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Smart Dairy Farming: Automatic Monitoring for Dairy Farm Sustainability, Efficiency...
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Smart Dairy Farming: Automatic Monitoring for Dairy Farm Sustainability, Efficiency and Environmental Impact

A special issue of Dairy (ISSN 2624-862X). This special issue belongs to the section "Dairy Farm System and Management".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 1306

Special Issue Editor

Dr. Olivier Debauche

E-Mail Website
Guest Editor
BioDyne, University of Liège (GxABT), 5030 Gembloux, Belgium
Interests: Internet of Things; cloud architecture; applied artificial Intelligence; smart farming

Special Issue Information

Dear Colleagues,

Smart dairy farming leverages advanced technologies to automatically monitor dairy farms, significantly enhancing their sustainability, efficiency, and environmental impact. Dairy animals are essential producers of nutritious food products worldwide, contributing to food and nutritional security for human populations. The most common animal species (cattle, sheep, goats, camels, buffaloes, yaks) and production systems (ranging from smallholders to large commercial farms, specialized dairy breeds, and dual-purpose animals) vary among regions or countries.

Automatic monitoring systems address critical challenges in all settings, including infectious diseases impacting production, the wider economy, and farmers' livelihoods. These systems can detect health issues (e.g., mastitis, high fever, and painful mammary gland lesions in lactating animals) early, preventing direct and indirect effects on milk production, such as fertility problems, morbidity, and the mortality of animals in use. Additionally, some pathogens can be transmitted to humans through direct contact with animals or by consuming food products, posing significant public health threats.

Smart dairy farming leverages AI, machine learning, and precision technologies to improve productivity, sustainability, and animal welfare. Real-time monitoring and predictive analytics enable tailored interventions, optimizing resources and minimizing waste. These advancements reduce labor demands, enhance economic resilience, and support environmental goals by lowering emissions and managing resource use efficiently.

This Special Issue will welcome scientific contributions in smart dairy farming, with a particular emphasis on automatic monitoring technologies. These technologies encompass a wide range of innovative tools and systems designed to enhance the efficiency, sustainability, and environmental impact of dairy farming operations. Contributions may explore various aspects such as the development and implementation of sensor networks, data analytics, and real-time monitoring systems that track animal health, milk production, and environmental conditions. Additionally, studies on the integration of these technologies into existing farming practices, their economic benefits, and their role in improving animal welfare and reducing greenhouse gas emissions are highly encouraged. Focusing on these cutting-edge advancements, this Special Issue aims to provide a comprehensive overview of how automatic monitoring technologies transform the dairy industry and contribute to a more sustainable future.

Dr. Olivier Debauche
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Dairy is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart dairy farming
  • automatic monitoring
  • sustainability
  • efficiency
  • environmental impact
  • sensor networks
  • data analytics
  • real-time monitoring
  • animal health
  • milk production

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Published Papers (1 paper)

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Research

26 pages, 13810 KB  
Article
Efficient Prediction of Milk Yield with Machine Learning Models Using Cow Identification or Milk Quality Traits
by Aurelio Guevara-Escobar, Vicente Lemus-Ramírez, José Guadalupe García-Muñiz, Adolfo Kunio Yabuta-Osorio, Claudia Andrea Vidales-Basurto and Benjamín Valdés-Aguirre
Dairy 2026, 7(3), 31; https://doi.org/10.3390/dairy7030031 - 24 Apr 2026
Viewed by 461
Abstract
Modeling milk yield in dairy cows is essential for improving management decisions, but traditional lactation curve models often fail to capture individual variability. Machine learning approaches offer greater flexibility; however, their performance in small, within-herd datasets and their reliance on explicit cow identification [...] Read more.
Modeling milk yield in dairy cows is essential for improving management decisions, but traditional lactation curve models often fail to capture individual variability. Machine learning approaches offer greater flexibility; however, their performance in small, within-herd datasets and their reliance on explicit cow identification remain unclear, particularly in grazing systems. This study aimed to evaluate whether routinely measured biological traits can substitute for cow identification in machine learning models for predicting daily milk yield within a herd under limited data conditions. The dataset comprised 62 lactations from 48 Holstein–Friesian cows in a grazing system. Two machine learning models were developed: one including cow identification (With ID) and another excluding cow identification but incorporating milk quality traits, body weight, and body condition score (Without ID). Both models were compared with the Wood lactation model fitted to individual cows. The With ID and Without ID models achieved R2 values of 0.97 and 0.93 and RMSE values of 1.2 and 1.6 kg d−1, respectively. Both machine learning models outperformed the Wood model fitted individually to each cow (R2 < 0.90; RMSE > 2.03 kg d−1), which represents an implicitly cow-specific approach. The model including cow identification therefore served as a machine learning analogue to this benchmark. Importantly, the trait-based model closely matched the performance of the cow-specific model. These results demonstrate that machine learning models based on routinely measured traits provide a practical approach for predicting within-herd milk yield from small datasets, while retaining much of the accuracy of cow-specific models. Full article
(This article belongs to the Special Issue Smart Dairy Farming: Automatic Monitoring for Dairy Farm Sustainability, Efficiency and Environmental Impact)
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