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Segmenting Victoria's farmers Roger Wilkinson, Neil Barr and Carole Hollier

Segmenting Victoria’s farmers December 2011

Authors: Roger Wilkinson, Neil Barr and Carole Hollier Service Design Branch Farm Services Victoria Division Department of Primary Industries

Acknowledgments: The authors would like to give their appreciation to the 1300 farmers who gave up their time to participate in a state-wide survey to enable us to undertake this study. We thank our DPI colleagues Wayne Harvey for assistance with spatial analysis and Geoff Kaine for helpful comments. Project consultants, OpenMind Research Group and Market Metrics, for help with questionnaire design and data collection, are also acknowledged. The work has been funded under the Future Farming Statement, Action 1.2 “State of art services to farm businesses”, completed within the “Developing understanding of FSV segments” project.

Cover photograph by Roger Wilkinson, near Hopetoun, April 2010

If you would like to receive this information/publication in an accessible format (such as large print or audio) please call the Customer Service Centre on 136 186, TTY 1800 122 969, or email [email protected]. Published by the Department of Primary Industries Rutherglen, July 2011 © The State of Victoria 2011. This publication is copyright. No part may be reproduced by any process except in accordance with the provisions of the Copyright Act 1968. Authorised by the Department of Primary Industries 1 Spring Street, Melbourne 3000. Disclaimer This publication may be of assistance to you but the State of Victoria and its employees do not guarantee that the publication is without flaw of any kind or is wholly appropriate for your particular purposes and therefore disclaims all liability for any error, loss or other consequence which may arise from you relying on any information in this publication.

For more information about DPI go to www.dpi.vic.gov.au or phone the Customer Service Centre on 136 186.

Key points •

Segmentation is undertaken to understand the behaviour or potential behaviour of a target population and its component segments in response to a product or service. It is used to improve service delivery. The Department of Primary Industries (DPI) provides a range of services to Victorian farmers and the state as a whole, with the aim of achieving the vision in DPI’s Strategic Plan of “primary industry and energy sectors sustainably building Victoria’s wealth and wellbeing”.



This report uses data from a survey of 1300 Victorian farmers conducted for DPI in June and July 2010 to segment Victoria’s farmers with respect to productivity and farm family well-being.



The most important segmentation concerns farmers’ aspirations for farm productivity and their perceived ability to achieve these aspirations. 16% were expansion oriented, planning to expand their farm by purchasing land or water; 32% aspired to increase the productivity but not the scale of their farm; 14% were interested in increasing productivity but doubted they could achieve it; and 38% were not interested in productivity (or planning to sell all or part of their farm).



Expansion oriented farmers tended to have larger farms (measured by gross farm income). Plans to sell out or scale down were independent of property scale, and seem instead to be determined by age or family circumstances. It seems that older people on small farms are willing to remain in farming despite low incomes, whilst young people choose to enter farming only if the farm has sufficient scale to earn them a high income.



The second segmentation concerns economic security, combining estimated household income and dependence on the farm for income. 32% were low income farm dependent; 10% were low income off-farm dependent; 15% were medium income farm dependent; 7% were medium income off-farm dependent; 20% were high income farm dependent; and 16% were high income off-farm dependent.



High income farm dependent households (20%) represent the historical target audience for many of DPI’s technology-focused programs. Low income farm dependent households constitute a third of farm families. They earn within the bottom 30 per cent of Australian households, and at least half their income from the farm. Despite their limited means, these farm households have aspirations for their farm little different from those of higher income households. Almost half aspire to increase the scale or productivity of their farms. Such aspirations may not always be achievable. On the other hand, even small productivity increases by these farmers would provide a greater marginal improvement in financial security than would be the case on larger farms. The package of products for these farmers may well be different from that offered to other segments.



The third segmentation reveals that 40 per cent of farmers prioritise farming over income security. Many of these farmers have a gross income below $100,000. These farmers’ reticence to even partially abandon the farming lifestyle to supplement their income suggests even modest improvements in productivity on their farms will lead to a significant improvement in their economic security.



The fourth segmentation shows 29% of farmers use DPI as a main source of any kind of service or information. Only those with the largest scale farms (gross farm income over $400,000) make extensive use of paid private consultants.



If DPI were to focus on improving aggregate agricultural sector productivity, then the most effective strategy would be to target its limited resources towards the segment with greatest capacity as well as aspiration for productivity increase. This is the expansion oriented segment. The use of private consultants to assist in the delivery of DPI information to this segment is a sensible approach to this goal.



However, if DPI were to seek to maximise the impact of its services on the economic security and well-being of the rural sector as a whole, then it would focus on the much larger group of farmers interested in productivity within their existing farm scale. This would be a less efficient means of achieving gains in sectoral productivity, but would be more efficient in enhancing well-being across the rural sector. These farmers have limited use of paid advisory services, and the technologies appropriate to their needs may or may not be suitable for delivery by farm merchandise suppliers. DPI’s strategy in this case may need to include a facilitatory role that would seek to extend the resources of both DPI and the private sector to deliver the information.

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Executive summary Segmentation is undertaken to understand the behaviour or potential behaviour of a target population and its component segments in response to a product or service. The Department of Primary Industries (DPI) provides a range of services to Victorian farmers and the state as a whole, with the aim of achieving DPI’s vision of “primary industry and energy sectors sustainably building Victoria’s wealth and wellbeing”, via its three headline outcomes of “competitive businesses and efficient markets”, “engaged, safe and responsible communities”, and “sustainably managed natural resources” (DPI Strategic Plan 2010–2013). Victoria’s farmer population could be segmented around each of these three headline outcomes. This report focuses on segmenting Victoria’s farmer population with regard to productivity (which corresponds with “competitive businesses and efficient markets”) and farm family well-being (which corresponds with “engaged, safe and responsible communities”). Productivity is an umbrella term that covers the many farming technologies and management systems that DPI invests in, while farm family well-being is represented by DPI’s response to emergencies and exceptional circumstances. Using data from a survey of 1300 Victorian farmers we created three segmentations based upon farmers’ aspirations and capacity for increasing farm productivity, economic well-being and strategies to cope with difficult times. A fourth segmentation on the use of services was based on data from the survey and from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). The most important segmentation concerns farmers’ aspirations with regard to farm productivity and their ability to achieve these aspirations. The surveyed farmers associated increasing farm productivity with increasing farm income, which is a production-based definition that contrasts with the efficiency-based “total factor productivity” definition of productivity (physical outputs relative to physical inputs) used in DPI’s Agriculture and Fisheries fouryear strategy 2011–15 (April 2011 version, p. 43). This report must be read with that contrast in mind. The segmentation is:

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Selling out — Those actively seeking to exit farming (13%);



Phasing down — Those wishing to remain in farming but planning to sell land or water to reduce the scale of the farm and the consequent farming effort (17%);



Not productivity oriented — Farmers satisfied with the current scale of their farm, and uninterested in expanding productivity (8%);



Productivity constrained — Farmers interested in increasing farm productivity, but doubting their capacity to achieve this aim (14%);



Productivity but not scale oriented — Farmers aspiring to increased productivity, but not seeking to achieve this through increasing farm scale. This is by far the largest segment (32%);



Expansion oriented — These farmers share a desire to expand their farm through land or water purchase (16%).

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Two factors strongly influence the place that farmers occupy within this segmentation. These are age and capacity to invest. The young are much more likely to profess interest in productivity objectives. However capacity to invest is strongly related to farm scale. A key finding of our research to date is the disparity between family farm aspirations and the capacity limitations of the majority of farm businesses. Many farm families face a trade-off between investment and current consumption, but also know that investment in future income growth is necessary for future income security. A common strategy adopted to resolve this dilemma is to seek to increase productivity within the constraints of the existing scale of the farm business. This is the strategy of the largest segment of the farmer population. A second key finding is that plans to sell out or scale down are independent of property size. They are determined by age and personal or family circumstances. This is consistent with conclusions drawn from detailed examination of ABS Population Census data conducted by Barr (2004). What this survey shows that is undetectable in census data is that the average financial scale of farms decreases with age. It seems that older people on small farms are willing to remain in farming despite low incomes, whilst young people choose to enter farming only if the farm has sufficient scale to earn them a high income.

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The second segmentation concerns economic security, combining estimated household income and dependence on the farm for income: •

Low income farm dependent (32%);



Low income off-farm dependent (10%);



Medium income farm dependent (15%);



Medium income off-farm dependent (7%);



High income farm dependent (20%);



High income off-farm dependent (16%).

Families with gross farm income less than $50,000 per year seem to substitute off-farm income for farm income. Families with gross farm income more than $50,000 rely predominantly on the farm for their income. Many of these families would have very low incomes, but accept their low income as the price of being able to farm fulltime. High income and farm dependent farm households represent the historical target audience for many of DPI’s technology-focused programs. Whilst it is a large segment, it is not the largest. Low income and farm dependent farm households constitute a third of farm families. These are households earning within the bottom 30 per cent of Australian households, and earning at least half their income from the farm. Despite their limited means, these

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farm households have aspirations for their farm little different from those of higher income households. Almost half aspire to increase the scale or productivity of their farms. Such aspirations may not always be achievable. On the other hand, even small productivity increases would provide a greater marginal improvement in financial security than would be the case on larger farms. A third segmentation explores the trade-off between current consumption and investment. Famers were asked to choose their most preferred and least preferred strategies for dealing with a period of lower farm income. We created six groups based on preferred strategy and the reason for choosing that strategy: •

Work off-farm to maintain family income (15%);



Work off-farm to invest in the farm (13%);



Cut investment to maintain family income (17%);



Cut investment to keep farming (14%);



Live on less to invest in the farm (22%);



Live on less to keep farming (19%).

This segmentation reveals that 40 per cent of farmers prioritise farming over income security. The farm scale at which farmers choose this path cuts in somewhere between $50,000 and $100,000 annual gross farm income. Many of these farmers are clearly living on low incomes. The reticence to even partially abandon the farming

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lifestyle to supplement income suggests there is a group of farmers for whom even modest improvements in productivity on their farms will lead to a significant improvement in economic security. The package of products for this group may well be very different from that offered to other segments. The fourth segmentation concerns the use of services or information provided by DPI, paid consultants, and retail outlets or the field staff of product purchasers: •

Use neither DPI nor consultant nor retailer (36%);



Use DPI only (10%);



Use consultant only (12%);



Use retailer only (14%);



Use both consultant and retailer (9%);



Use DPI and either consultant or retailer or both (19%).

Our analysis of survey data showed larger scale farmers used more services. The largest scale farmers were particularly high users of consultants. Due to limitations in the questions available to us, we have also used ABARES data to examine the relationship between farm scale and the use of private advisory services. These data show that the use of paid advisory services is well established and rising rapidly on the largest ten or fifteen per cent of farms, but has made little penetration into the rest of the farm sector.

3000

Advisory fees

2500 2000 1500 1000 500

20 08

20 06

20 04

20 02

20 00

19 98

19 96

19 94

19 92

19 90

0

Gross farm income LT $100k

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$100k-$200k

$200k-$400k

GT $400k

DPI faces its own dilemma in seeking to target its services. If it is to concentrate upon improving aggregate agricultural sector productivity, then the most effective strategy for DPI would be to target its limited resources towards the segment with greatest capacity as well as aspiration for productivity increase. This is the relatively smaller expansion oriented segment. The use of private consultants to assist in the delivery of DPI information to this segment is a sensible approach to this goal. However, if DPI were to seek to maximise the impact of its services on the economic security and financial welfare of the rural sector as a whole, then it would focus on the much larger group of farmers interested in productivity within existing resources. These are the farmers with lesser capacity to invest, and limited income from off-farm sources. This would be a less efficient means of achieving gains in sectoral productivity; but would be more efficient in enhancing well-being across the rural sector. These farmers have limited use of paid advisory services, and the technologies appropriate to their needs may or may not be suitable for delivery by farm merchandise suppliers. DPI’s strategy in this case may need to include a facilitatory role that would seek to extend the resources of both DPI and the private sector to deliver DPI information. For DPI to optimise its effort across these quite different kinds of farms, it will not be able to afford the efficiency of allocating a predominance of resources to any one of these segments. It will need to direct resource to both large and small scale farmers. However, the messages and products for the two segments would in many cases need to be tailored to their quite different aspirations and strategies. Our segmentations do not represent definitive ways to view Victoria’s farmer population. Instead, they are tools that provide a framework for DPI to think about how it might best work with all parts of the farming community for the betterment of Victorian agriculture. DPI has an opportunity to think positively about the design and delivery of its services. This report is one step in the journey.

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Contents Key points

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Executive summary

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List of Figures

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List of Tables

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The purpose of the study

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How the study was undertaken

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Segmentation based on farm productivity aspirations

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Farm productivity aspirations segmentation

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Age drives aspirations for productivity

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Farm scale constrains capacity to invest in increased productivity

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The critical intersection: farmer age and farm scale

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Segmentation based on financial welfare

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Large number of small farms

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Is off-farm income supplementing farm income?

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Prioritising a farming lifestyle over household income

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Household income estimation

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Economic security segmentation

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Many in the low income farm dependent segment have nascent aspirations for increasing farm productivity

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Segmentation based on response to a downturn

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“Calamity segmentation”

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Choosing to live on less to remain a farmer

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The oldest farmers are the most willing to live on less

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Full-time farmers don’t want off-farm work

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Segmentation based on use of services and information

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Service use segmentation

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Information seeking behaviour of productivity segments

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Only the largest scale farmers make substantial use of paid consultants

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A few large farms are responsible for more than half the expenditure on consultants

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Segmentations by industry, social landscape and state government region

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Discussion and implications

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The place of attitudes and landholder identity

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Limitations

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How might DPI use the segmentations?

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Sectoral productivity as an objective

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Well-being as an objective

29

Reconciling sectoral productivity and well-being

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Would a focus on well-being slow agricultural restructuring?

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Conclusion

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References

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Appendix 1. Method

36

Design

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Sampling

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Administration

36

Analysis

37

Reporting

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Appendix 2. Questionnaire

38

Appendix 3. Profile of surveyed farmers

48

Appendix 4. Raw data tables

51

Appendix 5. Segmentations by industry

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Appendix 6. Segmentations by social landscape

60

Appendix 7. Segmentations by state government region

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List of Figures Figure 1 Segmentation based on farm productivity expectations...............................................................................3 Figure 2 Farm productivity expectations by age.........................................................................................................4 Figure 3 Farm productivity expectations by farm financial scale................................................................................5 Figure 4 Farm financial scale by age..........................................................................................................................6 Figure 5 Farm financial scale and industry.................................................................................................................7 Figure 6 Farm financial scale and farm dependence .................................................................................................8 Figure 7 Substitution of off-farm income for farm income by farm financial scale......................................................9 Figure 8 Estimated farm household income by national household income deciles................................................11 Figure 9 Segmentation based on economic security ...............................................................................................13 Figure 10 Aspirations of the low income farm dependent segment .........................................................................14 Figure 11 Segmentation based on response to a downturn.....................................................................................16 Figure 12 Response to a downturn by economic security segment.........................................................................17 Figure 13 Response to a downturn by age...............................................................................................................18 Figure 14 Response to a downturn by balance of farm and off-farm income ..........................................................19 Figure 15 Segmentation based on use of services ..................................................................................................20 Figure 16 Use of services by farm productivity expectations .................................... Error! Bookmark not defined. Figure 17 Average annual amount spent on advisory fees by gross farm income, Victorian broadacre farms .......23 Figure 18 Percentage of expenditure on advisory fees by gross farm income, Victorian broadacre farms, 2009...24 Figure 19 Industry profile of weighted sample..........................................................................................................48 Figure 20 Gross farm income profile of weighted sample by industry......................................................................49 Figure 21 Profile of weighted sample compared with all Victorian farmers..............................................................50 Figure 22 Farm productivity expectations segmentation by industry .......................................................................58 Figure 23 Economic security segmentation by industry ...........................................................................................58 Figure 24 Downturn segmentation by industry .........................................................................................................59 Figure 25 Use of services segmentation by industry................................................................................................59 Figure 26 Farm productivity expectations segmentation by social landscape .........................................................60 Figure 27 Economic security segmentation by social landscape .............................................................................60 Figure 28 Downturn segmentation by social landscape ...........................................................................................61 Figure 29 Use of services segmentation by social landscape..................................................................................61 Figure 30 Farm productivity expectations segmentation by state government region .............................................62 Figure 31 Economic security segmentation by state government region.................................................................62 Figure 32 Downturn segmentation by state government region...............................................................................63 Figure 33 Use of services segmentation by state government region .....................................................................63

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List of Tables Table 1 Raw data for Figure 1 ..................................................................................................................................51 Table 2 Raw data for Figure 2 ..................................................................................................................................51 Table 3 Raw data for Figure 3 ..................................................................................................................................52 Table 4 Raw data for Figure 4 ..................................................................................................................................52 Table 5 Raw data for Figure 5 ..................................................................................................................................53 Table 6 Raw data for Figure 6 ..................................................................................................................................53 Table 7 Raw data for Figure 9 ..................................................................................................................................54 Table 8 Raw data for Figure 10 ................................................................................................................................54 Table 9 Raw data for Figure 11 ................................................................................................................................55 Table 10 Raw data for Figure 12 ..............................................................................................................................55 Table 11 Raw data for Figure 13 ..............................................................................................................................56 Table 12 Raw data for Figure 14 ..............................................................................................................................56 Table 13 Raw data for Figure 15 ..............................................................................................................................57

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Segmenting Victoria's farmers

The purpose of the study Segmentation is undertaken to understand the behaviour or potential behaviour of a target population in response to a product or service. The Department of Primary Industries (DPI) provides a range of services to Victorian farmers and the state as a whole, with the aim of achieving DPI’s vision of “primary industry and energy sectors sustainably building Victoria’s wealth and wellbeing”, via its three headline outcomes of “competitive businesses and efficient markets”, “engaged, safe and responsible communities”, and “sustainably managed natural resources” (DPI Strategic Plan 2010–2013). Victoria’s farmer population could be segmented around each of these three headline outcomes. This report focuses on segmenting Victoria’s farmer population with regard to productivity (which corresponds with “competitive businesses and efficient markets”) and farm family well-being (which corresponds with “engaged, safe and responsible communities”). Productivity is an umbrella term that covers the many farming technologies and management systems that DPI invests in, while farm family well-being is represented by DPI’s response to emergencies and exceptional circumstances. The main approach to productivity is to understand the aspiration and the capacity to invest in farm productivity improvement. We do not consider individual productivity innovations, but the aspiration and capacity to invest in future productivity growth. A secondary approach is to understand well-being on farms as it relates to productivity. Many farm families face a trade-off between investment and current consumption, but also know that investment in future income growth is necessary for future income security. This second approach to segmentation is justified on our assumption that productivity growth is not a policy end in itself, but is a tool employed by government to achieve the vision of sustainably building Victoria’s wealth and well-being. If this is the case, then one needs to consider how DPI might best balance the investment goal of optimising the productivity of the farm sector with the goal of optimising the well-being of the farm sector and the general community. The research aims to improve DPI’s collective understanding of Victoria’s farmers to enable more targeted, accessible and relevant service delivery. The intent is to move thinking beyond simple, broad segmentation based on farm scale, industry and geography towards a collection of segmentations fit for the range of possible DPI purposes.

How the study was undertaken The research was undertaken by Farm Services Victoria (FSV) Service Design branch, using data collected for DPI by Open Mind Research Group. The research was funded under the Future Farming Statement, Action 1.2, State of Art Services to Farm Businesses, as part of the ‘Developing Understanding of FSV Segments’ project. A total of 1300 Victorian farmers were interviewed by telephone in June and July 2010. A quota sampling approach was used to ensure adequate representation of different industries and age groups. All data were weighted for industry and gross farm income, which means that the graphs and tables in this report accurately reflect the industry and income makeup of Victoria’s farm population. The research methods are described in detail in Appendix 1. The interview schedule is included in Appendix 2. The schedule is a combination of questions designed for two separate projects. Questions 8 to 13 were designed by the DPI evaluation team as part of an on-going evaluation of DPI service delivery. Responses to these questions were available for analysis in this project. A demographic profile of the farmer respondents is provided in Appendix 3 and raw data tables are provided in Appendix 4.

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Segmentation based on farm productivity aspirations Farm productivity aspirations segmentation Many in the sample of farmers aspired to achieve increased farm productivity. The surveyed farmers had a strong stated interest in farm productivity. When asked how important it was to them, 73% rated it 8 or higher on a 1-to10 scale, including 38% rating it extremely important (score 10). In the survey, importance of increasing farm productivity was highly correlated with importance of increasing farm 2 income (r =0.32), with 72% rating increasing farm productivity 8 or higher and 46% rating it 10. This suggests farmers see these concepts as closely related. The meaning farmers gave to productivity, in associating it with income and production, contrasts with the efficiency-based “total factor productivity” definition of productivity (physical outputs relative to physical inputs) used in DPI’s Agriculture and Fisheries four-year strategy 2011–15 (April 2011 version, p. 43). This report must be read with that contrast in mind. We developed a segmentation based upon interest in farm productivity and perceived ability to achieve it. (As a check, we produced alternative versions of Figure 1, Figure 2 and Figure 3 using the income variables, and they were almost identical to the figures shown.) We used the following steps: •

First we quarantined those who plan to sell out and leave. They will not be around long;



Next we made a segment of those who plan to cut back on work and/or sell part of their farm without intention to buy elsewhere. These people are scaling back the intensity of their farming;



Then we identified those who plan to expand their farm by purchase, lease or share-farming of additional land.

We then had four groups: those intending to sell out, those intending to scale down, those intending to expand, and the rest. The rest was divided into three sub-groups:

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Those with low interest in increasing their farm productivity;



Those with a desire to increase farm productivity but little expectation it can be achieved in the next five years;



Those who both desire and expect to increase their farm productivity, but do not expect to expand farm scale.

Segmenting Victoria's farmers

This created a six-group segmentation (Figure 1): •

Selling out — Those actively seeking to exit farming (13%);



Phasing down — Those wishing to remain in farming but planning to sell land or water to reduce the scale of the farm and the consequent farming effort (17%);



Not productivity oriented — Farmers satisfied with the current scale of their farm, and uninterested in increasing productivity (8%);



Productivity constrained — Farmers interested in increasing farm productivity, but doubting their capacity to achieve this aim (14%);



Productivity but not scale oriented — Farmers aspiring to increased productivity, but not seeking to achieve this through increasing farm scale. This is by far the largest segment (32%);



Expansion oriented — These farmers share a desire to expand their farm through land or water purchase (16%).

Figure 1 Segmentation based on farm productivity expectations

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Age drives aspirations for productivity There are substantial differences in farm productivity expectations between farmers of different ages. There were few farmers aged under 25 in the survey sample so they have been removed from the analysis. The proportion of farmers interested in expansion is greatest among the youngest farmers and decreases with increasing age. Conversely, the proportion of farmers interested in contraction (both selling out and phasing down) is lowest among the youngest farmers and increases with increasing age (Figure 2). The most significant relationship is between age and aspiration to increase farm scale. Whilst ambition is highest amongst the youngest, it appears that ambition declines as years of working life reduce and as family responsibilities place constraints upon the capacity to fund farm expansion.

Figure 2 Farm productivity expectations by age

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Farm scale constrains capacity to invest in increased productivity Ambition is obviously mediated by capacity. The financial scale of farm businesses is an indicator of capacity to invest in farm productivity enhancement. This relationship is clear in Figure 3, which portrays the relationship between farm scale (measured by gross farm income) and aspirations for increased farm productivity. Plans to increase farm scale are strongly related to the scale of the existing farm. Those with large farms already are much more likely to aspire to further increases in farm scale. Plans to sell out or scale down are independent of property size. As Figure 2 shows, they are determined by age, and perhaps by health and family circumstances. Where the smallest farms, with less than $50,000 annual gross farm income, differ from larger farms is that they include the majority of those who are not productivity oriented. Farms with gross farm income between $50,000 and $100,000 differ from smaller and larger farms in that they include the largest proportion of farmers that would like to increase productivity but feel they are unlikely to achieve it. The ‘Productivity but not scale oriented’ group is found across all farm scales.

Figure 3 Farm productivity expectations by farm financial scale

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The critical intersection: farmer age and farm scale The results suggest that larger farms and younger farmers are associated with plans for increasing farm productivity through farm expansion in particular. It might be suggested that younger farmers are interested in expansion because they are in the early stages of their farming careers and have smaller farms than those who have been established in farming for longer. The data tell a different story. Younger farmers are more likely to have larger farms than older farmers (Figure 4). The age group with the largest mean farm scale is the youngest. This suggests that the younger generation is much less likely to enter farming without a good chance of earning a high income, and a farm that will earn a high income is necessarily a large-scale one. In contrast, the older generation will often choose to remain in farming despite the low returns from a small farm.

Figure 4 Farm financial scale by age

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Segmentation based on financial welfare Large number of small farms In common with earlier studies using Australian Bureau of Statistics data, this survey found the farm sector to be composed of a large number of relatively small farms with limited potential to generate income, with small numbers of large farms having sufficient scale to generate a good income. The relationship between farm scale and industry is shown in Figure 5. There are large numbers of farms with gross income less than $100,000. Two questions immediately arise: •

Is this indicative of a low income problem?



Is there any role for enhanced productivity as one strategy to increase low incomes?

In this section we attempt to answer these questions.

Figure 5 Farm financial scale and industry

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Is off-farm income supplementing farm income? Off-farm income is being used to supplement farm income, but not as much as would be suggested by farm scale. For example, half of those operators of farms with gross farm income between $25,000 and $50,000 said they gained at least half their household income from the farm. Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data suggest that farms of this scale are unlikely to produce a net income of greater than $10,000 in most years. This is strong evidence of a long-term low income issue in a significant segment of the farm sector (Figure 6).

Figure 6 Farm financial scale and farm dependence

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Prioritising a farming lifestyle over household income Plotting the average percentage of household income generated from farming for each of the categories of annual gross farm income measured in the survey, we find that a gross farm income of $50,000 is the pivot point (Figure 7). Below this point, the proportion of household income derived from farming drops steeply with decreasing gross farm income, suggesting that at these small scales farm income and off-farm income substitute for each other. Above this point, the proportion of household income generated from farming rises only slightly with increasing gross farm income, suggesting only a limited degree of substitution of off-farm income for farm income. In other words, farm households with gross farm income as low as $50,000 to $100,000 behave like those with higher gross farm income in their reliance on off-farm work. Considering only a fraction of gross farm income ends up as net income, these farm families must be living on a low income.

Figure 7 Substitution of off-farm income for farm income by farm financial scale

This suggests that farms with a gross farm income of less than $50,000, as well as those with a gross farm income between $50,000 and $100,000, need to be treated separately from larger scale farms. The segment with a gross farm income of less than $50,000 is twice the size of each of the other segments.

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Household income estimation To further explore the household income structure of the farm sector we built an indicator of net household income. We created a simplified conceptual model of farm investment by assuming that there is a hierarchy of farm objectives: •

The first is to generate sufficient income to provide for current living expenses. If this is not achieved, then income will be supplemented with off-farm work;



Second, additional income in good years will be invested in building financial reserves or investing in improving farm productivity. To keep pace with the decline in the terms of trade, a farm must achieve the equivalent of 1 per cent per annum increase in income;



Third, further additional income is invested in increasing farm scale through land purchase to provide future income protection and perhaps achieve inter-generational transfer.

These goals can be used as the basis of some notional hierarchies of farm capacity to invest in productivity. We can make estimates for at least four types based upon three scale and income thresholds. •

Farms too small to generate sufficient to allow a community median disposable income;



Farms large enough to generate a community median income for current consumption;



Farms large enough to support investment to maintain pace with the decline in terms of trade;



Farms large enough to support investment to keep pace with rises in income in the rest of the economy.

These thresholds are illustrative only and cannot take account of all individual circumstances. The thresholds will differ by industry and region. This is mainly a function of relative land values. In the high rainfall zone where land values are high, very large investments are needed to generate an equivalent increase in scale. We used regression equations derived from ABARES data to estimate net farm income from reported gross farm income. By combining the estimated annual net farm income with the percentage of household income generated from farming we were able to estimate the annual household income of our survey respondents. The resulting farm household income estimates should be viewed as indicative only. Then we compared the estimated household income of our survey respondents with the income distribution of all Australian households. The income distribution of our respondents was bimodal, dominated by households in the lowest income and highest income deciles (Figure 8). This suggests that many Victorian farm households have very low incomes, some have very high incomes, and only a small number have moderate incomes.

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Segmenting Victoria's farmers

Figure 8 Estimated farm household income by national household income deciles (decile 1 lowest; decile 10 highest)

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Economic security segmentation Our economic security segmentation used two variables: estimated household income and dependence on the farm for income. We defined a high income household as being in the top three deciles for Australian household incomes (for a couple with two children, this is greater than $91,000). A low income household was one in the bottom three deciles (less than $51,000), and a medium income household was in the middle four deciles. For respondents aged 55 or older we used the figures for a couple with no children ($68,000 and $38,000). This divides farmers into three income groups, the highest assumed to be where the capacity to invest is greatest, the lowest where there appears to be an income problem, although for those over 65 this can be overlooked if we assume the farm can be gradually sold off. In between is an income level where there is a constrained capacity to invest in increasing scale or major productivity enhancement without making sacrifices to current living standards. We defined a farm-dependent household as one that earned at least half its income from farming, and an off-farm dependent household as one that earned less than half its income from farming. This resulted in a six-way segmentation (Figure 9): •

Low income farm dependent (32%);



Low income off-farm dependent (10%);



Medium income farm dependent (15%);



Medium income off-farm dependent (7%);



High income farm dependent (20%);



High income off-farm dependent (16%).

In all three income groups there are more farm-dependant households than off-farm dependent households. In the low and medium income groups farm-dependant households predominate. The low income farm dependent segment is by far the largest.

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Segmenting Victoria's farmers

Figure 9 Segmentation based on economic security

Segmenting Victoria's farmers

13

Many in the low income farm dependent segment have nascent aspirations for increasing farm productivity Many families in the low income farm dependent segment will be relatively wealthy with a significant asset base in farm land ownership. However, they will have very limited capacity to fund investments in increased farm productivity. One would expect them to have limited interest in enhancing farm productivity, recognising their limited capacity to fund investment in improving the income earning potential of their farm. Surprisingly, though, nearly half have an interest in increasing farm productivity or expanding their operation (Figure 10). This proportion is only slightly less than the proportion of all surveyed farmers interested in enhancing farm income or expanding (Figure 1). For the low income farm dependent farmers, surely this interest in farm enhancement is tempered by some recognition of the constraints of their business.

Figure 10 Aspirations of the low income farm dependent segment

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Segmenting Victoria's farmers

Segmentation based on response to a downturn “Calamity segmentation” Our third segmentation explores the trade-off between current consumption and investment. Famers were asked to choose their most preferred and least preferred strategies for dealing with a period of lower farm income. We created six groups based on preferred strategy and the reason for choosing that strategy. The three strategies we offered were: •

The respondent or someone else in their family picks up off-farm work or increases their off-farm work;



They stop or cut back on investments in farm improvement or expansion; and



They make do with a lower income.

We assumed the least preferred option was an indicator of the thing they were trying to protect. For example, if making do with a lower income was the least preferred option, we took that to mean the respondent wanted to maintain family income.

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The resulting six-group segmentation was (Figure 11): •

Work off-farm to maintain family income (15%);



Work off-farm to invest in the farm (13%);



Cut investment to maintain family income (17%);



Cut investment to keep farming (14%);



Live on less to invest in the farm (22%);



Live on less to keep farming (19%).

Figure 11 Segmentation based on response to a downturn

This segmentation reveals that 40 per cent of farmers prioritise farming over income security. The farm scale at which farmers choose this path cuts in somewhere between $50,000 and $100,000 annual gross farm income. Many of these farmers are clearly living on low incomes. The reticence to even partially abandon the farming lifestyle to supplement income suggests there is a group of farmers for whom even modest improvements in productivity on their farms will lead to a significant improvement in economic security. The package of products and services offered to this group may well be very different from that offered to other segments.

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Segmenting Victoria's farmers

Choosing to live on less to remain a farmer This choice to preference farming over income security is emphasized by the choices our farmers nominated for difficult times in their industry. Those on lower household incomes were no less likely to choose to survive by living on less than those with higher household incomes (Figure 12). Among the two low income segments, the off-farm dependent clearly favoured increasing their off-farm work: those in this group are used to adjusting their level of off-farm work as required by the performance of their farm. The low income farm dependent, in contrast, preferred to live on less. Generally, the farm dependent households exhibited less enthusiasm for off-farm work than the off-farm dependent. (The medium income off-farm dependent segment, being the smallest, may be exhibiting some data instability in this cross-tabulation.)

Figure 12 Response to a downturn by economic security segment

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The oldest farmers are the most willing to live on less Willingness to live on a lower income is strongly influenced by age. More than half of farmers aged 65 and over said they would prefer to make do with a lower income if their farm income reduced (Figure 13). Few favoured obtaining or increasing off-farm work. These older farmers would no longer have the expense of raising children and are likely to have lower income needs than younger farmers. Their priorities seem to involve being a farmer rather than having a high income. In contrast, among farmers aged under 55, less than 30% favoured living on less and about 40% favoured offfarm work. The even mix of choices made by these younger farmers indicates the diversity of motivations and identities held by farming families.

Figure 13 Response to a downturn by age

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Segmenting Victoria's farmers

Full-time farmers don’t want off-farm work The balance of farm and off-farm income makes a large difference to how people would respond during a downturn in farm incomes (Figure 14).For those who mostly earn off-farm income there is little incentive to increase off-farm work if farm income falls. A large fall in a very small farm income for these people would result in so little change in total household income that it would barely impact on family income security. Thus, the left hand column of the graph can be ignored. The important comparison is between those with a mix of farm and offfarm income (column 2, those with between 40% and 60% of their household income from farming) and those with only farm income (column 4). That comparison is stark. Those who live on a mix of farm income and off-farm work are the most willing to increase their off-farm work, suggesting they adjust their amount of off-farm work to maintain their total household income in the face of fluctuations in farm income. In contrast, those who farm fulltime, and have no off-farm work, do not want to cross a threshold and obtain any off-farm work at all. This suggests they see themselves as full-time farmers and do not want to become anything less than a full-time farmer, even if obtaining off-farm work would increase their income.

Figure 14 Response to a downturn by balance of farm and off-farm income

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Segmentation based on use of services and information Service use segmentation The separate evaluation project funded a number of questions about farmers’ use of services. The questions asked the farmers about 14 different kinds of services or information they might have used. For each kind of service or information, the farmers were asked whether they used it, and if so who was the main provider. They were also asked to evaluate each service they had used. In this analysis we are interested in three main providers, named in the questionnaire as “Department of Primary Industries”, “Paid consultant”, and “Retail outlet and field staff from product purchasers”. The third category of provider combines what we would term the ‘indirectly paid advisers’: whilst it mostly comprises people from retail outlets who sell inputs to farmers (e.g., agronomic advisers who work for rural merchandise retailers), it also includes people from firms that purchase the farmer’s product (e.g., field officers of dairy companies). We are interested in paid consultants and indirectly paid advisers because of their possible role in distributing DPI information to farmers. The 6-way segmentation was (Figure 15): •

Use neither DPI nor consultant nor retailer (36%);



Use DPI only (10%);



Use consultant only (12%);



Use retailer only (14%);



Use both consultant and retailer (9%);



Use DPI and either consultant or retailer or both (19%).

Figure 15 Segmentation based on use of services

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Segmenting Victoria's farmers

About one third of the farmers said that, among the 14 topics we asked about, they had obtained no services or information from DPI, paid consultants, or retailers or field staff. About another one third of the farmers said they used DPI as a main source of services or information on at least one of the topics. A further third of farmers used either or both of paid consultants or retailers or field staff as main information sources, but not DPI. DPI has the potential to expand its reach into this last third of farmers, but only to the extent that consultants and retailers take up information or services provided to them by DPI. The potential for DPI to expand into the first segment (those who use neither DPI nor paid consultants nor retailers) is limited because most of this segment (61%) said they found all their information themselves, and only 28 per cent of this segment used other farmers or farmer groups as a main source of any information.

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Information seeking behaviour of productivity segments Information seeking behaviour varies substantially by farm productivity expectations. Figure 16 shows the extent of farmer use of services or information provided by DPI, paid consultants, and retailers or field staff. The graph shows the mean number of services or information provided mainly by the different sources that the farmers had used. The general shape of the graph is the same as a graph of the proportion of farmers who had used any service provided by the sources. It is also similar in shape to a graph of the mean number of services used in total, from any source. Those selling out and phasing down have similar levels of service or information use, and those levels are about average, suggesting yet again that selling out and phasing down are dependent more on stage of life or circumstances than on financial scale. Among continuing farmers, the more their expansionary focus, the more they used consultants for information or services. The pattern of use of DPI information or services was slightly different: those not interested in productivity or who felt constrained from increasing productivity were low DPI users, whilst the two productivity oriented segments had high levels of DPI use. The pattern of use of information or services provided by retail outlets or field staff was similar to the pattern for DPI.

Figure 16 Use of services mainly provided by DPI, paid consultants and retailers by farm productivity expectations

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Segmenting Victoria's farmers

Only the largest scale farmers make substantial use of paid consultants The questions on the use of services provided only a limited ability to detect variation in farmer behaviour. For this reason we chose to review ABARES farm survey data on the average amount of money spent on professional fees by Victorian broadacre farmers of different scales. The ABARES data give much more accurate reporting of money spent buying private advisory services. Figure 17 shows trends in advisory expenditure for the four standard ABARES farm scales between 1990 and 2008 (in nominal dollars). Two points are apparent. First, those with larger farms spent much more on consultants than those with smaller farms. Second, there has been significant growth in consultant expenditure on large farms over the past decade.

3000

Advisory fees

2500

2000

1500 1000

500

20 08

20 06

20 04

20 02

20 00

19 98

19 96

19 94

19 92

19 90

0

Gross farm income LT $100k

$100k-$200k

$200k-$400k

GT $400k

Figure 17 Average annual amount spent on advisory fees by gross farm income, Victorian broadacre farms (ABARES data)

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A few large farms are responsible for more than half the expenditure on consultants The point that only large scale farmers make substantial use of paid consultants is made even more starkly in Figure 18, which depicts the proportion of expenditure on advisory services by Victorian broadacre farmers with different farm scales. Farmers with a gross farm income of $400,000 or more were responsible for almost 60 per cent of the expenditure on paid farm advisors.

70

% of advisory expenditure

60 50 40 30 20 10 0 LT $100k

$100k-$200k

$200k-$400k

GT $400k

Gross farm income

Figure 18 Percentage of expenditure on advisory fees by gross farm income, Victorian broadacre farms, 2009 (ABARES data)

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Segmenting Victoria's farmers

Segmentations by industry, social landscape and state government region Several existing segmentations are used by DPI to varying degrees. These include agricultural industry sector, social landscape and state government region. We have graphed each of our four new segmentations by each of these three existing segmentations. The graphs themselves are presented in appendices to avoid disrupting the narrative of this report. The industry graphs are in Appendix 5 (Figure 22 to Figure 25), the social landscape graphs are in Appendix 6 (Figure 26 to Figure 29), and the state government region graphs are in Appendix 7 (Figure 30 to Figure 33). Following is a brief description of the important points arising from the 12 graphs. Agricultural industry is basic to the way DPI engages with Victoria’s farm community and the structure of the department reflects this. The major industries vary substantially in their financial performance, farmer workload and contact with advisory services. Dairy is the strongest industry financially, with the largest proportion of high income households. The grains industry sits in second place. Beef, the industry with the lowest income, is buffered by having the highest proportion of farm households with substantial off-farm income. The sheep industry has the highest proportion of low income farm dependent households. The beef industry has the lowest use of services and information. Social landscape is a relatively recent addition to the way DPI understands Victoria’s rural sector and structures its engagement with rural families (Barr 2008). Production landscapes, where natural and social amenity is low and land use is dominated by production agriculture, contain the highest proportion of farmers who are oriented toward increasing farm productivity and expanding their operation. They also contain the highest proportion of high-income farmers. Production landscape farmers are the highest users of services and information. Farmers in high amenity landscapes are the most likely to consider taking off-farm work; they live in the landscape where such work is most readily available. The highest proportion of low-income farmers is in the transitional landscape. The segmentations broken down by state government region are harder to interpret, as there is either little difference between regions or seemingly random variation. State government regions are based on administrative convenience rather than agriculture or land use. Grampians has the highest proportion of farmers focused on increasing productivity or expanding. Gippsland has the highest proportion of low-income farmers. Metropolitan farmers are the least likely to use services or information from DPI or paid consultants.

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Discussion and implications Segmenting Victoria’s farmers It is not easy to segment Victorian farmers according to how they might respond to a new product or service. Farmers operate in dynamic complex systems and farming is both a social and entrepreneurial activity impacted on by a wide range of factors. There are countless ways to segment the farming population based on a large variety of variables. Segmentation is context dependent. Different scales of policymaking or intervention program require different segmentations (Landais 1998). Each new technology, product or service is different and the context in which farmers might consider adopting it is different. In other words, segmentation must be “fit for purpose”. A grand universal segmentation is unlikely to be useful. The most sustained attempt at a universal segmentation of Australian farmers involved the Dutch idea of farming styles, but was ultimately unsuccessful (Vanclay et al. 2006). That paper concluded that, while farming styles could be identified, they were not complete ways of farming but were the basis for stories by which farmers describe each other. They are thus of limited use for segmentation purposes. An alternative form of universal segmentation was attempted by Don Thomson (2002), based on the work of Austin et al. (1996). His styles, which were based on attitudinal data, lacked obvious meaning and Thomson chose not to label them, instead referring to them by numbers. Despite the lack of a genuine universal segmentation scheme, there are some commonalities among existing segmentations. A review of several pieces of research that involved building a typology of farmers based on their adoption characteristics suggested the typologies or segmentations contained several common types: a traditional or conservative type, a smallholder or hobby farmer type, a progressive type, a resource-limited type and a comfortable type (Emtage et al. 2006). In this study we have produced four different but related segmentations of Victoria’s farmer population. Our main approach is to understand farmers’ aspiration and capacity to invest in improving farm productivity, which the farmers in our survey saw as being closely related to improving their farm income. We do not consider individual productivity innovations, rather the aspiration and capacity to invest in future growth of farm productivity. The second approach is to understand financial well-being of farm families. Many farm families face a trade-off between investment and current consumption, but also know that investment in future income growth is necessary for future income security. The third segmentation involves the choices farmers make in times of reduced income from farming. Understanding these choices tells us a lot about their priorities and a little about their identity. In the fourth segmentation we develop an understanding of the use of DPI, consultant and retailer services by Victoria’s farmers. Importantly, the segmentations cut across each other rather than operating in parallel. The productivity expectations of the low income farm dependent segment are little different from the expectations of other segments. The low income farm dependent are no more likely to be willing to live on a lower income to remain farming than the high income farm dependent. This cross-cutting of segmentations reinforces the need for multiple segmentation schemes to fully understand the diversity of Victoria’s farmers. Other variables also segment Victoria’s farmers in ways that may be useful for informing service design and delivery. These variables include industry, age, region and social landscape. On their own, each of these variables provides a simple segmentation of Victoria’s farmers. Such simple, one-dimensional segmentations are useful, but only to a limited extent. The true power of these variables emerges when they are tabulated against each other. The most obvious finding from the research is the disparity between family farm aspirations and the capacity limitations of the majority of farm businesses. Many farmers with small-scale operations appear to have only limited off-farm income, yet almost half of them say they are interested in increasing their farm productivity, expectations that in many cases may be unrealistic.

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A second key finding is that plans to sell out or scale down are independent of property size. They are determined by age and personal or family circumstances. This is consistent with conclusions drawn from a detailed examination of ABS Population Census data by Barr (2004). What this survey shows that is undetectable in census data is that the average financial scale of farms decreases with age. It seems that older people on small farms are willing to remain in farming despite low incomes, whilst young people choose to enter farming only if the farm has sufficient scale to earn them a high income. This finding is neither new nor isolated: it has been observed at other times and in other countries (Barr et al. 1980; Clawson 1963).

The place of attitudes and landholder identity This report does not contain a segmentation that is based explicitly on farmers’ attitudes or personal identity. We did measure some attitudinal variables in the survey for this purpose, but the findings from them were inconclusive. We have produced segmentations that involve for the most part structural factors. One reason is that attitudes are only a partial influence on behaviour (Eagly and Chaiken 1993). Farmers’ attitudes are mediated by capacity. Our productivity aspirations segmentation has a component that is to some extent attitudinal, in that it uses farmers’ aspirations, yet for many farmers in our study those aspirations were clearly tempered by the realities of business scale, finance and other constraints. Another reason for using structural segmentations is that structural factors are easy to measure and, once measured, can be used to produce segmentations that can be used predictively. A segmentation based on intangible characteristics that are hard to measure may not be useful (Howden and Vanclay 2000). Our segmentations can be used without knowing the exact details of individual farmers. In contrast, an attitudinal segmentation requires a survey to determine farmers’ attitudes before they can be segmented. The traditional farmer identity is that of the yeoman, focused on lifestyle and tasks and seeing farming as a special occupation. There is a diversity of non-traditional identities, but generally people with non-traditional identities don't see farming as a special occupation (Bryant 1999). However, even commercial or production oriented landholders may have non-commercial or lifestyle motivations (Pannell and Wilkinson 2009). Even for commercial landholders, profit may be only a minor motivator, particularly in high amenity areas (Gentner and Tanaka 2002). Lifestyle and economic motivations are relatively independent (Maybery et al. 2005). The traditional “yeoman” identity and the non-traditional “entrepreneur” identity can co-exist and are not necessarily mutually exclusive (Austin et al. 1996). This suggests that attempts to build a typology or segmentation that separates the two identities may not work. Perhaps the key implication is that farmers' attitudes and motives are complex. Any simple typology risks over-simplifying the complexity of the farmer community.

Limitations The survey was conducted at the end of a period in which some agricultural industries had experienced low commodity prices and some areas of Victoria had experienced prolonged drought. Farm incomes of our respondents may well have been unusually low. However, not all industries experienced low prices, and not all parts of the state were in drought. Also, it could be argued that, for farming, there is never a “usual” state. Some respondents may have confused gross income with net income. ABARES Farm Survey data for 2010 are now available, and it would be worth comparing them with the responses to this survey to set the survey responses in context. In many places in this report, we have generalised across industries, across family sizes and types, and across cost structures. We were not able to determine in a brief survey the exact circumstances of each respondent farm family. Some respondents may therefore have been misallocated to particular segments. The segments generally

Segmenting Victoria's farmers

27

do not have clearly defined boundaries, and there is likely to be at least as much variation within each segment as between them. The purpose of our segmentations is not to be definitive but to explore trends and to provide a series of tools to stimulate thinking. In a survey of 1300 respondents there is some scope for cross-classifying segments. Many of the graphs in this report depict two-way splits of the respondents. In some cases we have had to adjust the categories into which one or both of the variables in a graph were collapsed to ensure sufficient numbers in each cell of the underlying cross-tabulation so that the variation depicted was meaningful. We have avoided reporting three-way splits, however, as the cell sizes were often too small to provide for meaningful analysis. To provide three-way splits (say, industry by age by aspirations) would require a much larger sample, of perhaps 3000 or 4000 respondents.

How might DPI use the segmentations? Segmentations are interesting, but only useful if they are a guide to the client’s objective, in this case, to assist in the design of better targeted, more accessible and more relevant services to farmers. It is only possible to take this next step if one has clarity as to the objectives of FSV, DPI and the government. The DPI Strategic Plan 2010–2013 describes a vision of “primary industry and energy sectors sustainably building Victoria’s wealth and wellbeing”, which is to be achieved through three headline outcomes of “competitive businesses and efficient markets”, “engaged, safe and responsible communities”, and “sustainably managed natural resources”. How these outcomes are to be turned into things done by DPI is less clear. Among the strategic priorities in the FSV 2010–11 and 2011–12 Business Plans are “driving agricultural sector productivity improvements in an environmentally sustainable manner” and “strengthening the capability of rural industries, communities and rural service providers to anticipate and respond to change”. The attempt to balance productivity improvement with environmental sustainability is clear, but the priority on social sustainability mentioned in the FSV Business Plan for 2009–10 has been removed, leaving community capability to respond to change as the only social priority with no indication of how it might be balanced against productivity improvement. As a step to understanding the balancing of the various investment objectives, it is worth considering how our segmentation analysis might apply to the overall DPI vision of sustainably building Victoria’s wealth and well-being by focusing separately on the two objectives of sectoral productivity and well-being.

Sectoral productivity as an objective Implication 1. A goal of increasing aggregate farm sector productivity would be best met by focusing extension efforts on larger scale farm operators. The objective of conventional utilitarian economics is to maximise the sum of individual utility (or relative satisfaction) within society. Utility itself is not measured, based on the argument that utility is not measurable and that the choices people make (and thus the preferences they reveal) provide all the information needed to infer utility. It then follows that disposable income can be a good proxy measure of utility. Based on this assumption, a policy objective of maximising gross income within society has traditionally been seen as a means of maximising utility within a society (Layard 2005). The farmers best able to increase their income are those with the largest scale. As part of another project related to this one, our research group has been exploring the relationship between farm scale and the capacity to fund investment in productivity. From that work we have concluded there is a benchmark of approximately $400,000 gross farm income above which a farm business will on average be capable of generating a sustainable income for an average family with no off-farm income. Australian literature on the relationship between farm scale and

28

Segmenting Victoria's farmers

productivity has generally shown that most productivity gains in Australian agriculture are captured by the largest 10 or 20 per cent of farms (Ha and Chapman 2000; Knopke et al. 1995). Within our sample this group is best represented by the high income farm dependent and the expansion oriented segments. If DPI were to maintain a focus on productivity gains as measured by an increase in overall sector productivity, then the most effective strategy would be to target these larger scale farmers, for several reasons. These farmers have the greatest aspiration and capacity to achieve productivity increases. Technologies with the greatest potential for productivity increases tend to be complex and involve transformation of farming systems, and these technologies favour large farms (Dolling and Wilkinson 2010; O'Donoghue et al. 2011). Also, dealing with larger scale landholders minimises transaction costs (Pannell and Wilkinson 2009). The largest 10 percent of Victorian farms produce about 50 percent of agricultural output (Barr 2005, pp. 4–5). Because grains and dairy farms tend to be larger in financial scale than farms in other industries, a focus on farm sector productivity would be effectively a choice to concentrate investment on the grains and dairy industries. Even if a choice to focus attention on farms of large financial scale were not a deliberate part of a productivity strategy, it would be an inevitable result. Currently it appears that DPI is focusing mainly on this ‘sectoral productivity’ strategy. The DPI Agriculture and Fisheries four-year strategy 2011–15 states that “DPI’s role is primarily economic” (April 2011 version, p. 36). Also, although many of DPI’s services concern natural resource management, they are directed toward sustaining the productive resource rather than conserving nature. One of DPI’s policy options is to adopt a model of supplying information and services to other providers, who would deal directly with farmers. These providers could include private consultants who are paid directly by farmers, as well as indirectly paid advisers such as agronomists employed by farm suppliers and merchandise firms. According to the survey, farmers in the expansion oriented segment already make more use of paid consultants than farmers in the other segments. According to ABARES Farm Survey data, the largest farm businesses spend about five times as much per business on commercial advice as other farm businesses, suggesting that engaging with private consultants as intermediaries would be a sensible strategy for supplying DPI information to large farms.

Well-being as an objective Implication 2. Relatively small increases in productivity on expansion but not scale oriented farms may yield a high return in the well-being of Victoria’s farm families, despite a relatively modest return in farm sector productivity. FSV’s focus on “driving agricultural sector productivity improvements in an environmentally sustainable manner” is not a goal in itself but a means of achieving DPI’s strategic vision of “primary industry and energy sectors sustainably building Victoria’s wealth and wellbeing”. A focus on well-being is not an alternative to a focus on productivity; rather, well-being is the outcome or ultimate objective of a focus on productivity. The assumptions behind conventional utilitarian economics of non-observability and of revealed preference as a proxy for utility are now under challenge (Kahneman 1994; Kahneman et al. 1997). The discipline of behavioural economics has approached the utilitarian objective by asking if there is a better way of measuring utility than just assuming income as a proxy measure. One approach has been to measure well-being and happiness and then explore the relationship of these measures with income (Layard 2005). Three interesting and important findings have emerged:

Segmenting Victoria's farmers

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Income does not appear to be linearly correlated with happiness or well-being. Increases in income have a greater impact on the well-being of those on a lower income than they do on higher income individuals (Inglehart and Klingemann 2000);



Income is a rivalrous commodity. A person’s well-being is influenced not only by their own income, but their perception of the incomes of those around them. An observable increase in relative income for a higher income earner can decrease the sense of well-being of those around them who do not share a similar income increase (Clark and Oswald 1996);



Income increases are subjected to the impacts of habituation. Humans rapidly adjust to income increases and so any gains in happiness and sense of well-being from an increase in income are eroded quickly (Easterlin 2001).

These three findings complicate the utilitarian objective that lies at the heart of conventional economics. The maximisation of gross income in society cannot be assumed to maximise utility within that society (Layard 2006). Social objectives that have been notoriously difficult to link to monetary measures of progress have been empirically linked to measures of social well-being and happiness using international cross-cultural studies (Helliwell 2003). This theoretical and empirical re-assessment of utility has implications for the objectives of a productivity-oriented service organisation like DPI. The DPI Agriculture and Fisheries four-year strategy 2011–15 states that “The DPI’s role is primarily economic … A focus on production and productivity is therefore central to the DPI’s work, but so too is a complementary focus on social outcomes including community resilience, public and employee safety, and community expectations for animal welfare and the natural environment” (April 2011 version, p. 36). How a focus on productivity might be complemented by a focus on community resilience is not clarified, nor is there any mention of how DPI might work to improve the well-being of rural Victorians beyond responding to emergencies. In the absence of any guidance, we suggest that the interaction between farm productivity and the well-being of rural Victoria is most likely to be found in thinking about income security of farm households. The policy objective of increasing productivity to increase the gross income of Victoria is a policy founded in the classical economic paradigm. But increased productivity may not have the same utility pay-off across all segments of the Victorian farm sector. Different segments may gain quite different returns in well-being from the same investment in productivity. Our segmentation study has revealed a significant group of farm households that are interested in increasing the productivity of their farm but not its land area or water allocation. This is the productivity but not scale oriented segment, which constitutes almost one third of Victoria’s farmers. The productivity but not scale oriented can be found across all financial scales, but almost half of them are low income farm dependent or medium income farm dependent. Relatively small increases in productivity on these farms may yield a high return in improved household income security and well-being across a substantial proportion of Victoria’s farm families, despite a relatively modest return in measures of farm sector productivity. How DPI might involve private sector service providers in supplying information to farmers in the productivity but not expansion oriented segment is less clear than for expansion oriented farmers. ABARES Farm Survey data suggest farms of small and medium business scale have minimal contact with private consultants, so a strategy of engaging with private consultants is unlikely to increase DPI’s reach among such farmers. A strategy of supplying DPI information to retail outlets such as farm suppliers and merchandise firms has some potential to increase DPI’s market penetration among the productivity but not expansion oriented segment, as some operators of farms in this segment deal with private sector service providers but not with DPI. This strategy needs to be considered carefully. Many of DPI’s products and services are complex and not related to saleable products, and these firms would appear to have no financial incentive to offer such products. To maximise market penetration within this

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Segmenting Victoria's farmers

segment and assure the independence of its information, DPI would need to supply information to multiple farm supply and merchandise firms. An answer to the question of which DPI products might be suited to which retailers would require further research. The best channels for conveying services and information to the productivity but not scale oriented segment may vary depending on the product. This segment is likely to require a different service offer from the expansion oriented, because its technology needs will probably be different. Data from the questions in this survey on use of services are neither sufficiently robust nor detailed to match private sector providers to products. In-depth qualitative research is needed. The most useful approach would be along the lines of the farmer decision-making study by Phillips (1985). We would recruit a small number of farmers who fit the productivity but not scale oriented segment and follow their decision-making process for different kinds of adoption decisions. This would allow us to identify their sources of information and advice at different stages of the decision-making process, the purpose for which they obtain each piece of information or advice, how they evaluate the quality of each piece of information or advice, how they evaluate each provider of information or advice, and the extent to which each provider is trusted. We would then be in a position to suggest which kinds of DPI products or services might be able to be supplied to farmers through indirectly paid advisers (and which advisers in particular), which products and services would need to be supplied through private consultants, and whether any DPI products or services were not amenable to being supplied to farmers though private sector providers at all. The degree to which a farmer trusts a non-DPI provider of DPI information may be critical to the choice of private sector provider: an adviser who is not trusted can expect to be only a provider of information that is later evaluated within a closer circle of trusted contacts and advisers (Anderson 1981).

Reconciling sectoral productivity and well-being Implication 3. Many farmers in the productivity-oriented but capacity-constrained segment are receptive to a productivity message but such a message would need to be tailored to their needs and constraints. It may be that the reconciliation of well-being and productivity objectives can be found in the provision of services to the productivity constrained segment. These farmers can be found among all financial scales and among both the farm dependent and off-farm dependent, but the largest proportion of them are low income farm dependent. They are still receptive to a productivity message but their ability to act on such a message is constrained. The productivity constrained segment presents a challenge to DPI to point to practical pathways towards increased productivity and income generation within the constraints of these businesses. This will require a whole-of-farm system approach and a conscious effort to understand the constraints of these farms and communicate pragmatic investment pathways towards improved farm performance. There are several questions to be answered before DPI can engage effectively with the productivity constrained. The first question to ask is whether it is feasible for these businesses to improve their productivity. Our assessment of Farm Monitor data suggests the monitor farms demonstrate better than average conversion of capital into receipts and income. The self-selected monitor farms are performing better than average for their size, despite sharing the same limitation of small scale and capital base. Thus, there does seem to be some scope for productivity increases on productivity constrained farms. The next question is whether there are feasible paths available for the farms in this segment to increase their productivity and incomes within the constraints of their limited resources. We do not have an answer to this question, but we can point to two projects that suggest an answer. The Farm Monitor project contains valuable data to help identify the strategies that make a difference. The Dairy Directions project within Future Farming Systems Research is attempting to explore pragmatic pathways towards improved farm performance in the dairy sector. Finally, the low income farm dependent segment is most strongly represented within the sheep industry.

Segmenting Victoria's farmers

31

The BESTWOOL/BESTLAMB program is an obvious vehicle to trial a targeted approach within the sheep industry. To ensure any services provided to productivity constrained farmers were relevant to their needs, DPI would need to indentify and clarify their constraints. Data from the present survey are not sufficient to answer this question. Further research is required. The best approach would be to identify a small number of farmers in the productivity constrained segment and interview them in depth. We would ask what they thought was constraining them and what options they felt were open to them. Farmers in this segment in different industries and at different scales may well face different constraints. We would then consider whether the constraints faced by different farmers might be overcome and what would be required to overcome them. This would enable us to suggest a service offer for productivity constrained farmers that was appropriate to their constraints and needs.

Would a focus on well-being slow agricultural restructuring? Implication 4. Assisting the small scale operators is unlikely to slow or impede agricultural restructuring. There has been a long-held view that improved productivity in agriculture requires the operators of lowerperforming farms within the sector to exit to make way for the growth of the higher performers (Anderson 1972, p. 276). It follows then that any attempts to assist the operators of these farms will merely prolong their stay and impede restructuring. Policies should not mute the economic signals that these farmers’ labour would be better employed in other sectors of the economy where wages are higher and their own well-being would be enhanced. This is the logic behind the Commonwealth Drought-Preparedness Pilot Program. The use of farm financial planning and mutual obligation requirements seems designed to force a confrontation with the reality of low farm performance and limited viability. The expectation seems to be that this will stimulate some form of sensible adjustment to the new realities of farming and increased exit from farming or uptake of off-farm work. This argument is reasonable in theory, but it is not an argument against providing modified productivity services to the productivity constrained segment or the low income farm dependent segment. The lesson of numerous restructuring and adjustment programs is that governments have generally been unable to influence decisions to exit farming (Botterill 2001; Gregory 1972; McColl et al. 1997, p. 71). Re-settlement and re-training grants generally compensate farmers only for economic losses, not social losses, yet it is the social losses that are likely to prevent farmers from leaving their farm (Burton 2004; Core 1973, p. 159). The evidence of our survey is that there is no difference in the planned exit rates of operators of small or large farms. Unless the situation is extreme, exit from farming is generally a decision that is influenced by stage of life rather than economic circumstances. Further, one of our segmentations makes it clear that many operators are willing to bear the burden of lower incomes to remain in farming. Given this behaviour, the provision of assistance to improve farm performance is unlikely to have any impact on sectoral restructuring but may improve the welfare of those who choose to remain in farming until they are ready to retire. Dealing with large scale farmers through private consultants may well release some capacity that could be used to provide services and information to these smaller scale farmers.

Conclusion DPI is a productivity oriented organisation and its main extension messages are around increasing productivity in an environmentally sustainable manner. Yet this survey shows that a large number of Victoria’s farmers do not have sufficient scale to fully capture productivity increases. Although the provision of DPI services to these farmers is likely to provide a lesser return in aggregate farm sector productivity than providing services to the very

32

Segmenting Victoria's farmers

largest scale farmers, it may well provide a greater return in total well-being of Victoria’s farm sector. As the DPI Strategic Plan makes clear, increased wealth and well-being is the purpose of promoting productivity increases. Another substantial segment of farmers is interested in productivity but feels constrained by scale and unable to respond by increasing productivity in the same way as large-scale farmers. An extension message that promotes productivity to these farmers would need to be modified to allow for their constraints. The state government’s aims for Victoria’s farm sector may be better served by broadening DPI’s extension message from one focused mainly on total factor productivity in the farm sector as a whole to one that more explicitly addresses other aims such as resilience and well-being. The most effective use of the segmentation schemes we develop is likely to involve differentiation not only of the extension medium, but also of the message. Discussion within DPI is needed to determine how the findings from this research are best used. Our segmentations do not represent definitive ways to view Victoria’s farmer population. Instead, they are tools that provide a framework for DPI to think about how it might best work with all parts of the farming community for the betterment of Victorian agriculture and the wealth and well-being of Victoria as a whole. DPI has an opportunity to think positively about the design and delivery of its services. This report is one step in the journey.

Segmenting Victoria's farmers

33

References Anderson AM (1981) Farmers' expectations and use of agricultural extension services. Hawkesbury Agricultural College, Richmond, NSW. Anderson R (1972) 'Crisis on the land.' (Sun Books: Melbourne). Austin EJ, Deary IJ, Gibson GJ, McGregor MJ, Dent JB (1996) Attitudes and values of Scottish farmers: "yeoman" and "entrepreneur" as factors, not distinct types. Rural Sociology 61, 464–474. Barr NF (2004) The micro-dynamics of occupational and demographic change in Australian agriculture: 1976–2001. Report No. 2055.0. Australian Bureau of Statistics, Canberra. Barr NF (2005) 'The changing social landscape of rural Victoria.' (Department of Primary Industries: Tatura). Barr NF (2008) The social landscapes of rural Victoria. In 'Landscape analysis and visualisation: spatial models for natural resource management and planning'. (Eds C Pettit, W Cartwright, I Bishop, K Lowell, D Pullar, D Duncan) pp. 305–325. (Springer-Verlag: Berlin). Barr NF, Weston RE, Cary JW (1980) Farmers looking to the future: labour mobility and adjustment strategies in the 1970's. School of Agriculture and Forestry, University of Melbourne, Parkville, VIC. Botterill L (2001) Rural policy assumptions and policy failure: the case of the re-establishment grant. Australian Journal of Public Administration 60 (4), 9–16. Bryant L (1999) The detraditionalization of occupational identities in farming in South Australia. Sociologia Ruralis 39, 236–261. Burton RJF (2004) Seeing through the 'good farmer's' eyes: towards developing an understanding of the social symbolic value of 'productivist' behaviour. Sociologia Ruralis 44, 195–215. Clark AE, Oswald AJ (1996) Satisfaction and comparison income. Journal of Public Economics 61, 359–381. Clawson M (1963) Aging farmers and agricultural policy. Journal of Farm Economics 45, 13–30. Core PT (1973) Retraining and off-farm migration rates. Wool Adjustment Study Final Report No. 2. Department of Agricultural Economics and Business Management, University of New England, Armidale, NSW. Dolling PJ, Wilkinson RL (2010) Farmers’ experiences with lucerne in Western Australia. Paper presented to the Australian Agronomy Conference, Lincoln, New Zealand, 15–19 November. Eagly AH, Chaiken S (1993) 'The psychology of attitudes.' (Harcourt Brace Jovanovich: Fort Worth, Texas). Easterlin RA (2001) Income and happiness: towards a unified theory. The Economic Journal 111, 465– 484. Emtage N, Herbohn J, Harrison S (2006) Landholder typologies used in the development of natural resource management programs in Australia — a review. Australasian Journal of Environmental Management 13, 79–94. Gentner BJ, Tanaka JA (2002) Classifying federal public land grazing permittees. Journal of Range Management 55, 2–11.

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Gregory GNF (1972) A Pommie's reaction to the Australian rural recession. Progress report to the Australian Wool Board. University of New England, Armidale, NSW. Ha A, Chapman L (2000) Productivity growth trends across Australian broadacre industries. Australian Commodities 7, 334–340. Helliwell JF (2003) How's life? Combining individual and national variables to explain subjective wellbeing. Economic Modelling 20, 331–360. Howden P, Vanclay F (2000) Mythologisation of farming styles in Australian broadacre cropping. Rural Sociology 65, 295–310. Inglehart R, Klingemann H-D (2000) Genes, culture, democracy and happiness. In 'Culture and subjective well-being'. (Eds E Diener, EM Suh) pp. 165–183. (MIT Press: Cambridge, MA). Kahneman D (1994) New challenges to the rationality assumption. Journal of Institutional and Theoretical Economics 150, 18–36. Kahneman D, Wakker PP, Sarin R (1997) Back to Bentham? Explorations of experienced utility. The Quarterly Journal of Economics 112, 647–661. Knopke P, Strappazzon L, Mullen J (1995) Productivity growth: total factor productivity on Australian broadacre farms. Australian Commodities 2, 486–497. Landais E (1998) Modelling farm diversity: new approaches to typology building in France. Agricultural Systems 58, 505–527. Layard R (2005) 'Happiness: lessons from a new science.' (Allen Lane: London). Layard R (2006) Happiness and public policy:a challenge to the profession. The Economic Journal 116, C24–C33. Maybery D, Crase L, Gullifer C (2005) Categorising farming values as economic, conservation and lifestyle. Journal of Economic Psychology 26, 59–72. McColl JC, Donald R, Shearer C (1997) Rural adjustment: managing change. Mid-term review of the Rural Adjustment Scheme. Department of Primary Industries and Energy, Canberra. O'Donoghue EJ, Hoppe RA, Banker DE, Ebel R, Fuglie K, Korb P, Livingston M, Nickerson C, Sandretto C (2011) The changing organization of U.S. farming. Economic Research Service Report No. EIB-88. U.S. Department of Agriculture, Washington, D.C. Pannell DJ, Wilkinson R (2009) Policy mechanism choice for environmental management by noncommercial “lifestyle” rural landholders. Ecological Economics 68, 2679–2687. Phillips TI (1985) The development of methodologies for the determination and facilitation of learning for dairy farmers. Master of Agricultural Science thesis, University of Melbourne. Thomson D (2002) Understanding diversity in farming behaviour using "farming styles". Wool Technology and Sheep Breeding 50, 280–286. Vanclay F, Howden P, Mesiti L, Glyde S (2006) The social and intellectual construction of farming styles: testing Dutch ideas in Australian agriculture. Sociologia Ruralis 46, 61–82.

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Appendix 1. Method Design •

The survey was combined with one by the DPI evaluation group as part of an ongoing evaluation of DPI service delivery. Both sets of questions were asked in the one questionnaire instrument. Questions 8 to 13 were developed by Open Mind Research Group for the evaluation survey.



The segmentation questions were initially designed by experienced social researchers Roger Wilkinson, Neil Barr and Carole Hollier (Service Design, FSV).



We commissioned Open Mind Research Group to manage the survey.



Questionnaire design was informed by a review of farmer segmentation research (the SeScan annotated bibliography produced by Roger Wilkinson), qualitative focus-group research conducted by Open Mind, and the extensive farmer survey experience of the DPI research team.

Sampling •

Sample size: 1300 farmers.



Source of sample: FARMbase, owned by Axiom Australasia Pty Ltd. This is the most comprehensive database of Australian farmers available, and is widely used in market research surveys.



Respondents: the person mainly responsible for making decisions regarding the daily operations of the farm.



Quotas were set for each of 6 age groups and the 4 industry sectors (meat and wool, dairy, grains, horticulture) to ensure sufficient responses from each cell.



Sample was weighted by industry and gross farm income to ensure it reflected accurately the composition of Victoria’s farmer population.

Administration

36



The survey was administered by telephone, which is the most cost-effective way to obtain accurate responses.



Open Mind subcontracted administration of the survey to Market Metrics, who conducted all interviews from their call centre in Frankston.



Roger Wilkinson briefed the interviewers in person prior to commencement of the survey and was present in the call centre during the first evening of calling.



Following pilot testing of the questionnaire on the first evening, some minor amendments were made to the questionnaire to shorten and tighten it, and improve flow and language. These amendments were agreed between the DPI research team, Open Mind and Market Metrics.



Average interview length was about 20 minutes, which is as long as is generally recommended for telephone interviews.



Interviews were conducted during June and July 2010.

Segmenting Victoria's farmers

Analysis •

Open Mind produced a basic report, but detailed analysis has been done by Roger Wilkinson and Neil Barr.



Margin of Error (95% confidence interval) in percentage points: whole sample 3%, most age groups about 5%, industry sectors between 5 and 8%.

Reporting •

We conducted an extensive series of briefings for DPI staff, to test our initial findings and interpretations, and seek further ideas and insights.

Segmenting Victoria's farmers

37

Appendix 2. Questionnaire DEPARTMENT OF PRIMARY INDUSTRIES FARMING SURVEY QUESTIONNAIRE INTRODUCTION Good morning \ afternoon \ evening. My name is .... (NAME) and I am calling on behalf of the Open Mind Research Group. We are conducting a research project on behalf of the Victorian Department of Primary Industries. The research will help DPI better understand different types of landholders, this information will be used to improve service delivery. IF YES CONTINUE OR MAKE APPOINTMENT TO RING BACK Could I please speak to the property owner or manager responsible for making decisions regarding the daily operations of the farm? IF NOT PRIMARY DECISION MAKER, ASK TO SPEAK TO THE PERSON WHO IS/WOULD BE REPEAT INTRODUCTION, MAKE APPOINTMENT OR TERMINATE WITH THANKS WHEN SPEAKING TO RELEVANT PERSON, REPEAT INTRODUCTION The survey should take about 20 minutes of your time. If you’re willing to participate in this survey, could I please start with your first name? This survey is carried out in compliance with the Privacy Act, the information and opinions you provide will be used only for research purposes. Your responses will be treated in the strictest confidence. The responses of everyone who participates in the survey will be combined and no individual information will be made available. This survey may be monitored for quality control. If you do not wish for this to occur please let me know.

SCREENING QUESTIONS S1

Which of the following describes your role on the property? READ OUT SINGLE RESPONSE 1 2 3 97

Owner & Manager Manager Family Member Other (specify)

IF OTHER (CODE 97), TERMINATE WITH THANKS S2

Can I confirm that you are/would you be the person mainly responsible for making decisions regarding the daily operations of the farm? DO NOT READ OUT SINGLE RESPONSE 1 2 99

Yes No Don’t know/can’t say

IF NOT PRIMARY DECISION MAKER (CODE 2 or 99), ASK TO SPEAK TO THE PERSON WHO IS THE PRIMARY DECISION MAKER

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Segmenting Victoria's farmers

QUOTAS S3

So that we can ensure that we speak to a broad cross-section of people, could you please tell me which of the following age groups you fall into? READ OUT SINGLE RESPONSE 1 2 3 4 5 6 7 8 9

Under 18 years 18-24 years 25-34 years 35-44 years 45-54 years 55-64 years 65-74 years 75+ years DO NOT READ Refused

CODES 1 AND 9 TERMINATE WITH THANKS NEED A MINIMUM OF N=50 IN EACH AGE GROUP S4

And could you please tell me your postcode? SINGLE RESPONSE RESTRICT TO 4 DIGITS _________________

S5.

Of the following, which is the most accurate description of your MAIN farm enterprise? READ OUT SINGLE RESPONSE 1 2 3 4 5 6 7 8 9 97 99

Sheep for wool Sheep for meat Beef cattle Dairy cattle Broad acre cropping – grains Mixed – cropping and grazing Horticulture Forestry No primary commercial production Other (specify) Don’t know/can’t say

IF NO PRIMARY COMMERCIAL PRODUCTION (CODE 9), TERMINATE WITH THANKS CHECK QUOTAS

CONTEXT I am now going to ask you some questions about the farm you currently own and/or manage … Q1b.

What is the total area of the farm you manage, including any land that you lease or rent from others? (INTERVIEWER NOTE: This does not include any land that you lease or rent to others) RESPONDENT TO ANSWER IN EITHER ACRES OR HECTARES SINGLE RESPONSE ENTER NUMBER __________________ Acres OR __________________Hectares

Segmenting Victoria's farmers

39

ASK ALL Q2a Do you currently hold an irrigation water entitlement? DO NOT READ OUT SINGLE RESPONSE 1 2 98 99

Yes No Don’t know/unsure Refused

ASK IF YES AT Q2a Q2b In megalitres, how big is your irrigation water entitlement? ____________

PERCEPTIONS AND ATTITUDES TO FARMING ASK IF OWN FARM (CODE 1 ON S1) Q3a. I am going to read out a list of various things that other farmers have said they are planning to do in the next five years. On a 10 point scale where 1 is not at all likely and 10 is highly likely, can you tell me how likely it is that you will do each in the next 10 years? READ OUT SINGLE RESPONSE PER ROW ROTATE ROWS

40

A

The entire farm will be sold

Not at all likely 1 2 1 2

B

Part of the farm will be sold

1

2

3

4

5

6

7

8

9

10

98

C

Ownership of the farm will stay within the family

1

2

3

4

5

6

7

8

9

10

98

D

All or most of the farm will be leased out or worked by a share farmer

E

We will purchase, lease or share farm additional land

F

The enterprise mix will be changed to reduce my farm workload

1

2

3

4

5

6

7

8

9

10

98

G

The enterprise mix will be changed to more intensive enterprises

1

2

3

4

5

6

7

8

9

10

98

H

I will seek additional off-farm work

1

2

3

4

5

6

7

8

9

10

98

I

I will reduce the extent of my off-farm work

1

2

3

4

5

6

7

8

9

10

98

Segmenting Victoria's farmers

3 3

4 4

5 5

6 6

7 7

8 8

9 9

Highly likely 10 10

N/A 98

ASK ALL Q4a. Which of the following options would be the MOST appealing to you when faced with lower farm income? READ OUT SINGLE RESPONSE FOR EACH ROW 1 Myself or someone else in my family picks up off-farm work, or increases their off-farm work 2 I stop or cut back on investments in farm improvement or expansion 3 We make do with a lower income 98 None of the above 99 Unsure/ Don’t know Q4b.

Which of the following options would be the LEAST appealing to you when faced with lower farm income? READ OUT SINGLE RESPONSE FOR EACH ROW 1 Myself or someone else in my family picks up off-farm work, or increases their off-farm work 2 I stop or cut back on investments in farm improvement or expansion 3 We make do with a lower income 98 None of the above 99 Unsure/ Don’t know

Q5.

I’m going to read out a list of things some farmers have told us about their farming operations. For each I would like you to indicate how much you personally agree on a scale of 1 to 10, where 1 is completely disagree and 10 is completely agree. READ OUT NGLE RESPONSE FOR EACH ROW ANDOMISE ROWS Completely Completely disagree agree 1 2 3 4 5 6 7 8 9 10 PLANNING A It is critical to plan ahead 1 2 3 4 5 6 7 8 9 10 when managing my farm B I need a written business plan 1 2 3 4 5 6 7 8 9 10 to guide my farm management BUSINESS C Increasing the profitability or 1 2 3 4 5 6 7 8 9 10 net worth of my farm is critical D Farming is a business just like 1 2 3 4 5 6 7 8 9 10 any other FINANCE E I am likely to borrow heavily to finance increasing the size of 1 2 3 4 5 6 7 8 9 10 my farm F I am likely to borrow heavily to finance diversifying my 1 2 3 4 5 6 7 8 9 10 farming operation KNOWLEDGE G I need more information to 1 2 3 4 5 6 7 8 9 10 better manage my farm TECHNOLOGY H I prefer to leave experimenting 1 2 3 4 5 6 7 8 9 10 with new ideas to someone else I I am always one of the first in 1 2 3 4 5 6 7 8 9 10 the district to try something new TRADITION J I farm because I am committed to the tradition in 1 2 3 4 5 6 7 8 9 10 our family PREFERRED WORK K I much prefer looking after the management of the farm than 1 2 3 4 5 6 7 8 9 10 the manual labour

Segmenting Victoria's farmers

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Q6.

Can you tell me which of the following statements best describes you and your approach to farming? READ OUT SINGLE RESPONSE ROTATE STATEMENTS I tend to experiment with the way I run my farm as I want to try innovative ways of farming and I like to stay up to date with the latest research While I've been exposed to farming over a long period I think it is important to trial new methods of farming. I value going to training programs and am involved with my local farming community I am open to new ideas but would only start using new practices once I have seen clear evidence of its effectiveness, while I like being a part of the farming community, I would not call myself an active member My experience and history of farming have given me the know how I need, I know what works and what doesn't and would prefer to stay true to my knowledge

Q7a.

B C D E F G

2 3 4

I’m now going to read out some things that other farmers have said are important to them. For each, could you please tell me how important it is to you personally on a scale of 1 to 10 where 1 is not at all important to me and 10 is extremely important to me. READ OUT SINGLE RESPONSE FOR EACH ROW ROTATE ROWS Not at all important to me 1 2

A

1

Increasing the productivity of the farm Increasing your land area Increasing your farming knowledge Increasing your farm income Increasing your off-farm income Increasing the asset value or net worth of your farm Leading a healthy open air life

3

4

5

6

7

8

Extremely important to me 9 10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

ASK Q7b FOR STATEMENTS A – F RATED AS 8-10 IN Q7a Q7b On a scale of 1 to 10 where 1 is not at all likely and 10 is extremely likely could you please tell me how likely it is that you will in the next 5 years? And, again on the same scale, how likely is it that you will … etc. READ OUT SINGLE RESPONSE FOR EACH ROW ROTATE ROWS Not at all likely 1 2 A B C D E F

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Increase the productivity of the farm Increase your land area Increase your farming knowledge Increase your farm income Increase your off-farm income Increase the asset value or net worth of your farm

Segmenting Victoria's farmers

3

4

5

6

7

8

Extremely likely 9 10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

1

2

3

4

5

6

7

8

9

10

AWARENESS OF SERVICES Q8.

What services and/or information have you used in the last 12 months to help make decisions in managing your farm and/or your land? DO NOT READ OUT MULTIPLE RESPONSE Productivity improvement, including producing more crops, pasture meat, wool, milk, etc Adapting to climate variability Sustainability, including soil management, erosion control, salinity, revegetation Chemical use Animal welfare Weeds and pest animals, e.g. rabbits, foxes, wild dogs Support for the preparation or recovery from natural disasters Pests and diseases of crops or livestock Efficient water usage or irrigation management Marketing information Farm business decision support such as adapting to social, economic, environmental and technological change Quality assurance systems Farmer health and safe work practices Whole Farm planning Other (specify) None Don’t know/unsure

Q9b.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 97 98 99

I’m now going to ask you if you have used information or services related to the following areas… READ OUT SINGLE RESPONSE FOR EACH ROW RANDOMISE ROWS

A B C D E F G H I J K L M N

Productivity improvement, including producing more crops, pasture meat, wool, milk, etc Adapting to climate variability Sustainability, including soil management, erosion control, salinity, revegetation Chemical use Animal welfare Weeds and pest animals, e.g. rabbits, foxes, wild dogs Support for the preparation or recovery from natural disasters Pests and diseases of crops or livestock Efficient water usage or irrigation management Marketing information Farm business decision support such as adapting to social, economic, environmental and technological change Quality assurance systems Farmer health and safe work practices Whole Farm planning

Used

Not used

N/A 98

1

2

1

2

98

1

2

98

1 1

2 2

98 98

1

2

98

1

2

98

1 1 1

2 2 2

98 98 98

1

2

98

1 1 1

2 2 2

98 98 98

Segmenting Victoria's farmers

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ASK Q9c AND Q9d FOR EACH SERVICE USED AT Q9b Q9c. What was the MAIN way you got the service or information you used for ? READ OUT SINGLE RESPONSE FOR EACH ROW RANDOMISE ROWS Department of Primary Industries Paid consultant Retail outlet and field staff from product purchasers Farming, industry or community groups, including other farmers Found it myself (e.g. looked on internet, newspapers, training and seminars) Other (specify) Unsure

Q9d.

A B C

1 2 3 4 5 97 99

On a scale of 1 to 10 where 1 is completely disagree and 10 is completely agree, how much do you agree with the following statements about the you used in the last 12 months? READ OUT SINGLE RESPONSE FOR EACH ROW RANDOMISE ROWS

It was relevant to my farm I found it easy to get the information or service It was very important for the operation of my farm

Completely disagree 1 2 1 2

3 3

4 4

5 5

6 6

7 7

8 8

Completely agree 9 10 9 10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

ASK ALL Q10. Who or what sources do you trust the most for accessing information or support for helping you operate your farm business? DO NOT READ OUT MULTIPLE RESPONSE Fee-for-service agronomist, farm adviser or consultant Retail agronomist Other farmers Farmers groups or forums Rural weeklies or other rural media Department of Primary Industry Other government department Accountant Other (specify) None Unsure

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Segmenting Victoria's farmers

1 2 3 4 5 6 7 8 97 98 99

Q11a.

Are you aware of any information, products or services that the Victorian Department of Primary Industries provides to farmers? DO NOT READ OUT SINGLE RESPONSE 1 2 97 99

Yes No Refused Don’t know

ASK IF AWARE OF DPI PRODUCTS OR SERVICES (CODE 1 AT Q11a) CONTINUE Q11b. Could you tell me what DPI information, products and services you are aware of? PROBE FULLY _____________________________________________________________________________________________ _____________________________________________________________________________________________ _________________________________________________________________________________ ASK ALL Q12a. Have you heard of the Victorian Department of Primary Industries working with private providers (such as fee for service consultants, agronomists, advisers associated with rural merchandise stores, or industry associations, farmer groups etc) to provide any information, products or services for farmers? DO NOT READ OUT SINGLE RESPONSE 1 2 97 99

Yes No Refused Don’t know

ASK IF AWARE OF DPI COLLABORATIONS (CODE 1 AT Q12a) CONTINUE Q12b. Could you tell me about the products, information or services that you are aware of provided by DPI working with private providers? PROBE FULLY _____________________________________________________________________________________________ _____________________________________________________________________________________________ _________________________________________________________________________________ ASK ALL Q13. Overall, how would you rate the Department of Primary Industry’s performance in working with private consultants and providers to ensure farmers are receiving the information, support and services they need. Please answer on a 10 point scale where 1 is extremely poor and 10 is extremely good. READ OUT SINGLE RESPONSE FOR EACH ROW RANDOMISE ROWS Extremely poor 1

2

3

4

5

6

7

8

9

Extremely good 10

DK 99

Segmenting Victoria's farmers

45

DEMOGRAPHICS Finally to ensure we have spoken to a wide range of people I’ll ask you some questions about yourself and the farm you own/manage… D1

What is the highest level of formal education you have completed? READ OUT SINGLE RESPONSE 1 2 3 4 5 6 97 99

D2

Year 9 or below Year 10 or 11 Year 12 or high school equivalent TAFE certificate or diploma Bachelors degree Postgraduate qualification Refused Can’t say

RECORD GENDER SINGLE RESPONSE 1 2

D3

Which of the following best describes your farm business structure? READ OUT SINGLE RESPONSE 1 2 3 4 5

D4

Sole trader Family partnership Family trust Family company or private company Public company

In total, how many years have you been involved with farming? This includes working on, managing and owning farms. DO NOT READ OUT SINGLE RESPONSE 1 2 3 4 5 99

46

Male Female

0-2 years 3-5 years 6-10 years 11-20 years More than 20 years Don’t know/can’t say

Segmenting Victoria's farmers

D6 Over the past 5 years, could you estimate the average annual total gross income from your farm operations? [INTERVIEWER NOTE: If someone has owned/managed their farm for less than 5 years, this should be the average of the years they have been in operation] READ OUT SINGLE RESPONSE 1 $0 - $25,000 2 $26,000 to $50,000 3 $51,000 to $100,000 4 $101,000 to $200,000 5 $201,000 to $300,000 6 $301,000 to $400,000 7 $401,000 to $500,000 8 $501,000 to $1 million 9 More than $1 million 97 DO NOT READ Refused 99 DO NOT READ Don’t know D7

Could you estimate the percentage of your household income that is generated by your farming operation? SINGLE RESPONSE 0% 0

10% 1

20% 2

30% 3

40% 4

50% 5

60% 8

70% 7

80% 8

90% 9

100% 10

CLOSE Just to remind you, my name is ... (NAME). As this is market research, it is carried out in compliance with the privacy act and the information provided will only be used for research purposes. We are conducting this research project on behalf of the Victorian Government Department of Primary Industries. If you would like details about privacy or phone numbers to contact the Open Mind Research Group, I can give you those now. Would you like them? IF “YES”, CLARIFY IF PRIVACY OR PHONE NUMBERS AND READ APPROPRIATE SCRIPT BELOW. PRIVACY Your phone number was drawn randomly from a list of farmers contact details. Your personal details will be removed from your responses in about two weeks. Within this time, however, you may request that your personal details be deleted. PHONE NUMBERS If you have a pen and paper handy, the numbers are: Market Research Society: 1300 364 830 Open Mind (Gary Colquhoun): (03) 9662 9200

Thank you very much for your time and assistance, ...(RESPONDENT NAME). DID THE RESPONDENT WISH TO HAVE THEIR DETAILS REMOVED IMMEDIATELY?

1 2

PROG NOTE: - SINGLE RESPONSE Yes No I certify that this is a true, accurate and complete interview, conducted in accordance with industry standards and the AMSRS Code of Professional Behaviour (ICC\ESOMAR). I will not disclose to any other person the content of this questionnaire or any other information relating to this project.

1 2

PROG NOTE: - SINGLE RESPONSE Accept Not accept

Segmenting Victoria's farmers

47

Appendix 3. Profile of surveyed farmers The sample was weighted by industry and gross farm income to represent the balance of farmers in the 2006 Agricultural Census. The industry and gross farm income profiles of the weighted sample are thus identical to the profiles of Victoria’s farmer population. Figure 19 shows the industry profile of the weighted sample. The beef industry contains the largest proportion of farmers in the sample, but many of them are very small scale businesses

30.0 25.0

Percent

20.0 15.0 10.0 5.0 .0 Dairy

Sheep

Beef

Cropping Industry

Figure 19 Industry profile of weighted sample (n=1300)

48

Segmenting Victoria's farmers

Horticulture

Other

Combining the industry profile of the weighted sample with the gross farm income profile shows that half the farmers in the weighted sample had an average annual gross farm income of $100,000 or less (Figure 20). Among these smallest scale farms, beef predominates. The sheep industry is also disproportionately represented among small scale farms. In contrast, dairy and cropping farms tend to be larger in scale. (The number of farmers in this figure is lower than the whole sample because many farmers said they did not know their gross farm income.)

Figure 20 Gross farm income profile of weighted sample by industry (n=1071)

Segmenting Victoria's farmers

49

Although the sample could not be weighted by age, the age profile of the weighted sample could be compared with the profile of all Victorian farmers. As is typical of surveys, younger ages (under 45) are under-represented and older ages (55–74) over-represented (Figure 21). If we were to weight the sample by age we would not also be able to weight it by industry and gross farm income, so the slightly biased age structure must remain.

35 30

Percent

25 20

Sample ABS 2006

15 10 5 0 18-24

25-34

35-44

45-54

55-64

65-74

75+

Age group

Figure 21 Profile of weighted sample (n=1300) compared with all Victorian farmers

Key points: The beef and sheep industries are dominated by small scale producers but in the cropping and dairy industries larger scale farmers predominate. Service design and delivery in the sheep and beef industries will need to be framed with the needs of this large number of small scale producer in mind. Half of Victoria’s farmers are between the age of 45 and 64 years. Service needs and delivery may or may not be linked to age, but it is likely that age is a poor indicator of industry participation and enterprise management. DPI would benefit from assessing the age profile of the farmers who currently use extension services. If the age profile does not match the age profile of Victorian farmers then DPI may need to reshape its service design and delivery to better connect with certain age groups of farmers.

50

Segmenting Victoria's farmers

Appendix 4. Raw data tables Here are the raw data from which most of the Figures in this report were created. In each case, the data represent the number of farmers in each cell of the table. Grand totals less than 1300 indicate missing data.

Table 1 Raw data for Figure 1

Segment

Number of farmers

Selling out

164

Phasing down

225

Not productivity oriented

104

Productivity constrained

185

Productivity but not scale oriented

415

Expansion oriented

205

Total

1299

Table 2 Raw data for Figure 2 (number of farmers)

Segment

Age group 25–34

35–44

45–54

55–64

65–74

75+

Total

Selling out

1

9

33

68

44

9

164

Phasing down

2

17

47

81

63

14

224

Not productivity oriented

3

9

19

22

30

21

104

Productivity constrained

4

15

40

67

45

14

185

Productivity but not scale oriented

13

48

125

134

78

17

415

Expansion oriented

14

43

64

55

23

5

204

Total

37

141

328

427

283

80

1296

Segmenting Victoria's farmers

51

Table 3 Raw data for Figure 3 (number of farmers)

Segment

Gross farm income < $50k

$51–100k

$101–200k

$201– 400k

>$400k

Total

Selling out

43

22

24

24

14

127

Phasing down

73

33

36

30

22

194

Not productivity oriented

38

12

16

7

4

77

Productivity constrained

61

35

23

23

10

152

Productivity but not scale oriented

119

57

62

60

47

345

Expansion oriented

32

17

36

48

42

175

Total

366

176

197

192

139

1070

Table 4 Raw data for Figure 4 (number of farmers)

Gross farm income

52

Age group $101k

56

112

193

183

544

$101–200k

25

63

66

44

198

$201–400k

43

52

54

43

192

>$400k

25

53

44

18

140

Total

149

280

357

288

1074

Segmenting Victoria's farmers

Table 5 Raw data for Figure 5 (number of farmers)

Gross farm income

Industry Dairy

Sheep

Beef

Crop

Hort

Other

Total

$1M

4

0

1

5

11

5

26

Total

191

187

297

201

149

46

1071

Table 6 Raw data for Figure 6 (number of farmers)

Balance of farm and off-farm income

Gross farm income $400k

Total

Mostly off-farm income

155

39

40

32

15

281

Mix of farm and off-farm income

67

36

40

23

13

179

Mostly farm income

11

33

52

40

36

172

Only farm income

57

54

59

87

69

326

Total

290

162

191

182

133

958

Segmenting Victoria's farmers

53

Table 7 Raw data for Figure 9

Segment

Number of farmers

Low income farm dependent

306

Low income off-farm dependent

101

Medium income farm dependent

145

Medium income off-farm dependent

63

High income farm dependent

192

High income off-farm dependent

150

Total

957

Table 8 Raw data for Figure 10

Segment

54

Number of farmers

Selling out

41

Phasing down

71

Not productivity oriented

19

Productivity constrained

44

Productivity but not scale oriented

93

Expansion oriented

37

Total

305

Segmenting Victoria's farmers

Table 9 Raw data for Figure 11

Segment

Number of farmers

Work off farm to maintain income

159

Work off farm to invest in farm

138

Cut investment to maintain income

176

Cut investment to keep farming

149

Live on less to invest in farm

229

Live on less to keep farming

200

Total

1051

Table 10 Raw data for Figure 12 (number of farmers)

Calamity segment

Economic security segment Low income farm dependent

Low income off-farm dependent

Medium income farm dependent

Medium income offfarm dependent

High income farm dependent

High income offfarm dependent

Total

Work off farm to maintain income

44

21

12

7

13

23

120

Work off farm to invest in farm

27

20

16

4

10

15

92

Cut investment to maintain income

40

5

28

8

29

33

143

Cut investment to keep farming

33

7

20

4

34

18

116

Live on less to invest in farm

62

15

28

11

36

20

172

Live on less to keep farming

58

13

18

19

33

20

161

Total

264

81

122

53

155

129

804

Segmenting Victoria's farmers

55

Table 11 Raw data for Figure 13 (number of farmers)

Calamity segment

Age group