Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE

Incorporating data into grazing management decisions: supporting farmer learning

Lydia Turner A C , Lesley Irvine A and Sue Kilpatrick B
+ Author Affiliations
- Author Affiliations

A Tasmanian Institute of Agriculture, University of Tasmania, Cradle Coast Campus, Private Bag 3523, Burnie, Tas. 7320, Australia.

B Faculty of Education, University of Tasmania, Newnham Campus, Locked Bag 1307, Launceston, Tas. 7250, Australia.

C Corresponding author. Email: Lydia.Turner@utas.edu.au

Animal Production Science 60(1) 138-142 https://doi.org/10.1071/AN18533
Submitted: 30 August 2018  Accepted: 1 March 2019   Published: 3 May 2019

Abstract

Pasture consumption is an important contributor to farm business profitability in pasture based dairy systems around the world, including Tasmania. Research, development and extension prioritises further increasing pasture consumption in the Tasmanian dairy industry, through progressing technical innovations and providing services to support increased farmer adoption of proven practices. Increasing farmer adoption of best practice grazing management recommendations relies on the continued development of extension delivery to meet farmer information and skill-development needs. A social research study identified some of these needs by exploring pasture management approaches and associated learning processes of farmers whose practices were more aligned versus less aligned to recommended practices. The aim was to improve understanding of the grazing management learning process and implications for extension in the context of data made available through new technology. Qualitative interview data showed that pasture managers whose practices are more closely aligned to recommended practices have used pasture measurement tools and performed associated calculations intensively for an extended period (≥1 year), before adapting best practices to suit their farm management approach. Less aligned pasture managers were aware of the importance of grazing management but were less aware that they lacked knowledge and skills required to implement recommended practices. These findings suggest that introducing innovative ways to acquire pasture growth data will not result in practice change unless dairy farmers have progressed through the grazing management learning process and come to understand how to use data effectively.

Additional keywords: dairying, extension, farmer adoption, innovation, pasture utilisation.


References

Beukes PC, McCarthy S, Wims CM, Gregorini P, Romera AJ (2019) Regular estimates of herbage mass can improve profitability of pasture-based dairy systems. Animal Production Science 59, 359–367.
Regular estimates of herbage mass can improve profitability of pasture-based dairy systems.Crossref | GoogleScholarGoogle Scholar |

Cliffe N, Stone R, Coutts J, Reardon-Smith K, Mushtaq S (2016) Developing the capacity of farmers to understand and apply seasonal climate forecasts through collaborative learning processes Journal of Agricultural Education and Extension 22, 311–325.
Developing the capacity of farmers to understand and apply seasonal climate forecasts through collaborative learning processesCrossref | GoogleScholarGoogle Scholar |

Cooper JP (1964) Climatic variation in forage grasses. 1. Leaf development in climatic races of Lolium and Dactylis. Journal of Applied Ecology 1, 45–61.
Climatic variation in forage grasses. 1. Leaf development in climatic races of Lolium and Dactylis.Crossref | GoogleScholarGoogle Scholar |

Dairy Australia (2017) ‘Strategic plan 2016/17 to 2018/19.’ (Dairy Australia Ltd: Melbourne)

Eastwood C, Kenny S (2009) Art or Science? Heuristic versus data driven grazing management on dairy farms. Extension Farming Systems Journal 5, 95–102.

Fulkerson WJ, Donaghy DJ (2001) Plant soluble carbohydrate reserves and senescence: key criteria for developing an effective grazing management system for ryegrass-based pastures: a review. Australian Journal of Experimental Agriculture 41, 261–275.
Plant soluble carbohydrate reserves and senescence: key criteria for developing an effective grazing management system for ryegrass-based pastures: a review.Crossref | GoogleScholarGoogle Scholar |

Hall A, Turner L, Irvine L, Kilpatrick S (2017) Pasture management and extension on Tasmanian dairy farms: who measures up? Rural Extension and Innovation Systems Journal 13, 32–40.

Huberman AM, Miles MB (1994) Data management and analysis methods. In ‘Handbook of qualitative research’. (Eds NK Denzin, YS Lincoln) pp. 428–444. (Sage Publications: London)

Irvine L, Turner L (2018) The value of measuring pasture. In ‘Proceedings of the 8th Australasian dairy science symposium’. (Eds D Donaghy, D Pacheco, C Eastwood, J Roche, R Bryant, S Lomax, Y Garcia, B Wales, K DiGiacomo, R Rawnsley, D Barber) pp. 12–17. (Massey University: Palmerston North, New Zealand)

Kilpatrick S, Johns S (2003) How farmers learn: different approaches to change Journal of Agricultural Education and Extension 9, 151–164.
How farmers learn: different approaches to changeCrossref | GoogleScholarGoogle Scholar |

Lincoln YS, Guba EG (1985) ‘Naturalistic inquiry.’ (Sage Publications: Newbury Park, CA)

Maher J, Bogue F (2018) Grass 10: an extension campaign to improve the level of grass production and utilisation. In ‘Sustainable meat and milk production from grasslands. Proceedings of the 27th general meeting of the European Grassland Federation’. (Eds B Horan, D Hennessy, M O’Donovan, E Kennedy, B McCarthy, JA Finn, B O’Brien) pp. 956–958. (Teagasc, Animal & Grassland Research and Innovation Centre: Cork, Ireland)

Marra M, Pannell DJ, Abadi Ghadim A (2003) The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve? Agricultural Systems 75, 215–234.
The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve?Crossref | GoogleScholarGoogle Scholar |

National Research Council (2001) ‘Nutrient requirements of dairy cattle.’ 7th edn. (National Academies Press: Washington, DC)

O’Donovan M, Dillon P, Rath M, Stakelum G (2002) A comparison of four methods of herbage mass estimation. Irish Journal of Agricultural and Food Research 41, 17–27.

Öhlmér B, Olson K, Brehmer B (1998) Understanding farmers’ decision making processes and improving managerial assistance. Agricultural Economics 18, 273–290.
Understanding farmers’ decision making processes and improving managerial assistance.Crossref | GoogleScholarGoogle Scholar |

Pasture Plus (2006) ‘Pasture management for Tasmanian dairy farmers.’ (Crown in Right of the State of Tasmania: Hobart)

Robinson WL (1974) Conscious competency: the mark of a competent instructor. The Personnel Journal 53, 538–539.

Rodriguez J, Molnar J, Fazio R, Sydnor E, Lowe M (2009) Barriers to adoption of sustainable agriculture practices: change agent perspectives Renewable Agriculture and Food Systems 24, 60–71.
Barriers to adoption of sustainable agriculture practices: change agent perspectivesCrossref | GoogleScholarGoogle Scholar |

Ryan P, Bernard H (2000) Data management and analysis methods. In ‘Handbook of qualitative research’. (Eds NK Denzin, YS Lincoln) pp. 769–802. (Sage Publications: London)

Turner L, Irvine I (2017) Tasmanian dairy farmers and the pasture management learning process: case study findings on the role of coaching in achieving practice change. Rural Extension and Innovation Systems Journal 13, 31–40.

Waters W, Thomson D, Nettle R (2009) Derived attitudinal farmer segments: a method for understanding and working with the diversity of Australian dairy farmers Extension Farming Systems Journal 5, 47–57.