Precision Livestock Farming Research: A Global Scientometric Review
Abstract
:Simple Summary
Abstract
1. Introduction
2. Concept of PLF
3. Materials and Methods
3.1. Data Collection
3.2. Research Methodology
4. Results and Discussion
4.1. Analysis of Main Features
4.1.1. Annual Scientific Production
4.1.2. Countries
4.1.3. Institutions
4.1.4. Authors
4.1.5. Journals
4.2. Analysis of Research Cores
4.2.1. Research Hotspots
4.2.2. Research Directions
4.2.3. Research Frontiers
4.3. Analysis of Hot Topics
4.3.1. Social Science in PLF
4.3.2. The Environmental Impact of PLF
4.3.3. Information Technology in FLF
4.3.4. Animal Welfare in PLF
5. Conclusions and Insights
5.1. Conclusions
- (1)
- From the perspective of the characteristics of publishing, the number of research papers on topics related to PLF generally shows an increasing trend. The international cooperation of this research is strong, and the developed countries of livestock farming in Europe and America have a large number of papers and close cooperation among countries; the research institutions of this study are mainly universities, involving agriculture-related institutions in individual countries, and the Inter-institutional cooperation network is relatively loose but the group characteristics are obvious; Daniel Berckmans and his team have published the most articles, and the overall cooperation among scholars is characterized by “small concentration and large dispersion”, and the cooperation among scholars is weak; the research belongs to a multidisciplinary cross-fertilization research field, mainly including animal science, veterinary science, computer science, agricultural engineering and environmental science.
- (2)
- Research hotspots in PLF include precision dairy technology, precision cattle technology, intelligent systems, and animal behavior research. The hot words can be categorized into PLF technology, technology application objects, and technology use, with research directions focused on deep learning, accelerometer, automatic milking systems, lameness, estrus detection, and electronic identification, and the specific research contents intersecting. Scholars have paid more attention to deep learning and machine learning since 2021.
- (3)
- From the perspective of hot topics, the research on PLF mainly includes four hot topics: social science, environmental impact, information technology, and animal welfare. The literature on PLF from a social science perspective can be divided into five categories: adoption of PLF on farms, effects of PLF on farmer identity, farmer work and farm work, ethical concerns in PLF, economics and management of PLF, and transformation of PLF. PLF provides a valuable tool for mitigating the impact of animal husbandry on the environment by optimizing livestock management. The new generation of information technology represented by the Internet of Things, blockchain, and machine learning plays an important role in promoting the stable and sustainable development of animal husbandry. The combination of sensors and algorithms can effectively extract and analyze the images, sounds, movements and vital signs of animals, facilitating early detection of diseases and improving animal welfare.
5.2. Insights
- (1)
- Strengthen the exchange and cooperation of PLF research. Universities with outstanding contributions in the field of PLF, such as Wageningen University and Research, University of Guelph and Katholieke Universiteit Leuven, should actively hold academic exchange conferences or exchange programs, and other institutions should actively establish friendly exchange relations with these universities and carry out related project cooperation. Scholars should actively participate in international exchange meetings and forums on PLF, discuss and learn the research experience and latest achievements of PLF with scholars from all over the world, and strengthen extensive exchanges and cooperation among scholars.
- (2)
- Pay attention to the combination of multi-disciplines and multi-methods. The research content needs the intervention of many disciplines and fields such as animal science, veterinary science, computer science, agricultural engineering and environmental science; in terms of research methods, it is necessary to organically integrate various methods such as big data analysis and model analysis, and strengthen the innovative integration of information technology in animal husbandry, in order to promote the research and development of PLF.
- (3)
- Strengthen the application of deep learning, machine learning, and other technologies. Develop integrated intelligent detection channels that integrate lameness recognition, body condition scoring, weight estimation, respiratory heart rate sign measurement and other multi-functional features; give full play to the role of data decision support, and realize refinement management to improve animal welfare. In addition, it is necessary to consider the relationship between PLF and humans and animals from the perspective of social science and to guide farmers to change the concept of responsibility to deeply explore and give full play to the value of PLF.
- (4)
- Strengthen the focus and exploration of DLF. PLF is gradually transforming into DLF, and farmers, scientists and consumers as well as other stakeholders should consciously participate in and pay attention to following this process in order to accelerate the innovative integration of digital technology and animal husbandry and promote the development of animal husbandry.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No | Country | Year | Papers | Centrality |
---|---|---|---|---|
1 | USA | 1973 | 698 | 0.24 |
2 | China | 2000 | 419 | 0.09 |
3 | UK | 1992 | 325 | 0.22 |
4 | Australia | 2000 | 301 | 0.13 |
5 | Germany | 1990 | 300 | 0.11 |
6 | Canada | 1997 | 270 | 0.10 |
7 | Netherlands | 1992 | 237 | 0.18 |
8 | Italy | 1998 | 202 | 0.07 |
9 | Belgium | 2001 | 196 | 0.04 |
10 | Brazil | 2003 | 193 | 0.03 |
11 | France | 1986 | 140 | 0.18 |
12 | Spain | 1999 | 131 | 0.23 |
13 | Denmark | 1996 | 110 | 0.01 |
14 | Switzerland | 2003 | 102 | 0.02 |
15 | Japan | 2005 | 95 | 0.02 |
16 | New Zealand | 1996 | 89 | 0.03 |
17 | Republic of Ireland | 2001 | 80 | 0.01 |
18 | South Korea | 2009 | 79 | 0.06 |
19 | India | 1998 | 76 | 0.03 |
20 | Austria | 2004 | 72 | 0.01 |
No | Institution | Affiliated Country | Year | Papers | Centrality |
---|---|---|---|---|---|
1 | Wageningen University and Research | Netherlands | 1996 | 143 | 0.17 |
2 | Katholieke Univ Leuven | Belgium | 2004 | 117 | 0.07 |
3 | University Guelph | Canada | 2007 | 99 | 0.12 |
4 | China Agricultural University | China | 2013 | 87 | 0.07 |
5 | Univ Sydney | Australia | 2012 | 65 | 0.03 |
6 | USDA | USA | 1973 | 64 | 0.08 |
7 | University of Milan | Italy | 2008 | 64 | 0.04 |
8 | Univ British Columbia | Canada | 2009 | 63 | 0.02 |
9 | Agr and Agri Food Canada | Canada | 1998 | 60 | 0.04 |
10 | Univ Wisconsin | USA | 2010 | 59 | 0.03 |
11 | Aarhus University | Denmark | 2008 | 58 | 0.08 |
12 | INRAE | France | 1986 | 53 | 0.09 |
13 | Univ New England | Australia | 2000 | 51 | 0.06 |
14 | Ghent University | Belgium | 2001 | 48 | 0.02 |
15 | Iowa State University | USA | 1999 | 45 | 0.03 |
16 | Agr Res Org | Israel | 1997 | 40 | 0.03 |
17 | Univ Kentucky | USA | 2000 | 40 | 0.02 |
18 | Swedish Univ Agr Sci | Sweden | 2001 | 34 | 0.04 |
19 | Northwest A&F Univ | China | 2018 | 32 | 0.01 |
20 | Univ Bern | Switzerland | 2016 | 31 | 0.01 |
No | Author | Affiliated Institutions | Year | Records | Centrality |
---|---|---|---|---|---|
1 | Daniel Berckmans | Katholieke Univ Leuven | 2004 | 67 | 0.02 |
2 | Tomas Norton | Katholieke Univ Leuven | 2017 | 36 | 0.01 |
3 | Claudia Bahr | Katholieke Univ Leuven | 2010 | 33 | 0.01 |
4 | Ilan Halachmi | Agr Res Org | 2000 | 30 | 0.03 |
5 | Marcella Guarino | University of Milan | 2008 | 28 | 0.02 |
6 | Jeffrey Rushen | Univ British Columbia | 2009 | 27 | 0.01 |
7 | Trevor J. Devries | Univ Guelph | 2016 | 26 | 0.00 |
8 | Henk Hogeveen | Wageningen Univ and Res | 1994 | 22 | 0.01 |
9 | Jeffrey M. Bewley | Univ Kentucky | 2001 | 20 | 0.01 |
10 | Sergio C. Garcia | Univ Sydney | 2014 | 19 | 0.00 |
No | Keywords | Year | Papers | Centrality |
---|---|---|---|---|
1 | dairy cow | 1992 | 530 | 0.10 |
2 | cattle | 1991 | 526 | 0.13 |
3 | behavior | 1997 | 491 | 0.08 |
4 | system | 1996 | 457 | 0.15 |
5 | performance | 1993 | 291 | 0.07 |
6 | cow | 1992 | 279 | 0.08 |
7 | machine learning | 1996 | 279 | 0.03 |
8 | precision livestock farming | 2008 | 271 | 0.01 |
9 | dairy cattle | 1995 | 216 | 0.07 |
10 | health | 1999 | 198 | 0.03 |
11 | time | 2001 | 194 | 0.02 |
12 | deep learning | 2018 | 184 | 0.01 |
13 | management | 2004 | 179 | 0.03 |
14 | classification | 2000 | 176 | 0.03 |
15 | animal welfare | 1997 | 170 | 0.06 |
16 | feeding behavior | 1997 | 153 | 0.04 |
17 | model | 2004 | 136 | 0.03 |
18 | computer vision | 1995 | 131 | 0.01 |
19 | milk yield | 1999 | 125 | 0.02 |
20 | welfare | 2007 | 125 | 0.02 |
Cluster Serial Number | Cluster Name | Document Number | Keywords (Logarithmic Likelihood Ratio, p-Value) |
---|---|---|---|
#0 | deep learning | 168 | deep learning (174.71, 1.0 × 10−4); computer vision (118.87, 1.0 × 10−4); image processing (68.37, 1.0 × 10−4); precision livestock farming (58.38, 1.0 × 10−4); object detection (53.56, 1.0 × 10−4) |
#1 | accelerometer | 145 | accelerometer (36.26, 1.0 × 10−4); gps (35.94, 1.0 × 10−4); feeding behaviour (30.08, 1.0 × 10−4); grazing (29.56, 1.0 × 10−4); sheep (28.9, 1.0 × 10−4) |
#2 | automatic milking system | 122 | automatic milking system (57.92, 1.0 × 10−4); mastitis (47.72, 1.0 × 10−4); robotic milking (46.23, 1.0 × 10−4); somatic cell count (40.29, 1.0 × 10−4); automatic milking (46.98, 1.0 × 10−4) |
#3 | lameness | 109 | lameness (126.76, 1.0 × 10−4); locomotion score (40.18, 1.0 × 10−4); heat stress (39.21, 1.0 × 10−4); animal welfare (37.95, 1.0 × 10−4); locomotion (33.11, 1.0 × 10−4) |
#4 | estrus detection | 105 | estrus detection (56.1, 1.0 × 10−4); estrus (55.48, 1.0 × 10−4); machine learning (32.73, 1.0 × 10−4); reproductive performance (31.18, 1.0 × 10−4); ovulation (28.8, 1.0 × 10−4) |
#5 | electronic identification | 53 | electronic identification (42.82, 1.0 × 10−4); feeding behavior (36.53, 1.0 × 10−4); transponder (30.98, 1.0 × 10−4); traceability (25.81, 1.0 × 10−4); performance (15.7, 1.0 × 10−4) |
First Author | Title | Journal | Year | Cited Frequency |
---|---|---|---|---|
Laurens Klerkx | A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda | NJAS-Wageningen Journal of Life Sciences | 2019 | 344 |
Emanuela Tullo | Review: Environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy | Science of the Total Environment | 2019 | 137 |
Mohamed Torky | Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges | Computers and Electronics in Agriculture | 2020 | 98 |
Lefteris Benos | Machine Learning in Agriculture: A Comprehensive Updated Review | Sensors | 2021 | 95 |
Ricardo S. Alonso | An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario | Ad Hoc Networks | 2020 | 91 |
Ilan Halachmi | Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production | Annual Review of Animal Biosciences | 2019 | 91 |
Abhinav Sharma | Machine Learning Applications for Precision Agriculture: A Comprehensive Review | Sensors | 2020 | 86 |
Qiao Yongliang | Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming | Computers and Electronics in Agriculture | 2019 | 80 |
Callum Eastwood | Making sense in the cloud: Farm advisory services in a smart farming future | NJAS-Wageningen Journal of Life Sciences | 2019 | 70 |
Madonna Benjamin | Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners | Animals | 2019 | 67 |
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Jiang, B.; Tang, W.; Cui, L.; Deng, X. Precision Livestock Farming Research: A Global Scientometric Review. Animals 2023, 13, 2096. https://doi.org/10.3390/ani13132096
Jiang B, Tang W, Cui L, Deng X. Precision Livestock Farming Research: A Global Scientometric Review. Animals. 2023; 13(13):2096. https://doi.org/10.3390/ani13132096
Chicago/Turabian StyleJiang, Bing, Wenjie Tang, Lihang Cui, and Xiaoshang Deng. 2023. "Precision Livestock Farming Research: A Global Scientometric Review" Animals 13, no. 13: 2096. https://doi.org/10.3390/ani13132096