Regional Economic Profile of South West Victoria

Esther Liu an d Patricia Fitzsimons Future Farming Systems Research and Farm Services Victoria

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Published by: Department of Primary Industries 32 Lincoln Square North, Carlton VIC 3053 November, 2009

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Acknowledgements

We would like to thank our reviewers, Mark Taylor and Anthony Sims, and industry specialists, Sean Kenny and Martin Dunstan, for their time and suggestions to make this a better report. We would also like to thank BITRE and ABARE for the data and information that was provided to us.

Executive Summary

The purpose of this report is to understand the key characteristics and drivers of change on the regional economy of south west Victoria between 1996 and 2006. A series of economic indicators have been selected to undertake an economic analysis of the region. The indicators include: major employing industries, top industry specialisation, industrial diversity, the value and volume of agricultural commodities produced, farm business profit and farm equity ratio. The majority of the indicators are presented in the form of maps to allow a spatial analysis and to identify regional variation within the south west region, others include tables and graphs.

The main findings in this report are: • The south west region of Victoria has a diverse population base due in large part to the major regional centres of Ballarat and Geelong. It is in these regional centres that the trend, evident in much of Australia, towards an ageing population is not so pronounced. In addition, a higher proportion of their population are born overseas, particularly significant in Geelong (16.5%). • Three regional centres reveal significant population growth over the period from 1996 to 2006. The first occurs in Geelong and its environs including the townships of Torquay, Lara and Barwon Heads. The second area is Warrnambool and the third Ballarat. This contrasts with the smaller inland centres of Colac, Hamilton and Ararat which show minimal growth and population declines occurring in Portland. • Two distinct demographic features of south west Victoria are the out- migration of young people between the ages of 10 and 34 years, particularly noticeable between the ages of 25-29 years. The other feature is a low birth rate. • Whilst employment growth varies across south west Victoria, it is strongest in the working zones of Geelong, Ballarat, Warrnambool and Colac-Otway South. • South west Victoria has 7% of their labour force employed in the agriculture, forestry and fishing industries which is significantly higher than the state (2.8%) and national (3.2%) levels. Two other industries with high employment rates are the health and community services sector which employs 12.4% of the workforce in the south west compared to the state (10.9%) and national (11.0%) levels and retail trade which employ 16% in the south west which has a slightly higher share than the state (14.9%) and national (14.7%) levels. This highlights the role of regional centres in providing a range of property, business, retail and social services to agricultural communities. • In general, working zones with a relatively high employment share in primary industries are comparatively low in total employment levels and show little or no change in employment growth. Most of these working zones have no large regional service centres that offer a diversity of employment opportunities. • The employment results reflect long term structural changes taking place in Australia, as farmers exit the industry due to increasing exposure to international competition. Whilst farm numbers declined, the remaining farms increased in size, capital investment, and the adoption of new technologies. By substituting capital for labour, there were gains in productivity and a decline of agricultural employment in regional Australia (Nossal et al., 2009). • Overall for south west Victoria, retail trade has the largest share of the labour force in 2005-06. However, when the region is disaggregated to smaller areas, two thirds of the working zones in south west Victoria is found to have agriculture as their major employing industry. This suggests that most of the

1 local economies in the study region have more variable income sources than those in for example health services. • Most areas in the south west is highly specialised in primary industries, these include agriculture , services to agriculture; hunting and trapping, forestry and logging , and commercial fishing . While agricultural specialisation is strong across most of the region, high specialisation in forestry and logging is concentrated in fewer working zones. However interestingly the forestry and logging employment in Glenelg North is 23.5 times greater than the national industry level. • The industrially diverse working zones of Geelong and Ballarat have shown little or no change in their industrial diversity index from 1996 to 2006 whilst several working zones which have a predominantly agricultural base have increased their industrial diversity. Higher level of industrial diversity has been linked to more stable economic performance, e.g. low unemployment rates, and less variation in income and employment, this may be more preferable than a highly variable income source from specialisation in a few industries. • Average farm business profits in south west Victoria have fluctuated with the occurrences of droughts and commodity prices. Although profits among Victorian dairy farmers are higher than those in broadacre industry, the national trend suggests that the latter have persisted with higher levels of off-farm income. • Average farm equity ratios for the Victorian broadacre industries have performed more favourably than the Victorian dairy industry. In south west Victoria, the difference can be explained by a strong and growing demand for broadacre land (Martin et al., 2005). Another reason for the higher debt levels is that dairy farms tend to be more capital intensive and they require larger investments into the maintenance and expansion of their establishments (Bailey, 1997). • Different areas of the south west region concentrate on producing a diverse range of agricultural commodities. For example, areas in and around Ararat concentrate on the production of sheep, lambs, wool, wheat and canola, Corangamite South and Moyne South concentrate on milk production, and the Glenelg region focuses on meat cattle production. • The south west dairy industry in Victoria is a significant contributor to the regional, state and national economy. In 2005-06, it contributed to 31.8% and 21% of the total value of milk in Victoria and Australia respectively. Important factors that influence this region’s dairy industry include climate and market conditions in importing countries. This is due to the predominance of pasture based dryland dairy farming system and their export orientated production. • Beef is south west region’s second most valuable commodity. In 2005-06, it contributed to 29.6% of the state’s total value of cattle and calves slaughtered. Meat cattle numbers in the region have steadily increased over recent years as domestic expenditure increased and world beef price rose due to a case of ‘mad cow’ disease in the United States of America, a ban on their beef export for 1.5 years and an increase demand for Australian beef from Japan and Korea. • The collapse of the wool price scheme in the early 1990s has sparked many flow-on effects on the spatial distribution of agricultural commodities produced in south west Victoria. While the number of sheep in the region is still relatively high in 2005-06 compared to other parts of Victoria, its number has continued to drop over 2000-01 and 2005-05 due to the negative trend in wool prices. Many wool producers have turned to the following options: start or increase crop production, convert to prime lamb

2 production, sell and/ or lease land to other farmers or to forestry plantation companies. • Drier climate has allowed more farmers in south west Victoria to grow more crops as water logging has previously prevented crop production. This is also encouraged by the strong prices for wheat and canola over the recent years. Traditionally, major crop production came from Mallee and Wimmera in Victoria. However, in 2005-06, Ararat was the top canola producing SLA in Victoria, it alone produced a volume of $9.9 million.

3 Table of contents

Executive Summary ...... 1 List of figures ...... 5 List of tables ...... 7 Abbreviations ...... 8 1. Introduction ...... 9 2. Background of South West Victoria ...... 10 2.1. Spatial unit of analysis...... 10 2.2. Demographic profile...... 12 3. Industry Structure...... 16 3.1. Introduction ...... 16 3.2. Data ...... 16 3.3. Methodology ...... 16 3.4. Overview ...... 17 3.5. Results and analysis ...... 23 3.5.1. Major employing industries...... 23 3.5.2. Industry specialisation ...... 26 3.5.3. Industrial diversity ...... 31 3.6. Summary ...... 34 4. Farm Economic Performance...... 35 4.1. Introduction ...... 35 4.2. Data ...... 35 4.3. Results and analysis ...... 37 4.3.1. Farm business profit...... 37 4.3.2. Farm equity ratio ...... 40 4.4. Summary ...... 43 5. Agricultural Commodities Produced...... 44 5.1. Introduction ...... 44 5.2. Data ...... 44 5.3. Overview ...... 45 5.4. Results and analysis ...... 46 5.4.1. Milk ...... 46 5.4.2. Beef cattle...... 50 5.4.3. Sheep and lambs ...... 54 5.4.4. Wheat for grain...... 58 5.4.5. Canola ...... 60 5.5. Summary ...... 62 6. Conclusion ...... 63 References ...... 64 Appendix 1 ...... 66 Appendix 2 ...... 68

4 List of figures

Figure 2.1. BITRE 2006 working zones in South West Victoria ...... 10 Figure 2.2. ABS Statistical Location Areas for south west Victoria, 2006...... 11 Figure 2.3. AAGIS regions in Victoria ...... 11 Figure 2.4. Population change, Victorian towns (excluding Melbourne) 2001-200613 Figure 2.5. Percentage of rural population in the south west LGAs, 2006...... 14 Figure 2.6. Age pyramid for south west Victoria, 2006 ...... 15 Figure 3.1. Employment share by industry, 2006...... 18 Figure 3.2. Total employment for the working zones in South West Victoria, 2006 19 Figure 3.3. Change in total employment for the SW working zones, 1996 to 2006. 19 Figure 3.4. Change in total employment for the SW working zones, 1996 to 2001. 20 Figure 3.5. Change in total employment for the SW working zones, 2001 to 2006. 21 Figure 3.6. Primary industry employment shares for SW working zones, 2006 ...... 22 Figure 3.7. First major employing industry, 2006 ...... 24 Figure 3.8. Second major employing industry, 2006 ...... 24 Figure 3.9. Top specialisation industry for the South West working zones, 2006 ... 27 Figure 3.10. Level of industry specialisation in agriculture, 2006...... 28 Figure 3.11. Level of industry specialisation in forestry and logging, 2006 ...... 28 Figure 3.12. Level of industry specialisation in food, beverage and tobacco manufacturing, 2006...... 30 Figure 3.13. Industrial diversity index for the South West working zones, 2006..... 32 Figure 3.14. Change in Industrial Diversity Index, 1996 to 2001...... 33 Figure 3.15. Change in Industrial Diversity Index, 2001 to 2006...... 33 Figure 4.1. ADIS regions for New South Wales and Victoria ...... 36 Figure 4.2. Average business profits for the broadacre farms in south west Victoria and Victoria, 1995-96 to 2006-07 ...... 38 Figure 4.3. Average business profits for dairy farms in Victoria ...... 39 Figure 4.4. Off-farm income earned on Australian broadacre and Australian dairy farms 1980 to 1998, constant 1996 dollar terms ...... 40 Figure 4.5. Average equity ratios for broadacre farms, 1995-96 to 2006-07...... 41 Figure 4.6. Average equity ratios for the dairy farms in Victoria...... 42 Figure 5.1. Gross value of selected agricultural commodities in south west Victoria, 1995-96 to 2005-06 ...... 45 Figure 5.2. Export shares of principal agricultural commodities produced in south west Victoria, by volume, 2004-05...... 46 Figure 5.3. Gross value of milk, 2005-06 ...... 47 Figure 5.4. Number of cows in milk and dry, 2005-06...... 47 Figure 5.5. Change in number of cows in milk and dry, 2000-01 to 2005-06...... 48 Figure 5.6. Monthly averages of world skim milk powder prices, 1995 to 2009 ..... 49 Figure 5.7. Warrnambool weather station’s total monthly rainfall in 2002 and 2003 compared to average monthly rainfall for 1983 to 2000 ...... 49 Figure 5.8. World production and consumption of skim milk powder, 1988-2008. 50 Figure 5.9. Gross value of cattle and calves slaughtered, 2005-06 ...... 51 Figure 5.10. Total number of meat cattle, 2006...... 51 Figure 5.11. Change in total number of meat cattle, 2000-01 to 2005-06 ...... 52 Figure 5.12. Monthly averages of the world indicator price for beef, 1992-2009 ... 53 Figure 5.13. Domestic consumer expenditure on beef, 1990-91 to 2005-06 ...... 53 Figure 5.14. Total number of sheep (excluding lambs), 2005-06...... 54 Figure 5.15. Number of lambs under one year, 2005-06 ...... 55 Figure 5.16. Change in total number of sheep (excluding lambs), 2001-2006...... 56 Figure 5.17. Change in the sales of lambs (under one year of age), 2001-2006..... 57 Figure 5.18. Index of prices received for lambs, sheep and wool ...... 58 Figure 5.19. Gross value of wheat, 2005-06...... 59

5 Figure 5.20. World indicator price for wheat, 1998 to 2009...... 60 Figure 5.21. Gross value of canola, 2005-06...... 61 Figure 5.22. Australian grain prices, 1995-96 to forecast for 2013-14 ...... 62

6 List of tables

Table 2.1. Towns in Time: Large towns in south west Victoria, 2006...... 12 Table 3.1. Major employing industries...... 23 Table 3.2. Industry specialisation ...... 26 Table 3.3. Industrial diversity ...... 31 Table 4.1. Farm business profit ...... 37 Table 4.2. Farm equity ratio...... 40 Table 5.1. SLAs with the highest number of cows in milk and dry in SW Victoria, 2000-01 and 2005-06...... 48 Table 5.2. SLAs with the highest meat cattle number in SW Victoria, 2000-01 and 2005-06 ...... 52 Table 5.3. SLAs with the highest sheep production in SW Victoria, 2000-01 and 2005-06 ...... 55 Table 5.4. SLAs with the highest sales of lambs in SW Victoria, 2000-01 and 2005- 06 ...... 56 Table 5.5. SLAs with the highest wheat production in SW Victoria, 2005-06 ...... 59 Table 5.6. SLAs with the highest canola production in SW Victoria, 2005-06 ...... 61

Table A. 1. Survey sample sizes for the Australian Agricultural and Grazing Industries Survey (AAGIS)...... 68 Table A. 2. Survey sample sizes for the Australian Dairy Industry Survey (ADIS) ..... 68 Table A. 3. Average and relative standard error (RSE) for AAGIS profit and equity ratio ...... 68 Table A. 4. Average and relative standard error (RSE) for ADIS profit and equity ratio ...... 69

7 Abbreviations

AAGIS Australian Agricultural and Grazing Industries Survey ADIS Australian Dairy Industry Survey ABARE Australian Bureau of Agricultural and Resource Economics ABS Australian Bureau of Statistics ANZSIC Australian and New Zealand Standard Industrial Classification ASGC Australian Standard Geographical Classification BITRE Bureau of Infrastructure, Transport and Regional Economics BTRE Bureau of Transport and Regional Economics DEW Department of the Environment, Water, Heritage and the Arts DOT Department of Transport DPCD Department of Planning and Community Development FBTM Food, beverage and tobacco manufacturing ID Industrial diversity LQ Location quotient NLWRA National Land and Water Resources Audit SLA Statistical Local Area SW South West TCFM Textile, clothing, footwear and leather manufacturing

8 1. Introduction

In regional and rural Australia, the majority of people are employed in primary industries or industries associated with primary industries, populations are smaller in size and are often ageing, as young people migrate to urban areas (BITRE, 2008). Without an evidence based assessment of the region’s economy, there will be limitations in determining and exploring possible climate change impacts and adaptation strategies.

The Victorian Climate Change Adaptation Program (VCCAP) within the Department of Primary Industries (DPI) has developed a research program to ensure Victoria’s agricultural industries can adapt to a changing climate. The aim of the research is to increase the knowledge and capabilities of government, the agriculture sector and farming businesses to undertake sound and informed planning and policy decisions that maximise the benefits and minimise the economic, social and environmental costs of climate change. VCCAP’s research is focused on south west Victoria with the aim of identifying the distinctive characteristics of Victoria’s regions. In line with the VCCAP research program, this report is focused on south west Victoria.

The main objective of this study is to highlight the patterns of production and performance in the regional economy of south west Victoria between 1996 and 2006. Several economic indicators have been selected following consultation with regional stakeholders, in particular, the South West Climate Change Forum (a group of primary industry and planning groups formed to help south-west Victoria’s primary producers adapt and prepare for changes in climate and climate variability), economic analyst in the Australian Bureau of Agricultural and Resource Economics (ABARE) and economists in the Victorian Department of Primary Industries. This expert advice has been combined with a review of the literature to identify a key set of indicators and spatial maps to highlight regional variation within our study region (BTRE, 2003, BITRE, 2008, Shaffer et al., 2004, Cary et al., 2002, Martin et al., 2005).

The selected economic indicators fall under three themes. The first theme is industry structure which considers all the industries that operate within south west Victoria. BITRE (2008) have found that industrial structure is one of several important drivers that influence an economy. Three indicators selected around the theme of industry structure are: major employing industries, top specialisation industries, and industrial diversity. Our results, presented in chapter 3, are based on industry employment data from the population census conducted by the Australian Bureau of Statistics (ABS).

The importance of agriculture in regional Australia demands that we carefully look at the financial viability of farms and the variety of agricultural commodities produced in south west Victoria. Hence, the second theme, chapter 4 of this report, is farm economic performance. Data collected by the Australian Bureau of Agricultural and Resource Economics (ABARE) are used to consider some of the trends in farm business profit and farm equity ratio. For the third theme, we will examine the values or volumes of key agricultural commodities produced in south west Victoria through the ABS agricultural census data. The commodities considered include: milk, beef cattle, sheep and lambs, wheat, and canola. This theme, which is found in chapter 5, will help us to understand how different areas have used their land under changing economic and environmental conditions.

9 2. Background of South West Victoria

2.1. Spatial unit of analysis

The region of interest is south west Victoria. As the emphasis of an economic analysis is on changes in terms of employment by industry, the data presented under the industry structure theme is based on the 2006 Bureau of Infrastructure, Transport and Regional Economics (BITRE) working zones (BITRE, 2009). BITRE working zones are based on 2006 SLA boundaries and reflect the area within which people are willing to commute from their place of residence to their workplace and therefore provide us with a useful boundary to inform our research (BITRE, 2009). The BITRE has defined 391 working zones across Australia from approximately 1400 SLAs based on commuting patterns revealed by the 2006 census. The BITRE working zones relevant to south west Victoria is outlined in Figure 2.1. The value added of providing working zone employment data is that each working zone forms a functional labour market as reflected in commuting patterns.

Figure 2.1. BITRE 2006 working zones in South West Victoria Note: (S) denotes Shire and (RC) denotes Rural City

The Australian Bureau of Statistics uses the SLA as the base spatial unit to collect and disseminate statistics other than those collected from the Population Censuses. In non-census years, the SLA is the smallest unit defined in the Australian Standard Geographical Classification (ASGC). In aggregate, SLAs cover the whole of Australia without gaps or overlaps. Populations for SLAs are estimated as at 30 June each year. The population estimates for LGAs and other regions are built up from the SLA-level estimates. The Victorian SLAs are identified in Figure 2.2 and are to be found throughout the third theme of this report to inform a spatial analysis using the ABS agricultural census data.

Figure 2.2. ABS Statistical Location Areas for south west Victoria, 2006

Each year ABARE interviews producers from the broadacre and dairy sectors of Australian agriculture as part of its annual survey program. The information collected provides the basis for analysing the current financial position of farmers in these industries to identify anticipated changes that occur in the short term. Data from ABARE’s Australian Agricultural and Grazing Industries Survey (AAGIS) and Australian Dairy Industry Survey (ADIS) are utilised in this profile to gain insights into the performance of broadacre and dairy farms over the period from 1996 to 2006. ABARE have identified five regional zones within Victoria. The south west region of Victoria is referred to as the Western District and Figure 2.3 identifies the five zones for Victoria along with the Western District.

Figure 2.3. AAGIS regions in Victoria

11 2.2. Demographic profile

Major cities in the south west include Geelong, Ballarat and Warrnambool (DPCD, 2008). Table 2.1 provides the population statistics for the top 8 towns in south west Victoria. The southern part of the region is bounded by the Indian Ocean and two of the four Victorian ports are located along this coastline. One port is in Geelong and the other is in Portland. About a quarter of Victoria’s overseas exports go through the port of Geelong, and they mainly consist of raw materials such as petroleum products (gas from Otway Basin), grain and woodchips (DOT, 2009, DPI, 2009). Portland handles around four million tonnes of cargo each year, which includes grain, livestock, woodchips, logs, aluminium ingots, and mineral sands (DOT, 2009). Overall, the south-west region of Victoria has a diverse economic base and contains areas of great scenic beauty, a magnificent coastline and significant national parks such as the Grampians 1 and the Great Otways 2. The southern parts of the region are characterised by flat volcanic plains, while the northern parts are dominated by the Grampians. There are seven major river basins, extensive wetlands and three regional groundwater systems. These natural assets support the important resource-based industries which broadly include agriculture, forestry, and fisheries. Agriculture dominates the region’s land use pattern; however, there are a number of changes occurring to the landscape in the form of urbanisation and industrialisation (Sposito et al., 2008, forthcoming).

Table 2.1. Towns in Time: Large towns in south west Victoria, 2006 Year Geelong Ballarat Warrnambool Colac Portland Hamilton Torquay Ararat 2006 136,518 75,015 28,029 10,562 9,716 9,484 9,468 7,067 2001 131,295 71,618 25,882 10,463 9,584 9,233 8,018 7,068 1996 128,307 68,084 25,412 10,211 9,752 9,389 6,567 7,032 Source: Department of Planning and Community Development, Towns in Time, derived from ABS Census of Population, 2006, 2001 & 1996.

The land and its geographic location have offered a range of opportunities for different industries to prosper. Examples include: timber from forestry plantations and the tourism industry which attracts many visitors for its unique environment, particularly the coastal environment. However, this points to increasing pressure for farm adjustment and demands for land based on its amenity characteristics (Fitzsimons and Cherry, 2008)

The south west region has a diverse population structure that includes Victoria’s two major regional centres of Geelong and Ballarat. Geelong is the largest regional centre in Victoria and acts as a major service provider for the Barwon region. It houses a major port and has a strong industrial base. Out of a total workforce of 59,138, the following four areas employed the majority of the labour force: manufacturing (8792 or 14.9%), retail trade (8467 or 14.3%), health care and social assistance (6979 or 11.8%) and education and training (5163 or 8.7%) (DPCD, 2008). Whilst Geelong was impacted by the economic recession of the 1990s it achieved sustained economic growth of 4% between 2001 and 2006 (DPCD, 2008). In addition

1 The Grampians National Park is one of Victoria’s most popular parks covering over 168,000 ha with a rich diversity of plants and animals, some endemic to the Grampians. 2 The Great Otways National Park covers some 103,000 ha with ancient rain forests, a spectacular coastline, heathlands and woodlands. 12 to economic growth, the population grew by 3.9% between 2001 and 2006 following on from growth of 2.3% from 1996 to 2001. By way of comparison, the second largest regional centre of Ballarat plays a role as an important freight, transport and tourist centre due to its strategic position. Ballarat recorded a 4.7% increase in population between 2001 and 2006 and growth of 5.1% between 2001 and 2006. The labour force profile of Ballarat shows some slight variation from Geelong and includes: health care and social assistance (4,569), retail trade (4,470), manufacturing (4,119), education and training (3,254). These and other regional centres, such as Warrnambool, Portland, Ararat, Colac and Hamilton provide important services to support agricultural industries.

A prevalent trend across much of Australia is the ageing population and this is particularly enhanced in regional Australia. This increase in the proportion of persons in older age groups relative to younger age groups in regional areas is a trend caused by several factors such as: an increase in life expectancy; fall in fertility rates; ageing of the baby boomers; out-migration of youth; changes in land use patterns. However, the ageing profile is not as pronounced in the larger regional centres of Geelong and Ballarat. One additional factor of interest in large regional centres is their capacity to attract a more diverse population group, as a higher percentage of their population is born overseas. This is particularly pronounced in Geelong as 16.5% of the population are born overseas. By way of comparison, 7% of Ballarat’s population are born overseas, 6% of Warrnambool’s population are born overseas whilst 6% are born overseas in Colac (DPCD, 2008).

Figure 2.4. Population change, Victorian towns (excluding Melbourne) 2001-2006

Figure 2.4 highlights towns in south west Victoria that are experiencing population increases and decreases. There are two distinct regions experiencing population growth. The first occurs around Geelong and includes a number of seaside towns which reflect Australia’s love affair with the coast. Rich in natural and cultural resources, the coastal zone is a major area of economic, social, tourism and recreational activity that supports about 86% of Australia’s population (Fitzsimons and Cherry, 2008). The towns in the major growth centre around Geelong

13 experiencing significant population growth between 2001 and 2006 include (DPCD, 2008): Torquay (18% growth), Lara (2.3% growth), Barwon Heads (2% growth) and Ocean Grove (1% growth). In addition, Warrnambool experienced a population growth of 8.3% between 2001 and 2006. This compares to relatively stable populations in the inland towns of Ararat, -0.01%, and Colac, 0.9%, whilst Hamilton recorded a moderate growth rate of 2.7%.

Figure 2.5. Percentage of rural population in the south west LGAs, 2006

Local government areas in south west Victoria with the highest percentage of people living in rural areas are contrasted with areas with a low percentage of rural population, see Figure 2.5. The highest percentages of rural population are in the Moyne Shire which surrounds Warrnambool, the Golden Plains Shire which is to the north west of Geelong and the Pyrenees which is to the north east of Ballarat. Moyne Shire has a successful dairy industry as well as a long coastline that supports a number of tourism activities. Golden Plains as the name suggests is predominantly a cropping region with wheat, barley and canola grown. The Pyrenees is a productive agricultural region with cereal and hay crops, wool, viticulture and forestry activities along with gold, sand, gravel and slate, all contributing to the region’s economy. Areas with the lowest percentages of rural population are the Ballarat City Council and Greater Geelong which include large regional centres.

The age pyramid for south west Victoria for 2006 highlights two distinct features of the region (Figure 2.6). The loss of young people between 20 and 34 years, which is particularly noticeable in the age group 25-29 years and a lower birth rate reflected in the 0-4 year age group. This supports trends prevalent across Australia, falling fertility rates and the out migration of young people following completion of schooling. This highlights a lack of employment opportunities in rural areas. This demographic analysis provides an overview of the population dynamics that underpins the regional economy of south west Victoria. The feature of the pyramid,

14 whilst highlights a natural progression over the age of 45-49, highlights the lack of young people to sustain retirees which can impact on the future planning.

South West Victoria - Age Pyramid, 2006

Age % male % female

100 years and over 95-99 years 90-94 years 85-89 years 80-84 years 75-79 years 70-74 years 65-69 years 60-64 years 55-59 years 50-54 years 45-49 years 40-44 years 35-39 years 30-34 years 25-29 years 20-24 years 15-19 years 10-14 years 5-9 years 0-4 years

4 3 2 1 0 1 2 3 4 Percent of Population

Figure 2.6. Age pyramid for south west Victoria, 2006 Source: ABS (2008b)

15 3. Industry Structure

3.1. Introduction

Industry structure is defined as the spread of economic activity across different industries. One way of measuring the industrial mix of economic activity for a particular region is through the distribution of employment across industries. It is important to understand the industrial structure of a region because it determines the region’s level of income, economic stability and growth (BTRE, 2003). Therefore, the current and future economic performances of a regional economy needs to be understood and explored in terms of its sectoral characteristics as well as its comparative advantage or disadvantage to the national or state economy.

The following aspects of industry structure will be examined through a series of indicators which include: major employing industries, industry specialisation, and industrial diversity. Information on the data and methodology will be presented in the next two sections, followed by an overview of the data in section 3.4. The results and analysis for these indicators are presented in section 3.5. A summary is given in the last section of this chapter.

3.2. Data

To examine how regional economies have changed over a period of time, the employment by industry data are sourced from the 1996, 2001 and 2006 ABS Census of Population and Housing (ABS, 2008b and ABS data available on request). The data are specified as below:

• Geographic unit – Statistical Local Area (SLA) • Count method – Place of usual residence • Industry classification – 53 industry subdivisions found in the 1993 edition of the Australian and New Zealand Standard Industrial Classification (ANZSIC)

SLA boundaries can change from one census to another and this is problematic for those who wish to compare data over time. A method that is used to adjust for these differences is presented in the next section.

3.3. Methodology

The base year and geographic unit used is the year 2006 and the 2006 SLA boundaries respectively. To make the 1996 and 2001 data comparable to the base unit, the following concordance files are obtained from ABS (2006a):

• 1996 SLA to 2006 SLA Concordance • 2001 SLA to 2006 SLA Concordance

These are area based concordance files that show how SLAs have changed in area size and names between the census years. It records, for example, the ratio of land for a particular 2001 SLA that is now part of a neighbouring 2006 SLA.

16 Below are the steps used to adjust the 1996 and 2001 industry employment data to the 2006 SLA boundaries:

1. The ratio of a 1996 (or 2001) SLA named ‘X’ will be multiplied with each of the industry employment counts for ‘X’ in 1996. The products are the new partial employment counts for each industry of a SLA named ‘Y’ in 2006. These are partial estimates as SLA ‘Y’ may be composed of other 1996 SLAs. 2. The partial employment counts for each industry are added together for all 1996 SLAs that contribute to the area of SLA ‘Y’. The result is the total employment count for each industry of SLA ‘Y’.

The assumption behind this method is that the population is uniformly distributed over the SLA. However, it is likely that there may be higher, lower or no population in the areas that have transferred from one SLA to another. The SLAs in the South West region of Victoria have stayed relatively the same between 2001 and 2006, while more boundary changes occurred between 1996 and 2001. Hence, data comparison between 1996 and 2001 or 2006 will not be as reliable as those between 2001 and 2006.

Data presented at the SLA level reflect where people with certain characteristics live. Our interest is in regional economies and therefore areas where people live and work. As a consequence, the data will be presented through the BITRE 2006 working zones as shown on Figure 2.1 on page 10. These regions reflect the area in which people are willing to travel from their home to their workplace (BITRE, 2009). Each working zone consists of one or more adjoining SLA, and it is a reasonable geographic unit for analysing regional economies.

Each indicator under industry structure relies on the estimation of industry employment share. To ensure that employed people under the ‘not further defined’ categories are included in the estimation, they are spread proportionately across the subdivisions of the main ANZSIC division in which it is listed. Similarly, those who are employed but listed in ‘not stated’ or ‘non-classifiable economic units’ categories are spread proportionately across the 53 industry subdivisions. BTRE (2003) also applied this method to determine the industry employment shares for their data.

3.4. Overview

Figure 3.1 below provides an overview of the 2006 industry employment share for Australia, Victoria and South West Victoria. Instead of using the 53 ANZSIC subdivisions, the 17 ANZSIC divisions (on the horizontal axis) are used to present the data. The main features of the figure are:

• Retail trade is a major employing industry at all geographic levels, but the SW region has a slightly higher share (16%) than the state (14.9%) and Australia (14.7%). • Victoria and the SW region have around 13.3% of their labour force in the manufacturing industry, which is higher than the nation’s level of 11.3%. • Employment shares in the property and business industry are significantly higher at the state and national level (both at around 11%) than compared to the SW region (7.4%). Similarly for the finance and insurance industry, the national and state shares are at around 4%, while the SW has 2%. • In contrast to the last point, SW Victoria has 7% of their labour force in the agriculture, forestry and fishing industry. This is significantly higher than the industry employment share at the state (2.8%) and national level (3.2%).

17 • The employment share of the health and community services industry for SW Victoria is 12.4%. Interestingly, this is higher than state (10.9%) and national levels (11.0%) and reflects the significant role played by the larger regional centres of Ballarat and Geelong, in the provision of health services.

This graph is unable to show the variation within the South West region. Hence, the employment data will be spatially analysed by the SW subregions.

18% 16% 14% 12% 10% 8% 6%

Employment Share 4% 2% 0% e ng ts ce ing ices ini vices fenc rvices M cturing rvices d fish struction le Trade se ufa ter supply ail Trade Education on sa d insuran C Ret restauran ion Ser nal se ther serv Man wa ort and storage e an ion and de Whole and p orestry an unicat trat and o nd business recreatio re, f Trans Financ a ity, gas and Comm tion, cafes l and ersonal tric P ealth and community services Agricultu oda Property ent adminis H Elec rnm Cultura Accom Gove Australia Victoria SW Victoria Figure 3.1. Employment share by industry, 2006 Source: ABS (2008b)

Figure 3.2 is a map showing the spatial distribution of those employed in the labour force in 2006 for the South West region. The red coloured working zones highlight where employment levels are particularly high and these include the city regions of Geelong and Ballarat. The light yellow working zones, on the other hand, have lower employment number.

Figure 3.3 shows that employment growth from 1996 to 2006 has been particularly high in the working zones of Geelong, Ballarat, Warrnambool and Colac-Otway South. The percentage increase in total employment is 21.7% for Warrnambool (where community services industry recorded the greatest employment increase), 25.2% for Colac-Otway South (highest employment growth was in accommodation, cafes and restaurants industry), 26.1% in Ballarat (where largest employment growth was in business services ), and 26.7% for Geelong (where most increases was in the personal and household good retailing industry).

Figure 3.2. Total employment for the working zones in South West Victoria, 2006

Figure 3.3. Change in total employment for the SW working zones, 1996 to 2006

19 As previously mentioned, significant geographic boundary changes have meant that the comparison of data between 1996 and 2006 or 1996 and 2001 is not accurate. Hence we will now look at the percentage change in total employment from 1996 to 2001 (Figure 3.4), and 2001 to 2006 (Figure 3.5). These two maps provide greater spatial and temporal information than our previous map. For example, we can see that Corangamite North have experienced an increase in total employment from 1996 to 2001 and a decline from 2001 to 2006. If however we only looked at the percentage change from 1996 to 2006, we would not have been able to detect this variation.

Figure 3.4 shows that from 1996 to 2001 the following SW working zones have experienced the most increase in the percentage of employment: Ballarat and surrounds, Geelong and surrounds, and Colac-Otway South. Working zones that have declined in percentage terms for their employment level include: Glenelg North, South Grampians Wannon, Ararat, and Corangamite South.

Figure 3.4. Change in total employment for the SW working zones, 1996 to 2001

Figure 3.5 shows that Ballarat and Geelong have continued to experience strong percentage increase in total employment between 2001 and 2006. A few working zones that have been previously declined between 1996 and 2001 have seen some small increase in employment, namely: South Grampians Wannon, Ararat, and Corangamite South. Moreover, there are only two areas with some slight employment decrease, namely Glenelg North and Corangamite North.

Figure 3.5. Change in total employment for the SW working zones, 2001 to 2006

Primary industry employment shares for 2006 are shown in Figure 3.6 on page 22. Industries included here are the agriculture, forestry and fishing , and mining ANZSIC divisions. It can be noticed from this map and those shown previously, that the working zones with relatively high primary industry employment shares (greater than 16%) are also areas generally:

• declining and/ or increasing slowly in percentage change of employment levels or 1996 to 2006, see Figure 3.4 and Figure 3.5 • low in total employment levels (less than 7,742 employed in each working zone for 2006), see Figure 3.2

These results may reflect the long term structural changes which have been taking place in Australia. Over the past few decades, many farms began to exit the industry as they became more exposed to international competition. While farm numbers declined, the remaining farms increased in size, capital investment, and adoption of new technologies (Nossal et al., 2009). By substituting capital for labour, there were gains in productivity and a decline of agricultural employment in regional Australia (Nossal et al., 2009).

Figure 3.6. Primary industry employment shares for SW working zones, 2006

22 3.5. Results and analysis

All the results in this section are based on the ANZSIC 1993 subdivision level (see appendix 1). For each indicator, their definition, equation, importance, interpretations and limitations are presented as a table. This is followed by a discussion of their results along with a spatial analysis.

3.5.1. Major employing industries

Major employing industries that will be examined are those with the first and second highest share of regional employment. Table 3.1 below provides a short summary about this indicator.

Table 3.1. Major employing industries Definition Industries with the top two highest percentage of employment. Importance To understand where most of the labour force are employed and their sources of income. Limitation The percentage varies with changes in the level of industry classification used. This affects the estimations for other indicators.

Agriculture industry is the largest source of employment for two thirds of the working zones in South West Victoria (see Figure 3.7) Health services is featured as the 1st major employing industry for Ballarat and Geelong working zones. Figure 3.8 shows that health services are also the 2nd major employing industry for most of the working zones in the South West. These findings are similar to those by BTRE (2003) where, in 2001, agriculture is the top employing industry for 64.9% of the working zones in Australia and health services regularly appeared as the top 3 employing industries for 40.0% of the working zones. Part of the reason for this occurrence is that agriculture and health services as defined in ANZSIC 1993 encompass a broad range of occupations. Health services include hospitals, nursing homes, dental services, community health centres, and even veterinary services. Agriculture include horticulture, fruit growing, crop growing, dairy cattle farming, and livestock farming.

It is interesting to have these very different industries as the major employers of the SW working zones. For instance, those in the agriculture industry have variable income levels which are influenced by a range of supply and demand factors, such as: rainfall, temperature, world economy, and commodity prices. By comparison, workers in the health services industry have more stable incomes due to an inelastic or constant demand for health care services and the fact that many services operate in the public sector.

Figure 3.7. First major employing industry, 2006

Figure 3.8. Second major employing industry, 2006

24 Other major employing industries in the South West working zones are: • Metal product manufacturing for Portland and surrounds, • Accommodation, cafes and restaurants for Colac-Otway South, • Food, beverage and tobacco manufacturing (FBTM) for Corangamite South, and • Education for Ballarat and Geelong working zones.

Metal product manufacturing is a major employer in Portland because of the presence of the Portland Aluminium smelter. The aluminium smelter is operated by Alcoa and it directly employs more than 640 people, and around 200 contractual workers (Alcoa, 2009b). The bauxites that are processed and refined here to produce aluminium ingot is actually mined in Western Australia and imported through the deep water port of Portland (Alcoa, 2009a). Nearly all of the aluminium produced at this site is exported to the Asian market (Alcoa, 2009b). The importance of the accommodation, cafes and restaurants industry in Colac- Otway South is linked to the large number of tourists attracted to the area. The area offers a variety of recreational activities, such as fishing, swimming, boating, hiking, wineries and dining (Colac Otway Shire, 2004). Tourism attractions include: the Great Ocean Road, Otway National Park, beaches, wetlands, rainforests, music festivals, and the Cape Otway Lighthouse. The SLAs within and around Corangamite South are key dairy production areas. Thus it is not a surprise to find that many people living in the region work in some of the milk processing factories located in the Corangamite Shire. For example, Bonlac Foods has a milk drying plant in Cobden, Dairy Farmers has a cheese manufacturing plant in Simpson, and Timboon Farmhouse Cheese, which closed in March 2009, manufactured gourmet organic cheeses in Timboon (Corangamite Shire, 2009, Smith, 2009) There are several tertiary institutions located in the working zones of Ballarat and Geelong. They provide important training courses, many of which have excellent linkage to the local industries, for the communities in south west Victoria (DPI and City of Ballarat, 2003). Some institutions in the Ballarat area include: the University of Ballarat, Aquinas Campus of Australian Catholic University, University of Melbourne’s Creswick Campus, and Da Silva College of Business and Tourism. In the Geelong region, there are: Deakin University with two campuses, Gordon Institute of TAFE, Broughman School of Art and Photography, and Marcus Oldham College. Furthermore, a number of elite private and public schools are located in these two regions providing educational opportunities as well as employment.

25 3.5.2. Industry specialisation

Our next indicator is industry specialisation and its key information is given in Table 3.2 below.

Table 3.2. Industry specialisation Definition An industry is assumed to have a high degree of specialisation if the regional industry employment percentage is greater than the national/ state industry employment percentage (Shaffer et al., 2004). Equation Location quotient is a useful measure of specialisation: i t i (er er ) LQ r = i t (2) ()en en i where LQ r = industry i’s location quotient for region r i er = number of employed in industry i for region r t er = total number ( t) of people in the labour force for region r i en = number of employed in i for the national economy n t en = total number ( t) in the labour force for the nation ( n) Importance Reflects a region’s competitiveness and export capability for each industry. Interpretation If we assume that the benchmark economy is self-sufficient (Schaffer, 1999): i • LQ r =1 implies that the region’s employment share in sector i is the same as the national economy, and the sector is able to produce just enough to meet the local needs. i • LQ r <1 indicates that the sector is not producing enough to satisfy regional demands and that particular good or service must be imported. i • LQ r >1 suggests that sector i in the region is specialised and that there is enough people employed to support the local demands as well as to export their surplus goods or services to other economies. Limitations It may not be appropriate to compare one region’s employment share to the national or state economy due to differences in (Shaffer et al., 2004): • tastes, preferences, and demand • income levels • labour productivity, production practices and technologies • marginal propensities to consume local goods

Figure 3.9 shows the most specialised industry (i.e. industry with the highest location quotient and more than 15 persons are employed) for each working zone. The location quotient value for the most specialised industry is as labelled on the map in bold numbers. It is easy to see that the top specialisation industries for most of the South West region involve primary industries. For example: • agriculture • services to agriculture; hunting and trapping • forestry and logging • commercial fishing

26 In addition, the more urbanised working zones in the east, which include Ballarat and Geelong, have industries associated with primary industries. The top specialisation industry of Ballarat is food, beverage and tobacco manufacturing (FBMT), whilst textile, clothing, footwear and leather manufacturing (TCFM) is highest in Geelong.

Figure 3.9. Top specialisation industry for the South West working zones, 2006 Note: The bold numbers are the location quotient for the most specialised industry

We will now examine the level of specialisation for a few key industries in the SW region. First, the level of specialisation for agriculture in 2006 is shown on Figure 3.10. All the SW working zones, except for Geelong, have agricultural employment percentage greater than the national percentage. The mean and median for LQ are found to be 6.78 and 5.29 respectively. Working zones with particularly high specialisation level in agriculture are: Glenelg North, Grampians Wannon, and Corangamite South. This equates to the agriculture employment shares being ten to fifteen times greater than the national average. Overall, the results suggest that most of the regions have high numbers of agricultural workers, and that they are able to export their production to other areas within and outside of the SW region.

Figure 3.10. Level of industry specialisation in agriculture, 2006

Figure 3.11. Level of industry specialisation in forestry and logging, 2006

28 Figure 3.11 (see page 28) is a map of the specialisation level in forestry and logging for 2006. The information provided here through the employment data is quite useful given the lack of data on regional forestry production. This map shows that specialisation in forestry and logging is not as widespread across the South West as compared to agriculture . The mean and the median for LQ are also lower than those for agriculture ; they are 4.25 and 2.04 respectively. For five working zones in the South West, all their forestry and logging employment percentage are below the national percentage. However, there are also a number of working zones with very high specialisation level, those that are above the mean include:

• Portland and surrounds, LQ = 5.52 • Colac and surrounds, LQ = 6.22 • Colac-Otway South, LQ = 6.61 • Glenelg North, LQ = 23.51

The specialisation level of forestry and logging for Glenelg North is extremely high, and it may indicate the existence of large forestry plantation and logging activities. You may also recall that this working zone has a high specialisation in agriculture (LQ = 11.89), a low total employment number in 2006, and declining employment over the past decade.

The 2006 industry specialisation level for food, beverage and tobacco manufacturing is shown on Figure 3.12 (see page 30). The map shows that specialisation in the food, beverage and tobacco manufacturing industry is highest in the central south of the study region. These include Warrnambool & surrounds, Corangamite South, and Colac & surrounds. The specialisation level range between 0.2 and 2.9, which is a narrower range than those presented before. This may be due to the fact that the classification for this category is very broad and general. Therefore, a region may be highly specialised in manufacturing a particular food product, but this is not revealed in the way we are measuring specialisation, that is the estimated LQ.

Figure 3.12. Level of industry specialisation in food, beverage and tobacco manufacturing, 2006

30 3.5.3. Industrial diversity

Some insight into the regions’ economic stability can be gained through our next sub-indicator, industrial diversity. Please see Table 3.3 below for key information about this indicator.

Table 3.3. Industrial diversity Definition The presence of a variety of different economic activities and that these activities are evenly distributed across industries (Shear, 1965 as quoted by Malizia and Ke, 1993). Note that economic activities are measured by employment numbers for this report. Equations A measure of diversity is the modified Herfindahl index as used by BTRE (2003) with the following equation (3) 2 k  ei  1  r  ID r = − ∑ t  (3) i=1  er 

where ID r = industrial diversity index for region r i er = number of employed people in industry i for region r t er = total number ( t) of people in the labour force for region r k = total number of industries Importance Industrially diverse regions have been found to be more stable in their economic performances (that is, low unemployment rates, and less variation in income and employment) than those that are less diverse (BTRE, 2003, Malizia and Ke, 1993). Hence the above indices will provide a general indication of how well the region is insulated from positive and negative shocks in the domestic and global economy.

Interpretation ID is a number between 0 and 1 (or 0 – 100%). A small value indicates that there are few employing industries and/ or the distribution of employment is highly concentrated in a few industries, and vice versa.

Limitations The industry classification level used here is ANZSIC 1993 subdivision. At this level, there is no differentiation between the following under the agricultural industries: ‘horticulture and fruit growing’, ‘grain, sheep and beef cattle farming’, ‘dairy cattle farming’, ‘poultry farming’, etc. Therefore, a low ID value may not reflect an area’s diverse agricultural production activities.

BTRE’s industrial diversity index for the South West working zones in 2006 is shown as Figure 3.13. There are no benchmark values that can tell us what a safe level of ID is; hence we will compare the values for south west working zones to the following 2006 IDs derived for the working zones in Victoria: • minimum – 72.0 in Mount Buller Alpine Resort, • maximum – 95.4 in Albury-Wodonga & surrounds • mean – 90.1 • median – 93.2 • ID for Melbourne & surrounds – 95.3

Figure 3.13. Industrial diversity index for the South West working zones, 2006

The most industrially diverse regions in the SW are the working zones of Geelong (95.2%) and Ballarat (95.0%). Their index values are quite high compared to the maximum found in Victoria. Figure 3.13 further shows that more than half of the SW working zones have near or above the Victorian median. The least diverse regions in the SW are: Grampians Wannon at 80.4%, Corangamite South at 81.5%, and Glenelg North at 86.3%. We have shown in section 4 that these regions have a high percentage of employment in the primary industries (Figure 3.6), high specialisation in agriculture (Figure 3.10), and negative and/ or slow growth in total employment (Figure 3.4 and Figure 3.5). This is in contrast to the industrially diverse working zones of Geelong and Ballarat where they have been identified as areas providing and generating a high proportion of employment in the SW.

Figure 3.14 and Figure 3.15 are maps showing the percentage of change in the BTRE industrial diversity index from 1996 to 2001, and 2001 to 2006 respectively. In the former time period: • Ballarat and Geelong have both experienced a small decline in their industrial diversity index as shown in light yellow • areas in light orange (e.g. Ararat, Colac-Otway North, Hamilton, Portland, Warrnambool) have stayed relatively unchanged in their industrial diversity index • Corangamite South has experienced the greatest increase with 6.35 percentage points • other working zones that have also diversified considerably include: Glenelg North (+1.41%), Corangamite North (+2.19%), and Grampians Wannon (+3.79%)

Figure 3.14. Change in Industrial Diversity Index, 1996 to 2001

Figure 3.15. Change in Industrial Diversity Index, 2001 to 2006

33 Figure 3.15 shows that, between 2001 and 2006, some minor decrease in the industrial diversity index (-0.35% to 0.00%) are found for the working zones of Geelong, Ballarat and Portland. Slight increases (0.01% to 0.5%) are found in four working zones in the South West, namely Hamilton, Warrnambool, Corangamite North and Colac-Otway South. For this time period, working zones that have experienced notable increases in their industrial diversity index include: Ararat by 1.02 percentage point, Corangamite South by 3.97, and Grampians Wannon by 4.40.

A map of total change between 1996 and 2006 is not shown due to significant geographic boundary changes. However, overall, areas that have experienced significant diversification during the first and second time periods are: Glenelg North, Grampians Wannon, Corangamite North, and Corangamite South. Although these areas have relatively low ID values in 2006 (see Figure 3.13), our last two figures suggest that they have diversified extensively over the period between 1996 and 2006. The increase may be seen as beneficial for those who prefer ‘slow and steady growth over a boom and bust economy’ (BTRE, 2003, p.40). This is because higher industrial diversity has been linked to stable economic performance; and insulation from positive and negative shocks (BTRE, 2003).

3.6. Summary

This chapter has shown how the regional economies in South West Victoria are structured in terms of employment and how they have changed over the years. Generally, most areas of the SW have strong employment and specialisation in the primary industries. Employment seems to decline or stay relatively the same in regions that are less industrially diverse and have a high employment share in agriculture, forestry and logging. This is in contrast to areas with strong employment and specialisations in the secondary and tertiary industries, namely Geelong and Ballarat working zones. These urbanised areas are more industrially diverse and are generating higher employment growth over time. Our spatial analysis has shown that regional differences are greatest between those in the urbanised city regions and rural areas without major town centres.

34 4. Farm Economic Performance

4.1. Introduction

The agriculture sector plays an important role in the regional economy of south west Victoria. It is a major employer and is the top specialisation industry among many of the working zones in south west Victoria. The indicators in this chapter have been selected to show the long term viability of farm businesses and their ability to make new farm investments. The two indicators are farm business profit and farm equity ratio.

Information about the data will be presented in the next section. The results and analysis are provided in section 4.3, and a summary is given in section 4.4.

4.2. Data

Farm economic performance indicators for the broadacre industries are sourced from ABARE’s Australian Agricultural and Grazing Industries Survey (AAGIS). The survey outputs are given in average values and they are obtained for the state of Victoria and the AAGIS region labelled as Western Districts in Figure 2.3 (see page 11). It can be seen from the figure that our study region lies within the Western Districts defined by ABARE, therefore we will refer to this area as SW Victoria.

The broadacre industries are analysed for the period from 1995-96 to 2006-07. ABARE defines the broadacre industries as farms assigned with the following ANZSIC 1993 classes:

• Grain Growing (0121) • Grain-Sheep and Grain-Beef Cattle Farming (0122) • Sheep-Beef Cattle Farming (0123) • Sheep Farming (0124) • Beef Cattle Farming (0125)

ABARE’s Australian Dairy Industry Survey (ADIS) provided the farm economic performance data for the dairy industry. Due to boundary changes in the ADIS regions, the time period for 1995-96 to 2000-01 has to be examined separately from the period for 2001-02 to 2006-07. Figure 4.1 (a) shows the survey regions before 2001-02, while Figure 4.1b is the current regions. Average farm business profits and equity ratios are obtained for the state of Victoria and the region labelled as 21 on Figure 4.1 (a) and (b).

AAGIS and ADIS have been designed to provide statistically reliable estimates at the national and state level. Hence, average values for south west Victoria must be used with extreme caution. They are only presented in this report to show the trend in farm economic performance. The survey sample sizes for the region over the period between 1995-96 and 2006-07 are shown in Table A. 1 and Table A. 2 of the Appendix 2.

(a) Before 2001-02 (b) Since 2001-02

Figure 4.1. ADIS regions for New South Wales and Victoria Source: ABARE (2003)

Data quality

To determine the reliability of ABARE’s survey estimates, the relative standard errors were obtained for the farm business profits and equity ratios of each year between 1995-96 and 2006-07 (see Table A. 3 and Table A. 4 of Appendix 2). The relative standard errors (range: 1 to 7) suggests that the farm equity ratios are reliable, while the farm business profits data have very low reliability (range: 12 to 531). Hence, our comparison and analysis of regional farm business profits will only provide a partial picture of what has taken place across those farms.

36 4.3. Results and analysis

Farm business profit will be examined in section 4.3.1 and farm equity ratio will be analysed in section 4.3.2.

4.3.1. Farm business profit

Farm business profit is an important measure for efficiency. Table 4.1 below contains some key information about this indicator.

Table 4.1. Farm business profit Definition Profit = Total Revenue – Total Cost where total revenue and total cost are defined by ABARE (2003) as: • Total Revenue = total cash receipts + closing value of all changes in the inventories of trading stocks • Total Cost = total cash costs + capital depreciation + imputed value of owner manager and family labour Importance This indicator can show whether a farm is able to cover all the costs incurred in producing their outputs. It also provides a good indication of efficiency because profit is maximised by increasing production and/ or reducing cost of production, and most farmers seek to maximise profit as a means of achieving utility or satisfaction. Interpretations Most farm businesses would prefer to operate above zero profit and at maximum profit. If however costs exceed revenues for several consecutive seasons, the long term viability of a farm may be threatened. This could be the consequence of a rise in the price of input, decline in the price of output, or unfavourable weather condition. Limitations There are cases where profit will not truly reflect the utility and welfare of a farmer. They may occur when farmers have alternative goals other than maximising single season profit. For example, maximising long term profits (20 year period), accumulation of farmland, having the best set of farm machinery, or enjoyment of an amenity lifestyle (Debertin, 1986).

Average farm business profit for the broadacre farms in Victoria and the SW region are shown in Figure 4.2 below. Please note that all ABARE data have been adjusted for inflation and they are in 2007-08 dollars. From 1995-96 to 2006-07, the trend in Victoria and the SW region is as follow:

• Average profit declined from 1995-96 to 1998-99. This is due to lower average prices as well as lower production from the dry seasonal conditions (ABARE, 1998, ABARE, 1999). • Average profit then rose considerably to a peak in 2001-02. This is related to the higher average prices for crops, and increased livestock production (ABS, 2003). • A significant fall in profit occurred in 2002-03. Although average prices for all agricultural commodities was higher, a drought caused production levels to decrease sharply (ABS, 2004). • Profit increased in 2003-04 as drought eased, and livestock slaughtering and crop production increased (ABS, 2005).

37 • In 2004-05, profit continued to increase for some areas (Wimmera, Gippsland and SW), while other areas experienced a decline (Mallee and Central Northern). These differences relate to the agricultural commodities produced (ABS, 2006b). • Another drought in 2006-07 led to negative average profits across all regions in Victoria (ABS, 2008c).

2007-08 Broadacre Farm Business Profit $'000 60

-20

-40

-60

6 7 8 9 9 9 99 00 01 02 03 04 05 06 6-07 03- 04- 05- 0 1995- 1996- 1997- 1998- 1999- 2000- 2001- 2002- 20 20 20 20 Year South West Victoria Victoria

Figure 4.2. Average business profits for the broadacre farms in south west Victoria and Victoria, 1995-96 to 2006-07 Source: ABARE 2009. ABARE data available on request

The Victorian and south west region’s average business profits for dairy farms are shown in Figure 4.3. Similar to the trend for broadacre farms, Victorian dairy farms’ average profits fell in 1996-97 and gradually increased over the next four years. The profits peaked in 2001-02 and fell during the drought in 2002-03. An improvement was seen from 2003-04 to 2005-06, which is mainly due to an increase in the average price of milk (ABS, 2005, ABS, 2006b, ABS, 2008c). But average profits declined again with the drought in 2006-07. Similar trend is found for the SW dairy farms. Although Figure 4.3b suggests that the SW region has performed better than the state, such conclusion cannot be made given that the data quality for the region is quite poor.

38 2007-08 Dairy Farm Business Profit $'000 150

100

-50

-100 1995-96 1996-97 1997-98 1998-99 1999-2000 2000-01 Year South West Victoria Victoria

(a) 1995-96 to 2000-01 2007-08 Dairy Farm Business Profit $'000 150

100

-50

-100 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 Year South West Victoria Victoria

(b) 2001-02 to 2006-07

Figure 4.3. Average business profits for dairy farms in Victoria Source: ABARE 2009. ABARE data available on request

By comparing the profit achieved by Victorian dairy farms to the broadacre farms, we can see that the former have often been able to achieve higher levels of profit than the latter. However, NLWRA (2002) shows that broadacre farms in Australia have persisted with higher amounts of off-farm income.3 Figure 4.4 below shows that, since the 1980s, Australian broadacre farms in the high rainfall zone have significantly increased their level of off-farm income as compared to those in the dairy industry. Nelson and Kokic (2004) have found that increasing the diversity of on-farm and off-farm income sources is an effective way to reduce the vulnerability of farm households to climate variability. Therefore, although average profits for the dairy industry have performed better than the broadacre industries, broadacre industries have been supported by higher levels of off-farm income.

3 Off-farm income is defined by ABARE (2003) as income from wages, other businesses, investments and social welfare payments collected by the owner manager and spouse only. The data is sourced from ABARE’s farm survey. 39

Figure 4.4. Off-farm income earned on Australian broadacre and Australian dairy farms 1980 to 1998, constant 1996 dollar terms Source: NLWRA (2002)

4.3.2. Farm equity ratio

Farm equity ratio is a useful indicator that can help us to understand the level of debt faced by a farm business and their ability to make further investments, e.g. adoption of sustainable practices (Cary et al., 2002). Table 4.2 below contains key information about this indicator.

Table 4.2. Farm equity ratio Definition Farm equity ratio is the proportion of owned capital minus debt divided by total farm capital.

Equation Farm equity ratio = [(value of owned capital – farm business debt at 30 June) / total farm capital at 30 June] ×100

Importance To understand the level of debt faced by a farm business and their ability to make further investments, e.g. adoption of sustainable practices (Cary et al., 2002). Interpretations • Low equity ratio shows that a farm has a relatively high share of debt which may reflect a recent large financial investment on the farm or loss of production through crop failure. • High equity ratio, on the other hand, indicates a lower share of debt and a greater ability to make further investments. • Based on ABARE’s farm survey data, the average equity ratio for Australian broadacre farms is 87.9% from 1995 to 2007, and 82.6% for Australian dairy farms. We will assume that these national averages for the respective industries would indicate more security than ratios below these levels.

Average equity ratios for broadacre farms are shown in Figure 4.5. From 1995-96 to 2006-07, the ratios have ranged between a minimum of 83.37% and a maximum of 91.76% in 1998-99 and 2005-06 respectively. A negative trend can be seen at the start of this time period as dry seasonal conditions resulted in crop failure, lower

40 agricultural production and greater difficulty for some to meet debt repayments. The ratio has then steadily increased over the years, before descending with another drought in 2006-07. A similar trend can be observed for south west Victoria.

Farm equity ratios in Victoria have been on an upward trend from 2000-01. An important reason for this is rising land values (Martin et al., 2005, p.5). ABARE’s broadacre survey showed that the south west region in particular have seen a 70% increase in average broadacre land values from 1999-2000 to 2003-04 (Martin et al. 2005, p.10). Therefore, although there was a drought in 2002-03, the rising land values have masked some of the trend in farm debts.

Equity Broadacre Farm Equity Ratio Ratio (%) 100

7 9 1 6 -9 -9 0 -02 -04 -0 5-96 0- 2-03 4-05 6-07 9 96 98 0 0 0 05 0 9 9 0 0 19 1 1997-98 1 1999-00 2 2001 20 2003 20 2 20 Year South West Victoria Victoria

Figure 4.5. Average equity ratios for broadacre farms, 1995-96 to 2006-07 Source: ABARE 2009. ABARE data available on request

Figure 4.6 (a) on page 42 shows the average dairy farm equity ratio from 1995-96 to 2000-01. Over this time period, the Victorian ratios have been declining gently with a maximum of 82.3% in 1995-96 and a minimum of 78.6% in 2000-01. The trend for south west Victoria is an initial decline during the seasons of low rainfall and a general rise for the rest of the period.

41 Equity Dairy Farm Equity Ratio Ratio (%) 100

70 1995-96 1996-97 1997-98 1998-99 1999-2000 2000-01 Year South West Victoria Victoria

(a) 1995-96 to 2000-01

Equity Dairy Farm Equity Ratio Ratio (%) 100

70 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 Year South West Victoria Victoria

(b) 2001-02 to 2006-07

Figure 4.6. Average equity ratios for the dairy farms in Victoria Source: ABARE 2009. ABARE data available on request

Average equity ratios for dairy farms in 2001-02 to 2006-07 are shown on Figure 4.6 (b). In contrast to Figure 4.6 (a), there have been greater fluctuations for the Victorian and south west region’s ratio. The ratio for Victoria declined to 72.7% in 2003-04, then for the following two years it rose to a maximum of 82.5% before dropping to a minimum of 71% in 2006-07.

The results above show that farm equity ratios for the dairy industry are lower than those for broadacre industries in Victoria. This is expected as dairy farms require large investments in a range of facilities and equipment including: milking sheds, milking equipment, feeding systems, housing for cows, waste management, etc (Bailey, 1997). According to DEW (2002), dairy farms increased their borrowings throughout the 1990s to acquire more land and new technologies. Consequently, these debts have been reflected as lower equity ratios. Although the equity ratios for

42 dairy farms are low, section 4.3.1 showed that average dairy profits have a higher performance for most years. This suggests that a more reliable source of income has allowed dairy farms to pursue new investments and meet the required debt payments.

4.4. Summary

This chapter has examined the trends of farm business profit and farm equity ratio for the broadacre and dairy industries in Victoria and the south west. During 1995- 96 and 2006-07, the first indicator fluctuated greatly with the occurrence of droughts. Higher profits have been received by the dairy industry than broadacre industries in Victoria, but the latter have persisted with a greater reliance on off- farm income sources (NLWRA, 2002). The farm equity ratio for Victorian broadacre industries has been higher and more stable than those in the dairy industries. Firstly, this is due to a growing demand for land and particularly for broadacre crops in south west Victoria (Martin et al., 2005). Secondly, dairy farms have faced a higher share of debt because they are very capital intensive and large investments are required for the maintenance and expansion of their establishments (Bailey, 1997).

43 5. Agricultural Commodities Produced

5.1. Introduction

In this chapter, we examine the spatial distribution of several agricultural commodities produced in south west Victoria. This assists in understanding how different areas within the SW region have sustained and adapted through changing economic and environmental conditions. The following commodities are investigated: sheep and lambs, milk, beef cattle, wheat, and canola.

The next section contains information about the data used for analysis. An overview of agricultural production in south west Victoria is provided in section 5.3. Results and analysis are given in section 5.4 and the summary is in section 5.5.

5.2. Data

Data on the value and production of agricultural commodities are sourced from the Agricultural Census conducted by ABS (2008a). The census years that we will focus on are 2000-01 and 2005-06. The 1995-96 Agricultural Census data is not considered because the Statistical Local Area (SLA) boundaries used for that year are very different from those used in more recent years. Some small changes have also occurred between the 2001 and 2006 SLA boundaries. The relevant changes are:

• Colac-Otway Colac – the 2006 SLA is now made up of 98.9% of its 2001 SLA and 0.35% of the 2001 SLA of Colac-Otway North • Colac-Otway North – the 2006 SLA now consists of the following 2001 SLAs: 99.65% of Colac-Otway North, 1.1% of Colac-Otway Colac, and 0.06% of Surf Coast West • Surf Coast West – the 2006 SLA is now the remainder of 99.94% of its 2001 SLA

To make the 2001 data comparable with the 2006 SLA boundaries and data, they have been adjusted using the same approach as described in the chapter for industry structure (section 3.3).

ABS (2008c) estimated the value of each commodity by multiplying the quantity with the average price in Victoria. Hence, areas with the highest volume of production will have the highest value. Note that there is no price differentiation for the quality of an agricultural commodity,

In 2005-06, ABS began to use a new register of agricultural businesses maintained by the Australian Taxation Office (ABS, 2008c). Production data for the following commodities continue to be comparable across time: apples and pears, grapes, livestock disposals, and livestock products except for eggs (ABS, 2008c). This is because the data are sourced from other ABS collections. However, the 2005-06 data for crops and for eggs are no longer directly comparable to census data from the past (ABS, 2008c).

44 5.3. Overview

Figure 5.1 shows the gross value of a range of agricultural commodities produced in south west Victoria. Over the years, milk has been the region’s most valuable commodity. In 2005-06, it alone contributed $694.5 million (31%) to the region’s $2.2 billion worth of agricultural production. Furthermore, this is around 21% of the nation’s total value of whole milk produced (ABS, 2008c).

Gross value of selected agricutlural commodities in South West Victoria

Milk

Cattle and calves slaughtered

Sheep and lambs slaughtered

Wool

Hay

Wheat for grain

Poultry slaughtered 2005-06 Potatoes 2000-01 1995-96 Barley for grain

Canola

0 100 200 300 400 500 600 700 800 $m (nominal value) Figure 5.1. Gross value of selected agricultural commodities in south west Victoria, 1995-96 to 2005-06 Source: ABS (2008a) and ABS 2009. ABS data available on request

Other valuable commodities produced in 2005-06 are listed below and the percentages in brackets are the contribution of each commodity to the region’s total value of agricultural production:

• beef cattle – $375 million (17%) • sheep and lambs – $346 million (16%) • wool – $216 million (10%) • crops for hay – $183 million (8%)

Total cereals for grain accounted for around $123 million (6%) in 2005-06. Wheat is the most significant out of all the grain crops, followed by barley. The gross value of canola has increased over the past few years; it contributed almost $30 million (1.3%) to the south west’s agricultural production value.

The regional economy of south west Victoria is highly export orientated (Martin et al., 2005). Figure 5.2 below shows that, in 2004-05, large proportions of the top agricultural commodities produced are sold to overseas markets. For example, about 92% of wool, 82% of wheat, 77% of skim milk powder, 45% of beef, and 35% of lamb production in the SW are exported. This direct link between the SW economy and the international market implies that the SW agriculture sector can be strongly influenced by changes in the conditions of world markets (Martin et al., 2005).

Figure 5.2. Export shares of principal agricultural commodities produced in south west Victoria, by volume, 2004-05 Source: Martin et al. (2005)

5.4. Results and analysis

We will now examine the following commodity groups in detail: milk, beef cattle, sheep and lambs, wheat, and canola. For each group, there will be a spatial analysis followed by an industry analysis. The first is based on what the data and maps are showing, while the second seeks to understand the relationship between the trend and other contextual information provided by experts in the field. Please look at Figure 2.2 (page 11) to identify the location and name of the SLA that is referred to in our analysis.

5.4.1. Milk

As highlighted in the overview, for the past three agricultural censuses, the gross value of milk produced in SW Victoria has been ranked as the highest among all other agricultural commodities produced in the region. In 2005-06, 31.8% of Victoria’s total value of milk produced came from the south west. This important agricultural commodity is spatially and temporally analysed below for our study region.

Spatial Analysis

Figure 5.3 is a map of the distribution of milk in dollar terms in 2005-06. Corangamite South has the highest milk production valued at $226.8 million, followed by Moyne South with $158 million (please see Figure 2.2 for a map of the ABS SLAs in SW Victoria). Areas surrounding the aforementioned SLAs also have a substantially high value of milk. For example:

• Colac-Otway North - $89.8m • Corangamite North - $76.8m • Moyne North-East - $34.1m • Moyne North-West - $32.8m • Glenelg Heywood - $28.7m

The distribution of dairy cattle in 2005-06 (Figure 5.4) is similar to those for the distribution of milk. This is expected as areas with higher numbers of dairy cattle will be able to generate higher revenues from greater volumes of milk produced.

Figure 5.3. Gross value of milk, 2005-06

Figure 5.4. Number of cows in milk and dry, 2005-06

Figure 5.5. Change in number of cows in milk and dry, 2000-01 to 2005-06

For all key milk producing SLAs, except for Moyne South, total dairy cattle numbers in 2005-06 are lower than those in 2000-01 (see Figure 5.5 and Table 5.1). However, the percentage change is relatively small. For example, Corangamite South recorded a -3.9% change, Colac-Otway North with -8.2% change, -12.7% at Corangamite North, -3.9% for Moyne North-East, and -10.5% for Glenelg Heywood.

Table 5.1. SLAs with the highest number of cows in milk and dry in SW Victoria, 2000-01 and 2005-06 Rank Top producing SLAs, 2000-01 Top producing SLAs, 2005-06 1 Corangamite South 131,288 Corangamite South 126,154 2 Moyne South 76,088 Moyne South 87,983 3 Colac-Otway North 54,437 Colac-Otway North 49,947 4 Corangamite North 48,935 Corangamite North 42,715 5 Moyne North-West 34,102 Moyne North-East 18,965 6 Moyne North-East 19,646 Moyne North-West 18,222

Industry Analysis

The decrease in total herd size is most likely related to two important factors. One is the drop in world dairy prices from 2001 to 2002 (see Figure 5.6) and secondly, the reduction in annual rainfall during 2002-03, particularly in the crucial spring period (Figure 5.7) (Kenny, 2009). The typical management response to both events is to reduce cow numbers to manage the risk of such externalities (S. Kenny [DPI] pers. comm., 13 July 2009). Typically there is a lag phase following such herd culling with total milk production taking some time to follow the upward trend in world dairy prices (S. Kenny [DPI] pers. comm., 13 July 2009). Therefore our map shows a negative change between 2000-01 and 2005-06. It is possible however that key producing areas have gradually increased their herd size on a year by year basis from 2002-03 to 2005-06.

48 World Indicator Price for Skim Milk Powder - Monthly Averages

6,000

5,000

4,000

3,000

US$/Ton 2,000

1,000

5 7 9 1 3 9 -96 9 -98 9 -00 0 -02 0 -04 -06 -08 Jan- Jan Jan- Jan Jan- Jan Jan- Jan Jan- Jan Jan-05 Jan Jan-07 Jan Jan-09 Year Figure 5.6. Monthly averages of world skim milk powder prices, 1995 to 2009 Source: FAO (2009)

120

100

40 rainfall (mm) rainfall

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2002 2003 Average 1983 - 2000

Figure 5.7. Warrnambool weather station’s total monthly rainfall in 2002 and 2003 compared to average monthly rainfall for 1983 to 2000 Source: BOM (2009)

An interesting result shown on Figure 5.5 and Table 5.1 is the 15.6% increase in dairy cattle numbers from 2000-01 to 2005-06 in Moyne South, the second major milk producing SLA. Some questions we may be asking include: how were the dairy farmers in this area able to increase their stock numbers so quickly? Were there more favourable seasonal conditions in this area that have allowed dairy farmers to increase dairy herd size?

While world dairy prices have a strong influence on the production and revenue of milk in Australia and in south west Victoria, the direction of influence also works the other way round. Take for instance, the rapid rise in world dairy prices from 2006 to 2007 as shown on Figure 5.6. A combination of past and present factors resulted in this price hike, and this is effectively conveyed through Figure 5.8. This graph shows changes in the level of world production and consumption of skim milk powder as well as the analysis provided by Page (2008) from Fonterra Australia. The following events have contributed to price increases: • the 2007 global economic boom that saw rising income levels and a strong demand for milk from a number of importing countries (Page, 2008) • the 2006-07 drought in Australia which resulted in a decline of world milk supply (Page, 2008) • the elimination of global stockpiles of dairy commodities in the preceding time period (Kenny, 2009) • a reduction of subsidies by the European Union in 2003 leading to lower milk production (Page, 2008)

49 Conclusions

Overall, the total number of dairy cattle in SW Victoria has been lower in 2005-06 than in 2000-01. This can be attributed to falling world dairy prices in 2002 and the widespread drought in 2002-03.

Australia & NZ World production and consumption of skim milk powder meant to fill gap yet drought meant a significant shortfall 6,000 Declining product and hence world Strong demand in as EU reduce wide stock GATT raising importing countries, subsidies reductions. minimum export limited supply in 5,000 prices importing countries.

4,000

3,000 Consumption linked to income levels Stocks sold due Quantity(kt) to product 2,000 shortage resulting from drought. Reduced SMP stock levels and the influence of Austalia & NZ currency devaluation coupled drought in major SMP with increase in herd 1,000 producing countries together with higher stock and production levels feed costs.

9 1 3 6 7 0 2 4 6 7 90 95 08 988 001 1 198 19 199 1992 199 1994 19 199 199 1998 1999 200 2 200 2003 200 2005 200 200 20 Year

Production kt Consumption kt

Figure 5.8. World production and consumption of skim milk powder, 1988-2008 Sources: USDA (2008) and Page (2008) Notes: GATT denotes General Agreement on Tariffs and Trade, SMP is skim milk powder, and EU is for European Union

5.4.2. Beef cattle

In 2005-06, south west Victoria contributed to 29.6% of the state’s total value of cattle and calves slaughtered. It is also the region’s second most valuable commodity. We will now examine how meat cattle production has changed spatially and temporally in the south west.

Spatial Analysis

Figure 5.9 is a map of the value of cattle and calves slaughtered in 2005-06. Similar to the distribution of milk value, significantly higher values are found in the southern part of the study region. For example: • Moyne South - $46.2m • Corangamite South - $44.9m • Moyne North-West - $38.7m • Colac-Otway North - $33.0m The western parts of SW Victoria also have a considerable number of cattle and calves slaughtered. Areas with relatively low numbers of cattle and calves slaughtered are mainly in and around the cities of Ballarat and Geelong.

Figure 5.10 shows the total number of (live) meat cattle in the south west region in 2005-06. Unlike the previous figure, meat cattle numbers are highest in the western region and the numbers decrease towards the east. Areas with high numbers of meat cattle include: Glenelg Heywood, Moyne North-West, Glenelg North, and South

50 Grampians Balance. The apparent difference between Figure 5.9 and Figure 5.10 may be due to the fact that some meat cattle are sold to feedlots or for live export trade, while some dairy cows are slaughtered for meat.

Figure 5.9. Gross value of cattle and calves slaughtered, 2005-06

Figure 5.10. Total number of meat cattle, 2006

Figure 5.11. Change in total number of meat cattle, 2000-01 to 2005-06

Figure 5.11 shows the percentage change in beef cattle number between 2000-01 and 2005-06. The map shows that most south west SLAs in general have increased their beef cattle number. Some key production areas saw small percentage increases, e.g.: • Glenelg North (3.1%) • Glenelg Heywood (8.9%) • S. Grampians Bal. (2.7%) • Corangamite North (2.1%) There are also some important areas with medium percentage increases, e.g.: • Moyne North-West (26%) • Moyne North-East (35.5%) • Colac-Otway North (30.5%) High percentage increases are recorded for: Moyne South (49.6%), Ararat (59.7%), and areas around Ballarat and Geelong. Table 5.2 identifies six south west SLAs with the highest meat cattle number in 2000-01 and 2005-06, and it clearly shows that top producing areas have higher number of meat cattle in 2000-01 than in 2005- 06.

Table 5.2. SLAs with the highest meat cattle number in SW Victoria, 2000-01 and 2005-06 Rank Top producing SLAs, 2000-01 Top producing SLAs, 2005-06 1 Glenelg Heywood 93,416 Glenelg Heywood 101,738 2 Glenelg North 91,513 Moyne North-West 100,451 3 Moyne North-West 79,735 Glenelg North 94,390 4 S. Grampians Bal 76,480 S. Grampians Bal 78,510 5 Corangamite North 52,821 Moyne South 68,680 6 Moyne South 45,923 Moyne North-East 55,677

52 Industry Analysis

The positive trend in beef production has been driven by the strong demand for beef from the Japanese and Korean markets (Drum et al., 2006). Figure 5.12 below shows that the world indicator price for beef has been on an upward trajectory since 1996. The discovery of ‘mad cow’ disease led to a ban of US beef in a number of countries from December 2003 to July 2006 (Drum et al., 2006). Thus the demand for Australian beef increased during that period and farmers in south west Victoria increased their beef cattle numbers as they were less affected by drought than those in other parts of Australia.

World Indicator Price for Beef - Monthly Averages

4,500 4,000 3,500 3,000 2,500 2,000

US$/Ton 1,500 1,000 500 0

2 5 8 9 94 9 -97 9 -99 00 -02 03 -05 06 09 n- Jan- Jan-93 Ja Jan- Jan-96 Jan Jan- Jan Jan- Jan-01 Jan Jan- Jan-04 Jan Jan- Jan-07 Jan-08 Jan- Year Figure 5.12. Monthly averages of the world indicator price for beef, 1992-2009 Source: FAO (2009)

Besides the growing international demand, domestic consumer expenditure on beef has also been increasing steadily over the years (MLA, 2006). As shown on Figure 5.13 below, consumer spending on beef has grown by almost 50% from 2000-01 to 2005-06 (MLA, 2006). Thus this has provided another incentive for farmers in south west Victoria to increase meat cattle numbers.

Figure 5.13. Domestic consumer expenditure on beef, 1990-91 to 2005-06 Source: MLA (2006) Note: e denotes preliminary estimate

Conclusions

Meat cattle numbers in south west Victoria have steadily increased over 2000-01 and 2005-06. This has been in response to growing international demand for Australian beef, as United States, an important producer and exporter, faced trade restrictions. Moreover, there has been increasing domestic expenditure on beef over recent years.

53 5.4.3. Sheep and lambs

The production of sheep and lambs in SW Victoria generate significant revenue for the region’s agricultural sector as well as for the state of Victoria. For example, in 2005-06, the value of sheep and lambs slaughtered in south west Victoria contributed to 45.5% of Victoria’s total value of sheep and lambs slaughtered. Furthermore, 50.6% of Victoria’s total value of wool produced also came from the south west in 2005-06.

Spatial Analysis

Figure 5.14 and Figure 5.15 below show that the distribution of sheep is very similar to the distribution of lambs in 2005-06. Out of the 37 SLAs, South Grampians Balance has the highest number of sheep at 1.36 million and around 344,000 lambs under one year of age. Ararat has the second highest number of sheep and lambs at 884,000 and 302,000 respectively. SLAs with cities or large towns tend to have lower numbers of sheep and lambs. For examples, Geelong has 424 sheep and 243 lambs, while Ballarat Central has 325 sheep and 30 lambs. A different type of agricultural practice (i.e. hobby farms) is prevalent in these peri-urban areas.

Figure 5.14. Total number of sheep (excluding lambs), 2005-06

Figure 5.15. Number of lambs under one year, 2005-06

Figure 5.16 is a map showing the percentage change in the number of sheep (excluding lambs) over the period between 2000-01 and 2005-06. Although our attention may be first drawn to the high percentage increase near Ballarat and Geelong, these areas have very low total number of sheep compared to Ararat and S. Grampians. An important point worth further consideration is that most SLAs have reduced sheep production; this is as shown on the map where most SLAs are in sandy yellow and orange. Table 5.3 shows six SLAs in the SW with the highest sheep production (excluding lambs) in 2000-01 and 2005-06. It also shows a lower number of sheep for the major producing SLAs in 2005-06 as compared to 2000- 01.

Table 5.3. SLAs with the highest sheep production in SW Victoria, 2000-01 and 2005-06 Rank Top producing SLAs, 2000-01 Top producing SLAs, 2005-06 1 S. Grampians Bal 1,609,754 S. Grampians Bal 1,361,390 2 Ararat 1,224,168 Ararat 884,008 3 S. Grampians Wannon 951,276 S. Grampians Wannon 675,410 4 Moyne North-West 615,727 Moyne North-West 502,664 5 Corangamite North 611,667 Pyrenees South 405,739 6 Pyrenees South 515,365 Corangamite North 379,181

Figure 5.16. Change in total number of sheep (excluding lambs), 2001-2006

In contrast to the trend for sheep production, Table 5.4 shows there is higher sales of lambs for the six major production areas in 2005-06 than in 2000-01. Similarly, Figure 5.17 shows that most areas are in orange and red indicating a percentage increase in the sales of lambs between 2000-01 and 2005-06.

Table 5.4. SLAs with the highest sales of lambs in SW Victoria, 2000-01 and 2005- 06 Rank Top producing SLAs, 2000-01 Top producing SLAs, 2005-06 1 S. Grampians Bal 239,467 S. Grampians Bal 295,598 2 Glenelg North 222,710 Moyne North-West 231,724 3 Moyne North-West 177,611 Glenelg North 194,817 4 Glenelg Heywood 174,426 Ararat 175,548 5 Ararat 117,474 Glenelg Heywood 165,670 6 Corangamite North 100,393 Moyne North-East 130,806

Figure 5.17. Change in the sales of lambs (under one year of age), 2001-2006

The above results suggest that some farmers in south west Victoria have decreased the number of sheep bred for wool and switched to the production of other agricultural commodities, such as prime lamb. The next section will consider these changes in relation to the commodities’ price trends and other contextual information provided by industry experts.

Industry Analysis

Figure 5.18 is a graph of the prices for lambs, sheep and wool, and it seems to support this interpretation. Over the past few years, the prices for lamb have climbed to historically high levels. Sheep prices have also followed the same positive trend but they are not as high. Wool prices, on the other hand, have been on a downward trajectory since the end of the wool reserve price scheme in the early 1990s and competition from alternative fibres significantly increased (Drum et al., 2006). Under such circumstances, it has become less profitable for farmers to stay in wool production. Hence, wool producers in south west Victoria have turned to some of the following more profitable options (M. Dunstan [DPI] pers. comm., 13 July 2009):

• cross breed merino sheep with other breeds for prime lamb production • start or increase crop production • sell and/ or lease land to forestry plantation companies

The last option has been particularly attractive to some aging wool farmers (N. Barr [DPI] pers. comm., 5 December 2008). This is because of the difficulty they faced in adapting to changing economic and climate conditions, but also due to the rising land values in the south west region. ABARE’s broadacre survey showed that south west Victoria have seen a 70% increase in average broadacre land values from 1999- 2000 to 2003-04 (Martin et al., 2005). The strong demand for land has come from

57 “new entrants to agriculture, existing farmers and from forest industries” (Martin et al., 2005, p.10).

Figure 5.18. Index of prices received for lambs, sheep and wool Source: ABARE (2007)

Conclusions

In this section, we have seen a reduction of sheep number as linked to the decline of wool prices. This has sparked further changes, such as increase prime lamb and crop production, and more forestry plantations as suggested by some industry specialists.

5.4.4. Wheat for grain

A critical issue that can inhibit wheat production is waterlogging. Areas with high rainfall, level topography and/ or inadequate soil drainage are prone to waterlogging, and this can reduce wheat yield as plants become oxygen deficient (Collaku and Harrison, 2002). Over the past decade, south west Victoria have experienced a much drier climate which provided an opportunity for some to increase the planting of wheat (M. Dunstan [DPI] pers. comm., 13 July 2009).

Spatial Analysis

In south west Victoria, Ararat has the highest gross value and production of wheat (see Figure 5.19). Here, the gross value stands at $21.6 million in 2005-06. Areas around Ararat also have significant revenue generated from wheat production, for instance, Corangamite North ($9.3m), Pyrenees South ($8.0m), and Golden Plains South-East ($5.7m). Relatively lower values and production of wheat are found in Geelong, and the western and southern parts of the study region. Similar to Figure 5.19, Table 5.5 below shows the top four wheat producing areas in south west Victoria are Ararat, Corangamite North, Pyrenees South, and Golden Plains South- East.

Figure 5.19. Gross value of wheat, 2005-06

Table 5.5. SLAs with the highest wheat production in SW Victoria, 2005-06 Rank Top producing SLAs, 2005-06 Tonnes 1 Ararat 105,171 2 Corangamite North 45,420 3 Pyrenees South 38,942 4 Golden Plains South-East 27,803 5 Moyne North-East 19,270 6 S. Grampians Bal 17,733

ABS (2008c) has warned that the crop data from 2005-06 Agricultural Census are not directly comparable to past data. Thus we will not compare the change in wheat production nor for the canola production.

Industry Analysis

In our analysis of the sheep and lambs industry in south west Victoria, one of the options that some wool growers have turned to, as the price of their commodity declined, is the production of wheat. Figure 5.20 shows the movement of world wheat prices since 1998. Wheat prices steadily increased from around 114 to 158 US$/tonne over a six year period (1999 to 2005). The prices have since increased twofold to about 345 US$/tonne in 2008. Currently, it seems to be declining but the indicator price is still high compared to those of four years ago. The favourable international prices and the drier climate in south west Victoria have encouraged some farmers to consider growing this crop.

59 World Indicator Price for Wheat - Monthly Averages

600

500

400

300

US$/Ton 200

100

8 1 4 7 99 n-02 n-05 n-08 Jan-9 Jan- Jan-00 Jan-0 Ja Jan-03 Jan-0 Ja Jan-06 Jan-0 Ja Jan-09 Year

Figure 5.20. World indicator price for wheat, 1998 to 2009 Source: FAO (2009)

Conclusions

Wheat production has become a more attractive option for farmers in south west Victoria. This is related to the drier climate conditions, rising international prices, and the decline of the wool industry in the SW region.

5.4.5. Canola

In the past, water logging has prevented the production of canola in most parts of south west Victoria (Bluett and Wightman, 1999). Areas known for the production of canola have been: Wimmera, north-central and the north-eastern regions and the southern part of the Mallee (Bluett and Wightman, 1999). However, changes in weather patterns and/ or the adoption of raised bed cropping to reduce water logging damage have meant that some SLAs in south west Victoria are now producing significant quantities of canola. In 2005-06, the SW region alone contributed to 35.3% of the total value of canola produced in Victoria.

Spatial Analysis

Similar to the distribution of wheat in south west Victoria, in 2005-06, Ararat generated the greatest volume of canola worth $9.9 million (see Figure 5.21). Canola production is also high in SLAs adjacent to Ararat. For example: Corangamite North ($3.4m), Golden Plains South-East ($2.4m), and Pyrenees South ($2.3m). Table 5.6 lists the top SLAs by the tonnes of canola produced in 2005-06.

Even though the gross value of canola in Ararat is lower than its gross value for wheat, this single SLA actually has the highest canola production and value in the state of Victoria. In 2005-06, it contributed 11.7% to Victoria’s total value of canola production of $84.6m. Other significant canola production areas are West Wimmera and Horsham Balance, these SLAs are very close to our defined SW region. Corangamite North is 6 th in rank for Victoria.

Figure 5.21. Gross value of canola, 2005-06

Table 5.6. SLAs with the highest canola production in SW Victoria, 2005-06 Rank Top producing SLAs, 2005-06 Tonnes 1 Ararat 31,876 2 Corangamite North 10,835 3 Golden Plains South-East 7,625 4 Pyrenees South 7,311 5 S. Grampians Bal 6,690 6 Moyne North-East 5,990

Industry Analysis

Figure 5.22 below is a graph tracing the price movements of Australian crops. Since 1995-96, the price for canola has always been higher than the price for wheat. In recent years, grain and oilseed prices have risen sharply due to relatively low world grain stocks and an increasing demand for bio-fuels production (Barrett et al., 2009). These factors have encouraged and resulted in higher production levels of canola in many parts of Australia. However, ABARE has forecast declining grain prices as currently there are increasing domestic grain supplies and lower world grain prices (Barrett et al., 2009).

Figure 5.22. Australian grain prices, 1995-96 to forecast for 2013-14 Source: Barrett et al. (2009) Note: f denotes ABARE forecast

Conclusions

Ararat is the top canola producing SLA in Victoria in 2005-06. The northern parts of Victoria have traditionally been areas for canola production. Therefore our results show that the favourable prices for oilseeds, drier climate and/ or new production techniques have encouraged greater canola production in south west Victoria.

5.5. Summary

This chapter has shown the spatial distribution of several important agricultural commodities produced in SW Victoria. Changes in the spatial distribution of these commodities were examined together with international or domestic commodity prices. Below is a summary for each commodity in the south west region: • Major milk production areas are predominantly in the south at Corangamite South and Moyne South. Between 2000-01 and 2005-06, most of these key production areas have experienced small declines (0 to 10%) in the number of dairy cattle. • Beef cattle production is mainly distributed around the west, in areas such as Glenelg Heywood and Moyne North-West. Between 2000-01 and 2005-06, most areas in south west Victoria have increased their beef cattle numbers. • Sheep and lamb production is concentrated in the central north, particularly South Grampians Balance and Ararat. Sales of lambs have increased as its price has risen to historically high levels. In contrast, sheep numbers have declined as the price of wool has been on a downward trend over the past two decades. • Production of wheat and canola is highest around Ararat and Corangamite North. These have become more profitable to produce as prices for grains and oilseeds have increased over recent years.

62 6. Conclusion

Every regional economy is unique in terms of their industry structure, agricultural production, and farm economic performance. Our spatial analysis shows that employment growth varies across different parts of south west Victoria and this is influenced by a combination of factors such as industrial diversity, and level of specialisation in agriculture, and forestry and logging. This report concludes that the different areas of south west Victoria concentrate on producing a range of agricultural commodities. For example, areas in and around Ararat concentrate on the production of sheep, lambs, wheat and canola, Corangamite South and Moyne South concentrate on milk production, and the Glenelg region focuses on meat cattle production.

Over the past few years, a range of interacting drivers has affected the values and volumes of agricultural commodities produced. On the international and national scale, factors such as the end of the wool reserve price scheme, mad cow disease, changing consumer preferences and taste, increasing demands for bio-fuels, have had significant influences on commodity prices. These in turn have influenced what and how much certain commodities are produced, and profit and debt levels of farm businesses in south west Victoria.

This report has highlighted the key characteristics of the south west regional economy. To further understand the relationship between climate change and economic resilience, research is required to relate the economic indicators in this report to social, biophysical and climate factors, including age structure, migration flows, land topography, dominant soil types and texture, temperature and rainfall as well as investments in the effects of long-term infrastructure and education.

63 References

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65 Appendix 1

ANZSIC subdivision titles and codes

A Agriculture, Forestry and Fishing 01 Agriculture 02 Services to Agriculture; Hunting and Trapping 03 Forestry and Logging 04 Commercial Fishing B Mining 11 Coal Mining 12 Oil and Gas Extraction 13 Metal Ore Mining 14 Other Mining 15 Services to Mining C Manufacturing 21 Food, Beverage and Tobacco Manufacturing 22 Textile, Clothing, Footwear and Leather Manufacturing 23 Wood and Paper Product Manufacturing 24 Printing, Publishing and Recorded Media 25 Petroleum, Coal, Chemical and Associated Product Manufacturing 26 Non-Metallic Mineral Product Manufacturing 27 Metal Product Manufacturing 28 Machinery and Equipment Manufacturing 29 Other Manufacturing D Electricity, Gas and Water Supply 36 Electricity and Gas Supply 37 Water Supply, Sewerage and Drainage Services E Construction 41 General Construction 42 Construction Trade Services F Wholesale Trade 45 Basic Material Wholesaling 46 Machinery and Motor Vehicle Wholesaling 47 Personal and Household Good Wholesaling G Retail Trade 51 Food Retailing 52 Personal and Household Good Retailing 53 Motor Vehicle Retailing and Services H Accommodation, Cafes and Restaurants 57 Accommodation, Cafes and Restaurants I Transport and Storage 61 Road Transport 62 Rail Transport 63 Water Transport 64 Air and Space Transport 65 Other Transport 66 Services to Transport 67 Storage J Communication Services 71 Communication Services K Finance and Insurance 73 Finance 74 Insurance

66 75 Services to Finance and Insurance L Property and Business Services 77 Property Services 78 Business Services M Government Administration and Defence 81 Government Administration 82 Defence N Education 84 Education O Health and Community Services 86 Health Services 87 Community Services P Cultural and Recreational Services 91 Motion Picture, Radio and Television Services 92 Libraries, Museums and the Arts 93 Sport and Recreation Q Personal and Other Services 95 Personal Services 96 Other Services 97 Private Households Employing Staff Source: ABS (1993)

67 Appendix 2

Table A. 1. Survey sample sizes for the Australian Agricultural and Grazing Industries Survey (AAGIS) Western Total in Year Districts Victoria 1995-96 72 298 1996-97 53 206 1997-98 61 228 1998-99 40 205 1999-00 55 227 2000-01 48 217 2001-02 46 197 2002-03 62 244 2003-04 46 221 2004-05 84 267 2005-06 75 262 2006-07 82 276 Source: ABARE 2009. ABARE data available on request

Table A. 2. Survey sample sizes for the Australian Dairy Industry Survey (ADIS) Western Year Victoria Victoria 2001-02 27 87 2002-03 27 85 2003-04 22 66 2004-05 27 80 2005-06 24 77 2006-07 26 85 Source: ABARE 2009. ABARE data available on request

Table A. 3. Average and relative standard error (RSE) for AAGIS profit and equity ratio Western Districts Victoria Profit Equity Ratio Profit Equity Ratio Year Mean RSE Mean RSE Mean RSE Mean RSE 1995-96 -8,154 (63) 90.5 (2) 14,412 (60) 89.5 (1) 1996-97 -25,047 (30) 89.4 (2) -9,298 (46) 89.7 (1) 1997-98 -28,602 (48) 81.4 (6) -39,662 (14) 86.1 (2) 1998-99 -31,558 (54) 85.1 (7) -35,816 (14) 87.4 (2) 1999-00 -6,362 (166) 93.6 (3) -15,196 (29) 90.4 (1) 2000-01 11,454 (83) 92.9 (4) 16,303 (31) 89.3 (2) 2001-02 37,129 (43) 94.3 (2) 40,496 (17) 91.2 (2) 2002-03 7,100 (171) 94.1 (2) -26,696 (24) 92.3 (1) 2003-04 -17,885 (112) 92.7 (2) 5,242 (109) 90.5 (1) 2004-05 -5,071 (294) 92.8 (1) 8,784 (83) 92.3 (1) 2005-06 -2,741 (531) 93.0 (1) -6,570 (105) 91.8 (1) 2006-07 -60,552 (22) 92.9 (1) -68,841 (10) 90.7 (1) Source: ABARE 2009. ABARE data available on request

68 Table A. 4. Average and relative standard error (RSE) for ADIS profit and equity ratio Western Victoria Victoria Profit Equity Ratio Profit Equity Ratio Year Mean RSE Mean RSE Mean RSE Mean RSE 1995-96 32,800 (28) 82.1 (3) 26,285 (28) 82.3 (2) 1996-97 -10,422 (133) 78.5 (4) -18,573 (43) 79.7 (3) 1997-98 8,538 (194) 75.7 (5) -11,637 (68) 78.6 (2) 1998-99 6,794 (210) 78.1 (5) 2,458 (265) 80.3 (3) 1999-00 -19,287 (78) 78.5 (4) -3,218 (239) 79.6 (3) 2000-01 38,324 (33) 77.0 (4) 35,131 (17) 78.6 (2) 2001-02 120,900 (22) 78.9 (3) 100,294 (12) 79.6 (2) 2002-03 -4,515 (270) 83.2 (3) -66,915 (12) 82.4 (2) 2003-04 10,593 (110) 81.7 (5) -18,002 (50) 81.0 (3) 2004-05 41,100 (52) 86.9 (2) 32,366 (30) 83.1 (2) 2005-06 34,099 (57) 84.8 (4) 24,270 (43) 82.5 (3) 2006-07 -14,137 (140) 82.8 (4) -34,073 (27) 76.2 (6) Source: ABARE 2009. ABARE data available on request