Animal Management

Animal management encompasses the strategies, technologies and husbandry practices employed to optimise the health, welfare and productivity of animals under human care, whether in agricultural, research or conservation contexts. Core objectives include ensuring appropriate nutrition, minimising stress and disease, and aligning management protocols with both economic viability and ethical standards. Advances in sensor technology, machine learning and data analytics have transformed decision-making, allowing real-time monitoring of physiological and behavioural indicators. Simultaneously, refinements in housing design and environmental enrichment seek to support natural behaviours, while targeted reproductive and veterinary interventions aim to improve efficiency and resilience. The integration of biological understanding with engineered solutions fosters systems that balance animal well-being, resource efficiency and societal expectations.

Research from Nature Portfolio

A novel video-based monitoring system applies convolutional neural networks to segment, detect and track individual cattle in real farm settings, combining appearance, positional and motion cues to maintain identity over time. The approach achieved robust multi-object tracking accuracy under variable lighting and stocking densities, offering a scalable tool for continuous herd surveillance without physical tagging. Another study demonstrated that pseudo-labelled deep neural networks can markedly improve individual Holstein cow identification from coat-pattern images by leveraging large unlabeled datasets. Semi-supervised training increased recognition accuracy by over 20 percentage points compared with fully supervised baselines, reducing annotation effort and enabling broader deployment. In dense barn scenes, a lightweight detection model based on an improved YOLOv5s framework, incorporating depth-wise separable convolutions and attention mechanisms, achieved over 87 % mean average precision for recognising cow mounting behaviour at speeds exceeding 300 frames per second. Such rapid inference supports all-weather, multi-camera surveillance for timely reproductive and welfare management.

Research from all publishers

An artificial intelligence-driven oestrus detection system evaluated across multiple commercial sow farms used connected sensors and cameras to monitor behavioural and physiological patterns. Implementation shortened the weaning-to-oestrus interval by roughly 20 hours, stabilised oestrus duration and improved farrowing rates, while reducing insemination frequency and labour requirements. In a tropical environment, inclusion of a GnRH agonist, buserelin, in boar semen doses at the first insemination significantly increased total and live-born piglets per litter and enhanced birth-weight outcomes. Treated gilts synchronised with oral progestogen and intrauterine artificial insemination yielded larger and heavier litters under high-temperature conditions. A vaginal gel containing a GnRH agonist (triptorelin) enabled a single fixed-time insemination protocol 96 hours after weaning, achieving farrowing rates comparable to traditional multi-insemination regimens. Economic modelling estimated an added benefit of EUR 15–20 per sow by combining the gel with a single-service approach, demonstrating both reproductive and financial advantages on commercial farms.

Animal Management publication trend

The graph below shows the total number of articles in animal management across all publications each year (not limited to Nature Index journals).

Technical terms

Fixed-time artificial insemination (FTAI): Hormonal synchronisation of ovulation to permit insemination at a predetermined time without repeated oestrus detection. Gonadotropin-releasing hormone (GnRH) agonist: Synthetic compound that stimulates luteinising hormone release, used to induce or synchronise ovulation. Semi-supervised learning: A machine-learning technique combining a small set of labeled data with a larger pool of unlabeled data, often using pseudo-label generation to improve model performance. Depth-wise separable convolution: A convolutional neural network operation that factors standard convolution into spatial and channel-wise steps, reducing parameters and accelerating inference. Mean average precision (mAP): A metric for object-detection models that averages precision across multiple recall levels and object classes to summarise detection accuracy.

References

1. The evaluation of an artificial intelligence system for estrus detection in sows. Porcine Health Management (2023).
2. Buserelin Acetate Added to Boar Semen Enhances Litter Size in Gilts in Tropical Environments. Animals (2024).
3. AI-enhanced real-time cattle identification system through tracking across various environments. Scientific Reports (2024).
4. Using pseudo-labeling to improve performance of deep neural networks for animal identification. Scientific Reports (2023).
5. A lightweight cow mounting behavior recognition system based on improved YOLOv5s. Scientific Reports (2023).