Animal Production

Animal production integrates genetics, nutrition, health and husbandry to convert biological inputs into meat, milk, eggs and other outputs. Modern systems rely on selective breeding and assisted-reproduction techniques—artificial insemination, embryo transfer and genomic selection—to improve fertility, growth and disease resistance while reducing the generation interval. Nutrition is formulated to match energy and amino-acid requirements, optimising feed conversion and minimising nutrient excretion. Housing design, ventilation and bedding aim to safeguard welfare and performance, and biosecurity measures control infectious disease. Precision livestock farming deploys sensors, imaging and data analytics to monitor individual animals in real time, enabling early disease detection, automated feeding and welfare assessment. Concurrently, life-cycle assessment informs choices in feed sourcing, manure management and energy use to reduce greenhouse-gas emissions, water use and land demand. Emerging challenges include antimicrobial-resistance management, climate adaptation, resource competition between feed and food, and the need to balance productivity with environmental stewardship and societal expectations. Advances in engineering, data science and microbial ecology are reshaping production, fostering systems that enhance animal well-being, resource efficiency and economic viability on a global scale.

Research from Nature Portfolio

Recent studies have applied deep-learning to detect lameness in dairy cows by combining pose-estimation of anatomical landmarks—back arch, head position—with real-time tracking. A modified Mask R-CNN network and gradient-boosting classifier achieved over 94 % accuracy in distinguishing lame from sound cows, enabling continuous, non-invasive welfare surveillance. Another advance used semi-supervised learning to identify individual Holstein cattle by coat-pattern images. By pseudo-labelling large unlabeled datasets, a convolutional neural network improved recognition accuracy by over 20 percentage points compared with fully supervised models, reducing annotation effort and enabling scalable herd monitoring without physical tagging. In dense barn scenes, a lightweight detection model based on an improved YOLOv5s framework—augmented with depth-wise separable convolutions and an efficient attention mechanism—was used to recognise cow mounting behaviour at over 300 fps with mean average precision above 87 %. Such rapid inference under variable conditions supports timely detection of oestrus and welfare events across multi-camera installations.

Topic trend for the past 5 years

The graph below shows the article count in Nature Index journals for animal production.

Technical terms

Semi-supervised learning: A machine-learning approach that uses a small set of labelled examples together with a larger pool of unlabeled data, often via pseudo-label generation, to improve model performance.

Convolutional neural network (CNN): A deep-learning architecture composed of convolutional layers that automatically learn spatial hierarchies of features from images, widely used for object detection and classification.

YOLOv5: You Only Look Once version 5, a real-time object-detection model that predicts bounding boxes and classes in a single forward pass, optimised for speed and accuracy.

Fixed-time artificial insemination (FTAI): Hormonal synchronisation of ovulation so that insemination can be performed at a predetermined time without the need for visual oestrus detection.

Gonadotropin-releasing hormone (GnRH) agonist: A synthetic compound that stimulates the pituitary to release luteinising hormone, used to induce or synchronise ovulation in livestock.

Notable articles in animal production

1. Olfactory exposure to males, including men, causes stress and related analgesia in rodents. Nature Methods (2014).
2. Global diets link environmental sustainability and human health. Nature (2014).
3. The Ratio of Macronutrients, Not Caloric Intake, Dictates Cardiometabolic Health, Aging, and Longevity in Ad Libitum-Fed Mice. Cell Metabolism (2014).
4. Quantification of GDF11 and Myostatin in Human Aging and Cardiovascular Disease. Cell Metabolism (2016).
5. Dietary Fat, but Not Protein or Carbohydrate, Regulates Energy Intake and Causes Adiposity in Mice. Cell Metabolism (2018).
6. Sex reversal following deletion of a single distal enhancer of Sox9. Science (2018).
7. BTG4 is a meiotic cell cycle–coupled maternal-zygotic-transition licensing factor in oocytes. Nature Structural & Molecular Biology (2016).

Position of Animal Production in Nature Index by Count

Top 5 leading collaborators in Animal Production

Collaborating institutions