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Using the WACC methodology to improve the assessment of projects in the french farming industry. Empirical evidences from farm's results of Isère

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par Anaël BIBARD
Grenoble Graduate School of Business - MBA 2012
  

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Title : Using the WACC Methodology to Improve the

Assessment of Projects in the French Farming

Industry. Empirical Evidences from Farm's Results

of Isère

Program: MBA - PT 6 - Grenoble (2009 - 2012)

Academic Year: 2011-2012

Dissertation / Project / Internship Report: International Management Project 2011-2012 Student Name: Bibard Anaël

School Tutor / Evaluator Name: Lominadze Anton

To fill in for Internship only:

Company Name:

Town:

Country:

Position occupied during internship:

Summary: The Purpose of this Thesis is to calculate the cost of capital for a typical farm in Isère. This cost of capital is use as an actualization rate to estimate the NPV of projects or the value of a farm based on this profitability.

Keywords: https://library.grenoble-em.com/Thesaurus/Thesaurus.html CORPORATE FINANCE - INVESTMENT

COMPANY - AGRICULTURAL COMPANY

ACCOUNTING - EVALUATION OF A COMPANY

FRENCH REGION - RHONE ALPES

MBA PT 6 - Grenoble Graduate School of Business

Using the WACC Methodology to

Improve the Assessment of Projects

in the French Farming Industry

Empirical Evidences from Farm's Results of

Isère

Anaël BIBARD
30/09/2012

Final Management Project Tutor: Anton Lominadze

Table of Content

Abstract 6

Abbreviations 7

Table of Illustrations 8

Introduction 11

1 Significance of the Research 12

1.1 The Farming Business in France 13

1.2 The Profitability Heavily Relies on Subsidies, Not on Investment Decision 14

1.3 A New Approach to Find 17

2 Literature Review 20

2.1 Risk 20

2.1.1 Risk in Agriculture 20

2.1.2 Risk Premium 20

2.2 The WACC Theory 25

2.2.1 The Importance of the WACC Theory. 25

2.2.2 The Betas for Micro-Capitalizations 25

2.3 Discount Rates and the CAPM Used in Agriculture 26

3 Research Methodology 28

3.1 Survey Construction 28

3.1.1 Questionnaire 28

3.1.2 Results Analysis 28

3.2 Historical Analysis of the Results of Farms in Isère 28

3.2.1 Data Collection 28

3.2.2 Data Processing 30

3.2.3 Research Questions 31

3.2.4 Data Analysis 31

3.3 Bond Yield Plus Risk Premium Model 33

3.3.1 Risk-Free Rate 33

3.3.2 Beta and Risk premium 33

3.4 WACC Estimation 34

4 Data Analysis 36

4.1 Results of the Survey 36

4.2 Calculation of the Historical Results Using the Data from CERFRANCE Isère 38

4.2.1 Normality of the Datasets 38

4.2.2 ROE 39

4.2.3 ROA 44

4.3 Calculation of the Capitalization Rate Using Bond Yields Plus Risk Premium 49

4.4 Approach of the WACC for Farming in Isère 50

4.4.1 Calculation of the Betas 50

4.4.2 Calculation of the WACC for the Different Specialization 52

5 Discussion 55

5.1 Survey Results 55

5.2 ROE and ROA Analysis the Farms of Isère 55

5.3 Capitalization Using the Bond Yield Plus Risk Premium 56

5.4 The WACC Methodology 56

6 Utilization of These Results 58

6.1 Managerial Implications 58

6.2 Agricultural Policies Implications 59

7 Limitations and Further Research Implications 61

7.1 Survey Construction 61

7.2 ROE and ROA Analysis 61

7.3 Bond Yield Plus risk Premium Model 61

7.4 The WACC Methodology 62

7.5 Further Research Implications 62

Conclusion 63

Bibliography 64

Appendices 70

Abstract

The remarkable rise of the soft commodities prices in 2007/2008 represented just a reminder of the importance of agriculture for humanity. The Arab spring found its base on this mold and pulled down many regimes considered as stable few months before, like Tunisia or Egypt. Eventually, these price records could be reached again in 2012, due to a severe drought in the USA. To meet the challenge of feeding the world while developing more sustainable forms of production, advisory services must play a great role to help farmers in their decisions regarding their production as well as their investments. However, it appears that some financial consultants use really low actualization rates for agriculture, even lower than the risk-free rate. The purpose of this paper is therefore to present the different methods that could be used to determine more appropriate discount rates for farming businesses, and particularly the Weighted Average Cost of Capital and the bond yield plus risk premium model as presented by the French tax authority.

Historical results of farms from Isère over 5 years were studied to determine if the leverage has a significant impact on the profitability, and if signs of financial distress could be identified for the higher leverages. The statistical tests performed confirmed the underlying hypothesis of the WACC theory: the cost of capital is reduced by the increase of the leverage, up to a limit where financial distress overcomes the advantage of the lower cost of debt. The optimal capital structure for farms of Isère seems to range between 40-60% leverage, and up to 60-80% in the cases of the most stable production such as dairy farms.

This paper provides indications about how to establish the actualization rates for agricultural consultants, based on the WACC methodology and the method recommended by the tax authorities. The data analysis confirmed that the actual rates used by practitioners are clearly undervalued, leading to an over-valuation by two to four times in their asset valuation studies. The WACC and the bond yield plus risk premium methodology both seem to be applicable for small and medium farming businesses. However, further researches are necessary to validate the robustness of the WACC methodology in the agricultural context for non listed companies.

Abbreviations

ANOVA: Analysis Of Variance

CAP: Common Agricultural Policy

CAPM: Capital Asset Pricing Model

CNCER: Conseil National des CERFRANCE (national council of the CERFRANCE) EBITDA: Earnings Before Interest, Taxes, Depreciation and Amortization

EEP: Expected Equity Premium

FADN: Farm Accountancy Data Network, or RICA: Réseau d'Information Comptable Agricole FAO: Food and Agriculture Organization

FTE: Full-Time Equivalent

GDP: Gross Domestic Product

Ha: hectare, 10 000 m2 or 2.47105 acres

HEP: Historical Equity Premium

IEP: Implied Equity Premium

IMF: International Monetary Fund

NPV: Net Present Value

NSP: Ne Se prononce Pas (No Answer)

OECD: Organization for Economic Cooperation and Development REP: Required Equity Premium

ROA: Return On Asset

ROE: Return On Equity

SAFER: Société d'Aménagement Foncier et d'Etablissement Rural SAS: Statistical Analysis System

SMIC: Salaire Minimum Interprofessionnel de Croissance. The minimum salary in France. SPSS: Statistical Package for the Social Sciences

TCA: Total Cultivated Area

USD: US dollars

WACC: Weighted Average Cost of Capital

WTO: World Trade Organization

Table of Illustrations

Table 1: Subsidies and recurring net profit before tax in France per farm, in K€Source: FADN (RICA) 16 Table 2: Assumptions and recommendations of the 129 books that assume that REP = EEP. Source:

Fernandez 2010 21

Table 3: Equity premiums recommended and used in textbooks. Source: Fernandez 2010 22

Table 4: Historical Equity Premium for the French market 23

Table 5: Historical Equity Premium for small and micro-cap 24

Table 6: Market capitalization effect on the beta for micro-capitalization 26

Table 7: Primary data for the analysis of farms' results in Isère 29

Table 8: Cost of each labor unit per month for the SMIC 30

Table 9: Social security contributions 31

Table 10: Groups of debt level 32

Table 11: Number of companies studied for the Beta estimation. Source: Bloomberg 35

Table 12: Age and gender of the respondents 36

Table 13: answers to question 4, 5 and 6 36

Table 14: Answers of question 7: «how do you choose the discount factors you use?» 37

Table 15: Answers of question8 «what discount factor do you use usually for your customers? (ex given: 11%)» 37 Table 16: Answers of question 9: «If you were proposed a tool to estimate the WACC of the farms of your region to use it as a discount factor, how would you consider this tool?» 37 Table 17: Results of the Mood's median tests summary for ROE by groups of leverage for each years

43

Table 18: Weighted average ROE for all specialization and by years 44

Table 19: Average ROE by specialization and by group 44

Table 20: results of the Mood's median tests summary for ROA by groups of leverage for each years

48

Table 21: Average ROA by specialization and by years 49

Table 22: Average ROA by specialization and by groups 49

Table 23: Calculation of the Beta for the grain specialization (oilseed is considered as grain farming in

France). Source: Bloomberg 51

Table 24: Calculation of the beta for the cattle ranching and farming specialization. Source:

Bloomberg 51

Table 25: Calculation of the beta for poultry and fruits & vegetables Source: Bloomberg 52

Table 26: Summary of the beta for the different specialization and regions Source: Bloomberg 52

Table 27: Characteristic of each specialization in Isère *EBITDA/total annuity 53

Table 28: WACC estimation for each specialization (leverage is based on table 27) 53

Table 29 : WACC estimation for each specialization for group 5 (0 to 20% leverage) 54

Table 30: WACC estimation for each specialization for group 2 (60 to 80% leverage) 54

Table 31: NPV of a refinancing operation to reduce the WACC and increase the wealth of a dairy farm

59

Figure 1: The optimal amount of debt to reduce the company cost of capitalSource: Principles of

Corporate Finance 12

Figure 2: Agricultural production in France, 2010 13

Figure 3: Agricultural production in Isère, 2010 14

Figure 4: The percentage of the EU budget allocated to the CAP Source: Eurostat, in Clipici 2011 15

Figure 5: Historical Development of the CAP Source: Clipici E., 2011 15

Figure 6: CAP spending evolution Source: DG Agri 16

Figure 7: Average asset value per farm by member state 2007 Source: FADN 17

Figure 8: Volatility of the price of wheat after application of Agenda 2000Source: Offre et Demande Agricole 18 Figure 9: Fluctuation of the share of agro-food exports by key countries in the global volume of agrofood exports 18 Figure 10: Moving average (last 5 years) of the REP used or recommended in 150 finance and valuation textbooks. Source: Fernandez 2010 22 Figure 11: Small firm premium (bottom 10% of market cap in US) between 1927-2010. Source:

Damodoran 2010 24

Figure 12: Number of farm for each specialization 30

Figure 13: Region of the CERFRANCE of the respondents 36

Figure 14: histogram of frequency for ROE 38

Figure 15: Histogram of frequency for ROA 39

Figure 16: Box and whisker plots with median notch for ROE by year (means are shown with a red cross) 40 Figure 17: Box and whisker plots with median notch for ROE by specialization (means are shown with a red cross) 40 Figure 18: Median ROE for each year and each group of leverage for dairy* means significantly higher than ** 41

Figure 19 : Median ROE for each year and each group of leverage for grain* means significantly

higher than ** 42

Figure 20: Median ROE for each year and each group of leverage for cattle* means significantly

higher than ** 43 Figure 21 : Box and whisker plots with median notch for ROA by years (means are shown with a red cross) 45 Figure 22 : ROA for each year and each group of leverage for dairy producers* means significantly higher than ** 46 Figure 23: ROA for each year and each group of leverage for grain producers* means significantly higher than ** 47

Figure 24: ROA for each year and each group of leverage for cattle farming* means significantly

higher than ** 48

Appendix 1: Illustration of the database extraction. File name: "extraction 2006_1" 70

Appendix 2: Illustration of the database extraction, balance sheet information. File name: "extraction

2006_2" 71

Appendix 3: Questionnaire 72

Appendix 4: Normality test of the datasets for ROE, extraction from statgraphics 75

Appendix 5: Normality tests of the datasets for ROA, extraction from statgraphics 77

Appendix 6: ROE by year 79

Appendix 7:ROA by year 84

Appendix 8: ROE by groups for dairy production 89

Appendix 9: ROE by groups for cattle specialization 95

Appendix 10: ROE by groups for grain specialization 99

Appendix 11: ROA by groups for dairy specialization 103

Appendix 12: ROA by groups for cattle specialization 107

Appendix 13: ROA by groups for grain specialization 111

Appendix 14: Cost of debt for farms of Isère 115

Introduction

Food safety and agricultural commodities prices played a great role in the recent Arab spring (Breisinger, Ecker, & Al-Riffai, 2011). The Arab spring, also called the Arab revolution, is a wave of protestations that started in Tunisia in December 2010 with the suicide of a student who couldn't pay for the fruits he was trying to sell in the street as a subsistence job. This situation is not a surprise for many observers, as the farming business is facing once again one of its historical challenges: feed the world and its increasing population. The access to the essential commodities, such as energy and food, is taken for granted nowadays in the developed countries. However, the land resource is finite and even subject to a regular contraction due to urbanization and salinization. Moreover, the increasing consumption of meat is less efficient in terms of global productivity than the direct human consumption of grain. The remarkable rise of the soft commodities prices in 2007/08 represented just a reminder of the importance of agriculture for humanity and the inelasticity of this market. The world market prices are oriented in the same direction in 2012, and the records reached in 2007 are getting closer due to a severe drought in the USA (Damgé, 2012). The OECD and the FAO consider that these high prices will become usual in the next decade (OECD-FAO, 2010).

Some new challenges arose more recently, and they are not easy to meet. The public opinion demands farmers to develop new forms of production more sustainable and respectful to the natural environment (Faure & Compagnone, 2011). Moreover, they have to maintain an economic activity in rural areas (Mundler, Labarthe, & Laurent, 2006). To achieve these challenges, the advisory services play a great role to deliver the best advices, for agronomical innovation as well as economical consulting (Faure, Desjeux, & Gasselin, 2011).

However, some financial consultants for farming businesses use low discount rates. This empirical observation has been made in a company member of the network of CERFRANCE, the leading accounting network in France, even ahead of the big four for the French accounting market. This leading position is due to the really high market share in agriculture and small businesses. Someone could have expected to find really innovative financial methods to produce better financial advices for farmers, but the consultants do not use the NPV method a lot, mainly because of the lack of information about the calculation of appropriate discount factors. Research in the field seems to be not sufficient to promote this method among consultants.

The purpose of this paper is therefore to do an exploratory research about the Weighted Average Cost of Capital (WACC) methodology and its applicability to the agricultural sector. To reach this main objective, many other research questions are addressed:

- Which methods are used by the practitioners to determine actualization rates for farming project in France?

- Does leverage has an impact on the financial performances of farms?

- What is the optimal structure for a small or medium farm, considering the risk of financial

distress linked with high leverages?

This research is based on the existing work produced by other authors, mainly in other economic sectors, but also on primary data extracted from the database of the CERFRANCE Isère. This exploratory work is subject to caution, because its subject hasn't been enough covered yet for the agricultural sector and the assumptions taken have a strong impact on the results. However, it is essential to improve the current knowledge about the cost of capital and the actualization rates in

agriculture. This paper aims to rough out this subject and to improve immediately the practices of the agricultural consultants of the CERFRANCE.

1 Significance of the Research

«Risk is like love; we all know what it is, but we don't know how to define it»

J. STIGLITZ

According to Allen, Myers & Brealey (2008, pp. 966-967) the capital asset pricing model (CAPM) and the Net Present Value (NPV) are ones of the most important concepts in finance. The CAPM allows investors to identify the non-diversifiable risks, by measuring the impact of a change in the aggregate value of the overall economy on the value of their investments. The NPV on the other side helps managers to choose between different projects, or even to choose to kill or not a project. The CAPM relies on the calculation of betas, and NPV relies on the choice of the discount factor. However, there are many ways to evaluate this discount factor. First, when a manager has many options or projects, he can set the discount factor at the opportunity cost of capital which is the profitability of the best project to evaluate each project. But this method does not take into account the risk associated with each projects and is not helpful when a manager has only one project, or when he just wants to evaluate the economic profitability of its company at a fair market price. In the last case, the appropriate way to set the discount factor is the CAPM and the calculation of the Weighted Average Cost of Capital (WACC) formula (Brealey, Myers, & Allen, 2008).

The WACC formula is of particular interest for our concerns, because it gives a good estimation of the company cost of capital, which can be reduced by financing the company using the optimal amount of debt as the traditional approach suggests (Brealey, Myers, & Allen, 2008, p. 504). Figure 1 illustrates this theory.

Figure 1: The optimal amount of debt to reduce the company cost of capital Source: Principles of Corporate Finance

In the French farming industry, the NPV methodology is nearly never used. When this method is
used, discount factors are chosen without clear explanations, and we can observe important

variations between practitioners, even in the same business unit. Is it because everyone considers that there is merely no risk in farming business? However, such hypothesis does not make sense considering that a farm manager needs to handle the weather risk, the crop's pests risk and the price volatility risk. Is the WACC methodology not applicable in farming business, or just the NPV method is not enough known and risks related to farming business poorly assessed?

1.1 The Farming Business in France

French agricultural exportations represent USD 17.5 billion (€ 13.2 billion) (Agreste, 2011; IMF.Stat, 2012), making France the 14th biggest exporter of agricultural products in 2010 (WTO, 2011). Regarding food, France is the number three exporter in 2010, with USD 47.8 billion (€ 36.2 billion)(Agreste, 2011; WTO, 2011; IMF.Stat, 2012). France is also the number one agricultural producer in Europe, with € 62.0 billion in 2009 over a global European production of € 327 billion (Agreste, 2010). This high level of production and exportations is allowed by two factors: productivity and surface. The total cultivated area (TCA) in France represents 29.3 M ha in 2009, over the total 54.9 M ha of France (Agreste, 2010), and wheat yields of 7.04 T/ha are 88% superior to the world average of 3.74 T/ha (FAO, 2012).

However, the high level of production in France is not due to the size of the farms, because the average farm in France is only 55 ha in 2010 (Agreste, 2011 b) against 350 ha for US farms (Ambassade de France à Washington, 2009). The diversity of production is really high in France as we can see in Figure 1, as no production represent more than 16% of the overall production in value.

Grains

Oilseeds and soyabean Sugar beet

Other plants

Fruits & vegetables Wines

Forage crops

Cattle

Poultry

Milk

Source: Insee 2011 in Billion euros

Agricultural production in France, 2010

4,1

10,1

8,8

7,3

3,6

10,5

9,4

7,8

2,8

0,8

0,4

Figure 2: Agricultural production in France, 2010

Regarding Isère, the production is also really diversified as we can see in Figure 3. The overall production represents € 516 million for 2010, for a total GDP of around € 33 billion in Isère for 2009 (CCI Rhône Alpes, 2011). The average size of the farms in the department is smaller compare to France with only 38 ha in 2010 (Agreste, 2011 c), and the TCA in Isère (241 300 ha) represents only 0.8% of the national TCA.

3,0

Source: Agreste 2011 in Million euros

21,0

75,0

Agricultural production in Isère, 2010

69,0

6,0

57,0

36,0

72,0

93,0

84,0

Grains

Other plants

Fruits & vegetables Wines

Forage crops

Cattle Poultry Milk

Other animal productions Services

Figure 3: Agricultural production in Isère, 2010

1.2 The Profitability Heavily Relies on Subsidies, Not on Investment Decision

To understand the farming business in France, it is necessary to know the Common Agricultural Policy (CAP) and its impacts on Agriculture. The CAP is the first and most important common policy for the European Union, and its cost jeopardized the overall EU budget for years (Figure 4). The CAP finds its origins in the 1950's, when European agriculture was on its knee after World War II. Charles De Gaulle considered this policy as essential to prevent major social events in France (Moravcsik, 1999), as he said that agriculture was as important as the troubles in Algeria. This Policy helped to increase the productivity, stabilized the agricultural markets, and secured food supplies of all European countries (Clipici, 2011; Zaharia, Tudorescu, & Zaharia, 2009; Howarth, 2000). According to Howarth (2000), its main objectives given at its start in 1962 were:

- Agricultural productivity improvements

- Fair incomes for farmers

- Agricultural markets stability

- Secure food supplies

- Reasonable prices for customers

Figure 4: The percentage of the EU budget allocated to the CAP Source: Eurostat, in Clipici 2011

However, the European CAP had been heavily criticized over its history, either by European partners such as the Cairns group (Australia, Brazil...) and the USA (Spencer, 2003), or by European countries also such as the United Kingdom or Germany (Howarth, 2000; Elekes & Halmai, 2009; Moravcsik, 1999). The major points of friction are the market distortions induced by the CAP, the cost of this policy and the impact on the poorest economies (Howarth, 2000; Spencer, 2003; Borrel & Hubbard, 2000; Rickard, 2001). Some authors even argued that world agricultural prices could rise by 38% if subsidies were turned off (Borrel & Hubbard, 2000). Many negotiations rounds took place over the last decades to reduce these distortions, and the CAP was reformed many times since 1992 (see Figure 5).

Figure 5: Historical Development of the CAP Source: Clipici E., 2011

In 1992, the CAP was reformed to decrease export subsidies and market supports to shift to coupled
direct payments. Those payments (direct aids in Figure 6) were proportional to the surface cultivated

by farmers, but not directly to the volume of production. This reform helped to reduce the production surpluses, and world agricultural markets distortions were reduced. However, the distortions were still significant after the reform as the overall amount of subsidies was really high, around € 35 billion in 1992 (Clipici, 2011). All the other reforms had the objective to reduce these distortions, such as the decoupled payments. New conditions for granting were also added to the CAP such as environmental protection, risk management, animal welfare, budget reduction, etc...

Figure 6: CAP spending evolution Source: DG Agri

According to the Farm Accountancy Data Network (FADN), the amount of subsidies represents 80% of the average recurring net profit for French farms (see Table 1). Besides, grain producers depend even more on the CAP than other types of farms.

 

2006

2007

2008

2009

2010

Subsidies

Recurring net profit before tax

Dependence on

subsidies

30
37

81%

29
46

63%

29
36

81%

29
21

138%

31
45

69%

Table 1: Subsidies and recurring net profit before tax in France per farm, in K€ Source: FADN (RICA)

This dependence on subsidies is negative for innovation, as all the production is oriented by the CAP and not by the market demand. Moreover, the profitability depends on the capacity of the farmer to maximize the subsidies. New-Zealand is an example of a country that stopped all subsidies in 1984 (Gardner, 1994; Saunders, Wreford, & Cagatay, 2006; Sandrey & Scobie, 1994), and enjoys nowadays an enviable situation in the world agricultural market. This small country is the world largest milk exporter (Evans, 2008), and the second largest sheep producer (FAO, 2012).

Another element limits the innovative capacity of French farms, and also their adaptability: the control of the surface. As a matter of fact the SAFERs, Societé d'Aménagement Foncier et d'Etablissement Rural, have the power to control the land market. According to their mission, these structures have 3 main objectives:

- Protect and revitalize agriculture: this objective means to help young farmers to start their

activity, avoid the concentration on big farms, and develop a peri-urban agriculture and organic farming. The major tool used here is the preemption, which allows the SAFER to break a sale between to farmer to sell the land to another farmer. Small and young farmers have the priority in this system.

- Develop the vitality of the town and country planning policy: the SAFER has the ability to

preempt some properties to help young entrepreneurs to start their activity.

- Protect the environment: this last objective is really wide, and goes from rehabilitation of

swamps to biodiversity protection and restructuration of the forests...

The result of this policy is easily presented in Figure 7. More liberal countries such as Denmark, the Netherlands or the United Kingdom have much bigger farms, with an average capital assets per farm respectively of 1 820 000 €, 1 700 000 € and 1 370 000 €. On the contrary, French farms have an average capital asset per farm below 400 000 €.

Figure 7: Average asset value per farm by member state 2007 Source: FADN

Some investments cannot be amortized in such small structures, like methanation units for milk farms or precision farming for field crops. For a methanation unit, which allows to reduce the impact on the environment by producing electricity, the total investment is around 1 200 000 € for 250 kWe (AREC, 2011). This type of unit is not very important in terms of productivity and can be 10 to 20 times bigger, but is already out of reach for a small farm.

1.3 A New Approach to Find

The CAP has created an artificial market for French farmers. As seen in Figure 8, the guaranteed wheat price (in blue) was well above the world market price (in red) before 1992. The price volatility of agricultural commodities has always existed, with a fall from 165 €/T in 1988 to 80 €/T at the end of 1990, but French farmers were protected from this high volatility.

Figure 8: Volatility of the price of wheat after application of Agenda 2000 Source: Offre et Demande Agricole

In fact, French farmers were affected by the world market price in 1995/96, when the prices reached 180 €/T, and in 2007 when all agricultural commodities prices soared... The price fall of 2008 and the new peak in 2011 and 2012 illustrates well that the volatility is not coming to an end.

Another element will modify the market for French farmers: the end of the quotas for milk and sugar beet producers in 2015 (Pinson, 2012; Hénin, 2011). Nowadays, the volume of production is limited for each European milk and sugar producers, in order to avoid surpluses and price falls. This suppression of the quotas will modify the milk and sugar market, and price volatility will surely increase ( terrenet.fr, 2012; Hénin, 2011).

Moreover, many other countries are more competitive than France for milk production (Rhein, 2009). The example of Germany is often cited in France because the price of milk is lower, farms are bigger, more productive and specialized (You, 2010; Waintrop, 2010). However, even Germany is considered as non-competitive in the world market (Rhein, 2009). In fact, both countries are declining in terms of market share for total agricultural products exportation (see Figure 9). However, the case of France is more complicated, as from the second world exporter in 1995 we lost market shares almost continuously because of a lack of competitiveness (Momagri, 2012).

Figure 9: Fluctuation of the share of agro-food exports by key countries in the global volume of agro-food exports

Finally, the CAP shall be reformed once more in 2014-2015: the decoupled payments should converge at the European average and more dependant to environmental conditions (Le Boulanger, 2011; Hénin, 2012). The effect will be important for French producers, as the decoupled payments will be reduced on average, particularly for grain producers. Moreover, these payments will be conditioned to environmental obligations which are not always respected so far.

Therefore, this is essential for farmers to analyze the source of their actual profitability if they want to face the challenge of a globalized agriculture combined with the reduction of subsidies! It is necessary to evaluate each projects and farms with more accuracy than before, because all indicators suggest that the market risk will increase.

2 Literature Review

2.1 Risk

2.1.1 Risk in Agriculture

The risks associated with the agricultural activities can be divided into 4 types of risks according to the OECD (Harwood, Heifner, Coble, Perry, & Somwaru, 1999; Holzman & Jorgensen, 2001):

- Market/price risks: these risks can affect directly a farm or a region through the variation of

the prices of land, inputs, production...

- Production risks: hail or frost can affect directly a plot, but other climatic hazards can have a

strong impact on production. Technology evolution also can affect the production risks, by

controlling it (dams to prevent flood, anti-hail net, drainage, phytosanitary products...)

- Financial risks: some farmers have non agricultural revenues. A change in these revenues may affect the profitability. Farms are also exposed to financial risks through the interest rate on their loans.

- Institutional or legal risks: these risks are really hard to measure, as it goes from the

responsibility risk to the legislation risk and the CAP reform risk.

Overall, the risks in agriculture are really wide, and farmers cannot handle them all by themselves. Production risks for example cannot be covered only by insurance, because reinsurance companies refuse to cover the risk of insurance companies for this type of multi-risks insurance. The portfolio risks of these insurance is ten times higher than more conventional sectors (Chambers, 1989; Miranda & Glauber, 1997). Therefore, policy makers should integrate these elements into the CAP reform of 2014-2015.

On the other side, the market risks can be managed by the farmers themselves, the futures markets can be used by farmers to reduce their risk exposition (OECD, 2009). Actually, only 1% of all transactions made on the futures markets are effectively physically traded, but not many farmers use this tool (Rose, 2008). Even if the futures markets are not an efficient tool to predict the future physical prices, they remain efficient to reduce the risk exposure (OECD, 2009).

Regarding financial risks, it is harder to handle with agricultural policies or tools such as the future markets. However, farmers can with the help of their consultants estimate their financial exposition and reduce it by choosing fixed interest rates when they subscribe new loans. It is also possible to act on the optimal leverage level by choosing the appropriated financing decision for new projects or equipments.

Finally, institutional or legal risks are really hard to predict or reduce. For responsibility risk of course, it can be reduced through insurances and the choice of equipments in accordance with the legislation. However, institutional risks such as CAP reforms cannot be easily managed by farmers themselves.

2.1.2 Risk Premium

The equity risk premium, also called the risk premium, represents four different concepts according to the literature as stated by Fernandez (2010):

- Historical equity premium (HEP): it represents the difference between the historical results

of the stocks over government bonds or treasuries. This value really depends on the time-frame chosen. Damodoran (2011) recommends using a long time frame, as well as the French tax authority (2006).

- Expected equity premium (EEP): it is the expected difference between the same elements

than the HEP. However, in this case the historical results are not the base of the calculations. - Required equity premium (REP): this method is used by investors to estimate the

«incremental return of a diversified portfolio (the market) over the risk-free rate».

- Implied equity premium (IEP): it is in fact the REP assuming the market price of the stock

represents the fair and true value of the stock.

Out of the 150 textbooks studied by Fernandez (2010), 129 authors consider that REP has to be equal to EEP. This is the most common assumption as the CAPM states this assumption too, and 119 authors recommend using the CAPM (Fernandez, 2010). Out of those 129, 82 consider that EEP is also equal to HEP. We can consider that this position is the most commonly used, as 27 authors do not explain in their textbooks how they obtained their EEP (see Table 2). The overall recommended risk premium average is 6.7%. However, we can see than the difference between the min and max is really wide, ranging from 3.0% to 10.0%.

Table 2: Assumptions and recommendations of the 129 books that assume that REP = EEP. Source: Fernandez 2010

For our calculation, we will retain from this meta-analysis the minimum and maximum average for the most common assumption which is EEP = HEP. Therefore, the most probable range for the risk premium would be [5.5% : 6.7%] for the geometric mean and [7.0% : 8.5%] for the arithmetic mean. The geometric mean is presented by many authors as less dependent of the fluctuations and somehow more reliable. Fernandez itself recommends 3.8% to 4.3% for Europe and US.

According to the famous authors Brealey & Myers with Allen in their editions of Principles of Corporate Finance, they recommend to use REP between 5.0% and 8.5% (see Table 3). However, the major tendency is to use a REP between 6.0% and 8.0%.

Table 3: Equity premiums recommended and used in textbooks. Source: Fernandez 2010

What is more interesting is the reduction in the recommended risk premium in the textbooks over the last years. Figure 10 shows that this reduction was drastic between 1988 and 1993, and after 2002. Nowadays, the recommendation seems to be just below 6%. It is common to see in the literature some claims that these EEP are overvalued at 5.0 or 6.0%. Claus & Thomas (2001) states that after 1985 it is not realistic to take long period of centuries, and that the EEP is closer to 3.0% in reality. For France, for the period 1985-1998, they consider that EEP should be 2.6%, basing their calculation on the abnormal earnings approach. Quiry & Le Fur (2012) come to a similar conclusion even with a long time frame (111 years) with a 3.2% HEP.

Figure 10: Moving average (last 5 years) of the REP used or recommended in 150 finance and valuation textbooks. Source: Fernandez 2010

The French tax authority does not have a clear position on the subject (Direction Générale des Impots, 2006), however it gives the precision that someone could use the HEP of a long-run period: 5% over the last 100 year in France. For the French market, Damodoran (2011) and Salomons & Grootveld (2003) recommend to use 4.91%, using a 1976-2001 timeframe. The Credit Suisse in its Global Investment Returns Sourcebook of 2011 (Damodoran, 2011) recommends to use 6.0% for France (for a timeframe of 1900-2010, including the 2008 crisis for short-term Governments). Table 8 summarizes the different HEP obtained for the French market. Most of the observations are in the range between 3.8% and 5%.

Claus & Thomas (2001)

Direction Générale des Impôts (2006)

Damodoran (2011)

Salomons & Grootveld (2003) Quiry & Le Fur (2012)

Fernandez (2010) for European markets

1985-1998 2.6%

100 years 5.0%

1900-2010 6.0%

1976-2001 4.91%

1900-2010 3.2%

Long term 3.8% to 4.3%

Average

4.26%

Table 4: Historical Equity Premium for the French market

The small-capitalization premium is subject to controversies in the early literature (Damodoran, 2011), however it seems that overall we can observe some significant premia for small or micro-capitalization. Figure 11 represents the premium observed for micro-capitalization compare to all-US stocks. We can easily see that during the 1980's and the major part of the 1990's the small firms did not perform more than the overall US stocks. However, since 1999 it seems that this small firm premium is starting again to be observed.

The other element we can learn from Figure 11 is that the variability is really strong. Therefore, it explains easily that we find really different positions in the literature, ranging from no premium to extreme values. For example Bergstrom, Frashure & Chisholm (1991) consider that for the French market this premium is 8.8% for a timeframe of 1955 to 1984. For the same time frame, they found only 3.0% for the German market.

Dimson & Marsh (2001) considers that the micro-cap geometric premium is 5.4% over all equities for a time-frame of 1955-1999 in UK (the EEP obtained is 6.2% for all equities for the same time frame). This premium for the US market seems to be lower according to Leonard (2009), with a premium ranging from 2.38% to 2.47% for US micro-cap stocks over the overall market (time frame: 1938- 2007). This micro-cap US premium has increased after the Dotcom crisis, reaching 5.63 to 5.83% for the period Oct-2002 to Sept-2007 (Leonard, 2009). Israelsen (2009) found 1.81% premium for small-cap compare to mid-cap and 2.8% for small-cap compare to large-cap for a 1980-2008 time frame. Switzer (2010) considers that this premium has even increased more when we consider a 2001-2010 time frame reaching 7.74% in the US. For the 1926-2010 time frame, this premium is 2.03% for the US. For Canada, this small-cap premium seems to be lower, ranging from 0.10% (1987-2010) to 4.12% (2001-2010). The most important finding of this work is that small-stocks have better premia during the periods of recoveries (after the dotcom crisis, the 2008 crisis, or the 1975 crisis). This is particularly essential today, as the overall economy is still recovering from the 2008 financial crisis.

Figure 11: Small firm premium (bottom 10% of market cap in US) between 1927-2010. Source: Damodoran 2010

Overall, the small and micro-cap premium observed ranges from 0.1% to 8.8%, depending on the market and the time frame studied. However, it seems that this premium is increasing since 2000, because the economy has experienced two important crisis since 2001. Moreover, it seems that the premium seems to be higher in France from what have been observed by Bergstrom, Franshure and Chisholm (1991). Table 5 presents a summary of our findings in the literature.

Authors

 

Time frame

Small and micro-cap premium

Bergstrom, Frashure

&

1955-1984, France

8.8 %

Chisholm (1991)

 
 
 

Israelsen (2009)

 

1980-2008 US

1.8% to 2.8%

Leonard (2009)

 

2001-2007 US

5.63% to 5.83%

Leonard (2009)

 

1938-2007 US

2.38% to 2.47%

Switzer

 

2001-2010 Canada

4.12%

Switzer

 

1926-2010 Canada

0.10%

Switzer

 

2001-2010 US

7.74%

Switzer

 

1926-2010 US

2.03%

Mean

 
 

3.97%

Table 5: Historical Equity Premium for small and micro-cap

For the recent time frame, which is the most appropriate time frame considering the fact that the economy is in recovery since the major crisis of 2008, authors considers that the HEP is ranging from 4.12% to 7.74% (Table 5, Switzer, 2010). Moreover, this choice is supported by the fact that many analysts agree on the fact that the soft commodities prices should remain at a higher price in the coming years (OECD-FAO, 2010). The expected market return should increase in the following years.

2.2 The WACC Theory

2.2.1 The Importance of the WACC Theory.

The WACC is not considered easy to compute, but is one of the fundamental elements in modern finance (Quiry & Le Fur, 2012; Brealey, Myers, & Allen, 2008). The NPV calculation and the determination of the value of the stock are based on the results of the WACC, which underlines its importance (Quiry & Le Fur, 2012, p. 699). The underlying theory of the WACC is the CAPM. This model objective is to measures capital assets value depending on the risk and the expected return of the company. According to Brealey, Myers & Allen (2008), the after-tax WACC represents more the reality of the company and can be described as follow:

E

WACC = K(D) . (1 -- Tc) r, + K(E)

K(D): cost of debt, at market value.

Tc: corporate tax rate.

D/V: total debt divided by total firm value. K(E): cost of equity.

E/V: total equity divided by firm value.

The cost of equity in the WACC theory has to be calculated using the CAPM, which can be described as follow:

E (Re) = Rf + f3. [E (Rm) -- Rf]

E(Re): expected return on equity. It corresponds to K(E) in the WACC formula [3: beta. It represents the risk.

E(Rm): expected return of the market in which the company operates. See part 2.1.2 page 20 on risk premium for the literature review dealing with risk premium. However, the risk premium is defined as the premium obtained compare to a risk-free asset.

Rf: risk free rate of return. The risk-free rate of return can be estimated by the long-term government bond yield (Brealey, Myers, & Allen, 2008; Quiry & Le Fur, 2012).

2.2.2 The Betas for Micro-Capitalizations

In this research, financial techniques are used to estimate the risk for listed companies to apply it for non-listed small companies. The bias introduced is consequent, as the beta has a negative relationship with the size of firms (Binder, 1992; Al-Rjoub, Varela, & Hassan, 2005; Shomir, Pat, & Jeong-gil, 2011). Therefore, small farms should have a higher Beta compared to listed companies operating in agriculture, as smaller firms present little or no diversification (Drew & Veeraraghavan, 2003). Table 6 presents the results found in the literature comparing large capitalizations and micro-capitalizations, which can be considered as the right comparison between the listed companies operating in agriculture which have activities in many countries and the small farms of Isère.

Authors

Time frame

Number of groups

micro-cap Beta

premium

Al-Rjoub, Varela & Hassan (2005)

1970-2000

1st decile vs 10th d

 

0.51

Al-Rjoub, Varela & Hassan (2005)

1982-2000

1st decile vs 10th d

 

0.36

Al-Rjoub, Varela & Hassan (2005)

1990-2000

1st decile vs 10th d

 

0.24

Shomir, Pat & Jeong-gil (2011)

1980-2003

1st quartile vs 4th q

 

0.21

Bhardwaj & Brooks (1993)

1926-1988

1st group vs 5th g

 

0.72

Chan & Chen (1988)

1949-1983

1st group vs 20th g

 

0.69

Dongcheol (1993)

1926-1990

1st decile vs 10th d

 

0.58

Jegadeesh (1992)

1954-1989

1st group vs 20th g

0.32

- 0.62

Mean

 
 
 

0.47

Table 6: Market capitalization effect on the beta for micro-capitalization

As presented in Table 6, the effect of size is quite important on the beta. It is important to notice that the standard deviation also increases with the beta (Drew & Veeraraghavan, 2003; Dongcheol, 1993; Chan & Chen, 1988). From the results found in the literature, for micro-capitalizations compare to large capitalizations, the beta increases on average by 0.47, which is really significant regarding the impact of the beta on the WACC formula.

2.3 Discount Rates and the CAPM Used in Agriculture

The CAPM and the actualization rates have already been used in agriculture long ago. The Capital Asset Pricing Model has been tested for example to test the farm real estate market by Barry P. J. in the 1980's (Barry P. J., 1980a). His conclusions were that the low betas (0.19 for the US) observed for farm real estate made this investment valuable as a diversification tool for portfolios and that the required rate of return for the US farmland was 8.76%. Other studies have been done on the same subject to test the interest for investors to diversify into farmland (Hanson & Myers, 1995). It seems that these conclusions remain true nowadays, as the foreign investments in farmland increased strongly after the rise of the grain market price in 2007.

Since then, the diversification into agricultural commodities have been tested either. The results are interesting, because thanks to negative correlation with other markets, this diversification reduces the risk and are considered as profitable for investors (Sminou, 2010). The opposite have been tested also, to see if farmers who own their lands have an interest to diversify their investments with traditional financial tools (Nartea & Webster, 2008). According to their results for New Zealand after the deregulation of the market, the expected return of the farmers would increase thanks to diversification, even if the expected return on farmland due to capital gains (9.83%, 1988-2003) were higher than the expected returns of ordinary shares (5.59%, for the same period). This potential gain comes from the good diversification of the portfolio which reduces the risk but not the overall return according to the authors.

On the other side, some studies have been done in agriculture using the NPV method. Most of them
do not explain clearly how they chose their discount factors. For example, investments in peach
orchards in India have been tested with three discount rates proposed by NGOs: 6, 9 and 12% to

compare the NPV method with the amortization method (Gangwar, Singh, & Mandal, 2008). Finally the authors retained the 12% discount factor.

Barry P. J., at the beginning of the 1980's, has done a research to evaluate the impacts of government support price programs on the financial structure of the farms of US (Barry P. J., 1980b). In his calculation, he used three pre-tax discount factors of 10%, 12% and 14%, corresponding to an after tax discount factor of 7.8%, 8.16% and 7.0% respectively. However, here the author selected the three pre-tax discount factors arbitrarily.

More recently, Johnson M. R. (Johnson, 2002) presented its method to calculate the capitalization rate for Kansas in a case study. He starts first with the loan rate offered to farmers, and then he adds a discretionary increase based on the risk premium and the tax effect. He obtains a capitalization rate of 14.71% in 2000 (with a loan rate level of 8.94% and a corporate tax rate at 30%). Using his method we would obtain around 10.5% of capitalization rate for France (with a loan rate level of 3.5% and a corporate tax rate at 33%). The discount factors found in the literature are therefore ranging from 7.0% for the lowest, to 14.71% for the most recent study found from the US. However, it would be hard to apply these methods to the French context, because most of these discount factors are somehow based on discretionary risk premiums or they are chosen totally arbitrarily.

3 Research Methodology

One of the main objectives of this research is to give new decision tools for farmers' consultants to evaluate the expected profitability of their customers' projects or farms. In this section, the preparation of this research will be presented.

3.1 Survey Construction

A small survey designed to collect different perspectives from accounting and consulting companies, mainly from the network CEFRANCE:

- First, the network of the CERFRANCE represents more than 50% of the market of farming consulting. The remaining market shares are shared by many different organizations with no comparable size to the leader.

- Then, the methodologies used in the different CERFRANCE are based on the same recommendations from the head of the network (the CNCER). Therefore, we can obtain a clear view of the methodologies used by consultants without a large survey.

3.1.1 Questionnaire

The purpose of this questionnaire is twofold:

- Confirm of reject my initial observation which stands that farm consultants use discount rates based on loan rates plus a risk premium around 2%.

- Test the willingness of farm consultants to use the WACC to approximate the cost of capital of their customers in their region.

To test these hypotheses, an internet-based survey of 9 questions was sent to 25 consultants in France. In order to improve the answer rate, the questionnaire was sent only to farm consultants who were linked to me in a professional social network (Viadéo® or Linked In®). As these consultants are all French, it has been written in French (see Appendix 3).

This choice of consultants introduces a first bias: mostly young professionals use professional social networks. However, as young consultants are less experimented, it is common sense to believe that they are taught with the last methods developed or chosen by their consulting companies. Therefore, this choice of population was motivated by its efficiency to collect up to date information at the minimal cost.

3.1.2 Results Analysis

As the number of participants was reduced, analysis of the results was only descriptive (percentages, average and medians) and no statistical tests were performed. However, these results were sufficient to be conclusive.

3.2 Historical Analysis of the Results of Farms in Isère

3.2.1 Data Collection

The data used in this part of the research have been extracted from the database of the CERFRANCE
Isère. This database has 5 years of results available, for 2 000 farms operating in Isère. CERFRANCE
Isère is the leading accounting company in the area, serving more than 50% of the 3 999 professional

farms of the department (Agreste, 2011). Therefore, we can consider the results extracted from this sample as representative of the population of farmers of Isère. This is why the inclusion criteria for the extractions were as wide as possible:

- All companies which are taxed at the «bénéfice réel», which means that they are obligated to produce a balance sheet, a profit and loss statement and a cash flow statement to the tax administration. All the companies with a turnover above 76 300 € are concerned by this obligation. This inclusion criterion is obvious because without books accounts profitability or leverage ratios cannot be calculated.

- All companies that are taxed under the «Bénéfice agricole» regime, which means that more than 70% of their turnover comes from agricultural activities. This inclusion criteria has been chosen to minimize the impact of forestry, environmental services and other activities practiced by some farmers.

The software used at the CERFRANCE Isère (Panda) cannot produce extractions for several years at one time neither an extraction with all the data about a year in one file. Therefore, two extractions were made for each year:

- One excel sheet with all the balance sheet information available (146 data per farm, from

surface, number of labor units to asset valuation. See Appendix 1 for illustration),

- A second excel sheet with all profit & loss and cash flow statement information (143 data per farm. See Appendix 2 for illustration)

Years of extraction

 

Number of farms after extraction Number of farms selected

2006 2007 2008 2009 2010

1 060 620

1 231 620

1 199 620

919 620

1 054 620

Table 7: Primary data for the analysis of farms' results in Isère

As presented in Table 7, the number of companies available is not constant. This effect is due to the information system structure: depending on the way the accounting books are send to the tax administration, the results of the farm are automatically or manually uploaded into the database. The only exclusion criterion was to keep a constant sample over the five years. Therefore all farms which did not have results for 1 or more year were excluded. This exclusion criterion reduced the population of observed farms to 620.

The 620 farms have been classified into 7 specializations, based on the production most observed in the department of Isère (see Figure 12). A farm generating 2/3 of its turnover from one main production was considered as specialized in this production. Cattle ranching corresponds to the beef production (sheep or other ruminants were considered as diversified production as they do not represent enough farms to constitute a group). Walnut productions, vegetables and fruit growing

were not analyzed as the number of farms observed was too low to make groups based on their financial leverage.

256

13 28 11

45

141

Dairy farming

Grain

Cattle ranching Diversified production Vegetables

126 Walnut production

Fruit growing

Figure 12: Number of farm for each specialization

3.2.2 Data Processing

The raw data extracted from the database were to be analyzed. First, the self-employed labor unit costs are not always taken into accounts into the books, due to accounting regulations. Therefore, the data have been transformed to include a labor cost for each labor unit at the SMIC (minimum salary level in France for a FTE, Table 8):

Years

2006

2007

2008

2009

2010

SMIC before social tax per month

1 254.28€

1 280.07€

1 308.88€

1 337.70€

1 343.77€

Table 8: Cost of each labor unit per month for the SMIC

Another element had to be transformed in order to have reliable results: social security contributions. This cost is tax deductible, but depending on the legal form of the company it can be treated outside of the balance sheet and profit & loss statements. The calculation of these contributions depends on many factors, such as the year of installation of the farmer, his average profits for the last 3 years (an option exists to calculate it only with last year results). An important element is that only farmers who operate in limited liability legal form have the possibility to treat these contributions off-balance sheet. Their results are most of the time significantly higher, justifying that the added contributions should be on average higher than the contributions in books. The contributions were calculated based on the results of the annual accounting results (in reality, the years' results are taxed over 3 years: N-1, N-2 and N-3 for most farms). This recalculation introduced a bias in this research, but the accounting principles of the company have been adapted to avoid this bias for future researches. For 2012 and after, all social contributions will be recorded in the database.

Years

# of companies without

social security contribution in books

Average contribution added

 

Average contribution in
books

Average

contribution for the population

2006

77

(12%)

7

061

7

193 €

7

176 €

2007

110

(18%)

11

871

7

009 €

7

871 €

2008

139

(22%)

12

633

7

371 €

8

551 €

2009

153

(25%)

10

124

8

230€

8

697 €

2010

162

(26%)

6

539

7

488 €

7

241 €

Table 9: Social security contributions

3.2.3 Research Questions

The main goal of this analysis is to test the traditionalist approach which states that an optimal level of debt helps to reduce the WACC (Brealey, Myers, & Allen, 2008, p. 485). If this theory holds in agriculture, a positive link between the debt level and profitability or productivity should be observed. Therefore, the research question can be stated with two hypotheses.

The dependant variables tested are:

- ROA: return on asset.

- ROE: return on equity.

The Independent variables tested is:

- Level of debt of the company.

First hypothesis:

H0: ROA and ROE tend to increase with the level of debt of the farms in Isère.

H1: ROA and ROE have no positive links with the level of debt of the farms in Isère

Second hypothesis:

H0: Financial distress may occur when leverage increases, reducing ROA or ROE. H1: No signs of financial distress are clearly observed when the leverage increases.

The second hypothesis is common sense. However it has to be tested for agriculture. Milk production was really stable for years, because the market was controlled thanks to the system of quotas. Therefore, as the volume of production is somehow easy to control, financial performances of the farms are easy to predict, limiting strongly the risks of financial distress.

3.2.4 Data Analysis

The WACC theory assumes that there is an optimal debt level to reduce the cost of capital of the company and then increase its overall performance (see Figure 1 page 12). Therefore, ROE and ROA were analyzed by groups of leverage in order to verify if this theory holds in agriculture. The groups more leveraged should have better results in terms of ROE and ROA than low leverage groups. We should also observe more variability and/or lower results for groups with really high leverages like group 1 or 2 (Table 10). This observation would be considered as financial distress.

As presented in part 3.2.2, the data had to be processed to be exploited. The ROE, ROA and leverage also have to be calculated considering the specificity of the accounting standards used at the CEFRANCE Isère. Therefore, ROE and ROA were calculated as follow:

E

P -- w E + P c P -- w

-- P

L

- P: Profit,

- Wc: cost of labor (each FTE self-employed unit of labor received a cost of the SMIC),

- E: shareholders equity. As no financial market exists to estimate a market value of farm's
equity in agriculture, the book value had to be retained,

- Pca: partners current accounts. These amounts are considered as debt from an accounting perspective. However, it is not considered by convention as a debt but as shareholder's equity from a consultant perspective because these «partners» are the shareholders,

- A: total assets,

- L: leverage,

- D: total debts. The value retained was book value, as the database do not includes

information allowing a recalculation at market value. However, the interest rate was analyzed at the market value,

- tl: total liabilities.

Leverage

L < 20%

20%= L <40%

40%= L <60%

60%= L <80%

80% < L

Group #

5

4

3

2

1

Table 10: Groups of debt level

In order to compare the groups, a set of statistical tests were performed with the software Statgraphic Centurion:

- Shapiro Wilk test and Kolmogorov test for the normality of the datasets. Density trace and histograms were also used for sample sizes bigger than 2 000 farms (Shapiro Wilk test cannot be performed for this size of samples). Normality was tested before each test.

- One-way ANOVA and multiple range comparison to compare the means of the different groups when the assumptions of normality and homogeneity of variance were verified.

- When the assumption of normality or homogeneity of variance was rejected, a non parametric test was used (Kruskal-Wallis). As normality and equivalence of the variance are the base-assumptions of the ANOVA, the means cannot be compared if these two hypotheses are not verified. The Kruskal-Wallis test compares the rank of each observation to overcome this limitation, and tests if the medians are significantly different or not.

- Mood's and Median test to analyses the medians. It is a non-parametric test which compares the distribution of each sample around the overall median of all the groups. Therefore, this test can estimate if the medians are significantly different.

First, the effect of time was tested. As time has an effect on ROE or ROA, separate tests were performed for each year in order to isolate the time effect and analyze the effect on leverage more accurately. Then, the effect of specialization was tested. As specialization has an effect on ROE or ROA, separate tests were performed for each specialization to improve the robustness of the results. The risk error (alpha) represents the risk to reject the null hypothesis tested when it should be

considered actually true. Because 10% is an acceptable level of risk error in finance, this alpha was used for all statistical tests in this research.

3.3 Bond Yield Plus Risk Premium Model

The first method to estimate the capitalization rate was the method used by the French tax authority. Farming consultants have to keep in mind that their valuation methods can be used as the base of the calculation for a capital gain on the sale of a company. The tax authority therefore has the right to modify the tax base if it considers that the results do not represent a fair value at the time of the transaction. The most common tax adjustment in those cases is the hidden donation, when the tax authority considers that the company or its shares (not publicly tradable in the case of most farms) are undervalued. This is why it is necessary to include the methods used by the administration in this study. In reality, this method is based on the CAPM, as the risk premium is the difference with the total expected return minus the risk-free return. The actualization rate formula can be presented in this way:

A: Actualization rate,

Rf: Risk free rate,

â: Beta,

Rp: Risk premium for the French market.
3.3.1 Risk-Free Rate

As presented by Sharpe (1964) it was assumed that everyone can borrow or lend its funds at a «pure rate of interest». This risk-free rate is considered by the tax authority as the government bonds rate (Direction Générale des Impots, 2006), and was assumed in this paper as equivalent to the French government debt reference rate, given by the French central bank (a 12 month average of the 10 years bonds monthly average was used).

3.3.2 Beta and Risk premium

The risk free rate has to be increased by a risk premium, justified by the necessary supplementary yield that a risky asset needs to deliver compare to a pure interest rate. The tax authority recommends using the historical risk premium observed in the French economy, which can be increased to consider the size of the company studied. For the calculations, the references of the tax authority were used, and an increase to consider that we are dealing with micro-capitalization.

The first bias that arises is easy to consider, as stated by Damodoran (2011), would be to include 2008 stock results in our time-frame: the major crash observed this year would diminish the results significantly, leading investors to think that the risk would be lower after the 2008 crisis than before. However, from a pragmatic point of view, this could be considered as logical: there is less risk of a major historical crisis after it happens than before, like there are less risks of a centenary flood after ones happen.

The Beta is used to consider the risk relative to the company and the sector in which it operates (Direction Générale des Impots, 2006). Once again, the tax authority produced some recommendation and we will base our calculation on it. We will compare this beta with the beta observed by another author for agriculture in France.

3.4 WACC Estimation

Regarding the WACC estimation, 2 types of data will be used: primary and secondary data. For primary data, we will use:

- Results of the farms from Isère extracted from the database of the CERFRANCE Isère to calculate the weighted average leverage by specialization. The time frame of the dataset is 2006-2010, with 620 farms. The characteristics of this dataset are presented in detail in part 3.2.

- Sample collection of 46 recent loan's rate for all specialization in the database of the

CERFRANCE Isère, to determine the market value of the cost of debt.

Regarding secondary data, we will use:

- The market capitalization and the beta calculated by Bloomberg for all the companies listed

in North America and Western Europe for the following activities: Oilseed farming, grain farming, cattle ranching and farming, poultry, fruits & vegetables. Those activities have been chosen because they are really close to the specialization of the farms of Isère.

- Risk-free rate of return: the 10 year government bonds yield calculated by the French central bank will be used. The 12 month average of this rate will be used. This choice of long term maturity was determined by the timeframe of the investments of French farms. Even in grain production, investments are made for 7 years on average, and the rotation timeframe for the crops is 3 to 4 years. For dairy farms, constructions are amortized over 20 years on average.

- Risk premium for small companies, as determined in part 2.1.2.

The extraction from Bloomberg helped to identify 41 companies specialized in agriculture in Western Europe and US (see Table 11). The grain market is globalized today, and the meat market is really influenced by world prices either. These statements are not so true for the dairy market, because the cost of refrigerated transportation is quite high regarding the price of milk. The European market is a more representative market for dairy production therefore.

The beta of each firm will be weighted averaged with the market capitalization, in order to obtain a beta for each specialization and each region. However, for grain farming we will retain the beta of oilseed and grain farming for Western Europe and the US together. The grain market is so interconnected between Europe and US that the market-risk is really close. For the grain farming in Isère, we will retain the beta of oilseed farming and grain farming, because oilseed production is considered as grain production in the French classification. For dairy and cattle farming, we will retain the beta of cattle ranching and farming of Western Europe. For diversified production and the average farm of Isère, we will retain the average beta of the 41 companies extracted from the Bloomberg database.

Specialization

Total market cap

(M $)

# of companies in western Europe

# of companies in

the US

Total # of companies

Oilseed farming

5 810

11

0

11

Grain

14 421

9

4

13

Cattle ranching

8 196

4

3

7

poultry

2 321

1

2

3

Fruits & vegetables

2 224

3

4

7

Total

32 971

28

13

41

Table 11: Number of companies studied for the Beta estimation. Source: Bloomberg

The last element of the WACC formula is the corporate tax rate. This element is really hard to determine in agriculture, because farmers have the choice between the corporate tax rate at 15% (up to 38 120 € of net income and 33.33% after) and the personal income tax with its progressive tax rate from 0% to 41%, depending on the total income per capita of the household. Moreover, the net income is also subject to social tax, at an average rate of 32.1%. Therefore, to simplify our calculation we will assume a corporate tax rate at 33.33%, which is the usual corporate tax rate in France (only companies held at 75% by persons have the benefit of the 15% tax rate up to 38 120 €). This rate is chosen in most studies about profitability for other industries in France. Therefore, it will simplify comparisons. As the French farms are small companies, it is not obvious that this tax rate could increase in the coming years. The actual government considers that SEM's are too much taxed compare to the blue chips.

4 Data Analysis

4.1 Results of the Survey

Fourteen farm consultants answered to the survey, from almost all regions of France (Figure 13). Therefore, the results are not influenced by only one CERFRANCE.

1 Region of the respondents

2

1

4

1

3

3

 
 
 

West North

North west East

South east South west

Center

Figure 13: Region of the CERFRANCE of the respondents

Regarding the age and the gender, the major part of the respondents are male consultants aged between 26 and 35 years old as we can see in Table 12.

 

18-25 y

26-35 y

35-50 y

51- and more y

Age

1

11

2

-

Male

1

10

2

-

Female

-

1

-

-

Table 12: Age and gender of the respondents

Out of the 14 respondents, 13 said that they realized project business plans for their customers. However, only 79% said that they used discount factors to evaluate the value of their customers' companies in question 5. Less surprising are the answers to question 6, where 89% answered that they used the NPV method to estimate the profitability of a project (see Table 13).

Do you realize project business plans for your customers?

Do you use discount factors to evaluate the value of your customers' companies?

Do you use NPV to evaluate the profitability of your customers' projects?

# of answers

Yes

No

NSP

14

13

1

-

14

11

2

1

9

8

1

-

Table 13: answers to question 4, 5 and 6

Regarding question 7, only 9 out of the 14 respondents answered to the question (Table 14). It seems that the most popular method among the farm consultants is to choose a discount factor based on the experience, and loan's rate plus risk premium is the second most popular method.

 

Percentage

# of answers

Loan's rate + risk premium

44%

4

T-bills + risk premium

33%

3

WACC

33%

3

Estimation based on experience

56%

5

Other (please specify)

11%

1

TOTAL

100%

9

Table 14: Answers of question 7: «how do you choose the discount factors you use?»

Regarding the most important question of the survey, the question 8, not all consultants are specialized in all the fields concerned by the question. This explains why the number of answers is reduced (9 consultants gave at least one answer for this question). However, the answers tend to confirm that almost all farm consultants use really low discount factors (see Table 15). The highest answer is 15% for grain producers. However, even this answer is surprising, because this consultant uses a 5% discount factor for cattle ranching, which seems to be a difference too important. There are merely no reasons for such a difference in terms of risk premium between the two different productions, and it is probable that this answer is only a typing error.

 

# of answers

Mean

Median

Min

Max

Grain producers

6

5,5%

4,0%

1,0%

15,0%

Cattle ranching

6

4,0%

4,5%

1,0%

9,0%

Wine production

4

2,8%

2,5%

1,0%

6,0%

Other

7

3,3%

4,0%

1,0%

7,0%

Table 15: Answers of question8 «what discount factor do you use usually for your customers? (ex given: 11%)»

Another element is surprising: means and medians are really low for all production, with discount factors equivalent to the French 10 years Government bond yield. This rate was recently at its record low at 2.422% (Dobson, 2012). The risk premiums used by some practitioners are actually lower than the 2% used at the CERFRANCE Isère. The low number of bankruptcy observed in the sector (1.6%o in 2011 for Rhone Alpes) may explain that practitioners using rates based on experience use really low risk premium. Most of the consultants do not use a loan's rate plus 2% risk premium, as this method would give results closer to a 5 to 6% interval (regarding the loan's rate observed in the sector).

 

# of

answers

percentage

Necessary for the discernment of your recommendations

7

78%

Useful to improve the discernment of your recommendations

2

22%

Not useful regarding the efficiency of the tools you already use

-

-

The WACC and the discount factors are not useful in your recommendations

-

-

TOTAL

9

100%

Table 16: Answers of question 9: «If you were proposed a tool to estimate the WACC of the farms of your region to use it as a discount factor, how would you consider this tool?»

Finally, question 9 (Table 16) implicitly confirmed that consultants are not really secured with the tools they use. 78% of the respondents considered that a tool to estimate the WACC of the farms of their region would be necessary to improve the discernment of their recommendation, and the remaining 22% considered it as useful.

The major highlights of the survey are:

- Consultants use the loan rate plus risk premium model. The risk premiums used seem to be lower than 2%,

- The most popular method it to choose estimation based on the experience,

- Most of the consultants would find necessary to have new tools to improve their methods.

4.2 Calculation of the Historical Results Using the Data from CERFRANCE Isère

The objective of these calculations is to see if the hypothesis of the traditionalist's approach is verified in agriculture: does the performance of the firm increases with leverage? Therefore, the results of firms divided in 5 groups of leverage were analyzed (group 1 high leverages, group 5 lowest leverages. See section 3.2.4 page 31 for details).

4.2.1 Normality of the Datasets

Figure 14 presents the distribution of values for ROE to compare it with a normal distribution. The distribution does not fit with the normal distribution line looking at the histogram, because the central values have an abnormally high frequency. This impression is confirmed by the KolmogorovSmirnov test, because the P-Value is lower than 0.000. We can reject with 99% confidence that ROE comes from a normal distribution. The Shapiro-Wilk test cannot be performed because the sample is bigger than 2000 values.

Figure 14: histogram of frequency for ROE

The distributions of ROE have been tested for each year's separately, and within each year's for each
production either. Over the 25 tests, ROE could come from a normal distribution only for dairy

Dstrbuon
Normal

producers and cattle producers in 2010. Therefore, only the Kruskal-Wallis and Mood's Median tests were used to analyze ROE, and the results of the Anova's were not presented.

Figure 15 presents a histogram of the frequency of the distribution for ROA to compare it with a normal distribution. From a graphic perspective, it seems that ROA is closer than ROE to a normal distribution, as the normal distribution line is closer to the frequency histogram. However, the Kolmogorov-Smirnov test P-Value was lower than 0.000 as well, therefore we can also reject at 99% confidence that ROA comes from a normal distribution.

-3 -1 1 3 5 7

Figure 15: Histogram of frequency for ROA

Same as the ROE, the distributions of ROA have been tested for each year and within each year for each production separately. All the tests performed reported the same results: ROA does not come from a normal distribution at 99% confidence. Therefore, only the results of the Kruskal-Wallis and Mood's Median tests were discussed for ROA.

4.2.2 ROE

4.2.2.1 ROE Tested by Time and Specialization

The dataset have been tested first to see if time had an effect on ROE. The results of the Mood's
median and Kruskal-Wallis tests were quite logical: as someone could have predicted, time has a
significant effect on the ROE, with really low P-Values. The years 2006 and 2009 were two years of

isra A

bad results for farmers in Isère. The price of agricultural commodities soared in 2007 and 2008,
explaining that these two years were better. The effect was similar in 2010, when commodity prices

Dstributon

recovered from the severe fall of 2009. .

Mood's Median Test for ROE by YEAR Total n = 2840

Grand median = 0,0583012

YEAR

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

2006

568

326

242

0,0195117

0,00305438

0,041546

2007

568

245

323

5

0,0910952

55

0,0759334

0,110756

2008

568

250

318

0,0837851

0,065602

0,0957051

2009

568

328

240

0,0176906

0,00193535

0,0337506

2010

568

271

297

0,0734001

0,0512998

0,091961

Test statistic = 46,0986 P-Value = 2,3492E-9 Kruskal-Wallis Test for ROE by YEAR

YEAR

Sample Size

Average Rank

2006

568

1275,31

2007

568

1547,93

2008

568

1517,84

2009

568

1287,11

2010

568

1474,32

Test statistic = 57,0073 P-Value = 1,2328E-11

Figure 16: Box and whisker plots with median notch for ROE by year (means are shown with a red cross)

Figure 16 presents the box and whisker plots with median notch at 90% confidence intervals, showing that the results of the years 2006 and 2009 were significantly lower than in 2007, 2008 or 2010. Therefore, in order to isolate the time effect, ROE has to be analyzed year by year.

ROE has been analyzed by specialization:

- Dairy production,

- Cattle ranching,

Bnd

- Grain production,

- Diversified production.

Mood's Median Test for ROE by SPECIALIZATION Total n = 2840

Grand median = 0,0583012

SPECIALIZATION

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

Dairy

705

326

379

0,0747834

0,0595245

0,0896643

Cattle

225

154

71

-0,00274151

-0,0266764

0,0162487

Grains

630

299

331

0,0769898

0,0493805

0,103964

Diversified

1280

641

639

0,0580287

0,0458927

0,0699453

Test statistic = 36,2307 P-Value = 6,69281E-8

Dairy
Cattle
Grains
Diversified

 

Figure 17: Box and whisker plots with median notch for ROE by specialization (means are shown with a red cross)

The Mood's median test for ROE by specialization illustrates that the specialization has a significant impact on the ROE, with a really low P-Value illustrating that we cannot reject the hypothesis that the medians are different at 99% confidence. The cattle production has a median ROE lower than the other productions. The P-Value for the Kruskal-Wallis test is 3,96451E-8, giving the same results. Therefore, ROE was tested separately for each specialization and each year, in order to isolate the effect of time and specialization.

4.2.2.2 ROE for Dairy Producers

The group of dairy producers represents 141 different farms studied over the 5 years. The Figure 18 presents the results of the Mood's median tests performed for the ROE of the different groups. First, we can see that the groups 4 and 5, which are the lowest leverages, have the lowest medians. Then, the group 1 shows sign of financial distress: it has the best results in 2007 with a ROE higher than 15%, but is close to be the worst group in 2006 with a ROE close to 0,0%. Groups 2 and 3 seem to be more stable and closer in terms of results, with an advantage for group 2 which is higher than group 3 (except in 2006, a bad year for agriculture in Isère as already presented, explaining that financial distress may have occurs for the most leveraged farms).

Figure 18: Median ROE for each year and each group of leverage for dairy * means significantly higher than **

In 2007, group 1, 2 and 3 were significantly better than group 5. In 2009, it was group 2 and 3 which were significantly better than groups 4 and 5. In 2010, only the group 2 over performed the group 4. For all the other years, there is no statistical difference between groups, and the differences between the medians cannot be interpreted due to high standard deviation (see Appendix 8 for details page 89).

4.2.2.3 ROE for Grain Producers

The group of grain producers represents 126 farms of Isère. The sample is constant over the 5 years.
The Figure 19 represents the ROE for each year and each group. First, group 1 over performed in
2006, 2009 and 2010. This is quite surprising, because 2009 wasn't a good year in terms of price of

grains, and we could have expected signs of financial distress. However, except in 2010, there is no statistical difference showing that group 1 is better than any other groups. This could be surprising regarding the major difference seen in the histograms, but standard deviations are really high for group 1, with a lot of extreme values (see Appendix 10 page 99 for standard deviations).

Figure 19 : Median ROE for each year and each group of leverage for grain * means significantly higher than **

In 2007, the group 2 has significantly better results than the group 5. For 2010, it is group 1 which has better results than groups 4 and 5. For 2006, 2008 and 2009, no statistical conclusion can be made because the Mood's Median and Kruskal-Wallis tests did not reveal any significant results, with P-Values higher than 10% (see Appendix 10 page 99).

4.2.2.4 ROE for Cattle Ranching

For the group of cattle producers, only 45 specialized producers were represented. As the sample is really small, the differences between the groups are in most cases not significant due to the limited number of values in each group of leverage (see Appendix 9 page 95). In figure 18 group 2 seems to over perform the other groups. Years 2007, 2008 and 2010 seem to illustrate that performance increases with leverage, up to a limit at 80% where financial distress may occurs. However, Group 2 is significantly higher than group 5 only in 2008. We cannot conclude anything else from the results of the statistical tests.

Figure 20: Median ROE for each year and each group of leverage for cattle * means significantly higher than **

4.2.2.5 ROE for diversified production

This group is the biggest in terms of number of values, with 256 farms analyzed for 5 years. However, this is a really heterogeneous group, with farms that cannot be compared between each others. Therefore the diversified group was not analyzed in details because the results would not be useful in terms of managerial implications.

4.2.2.6 Results Summary for ROE by Groups of Leverage

Table 17 presents a summary of the results of the statistical tests. The group 2 showed the best results in terms of ROE, followed by group 3. Groups 4 and 5 on the contrary have really often significantly lower results than groups 2, 3 and in one case group 1.

Year

 

Dairy

 
 

Grain

Cattle

 

Diversified

2006 2007 2008 2009 2010

1

2

& 2

& 3

2

-

& 3 -

> 4
> 4

>

&

5
5

-

2 >

- 2 > 2 >

5

4
4

-
-
2 > 5

-
-

2

& 3

2
2

-

> 4 -

> 5
> 5

& 5

Table 17: Results of the Mood's median tests summary for ROE by groups of leverage for each years

Table 18 presents the average ROE for all specializations and all years. Diversified and dairy farming have really close results regarding their overall means. However, it seems that diversified production is much more volatile, moving from 13.9% ROE on average in 2007 to 4.0% in 2009. For milk, the range starts from 5.4% in 2006 and goes to 11.7% in 2008. It seems that this specialization is less risky than diversified production or grain farming. Cattle's farming, on the contrary, has always the lowest results except in 2010. It seems that the economical performances of these farms are

chronically insufficient. 2010 is an exception, with the rise of the price of meat linked with the new export opportunities to Turkey. This improvement of the market conditions for cattle farming still has to be confirmed, because it depends so far on a customer representing more than 50% of the exports: if Turkey stops its importations as it did in the past, prices could fall sharply.

Specialization

2006

2007

2008

2009

2010

Mean

Dairy

5,4%

9,3%

11,7%

7,8%

5,6%

8,4%

Cattle

-5,4%

3,5%

3,6%

1,5%

13,7%

2,2%

Grain

2,0%

21,2%

18,6%

2,7%

7,7%

11,3%

Diversified

4,0%

8,7%

13,9%

4,3%

12,3%

8,3%

Mean

3,2%

10,3%

13,0%

4,8%

10,1%

8,2%

Table 18: Weighted average ROE for all specialization and by years

However, a really different outcome can be observed looking at Table 19, which presents the ROE by specialization and by groups of leverage (all years are considered in this table). The cattle specialization, which obtains low results every year except in 2010, is profitable when the leverage is higher than 40%. On average, the farms of Isère are economically more profitable when the leverage increases, particularly for milk and cattle. We can observe a fall in the results for group 1, particularly for dairy farms, which can be explained by the financial distress that can occurs when leverage is too high.

Specialization

Group 1

Group 2

Group 3

Group 4

Group 5

Mean

Dairy

-2,4%

11,7%

9,3%

6,3%

3,7%

8,4%

Cattle

1,2%

7,3%

7,4%

1,3%

-4,8%

2,2%

Grain

9,4%

12,5%

11,5%

12,0%

10,5%

11,3%

Diversified

5,7%

12,4%

11,1%

4,4%

5,4%

8,3%

Mean

4,6%

11,8%

10,2%

5,9%

4,8%

8,2%

Table 19: Average ROE by specialization and by group

These results are totally consistent with the initial hypothesis which states that there is an optimal level of debt for farming. From the ROE results, group 2 and group 3 obtain the best results, which is an indication that the optimal leverage lies between 40% and 80%. Group 3 has similar results than group 2, except for dairy production. Therefore, the optimal leverage must be higher for this specialization.

The major highlights for the analysis of the ROE are:

- Performance increases significantly with financial leverage,

- Financial distress may occur for all production for leverage higher than 80%.

4.2.3 ROA

4.2.3.1 ROA Tested by Time and Specialization

We applied the same tests for ROA than for ROE. The time effect was first tested as well as
specialization to see if it was necessary to isolate their effects on the ROA. The results were very
similar than for the ROE, which is really logical as ROE and ROA have the same numerator. Again, the

box and whisker plot in Figure 21 illustrates that year 2006 and 2009 were bad in terms of results for farming in Isère with ROA median close to 1%, which is lower than the 1.5% inflation in 2006 (Le Monde.fr, 2007).

Kruskal-Wallis Test for ROA by YEAR

YEAR

Sample Size

Average Rank

2006

568

1253,72

2007

568

1552,39

2008

568

1545,22

2009

568

1269,77

2010

568

1481,39

Test statistic = 73,6591 P-Value = 0 Mood's Median Test for ROA by YEAR Total n = 2840

Grand median = 0,034619

YEAR

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

2006

568

339

229

0,00945275

-0,000555481

0,0218933

2007

568

242

326

0,0570648

0,0460463

0,0689174

2008

568

248

320

0,0528593

0,0436125

0,0621654

2009

568

327

241

0,0121847

0,000872383

0,0184358

2010

568

264

304

0,044225

0,0338877

0,0561194

Test statistic = 58,6901 P-Value = 5,46618E-12

B-andW hier P

Figure 21 : Box and whisker plots with median notch for ROA by years (means are shown with a red cross)

Therefore, we had to isolate the time effect to isolate its effect on the ROA.

Regarding the effect of specialization, once again the P-Values were really low, illustrating that we cannot reject the hypothesis that medians are significantly different at 99% confidence. The two tables of the Kruskal-Wallis and Mood's median tests illustrate that cattle's ranching presented significantly lower results than all other production in Isère.

Kruskal-Wallis Test for ROA by SPECIALIZATION

SPECIALIZATION

Sample Size

Average Rank

Dairy production

705

1487,12

Cattle ranching

225

1124,15

-0i4 -0i3 -0i

Grains production

630

0i2

1494,56

Diversified production

RO

1280

1399,45

Test statistic = 40,0263 P-Value = 1,05194E-8

Mood's Median Test for ROA by SPECIALIZATION Total n = 2840

Grand median = 0,034619

SPECIALIZATION

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

Dairy production

705

331

374

0,0427384

0,0344119

0,0510433

Cattle ranching

225

153

72

-0,00207533

-0,0192198

0,012193

Grains production

630

292

338

0,0464268

0,0350341

0,0583288

Diversified production

1280

644

636

0,0337455

0,0277409

0,0419674

Test statistic = 35,1914 P-Value = 1,10992E-7

Therefore, we had to isolate the effect of specialization also on ROA. The tests were performed for each specialization and each year separately.

4.2.3.2 ROA for Dairy Producers

The farms analyzed here are the same than the ones for the ROE for dairy producers: 141 dairy farms of Isère. Figure 22 presents the results of the different tests performed to compare the median ROA for dairy farms between 2006 and 2010. The results are close to what can be observed for the ROE: groups 2 and 3 seem to over-perform the other groups.

Figure 22 : ROA for each year and each group of leverage for dairy producers * means significantly higher than **

For year 2007, the group 2 and 3 are significantly better than the group 5. For 2009 and 2010, group

2 is significantly higher than group 4 and 5. Group 1 median on the contrary is lower than group 2 or

3 for all years, and has the lowest median of all groups in 2006, 2008 and 2009. We cannot affirm that this difference is significant as the standard deviations are really high for group 1 (Appendix 11 page 103). However, these elements are clear signs of financial distress.

4.2.3.3 ROA for Grain Producers

Figure 23 represents the ROA medians for each group of leverage for the grain producers. For this specialization, the results are not really similar with what we can observe for the ROE. First of all, group 2 is never statistically higher than any other groups, and is even the lowest performance in 2008 and 2009 (without a statistical difference however). Only 2006 and 2010 tend to be consistent with the theory which states that performance increases with leverage. In 2010 group 1 was

significantly higher than group 4. However, there was no significant difference with group 5 even if its median was lower, because of the higher standard deviation in group 5 (see appendix Appendix 13 page 111).

Figure 23: ROA for each year and each group of leverage for grain producers * means significantly higher than **

The only statistical difference between groups tends to prove that leverage increases ROA, but only one year out of five. Therefore, it will be hard to make conclusions on these results for grain producers, except that it seems that the optimal leverage range is 40-60% for this specialization.

4.2.3.4 ROA for Cattle Farming

The results for the ROA are consistent with what have been observed for the ROE for cattle farming. Group 2 has a higher median than group 4 and 5 in 2008, and performance seems to increases with the leverage (see Figure 24). Once again, group 1 do not present stable results over the year, which could be the consequence of financial distress for those farms which are more leveraged than 80%. However, it will be hard to make more interpretation from these results as only 45 farms are studied for this specialization, and significant differences are observed only in 2008.

Figure 24: ROA for each year and each group of leverage for cattle farming * means significantly higher than **

4.2.3.5 Results Summary for ROA by Groups of Leverage

The median of group 2 is usually significantly higher than the medians of groups 4 and 5 (Table 20). Group 3 in two cases over-performs group 4 or 5, and group 1 is better than group 4 in one case. The same elements are observed for the ROA than for the ROE: the performance increases with the leverage, even if the differences between groups are not always significant. The high heterogeneity intragroup and between groups, illustrated by the high standard deviations (see Appendix 11, Appendix 12 and Appendix 13 page 103, 107 and 111) explains why the differences cannot be considered as significant even if it could have been anticipated so only by looking at the means and medians of the different groups.

Year

 

Dairy

 

Grain

 

Cattle

 

Diversified

2006

 

-

 
 

-

 

-

 

-

 

2007

2

&3

>

5

-

 

-

 

3 >

4

2008

 

-

 
 

-

2

> 4 &

5

-

 

2009

2

> 4

&

5

-

 

-

 

2 >

5

2010

2

> 4

&

5

1 > 4

 

-

 

-

 

Table 20: results of the Mood's median tests summary for ROA by groups of leverage for each years

Table 21 presents the average ROA for all specializations and all years for the studied farms. The first point that can be noticed is that the average ROA is 3.1% for the average farm in Isère. The ROA reaches 13% for grain farms in 2007, but fall to 1.7% in 2009 for the same specialization. It is interesting to compare the dairy and the grain production: both have a close weighted average for ROA, but variability is really higher for grain than for dairy farming (standard deviation for dairy is 1.7%, but reaches 5.6% for Grain). Grain farming can be considered as a riskier specialization. In 2008, the average farm in Isère reaches an ROA of 8.2%, 6.4% in 2007, and 6.2% in 2010. However,

the average cattle farm on the long term has an ROA close to the level of inflation. 2010 is the exception with the best result of all specialization with 9.0% ROA.

Specialization

2006

2007

2008

2009

2010

Mean

Dairy

3,3%

5,8%

7,3%

4,8%

3,4%

5,2%

Cattle

-3,6%

2,4%

2,5%

1,0%

9,0%

1,5%

Grain

1,1%

13,0%

11,9%

1,7%

5,1%

6,9%

Diversified

2,4%

5,3%

8,5%

2,5%

7,3%

5,0%

Mean

1,9%

6,4%

8,2%

2,9%

6,2%

3,1%

Table 21: Average ROA by specialization and by years

Table 22 presents the results from a different perspective: the high leverage groups obtain better results, except for group 1 which presented strong signs of financial distress. The grain specialization on the contrary have opposite results, with an ROA decreasing with the leverage. However, when we consider this element year by year, this result cannot be observed, showing that time has to be taken in consideration. Except for the grain specialization, these results are consistent with what we observed for the ROE, showing that the economical performance increases with the leverage, and that 80% is a higher limit. Even cattle production obtains positive results when the leverage is between 40% and 80% (group 2 and 3). In the case of this specialization, the results of group 3 are better (without significant difference however) than group 2.

Specialization

Group 1

Group 2

Group 3

Group 4

Group 5

Mean

Dairy

-1,1%

6,1%

5,7%

4,5%

3,3%

5,2%

Cattle

0,7%

4,0%

4,8%

0,9%

-4,2%

1,5%

Grain

3,8%

5,8%

6,8%

8,6%

9,0%

6,9%

Diversified

2,2%

6,6%

6,6%

3,2%

4,8%

5,0%

Global Mean

2,0%

6,2%

6,2%

4,3%

4,2%

3,1%

Table 22: Average ROA by specialization and by groups

Finally, we can say that H0 cannot be rejected for the first hypothesis and the second hypothesis tested:

- ROA and ROE tend to increase with the level of debt of the farms in Isère.

- Financial distress may occur when leverage increases, reducing ROA or ROE. Looking at the ROE, 80% seem to be the upper limit. The results for the ROA tend to show that 60% is probably more appropriate (except for dairy production).

4.3 Calculation of the Capitalization Rate Using Bond Yields Plus Risk Premium

This method, presented in part 3.3, estimates the capitalization or actualization rate through this equation:

A: Actualization rate Rf: Risk free rate

â: Beta

Rp: Risk premium for the French market. It has to be increased for the micro-capitalization premium

The risk-free rate of the 10 years Governments bonds average is 3.00% (Source French central bank, average of monthly average from July 2011 to June 2012). It is interesting to notice that this rate reached an average of 2.569 % in June 2012, and it has never been so low (Dobson, 2012). The 12 month average will surely decrease in the following months, but on the long term it cannot be considered that this rate will remain at this low level.

Regarding the Beta, the tax authority recommends 0.8 to 1.1 for the food-processing and agricultural sector (Direction Générale des Impots, 2006). On their side, Fama & Fench (Laur, 2010) consider that the beta for the agricultural sector is 0.81 (time frame 1929-2009). For shorter periods of time, the agricultural sector beta remains around 0.70 to 0.91 (Laur, 2010). In our calculation the value of 0.8 was retained because it both answers to the necessity to cope with the recommendation of the tax authority and the necessity to be easy to used for consultants (these information are easy to access).

The Risk premium is composed with two elements:

- The risk premium for the French market: from the literature review, we found a range of [3.8% : 5.0%] (see part 2.1.2)

- The small or micro-cap premium: from the literature review, we found a range of [4.12% : 7.74%] (see part 2.2.2).

Therefore the actualization rate has to be contained in the following range:

The results obtained with this method are really higher than what consultants use normally. The benefit of this fact is the following: consultants are not exposed to tax adjustment, because the administration cannot consider their results as hidden donation. The administration itself would retain a lower tax base for its calculations (as the overall price of the company is reduced by a higher actualization rate). However, this method is only an approach based on secondary data, and need to be compared with other approaches such as the WACC.

4.4 Approach of the WACC for Farming in Isère

4.4.1 Calculation of the Betas

Table 23 presents the results of the calculation of the betas for the grain farming specialization. Over the 24 companies concerned, the average beta obtained is 0.804, really close to the 0.8 to 1.1 proposed by the tax authorities. However, it is important to notice that this beta is a weighted average for the companies operating in grain and oilseed, with an average market capitalization of 843 M$ (around 693 M€ for 2012). The average total assets of the farms concerned in our study is 279 000 €.

Another element is worth noticing: the company size. The Average grain company in Western Europe has a market capitalization of 714 M$, compared to 1 997 M$ for North America.

Member Name

Total Mkt Cap ($)

adjusted beta

R2

SE beta

â weigthed average

SOCFIN

440 363 745

1.106

0.425

0.134

 

ODET

1 990 689 644

0.622

0.176

0.093

 

SIPEF NV

550 707 800

0.729

0.152

0.139

 

SOCFINASIA

598 033 371

0.419

0.010

0.126

 

ANGLO-EAST PLNTS

395 519 023

0.574

0.056

0.147

 

SOCFINAF

383 174 522

0.626

0.100

0.131

 

REA HOLDINGS

224 078 524

0.641

0.054

0.191

 

ADECOAGRO SA

834 190 505

1.220

0.246

0.289

 

M P EVANS GROUP

331 529 995

0.517

0.048

0.123

 

CONTINENTAL FARM

47 960 000

0.289

0.003

0.173

 

NARBOROUGH PLTNS

14 030 701

0.352

0.000

0.137

 
 

5 810 277 829

 
 

0.144*

0,722

ADECOAGRO SA

834 190 505

1.220

0.246

0.289

 

TRIGON AGRI A/S

106 214 029

0.870

0.175

0.174

 

BLACK EARTH-SDR

129 321 577

1.017

0.217

0.194

 

SOCFINASIA

598 033 371

0.419

0.010

0.126

 

SOCFIN

440 363 745

0.499

0.020

0.182

 

BONIFICA FERR

190 783 713

0.574

0.010

0.353

 

CONTINENTAL FARM

47 960 000

0.289

0.003

0.173

 

AGRICOLE DE CRAU

29 847 642

-0.166

0.008

1.226

 

BOLLORE

4 053 210 291

0.785

0.344

0.093

 
 

6 429 924 873

 
 

0.145*

0,780

ANDERSONS INC

663 031 853

1.132

0.377

0.153

 

VITERRA INC

4 573 354 378

0.834

0.142

0.184

 

SANDERSON FARMS

928 292 250

0.501

0.023

0.162

 

SEABOARD CORP

1 826 143 352

1.106

0.425

0.134

 
 

7 990 821 833

 
 

0.167*

0.882

 

20 231 024 535

 
 

0.154*

0.804

industry

oilseed farming in w europe

oilseed farming in w europe

oilseed farming in w europe

oilseed farming in w europe

oilseed farming in w europe

oilseed farming in w europe

oilseed farming in w europe

oilseed farming in w europe

oilseed farming in w europe

oilseed farming in w europe

oilseed farming in w europe

Total oilseed farming in WE

grain western europe

grain western europe

grain western europe

grain western europe

grain western europe

grain western europe

grain western europe

grain western europe

grain western europe

Total grain western europe

grain north america

grain north america

grain north america

grain north america

Total grain north america

TOTAL grain & Oilseed

Table 23: Calculation of the Beta for the grain specialization (oilseed is considered as grain farming in France). Source: Bloomberg

*weighted average of the SE for each company

For the cattle ranching, presented in Table 24, we see that there is more difference between Europe and North America. The average beta is really higher for the four European companies, with a beta close to 1. Once more, the range of references proposed by the Tax authorities corresponds to what is observed in Bloomberg. It is worth noticing the difference of the size of the companies between the two continents, but in the opposite direction with the grain specialization. European companies are more important, with an average market capitalization of 319 M$ compared with an average of 68 M$ for North America. However, the market capitalization of the listed companies operating in cattle ranching and farming is smaller than the companies operating in grain.

Member Name

Total Mkt Cap ($)

adjusted beta

R2

SE beta

â weigthed average

YASHENG GROUP

71 968 101

0.517

0.007

0.331

 

ALICO INC

133 115 937

0.929

0.259

0.150

 

CANAL CAPITAL CO

39 135

-2.083

0.018

3.955

 
 

205 123 173

 
 

0.214*

0.784

NATIONAL MILK

3 871 194

0.274

0.011

0.085

 

ADECOAGRO SA

834 190 504

1.222

0.246

0.289

 

TRIGON AGRI A/S

106 214 028

0.870

0.175

0.174

 

M P EVANS GROUP

331 529 994

0.517

0.048

0.123

 
 

1 275 805 720

 
 

0.236*

1.007

 

1 480 928 893

 
 

0.233*

0.976

industry

cattle ranching and farming NA
cattle ranching and farming NA
cattle ranching and farming NA

Total cattle ranching and farming NA

cattle ranching and farming WE cattle ranching and farming WE cattle ranching and farming WE cattle ranching and farming WE

Total cattle ranching and farming WE

TOTAL cattle ranching

Table 24: Calculation of the beta for the cattle ranching and farming specialization. Source: Bloomberg

*weighted average of the SE of each companies

Table 25 presents the companies specialized in poultry and fruits and vegetables. These
specializations are not studied in this paper, because the number of farms concerned is lower in

Isère. However, some regions are more concerned with these types of farms, and some consultants will find useful to work on these beta.

Member Name

Total Mkt Cap ($)

adjusted beta

R2

SE beta

â weigthed average

CAL-MAINE FOODS

 

715 100 119

0.814

0.238

0.128

 

SANDERSON FARMS

 

928 292 250

0.501

0.023

0.162

 
 

1

643 392 369

 
 

0.147*

0.637

LDC

 

677 446 072

0.512

0.083

0.088

 
 
 

677 446 072

 
 

0.088*

0.512

DOLE FOOD CO INC

 

633 782 418

1.081

0.254

0.191

 

VILLAGE FARMS IN

 

27 137 995

0.479

0.014

0.182

 

FRESH DEL MONTE

1

106 613 718

0.881

0.401

0.100

 

ALICO INC

 

133 115 937

0.929

0.259

0.150

 
 

1

900 650 068

 
 

0.135*

0.945

CAMPOSOL HOLDING

 

84 172 880

0.558

0.043

0.159

 

CONTINENTAL FARM

 

47 960 000

0.289

0.003

0.173

 

BONIFICA FERR

 

190 783 713

0.574

0.010

0.353

 
 
 

322 916 593

 
 

0.276*

0.528

industry

poultry north america
poultry north america

Total poultry north america

poultry western europe

Total poultry western europe

fruits&vegetables NA fruits&vegetables NA fruits&vegetables NA fruits&vegetables NA

Total fruits&vegetables NA

fruits&vegetables WE
fruits&vegetables WE
fruits&vegetables WE

Total fruits&vegetables WE

Table 25: Calculation of the beta for poultry and fruits & vegetables Source: Bloomberg

* weighted average of the SE of each company

Table 26 presents a summary of the beta for the companies specialized in agriculture for each specialization. It is interesting to notice that the average market capitalization of North American companies is bigger than the Western European ones, but their beta is higher. This element is not consistent with what can be found in the literature about the relationship between the market size of a company and its beta. Therefore, we can conclude that the risk must be lower for farming in Western Europe than in North America.

industry

 
 

# of companies Average Mkt

Cap ($)

SE beta* â weigthed

average

TOTAL grain & Oilseed western Europe and North America

TOTAL cattle ranching western Europe and North America

TOTAL Western Europe

Total oilseed farming in WE Total grain western europe Total grain north america

Total poultry North America

Total poultry Western Europe

Total fruits&vegetables North America Total fruits&vegetables Western Europe

11

9

4

24

3

4
7

2
1
4

3

28

1

528 714 997 842

68 318 211

821 677 475 107

518

207 436 705 959

374
951
561

696 446 162 638

441

075 097 458 356

391
430
270

185 072 517 864

825

0.144 0.145 0.167 0.154

0.214
0.236
0.169

0.147 0.088 0.135 0.276

0.153

Total cattle ranching and farming NA
Total cattle ranching and farming WE

0.722

0.780

0.882

0.804

0.784

1.007

0.976

0.637

0.512

0.945

0.528

0.758

13 903 075 957 0.160 0.856

41 804 179 869 0.156 0.802

TOTAL North America

TOTAL North America + Western Europe

Table 26: Summary of the beta for the different specialization and regions Source: Bloomberg

* weighted average of the SE of each company

4.4.2 Calculation of the WACC for the Different Specialization

Table 27 presents the results of the different leverage for each specialization. This leverage was be
used in the calculation of the estimation of the WACC for each specialization. Other financial
information are presented in this table, such as the net salary per capita (in €/month) which can be

compared to the SMIC which is around 1 029 € net per month for the time period 2006-2010. Therefore, the average cattle farmers in Isère earn less than the minimum salary in France.

The average leverage is really similar for each specialization, around 50%, but the debt coverage on the contrary is different depending on the specialization. The grain farmers have a debt coverage of 3.16, which is really different than the 2.08 for the cattle farmers. It shows again that cattle farming was the less profitable specialization in Isère for 2006-2010.

Specialization

Leverage

Net salary per capita

EBITDA/turnover

Debt coverage*

Dairy

52.7%

1 230 €

41%

2.44

Cattle farming

49.5%

1 015 €

42%

2.08

Grain

49.9%

1 478 €

46%

3.16

Diversified

54.6%

1 277 €

35%

2.49

Weighted average

52.8%

1 279 €

39%

2.54

Table 27: Characteristic of each specialization in Isère *EBITDA/total annuity

Regarding the WACC, Table 28 presents the details of the calculation and the range of WACC for each specialization. First, the cost of debt is really low for farmers in France (see Appendix 14 page 115). The first element which could explain this fact is that loans are guaranteed by the assets in the farming business, reducing therefore the risk for the bank and the interest rate as well. The other element which can explain this fact is that the first bank in agriculture is the Crédit Agricole, which is really linked with the agricultural sector. His regional boards are mostly composed with farmers.

One could object that this low cost of debt doesn't reflect the real risk associated with the debt in Agriculture because of the specificity of this sector. However, the rate bankruptcy is really low: 1.6%o in Rhone Alpes (Coface, 2011). This element is linked with the culture of the French farmers. Going bankrupt is clearly not conceivable for most of them, and we can put this statement in relation with their level of salary. As presented in table 27, cattle farmers on average earn less than the minimum salary in France, but they maintain their activity and continue to pay their annuities. From an economical perspective, this attitude is totally counter-productive, because selling the farm and getting whatever job would generates higher returns. This fact illustrates the farmers' attachment to their farm and may explain the really good interest rates they obtain from their banks.

Specialization

rE. E/V (low)

rE. E/V (high)

rD.(1-Tc).D/V

WACC (low an high range)

Dairy

7,0%

10,3%

1,3%

8,3%

11,7%

Cattle

7,4%

11,0%

1,2%

8,6%

12,2%

Grain

6,6%

9,6%

1,2%

7,7%

10,8%

Diversified

5,9%

8,7%

1,3%

7,2%

10,0%

Mean

6,2%

9,1%

1,3%

7,4%

10,3%

Table 28: WACC estimation for each specialization (leverage is based on table 27)

The cost of equity is higher for dairy and cattle farms (see Table 28), linked with the higher beta observed for these specializations. Finally, the WACC range for the average farm in Isère is [7.4% ; 10.3%]. This method gives lower results than the method proposed by the tax authorities. However, for some specializations, the range is almost similar, particularly for cattle and dairy production.

Another element has to be considered. As a matter of fact, the WACC methodology takes into
account the leverage, which reduces the overall cost of capital. Table 29 presents the WACC for the

farms of the group 5, for which the leverage range is 0-20%. Here the WACC increases tremendously, reaching almost 20% for the higher estimation for cattle and dairy production.

Specialization

rE. E/V (low)

rE. E/V (high)

rD.(1-Tc).D/V

WACC (low and high range)

Dairy

13,2%

19,6%

0,3%

13,5%

19,9%

Cattle

13,2%

19,6%

0,2%

13,5%

19,9%

Grain

11,8%

17,3%

0,2%

12,0%

17,5%

Diversified

11,8%

17,3%

0,2%

12,0%

17,5%

Mean

11,8%

17,3%

0,2%

12,0%

17,5%

Table 29 : WACC estimation for each specialization for group 5 (0 to 20% leverage)

Group 2 (60-80% leverage) was usually the best group in terms of results (see parts 4.2.2 and 4.2.3). Regarding this group, the WACC falls to lower levels, between 5.6% and 7.4% for the average farm in Isère. This massive reduction of the WACC is due to the really low cost of debt observed in agriculture in France. The level is still higher for cattle and dairy farms of course, pushed by the higher beta for this production. The WACC for this group of leverage is closer to the actualization rate used by practitioners (2.5-4.5%), but still significantly higher.

Specialization

rE. E/V (low)

rE. E/V (high)

rD.(1-Tc).D/V

WACC (low and high range)

Dairy

4,4%

6,5%

1,8%

6,2%

8,3%

Cattle

4,4%

6,5%

1,7%

6,1%

8,2%

Grain

3,9%

5,8%

1,6%

5,5%

7,4%

Diversified

3,9%

5,8%

1,7%

5,6%

7,4%

Mean

3,9%

5,8%

1,7%

5,6%

7,4%

Table 30: WACC estimation for each specialization for group 2 (60 to 80% leverage)

As presented in the previous sections, the signs of financial distress are strong in group 1 (leverage higher than 80%), and even for the group 2. Therefore, the real optimal WACC is maybe closer to the results presented in Table 28 for leverages around 50%. Farming consultants should use these ranges of WACC to determine their actualization rates in their feasibility studies.

5 Discussion

5.1 Survey Results

The results of the survey confirmed my initial observations: farm consultants do not often use the WACC method, and the discount factors used are really low generally, around 2.5% to 4.5%. Moreover, the estimations based on experience are the most popular, and we can make the assumption that they are influenced by the low level of bankruptcy in the sector. The means of the discount factors used by practitioners was compared to the ROE and ROA statistically observed in Isère and the required risk premium based on our calculation. As practitioners use lower discount factors than the required risk premium, it signifies that the estimations they produce are overestimated, which would lead farmers to base their decisions on overoptimistic simulations.

5.2 ROE and ROA Analysis the Farms of Isère

The statistical tests performed in part 4 confirm the underlying hypothesis of the WACC methodology: firm's performance increases with leverage up to a certain limit where financial distress occurs. The standard deviation increases in group 1, with lower median results. It can be safely assumed that 80% leverage is a limit that should not be exceeded in agriculture.

Groups 2 and 3 are the groups which have results significantly better than the lower groups, meaning that the optimal level of debt could be between 40% and 80%. As group 3 shows good results either, better than group 1 which shows strong signs of financial distress some years, it is common sense to think that the optimal debt level is closer to 60% than 80%. Except for the dairy specialization, it could be recommended to target a leverage of 40 to 60%.

For grain and cattle producers, the optimal level of debt is surely between 40% and 60%. The median ROA of the group 2 is never significantly higher than any other groups, and is even lower some years (without significant differences). The group 3 on the contrary presents more stable results in terms of ROA for these specializations (grain vs cattle). This element may be explained by the difference in nature between these two productions: dairy farms have to invest in expensive buildings and equipments, necessary to increase the volume of production (the size of the animal housing is directly linked with the level of production); However, for grain production, the most expensive equipments are the tractors, and there is no such linear relationship between the investment and the productivity. Land is more linked with the productivity, but availability of land is not linked with the financial capacity of farmers, and most of the time farmers rent the land. Therefore, lands are most of the time not financed by loans and have no impact on the leverage. For cattle farming, the animal housing is less necessary than for dairy farms, depending on the surface of pasture available for the herd. Therefore, a high leverage for these farms does not directly guarantee that the farm will benefit of a higher potential of production.

Other elements can be learned from the results of the analysis of the ROE and ROA of farms in Isère. The average farm in Isère has not so low results in terms of ROE and ROA: 8.2% and 3.1% respectively. ROE and ROA reaches respectively 11.8% and 6.2% for group 2, which is higher than what consultants use. If we consider that the historical ROA should be the minimum actualization rate, the difference with the actualization rate used by practitioners is quite high.

On average, the respondents answered that they used discount factors around 2.5% to 4.5% depending on the specialization. Moreover, the results obtained in this analysis are based on historical results: it does not take all the risks into account and reflects only the past results of the farms of Isère. Therefore, most agricultural consultants use overoptimistic discount factors, leading them to consider that some projects are profitable when not.

5.3 Capitalization Using the Bond Yield Plus Risk Premium

The range obtained using this method is [9.3% ; 12.7%], far from the 2.5% to 4.5% commonly used by the practitioners in agriculture, and even the historical ROA around 4.0% to 6.8% observed for the groups 2 and 3 of the farms from Isère (leverage between 40 and 80%). On the contrary, some specializations reached this range in terms of ROE, with 11.3% for grain. The first conclusion we can draw from these results is that farmers of Isère do not reach the required rate of return calculated for some specializations. The second conclusion is that we can now safely assume that most practitioners do not use the appropriate actualization rate in their consulting activities, leading farmers to base their decisions on overoptimistic results.

It is common sense to think that the first observation comes partially from the second. As a matter of fact, most of the farmers from the database studied in this research based their financial decision partially on the business plans calculated by the CERFRANCE Isère. Now, it is established that the risk premium was not enough taken into account in these business plans, leading some farmers to start a project without an appropriate economical assessment. This statement is particularly true when farms are sold to new farmers, because the valuation methods used by practitioners tend to overestimate the value of the company. In this case, the new farmer buys a farm which is too expensive regarding its expected performances, which decreases strongly the net present value of his investment. Eventually, this leads the overall sector to underperform on the long run.

This eventuality is hard to prove, but has to be considered. In Isère, the consultants calculate the price of farm using two valuation methods, as recommended by the tax authorities (Direction Générale des Impots, 2006). The most commonly used are the patrimonial method and the profitability method. The first one is easy to implement and requires the evaluation of the net assets of the farm. Buildings and equipments are valued at their resale price. In one sense, this is the value of the company if it had to be sold parts by parts. The second one is based on the actualization rate: we divide the reproducible net cash flow generated by the actualization rate. It is the NPV of a perpetual cash flow. A difference between 3% and 9% multiplies the value of the farm by 3 with this method! The most usual discount factors used by practitioners are 2 to 4 times lower than the range [9.3% ; 12.7%] obtained with the method recommended by the tax authorities, which means that the difference can be really important.

5.4 The WACC Methodology

The elements observed in part 5.3, regarding the results of the method bond yield plus risk premium proposed by the tax authorities, are consistent but higher than what is obtained with the WACC methodology for the average farm in Isère. This method confirms that the actualization rates used by practitioners are undervalued. However, it gives really different results depending on the leverage of the companies. Therefore, it gives a strong advantage to this approach for the practitioner, who will be able to adapt his actualization rate to the particularity of each farm. Another advantage is the possibility to choose the best financing solution for a new investment, considering the overall

leverage of the farm in order to modify its capital structure to reduce the WACC. The consequence of this modification should be the increase of the profit generated by the farm.

On the other side, the method proposed by the tax authorities has the strong advantage to be easier to implement and to learn for the consultants. The other advantage of the bond yield plus risk premium as presented by the French tax authority is its origin itself: in case of litigation with the administration, the choice of the actualization rate will be easier to defend with the method they recommend. The power of this argument is reduced by the recognition of legitimacy of other methods from the administration itself, without describing or citing them however (Direction Générale des Impots, 2006).

Finally, we can observe that the WACC methodology should be more accepted by the practitioners, as it gives lower results for the average farms in Isère. This element has to be considered, because such a strong modification of the actualization rates used by the consultants would signify that all their previous studies were significantly wrong. It could lead to a movement of rejection from these practitioners.

6 Utilization of These Results

6.1 Managerial Implications

The first conclusion that can be learned from this research is that agricultural consultants do not use adapted discount factors. The lack of research and publication on the subject is leading consultants to adopt discount factors based on the cost of debt or inflation or even only on risk-free assets, and not on the cost of capital or a real risk premium. The first managerial implication that should be drawn is the necessity of further research on the topic. CERFRANCE developed a partnership with a network of engineering school in agriculture to specialize 40 students in consulting for farmers every year (CNCER, 2012). This partnership with teaching and research facilities could be the base of the discussion about the necessity to improve the knowledge on the topic. The engineering schools in agriculture are the perfect support for a deeper research on the subject, and this option should be considered as the partnership with the CERFRANCE would help the researchers to access a unique database that contains all economical results of more than 50% of all French farmers. Finally, the CNCER (the central element of the CERFRANCE network) should initiate a project to uniform the practices among the different members of the network. It is clearly a weakness of the network so far.

The other implication induced by this research is the necessity to improve the methods actually used by most practitioners of the CERFFRANCE. Our survey is not exhaustive, therefore it cannot be said that all the CERFRANCE are concerned, neither all its consultants. Some answers prove that some of them use higher rates. However, generally speaking it is obvious that the different methods to determine appropriate actualization rate are not well understood by most of the consultants. In-house training should be more focused on these methods, as well as on the consequences of choosing extremely low discount factors. This type of training could start without waiting for other results, because even the descriptive statistics show that for some groups of leverage the average ROE and ROA are really higher than the discount factors used by practitioners. The ROE cannot be the right estimation for a discount factor. However, it seems to be misleading to choose it two times lower than the ROA for the best groups of farms.

Consultants and farmers should also adapt their methodologies for the management of the working capital. This element is really important in agriculture, and represents 34.5% of the total assets in dairy farming in Isère and 43% for the cattle farms. This working capital is principally composed by the animals (66% for dairy, and 79% for cattle farms). This working capital, for the farms that are not enough leveraged, could be financed with debt instead of being financed with equities, which is usually the case in agriculture. This financing is possible to put in place, as the financial institutions could take a guarantee on the herd of cattle. The farmers would be able to recuperate in cash a part of its equities, as well as reducing its overall cost of capital. The benefit could be important, both for the financial institution which would increase its activities, and also for the farmers who would be able to increase the NPV of their farms and their personal wealth. An estimation of the possible gain for the farmer is presented in Table 31. The hypotheses of this calculation are:

- 4% interest rate, higher than the average rate for farms from Isère, because the loan would be riskier for the bank than a loan guaranteed by lands or constructions,

- 40 000 € total loan, (45% of the total value of the animals for an average dairy farm), - 10% actualization rate,

- 6 years maturity, considering that a herd is renewed at 25% each year on average and calves become productive in their third year.

Year

net cash flow (total)

Present Value at 10%

Positive cash flow

annual payment (4% interest) tax relief on interests (at 33%)

discount rate

NPV

 

0

 

1

 

2

 

3

 

4

 

5

 

6

40

000

 
 
 
 
 
 
 
 
 
 
 
 
 
 

-7

510

-7

510

-7

510

-7

510

-7

510

-7

510

 
 
 

429

 

356

 

281

 

203

 

122

 

44

40

000

-7

080

-7

153

-7

229

-7

307

-7

388

-7

466

1,000

0,909

0,826

0,751

0,683

0,621

0,564

40 000

-6 437

-5 912

-5 431

-4 991

-4 587

-4 214

8 429 €

 
 
 
 
 
 

Table 31: NPV of a refinancing operation to reduce the WACC and increase the wealth of a dairy farm

The example presented in table 31 would not be interesting if the actualization rate was set at 3% like most consultants do. However, it becomes really interesting when the actualization rate is set in a normal range for dairy farms (between 8.3% and 11.7% as presented in section 4).

The only limitation of this strategy is to get the acceptation of the financial institution which would increase its risk exposure. So far, the risk associated with the working capital was mostly supported by the farmer itself. However, this limitation could be reduced if the farmers subscribe to a predefined percentage of the cash generated by the new loan on a financial product offered by the institution. This solution would also be interesting for the preparation of the retirement of the farmers, who are most of the time not well prepared considering the low level of the pensions in the sector.

6.2 Agricultural Policies Implications

The actual Common Agricultural Policy (CAP) does not take into consideration many elements of the risk in agriculture:

- The climate risk (some subsidies are now conditioned to the subscription to climatic

insurance, but this is a marginal phenomena yet),

- The variability of the soft commodities price.

Our results show that some years are profitable, when we consider 2007, 2008 or even 2010. However, 2006 and 2009 were really poor in terms of results. The future CAP will be reformed in 2014 or 2015, depending on the complexity of the negotiations to reach a consensus among European countries. It could be a great opportunity to consider the evolution of the risk in agriculture during this reform. Some analysts suggest that countercyclical tools could help farmers to maintain their revenue during downturns and limit the overall cost of the CAP (Momagri, 2012b). This paper shows that cyclicality of revenues and profitability are really strong. Instability comes from the price inelasticity regarding the soft commodities: 1 or 2% change in the level of global production can lead to a price variation of 50 to 100%, and even more (Momagri, 2012b). Therefore, the opportunity of changing the CAP to integrate countercyclical tools should be strongly considered, even if this would lead to add complexity to a system already considered as opaque and complex by non-initiated observers.

Other policies should be included in the CAP, which are the public subsidies for new farmers. So far, only the French government subsidizes the new farmers through loans at low interest rates and a front subsidy for the first year of activity. These elements could be generalized at the European level, particularly for the loan subsidy which could be an easy way to redirect the CAP payments to more socially acceptable subsidies. Lower interest rates and public support for loan's access can be as profitable in the long term for farming activities as decoupled payments or direct aids which represent a large part of the cost of the CAP (see Figure 6, page 16).This type of support would have a lower impact on the price of the grains than direct payments, as it would be more oriented on maintaining the number of young farmers high enough to maintain the age pyramid of this population.

7 Limitations and Further Research Implications

7.1 Survey Construction

The purpose of this survey is not to have an exhaustive view of the tools used by the practitioners in the field. As the questionnaire was sent to consultants of different CERFRANCE, the answers cannot be considered as representative for all accounting companies working with farmers. However, the CERFRANCE has a dominant position in this particular market all over the country and serve more than 50% of the farmers. Therefore, it could be appropriate for the CNCER to intensify its researches on the subject to uniform the practices across all CERFRANCE.

7.2 ROE and ROA Analysis

As we can see in Table 7 page 29, some years like 2009 count fewer companies available for analysis, even if the number of books accounts produced by the CERFRANCE Isère increased constantly over the 5 years. Therefore, some companies were not included in the research because of limitations of the information system. However, this limitation cannot be considered as enough relevant to invalidate the results obtained, mainly because the non-inclusion in the database for a company occurs randomly (there is no reason not to include results in the database).

Another limitation comes from the decision to work on a constant population. This constant population enables an interpretation of the results over several years, but it also removes the companies that went bankrupt during the studied period. Therefore, this exclusion criterion may have an effect on the results for the hypothesis 2 which analyses the effect of financial distress. However, the number of bankruptcy in agriculture is really low: only 63 farms went bankrupt in 2011 in Rhone Alpes (Coface, 2011) over 39 020 farms (Agreste, 2011), which represents 1.6%o bankruptcy in the sector for the year.

Another limitation comes from the number of farms available for the cattle specialization. These farms, specialized in beef production are not enough represented in Isère to obtain reliable results. Other regions such as Côtes d'Or or Saône-et-Loire should work on the subject.

Finally repeated measure statistical test could have been performed to test the data to try to evaluate more precisely the ideal debt leverage ratio. However, these mixed models are not available with traditional statistical software as they do no compare means or medians but probabilities (here probability to fall in financial distress in year N+1 depending on the debt level of year N). As this research paper tries to serve as a base for all the consultants of the CERFRANCE network, this analysis needs to be replicable with the results of other departments without expensive tools such as SAS or SPSS. However, this limitation justifies further researches on the topic.

7.3 Bond Yield Plus risk Premium Model

The bond yield plus risk premium model is easier to calculate than the WACC for the farms, which represents a strong advantage for this method. However, this model does not take into account the financial structure of the companies studied. This strong limitation reduces its applicability to the farms of Isère, which are really diversified.

The research was based on elements found in the literature for this part of the research. It has been
decided to choose this method in order to try to estimate what actualization rate could be chosen

without exhaustive researches on the subject. The purpose of this choice is to compare the actualization rate used by practitioners with the rates the tax authorities could use in case of litigation.

7.4 The WACC Methodology

The first limitation of this methodology is really easy to identify: this method, based on the CAPM, is really dependent on the beta of the company studied. However, the beta cannot be calculated for non listed companies, and it is necessary to extrapolate the results of listed companies to obtain an appropriate beta for the small and medium farming businesses studied in this paper. The other limitation of the method is directly linked to the first one: the market value of the firm should be estimated by its market capitalization. However, for non-listed companies this value cannot be calculated. The book value used in our research cannot replace fully the market capitalization, because it does not reflect the opinion of the market on the company.

The second limitation of this method is the size of the farms studied compare to the size of the listed companies used to calculate the betas. Even if a premium was added to the betas of the listed companies, it remains a bias not considered by the WACC methodology.

The last limitation is more linked with the choice of the expected return. It has been chosen on purpose to adopt the risk premium to estimate E(Rm) - Rf, in order to be able to compare the results of the two methodologies. However, it could have chosen to use the historical results of the farms from Isère of the first quartile (the 25% most performing farms).

7.5 Further Research Implications

First, there is a clear necessity to intensify the research about the optimal level of debt in Agriculture. Repeated measure statistical tests could be used to work deeper on the subject, in order to identify more clearly the optimal level of debt, but also to identify what are the other factors that have a strong impact on the financial performance of farms. It seems obvious that the productivity of farmers (number of hectares or cows per FTE) and the surface of the farm play a great role in the results. However, it is really hard to find quantitative research on the subject in France, at least researches that could be used easily by financial consultants.

The other further research implication is the necessity to test the methods presented here in the long run, in order to compare them. It would be interesting to follow many projects evaluated with the NPV method in order to see finally which method gave the most appropriate actualization rate. Moreover, other methods should be tested either to identify which ones should be chosen by practitioners. So far, this research tends to prove that the WACC methodology can be used by consultants, even if some theoretical limitations arose.

Finally, the same research methodology should be applied in other departments than Isère, with other specializations also. This would give further indications on the adaptability of the WACC methodology to the agricultural sector. It would allow also consultants to estimate the appropriate discount factors for farms specialized in fruits, wines or vegetables.

Conclusion

The WACC methodology tested in this research presents real limitations due to its underlying theories based on the market efficiency. This assumption holds only for the listed companies, introducing a bias in the calculation presented in this paper. However, the results obtained are consistent with the initial expectations regarding the relationship between leverage and financial performance or financial distress. Therefore, consultants should take more consideration for this methodology and use it in their feasibility studies.

The other achievement of this paper concerns the discount rates actually used by practitioners. All signs clearly indicate that many of them use abnormally low actualization rates, ranging from 2.5 to 4.5%. The impact of these low actualization rates in valuation methods, such as the profitability method, can be really important. The NPV of a farm can be over-estimated by two to four using such discount factors! Some consultants use more appropriate rates, but it is far from being a generality according to the results of the survey.

Then, it appears clearly that leverage has a positive impact on the financial performance of the farms of Isère. These expected results confirm that the WACC methodology should hold in the context of small and medium farming business. Therefore, consultants should consider the capital structure of the farms into consideration not only to avoid the risk of financial distress, but also to look for the optimal leverage, which appears to range between 40 and 60%. From the results presented in this paper, the 60-80% leverage group presented really good performances, but also more variability. Moreover, the bankruptcy risk was not studied because a sample of farms studied had to be constant. Therefore, it is safer to consider that the optimal debt level lies in the group 40-60%, may be closer to 60% regarding the good performance of the 60-80% group, particularly for the dairy specialization.

Finally, the consultants of the CERFRANCE Isère do not need to wait for other researches on the field to modify their methods. Main recommendation is to increase significantly their actualization rates. On the other side, it must be acknowledge that further researches on the topic are necessary to improve the methodology and determine more precisely the hypothesis that should be taken, regarding the risk premiums and the beta for example. The productivity constraints of the consultants working in the different CERFRANCE militate in favor of a partnership with the engineering schools specialized in agriculture. This partnership, already implemented in some schools, could be the starting point of others researches about the financial performances of farming businesses.

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Appendices

Appendix 1: Illustration of the database extraction. File name: "extraction 2006_1"

 
 

70

 

Appendix 2: Illustration of the database extraction, balance sheet information. File name: "extraction 2006_2"

 
 

71

Appendix 3: Questionnaire

L'objectif de cette enquête est d'avoir un aperçu des techniques utilisées par les conseillers d'entreprise agricole pour actualiser les flux. Les résultats de cette enquête seront analysés de manière anonyme, et seront utilisés dans le mémoire « L'utilisation du CMPC en vue d'optimiser l'évaluation du coût du capital dans les exploitations agricoles en France. Constatations empiriques à partir des résultats économiques d'exploitations Iséroises ». Une version électronique de ce mémoire pourra être distribuée aux personnes intéressées d'ici octobre.

Nom de votre compagnie: Age: Sexe:

Question 1: Quelle est votre profession ?

Question 2: Pouvez-vous décrire succinctement votre activité ?

Question 3: Dans le cadre de votre profession, rédigez vous des études prévisionnelles pour vos adhérents afin d'estimer la rentabilité de leurs projets ?

Question 4: est-ce que vous utilisez des taux d'actualisation pour évaluer la valeur d'une entreprise agricole ? (actualiser un flux perpétuel: flux prévisionnel reproductible / taux d'actualisation)

Question 5: Est-ce que vous utilisez la méthode de la Valeur Actuelle Nette (VAN) pour estimer la rentabilité d'un projet ? (projet photovoltaïque ou méthanisation par exemple)

Question 6: comment choisissez vous un taux d'actualisation ? Cochez les méthodes utilisées dans votre structure.

 

u ben e dhnt

Question 7: Dans quelle fourchette de pourcentage se situent les taux d'actualisation que vous utilisez principalement pour votre clientèle agricole ?

Question 8: Comment qualifieriez vous les méthodes utilisées dans votre structure pour comparer et évaluer des projets d'investissement ?

Question 9: Si l'on vient a vous proposer un outil pour estimer le coût moyen pondéré du capital des exploitations de votre région afin de l'utiliser comme taux d'actualisation, comment considéreriez vous cet outil ?

P-Value

Appendix 4: Normality test of the datasets for ROE, extraction from statgraphics

Uncensored Data - ROE

Data variable: ROEc

The Shapiro Wilk test for normality cannot be performed. Therefore we observed the data graphically, and considered that the data follow a normal distribution (density trace and histogram of frequency look normal).

2800 values ranging from -4,78806 to 4,81454 Fitted Distributions

Normal

mean = 0,0444249

standard deviation = 0,628166

The StatAdvisor

This analysis shows the results of fitting a normal distribution to the data on ROEc. The estimated parameters of the fitted distribution are shown above. You can test whether the normal distribution fits the data adequately by selecting Goodness-ofFit Tests from the list of Tabular Options. You can also assess visually how well the normal distribution fits by selecting Frequency Histogram from the list of Graphical Options. Other options within the procedure allow you to compute and display tail areas and critical values for the distribution. To select a different distribution, press the alternate mouse button and select Analysis Options.

Tests for Normality for ROEc

Den

Test

Shapiro-Wilk W

0,4

Statistic

Too much data

The StatAdvisor

This pane shows the results of several tests run to determine whether ROEc can be adequately modeled by a normal

03

distribution. The Shapiro-Wilk test is based upon comparing the quantiles of the fitted normal distribution to the quantiles of the data. The Shapiro-Wilk test was not performed because the sample size was greater than 2000.

Goodness-of-Fit Tests for ROEc Kolmogorov-Smirnov Test

 

Normal

DPLUS

0,187571

DMINUS

0,203456

DN

0,203456

P-Value

0,0

The StatAdvisor

This pane shows the results of tests run to determine whether ROEc can be adequately modeled by a normal distribution. Since the smallest P-value amongst the tests performed is less than 0,05, we can reject the idea that ROEc comes from a normal distribution with 95% confidence.

ce for RO

P-Value

Appendix 5: Normality tests of the datasets for ROA, extraction from statgraphics Uncensored Data - ROA by time

Data variable: ROA

Too much data for the Shapiro test. We do it visually, and we can conclude that it is normally distributed! 3046 values ranging from -4,37674 to 3,08145

Fitted Distributions

Normal

mean = 0,0569658

standard deviation = 0,485766

The StatAdvisor

This analysis shows the results of fitting a normal distribution to the data on ROA. The estimated parameters of the fitted distribution are shown above. You can test whether the normal distribution fits the data adequately by selecting Goodness-ofFit Tests from the list of Tabular Options. You can also assess visually how well the normal distribution fits by selecting Frequency Histogram from the list of Graphical Options. Other options within the procedure allow you to compute and display tail areas and critical values for the distribution. To select a different distribution, press the alternate mouse button and select Analysis Options.

Tests for Normality for ROA

Test

Shapiro-Wilk W

Statistic D

Too much data

The StatAdvisor

This pane shows the results of several tests run to determine whether ROA can be adequately modeled by a normal
distribution. The Shapiro-Wilk test is based upon comparing the quantiles of the fitted normal distribution to the quantiles of

0,3

the data. The Shapiro-Wilk test was not performed because the sample size was greater than 2000.

G

K

xn

oodness-of-Fit Tests for ROA olmogorov-Smirnov Test

,

 

Normal

DPLUS

0,148105

0,1

DMINUS

0,170935

DN

0,170935

P-Value

0

0,0

The StatAdvisor

This pane shows the results of tests run to determine whether ROA can be adequately modeled by a normal distribution.

Since the smallest P-value amongst the tests performed is less than 0,05, we can reject the idea that ROA comes from a normal distribution with 95% confidence.

Appendix 6: ROE by year

One-Way ANOVA - ROE by ANNEE

Dependent variable: ROE Factor: ANNEE

Number of observations: 2840 Number of levels: 5

The StatAdvisor

This procedure performs a one-way analysis of variance for ROE. It constructs various tests and graphs to compare the mean values of ROE for the 5 different levels of ANNEE. The F-test in the ANOVA table will test whether there are any significant differences amongst the means. If there are, the Multiple Range Tests will tell you which means are significantly different from which others. If you are worried about the presence of outliers, choose the Kruskal-Wallis Test which compares medians instead of means. The various plots will help you judge the practical significance of the results, as well as allow you to look for possible violations of the assumptions underlying the analysis of variance.

Summary Statistics for ROE

ANNEE

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

2006

568

-0,0079864

0,541224

-6776,82%

-3,25

3,7011

6,9511

1,84708

2007

568

0,0882099

0,498518

565,15%

-3,90675

3,31882

7,22556

-5,94064

2008

568

Scat

0,07103

by Leve

0,60985

858,581%

-3,3045

3,703

7,0075

-0,658518

2009

568

0,00166802

0,489926

29371,7%

-3,68929

3,90305

7,59234

-11,2575

4 2010

568

0,0906529

0,628869

693,71%

-3,47483

3,57

7,04483

3,45006

Total

2840

0,0487149

0,557841

1145,11%

-3,90675

3,90305

7,80979

-2,20759

2

ANNEE

Stnd. kurtosis

2006

72,8003

2007

101,535

0

2008

78,8042

2009

114,527

2010

2

67,3229

Total

187,103

The StatAdvisor

4

This table shows various statistics for ROE for each of the 5 levels of ANNEE. The one-way analysis of variance is

2006 2007 2008 2009 2010

primarily intended to compare the means of the different levels, listed here under the Average column. Select Means Plot

ANNEE

from the list of Graphical Options to display the means graphically.

WARNING: The standardized skewness and/or kurtosis is outside the range of -2 to +2 for 5 levels of ANNEE. This indicates some significant nonnormality in the data, which violates the assumption that the data come from normal distributions. You may wish to transform the data or use the Kruskal-Wallis test to compare the medians instead of the means.

ANOVA Table for ROE by ANNEE

Source

Sum of Squares

Df

Mean Square

F-Ratio

P-Value

Between groups

5,2512

4

1,3128

4,24

0,0020

Within groups

878,206

2835

0,309773

 
 

Total (Corr.)

883,457

2839

 
 
 

The StatAdvisor

The ANOVA table decomposes the variance of ROE into two components: a between-group component and a within-group component. The F-ratio, which in this case equals 4,23794, is a ratio of the between-group estimate to the within-group estimate. Since the P-value of the F-test is less than 0,05, there is a statistically significant difference between the mean ROE from one level of ANNEE to another at the 95,0% confidence level. To determine which means are significantly different from which others, select Multiple Range Tests from the list of Tabular Options.

ANOVA for ROE

2

Table of Means for ROE by ANNEE with 90,0 percent LSD intervals

0,04

 
 
 

Stnd. error

 
 

ANNEE

Count

Mean

(pooled s)

Lower limit

Upper limit

0

2006

568

-0,0079864

0,0233533

-0,0351483

0,0191755

2007

568

0,0882099

0,0233533

0,061048

0,115372

2008

004

568

0,07103

0,0233533

0,043868

0,0981919

2009

568

0,00166802

2007

,233533

2008 2

-0,0254939

2010

0,0288299

2010

568

0,0906529

0,0233533

NNEE

0,063491

0,117815

Total

2840

0,0487149

 
 
 

The StatAdvisor

This table shows the mean ROE for each level of ANNEE. It also shows the standard error of each mean, which is a measure of its sampling variability. The standard error is formed by dividing the pooled standard deviation by the square root of the number of observations at each level. The table also displays an interval around each mean. The intervals currently displayed are based on Fisher's least significant difference (LSD) procedure. They are constructed in such a way that if two means are the same, their intervals will overlap 95,0% of the time. You can display the intervals graphically by selecting Means Plot from the list of Graphical Options. In the Multiple Range Tests, these intervals are used to determine which means are significantly different from which others.

Multiple Range Tests for ROE by ANNEE Method: 90,0 percent LSD

ANNEE

Count

Mean

Homogeneous Groups

2006

568

-0,0079864

X

2009

568

0,00166802

X

2008

568

0,07103

X

2007

568

0,0882099

X

2010

568

Box-

0,0906529

W h

X

Contrast

Sig.

Difference

+/- Limits

2006 - 2007

2006

*

-0,0961963

0,0543239

2006 - 2008

*

-0,0790164

0,0543239

2006 - 2009

 

-0,00965442

0,0543239

2007

2006 - 2010

*

-0,0986393

0,0543239

2007 - 2008

 

0,0171799

0,0543239

2008

2007 - 2009

*

0,0865419

0,0543239

2007 - 2010

 

-0,00244305

0,0543239

28 - 2009

2009

*

0,0693619

0,0543239

2008 - 2010

 

-0,019623

0,0543239

2009 - 2010

2010

*

-0,0889849

0,0543239

* denotes a statistically significant difference.

The StatAdvisor

This table applies a multiple comparison procedure to determine which means are significantly different from which others. The bottom half of the output shows the estimated difference between each pair of means. An asterisk has been placed next to 6 pairs, indicating that these pairs show statistically significant differences at the 95,0% confidence level. At the top of the page, 2 homogenous groups are identified using columns of X's. Within each column, the levels containing X's form a group of means within which there are no statistically significant differences. The method currently being used to discriminate among the means is Fisher's least significant difference (LSD) procedure. With this method, there is a 5,0% risk of calling each pair of means significantly different when the actual difference equals 0.

Variance Check

 

Test

P-Value

Levene's

1,56665

0,180425

Comparison

Sigma1

Sigma2

F-Ratio

P-Value

2006 / 2007

0,541224

0,498518

1,17867

0,0506

2006 / 2008

0,541224

0,60985

0,787605

0,0045

2006 / 2009

0,541224

0,489926

1,22038

0,0179

2006 / 2010

0,541224

0,628869

0,740685

0,0004

2007 / 2008

0,498518

0,60985

0,668216

0,0000

2007 / 2009

0,498518

0,489926

1,03539

0,6790

2007 / 2010

0,498518

0,628869

0,628409

0,0000

2008 / 2009

0,60985

0,489926

1,54948

0,0000

2008 / 2010

0,60985

0,628869

0,940428

0,4649

2009 / 2010

0,489926

0,628869

0,606932

0,0000

The StatAdvisor

The statistic displayed in this table tests the null hypothesis that the standard deviations of ROE within each of the 5 levels of

4

ANNEE is the same. Of particular interest is the P-value. Since the P-value is greater than or equal to 0,05, there is not a

statistically significant difference amongst the standard deviations at the 95,0% confidence level.

The table also shows a comparison of the standard deviations for each pair of samples. P-Values below 0.05, of which there are 7, indicate a statistically significant difference between the two sigmas at the 5% significance level.

Kruskal-Wallis Test for ROE by ANNEE

ANNEE

Sample Size

Average Rank

2006

568

1275,31

2007

568

1547,93

2008

568

1517,84

2009

568

1287,11

2010

568

1474,32

Test statistic = 57,0073 P-Value = 1,2328E-11

The StatAdvisor

The Kruskal-Wallis test tests the null hypothesis that the medians of ROE within each of the 5 levels of ANNEE are the same. The data from all the levels is first combined and ranked from smallest to largest. The average rank is then computed for the data at each level. Since the P-value is less than 0,05, there is a statistically significant difference amongst the medians at the 95,0% confidence level. To determine which medians are significantly different from which others, select Box-and-Whisker Plot from the list of Graphical Options and select the median notch option.

Mood's Median Test for ROE by ANNEE Total n = 2840

Grand median = 0,0583012

0,12

0,1

ANNEE

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

2006

568

326

242

0,0195117

0,00305438

0,041546

0,08

2007

568

245

323

0,0910952

0,0759334

0,110756

2008

568

250

318

0,0837851

0,065602

0,0957051

0,06

2009

568

328

240

0,0176906

0,00193535

0,0337506

2010

004

568

271

297

0,0734001

0,0512998

0,091961

Test statistic = 46,0986 P-Value = 2,3492E-9

The StatAdvisor

Mood's median test tests the hypothesis that the medians of all 5 samples are equal. It does so by counting the number of

0

observations in each sample on either side of the grand median, which equals 0,0583012. Since the P-value for the chi-

2006 2007 2008 2009 2010

square test is less than 0,1, the medians of the samples are significantly different at the 90,0% confidence level. Also

ANNEE

included (if available) are 90,0% confidence intervals for each median based on the order statistics of each sample.

Appendix 7:ROA by year

One-Way ANOVA - ROA by ANNEE

Dependent variable: ROA Factor: ANNEE

Number of observations: 2840 Number of levels: 5

The StatAdvisor

This procedure performs a one-way analysis of variance for ROA. It constructs various tests and graphs to compare the mean values of ROA for the 5 different levels of ANNEE. The F-test in the ANOVA table will test whether there are any significant differences amongst the means. If there are, the Multiple Range Tests will tell you which means are significantly different from which others. If you are worried about the presence of outliers, choose the Kruskal-Wallis Test which compares medians instead of means. The various plots will help you judge the practical significance of the results, as well as allow you to look for possible violations of the assumptions underlying the analysis of variance.

Summary Statistics for ROA

ANNEE

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

2006

568

-0,013372

0,223258

-1669,59%

-2,05464

0,701545

2,75619

-31,7124

2007

568

0,045657

0,198647

435,086%

-1,41503

0,677594

2,09262

-20,8092

2008

568

Scatte

0,0677319

by Level

0,328621

485,179%

-2,07651

2,83152

4,90803

33,4605

2009

568

-0,0042941

0,204192

-4755,18%

-1,50563

0,600911

2,10654

-23,9402

2,9

2010

568

0,0572362

0,292304

510,697%

-1,41254

2,82314

4,23567

28,9587

Total

2840

0,0305918

0,256688

839,075%

-2,07651

2,83152

4,90803

33,8379

ANNEE

Stnd. kurtosis

2006

118,296

0,9

2007

63,4508

2008

169,523

-0,1

209

65,4003

2010

147,887

-1,1

Total

386,057

The StatAdvisor

21

This table shows various statistics for ROA for each of the 5 levels of ANNEE. The one-way analysis of variance is

2006 2007 2008 2009 2010

primarily intended to compare the means of the different levels, listed here under the Average column. Select Means Plot

ANNEE

from the list of Graphical Options to display the means graphically.

WARNING: The standardized skewness and/or kurtosis is outside the range of -2 to +2 for 5 levels of ANNEE. This indicates some significant nonnormality in the data, which violates the assumption that the data come from normal distributions. You may wish to transform the data or use the Kruskal-Wallis test to compare the medians instead of the means.

ANOVA Table for ROA by ANNEE

Source

Sum of Squares

Df

Mean Square

F-Ratio

P-Value

Between groups

3,10475

4

0,776188

11,96

0,0000

Within groups

183,953

2835

0,0648865

 
 

Total (Corr.)

187,058

2839

 
 
 

The StatAdvisor

The ANOVA table decomposes the variance of ROA into two components: a between-group component and a within-group component. The F-ratio, which in this case equals 11,9622, is a ratio of the between-group estimate to the within-group estimate. Since the P-value of the F-test is less than 0,05, there is a statistically significant difference between the mean ROA from one level of ANNEE to another at the 95,0% confidence level. To determine which means are significantly different from which others, select Multiple Range Tests from the list of Tabular Options.

ANOVA for ROA

0,2 0,8 1,

Table of Means for ROA by ANNEE with 90,0 percent LSD intervals

0,03

 
 
 

Stnd. error

 
 

0,01

ANNEE

Count

Mean

(pooled s)

Lower limit

Upper limit

2006

0,01

568

-0,013372

0,0106882

-0,0258033

-0,000940728

2007

568

0,045657

0,0106882

0,0332257

0,0580883

2008

003

568

0,0677319

0,0106882

0,0553006

0,0801632

2009

568

-0,0042941

2007

0,106882

2008 2

-0,0167254

2010

0,00813719

2010

568

0,0572362

0,0106882

NNEE

0,0448049

0,0696675

Total

2840

0,0305918

 
 
 

The StatAdvisor

This table shows the mean ROA for each level of ANNEE. It also shows the standard error of each mean, which is a measure of its sampling variability. The standard error is formed by dividing the pooled standard deviation by the square root of the number of observations at each level. The table also displays an interval around each mean. The intervals currently displayed are based on Fisher's least significant difference (LSD) procedure. They are constructed in such a way that if two means are the same, their intervals will overlap 95,0% of the time. You can display the intervals graphically by selecting Means Plot from the list of Graphical Options. In the Multiple Range Tests, these intervals are used to determine which means are significantly different from which others.

Multiple Range Tests for ROA by ANNEE Method: 90,0 percent LSD

ANNEE

Count

Mean

Homogeneous Groups

2006

568

-0,013372

X

2009

568

-0,0042941

X

2007

568

0,045657

X

2010

568

0,0572362

X

2008

568

Box-

0,0677319

X h

X

Contrast

Sig.

Difference

+/- Limits

2006 - 2007

2006

*

-0,059029

0,0248626

2006 - 2008

*

-0,0811039

0,0248626

2006 - 2009

 

-0,00907792

0,0248626

2007

2006 - 2010

*

-0,0706082

0,0248626

2007 - 2008

 

-0,0220748

0,0248626

2008

2007 - 2009

*

0,0499511

0,0248626

2007 - 2010

 

-0,0115791

0,0248626

28 - 2009

2009

*

0,072026

0,0248626

2008 - 2010

 

0,0104957

0,0248626

2009 - 2010

2010

*

-0,0615303

0,0248626

* denotes a statistically significant difference.

The StatAdvisor

This table applies a multiple comparison procedure to determine which means are significantly different from which others. The bottom half of the output shows the estimated difference between each pair of means. An asterisk has been placed next to 6 pairs, indicating that these pairs show statistically significant differences at the 95,0% confidence level. At the top of the page, 2 homogenous groups are identified using columns of X's. Within each column, the levels containing X's form a group of means within which there are no statistically significant differences. The method currently being used to discriminate among the means is Fisher's least significant difference (LSD) procedure. With this method, there is a 5,0% risk of calling each pair of means significantly different when the actual difference equals 0.

Variance Check

 

Test

P-Value

Levene's

2,58661

0,0352025

Comparison

Sigma1

Sigma2

F-Ratio

P-Value

2006 / 2007

0,223258

0,198647

1,26314

0,0055

2006 / 2008

0,223258

0,328621

0,461557

0,0000

2006 / 2009

0,223258

0,204192

1,19546

0,0337

2006 / 2010

0,223258

0,292304

0,583375

0,0000

2007 / 2008

0,198647

0,328621

0,365405

0,0000

2007 / 2009

0,198647

0,204192

0,946424

0,5123

2007 / 2010

0,198647

0,292304

0,461846

0,0000

2008 / 2009

0,328621

0,204192

2,59007

0,0000

2008 / 2010

0,328621

0,292304

1,26393

0,0054

2009 / 2010

0,204192

0,292304

0,48799

0,0000

The StatAdvisor

The statistic displayed in this table tests the null hypothesis that the standard deviations of ROA within each of the 5 levels of

3

ANNEE is the same. Of particular interest is the P-value. Since the the P-value is less than 0,05, there is a statistically significant difference amongst the standard deviations at the 95,0% confidence level. This violates one of the important

2

assumptions underlying the analysis of variance and will invalidate most of the standard statistical tests.

The table also shows a comparison of the standard deviations for each pair of samples. P-Values below 0.05, of which there are 9, indicate a statistically significant difference between the two sigmas at the 5% significance level.

0

Kruskal-Wallis Test for ROA by ANNEE

ANNEE

Sample Size

Average Rank

2006

568

1253,72

2007

568

1552,39

2008

568

1545,22

2009

568

1269,77

2010

568

1481,39

Test statistic = 73,6591 P-Value = 0

The StatAdvisor

The Kruskal-Wallis test tests the null hypothesis that the medians of ROA within each of the 5 levels of ANNEE are the same. The data from all the levels is first combined and ranked from smallest to largest. The average rank is then computed for the data at each level. Since the P-value is less than 0,05, there is a statistically significant difference amongst the medians at the 95,0% confidence level. To determine which medians are significantly different from which others, select Box-and-Whisker Plot from the list of Graphical Options and select the median notch option.

Mood's Median Test for ROA by ANNEE Total n = 2840

0001

Grand median = 0,034619

79

ANNEE

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

59

2006

568

339

229

0,00945275

-0,000555481

0,0218933

2007

568

242

326

0,0570648

0,0460463

0,0689174

2008

568

248

320

0,0528593

0,0436125

0,0621654

39

2009

568

327

241

0,0121847

0,000872383

0,0184358

2010

568

264

304

0,044225

0,0338877

0,0561194

Test statistic = 58,6901 P-Value = 5,46618E-12

19

The StatAdvisor

Mood's median test tests the hypothesis that the medians of all 5 samples are equal. It does so by counting the number of

1

observations in each sample on either side of the grand median, which equals 0,034619. Since the P-value for the chi-square

2006 2007 2008 2009 2010

test is less than 0,1, the medians of the samples are significantly different at the 90,0% confidence level. Also included (if

ANNEE

available) are 90,0% confidence intervals for each median based on the order statistics of each sample.

Appendix 8: ROE by groups for dairy production

One-Way ANOVA - ROE by Groups (ANNEE=2010&SPECIALISATION="Bovins lait")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2010&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

10

-0,0069795

0,262229

-3757,13%

-0,484615

0,252975

0,73759

-1,14121

2

48

0,142481

0,180874

126,946%

-0,412968

0,474718

0,887686

-2,98642

3

43

0,123011

0,208889

169,813%

-0,37387

0,642566

1,01644

0,773958

4

28

0,0362009

0,195441

539,878%

-0,268354

0,67475

0,943103

2,75521

5

12

0,0452324

0,0883501

195,325%

-0,0649112

0,241946

0,306857

1,52055

Total

141

0,0965617

0,197818

204,861%

-0,484615

0,67475

1,15936

-0,42451

Groups

Stnd. kurtosis

1

-0,367154

2

2,91906

3

0,924502

4

3,11992

5

0,672821

Total

2,31948

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

2

1

10

59,3

2

Cs)

48

83,875

3

43

75,2093

4

28

53,7857

5

12

54,3333

Test statistic = 13,0166 P-Value = 0,0111951

Mood's Median Test for ROE by Groups Total n = 141

6 04

Grand median = 0,104137

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

10

6

4

0,0749859

-0,379136

0,23285

2

48

17

31

0,162987

0,111378

0,214087

3

43

20

23

0,121517

0,0528025

0,196325

4

28

19

9

0,0175846

-0,083556

0,106835

5

12

9

3

0,0220671

-0,0257561

0,128591

Test statistic = 11,2575 P-Value = 0,0238171

One-Way ANOVA - ROE by Groups (ANNEE=2009&SPECIALISATION="Bovins lait")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2009&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

10

-0,17879

1,37918

-771,394%

-3,68929

1,89707

5,58636

-2,33501

2

42

0,11621

0,185857

159,932%

-0,376288

0,596295

0,972584

0,502

3

39

0,0630057

0,137776

218,672%

-0,181477

0,29342

0,474897

-0,76982

4

36

0,0100035

0,175893

1758,32%

-0,318248

0,462527

0,780774

2,18408

5

14

-0,0197315

0,0917217

-464,848%

-0,213078

0,123559

0,336637

-0,648606

Total

141

0,0399575

0,389822

975,59%

-3,68929

1,89707

5,58636

-26,7144

Groups

Stnd. kurtosis

1

3,83774

2

1,17713

3

-1,46433

4

0,88851

5

0,0493783

Total

153,54

Kruskal-Wallis Test for ROE by Groups

Groups

3

Sample Size

Average Rank

1

10

65,2

2

42

85,5952

3

39

76,3077

4

36

57,1389

5

14

52,2143

Test statistic = 13,3289 P-Value = 0,00977558 Mood's Median Test for ROE by Groups

ROE

Total n = 141

Grand median = 0,0435052

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

10

5

5

0,0550638

-0,618245

0,387143

2

42

13

29

0,099151

0,0719154

0,155127

3

39

16

23

0,0898162

0,0108911

0,150199

4

36

26

10

-0,00936615

-0,0647502

0,0233567

5

14

11

3

-0,0151896

-0,103506

0,0445331

Test statistic = 19,0281 P-Value = 0,000776032

One-Way ANOVA - ROE by Groups (ANNEE=2008&SPECIALISATION="Bovins lait")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2008&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

6

0,075066

2,21034

2944,52%

-3,267

3,703

6,97

0,288721

2

33

0,106858

0,284379

266,127%

-1,04792

0,891704

1,93962

-3,53472

3

48

0,103257

0,150159

145,423%

-0,177508

0,584271

0,761778

2,14386

4

40

0,0920779

0,125932

136,767%

-0,156094

0,329752

0,485846

-0,006572

5

14

0,121409

0,358122

294,972%

-0,115502

1,34073

1,45623

5,31902

Total

141

0,101531

0,4658

458,776%

-3,267

3,703

6,97

3,27998

Groups

Stnd. kurtosis

1

1,24469

2

10,951

3

1,51367

4

-0,748698

5

9,66003

Total

108,366

Kruskal-Wallis Test for ROE by Groups

3

Groups

Sample Size

Average Rank

1

6

58,1667

2

33

79,0303

3

48

71,7083

4

40

70,575

5

14

56,3571

0

, ,

Test statistic = 3,68552 P-Value = 0,450235

ROE

Mood's Median Test for ROE by Groups Total n = 141

Grand median = 0,0900887

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

6

3

3

0,0677642

 
 

2

33

11

22

0,125305

0,0811245

0,185447

3

48

25

23

0,0847578

0,0506366

0,131847

4

40

22

18

0,0781741

0,0264757

0,146814

5

14

10

4

0,0356748

-0,0276925

0,118881

Test statistic = 6,71467 P-Value = 0,151757

One-Way ANOVA - ROE by Groups (ANNEE=2007&SPECIALISATION="Bovins lait")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2007&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

7

-0,20666

1,02638

-496,653%

-2,52135

0,386503

2,90785

-2,79018

2

34

0,177157

0,250627

141,472%

-0,362507

1,06101

1,42352

3,11097

3

49

0,0841043

0,146422

174,096%

-0,481832

0,37319

0,855022

-3,64391

4

38

0,0371798

0,147275

396,114%

-0,270465

0,39789

0,668355

0,0203899

5

13

-0,0100111

0,0819924

-819,015%

-0,105939

0,165918

0,271857

1,15795

Total

141

0,0707839

0,28493

402,535%

-2,52135

1,06101

3,58236

-24,7291

Groups

Stnd. kurtosis

1

3,6496

2

5,01567

3

4,98283

4

0,0175206

5

0,00589768

Total

120,25

Kruskal-Wallis Test for ROE by Groups

4

Groups

Sample Size

Average Rank

1

7

84,1429

2

34

87,8529

3

49

74,4898

4

38

1 0

59,1053

5

13

41,4615

Test statistic = 16,8905 P-Value = 0,00202995

Mood's Median Test for ROE by Groups Total n = 141

Grand median = 0,0919618

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

7

2

5

0,1569

 
 

2

34

10

24

0,140842

0,102307

0,193956

3

49

23

26

0,120696

0,0469401

0,164298

4

38

24

14

0,0287984

-0,0153515

0,119707

5

13

12

1

-0,0275842

-0,0825783

0,0486049

Test statistic = 19,1672 P-Value = 0,000728654

One-Way ANOVA - ROE by Groups (ANNEE=2006&SPECIALISATION="Bovins lait")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2006&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

8

-0,143292

0,641699

-447,826%

-1,38481

0,676675

2,06148

-1,25734

2

35

0,0225383

0,240142

1065,48%

-1,09395

0,276005

1,36996

-7,54682

3

50

0,0475295

0,194926

410,116%

-0,699781

0,443904

1,14368

-3,02011

4

34

0,00565164

0,126919

2245,71%

-0,254159

0,257111

0,511269

0,526975

5

14

0,0271876

0,10212

375,611%

-0,0965745

0,303625

0,4002

2,70992

Total

141

0,0183813

0,233466

1270,13%

-1,38481

0,676675

2,06148

-12,3857

Groups

Stnd. kurtosis

1

0,679283

2

16,2943

3

4,88459

4

-0,227566

5

2,80584

Total

31,5881

Kruskal-Wallis Test for ROE by Groups

Groups

5

Sample Size

Average Rank

1

8

67,5

2

35

76,2286

-02

3

50

75,86

4

34

61,1471

5

14

66,5

Test statistic = 3,48819 P-Value = 0,479676

Mood's Median Test for ROE by Groups Total n = 141

Grand median = 0,0333134

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

8

5

3

-0,000383141

-1,3004

0,615859

2

35

14

21

0,0600382

-0,0171497

0,142537

3

50

20

30

0,0771671

0,00399078

0,126858

4

34

22

12

-0,0159893

-0,0583432

0,0413279

5

14

10

4

0,0124171

-0,0434617

0,0456193

Test statistic = 9,40599 P-Value = 0,0517153

Appendix 9: ROE by groups for cattle specialization

One-Way ANOVA - ROE by Groups (ANNEE=2006&SPECIALISATION="Bovins viande")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2006&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

7

-0,0824401

0,59067

-716,484%

-1,40816

0,248772

1,65693

-2,73727

2

6

-0,133896

0,230839

-172,402%

-0,567189

0,0267818

0,593971

-1,70149

3

7

-0,100753

0,239325

-237,537%

-0,526313

0,16275

0,689062

-1,00633

4

16

-0,158071

0,283015

-179,042%

-1,0385

0,149503

1,188

-3,41863

5

9

-0,138262

0,411863

-297,886%

-1,21844

0,131187

1,34963

-3,43557

Total

45

-0,130205

0,34697

-266,48%

-1,40816

0,248772

1,65693

-6,25348

Groups

Stnd. kurtosis

1

3,53339

2

1,45072

3

0,225223

4

4,876

5

5,01709

Total

7,65586

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

7

33,7143

2

6

20,6667

3

7

22,2857

4

16

19,1875

5

9

23,5556

Test statistic = 6,23275 P-Value = 0,182427

Mood's Median Test for ROE by Groups Total n = 45

Grand median = -0,0364259

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

7

1

6

0,149788

 
 

2

6

3

3

-0,0463576

 
 

3

7

4

3

-0,0429657

 
 

4

16

10

6

-0,0797749

-0,286345

0,0373879

5

9

5

4

-0,0364259

-0,719997

0,122984

Test statistic = 4,80555 P-Value = 0,307838

One-Way ANOVA - ROE by Groups (ANNEE=2007&SPECIALISATION="Bovins viande")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2007&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

5

0,350147

0,452136

129,127%

-0,0783535

0,965862

1,04422

0,501517

2

4

0,0319552

0,274144

857,902%

-0,374624

0,203506

0,57813

-1,52757

3

10

-0,00885641

0,230547

-2603,16%

-0,436945

0,272862

0,709807

-1,60867

4

14

-0,0810269

0,28336

-349,711%

-1,03602

0,134314

1,17033

-5,12914

5

12

-0,119093

0,347957

-292,173%

-1,08706

0,167536

1,25459

-3,22219

Total

45

-0,0171888

0,329044

-1914,29%

-1,08706

0,965862

2,05292

-2,20465

Groups

Stnd. kurtosis

1

-0,792416

2

1,43611

3

0,476563

4

9,17569

5

3,97617

Total

6,68529

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

5

31,0

2

4

30,25

3

10

26,1

4

14

18,1429

5

12

20,3333

Test statistic = 6,0404 P-Value = 0,196151

Mood's Median Test for ROE by Groups Total n = 45

Grand median = 0,0260234

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

5

2

3

0,250584

 
 

2

4

1

3

0,149469

 
 

3

10

3

7

0,0563751

-0,408074

0,18652

4

14

10

4

-0,0117706

-0,0781489

0,0366616

5

12

7

5

0,0195701

-0,364224

0,0845263

Test statistic = 5,68535 P-Value = 0,223911

One-Way ANOVA - ROE by Groups (ANNEE=2008&SPECIALISATION="Bovins viande")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2008&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

3

-0,137126

0,268858

-196,066%

-0,442407

0,0643751

0,506782

-1,04524

2

6

0,200511

0,0814852

40,6389%

0,124808

0,341432

0,216623

1,16574

3

12

0,193629

0,925176

477,808%

-0,481933

3,053

3,53493

4,4236

4

10

-0,0391609

0,0765327

-195,431%

-0,166521

0,0709146

0,237435

-0,429557

5

14

-0,233825

0,391322

-167,357%

-1,13931

0,081154

1,22046

-2,50106

Total

45

-0,0122204

0,546856

-4474,94%

-1,13931

3,053

4,19231

10,3766

Groups

Stnd. kurtosis

1

 

2

0,433379

3

7,37526

4

-0,621184

5

1,23119

Total

31,9128

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

3

19,0

2

6

40,8333

3

12

24,6667

4

10

21,0

5

14

16,2143

Test statistic = 15,5023 P-Value = 0,0037652

Mood's Median Test for ROE by Groups Total n = 45

Grand median = -0,00954206

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

3

2

1

-0,0333468

 
 

2

6

0

6

0,177259

 
 

3

12

5

7

0,0251912

-0,299153

0,149681

4

10

6

4

-0,0288041

-0,128674

0,0316859

5

14

10

4

-0,0589653

-0,468499

0,0204429

Test statistic = 9,62062 P-Value = 0,0473268

One-Way ANOVA - ROE by Groups (ANNEE=2009&SPECIALISATION="Bovins viande")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2009&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

7

0,0674309

0,133599

198,127%

-0,0885807

0,339021

0,427602

1,678

2

6

-0,00554691

0,302544

-5454,28%

-0,536908

0,316999

0,853907

-1,1561

3

7

0,0118722

0,235413

1982,89%

-0,321133

0,434852

0,755984

0,734801

4

13

-0,0072047

0,257595

-3575,37%

-0,37133

0,729829

1,10116

2,94153

5

12

-0,204717

0,385528

-188,322%

-1,13015

0,0444478

1,1746

-2,77536

Total

45

-0,0450762

0,292856

-649,69%

-1,13015

0,729829

1,85998

-3,41959

Groups

Stnd. kurtosis

1

1,87505

2

0,748515

3

0,758146

4

4,36867

5

1,95883

Total

7,26813

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

7

30,2857

2

6

26,1667

3

7

24,4286

4

13

21,3077

5

12

18,1667

Test statistic = 4,4266 P-Value = 0,35134

Mood's Median Test for ROE by Groups Total n = 45

Grand median = -0,030158

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

7

1

6

0,0295297

 
 

2

6

3

3

0,0492473

 
 

3

7

4

3

-0,0559836

 
 

4

13

8

5

-0,0533762

-0,147412

0,0818418

5

12

7

5

-0,0593088

-0,518098

0,0304725

Test statistic = 4,72004 P-Value = 0,317248

One-Way ANOVA - ROE by Groups (ANNEE=2010&SPECIALISATION="Bovins viande")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2010&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

5

-0,0655322

0,312964

-477,572%

-0,608197

0,156568

0,764764

-1,74174

2

8

0,0281413

0,327645

1164,28%

-0,58715

0,465494

1,05264

-0,979147

3

7

0,058023

0,17472

301,122%

-0,122793

0,384158

0,506951

1,1234

4

15

0,033406

0,200963

601,577%

-0,152073

0,678656

0,830729

4,12163

5

10

-0,111302

0,656989

-590,277%

-1,49246

1,18739

2,67985

-0,314283

Total

45

-0,00685106

0,368239

-5374,92%

-1,49246

1,18739

2,67985

-2,12124

Groups

Stnd. kurtosis

1

1,74629

2

0,476915

3

0,634647

4

6,42088

5

2,12482

Total

10,2089

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

5

23,6

2

8

27,125

3

7

25,0

4

15

22,2

5

10

19,2

Test statistic = 1,85464 P-Value = 0,762472

Mood's Median Test for ROE by Groups Total n = 45

Grand median = 0,0147703

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

5

2

3

0,0190313

 
 

2

8

3

5

0,0835481

-0,544271

0,440487

3

7

3

4

0,094098

 
 

4

15

9

6

-0,00436701

-0,0863766

0,091969

5

10

6

4

-0,00584826

-0,622008

0,214118

Test statistic = 1,82153 P-Value = 0,76854

Appendix 10: ROE by groups for grain specialization

One-Way ANOVA - ROE by Groups (ANNEE=2006&SPECIALISATION="Grandes cultures")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2006&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

26

0,179213

1,2686

707,873%

-2,7905

3,7011

6,4916

1,73148

2

25

-0,0607359

0,608729

-1002,26%

-1,40621

1,22074

2,62696

-0,970494

3

28

-0,229362

0,671868

-292,929%

-3,25

0,240467

3,49047

-7,95841

4

28

-0,0463455

0,30485

-657,778%

-0,85709

0,622275

1,47936

-0,288213

5

19

0,00180915

0,119182

6587,76%

-0,343083

0,196914

0,539997

-1,78401

Total

126

-0,0360658

0,728705

-2020,49%

-3,25

3,7011

6,9511

2,59487

Groups

Stnd. kurtosis

1

3,16843

2

0,267322

3

17,0465

4

1,31149

5

2,74246

Total

26,5316

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

26

73,5769

2

25

66,48

3

28

53,6071

4

28

59,75

5

19

65,8947

Test statistic = 4,57832 P-Value = 0,333361

Mood's Median Test for ROE by Groups Total n = 126

Grand median = -0,00516469

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

26

9

17

0,127062

-0,323749

0,515738

2

25

11

14

0,0162612

-0,214373

0,217584

3

28

16

12

-0,0916287

-0,169954

0,051114

4

28

17

11

-0,0583804

-0,168101

0,083301

5

19

10

9

-0,00722626

-0,0502325

0,077573

Test statistic = 4,73131 P-Value = 0,315994

One-Way ANOVA - ROE by Groups (ANNEE=2007&SPECIALISATION="Grandes cultures")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2007&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

17

0,313318

1,35468

432,366%

-2,42969

3,31882

5,74851

-0,0972392

2

20

0,387628

0,374057

96,4989%

-0,302679

1,03553

1,33821

0,0513675

3

28

0,224728

0,341117

151,791%

-0,76952

0,759663

1,52918

-2,12864

4

30

0,0938323

0,434063

462,594%

-1,607

0,727135

2,33414

-4,80535

5

31

0,144244

0,188682

130,808%

-0,161782

0,682598

0,84438

1,8683

Total

126

0,211571

0,58687

277,387%

-2,42969

3,31882

5,74851

0,152769

Groups

Stnd. kurtosis

1

0,947297

2

-0,338419

3

1,27225

4

8,34027

5

1,03053

Total

24,1497

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

17

68,4118

2

20

80,1

3

28

68,5

4

30

56,2333

5

31

52,6129

Test statistic = 8,90878 P-Value = 0,0634204

Mood's Median Test for ROE by Groups Total n = 126

Grand median = 0,217189

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

17

9

8

0,212239

-0,312194

1,10676

2

20

4

16

0,349002

0,227342

0,615027

3

28

11

17

0,324976

0,0618571

0,425479

4

30

18

12

0,159329

0,0492383

0,243204

5

31

21

10

0,110928

0,0475332

0,22415

Test statistic = 13,6478 P-Value = 0,0085084

One-Way ANOVA - ROE by Groups (ANNEE=2008&SPECIALISATION="Grandes cultures")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2008&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

11

-0,0327873

1,60727

-4902,11%

-2,50663

3,0111

5,51773

0,0378886

2

19

-0,0564867

0,93133

-1648,76%

-2,69078

0,844573

3,53535

-3,89846

3

31

0,140444

0,397205

282,82%

-0,854586

0,968828

1,82341

-1,36906

4

36

0,173722

0,249045

143,359%

-0,480456

0,918507

1,39896

-0,402512

5

29

0,270691

0,585796

216,408%

-0,181367

3,181

3,36237

10,2369

Total

126

0,13511

0,690238

510,871%

-2,69078

3,181

5,87178

-2,50975

Groups

Stnd. kurtosis

1

0,226125

2

3,87705

3

0,740814

4

3,45077

5

25,9989

Total

23,3753

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

11

59,0909

2

19

64,8421

3

31

64,2903

4

36

64,1111

5

29

62,6897

Test statistic = 0,224908 P-Value = 0,994132

Mood's Median Test for ROE by Groups Total n = 126

Grand median = 0,158119

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

11

6

5

0,106996

-2,13698

1,11392

2

19

9

10

0,222666

-0,0259652

0,468206

3

31

13

18

0,19585

0,00799357

0,346097

4

36

18

18

0,157943

0,121049

0,248522

5

29

17

12

0,140075

0,0642507

0,268611

Test statistic = 1,81206 P-Value = 0,770275

One-Way ANOVA - ROE by Groups (ANNEE=2009&SPECIALISATION="Grandes cultures")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2009&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

16

-0,125603

0,867811

-690,918%

-3,0875

0,623308

3,71081

-4,80476

2

30

-0,127775

0,699474

-547,427%

-3,04993

1,08441

4,13434

-5,23813

3

24

-0,169858

0,567433

-334,063%

-2,0637

0,486368

2,55007

-3,81864

4

29

0,00564268

0,177867

3152,17%

-0,226196

0,608902

0,835098

4,06886

5

27

-0,0230683

0,343156

-1487,57%

-1,23032

0,707247

1,93757

-2,58084

Total

126

-0,0823706

0,547235

-664,357%

-3,0875

1,08441

4,17191

-13,5848

Groups

Stnd. kurtosis

1

8,1581

2

11,4772

3

4,52777

4

4,66555

5

5,88206

Total

32,3185

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

16

72,8125

2

30

56,8667

3

24

60,2083

4

29

66,5172

5

27

65,037

Test statistic = 2,47127 P-Value = 0,649788

Mood's Median Test for ROE by Groups Total n = 126

Grand median = -0,045463

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

16

6

10

0,0520806

-0,166633

0,346845

2

30

19

11

-0,0973981

-0,308864

0,0792782

3

24

13

11

-0,0574204

-0,193187

0,139505

4

29

12

17

-0,0339269

-0,0779227

0,0381452

5

27

13

14

-0,0370192

-0,128781

0,0722118

Test statistic = 4,19911 P-Value = 0,37973

One-Way ANOVA - ROE by Groups (ANNEE=2010&SPECIALISATION="Grandes cultures")

Dependent variable: ROE

Factor: Groups

Selection variable: ANNEE=2010&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROE

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

23

0,365149

1,55797

426,666%

-3,43153

3,04051

6,47204

-1,30814

2

28

-0,0165585

0,803741

-4853,95%

-2,79716

1,43732

4,23449

-4,06086

3

27

0,0836475

0,442804

529,369%

-0,876775

1,12613

2,0029

0,527303

4

22

0,165111

0,778352

471,412%

-0,764529

2,94949

3,71402

4,66934

5

26

0,0409212

0,394668

964,46%

-0,51794

1,15891

1,67686

3,03462

Total

126

0,118172

0,870417

736,569%

-3,43153

3,04051

6,47204

-0,99077

Groups

Stnd. kurtosis

1

1,41263

2

5,76644

3

1,80958

4

7,20079

5

2,1899

Total

13,7459

Kruskal-Wallis Test for ROE by Groups

Groups

Sample Size

Average Rank

1

23

82,2174

2

28

64,6786

3

27

62,3333

4

22

56,0455

5

26

53,1923

Test statistic = 9,08773 P-Value = 0,0589437

Mood's Median Test for ROE by Groups Total n = 126

Grand median = 0,0469154

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

23

7

16

0,320624

0,0355358

0,546584

2

28

12

16

0,10634

-0,102615

0,378908

3

27

12

15

0,0793623

-0,0679452

0,247933

4

22

15

7

-0,0154434

-0,147291

0,088839

5

26

17

9

-0,0381151

-0,184124

0,10304

Test statistic = 9,79713 P-Value = 0,0439872

Appendix 11: ROA by groups for dairy specialization

One-Way ANOVA - ROA by Groups (ANNEE=2006&SPECIALISATION="Bovins lait")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2006&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

8

-0,0276876

0,142156

-513,428%

-0,286952

0,158607

0,445559

-0,534166

2

35

0,0286671

0,0880925

307,295%

-0,264655

0,154997

0,419652

-2,50623

3

50

0,0389244

0,121565

312,31%

-0,456612

0,275404

0,732016

-3,21185

4

34

0,00725538

0,0943411

1300,29%

-0,17114

0,224912

0,396052

1,06473

5

14

0,0252457

0,0962117

381,101%

-0,0883339

0,303035

0,391369

3,15877

Total

141

0,0236042

0,106445

450,959%

-0,456612

0,303035

0,759647

-2,64697

Groups

Stnd. kurtosis

1

0,348903

2

2,46083

3

6,51958

4

-0,0314555

5

4,01228

Total

6,72696

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

8

54,625

2

35

74,8

3

50

78,26

4

34

61,9118

5

14

67,0

Test statistic = 4,98542 P-Value = 0,288797

Mood's Median Test for ROA by Groups Total n = 141

Grand median = 0,0220625

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

8

6

2

-0,0301937

-0,263351

0,155944

2

35

15

20

0,0256582

-0,00587675

0,0856697

3

50

20

30

0,0500655

0,00212655

0,0763213

4

34

21

13

-0,0110551

-0,0471129

0,0310625

5

14

9

5

0,0102684

-0,0378927

0,0391715

Test statistic = 7,73279 P-Value = 0,101872

One-Way ANOVA - ROA by Groups (ANNEE=2007&SPECIALISATION="Bovins lait")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2007&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

7

0,0431667

0,117686

272,63%

-0,206161

0,141439

0,3476

-2,13626

2

34

0,091015

0,0987241

108,47%

-0,089323

0,353563

0,442886

2,18268

3

49

0,0587761

0,0951325

161,856%

-0,250523

0,336835

0,587358

-0,867226

4

38

0,0305541

0,107431

351,609%

-0,209863

0,300593

0,510456

0,346439

5

13

-0,00667726

0,0742954

-1112,66%

-0,0941891

0,158182

0,252371

1,30607

Total

141

0,0521344

0,101742

195,153%

-0,250523

0,353563

0,604086

0,494216

Groups

Stnd. kurtosis

1

2,35332

2

1,70826

3

3,09405

4

0,132081

5

0,297842

Total

2,51387

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

7

76,8571

2

34

84,4706

3

49

75,1633

4

38

61,9737

5

13

43,3077

Test statistic = 12,1811 P-Value = 0,0160539

Mood's Median Test for ROA by Groups Total n = 141

Grand median = 0,0550632

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

7

3

4

0,0682113

 
 

2

34

11

23

0,0789014

0,0562679

0,103172

3

49

22

27

0,0688806

0,0244087

0,0981639

4

38

24

14

0,0202312

-0,0107817

0,0908195

5

13

11

2

-0,0253725

-0,069592

0,0417187

Test statistic = 13,7443 P-Value = 0,00815748

One-Way ANOVA - ROA by Groups (ANNEE=2008&SPECIALISATION="Bovins lait")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2008&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

6

-0,13581

0,22382

-164,803%

-0,458807

0,0627273

0,521534

-0,719548

2

33

0,061246

0,11112

181,432%

-0,315704

0,356522

0,672226

-1,49312

3

48

0,0709067

0,102503

144,56%

-0,1078

0,38364

0,49144

2,8109

4

40

0,0697293

0,0930542

133,451%

-0,106346

0,250123

0,356469

0,516

5

14

0,114182

0,339191

297,062%

-0,0959154

1,27192

1,36783

5,36768

Total

141

0,063812

0,152396

238,82%

-0,458807

1,27192

1,73072

15,6065

Groups

Stnd. kurtosis

1

-0,816362

2

4,77391

3

1,78309

4

-0,706596

5

9,78065

Total

69,5517

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

6

33,8333

2

33

74,303

3

48

72,7917

4

40

74,825

5

14

62,0714

Test statistic = 6,29522 P-Value = 0,178159

Mood's Median Test for ROA by Groups Total n = 141

Grand median = 0,0501904

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

6

5

1

-0,0544237

 
 

2

33

14

19

0,0606138

0,0374828

0,106412

3

48

24

24

0,0527917

0,024406

0,0916713

4

40

20

20

0,0551586

0,0217964

0,10954

5

14

8

6

0,0311118

-0,0237862

0,107481

Test statistic = 3,70305 P-Value = 0,447682

One-Way ANOVA - ROA by Groups (ANNEE=2009&SPECIALISATION="Bovins lait")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2009&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

10

-0,0530749

0,174992

-329,708%

-0,464359

0,116707

0,581066

-1,98846

2

42

0,0624122

0,0839266

134,471%

-0,101321

0,31074

0,41206

1,26242

3

39

0,0469517

0,0859704

183,104%

-0,0800366

0,221199

0,301236

0,211634

4

36

0,0117456

0,132892

1131,42%

-0,194892

0,350478

0,54537

2,56668

5

14

-0,0183905

0,0860311

-467,801%

-0,20497

0,115921

0,320892

-0,71293

Total

141

0,0289862

0,111116

383,342%

-0,464359

0,350478

0,814837

-1,10943

Groups

Stnd. kurtosis

1

1,81784

2

0,905741

3

-1,26116

4

0,830834

5

0,270994

Total

5,91865

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

10

54,8

2

42

85,3571

3

39

78,7179

4

36

57,6944

5

14

52,2143

Test statistic = 14,9349 P-Value = 0,00483818

Mood's Median Test for ROA by Groups Total n = 141

Grand median = 0,0264797

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

10

6

4

-0,0185376

-0,202246

0,112088

2

42

14

28

0,0601934

0,0371256

0,0825121

3

39

16

23

0,0513118

0,00540596

0,0953483

4

36

26

10

-0,00644387

-0,0427011

0,0156736

5

14

9

5

-0,0137695

-0,0925586

0,0372823

Test statistic = 14,5707 P-Value = 0,00567971

One-Way ANOVA - ROA by Groups (ANNEE=2010&SPECIALISATION="Bovins lait")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2010&SPECIALISATION="Bovins lait"

Number of observations: 141 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

10

0,0334214

0,0841506

251,786%

-0,112091

0,130723

0,242815

-0,717725

2

48

0,0807499

0,0909653

112,651%

-0,197911

0,323128

0,521039

-1,2612

3

43

0,0720914

0,112581

156,164%

-0,150311

0,328919

0,47923

0,716939

4

28

0,0258738

0,139871

540,588%

-0,206584

0,454306

0,66089

2,57093

5

12

0,041393

0,0791111

191,122%

-0,0596758

0,211972

0,271648

1,42801

Total

141

0,0605058

0,108654

179,575%

-0,206584

0,454306

0,66089

1,67491

Groups

Stnd. kurtosis

1

-0,512573

2

2,28223

3

-0,339197

4

2,46287

5

0,44387

Total

2,09408

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

10

61,2

2

48

81,4792

3

43

74,6512

4

28

55,25

5

12

60,9167

Test statistic = 8,97242 P-Value = 0,0617925

Mood's Median Test for ROA by Groups Total n = 141

Grand median = 0,0608248

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

10

5

5

0,0441637

-0,0820523

0,123533

2

48

19

29

0,0855747

0,0591239

0,11358

3

43

21

22

0,0697366

0,0250791

0,111734

4

28

18

10

0,0125945

-0,0636708

0,0666888

5

12

8

4

0,019074

-0,0212725

0,120007

Test statistic = 5,71883 P-Value = 0,221153

Appendix 12: ROA by groups for cattle specialization

One-Way ANOVA - ROA by Groups (ANNEE=2006&SPECIALISATION="Bovins viande")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2006&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

7

-0,105765

0,453978

-429,234%

-1,13039

0,140396

1,27079

-2,80058

2

6

-0,0453944

0,078609

-173,169%

-0,177484

0,0229301

0,200414

-1,00598

3

7

-0,0473511

0,111711

-235,92%

-0,212802

0,0912202

0,304023

-0,371181

4

16

-0,111976

0,203313

-181,569%

-0,736663

0,11937

0,856032

-3,26129

5

9

-0,112163

0,336903

-300,368%

-0,993887

0,114128

1,10801

-3,40561

Total

45

-0,0921169

0,257116

-279,119%

-1,13039

0,140396

1,27079

-7,86363

Groups

Stnd. kurtosis

1

3,66696

2

0,113159

3

-0,655198

4

4,54897

5

4,95777

Total

12,0237

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

7

31,4286

2

6

21,8333

3

7

22,5714

4

16

19,5

5

9

23,7778

Test statistic = 4,10541 P-Value = 0,391929

Mood's Median Test for ROA by Groups Total n = 45

Grand median = -0,0284627

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

7

1

6

0,0516395

 
 

2

6

3

3

-0,0247187

 
 

3

7

3

4

-0,0179525

 
 

4

16

10

6

-0,0646852

-0,187257

0,028068

5

9

6

3

-0,0293466

-0,589257

0,107523

Test statistic = 5,69488 P-Value = 0,223123

One-Way ANOVA - ROA by Groups (ANNEE=2007&SPECIALISATION="Bovins viande")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2007&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

5

0,0746187

0,110029

147,455%

-0,0409659

0,217745

0,258711

0,141305

2

4

0,0162684

0,168061

1033,05%

-0,230272

0,138248

0,36852

-1,42673

3

10

0,00954246

0,130679

1369,44%

-0,222714

0,215079

0,437793

-0,964728

4

14

-0,0609528

0,218634

-358,694%

-0,79908

0,099552

0,898632

-5,15769

5

12

-0,0972349

0,295406

-303,806%

-0,922824

0,153846

1,07667

-3,2495

Total

45

-0,0330348

0,213964

-647,692%

-0,922824

0,217745

1,14057

-7,61542

Groups

Stnd. kurtosis

1

-0,824357

2

1,27345

3

0,42576

4

9,25855

5

4,09935

Total

12,5719

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

5

29,6

2

4

29,5

3

10

26,3

4

14

18,2857

5

12

20,8333

Test statistic = 5,00392 P-Value = 0,286896

Mood's Median Test for ROA by Groups Total n = 45

Grand median = 0,0218779

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

5

2

3

0,0907909

 
 

2

4

1

3

0,0785487

 
 

3

10

3

7

0,0371746

-0,201921

0,112828

4

14

10

4

-0,00844839

-0,0582445

0,0252546

5

12

7

5

0,0165747

-0,297092

0,0768496

Test statistic = 5,68535 P-Value = 0,223911

One-Way ANOVA - ROA by Groups (ANNEE=2008&SPECIALISATION="Bovins viande")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2008&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

3

-0,0681985

0,155976

-228,708%

-0,241266

0,0615116

0,302777

-0,81616

2

6

0,0936297

0,0367747

39,2768%

0,0291386

0,137466

0,108327

-1,05367

3

12

0,190185

0,765362

402,431%

-0,228707

2,5915

2,82021

4,68812

4

10

-0,027013

0,0593021

-219,532%

-0,128615

0,0663722

0,194987

-0,361463

5

14

-0,197005

0,329566

-167,288%

-0,941719

0,0759867

1,01771

-2,45214

Total

45

-0,00864

0,452028

-5231,81%

-0,941719

2,5915

3,53322

11,3001

Groups

Stnd. kurtosis

1

 

2

0,926372

3

7,97102

4

-0,321322

5

1,13895

Total

35,7779

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

3

19,0

2

6

39,1667

3

12

25,3333

4

10

21,0

5

14

16,3571

Test statistic = 13,5611 P-Value = 0,00883607

Mood's Median Test for ROA by Groups Total n = 45

Grand median = -0,00847679

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

3

2

1

-0,0248415

 
 

2

6

0

6

0,0991103

 
 

3

12

5

7

0,0165927

-0,183294

0,117365

4

10

6

4

-0,019054

-0,0979158

0,0233145

5

14

10

4

-0,0486992

-0,393363

0,0173552

Test statistic = 9,62062 P-Value = 0,0473268

One-Way ANOVA - ROA by Groups (ANNEE=2009&SPECIALISATION="Bovins viande")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2009&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

7

0,0219456

0,051473

234,548%

-0,0646818

0,0906458

0,155328

-0,277781

2

6

0,0469839

0,122463

260,649%

-0,114153

0,186734

0,300888

-0,157948

3

7

0,0075369

0,128871

1709,86%

-0,171113

0,230123

0,401236

0,640108

4

13

-0,00644273

0,165268

-2565,19%

-0,223332

0,462336

0,685667

2,92308

5

12

-0,191032

0,366677

-191,946%

-1,05433

0,0394772

1,09381

-2,77349

Total

45

-0,0419523

0,232259

-553,625%

-1,05433

0,462336

1,51666

-7,34034

Groups

Stnd. kurtosis

1

0,277796

2

-1,10656

3

0,33783

4

4,0552

5

1,87133

Total

14,9154

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

7

28,4286

2

6

29,3333

3

7

25,0

4

13

20,9231

5

12

17,75

Test statistic = 4,99582 P-Value = 0,287726

Mood's Median Test for ROA by Groups Total n = 45

Grand median = -0,0214517

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

7

1

6

0,0143045

 
 

2

6

2

4

0,0517442

 
 

3

7

4

3

-0,0355858

 
 

4

13

8

5

-0,0368864

-0,106846

0,0743713

5

12

8

4

-0,0506681

-0,489059

0,0293248

Test statistic = 6,38753 P-Value = 0,172017

One-Way ANOVA - ROA by Groups (ANNEE=2010&SPECIALISATION="Bovins viande")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2010&SPECIALISATION="Bovins viande"

Number of observations: 45 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

5

0,0150524

0,0949592

630,858%

-0,121916

0,107682

0,229598

-0,543456

2

8

0,0492815

0,133

269,878%

-0,13138

0,25763

0,38901

0,0269102

3

7

0,0338206

0,105709

312,558%

-0,0764125

0,221189

0,297601

0,818161

4

15

0,0330233

0,173375

525,008%

-0,115176

0,613893

0,729069

4,73907

5

10

-0,120915

0,592286

-489,838%

-1,41254

0,982268

2,39481

-0,732483

Total

45

-0,000167523

0,301365

-179895,%

-1,41254

0,982268

2,39481

-4,15775

Groups

Stnd. kurtosis

1

-0,285199

2

-0,375168

3

0,169954

4

8,06292

5

2,10848

Total

17,7737

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

5

24,6

2

8

27,375

3

7

25,0

4

15

22,1333

5

10

18,6

Test statistic = 2,31184 P-Value = 0,678615

Mood's Median Test for ROA by Groups Total n = 45

Grand median = 0,0134522

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

5

2

3

0,0136008

 
 

2

8

3

5

0,0500813

-0,129276

0,244492

3

7

3

4

0,0634198

 
 

4

15

9

6

-0,00312077

-0,0566846

0,0635826

5

10

6

4

-0,00640764

-0,597499

0,188712

Test statistic = 1,82153 P-Value = 0,76854

Appendix 13: ROA by groups for grain specialization

One-Way ANOVA - ROA by Groups (ANNEE=2006&SPECIALISATION="Grandes cultures")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2006&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

26

-0,0210406

0,264087

-1255,13%

-0,717935

0,385031

1,10297

-1,9433

2

25

0,00810686

0,217864

2687,41%

-0,415281

0,488035

0,903316

-0,103519

3

28

-0,116418

0,412285

-354,143%

-2,05464

0,185067

2,23971

-8,91681

4

28

-0,031409

0,209427

-666,773%

-0,52968

0,400579

0,930259

0,126946

5

19

0,00295864

0,109342

3695,66%

-0,315782

0,194464

0,510247

-1,81182

Total

126

-0,0351374

0,270354

-769,419%

-2,05464

0,488035

2,54268

-16,0997

Groups

Stnd. kurtosis

1

1,11277

2

-0,0307004

3

20,8898

4

0,554087

5

2,91151

Total

55,7841

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

26

66,9231

2

25

68,48

3

28

56,8214

4

28

59,3571

5

19

68,2105

Test statistic = 2,3065 P-Value = 0,679586

Mood's Median Test for ROA by Groups Total n = 126

Grand median = -0,0106826

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

26

11

15

0,026324

-0,102639

0,0944078

2

25

11

14

0,00499102

-0,0805615

0,132682

3

28

16

12

-0,0494749

-0,0861457

0,038731

4

28

17

11

-0,0426167

-0,114584

0,0563211

5

19

8

11

-0,00581426

-0,04485

0,0720474

Test statistic = 3,30621 P-Value = 0,507949

One-Way ANOVA - ROA by Groups (ANNEE=2007&SPECIALISATION="Grandes cultures")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2007&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

17

-0,007065

0,34629

-4901,48%

-1,09085

0,420565

1,51142

-3,33089

2

20

0,153102

0,161233

105,311%

-0,146563

0,424526

0,571089

0,162655

3

28

0,142208

0,210249

147,846%

-0,374676

0,677594

1,05227

-0,432352

4

30

0,0629824

0,333997

530,302%

-1,35464

0,542145

1,89679

-6,01841

5

31

0,124657

0,159385

127,859%

-0,131722

0,550014

0,681736

1,66031

Total

126

0,100616

0,252418

250,873%

-1,35464

0,677594

2,03224

-10,5448

Groups

Stnd. kurtosis

1

4,71157

2

-0,605725

3

1,35227

4

12,217

5

0,561296

Total

26,2384

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

17

51,0588

2

20

69,85

3

28

70,0714

4

30

60,9

5

31

62,8065

Test statistic = 3,648 P-Value = 0,455734

Mood's Median Test for ROA by Groups Total n = 126

Grand median = 0,119947

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

17

10

7

0,0704004

-0,154593

0,168393

2

20

11

9

0,101369

0,0783039

0,269983

3

28

9

19

0,188491

0,0397537

0,225054

4

30

16

14

0,101664

0,0372794

0,181625

5

31

17

14

0,0994869

0,0423933

0,189778

Test statistic = 4,7245 P-Value = 0,316751

One-Way ANOVA - ROA by Groups (ANNEE=2008&SPECIALISATION="Grandes cultures")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2008&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

11

0,132713

0,42949

323,624%

-0,372089

1,10397

1,47606

1,43867

2

19

-0,00170479

0,334047

-19594,6%

-1,11512

0,344811

1,45993

-4,38283

3

31

0,0933312

0,227565

243,825%

-0,452873

0,441864

0,894738

-1,51654

4

36

0,120531

0,176681

146,586%

-0,357934

0,7116

1,06953

0,39532

5

29

0,232773

0,487943

209,622%

-0,152033

2,64574

2,79777

10,0901

Total

126

0,122303

0,332737

272,058%

-1,11512

2,64574

3,76086

14,46

Groups

Stnd. kurtosis

1

1,03476

2

6,07373

3

-0,0995506

4

5,01875

5

25,4604

Total

63,1311

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

11

61,4545

2

19

53,1053

3

31

63,129

4

36

64,7778

5

29

69,8966

Test statistic = 2,51113 P-Value = 0,642644

Mood's Median Test for ROA by Groups Total n = 126

Grand median = 0,112204

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

11

6

5

0,0986874

-0,315322

0,49457

2

19

12

7

0,0782904

-0,00894632

0,17541

3

31

15

16

0,112627

-0,000184656

0,246034

4

36

17

19

0,123432

0,0862352

0,170685

5

29

13

16

0,127359

0,0548796

0,224914

Test statistic = 1,86041 P-Value = 0,761413

One-Way ANOVA - ROA by Groups (ANNEE=2009&SPECIALISATION="Grandes cultures")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2009&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

16

0,011712

0,133853

1142,87%

-0,354306

0,234759

0,589065

-2,07989

2

30

-0,0478995

0,322939

-674,201%

-1,50563

0,400454

1,90608

-7,00551

3

24

-0,075446

0,303661

-402,488%

-1,08988

0,3173

1,40718

-3,70352

4

29

0,00888732

0,131824

1483,28%

-0,146988

0,436751

0,583739

4,25596

5

27

-0,0184289

0,289708

-1572,03%

-1,01514

0,555053

1,57019

-2,44534

Total

126

-0,0261916

0,256366

-978,807%

-1,50563

0,555053

2,06068

-10,8935

Groups

Stnd. kurtosis

1

2,45812

2

16,3957

3

4,61302

4

4,43736

5

5,06561

Total

26,7243

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

16

74,3125

2

30

58,3

3

24

60,4167

4

29

66,069

5

27

62,8519

Test statistic = 2,3342 P-Value = 0,674549

Mood's Median Test for ROA by Groups Total n = 126

Grand median = -0,0253879

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

16

4

12

0,028231

-0,0257411

0,100183

2

30

19

11

-0,057458

-0,109656

0,0350027

3

24

12

12

-0,0297157

-0,100399

0,0758477

4

29

14

15

-0,0245652

-0,0545986

0,0302802

5

27

14

13

-0,0315569

-0,112985

0,0662393

Test statistic = 6,20485 P-Value = 0,184363

One-Way ANOVA - ROA by Groups (ANNEE=2010&SPECIALISATION="Grandes cultures")

Dependent variable: ROA

Factor: Groups

Selection variable: ANNEE=2010&SPECIALISATION="Grandes cultures"

Number of observations: 126 Number of levels: 5

Summary Statistics for ROA

Groups

Count

Average

Standard deviation

Coeff. of variation

Minimum

Maximum

Range

Stnd. skewness

1

23

0,14274

0,225007

157,634%

-0,414526

0,629534

1,04406

-0,951261

2

28

0,0441773

0,235867

533,911%

-0,61943

0,448297

1,06773

-1,69751

3

27

0,0456094

0,239838

525,851%

-0,501173

0,67582

1,17699

0,174262

4

22

0,103995

0,495929

476,878%

-0,574792

1,77651

2,35131

4,02694

5

26

0,0271355

0,336543

1240,23%

-0,436735

0,900406

1,33714

2,7171

Total

126

0,0694037

0,314165

452,664%

-0,61943

1,77651

2,39595

7,10176

Groups

Stnd. kurtosis

1

1,41796

2

1,15054

3

2,04443

4

5,40591

5

1,53559

Total

15,4533

Kruskal-Wallis Test for ROA by Groups

Groups

Sample Size

Average Rank

1

23

80,5217

2

28

64,7143

3

27

63,5185

4

22

56,4545

5

26

53,0769

Test statistic = 7,96551 P-Value = 0,0928499

Mood's Median Test for ROA by Groups Total n = 126

Grand median = 0,0352505

Groups

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

1

23

6

17

0,153682

0,0569592

0,230257

2

28

14

14

0,0352505

-0,0468007

0,207821

3

27

12

15

0,0373107

-0,0316293

0,116303

4

22

14

8

-0,012042

-0,110281

0,0668334

5

26

17

9

-0,0347476

-0,162994

0,0882766

Test statistic = 9,6921 P-Value = 0,0459461

Appendix 14: Cost of debt for farms of Isère

Specialization

number of observation cost of debt (market value)

15 3,8%

8 3,6%

15 3,5%

8 3,5%

Dairy Cattle Grain Diversified

46 3,5%

Mean

The cost of capital has been estimated using the market value for the cost of debt. For some specialization like cattle farming, we do not dispose of enough farms to collect 15 recent interest rates.






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"I don't believe we shall ever have a good money again before we take the thing out of the hand of governments. We can't take it violently, out of the hands of governments, all we can do is by some sly roundabout way introduce something that they can't stop ..."   Friedrich Hayek (1899-1992) en 1984