- Dr Jean-Marc Schwartz (Senior Lecturer - University of Manchester)
|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Forum posts (1,500 words)||25%|
|Semester Assessment||Data analysis task - on provided dataset (2,000 words)||30%|
|Semester Assessment||Case study (2,500 words)||45%|
|Supplementary Assessment||Students must retake elements of assessment equivalent to those that led to module failure.||100%|
On successful completion of this module students should be able to:
1. Demonstrate an advanced understanding of the principles of plant breeding, and the relationship between plant reproductive biology and commercial opportunity for varieties.
2. Compare and contrast the way varieties are produced in crop species that differ in reproductive biology.
3. Analyse a progeny trait dataset and explain the decision making process for progeny selection in a composite variety.
4. Appraise the plant variety commercialisation pathway in the UK.
5. Critically evaluate an existing breeding system or present a rationalized plan for commencement of a new breeding system.
This module is designed to give those with an interest in plant breeding or genetics a comprehensive understanding of what it takes to bring a variety from a concept to market. It will consider the what, why, where, when, whom and how of what makes a commercial plant breeding program successful. Real datasets will demonstrate the challenges a breeder faces when making selections and a case study will enable students to look in depth at the process of breeding a crop of their choice.
• Plant reproductive biology
• Control of pollination and uses in breeding
• Breeding systems for self and cross-pollinated plant species
• Defining breeding objectives
• Trait measurement and selection methods
• Agricultural experimentation and trial design
• Defining breeding objectives
• Variety legislation and commercialisation
• Marker-assisted and genome wide selection
• Utilising diversity, mutant and biotechnology in breeding
The module will deliver the content through a series of recorded lectures and interviews between researchers from IBERS and representatives from the seed marketing industry. Students will be given a reading list comprising reference texts and articles. Students will be expected to interact through forums with other students and the course tutor/researcher. The module will also contain assessments which will encourage the students to apply the knowledge gained to real-life scenarios.
|Skills Type||Skills details|
|Application of Number||Numeracy will be demonstrated in the assessed selection task using a real dataset. Resource deployment will also be discussed as a rationalization of breeding objectives in relation to potential commercial return.|
|Communication||Students will be expected to be able to express themselves appropriately in their assignments.|
|Improving own Learning and Performance||Detailed feedback will be given for assignment work. This will be assessed through the feedback providing general guidance towards the student’s next assignment.|
|Information Technology||Students will be required to source information from a variety of scientific publication data bases and to use Blackboard for all aspects of the module.|
|Personal Development and Career planning||This module will provide the theory required to undertake research projects in plant breeding, and provide the platform for breeding program management. The knowledge will also be invaluable to those in seed marketing and related industries when talking about the importance of variety selection for arable and forage production systems.|
|Problem solving||Problem based learning challenges alongside online forum posts will be used throughout the module to help develop and improve student’s problem solving skills.|
|Research skills||Students will be required to undergo directed self-study, so will develop their literature research skills.|
|Subject Specific Skills||Students will gain a theoretical knowledge of plant breeding and experience of selection via the dataset challenge.|
|Team work||Online assessments will require students to debate among themselves to develop a consensus of opinion.|
This module is at CQFW Level 7