Full Title:GIS, Data Management and Statistics
Language of Instruction:English
Module Code:AGRI S9Z09
Credits: 7.5
Valid From:Semester 1 - 2019/20 ( June 2019 )
Module Delivered in 2 programme(s)
Module Description:The aim of this module is to equip students with the knowledge and skills to conduct applied research in agricultural biotechnology and to interpret and synthesise existing research to inform their Project. This course will increase student awareness of research design and the main ethical issues associated with modern research. This course will provide an in depth analysis of data handling techniques, providing information on appropriate methods for sorting, storing and using data, particularly in the manipulation of large complex datasets. Students will become proficient in applying scientific data to appropriate statistical tests and how to import data to data-handling programs, manipulate and graph them. This module aims to provide students with an appreciation of ICT in Agriculture production and processing level. It will also enable them to assess current novel precision (smart) techniques including the area of advanced biotechnology specific to Agri-Food and Environment.
Learning Outcomes:
On successful completion of this module the learner should be able to
  1. Interpret and appraise various forms of technology data acquisition within precision agriculture (biosensors, remote sensing, yield monitoring)
  2. Demonstrate an appropriate use of GIS and GPS software in agricultural practices (eg crop and/or livestock management)
  3. Evaluate the importance and relevance of intellectual property (IP) within a scientific context and to critically analyse the main ethical issues associated with current and future developments in the biotechnology field.
  4. Critically synthesise the importance of different types of data and assess the rationale for different methods of data collection, sorting, storage, visualisation, presentation and analyses, including quantitative and qualitative approaches.
  5. Select appropriate qualitative and quantitative data analysis techniques, report and critically interpret the outcomes of these analyses.

Module Content & Assessment

Indicative Content
Research Design
Evaluation of reliability and validity of experimental design. Rationale behind random sampling, and issues such as sample selection bias and endogeneity will also be raised. Choosing between qualitative and quantitative approaches. Understanding the limitations of data and how to avoid drawing tentative conclusions. Critically evaluate the ethical and political dimension to research and its implications for the researcher. Introduction will be given to DOE software (e.g. Stat-Ease).
Ethical Framework
The application of ethical and regulatory frameworks to experimental design, the application of national and international legislation, agreements, conventions and guidelines, decision making and whistle blowing.
Intellectual Property (IP)
Differences between artistic (copyright) and industrial property (including trademarks, patents, etc.). Overview of the importance and application of industrial property in the area of Biotechnology.
Data Collection, Analysis and Interpretation
Defining data, data collection, ensuring reliability and validity of collected data. Identifying appropriate statistical analyses, introduction to statistical software, selecting and extracting raw data in appropriate formats for interpretation of specific statistical analyses using a range of statistical software, dealing with outliers and incomplete or missing data. Examining the relationship between design and analysis, describing and illustrating quantitative data, content analysis, understanding variation, probability and inferential statistics.
Information/communication and Location Technology;
Information/communication and Location Technology; Geographical Information Systems: capture, store, manipulate, analyze, manage, and present all types of geographical data. Global Positioning Systems, Differential Global Positioning Systems, coordinate formats. Information/communication and Geographical Information Systems: capture, store, manipulate, analyze, manage, and present all types of geographical data. Global Positioning Systems, Differential Global Positioning Systems, coordinate formats.
Percision agriculture and data management
Overview of current (incl historic timeline) ICT in crop and livestock management, emerging technologies, future farming, cloud infrastructure. Data base management and analytics. Data logging, yield monitors, remote sensing. Importance of historical and real time data on farm business metrics such as livestock/crop production performance. Typical web interfaces, algorithm development, data interpretation and scenario predictions.
Assessment Breakdown%
Course Work30.00%

Full Time

Course Work
Assessment Type Assessment Description Outcome addressed % of total Marks Out Of Pass Marks Assessment Date Duration
Class Test The students carry out a variety of practical exercises using suitable software relating to the sorting, storage, visualisation, and analyses of complex datasets while addressing ethical concerns related to data collection and handling. 3,4,5 30.00 0 40 n/a 0
Assessment Type Assessment Description Outcome addressed % of total Marks Out Of Pass Marks Assessment Date Duration
Project Project Proposal: The student will submit a proposal for a research project, which will include a work plan, project objectives and a timeline as a Gantt chart, for their research project. Where feasible projects which include links to on-going funded research projects, to industry and to local stakeholders, including links to communities will be encouraged. The plan should include reference to the methods to be employed in the practical component and any ethical or IP issues which may arise. 2,3,4,5 50.00 0 40 n/a 0
Assessment Type Assessment Description Outcome addressed % of total Marks Out Of Pass Marks Assessment Date Duration
Practical/Skills Evaluation Practicals such as GIS and Data logging / gathering. 1,2 20.00 0 40 n/a 0
No End of Module Formal Examination
Reassessment Requirement
No repeat examination
Reassessment of this module will be offered solely on the basis of coursework and a repeat examination will not be offered.

DKIT reserves the right to alter the nature and timings of assessment


Module Workload & Resources

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Weekly lectures will be delivered which will place an emphasis on deep learing and student- centered approaches. All lecture material will made available to the students through DkITs virtual learning environment (Moodle). 1.00 Every Week 1.00
Practical Weekly 2 hour computer laboratory sessions will enable application and use of data handling techniques, statistics and experimental design software 2.00 Every Week 2.00
Independent Study No Description 7.50 Every Week 7.50
Directed Reading No Description 3.00 Every Week 3.00
Online Contact Online Discussion Forum 0.50 Every Week 0.50
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 3.50
This course has no Part Time workload.
Recommended Book Resources
  • Field, Andy 2012, Discovering Statistics Using R, 1st Ed., SAGE Publications Ltd. London [ISBN: 978-1-4462-00]
  • Zuur, Alain, Ieno, Elena N., Meesters, Erik, 2009, A Beginner's Guide to R, Springer [ISBN: 978-0-387-938]
  • Goddard, W. and Melville, S. 2007, Research Methodology: An introdution, 2nd Ed., McGraw-Hill
  • Dawson, C. 2009, Introduction to research methods: A practical guide for anyone undertaking a research project, 4th Ed., Oxford: how to Books Ltd. [ISBN: 1845283678]
  • Box, G.E.P., Hunter, W.G. and Hunter, J.S. 2005, Statistics for experimenters, John Wiley & Sons [ISBN: 0471093157]
This module does not have any article/paper resources
Other Resources

Module Delivered in

Programme Code Programme Semester Delivery
DK_SAGBI_9 Master of Science in Agricultural Biotechnology 1 Mandatory
DK_SAGPD_9 Postgraduate Diploma in Agricultural Biotechnology 1 Mandatory