Engaging Entry Level Researchers in Agriculture in Statistical Communication and Collaboration: Why? and How?

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 81)


The Biometry Hub, a statistics research and consulting group in an agricultural science school, has commenced a project to enhance the statistical capacity of graduates in agricultural sciences. The project engages with students undertaking research projects as they are completing their undergraduate degrees and considering their careers, possibly in research. This group is motivated learners focused on delivering outcomes in solving real-life problems, and respond well to opportunities for their broad professional development. The project will help them become familiar with the culture of cross-disciplinary collaboration, an essential component of modern agricultural research.

The teaching and learning framework of the project consists of four elements: (1) group workshops in quantitative methods; (2) individual attention from a statistics consultant throughout the research project; (3) targeted guidance with peer-reviewed resources in statistical methods, experimental design and data management specific to the students’ research topics and (4) supervisor and peer support encouraged through the dissemination of ‘good statistics practice’ in the research group hosting a student.

This chapter summarises the problems addressed by the project, presents the project framework and discusses performance measures for the project’s elements and potential impact.


Multidisciplinary collaboration Enquiry-based learning Academic workplace culture 


  1. ABS. (2010a). 1500.0—A guide for using statistics for evidence based policy, 2010. Canberra, ACT, Australia: Australian Bureau of Statistics.Google Scholar
  2. ABS. (2010b). Understanding statistics. Why statistics matter. Canberra, ACT, Australia: Australian Bureau of Statistics.Google Scholar
  3. Acker, D. (2008). Research and Education Priorities in Agriculture, Forestry, and Energy-Working toward achieving the 25x’25 renewable energy vision, a recent paper by the National 25x’25 Agriculture/Forestry Steering Committee provides an update. Resource, 15, 12–13.Google Scholar
  4. AIAST. (2013). Code of ethics. Crows Nest, NSW, Australia: Australian Institute of Agricultural Science and Technology.Google Scholar
  5. Allan, C. (2011). Exploring the experience of ten Australian Honours students. Higher Education Research and Development, 30, 421–433.CrossRefGoogle Scholar
  6. ARC. (2013). Excellence for research in Australia. Australian Research Council, Australian Government. Retrieved August 26, 2013, from (http://www.arc.gov.au/era/
  7. Belli, G. M. (1998). The teaching aspect of consultancy. In Proceedings of the International Conference on Teaching Statistics (ICOTS’5). Retrieved August 26, 2013, from http://iase-web.org/Conference_Proceedings.php?p=ICOTS_5_1998
  8. Bidgood, P. (2009). Helping students prepare for their future working lives. In Proceedings of the International Association for Statistics Education and International Statistics Institute Satellite Conference.Google Scholar
  9. Bishop, G., & Talbot, M. (2001). Statistical thinking for novice researchers in the biological sciences. In C. Batanero (Ed.), Training researchers in the use of statistics. International Association for Statistical Education and International Statistical Institute: Granada, Spain.Google Scholar
  10. Buch, K., & Bartley, S. (2002). Learning style and training delivery mode preference. Journal of Workplace Learning, 14, 5–10.CrossRefGoogle Scholar
  11. Cargill, M. (1996). An integrated bridging program for international postgraduate students. Higher Education Research and Development, 15, 177–188.CrossRefGoogle Scholar
  12. Cargill, M., & Cadman, K. (2005). Revisiting quality for international research education: Towards an engagement model. In 2005 Australian Universities Quality Forum: Citeseer.Google Scholar
  13. CSIRO. (2011). Plant phenomics facilities. Retrieved August 26, 2013, from http://www.csiro.au/en/Organisation-Structure/Divisions/Plant-Industry/Phenomics-Facility.aspx
  14. Cullis, B. (2012). Progress Report 2011-2012 for UOW capacity building project. In Statistics for the Australian Grains Industry Technical Report Series. Wollongong, NSW, Australia: University of Wollongong.Google Scholar
  15. Davies, M., Devlin, M., & Tight, M. (Eds.). (2010). Interdisciplinary higher education: Perspectives and practicalities. International perspectives on higher education research (Vol. 5). Bingley, England: Emerald.Google Scholar
  16. DIISR. (2011). Focusing Australia’s publicly funded research review. Department of Innovation, Industry, Science and Research, Australian Government. Retrieved August 26, 2013, from http://www.innovation.gov.au/research
  17. Faulkner, P., Gray, B., & Thomas, T. (2009). New skills for a new era: Ideas for preparing professionals for service in twenty first century agriculture. International Journal of Applied Educational Studies, 4, 34–46.Google Scholar
  18. Gilmour, A. R., Gogel, B. J., Cullis, B. R., & Thompson, R. (2009). ASReml user guide release 3.0. Hemel Hempstead, England: VSN International Ltd.Google Scholar
  19. GRDC. (2008). Number crunching to yield improved variety information. Kingston, ACT, Australia: Grains Research and Development Corporation, Australian Government.Google Scholar
  20. GRDC. (2012). Strategic research and development plan, 2012-2017. Grains Research and Development Corporation, Australian Government. Retrieved August 26, 2013, from http://strategicplan2012.grdc.com.au
  21. Hall, P. (2004). The sum and the product of our difficulties: Challenges facing the mathematical sciences in Australian universities. The Australian Mathematical Society Gazette, 31, 6–11.Google Scholar
  22. Hughes, M., & Bennett, D. (2013). Survival skills: The impact of change and the ERA on Australian researchers. Higher Education Research and Development, 32, 340–354.CrossRefGoogle Scholar
  23. Hussey, T., & Smith, P. (2003). The uses of learning outcomes. Teaching in Higher Education, 8, 357–368.CrossRefGoogle Scholar
  24. Johnson, I. H. (1996). Access and retention: Support programs for graduate and professional students. New Directions for Student Services, 1996, 53–67.CrossRefGoogle Scholar
  25. Johnson, H. D., & Warner, D. A. (2004). Factors relating to the degree to which statistical consulting clients deem their consulting experience to be a success. American Statistician, 58, 280–289.CrossRefMathSciNetGoogle Scholar
  26. Kiley, M., Moyes, T., & Clayton, P. (2009). ‘To develop research skills’: Honours programmes for the changing research agenda in Australian universities. Innovations in Education and Teaching International, 46, 15–25.CrossRefGoogle Scholar
  27. Kogler Hill, S. E., Bahniuk, M. H., & Dobos, J. (1989a). The impact of mentoring and collegial support on faculty success: An analysis of support behavior, information adequacy, and communication apprehension. Communication Education, 38, 15–33.CrossRefGoogle Scholar
  28. Kogler Hill, S. E., Hilton Bahniuk, M., Dobos, J., & Rouner, D. (1989b). Mentoring and other communication support in the academic setting. Group and Organization Management, 14, 355–368.CrossRefGoogle Scholar
  29. Lancaster, G. (2010). Communicating the value of statistical thinking in research. In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceeding of the Eights International Conference on Teaching Statistics (ICOTS8, July, 2010), Slovenia.Google Scholar
  30. Lancaster, S., Di Milia, L., & Cameron, R. (2013). Supervisor behaviours that facilitate training transfer. Journal of Workplace Learning, 25, 6–22.CrossRefGoogle Scholar
  31. Lindsay, B. G., Kettenring, J. R., & Siegmund, D. O. (2004). A report on the future of statistics (with discussion). Statistical Science, 19, 387–412.CrossRefMATHMathSciNetGoogle Scholar
  32. Manathunga, C., Kiley, M., Boud, D., & Cantwell, R. (2011). From knowledge acquisition to knowledge production: Issues with Australian honours curricula. Teaching in Higher Education, 17, 139–151.CrossRefGoogle Scholar
  33. Meng, X.-L. (2009). Desired and feared—What do we now and over the next 50 years? The American Statistician, 63(3), 202–210.CrossRefMATHMathSciNetGoogle Scholar
  34. Molenberghs, G. (2005). Biometry, Biometrics, Biostatistics, Bioinformatics, …, Bio-X. Biometrics, 61, 1–9.CrossRefMathSciNetGoogle Scholar
  35. Munack, A., & Speckmann, H. (2001). Communication technology is the backbone of precision agriculture. Agricultural Engineering International: The CIGR Journal of Scientific Research and Development, 3, 1–12.Google Scholar
  36. NIASRA. (2013). Capacity building. Retrieved October 16, 2013, from www.niasra.uow.edu.au
  37. Olkin, I., Sacks, J., Blumstein, A., Eddy, A., Eddy, W., Jurs, P., Kruskal, W., Kurtz, T., Mcdonald, G. C., Peierls, R., Shaman, P., & Spurgeon, W. (1990). IMS panel on cross-disciplinary research in the statistical sciences. Statistical Science, 5, 121–146.Google Scholar
  38. Pfannkuch, M., & Wild, C. J. (2000). Statistical thinking and statistical practice: Themes gleaned from professional statisticians. Statistical Science, 15, 132–152.CrossRefGoogle Scholar
  39. Pratley, J. (2012). Professional Agriculture-a case of supply and demand. Surry Hills, NSW, Australia: Australian Farm Institute. Retrieved February 24, 2012, from www.farminstitute.org.auGoogle Scholar
  40. Quinn, C., Burbach, M. E., Matkin, G. S., & Flores, K. (2009). Critical thinking for natural resource, agricultural, and environmental ethics education. JNRLSE, 38, 221–227.Google Scholar
  41. R Core Team. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
  42. Raney, T., Gerosa, S., Khwaja, Y., Skoet, J., Steinfeld, H., Mcleod, A., et al. (2009). Livestock in the balance. In The State of Food and Agriculture. Rome, Italy: Food and Agriculture Organisation of the United Nations. Retrieved August 26, 2013, from http://www.fao.org/docrep/012/i0680e/i0680e.pdf
  43. Ransom, C., Patricka, C., Ando, K., & Olmstead, J. (2006). Report of breakout group 1. What kind of training do plant breeders need, and how can we most effectively provide that training? HortScience, 41(1), 53–54.Google Scholar
  44. SAS Institute Inc. (2010). SAS OnlineDoc® 9.2. Cary, NC: SAS Institute Inc.Google Scholar
  45. Schuyten, G., Batanero, C., & Cordani, L. (2006). Working cooperatively in statistics education. In 7th International Conference on Teaching Statistics. Salvador, Brazil: ISI.Google Scholar
  46. Schwartz, M. A. (2008). The importance of stupidity in scientific research. Journal of Cell Science, 121, 1771–1771.CrossRefGoogle Scholar
  47. Silva, P. L. D. N. (2006). Statistical education for doing statistics professionally: Some challenges and the road ahead. In Seventh International Conference on Teaching Statistics, Salvador, Brazil.Google Scholar
  48. Sowey, E. R. (2006). Letting students understand why statistics is worth studying. In 7th International Conference on Teaching Statistics, Salvador, Brazil.Google Scholar
  49. Sprent, P. (1970). Some problems of statistical consultancy. JRSS. Series A, 133(2), 139–165.Google Scholar
  50. SSAI. (2005). Statistics at Australian Universities, an SSAI-sponsored review. Brando, ACT, Australia: Author.Google Scholar
  51. Stubbs, E. (2012). The changing skills graduates need to be successful in applying analytics. In Australian Statistical Conference, Adelaide, SA, Australia.Google Scholar
  52. Tishkovskaya, S., & Lancaster, G. A. (2012). Statistical education in the 21st century: A review of challenges, teaching innovations and strategies for reform. Journal of Statistics Education, 20. Retrieved August 26, 2013, from www.amstat.org/publications/jse/v20n2/tishkovskaya.pdf
  53. UA. (2013). Honours at Adelaide. University of Adelaide. Retrieved August 26, 2013, from http://www.adelaide.edu.au/study/honours/
  54. VSN International. (2012). Genstat. Hemel Hempstead, England: VSN International.Google Scholar
  55. Willison, J., & O’regan, K. (2007). Commonly known, commonly not known, totally unknown: A framework for students becoming researchers. Higher Education Research and Development, 26, 393–409.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Agriculture, Food and WineUniversity of AdelaideAdelaideAustralia
  2. 2.Biometry Hub, School of AgricultureFood and Wine University of AdelaideAdelaideAustralia

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