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DRAFT – PUTTI – Identifying factors influencing land management practices Pag

Partnerships and Understanding Towards Targeted Implementation Identifying factors influencing land management practices Natasha B. Porter, David I. Tucker, Zoe Leviston, Simon N. Russell, Murni Po, Alexandra J. Fry, Wendy McIntyre, Blair E. Nancarrow and Lorraine E. Bates

CSIRO Land and Water Science Report 29/07 June 2007

DRAFT – PUTTI – Identifying factors influencing land management practices Page 2

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ISSN: 1883-4563

ISBN: 978 0 643 09483 3

PUTTI – Identifying factors influencing land management practices Page i

Partnerships and Understanding Towards Targeted Implementation Identifying factors influencing land management practices Natasha B. Porter, David I. Tucker, Zoe Leviston, Simon N. Russell, Murni Po, Alexandra J. Fry, Wendy McIntyre, Blair E. Nancarrow and Lorraine E. Bates

CSIRO Land and Water Science Report 29/07 June 2007

PUTTI – Identifying factors influencing land management practices Page ii

Acknowledgements We would like to acknowledge the generosity of the Central West catchment community for their participation in our research and on-going interest in the project. The support and assistance of our research partners, the Central West Catchment Management Authority, is highly appreciated. The PUTTI project is a collaborative research initiative between the CSIRO and the Central West Catchment Management Authority, funded by the Australian Government’s National Action Plan for Salinity and Water Quality/National Heritage Trust Program.

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Executive Summary This report details the findings of the first stage of the Partnerships and Understanding Towards Targeted Implementation (PUTTI) research project. PUTTI is funded by the Australian Government’s National Action Plan for Salinity and Water Quality/National Heritage Trust Program and is a collaboration between the Central West Catchment Management Authority, landholders, the broader Central West community and researchers at the CSIRO. Past experience has shown that best practice farm management as identified by Catchment Management Authorities (CMAs) and government agencies does not always mesh well with what landholders think is best for their farms or lifestyles (eg. Barr & Cary 2000; Curtis & Byron 2002). It is well known that farming practice can be more than just an economic activity; it is underpinned by experience, local understanding, local knowledge about the environment shared by landholders and others in the community, and a set of beliefs, values and attitudes which are specific to individual landholders. This research aimed to further our understanding of the features and context of land management practices at a farm based level, so that catchment management can better reflect and address the drivers of decision-making at the individual property level. An hypothesised model of decision-making in regards to land management practices was developed and tested through extensive face-to-face scoping interviews and delivery of a large scale telephone survey of landholders in two sub-catchments of the Central West Catchment of New South Wales. The findings suggest that there are several determinants of whether a range of desirable land management practices are likely to be undertaken, including:

• the reported innovativeness with which farm management is approached;

• the level to which financial cost is seen as a barrier to certain practices;

• whether properties are operating under a formal written farm plan; and

• the perceived effectiveness of specific land management practices.

Other influences on land management practices included considerations of lifestyle, environmental values, attitudes towards science and technology, the presence of agricultural qualifications and perceptions of the environmental condition of both individual properties and the surrounding area. Conversely levels of engagement with the community, succession planning and social influences were found to have relatively little influence over land management practices.

There were several other findings that were of interest. Firstly, large differences in trust levels for sources of information were revealed – most notably, high levels of trust in agronomists and other farmers, and relatively low levels of trust in the local CMA. Also, there were very few differences in responses between the two sub-catchment areas. This consistency suggests that the findings of the present study could be generalised to the larger dryland farming population. This phase of the PUTTI project succeeded in creating an adequate exploratory model of land management practices that promises applicability across broad geographical areas. It also provides some clear recommendations for developing partnered change and incentive programs to encourage sustainable land management. These will be progressed over the coming months. It is intended to apply this model in other catchments in NSW to test areas of refinement and modification that have been suggested here.

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Table of Contents 1. Introduction.........................................................................................................1 2. Scoping Study.....................................................................................................2

2.1. Methodology.............................................................................................................. 2 2.2. Results ...................................................................................................................... 2

2.2.1. What farmers do on their farms and why ........................................................... 2 2.2.2. Who influences farmers and their decisions? .................................................... 3 2.2.3. Knowledge of and views on the CMA and the Incentives Program ................... 5 2.2.4. The future........................................................................................................... 5

2.3. Building on the scoping study findings ...................................................................... 5 3. An Hypothesised Model of Land Management Practice..................................6

3.1. Past research ............................................................................................................ 6 3.2. Model description ...................................................................................................... 6

4. Testing the Model .............................................................................................10 4.1. Methodology............................................................................................................ 10

4.1.1. Study area........................................................................................................ 10 4.1.2. Respondents and refusal rates ........................................................................ 11 4.1.3. The questionnaire ............................................................................................ 12

5. Results...............................................................................................................13 5.1. Preliminary analyses ............................................................................................... 13

5.1.1. Background farming information ...................................................................... 13 5.1.2. Farm plan ......................................................................................................... 17 5.1.3. Decision making............................................................................................... 18 5.1.4. Sources of information ..................................................................................... 19 5.1.5. Involvement in community groups.................................................................... 22 5.1.6. Social norm ...................................................................................................... 23 5.1.7. Attitudinal statements....................................................................................... 24 5.1.8. General environmental statements .................................................................. 27 5.1.9. Environmental condition................................................................................... 28 5.1.10. Land management practices............................................................................ 28 5.1.11. Future involvement........................................................................................... 37

5.2. Group comparisons ................................................................................................. 37 5.3. Predicting land management practices – the structural equation model................. 40

6. Summary and Discussion ................................................................................45 6.1. Study participants.................................................................................................... 45 6.2. Preliminary results................................................................................................... 45 6.3. Structural equation model ....................................................................................... 45

7. Recommendations............................................................................................48 8. References ........................................................................................................50 APPENDIX A ............................................................................................................52 APPENDIX B ............................................................................................................54 APPENDIX C ............................................................................................................56 APPENDIX D ............................................................................................................59 APPENDIX E.............................................................................................................64 APPENDIX F.............................................................................................................67 APPENDIX G ............................................................................................................71

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1. Introduction Effective catchment management requires both local knowledge and scientific understanding of the biophysical, social and economic features of an area. Therefore, the support and cooperation of the agricultural community is essential to implement change. Matching landholder and broader catchment management priorities is an issue facing Catchment Management Authorities (CMAs) throughout New South Wales. CMAs are required to formulate targets for natural resource management in their region as part of their Catchment Action Plans (CAPs). Target areas include soil, water, vegetation, biodiversity and salinity. The targets in these areas have historically been based on the CMA’s considerations of ‘good’ farm management practices. To encourage landholders to assist in meeting these management targets, incentive programs have been designed which offer financial assistance for eligible landholders. Without the integration of local and scientific knowledge, however, catchment management initiatives can be limited in their effectiveness. Effective implementation of CAPs (including uptake of incentive programs) is essential for all CMAs as these plans are designed to ensure natural resources are maintained and improved for the whole community. This can only be effectively achieved by working with the community to identify their priorities for their properties and their regions and understanding what factors affect the way landholders manage their farms – what matters to them and what influences their farm management decisions. This is the focus of the current project - Partnerships and Understanding Towards Targeted Implementation (PUTTI). It is funded by the Australian Government’s National Action Plan for Salinity and Water Quality/National Heritage Trust Program. It is a collaborative project between the Central West Catchment Management Authority, landholders, the broader community and researchers at the CSIRO. The ultimate aim of the PUTTI research is to create and facilitate a genuine partnership between catchment managers, scientists and the catchment community to develop and implement an ongoing program to encourage community change and build mutual trust between community members, scientists and decision-makers. The current research was undertaken in the Central West catchment area, specifically the Bell and Cudgegong sub-catchments. The focus is on dryland farming and is comprised of the following three stages.

Stage 1: Scoping Study

Semi-structured interviews with members of the Bell and Cudgegong sub-catchments to understand landholders’ attitudes, beliefs and values relating to farming practices and decision-making. Stage 2: Community Survey

A survey of landholders in the Bell and Cudgegong sub-catchments to collect further data on and identify what drives farming practices and decision-making on landholder’s properties. Stage 3: Partnered Change

Having identified the major drivers in Stage 2, a change program will be developed in partnership with the community, followed by implementation in the Bell and Cudgegong sub-catchments. The program will focus on addressing the key aspects that underpin land management decisions and practices to encourage the uptake of approaches that lead to better environmental outcomes at the local and regional scale.


2. Scoping Study The scoping phase of the PUTTI project was designed to:

• provide an indication of the range of land management practices, issues, hopes, concerns and influences across the study area to inform later stages of the research;

• provide the project team with first hand experience of the catchment community;

• contribute to the development of a project communication plan; and

• act as an ongoing reference source for the project.

2.1. Methodology The scoping study involved semi-structured face-to-face interviews during the period 21st August to 15th September 2006, across the two identified sub-catchment areas of the Bell and Cudgegong Rivers. Participants were identified through lists supplied by the Central West CMA and additional nominations from landholders and other sources. Project team members telephoned individuals to describe the project, invite participation and organise an interview time. Confirmation letters were sent to each participant. Project team members visited the catchment and conducted the interviews in pairs. An information sheet describing the project was provided to participants, a copy of which can be found in Appendix A. Forty-two landholders were interviewed in the Cudgegong sub-catchment and thirty-seven in the Bell sub-catchment. A summary of characteristics of landholders is provided in Appendix B.

2.2. Results The interviews elicited a large and diverse range of perspectives and ideas. As the majority of discussion was related to both the Bell and Cudgegong sub-catchments, results for the two sub-catchment areas have been combined. Where this was not appropriate, separate reporting has been provided and is noted as such. Participants in the interviews were overwhelmingly generous with their contributions. They provided information about their views, practices, hopes and concerns, including what they did on their farms and why; whom they look to for information and advice (and whom they did not) and what they wanted for the future (a copy of the interview checklist can be found in Appendix C).

2.2.1. What farmers do on their farms and why When questioned about what they do on their farms and why, participants gave a mix of direct on-ground type actions, such as pasture and stock management and broader management type activities such as farm planning. The on-ground type actions nominated in the interviews and some of the activities covered in these included:

• Pasture and weed management - developing grass cover - cell grazing - pasture improvement - weed eradication

• Crop management - direct drilling - zero/minimal tillage - stubble retention


• Soil management - monitoring soil structure

- erosion control - soil improvement - machinery conversions

• Stock management - stocking rates - breeding programs - fencing

• Vegetation - tree planting - native vegetation protection and restoration Activities relating to water management were also discussed (notably in the Bell sub-catchment) including improved water use efficiency, the removal of willows from water courses, fencing off the river and in some cases providing access for light stock along the river to control weeds. At a broader management level, actions that were raised in the interviews focussed on farm planning to cover a range of activities, including whole farm planning (whether written or informal), succession planning, land use rotation, financial planning and chemical or fertiliser use. A focus was also placed on being versatile and flexible in the range of activities and approaches on the farm. These activities included; decisions to reduce stock numbers before needing to invest time and money in hand feeding, or having multiple income sources – whether this be off-farm employment or a bed and breakfast enterprise associated with the farm. The actions and activities discussed above provide a close fit with priorities under the Catchment Action Plan and the incentives program. The reasons people gave for focussing on these activities were diverse and covered social, environmental and economic motivations. Managing through drought was an obvious and key motivation for many. From an environmental and land and water management perspective, the reasons people pursued different actions included salinity, weed and erosion control; maintenance or improvement of soil nutrients and soil condition; stock quality and quantity; increased availability and quality of water for the river system; and provision of wildlife corridors and windbreaks. Social and financial motivations for adoption of farm management approaches included more leisure time; family life stage (i.e. whether children were grown and largely independent or young and likely to require greater commitments of time and money); farm productivity and financial returns; providing for the future and being in a position to respond or adapt to diverse conditions; and the aesthetic impact of different activities. For some there was a focus on adopting an approach that recognised farming as a business. For others, particularly those on lifestyle or hobby blocks, the motivation for different activities focussed more on lifestyle choices.

2.2.2. Who influences farmers and their decisions? Participants were asked about the type of information they sought, where they went and who they turned to for information about managing their farms. The responses showed a wide range of influences and information sources (see Table 1).


Table 1. Sources and types of information participants sought

Information source Information type

Government (or identified as government)

e.g. CMA, Dept for Planning and Infrastructure (DPI), Dept of Soil Conservation, Dept of Agriculture, Dept of Natural Resources

Technical advice

Financial and personal support

Information/research

Legislative requirements

Industry

e.g. agronomists; agricultural/rural suppliers; specific advisers and experts – eg. financial consultants, farm plan consultants; marketing agents; industry bodies

Farm business management (including environment)

Technical advice

Market situation and opportunities

Financial advice and analysis

Occupational health and safety and other legislative requirements

Crop and stock management

Community

e.g. other farmers and peers; field days; Landcare; neighbours; bus tours; ‘down the pub’

Farm business management (including environment)

Technical advice

Personal support

Crop and stock management

Market situation and opportunities

Media

e.g. internet, TV; books, journals and newspapers; seminars and courses

Technical advice

New/emerging market opportunities

Weather forecasts

Farm practice trends and recommendations

Discussion about sources of information and influences also elicited references to particular individuals, including local farmers, CMA, Soil Conservation or DPI staff and specific consultants. The ongoing changes in arrangements and responsibilities in the government and CMA were cited as causing problems in accessing information due to the loss of particular and valued staff members and a lack of clarity about who to approach for information. Changes in Landcare and Landcare funding were cited as problematic by some respondents in both sub-catchments. A number of respondents in both sub-catchments referred to the fact that they no longer know who to contact and that access to people with the relevant skills and knowledge that would come to the property to discuss issues had become increasingly difficult. For some respondents (more so in the Cudgegong), access to good and reliable internet sources was raised as an issue. Changing demographics and the increase in hobby farm arrangements and reliance on off-farm income were identified as resulting in decreased opportunities to learn from, or to have discussions with neighbours about different farm


management approaches. Programs like the ‘Ripple Effect’ program1 were seen as providing good opportunities for learning and development in the region.

2.2.3. Knowledge of and views on the CMA and the Incentives Program Most respondents had heard of the CMA, but often there was limited knowledge about the role of the CMA within the catchment. For those who knew about the CMA, the majority had previously applied for incentive funding. There were mixed views about the incentives program. For some it was viewed as positive because it allowed people to undertake land management activities that they would not otherwise have achieved. For others, there was a view that the program lacked the flexibility to cater for the requirements of different land and business enterprises; that the application form and process were confusing and time consuming; and that the monitoring and accountability requirements could be a deterrent. For some, there was also a view that decisions regarding incentive funding allocation were pre-determined. There were also requests, mostly from the Cudgegong, for more information on the science behind priority areas and incentives.

2.2.4. The future As would be expected, there were mixed responses about the future for individuals and the district. Throughout the Cudgegong, views on the future for individuals were mixed. Respondents thoughts included whether they would sell up, subdivide, be forced to leave because of reduced profitability, or continue to farm, improving their practices and providing for future sustainability. At a district level, attention focussed on the impact of subdivision and lifestyle blocks; a decline in farmers and family farms with young people leaving the district and an ageing farming population; an increase in corporate farms and farm managers; and water, and water scarcity, becoming an increasingly important issue. There was optimism about the diversity and popularity of the area to provide for its future. In the Bell, individual futures generally involved staying on the farm until retirement – in some cases selling and using the proceeds for superannuation or, less commonly, passing the farm on to the children. In terms of the future for the district, there was a strong focus on local government planning legislation with associated increases in lifestyle blocks and pressures on agricultural land prices. Both sub-catchments identified the drought and/or climate change as significant influences on the future, for individuals and the district. Conversely, some respondents in both sub-catchments also compared the present (and the potential for the future) favourably with the situation, say, ten years ago seeing this as providing optimism for the future.

2.3. Building on the scoping study findings The scoping study results were used to inform future stages of the project, providing the basis for development of the questionnaire to survey landholders across the Bell and Cudgegong sub-catchments and generally providing a rich source of information for future reference in the project. The next stage involved the development of an hypothesised model of the factors influencing farmers’ land management practices.

1 The Ripple Effect program is an initiative of the Mid Macquarie Landcare Group. The program uses peer support and on-ground training to help landholders influence their colleagues. The aim is to change large, priority areas of the catchment through revegetation and the increased use of perennials.


3. An Hypothesised Model of Land Management Practice An hypothesised model was developed as a ‘starting point’ to identify the factors that influence the decision making and behaviours of rural landholders, and the barriers to the adoption of sustainable practices. The model was based upon:

• results from scoping interviews;

• results from previous similar studies; and

• a review of previous research carried out by ARCWIS.

3.1. Past research There have been numerous studies internationally that have attempted to account for farmers’ decision-making in regards to land stewardship behaviours. These have revealed a large variety of potential drivers. For instance, in their research into English farmers’ attitudes and motivations towards conservation-related land management practices, Beedell and Rehman (1999) found that farmers were more likely to engage in these practices if they perceived a greater social pressure to do so. In addition to overseas studies, there have been a number of projects of this kind conducted locally. In an investigation into the implementation of the NSW Murray Darling Blueprint, Crase and Mayberry (2002) found that financial concerns, age and evidence of on-farm environmental degradation were significant predictors of land stewardship behaviour. In a meta-analysis of studies, Cary, Webb and Barr (2002) found a number of significant predictors of land management practices, including Landcare membership, environmental concern, technical concern, farm size and the existence of a formal farm plan. The project team itself has extensive experience in the use of advanced statistical modelling techniques to develop models that describe key factors that influence individual decision making in both rural and urban settings. Recent research undertaken in the South Australian Riverland region (Leviston, Porter, Jorgensen, Nancarrow & Bates, 2005) explored key individual psychological and social factors in irrigators’ decision-making with regards to water use and the adoption of water-efficient technologies. It was found that evaluations of potential outcomes and trust in information provided by regional authorities were key to predicting water-using practices for irrigators in the chosen study area. Further, Tucker, Johnston, Leviston, Jorgensen and Nancarrow (2006) found that the perceived effectiveness of protective management measures was a significant predictor of people’s intentions to perform those measures. Further variables in the hypothesised model were directly drawn from the scoping phase of the study, many of which are consistent with literature. For instance, during scoping interviews, many participants referred to barriers that prevented them from achieving desired goals on their properties. This is in keeping with theoretical models of behaviour such as ‘protection motivation’ theory, which suggests that behavioural change can be hindered by perceived or actual barriers (see Grothmann & Ruesswig, 2006; Neuwirth, Dunwoody and Griffin 2000).

3.2. Model description Figure 1 outlines the hypothesised model. The model contained 16 latent variables and was intended to be exploratory in nature. As such, many potential relationships between variables were included in the model for examination, with the expectation that many of the relationships between latent variables would not reach statistical significance.


Succession Planning

PerceivedEffectiveness Of

Land Management Practices

SocialNorm

Perceived Environmental

Condition

Farm Plan

Farm experience

Age

Lifestyle

AgriculturalQualifications

Trust in and Influence of

Information Sources

Community Engagement

Land Management

Practices

Science & Technology

Environmental Values

Innovator

Perceived Barriers To Change

Farm Size

Figure 1. Hypothesised model

The following sections provide an overview of the hypothesised model’s components.

Land Management Practices The Land Management Practices variable is comprised of responses to a number of different land management questions, pertaining to the following areas of practice: weed control; native vegetation management; perennial pasture management; stock management (where applicable); and soil management and testing. An overall land management score was calculated.

Farm Size The Farm Size component of the model is the number of hectares of land that each respondent farms.

Farm Experience The Farm Experience component of the model refers to the number of years respondents have spent as dryland farmers.

Age The Age component of the model is the respondent’s age in years.

Lifestyle The Lifestyle component of the model refers to the extent to which respondents indicate that they are farming for the lifestyle as opposed to a business venture. Higher scores in this variable indicate that a respondent is farming more for the lifestyle it affords than for business purposes.


Agricultural Qualifications The Agricultural Qualifications component of the model refers to any formal qualifications that respondents have that are relevant to land management and farming.

Community Engagement The Community Engagement component of the model refers to respondents’ engagement with the community, including non farm related community groups such as sporting clubs.

Farm Plan The Farm Plan component of the model refers to whether or not respondents have a formal written farm plan.

Environmental Values The Environmental Values component of the model refers to the respondents’ general views of the environment and the interaction humans have with it.

Succession Planning This component measures whether the respondent intends to pass the farming property down to the next generation in the family.

Science and Technology The Science and Technology component of the model refers to respondents’ beliefs that science and technology can aid in finding useful solutions to land management issues.

Perceived Barriers to Change The Perceived Barriers to Change component of the model refers to the extent that cost, time and government regulations are perceived to be barriers to undertaking desirable land management practices.

Perceived Environmental Condition The Perceived Environmental Condition component of the model refers to respondents’ perception of the condition of their property and the region as a whole. It covers perceptions of water quality, soil health, weed presence and erosion.

Trust in and Influence of Information Sources The Trust in and Influence of Information Sources component of the model indicates how much trust respondents have in information provided by various organisations, businesses and institutions, and how much these various sources of information influence what they do on the farm.

Social Norm The Social Norm component of the model measures the extent to which the views of other farmers in the area influence respondents’ behaviours.


Perceived Effectiveness of Behaviour The Perceived Effectiveness of Behaviour component of the model refers to the extent to which respondents believed that the stated land management practices are effective components of overall good land management.

Innovator The Innovator component of the model measures the extent to which respondents report they enjoy trying new things, and being innovative in the way they manage their farm.


4. Testing the Model

4.1. Methodology Data was collected by a trained team of field interviewers who conducted a telephone survey with dryland farmers in the Bell and Cudgegong sub-catchment areas of the Central West Catchment. A sample size of 400 respondents – 200 from each of two sub-catchments – was the design target. The sample population allows for comparative analysis between sub-catchments and enables Structural Equation Modelling (SEM) to be undertaken.

4.1.1. Study area The Bell and Cudgegong sub-catchments, at the southern end of the Central West region, were used in this project. Their boundaries are defined by the Bell and Cudgegong Rivers which flow through the centre of each sub-catchment and are tributaries of the Macquarie River. Figure 2 shows the whole Central West Catchment, the Cudgegong and Bell Rivers and the major urban settlements of the region. The majority of the Bell and Cudgegong sub-catchments fall within the NSW electoral district of Orange. The population within this boundary is 68,527. Eleven percent of the industry effort in the region is involved in agriculture, forestry and fishing (NSW Electoral Commission, 2007). The Bell sub-catchment begins at Wellington in the north and runs south along the river to the Molong district and ends before Orange (see Figure 2). The Orange LGA was not included in this study as it has a higher residential density than normal rural settlements and as such involves many farmers with fewer than 10 acres, who were not classed as dryland farmers for the purpose of this study. The Wellington and Cabonne LGA’s house the bulk of the Bell sub-catchment study area. In 2001 Cabonne had a population of 5,427, and 28.8% of the area’s population was within the Bell study area. The Wellington LGA had a population of 41,368 in 2004 and the value of Agricultural commodities produced in 2001 totalled $32.4 million (Australian Bureau of Statistics, 2006). The Cudgegong sub-catchment runs parallel to the Cudgegong River and within the geographical confine of that river catchment. The northern point, as identified in Figure 2, is Gulgong and runs south through Mudgee to the Rylstone/Kandos area. The Mid-West Regional Local Government Area (LGA) includes the bulk of the Cudgegong sub-catchment study area and had an estimated population of 22,289 in 2003. The largest industry employer was Agriculture, Forestry and Fishing (16.3%). The sheep-beef cattle farming industry had the highest growth rate between 1996 and 2001, employing 100 of the 571 new jobs in this period (Mid-Western Regional Council, 2006). The Rylstone LGA also includes a portion of the Cudgegong sub-catchment. In 2001 it had a population of 3,784 (Community Relations Commission for a Multicultural NSW, 2007).


Central West Catchment, NSW

CWCMA Boundary

Bell Catchment

Cudgegong Catchment

Towns

Q

0 40 80 120 16020Kms

Dubbo

Wellington Mudgee

Orange

Bathurst

Data Source: CWCMA Boundary - supplied by DNR

Map complied by CSIRO Land & Water 6/7/07 Stressed Rivers - supplied by DLWC 1998

Figure 2. NSW Central West Catchment

4.1.2. Respondents and refusal rates Landholders were randomly selected from telephone lists for both the Bell and Cudgegong sub-catchments. In total 407 landholders were surveyed – 221 (54.3%) from the Cudgegong sub-catchment and 186 (45.7%) from the Bell sub-catchment. Interviewers were directed to ask to speak to the principal decision-maker for the property (this may have been the owner or farm manager). Interviewers were further instructed to contact each property on their lists at least five times, at different times of the day and across different days, before the property could be classed as a ‘non-contact’. Table 2 depicts the refusal rate over the two sub-catchments and as a whole. The refusal rate for Cudgegong and Bell sub-catchments was 42% and 39% respectively, which by recent standards, indicates good involvement in the project.


Table 2. Refusal details

Reason Bell Cudgegong TOTAL

Not Interested 59 80 139

Too Busy 39 47 86

Elderly/Too Old 13 8 21

Ill/Sick 5 9 14

Little/No English 2 12 14

Visitor 1 7 8

TOTAL 119 163 282

Respondent gender was also recorded by the interviewers. The breakdown is presented in Table 3.

Table 3. Respondent gender

Bell Cudgegong Total Option n

(187) % n (220) % n

(407) %

1 – Female 50 26.7 71 32.3 121 29.7

2 – Male 137 73.3 149 67.7 286 70.3

4.1.3. The questionnaire The questionnaire was designed to measure each of the variables in the hypothesised model (as outlined in Section 3.3). As a summary, the questionnaire covered the following areas.

• Background information relating to farming/property details – including farming activities, property size, years in farming, and property ownership.

• Socio-demographic information.

• Trust in and influence of sources of information and advice.

• Individuals’ role in and views of the community.

• A series of attitudinal statements.

• Perception of the environmental condition of the wider farming region and their property(s).

• The existence of farm plans.

• A range of questions on weed management, soil management, perennial pasture management, native vegetation management and stocking rate management.

The questionnaire was pre-tested with a number of landholders and appropriate changes were made before finalising and implementing.


5. Results Preliminary analyses were undertaken using correlation, analysis of variance (ANOVA), cross-tabulation and reliability analysis. This was followed by investigation of the causal relationships between the components of the hypothesised model using the robust maximum likelihood estimation method in LISREL 8.72 (Joreskorg, Sorbom, du Toit & du Toit, 2000). For the preliminary analyses, and to ensure robustness of results, a significance level of p < 0.01 was applied. Differences labelled “significant” in the results section refer to statistical significance at this level. The number of respondents answering a question is shown and denoted as “n” and/or as a percentage of the whole sample. For a number of open-ended questions, more than one answer was allowed, hence percentages do not always add up to 100%. Results of open-ended questions are presented with the number of times this response was offered (n) and as a percentage of the number of people responding.

5.1. Preliminary analyses 5.1.1. Background farming information A series of questions were asked initially of respondents to collect general information regarding their properties.

Types of farming activities Respondents were asked to nominate the main farming activities that were carried out on their property(s). The most common responses are shown in Table 4 below.

Table 4. Main farming activities

Response n (407) %

Cattle 270 66.3

Sheep 215 52.8

Broad acre crops 100 24.6

Goats 17 4.2

Horses 16 3.9

Other animals (incl. alpacas, pigs, deer) 13 3.2

Olives 8 2.0

Other fruit 7 1.1

The main farming activities nominated by respondents were cattle (66.3%), followed by sheep (52.8%) and broad acre crops (24.6%). About half of the respondents (n = 202) stated engaging in more than one farming activity on their property (eg. cattle and sheep faming; cattle farming and broad acre crops).


Years in dryland farming Respondents were asked how many years they had been dryland farming. Length of time stated ranged from one year to seventy-eight years with a mean of 22.3 years. For ease of presentation and use in further analysis the responses were grouped as shown in Table 5.

Table 5. Summary of number of years respondents stated they had been dryland farming

Number of Years n (406) %

Up to 12 years 149 36.7

13 to 30 years 157 38.7

More than 30 years 100 24.6

Mean = 22.25

Respondents were also asked how many years their families had been farming in the area. The responses to this question ranged from one year to 219 years. On average, respondents’ families had been farming in the area for more than half a century (mean = 59.3 years). Again for ease of presentation, responses were grouped as shown in Table 6 below.

Table 6. Summary of the number of years respondents stated their families had been farming in the area

Number of Years n (393)* %

Up to 10 years 82 20.9

11 to 50 years 148 37.7

51 to 100 years 92 23.4


Mean = 59.25

* Thirteen respondents could not give the length of time in years, however stated that their families had been farming in the area for generations.

Farm size and number of properties Respondents were asked about the size of their farm and the number of properties involved. A wide range of farm sizes were reported, with a mean of 577.0ha. Responses were grouped as shown in Tables 7 and 8.


Table 7. Respondents’ farm sizes

Farm size (ha) n (407) %

Less than 40 83 20.4

41 to 100 76 18.7

101 to 400 94 23.1

401 to 1000 78 19.1

1001 and above 76 18.7

Mean = 577.0

More than one-third of respondents (39.1%) reported having 100ha or less of farm land while nearly one-fifth (18.7%) with farm land greater than 1000ha.

Table 8. The number of properties respondents farm

Number of properties

n (405) %

1 293 72.3

2 75 18.5

3 26 6.4

4 4 1.1

5 6 1.5

6 1 0.2

Mean = 1.41

The majority of respondents (72.3%) farmed only one property while less than one-fifth of respondents (18.5%) reporting having two properties (see Table 8). Very few respondents in the study had more than two properties (9.2%).

Farm ownership A series of questions was asked regarding farm ownership and management. Respondents first were asked whether they were the owner and/or manager, or to nominate their alternative roles on the property (see Table 9 for results).


Table 9. Respondents’ role on the property

Role n (407) %

Owner (incl. ‘owner managers’) 365 89.7

Owner and lessee 5 1.2

Owner and caretaker 1 0.2

Lessee 2 0.5

Manager 23 5.7

Owner’s family 10 2.5

Caretaker 1 0.2

The vast majority of respondents (91.1%) were owners of the properties. Very few respondents were the managers (5.7%). Other roles nominated by respondents included owner’s family or extended family members, lessee and caretaker. Respondents who said they were owners of a property and those who nominated other roles were further asked whether they employed a farm manager. Most of these respondents did not employ a farm manager (97.1%). Of those with farm managers (n = 11), eight worked full-time, while the remaining three worked on a part-time basis.

Socio-demographics Respondents were asked a number of socio-demographic questions. Responses are detailed in Tables 10 to 12. Age

Table 10 provides a breakdown of respondents’ age groups.

Table 10. Details of respondents’ age

Age group n (407) %

Less than 24 6 1.5

25 to 34 years 26 6.4

35 to 44 years 68 16.7

45 to 54 years 103 25.3

55 to 64 years 116 28.5

65 to 74 years 72 17.7



Education

Table 11 provides a breakdown of respondents’ highest completed levels of formal education.

Table 11. Details of respondents’ highest level of education

Option n (404) %

All or some of primary school 12 3.0

All or some of secondary school 133 32.9

Partial trade or technical qualification 31 7.7

Trade or technical qualification 107 26.5

Partial university qualification 19 4.7

University qualification 102 25.2

Respondents were also asked if they had any formal agricultural qualifications. A little over one-third of respondents (n = 153, 37.7%) said they did possess these qualifications. Table 12 presents details of the most common agricultural qualifications held by respondents, attained at a university, TAFE or from another source (eg. field days, industry courses). Some respondents held qualifications from more than one institution. Appendix D contains the full list of agricultural qualifications reported.

Table 12. Details of respondents’ agricultural qualifications*

n % n % n % University

40 26.1 TAFE

88 57.5

Other (eg. field days, industry courses) 34 22.5

Qualification n % Qualification n % Qualification n %

Bachelor of Agriculture/Degree of Agriculture

7 17.9 Wool Classing 29 33.7 Wool Classing 8 23.5

Diploma of Agriculture 7 17.9 Farm

Management 6 7.0 Diploma of Agriculture 4 11.8

Agricultural Economics 3 7.7 Wool Classing &

OH&S Course 4 4.7 Diploma of Farm Management 4 11.8

Bachelor of Farm Management 3 7.7 Animal Husbandry 3 3.5 Chemical Training 4 11.8

* Some respondents had more than one qualification from the same institution. In these instances, qualifications were combined (eg. “Wool Classing & OH&S Course”).

5.1.2. Farm plan Respondents were asked questions relating to farm plans for their property(s) including whether they had a written farm plan, if it was prepared by professionals, and whether it was funded by the CMA (see Table 13 for results).


Table 13. Formal farm plan details Do you have a written

farm plan? Assistance from a

professional consultant? Funded by the CMA?

Option n (407) %

Option n

(121) %

Option n (121) %

Yes 121 29.7 Yes 62 51.2 Yes 13 10.7

No 286 70.3 No 59 48.8 No 108 89.3

Nearly three quarters (70.3%) of respondents said that they did not have a written farm plan. Of the 121 respondents who stated they did have a written farm plan, just over half (n = 62) stated that it was created with the aid of a professional consultant. Interestingly, only 13 out of the 121 respondents with farm plans reported receiving funding from the CMA to develop them. For the 286 respondents who stated they did not have a written farm plan, a wide variety of responses were recorded as to what they did in place of one. From the 426 responses to this question, the most common responses were rely on experience/do what I’ve always done/it’s in my head (n = 68), weather dictates what is done (n = 55), day by day decisions (n = 48), and do things when they’re needed (n = 40). Appendix E contains the full list of responses recorded.

5.1.3. Decision making Making key decisions Respondents were asked unprompted to nominate people or agencies they would go to if they were making a key decision for their farm. The most common responses recorded are shown in Table 14.

Table 14. People or agencies respondents would consult when making key decisions

Response n (406) %

Family members 339 83.5

Other farmers 117 28.8

Agribusinesses 74 18.2

Agronomists 64 15.8

Government departments 61 15.0

Financial/legal bodies 52 12.8

Media (eg. print, internet) 22 5.4

Specific person 22 5.4

Owner/board member 10 2.5

CMA 9 2.2

Organised courses 3 0.7

Industry groups 2 0.5

Landcare 1 0.2


Respondents most often said that they would go to family members (83.5%) to discuss key decisions they were making for their farm. The other main group consulted for key decisions was other farmers (28.8%).

Providing advice to others Respondents were asked whether anyone came to them for advice about making decisions on their farms and who these people were. More than half of respondents (n = 242; 59.5%) said that other people asked them for advice. The advice was mainly sought by other farmers or people in a similar venture (99.2%). Some of the advice was sought by family members (7.9%), hobby farmers (7.1%) and new farmers (7.1%). Other responses obtained for the question included young farmers; students or schools and agribusinesses.

5.1.4. Sources of information Trust in information from a variety of sources A list of potential information sources was presented to respondents who were then asked to rate how much they would trust information on farm management from each source using a five-point scale (1 = no trust to 5 = complete trust). Results can be seen in Table 15.

Table 15. Levels of trust in various sources of information

Sources 1

no trust at all (%)

2

(%)

3 some trust (%)

4

(%)

5 complete

trust (%)

Mean

Agronomists (n = 401) 3.0 7.0 20.2 50.1 19.7 3.77

Other farmers (n = 403) 2.0 5.0 38.2 39.2 15.6 3.62

Field days (n = 405) 2.0 5.2 41.5 39.5 11.8 3.54

Organised courses (n = 390) 1.8 11.0 34.4 42.8 10.0 3.48

Scientists (eg. CSIRO, Universities) (n = 398) 3.0 9.3 43.0 33.9 10.8 3.40

Tafe/university/educational institutions (n = 395) 4.1 11.8 43.3 32.4 8.4 3.29

Landcare (n = 389) 6.2 16.7 41.1 30.3 5.7 3.13

Agribusinesses (n = 400) 4.8 18.2 45.5 25.0 6.5 3.10

Industry groups (eg. NSW Farmers Federation) (n = 392) 7.4 16.8 43.6 26.0 6.2 3.07

Government departments (n = 401) 11.5 18.5 37.3 26.7 6.0 2.97

The CMA (Catchment Management Authority) (n = 372) 10.7 19.6 42.5 22.6 4.6 2.91

Community groups (n = 383) 9.9 21.9 42.7 21.1 4.4 2.88


Overall, more than half of the respondents (>50%) felt at least some trust in the farm management information provided to them from the twelve sources. In particular, over two-thirds of respondents reported high levels of trust (responding either 4 or 5) in the farm management information obtained from agronomists (69.8%). Just over half of the respondents also rated a high level of trust in the information obtained from other farmers (54.8%), field days (51.3%), and organised courses (52.8%). On the other hand, one-third of respondents felt little or no trust in information provided by government departments (30.0%), the CMA (30.3%) and community groups (31.8%). Other sources of information were also nominated by some respondents (n = 39). The most common source was media (n = 31) with a mean trust rating of 3.52.

3.623.54 3.48 3.40

3.293.13 3.10 3.07

2.97 2.91 2.88

3.77

1

2

3

4

5

Agronomists Other farmers Field days Org courses Scientists Educationalinstitutions

Landcare Agribusinesses Industry grps Governmentdepts

CMA Comm grps

Complete trust

Some trust

No trust at all

Figure 3. Mean trust ratings across different information sources Statistically significant differences were identified in respondents’ mean ratings of trust across different sources of information (see Figure 3 for a graphical display of mean ratings). These differences are indicated by the broken lines in Figure 3 and outlined below. Respondents had significantly more trust in the information provided by:

• agronomists than in any other sources;

• other farmers, field days and organised courses when compared with the remaining sources; and

• scientists and educational institutions when compared with the remaining sources.

Influence of information sources Respondents were further asked to rate the influence that each of the information sources had on what they did on their farms, on a five-point scale (1 = no influence to 5 = significant influence). Results can be seen in Table 16.


Table 16. The level influence each source had on what respondents did on their farms

Sources 1 no

influence

(%)

2

(%)

3 some

influence

(%)

4

(%)

5 significant influence

(%) Mean

Agronomists (n = 400) 13.8 7.3 21.7 41.7 15.5 3.38

Other farmers (n = 405) 6.2 11.6 34.1 34.8 13.3 3.38

Field days (n = 403) 8.9 16.9 41.9 26.6 5.7 3.03

Organised courses (n = 392) 15.8 17.4 34.4 26.5 5.9 2.89

Scientists (eg. CSIRO, Universities) (n = 400) 16.7 18.5 36.5 24.0 4.3 2.80

Tafe/university/educational institutions (n = 392) 17.6 22.4 36.5 19.2 4.3 2.70

Agribusinesses (n = 400) 20.0 21.5 38.2 16.5 3.8 2.63

Government departments (n = 400) 21.5 21.8 34.0 19.2 3.5 2.62

Landcare (n = 391) 24.6 19.4 34.7 18.2 3.1 2.56

Industry groups (eg. NSW Farmers Federation) (n = 390)

25.4 24.4 33.3 13.3 3.6 2.45

The CMA (Catchment Management Authority) (n = 381) 30.7 17.6 32.3 15.5 3.9 2.44

Community groups (n = 389) 31.6 22.4 33.7 10.8 1.5 2.28

Over half of the respondents thought agronomists strongly (responding either 4 or 5) influenced their farming practises (57.2%), while nearly half (48.8%) were strongly influenced by other farmers. Community groups were again rated lowest, and nominated by more than half of respondents (54.0%) to have little or no influence over their decisions. Also, the CMA was thought to have little or no influence by 48.3% of the respondents. Those respondents who nominated media as another source of information (n = 31) gave it a mean influence rating of 3.40.


3.38

3.032.89

2.802.70 2.63 2.62 2.56

2.45 2.442.28

3.38

1

2

3

4

5

Agronomists Other farmers Field days Org courses Scientists Educationalinstitutions

Agribusinesses Governmentdepts

Landcare Industry grps CMA Communitygrps

Significantinfluence

Some influence

No influence

Figure 4. Mean influence ratings across different information sources Statistically significant differences were identified in respondents’ mean ratings of influence across different information sources (see Figure 4 for a graphical display of mean ratings). These differences are indicated by the broken lines in Figure 4 and outlined below. Respondents felt information provided by:

• agronomists and other farmers was more influential to them than in any other sources;

• field days was more influential to them than the remaining sources; and

• community groups was less influential to them than any other sources.

5.1.5. Involvement in community groups Respondents were asked about their involvement in community groups and in how many groups they participated. More than half of the respondents (n = 243; 59.7%) were involved in at least one community group, with the remaining 164 (40.3%) not involved in any. The number of community groups in which respondents participated ranged from one to eight. See Table 17 for details.

Table 17. The number of community groups respondents were involved in

Number of groups n (243) %

1 92 37.9

2 74 30.5

3 38 15.6

4 25 10.3

5 11 4.5

6 2 0.8

8 1 0.4

Mean = 2.18


The main groups with whom respondents associated were sporting clubs (44.9%), fire fighting groups (42.0%), community services (35.4%), conservation groups (eg. Landcare, CMA) (30.0%), field days or professional associations (26.3%), and general community groups (10.7%). Other responses included religious; arts/crafts/heritage; environmental recreation and local council groups.

5.1.6. Social norm Two questions were asked to ascertain the influence of other people’s views on respondents. Respondents were firstly asked to rate on a five-point scale how much they valued the views of other farmers in their community (1 = no value at all to 5 = immense value).

Table 18. The value of other farmers’ views

Rating n (407) %

1 - no value at all 3 0.7

2 - little value 9 2.3

3 - some value 126 31.0

4 - a lot of value 187 45.9

5 - immense value 82 20.1

Mean = 3.83

The majority of respondents (97.0%) felt that the views of other farmers had at least some value to them (see Table 18). The mean rating indicated that respondents valued the views of other farmers in their community a lot. Very few respondents (3.0%) said that the views of other farmers were of little or no value to them. The second question asked respondents to rate how important they believed sustainable land management was to other farmers in their community (see Table 19).

Table 19. The importance of sustainable land management to other farmers in the community

Option n (403) %

1 – Not at all important 4 1.0

2 11 2.7

3 – Somewhat important 66 16.4

4 121 30.0

5 – Extremely important 201 49.9

Mean = 4.25

Just under half of the respondents (49.9%) believed that sustainable land management was extremely important to other farmers in their community. In contrast only four respondents said that it was not at all important to farmers.


5.1.7. Attitudinal statements A series of attitudinal statements were presented to respondents which later formed the attitudinal scales in this study. Respondents were asked whether they agreed or disagreed with each statement, using a five-point scale from 1 = strongly disagree to 5 = strongly agree. Details are listed in Table 20.

Table 20. Attitudinal statements

Statement

1 strongly disagree

(%)

2 mildly

disagree

(%)

3 unsure

(%)

4 mildly agree

(%)

5 strongly

agree

(%)

Mean

Making a living from my farm now is more important than worrying about the farm’s long-term future

33.4 37.1 15.5 10.6 3.4 2.14

I am confident that my family will be farming in this area in 50 years 38.1 22.4 19.2 12.3 8.0 2.30

Overall I am very happy to be living in this community 0.2 1.0 3.2 28.9 66.7 4.61

I feel like I belong in this community 0.7 2.5 7.9 34.5 54.4 4.39

Strong friendships exist between me and others in this community 0.5 3.4 8.2 34.7 53.2 4.37

Scientists don’t provide useful information on land management 13.3 39.5 29.2 16.0 2.0 2.54

I trust scientists to provide me with accurate information on new ways to manage land 4.7 14.0 26.8 44.2 10.3 3.41

I trust technology to provide answers to land management issues 6.0 15.0 34.0 40.0 5.0 3.23

Advances in technology will be essential to improving land management in the future 1.5 5.7 16.5 46.4 29.9 3.98

The use of new technology causes more problems than it solves 15.4 37.3 28.9 14.9 3.5 2.54

To me, farming is more of a lifestyle than a business 8.6 20.9 13.3 32.4 24.8 3.44

I see farming as a business only 28.1 48.6 10.4 9.9 3.0 2.11

The lifestyle is what keeps me in farming 5.8 9.4 11.1 42.4 31.3 3.84

I enjoy being the first to try new things in farming 6.9 22.4 21.9 35.5 13.3 3.26

I prefer to wait and see what works on other properties before trying something new 13.0 36.7 17.5 28.6 4.2 2.74

I am happy to try new farming methods that aren’t used a lot 1.5 14.7 16.0 51.3 16.5 3.67


Attitudinal Scales The sixteen attitudinal statements were reduced to four scales2 with sufficient reliability for further analysis. Each scale was constructed by summing the agree/disagree scores of each scale item. Descriptive labels were applied as follows. Community Attachment

The Community Attachment scale had a Cronbach’s alpha3 coefficient of 0.79 and consisted of three items. Higher scores indicate greater feelings of attachment (eg. friendship, belonging) to the community the respondent lives in. The scale is summarised in Table 21.

Table 21. Summary for the Community Attachment Scale

n (403)

Minimum Score 5 (3)*

Maximum Score 15 (15)

Mean Score 13.4

Number of Items 3

Cronbach’s α Coefficient 0.79 * numbers in brackets represent possible minimum/

maximum scores achievable The mean score suggests that respondents generally felt a part of the community they lived in. Value of Science and Technology

The Value of Science and Technology scale had a Cronbach’s alpha coefficient of 0.63 and consisted of five items. Higher scores indicate higher levels of agreement that science and technology provides value for land managers and good land management solutions. The scale is summarised in Table 22.

Table 22. Summary for the Value of Science and Technology Scale

n (401)

Minimum Score 8 (5)


Mean Score 17.5

Number of Items 5

Cronbach’s α Coefficient 0.63

2 Each scale comprised of several attitudinal statements measuring the same underlying construct. 3 Cronbach’s alpha tests for a scales internal consistence or reliability. A Cronbach’s alpha of 0.6 or above is deemed as acceptable.


The mean score suggests that respondents generally felt that science and technology provided value to land management issues. Lifestyle

The Lifestyle scale had a Cronbach’s alpha coefficient of 0.68 and consisted of two items. Higher scores indicated greater agreement that farming was more of a lifestyle than a business. The scale is summarised in Table 23.

Table 23. Summary for the Lifestyle Scale

n (406)

Minimum Score 2 (2)


Mean Score 7.28

Number of Items 2


The mean score suggests respondents generally felt that the lifestyle of farming was more important to them than the business aspect. Innovation

The Innovation scale had a Cronbach’s alpha coefficient of 0.66 and consisted of three items. Higher scores indicated respondents who reported they tended towards being innovators (liked being the first to try new things/methods) in farming. The scale is summarised in Table 24.

Table 24. Summary for the Innovation Scale

n (405)

Minimum Score 3 (3)


Mean Score 10.19

Number of Items 3



5.1.8. General environmental statements A series of six statements were presented to respondents to elicit their general attitudes towards the environment. Table 25 provides the distributions of responses to each statement.

Table 25. General Environmental Statements

Statement

1 strongly disagree

(%)

2 mildly

disagree

(%)

3 unsure

(%)

4 mildly agree

(%)

5 strongly

agree

(%)

Mean

People have the right to change the environment to suit their needs (n = 407)

29.2 34.2 20.6 12.8 3.2 2.27

When people interfere with nature it often ends in disaster (n = 407)

2.5 12.2 18.2 40.3 26.8 3.77

People are severely abusing the environment (n = 406)

8.9 23.2 20.7 34.5 12.7 3.19

The Earth has plenty of resources if we just learn how to develop them (n = 406)

6.2 13.5 14.5 48.8 17.0 3.57

People will eventually learn enough about how nature works to be able to control it (n = 405)

28.6 29.6 18.5 21.3 2.0 2.38

If we continue the way we have there will soon be an environmental catastrophe (n = 406)

8.1 17.7 20.4 29.6 24.2 3.44

Almost two-thirds (63.4%) of respondents mildly or strongly disagreed that people have the right to change the environment to suit their needs. Additionally, 67.1% agreed that when people interfere with nature it often ends in disaster. Of the 406 respondents to the statement, ‘People are severely abusing the environment’, 15.2% more respondents agreed with the statement than disagreed. About two-thirds (65.8%) of respondents mildly or strongly agreed that the Earth has plenty of resources if we just learn how to develop them. Only 2% of respondents strongly agreed that people will eventually learn enough about how nature works to be able to control it (28.6% strongly disagreed with the statement). Just over half of the respondents (53.8%) believed that if we continue the way we have there will soon be an environmental catastrophe.


5.1.9. Environmental condition Respondents were asked to rate the environmental condition of their area as well as their particular property. Responses are shown in Table 26.

Table 26: Ratings of environmental condition of the area and property

Question

1 severely degraded condition

(%)

2

(%)

3 fair

condition

(%)

4

(%)

5 extremely

good condition

(%)

Mean

Soil Health

Area (n = 405) 4.4 16.3 49.2 26.4 3.7 3.09

Property (n = 407) 2.5 9.8 35.8 41.3 10.6 3.48

Weeds

Area (n = 405) 11.6 28.5 40.2 17.3 2.5 2.71

Property (n = 407) 5.4 16.5 29.7 37.1 11.3 3.32

Water Quality

Area (n = 403) 7.2 19.7 30.3 37.8 5.2 3.14

Property (n = 405) 4.7 12.3 22.2 45.7 15.1 3.54

Erosion

Area (n = 405) 4.7 17.6 42.3 30.6 5.0 3.14

Property (n = 407) 4.7 9.1 25.3 35.8 25.1 3.68

Mean ratings for the wider area of soil health (mean = 3.09), weeds (mean = 2.71), water quality (mean = 3.14) and erosion (mean = 3.14) indicated a view of fair condition. For respondents’ properties the ratings of soil health (mean = 3.48), weeds (mean = 3.32), water quality (mean = 3.54) and erosion (mean = 3.68) indicated respondents viewed them to be in fair to good condition. According to the means, respondents generally rated the environmental condition of their own properties to be in better condition than the wider area. However, these differences were only statistically significant (p < 0.01) in the case of the soil health rating (area mean = 3.09; property mean = 3.48).

5.1.10. Land management practices A series of questions was asked relating to five land management practices common to dryland farming. These practices were identified as contributing to good land management during the scoping phase of the study and reviews of past research (see Section 3). The


land management areas explored were soil management, perennial pasture management, native vegetation management, weed management and stock management (where applicable to respondents). Respondents were firstly asked questions regarding their land management practices in the five identified areas. These were followed by questions asking respondents whether they considered these practices to be an effective part of land management and if there were any barriers (cost, time and government regulations) impeding the uptake of these practices.

Soil management This section relates to the management of soil on the respondent’s property. Respondents were firstly asked a series of questions about soil testing. Out of the 407 respondents, 158 (38.8%) reported undertaking regular soil testing on their properties. Of the respondents who tested their soil, almost half (48.7%) used a consultant to test soil on their property, 18.4% conducted the soil test themselves, and 32.9% said they used a combination of consultants and themselves to test the soil on their property. When these 158 respondents were asked what soil testing was conducted, the majority of respondents said they tested for pH (n = 114) and fertiliser requirements (n = 112). Only twenty-one of the respondents said they tested the soil for moisture retention/infiltration, and sixty-six respondents said they undertook tests for trace elements/long term soil health. Respondents were then asked how often they tested their soil. Test frequency is shown in Table 27.

Table 27. Soil test frequency

Frequency n (154) %

Every month 2 1.3

Every 6 months 9 5.8




Every 2 years 36 23.4

Every 2.5 years 4 2.6



Every 4 years 8 5.2



Every 6 years 2 1.3

Less than once every 8 years 1 0.6

Mean = 25.1 months


On average respondents conducted soil testing approximately every two years (mean = 25.1 months). Over a third of respondents who did soil testing (42.8%) indicated that they did so at least every 12 months or less. Respondents were asked about several possible barriers in undertaking regular soil testing on their properties, as well as whether they believed it was a effective part of good land management. Results are presented in Table 28.

Table 28. The effectiveness of and perceived barriers to soil management

Soil Testing

1 not at all effective

(%)

2

(%)

3 somewhat effective

(%)

4

(%)

5 extremely effective

(%)

Mean

An effective part of good land management? (n = 407)

0.5 4.7 31.2 32.4 31.2 3.89

1 no concern

at all

(%)

2

(%)

3 a slight concern

(%)

4

(%)

5 a major concern

(%)

Mean

Cost a concern? (n = 407) 21.9 10.6 25.1 20.4 22.0 3.10

Time a concern? (n = 406) 60.8 9.9 16.7 8.7 3.9 1.85

Government regulations a concern? (n = 406)

70.0 6.9 11.1 5.4 6.6 1.72

Almost one-third (31.2%) of respondents believed that regular soil testing is an extremely effective part of good land management. Results showed a fairly even spread across responses for cost as a barrier, with 21.9% indicating no concern at all and 22.0% indicating a major concern. The mean rating indicates that, overall, respondents felt it was a slight concern (mean = 3.10). The mean rating for time and government regulations indicate that respondents generally felt there was less than a slight concern but more than no concern at all (time mean = 1.85; government regulations mean = 1.72).

Perennial pasture management This section relates to perennial pastures on the respondent’s property. Respondents were asked a series of questions about setting aside land for perennial pastures. The majority of respondents said yes (n = 325; 79.9%) when asked if any of their property is under perennial pastures. One-fifth of respondents (n = 82; 20.1%) reported not having any land under perennial pasture. For the 325 respondents who had perennials planted, the most common varieties were phalaris (28.4%), lucerne (19.6%), native grasses/pastures (15.2%) and coxfoot (13.7%). Respondents were then asked why they planted perennial pastures. Responses were categorised into four groups: ‘stock feed and production purposes’; ‘positive environmental impact’ (including erosion control/groundcover retention); ‘land recovery’ (including regeneration/maintenance of groundcover/best perennial for area); and ‘soil/pasture improvement’ (see Table 29).


Table 29. Reasons for planting perennial pastures*

Reason n (325) %

Stock feed and production 264 81.2

Positive environmental impact (incl. erosion control/groundcover retention) 59 18.2

Land recovery (incl. regeneration/maintenance of groundcover/best perennial for area);

50 15.4

Soil/pasture improvement 34 10.5

*Respondents could be categorised into more than one group according to the responses offered. The most commonly reported responses regarding why they planted perennial pastures fell under the stock feed and production category (81.2% of respondents). Again, respondents were asked about possible barriers in undertaking the practice, as well as its effectiveness in good land management. Results are presented in Table 30.

Table 30. The effectiveness of and perceived barriers to perennial pasture management

Perennial pasture planting


(%)

2

(%)


(%)

4

(%)


(%)

Mean


1.0 2.0 15.1 24.4 57.5 4.36

1 no concern

at all

(%)

2

(%)

3 a slight concern

(%)

4

(%)

5 a major concern

(%)

Mean

Cost a concern? (n = 405) 24.9 6.2 23.7 21.2 24.0 3.13

Time a concern? (n = 406) 41.6 14.0 22.9 15.3 6.2 2.30


66.7 9.6 12.8 5.2 5.7 1.73

Over half of the respondents (57.5%) believed having perennial pastures is an extremely effective part of good land management, with only 1% stating perennial pastures are not effective at all (Table 30). When respondents were asked about whether cost was a concern when setting aside land for perennials, a mixed response was received, with 24.9% stating that it was no concern at all, 23.7% a slight concern and 24.0% a major concern. The mean rating indicates respondents generally felt that it was a slight concern (mean = 3.13). A large proportion of respondents (41.6%) regarded time to be no concern at all when setting aside land for perennials, with two-thirds (66.7%) stating government regulations were no concern at all.


Mean ratings for time and government regulation barriers again fell between being a slight concern and no concern at all (time mean = 2.30; government regulations mean = 1.73).

Native vegetation management This section relates to the native vegetation on the respondent’s property. Over half of respondents (n = 216; 53.1%) said they had planted native vegetation on their properties. The amount planted ranged from 1% to 100% of their total property size, with an average of 7.2%. The vast majority (82.8%) of respondents said they had planted 10% or less, with 37.3% planting 1% or less. Respondents were also asked what percentage of their property was currently under native vegetation (including both what they had planted and remnant vegetation). The amounts ranged from 0% to 100% with an average of 35.7%. Over half of the respondents (51.1%) indicated that they had between none and one-quarter of their land under native vegetation, 23.0% said between one-quarter and half of the property was under native vegetation, 11.7% said between half and three-quarters, and 13.0% said between three-quarters and wholly under native vegetation. Respondents were asked about possible barriers in undertaking the practice, as well as its effectiveness in good land management. Results are presented in Table 31.

Table 31. The effectiveness of and perceived barriers to native vegetation management

Native vegetation


(%)

2

(%)


(%)

4

(%)


(%)

Mean


3.0 5.2 27.5 25.5 38.8 3.92

1 no concern

at all

(%)

2

(%)

3 a slight concern

(%)

4

(%)

5 a major concern

(%)

Mean

Cost a concern? (n = 407) 22.6 9.1 24.1 20.9 23.3 3.13

Time a concern? (n = 407) 34.4 11.5 23.8 16.5 13.8 2.64


68.1 7.6 14.2 4.7 5.4 1.72

When respondents were asked if they thought planting natives was an effective part of good land management, only 3% stated that it was not at all effective, whereas 38.8% said it was extremely effective. The mean frequency shows that respondents view cost (mean = 3.13) and time (mean = 2.64) as only a slight concern. Just over two-thirds of the respondents (68.1%) have no concern at all about government regulations when thinking about planting native vegetation.


Weed management Respondents were asked about weed management on their properties. In particular, they were asked to nominate the level of focus weeds received in their farm management, and the extent to which their property was affected by weeds. Results are presented in Table 32.

Table 32. Weed management on property

Question

1 none at all

(%)

2

(%)

3 some focus

(%)

4

(%)

5 large focus

(%)

Mean

Amount of farm management focussed on weed control (n = 407)

0.7 6.9 23.4 30.2 38.8 4.00

1 not at all affected

(%)

2

(%)

3 somewhat affected

(%)

4

(%)

5 severely affected

(%)

Mean

Amount of property affected by weeds (n = 406)

5.4 30.5 48.4 10.8 4.9 2.79

Less than 1% of respondents said that they did not focus any of their farm management time on weed control. In contrast 38.8% of respondents rated weed control as a large focus. When discussing how much of their property was affected by weeds, nearly half the respondents (48.4%) indicated that their property was somewhat affected. Respondents were then asked what weed control techniques they used on their properties. A wide variety of responses were recorded and grouped into four categories – chemical spraying4 only; chemical spraying (excluding spot spraying) and other non-chemical techniques5; chemical spot spraying and other non-chemical techniques; and non-chemical techniques. Table 33 shows the number of respondents in each category.

Table 33. Weed control techniques used

Weed Control Technique n (407) %

Chemical spraying only 99 24.3

Chemical spraying and other non-chemical control techniques 234 57.5

Chemical spot spraying and other non-chemical control techniques 23 5.7

Non-chemical techniques 51 12.5

More than half the respondents (57.5%) incorporated a combination of both chemical spraying and other non-chemical techniques to control weeds on their property.

4 Chemical spraying included commercial and ‘boom’ spraying 5 Non-chemical techniques include rotational grazing, increase ground cover, ripping, burning, mulching, minimum till


Respondents were asked about possible barriers in undertaking weed control, as well as its effectiveness as part of good land management. Results are presented in Table 34.

Table 34. The effectiveness of and perceived barriers to weed management

Weed management


(%)

2

(%)


(%)

4

(%)


(%)

Mean


1.0 0.5 6.6 22.6 69.3 4.59

1 no concern

at all

(%)

2

(%)

3 a slight concern

(%)

4

(%)

5 a major concern

(%)

Mean

Cost a concern? (n = 407) 15.5 8.4 21.1 23.1 31.9 3.48

Time a concern? (n = 407) 23.6 8.1 24.8 21.6 21.9 3.10


37.3 9.3 24.3 17.7 11.4 2.56

Over two-thirds (69.3%) of respondents rated weed control as an extremely effective part of good land management, 6.6% somewhat effective and 1% not at all effective. When presenting the barriers respondents had when thinking about weed control, frequencies were spread across the 5-point scale, and the means indicated that the three potential barriers were generally a slight concern for respondents (mean cost concern = 3.48; mean time concern = 3.10; mean government regulations concern = 2.56).

Stock management For those who had stock on their properties, a series of questions were asked regarding management of stocking rates on the property, in particular if they had undertaken activities to minimise the environmental impact of stock at a property scale.

Table 35. Minimising the environmental impact of stock

Activity Yes (%)

No (%)

Reduced stocking rates (n = 397) 80.6 19.4

Erected fences to exclude stock from rivers and waterways (n = 397)

45.8 54.2

Erected fences to exclude stock from re-vegetation areas (n = 397)

60.7 39.3


As shown on Table 35, 80.6% of respondents indicated they had reduced their stocking rates, responses were split roughly in half (yes = 45.6%, no = 54.2%) for erected fences to exclude stock from rivers and waterways, and 60.7% of respondents erected fences to exclude stock from revegetated areas. However, it is unknown how many of these respondents had no land adjoining waterways or areas of revegetation to fence. Again, respondents were asked about possible barriers in undertaking the practice, as well as its effectiveness as part of good land management. Results are shown in Table 36.

Table 36. The effectiveness of, and perceived barriers to, stock management

Stock management


(%)

2

(%)


(%)

4

(%)


(%)

Mean


2.0 2.3 17.9 30.1 47.7 4.19

1 no concern

at all

(%)

2

(%)

3 a slight concern

(%)

4

(%)

5 a major concern

(%)

Mean

Cost a concern? (n = 397) 10.3 6.3 19.4 30.2 33.8 3.71

Time a concern? (n = 397) 23.2 8.6 25.2 24.4 18.6 3.07


58.7 10.0 16.1 7.6 7.6 1.95

Just under half of the respondents (47.7%) agreed that reducing stocking rates, erecting fences to exclude stock from rivers, waterways and re-vegetation areas are extremely effective, with only 2.0% believing they are not effective at all. While the majority of respondents felt that cost (83.4%) and time (68.2%) were at least a slight concern when thinking about stock management, the majority of respondents thought that government regulations (58.7%) were no concern at all when considering stocking rate management activities.

Comparing the Effectiveness of, and Barriers to, Land Management Practices The reported effectiveness of the five land management practices was compared. The following statistically significant differences were found:

• weed control was seen as a more effective component of good land management than any of the other practices;

• perennial pasture management was seen as more effective than stock management, native vegetation management and soil management; and

• stock management was seen as more effective than native vegetation management and soil management.

For all land management practices, cost was seen as a significantly larger barrier than both time and government regulations. In turn, time was seen as a significantly larger barrier than


government regulations, with the exception of soil management, where time and government regulations were viewed similarly. Each of the barriers was combined to identify any differences in overall perceptions of barriers to land management practices. The following statistically significant differences were found:

• perceived barriers affecting weed management were larger than for each of the other land management practices;

• perceived barriers affecting stock management were larger than for native vegetation management, perennial pasture management, and soil management; and

• perceived barriers affecting perennial pasture management were larger than for soil management.

Additional activities Respondents were asked if there was anything else they thought they could do on their properties to reduce environmental impacts. The most common response recorded was nothing else (n = 153). Other common responses are given in Table 37 (a complete list of additional activities can be seen in Appendix F).

Table 37. Other activities undertaken on properties to reduce environmental impact

Activity n %

no/nothing 153 27.9%

contour banking/grading to maintain/control water flows 39 7.2%

minimum till (including direct drilling)/stubble retention 32 5.8%

stock rotation 28 5.1%

erosion control/water diversion to stop erosion 26 4.7%

cell grazing 20 3.6%

feral animals/pest control 16 2.9%

rotational grazing 9 1.6%

grazing management 8 1.5%

Additional barriers When respondents were asked if there was anything that stopped them from doing what they wanted to do on their farm, the most common responses were rain/lack of water/drought (n = 97) and age (n = 21). A number of respondents (n = 64) indicated that nothing has stopped them from doing the things on the farm they want (refer to Appendix G for a full list of barriers as stated by respondents). While this question was designed to elicit responses that were in addition to the perceived barriers of cost, time and government regulations, these three barriers were reiterated by a large proportion of respondents, most notably, cost, which was mentioned by 213 respondents.


5.1.11. Future involvement As a final question respondents were asked if they would be interested in being involved in any future stages of the project. The vast majority of respondents (n = 375; 93.3%) agreed to be involved, which is very encouraging for future partnerships.

5.2. Group comparisons Statistical analyses were performed to identify any differences in responses to particular questions based on key characteristics of the respondents. These included the respondents’ age groups, sub-catchment area, agricultural qualifications, farm size, involvement in community groups and whether they had a written farm plan. Only a small number of significant differences, were identified. These are reported below.

Differences between age groups • Respondents aged less than 24 years generally thought that erosion on their

property was significantly worse than did older respondents (Table 38).

Table 38. Mean environmental condition ratings across age groups

Age group Mean*

Less than 24 1.67

25 to 34 years 3.73

35 to 44 years 3.47

45 to 54 years 3.65

55 to 64 years 3.78

65 to 74 years 3.85

More than 75 3.88

*higher scores denote better environmental condition and the dotted lines indicate where the significant differences were

Differences between sub-catchments • Respondents living in the Cudgegong sub-catchment (mean = 4.12) scored

significantly higher on the native vegetation management component of the land management practices measure than respondents living in the Bell sub-catchment (mean = 2.92).


Differences based on agricultural qualifications Respondents who possessed formal agricultural qualifications:

• were significantly less trusting of farm management information from other farmers than respondents without qualifications (with qualifications: mean = 3.38 and without qualifications: mean = 3.75);

• felt that other farmers had significantly less influence on their farming practices than respondents without qualifications (with qualifications: mean = 3.20 and without qualifications: mean = 3.49);

• thought that educational institutions had significantly more influence on their farming practices than respondents without qualifications (with qualifications: mean = 2.93 and without qualifications: mean = 2.56);

• valued the views of other farmers significantly less than respondents without qualifications (with qualifications: mean = 3.68 and without qualifications: mean = 3.92);

• placed significantly higher value in science and technology than respondents without qualifications (with qualifications: mean = 18.15 and without qualifications: mean = 17.16);

• were significantly less likely to see farming as a lifestyle than respondents without qualifications (with qualifications: mean = 6.69 and without qualifications: mean = 7.63); and

• were less concerned about the environment in general than respondents without qualifications (with qualifications: mean = 13.47 and without qualifications: mean = 14.55).

While the above differences are statistically significant, it should be noted that this does not imply an opposing viewpoint. For example, although statistically those who had agricultural qualifications valued the views of other farmers less than others, generally speaking, both groups valued the opinions of other farmers.

Differences based on farm size • Respondents with less than 40ha of land (mean = 2.93) rated land management

information from agronomists as significantly less influential to their farming practices than did respondents with more than 1000ha (mean = 3.60).

• Respondents with more than 400ha of land were significantly less likely to see farming as a lifestyle (see Table 39) than respondents with less than 400ha.

Table 39. Mean lifestyle scores across farm sizes

Farm size (ha) Mean*

0 to 40 8.21

41 to 100 8.16

101 to 400 7.38

401 to 1000 6.22

More than 1000 6.34

* The dotted lines indicate where the significant differences were


• Environmental values were higher for respondents who farmed up to 100ha of land (0 to 40ha: mean = 14.76 and 41 to 100ha: mean = 15.04) than those who had more than 1000ha (mean = 12.96).

• Respondents who had 40ha or less (mean = 2.25) were significantly less likely to

see time as a barrier to good land management practices than respondents who had more than 1001ha of land (mean = 2.79).

Differences based on community group involvement Generally, respondents who were involved in community groups:

• were significantly more trusting of land management information given by the CMA than respondents who were not involved (involved: mean = 3.02; not involved: mean = 2.74);

• reported that the CMA had significantly more influence on their farming practices than respondents who were not involved (involved: mean = 2.59; not involved: mean = 2.24);

• expressed a significantly greater sense of community engagement than respondents who were not involved (involved: mean = 13.69; not involved: mean = 12.93); and

• scored significantly higher for the stock management component of the land management practice measure (mean = 8.35) than those who were not involved (mean = 7.28).

Differences based on written farm plans Respondents who had a written farm plan:

• were significantly more trusting of information provided by the CMA (mean = 3.12) and Landcare (mean = 3.34) than respondents who did not have a written farm plan (CMA mean = 2.81; Landcare mean = 3.03);

• reported that the CMA (mean = 2.76), Landcare (mean = 2.80) and organised courses (mean = 3.16) had significantly more influence on what they did on their farm than respondents who did not have a written farm plan (CMA mean = 2.30; Landcare mean = 2.45; organised courses mean = 2.78);

• were significantly less likely to see farming as a lifestyle (mean = 6.32) than those who did not have a farm plan (mean = 7.68);

• were significantly more willing to try new things in farming (mean = 10.98) than respondents who did not have a written farm plan (mean = 9.86);

• scored significantly higher for the soil management component of the land management practice measure (mean = 3.47) than respondents who did not have a written farm plan (mean = 1.62);

• scored significantly higher for the stock management component (mean = 8.81) of the land management practice measure than respondents who did not have a written farm plan (mean = 7.54); and

• were more likely to undertake desirable land management practices (mean = 3.43) than respondents who did not have a written farm plan (mean = 3.07).


5.3. Predicting land management practices – the structural equation model

LISREL 8.72 software (Joreskog, Sorbom, du Toit & du Toit, 2000) and Robust Maximum Likelihood Estimation were used to create an initial exploratory structural equation model containing all of the hypothesised variables. As an initial step, non-significant relationship pathways were removed, as were any variables with no significant relationship with any other variables. In this stage of exploration the following variables were removed from the model:

• Community Engagement

• Age

• Succession Planning

• Social Norm

Two further components of the model contained no significant pathways with the remaining model components. These were Perceived Environmental Degradation and Perceived Barriers to Change. Both of these components appeared to measure multiple aspects of a concept. Perceived Environmental Degradation looked at individual properties as well as the larger area, and Perceived Barriers to Change included Cost Barriers, Time Barriers and Government Regulations. It was therefore decided to break these components into sub-components and see if they had a significant impact on the model in this way. Perceived Environmental Degradation was split into two components, Perceived Environmental Condition of Individual Property, and Perceived Environmental Condition of the Area. Both of these had significant pathways within the model and were retained for further analysis. Perceived Barriers to Change became three components: Perceived Cost Barriers, Perceived Time Barriers and Perceived Government Regulation Barriers. Of the three new components, only Perceived Cost Barriers had significant relationships within the model, hence Perceived Time Barriers and Perceived Government Regulation Barriers were dropped from the model. The final model can be seen in Figure 5 which shows the relationships between the latent variables (shown in the model as ellipses) and their respective indicators (shown in the model as rectangles). This reveals how well the indicators (eg. ATT7, an attitudinal statement regarding people’s attitude towards science and technology) measure the latent variables of interest (eg. trust in science & technology, SCITECH). Coefficients on these paths can range from -1.0 (ie. a strong negative relationship between the latent variable and the indicator) to +1.0 (ie. a strong positive relationship between the latent variable and the indicator). Figure 5 shows that all indicators in the model have at least moderate relationships with the latent variables they were hypothesised to measure.


HECTARE0.05

ATT6R0.86

ATT70.63

ATT80.50

ATT90.76

ATT10R0.84

ATT110.29

ATT130.60

ATT140.48

ATT15R0.85

ATT160.49

CONDPR10.50

CONDPR20.82

CONDPR30.71

CONDPR40.66

AGQUALSC0.25

FARMSIZE

LIFESTYL

AGQUAL

SCITECH

PROPCOND

INNOVATE

FARMPLN

ENVALS

AREACOND

TRSTINFL

COSTB

EFFECT

LMP

TSTINF1

0.62

TSTINF2

0.66

TSTINF3

0.73

TSTINF4

0.65

TSTINF5

0.91

TSTINF6

0.63

TSTINF7

0.71

TSTINF8

0.63

TSTINF9

0.60

TSTINF10

0.59

TSTINF11

0.52

TSTINF12

0.64

CONDAR1

0.44

CONDAR2

0.92

CONDAR3

0.83

CONDAR4

0.66

NEP1R

0.91

NEP2

0.80

NEP3

0.41

NEP6

0.50

FARMPLAN 0.09

EFFECTIV 0.24

COSTBAR 0.24

LMPRAC

0.21

0.610.590.52 0.600.310.610.530.610.630.640.700.60

0.750.29 0.420.59

0.29 0.450.77 0.71

0.95

0.87

0.87

0.89

0.97

0.38

0.61

0.71

0.49

0.39

0.84

0.63

0.72

0.39

0.71

0.710.420.540.58

0.86

-0.38-0.19

0.39

0.15

-0.14

0.20

-0.36

0.17

0.21

0.57

0.18

0.30

0.25

Figure 5. Model 1: Estimated Structural Equation Model for Land Management Practices with

Scale Items

Variable labels in the Estimated Structural Equation Model (Figure 5) are as they appear in Lisrel 8.72, and differ from the descriptive labels in both the simplified (see Figure 6) and hypothesised model (Figure 1). Table 40 shows the variable names as they appear in the Estimated Model (Figure 5) and the model component names they correspond with. Descriptions of the model component variable names can be found in Section 3.3.

Table 40. Variable names and descriptive labels for the Estimated Structural Equation Model (Figure 5)

Estimated Structural Equation Variable Name Model Component Name

FARMSIZE Farm Size

SCITECH Science and Technology

LIFESTYL Lifestyle

AGQUAL Agricultural Qualifications

INNOVATE Innovator

TRUSTINFL Trust in and Influence of Information Sources

ENVALS Environmental Values

FARMPLN Farm Plan


Table 40. (contd.)

Estimated Structural Equation Variable Name Model Component Name

COSTB Perceived Cost Barriers to Change

AREACOND Perceived Environmental Condition – Area

PROPCOND Perceived Environmental Condition – Property

EFFECT Perceived Effectiveness of Behaviour

LMP Land Management Practices

Figure 6 shows a simplified version of the structural equation model, and displays the relative strength and significance of pathways. Here, all the pathways shown are statistically significant, with relatively larger effects shown as thick red arrows, relatively moderate effects as thinner blue arrows, and relatively weaker effects as thin green arrows.

PerceivedEffectiveness Of

Land Management Practices

Perceived Barriers toChange – Cost


Condition – Area

Farm Plan

Farm Size

Lifestyle

Science & Technology

AgriculturalQualifications

Trust in and Influence of

Information Sources

Land Management

Practices

EnvironmentalValues

Innovator


Condition - Property

0.18

-0.14

-0.36

0.17

0.21

0.57

0.30

0.25

0.15

0.20

-0.38

0.39

-0.19

14% OF VARIANCE EXPLAINED

Strong Effect

Moderate Effect

Weak Effect

Figure 6. Model 1 Simplified: Estimated Structural Equation Model for Land Management

Practices

Figure 6 also shows the relationships between the independent variables and the dependent variable (ie. Land Management Practices). The coefficients on these paths can range from -1.0 (ie. a strong negative relationship between the predictor and Land Management Practices) to +1.0 (ie. a strong positive relationship between the predictor and Land Management Practices). Figure 6 shows that four latent variables had significant direct relationships with Land Management Practices – Innovator, Farm Plan, Perceived Effectiveness and Perceived Cost


Barriers. Although the other latent variables did not have a significant direct effect on Land Management Practices, they all imparted a mediated influence through their direct relationship with other variables. The relationships between the variables in the model can be summarised as follows.

• Land Management Practices

The following conditions lead directly to an increased likelihood of undertaking desirable Land Management Practices:

a reduced perception that cost is a barrier to undertaking desirable land management practices;

a greater belief that particular practices are an effective means of good land management;

having a written farm plan; and

a reported emphasis on innovative practices.

• Farm Size

Larger farm size leads directly to:

increased perception that cost is a barrier to undertaking desirable land management practices.

• Science and Technology

Greater belief in the relevance of science and technology leads directly to:

greater levels of trust in, and influence taken from sources of information regarding land management practices.

• Lifestyle

Seeing farming as more of a lifestyle than a business leads directly to:

a decreased likelihood of having a written farm plan; and

an increase in environmental values.

• Agricultural Qualifications

The possession of agricultural qualifications leads directly to:

an increased likelihood of having a written farm plan.

• Trust in and Influence of Sources of Information

Greater trust in and influence of sources of land management information lead directly to:

a greater belief that particular land management practices are an effective means of good land management.

• Environmental Values

Greater environmental values lead directly to:

increased perceptions of environmental degradation of the area as a whole.


• Perceived Environmental Condition – Area

Perceptions of degraded environmental condition of the area lead directly to:


• Perceived Environmental Condition – Property

Perceptions of good environmental condition of property lead directly to:


The model accounted for 14% of the variance in land management behaviour and its overall goodness-of-fit indices were adequate given the exploratory nature of the study and model (see Table 41). The Chi-Square was significant at the .05 level indicating that the model could not reproduce the relationships among the indicators observed in the sample within a .05 level of significance. As the chi-square statistic is known to be upwardly biased in samples of 200 cases or more (Hair et al., 1995) a number of other goodness-of-fit measures to test the overall fit of the model were applied. The Root Mean Square Error of Approximation (RMSEA) measure was well within the suggested standards, and although the GFI and CFI were slightly outside of recommended values (Kline, 2005) they were acceptable given the exploratory nature of the study.

Table 41. Model fit indices for initial structural equation model

Fit Statistics Obtained Value Recommended Value

Chi-square (df) 1660.26 (730), p < .05 p > .05

CFI 0.86 ≥ .90

GFI 0.80 ≥ .90

RMSEA 0.06 ≤ .08


6. Summary and Discussion

6.1. Study participants A diverse range of dryland farmers were interviewed as part of this study. It is interesting to note that aside from an expected relationship between age and years farming, that there were no significant differences between these socio-demographic elements and any of the major variables (such as trust in information, environmental values and so on). Consequently, the findings can be confidently extrapolated to the dryland farming populations of these sub-catchment. Also, sub-catchment comparisons revealed very few significant differences between respondents living in the Bell sub-catchment with those in the Cudgegong sub-catchment, suggesting that the model presented here may have broader geographical applicability.

6.2. Preliminary results Land Management Practices Across land management practices, there were significant differences in the the level to which cost, time and government regulations were seen as barriers to implementing change. In all cases of land management practices, significant differences were found in the level of impediment that cost, time and government regulations posed in relation to carrying the practices out. In all cases cost was perceived as the biggest barrier, followed by time (although time was notably of lower concern with regards to soil testing). Government regulations were consistently rated of little concern.

Trust and Influence An unexpected finding was the low level of trust with which respondents viewed information on land management practices received from the CMA (relative to the other groups asked about). The CMA was rated second last in both trust and influence only to Community Groups (which may or may not have included general, non land-management-related community groups). That farmers are reporting, as a whole, that they have little trust in the information coming from the CMA, and further that it has little influence, is of some concern and this needs to be addressed in developing partnerships in the PUTTI project. By contrast, there was generally a high level of trust and influence relating to agronomists. It is conceivable that this is influenced by the tendency to instil more worth in services that are paid for (Arkes & Blumer, 1985; Aronson & Mills, 1959). Regardless, it does provide some interesting possibilities for CMA’s in regards to their links with agronomists and the wider farming community. Results also indicated high levels of trust in other farmers, suggesting a cooperative, collaborative approach to land management, which could be highly beneficial for building partnerships. This collaboration is supported by other responses throughout the questionnaire, such as who is sought out for advice when making decisions affecting their properties.

6.3. Structural equation model The exploratory model produced in this stage of the study was able to explain 14% of the variance in Land Management Practices. While relatively low, the variance is acceptable given the exploratory nature of the study and subsequent opportunities for future refinement. It is important to stress that there are a number of key factors that may have contributed to the modest predictive ability of the model that are not linked to its theoretical foundation.


One such limitation is the challenge of developing a measure of desirable land management practices that is dependent on self-report. While the fit statistics identified in the model were not ideal, some were acceptable and others were very close. By pairing down the model it was possible to improve the fit statistics; however it was felt that, at this exploratory stage, any significant pathways should remain and be refined for future stages of the study. Despite both the low variance explained and the methodological challenges, the model does provide a solid base upon which to build.

Omitted variables There were a number of interesting omissions in the resulting model. Age failed to significantly predict Land Management Practices, either indirectly or directly. This is in contrast with other studies which have suggested that younger farmers are more likely to engage in desirable Land Management Practice (eg. Crase & Mayberry, 2002). In fact, age didn’t correlate with Land Management Practices at all, suggesting that its omission can not be explained by a shared variance with the variables remaining in the model (that is, that the effects of age are accounted for by other variables). This seeming anomaly could be due to characteristics specific to farmers in the catchment. Further investigation in a different catchment may provide further insight. Another variable not appearing in the final model was Succession Planning. The failure of this variable to strongly relate to Land Management Practices may be attributable to the widespread uncertainty about the future viability and desirability of land management in the area as a consequence of prolonged drought – a sentiment elicited in the scoping phase. Social Norm also fails to appear in the model. Again, this is at odds with much of the literature (Beedell & Rehman, 1999; Lam, 1999). Qualitative responses appear to suggest that farmers are instrumental in each others decision-making, which suggests that the problem might be one of measurement (a valid social norm measure can provide challenges to construct in self-report style questionnaires). Future stages will continue to investigate the role of social norm whilst attempting to account for any possible measurement uncertainty.

Model variables The Perceived Cost Barriers component was found to have a significant and direct effect on Land Management Practice (ie. the more people felt that cost barriers existed, the less likely they were to engage in the Land Management Practices). In light of qualitative results suggesting that cost and/or money was the number one factor stopping them from doing things on their property, the contribution that Perceived Cost Barriers makes in the model could be viewed as rather modest. This suggests that there are more important factors in people’s decision-making in relation to Land Management Practices than affordability (as evidenced in the structural equation model). Interestingly, there was no significant relationship between those who mentioned money in response to the qualitative question, and Land Management Practices. Perhaps then it is more to do with the personal view of whether a practice is ‘worth spending the money on’ than on its actual affordability relative to financial means. Of further interest is that it appears to be those with larger farms that are more concerned with cost – somewhat against the concept of ‘economy of scale’. One of the striking features of the model is the role that the Innovator variable plays. There is a significant and relatively strong direct link between the amount of innovation in land practices the respondent reports as having and the likelihood of engaging in desirable Land Management Practices. An obvious explanation for this is that many sustainable techniques require some level of proactive management and the willingness to ‘risk’ the adoption of new or innovative techniques and technologies. It is one of the intentions of the next stage of the


study to explore whether this Innovator variable itself can be modelled. That is, can we establish what the precursors to innovation are, and so understand what makes some farmers more predisposed to being more innovative than others? The model also suggests that having a farm plan is a good and direct predictor of desirable Land Management Practices, as hypothesised. However, the preliminary results highlight a dearth of formal farm plans in the region, particularly ones which the CMA has helped fund. Both lifestyle, and whether or not respondents possessed agricultural qualifications, were strong determinants of who was likely to have developed a formal farm plan. The model suggests that those farming for the lifestyle are less likely to formally plan, as are those without formal agricultural qualifications. Perhaps the challenge for the CMA lies in encouraging the smaller or ‘hobby’ farmer to develop a more structured approach to their land management activities. Also, there may be merit in encouraging people to engage in some form of agricultural education, whether it is through formal institutions or less intensive means such as field and training days. As expected, Perceived Effectiveness of Behaviour was a direct predictor of Land Management Practices. What is interesting, however, is the relationship between Perceived Environmental Condition of the Area and Perceived Environmental Condition of the Property with perceptions of effectiveness. While perceptions of area degradation are linked with high levels of effectiveness, property degradation is linked with a low level of effectiveness. This may be at least partly attributable to a feedback effect of people who have initiated desirable practices noting that it has had a positive impact on their own property. Due to these issues of causality, the role of perceived effectiveness will need to be re-evaluated before inclusion in the next stage of the study. In conclusion, this stage of the PUTTI project provides insight into factors that influence desirable land management practices. Future stages of the project will re-evaluate and refine the model presented here and test its ability to be generalised to other catchment areas in NSW. However, this model will provide a useful basis to focus future partnership activities and incentives


7. Recommendations The following recommendations are made as a result of the major findings in this report and the subsequent discussion with the project Coaching Committee.

Refine survey measures Questions in the survey should be refined for future surveys to better measure some of the key variables as were identified in Section 6 in this report.

• The key land management questions should ensure a measure of why, and in some cases where in the landscape of their farms, and when people perform the practices they do, as well as what they do.

• A better measure of Perceived Barriers to Change is required in an attempt to strengthen the contribution of this variable as a predictor of Land Management Practices. This may include an indication of farmers’ responses to perceived barriers.

• A more precise measure of Perceived Effectiveness of the Land Management Practices is also required and its relationship with Perceived Environmental Condition of the Area and of the Property needs greater clarification.

• The measurement of Social Norm should be investigated to determine its role in the prediction of Land Management Practices.

Develop a greater understanding of Farm Plans While the presence of Farm Plans is a significant predictor of Land Management Practices, there needs to be a greater understanding of the nature and degree of usefulness of the Plans. The next stage of the project should investigate this issue to improve measures in the following surveys, and in-depth discussions with farmers should include:

• why they have farm plans and how they were developed;

• what the plans look like/what information is included;

• what role the plan plays in the day to day management of their farms;

• how often the plans are referred to; and

• how often the plans are updated.

Information from these discussions should be used to review the CMA’s incentive program in regard to Farm Plans to ensure it targets farmers’ needs and clarifies the reason why farmers are encouraged to develop farm plans.

Develop an understanding of trust to better utilise currently trusted sources of information and to build trust in key organisations where it is lacking

Trust in organisations and in science and technology were important predictors of farmers’ perceptions of effectiveness of land management practices. At the same time, it was evident that trust in the CMA was lacking. In-depth discussions with a range of farmers should be held to explore the issue of trust, and what leads them to trust individuals and organisations. Discussions should also explore how trust in the CMA could be built, and how scientists could better provide information that would be trusted to the extent that changes would be made on farms. How people distinguish between government agencies and the CMA and how this relates to trust should also be explored.


Insights gained from these discussions should be used to develop a trust building program, and also to better utilise the sources of information that are currently trusted by farmers to promote useful information exchange on land management practices. The findings of the social network analysis currently being conducted should provide a better understanding of how information is exchanged throughout and between the farming communities to assist with this recommendation.

Develop an understanding of the components that make up an innovator to better focus incentive and communication programs

By understanding what makes an innovative farmer, communication and incentive programs may be able to be developed to target some of the key components. This in turn may encourage farmers to “take some chances” with previously untried land management practices.

Develop a communication program to address attitudinal change Attitudes and values in relation to both farming lifestyle and the environment indirectly influenced the performance of Land Management Practices. These should be investigated in more detail with a view to developing a communication program to address change.

Investigate ways to better focus the Incentives Programs to meet the needs of both the CMA and the farmers

Progress on all of the above recommendations will provide valuable information to develop appropriate directions and priorities as a basis for the design of incentives programs such that subscription is increased and broadened. The Project Team and the CMA should work together to achieve this.


8. References Arkes, H.R. & Blumer, C. (1985). The psychology of sunk cost. Organizational Behavior and

Human Decision Processes, 35, 1,124-140. Aronson, E. & Mills, J. (1959). The effect of severity of initiation on liking for a group. Journal

of Abnormal and Social Psychology, 59, 177-181. Australian Bureau of Statistics. (2006). National regional profile: Wellington (S) (Local

Government Area). Retrieved March 20th, 2007, from http://www.abs.gov.au/AUSSTATS/[email protected]/9fdb8b444ff1e8e2ca25709c0081a8f9/0d53958bb0d596b6ca2571cb000c60dc!OpenDocument,

Barr, N. & Cary, J. (2000). Influencing improved natural resource management on farms: a

guide to understanding factors influencing the adoption of sustainable resource practices. Bureau of Rural Sciences: Canberra

Beedell, J.D.C & Rehman, T. (1999). Explaining farmers’ conservation behaviour: Why do

farmers behave the way they do? Journal of Environmental Management, 57, 165-176. Cary, J., Webb, T. & Barr, N. (2002). Understanding landholders’ capacity to change to

sustainable practices. Department of Agriculture, Fisheries and Forestry – Australia. Community Relations Commission for a Multicultural NSW, 2007, Rylstone LGA. Retrieved

March 20th, 2007, from http://www.crc.nsw.gov.au/statistics/Nsw/RylstoneLGA2pp.pdf, Crase, L. & Maybery, D.J. (2002). Social research to underpin the regional catchment plan

implementation for the NSW Murray: Understanding the status quo. Department of Land and Water Conservation, NSW.

Curtis, A. & Byron, I. (2002). Understanding the social drivers of catchment management in

the Wimmera region. The Johnstone Centre: Albury. Grothmann, T. & Ruesswig, F. (2006). People at risk of flooding: why some residents take

precautionary action while others do not. Natural Hazards, 38, 101-120. Lam, S-P. (1999). Predicting intentions to conserve water from the Theory of Planned

Behaviour, perceived moral obligation, and perceived water right. Journal of Applied Social Psychology, 29, 1058-1071.

Leviston, Z., Porter, N.B., Jorgensen, B.S., Nancarrow, B.E. & Bates, L.E. (2005). Towards

sustainable irrigation practices: Understanding the irrigator. A case study in the Riverland – South Australia. CSIRO Land & Water: Perth.

Neuwirth, K., Dunwoody, S. & Griffin, R. (2000). Protecction motivation and risk

communication. Risk Analysis, 20, 721-734. Mid-Western Regional Council. (2006). Mid Western regional council: In profile. Retrieved

March 20th, 2007, from http://midwestern.local-e.nsw.gov.au/files/1595/File/profil.pdf 200307

New South Wales electoral commission. (2007). Electoral district of Orange. Retrieved

March 20th, 2007, from http://www.elections.nsw.gov.au/__data/assets/pdf_file/31497/Orange_Profile.pdf


Tucker, D., Johnston, C., Leviston, Z., Jorgensen, B.S., & Nancarrow, B.E. (2006). Sense of place: Towards a methodology to value externalities associated with urban water systems. CSIRO: Water for a Healthy Country National Research Flagship, Land and Water: Perth.


APPENDIX A

Project Information Sheet



APPENDIX B

Characteristics of Interviewed Landholders Scoping Study


Cudgegong Sub-Catchment 42 interviews with Landholders

Landuse n (42)

Grazing 9

Grazing/Cropping 23

Horticulture 5

Mixed land use 2

Hobby farm 3

Property size (ha) n (42)

0-19 2

20-99 4

100-499 11

500-999 9

1000+ 11

Not specified 5

Time in the area (years) n (42)

0-4 3

5-9 2

10-19 9

20-49 10

50+ 3

Not specified 15

History n (42)

2nd Generation 2

3rd Generation 3

4th Generation 3

5th Generation 3

Farming since settlement 3

Family connection 6

Moved from Sydney 7

Not specified 15

Bell Sub-Catchment 37 interviews with landholders

Landuse n (37)

Grazing 20

Grazing/Cropping 7

Horticulture 7

Mixed land use 2

Other 1

Property size (ha) n (37)

0-19 1

20-99 9

100-499 4

500-999 8

1000+ 14

Not specified 1

Time in the area (years) n (37)

0-4 1

5-9 2

10-19 6

20-49 10

50+ 6

Whole life 4

Not specified 8

History n (37)

4th Generation 3

5th Generation 1

Family connection 9

Moved from Sydney 1

Moved to catchment 10

Not specified 13


APPENDIX C

Interview Summary Scoping Study


FARMERS – prompts are in italics PUTTI Scoping – Central West Cudgegong and Bell River sub-catchments Introduction/background How long have you been in the area? Why did you become a farmer? What do you farm? What is the size of your property? Community How close is the farming community in the Cudgegong/Bell? Are there particular people or groups who are respected and lead the way in terms of farming? If yes, why and how? Land & Water Management What do you think has changed in farming in the district over time? What things haven’t changed? What do you think you do well on your farm? What things would you like to do but can’t? Why not? Can you describe some of the main things (including people or organisations) that influence what you do on your farm and the way you manage it?

Past and potential future influences How much is about money Are broader catchment impacts an influence – on you/on others What role does family play Where do you get information from, how useful is it Who else influences you Off-farm income

How easy is it to change the way you do things on your property? How easy do you think it is for others to change? What sorts of activities and tools are important for the operation of your farm/property?

how and what you grow/graze; whether you plant trees; soil programs succession planning, property management plans, investment in environmental management etc

What are the key things that you think describe a well managed farm?


Catchment Management What are the two key issues for the catchment? (Bell/Cudgegong) Do you know about the Catchment Action Plan and the Incentive Programs of the Central West CMA? What do you think about them– what works/what doesn’t – for your farm and for the catchment? How much do people talk about advice or information that comes from the Catchment Management Authority? What is the single most important thing that science should be doing for you and the farming community?

(Might be developing more disease resistance or higher yielding grains, weed eradication or social scientists working out how to get a better match between on farm behaviour and catchment plans and targets!)

The Future How do you see the future for farming in this district – and the future for the community? Where do you want to be in the future – in 10 years, in 20 years? Ongoing Participation Are you interested in participating in future surveys or discussions about this project or are there others who you think might be interested?


APPENDIX D

Respondents’ Agricultural Qualifications


University Agricultural Qualifications n %

Bachelor of Agriculture/Degree of Agriculture 7 17.9

Diploma of Agriculture 7 17.9

Agricultural Economics 3 7.7

Bachelor of Farm Management 3 7.7

Agriculture Course 2 5.1

Veterinarian PhD 2 5.1

Soil Science PhD 1 2.6

Associate Diploma in Farm Management 1 2.6

Diploma of Farm Management 1 2.6

Bachelor of Rural Science, Master of Applied Science 1 2.6

Diploma In Agriculture, Part Way Through Master In Sustainable Land Management Course 1 2.6

Masters of Agribusiness 1 2.6

Advanced Diploma in Farm Management 1 2.6

Bachelor of Applied Science (Rural Technology) 1 2.6

Bachelor of Science (Wool and Pastoral Science) 1 2.6

Bachelor of Science 1 2.6

Bachelor of Science (Forestry) 1 2.6

Masters in Agriculture 1 2.6

Sustainable Agriculture 1 2.6

Bachelor of Science (Agriculture) 1 2.6

Bachelor of Rural Science 1 2.6

* Some respondents had more than one qualification from the same institution. In these instances, qualifications were combined.


TAFE Agricultural Qualifications n %

Wool Classing 29 33.7

Farm Management 6 7.0

Wool Classing, OH&S Course 4 4.7

Animal Husbandry 3 3.5

Chemical Training 3 3.5

Wool Classing, Sheep Classing, Horse Shoeing, Shearing, Wool Handling 3 3.5

Viticulture 3 3.5

Advanced Certificate in Urban Horticulture 2 2.3


Sheep Classing/Grading 2 2.3

Agricultural College 1 1.2

Agricultural Explosives Course 1 1.2

Associate Diploma in Farm Management 1 1.2

Associate Diploma in Sheep & Wool Technology 1 1.2

Beef Husbandry 1 1.2

Business Management 1 1.2

Certificate IV Work Place Trainer 1 1.2

Certificate of Agriculture 1 1.2

Certificate for Beef Production 1 1.2

Certificate in Land Management 1 1.2

Certificate IV in Horticulture 1 1.2

Chemicals Certificate, Wool Classing, Agricultural Blasting 1 1.2

Chemical Course, Machinery Course 1 1.2

Diploma of Applied Science 1 1.2


Contd.

TAFE Agricultural Qualifications n %


Diploma of Horse Management 1 1.2

Farm Chemicals, Farm Safety 1 1.2

Native Plant and Land Conservation 1 1.2

Organic Farming 1 1.2

Rural Management 1 1.2

Veterinary Course; Agronomy; Tree Care 1 1.2

Water Management 1 1.2

Wine Manufacture 1 1.2

Wool Classing, Farm Management 1 1.2

Wool Classing, Farm Mechanics 1 1.2

Wool Classing, Farm Mechanics, Carpentry 1 1.2

Wool Classing, Sheep Management 1 1.2

Wool Classing, Soil Technology, Viticulture 1 1.2



Other Educational Sources for Agricultural Qualifications (eg. field days, industry courses) n %

Wool Classing 8 23.5



Chemical Training 4 11.8

Agriculture Course 3 8.8

Farm Management 1 2.9

Carcass Grading (Chiller) 1 2.9

Agriculture Safety, Chemical Handling Course 1 2.9

Chemical Classification, Wool Classer 1 2.9

Rural Skills Certificate III, Chem Smart 1 2.9

Certificate of Agriculture 1 2.9

Grazing for Profit Graduate Link 1 2.9

Soil Management 1 2.9

Artificial Insemination 1 2.9

Certificate II in Land Management 1 2.9

Conservation and Land Management 1 2.9



APPENDIX E

What respondents use/do in lieu of using a formal written farm plan


Response n %

rely on experience/do what I’ve always done/it’s in my head 68 15.9%

weather dictates what is done 55 12.9%

day by day 48 11.3%

do things when they're needed/look at property daily to assess 40 9.4%

discuss with spouse/family 34 7.9%

muddle along/fly by the seat of our pants/go with the flow 24 5.7%

year by year/month by month/week by week planning 20 4.7%

seasonal - do things at a particular time of the year 17 4.0%

local knowledge/talk to neighbours/local farmers 10 2.4%

instinct 8 1.9%

I know what I want to do and do it 7 1.6%

nothing/no need for a plan 6 1.4%

work with money available 5 1.2%

keep diary/record 4 0.9%

respond to changes 4 0.9%

work off maps 4 0.9%

work on priority projects 4 0.9%

consult contractors 3 0.7%

have a general plan 3 0.7%

have a long term plan between us 3 0.7%

internet 3 0.7%

plan verbally 3 0.7%

property to small to require one 3 0.7%

carry out ideas without having to write them down 2 0.5%


Contd.

Response n %

good memory 2 0.5%

keep an eye on everything 2 0.5%

use judgement 2 0.5%

a hotch-potch of other written policies 1 0.2%

buy and sell stock 1 0.2%

do what has worked in the past 1 0.2%

do what my father did 1 0.2%

follow basic guidelines 1 0.2%

general agreement 1 0.2%

get advice from the department of agriculture 1 0.2%

graze according to production potential 1 0.2%

guided by our manager 1 0.2%

library 1 0.2%

make changes based on possibility 1 0.2%

plan specific projects 1 0.2%

plans informally 1 0.2%

prefer to remain flexible 1 0.2%

set routine around shearing and mating times 1 0.2%

strategy document 1 0.2%

use budgets 1 0.2%

use rotation method 1 0.2%

work things out myself 1 0.2%


APPENDIX F

Additional Activities Undertaken to Reduce Environmental Impact


Activities n %

no/nothing 153 27.9%

stock rotation/rotational grazing/grazing management 45 8.2%

contour banking/grading to maintain/control water flows 39 7.2%

minimum till (including direct drilling)/stubble retention 32 5.8%

erosion control/water diversion to stop erosion 26 4.7%

cell grazing 20 3.6%

feral animals/pest control 16 2.9%

control soil compaction 6 1.1%

fencing 6 1.1%

plant windbreaks 6 1.1%

limit chemical use 5 0.9%

mulching 5 0.9%

'solar passive house'/solar energy 5 0.9%

recycling 4 0.7%

road design to minimise erosion/laneways, etc. 4 0.7%

choosing the right animals for the climate 3 0.5%

fencing water ways 3 0.5%

harrow out cow/horse manure 3 0.5%

more dams 3 0.5%

organic practices 3 0.5%

pasture management/improvement 3 0.5%

pumping out water to troughs (stock off waterways) 3 0.5%

salinity management 3 0.5%

sub-soil conditioning/soil improvement 3 0.5%


Contd.

Activities n %

buying feed for cattle 2 0.4%

drought proofing 2 0.4%

grading to maintain water flows/control water flows 2 0.4%

improve water catchment 2 0.4%

no burning at all/reduced burning 2 0.4%

reduce litter and garbage 2 0.4%

refuse to have sheep 2 0.4%

regular fertiliser 2 0.4%

resting paddocks 2 0.4%

water conservation in the house 2 0.4%

biodiversity 1 0.2%

biological toilet/septic system 1 0.2%

burn off to reduce fire risk 1 0.2%

cleaning out dams 1 0.2%

composting 1 0.2%

correct disposal of waste (eg. drums) 1 0.2%

crop rotation 1 0.2%

cultivation techniques 1 0.2%

decommission bores 1 0.2%

drought yards (feed lots) 1 0.2%

fodder conservation 1 0.2%

hand water 1 0.2%

maintain driveways 1 0.2%

management plan 1 0.2%


Contd.

Activities n %

minimise contours in paddocks 1 0.2%

no cell grazing 1 0.2%

no genetically modified seeds 1 0.2%

not re-daming 1 0.2%

permaculture 1 0.2%

piping water to reduce evaporation 1 0.2%

plant by the moon 1 0.2%

ploughing at the right time 1 0.2%

ploughing stubble back into ground 1 0.2%

pray for rain 1 0.2%

regular checking of pastures 1 0.2%

silt traps 1 0.2%

top dressing 1 0.2%

using horses 1 0.2%

using small stock areas 1 0.2%

walls to stop erosion 1 0.2%

water regulation / management 1 0.2%


APPENDIX G

Additional Barriers Stopping Respondents Undertaking Activities on Their Farms


Barriers n %

rain/lack of water/drought 97 16.9%

no/nothing 64 11.1%

age 21 3.7%

labour 5 0.9%

land size 5 0.9%

physically unable/energy/motivation 5 0.9%

neighbours 4 0.7%

pests (ie locusts and kangaroos) 4 0.7%

the market/stock/produce prices when selling 4 0.7%

wife/family 4 0.7%

contour of the land (hilly/rocky)/not enough flat land 3 0.5%

need off farm income 3 0.5%

lack of knowledge 2 0.3%

machinery (lack or appropriate) 2 0.3%

not being able to clear lands 2 0.3%

weather/seasons 2 0.3%

weeds 2 0.3%

agro-forestry 1 0.2%

allocation of funding 1 0.2%

clay soils 1 0.2%

family succession 1 0.2%

health 1 0.2%

land subdivision 1 0.2%

Landcare 1 0.2%


Contd.

Barriers n %

licences 1 0.2%

neighbours spraying 1 0.2%

people not accepting of regular farming practices 1 0.2%

produce prices when selling 1 0.2%

second job 1 0.2%

stock prices when selling 1 0.2%

the owner 1 0.2%

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