# Group map data for all 20 farmers

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## Group map data for all 20 farmers

When the data for all 20 cases had been entered into individual Excel worksheets it was possible to create an equivalent data matrix for the group map by calculating the average score for each cell in the group matrix. These average scores then formed the basis of further calculations. The complete matrix for the group map data shows that for the average group map there were a total of 194 separate connections between factors, considerably short of the theoretical maximum of 41 times 40 or 1,640 connections (12 per cent), but still rather too many to represent easily on a single map (see later). In this section of the report the data are presented by first focusing on the group map data and then focusing on the group map generated by these data.

Group map data for each panel

The assessment of differences between the group maps created for each of the ARGOS management panels involved the identification of significant differences for centrality scores among the panels. To facilitate the analysis, the data from the individual maps were combined into one table that listed the 20 farmers and collated the 41 factors in 41 columns of data. These data were examined using ANOVA and the results are shown in Table 51 . Two of the significant levels are just over the 0.05 level. Bolding is used to show which panel has the higher average score for that factor.

### Group Data for Each Q-sort type

The Q-sort data provided the basis for an examination of groups of farmers based on how they rated the importance of the factors. The factor analysis result that gave two Q-sort types was the best solution in terms of readily interpretable types with reasonable numbers of farmers being associated with each type. Table 6 shows the number of farmers who loaded significantly on each factor. Q-sort type 1 had 12 significant loaders and Q-sort type 2 had eight significant loaders. The table shows that Q-sort type 1 has a small majority of conventional farmers and Q-sort type 2 was mainly converting farmers. It is usual in Q-sort analysis to examine the type arrays, those factors that make up the underlying or prototypical characteristics of each Q-sort type. However, before presenting these data, it is necessary to examine the centrality scores and map characteristics to see if in fact there are differences in the maps for each type.

Group map

The overall group map shows that dairy farming involves the management and response to a wide variety of factors, including economic, environment and social ones. At the core of the map are personal (farmer decision maker and satisfaction) and production factors surrounded by soil, environmental, climatic, family and financial factors. True to the family farm structure of much of New Zealand farming, the map shows the closely integrated role of family in the farming system expressed as family needs. The map is not insular since there are connections extending outwards including other people and related factors, especially the marketing or processing organisation and considerations of time in farm work. There is a strong production orientation in the map with some of the strongest connections from farmer decision maker to fertiliser and soil fertility health and to production. However, the environment is also important, reflected in farm environmental health and, to a lesser extent, of farm environment as a place to live. The sources of satisfaction (production, fertiliser and soil fertility health, farmer decision maker, family needs farm environment as a place to live and net profit before tax) are quite varied and reflect the broad mix of factors at the core of the map.

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#### Discussion and Interpretation of Results

There are some general points among the results that bear a discussion similar to that provided in the sheep/beef report and can now be considered briefly. First, the preceding results confirm core similarities across panels. While we have emphasised the differences across panels and across Q-sort types, it is still the case that the maps have many factors which had similar levels of importance and are connected in similar ways. Second, the maps show the key role of family in farming. There are complex interactions among the central factors in the map and one of these is family needs. Thus, any change in family situation, such as a birth or a death, could affect family needs and this, in turn, has a major influence on farmer decision maker. Third, environmental factors were important in the maps. The emphasis on environmental factors is interesting because it did not come up in pre-testing. Fourth, the group map results also show that many dairy farmers see their farm as a complex system, although there is differentiation among farmers in their appreciation of farm complexity. Converting farmers and Q-sort type 2 farmers have more complex maps.

Contents :

• List of Tables
• List of Figures
• Summary
• Acknowledgements
• Chapter 1 Introduction: Background, Research Objectives and Method
• 1.1 Background and research objectives
• 1.2 Method
• Chapter 2 Dairy Results
• 2.1 Introduction
• 2.2 Group map data for all 20 farmers
• 2.3 Group map data for each panel
• 2.4 Group Data for Each Q-sort type
• 2.5 Conclusion
• Chapter 3 Key Findings and Discussion
• 3.1 Summary of results
• 3.2 Discussion and Interpretation of Results
• References
• Appendix 1: Responses to questions asked after causal mapping

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Causal mapping of ARGOS dairy farms and comparisons to sheep/beef farms