Artificial Intelligence Techniques in Enhancing Livestock Adaptation to Climate Change: A Systematic Literature Review
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Climate change is increasingly affecting livestock systems worldwide by exacerbating heat stress, water shortages, and disease prevalence, which are critical challenges that can jeopardize the productivity and sustainability of this sector, particularly in vulnerable areas. In this context, this research aims to conduct a systematic literature review of 60 reviewed articles to examine the contribution of artificial intelligence (AI) in enhancing the adaptation of the livestock sector to climate change. The literature review has synthesized the major opportunity domains, factors of adoption, barriers to adoption, areas of application, and theory base. The research has found that AI is mainly used in heat stress detection, disease monitoring, and productivity optimization. However, its adoption is significantly influenced by technological and economic feasibility. On the other hand, the major barriers to adopting AI are high implementation costs and lack of infrastructure. A critical gap in research is that most literature has not adopted a comprehensive theory base to explain the complex factors affecting AI adoption in the livestock sector. To this end, a new conceptual framework is introduced that incorporates AI attributes, climate stressors, adaptation mechanisms, production results, and adoption factors. This framework is envisioned to serve as a holistic window for forthcoming studies and practice, recognizing that successful AI implementation is not only about technological advancements but also about facilitating infrastructure, capacity development, and policy support. This paper makes a theoretical and practical contribution to the field by synthesizing information in an organized manner and informing future studies that can deliver a more comprehensive AI-driven solution.
H. Bashiru and S. Oseni, “Simplified climate change adaptation strategies for livestock development in low-and middle-income countries,” Front. Sustain. Food Syst., 2025, Doi: 10.3389/fsufs.2025.1566194.
T. Taye et al., “Scientific Advances in Climate-Resilient Livestock Production with Emphasis on Sustainability: A Review,” Journal of Experimental Agriculture International, 2025, Doi: 10.9734/jeai/2025/v47i83716.
M. Passamonti et al., “The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock,” Animals (Basel)., vol. 11, 2021, Doi: 10.3390/ani11102833.
A. Meirmanova, M. H. Miraz, H. J. Hwang, and D. Kaibassova, “Modeling the Factors of Users’ Intention to Adopt AI-Driven Livestock Recognition Systems for Transparency in G2B Transactions: An Extension of the TAM-UTAUT Integrated Model,” IEEE Access, vol. 13, pp. 183598–183616, 2025, Doi: 10.1109/access.2025.3624124.
R. Ayadi, Y. Forouheshfar, and O. Moghadas, “Enhancing system resilience to climate change through artificial intelligence: a systematic literature review,” Frontiers in Climate, 2025, Doi: 10.3389/fclim.2025.1585331.
M. Sairam and R. Egala, “Artificial Intelligence-Powered Cyclone Classification Framework Using Mobilenetv1 and Goose Optimizer: Climate-Resilient Farming,” International Research Journal on Advanced Science Hub, 2025, Doi: 10.47392/irjash.2025.036.
V. Georgopoulos, D. Gkikas, and J. Theodorou, “Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020,” Sustainability, 2023, Doi: 10.3390/su152316385.
U. Nawaz, M. Z. Zaheer, F. Khan, H. Cholakkal, S. Khan, and R. Anwer, “AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock,” ArXiv, vol. abs/2507.2, 2025, Doi: 10.48550/arxiv.2507.22101.
L. Tedeschi, P. G. Lopez, H. Menendez, and S. Seo, “Advancing Precision Livestock Farming: Integrating Artificial Intelligence and Emerging Technologies for Sustainable Livestock Management.,” Anim. Biosci., 2025, Doi: 10.5713/ab.25.0289.
A. Sood, A. Bhardwaj, and R. Sharma, “Towards sustainable agriculture: key determinants of adopting artificial intelligence in agriculture,” J. Decis. Syst., vol. 33, pp. 833–877, 2022, Doi: 10.1080/12460125.2022.2154419.
B. Mpande and S. Dube, “Issue 3 www.jetir.org (ISSN-2349-5162),” 2026. [Online]. Available: www.jetir.org
A. Hernandez, S. Manajit, J. Festijo, and J. Tucpi, “Examining small farm holders’ adoption intention on artificial intelligence of things for sustainable agriculture in developing country: A structural equation modelling assessment,” Edelweiss Applied Science and Technology, 2025, Doi: 10.55214/2576-8484.v9i9.10125.
M. M. Passamonti et al., “The quest for genes involved in adaptation to climate change in ruminant livestock,” Animals, vol. 11, no. 10, pp. 1–25, 2021, Doi: 10.3390/ani11102833.
I. Paparamborda, S. Dogliotti, P. Soca, and W. A. H. Rossing, “A conceptual model of cow-calf systems functioning on native grasslands in a subtropical region,” Animal, vol. 17, no. 10, p. 100953, 2023, Doi: 10.1016/j.animal.2023.100953.
R. Cresci, B. A. Balkan, L. O. Tedeschi, A. Cannas, and A. S. Atzori, “A system dynamics approach to model heat stress accumulation in dairy cows during a heatwave event,” Animal, vol. 17, p. 101042, 2023, Doi: 10.1016/j.animal.2023.101042.
J. M. Rigby et al., “Exploring the Information Needs of Somaliland Pastoralists: Design Considerations for Digital Climate Adaptation Services,” pp. 1548–1565, 2023, Doi: 10.1145/3563657.3596061.
D. Dell’Unto, R. Selvaggi, G. Pappalardo, and R. Cortignani, “Adoption of precision livestock farming devices in the dairy cattle sector: An assessment based on agroeconomic modelling,” Science of the Total Environment, vol. 1002, no. September 2025, p. 180555, 2025, Doi: 10.1016/j.scitotenv.2025.180555.
L. O. Tedeschi, P. L. Greenwood, and I. Halachmi, “Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming,” J. Anim. Sci., vol. 99, no. 2, pp. 1–11, 2021, Doi: 10.1093/jas/skab038.
A. Chlingaryan, P. C. Thomson, S. C. Garcia, and C. E. F. Clark, “An AI-based hybrid model for dairy cattle heat tolerance phenotype,” Smart Agricultural Technology, vol. 12, no. August, p. 101455, 2025, Doi: 10.1016/j.atech.2025.101455.
E. B. Rebez et al., “Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock,” Sensors, vol. 24, no. 18, pp. 1–33, 2024, Doi: 10.3390/s24185890.
C. Griffin, A. Wreford, and N. A. Cradock-Henry, “‘As a farmer you’ve just got to learn to cope’: Understanding dairy farmers’ perceptions of climate change and adaptation decisions in the lower south Island of Aotearoa-New Zealand,” J. Rural Stud., vol. 98, no. December 2022, pp. 147–158, 2023, Doi: 10.1016/j.jrurstud.2023.02.001.
E. Estefania-Salazar and E. Iglesias, “Assessing vegetation phenology dynamics in West African rangelands: Implications for livestock sustainability and transhumance,” Ecol. Inform., vol. 88, no. November 2024, p. 103138, 2025, Doi: 10.1016/j.ecoinf.2025.103138.
O. F. Akinmoladun, C. T. Mpendulo, and M. O. Ayoola, “Assessment of the adaptation of Nguni goats to water stress,” Animal, vol. 17, no. 8, p. 100911, 2023, Doi: 10.1016/j.animal.2023.100911.
M. C. Bianchi et al., “Can technology mitigate the environmental impact of dairy farms?,” Cleaner Environmental Systems, vol. 12, no. October 2023, p. 100178, 2024, Doi: 10.1016/j.cesys.2024.100178.
D. Lovarelli, L. Leso, M. Bonfanti, S. M. C. Porto, M. Barbari, and M. Guarino, “Climate change and socio-economic assessment of PLF in dairy farms: Three case studies,” Science of the Total Environment, vol. 882, no. April, p. 163639, 2023, Doi: 10.1016/j.scitotenv.2023.163639.
K. Tuyishime and M. A. Adewusi, “Data-driven prediction of cattle weight gain for evaluating key growth factors with machine learning approaches,” Discover Computing, vol. 28, no. 1, 2025, Doi: 10.1007/s10791-025-09798-6.
A. M. Pranta, S. M. A. Islam, and R. I. Khan, “Development of a sensor-integrated AI automation model for decision-based heat stress management in layer chickens under subtropical climate conditions,” Smart Agricultural Technology, vol. 12, no. August, p. 101306, 2025, Doi: 10.1016/j.atech.2025.101306.
M. C. Bianchi et al., “Diffusion of precision livestock farming technologies in dairy cattle farms,” Animal, vol. 16, no. 11, p. 100650, 2022, Doi: 10.1016/j.animal.2022.100650.
B. Horváthné Kovács and Z. Zörög, “Digital livestock farming in climate-smart agriculture: an overview to advance the SDGs,” Discover Sustainability, vol. 6, no. 1, 2025, Doi: 10.1007/s43621-025-01866-7.
S. R. Silva et al., “Extensive Sheep and Goat Production: The Role of Novel Technologies towards Sustainability and Animal Welfare,” Animals, vol. 12, no. 7, pp. 1–28, 2022, Doi: 10.3390/ani12070885.
S. Y. Chen, J. P. Boerman, L. S. Gloria, V. B. Pedrosa, J. Doucette, and L. F. Brito, “Genomic-based genetic parameters for resilience across lactations in North American Holstein cattle based on variability in daily milk yield records,” J. Dairy Sci., vol. 106, no. 6, pp. 4133–4146, 2023, Doi: 10.3168/jds.2022-22754.
G. Laible et al., “Holstein Friesian dairy cattle edited for diluted coat color as a potential adaptation to climate change,” BMC Genomics, vol. 22, no. 1, pp. 1–12, 2021, Doi: 10.1186/s12864-021-08175-z.
H. A. E. H. Mansour, “Integration of assisted reproductive technologies and artificial intelligence for optimizing fertility and genetic selection in livestock production,” Discover Applied Sciences, vol. 7, no. 10, 2025, Doi: 10.1007/s42452-025-07380-9.
R. M. F. Silveira, A. M. de Vasconcelos, C. McManus, L. P. Fávero, and I. J. O. da Silva, “Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change,” Smart Agricultural Technology, vol. 12, no. November 2024, 2025, Doi: 10.1016/j.atech.2025.101128.
N. Piscopo et al., “Investigation of Climate Effects on the Physiological Parameters of Dairy Livestock (Cow vs. Buffalo),” Sensors, vol. 24, no. 4, pp. 1–9, 2024, Doi: 10.3390/s24041164.
A. C. Castonguay et al., “Projecting the impacts of climate and land-use change on avian influenza suitability in Bangladesh,” One Health, vol. 21, no. October, 2025, Doi: 10.1016/j.onehlt.2025.101238.
D. Lovarelli, M. Bovo, C. Giannone, E. Santolini, P. Tassinari, and M. Guarino, “Reducing life cycle environmental impacts of milk production through precision livestock farming,” Sustain. Prod. Consum., vol. 51, no. September, pp. 303–314, 2024, Doi: 10.1016/j.spc.2024.09.021.
S. Cesco et al., “Smart management of emergencies in the agricultural, forestry, and animal production domain: Tackling evolving risks in the climate change era,” International Journal of Disaster Risk Reduction, vol. 114, no. August, 2024, Doi: 10.1016/j.ijdrr.2024.105015.
A. Bionda, A. Negro, M. Barbato, L. Liotta, S. Grande, and P. Crepaldi, “Spatio-Temporal Genomics of Goats: Recent Evolution, Adaptation, and Future Vulnerability,” animal, p. 101732, 2025, Doi: 10.1016/j.animal.2025.101732.
C. Adetola, F. Egbinola, O. Alabi, M. Adebayo, and R. Ojo, “Strategies for adaptation and mitigation in climate-smart animal production in Africa, Asia and South America,” Discover Agriculture, vol. 3, no. 1, p. 194, 2025, [Online]. Available: https://link.springer.com/10.1007/s44279-025-00362-w
B. E. Akinyemi, J. M. Siegford, L. Jessiman, S. P. Turner, A. K. Johnson, and F. Akaichi, “Precision livestock farming usage among a subset of U.S. swine producers: Insights through a structural equation modeling approach,” Smart Agricultural Technology, vol. 10, no. November 2024, p. 100839, 2025, Doi: 10.1016/j.atech.2025.100839.
B. Horváthné Kovács and Z. Zörög, “Digital livestock farming in climate-smart agriculture: an overview to advance the SDGs,” Discover Sustainability, vol. 6, no. 1, p. 940, Sep. 2025, Doi: 10.1007/s43621-025-01866-7.
C. Duku, G. T. Diro, T. Demissie, and S. Dawit, “Climate change impacts livestock carrying capacity in East Africa,” Reg. Environ. Change, vol. 25, no. 3, Sep. 2025, Doi: 10.1007/s10113-025-02440-7.
D. Kaur and A. K. Virk, “An IoT-Driven hybrid AI model for health monitoring of cows,” Discover Artificial Intelligence, vol. 5, no. 1, Dec. 2025, Doi: 10.1007/s44163-025-00610-4.
R. Eckhardt, R. Arablouei, A. Ingham, K. McCosker, and H. Bernhardt, “Livestock behaviour forecasting via generative artificial intelligence,” Smart Agricultural Technology, vol. 11, Aug. 2025, Doi: 10.1016/j.atech.2025.100987.
N. H. Chapman et al., “A deep learning model to forecast cattle heat stress,” Comput. Electron. Agric., vol. 211, Aug. 2023, Doi: 10.1016/j.compag.2023.107932.
H. Shu et al., “Predicting physiological responses of dairy cows using comprehensive variables,” Comput. Electron. Agric., vol. 207, Apr. 2023, Doi: 10.1016/j.compag.2023.107752.
H. Xie and S. P. Ardakani, “A Machine Learning Enabled Mobile Application to Analyse Ambient-Body Correlations,” SN Comput. Sci., vol. 3, no. 2, Mar. 2022, Doi: 10.1007/s42979-022-01027-x.
E. M. Ibeagha-Awemu et al., “The Role of Modern Technologies for Improving the Production Environment of Livestock in Africa,” 2026, pp. 909–989. Doi: 10.1007/978-3-031-92076-9_21.
L. Zhu et al., “Empowering precision livestock farming: Artificial intelligence applications in animal genomic breeding and multi-dimensional phenotypic measurement,” Dec. 01, 2025, Elsevier B.V. Doi: 10.1016/j.atech.2025.101655.
R. Matera et al., “Precision livestock farming in buffalo species: a sustainable approach for the future,” Aug. 01, 2025, Elsevier B.V. Doi: 10.1016/j.atech.2025.101060.
X. Lu, L. Wang, S. Li, and W. Gao, “Quick Detection of Metabolic Status in Dairy Cows Based on Near-Infrared Spectral Analysis,” in VSIP 2025 - Proceedings of the 2025 7th International Conference on Video, Signal and Image Processing, Association for Computing Machinery, Inc, Jan. 2026, pp. 9–14. Doi: 10.1145/3784713.3784715.
F. Chang, Z. Gu, Z. Shi, and W. Tang, “Advancing Cattle Health Monitoring through ACI-Driven Wearable Sensor Technology: A Case Study of Leg-Worn System Development,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Dec. 2023. Doi: 10.1145/3637882.3637893.
L. T. Gwaka, “Computer Supported Livestock Systems: The Potential of Digital Platforms to Revitalize a Livestock System in Rural Zimbabwe,” Proc. ACM Hum. Comput. Interact., vol. 6, Nov. 2022, Doi: 10.1145/3555085.
M. M. Passamonti et al., “The quest for genes involved in adaptation to climate change in ruminant livestock,” Oct. 01, 2021, MDPI. Doi: 10.3390/ani11102833.
A. K. Shirley, P. C. Thomson, A. Chlingaryan, and C. E. F. Clark, “The diversity in dairy cattle reticulorumen temperature: Identifying water intake events,” Comput. Electron. Agric., vol. 235, Aug. 2025, Doi: 10.1016/j.compag.2025.110357.
M. Taneja, N. Jalodia, and B. Dezfouli, “Towards Building a Data-Driven Framework for Climate Neutral Smart Dairy Farming Practices,” in 2021 IEEE Global Humanitarian Technology Conference (GHTC), IEEE, Oct. 2021, pp. 213–218. Doi: 10.1109/GHTC53159.2021.9612417.
S. Singh et al., “AI Powered Livestock Recognization System,” in 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), IEEE, Apr. 2024, pp. 1–8. Doi: 10.1109/I2CT61223.2024.10544235.
T. Todic, L. Stankovic, V. Stankovic, and J. Shi, “Quantification of Dairy Farm Energy Consumption to Support the Transition to Sustainable Farming,” in 2022 IEEE International Conference on Smart Computing (SMARTCOMP), IEEE, Jun. 2022, pp. 368–373. Doi: 10.1109/SMARTCOMP55677.2022.00082.
H. Mahmoud et al., “V-FARM: Virtualized Farming Analytics for Smart Decision-Making,” in 2025 IEEE 11th Conference on Big Data Security on Cloud (BigDataSecurity), IEEE, May 2025, pp. 186–191. Doi: 10.1109/BigDataSecurity66063.2025.00034.
C. Davison, A. Hamilton, C. Tachtatzis, C. Michie, and I. Andonovic, “Keynote Speech 2: Data-driven Machine Learning Precision Livestock Farming Technologies and Applications,” in 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), IEEE, Dec. 2021, pp. 29–29. Doi: 10.1109/ICICIS52592.2021.9694131.
M. Ferrero et al., “Transfer Learning and Sensor Fusion for Cattle Monitoring: An IoT-Driven Approach for Sustainable Livestock Farming,” in 2025 IEEE 11th World Forum on Internet of Things (WF-IoT), IEEE, Oct. 2025, pp. 1–6. Doi: 10.1109/WF-IoT64238.2025.11270753.
S. R. Silva et al., “Extensive Sheep and Goat Production: The Role of Novel Technologies towards Sustainability and Animal Welfare,” Animals, vol. 12, no. 7, p. 885, Mar. 2022, Doi: 10.3390/ani12070885.
J. Li et al., “Barriers to computer vision applications in pig production facilities,” Comput. Electron. Agric., vol. 200, p. 107227, Sep. 2022, Doi: 10.1016/j.compag.2022.107227.
Shu et al., “Evaluation of environmental and physiological indicators in lactating dairy cows exposed to heat stress,” Int. J. Biometeorol., vol. 66, no. 6, pp. 1219–1232, Jun. 2022, Doi: 10.1007/s00484-022-02270-w.
Y. Li et al., “Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods,” Animals, vol. 12, no. 9, p. 1060, Apr. 2022, Doi: 10.3390/ani12091060.
D. Pavlovic et al., “Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks,” Sensors, vol. 21, no. 12, p. 4050, Jun. 2021, Doi: 10.3390/s21124050.
S. L. Mon, T. Onizuka, P. Tin, M. Aikawa, I. Kobayashi, and T. T. Zin, “AI-enhanced real-time cattle identification system through tracking across various environments,” Sci. Rep., vol. 14, no. 1, p. 17779, Aug. 2024, Doi: 10.1038/s41598-024-68418-3.
O. F. Akinmoladun, C. T. Mpendulo, and M. O. Ayoola, “Assessment of the adaptation of Nguni goats to water stress,” animal, vol. 17, no. 8, p. 100911, Aug. 2023, Doi: 10.1016/j.animal.2023.100911.
U. Nawaz, M. Z. Zaheer, F. Khan, H. Cholakkal, S. Khan, and R. Anwer, “AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock,” ArXiv, vol. abs/2507.22101, p., 2025, Doi: 10.48550/arxiv.2507.22101.
A. Prusty, P. Saha, N. Das, and S. Suman, “Implementation and adoption of smart technologies in agri-allied sectors,” Plant Science Today, p., 2025, Doi: 10.14719/pst.3467.
A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning Applications for Precision Agriculture: A Comprehensive Review,” IEEE Access, vol. 9, pp. 4843–4873, 2021, Doi: 10.1109/access.2020.3048415.
D. P. De Castro et al., “Artificial intelligence applied to animal production,” Ciência Rural, p., 2025, Doi: 10.1590/0103-8478cr20230520.
R. Ayadi, Y. Forouheshfar, and O. Moghadas, “Enhancing system resilience to climate change through artificial intelligence: a systematic literature review,” Frontiers in Climate, p., 2025, Doi: 10.3389/fclim.2025.1585331.
S. O. Araújo, R. S. Peres, J. Ramalho, F. Lidón, and J. Barata, “Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives,” Agronomy, p., 2023, Doi: 10.3390/agronomy13122976.
V. Georgopoulos, D. Gkikas, and J. Theodorou, “Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020,” Sustainability, p., 2023, Doi: 10.3390/su152316385.
M. Cabanillas-Carbonell and J. Zapata-Paulini, “Artificial intelligence applications in agriculture: a systematic review of literature,” IAES International Journal of Artificial Intelligence (IJ-AI), p., 2025, Doi: 10.11591/ijai.v14.i5.pp3503-3519.
J. A. M. Valencia, “Innovaciones para una Ganadería Climáticamente Inteligente: Impacto de Suplementos, Genética y Manejo del Estiércol en las Emisiones de GEI,” Ibero Ciencias - Revista Científica y Académica - ISSN 3072-7197, p., 2025, Doi: 10.63371/ic.v4.n2.a89.
S. Bangura, T. Chikukwa, and M. Lourens, “Scoping Review: Artificial Intelligence Applications for Climate Mitigation and Adaptation in Developing Nations: Opportunities, Technical Challenges, and Associated Risks,” Advances in Networks, p., 2025, Doi: 10.11648/j.net.20251202.11.
O. B. Akintuyi, “Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data,” Open Access Research Journal of Multidisciplinary Studies, p., 2024, Doi: 10.53022/oarjms.2024.7.2.0023.
