Award details

Collaborative statistical investigations into biological processes

Reference	BBS/E/C/00144034
Principal Investigator / Supervisor	Professor Robin Thompson
Co-Investigators / Co-Supervisors
Institution	Rothamsted Research
Department	Rothamsted Research Department
Funding type	Research
Value (£)	26,463
Status	Completed
Type	Institute Project
Start date	01/04/1997
End date	31/03/1999
Duration	24 months

Abstract

To aid consulting efficiency, to provide continuity and to permit specialisation in relevant biometric methods, individual statisticians are allocated on a long- term basis, to liaise with scientific staff in other IACR departments. Collaboration is fostered through long-term relationships between biometricians and client scientists, to encourage an innovative multidisciplinary approach and an early involvement for the statistician in project planning. Statisticians are involved in the planning of projects as well as in analysis and interpretation. The department has strong skills in experimental design which are deployed to ensure that investigations are able properly to address the questions of interest and that resources are used most effectively. These skills include the conventional techniques of blocking, confounding and randomization, as well as more recent techniques for example for assessment of neighbour effects. Field experiments are all checked by statisticians when they are presented to the Institute's Commodity Groups. Advice on other experiments, for example in glasshouses and growth cabinets, is provided by the liaison statisticians. Research in relevant biometric methodology is done, arising from these collaborations or anticipating them. Objectives 1996 Develop parsimonious accurate models in a wide range of biological processes. These include: Estimation of relationships between weed density and yield loss 1. Modelling the interaction between crop-weed interaction and herbicide dose; 2. Modelling distributions of plant dispersal and insects in space and time; 3. Estimation of relationships between disease severity and yield loss; 4. Modelling the effects of gravitropism on root growth; 5. Characterise the effect of cultivation treatment over time on chemical leaching from agricultural crops; 6. Analysis of NO2 time series from the Environmental Change Network and relationship with meteorological variables; 7. Studies of distance measures for molecular marker data; 8. Combination of information over time from lupin experiments; 9. Multivariate analysis of plant communities. 1997 Develop parsimonious accurate models in a wide range of biological processes. These include: 1. Estimation of relationships between weed density and yield loss; 2. Modelling the interaction between crop-weed interaction and herbicide dose. Further experimentation will allow validation and extension of the models; 3. Modelling distributions of plant dispersal and insects in space and time, including using wavelet analysis; 4. Estimation of relationships between disease severity and yield loss; 5. Modelling the effects of gravitropism on root growth, including extensions to three dimesions; 6. Characterise the effect of cultivation treatment over time on chemical leaching from agricultural crops; 7. Analysis of NO2 time series from the Environmental Change Network and relationship with meteorological variables; 8. Studies of distance measures for molecular marker data; 9. Combination of information over time from lupin experiments; 10. Multivariate analysis of plant communities.

Summary

unavailable

Committee	Closed Committee - Engineering & Biological Systems (EBS)
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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