Using a ‘network of practice’ approach to match grazing decision-support system design with farmer practice
C. R. Eastwood A C , B. T. Dela Rue A and D. I. Gray BA DairyNZ Ltd, Hamilton 3240, New Zealand.
B Massey University, Palmerston North 4474, New Zealand.
C Corresponding author. Email: callum.eastwood@dairynz.co.nz
Animal Production Science 57(7) 1536-1542 https://doi.org/10.1071/AN16465
Submitted: 19 July 2016 Accepted: 11 November 2016 Published: 21 December 2016
Abstract
The use of pasture measurement tools and decision-support systems (DSS) for grazing management remains limited on New Zealand dairy farms. However, effective use of such tools provides opportunities to optimise pasture grown and pasture harvested. The present study used a mixed-method qualitative research approach to investigate pasture data and technology use for grazing decision making, through interviews and workshops with farmers, rural professionals, commercial software developers and a panel of farming-system specialists. Results suggest that different drivers for use of pasture data and DSS exist between farm owner-operators and corporate farming operations. Larger multi-farm businesses are collecting pasture data for use at a governance level as well as for operational decision making. Understanding the seasonal influences on decision making, and incorporating major regional differences such as pasture growth rates and impact of irrigation use, provides guidance on how to better match DSS to farmer practice. Study participants identified a need for greater integration of software tools to connect in-paddock data capture with real-time feedback. Also, data integration is needed to enable the transfer of information across different platforms for corporate farming operations. Rural professionals used commercial grazing DSS products, but also constructed their own spreadsheets to enable functionality and reporting not available in the DSS products. The research highlighted a need for farmer-orientated tools that are flexible to incorporate differences in user goals, decision making, mobility and desired outputs. Key attributes identified were seasonality, simplicity, ability to trial before purchase, flexibility in application, scalability to match farm systems, and integration with other tools. Future research and design of DSS tools requires a focus on co-creation with farmers, to merge scientific and practical knowledge.
Additional keywords: agricultural innovations, farm management, grazing management, precision farming.
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