This is a 4 year studentship based at the School of Computing, Jordanstown, funded by Department of Agriculture, Environment and Rural Affairs of Northern Ireland through a DAFM/DAERA joint research programme.
Each year, DAFM and DAERA produce a list of white clover varieties recommended for farmers to use in ROI (e.g., DAFM, 2017) and NI (e.g., DAERA, 2016), respectively. Both lists include a range of varieties with leaf sizes from small to very large, however, do not give any guided information on nutritive values of these recommended varieties or differences in nutritive values among those varieties. White clover normally contains high levels of protein and low levels of fibre in comparison with fresh grass (Hynes et al., 2018). The efficiency of use of this legume forage relies on robust prediction of their nutritive values.
However, at present in UK and Ireland, there is no accurate and quick evaluation technique for estimating nutritive values of fresh white clover, such as that adopted for grass silage using the NIRS (Near Infra-red Reflectance Spectroscopy) techniques (Park et al., 1998). Therefore, this project is designed to address these knowledge gaps with the objectives to establish a novel database for nutritive values of white clover of different varieties, and develop an innovative evaluation system for robust and quick prediction of feeding values (e.g., chemical contents, metabolisable energy (ME) and net energy (NE) concentration and rumen degradability) of white clover using the NIRS technique.
Task 1. Developing a novel database on nutritive values of white clover of different varieties A range of fresh samples of white clover of different varieties which are commonly used in the commercial farms in both ROI and NI, will be collected. Each variety will be samples in simulated grazing conditions from early, mid and late grazing season. The collected samples will be used in the following trials, with the objective to collect data for establishing a novel nutritive value database of white clover of different varieties. 1. NIRS scan to collect scanning spectra data of each fresh and dry sample 2. Wet chemical analysis for chemical composition of each sample. 3. In vivo measurements of rumen nutrient degradability of white clover using rumen fistulated dry cows
Task 2. Development of a novel feed evaluation tool using the NIRS technique Data obtained in Task 1 will also be used to develop the following relationships (1), between NIRS scanning spectra data and chemical composition variables, including., DM, OM, GE, CP, ADF, NDF, lipid, WSC, etc. (2), between NIRS scanning spectra data and rumen nutrient degradability variables (3), between NIRS scanning spectra data and whole tract digestibility of each nutrient and energy, ME and NE concentration, and MP supply from microbial protein and rumen bypass protein. These relationships will be used to develop a novel feed evaluation system for robust and quick prediction of nutritive values of white clover using the NIRS technique.
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
- Experience using research methods or other approaches relevant to the subject domain
- Sound understanding of subject area as evidenced by a comprehensive research proposal
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- First Class Honours (1st) Degree
- Masters at 65%
This project is funded by: Department of Agriculture, Environment and Rural Affairs of Northern Ireland through a DAFM/DAERA joint research programme
This scholarship will cover tuition fees at home rate and for UK/EU residents only a maintenance allowance of not less than £14,777 per year for four years. Non-EU applicants would have to cover the difference between UK/EU and overseas tuition fees.
The Doctoral College at Ulster University
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video