Course description for 2026/27
Precision Livestock Farming
HUS5002
Course description for 2026/27
Precision Livestock Farming
HUS5002
This course on Precision Livestock Farming (PLF) covers the principles, main sensor technologies, data analytics, and sustainability of, and ethical considerations involved in, PLF with focus on improving animal health, welfare and productivity.
This course provides an in-depth exploration of the technologies used in Precision Livestock Farming (PLF). Students will learn about the latest advancements in sensor technology, data analytics, and automation, and how these technologies are applied to improve animal health, welfare, and productivity. The course will include lectures, seminars, and self-studies to provide a comprehensive understanding of the PLF technologies.
Students must meet current admission requirements for the associated study programme.
Upon successful completion of this course, it is expected that the student will have reached the following learning objectives:
Knowledge:
- Understand the fundamental principles and concepts of Precision Livestock Farming (PLF).
- Identify and describe various sensor technologies used in PLF, including their applications and integration.
- Explain the role of data analytics and big data in livestock farming.
- Understand the impact of environmental conditions on livestock and the technologies used for environmental monitoring.
- Comprehend the ethical and regulatory considerations in the implementation of PLF technologies.
Skills:
- Analyze and interpret data collected from PLF technologies.
- Integrate multiple PLF technologies into a cohesive and functional system.
- Conduct case studies and practical applications of PLF technologies in real-world scenarios.
- Evaluate and address ethical and regulatory challenges in PLF.
General competence:
- critically reflect on the research practices relevant to precision livestock farming.
- independently apply scientific methods in interdisciplinary contexts, contributing to innovation and problem-solving in livestock production systems.
In addition to the semester fee and curriculum literature, it is assumed that the student has a laptop computer at disposal.
Elective for master in biosciences
Lectures, exercises, group and individual work and student presentations.
Evaluated annually by students through course surveys. These evaluations are included in the university’s quality assurance system.
Compound assessment (Grading rule A-F)
- Submission of up to 3 reports, grading rule: Pass/Fail. Prerequisite for receiving a final grade in the course.
- Written assignment, weights 60/100 of the grade. Grading rule: A-E, best A, fail F.
- Oral exam, weights 40/100 of the grade. Grading rule: A-E, best A, fail F.
Generating an answer using generative artificial intelligence (AI) and submitting it wholly or partially as one's own answer is considered cheating. AI may be used as a discussion partner, and for language editing. This should be clearly described in the methodology section of the submission. The actual response must be prepared by the students themselves with sources. You can read more about the correct use of AI in studies here: www.nord.no/en/about/generative-artificial-intelligence/use-of-generative-intelligence-in-studies
It is recommended that students have basic knowledge in the subject areas of livestock production and ethology.
Overlap refers to a similarity between courses with the same content. Therefore, you will receive the following reduction in credits if you have taken the courses listed below:
HUS5000 - Precision Livestock Farming - 7.5 credits
Credits
7.5
Study location
Online
Teaching semesters
Spring 2026
Responsible faculty
Faculty of Biosciences and Aquaculture
Course coordinator
