmark), the Danish Agricultural Agency (Denmark), and the Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania). This work has been also partially supported by the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125.

Funding Information:
We would like to express our gratitude to the FarmSustainaBl project, which is administered through the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 618123 [ICT-AGRI 2]. The project has received funding from the General Secretariat for Research and Technology (Greece), the Ministry of Environment and Food (Denmark), the Danish Agricultural Agency (Denmark), and the Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania). This work has been also partially supported by the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125.

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

Data analytics
Data-driven decision support
GHG emission
Modelling and simulation
Precision livestock farming

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Niloofar, P., Francis, D. P., Lazarova-Molnar, S., Vulpe, A., Vochin, M. C., Suciu, G., Balanescu, M., Anestis, V., & Bartzanas, T. (2021). Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges. Computers and Electronics in Agriculture, 190, [106406]. https://doi.org/10.1016/j.compag.2021.106406

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abstract = "Precision Livestock Farming (PLF) is a concept that allows real-time monitoring of animals, by equipping them with sensors that surge livestock-related data to be further utilized by farmers. PLF comes with many benefits and ensures maximum use of farm resources, thus, enabling control of health status of animals, while potentially mitigating Greenhouse Gas (GHG) emissions. Due to the complexity of the decision making processes in the livestock industries, data-driven decision support systems based on not only real-time data but also expert knowledge, help farmers to take actions in support of animal health and better product yield. These decision support systems are typically based on machine learning, statistical analysis, and modeling and simulation tools. Combining expert knowledge with data obtained from sensors minimizes the risk of making poor decisions and helps to assess the impact of different strategies before applying them in reality. In this paper, we highlight the role of data-driven decision support tools in PLF, and provide an extensive overview and categorization of the different data-driven approaches with respect to the relevant livestock farming goals. We, furthermore, discuss the challenges associated with reduction of GHG emissions using PLF.",

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author = "Parisa Niloofar and Francis, {Deena P.} and Sanja Lazarova-Molnar and Alexandru Vulpe and Vochin, {Marius Constantin} and George Suciu and Mihaela Balanescu and Vasileios Anestis and Thomas Bartzanas",

note = "Funding Information: We would like to express our gratitude to the FarmSustainaBl project, which is administered through the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 618123 [ICT-AGRI 2]. The project has received funding from the General Secretariat for Research and Technology (Greece), the Ministry of Environment and Food (Denmark), the Danish Agricultural Agency (Denmark), and the Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania). This work has been also partially supported by the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125. Funding Information: We would like to express our gratitude to the FarmSustainaBl project, which is administered through the European Union{\textquoteright}s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 618123 [ICT-AGRI 2]. The project has received funding from the General Secretariat for Research and Technology (Greece), the Ministry of Environment and Food (Denmark), the Danish Agricultural Agency (Denmark), and the Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania). This work has been also partially supported by the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125. Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

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Niloofar, P, Francis, DP, Lazarova-Molnar, S, Vulpe, A, Vochin, MC, Suciu, G, Balanescu, M, Anestis, V & Bartzanas, T 2021, 'Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges', Computers and Electronics in Agriculture, vol. 190, 106406. https://doi.org/10.1016/j.compag.2021.106406

TY - JOUR

T1 - Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions

T2 - Overview and challenges

AU - Niloofar, Parisa

AU - Francis, Deena P.

AU - Lazarova-Molnar, Sanja

AU - Vulpe, Alexandru

AU - Vochin, Marius Constantin

AU - Suciu, George

AU - Balanescu, Mihaela

AU - Anestis, Vasileios

AU - Bartzanas, Thomas

N1 - Funding Information: We would like to express our gratitude to the FarmSustainaBl project, which is administered through the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 618123 [ICT-AGRI 2]. The project has received funding from the General Secretariat for Research and Technology (Greece), the Ministry of Environment and Food (Denmark), the Danish Agricultural Agency (Denmark), and the Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania). This work has been also partially supported by the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125. Funding Information: We would like to express our gratitude to the FarmSustainaBl project, which is administered through the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 618123 [ICT-AGRI 2]. The project has received funding from the General Secretariat for Research and Technology (Greece), the Ministry of Environment and Food (Denmark), the Danish Agricultural Agency (Denmark), and the Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania). This work has been also partially supported by the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125. Publisher Copyright: © 2021 Elsevier B.V.

PY - 2021/11

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N2 - Precision Livestock Farming (PLF) is a concept that allows real-time monitoring of animals, by equipping them with sensors that surge livestock-related data to be further utilized by farmers. PLF comes with many benefits and ensures maximum use of farm resources, thus, enabling control of health status of animals, while potentially mitigating Greenhouse Gas (GHG) emissions. Due to the complexity of the decision making processes in the livestock industries, data-driven decision support systems based on not only real-time data but also expert knowledge, help farmers to take actions in support of animal health and better product yield. These decision support systems are typically based on machine learning, statistical analysis, and modeling and simulation tools. Combining expert knowledge with data obtained from sensors minimizes the risk of making poor decisions and helps to assess the impact of different strategies before applying them in reality. In this paper, we highlight the role of data-driven decision support tools in PLF, and provide an extensive overview and categorization of the different data-driven approaches with respect to the relevant livestock farming goals. We, furthermore, discuss the challenges associated with reduction of GHG emissions using PLF.

AB - Precision Livestock Farming (PLF) is a concept that allows real-time monitoring of animals, by equipping them with sensors that surge livestock-related data to be further utilized by farmers. PLF comes with many benefits and ensures maximum use of farm resources, thus, enabling control of health status of animals, while potentially mitigating Greenhouse Gas (GHG) emissions. Due to the complexity of the decision making processes in the livestock industries, data-driven decision support systems based on not only real-time data but also expert knowledge, help farmers to take actions in support of animal health and better product yield. These decision support systems are typically based on machine learning, statistical analysis, and modeling and simulation tools. Combining expert knowledge with data obtained from sensors minimizes the risk of making poor decisions and helps to assess the impact of different strategies before applying them in reality. In this paper, we highlight the role of data-driven decision support tools in PLF, and provide an extensive overview and categorization of the different data-driven approaches with respect to the relevant livestock farming goals. We, furthermore, discuss the challenges associated with reduction of GHG emissions using PLF.

KW - Data analytics

KW - Data-driven decision support

KW - GHG emission

KW - Modelling and simulation

KW - Precision livestock farming

U2 - 10.1016/j.compag.2021.106406

DO - 10.1016/j.compag.2021.106406

M3 - Review

AN - SCOPUS:85114928028

VL - 190

JO - Computers and Electronics in Agriculture

JF - Computers and Electronics in Agriculture

SN - 0168-1699

M1 - 106406

ER -

Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges

Abstract

Bibliographical note

Keywords

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