Impact of Debt on Ontario Swine Farms

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1 Impact of Debt on Ontario Swine Farms Prepared by: Randy Duffy and Ken McEwan University of Guelph, Ridgetown Campus November 2011

2 Funding support for this project. This project is funded in part through the Agricultural Management Institute (AMI). The AMI is part of the Best Practices Suite of programs for Growing Forward, a federal provincial territorial initiative. This report was also made possible with the support of Ontario Pork.

3 Acknowledgements The report Impact of Debt on Ontario Swine Farms was made possible through the support of many individuals and organizations. The authors wish to thank the following for their contributions: Ontario Pork Ontario Ministry of Agriculture, Food and Rural Affairs Steve Duff (OMAFRA) Greg Pate (OMAFRA) Ken Poon (University of Guelph) Carolyn Lucio (University of Guelph, Ridgetown Campus) Lynn Marchand (University of Guelph, Ridgetown Campus) Statistics Canada United States Department of Agriculture University of Guelph, Ridgetown Campus Page i

4 Executive Summary Total Canadian farm debt has been increasing since 1992 and as of December 31, 2010 it was estimated the total liabilities on all Canadian farms was $66.4 billion. The three farm types of grains and oilseeds (33%), dairy (25%), and beef (15%) combined account for 73% of the total farm debt in The Canadian swine industry accounted for a 6% share of the total Canadian farm debt in 2009, the most recent data available. This was an average of $850,435 per farm or $2.8 billion in total. The total Ontario farm debt for the entire agricultural industry was estimated to be $16.4 billion in Debt on Ontario swine farms has become a concern as the industry endured a prolonged period of financial losses from late 2006 to early 2010 as a result of many factors that caused revenue and costs to fluctuate widely. According to the Farm Financial Survey, the average Ontario swine farm experienced an increase in total liabilities from $551,288 in 2006 to $749,126 in Fortunately, interest rates have stayed relatively low which has helped producers keep their interest expenses lower. The other factor during this time that has helped swine farms that own land is the increased value of this farm land. This increased value has provided additional collateral for assuming higher debt levels. However, there is potential risk that when interest rates start to increase this will cause extra financial burden and hardship on Ontario swine farms. It is beneficial for the Ontario swine industry to know how it compares relative to producers in other major swine producing regions and other Ontario commodities in terms of their debt levels and their ability to meet financial repayments should interest rates rise from their current historically low levels. As well, it is important to know how Ontario swine producers compare across different farm sizes and production types. The specific objectives for this project were to: 1. Calculate Ontario industry averages for debt related financial ratios using a variety of data sources. These could include Ontario Farm Income Database (OFID), Ontario Data Analysis Project (ODAP), Farm Financial Survey or Canadian Farm Financial Database data. This will be done for the entire industry as well as by production system and farm size. 2. Calculate financial ratios for pig producers in other provinces and the U.S. These other jurisdictions are competitors to the Ontario pig industry so it is important to understand their financial situation. 3. Identify differences, if any, in farm debt repayment ability for the various production stages or for different farm sizes. This will help show if there are certain types of farm sizes or production types that may be more vulnerable to interest rate increases. 4. Develop a tool that producers and industry partners can use to input individual farm data that allows for comparison to the industry averages. Use of the tool will give producers information on their financial situation with respect to debt and repayment ability and how their farm business compares to others in the industry. Depending on what the comparison reveals to the producer it may encourage them to develop business risk management strategies, or seek out training and business management advice. University of Guelph, Ridgetown Campus Page ii

5 Various data sources were used in the analysis. These sources included the Farm Financial Survey (i.e. FFS, Statistics Canada), the Canadian Farm Financial Database (i.e. CFFD, Statistics Canada), the Agricultural Resource Management Survey (i.e. ARMS, United States Department of Agriculture), Ontario Data Analysis Project (i.e. ODAP, University of Guelph, Ridgetown Campus), and the Ontario Farm Income Database (i.e. OFID, Ontario Ministry of Agriculture, Food and Rural Affairs). Each of these sources is considered to be representative indicators of the financial performance of farms in Ontario and other regions but they also have some limitations. By using several different sources, this allows for a more complete picture of the debt situation on Ontario swine farms. Specifically, the results of the analysis using the FFS/CFFD/ARMS data showed that: Relative to swine farms in other regions and other Ontario farm types, Ontario swine farms typically have higher debt to equity ratios than swine farms in Manitoba and the U.S. but lower ratios than Quebec swine farms. Debt to equity ratios are also higher than most other farm types in Ontario. Beef and grains & oilseeds farms in Ontario have the lowest ratios. Ontario swine farms have lower % equity positions than U.S. swine farms and other Ontario farm types, similar positions to Manitoba, and higher positions than Quebec. Ontario swine farms have more of their debt structured for long term repayment rather than short term relative to the other swine regions and most other Ontario commodities. Dairy and poultry & egg farms have relatively more of their debt structured long term than Ontario swine farms. The financially difficult period from 2007 to 2009 caused current ratios (i.e. current assets to current liabilities) to drop to 1.4 at the end of Ratios had been close to the 2.0 level from 2003 to Debt to total revenues averaged 1.25 during the period which is higher than all other Ontario farm types except dairy. The interest expense to total revenue ratio has averaged 7% on Ontario swine farms during the period, well below the recommended 20 25% guideline. In other words, for every $1.00 of total revenues, $0.07 was paid out in interest expense. The ratio of working capital to total revenue has averaged 21% during the period, well within the recommended 10 30% guideline. However, the ratio slipped to 11% in Total estimated debt using the CFFD source for the Ontario swine industry in 2009 was $944 million while total interest expenses were estimated to be $74 million. The impact of a 2% interest rate increase suggested that this would have resulted in an average increase of $3 $7 per pig in interest expenses over the 2003 to 2009 period. This would have translated into an additional cost of $17.0 $43.6 million for the entire Ontario swine industry depending on the year. The results of the analysis using the ODAP data showed that on a small sample of land based Ontario farrow to finish farms: Debt to equity ratios, % equity positions, and current ratios all worsened in 2009 compared to the 2003 to 2008 levels. Debt levels per sow have increased during the period. Equity levels have decreased during the period but at the end of 2009 were still higher than levels in Appreciating asset values (i.e. land) have kept pace with the increased debt levels. This has provided additional collateral for farms with secured debt. The debt servicing requirement ratio in 2009 at 16% of total revenues was well within the historical range. University of Guelph, Ridgetown Campus Page iii

6 Return on assets was in the 3 7% range from 2003 to 2008 but dropped to slightly above 0% in Return on equity was in the 2 8% range from 2003 to 2008 but was 2% in The overall trend during the 2003 to 2009 period has been downward. The impact of a 2% interest rate increase suggested that this would have resulted in an average increase of $3 $4 per pig or $10,000 $21,000 per farm in interest expenses over the 2003 to 2009 period depending on the year. Previous research using the ODAP data to analyze profitability has shown that economic size, debt levels, and pigs sold (i.e. productivity) are each important but were not found to be statistically significant as determining factors in terms of a farm being profitable. This is likely due to the variability that exists within the industry. The results of the analysis using the OFID data showed that: There is a great deal of variability that exists within the data. Often, more variability exists within categories than across categories (i.e. different farm sizes or production types). Total estimated debt for the industry has increased from 2007 to 2009 as a result of the large losses experienced by the industry. Total estimated debt in 2009 was $1.1 billion while total interest expenses were $38 million. In terms of farm size, a farm likely needs to have at least $300,000 in total revenue to generate sufficient funds to provide an operating profit margin that allows for debt repayment and provide a return to the farm owner. Profitability is not necessarily related to farm size, production type, or debt level. Data disaggregated into quintiles by operating profit margin generally showed that regardless of size, type or year the bottom 20% of farms were not profitable while the top 20% of farms were very profitable. Debt levels and interest expenses per farm increase as farm size increases. Debt levels are in acceptable ranges if proportional to farm size but achieving the appropriate balance can be difficult. Debt coverage ratios were fairly consistent over the period. Depending on the year, the 40% 60% most profitable farms for the most part can pay their debt. Statistical significance tests showed that comparisons across farm sizes, production types, the top/bottom 20% of farms vs. the other 80% of farms were not significantly different in most instances. This is likely due to the high variability existing within the data. The impact of a 2% interest rate increase suggested that this would have resulted in an average increase of $2 $3 per pig or $13,000 $29,000 per farm in interest expenses over the 2003 to 2009 period depending on the year. In summary, there are several key observations from this analysis: 1. The total debt estimated for the Ontario swine industry in 2009 ranged from $944 million with interest expenses of $74 million using the CFFD database to $1.1 billion of debt and interest expenses of $38 million using the OFID database. The exact reasons why these interest expense numbers vary are unknown, but remember both are calculated numbers. A significant portion of swine debt would be in long term assets (ie. land and buildings) which would have a higher interest rate than that which was used in the calculation. 2. Overall, the detailed data by farm size (gross revenue) seems to indicate that debt levels increase as farm size increases, debt levels do not necessarily relate to profitability, and the relative balance between manageable debt levels and farm size is important. Using the OFID, it University of Guelph, Ridgetown Campus Page iv

7 was shown that the farm size categories of $500,000 $1,000,000 (22%) and greater than $1,000,000 (60%) carried over 82% of industry debt. 3. In general, the detailed data by production type seems to indicate that debt levels do not necessarily relate to production type or profitability. In 2009, 87% of industry debt was carried by the following production types: farrow to finish (48%), finish (22%) and farrow to feeder (17%). 4. There is a great deal of variability within the OFID data in terms of estimated debt, EBITA and operating profit margins. As a result of this variability, there are no clear patterns or statistically significant differences between farms of different gross revenue sizes or production types. This variability can be seen by the range in quintile averages and standard deviations. However, when EBITA per farm by gross revenue category from 2003 to 2009 was analyzed, the farm size that performed best financially through the tough 2007 to 2009 time period was the $500,000 $1,000,000 category. When EBITA per farm by production type was reviewed, all farm types experienced small to negative returns during the 2007 to 2009 time period. 5. On an aggregate industry level, debt levels and debt servicing requirements on average do not appear to be the major determining factor in profitability. However, due to the variability within the industry, there are quintiles (i.e. farms) within each gross revenue or production type category that are struggling financially and there are quintiles within each category that are doing very well financially, regardless of the year. The 40% least profitable farms are facing a lot of financial pressure. This may be a result of high debt levels, low equity positions, high debt servicing requirements, swings in hog prices and revenue, swings in costs of production or a combination of these factors. 6. For farms of any gross revenue size or production type it is important to not extend their debt servicing capacity beyond levels that are sustainable. A key factor appears to be the ability to maintain a debt level that is balanced with the farm s ability to generate revenue and control costs. The ratio or balance of total debt to total revenue will be unique to each farm due to the many other variables (i.e. management, productivity, importance of off farm income, etc.) that can affect profitability. While there was no statistical evidence to suggest a standard rule of thumb between total debt and total revenue, the simple average between 2003 and 2009 of the three farm size categories with gross revenues over $300,000 showed the ratio was close to 1.00 or greater. This would mean that a reasonable target would be one dollar of farm revenue for every dollar of debt. To conclude, given the information available Ontario swine farms do carry more debt than their U.S. counterparts. This heavier debt load makes the Ontario industry more vulnerable during high interest rate times. However, the evidence supplied in this report illustrates tremendous variability in profitability and debt load regardless of production type or economic size. There was no statistical correlation between high profit farms and low debt levels. While the analysis of current ratios show the values are above 1.0, the trend has been downward and it is important to remember that livestock inventory values do vary considerably from year to year. The tough economic times of 2007 to 2009 have eroded equity and increased debt on most farms. Still the debt levels as of 2009, appear manageable on an industry basis. University of Guelph, Ridgetown Campus Page v

8 Table of Contents 1.0 Introduction Canadian Farm Debt Situation Ontario Farm Debt Situation A Brief Summary of Recent Research and Articles on North American Farm Debt Objectives Methodology Results FFS/CFFD/ARMS ODAP OFID Results by Gross Revenue Range Results by Production Type Statistical Significance of OFID Results Impact of a 2% Interest Rate Increase Comparison of Results from the Different Data Sources Summary References Appendix A Ontario Farm Income Database Tables by Gross Revenue (Farm Size) Appendix B Ontario Farm Income Database Tables by Production Type Appendix C Examples of Selected Variable Calculations For Ontario 2009 Figures Appendix D Selected Methodology, Ontario Farm Income Database List of Figures Figure 1 Total Canadian Farm Debt Outstanding by Lender, at December 31, 2003 vs Figure 2 Provincial Share of Total Canadian Farm Debt Outstanding, at December 31, 2003 vs Figure 3 Estimated Commodity Share of Total Canadian Farm Debt, 2003 vs Figure 4 Average Total Farm Debt Per Farm by Commodity, Canada, 2003 vs Figure 5 Ontario Monthly Revenue and Cost Per Market Hog, 2005 to Figure 6 Total Operating Revenues, Ontario Swine Industry, 2003 to Figure 7 Total Liabilities and Equity, Ontario Swine Industry, 2003 to Figure 8 Total Liabilities and Equity Per Pig Produced, Ontario Swine Industry, 2003 to Figure 9 Total Interest Expenses, Ontario Swine Industry, 2003 to Figure 10 Total Interest Expenses Per Pig Produced, Ontario Swine Industry, 2003 to Figure 11 Debt to Equity Ratio, Swine Farms, 2003 to Figure 12 Debt to Equity Ratio, Various Ontario Farm Types, 2003 to Figure 13 % Equity Ratio, Swine Farms, 2003 to Figure 14 % Equity Ratio, Various Ontario Farm Types, 2003 to Figure 15 Debt Structure Ratio, Swine Farms, 2003 to Figure 16 Debt Structure Ratio, Various Ontario Farm Types, 2003 to Figure 17 Current Ratio, Swine Farms, 2003 to Figure 18 Current Ratio, Various Ontario Farm Types, 2003 to Figure 19 Debt to Total Revenues Ratio, Swine Farms, 2003 to Figure 20 Debt to Total Revenues Ratio, Various Ontario Farm Types, 2003 to University of Guelph, Ridgetown Campus Page vi

9 Figure 21 Interest Expenses to Total Revenues Ratio, Swine Farms, 2003 to Figure 22 Interest Expenses to Total Revenues Ratio, Various Ontario Farm Types, 2003 to Figure 23 Working Capital to Total Revenues Ratio, Swine Farms, 2003 to Figure 24 Working Capital to Total Revenues Ratio, Various Ontario Farm Types, 2003 to Figure 25 Debt to Equity Ratio, Ontario Farrow to Finish Swine Farms, 2003 to Figure 26 % Equity Ratio, Ontario Farrow to Finish Swine Farms, 2003 to Figure 27 Current Ratio, Ontario Farrow to Finish Swine Farms, 2003 to Figure 28 Debt Servicing Requirement Ratio, Ontario Farrow to Finish Swine Farms, 2003 to Figure 29 Debt per Sow and Equity per Sow, Ontario Farrow to Finish Swine Farms, 2003 to Figure 30 Debt per Pig Produced and Equity per Pig Produced, Ontario Farrow to Finish Figure 31 Swine Farms, 2003 to Return on Assets and Return on Equity, Ontario Farrow to Finish Swine Farms, 2003 to Figure 32 Total Operating Revenue, Ontario Swine Industry, 2003 to Figure 33 Total Estimated Debt, Ontario Swine Industry, 2003 to Figure 34 Total EBITA, Ontario Swine Industry, 2003 to Figure 35 Share of Total Industry Operating Revenue by Gross Revenue Range, Figure 36 Share of Total Industry Operating Revenue by Gross Revenue Range, Figure 37 Estimated Debt per Farm by Gross Revenue Range, 2003 to Figure 38 Figure 39 Estimated Debt to Total Operating Revenue Ratio by Gross Revenue Range, 2003 to Interest Expense to Total Operating Revenue Ratio by Gross Revenue Range, 2003 to Figure 40 EBITA per Farm by Gross Revenue Range, 2003 to Figure 41 Share of Total Industry Operating Revenue by Production Type, Figure 42 Share of Total Industry Operating Revenue by Production Type, Figure 43 Estimated Debt per Farm by Production Type, 2003 to Figure 44 Estimated Debt to Total Operating Revenue Ratio by Production Type, 2003 to Figure 45 Interest Expense to Total Operating Revenue Ratio by Production Type, 2003 to Figure 46 EBITA per Farm by Production Type, 2003 to Figure 47 EBITA by Operating Profit Margin Quintile, Farrow to Finish Farms, 2003 to Figure 48 Figure 49 Estimated Debt by Operating Profit Margin Quintile, Farrow to Finish Farms, 2003 to Scatter Diagram of EBITA vs. Estimated Debt, Farrow to Finish Farms by Operating Profit Margin Quintile, 2003 to Figure 50 Scatter Diagram of EBITA vs. Estimated Debt, $500,000 $1,000,000 Gross Revenue Farms by Operating Profit Margin Quintile, 2003 to List of Tables Table 1 Data Sources Table 2 List of Farm Debt and Profitability Measures Used in the Analysis Table 3 Average Number of Farms Used in the Analysis, 2003 to Table 4 Share of Total Industry Debt by Gross Revenue Range, 2003 vs Table 5 Share of Total Industry Debt by Production Type, 2003 vs Table 6 % of Variation in EBITA Explained by Variation in Estimated Debt by Production Type Table 7 % of Variation in EBITA Explained by Variation in Estimated Debt by Gross Revenue Range University of Guelph, Ridgetown Campus Page vii

10 Table 8 Scenario of a 2% Increase in Interest Rates Using CFFD Table 9 Scenario of a 2% Increase in Interest Rates Using ODAP Table 10 Scenario of a 2% Increase in Interest Rates Using OFID Table A1 Number of Ontario Swine Farms by Gross Revenue Range and Operating Profit Margin Quintile, 2003 to Table A2 Estimated Total Pigs Sold Per Farm by Gross Revenue Range and Operating Profit Margin Quintile, 2003 to Table A3 Operating Profit Margin Per Farm by Gross Revenue Range and Operating Profit Margin Quintile, 2003 to Table A4 EBITA Per Farm by Gross Revenue Range and Operating Profit Margin Quintile, 2003 to Table A5 Estimated Debt Per Farm By Gross Revenue Range and Operating Profit Margin Quintile, 2003 to Table A6 Debt Coverage Ratio Per Farm by Gross Revenue Range and Operating Profit Margin Quintile, 2003 to Table B1 Number of Ontario Swine Farms by Production Type and Operating Profit Margin Quintile, 2003 to Table B2 Estimated Total Pigs Sold per Farm by Production Type and Operating Profit Margin Quintile, 2003 to Table B3 Operating Profit Margin Per Farm by Production Type and Operating Profit Margin Quintile, 2003 to Table B4 EBITA Per Farm by Production Type and Operating Profit Margin Quintile, 2003 to Table B5 Estimated Debt Per Farm by Production Type and Operating Profit Margin Quintile, 2003 to Table B6 Debt Coverage Ratio per Farm by Production Type and Operating Profit Margin Quintile, 2003 to University of Guelph, Ridgetown Campus Page viii

11 1.0 Introduction 1.1 Canadian Farm Debt Situation Total farm debt levels on Canadian farms have been increasing since 1992 (Statistics Canada). As of December 31, 2010, it was estimated that total liabilities (both farm related and personal portion) on all Canadian farms was $66.4 billion. This is an increase of $19.5 billion or 42% since Figure 1 shows the distribution of farm debt by lender for 2003 and Figure 1. Total Canadian Farm Debt Outstanding by Lender, at December 31, 2003 vs $25,000,000, Farm Debt Outstanding at December 31 $20,000,000,000 $15,000,000,000 $10,000,000,000 $5,000,000,000 $0 Chartered banks Federal government agencies Source: Statistics Canada cansim Provincial government agencies Credit unions Insurance, trust Private and loan individuals and companies others Lender Advance payments for crops In 2010, chartered banks held the largest portion of debt at $23.6 billion or 36% of the total. This was an increase of $3 billion from 2003 but the share of total farm debt actually decreased from the 44% held in Farm debt has increased for every lender type from 2003 to 2010 but shares of the total have changed slightly. The major change is in debt held by federal government agencies, the second largest lender. The share has increased from 19% in 2003 to 28% in Credit unions are the third largest lender with a 16% share in University of Guelph, Ridgetown Campus Page 1

12 Figure 2 shows the provincial share of total Canadian farm debt in 2003 vs Shares are relatively similar for both years. Approximately two thirds of the total debt is held by three provinces in 2010, Quebec (18%), Ontario (25%) and Alberta (22%). Figure 2. Provincial Share of Total Canadian Farm Debt Outstanding, at December 31, 2003 vs % Provincial Share of Farm Debt Outstanding 20% 15% 10% 5% 0% Source: Statistics Canada cansim Province University of Guelph, Ridgetown Campus Page 2

13 Figure 3 shows the share by commodity of the total Canadian farm debt in 2003 vs Swine farms accounted for 7% of the total debt in 2003 and 6% of the total debt in The three farm types with the largest shares in 2009 were grains and oilseeds (33%), dairy (25%) and beef (15%). Combined, these three farm types held 73% of the total debt in Figure 3. Estimated Commodity Share of Total Canadian Farm Debt, 2003 vs Estimated Commodity Share of Total Canadian Farm Debt 35% 30% 25% 20% 15% 10% 5% 0% Farm Type Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey University of Guelph, Ridgetown Campus Page 3

14 Figure 4 shows the average total debt per farm by commodity in Canada in 2003 relative to 2009 as this is the most recent available data for this source. The average swine farm held $525,506 in This increased 62% to $850,435 per farm in The farm types with the highest average debt per farm in 2009 included potato ($1,249,543), dairy ($983,556) and poultry and egg ($824,568). Most farm types experienced an increase in total debt per farm from 2003 to The largest % increases from 2003 to 2009 were experienced by potato (87%), dairy (76%), fruit and tree nuts (75%), swine (62%), other vegetable and melon (59%), poultry and egg (58%), and grains and oilseeds (54%). Figure 4. Average Total Farm Debt Per Farm by Commodity, Canada, 2003 vs $1,400,000 Average Farm Debt Per Farm by Commodity $1,200,000 $1,000,000 $800,000 $600,000 $400,000 $200, $0 Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey Although debt levels per farm for most farm types have increased significantly it should be noted that data for all Canadian farms shows that total revenues, total assets and total equity have also increased during this period. This means that while the increased debt levels are concerning, it appears that the increased debt has likely been used to incorporate technology and/or expand production leading to increased farm revenue. Asset appreciation has for the most part been through increases in land values which has provided additional security for lenders. 1.2 Ontario Farm Debt Situation Farm Type Total farm debt levels for the entire Ontario agricultural industry have increased from $12.7 billion in 2006 to $16.4 billion in 2010, an increase of 29.5% (Statistics Canada). The increase from 2009 to 2010 alone was 10.0%. In 2010, the breakdown of major debt holders was: chartered banks (41.1%); federal government agencies (34.7%); private individuals and supply companies (14.7%); credit unions (6.2%); insurance, trust companies and other (1.2%); advance payment programs (1.8%); and provincial government agencies (0.2%). University of Guelph, Ridgetown Campus Page 4

15 Since 2006, times have been especially difficult for swine farms in Ontario and debt levels have increased significantly on some farms. According to the Farm Financial Survey (FFS), the average Ontario swine farm saw total liabilities increase from $551,288 in 2006 to $749,126 in 2009, an increase of $197,838 or 36%. As a comparison, the Ontario Data Analysis Project (ODAP) undertaken at University of Guelph, Ridgetown Campus showed the average farrow to finish farm had total liabilities of $905,504 in 2006 compared to $1,217,646 in This was an increase of $312,142 or 34%. As a result of many different factors, revenues and costs in the swine industry have fluctuated widely. Figure 5 shows the Ontario estimated monthly revenue and cost per market hog from 2005 to Profits were realized in the industry in parts of 2005 and 2006 but losses were incurred over the entire three and a half year period from late 2006 to early This explains why debt levels have increased significantly on some Ontario swine farms. Figure 5. Ontario Monthly Revenue and Cost Per Market Hog, 2005 to 2011 $200 $190 $180 Revenue Cost $170 $ / head $160 $150 $140 $130 $120 $110 $100 $90 $80 J FMAMJ JASONDJ FMAMJ JASONDJ FMAMJ JASONDJ FMAMJ JASONDJ FMAMJ JASONDJ FMAMJ JASONDJ FMAMJ JASOND Source: Ontario Ministry of Agriculture, Food and Rural Affairs, Monthly Swine Budgets. University of Guelph, Ridgetown Campus Page 5

16 Using data from the Canadian Farm Financial Database (CFFD), Figure 6 shows the estimated total operating revenues for the Ontario swine industry from 2003 to Total revenue has been over $1 billion each year but has fluctuated up to $200 million depending on the year to year comparison. Also shown in the figure is the % that the Ontario swine industry represents of the Ontario total operating revenues for all farms. This has varied from 12% in 2005 to 9% in Figure 6. Total Operating Revenues, Ontario Swine Industry, 2003 to 2009 $1,300,000,000 $1,250,000,000 Total Operating Revenues Total Operating Revenues % of ON 14% 12% Total Industry Operating Revenues $1,200,000,000 $1,150,000,000 $1,100,000,000 $1,050,000,000 10% 8% 6% 4% Swine Industry as % of Ontario $1,000,000,000 2% $950,000, Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey 0% University of Guelph, Ridgetown Campus Page 6

17 Figure 7 shows the estimated total liabilities for the entire Ontario swine industry from 2003 to Total liabilities for the industry have ranged from $834 million in 2003 to $1.2 billion in Total liabilities for the Ontario swine industry have accounted for 8% 11% of the total liabilities for all farms in Ontario during this period. Total swine debt in 2009 was estimated to be $944 million by Statistics Canada. Figure 7 also shows the estimated total equity for the entire Ontario swine industry from 2003 to Total equity for the industry has ranged from $1.5 billion to $2.1 billion depending on the year. From 2006 to 2009, total equity has decreased approximately $367 million. Total equity for the Ontario swine industry has accounted for 3% 5% of the total equity for all farms in Ontario during this period. Figure 7. Total Liabilities and Equity, Ontario Swine Industry, 2003 to 2009 $2,500,000,000 $2,000,000,000 Liabilities Liabilities % of ON Equity Equity % of ON 12% 10% Total Industry Liabilities Total Industry Equity $1,500,000,000 $1,000,000,000 8% 6% 4% Swine Industry as % of Ontario $500,000,000 2% $ Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey 0% University of Guelph, Ridgetown Campus Page 7

18 Figure 8 shows the same total liabilities and total equity as in Figure 7 only on a per pig produced basis. Total pigs produced for the entire Ontario industry were estimated as the sum of Ontario origin pigs slaughtered in Canada (AAFC) plus live feeder pigs and market hogs exported to the U.S. through Michigan and New York state border points (USDA). The estimated total pigs produced for the entire Ontario industry have ranged from a high of 8 million head in 2004 to a low of 6.5 million head in Total liabilities per pig produced were approximately $109 in 2003 and were $146 in 2009 after peaking in 2007 at $165 per head. On the equity side, total equity per pig produced was approximately $225 in 2003 and had increased to $270 in However, within the period, equity peaked at $292 in 2006 and dropped to $206 in Figure 8. Total Liabilities and Equity Per Pig Produced, Ontario Swine Industry, 2003 to 2009 $ Total Liabilities Total Equity $ Total Liabilities Per Pig Produced Total Equity Per Pig Produced $ $ $ $50.00 $ Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey, Agriculture and Agri Food Canada, University of Guelph Ridgetown Campus calculations University of Guelph, Ridgetown Campus Page 8

19 Looking at the interest expense only portion of debt servicing requirements, Figure 9 shows that total interest expenses for the entire Ontario swine industry were as low as $57 million in 2005 and as high as $74 million in Total interest expenses for the Ontario swine industry have accounted for 9% 12% of the total interest expenses for all farms in Ontario during this period. As a reference point, Figure 9 also shows the historical bank prime interest rate plus 1% during the 2003 to 2009 period. Despite the bank interest rate decreasing from 2007 to 2009, interest expenses have increased as a result of the higher debt levels. Figure 9. Total Interest Expenses, Ontario Swine Industry, 2003 to 2009 $80,000,000 Interest Expenses Interest % of ON Bank Prime Rate + 1% 14% $70,000,000 12% Total Industry Interest Expenses $60,000,000 $50,000,000 $40,000,000 $30,000,000 $20,000,000 10% 8% 6% 4% Swine Industry as % of Ontario Bank Prime Rate + 1% $10,000,000 2% $ Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey 0% University of Guelph, Ridgetown Campus Page 9

20 Figure 10 shows the same total interest expenses in Figure 9 only on a per pig produced basis using the same calculation mentioned in the discussion of Figure 8. Total interest expenses per pig produced were approximately $8.34 in 2003 but had increased to $11.43 per head in Figure 10. Total Interest Expenses Per Pig Produced, Ontario Swine Industry, 2003 to 2009 $12.00 $10.00 Interest Expenses Per Pig Produced $8.00 $6.00 $4.00 $2.00 $ Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey, Agriculture and Agri Food Canada, University of Guelph Ridgetown Campus calculations Fortunately, as shown in Figure 9 interest rates have stayed relatively low which has helped producers keep their interest expenses lower. The other factor during this time that has helped swine farms that own land is the increased value of this farm land. This increased value has provided additional collateral for assuming higher debt levels. Statistics Canada figures show that Ontario farm land values have gone from $4,201 per acre in 2006 to $4,767 per acre in 2009, an increase of 13%. If the period is extended to include 2010, the average value per acre is $5,061 or an increase of 20% from This is substantiated by figures from the FFS. The average total assets per farm increased from $1,875,082 in 2006 to $2,138,776 in 2009, an increase of $263,694 or 14%. The majority of this increase ($253,118) was in long term assets of which land is included. Accordingly, the ODAP results showed total assets increased from $2,765,701 per farm in 2006 to $2,902,379 in 2009, an increase of $136,678 or 5%. The land component of the ODAP asset values increased from $1,224,753 in 2006 to $1,405,161 in 2009, an increase of $180,408 or 15%. However, there is potential risk that when interest rates start to increase this will cause extra financial burden and hardship on Ontario swine farms. For example, what will happen to the ability of Ontario swine farms to cover the increased interest expense if interest rates rise 2% from their current levels? It is beneficial for the Ontario swine industry to know how it compares relative to producers in other major swine producing regions and other Ontario commodities in terms of their debt levels and their ability to meet financial repayments should interest rates rise from their current historically low levels. University of Guelph, Ridgetown Campus Page 10

21 As well, it is important to know how Ontario swine producers compare across different farm sizes and production types. University of Guelph, Ridgetown Campus Page 11

22 2.0 A Brief Summary of Recent Research and Articles on North American Farm Debt Included below are key points regarding farm debt reported in other publications. a. Robert A. Hoppe. U.S. Farm Structure. USDA, ERS. Amber Waves Volume 8, Issue 3, September 2010, pp Agricultural production continues to shift to larger operations, while the number of small commercial farms and their share of farm sales continue a slow, long term decline. There are a large number of small commercial farms that have negative operating profits however total household income on these farms is supplemented by off farm income. Large and very large farms typically have higher returns. This competitive edge will result in a continually decreasing number of small farms as production is shifted to the large farms. b. Brian C. Briggeman. The Role of Debt in Farmland Ownership. Choices, a publication of the Agricultural & Applied Economics Association. Accessed September 1, 2011 from magazine/theme articles/farmland values/the roleof debt in farmland ownership. Most farmers need access to credit in order to purchase land especially with land prices at historically high levels. Farm land debt in the US is held primarily by the Farm Credit system and commercial banks rather than being held privately by individuals. Total outstanding farm real estate debt in the US is estimated to be between $132 billion and $141 billion depending on the source. There is concern about how much land values and debt levels have increased however the USDA suggests that net farm income will increase by 20% in 2011 due to tight supplies and increased demand. c. Jason Henderson and Maria Akers. Agricultural Finance Conditions Turn. Agricultural Finance Databook, National Trends in Farm Lending July Federal Reserve Bank of Kansas City. Accessed September 1, 2011 from 07 ag findb.pdf, p. 1. The price of land has increased due to high commodity prices and farm profitability. Even though farm input costs have risen, agricultural banks have reported improved profits however, there is potential risk in the future with respect to the ability of farms to repay their debt if input costs remain high. d. USDA, ERS. Farm Income and Costs: Assets, Debt and Wealth. Accessed September 1, 2011 from There are three key factors that are increasing farm asset values. These factors are: 1) higher income from production assets; 2) low costs associated with borrowing; and 3) the anticipated growth of future returns on these investments. The values of real estate are expected to increase by 6.6% from $2.18 trillion in 2010 to $2.32 trillion in 2011 while farm debt is expected to decrease from $246.9 billion to $242.1 billion. This will result in an increase in farm equity from $1.93 trillion in 2010 to $2.08 trillion in A comparison of debt to asset and debt to University of Guelph, Ridgetown Campus Page 12

23 equity ratios for these two years indicates that the farm sector is more solvent in 2011 than in Interest rate movement is important to agriculture because the interest rate affects the value of outstanding debt and therefore the solvency of the sector. Also, agriculture is very capitalintensive so it is sensitive to movements in the interest rate. Short term financing costs incurred during the growing season are influenced by the interest rate as are the costs associated with longer term capital investments. e. Statistics Canada Value of Farm Capital Agriculture Economic Statistics, Catalogue no X, vol. 10 no. 1. May 2011, p. 6. The value of Canada s farm capital increased to $308 billion in 2010, an increase of 4.6% over 2009 and 16.4% higher than the previous five year average (2005 to 2009). The value of land and buildings accounted for 82.7% of total farm capital value and increased by 4.6% in 2010 over the 2009 level to reach $254.7 billion. On a per acre basis, the value of land and buildings for Eastern Canada and Western Canada averaged $3,953 and $1,112 respectively. Farm machinery and equipment accounted for $40.2 billion or 13.0% of total farm capital. Tractors, combines and other farm machinery represented 84.9% of the total value of this category and their value increased by 5.4% in Cattle and hog numbers have been declining in Canada due to a combination of rising input costs, weak slaughter prices and Country of Origin Labeling (COOL) legislation implemented in the United States in f. Statistics Canada Balance Sheet of the Agricultural Sector Agriculture Economic Statistics, Catalogue no X, vol. 10, no.1. June 2011, pp. 5, 6, 10, 39. Equity in Canada s farm sector continued to increase in 2010, rising 3.1% or $8.6 billion to reach $282.4 billion. Asset values outpaced an increase in liabilities when comparing 2009 and 2010 values. In 2010 total farm assets increased by $12.1 billion to reach a value of $343.3 billion. Farm land values had the largest impact on the total asset value. Land increased by $6.1 billion in 2010 to reach $232 billion. Farm liabilities increased to $60.9 billion, growing by $3.5 billion over This report also generates a historical balance sheet of the agricultural sector for each province. For comparison purposes, in 1981 Ontario farm assets were valued at $30.5 billion, liabilities $4.2 billion and equity $26.3 billion. By December 31, 2010 Ontario farm assets were estimated to be $86.4 billion, liabilities $15.2 billion and equity $71.2 billion. This means between 1981 and 2010 assets have increased in value by 183% while liabilities increased by 262% and equity rose by 171%. Therefore farm liabilities have risen by a greater percentage than the rise experienced in farm assets during this 30 year time period. The major asset class was farm real estate valued at $61.95 billion while most debt was held in the long term category. It is interesting to see that Ontario s quota value increased from $2.41 billion in 1981 to $10.7 billion in 2010 (i.e. 344% increase). The debt to asset ratio rose from 13.7% to 17.6% over the 1981 to 2010 time period. University of Guelph, Ridgetown Campus Page 13

24 g. Statistics Canada Farm Debt Outstanding Agriculture Economic Statistics, Catalogue no X, vol. 10, no. 1. May 2011, pp 5, 9, 15. At the national level, farm debt outstanding at December 31, 2010 rose to $66.4 billion up 6.1% from 2009, and continuing the steady trend upwards that has been occurring since It should be noted that this value of farm debt is slightly different than the estimate provided above in Statistic Canada s balance sheet report (i.e. $60.9 billion vs $66.4 billion). A partial explanation for this difference in debt estimates seems to depend on whether farm household liabilities are included or not. The main holders of mortgaged farm debt were: Federal government agencies eg. Farm Credit Corporation (45.1%); chartered banks (20.7%); individuals (16%); credit unions (9.9%); and provincial government agencies (4.2%). Non mortgaged debt was primarily owed to chartered banks (52.2%) and credit unions (22.4%). When debt levels for Ontario are examined, in 1981 the total outstanding debt was $4.8 billion with the three major lenders being chartered banks (49%); private individuals and supply companies (24%); and Federal government agencies (22%). In 2010, the total outstanding debt was $16.4 billion and the major lenders were: chartered banks $6.8 billion (41%); Federal government agencies $5.7 billion (35%); and private individuals $2.4 billion (15%). This means that during the 1981 to 2010 time period, banks have lost some market share while Federal government agencies have increased their relative proportion. Also, total farm debt for Ontario has increased 2.4 times during the 30 year time period. h. Mussell, Al, T. Moore, K. McEwan and R. Duffy. Testing the Structure of Canadian Farm Incomes. Report prepared for Canadian Agri Food Policy Institute (CAPI), September 1, This paper looks at the farm income issues at a disaggregated level. It shows that there can be more variability of farm profitability within a specific sales class, than there is between sales classes. While farm income in the aggregate has not been increasing, there are farm operations that are increasing their profitability, and size is not the only means to profitability. Farm management skills seem to be an underlying necessity for achieving farm level profitability. i. AgCanda.com. BMO Sounds Warning Bell On Interest Rates Canada's farmers owed $63B in both mortgage and non mortgage debt in 2009, a 4.7% rise from the previous year. Article written by Ron Friesen. Monday, June 21, Accessed September 1, 2011 from This article provides information on strategies the Bank of Montreal (BMO) is offering to help Canadian farmers manage their debt in preparation for higher interest rates over the next three to five years. BMO indicated that interest rates could increase by three to five percentage points by 2015 and this could really impact farmers, particularly those that are highly leveraged. In the article, a comparison of the amount of debt in Canada in 2010 is made to debt levels in 1981 and it has increased almost 3.5 times. In contrast, US farm debt increased 20 per cent during the same time. University of Guelph, Ridgetown Campus Page 14

25 Concern is expressed about the ability of farmers in Canada to carry increasing debt loads. In the 1980s the average debt to income ratio was seven to one and by 2007 it was 40 to one. It was recognized however that not every farm in Canada is dealing with high debt levels. j. McEwan, Ken and L. Marchand. Success Factors For Innovative Farmers: Case Study of Swine Farms In South Western Ontario. Report prepared for Agriculture and Agri Food Canada. May, For this project two workshops were held with progressive Ontario swine producers located in south western Ontario. The producers that were selected to participate have been in the pig production industry for many years; they have little, if any off farm income and rely primarily on income from the pig enterprise; they have grown their farm business over time; and these producers have had a long working relationship with the lead author. During the workshops five farm level factors were discussed with respect to how they may influence the competitiveness and long run success of Ontario swine farms. These factors were: adoption of technology; management process; entrepreneurship and risk taking; farm size; and family and community. It is important to stress that the information obtained through the workshops is not a statistical representative sample of the Canadian or Ontario swine industry. Rather, the findings are a collection of thoughts and ideas shared by a group of swine producers that have been successful in the business. Each participant was encouraged to share their own point of view however the producers seemed to agree with each other on most opinions expressed. The workshop participants all agreed that the essential ingredients for success relate to the manager having a positive attitude, having a plan or strategy of where the farm is going, and having the courage to implement the plan. k. George Morris Centre. Advancing a Policy Dialogue Series 1: Understanding the Structure of Canadian Farm Incomes. Prepared for the Canadian Agri Food Policy Institute (CAPI). February Accessed September 1, 2011 from icpa.ca/destinations/capi_advancingpolicydialogue.pdf. The Canadian Agri Food Policy Institute (CAPI) commissioned the George Morris Centre to complete a series of short papers in 2009 and 2010 that highlight what the key issues are relating to farm viability in Canada. In the sixth paper, Understanding Farm Debt in Canada, it is noted that in Canada and the US farm debt has been increasing over time but if farms are able to service the debt then it can be used to expand an operation. The series of papers found that farms in Canada carry almost two times the amount of debt that US farms do and farm debt in Canada is increasing faster than in the US. Also, it was reported that debt repayment likely takes longer in Canada because the farms have higher debt/earnings ratios. This is the situation for farms of all sizes although large farms in both countries can service debt better than small farms. The market value of assets has been increasing but earnings are low compared to the level of debt on farms. University of Guelph, Ridgetown Campus Page 15

26 It was also found that off farm income is very important. For example, on farms that have less than $100,000 in sales, off farm income represents 76% or more of the family s total income. Farming families have lower total income than the average Canadian family but their net worth is three times higher than the average. The appreciation of farm business assets contributes greatly to the economic well being of farmers. The research also found that farm size is not indicative of the amount of farm income generated. Farms that generate little income can be small or large in terms of economic size. In summary, there is concern within the U.S. about higher land values and input costs because of the potential risk in the future of farmers not being able to repay their loans. However, U.S. farm debt is expected to decrease from $246.9 billion to $242.1 billion between 2010 and Key factors that are increasing farm asset values are: 1) higher income from production assets; 2) low borrowing costs; 3) anticipated growth of agricultural returns. In Ontario, similar comments have been made, however liabilities have risen by a greater percentage than farm assets between 1981 and Further, there has been research done in Canada that shows there can be more variability of farm profitability within a specific sales class than there is between different sales classes. Farm management skills are a necessity to achieving farm level profitability. University of Guelph, Ridgetown Campus Page 16

27 3.0 Objectives The specific objectives for this project were to: 1. Calculate Ontario industry averages for debt related financial ratios using a variety of data sources. These could include Ontario Farm Income Database (OFID), Ontario Data Analysis Project (ODAP), Farm Financial Survey or Canadian Farm Financial Database data. This will be done for the entire industry as well as by production system and farm size. 2. Calculate financial ratios for pig producers in other provinces and the U.S. These other jurisdictions are competitors to the Ontario pig industry so it is important to understand their financial situation. 3. Identify differences, if any, in farm debt repayment ability for the various production stages or for different farm sizes. This will help show if there are certain types of farm sizes or production types that may be more vulnerable to interest rate increases. 4. Develop a tool that producers and industry partners can use to input individual farm data that allows for comparison to the industry averages. Use of the tool will give producers information on their financial situation with respect to debt and repayment ability and how their farm business compares to others in the industry. Depending on what the comparison reveals to the producer it may encourage them to develop business risk management strategies, or seek out training and business management advice. University of Guelph, Ridgetown Campus Page 17

28 4.0 Methodology Data Sources Various data sources were used in this project. Each of these are considered to be representative indicators of the financial performance of farms in Ontario and other regions but they also have some limitations. By using several different sources this allows for a more complete picture of the debt situation on Ontario swine farms. The data sources used were: Table 1. Data Sources Database/Survey Source Advantages Limitations Farm Financial Survey (FFS) Statistics Canada Canadian Farm Financial Database (CFFD) Agricultural Resource Management Survey (ARMS) Ontario Data Analysis Project (ODAP) Ontario Farm Income Database (OFID) Statistics Canada United States Department of Agriculture University of Guelph, Ridgetown Campus Ontario Ministry of Agriculture, Food and Rural Affairs Income statement and balance sheet information. Income statement and balance sheet information. Cash and accrual basis calculations possible for some variables. Income statement and balance sheet information. Cash and accrual basis calculations possible for some variables. Income statement, balance sheet and production data. Income statement and inventory figures. Cash and accrual basis calculations possible for some variables. Not all farms are included. Not same set of farms year to year. Cash basis with no accrual adjustments for depreciation and inventory changes. Not all farms are included. Not same set of farms for both income statement and balance sheet information. Not same set of farms year to year. Not all farms are included. Not same set of farms year to year. Small sample. Not same set of farms year to year. No balance sheet information. Debt has to be estimated. Production type has to be estimated using inventory figures. Not same set of farms year to year. Some commodities are considered to have close to the full farm population while others include only part of the industry. University of Guelph, Ridgetown Campus Page 18

29 Time Period The time period used in this project is from 2003 to 2009 due to the availability of data from the Ontario Farm Income Database. Earlier data is available for some of the other sources (eg. FFS, CFFD, ARMS, ODAP) but for consistency this period was used. This period is important to look at since the earlier years (2003 to 2006) saw the swine industry experience some profits while the latter years (2007 to 2009) brought significant losses to the industry due to low hog prices and rising feed costs. Variables Most of the variables used in this analysis are ratios. This allows for a comparison of farms from different regions, farm sizes or farm types. The differences between data sources does not allow for all of the variables to be calculated for each data source. The variables used in this analysis include: Table 2. List of Farm Debt and Profitability Measures Used in the Analysis Measure Calculation Used in Analysis Notes Debt : Equity Total Liabilities Total Equity Leverage measure that shows for every dollar in equity (paid assets), this % is still owing liabilities (unpaid assets). Indicates capacity of farm to repay debt. % Equity Total Equity Total Assets Alternative way of showing Debt : Assets Ratio 100% Debt Structure Current Liabilities Total Liabilities % of total liabilities due within one year. Return on Assets (Net Farm Income (accrual) + Interest Expenses) Total Assets x 100% Return on Equity Net Farm Income (accrual) Total Equity x 100% Current Ratio Debt per Sow Debt per Pig Produced Equity per Sow Equity per Pig Produced Debt Servicing Requirement Debt : Total Revenue Interest : Total Revenue Current Assets Current Liabilities Total Liabilities Average Number of Sows Total Liabilities Total Pigs Produced Total Equity Average Number of Sows Total Equity Total Pigs Produced (Principal + Interest Expenses) Total Revenues Total Liabilities Total Revenues Interest Expenses Total Revenues For every dollar in assets controlled by the farm, this % is available to pay a return to the farm owners. Profitability measure. For every dollar of equity, this % is available to pay a return to the farm owner s equity. Profitability measure. Liquidity measure of the farm s ability to pay shortterm obligations. Total liabilities of the farm per sow owned. Total liabilities of the farm per pig produced. Total equity of the farm per sow owned. Total equity of the farm per pig produced. % of total revenues that is required to service all debt obligations for the year. % of total liabilities relative to every dollar of revenue generated by the farm. Measure of farm s ability to generate revenue relative to it s debt load. Should not exceed 20 25% of total revenues. Upward trend could eventually lead to financial distress. University of Guelph, Ridgetown Campus Page 19

30 Measure Calculation Used in Analysis Notes Working Capital : Total Revenue (Current Assets Current Liabilities) Total Revenues Ratio of 30% considered strong, 10% 30% considered adequate. Ability to meet debt obligations and provide flexibility for future cash flow volatility. Operating Profit Margin (%) Number of Farms Estimated Pigs Sold EBITA Estimated Debt Debt Coverage Ratio Sources: various Earnings Before Interest and Taxes Total Operating Revenues Number of Farms in Category or Quintile Estimated Total Pig Sales from Producer Inventory Schedule Earnings Before Interest, Taxes and Amortization Interest Expenses Interest Rate (Interest Expenses + Net Income) Interest Expenses Profitability measure that shows operating income relative to total revenues. Operating income is difference between total operating revenues and total operating expenses. No interest expenses or accrual adjustments included. none none Accrual basis. Leasing expenses are included along with CCA (depreciation). Interest rate used is estimated to be bank prime + 1% Measures ability to pay for interest, principal and lease payments from farm income. Proxy for financial distress. Ratio over 1.0 indicates sufficient cash flow to meet debt obligations. Typically, a ratio of at least 1.2 is preferred. Calculation done using OFID data is cash basis and does not include principal payments due to data availability. The list of variables that were calculated by data source are as follows: Section 1: FFS/CFFD/ARMS 7 Financial Measures Calculated Debt : Equity % Equity Debt Structure Current Ratio Debt : Total Revenues Interest Expense : Total Revenues Working Capital : Total Revenues Appendix C contains selected examples for 2009 calculations for Ontario swine farms using the CFFD and FFS data. University of Guelph, Ridgetown Campus Page 20

31 Section 2: ODAP 10 Financial Measures Calculated Debt : Equity % Equity Current Ratio Debt Servicing Requirement Ratio Debt per Sow Equity per Sow Debt per Pig Produced Equity per Pig Produced Return on Assets Return on Equity Appendix C contains selected examples for 2009 calculations for Ontario swine farms using the ODAP data. Section 3: OFID 11 Financial Measures Calculated Total Operating Revenue Total Estimated Debt Total EBITA (Earnings Before Interest, Taxes and Amortization) Estimated Debt per Farm Estimated Debt to Total Operating Revenue Ratio Interest Expense to Total Operating Revenue Ratio EBITA per Farm Number of Farms Estimated Pigs Sold Operating Profit Margin Debt Coverage Ratio Appendix D contains further information on OFID methodology. The data used included all swine farms with >50% of their total operating revenue from swine sales. These farms were sorted by farm size (gross revenue range) and by swine farm production type. The five categories for farm size (based on total operating revenue or gross revenue) were: 1. >$0 to <$100, >$100,000 to <$300, >$300,000 to <$500, >$500,000 to <$1,000, >$1,000,000 These five gross revenue categories were selected because farm numbers were distributed relatively equally across them. University of Guelph, Ridgetown Campus Page 21

32 The five categories for farm production type (based on majority of pig sales and sow numbers) were: 1. Farrow to Finish (i.e. majority of sales are market hogs) 2. Finish (i.e. majority of sales are market hogs that were previously purchased as early wean piglets or feeder pigs) 3. Farrow to Feeder (i.e. majority of sales are feeder pigs, eg. weigh approx kg) 4. Farrow to Wean (i.e. majority of sales are early wean piglets, eg. weigh approx. 6 kg) 5. Mixed Production (i.e. sales spread across piglets, feeder pigs and market hogs) Farms were also sorted into quintiles (i.e. groups representing 20% of farm numbers) according to their operating profit margin (i.e. profitability) with quintile five containing the 20% most profitable farms and so on down to quintile one which contains the 20% least profitable farms. Quintiles were generated separately for each of the farm size and farm production type sorts. For example, quintile one for the farm size analysis contained a different set of farms than quintile one for the farm production type analysis. In summary, farm debt and profitability measures were used to analyze farms of different economic and production sizes. Farms were also sorted into quintiles according to their operating profit margin to compare farms of different economic class and production types. University of Guelph, Ridgetown Campus Page 22

33 5.0 Results The results are divided into three sections. Section 5.1 shows the financial performance of Ontario swine farms relative to swine farms in Manitoba, Quebec and the U.S. as well as other Ontario commodities (beef, grains and oilseeds, dairy, poultry and egg, greenhouse/nursery/floriculture, fruit and tree nut, and other vegetables and melons). Potato farms have been excluded due to data not being available for the entire 2003 to 2009 period. This comparison is done using data from the FFS, CFFD and ARMS. Section 5.2 discusses results from the Ontario Data Analysis Project Swine for land based Ontario farrow to finish swine farms. Section 5.3 shows a more detailed breakdown of Ontario swine farms by gross revenue range and production type. This comparison is done using data from the Ontario Farm Income Database. 5.1 FFS/CFFD/ARMS Table 3 shows the average number of farms over the 2003 to 2009 period that are reported in the data from the FFS, CFFD and ARMS sources by region and farm type. Table 3. Average Number of Farms Used in the Analysis, 2003 to 2009 Farm Type Region Number of Farms Swine Ontario 1,586 Swine Manitoba 520 Swine Quebec 1,505 Swine U.S. 24,488 Beef Ontario 8,523 Grains & oilseeds Ontario 11,172 Dairy Ontario 4,807 Poultry & egg Ontario 1,456 Greenhouse/nursery/floriculture Ontario 1,234 Fruit & tree nut Ontario 1,024 Other vegetables (excluding potato) and melons Ontario 846 Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey; USDA ARMS Figure 11 shows the debt to equity ratio for Ontario, Manitoba, Quebec and U.S. swine farms from 2003 to The trend for all regions over this period has seen an increase in debt levels relative to equity levels, especially during the 2007 to 2009 period. Ontario farms had increased from 0.48 in 2003 to 0.73 in 2008 but dropped to 0.54 in U.S. swine farms have consistently had the lowest ratio (0.18 to 0.28 range) over the time period while Quebec farms have had the highest ratio (0.74 to 0.89). At the end of 2009, Ontario and Manitoba farms had similar ratios. Figure 12 shows the debt to equity ratio for Ontario swine farms relative to Ontario beef, grains and oilseeds, dairy, poultry and egg, greenhouse/nursery/floriculture, fruit and tree nut, and other vegetables and melons farms from 2003 to The results show that over the time period, swine farms (0.42 to 0.73 range) and greenhouse/nursery/floriculture operations (0.40 to 0.61) have had the highest ratios. The trend has been the opposite for the greenhouse/nursery/floriculture industry as ratios have decreased from 2006 to The lowest ratios during this time have consistently been beef (0.14 to 0.17) and grain and oilseeds farms (0.15 to 0.20). University of Guelph, Ridgetown Campus Page 23

34 Figure 13 displays the % equity position for Ontario, Manitoba, Quebec and U.S. swine farms from 2003 to The equity position on Ontario farms has ranged from 71% in 2006 to 58% in U.S. farms have consistently had the highest % equity position. The range has been a high of 85% in 2005 to a low of 78% in Manitoba s range has been from 77% in 2004 to a low of 65% in Quebec has consistently been the lowest during this time. Their range has been from a high of 57% in 2005 to a low of 53% in Figure 14 compares the % equity position for Ontario swine farms relative to the other seven Ontario farm types in this analysis. Similar to the debt to equity ratio trends, swine farms (58% 71%) and greenhouse/nursery/floriculture operations (62 72% range) had the lowest equity positions during this period while beef (86 88% range) and grains and oilseeds (83 87% range) farms have had the highest. Ontario dairy farms have been in the 75 80% range while poultry and egg farms ranged from 75 84%. University of Guelph, Ridgetown Campus Page 24

35 Figure 11. Debt to Equity Ratio, Swine Farms, 2003 to Ontario Manitoba Quebec U.S.A Debt : Equity Ratio Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey; United States Department of Agriculture, Agricultural Resource Management Survey Figure 12. Debt to Equity Ratio, Various Ontario Farm Types, 2003 to Swine Beef Grains & Oilseeds Dairy Poultry & Egg Greenhouse/Nursery/Floriculture Fruit & Nut Vegetables & Melons Debt : Equity Ratio Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey University of Guelph, Ridgetown Campus Page 25

36 Figure 13. % Equity Ratio, Swine Farms, 2003 to % Ontario Manitoba Quebec U.S.A. 90% 80% % Equity Ratio 70% 60% 50% 40% 30% Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey; United States Department of Agriculture, Agricultural Resource Management Survey Figure 14. % Equity Ratio, Various Ontario Farm Types, 2003 to % 95% 90% Swine Grains & Oilseeds Poultry & Egg Fruit & Nut Beef Dairy Greenhouse/Nursery/Floriculture Vegetables & Melons 85% % Equity Ratio 80% 75% 70% 65% 60% 55% 50% Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey University of Guelph, Ridgetown Campus Page 26

37 Figure 15 shows the debt structure ratio (i.e. current liabilities to total liabilities) for Ontario, Manitoba, Quebec and U.S. swine farms from 2003 to Ontario has had the lowest ratio of short term debt to total debt over this time and has ranged from 14% in 2006 to 20% in This means most of Ontario swine farm debt (i.e. 80% in 2009) is structured to be repaid long term. The highest ratio is on U.S. farms and has ranged from 27% in 2004 to 36% in At the end of 2009, Manitoba had a ratio of 23% while Quebec was at 27%. It is unclear why U.S. hog farms would have more current liabilities relative to total liabilities. Perhaps land and buildings comprise a smaller percentage of farm debt. Figure 16 compares the debt structure ratio for Ontario swine farms with the other 7 Ontario farm types. The ratio for Ontario swine farms is about in the middle of the pack with the lowest ratios occurring in dairy (4 8% range) and poultry and egg (8 13% range). The highest ratios were in beef (24 35% range), greenhouse/nursery/floriculture (20 25%) and vegetables and melons (20 29%). For beef this makes sense given the large amount of operating capital required to purchase animal inventory. Figure 17 shows the current ratio of current assets to current liabilities for Ontario, Manitoba, Quebec and U.S. swine farms from 2003 to The trend over this time has seen this ratio drop, especially during the 2007 to 2009 period. At the end of 2009, Ontario s ratio was 1.4 while Quebec s was These ratios were down from a high of 2.5 for Ontario in 2006 and 2.27 for Quebec in Recall that a ratio of at least 1.0 is the preferred minimum guideline which allows short term liabilities to be covered. As a comparison, Manitoba was at 1.74 and U.S. farms were at 2.4 at the end of Figure 18 compares the current ratio for Ontario swine farms to the other 7 Ontario farm types. During the 2003 to 2009 period, fruit and nut farms have had the lowest average ratio at 1.53 while poultry and egg farms had the highest at Swine farms averaged 2.1 over the time period. University of Guelph, Ridgetown Campus Page 27

38 Figure 15. Debt Structure Ratio, Swine Farms, 2003 to Debt Structure Ratio Ontario Manitoba Quebec U.S.A Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey; United States Department of Agriculture, Agricultural Resource Management Survey Figure 16. Debt Structure Ratio, Various Ontario Farm Types, 2003 to Swine Grains & Oilseeds Poultry & Egg Fruit & Nut Beef Dairy Greenhouse/Nursery/Floriculture Vegetables & Melons 0.30 Debt Structure Ratio Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey University of Guelph, Ridgetown Campus Page 28

39 Figure 17. Current Ratio, Swine Farms, 2003 to Ontario Manitoba Quebec U.S.A Current Ratio Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey; United States Department of Agriculture, Agricultural Resource Management Survey Figure 18. Current Ratio, Various Ontario Farm Types, 2003 to Current Ratio Swine Beef Grains & Oilseeds Dairy 0.50 Poultry & Egg Greenhouse/Nursery/Floriculture Fruit & Nut Vegetables & Melons Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey University of Guelph, Ridgetown Campus Page 29

40 Figure 19 displays the ratio of total debt to total revenues for Ontario, Manitoba, Quebec and U.S. swine farms from 2003 to This ratio is calculated as estimated debt divided by total operating revenue. It is a measure of the ability to generate revenue relative to debt level. For example, a ratio of 1.0 means that debt level is the same as gross revenue while a ratio of 2.0 means that the debt level is twice as much as gross revenue. A smaller ratio indicates that it is able to generate more revenue based on the current debt level. Ontario had the highest ratio over this time averaging From 2003 to 2006, the range was from 1.04 to However, this had increased to the range of 1.34 to 1.57 during the 2007 to 2009 period. The averages for Manitoba, Quebec and the U.S. during this time were 0.74, 1.04 and 0.58 respectively. It is unknown why the U.S. consistently over the 2003 to 2009 period has less debt relative to farm revenue. Figure 20 compares Ontario swine farms to the other 7 Ontario farm types for the total debt to total revenues ratio. Swine farms had the second highest average during this time at The highest ratio has been on dairy farms at The lowest average ratios have been on greenhouse/nursery/ floriculture operations (0.54) and vegetable and melon farms (0.58). A possible explanation for dairy farms having the highest debt to revenue ratios is because of the stability and consistency in milk income. Figure 21 shows the ratio of interest expenses to total revenues for Ontario, Manitoba, Quebec and U.S. swine farms from 2003 to Recall that the rule of thumb for this ratio is that interest expenses should not be greater than 20 25% of total revenues and an upward trend could eventually lead to financial distress (University of Illinois 2011). With this in mind, results show that all four regions have been well below the 20 25% range over the time period. Ontario s ratio has averaged 7% which means that for every $1 of total revenue, $0.07 is paid out in interest expenses. Even during the 2007 to 2009 period, the range for Ontario was 6 8%. The averages for Manitoba, Quebec and the U.S. for the entire period have been 4%, 5% and 4% respectively. These ratios show that although interest expenses can be significant expenses on swine farms, there are other expenses (eg. feed, facility costs, labour, etc.) that are much higher. Figure 22 compares Ontario swine farms to the other 7 Ontario farm types for the interest expenses to total revenues ratio. All of the farm types have been well under the 20 25% range during the period. The farm type with the highest average ratio was dairy at 9% followed by swine at 7%. The lowest average ratio was 3% for vegetable and melon farms and greenhouse/nursery/floriculture operations. Figure 23 displays the ratio of working capital to total revenues for Ontario, Manitoba, Quebec and U.S. swine farms from 2003 to Recall that this ratio is a liquidity measure and the rule of thumb is that a ratio of 30% is considered strong while 10 30% is considered adequate. This ratio is useful because more working capital is required as revenue and farm size increases and it provides an idea of a farm s ability to meet debt obligations and financial flexibility for future cash flow variability (University of Illinois 2011). Results show that the ratio has decreased in all four regions during the 2007 to 2009 period. The averages for the entire period were Ontario 21%, Manitoba 25%, Quebec 14% and the U.S. 29%. University of Guelph, Ridgetown Campus Page 30

41 Figure 24 compares Ontario swine farms to the other 7 Ontario farm types for the working capital to total revenues ratio. At the end of 2009, four farm types (swine, dairy, greenhouse/nursery/floriculture, and fruit and nut) were all close to the 10% level. The lowest averages over the entire time period were in fruit and nut (9%), dairy (12%) and vegetable and melon (13%). The highest averages were in beef (36%), grains and oilseeds (27%) and swine (21%). University of Guelph, Ridgetown Campus Page 31

42 Figure 19. Debt to Total Revenues Ratio, Swine Farms, 2003 to Debt : Total Revenues Ratio Ontario Manitoba Quebec U.S.A Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey; United States Department of Agriculture, Agricultural Resource Management Survey Figure 20. Debt to Total Revenues Ratio, Various Ontario Farm Types, 2003 to Debt : Total Revenues Ratio Swine Beef Grains & Oilseeds Dairy Poultry & Egg Greenhouse/Nursery/Floriculture Fruit & Nut Vegetables & Melons Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey University of Guelph, Ridgetown Campus Page 32

43 Figure 21. Interest Expenses to Total Revenues Ratio, Swine Farms, 2003 to Interest Expenses : Total Revenues Ratio Ontario Manitoba Quebec U.S.A Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey; United States Department of Agriculture, Agricultural Resource Management Survey Figure 22. Interest Expenses to Total Revenues Ratio, Various Ontario Farm Types, 2003 to Interest Expenses : Total Revenues Ratio Swine Beef Grains & Oilseeds Dairy Poultry & Egg Greenhouse/Nursery/Floriculture Fruit & Nut Vegetables & Melons Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey University of Guelph, Ridgetown Campus Page 33

44 Figure 23. Working Capital to Total Revenues Ratio, Swine Farms, 2003 to Working Capital : Total Revenues Ratio Ontario Manitoba Quebec U.S.A (0.05) Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey; United States Department of Agriculture, Agricultural Resource Management Survey Figure 24. Working Capital to Total Revenues Ratio, Various Ontario Farm Types, 2003 to Swine Beef Grains & Oilseeds Dairy 0.50 Poultry & Egg Greenhouse/Nursery/Floriculture Fruit & Nut Vegetables & Melons 0.45 Working Capital : Total Revenues Ratio Year Source: Statistics Canada, Canadian Farm Financial Database, Farm Financial Survey University of Guelph, Ridgetown Campus Page 34

45 In summary, Figures 11 to 24 showed that over the 2003 to 2009 period Ontario swine farms: Had less debt relative to Quebec swine farms, more debt relative to U.S. swine farms and similar debt to Manitoba swine farms. Because Ontario swine farms have more debt than their U.S. counterparts, several of the debt measuring ratios are more favourable to the U.S.. Had more debt relative to seven other Ontario farm types. In terms of % equity position, even after the decrease experienced in 2007 to 2009, Ontario swine farms still have a strong equity position with the large majority of debt structured as long term. Interest expenses as a % of total revenues show that Ontario swine farms have ratios as good as other farm types or regions. Ratios dealing with liquidity (current ratio; working capital to total revenues ratio) show that, on average, Ontario swine farms are still within acceptable ranges. 5.2 ODAP Figures 25 to 31 display results using ODAP data from 2003 to The data is for a small sample of Ontario land based farrow to finish farms, approximately per year. A limitation with this data, besides the small sample, is that the group of farms is not the exact same group from year to year. Also, the pigs produced number in this analysis is a constructed number that does not match exactly the number of pigs sold. This constructed number attempts to transform pigs sold to a market hog basis and accounts for sales of early wean piglets and feeder pigs and takes into account all production and inventory changes. Figure 25 shows the trend in the debt to equity ratio from 2003 to For the 2004 to 2006 period the ratio was fairly stable in the 0.5 range. However, the ratio started to increase in 2007 and at the end of 2009 was This is a result of the combination of debt levels increasing and equity positions being eroded. Figure 26 shows the % equity position from 2003 to For most of the period (2004 to 2008), the % equity position was consistently in the 62 67% range. However, the losses incurred during the 2007 to 2009 period started to make an impact in 2009 when the equity position dropped to 58%. Figure 27 displays the trend in the current ratio on these farms from 2003 to The trend has been downward during this period but the ratio has remained acceptable (i.e or higher) for every year except 2009 when it fell to This still indicates that the farms have sufficient current assets to cover their current liabilities. Figure 28 shows the debt servicing requirement ratio from 2003 to Recall that this is the % of total revenues that goes toward principal repayment and interest expenses. From 2003 to 2006, the ratio was in the 13 16% range. There was a large jump to 21% in 2007 but 2008 saw the ratio back to 13%. In 2009 there was another increase but the 16% is well within the historical range experienced by the farms in the earlier years of the period. Figure 29 compares the average debt per sow and equity per sow for these farms from 2003 to The results here show that the debt levels were relatively stable in the $3,800 $4,200 range from 2003 to The 2007 to 2009 period has seen debt per sow rise to approximately $5,200 per sow at the end of The trend in equity per sow has been interesting during this period. Equity per sow jumped from $6,200 per sow in 2003 to $8,400 per sow in 2004 and remained strong until At the end of University of Guelph, Ridgetown Campus Page 35

46 2009, equity had dropped to $7,200 per sow but this is still higher than the level in This indicates that although debt levels have increased during the period, asset values (specifically land values) have also increased which has helped maintain the equity position. For the most part, the average number of sows on these farms has been relatively stable in the range during this time. Figure 30 compares the debt per pig produced and equity per pig produced from 2003 to This data will then incorporate productivity changes (i.e. pigs produced) into the numbers. The debt per pig produced during this time shows a slight upward trend. In 2003, it was $222 and at the end of 2009 it was up to $250 per pig. The equity per pig produced data shows that equity was built up from 2003 ($359 per pig) to 2005 ($502 per pig) but has since been eroded down to $346 per pig at the end of Pig productivity during this time experienced some variability due to production challenges (i.e. disease) but the 2008 and 2009 years showed increases in total pigs produced per farm. Figure 31 compares the return on assets (ROA) and return on equity (ROE) experienced by these farms from 2003 to This shows the relative profitability during this period and therefore the ability to generate a positive return and cover debt obligations. The ROA for these farms was 6 7% from 2003 to This dropped to the 3 4% range in 2006 to The ROE displayed similar trends. ROE was 7 8% from 2003 to 2005 but decreased to the 2 4% range from 2006 to was the least profitable year of the period as the ROA was slightly above 0% (i.e. 0.5%) while the ROE was negative (i.e. 2.2%). In summary, the ODAP results show that debt levels have been increasing since The equity on these swine farms has been eroded, especially in 2009, but the farms appear to still be in a good equity position as asset values have also increased during this time. Current ratios and debt servicing requirement ratios show that as a group, these farms appear to be in a reasonable position. However, the ratios for individual farms will vary. University of Guelph, Ridgetown Campus Page 36

47 Figure 25. Debt to Equity Ratio, Ontario Farrow to Finish Swine Farms, 2003 to Debt : Equity Ratio Year Source: Ontario Data Analysis Project, University of Guelph, Ridgetown Campus Figure 26. % Equity Ratio, Ontario Farrow to Finish Swine Farms, 2003 to % 70% 60% 50% % Equity Ratio 40% 30% 20% 10% 0% Year Source: Ontario Data Analysis Project, University of Guelph, Ridgetown Campus University of Guelph, Ridgetown Campus Page 37

48 Figure 27. Current Ratio, Ontario Farrow to Finish Swine Farms, 2003 to Current Ratio Year Source: Ontario Data Analysis Project, University of Guelph, Ridgetown Campus Figure 28. Debt Servicing Requirement Ratio, Ontario Farrow to Finish Swine Farms, 2003 to Debt Servicing Requirement Ratio Year Source: Ontario Data Analysis Project, University of Guelph, Ridgetown Campus University of Guelph, Ridgetown Campus Page 38

49 Figure 29. Debt per Sow and Equity per Sow, Ontario Farrow to Finish Swine Farms, 2003 to 2009 $9,000 Debt per Sow Equity per Sow $8,000 $7,000 $6,000 Debt per Sow Equity per Sow $5,000 $4,000 $3,000 $2,000 $1,000 $ Year Source: Ontario Data Analysis Project, University of Guelph, Ridgetown Campus Figure 30. Debt per Pig Produced and Equity per Pig Produced, Ontario Farrow to Finish Swine Farms, 2003 to 2009 $600 $500 Debt per Pig Produced Equity per Pig Produced Debt per Pig Produced Equity per Pig Produced $400 $300 $200 $100 $ Year Source: Ontario Data Analysis Project, University of Guelph, Ridgetown Campus University of Guelph, Ridgetown Campus Page 39

50 Figure 31. Return on Assets and Return on Equity, Ontario Farrow to Finish Swine Farms, 2003 to % Return on Assets Return on Equity 8% 6% Return on Assets Return on Equity 4% 2% 0% 2% 4% Year Source: Ontario Data Analysis Project, University of Guelph, Ridgetown Campus University of Guelph, Ridgetown Campus Page 40

51 5.3 OFID This section uses data from the Ontario Farm Income Database for Ontario swine farms with at least 50% of their total revenue from pig sales. Therefore, farms with pig sales comprising less than 50% of their total revenue will be excluded as well as swine farms who did not apply to the OFID programs during this period. However, even with these exclusions the data provides a good picture of the financial situation experienced by Ontario swine farms during the 2003 to 2009 period. Data for all swine farms was sorted into quintiles by operating profit margin with quintile 5 being the 20% most profitable farms and quintile 1 being the 20% least profitable farms. Appendix D contains further information on OFID methodology. One limitation with this data is that the debt levels for these farms are estimated based on actual interest expenses and historical interest rates. It should be noted that from 2003 to 2007, 95% of farms in the database reported an interest expense. This increased to 96% in 2008 and 97% in Another limitation is that the pigs sold figure is estimated using data from actual inventory and production schedules. The same is true for the swine farm production types as farms have been categorized based on the inventory and production schedule data (see Appendix D for further information on this methodology). Figure 32 shows the total estimated operating revenue (gross income) for all swine farms from 2003 to Since 2004 the trend appears to be gradually downward. In 2004, total estimated operating revenues were approximately $1.16 billion but had decreased to approximately $843 million in Swine related sales comprised about $765 million or 91% of total operating revenues in As a comparison, OMAFRA statistics show farm cash receipts for hogs equal to $700 million in Therefore, for the 2009 year estimated revenue of $765 million using the OFID is relatively close to the swine revenue reported by OMAFRA of $700 million. University of Guelph, Ridgetown Campus Page 41

52 Figure 32. Total Operating Revenue, Ontario Swine Industry, 2003 to 2009 $1,200,000,000 $1,000,000,000 $800,000,000 $600,000,000 $400,000,000 $200,000,000 $ Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: Includes only farms with swine revenue that comprises 50% or greater of the total farm gross revenue. Figure 33 shows the total estimated debt for all swine farms from 2003 to Recall that these are not actual debt levels but estimated using actual interest expenses divided by historical interest rates (i.e. bank prime +1%). These estimates may be underestimating or overestimating debt depending on a farm s individual average interest rate they are paying on their debt. During the period, total debt was at it s lowest in 2006 but has steadily increased until This makes sense as the large industry losses during the 2007 to 2009 period would have resulted in many farms taking on additional debt. It is interesting to note that total debt in 2009 of approximately $1.1 billion is similar to that of There were far fewer farms in 2009 (777) as compared to 2004 (1,370) therefore average debt per farm has subsequently increased from $796,000 in 2004 to $1.4 million in However, average gross income per farm has also increased from $844,000 in 2004 to $1.1 million in University of Guelph, Ridgetown Campus Page 42

53 Figure 33. Total Estimated Debt, Ontario Swine Industry, 2003 to 2009 $1,200,000,000 $1,000,000,000 $800,000,000 $600,000,000 $400,000,000 $200,000,000 $ Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: Estimated Debt = Interest Expenses / Interest Rate; Interest Rate is estimated to be bank prime + 1%; Includes only farms with swine revenue that comprises 50% or greater of the total farm gross revenue. Figure 34 shows the total estimated earnings before interest, taxes and amortization (EBITA) for all swine farms from 2003 to EBITA is basically the farm earnings available to cover debt servicing, accrual adjustments, amortization, and provide a return to the owner. While total operating revenues have been on a gradual downward trend, total EBITA for the sector has seen a significant downward trend. In 2004, total EBITA was approximately $183 million while in 2009 it was $9 million. University of Guelph, Ridgetown Campus Page 43

54 Figure 34. Total EBITA, Ontario Swine Industry, 2003 to 2009 $200,000,000 $180,000,000 $160,000,000 $140,000,000 $120,000,000 $100,000,000 $80,000,000 $60,000,000 $40,000,000 $20,000,000 $ Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: EBITA = Earnings Before Interest, Taxes and Amortization (accrual basis); Leasing expenses are included along with CCA; Includes only farms with swine revenue that comprises 50% or greater of the total farm gross revenue Results by Gross Revenue Range This section discusses the distribution of total operating revenue, debt and EBITA by gross revenue range. Further descriptions and results are located in Appendix A. Figure 35 shows that of the total operating revenues for all swine farms in 2003, farms with greater than $1,000,000 in gross revenue accounted for 63% of the industry total. Farms with $500,000 $1,000,000 comprised 18% while farms in the $300,000 $500,000 range made up 10% of the total. It is also important to note that farms with less than $300,000 in total operating revenue represented 9%. University of Guelph, Ridgetown Campus Page 44

55 Figure 35. Share of Total Industry Operating Revenue by Gross Revenue Range, % 8% 10% 63% 18% $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 36 shows that by 2009, farms with greater than $1,000,000 in gross revenue accounted for 74% of the industry total. Farms with gross revenue of $500,000 $1,000,000 comprised 16% while farms in the $300,000 $500,000 range made up 7% of the total. Altogether, farms with gross revenue greater than $300,000 accounted for 97% of all gross revenue. This means that farms with less than $300,000 in total operating revenues represented only 3% of the industry total. Figure 36. Share of Total Industry Operating Revenue by Gross Revenue Range, % 3% 7% 16% 74% $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Source: Calculations using the Ontario Farm Income Database, OMAFRA University of Guelph, Ridgetown Campus Page 45

56 Table 4 shows that of the total estimated debt for all swine farms in 2003, farms with greater than $1,000,000 in gross revenue accounted for 53% of the industry total. Farms with gross revenue of $500,000 $1,000,000 comprised 22% while farms in the $300,000 $500,000 range made up 13% of the total. Small farms with total operating revenue less than $100,000 had 2% of the estimated debt for the industry. Further, Table 4 shows that by 2009, farms with greater than $1,000,000 in gross revenue accounted for 60% of the industry total debt. Farms with $500,000 $1,000,000 comprised 22% while farms in the $300,000 $500,000 range made up 11% of the total. Altogether, farms with gross revenue greater than $300,000 accounted for 93% of all debt. Farms with less than $100,000 in gross revenue had only 1% of the estimated industry debt. Table 4. Share of Total Industry Debt by Gross Revenue Range, 2003 vs Gross Revenue Range $0 $100,000 2% 1% $100,000 $300,000 10% 6% $300,000 $500,000 13% 11% $500,000 $1,000,000 22% 22% >$1,000,000 53% 60% Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 37 shows the estimated debt per farm by gross revenue range from 2003 to The trend during the period for most farm sizes saw debt per farm decrease from 2003 to 2006 but increase steadily from 2006 to Figure 37. Estimated Debt per Farm by Gross Revenue Range, 2003 to 2009 $3,000,000 $2,500,000 $2,000,000 $1,500,000 $1,000,000 $500,000 $ $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Source: Calculations using the Ontario Farm Income Database, OMAFRA University of Guelph, Ridgetown Campus Page 46

57 Figure 38 shows the ratio of estimated debt to total operating revenue by gross revenue range from 2003 to Recall that the ratio is calculated as estimated debt divided by total operating revenue and is a measure of the ability to generate revenue relative to debt level. For example, a ratio of 1.0 means that debt level is the same as gross revenue while a ratio of 2.0 means that the debt level is twice as much as gross revenue. A smaller ratio for a specific category indicates that relative to other categories, it is able to generate more revenue based on the current debt level. It is interesting to note that from 2003 to 2008 the ratio had been relatively similar and stable for the three middle farm size categories. The ratio was smallest for the largest farm size (greater than $1,000,000) and largest for the smallest farm size ($0 $100,000). Ratios jumped in 2009 for all categories. Figure 38. Estimated Debt to Total Operating Revenue Ratio by Gross Revenue Range, 2003 to $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 39 shows the ratio of interest expense to total operating revenue by gross revenue range from 2003 to Recall that the ratio is calculated as interest expense divided by total operating revenue and the rule of thumb for this ratio is that interest expenses should not be greater than 20 25% of total revenues and an upward trend could eventually lead to financial distress (University of Illinois 2011). For example, a ratio of 0.5 means that for every $1.00 of total operating revenue, $0.50 is paid out in interest expense. It is interesting to note that the three middle farm size categories have generally been in the 6% 8% range with the exception of 2007 when they were between 8% and 10%. The ratio was smallest (in the 4% range) for the largest farm size (greater than $1,000,000) and largest for the smallest farm size ($0 $100,000). The smallest farm size has increased from 11% in 2006 to 17% in University of Guelph, Ridgetown Campus Page 47

58 Figure 39. Interest Expense to Total Operating Revenue Ratio by Gross Revenue Range, 2003 to $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 40 shows the EBITA per farm by gross revenue range from 2003 to Recall that EBITA is earnings before interest, taxes and amortization and is the margin available to cover debt obligations, taxes, amortization, and provide a return to the farm owner. Theoretically, in profitable periods, EBITA should be positive and large enough to cover debt obligations while in more difficult financial times EBITA will be smaller or negative indicating that the margin is not sufficient to cover debt obligations and provide a return to the owner. There was an upward trend from 2003 to 2005 but this reversed from 2005 to As times got financially tougher for swine farms from 2007 to 2009, it is interesting that the only category with a positive significant EBITA for all three years is the $500,000 $1,000,000 farm size category. University of Guelph, Ridgetown Campus Page 48

59 Figure 40. EBITA per Farm by Gross Revenue Range, 2003 to 2009 $400,000 $350,000 $300,000 $250,000 $200,000 $150,000 $100,000 $50,000 $0 ($50,000) $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: EBITA = Earnings Before Interest, Taxes and Amortization (accrual basis); Leasing expenses are included along with CCA Results by Production Type This section discusses the distribution of total operating revenue, total debt and total EBITA by production type and the changes that have occurred from 2003 to Further descriptions and results are located in Appendix B. Figure 41 shows that of the total operating revenues for all swine farms in 2003, farrow to finish farms accounted for 38% of the industry total. Finish farms comprised 34% while farrow to wean farms (selling piglets) were next with 12% of the total. These three farm types accounted for 84% of all gross revenue. University of Guelph, Ridgetown Campus Page 49

60 Figure 41. Share of Total Industry Operating Revenue by Production Type, % 34% 38% 8% 12% Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 42 shows that by 2009, farrow to finish farms accounted for 43% of the industry total. Finish farms comprised 26% while farrow to feeder farms (selling feeder pigs) were next with 14% of the total. Altogether, these three farm types accounted for 83% of all gross revenue. Figure 42. Share of Total Industry Operating Revenue by Production Type, % 14% 12% 43% 5% Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Source: Calculations using the Ontario Farm Income Database, OMAFRA University of Guelph, Ridgetown Campus Page 50

61 Table 5 shows that of the total estimated debt for all swine farms in 2003, farrow to finish farms accounted for 41% of the industry total. Finish farms comprised 26% while farrow to wean farms made up 17% of the total. These three farm types represented 84% of the total estimated industry debt in Table 5 also shows that by 2009, farrow to finish farms accounted for 48% of the industry total. Finish farms comprised 22% while farrow to feeder farms made up 17% of the total. Altogether, these three farm types accounted for 87% of all debt in Table 5. Share of Total Industry Debt by Production Type, 2003 vs Production Type Farrow to Feeder 10% 17% Farrow to Finish 41% 48% Farrow to Wean 17% 8% Mixed Production 6% 5% Finish 26% 22% Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 43 shows the estimated debt per farm by production type from 2003 to The trend during the period for most farm types saw average debt per farm decrease from 2004 to 2006, hold relatively steady through 2007, but increase in 2008 and Figure 43. Estimated Debt per Farm by Production Type, 2003 to 2009 $2,000,000 $1,800,000 $1,600,000 $1,400,000 $1,200,000 $1,000,000 $800,000 $600,000 $400,000 $200,000 $ Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 44 shows the ratio of estimated debt to total operating revenue by production type from 2003 to It is interesting to note that from 2003 to 2008 the ratio had been approximately 1 or less for farrow to finish, mixed production, and finish farms. During this time, farrow to feeder and farrow towean farms appeared to have more debt relative to gross revenue as their ratios were 1 or higher. The ratio was smallest for mixed production and finish farms. Ratios started to increase from 2007 to 2009 for all farm types except for mixed production farms which saw their ratios decrease. University of Guelph, Ridgetown Campus Page 51

62 Figure 44. Estimated Debt to Total Operating Revenue Ratio by Production Type, 2003 to Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 45 shows the ratio of interest expense to total operating revenue by production type from 2003 to All farms types have been well under the rule of thumb of 20 25% for this ratio with ratios generally in the range of 4% to 8%. During this period, it appears that farrow to feeder and farrow towean farms have had the highest ratio while mixed production or finish farms have had the lowest ratio. Figure 45. Interest Expense to Total Operating Revenue Ratio by Production Type, 2003 to Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Source: Calculations using the Ontario Farm Income Database, OMAFRA University of Guelph, Ridgetown Campus Page 52

63 Figure 46 shows the EBITA per farm by production type from 2003 to The 2003 to 2007 period showed positive EBITA of approximately $50,000 or greater for most farm types. Times got much tougher financially in the 2008 and 2009 periods as shown by the small positive or negative EBITA per farm. Figure 46. EBITA per Farm by Production Type, 2003 to 2009 $250,000 $200,000 $150,000 $100,000 $50,000 $0 ($50,000) ($100,000) Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: EBITA = Earnings Before Interest, Taxes and Amortization (accrual basis); Leasing expenses are included along with CCA. To further illustrate the variability that exists within the OFID data, Figures 47 to 50 show results for Ontario farrow to finish or $500,000 $1,000,000 gross revenue farms as examples. Figure 47 shows average EBITA per farm for farrow to finish farms by operating profit margin quintile from 2003 to The trends make sense as generally the 20% most profitable farms (quintile 5) have the highest EBITA which then decreases as you move down each less profitable quintile. In any given year, the difference between the average of the 20% most profitable farms (quintile 5) and the average of the 20% least profitable farms (quintile 1) ranged from $200,000 to $400,000 per farm. University of Guelph, Ridgetown Campus Page 53

64 Figure 47. EBITA by Operating Profit Margin Quintile, Farrow to Finish Farms, 2003 to 2009 $300,000 $200,000 $100,000 $0 ($100,000) ($200,000) ($300,000) Avg Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 48 shows the estimated debt per farm for farrow to finish farms by operating profit margin quintile from 2003 to The variability in this chart is interesting as the quintiles with the highest or lowest debt per farm vary from year to year. There were actually three years (2004, 2005 and 2008) where the least profitable quintile in terms of operating profit margin had the lowest estimated debt per farm. University of Guelph, Ridgetown Campus Page 54

65 Figure 48. Estimated Debt by Operating Profit Margin Quintile, Farrow to Finish Farms, 2003 to 2009 $2,000,000 $1,750,000 $1,500,000 $1,250,000 $1,000,000 $750,000 $500,000 $250,000 $ Avg Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 49 is a scatter diagram using the data from Figures 47 and 48. The average EBITA per farm was plotted vs. the average estimated debt per farm for farrow to finish farms and a line of best fit through the data was calculated. In theory, the line of best fit should slope upward to the right if increased debt is used to improve profitability (i.e. EBITA). The line of best fit will slope downward to the right if increased debt lowers profitability. It is interesting to see that only 23.26% of the variation in EBITA is explained by the variation in estimated debt (i.e. R squared = ). This means that there are other factors (e.g. rising input costs, poor productivity caused by disease, and etc.) besides debt that contribute to the variation in EBITA. Notice that the line of best fit slopes downward to the right which would appear to indicate that increased debt lowers profitability even at modest interest rates experienced during the 2003 to 2009 time period. However, recall that only 23.26% of the variation in the EBITA was explained by debt level. University of Guelph, Ridgetown Campus Page 55

66 Figure 49. Scatter Diagram of EBITA vs. Estimated Debt, Farrow to Finish Farms by Operating Profit Margin Quintile, 2003 to 2009 $300,000 $200,000 $100,000 EBITA $0 ($100,000) R² = ($200,000) ($300,000) $0 $500,000 $1,000,000 $1,500,000 $2,000,000 $2,500,000 Debt Farrow to Finish Linear (Farrow to Finish) Source: Calculations using the Ontario Farm Income Database, OMAFRA Similar calculations as that in Figure 49 for farrow to finish farms were done for the four other production types. The results in Table 6 indicate that the line of best fit revealed that the variation in estimated debt could only explain 26.67% or less of the variation in EBITA. Table 6. % of Variation in EBITA Explained by Variation in Estimated Debt by Production Type Production Type R Squared Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Source: Calculations using the Ontario Farm Income Database, OMAFRA Figure 50 is a scatter diagram for farms with gross revenues of $500,000 $1,000,000. This farm size category was selected because it represents commercial scale swine production. The average EBITA per farm was plotted vs. the average estimated debt per farm and a line of best fit through the data was calculated. Recall the explanation for the line of best fit for Figure 49. The results were a little better than those for farrow to finish farms but only 33.91% of the variation in EBITA was explained by the variation in estimated debt (i.e. R squared = ). Again, this means that there are other factors besides debt that contribute to the variation in EBITA. University of Guelph, Ridgetown Campus Page 56

67 Figure 50. Scatter Diagram of EBITA vs. Estimated Debt, $500,000 $1,000,000 Gross Revenue Farms by Operating Profit Margin Quintile, 2003 to 2009 EBITA $350,000 $300,000 $250,000 $200,000 $150,000 $100,000 $50,000 $0 ($50,000) ($100,000) ($150,000) ($200,000) R² = ,000 1,000,000 1,500,000 2,000,000 2,500,000 Debt $500,000 $1,000,000 Linear ($500,000 $1,000,000) Source: Calculations using the Ontario Farm Income Database, OMAFRA Similar calculations as that in Figure 50 for $500,000 $1,000,000 gross revenue farms were done for the four other gross revenue categories. The results in Table 7 indicate that the line of best fit revealed that the variation in estimated debt could only explain 36.41% or less of the variation in EBITA. Table 7. % of Variation in EBITA Explained by Variation in Estimated Debt by Gross Revenue Range Gross Revenue Range R Squared $0 $100, $100,000 $300, $300,000 $500, $500,000 $1,000, >$1,000, Source: Calculations using the Ontario Farm Income Database, OMAFRA In summary, the OFID results for both the gross revenue ranges and production types show there is a lot of variability within the data. Often, there is more variability within a category than across categories. Debt per farm increases as farm size increases but the level of debt is not necessarily related to profitability. Profitability is also not necessarily related to farm size, production type or the total pigs sold. Generally, the top 20% of farms are very profitable while the bottom 20% of farms are not profitable regardless of farm size, production type or year. In terms of the ability to generate sufficient earnings to cover debt obligations and provide a return to the farm owner, a farm likely needs to have total revenues of $300,000 or more. University of Guelph, Ridgetown Campus Page 57

68 5.4 Statistical Significance of OFID Results As part of this analysis it is important to test whether there are statistically significant differences between the gross revenue ranges or production types. Category averages were tested relative to each other and the results for the 20% most profitable farms (or 20% least profitable) were tested against the remaining 80% in the OFID data. The tests were performed at a 95% confidence level (i.e. 5% probability of error) using a Welch s t test for samples assumed to have unequal variances. For the testing of differences between farm sizes, an interesting pattern appears to be that interest expenses (and debt levels) increase as total farm revenue increases but debt coverage is independent of it. Therefore, there is no significant difference between gross revenue ranges. This implies that as farms generate more revenue they are also likely to borrow more money. The testing of differences between production types showed that the finish farm category seemed to have significantly more debt most years than the other production types. Generally, the results of the tests showed no clear patterns and were insignificant in many cases. This is likely due to the large amount of variability that exists within the data. In many instances, there were no statistically significant differences across gross revenue ranges, production types, or years. This was also true for many cases when comparing the 20% most profitable or 20% least profitable farms to the remaining 80% of farms. However, EBITA was shown to have statistically significant differences when comparing the top 20% or bottom 20% to the average for all farms. This was the case for both gross revenue range and production type. 5.5 Impact of a 2% Interest Rate Increase Since the Bank of Canada has indicated the intention to raise interest rates in the future, an important question regarding current debt levels on Ontario swine farms is how will the ability of these farms to cover debt obligations be impacted if interest rates rise 2% for example? To answer this, analysis was done using the CFFD, ODAP and OFID data from 2003 to The results using the CFFD data are in Table 8 and show that a 2% increase in interest rates would have added approximately $3 $7 per pig produced in additional interest expenses over the 2003 to 2009 period. This is an increase of approximately $17.0 $43.6 million for the entire Ontario swine industry depending on the year. University of Guelph, Ridgetown Campus Page 58

69 Table 8. Scenario of a 2% Increase in Interest Rates Using CFFD Year Interest Expense Per Pig Produced (Before) Total Interest Expense After Change Change in Interest Expense ($/Pig) Change in Interest Expense (Entire Industry) $22.5 million $23.1 million $21.1 million $18.8 million $17.0 million $20.9 million $43.6 million Average $23.9 million Source: University of Guelph, Ridgetown Campus calculations using the CFFD database, Statistics Canada Note: Calculations were done using an annual estimated total pigs produced figure. Estimated total pigs produced was calculated as Ontario origin pigs processed in Canada plus live exports to the U.S. of feeder pigs and market hogs through Michigan and New York border points. The results using the ODAP data are in Table 9 and show that a 2% increase in interest rates would have added approximately $3 $4 per pig produced in additional interest expenses over the 2003 to 2009 period. This is approximately $10,000 $21,000 per farm depending on the year. Table 9. Scenario of a 2% Increase in Interest Rates Using ODAP Year Interest Expense Per Pig Produced (Before) Total Interest Expense After Change Change in Interest Expense ($/Pig) Change in Interest Expense ($/Farm) , , , , , , ,889 Average ,409 Source: University of Guelph, Ridgetown Campus calculations The OFID results in Table 10 showed that a 2% increase in interest rates would have resulted in increased interest expenses of $2 $3 per pig sold. This is approximately $13,000 $29,000 per farm depending on the year. Further analysis shows that this additional expense would have caused an additional 3 6% of swine farms to move into a negative EBT (earnings before taxes) position depending on the year. This means that there would not be sufficient margin available to cover any debt principal payments or provide a return to the owner. In this scenario, depending on the individual farm situation, some or all of the interest expenses may not have been covered. Additional sensitivity analysis results in the data suggesting that a 5% increase in interest rates would have caused 63% of all farms (compared to the baseline of 54%) over the 2003 to 2009 period to shift into a negative EBT position. University of Guelph, Ridgetown Campus Page 59

70 Table 10. Scenario of a 2% Increase in Interest Rates Using OFID Year Interest Expense Per Pig Sold (Before) Total Interest Expense After Change Change in Interest Expense ($/Pig) Change in Interest Expense ($/Farm) % of Farms with Negative EBT (Before) % of Farms with Negative EBT (After) ,255 56% 61% ,013 36% 42% ,274 47% 52% ,084 49% 53% ,651 66% 69% ,880 64% 68% ,557 69% 73% Average ,396 54% 58% Source: University of Guelph, Ridgetown Campus calculations using the Ontario Farm Income Database, OMAFRA These results are important as margins in the swine industry have been slim since 2006 and another $2 $7 per pig (depending on the data source and year) in interest expenses will cause further financial stress. 5.6 Comparison of Results from the Different Data Sources The data from the three different sources does vary but it also shows similar trends. The CFFD data showed that the total Ontario swine industry debt ranged from $834 million to $1.2 billion while the OFID data calculations estimated total debt that ranged from $744 million to $1.1 billion depending on the year. On a per pig produced basis, the total debt ranged from $109 $165 using the CFFD data, $217 $250 using the ODAP data, and $85 $129 using the OFID data. Total interest expenses for the entire industry ranged from $57 million to $74 million using the CFFD data while the OFID data showed a range of $38 million to $56 million. On a per pig produced basis, the interest expenses ranged from $7.25 $11.43 using the CFFD data, $7.04 $11.23 using the ODAP data, and $4.36 $6.01 using the OFID data. University of Guelph, Ridgetown Campus Page 60

71 6.0 Summary Debt levels are an important issue for the Ontario swine industry. On an individual farm basis, debt levels vary widely and can potentially affect the farm s ability to be profitable and competitive. Producers have used different management strategies to survive during the last few years of negative margins in the swine industry. These various strategies include and are not limited to: Managing feed costs and facilities costs as these are still the major expenses in producing pigs. Debt servicing typically has only required approximately 15% of total revenues. Producers that have left the industry in recent years due to the financially difficult times appear to have had options. They cut losses by exiting pig production, selling land, focusing on cash crop production, finding off farm employment, etc. Anecdotal evidence suggests that there were not a lot of swine farms nearing bankruptcy during the 2007 to 2009 period in Ontario. The ratio of total debt to total revenue is higher for Ontario swine farms relative to U.S. farms. This is a concern because it affects relative competitiveness. As long as debt levels are proportional to farm size (total revenue) then farms seem to be able to cash flow their debt obligations. However, the timing of debt obligations and other cash flow requirements such as feed purchases can have an impact on the ability to meet debt obligations. Current ratios and the working capital to total revenue ratios show that Ontario swine producers overall appear to be in an acceptable range. Specifically, the results of the analysis using the FFS/CFFD/ARMS data showed that: Relative to swine farms in other regions and other Ontario farm types, Ontario swine farms typically have higher debt to equity ratios than swine farms in Manitoba and the U.S. but lower ratios than Quebec swine farms. Debt to equity ratios are also higher than most other farm types in Ontario. Beef and grains & oilseeds farms in Ontario have the lowest ratios. Ontario swine farms have lower % equity positions than U.S. swine farms and other Ontario farm types, similar positions to Manitoba, and higher positions than Quebec swine farms. Ontario swine farms have more of their debt structured for long term repayment rather than short term relative to the other swine regions and most other Ontario commodities. Dairy and poultry & egg farms have relatively more of their debt structured long term than Ontario swine farms. The financially difficult period from 2007 to 2009 caused current ratios to drop to 1.4 at the end of Ratios had been close to the 2.0 level from 2003 to Debt to total revenues averaged 1.25 during the period which is higher than all other Ontario farm types except dairy. Interest expenses to total revenues ratios have averaged 7% on Ontario swine farms during the period, well below the recommended 20 25% guideline. In other words, for every $1.00 of total revenues, $0.07 was paid out in interest expense. The ratio of working capital to total revenue has averaged 21% during the period, well within the recommended 10 30% guideline. However, the ratio slipped to 11% in Total estimated debt for the Ontario swine industry in 2009 was $944 million while total interest expenses were estimated to be $74 million. The impact of a 2% interest rate increase suggested that this would have resulted in an average increase of $3 $7 per pig in interest expenses over the 2003 to 2009 period. This would have translated into an additional cost of $17.0 $43.6 million for the entire Ontario swine industry. University of Guelph, Ridgetown Campus Page 61

72 The results of the analysis using the ODAP data showed that on a small sample of land based Ontario farrow to finish farms: Debt to equity ratios, % equity positions, and current ratios all worsened in 2009 compared to the 2003 to 2008 levels. Debt levels per sow have increased during the period. Equity levels have decreased during the period but at the end of 2009 were still higher than levels in Appreciating asset values (i.e. land) have kept pace with the increased debt levels. This has provided additional collateral for farms with secured debt. The debt servicing requirement ratio in 2009 at 16% of total revenues was well within the historical range. Return on assets was in the 3 7% range from 2003 to 2008 but dropped to slightly above 0% in Return on equity was in the 2 8% range from 2003 to 2008 but was 2% in The overall trend during the 2003 to 2009 period has been downward. The impact of a 2% interest rate increase suggested that this would have resulted in an average increase of $3 $4 per pig or $10,000 $21,000 per farm in interest expenses over the 2003 to 2009 period. Previous research using the ODAP data to analyze profitability has shown that economic size, debt levels, and pigs sold (i.e. productivity) are each important but were not determined to be statistically significant as determining factors in terms of a farm being profitable. This is likely due to the variability that exists within the industry. The results of the analysis using the OFID data showed that: There is a great deal of variability that exists within the data. Often, more variability exists within categories than across categories (i.e. different farm sizes or production types). Total estimated debt for the industry has increased from 2007 to 2009 as a result of the large losses experienced by the industry. Total estimated debt in 2009 was $1.1 billion while total interest expenses were $38 million. In terms of farm size, a farm likely needs to have at least $300,000 in total revenue to generate sufficient funds to provide an operating profit margin that allows for debt repayment and provide a return to the farm owner. Profitability is not necessarily related to farm size. Profitability is not necessarily related to production type. Data disaggregated into quintiles by operating profit margin generally showed that regardless of size, type or year the bottom 20% were not profitable while the top 20% were very profitable. Debt levels and interest expenses per farm increase as farm size increases. Debt levels are in acceptable ranges if proportional to farm size but achieving the appropriate balance can be difficult. Profitability is not necessarily related to debt level. Debt coverage ratios were fairly consistent over the period. Depending on the year, the 40% 60% most profitable farms for the most part can pay their debt. Statistical significance tests showed that comparisons across farm sizes, production types, the top/bottom 20% of farms vs. the other 80% of farms were not significantly different in most instances. This is likely due to the high variability existing within the data. The impact of a 2% interest rate increase suggested that this would have resulted in an average increase of $2 $3 per pig or $13,000 $29,000 per farm in interest expenses over the 2003 to 2009 period. University of Guelph, Ridgetown Campus Page 62

73 In summary, there are several key observations from this analysis: 1. The total debt estimated for the Ontario swine industry in 2009 ranged from $944 million with interest expenses of $74 million using the CFFD database to $1.1 billion of debt and interest expenses of $38 million using the OFID database. The exact reasons why these interest expense numbers vary are unknown, but remember both are calculated numbers. A significant portion of swine debt would be in long term assets (i.e. land and buildings) which would have a higher interest rate than that which was used in the calculation. 2. Overall, the detailed data by farm size (gross revenue) seems to indicate that debt levels increase as farm size increases, debt levels do not necessarily relate to profitability, and the relative balance between manageable debt levels and farm size is important. Using the OFID, it was shown that the farm size categories of $500,000 $1,000,000 (22%) and greater than $1,000,000 (60%) carried over 82% of industry debt as seen in Table In general, the detailed data by production type seems to indicate that debt levels do not necessarily relate to production type or profitability. The best table to provide evidence for this statement is B5 located in Appendix B. Further, Table 5 shows that farrow to finish (48%), finish (22%) and farrow to feeder (17%) carried 87% of industry debt in There is a great deal of variability within the OFID data in terms of estimated debt, EBITA and operating profit margins. As a result of this variability, there are no clear patterns or statistically significant differences between farms of different gross revenue sizes or production types. This variability can be seen by the range in quintile averages and standard deviations. Figure 49 is an example of this variability. However, when EBITA per farm by gross revenue category from 2003 to 2009 was analyzed, the farm size that performed best financially through the tough 2006 to 2009 time period was the $500,000 $1,000,000 category. Figure 40 displays this result. When EBITA per farm by Production Type (Figure 46) was reviewed, all farm types experienced small to negative returns. 5. On an aggregate industry level, debt levels and debt servicing requirements on average do not appear to be the major determining factor in profitability. However, due to the variability within the industry, there are quintiles (i.e. farms) within each gross revenue or production type category that are struggling financially and there are quintiles within each category that are doing very well financially, regardless of the year. The 40% least profitable farms are facing a lot of financial pressure. This may be a result of high debt levels, low equity positions, high debt servicing requirements, swings in hog prices and revenue, swings in costs of production or a combination of these factors. 6. For farms of any gross revenue size or production type it is important to not extend their debt servicing capacity beyond levels that are sustainable. A key factor appears to be the ability to maintain a debt level that is balanced with the farm s ability to generate revenue and control costs. The ratio or balance of total debt to total revenue will be unique to each farm due to the many other variables (i.e. management, productivity, importance of off farm income, etc.) that can affect profitability. While there was no statistical evidence to suggest a standard rule of thumb between total debt and total revenue, the simple average between 2003 and 2009 of the three farm size categories with gross revenues over $300,000 showed the ratio was close to 1.00 or greater. This is supported by the results in Figures 19, 38 and 44, which appear to show that for most farm sizes or production types the ratio was close to This would mean that a reasonable target would be one dollar of farm revenue for every dollar of debt. University of Guelph, Ridgetown Campus Page 63

74 To conclude, given the information available Ontario swine farms do carry more debt than their U.S. counterparts. This heavier debt load makes the Ontario industry more vulnerable during high interest rate times. However, the evidence supplied in this report illustrates tremendous variability in profitability and debt load regardless of production type or economic size. There was no statistical correlation between high profit farms and low debt levels. While the analysis of current ratios show the values are above 1.0, the trend has been downward and it is important to remember that livestock inventory values do vary considerably from year to year. The tough economic times of 2007 to 2009 have eroded equity and increased debt on most farms. Still the debt levels as of 2009, appear manageable on an industry basis. University of Guelph, Ridgetown Campus Page 64

75 7.0 References AgCanda.com. BMO Sounds Warning Bell On Interest Rates Canada's farmers owed $63B in both mortgage and non mortgage debt in 2009, a 4.7% rise from the previous year. Article written by Ron Friesen. Monday, June 21, Accessed September 1, 2011 from Briggeman, Brian C. The Role of Debt in Farmland Ownership. Choices, a publication of the Agricultural & Applied Economics Association. Accessed September 1, 2011 from magazine/theme articles/farmland values/the role of debtin farmland ownership. Canadian Agri Food Policy Institute. Measuring Farm Profitability and Financial Performance. March Ellinger, Paul. Farmdocdaily: Interest Rate Risk Know Your Exposure. Department of Agricultural and Consumer Economics, University of Illinois. June Ellinger, Paul. Farmdocdaily: Farm Liquidity A Buffer for Volatile Revenue and Costs. Department of Agricultural and Consumer Economics, University of Illinois. July Friesen, Ron. BMO Sounds Warning Bell on Interest Rates. June 21, George Morris Centre. Advancing a Policy Dialogue Series 1: Understanding the Structure of Canadian Farm Incomes. Prepared for the Canadian Agri Food Policy Institute (CAPI). February Accessed September 1, 2011 from icpa.ca/destinations/capi_advancingpolicydialogue.pdf. Henderson, Jason and Maria Akers. Agricultural Finance Conditions Turn. Agricultural Finance Databook, National Trends in Farm Lending July Federal Reserve Bank of Kansas City. Accessed September 1, 2011 from 07 ag fin db.pdf, p. 1. Hoppe, Robert A. U.S. Farm Structure. USDA, ERS. Amber Waves Volume 8, Issue 3, September 2010, pp McEwan, Ken and L. Marchand. Success Factors For Innovative Farmers: Case Study of Swine Farms In South Western Ontario. Report prepared for Agriculture and Agri Food Canada. May, Mussell, Al, T. Moore, K. McEwan and R. Duffy. Testing the Structure of Canadian Farm Incomes. Report prepared for Canadian Agri Food Policy Institute (CAPI), September 1, University of Guelph, Ridgetown Campus Page 65

76 Ontario Ministry of Agriculture, Food and Rural Affairs. Ontario Farm Income Database. Various years. Ontario Ministry of Agriculture, Food and Rural Affairs. Monthly Swine Budgets. Various years. Ontario Ministry of Agriculture, Food and Rural Affairs. Farm Cash Receipts from Farming Operations, Ontario, Statistics Canada Balance Sheet of the Agricultural Sector Agriculture Economic Statistics, Catalogue no X, vol. 10, no.1. June 2011, pp. 5, 6, 10, 39. Statistics Canada. Canadian Farm Financial Database. Various years. Statistics Canada. CANSIM Table Value per acre of farm land and buildings, annual. Statistics Canada. Farm Cash Receipts. Catalogue no X. May Statistics Canada Farm Debt Outstanding Agriculture Economic Statistics, Catalogue no X, vol. 10, no. 1. May 2011, pp 5, 9, 15. Statistics Canada. Farm Financial Survey. Catalogue no. 21F0008X. Various years. Statistics Canada Value of Farm Capital Agriculture Economic Statistics, Catalogue no X, vol. 10 no. 1. May 2011, p. 6. United States Department of Agriculture. Agricultural Resource Management Survey. Various years. United States Department of Agriculture, Economic Research Service. Farm Income and Costs: Assets, Debt and Wealth. Accessed September 1, 2011 from University of Guelph, Ridgetown Campus. Ontario Data Analysis Project Swine. Various years. University of Guelph, Ridgetown Campus Page 66

77 Appendix A Results from Ontario Farm Income Database Tables by Gross Revenue (Farm Size) University of Guelph, Ridgetown Campus Page 0

78 Tables A1 to A6 display more detailed results by gross revenue range (farm size) using OFID data from 2003 to Averages and standard deviations are presented to show variability in the data. Results for these tables are also sorted into quintiles by operating profit margin with the quintiles being gross revenue range specific with the number of farms not being equal for each quintile (i.e. not all quintiles have 20% of the farms). That is, the numbers in each quintile for a specific gross revenue range column relate to the number of farms originally in each operating profit margin quintile. For example, in 2003, 64 of 267 farms (24%) in the $300,000 $500,000 range were in the most profitable quintile. Table A1 shows the number of farms by gross revenue range and quintile from 2003 to Total farm numbers have decreased over the period from 1,343 in 2003 to 777 in This trend is evident in all of the gross revenue range categories except for the largest (>$1,000,000). The number of farms in this category increased from 219 in 2003 to 232 in Table A2 shows the estimated total pigs sold per farm by gross revenue range. Recall that these pig sales are estimated using production schedules and are not actual sales but should be relatively close to actual sales. Results show that the average farm appears to have gotten larger (i.e. sold more pigs) over time and that there is a lot of variability as indicated by the large standard deviations relative to the averages in the table. The large variation may be a result of large changes in productivity due to things such as disease, feeding or reproductive challenges, and etc.. Results also show that the most profitable farms don t necessarily sell the most pigs but they do have smaller variation (i.e. smaller standard deviations). In many instances, the least profitable farms actually sold the most pigs and had a lot more variability. Table A3 shows the operating profit margin per farm by gross revenue range. Operating profit margin is calculated as EBIT (earnings before interest and taxes, cash basis) divided by total operating revenues. Results show that for every year and farm size the top 20% had a positive operating profit margin while the bottom 20% were negative. Farms with at least $100,000 in gross revenue showed positive operating profit margins for the top 60% of farms from 2003 to However, from 2007 to 2009 only the top 40% of farms had a positive operating profit margin each year. Table A4 shows the average Earnings Before Interest, Taxes and Amortization (EBITA) per farm by gross revenue range. EBITA is on an accrual basis with amortization including capital cost allowance (CCA) and leasing expenses. This table provides a good measure of relative profitability and the ability to cover debt obligations and provide a return to the owner. Generally, for all categories, the top 20% are quite profitable while the bottom 20% are not profitable. For gross revenue ranges above $300,000, the top 40 60% are profitable most years. Depending on the year, the gross revenue ranges below $300,000 show mixed results. Overall, the results show a lot of variability exists in the data and profitability is not necessarily related to farm size. There are relatively large farms that make money and lose money and there are relatively small farms that make money and lose money. Table A5 displays the estimated debt per farm by gross revenue range. Recall that these are not actual debt levels but are estimated using actual interest expenses and historical interest rates (i.e. bank prime +1%). These estimates may be underestimating or overestimating debt depending on a farm s individual average interest rate they are paying on their debt. However, the overall trends should be reflective of what has happened on these farms during this time period. The data shows a lot of variability. Generally, the results show that the most profitable 20% of farms have less debt than the least profitable 20% of farms. There are instances, however, where the most profitable 20% of farms have more debt than the farms in quintiles two, three or four. Overall, the data seems to indicate that debt University of Guelph, Ridgetown Campus Page 0

79 levels increase as farm size increases, debt levels do not necessarily relate to profitability, and the relative balance between manageable debt levels and farm size is important. Table A6 shows the debt coverage ratio per farm by gross revenue range. This ratio provides an indicator of the farm s ability to cover debt obligations and can be used as a proxy for financial stress. A limitation in this particular calculation is that there is no principal repayment included but the results provide a good indicator of trends within the industry. Basically, a debt coverage ratio of 1.0 means that all of the farm earnings are going into debt payments and less than 1.0 means that an operation does not generate enough profit to cover the debt obligation for that year. The results show that in most years and gross revenue range categories, the bottom 40% struggle while the top 40% are okay. In 2007 to 2009, the bottom 60% of farms with less than $300,000 in total revenue experienced financial struggles. There is a great deal of variation that exists within the data. University of Guelph, Ridgetown Campus Page 1

80 Table A1. Number of Ontario Swine Farms By Gross Revenue Range and Operating Profit Margin Quintile, 2003 to 2009 Year $0 $100,000 $300,000 $500,000 Quintiles by >$1,000,000 ALL FARMS OpProfMrgn $100,000 $300,000 $500,000 $1,000, , , , , , Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: OpProfMrgn = Operating Profit Margin University of Guelph, Ridgetown Campus Page 2

81 Table A2. Estimated Total Pigs Sold Per Farm By Gross Revenue Range and Operating Profit Margin Quintile, 2003 to 2009 $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Year Quintiles by Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev OpProfMrgn ,465 1,249 2,992 2,204 6,272 4,926 27,231 38, ,509 1,378 1,139 2,802 1,965 6,765 5,203 44,066 72, , ,075 2,113 6,372 5,459 24,846 24, ,693 1,628 2,721 1,435 5,559 4,431 21,526 19, ,478 1,091 3,085 2,428 6,278 5,013 26,313 21, ,551 1,330 3,275 2,851 6,386 4,574 19,273 16, ,373 1,693 2,490 1,663 5,421 4,270 24,593 35, ,442 2,949 2,239 1,205 6,409 4,886 29,973 28, , ,431 1,534 5,084 4,227 32,553 45, , ,508 1,571 5,431 3,951 23,841 51, ,413 1,084 2,657 1,977 4,801 3,363 17,213 14, ,661 1,803 2,614 1,937 5,366 4,701 19,287 18, ,834 1, ,734 2,059 5,507 3,959 22,381 22, , ,468 1,544 5,304 4,248 24,066 17, , ,469 1,063 5,639 3,751 26,458 22, , ,990 2,333 5,360 3,461 20,727 33, ,452 1,014 2, ,412 3,054 16,885 14, ,856 8,464 1,456 1,049 3,486 3,253 6,818 4,792 23,742 19, ,481 1,015 3,130 2,038 5,963 4,202 25,101 27, , ,735 2,597 5,980 4,305 27,741 25, , ,806 1,136 5,253 2,561 34,058 47, , ,061 1,731 5,518 3,029 22,583 17, ,580 1,150 2,609 1,777 6,181 5,227 18,735 14, ,731 1,353 3,439 2,466 6,884 5,146 22,389 17, ,655 1,553 3,260 2,520 6,522 4,458 25,423 27, ,151 2,634 3,569 2,478 6,069 4,313 27,610 22, , ,296 3,025 7,375 4,662 32,677 46, , ,163 2,927 6,186 4,176 23,550 18, , ,071 1,901 5,998 3,651 22,986 20, ,914 1,690 3,201 2,197 6,979 5,304 20,255 16, ,317 4,010 3,853 3,498 7,139 6,128 25,343 31, ,341 8,297 5,194 5,168 11,655 9,187 30,680 31, ,793 1,176 3,622 2,894 7,054 5,198 36,630 50, , ,113 3,671 6,254 4,954 18,824 20,876 4 X X 1,704 1,173 3,204 2,533 5,811 3,839 20,343 20, ,054 2,010 3,109 2,075 4,900 3,389 20,239 18, ,025 2,637 3,949 3,458 7,342 5,885 27,124 36, ,826 5,099 4,592 4,590 10,057 8,748 46,171 65, , ,591 4,325 6,212 4,102 21,410 17,998 3 X X 1,935 1,733 3,315 2,271 7,553 5,436 28,100 24,731 4 X X 1,847 1,286 3,649 2,432 6,629 4,605 15,369 13, ,870 1,818 3,609 2,998 6,264 4,691 24,219 29,595 Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 3

82 Table A3. Operating Profit Margin Per Farm By Gross Revenue Range and Operating Profit Margin Quintile, 2003 to 2009 $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Year Quintiles by Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev OpProfM rgn 2003 (0.48) (3.08) 7.55 (0.27) 0.28 (0.14) 0.14 (0.20) 0.14 (0.19) (0.32) 0.08 (0.02) (0.01) (0.09) (0.67) (4.15) 8.61 (0.22) 0.25 (0.15) 0.15 (0.11) 0.19 (0.13) (0.50) (0.13) (21.01) (108.12) (0.39) 0.84 (0.15) 0.22 (0.08) 0.13 (0.08) (0.28) 0.09 (0.02) (0.06) (1.52) (8.00) (0.32) 0.28 (0.16) 0.15 (0.14) 0.10 (0.13) (0.34) 0.08 (0.05) (0.10) (0.70) 4.93 (0.03) (4.20) (0.52) 0.74 (0.19) 0.15 (0.30) 0.44 (0.17) (0.41) 0.09 (0.09) 0.03 (0.01) 0.03 (0.03) 0.03 (0.03) (0.14) (0.12) 0.57 (0.06) 0.30 (0.05) 0.22 (0.03) (1.65) 1.34 (0.76) 0.93 (0.44) 0.37 (0.38) 0.22 (0.31) (0.42) 0.07 (0.18) 0.06 (0.13) 0.04 (0.10) 0.04 (0.11) (0.22) 0.06 (0.04) 0.03 (0.03) 0.03 (0.01) 0.02 (0.01) X X (0.42) 2.37 (0.09) (0.03) (2.76) 4.40 (0.62) 0.67 (0.29) 0.21 (0.26) 0.16 (0.34) (0.44) 0.16 (0.13) 0.05 (0.07) 0.03 (0.07) 0.02 (0.10) X X (0.03) (0.01) X X Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: Operating Profit Margin = Earnings Before Interest and Taxes (cash basis) / Total Operating Revenue; OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 4

83 Table A4. EBITA Per Farm By Gross Revenue Range and Operating Profit Margin Quintile, 2003 to 2009 $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Year Quintiles by Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev OpProfMrgn 2003 (1,374) 28,955 26,856 49,807 78,249 83, , , , ,592 1 (24,103) 40,327 (13,185) 54,650 4,170 65,961 (15,208) 157,144 (444,294) 1,658,320 2 (10,421) 19,564 10,797 35,913 77,020 82,620 79,382 97, , , ,969 21,495 26,502 35,825 86,382 67, ,968 94, , , ,154 14,542 38,544 33,179 85,270 57, , , , , ,622 19,808 71,624 42, ,661 80, , , , , ,326 41,727 44,721 52,483 99,169 81, , , , ,122 1 (6,717) 38,508 17,037 71,843 64, ,769 83, ,079 45, , ,878 61,178 35,165 43,548 83,471 56, , , , , ,185 18,792 37,649 33,821 99,292 63, ,303 92, ,262 1,152, ,774 41,551 52,916 35, ,144 71, ,958 84, , , ,512 36,171 80,743 45, ,568 79, , , , , ,068 44,113 32, ,000 83,419 93, , , , ,906 1 (22,513) 62,897 (24,875) 252,231 21,413 96,467 47, , , ,508 2 (6,846) 21,507 11,765 48,052 67,641 61,395 94, , , , ,385 15,792 32,082 41,125 72,785 68, , , , , ,303 15,078 56,242 46, ,152 52, , , , , ,030 60,687 87, , , , , , , , ,986 41,391 39,486 59,668 93,048 92, , , , ,955 1 (4,107) 61,444 (1,693) 58,798 35,985 85,406 32, ,559 8, ,266 2 (3,508) 23,626 27,582 45,634 60,471 67,095 90, , , ,143 3 (887) 25,254 28,323 41,190 88,692 75, , , , , ,029 17,930 54,288 38, ,249 63, , , , , ,401 51,501 88,748 67, , , , , , , ,351 37,855 6,267 69,575 49,898 92,677 90, , , ,936 1 (17,566) 25,611 (45,019) 106,959 (33,128) 84,231 (99,345) 250,793 (314,621) 728,870 2 (12,355) 30,631 (19,524) 36,855 29,105 78,468 39, ,739 (103,130) 474,614 3 (4,562) 16,747 12,469 33,009 60,261 60, , , , , ,518 26,406 19,572 32,804 66,202 73, , , , , ,718 54,279 63,712 49, ,303 85, , , , , ,834 51,383 10,959 98,412 48, ,239 56, ,528 21, ,385 1 (29,406) 44,100 (88,995) 111,733 (46,877) 167,555 (97,912) 233,752 (552,229) 628,312 2 (4,614) 13,018 (5,884) 34,886 41,734 71,187 13, ,283 (311,240) 876,219 3 (3,607) 18,109 17,942 28,622 72,243 81,703 66, , , ,832 4 X X 34,169 57,382 83,630 66, , , , , ,820 79,724 97, ,970 92,005 87, , , , , (3,871) 49,290 (4,817) 79,987 14,968 99,097 69, ,475 (22,695) 781,328 1 (21,308) 67,668 (77,558) 90,558 (65,963) 112,258 (90,459) 195,076 (827,428) 1,101,995 2 (23,367) 17,170 (4,638) 40,479 (25,610) 83,523 (3,351) 95,486 (109,440) 266,421 3 X X (929) 38,060 41,342 73, , ,965 25, ,055 4 X X 13,964 91,961 52,752 53,146 93, , , ,285 5 (2,586) 73,970 45,728 67,585 72,233 90, , , , ,977 Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: EBITA = Earnings Before Interest, Taxes and Amortization (accrual basis); Leasing expenses are included along with CCA; OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 5

84 Table A5. Estimated Debt Per Farm By Gross Revenue Range and Operating Profit Margin Quintile, 2003 to 2009 $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Year Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev Quintiles by OpProfMrgn , , , , , , , ,546 2,346,893 2,626, , , , , , ,226 1,071, ,076 2,834,695 4,571, , , , , , , , ,099 2,181,853 1,819, , , , , , , , ,371 2,314,439 2,066, ,416 78, , , , , , ,296 2,593,184 2,128, , , , , , , , ,477 1,820,641 1,329, , , , , , , , ,320 2,290,660 2,928, , , , , , ,841 1,083, ,914 2,499,743 2,384, , , , , , , , ,945 1,957,660 1,991, , , , , , , , ,162 2,360,613 4,958, , , , , , , , ,656 2,212,244 1,637, , , , , , , , ,673 2,429,480 2,516, , , , , , , , ,734 1,763,886 1,477, , , , , , , , ,281 1,689,849 1,094, , , , , , , , ,319 1,645,057 1,682, , , , , , , , ,373 1,828,220 1,464, , , , , , , , ,572 1,616,773 1,205, , , , , , , , ,294 2,034,358 1,822, , , , , , , , ,217 1,614,219 1,454, , , , , , , , ,404 1,825,432 1,920, ,972 94, , , , , , ,018 1,708,128 1,247, ,065 99, , , , , , ,057 1,558,887 1,882, ,084 65, , , , , , ,226 1,650, , , , , , , , , ,469 1,328, , , , , , , , , ,076 1,635,800 1,459, , , , , , , , ,341 2,083,785 2,147, , , , , , , , ,849 1,551,221 1,162, , , , , , , , ,647 1,515,264 1,241, , , , , , , , ,412 1,509,037 1,295, , , , , , , , ,047 1,540,366 1,227, , , , , , , , ,412 1,998,721 1,670, , , , , , ,949 1,149, ,787 2,586,415 2,318, , , , , , ,313 1,057,045 1,075,174 2,272,546 1,674, , , , , , , , ,910 1,596,341 1,239,863 4 X X 269, , , , , ,343 1,713,502 1,121, , , , , , , , ,791 1,847,507 1,603, , , , , , ,900 1,305,121 1,226,252 2,928,081 2,837, , , , , , ,596 2,071,696 1,895,376 3,492,570 2,702, , , , , , ,056 1,233, ,975 2,733,633 1,908,879 3 X X 441, , , ,852 1,359, ,821 2,773,319 2,137,444 4 X X 461, , , , , ,606 2,171,255 1,750, , , , , , ,606 1,002, ,035 3,430,389 4,529,583 Source: Calculations using the Ontario Farm Income Database, OMAFRA Note Estimated Debt = Interest Expenses / Interest Rate; Interest Rate is estimated to be bank prime + 1%; OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 6

85 Table A6. Debt Coverage Ratio Per Farm By Gross Revenue Range and Operating Profit Margin Quintile, 2003 to 2009 $0 $100,000 $100,000 $300,000 $300,000 $500,000 $500,000 $1,000,000 >$1,000,000 Year Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev Quintiles by OpProfMrgn 2003 (0.4) ,256.3 (10.5) (8.6) 12.7 (36.4) 98.2 (6.5) 20.8 (10.7) 50.2 (17.9) (23.7) 38.6 (3.4) (3.5) 21.9 (132.5) (5.8) , , , (75.0) 1, (31.6) 56.0 (19.1) 45.1 (11.3) 48.2 (1.3) 3.4 (459.9) 2, (16.0) (23.3) , , , , (33.7) 96.8 (16.0) 34.2 (1.6) 3.7 (6.2) 23.4 (96.7) (45.0) (2.3) (34.3) , (13.7) (44.2) (33.5) 98.7 (6.4) 18.9 (10.6) 56.0 (26.3) (77.5) (39.9) (46.3) (2.0) , (8.3) (69.0) 1, , (57.5) 94.5 (312.3) 1,527.8 (22.9) 77.3 (6.9) 16.1 (49.4) (20.9) 45.8 (421.9) 2,721.4 (0.6) 3.7 (9.8) 65.4 (6.1) (31.9) , , (8.0) (13.1) (62.7) (18.6) 46.6 (61.7) (17.4) 73.2 (197.8) 1, (47.7) 64.1 (25.3) (34.8) 86.7 (2.7) 4.1 (8.7) (23.9) 45.5 (20.1) 49.1 (1.6) 4.1 (12.0) X X , , (2.9) , , (47.4) 89.9 (23.6) 36.7 (39.4) (7.0) 13.7 (127.4) (23.2) 44.6 (12.8) 28.2 (5.1) 15.6 (7.7) 25.4 (31.8) X X (10.4) , X X , , , Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 7

86 Appendix B Results from Ontario Farm Income Database Tables by Production Type University of Guelph, Ridgetown Campus Page 8

87 Tables B1 to B6 display more detailed results by swine production type using OFID data from 2003 to Averages and standard deviations are presented to show variability in the data. Results for this set of tables is also sorted into quintiles by operating profit margin with the quintiles being production type specific with the number of farms not being equal for each quintile (i.e. not all quintiles have 20% of the farms). That is, the numbers in each quintile for a specific production type column relate to the number of farms originally in each operating profit margin quintile. For example, in 2004, 144 of 632 farms (23%) in the farrow to finish category were in the most profitable quintile. Table B1 shows the number of farms by production type and quintile from 2003 to The trends in total farm numbers are the same as in Table A1. The farrow to feeder and mixed production categories have seen farm numbers increase slightly from 2003 to 2009 while the farrow to finish, farrow to wean, and finishing categories have all seen large decreases in farm numbers. Table B2 shows the estimated total pigs sold per farm by production type. Generally, farms have gotten larger (i.e. sold more pigs) over time and there is a lot of variability as seen in the standard deviations. This is likely due to changes in productivity that can occur due to disease, feeding and reproductive challenges and etc.. Similar to Table A2, results also show that the most profitable farms don t necessarily sell the most pigs. In some instances, the least profitable farms actually sold the most pigs and had more variability. Table B3 shows the operating profit margin per farm by production type. Operating profit margin is calculated as EBIT (earnings before interest and taxes, cash basis) divided by total operating revenues. Generally, from 2003 to 2006, the top 60% of farms showed a positive operating profit margin while the bottom 40% experienced a negative margin. From 2007 to 2009, this changed to only the top 40% of farms in each category had a positive operating profit margin. However, depending on the year and production type, some of the categories showed the top 60% of farms generated a positive operating profit margin. Table B4 shows the average Earnings Before Interest, Taxes and Amortization (EBITA) per farm by gross revenue range. EBITA is on an accrual basis with amortization including capital cost allowance (CCA) and leasing expenses. This table provides a good measure of relative profitability and the ability to cover debt obligations and provide a return to the owner. Similar to Table B3, generally for all categories, the top 20% of farms are quite profitable while the bottom 20% are not profitable. In 2007 to 2009, some categories saw the bottom 40 60% struggle. Overall, the results show a lot of variability exists in the data and profitability is not necessarily related to farm type. There are farm types that make money and lose money. Table B5 displays the estimated debt per farm by production type. Similar to Table A5, these are not actual debt levels but are estimated using actual interest expenses and historical interest rates (i.e. bank prime +1%). These estimates may be underestimating or overestimating debt depending on a farm s individual average interest rate they are paying on their debt. However, the overall trends should be reflective of what has happened on these farms during this time period. Similar to Table A5, the data shows a lot of variability. There are years where generally the results show the most profitable 20% of farms have less debt than those in some of the other four quintiles. There are instances, however, where the most profitable 20% of farms have more debt. Overall, the data seems to indicate that debt levels do not necessarily relate to production type or profitability. University of Guelph, Ridgetown Campus Page 9

88 Table B6 shows the debt coverage ratio per farm by production type. Similar to Table A6, this ratio provides an indicator of the farm s ability to cover debt obligations. The results show that in most years and production types, the bottom 40% struggle while the top 40% are okay regardless of the farm type. In 2008 and 2009, the bottom 60% of farms in the farrow to feeder, farrow to finish, and mixed production types experienced financial struggles. There is a lot of variation within the data. University of Guelph, Ridgetown Campus Page 10

89 Table B1. Number of Ontario Swine Farms By Production Type and Operating Profit Margin Quintile, 2003 to 2009 Year Farrow to Farrow to Farrow to Mixed Quintiles by Feeder Finish Wean Production OpProfMrgn Finish All Farms , , , , , Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation University of Guelph, Ridgetown Campus Page 11

90 Table B2. Estimated Total Pigs Sold Per Farm By Production Type and Operating Profit Margin Quintile, 2003 to 2009 Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Year Quintiles by Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev OpProfMrgn ,322 18,557 4,047 10,818 19,579 20,992 18,164 46,362 4,732 18, ,519 11,557 4,711 20,934 12,941 16,409 14,300 25,323 8,340 37, ,323 22,701 4,450 7,291 19,050 24,959 47, ,342 5,339 11, ,366 27,620 3,967 6,085 27,111 23,730 15,895 15,967 3,727 5, ,774 9,631 4,618 6,951 20,801 19,597 8,128 12,905 3,569 4, ,340 9,501 2,495 2,689 18,352 18,688 6,995 11,067 2,677 3, ,092 17,534 4,334 13,447 18,895 19,837 20,747 63,144 4,306 8, ,141 11,683 4,362 18,289 15,246 16,916 7,249 8,977 2,121 4, ,229 31,972 6,835 22,481 21,500 18,836 38,165 71,370 6,621 12, ,079 8,427 4,024 5,654 16,668 20,538 48, ,660 5,471 11, ,969 9,913 3,524 3,766 19,618 20,240 9,825 14,555 3,816 4, ,764 8,860 2,930 3,851 21,481 22,817 3,056 1,654 3,501 6, ,920 18,387 4,450 8,283 19,685 18,747 12,517 36,479 4,852 8, ,058 14,778 3,160 7,464 16,437 19,727 6,580 7,962 1,719 3, ,690 25,031 5,957 12,619 23,794 20,497 13,256 18,407 6,727 10, ,956 18,212 5,481 6,729 18,297 16,346 33,013 76,751 6,987 11, ,906 18,270 3,722 4,319 20,800 20,399 4,671 7,373 5,108 6, ,096 15,426 3,939 7,774 19,326 17,416 4,356 1,917 3,746 8, ,610 20,179 5,210 14,368 21,536 19,313 10,478 14,284 5,438 12, ,045 35,701 3,544 8,375 16,548 20,285 6,968 9,680 2,733 4, ,542 9,576 8,247 28,203 24,334 22,084 13,827 17,528 8,579 24, ,231 16,786 6,190 10,482 21,795 15,752 8,953 15,282 6,723 10, ,543 16,811 4,640 4,941 24,225 19,036 16,051 20,353 5,267 6, ,722 4,611 3,439 4,755 21,064 19,778 7,411 6,882 3,887 4, ,647 21,799 5,550 13,764 20,763 19,218 15,091 19,624 6,727 14, ,225 25,754 4,494 9,337 17,134 19,823 5,260 8,838 3,459 5, ,595 27,509 8,851 26,950 22,658 21,698 25,385 29,049 11,717 27, ,658 23,354 6,472 9,201 18,919 13,139 20,137 23,712 8,603 12, ,266 17,421 4,369 4,976 27,590 25,994 16,203 16,214 6,391 9, ,488 11,006 3,566 3,710 17,410 11,290 8,468 7,977 3,464 7, ,899 25,611 5,953 10,544 22,740 19,271 20,289 42,505 7,184 15, ,158 35,985 5,865 15,404 18,630 17,377 11,930 18,784 6,992 10, ,222 20,255 9,047 15,153 17,664 14,307 35,327 82,058 13,189 30, ,892 30,548 5,511 5,588 34,739 25,457 13,572 13,465 5,727 4, ,085 21,914 5,063 5,073 20,330 18,048 24,656 36,224 7,241 10, ,138 14,109 4,300 4,847 21,760 16,603 15,351 24,394 2,746 2, ,068 28,820 6,636 11,288 20,468 17,048 23,444 53,637 8,563 22, ,863 33,106 10,944 21,015 23,981 15,538 4,190 5,127 16,055 47, ,073 26,025 6,546 6,228 19,630 17,868 47, ,989 8,090 8, ,009 20,687 6,712 9,797 22,368 20,505 17,895 27,010 7,884 9, ,515 18,395 4,753 3,493 13,607 12,640 30,753 36,211 6,151 5, ,853 40,857 4,224 5,205 22,756 19,099 X X 4,566 5,420 Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 12

91 Table B3. Operating Profit Margin Per Farm By Production Type and Operating Profit Margin Quintile, 2003 to 2009 Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Year Quintiles by Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev OpProfMrgn 2003 (0.18) (0.04) (1.34) 2.68 (0.30) 0.42 (0.50) 0.55 (0.37) 0.32 (0.92) (0.02) 0.03 (0.02) (0.02) 0.05 (0.04) (0.13) (0.50) 0.54 (0.29) 0.41 (0.37) 0.27 (0.25) 0.21 (1.55) (0.01) (0.02) (0.22) (6.64) (0.16) 0.20 (1.72) (0.35) 0.68 (0.22) 0.21 (35.34) (0.03) (0.08) 0.42 (0.37) (0.43) 0.90 (0.25) 0.23 (0.22) 0.17 (0.62) 0.67 (2.42) (0.02) 0.03 (0.00) (0.05) 0.03 (0.04) (0.15) 1.30 (0.09) 2.14 (0.07) 0.71 (0.03) (1.23) 2.69 (0.80) 4.72 (0.87) 1.28 (0.28) 0.12 (0.50) (0.03) 0.04 (0.06) 0.04 (0.01) 0.03 (0.09) 0.04 (0.06) (0.03) (0.21) 0.65 (0.07) 0.41 (0.09) 0.32 (0.12) (1.02) 1.10 (0.49) 0.60 (0.57) 0.34 (0.47) 0.18 (0.54) (0.21) 0.06 (0.13) 0.04 (0.16) 0.07 (0.21) 0.04 (0.12) (0.07) 0.03 (0.02) (0.08) 0.02 (0.01) (0.09) 0.48 (0.04) 0.26 (0.03) 0.39 (0.17) 0.30 (0.04) (0.69) 0.80 (0.36) 0.32 (0.60) 0.52 (0.63) 0.37 (0.75) (0.09) 0.05 (0.10) 0.03 (0.07) 0.03 (0.20) 0.04 (0.07) (0.02) (0.10) (0.00) X X Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: Operating Profit Margin = Earnings Before Interest and Taxes (cash basis) / Total Operating Revenue; OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 13

92 Table B4. EBITA Per Farm By Production Type and Operating Profit Margin Quintile, 2003 to 2009 Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Year Quintiles by Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev OpProfMrgn , ,327 86, , , ,375 58, ,757 40, ,505 1 (36,338) 97,368 (55,035) 399,074 (96,023) 164,668 (137,659) 295,768 (108,873) 1,007, , ,219 86, , , ,930 73, ,193 12, , , ,744 95, , , , , ,577 69, , , , , , , , , , , , , , , , , ,623 89, , , , , , , , , ,741 17,294 1,181,329 99, ,465 1 (9,604) 151,144 8, ,403 (37,196) 315,285 73, ,314 3, , , , , , , ,086 98, ,687 86, , , , , , , ,790 (652,827) 2,501,535 96, , , , , , , , , , , , , , , , , , ,552 96, , , , , , , , , , , , , , ,656 (10,162) 261,778 (2,238) 182,882 64, ,219 1, , , , , , , , , ,735 54, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,128 96, , , ,945 (12,593) 133,508 (7,800) 220, , ,679 (677) 104,665 2 (25,639) 323,954 87, , , ,275 74, ,423 22, , , , , , , ,475 22, ,815 77, , , , , , , , , , , , , , , , , , , , , , , ,753 53, , , , , ,923 46, ,471 1 (296,304) 538,529 (113,985) 386,884 34, ,152 (153,186) 213,753 (74,254) 140, , ,209 (57,685) 337,203 46, , , ,468 (66,836) 283, , , , , , ,210 12, ,352 35, , , , , , , , , , , , , , , , , , , , , , (23,650) 384,157 57, ,220 67, ,269 (24,333) 663,340 20, ,495 1 (212,840) 328,955 (103,862) 294,083 (165,946) 294,568 (200,805) 208,980 (225,815) 505,578 2 (211,532) 442,004 (61,872) 427,599 (51,121) 234,671 (286,699) 908,632 (109,514) 602, , ,599 83, , , ,397 (2,838) 148,932 57, , , , , , , ,117 (139,262) 379, , , , , , , , , , , , , , ,342 8, ,173 6, ,806 (61,210) 911,848 29, ,654 1 (244,508) 427,343 (255,369) 656,967 (342,267) 374,337 (115,457) 124,749 (305,897) 552,622 2 (125,737) 190,318 (39,591) 163,635 27,149 76,781 (671,993) 1,785,202 (60,691) 252, , ,298 20, ,571 42, ,557 70, ,778 66, , , , , , , , , , , , , , , , , ,081 X X 308, ,353 Source: Calculations using the Ontario Farm Income Database, OMAFRA Note EBITA = Earnings Before Interest, Taxes and Amortization (accrual basis); Leasing expenses are included along with CCA; OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 14

93 Table B5. Estimated Debt Per Farm By Production Type and Operating Profit Margin Quintile, 2003 to 2009 Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Year Quintiles by Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev OpProfMrgn ,102,912 1,548, ,470 1,446,351 1,498,092 1,667,671 1,355,096 1,903, , , ,296 1,398, ,872 2,599, , ,075 1,827,703 2,981, ,649 1,728, ,765,513 2,116, ,122 1,186,846 1,567,735 1,168,631 1,154,944 1,028, , , ,293,059 1,999, ,873 1,194,556 2,131,737 2,562,893 1,715,491 2,135, , , ,189,441 1,021, , ,533 1,649,881 1,833,993 1,244,677 1,852, , , , , , ,826 1,343,490 1,228, ,162 1,104, , , ,211,732 1,558, ,512 1,023,206 1,635,758 1,915,078 1,946,485 5,644, ,729 1,070, , , , ,428 1,466,442 1,831,378 1,250,853 1,737, , , ,112,851 2,394, ,505 1,419,333 1,747,847 2,513,508 1,958,256 2,473, , , , , , ,241 1,360,517 1,736,748 5,977,574 13,506, , , ,877,894 1,768, , ,641 2,141,158 1,894,495 1,226,480 1,718, , , , , , ,483 1,487,270 1,537, , , ,264 1,923, ,093,753 1,319, , ,884 1,258,633 1,410, ,426 1,006, , , ,075, , , ,426 1,001,264 1,082,415 1,021, , , , ,244,184 2,030, , ,440 1,234,945 1,175,185 1,093,313 1,276, , , ,107,963 1,186, ,526 1,259,702 1,056, ,036 1,337, , , , ,250,489 1,216, , ,547 1,693,730 2,104,646 1,032,769 1,321, , , , , , ,324 1,314,385 1,456, , , , , ,365 1,022, , ,125 1,071,186 1,187,277 1,221,799 1,897, , , ,304,280 1,736, , , ,268 1,063,174 1,966,339 3,496, , , , , , , ,250 1,100,271 1,151,848 1,766, , , , , , ,843 1,649,525 1,752, ,283 1,123, ,280 1,326, , , , , , , ,589 1,008, , , , , , , , ,818 1,030,009 1,245, , , ,133,909 1,443, , ,021 1,088,985 1,405,177 1,146,377 1,044, , , ,565,697 2,246, , ,150 1,577,980 2,591, , , , , ,164, , , ,809 1,121,020 1,113,574 1,224, , , , ,107,758 1,046, , , , ,730 1,645,971 1,581, , , ,162,128 1,722, , ,976 1,087,876 1,055,002 1,119, , , , , , , , , ,623 1,129, , , , ,225,894 1,503, ,837 1,162,157 1,091,266 1,098,218 1,458,626 1,810, , , ,542,729 2,528, ,497 1,020,548 1,101, ,034 1,194,257 2,600, ,373 1,181, ,285,962 1,477,990 1,219,200 1,492,694 1,004,377 1,207,107 1,663,358 1,428, , , ,201,367 1,176, , ,306 1,121,942 1,090,840 2,350,516 2,423, , , ,162, , , ,039 1,205,357 1,365,662 1,033, ,576 1,055,857 1,085, , , ,572 1,255,078 1,025,007 1,171,390 1,132,525 1,146, , , ,947,810 3,261,611 1,453,854 1,716,031 1,680,880 2,625,123 1,385,332 1,519,761 1,241,276 1,463, ,138,348 2,573,988 1,952,971 2,357,319 2,382,163 2,192,005 1,184,455 1,427,712 1,193,049 1,863, ,420,409 1,710,898 1,537,227 1,457,842 1,004, ,606 1,566,531 2,139,721 1,457,428 1,742, ,350,469 1,333,038 1,509,581 1,920,755 1,165,911 1,257,118 1,128, ,809 1,286,896 1,197, ,042,021 1,673,778 1,166,523 1,370,839 1,139,650 1,187,596 1,648,677 1,692,092 1,075,653 1,065, ,736,274 6,219,156 1,117,734 1,133,422 2,712,109 5,134,411 X X 1,189,801 1,359,790 Source: Calculations using the Ontario Farm Income Database, OMAFRA Note Estimated Debt = Interest Expenses / Interest Rate; Interest Rate is estimated to be bank prime + 1%; OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 15

94 Table B6. Debt Coverage Ratio Per Farm By Production Type and Operating Profit Margin Quintile, 2003 to 2009 Farrow to Feeder Farrow to Finish Farrow to Wean Mixed Production Finish Year Quintiles by Average StdDev Average StdDev Average StdDev Average StdDev Average StdDev OpProfMrgn , (9.2) (4.9) 4.4 (15.0) 53.0 (17.9) 56.0 (69.9) (25.5) (9.0) 37.4 (0.2) 10.3 (6.8) 31.8 (2.8) 5.5 (68.3) , , (8.9) 13.1 (14.5) 36.0 (9.9) 22.7 (18.2) 32.5 (316.1) 2, (8.1) , , , (2.7) 4.6 (24.4) 85.5 (10.7) 26.8 (2.0) 1.9 (18.4) (65.1) , , , (5.9) (24.0) 91.6 (39.7) (12.7) 28.6 (3.7) 3.2 (40.2) (0.3) (69.8) (36.0) (10.6) (18.2) (21.2) , (236.0) (240.6) 2,011.8 (10.3) 26.2 (4.5) 3.7 (174.3) 1, (0.4) 0.9 (3.7) (102.0) (51.6) (0.5) , (12.0) (19.4) 53.1 (27.3) 88.7 (8.7) 14.2 (22.7) 37.3 (222.1) 1, (34.8) (14.7) 51.8 (6.4) 12.6 (63.9) (13.4) (10.7) 36.2 (2.2) (29.7) (7.4) , , , (47.3) (20.6) 52.3 (142.8) (9.4) 5.7 (123.0) (6.0) 9.0 (9.5) 31.3 (1.6) 0.7 (11.3) 13.3 (40.4) (0.1) 1.7 (4.4) (5.4) , , , , X X Source: Calculations using the Ontario Farm Income Database, OMAFRA Note: OpProfMrgn = Operating Profit Margin; StdDev = Standard Deviation; X = data suppressed due to less than five farms University of Guelph, Ridgetown Campus Page 16

95 Appendix C Examples of Selected Variable Calculations For Ontario 2009 Figures University of Guelph, Ridgetown Campus Page 17

96 Source: Canadian Farm Financial Database (CFFD) / Farm Financial Survey (FFS) Debt to Equity = Total Liabilities Total Equity = $749,126 $1,389,650 = 0.54 % Equity = Total Equity Total Assets 100% = $1,389,650 $2,138, % = 65% Debt Structure Current Ratio Debt to Total Revenues Interest Expense to Total Revenues Working Capital to Total Revenues = Current Liabilities Total Liabilities = $146,309 $749,126 = 0.20 = Current Assets Current Liabilities = $205,096 $146,309 = 1.40 = Total Liabilities Total Revenue = $749,126 $558,835 = 1.34 = Interest Expenses Total Revenue = $35,026 $558,835 = 0.06 = (Current Assets Current Liabilities) Total Revenue = ($205,096 $146,309) $558,835 = 0.11 University of Guelph, Ridgetown Campus Page 18

97 Source: Ontario Data Analysis Project (ODAP) Debt to Equity = Total Farm Liabilities Equity in Farm = $1,217,646 $1,684,732 = 0.72 % Equity = Equity in Farm Total Farm Assets 100% = $1,684,732 $2,902, % = 58% Current Ratio Debt Servicing Requirement Debt per Sow (Ending) Equity per Sow Debt per Pig Produced Equity per Pig Produced = Total Current Assets Total Current Liabilities = $404,180 $319,129 = 1.27 = (Principal + Interest Paid (All)) Total Farm Revenue = ($87,753 + $51,289) $747, 901 = 0.16 = Total Farm Liabilities (Ending) Average Number of Sows = $1,217, = $5,227 = Equity in Farm Average Number of Sows = $1,684, = $7,232 = Total Farm Liabilities (Ending) Pigs Produced = $1,217,646 4,865 = $250 = Equity in Farm Pigs Produced = $1,684,732 4,865 = $346 Return on Assets = (Net Farm Income (Accrual) + Interest Paid (All)) Total Farm Assets (Average of Beginning and Ending) 100% = 0.5% Return on Equity = Net Farm Income (Accrual) Owners Equity (Average of Beginning and Ending) = 2.2% University of Guelph, Ridgetown Campus Page 19

98 Appendix D Selected Methodology Ontario Farm Income Database (OFID) University of Guelph, Ridgetown Campus Page 20

99 Revenue, Expense, and Income Measures Revenue generated by each farm is measured by Total Operating Revenue (TtlOpRev), which captures revenue generated by commodity sales. For the purpose of this dataset, it is calculated as the eligible income, which includes both commodity sales and crop insurance payments (crop insurance payments cannot be completely decoupled from commodity sales in the dataset). Total Operating Expense (TtlOpExp) is calculated as all expenses incurred by the farm. This is calculated as eligible expense plus ineligible expense, minus non arm s length salary and quota rentals, and other tax adjusting line codes. Non arm s length salary and quota rental are taken out because they can be adjusted by the tax filer for tax purposes. Interest expense is represented by the variable Interest_EE, and is taken from the eligible expense lines 9607 and This variable captures interest paid to farm and non farm long term loans, including real estate and mortgage payments. This variable may not include repayment of some short term loans, such as interest payment portion of payment plans for feed and fertilizer. These short term loan repayments are generally hidden within their respective input expenses. Earnings Before Tax (EBT). It shows the absolute net income realized by the farm, and is equivalent to net income. It is calculated as Total Operating Revenue minus Total Operating Expenses. Earnings Before Interest and Tax (EBIT). It is calculated as the EBT without (i.e. before) interest expense. The next two measures are both Earnings before Interest, Tax, and Amortization (EBITA), and they differ between how amortization is estimated. The first of the two measures, EBITA1, assumes the cost of amortization is totally captured by the capital cost allowance. This assumption is based on full ownership of farm equipment and the capital cost allowance is a proxy for depreciation. The second measure, EBITA2, takes machinery rental expenditure as an additional proxy for depreciation. This assumes that an operator rents some farm machinery in place of paying initial capital cost and maintenance cost for owning machinery. Therefore, EBITA1 is calculated as EBIT minus capital cost allowance expense. EBITA2 is calculated as EBIT minus both capital cost allowance expense and machinery maintenance rental expense (note that for the purposes of this study EBITA2 is used). Accrual Adjustments Variables An individual files taxes on a cash basis, where revenue and expenses are reported at the time when money exchanges hands. However, operators can purchase large amounts of nonperishable inputs at any time to be used over a number of years, or receive payment from the previous year s sale. Therefore, indicators generated from cash data is not always reflective of the financial situation of a farm, as an operator can increase expenditures in a good year and reduce expenditure in years when cash flow may be an issue. Accrual adjustments are done to adjust for inventory carryovers and surpluses between years to get a more accurate picture of an operation s yearly financial situation, and is done by supplementing revenue and expense data with inventory data. For program administration purposes, accrual adjustment is done to limit an operator s power to adjust his or her income margin to trigger program payment. There are four sets of accrual adjustment variables: crop and livestock inventory adjustment, purchased inputs, accounts receivable, and accounts payable. University of Guelph, Ridgetown Campus Page 21

100 Financial Ratios Operating Profit Margin measures the net result of the business with profit as a percent of sales revenue, and is calculated as the EBIT divided by the Total Operating Revenue. Debt Coverage Ratio measure the farm s ability to generate enough cash to cover its debt payments. It is calculated as the Interest Expense plus Net Income, divided by Interest Expense. Estimated Debt provides an estimate of the level of debt a farm holds. It is calculated using the following equation: 1% The interest rate was taken as the annual average of the month end prime rate provided by the Bank of Canada plus 1%, which provides an estimate of the actual borrowing rate offered in the agricultural sector. The interest rates used for each year are as follows: Year Prime Rate (Annual Average of rate at Month End) Prime Livestock count Livestock count and prices are taken from inventory data. OFID participants are required to submit swine inventory numbers, and livestock are categorized into six types/stages: early weaners (EW_), weaners (WNR_), feeder hogs 90lb (FH90_), feeder hogs 130lb (FH130_), feeder hogs 170lb (FH170_), and marketed hogs (MKT_). The weights attached to the swine categories are also part of the categories the operators choose. The number of livestock sold in each category is taken from the reported sales in the inventory data, and the sum of all swine sold (variables with the suffix _SALES) is reported in CALC_SALES. A second variable that estimates the number of swine sold is reported in the variable EST_SALES. Instead of taking the sales figures directly, EST_SALES calculates sales figures based on the following equation: For approximately 91% of swine operations, there was no difference in CALC_SALES and EST_SALES. The difference between the two may be due to misreporting: for some records, sales are found to be misreported as TRANSFER OUT. The final variable documenting number of hogs sold is HOG_SOLD, which picks the larger number of CALC_SALES and EST_SALES (note that for the purposes of this study HOG_SOLD is used). University of Guelph, Ridgetown Campus Page 22

101 In addition to sales, the production data provides additional information on the productive capacity of a farm. Given the number of hogs sold at different stages, number of sows were divided into three different variables: Swine Farrow (i.e. referred to as Farrow to Feeder) tracks the number of sows producing farrowed swine sold at the weaner (or feeder pig) stage, Swine Farrow Early Weaner (i.e. referred to as Farrow to Wean) tracks the number of sows producing farrowed swine sold at the early weaner stage, and Swine Farrow Finish (i.e. referred to as Farrow to Finish) tracks the number of sows producing farrowed swine sold at any of the feeder and marketed stages. It is unclear what the difference between the Swine Farrow and Swine Farrow Early Weaner is, as the earliest reported stages of hog production in the inventory data is at the early weaner stage. These numbers are estimated by how many hogs in each stage are produced per farm on average, taking into account mortality rates. For operators where hogs are not farrowed on farm but are instead bought in at a later stage and finished to market, the number of livestock sold is recorded in the variable Swine Finish (i.e. referred to as Finish). Note that for hogs sold that are not farrowed on farm, the number is prorated in Swine Finish. For example, for a hog bought into the farm at 5kg and sold at the weight of 25kg, it would only count as ¼ of an animal in Swine Finish. This is because production data is mainly used for cost of production purposes: data is prorated to account for differences in feeding cost. Based on the sow numbers from production data, additional swine farm production types are constructed and recorded in variable FRRW_TYPE. There are four production types, depending on the share of sows on farm producing hogs sold at different stages: FFARROW (i.e. referred to as Farrow to Feeder) are assigned to farms where greater than two third of sows are slotted in the Swine Farrow category; FWEAN (i.e. referred to as Farrow to Wean) where greater than two third of sows are slotted in the Swine Farrow Early Weaner category; FFNSH (i.e. referred to as Farrow to Finish) where greater than two third of sows are slotted in the Swine Farrow Finish category; and MIX (i.e. referred to as Mixed Production) if the farm did not have any of the three categories greater than twothirds of the total number of sows; For years where an operation did not report any sows in the production data, a production type is not assigned (i.e. referred to as Finish). Strengths of the OFID dataset The uniqueness in the dataset lies in the detailed inventory and production data linked to the tax file, and that each item in the tax, inventory, and production data are identified by both an operator ID and a farm ID. The dual ID system allows for linking each operator with their associated farms without any loss of detail in the data, and subsequently allows for very detailed farm level analysis even though the data is collected at the individual farm operator level. Detailed access to specific tax lines and inventory lines allows for detailed disaggregation in revenue, expense, and production information, giving flexibility to calculate a number of financial measures and performance indicators. The ability to construct panel data is also a plus. The inclusion of detailed inventory data is also an advantage unique to the OFID data and the resulting datasets generated from it. Access to inventory data allows for nuanced and detailed accrual adjustments to certain financial measures. Ability to do accrual adjustment is a major advantage over other datasets when examining financial performance, since cash reporting, which is generated by most other tax and census based datasets from Statistics Canada and AAFC, can distort the relationships between revenue, expenditure, input, and production levels. Furthermore, for specific sectors like swine, inventory data can be (and is) used to construct production type. Production acreage, missing in most datasets with the exception of the Census of Agriculture, is also accessible in this dataset. Having University of Guelph, Ridgetown Campus Page 23

102 data on units of sale linking to data on revenue, expenses, and other farm characteristics for a large number of farms in Ontario is also extremely useful for cost of production and efficiency analyses. Lastly, detailed program payment breakdowns are taken directly from the calculations rather than from what the operators report. This means that program payments are linked to the year that payment is triggered, avoiding the issue of uncertain time lag in other datasets (where payments are received and reported in a later tax year; issues arise when program payments with different payout schedules are reported as one metric). Using calculated payments rather than payments reported in tax files also eliminate reporting errors. Weaknesses of the OFID dataset Shortcomings of the OFID dataset, in the context of farm level financial performance analysis, include sample selection bias, lack of balance sheet information, limited demographic data, and incompatibility with measures generated from other datasets. Sample selection bias exists because the dataset only includes farms where at least one operator applied for support programs such as OFID, and a variety of factors exist that potentially sway an operators participation in this program, including but not limited to: farm type, farm size, and production of supply managed commodities (OFID payment is reduced by the percentage of revenue generated by dairy or poultry sales). Since OMAFRA also collects tax data from non participants (but not inventory data or program payment data), data on farm size and sector can be constructed and used to test for sample selection bias between all tax filing farms in Ontario versus OFID participants. Lack of balance sheet information on the farms is another disadvantage of using the OFID datasets for financial performance analysis; though this would still be an issue if another dataset (other than the Farm Financial Survey) is used. Lack of information on asset, liability, and equity levels means it is impossible to generate important financial measures that require balance sheet data, including: capital turnovers, current ratio, debt to asset ratio, debt structure, return on assets, return on equity, leverage, and working capital ratio. While some ratios and measures that infer profitability, productive efficiency, and financial efficiency ratio can be constructed with revenue and expense data alone, liquidity and solvency ratios require balance sheet data and cannot be constructed without information on assets and/or liabilities held by the operation. These measures are important in measuring a firm s ability to pay off short term and long term debt. Limited demographic data is another issue, although most other datasets (with the exception of the Ag Pop linkage database) do not contain much demographic information either. Data on operator s characteristics like age, and education can have important implications in farm management. Lastly, converting information from operator level to a farm level dataset is unique amongst all other tax based datasets. As such, information on numbers, size, revenue, expenses, etc., will likely not match similar statistics generated from other datasets, which based their measurements from operator level data. However, since the dataset has shown that most farms in the dataset are associated with a single operator and that most operators are associated with one operation, it is unlikely that the difference will be drastic. University of Guelph, Ridgetown Campus Page 24