Data-led agriculture

Learn more about this course:

What will I learn?

You will gain expertise in the evaluation and deployment advanced sensor technologies, and place these within robust biometric designs for their implementation. This foundational skill will enable you to effectively integrate cutting-edge sensors into agricultural systems, ensuring precise and reliable data collection that drives informed decision-making.

In addition to sensor technology, you will develop the ability to analyse complex datasets, processing multivariate data within agricultural contexts. This skill is necessary for informing strategic decisions that enhance productivity, sustainability, and resource management. Through data analysis, you will be equipped to tackle the challenges of modern agriculture, using data to drive improvements in efficiency and sustainability.

The course will give you the skills to design, evaluate, and utilise predictive models to forecast agricultural events. These models will enable you to make data-driven recommendations, proactively managing and optimising agricultural operations.

By employing predictive analytics, you will enhance the efficiency, productivity, sustainability, and resilience of the agricultural industry, positioning you to implement data-led agriculture.

How will I learn?

The course is taught asynchronously through our virtual learning environment Moodle, allowing you to learn at your own pace. It includes:

• Weekly Video Lectures: Pre-recorded lectures that you can watch at your convenience.
  
  
• Reading Materials: Curated articles, research papers, and book chapters related to data analytics in agriculture.
  
  
• Interactive Tutorials: Step-by-step guides and exercises on using data analytics tools (e.g., Python, R, Excel).
  
  
• Interactive Content: Use of quizzes, interactive exercises, and polls to support your engagement.
  
  
• Case Studies: Analysis of real-world examples where data analytics improved agricultural outcomes.
  
  
• Hands-On Projects: Practical projects where you apply data analysis to agricultural datasets.
  
  
• Discussion forums: for you to ask questions, share insights, and collaborate.

How will I be assessed?

Your assessment will take place via a progressing project which demonstrates an application of data led agriculture. This can be shown to employers as proof of knowledge, ensuring that the course does not result in just a certificate but a provable and moveable body of work.

The project will include an evaluative report, data analysis and predictive modelling exercise. The assessment will follow the logical progression of the course: measuring and gathering data, analysing data, and feeding the result into a management decision.

By the end of the course what will I be able to do?

• Evaluate and deploy advanced sensor technologies, creating a robust biometric design for their implementation.
  
  
• Evaluate complex datasets, processing multivariate data within agricultural contexts, to inform strategic agricultural decisions that enhance productivity, sustainability, and resource management.
  
  
• Design, evaluate and use predictive models to forecast and predict agricultural events, making data-led recommendations to proactively manage and optimise agricultural operations for improved efficiency, productivity, sustainability, and resilience.

How long does the course last for and how much time will I need to spend on it each week?

Course duration:

• The course is self-paced, allowing you to progress at your own speed. 
• You have 52 weeks from the time of enrolment to complete the course. 

Typical completion time: The course is generally designed to be completed in 12 weeks. 

Weekly time commitment: To complete a course in 12 weeks, expect to spend 2-3 hours per week on directed online learning activities and at least one day per week dedicated to independent learning and assessment work. 

Why is SRUC a good place to study this micro-credential?

• Our staff have real-world experience working in the agricultural sector, and particularly in the area of data analytics. 
  
  
• All our courses blend theory with practice to ensure you are learning skills you can use in your own work. 
  
  
• Our data-led agriculture and decision-making course is designed to meet the global food demand through improved efficiency, lower inputs, and higher outputs.

Who will be teaching me?

Dr Holly Ferguson

Dr Ferguson's interests focus on bovine health and production, the use of precision livestock farming (PLF) tools to study health and nutrition problems of ruminants and innovative dairying.

• Dairy Sensors: Identifying best sensor technologies to deliver verifiable health and welfare, environmental and processing benefits for dairy products.
  
  
• GrASTech: Precision Livestock Farming (PLF) technologies to reduce greenhouse gas (GHG) emissions intensity of pasture-based systems.
  
  
• RESAS; Precision Livestock Farming: Use of PLF technologies and combinations of sensors to increase early detection of health and nutrition problems in dairy cows.

 

Dr Mia Aitchison
Dr Aitchison joined SRUC as a lecturer in Animal Science in 2013 whilst completing her PhD examining the energetic budgets of broiler chickens at Aberdeen University.  She has taught a range of subjects, but her focus now is statistics, physiology and nutrition.

 

Jeremy Taylor
A long-term interest in precision farming and AgTech initiated through using data to forecast tea plucking rounds in the world’s first large scale mechanical tea harvesting implementation over 1400 ha. Responsible for developing innovative courses in AgTech at SRUC.