Sustainability and production costs in the global farming sector : comparative analysis and methodologies.
Date: 2012-10-08
Institutions: Institute for Prospective Technological Studies
Subjects: agricultural production policy, production statistics, research report, statistical method, sustainable development

European
Commission

J R C   S C I E N T I F I C   A N D   P O L I C Y   R E P O R T S

Report EUR 25436 EN

Sustainability and 
Production Costs in 
the Global Farming 
Sector: Comparative 
Analysis and 
Methodologies

Authors: 

Stephen Langrell, Pavel Ciaian, 

Sergio Gomez y Paloma, Dan L. Cunningham,

Jean-Francois Garnier, Folkhard Isermeyer

and Ashok K. Mishra

Editors: 

Stephen Langrell, Pavel Ciaian and 

Sergio Gomez y Paloma 

2012

EUR 25436 EN

Acknowledgements

This report has been prepared by the IPTS editorial team comprising Stephen Langrell, Pavel Ciaian 

and Sergio Gomez y Paloma. We would like to thank Tassos Haniotis, Director, Directorate L, DG-AGRI, 

for his intervention in the workshop and for  setting  the scene  in  a political  and global context, as  well 

as Pierluigi Londero, Willie Schulz Greve, Hubertus Gay, Dangiris Nekrasius and Thierry Vard, also from 

DG-AGRI  (Directorate  L),  for  their  close  collaboration  in  the  realization  of  the  workshop  and  in  their 

valuable comments and further inputs to this report. We thank Jacques Delincé, Head of Agriculture and 

Life Sciences in the Economy Unit, for his continuing support and additional input to the production of 

this report.

We  would  like  to  thank  all  participants  who  readily  gave  of  their  experiences  and  expertise  of  the 

various production cost calculation methods and approaches used across the globe in the various sectors 

under consideration. Details of all external participants, including internal European Commission staff, are 

given in the Annex.

Finally we would like to thank Folkhard Isermeyer (Johann Heinrich von Thünen-Institute, Germany), 

Dan L. Cunningham (University of Georgia, USA), Ashok K. Mishra (Louisiana State University, USA) and 

Jean-François Garnier (ARVALIS, France) for acting as rapporteurs for each of the technical sessions and their 

contribution to respective synthesis reports towards the production and overall realisation of this JRC report.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

3

 
 
 
 
 
 
 
 
 
 
 
 
Table of Contents

Acknowledgements 

Preface 

Executive Summary 

Chapter 1. Introduction to Production Costs 

1.1. 

Introduction 

1.2.	 Classification	of	costs	

1.3.  Cost calculation approaches 

Chapter 2. Methodologies and Comparisons of Production Costs  

           – a Global Overview  

2.1. 

Introduction 

2.2.  Data Collection and Methodologies in National Surveys 

2.2.1.  European Union 

2.2.2.  USA 

2.2.3.  Brazil 

2.2.4.  Canada 

2.2.5.  Australia 

2.2.6.  New Zealand 

2.2.7.  Ukraine 

2.3.  Global comparisons 

2.4.  Summary, Conclusions, Recommendations 

Chapter 3. Production Cost Calculations and Methodologies Used for Dairy, 

Beef, Pork and Poultry Industries  

3.1. 

Introduction 

3.2.  Commodity Reports 

3.2.1.  Dairy 

3.2.2.	 Beef	

3.2.3.  Pork 

3.2.4.  Poultry 

3.3.  Conclusions and Recommendations 

3

11

13

21

21

21

23

25

25

26

26

28

30

32

34

35

36

37

43

51

51

51

51

53

59

65

70

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

5

 
 
 
 
 
 
 
 
 
 
 
 
Chapter 4. Production Cost Methodologies for Cereals and Arable Crops 

4.1. 

Introduction 

4.2.	 Which	type	of	production	cost?	

4.2.1.	 Perimeter	of	calculating	the	cost	of	production		

4.2.2.	 Cash	cost	or	Total	cost?	

4.3.	 Studies	of	competitiveness	for	different	purposes	

4.4.	 Different	methods	to	estimate	the	cost	of	production	

4.4.1.  Data Collection  

4.4.2.	 Main	differing	points	in	the	calculation	methods		

4.4.3.	 Challenges	and	opportunities	of	the	different	methods	

4.5.	

International	comparisons	of	crop	production	costs	

4.5.1.  Main challenges  

4.5.2.	 How	can	we	compare	results	from	different	methods?	

4.6.  Conclusions and Recommendations 

Chapter 5. Horizontal Technical Issues on Production Cost Methodologies 

5.1. 

Introduction and Background 

5.2.  Production Allocation Costs, Farm Heterogeneity and Exchange Rates 

5.3. 

Inputs and Policy Inter-Linkages 

5.4.	 Climate-adjusted	Productivity	and	Economies	of	Scale	

5.5.	 Opportunity	Cost	of	Family	Labour	in	the	United	States	

5.6.	 Family	Farm	Diversity	and	Opportunity	Cost	of	Family	Labour	in	Brazil	

5.7.  Production Costs and Farming Systems 

5.8.  Conclusions and Recommendations  

Chapter 6. Conclusions and Recommendations  

6.1. 

Introduction 

6.2.	 Key	Methodological	Challenges	for	global	comparison	of	production	costs	

6.3.  Recommendations 

References 

Annex 

73

73

74

74

74

76

77

77

81

85

87

87

89

91

95

95

95

96

97

98

100

100

101

105

105

105

106

111

115

s
t
n
e
t
n
o
C

f
o

e
l
b
a
T

6

 
 
List of Figures

Figure	3.1:	 Costs	and	Prices	of	Brazilian	Beef	(2004-2011)	

Figure	3.2:	 Costs	and	Returns	for	Typical	Brazilian	Cow-Calf	farm	

Figure	3.3:	 Costs,	Depreciation	and	returns	for	a	Typical	Brazilian	Farm	

Figure	3.4:	 Argentine	Beef	Consumption	

Figure	3.5:	 Argentine	Slaughter	of	female	Livestock	

Figure	3.6:	 Costs	Structure	and	Gross	Margin	for	Breeding	Farms	in	Argentina	

Figure	3.7:	 Cost	structure	and	Gross	Margins	for	fattening	Farms	in	Argentina	

Figure	3.8:	 Cost	and	Price	Evolution	for	Pork	in	Santa	Catarina,	Brazil	

Figure	3.9:	 Feed	Price	Evolution	in	Santa	Catarina	(2007-2010)	

Figure	3.10:	 Inflation	and	Currency	Exchange	Rate	(Brazil,	2007-2010)	

Figure	3.11:	 Estimated	Returns	for	Hog	Operations	in	Iowa	($/Head)	

Figure	3.12:	 Regional	Differences	in	Costs	to	produce	Poultry	in	Brazil	

Figure 3.13:  Feed Price Evolution in Santa Catarina Brazil 

54

55

55

56

56

58

58

60

61

61

64

67

68

Figure	4.1:	 Cost	of	production	of	ARVALIS	UNIGRAINS	French	wheat	CNCER	sample	(2000-2009)	75

Figure	4.2:	 Cumulative	distribution	of	U.S	wheat	farm	at	different	cost	levels	

Figure 4.3:	

	Total	cost	($/t)	and	gross	revenue	in	wheat	average	2008-2010	(Australia,	
Canada,	Ukraine,	and	UK)	

Figure 5.1:  Climate-adjusted productivity, Australia 

75

76

98

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

7

 
 
 
 
 
 
 
 
 
 
 
 
List of Tables

Table	1.1:	 Typology	of	production	costs		

Table	3.1:	 Beef	Cost	Structure	Example	-	Alta	Floresta	Farm,	Brazil	

Table	3.2:	 Summary	of	Variable	Costs	and	Calculations	for	Pork	

Table	3.3:	 Sources	of	Information	for	Pork	Costs	in	Brazil	

Table	3.4:	 Estimated	Returns	for	Hogs	in	Iowa		

Table	3.5:	 Cost	Percentages	for	Hogs	in	Iowa	(2001-2011)	

Table	3.6:	 Cost	Increases	for	Hogs	in	Iowa	(2001-2011)	

Table 3.7:  Poultry Production Systems and regions in Brazil 

Table	3.8:	 Variable	Cost	details	for	Poultry	in	Brazil	

Table	3.9:	 Sources	for	Poultry	Cost	Analysis	in	Brazil	

Table	3.10:	 Costs	and	Returns	for	Georgia	Broiler	Growers	(4	Houses)	

Table	3.11:	 Estimates	for	Georgia	Broiler	Integrator	Costs	and	Returns	

Table	4.1:	 Method	to	specify	a	typical	Agri	benchmark	farm	

Table 4.2:  Approaches used to estimate commodity costs in US  

(Economic	Research	Service	USDA)	

Table	4.3:	 Approaches	used	to	collect	the	data	in	different	production	cost	studies	

Table	4.4:	 Approaches	used	to	calculate	the	different	costs	in	different	methods	

Table 4.5:	 Approaches	used	to	allocate	the	different	costs	and	to	estimate	selling	prices	in	

different	method	

Table	4.6:	 Example	results	presentation	in	various	posts	of	charges	for	3	different	methods	

Table	5.1:	 Calcuation	of	farm	income	and	balance	sheet,	Australia	

Table 5.2:  Approaches used to estimate commodity costs in the US  

23

53

59

60

62

64

64

65

66

67

69

70

78

79

80

84

86

90

97

99

Table	5.3:	 Opportunity	cost	of	Unpaid	and	Hired	Labour	for	Dairy	Farms,	by	Farm	Size,	in	US		

100

Table	5.4:	 Opportunity	cost	of	Unpaid	and	Hired	Labour	for	Dairy	Farms,	by	region,	in	US	

100

s
t
n
e
t
n
o
C

f
o

e
l
b
a
T

8

 
 
List of Acronyms

AACREA 
ASRA 
CNCER 
Conab 
CoP 
Embrapa 
FACEPA 
FADN 
IFCN 
INTA 
MAF 
RoA 
RoE 
USDA 
50-sg 

Argentine Association of Agricultural Consortium for Agricultural Experimentation

Farm Income Stabilization Insurance

National Council of Rural Economy Centre

Companhia Nacional de Abastecimento

Cost of production

Brazilian Agricultural Research Corporation

Farm Accountancy Cost Estimation and Policy Analysis of European Agriculture

Farm Accountancy Data Network

International Farm Comparison Network

National Institute of Agricultural Technology

Ministry of Agriculture and Forestry in New Zealand

Return on assets

Return on equity

United States Department of Agriculture

State national wide survey

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

9

 
 
 
 
 
 
 
 
 
 
 
 
Preface

This  report  constitutes  a  comprehensive  compilation  and  synthesis  of  the  principal  issues  and 

outcomes  of  the  Joint  Institute  for  Prospective Technological  Studies/Directorate-General  for Agriculture 

and Rural Development workshop on “Sustainability and Production Costs in the Global Farming Sector: 

Comparative Analysis and Methodologies” held in Brussels between 21-22 June 2011.

Gathering a range of international experts and specialists in the field of production costs analysis and 

development, covering a range of strategic agricultural sectors of global importance, the workshop aimed 

to review methodologies and approaches to calculating production costs used in various sectors nationally 

and  globally,  with  emphasis  on  exploring  the  applicability  for  effective  international  comparisons. 

Particular attention was given to the methodologies and approaches for data collection and processing, 

factor market structure and policy inter-linkages, sectoral coverage, horizontal technical issues, and the 

implications for global agricultural markets. Based on participant deliberations and discussions, a number 

of practically based policy recommendations towards achieving such comparisons were highlighted.

The production of this report, following completion of the workshop, has been the responsibility of 

the IPTS. This task has been facilitated through collaboration with four internationally recognised experts 

(Folkhard  Isermeyer,  Johann  Heinrich  von Thünen-Institute,  Germany  (Chapter  2),  Dan  L.  Cunningham, 

University of Georgia, USA (Chapter 3), Jean-François Garnier, ARVALIS, France (Chapter 4), and Ashok K. 

Mishra, Louisiana State University, USA (Chapter 5)) acting as rapporteurs for each of the workshop’s four 

technical sessions, whose efforts in capturing the principle issues and outcomes of their respective session 

has been instrumental towards realisation of this report. Stephen Langrell, Pavel Ciaian and Sergio Gomez 

y Paloma acted as Editors and compiled Chapters 1 and 6.

This  report  constitutes  a  particular  and  comprehensive  technical  overview  of  the  state  of  production 

costs calculations for the sectors under consideration at global level, and a consideration of the prospects 

for  effective  international  comparison.  It  reviews  methodologies  applied  for  production  costs  calculation 

at national and global level followed by the discussion on methodologies used for animal and arable crop 

sectors.  Finally,  the  report  discusses  horizontal  issues  related  to  production  costs  calculations. The  report 

closes with expert opined policy-relevant conclusions as a basis for policy suggestions and recommendations. 

It is envisaged that this report will provide a valuable source of technical and conceptual information for on-

going policy considerations, both at EU and third country/international level.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

John Bensted-Smith
Director - IPTS

11

 
 
 
 
 
 
 
 
 
 
 
 
Executive Summary

This report synthesises the findings from the workshop on “Sustainability and Production Costs in the 

Global Farming Sector: Comparative Analysis and Methodologies” organised jointly by the IPTS-JRC and 

DG AGRI in Brussels on 21-22 June 2011.

The main objectives of the report are:

•	 To give a snapshot on available data on production costs in agriculture around the world.
•	 To give an overview on methodologies and approaches for data collection and processing at national 

and global level. 

•	 To explore the applicability of production cost data for an effective international comparative analysis. 
•	 To  provide  recommendations  for  conducting  an  effective  international  comparative  analysis  of 

production costs.

The report is organised in six Chapters. Four technical chapters (chapters 2-5) were written by a panel 

of  international  experts  charged  with  capturing  the  principle  outputs  of  the  respective  sessions  of  the 

workshop  and  to  consider,  from  their  own  perspective,  such  outputs  in  potential  policy  scenarios  and 

possible recommendations. Chapter 2, by Prof. Dr. Folkhard Isermeyer, President of the Thünen-Institute 

and  coordinator  of  agri  benchmark,  reviews  methodologies  applied  for  production  costs  calculation  at 

national and global level followed by chapters 3 and 4, by Dr. Dan L. Cunningham, Professor of Poultry 

Science,  University  of  Georgia,  USA  and  Dr.  Jean-François  Garnier,  Crop  Economist, Arvalis  Institut  du 

Vegetal,  France,  respectively,  who  discuss  methodologies  used  for  the  animal  and  arable  crop  sectors. 

Chapter 5, by Prof. Dr. Ashok Mishra, W. H. Alexander Professor of Agricultural Economics, Louisiana State 

University, addresses horizontal issues related to production costs calculations. Chapter 1, an introductory 

discussion on the problem of cost of production (CoP), and chapter 6, summarising expert opined policy-

relevant conclusions as a basis for possible policy suggestions and recommendations, were written and 

compiled by Stephen Langrell, Pavel Caiain and Sergio Gomez y Paloma of the IPTS (who also acted as 

editors). It is stressed that the views and opinions expressed in this report are those of the authors and do 

not in any way represent a view or opinion of the European Commission.

Chapter 1 (Pavel Ciaian, Stephen Langrell and Sergio Gomez y Paloma, European Commission, DG-

JRC, IPTS) provides a conceptual introduction to the problem of CoP. Firstly, it provides an introduction to 

the classification of production costs as used in economics and applied business sciences. Cost is defined 

as the value of a factor of production (input) employed in the production of final outputs. The classification 

of production costs can be made along several dimensions (i) whether costs are traceable to specific farm 

commodities  (direct  versus  indirect  costs);  (ii)  between  cash  costs  and  noncash  costs;  (iii)  according  to 

their  variation  with  respect  to  the  unit  of  production  (variable  and  fixed  costs);  (iv)  with  respect  to  the 

unit of comparison (total costs, average costs and marginal costs); (v) in terms of inputs usage during the 

production  process  (expendable  inputs,  capital  costs,  capital  services);  and  (vi)  with  respect  to  the  link 

they have with respect to farm operations (operating costs, overhead costs).

Secondly, chapter 1 reviews main cost calculation approaches. The methodology that tracks, studies 

and  analyses  all  costs  accrued  in  the  production  process  is  referred  to  as  product  costing. The  aim  is 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

13

 
 
 
 
 
 
 
 
 
 
 
 
y
r
a
m
m
u
S

e
v
i
t
u
c
e
x
E

to  classify  costs  by  their  nature  and  then  to  allocate  them  to  different  commodities  according  to  the 

destination when  consumed or used  (typically  referred  to as  analytical accounting). Three  key  methods 

used for allocation of costs to commodities in analytical accounting can be distinguished: direct costing, 

indirect costing, and activity based costing. Direct costing considers only variable costs. This type of costs 

is traceable, they can be relatively easily identified with the commodity on which they were actually used. 

Indirect  costing  considers  indirect  costs  and  applies  an  allocation  scheme  to  disaggregate  the  indirect 

costs to commodities. Activity based costing assigns costs to specific farm activities according their actual 

consumption  with  the  aim  to  allocate  cost  items  only  to  relevant  commodities  subject  to  charge.  Most 

applied  methodologies  combine  several  approaches  for  calculation  of  commodity  costs.  Direct  costing 

tends to be applied to account for traceable costs such as direct, cash, variable, or operating costs. Indirect 

or activity based costing is often used for allocation of other cost types. Although activity based costing is 

the most exact, it is almost never fully applied in practice but only for certain types of costs (e.g. certain 

fixed costs) mainly due to complexity of data needs.

Chapter 2 (Folkhard Isermeyer, President of the Thünen-Institute and coordinator of agri benchmark, 

Germany)  summarizes  the  workshop  presentations  and  discussions  on  the  national  farm  data  surveys 

as  used  in  the  EU,  Ukraine,  USA,  Canada,  Brazil,  Argentina,  Australia  and  New  Zealand  for  CoP 

calculation. In addition the chapter covers two worldwide networks which are conducting international 

CoP comparisons. The chapter also draws some conclusions and highlights several recommendations on 

international CoP comparison.

European  Union:  For  intra-EU-comparisons,  the  Farm Accountancy  Data  Network  (FADN)  offers  a 

harmonized data base. The great advantage of this data base is that it contains data of a large number of 

farms (stratified sample) and is updated annually. The main limitation is that this data base does not report 

CoP broken down by commodities.

USA: The Agricultural Resource Management Survey (ARMS) provides CoP data that are statistically 

representative. The data collection procedure is specifically designed for the calculation of CoP although 

there are some open questions regarding the quality of the data (especially on labour costs). The long time-

interval (update only every 4-8 years) and the non-existing possibility to interview the farmers in between 

are severely limiting the usability of the system to produce answers for the questions mentioned above. 

The Agricultural and Food Policy Centre (AFPC) at Texas A&M University is operating an alternative (so-

called) representative farm concept which is successfully used for farm-level policy impact assessment for 

the US congress. This system is based on the concept of typical (virtual) farms which are put together by 

regional panels on the basis of bookkeeping data and expert judgement.

Canada: There is neither a unified data collection system nor a harmonized farm data set which could 

be  used  for  standardized  CoP  calculations.  Instead  there  are  different  sour ces  of  information  available, 

some containing very detailed data. Most schemes are operated under the responsibility of the provinces.

Brazil:  CONAB,  a  national  agency  for  the  dissemination  of  information,  offers  CoP  data  for  many 

14

commodities, production systems and regions. The data are collected by focus groups for typical (virtual) 

farms. The experience has shown that this procedure leads to a higher-quality data (especially on labour 

costs) than could be collected by other data collection methods. The CONAB data base is not statistically 

representative.  CONAB  does  not  cover  the  whole  agricultural  sector.  Some  commodities  are  analyzed 

by  other  institutions,  and  they  also  apply  the  concept  of  typical  farms  based  on  focus  groups  (panel 

discussions).

 
Australia: The Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) is 

operating a representative survey on CoP. The survey is not covering all commodities; about 75% of total 

agricultural  production  is  included.  The  data  collection  system  (face-to-face  interviews)  is  specifically 

designed to calculate CoP. For some crops, the data base contains data going back 30 years which allows 

interesting time series analysis.

New Zealand: The traditional monitoring system with a relatively strong statistical basis is currently 

being  replaced  by  a  modern  system  which  is  based  on  the  principles  of  voluntary  participation,  strong 

farmer  and  advisor  involvement,  timeliness,  and  high  usability  of  the  benchmarking  data  by  farmers. 

Up-to-date data are collected annually by face-to-face interviews, and these interviews are also used to 

validate the data for the previous period. The data pool is not statistically representative. About 10% of all 

farmers are now taking part in the system.

Ukraine: Medium- and large-scale farms are reporting data on agricultural production and sales to 

the district-level bodies. This data source, however, is not suitable for a profound analysis of CoP. Detailed 

CoP data for a handful of typical farms are collected by the Ukrainian Agribusiness Club (UCAB); this is 

done within the framework of the glo bal network agri benchmark. The UCAB has also launched a project 

called AgriEfficiency, a national extension of the agri benchmark project that aims at collecting data from 

Ukrainian farms with less effort.

The chapter also presents two global networks, agri benchmark and International Farm Comparison 

Network (IFCN), specialised in collection and analysis of CoP worldwide. Agri benchmark and IFCN are 

the  only  institutions  who  provide  CoP  on  a  worldwide  basis. These  approaches  are  based  on  networks 

of  experts,  advisors  and  farmer  panels  located  in  different  parts  of  the  world  who  collect  and  process 

data  locally. The  comparison  of  “typical  farms”  is  the  core  concept  of  their  approach. The  methods  for 

farm selection, data collection and CoP analysis follow a standard operating procedure for all farms and 

all  countries. An  important  weakness  of  the  agri  benchmark  and  the  IFCN  is  low  representativeness  of 

collected data and coverage of limited number of commodities.

Overall as indentified in this chapter, very different concepts for the collection of farm-based CoP data 

have been implemented. In view of these extreme methodological differences on the international level, 

this chapter proposes three alternative conceptual strategies that could be pursued to conduct meaningful 

international comparison of CoP:

(1)  Take the different data bases as they are, build some interfaces, and compare the resulting CoP figures 

across nations and continents

(2)  Convince the administrations of various countries around the world to agree upon the establishment 

of one harmonized concept (data collection, CoP calculation).

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

(3)  Continue  the  development  of  global  networks  (e.g.  IFCN,  agri  benchmark)  which  have  developed 

internationally harmonized standards for CoP calculation and work on a stepwise evolution of their 

network concepts

15

After  taking  into  consideration  drawbacks  and  strengths,  strategy  three  was  identified  in  Chapter  2 

as  the  most  appropriate  to  be  applied  for  international  comparison  of  CoP.  Main  disadvantage  of  the 

first  strategy  is  that  there  are  fundamental  methodological  differences  between  the  national  schemes 

 
 
 
 
 
 
 
 
 
 
 
 
y
r
a
m
m
u
S

e
v
i
t
u
c
e
x
E

which make international comparison of CoP difficult to implement. The main disadvantage of the second 

strategy  might  be  low  interest  of  countries  to  implement  a  harmonised  system  due  to  the  fact  that  the 

existing data collection systems would need to be redesigned as well as countries which currently do not 

have a farm level data collection system in place may be left out (in particularly less developed countries), 

or, alternatively, it would require a significant amount of additional financial resources to introduce the 

harmonised system in these countries. The main limitations of the third strategy is that the approach relies 

on  non-representative  datasets  and  panel  processing  of  data  compilation  may  be  biased  by  subjective 

perception and expert judgments.

Chapter  3  (Dr.  Dan  L.  Cunningham,  Professor  of  Poultry  Science,  University  of  Georgia,  USA) 

provides  examples  of  production  cost  calculations  and  methodologies  utilized  in  various  countries  for 

the  dairy,  beef,  pork  and  poultry  industries. The  countries  covered  are Argentina,  Brazil,  New  Zealand 

and the United States. The chapter included information on reporting agencies, methodologies, sampling 

procedures and cost and returns calculations.

Based on the reports for dairy, beef, pork and poultry from the participating countries it is apparent 

that  the  methodologies  and  agencies  used  to  estimate  production  costs  vary  considerably  from  country 

to country. Agencies involved in collecting and analyzing cost and returns data across countries included 

governmental, private, academic, industry and farm owner groups. 

Different  commodities  have  different  business  models  that  make  cost  comparisons  difficult.  The 

methodologies used with regard to sampling vary considerably from country to country and represent a 

major obstacle in meaningful cost comparisons. Sampling size and reporting period also vary from country 

to  country,  as  well  as  commodity  to  commodity.  In  addition,  accuracy  and  validation  of  information 

collected is an issue for some reports. 

The  variability  between  methodologies  and  production  systems  makes  commodity  comparisons 

between  countries  difficult. The  following  is  a  summary  of  some  of  the  major  obstacles  for  meaningful 

comparisons:

•	

•	

The need for representative sample size

The need for accurate information

The need for discipline and timing of data collection

•	
•	 Defining the ‘typical’ farm
•	 Validation of data
•	

The need for consistent reporting cost categories

Although there are significant challenges to achieving meaningful international comparisons, it may 

be  possible  with  a  central  coordinating/directing  group  providing  responsibility  for  a  global  approach. 

The  IFCN  program  for  reporting  on  global  production  costs  for  dairy  is  an  example  of  the  feasibility  of 

such a program. A central coordinating/directing group could standardize methodologies, sampling size, 

reporting requirements and command the discipline necessary to produce meaningful results. Participation 

by various countries would depend, however, on available resources and commitment to the project.

16

Chapter 4 (Jean-François Garnier, Crop Economist, ARVALIS, France) firstly presents main differences 

between  different  CoP  methods  applied  for  cereals  and  arable  crops  in  the  USA,  Canada,  Ukraine  and 

agri benchmark. In order to have a wider analysis of the current context of production costs calculation, 

 
and especially their international comparisons, two French studies (France Arvalis-Unigrains observatory 

and  the  international Arvalis  observatory)  and  the  FADN  approach  were  also  included  in  the  analysis. 

Secondly, the chapter provides discussion on the main challenges in conducting international comparison 

of CoP for cereals and arable crops.

There are two different methodological approaches applied to calculate and compare production costs: 

the methods based on large representative samples and those based on a “typical farm” approach. Methods 

based on large representative samples are relevant for conducting policy impact analysis and therefore may 

represent  an  important  source  of  information  in  support  for  policy  decision  making.  Nevertheless,  these 

methods  are  less  suitable,  in  particular,  to  analyse  the  competitiveness  of  crop  production.  In  order  to 

analyse factors of competitiveness and technological improvement, it is necessary to collect more technical 

data  which  is  less  suitable  to  be  conducted  within  this  method. The  “typical  farm”  approach  of  the  agri 

benchmark network, the international Arvalis Observatory or the win-tops methods are more appropriate to 

analyse such issues but the representativeness of the data remains a main concern.

On the other hand, the approaches using representative samples permit conducting time series and 

trend  analysis. This  is  provided  the  sample  and  the  methodology  are  kept  constant  over  time.  For  the 

“typical farm” approach this may be problematic. Agri benchmark, or the Arvalis International observatory, 

adjusts the calculation method and the definition of the “typical farm” in regular intervals making the time 

series and trend analysis less accurate.

The methodologies applied for CoP calculation by national systems have different underplaying objectives, 

and thus the structure and method for collecting, processing and reporting CoP data differs accordingly. This 

makes international comparisons difficult to conduct. Some national data collection systems covered in this 

chapter (ARMS, win-tops, 50 sg report, and France Arvalis-Unigrains observatory) are interesting to study from a 

methodological point of view but are less relevant to be used for international comparison due to differences in 

sampling strategy, cost calculation methodology and the reporting of CoP results.

Agri  benchmark  and  the  Arvalis  international  observatory  both  employ  a  constant  methodology, 

although different from each other, to calculate and compare the production costs in different countries. 

It allows easier international comparison as compared to national data collection systems. The FACEPA 

project,  which  explores  the  FADN  data,  allows  CoP  comparisons  within  the  EU,  however,  there  is  no 

equivalent data available in other countries to conduct CoP comparisons with non-EU countries.

For  international  crop  competitiveness  comparison,  connection  between  the  two  methods 

would  certainly  permit  constructive  interaction  at  a  European  level.  For  example,  an  estimation  of  the 

representativeness of the typical farm, based on FADN data, using criteria such as the farm structure, crop 

rotation, yields etc., could be useful for the typical farm approach. On the other hand, the methods based 

on the “typical farm” would probably bring more field data like technical crop schedule, average doses of 

input, etc., that may likely refine the analysis and interpretations of international comparison.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Beyond  evaluation  of  the  economic  performance  of  farms  there  is  increasing  interest  to  analyse 

environmental  and  social  aspect  of  agricultural  production,  in  particular  the  social  and  environmental 

costs and benefits. To perform the evaluation of environmental and social sustainability, detailed technical 

17

data on issues such as technical processes, input intensities, soil and climate conditions, etc., are required 

to be collected. The approach based on the “typical farm” may be well-positioned to expand the valuation 

of farm performance in this respect.

 
 
 
 
 
 
 
 
 
 
 
 
y
r
a
m
m
u
S

e
v
i
t
u
c
e
x
E

Chapter 5 (Prof. Dr. Ashok K. Mishra, W. H. Alexander Professor of Agricultural Economics, Louisiana 

State  University,  USA)  presents  key  horizontal  challenges  and  methodological  issues  on  calculation  of 

CoP  related  to:  (1)  farm  heterogeneity  and  exchange  rates;  (2)  inputs  and  policy  linkages;  (3)  climate 

adjusted productivity and economies of scale; (4) opportunity cost of family labour in the United States; 
(5)  family  farm  diversity  and  opportunity  cost  of  family  labour  in  Brazil;  and  (6)  production  costs  and 

farming systems.

While the workshop provided a good understanding of various aspects of costs of production, it is 

abundantly  clear  that  international  comparisons  of  CoP  needs  to  have  a  good  foundation,  particularly 

with respect to the following: (1) a common definition of a farm; (2) common methodologies to calculate 

various aspects of CoP, income statement, and balance sheet. These items can then be used to calculate 

costs of production that is easily transportable and abundantly clear as to how to calculate costs across 

farm type, commodities, farming region, and country; (3) unit of data collection—farm-level, typical farm, 

regional  data,  aggregate  data,  regional  data;  (4)  unit  of  analysis—such  as  per  arable  acres,  per  unit  of 

output.  Amongst  all  the  workshop  presentations  a  common  thread  was  farm-level  data  being  used  for 

analysis which may imply that a common methodology could be developed to calculate CoP of agricultural 

commodities and compare them across countries. However, the main challenges in accomplishing these 

idea would be in terms of resource constraints, specifically, the willingness to conduct such data collation 

and resource allocation to do so - both budgetary and personnel.

There are several recommendations that can be gleaned from these presentations. First, farm-level data 

is the best option to compare costs of production across countries. Second, these costs should be adjusted 

frequently  to  adjust  for  government  subsidies  and  other  structural  changes  in  agriculture.  Secondly, 

any  policy  that  affects  use  of  inputs  should  also  be  noted  and  adjusted  for  in  the  final  CoP. Third,  CoP 

comparison should be adjusted for inflation and exchange rates when comparing costs across countries. 

Fourth, CoP should be calculated on a per unit basis, e.g. per kg beef, or per tonne of wheat, produced. 
This  option  of  collecting  data  and  taking  a  “typical	 farm”  into  consideration  can  prove  to  be  useful  in 
developing costs of production for agricultural commodities and comparing these costs across countries. It 

is an alternative that is cheaper and can be pursued in the future if counties and organization choose to do 

so. Finally, one has to be cognizant about several other factors when comparing costs of production across 

various countries. These include: (1) farm heterogeneity (size); (2) diversified farm enterprises; (3) climate-

adjusted productivity; and, (4) data requirements to derive an opportunity cost of unpaid labour.

One  of  the  most  controversial  issues  facing  economists  to  accurately  measure  CoP  is  valuation 

of  unpaid  farm  labour. Though  unpaid  farm  labour  does  not  generally  receive  a  wage,  it  does  have  an 

economic cost. The best method to obtain opportunity cost of unpaid labour is the implicit compensation 

for unpaid farm labour is based on the opportunity cost of off-farm work, or the return available in the next 

best alternative use of this labour time and effort. All adult unpaid farm labour (and salaried labour with 

ownership claims) should be valued at its opportunity cost, defined to be the maximum value for non-farm 

uses. However, this method would require survey data, and detailed data on demographics, local labour 

markets,  and  other  socio-economic  variables.  Consequently,  although  this  methods  is  economically 

sound,  it  would  require  additional  resources  in  terms  of  time  and  money.  In  light  of  this,  one  can  use 

18

alternative methods that may be cheaper, readily available, and consistent across countries. These include: 

(1) hired farm worker wage rate; (2) skilled worker wage rate; (3) replacement worker wage rate; and, (4) 

governments can set off-farm wage rates.

 
Chapter 6 (Stephen Langrell, Pavel Ciaian and Sergio Gomez y Paloma, European Commission, DG-

JRC, IPTS) summarises the main findings of Chapters 1 to 5 and attempts to formulate recommendations 

for conducting an effective global comparison of CoP.

The  different  concepts  and  approaches  currently  deployed  regionally  are  either  based  on  the  large 

representative  samples  (e.g.  FADN,  ARMS)  or  the  typical  farm  approach  (e.g.  agri  benchmark,  IFCN, 

CONAB). The  exiting  data  collection  systems  are  developed  to  address  multiple  policy  objectives  and 

are  not  solely  design  to  deliver  only  CoP  data. The  difference  in  objectives  of  national  and  global  data 

collection systems, and differences in their use, largely determines the methodology employed in general, 

and sampling strategy, in particular. If the goal is to evaluate an average production cost per country, per 

region, or for each major farming systems, representativeness of the studied sample will be critical (e.g. 

Methods ARMS,  50-sg  report,  France Arvalis-Unigrains  Observatory,  FADN).  However,  if  the  goal  is  to 

evaluate the production costs of performing farms, or to characterise the economic impact of innovative 

practices  (e.g.  minimum  tillage,  low  input  system,  organic  farming  etc.),  then  representativeness  is  still 

important, but is secondary compared to the needs of having detailed and specific economic and technical 

data on technology, farm practices, and timing of activities through the season, etc.

Conducting  robust  comparative  analysis  of  production  costs  across  agricultural  commodities  and 

across  countries  requires  availability  of  data  which  apply  similar  data  collection  approaches  and  cost 

calculation methodologies. Few statistical sources satisfy these requirements. The agri benchmark and the 

IFCN, based on the typical farm approach, are the only data sources currently available for international 

comparison  of  production  costs.  They  apply  a  common  methodology  for  costs  identification  and 

calculation across all covered countries. They can be applied without further methodological adjustments 

to compare production costs among available commodities and regions. However, their main disadvantage 

is that they are based on small and non-representative samples, unable to capture adequately variation of 

farming systems and structural change within regions, cover only a restricted set of commodities. Further, 

the involvement of experts/advisors introduces certain subjectivity and personal perceptions in the data 

collection process.

Most countries conduct their own collection of data on production costs as part of national agricultural 

data  gathering  exercises.  However,  methodological  approaches  vary  strongly  in  terms  of  collection 

approaches,  type  of  data  collected,  dis-aggregation  of  cost  items,  data  processing,  and  cost  calculation 

methodology. Hence it is problematic to use them for inter-country comparison. The application of national 

sources  for  international  comparison  would  require  further  data  processing  and/or  harmonization  of 

methodologies. This could be potentially achieved (following the analysis of Chapters 2 to 5) at three levels:

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

•	 Minimalistic  harmonization:  Exploits  existing  available  databases  and  harmonises  methodologies 
without altering the current system of data collection and type of data collected. This approach would 

lead to harmonization of certain aspects of methodologies such as structuring of cost categories and 

the  harmonization  of  certain  cost  items  not  connected  directly  to  data  collection  processes  which 

are less demanding in terms of resource requirements (e.g. accounting for opportunity costs of own 

inputs,  etc.).  Main  disadvantages  of  this  approach  would  be  the  comparability  of  CoP  data  across 

countries will remain an issue of concern due to differences in underlining methodologies.

19

•	 Partial  harmonization:  This  approach  proposes  to  harmonise  the  type  of  data  collected  and  cost 
calculation  methodology,  while  keeping  current  systems  of  data  collection  (e.g.  sampling  strategy) 

applied at national level unchanged. In principle this approach would require extension or adjustment 

 
 
 
 
 
 
 
 
 
 
 
 
of the current national systems in terms of questionnaire design (e.g. harmonization of the type of cost 

data collected) and cost calculation methodology (e.g. harmonization of the method to account for 

opportunity cost). The actual level of comparability of CoP data would depend on what aspect of the 

cost calculation methodology would be actually harmonized.

•	 Full harmonization: Application of common methodology for data collection and calculation of cost 
values  in  all  participating  countries.  In  principle  this  would  lead  to  a  redesign  of  whole  national 

systems  starting  from  harmonization  of  primary  data  collection  method  (e.g.  sampling  strategy)  to 

harmonization of costs calculation methodologies. Main constraints might be low interest of countries 

to join such schemes as the existing systems would need to be replaced by new harmonised system.

The successfulness of implementing harmonization of national methodologies requires cooperation 

among  national  authorities  and  the  level  of  cooperation  required  increases  with  the  degree  of 

harmonization.  One  of  the  main  limitations  of  the  harmonization  approach  is  that  in  many  countries 

farm  data  collection  systems  may  not  be  available,  nor  sufficient  financial  resources  that  would  enable 

their participation in the scheme. Many important global players might be left out as a result unless an 

alternative solution is found. A key challenge for this type of global data collection system, where many 

stakeholders  are  involved,  relates  to  practicalities  of  over-arching  coordination  problems  and  complex 

processing and validation of the final datasets or databases. Experience from national systems shows that 

such complexity may lead to delays in finalization and publication of CoP datasets or databases. 

y
r
a
m
m
u
S

e
v
i
t
u
c
e
x
E

20

 
Chapter 1. Introduction to Production Costs

Pavel Ciaian, Stephen Langrell and

Sergio Gomez y Paloma

•	 To provide recommendations for conducting 
international  comparative 

an  effective 

Institute for Prospective Technological Studies (IPTS) 

analysis of production costs.

Joint Research Centre (JRC)

European Commission

1.1. Introduction

Before  going  into  further  detail  of  these 

topics, 

this  chapter  provides  a  conceptual 

introduction  to  the  problem  of  CoP.  Firstly,  it 

provides  an  introduction  to  the  classification 

of  production  costs  as  used  in  economics  and 

The  structure  and  level  of  cost  of  production 

applied business sciences. Secondly, the chapter 

(CoP)  have  major  implications  for  global  food 

discuses  main  approaches  for  cost  calculation 

markets  and  food  security.  Production  costs  not 

and allocation to farm activities.

only  shape  the  development  of  farming  systems 

but  also  affect  their  sustainability  and  determine 

overall  food  production  potential.  The  availability 

1.2. Classification of costs

of good quality data on CoP is a key requirement 

for  conducting  comparative  analysis  at  national 

CoP  is  an  economic  indicator  assessing 

and global level. In view of this, this report aims to 

the  economic  performance  of  production.  Cost 

explore  data  availability  and  global  and  national 

is  defined  as  the  value  of  a  factor  of  production 

methodologies  for  production  cost  calculation, 

(input) employed in the production of final outputs. 

with specific focus on commodity production costs. 

The classification of production costs can be made 

In  particular,  the  report  aims  to  summarise  the 

along  several  dimensions.  Table  1.1  summarises 

methodologies and approaches for data collection 

and  processing  and  their  appropriateness  for  an 

six possible ways of categorising production costs 
(AAEA 2000; Cesaro et al., 2008).

effective 

international  comparative  analysis  of 

agricultural production costs.

Consequently,  the  main  objectives  of  the 

study are:

Firstly,  general  classification  is  based  on 

whether  costs  are  traceable  to  specific  farm 

commodities  (i.e.  direct  versus  indirect  costs). 
A  direct  cost  is  a  cost  that  can  easily  and 
conveniently  be  traced  to  the  particular  farm 

•	 To  give  a  snapshot  on  available  data  on 
production  costs  in  agriculture  around  the 

commodity under consideration. For example, the 

use of fertilizer is a direct cost of a particular crop 

world.

on  which  the  input  was  used.  An  indirect  cost 

is  a  cost  that  cannot  be  easily  and  conveniently 

•	 To  give  an  overview  on  methodologies 
and  approaches  for  data  collection  and 

traced  to  the  particular  farm  commodity  under 

consideration.  For  example,  if  a  farm  produces 

processing at national and global level.

several  crop  commodities,  the  cost  item  such 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

•	 To  explore  the  applicability  of  production 
international 
for  an  effective 

cost  data 

comparative analysis.

as  machinery maintenance is  an  indirect cost of 

all  crops  for  which  the  machinery  was  utilised. 

21

Here, the reason is that machinery maintenance 

costs  are  not  caused  by  any  specific  crop  but 

are  common  to  all.  Indirect  costs  are  incurred 

 
 
 
 
 
 
 
 
 
 
 
 
s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
o
t
n
o
i
t
c
u
d
o
r
t
n
I

.
1

r
e
t
p
a
h
C

to  support  multiple  activities  (e.g.  multiple  crop 

best  alternative  use  (e.g.  the  opportunity  cost  of 

commodities)  and  cannot  be  traced  to  each 
individually.1  Indirect  costs  are  usually  constant 
for  a  wide  range  of  outputs  and  are  grouped 

under fixed factors.

family  labour  is  off-farm  wage;  the  opportunity 

costs  of  own  land  is  market  rental  price).  The 

consideration  of  opportunity  costs  is  one  of  the 

key differences between the concepts of economic 

cost  and  accounting  cost. The  latter  usually  does 

It  is  possible  to  classify  almost  any  kind  of 

not consider opportunity costs because the actual 

cost  as  either  direct  or  indirect.  Labour  costs, 

payment  transactions  are  not  realised.  Economic 

for  example,  can  be  indirect,  as  in  the  case  of 

costs  consider  all  explicit  and  implicit  costs 

maintenance  personnel  and  managerial  labour; 

incurred by farms including opportunity costs.

or  can  be  direct,  as  in  the  case  of  hired  labour 

for  specialised  work  carried  out  on  a  particular 

Other  standard  cost  classifications  used 

commodity.  Similarly,  other  costs 

such  as 

extensively 

in  economic 

theory  are  used 

machinery and equipment maintenance costs, such 

as  for  tractor  depreciation,  are  typically  classified 

according  to  their  variation  with  respect  to 
the  unit  of  production.  Variable  costs  change 

as  indirect  costs,  while  machinery  and  equipment 

with  production  level,  whereas  fixed  costs  are 

used  for  a  specific  commodity  (e.g.  corn  sowing 

independent of production level. In other words, 

machine), are included in direct costs.

variable  costs  are  affected  by  the  farm’s  actions 

in the period under consideration, whereas fixed 

Considering  monetary  flows,  a  second 

farm  costs  incur  independently  of  the  actions 

distinction  is  made  between  cash  costs  and 

undertaken  by  the  farm  in  the  period  under 

noncash costs. For cash costs, monetary payments 

consideration.  Note  that  some  fixed  costs  may 

and  the  consumption  of  input  are  realised  in 

be  quasi-fixed  implying  that  they  are  flat  within 

the  same  period  (e.g.  cash  payments  for  fuel, 

a  certain  range  of  production  but  change  if  the 

fertilizer,  seed,  repairs,  and  similar  items).  For 

range is overshot (e.g. machinery). 

non-cash costs, either the payment is not realised 

(opportunity cost of own inputs) or there is a time 

lag  between  the  time  when  payment  was  made 

and when the input was used (e.g. depreciation). 

With respect to the unit of comparison, costs 
can  be  classified  as  total  costs,  average  costs  or 
marginal costs. The total costs represent the value 

Depreciation costs account for the declining value 

of  all  inputs  (cash  and  non-cash)  a  farm  uses  in 

of  farm  assets  such  as  machinery  and  buildings. 

a  given  period  and  they  are  the  sum  of  variable 

Opportunity  costs  (also  referred  to  as  implicit 

and fixed costs. Average costs are total costs split 

cost  and/or  imputed  cost)  represent  the  cost  of 

per  unit  of  measurement  such  as  per  hectare  or 

own  inputs  (e.g.  own  land,  labour  and  capital). 

per  unit  of  production  (e.g.  per  tonne).  Further, 

Because  own  inputs  are  used  at  farm  level,  they 

average  costs  can  be  distinguished  by  type  of 

forgo income which could be earned if they were 

costs  such  as  average  fixed  or  average  variable 

employed in non-farm activities. Thus opportunity 

costs. The marginal cost is the change in total cost 

cost represents the value of own inputs in the next 

22

1  Other  terminology  often  used  is  joint  costs.  Joint  costs  are 
costs incurred in a production process involving more than 
one  product  which  production  cannot  be  separated  from 
each other (e.g. wool and sheep meet production are joint 
products hence all sheep costs are joint costs). Joint costs can 
occur either as direct costs or as indirect costs. Some inputs 
such  as  fertilizer  or  lime,  which  are  normally  viewed  as 
direct costs and can be assigned to a particular commodity, 
may have an inter-temporal or residual carry-over effect that 
may impact the production of other commodities. 

that  arises  due  to  the  change  in  one  additional 
unit  of  output  or  input.2 The  marginal  cost  with 
respect  to  output  is  total  cost  change  when 

production changes by one unit. Equivalently, the 

marginal cost with respect to input is total costs 

change when input use changes by one unit (e.g. 

marginal cost of labour, marginal costs of land).

2  Expressed  mathematically,  the  marginal  cost  is  the  first 

derivative of the total production costs.

 
 
 
 
 
Table 1.1: Typology of production costs 

Classification 
description

Type of costs Description

Examples

In relation to farm 
activity

-Direct cost 
-Indirect cost

Direct cost can be assigned directly to a 
farm activity (e.g. commodity). Indirect costs 
are spent per group of products or per farm 
as whole.

-Direct cost: fertilizers, seeds
-Indirect cost: overheads, machinery 
maintenance, depreciation 

In relation to cash 
flow

-Cash cost 
-Noncash cost

Costs based on whether monetary payment 
follow input flow in a given period.

In relation to unit 
of production

-Variable cost 
-Fixed cost 

In relation to unit 
of comparison

-Total cost 
-Average cost 
-Marginal cost

Variable costs change with production level; 
fixed costs are independent of production 
level. 

The distinguishing criterion is unit of 
measurement with respect to which cost 
change, such as per farm, per hectare, per 
unit of production. 

In relation to usage

-Expendable
-Capital
-Capital services

Expendable are inputs consumed in a given 
period. Capital is a stock concept. Capital 
services are services obtained from the 
capital stock in a given period.

In relation to farm 
operations

-Operating costs
-Overhead costs

To what extent they related to operation of 
farm processes.

-Cash cost: fertilizers, seeds, hired labour, rental 
costs
-Noncash cost: depreciation, opportunity cost of 
own inputs 
-Variable cost: seed, fuel, machine repairs, 
fertilizer
-Fixed cost: depreciation on buildings and 
machinery  

-Expendable: seed, fuel, feed 
-Capital: machinery, buildings, equipment, land, 
human capital
-Capital services: services provided by 
equipment, labour, etc.
-Operating: seed, fuel, feed 
-Overhead costs: the purchase of land, buildings, 
machinery

Other  costs  distinctions,  or  terms,  reflect 

to  be  used  in  the  production  process.  Unlike 

input  usage  during 

the  production  process. 

operating  costs,  overhead  costs  are  one-time 

Expendable  are  inputs  that  are  completely  used 

expenses and ensure that a given farm production 

up  or  consumed  during  a  single  production 

process  is  in  an  operational  status.  Overhead 

period.  Capital  is  a  stock  that  is  not  used  up 

costs are fixed and are therefore independent of 

during  a  single  production  period  but  provides 

the level of production.

services  over  time.  Capital  services  are  the  flow 

of productive services that can be obtained from 

a given capital stock during a production period.

1.3. Cost calculation approaches

Finally,  cost  can  be  distinguished  in  the 

Farm  operations  conduct  a  variety  of 

link  they  have  with  respect  to  farm  operations. 

activities managing many cost categories ranging 

Operating  costs  are  related  directly 

to 

the 

from  explicit  cash  costs  on  variable  inputs, 

operation of farm activities. They can also refer to 

investment  expenditure  on  machinery  and 

the costs of operating a specific farm activity (e.g. 

fixed  assets  to  implicit  own  input  allocation. 

wheat production). Operating costs can be either 
variable or fixed costs.3 In contrast overhead costs 
are costs incurred on the purchase of factors such 

The 

intensity  of  cost 

levels  varies  widely 

depending  on  region,  utilised  technology,  farm 

specialisation,  farm  size,  etc.  Key  challenges 

as  land,  buildings,  machinery  and  equipment 

in  calculating  production  costs  accurately  is 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

3  For  example,  AAEA 

that  all 
expendable costs to be classified as operating costs and all 
other costs to be grouped as overheads in the commodity 
cost calculation method applied in the US. 

recommended 

(2000) 

to  assign  each  farm  cost  item  to  a  specific  farm 

activity 

(commodity).  The  methodology 

that 

23

tracks, studies and analyses all the costs accrued 

in the production process is referred to as product 

costing. The  measurement  of  CoP  is  done  using 

 
 
 
 
 
 
 
 
 
 
 
 
s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
o
t
n
o
i
t
c
u
d
o
r
t
n
I

.
1

r
e
t
p
a
h
C

24

accounting  methods  which  assigns  each  cost 

margin  share,  direct  cost  shares,  direct  labour 

item to specific farm commodities. The aim is to 

cost shares, direct labour hours shares, technical 

classify costs by their nature and then to allocate 

coefficients, engineering formulas, estimates from 

them  to  different  commodities  according  to  the 

pilot  surveys,  etc.  For  example,  the  USDA  uses 

destination  when  consumed  or  used  (typically 

operating  margin  shares  (value  of  production 

referred  to  as  analytical  accounting). The  nature 

less  operating  costs) 

to  allocate  overheads 

of  the  accounting  approach,  as  well  as  the  type 

to  commodities,  whereas  for  machinery  and 

of costs, typically determine the accuracy of the 

equipment the USDA applies survey information 

obtained cost values (FACEPA, 2011).

on production practices, technical information on 

Certain types of costs can be traced to specific 

determined from machinery tests (USDA 2011).

machine performance, and engineering formulas 

farm  commodities  relatively  easily  (e.g.  direct, 

cash, variable, operating costs), whereas this may 

Activity  based  costing  assigns  all  costs 

not be the case for other cost types (e.g. non-cash, 

to  farm  activities  conducted  to  produce  farm 

overheads, or fixed costs). Inputs that are directly 

commodities. The objective is to identify each cost 

linked  to  production  of  a  specific  commodity 

item  with  each  farm  commodity.  This  approach 

can  be  straightforwardly 

identified  with 

that 

is more exact than the indirect costing because it 

commodity. In this case the assignment of the costs 

avoids using allocation schemes. For example, the 

does  not  pose  a  significant  challenge  in  terms  of 

use  of  a  scheme  based  on  production  shares  for 

its  allocation  to  commodities  on  which  it  was 

allocation  of  machine  costs  may  fail  to  represent 

actually  used.  However,  some  inputs  particularly 

the true value of commodity costs if the production 

those which are incurred on multiple commodities 

shares do not correspond to the actual distribution 

cannot  be  directly  assigned  to  commodities.  In 

of machine needs between commodities. Activity 

this case an appropriate approach (e.g. allocation 

based  costing  requires  detailed  accounting  of  all 

scheme) must be developed for disaggregation of 

individual  farm  activities  and  their  distribution 

costs by commodity. The accuracy of the calculated 

between  farm  commodities  (for  example,  hours 

cost value then depends on the precession of the 

of 

labour  and  machines  used 

for  different 

chosen approach (AAEA, 2000; FACEPA, 2011). 

commodities).  In  principle,  each  farm  activity 

must  be  identified  with  each  specific  commodity 

Three  key  methods  used  for  allocation  of 

on which it was allocated.

costs to commodities in analytical accounting can 

be  distinguished:  direct  costing,  indirect  costing, 
and activity based costing (FACEPA, 2011).4

Most  applied  methodologies  combine  several 

approaches  for  calculation  of  commodity  costs. 

Direct  costing  tends  to  be  applied  to  account  for 

Direct costing considers only variable costs. 

traceable  costs  such  as  direct,  cash,  variable,  or 

Because  this  type  of  costs  is  traceable,  they  can 

operating  costs. Indirect  or activity based  costing is 

be relatively easy identified with the commodity 

often used for allocation of other cost types. Although 

on which they were actually used.

the  activity  based  costing  is  the  most  exact,  it  is 

Indirect costing considers indirect costs and 

types of costs (e.g. certain fixed costs). This is because 

applies an allocation scheme to disaggregate the 

this method requires a significant amount of human 

indirect costs to commodities. Standard allocation 

and  financial  resources  for  its  implementation, 

schemes  are  based  on  production  shares,  gross 

relying on the collection of a large size of information 

almost never fully applied in practice only for certain 

4  There  are  also  other  approaches  available  such  us 
standard costing, historical costing, etc. For more details 
see FACEPA (2011).

which is often difficult to obtain in reality given the 

fact that some cost categories may not be possible to 

distinguish  by  activity/commodity  due  to  their  joint 
nature (e.g. joint costs). 

 
 
 
 
 
Chapter 2.  Methodologies and Comparisons of 

Production Costs – a Global Overview 

Prof. Dr. Folkhard Isermeyer

for reliable information and assessments. Without 

President of the Thünen-Institute and coordinator 

such  information,  they  are  at  risk  to  invest  in 

of agri benchmark

agricultural  enterprises  that  will  no  longer  be 

Johann  Heinrich  von  Thünen-Institute,  Federal 

appropriate  for  their  location  in  the  agricultural 

Research  Institute  for  Rural  Areas,  Forestry  and 

world of tomorrow.

Fisheries

Germany

2.1. Introduction

The only way to provide reliable, scientifically 

sound  answers  to  such  questions  is  to  calculate 

CoP, broken down by regions, by commodities, by 

farm types, and by production systems.

In  many  conferences  around  the  world, 

In  many  developed  countries,  there  is  lot  of 

policymakers  are  currently  asking 

similar 

information  on  CoP  available  –  however,  only  in 

questions with regard to the status and prospects 

individual farms or in farm advisory groups. In most 

of the global farming sector:

cases  this  information  cannot  readily  be  used  for 

analysis on a global scale, for a number of reasons: 

- 

Is  the  increase  in  agricultural  commodity 

First,  it  is  private  and  confidential  information. 

prices  just  a  “flash  in  the  pan”?  Or  are  we 

Second, the data have been collected in different 

witnessing a fundamental change towards a 

ways  using  different  methods;  comparing  figures 

new world food crisis?

on  cost  of  production  from  different  sources 

without  methodological  harmonization  often 

-  How  quickly,  how  strongly  and  how 

means comparing apples and pears.

sustainably  will  different 

farm 

types 

in 

different  world  regions  respond  to  high 

Third, from the user´s perspective it may not 

commodity prices?

be sufficient to just have “one figure per farm” on 

cost  of  production.  Instead,  a  set  of  figures  may 

-  How will the expected growth of bioenergy 

be required that allow us to really understand the 

production  influence  the  division  of  labour 

agricultural production process, production cost, 

in the global farming sector?

sustainability  and  international  competitiveness 

-  How  will  those  agricultural  sectors  which 

is perhaps necessary to provide much more than 

have  been  highly  protected  in  the  past,  find 

just one figure on cost of production per farm.

in different world regions. To achieve this goal, it 

their way into a liberalized agricultural world?

Against 

this  background, 

the  question 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

-  How  can  regional  production  systems  be 

arises  whether  it  might  be  possible  to  draw  on 

improved 

(better  environmental 

impact, 

government-driven  farm  accountancy  networks 

more  animal  welfare)  and  still  remain 

that  are  available  in  some  countries.  In  these 

competitive in a globalized economy?

networks, individual farm data are collected and 

analyzed by using harmonized methods. Hence, 

Such  questions  are  not  only  raised  by 

it might possible to introduce a few interfaces in 

policymakers.  Entrepreneurs  around  the  world 

order  to  internationally  harmonize  the  national 

are  having  the  same  concerns  and  are  looking 

data  bases.  At  the  end  of  such  a  development, 

25

 
 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

one  might  arrive  at  a  globally  harmonized  farm 

The system was established in 1965 and has 

data base for the analysis of cost of production.

since  grown  considerably.  Currently  data  from 

about  81,000  commercial  farms,  covering  over 

During  the  workshop  in  Brussels,  speakers 

90%  of  EU  production,  are  collected  annually. 

from  the  EU,  Ukraine,  USA,  Canada,  Brazil, 

The  FADN  is  a  representative  sample,  with 

Argentina,  Australia  and  New  Zealand  reported 

regard  to  region,  farm  type  and  farm  size.  The 

about  their  national  farm  data  surveys  that 

internationally  harmonized  farm  accounts  are 

might  serve  as  a  source  for  an  internationally 

delivered by the member states.

harmonized  global 

farm  comparison. 

In  an 

additional  paper, 

two  worldwide 

initiatives 

A  wide  range  of  data  is  collected:  data 

of  research  institutions  have  been  presented 

on  location  of  the  holding  (municipality,  less 

which  are  already  conducting  international  CoP 

favoured  area,  altitude, 

...),  ownership  and 

comparisons for more than 10 years.

organizational 

form, 

information  on  crops 

and  livestock  (areas,  heads,  production,  sales, 

The  report  in  hand  has  the  main  task 

purchases,  stocks),  labour  force,  public  support, 

to  summarize 

the  presentations  and 

the 

financial situation (assets, liabilities).

discussions. This will be done in the following 

sections 1.2 and 1.3. The author has also been 

Information  on  commodity-specific  CoP 

asked  by  the  European  Commissions  to  draw 

cannot  be  taken  directly  from  the  data  files 

some  conclusions.  These  will  be  presented  in 

because  such  information  is  not  delivered  by 

section 1.4.

the  farms  or  by  the  member  states.  Instead,  it  is 

necessary  to  estimate  commodity-specific  CoP. 

For  this  exercise,  the  FADN  data  set  offers  farm 

2.2. Data Collection and Methodologies 

individual data on monetary inputs (e.g. expenses 

in National Surveys

for  fertilizer,  feed,  or  contractor  per  farm)  and 

farm  individual  data  on  production  (e.g.  tons  of 

This section summarizes seven reports about 

wheat, barley or sugar beet per farm). However, 

nation-wide  farm  surveys  (1  from  the  EU,  and  6 

FADN does not offer enterprise-specific data (e.g. 

from non-EU-countries). It should be noted right 

expenses of fertilizer per hectare of wheat).

at  the  outset  that  these  countries  represent  only 

one segment (rather the “developed” segment) of 

The  workshop  discussions  pointed  to  a 

the global farm sector. 

number of restrictions with regard to the usability 

of the FADN data set for CoP comparisons.

The  other  segment  of  global  agriculture 

which 

is  not 

reported  here 

is  mainly 

As  most  farms  are  multi-product  farms,  the 

characterized  by  developing  countries  where 

above mentioned data deficits represent an obstacle 

there is no nation-wide farm data base available 

for  a  commodity-specific  calculation  of  CoP.  The 

that  could  be  used  for  a  global  comparison  of 

allocation of expenses to certain enterprises must be 

cost of production.

2.2.1. European Union

done on the basis of additional information or on the 

basis of assumptions. Most of the cost components 

which  are  available  in  the  FADN  data  set  are  not 

broken  down  into  detailed  cost  components.  For 

26

The  Farm  Accountancy  Data  Network 

example, only data on fertilizer costs are available, 

(FADN) is an EU-wide system information system. 

but no breakdown into nitrogen, phosphorous etc., 

It  has  two  objectives,  (a)  to  determine  farm 

and  no  breakdown  into  input  quantity  and  input 

income and (b) to analyse business development 

price.  This  is  an  additional  limitation  for  direct 

in agricultural holdings (Vard 2011).

analytical work and its interpretation.

 
 
 
 
 
 
 
 
 
 
 
To  be  sure,  there  is  no  CoP  scheme  that 

These  issues  may  explain,  to  a  certain 

can  completely  solve  this  problem.  Phone  costs, 

degree, why the European Commission does not 

for  example,  are  not  allocated  by  the  farmers  to 

calculate farm-individual CoP data (in terms of €/t 

certain enterprises, and therefore the scientists are 

of  product).  Instead,  the  “CoP  estimate  method” 

forced  to  allocate  this  cost  component  to  certain 

applied aims at the calculation of margins (Vard 

enterprises  by  using  assumptions  (e.g.  allocation 

2011). This method has the following features:

by  output  share  of  the  different  enterprises). 

However,  modern  farms  do  keep  enterprise-

- 

Calculation  of  margins  and  cost  aggregates. 

specific  records  of  important  cost  components 

Different  margins  are  calculated.  (1)  gross 

such as fertilizer or chemicals, and the large-scale 

margin  =  revenue  minus  operating  costs, 

farms  go  even  further  and  monitor  enterprise-

(2)  net  margin  =  revenue  minus  operating 

specific data for labour and machinery costs. This 

costs  minus  depreciation,  (3)  net  economic 

enterprise-specific  information  is  very  important 

margin  =  revenue  minus  operating  costs 

for  a  realistic  assessment  of  production  cost,  and 

minus  depreciation  minus  external  factors 

the  ability  of  FADN  to  calculate  meaningful  CoP 

minus  own  factors  (estimated  family  wages, 

results  is  severely  limited  by  the  fact  that  only 

rent and interest).

whole-farm data are collected. This is an important 

disadvantage compared to most of the other CoP 

- 

Selection of specialized farms, and allocation 

data collection schemes discussed in this report.

of the cost components (for the whole farm) 

There are a number of further methodological 

keys (e.g. share of the commodity´s output in 

to  different  commodities  by  using  various 

issues 

that  complicate 

the  calculation  of 

total farm output).

meaningful  farm-individual  CoP  data  from  the 

FADN data file for international comparsions. Just 

- 

Calculation  of  opportunity  costs  for  farm-

two  examples  for  illustration:  (a)  The  enormous 

owned  factors,  using  regional  land  rents, 

variability of products poses a problem of finding 

wage  rates  of  regional  agricultural  workers, 

a comparable denominator (e.g.: calves with 140, 

and interest rate of ten year national treasury/

150, 160, ... kg?). (b) In the absence of additional 

LT bonds. 

information about the workforce (e.g. hours of a 

child, an entrepreneur, a retired farmer, etc.), it is 

- 

The  gross  margins  are  being  updated 

difficult to calculate true labour costs for certain 

annually to the year n-1. 

enterprises.

However,  these  issues  affect  the  usability 

applied for different “types of farms” (e.g. in 

of  FADN  data  for  CoP  calculations  for  some 

beef production: breeders, breeder-fatteners, 

-  When  needed, 

this  procedure  can  be 

commodities  more  than  others.  For  example, 

fatteners).

the  FADN  data  set  is  relatively  well-suited  to 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

calculate  cost  of  milk  production  in  specialized 

There are annual publications of margins and 

dairy  farms. These  farms  are  characterized  by  a 

incomes for some commodities, and there are ad-

rather uniform product, and joint production only 

hoc  studies  on  various  issues  (e.g.  international 

plays a minor role. In this case, the FADN-based 

comparisons  of  CoP).  Research  work  is  going 

CoP method can be advantageous because many 

on  in  order  to  develop  and  improve  the  CoP 

farms  are  included  and  farm-to-farm  differences 

calculation method.

can  be  analyzed.  For  commodities  such  as 

27

brewers  grain,  rapeseed  or  certain  varieties  of 

During the workshop, no results on margins 

beef, however, it will hardly be possible to arrive 

were  presented.  The  publications 

that  are 

at reliable, farm-individual CoP results. 

available  in  the  internet,  however,  demonstrate 

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

28

an  impressive  amount  of  empirical  results  on 

ARMS began in 1996 as a synthesis of former 

margins that have been computed on the basis of 

surveys on cropping practices, chemical use, and 

FADN data. These include box-plots, distribution 

farm  cost  and  returns,  which  dated  back  to  the 

curves etc. indicating that indeed farm-individual 

mid-70ies.

calculations  are  conducted 

(e.g.  European 

Commission 2009).

Data collection is done by field enumerators 

who  are  personally  interviewing  the  farmers. 

However, a strategic move from CoP towards 

ARMS 

is  a  series  of 

interviews  conducted 

margins  does  not  really  solve  the  issue.  The 

throughout  the  “survey  year”  which  runs  from 

computation  of  “net  economic  margins”  for  a 

June to April.

certain,  individual  FADN  farm  needs  the  same 

data  as  the  computation  of  “cost  of  production” 

- 

Phase  I  (Screening). The  selected  farms  are 

in  that  farm,  and  if  the  original  data  has  not 

screened  to  verify  their  operating  status 

been collected it remains difficult to improve the 

and  to  determine  whether  they  produce 

diagnostic power of the analysis by just changing 

commodities  targeted  for  data  collection. 

the method of computation.

This  helps  to  improve  survey  efficiency  in 

phases II and III.

This  issue  is  particularly  important  for  the 

measurement  of  family  farm  labour  costs.  Farm-

- 

In  Phase 

II,  randomly  selected  operating 

to-farm  differences  in  labour  productivity  are 

farms  from  phase  I  are  interviewed  to  obtain 

considered a major driver for structural change in 

information  on 

their  production  practices 

regional, national and global agriculture.

and  chemical  use.  Data  are  collected  at  the 

individual  field  or  production  unit 

level. 

The potential use of the FADN data for CoP 

Physical and economic input data are collected, 

calculations  could  be  substantially  enhanced  if 

so that a detailed analysis of CoP is possible.

the range of collected data (including enterprise-

specific data) could be widened. However, many 

-   Phase  III  data  are  collected  on  the  whole 

family farms do not have such data in their files. 

farm 

level.  Data  are  collected 

from  a 

Therefore  to  achieve  such  an  objective  it  may 

nationally  representative  sample  of  farmers 

be  necessary  to  financially  compensate  sample 

in order to analyze the farm-level economic 

farms for the extra data collection effort.

situation in the reference year. Reported data 

2.2.2. USA

for  phase  II  are  included,  so  that  data  from 

both surveys can be merged.

The United States Department of Agriculture 

The  producer  surveys  which  are  designed 

(USDA) 

regularly  conducts  an  Agricultural 

to  calculate  enterprise-specific  CoP  are  not 

Resource  Management  Survey  (ARMS)  (Lazarus 

done every year, but only every 4 to 8 years, and 

2011,  USDA  2011).  The  questionnaires  are 

they  do  neither  cover  all  crops  nor  all  livestock 

developed  by  the  National Agricultural  Statistics 

branches. Regional coverage also varies between 

Service  (NASS)  and  the  Economic  Research 

interview  years,  i.e.  in  some  years  more  states 

service of the USDA (ERS).

are  included  than  in  other  years.  In  those  years 

where  no  interviews  are  possible,  adjustments 

ARMS  is  the  USDA´s  primary  source  of 

with  regard  to  inflation  etc.  are  made  (without 

information  about  the  current  status  and  trends 

farm interview).

in  the  financial  condition,  production  practices, 

and  resources  use  of  farmers,  as  well  as  their 

Sample  sizes  are  in  the  magnitude  of  a 

households´ economic wellbeing.

couple  of  thousands,  e.g.  2.800  wheat  farms 

 
 
 
 
 
 
 
 
 
 
 
and 1.500 dairy farms. Obviously, the system is 

Like  in  other  countries,  there  are  many 

somewhat  limited  due  to  financial  restrictions. 

methodological issues that need to be solved (e.g. 

In  a  2002  internet  note,  NASS  and  ERS  stated 

allocation of fix costs, opportunity costs of labour, 

that – for reasons of statistical accuracy – they 

opportunity  costs  of  land,  joint  production), 

planned to increase the number of interviewed 

and  since  it  is  not  possible  to  find  completely 

farms  up  to  its  historical  level  of  18.000  to 

satisfactory solutions, the usefulness of the mere 

19.000 

farms  annually 

(USDA  2002).  The 

CoP results remains limited.

annual cost of the ARMS is currently estimated 

at 5 to 6 Mio. US-Dollars. Most of this money 

With  regard  to  labour,  there  are  serious 

is used for paying the local interviewers. Their 

doubts whether the reported differences in wage 

role in the system is crucial.

rates between grain farms and hog operations are 

reflecting the true situation. Such a question can 

Three  types  of  reports  are  being  published: 

become very important when the competitiveness 

(a) 

farm 

structure  and  finance, 

(b)  crop 

of grain vs. hog operations shall be assessed.

production  practices,  (c)  commodity  production 

costs and returns. These regular publications are 

With  regard  to  land,  opportunity  costs  are 

spreadsheet  tables  for  9  regions  covering  the 

measured  by  taking  the  regional  land  rent. This 

whole US. In addition, more detailed reports on 

is  not  satisfactory  because  it  creates  a  wrong 

special issues are being produced (e.g. impact of 

impression about the competitiveness of farming 

farm size on CoP), sometimes on the basis of time 

in a certain region.

series data.

The  spreadsheet  tables  on  CoP  of  certain 

workshop  speakers  stated  that  the  “circularity” 

crops display “$/acre”-figures for different regions 

of  agricultural  prices  and  land  costs  is  a  serious 

of  the  USA,  broken  down  into  the  following 

issue.  Time  series  data  clearly  indicate  that 

Lazarus  (2011)  and  a  number  of  other 

compartments:

land  prices  are  following  corn  prices;  the  ratio 

between  the  two  is  almost  constant.  If  high 

-  

gross  value 

(breakdown:  primary  and 

agricultural prices lead to high (computed) costs 

secondary production)

the question arises: What message can be derived 

from 

the  high 

(computed)  costs?  Definitely 

-   operating  costs  (breakdown:  seed,  fertilizer, 

not,  the  message  that  farming  has  become  less 

chemicals,  custom  services,  energy,  repairs, 

profitable and therefore agricultural policy has to 

irrigation, interest on operating capital)

give more support to farmers.

-  

allocated  overhead 

(breakdown:  hired 

Besides  ARMS,  there  are  also  other  sources 

labour,  opportunity 

costs  of  unpaid 

for CoP calculations in the USA. For example, CoP 

labour,  capital  recovery  of  machinery  and 

data  on  cash  crops  are  collected  by  institutions 

equipment,  opportunity  cost  of  land,  taxes 

in  Minnesota,  Kansas,  Illinois,  Iowa,  and  crop 

and insurance, general farm overhead) 

enterprise  budget  projections  are  available  for 

many  states.  Obviously,  the  ARMS  system  is  not 

Moreover,  they  provide  a  few  additional 

able to serve all purposes (Lazarus 2011). 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

figures  on  price,  yield,  enterprise  size,  percent 

irrigated land, percent dryland.

The ARMS  system  is  mainly  used  for  policy 

purposes  because  it  has  the  advantage  of  being 

29

The  method  of  CoP  calculation  has  been 

statistically 

representative.  A  weak  point, 

developed in 1995 by a task force of the American 

however,  is  that  in  ARMS  “real  farm  data”  are 

Association of Agricultural Economics (AAEA).

only collected every 4 to 8 years so that farmers 

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

30

do  not  get  the  kind  of  information  that  they 

demands. Hence, a detailed data base is available 

would  need  for  farm  management  decisions. 

which  can  be  used  both  for  calculating  CoP  and 

Furthermore,  in  the  ARMS  system  there  is  often 

for modelling various policy options.

a considerable time-lag (more than 5 years) until 

the data is analyzed and publicly available.

In  the  late  90ies,  the  AFPC  was  one  of  the 

Hence,  the  farming  sector  prefers  other 

US-concept  was  used  as  a  blueprint  when  the 

sources  of  information  although  these  other 

agri benchmark / IFCN concept was designed for 

sources  are  not 

statistically 

representative. 

the international level (see section 3).

founding members of the IFCN, and the national 

Extension 

services 

use 

non-representative 

regional  data  because  they  are  of  better  quality 

2.2.3. Brazil

and  more  up-to-date.  An  increasing  number 

of 

farms 

is 

involved 

in  consultancy-based 

CONAB, the national agency for supply, is a 

benchmarking  activities. This  could  theoretically 

public company that produces and disseminates 

be  a  source  for  “government-administered”  CoP 

information,  especially  information  about  the 

statistics,  too.  Yet  practically  the  source  is  not 

farming sector. CONAB acts nationwide and has 

captured,  and  it  is  doubtful  whether  the  private 

an office in every state in Brazil. CONAB assists 

organizations would make their data available for 

the  national  government  in  formulation  and 

public  use.  More  likely  (and  sometimes  already 

execution of policies.

practiced)  is  a  cooperation  between  private 

consultants and regional extension services.

CONAB  is  compiling  a  lot  of  information 

on  agricultural  CoP  in  Brazil  (Teixeira  2011). The 

It  is  also  remarkable  that  for  policy  advice 

service covers temporary cultures as well as semi-

an  alternative  farm-based  data  base  has  been 

perennial  and  permanent  crops,  products  related 

developed in the US. Only a few years after the 

to  poultry,  pork,  goats,  sheep,  dairy  farming,  and 

USDA had started the predecessor of ARMS (farm 

also  extraction,  biodiversity,  and  fish  (sardines). 

cost  and  returns  survey),  in  the  early  80ies  the 

In  total, 428  budgets  with agricultural CoP figures 

Agricultural  Food  and  Policy  Research  Centre 

are  available  (published  the  internet).  For  many 

(AFPC)  at  Texas  A&M  University  started  an 

commodities,  different  “technological  packages” 

alternative (so-called) representative farm concept 

(with different technical coefficients) are calculated.

(Richardson  et  al.,  2011). This  system  has  been 

successfully developed until today. It is based on 

The wider purpose of this exercise is to identify 

the  concept  of  typical  (virtual)  farms. These  are 

differences in competitiveness (a) between regions 

built, validated and discussed in regional panels 

and  (b)  between  technologies.  The  comparative 

of farmers, advisors and AFPC scientists.

analysis can provide useful information for services, 

investors,  policymakers  both  on  the  regional, 

The AFPC data set is used as a prime source 

national  and  international  level.  The  results  are 

of  information  for  the  US  congress.  Proposals  for 

produced for a wide range of users (policy, business, 

agricultural policy reforms are jointly analyzed by 

universities, …), including international institutions 

Texas A&M and FAPRI, and for the farm-level part 

(FAO,  USDA).  In  Brazil,  the  main  purpose  is  to 

of these assessments AFPC uses the FLIPSIM model 

support  government  decision  making.  It  is  felt 

(farm level income and simulation model) on the 

necessary to have an agency that provides uniform, 

basis  of  the  typical  farm  data  (Richardson  et  al., 

reliable and consistent information.

2011). Currently, 97 representative crop, livestock 

and dairy operations in major production areas in 

The  data  collection  procedure  is  similar  to 

27 states are in the systems. The typical farms are 

the procedure developed by the agri benchmark 

regularly updated to be able to meet future policy 

network: 

 
 
 
 
 
 
 
 
 
 
 
-  

Identification  of  the  regional  centres  of 

the figures presented at the workshop shows that 

production

the data allow a much more detailed analysis of 

CoP than, for example, would be possible in the 

-  Organize  panel  meetings  in  these  regions 

ARMS  and/or  FADN  system.  The  reason  is  that 

(15 participants)

the basic conceptual idea of the CONAB system 

is inspired by a commodity-oriented “engineering 

- 

Survey  the  technical  coefficients  of  the 

approach”  which  would  be  pursued  by  an 

selected enterprises

investor and his/her operating manager.

In  the  panel  meetings,  (a)  regional  experts 

The  comparison  of  CONAB  and  FADN 

(e.g.  advisors,  researchers),  (b)  producers  and 

reveals the advantages and disadvantages of two 

(c) the CONAB  analysts  are  taking part (Teixeira 

different  concepts.  CONAB  allows  an  in-depth 

2011). The  technical  and  economic  coefficients 

analysis  of  various  aspects  of  a  typical  farm, 

are collected on the basis of regional figures and 

and this leads to a  much  better “understanding” 

expert judgement, and the panel has to agree to 

of  production 

systems  and  possible 

farm 

one  coherent  set  of  coefficients  (“If  everybody 

adjustments.  However,  it  would  not  be  possible 

agrees, the cost is published”).

to  establish  so  many  typical  farms  (with  panels 

etc.)  that  this  approach  could  come  anywhere 

An  important  feature  of  this  system  is  the 

near  “statistical  representativity”.  In  contrast, 

selection  and  modelling  of  a  “modal  farm”.  It 

the  basic  conceptual  idea  of  the  FADN  system 

is  important  not  to  have  a  statistical  average  of 

is  a  farm-oriented  “income  monitoring  system” 

different farms in a region because the average of 

which has to place much more emphasis on mass 

different  farms  does  not  form  an  economic  unit 

statistics, representativity, and the profit and loss 

that  makes  sense  and  could  be  found  in  reality. 

account of the whole farm.

Therefore  a  decision  is  necessary  to  opt  for  one 

“technological  package”  which  is  best  suited  to 

The  technology  package  for  each  budget  is 

represent  the  farming  system  in  the  region,  and 

updated  every  3  years,  while  prices  are  updated 

then  this  technology  is  captured  and  modelled 

monthly. Therefore  CONAB  can  always  offer  up-

(instead of unrealistic statistical averages).

to-date  CoP  figures. This  has  been  demonstrated 

using a chart on soybean 2011/12 (Teixeira 2011).

Another advantage of this system is that both 

physical  data  (e.g.  amount  of  fertilizer)  and  price 

The  government  uses 

the  data 

for 

the 

data  (e.g.  fertilizer  price)  is  collected.  Compared 

minimum price policy of Brazil. For this purpose, 

to other systems which mainly rely on whole farm 

only the results on variable cost are needed. But 

data from the profit and loss account (e.g. fertilizer 

CONAB  goes  further,  covering  all  steps  on  the 

purchases  per  farm),  this  gives  a  much  more 

way to total CoP.

detailed picture of the production system and CoP. 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

The  same  is  true  with  regard  to  machinery 

is  done  in  a  similar  way  as  in  other  countries. 

costs. The detailed analysis does not only include 

Only 

the  component 

is  “external 

transport 

purchase  value  and  expected  life  time  (as  many 

costs” 

is  a 

rather  unique  one.  This  cost 

other approaches do), but also residual value and 

component,  however,  does  not  capture  the  cost 

The breakdown of CoP into cost components 

life cycle (in hours).

of  long-distance  transports  (which  would  be 

very  informative  with  regard  to  international 

31

Standardized  routines  are  used  to  transform 

competitiveness of Brazilian agriculture) but only 

the collected data (on input and output items) into 

the  transport  to  the  first  storage  centre,  with  a 

total CoP and cost components. A closer look at 

maximum distance of about 80 km.

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

The  main  challenges,  as  identified  by  the 

Catarina (south of Brazil) for many years; swine 

CONAB  administrators,  are  (a)  how  to  include 

production  cost  started  in  the  1980s,  poultry 

environmental  cost  and  (b)  how  to  integrate  cost 

production  cost  in  the  90ies  (Miele  2011  a, 

of  crop,  livestock  and  forest.  Another  challenge 

b). In 2005, this EMBRAPA CoP approach was 

might be seen in expansion of the system to more 

spread  to  11  states  throughout  Brazil,  using 

farm types, regions, and commodities. Until today, 

the  CONAB  infrastructure.  Recently,  Embrapa 

the  budgets  cover  many,  but  not  all  agricultural 

started  a  cooperation  with  slaughterhouses  in 

commodities.  For  example,  beef 
is  not  yet 
included  because  the  government  did  not  seem 
to  be  interested,  and  sugar  cane  is  not  available, 

order  to  include  contract  production  systems 

in  the  analysis.  Within  the  Brazilian  swine 

network,  different  typical  production  systems 

because this crop is covered by another institution.

in  various  regions  are  being  analyzed.  Labour 

costs  are  of  special 

importance.  At  first, 

Other 

institutions 

in  Brazil  are  also 

EMBRAPA  tried  to  collect  relevant  data  on 

collecting  and  analyzing  CoP  data  but  they 

labour  costs  from  real  farms.  This  approach 

cover  less  commodities  than  CONAB  does. 

failed  because  too  many  farms  delivered  low-

The  parallel  development  started  in  2003  when 

quality  data  that  did  not  lead  to  meaningful 

agri  benchmark  beef  came  to  Brazil  and  invited 

results.  Therefore, 

they  finally  decided 

to 

institutions  to  participate  in  the  world-wide 

follow  the  typical  farm  approach  where  data 

network. At that time, there were no Brazilian on-

for  a  typical  farm  of  the  region  are  generated 

farm data on beef production available. Therefore, 

by  a  panel  of  farmers  and  researchers.  This 

a  cooperation  between  agri  benchmark,  CNA 

approach has led to better results.

(National  Farmers  Association  of  Brazil)  and 

CEPEA  (Centre  of  Advanced  Studies  in  Applied 

Given  the  fact  that  so  many  important 

Economics  at  the  University  of  Sao  Paulo)  was 

Brazilian  institutions  are  now  involved  in  CoP 

established (de Carvalho and de Zen 2011).

analysis,  it  would  not  be  surprising  to  see  a 

From  the  very  beginning,  the  Brazilian  agri 

concepts developing in the foreseeable future.

stronger  cooperation  and  new  organisational 

benchmark  partners  adopted  the  international 

concept (typical farms; panel-based data analysis) 

2.2.4. Canada

for the intra-Brazilian network. The Brazilian beef 

network  currently  comprises  61  finishing  farms 

There  are 

four  primary 

reasons 

that 

and 56 cow-calf farms in 13 states of the country. 

governments  and  agencies 

in  Canada  are 

A  couple  of  these  farms  are  used  for  the  global 

collecting,  analyzing  and  publishing  CoP  data 

comparison  of  the  agri  benchmark  network  (see 

(Koroluk 2011):

section  3).  Besides  the  annual  CoP-related  data 

collection, information on prices is collected on 

- 

Providing a date base for farm management 

a monthly basis (telephone interviews). CEPEA is 

extension,  planning,  and  research 

(“the 

collecting  and  analyzing  not  only  CoP  for  beef 

traditional role”):

but also for dairy and for various cash crops.

(a) farm budgeting, crop planning and new 

A  third  player  that  should  be  considered 

(b) environmental beneficial management 

enterprise establishment 

32

is  Brazilian  Agricultural  Research  Corporation 

practices 

(EMBRAPA) which is also interested in building 

(c) biofuels production 

up more knowledge in the field competitiveness 

of certain production systems and commodities. 

-  Helping to understand market development, 

In  the  field  of  pig  and  poultry  production, 

regional  profitability,  and 

international 

Embrapa has done some CoP analysis in Santa 

competitiveness:

 
 
 
 
 
 
 
 
 
 
 
(a) assessing competitiveness, risks and 

- 

Budgets prepared by farm management and 

opportunities in the primary agricultural 

commodity specialists

sector

(b) improving policy decisions by 

-  Marketing board surveys of producer members

government and producer groups

(c) identifying and promoting potential new 

- 

Information  collected  as  part  of  analytical 

winners

studies or special projects

- 

Establishing “official” prices for commodities 

under the system of supply management

(a) relevant for dairy, broiler chickens, 

turkeys, eggs, and hatching eggs

- 

- 

Budgets modified from other jurisdictions

Program administrations

-  Meeting program administration requirements 

extension, accounting firms, farmers clubs)

-  Other  data  providers 

(e.g.  university 

at both federal and provincial levels

(a) used in calculating program payments

With  regard  to  data  processing  and  CoP 

(b) assists in new program design

calculation,  different  sources  make  different 

assumptions and apply different methods.

Most of the CoP information is collected by 

various agencies in the provinces for their specific 

Budgets  are  often  integrated  in  production 

purposes. This implies that data come from several 

planning  tools  and  therefore  delivered  per  unit 

sources and in several different formats. Some of 

(ha,  head,  kg).  Most  of  these  budgets  contain 

the  few  national  data  collections  are  conducted 

average  figures,  i.e.  they  do  not  take  account  of 

by the supply managed industries (dairy, poultry) 

farm-to-farm differences. In view of the big farm-

in the framework of national legislation.

to  farm  differences,  especially  with  regard  to 

Overall, there is a fairly good coverage of the 

CoP  data  that  give  a  realistic  and  representative 

main production regions. According to Agriculture 

survey of the full range of real farm situations.

livestock,  it  would  hardly  be  possible  to  obtain 

and  Food  Canada,  the  division  of  responsibilities 

makes sense under Canadian conditions, because 

Altogether,  around  1.524  budgets  are 

of  the  diversity  in  geography  and  consequent 

available, originating from the various sources.

differences  in  agronomic  condition,  production 

practices,  crops,  markets,  and  trading  patterns. 

Main challenges of the current system are:

Furthermore,  programs  are  increasingly  being 

delivered  by  provinces,  so  even  administrative 

- 

Primary  data  collection  is  costly,  and  the 

sources are increasingly province based.

high  cost  is  a  deterrent  to  broad-based 

initiatives.  Hence,  there  is  very  little  scope 

There  is  no  common,  standardized  method 

for financing additional surveys.

applied for data collection. Instead, different data 

suppliers use different approaches. Most common 

-  Differences  in  collection  methods,  variable 

data collecting methods are (Koroluk 2011):

definitions  and  reporting  formats  make  CoP 

information  difficult  to  compare  between 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

- 

Producer  and  retailer  surveys  (annual  or 

provinces.

periodic)

33

- 

Consensus  budgets  using  producer  focus 

updates is crucial for producing data that are 

groups, workshops and panels

regarded as relevant by the addressees.

- 

Timeliness of information and frequencies of 

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

- 

As  farms  become  more  complex,  integrated 

For  the  broadacre  sector  (cropping,  mixed 

and large-scale, CoP information may become 

crop-livestock,  beef,  sheep)  and  the  dairy  sector 

less relevant (commodity-specific CoP tend to 

(75%  of  all  farms),  the  Australian  Bureau  of 

neglect aspects of joint production).

Agricultural and Resource Economics and Sciences 

(ABARES) is doing farm surveys for 33 years.

- 

Especially 

in 

large  commercial 

farms 

willingness  to participate is decreasing over 

The system is not covering all sub-sectors of 

time.  They  question  whether  this  kind  of 

the agricultural sector. Pig, poultry and fruits are 

information may be useful for them.

missing, and sugar has only been covered once in 

a special study. Vegetable production is included 

- 

The  systems  in  place  do  not  deliver  enough 

only since 2005.

information  on  new  crops  and  technologies, 

beneficial  management 

practices, 

and 

Data collection is done by interviewers who 

environmental practices to encourage adoption.

visit  the  farms  (face  to  face  data  collection). The 

farms  are  randomly  selected  from  the  Australian 

- 

Budget averages do not reflect the variability 

business  register  (using  business  size,  enterprise 

in CoP from farm to farm.

type,  and  geographic  location),  and  the  stratified 

sample is followed over many years. This approach 

According  to  Koroluk  (2011),  the  Canadian 

has lead to a valuable data source for time series 

Farm  Business  Management  Council  (CFBMC) 

analysis. The  location  of  the  farms  is  geo-coded, 

has  tried  to  harmonize  data  (across  provinces) 

allowing interesting additional studies.

and present the budgets on the internet, in a very 

comprehensive  manner  (e.g.  including  budgets 

The  data  are  used  to  monitor  farm  cash 

for  farm  vacation  enterprise  and  on-farm  food 

costs, broken down into cost components (seed, 

processing).  However,  the  harmonization  and 

fertilizer,  etc.).  Furthermore,  whole  farm  data 

standardization  issue  (e.g.  comparability  across 

from  the  profit  and  loss  account  are  used  to 

commodities) remains partly unsolved.

calculate  (a)  farm  business  profit  and  (b)  rate  of 

return  to  capital,  both  excluding  and  including 

Regarding  the  financial  and  the  income 

capital appreciation.

status  of  farms,  Canada  has  established  other 

information  systems.  Yet  these  systems  are  not 

Three  critical  issues  have  been  identified 

able  to  deliver  meaningful  CoP  information 

(Foster 2011):

(Koroluk 2011).

2.2.5. Australia

-  How to calculate meaningful  CoP  in  mixed 

farming systems 

Australian  agriculture  is  highly  exported-

-  How to measure full cost of production (e.g. 

oriented,  with  about  58%  of  total  agricultural 

evaluation of family farm labour)

produce being exported to other countries (Foster 

2011).  Hence, 

international  competitiveness 

-  How  to  use  the  data  for  international  CoP 

is  an  important  issue.  It  is  not  easy,  however,  to 

comparisons

conduct  meaningful  CoP  analysis.  Except  from 

34

sugar  cane  production,  Australia´s  agricultural 

Inter-industry  comparisons  are  possible. 

sector is characterized by mixed farms. The nature 

This  is  an  advantage  of  a  broad  system 

of  farming  varies  a  lot  within  Australia  because 

covering  many  sub-sectors  of  agriculture  and 

the country extends over different climate zones, 

applying harmonized methods, at least within 

including tropical, temperate, and dry-grassland. 

a country.

 
 
 
 
 
 
 
 
 
 
 
Some interesting results have been presented 

monitoring 

system. 

In 

this 

system, 

farm 

in the workshop:

consultants  in  the  regions  are  monitoring  farms, 

together  with  farm  groups,  and  forecasting  the 

- 

The  one-year 

analysis 

for  different 

situation of next year. This is done for all important 

vegetables  (potatoes,  carrots,  onions,  etc.) 

farm types: dairy, beef, sheep, deer, cropping and 

shows  that  one  CoP  figure  per  country 

horticulture.  When  the  farm  consultants  come 

and  crop  is  not  informative,  unless  it  can 

again in the following year, they monitor the past 

be  compared  to  product  prices,  to  other 

year;  and  by  doing  so,  they  validate  the  system 

regions, or to other years.

and improve their ability to assess the situation of 

the farms (Shadbolt 2011).

- 

The  CoP  data  for  sugar  cane,  displayed  for 

a  number  of  regions  over  three  years,  give 

This  system 

is  relatively  new. 

It 

is  an 

a  good  example  for  a  better  CoP  analysis: 

alternative  to  an  older  system  called  “Economic 

It  shows, 

for  example, 

that  CoP  have 

Survey of sheep and beef”, which is also still being 

considerably  increased  between  2005  and 

applied. The  old  system  has  a  stronger  statistical 

2007,  especially  in  Queensland,  and  that 

basis, but tends to be always outdated. The farmers 

New South Wales continues to be the least-

complained about this system because the results 

cost producer in Australia.

were  not  useful  for  them.  In  general,  the  former 

systems  have  failed  to  give  farmers  exactly  those 

- 

CoP breakdowns by farm size groups (sugar 

results that they need for their daily business (with 

cane)  and  cropping  intensity  (sugra  cane, 

bankers, consultants, etc.).

grain) indicates that CoP per ton of product 

decreases  with  increasing  farm  size  and 

For  the  development  of  the  new  system, 

that  rate  of  return  increases  with  increasing 

(a)  timeliness,  (b)  cost  and  (c)  focus  have  been 

cropping intensity.

identified  as  crucial  success  factors.  Especially 

timeliness is very important. The  results must be 

A  long-term  time  series  analysis  of  the  data 

available as soon as the business year ends.

has lead to the result that total factor productivity 

(TFP)  in Australia´s  broadacre  sector  has  slowed 

For  the  dairy  industry,  the  development 

down  considerably  since  the  mid-90s  (1953-

process started in 2003 with a small group trying 

1994:  2,2%  p.a.,  1994-2008:  0,4%  p.a.). This  is 

to achieve all three purposes. At the beginning, the 

mainly caused by a strong TFP decrease in crop 

system and the data situation was very fragmented. 

production while the TFP trend in beef and sheep 

In  2006,  with  the  introduction  of  DairyBase  Ltd, 

production is still positive (Foster 2011).

a  web-based  data  base  was  built.  This  meant  a 

The  slowdown  of  agricultural  TFP  growth 

All the big companies and institutions of the New 

is  caused  by  an  number  of  reasons,  the  most 

Zealand dairy sector have been involved (Fonterra, 

important being: Drought, lack of new ` big gain´ 

Dexcel, Massey University), and the system is now 

huge effort in harmonization of previous systems. 

-technologies,  ageing  farm  population,  fewer 

generally accepted.

expansion  opportunities,  changing 

research 

priorities,  and 

falling  public 

investment 

in 

Important  characteristics  of  the  new  system 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

research and development.

2.2.6. New Zealand

are (a) voluntary participation of farmers and (b) 

a  high  degree  of  farmer  involvement. Therefore, 

it is not possible to make this system statistically 

representative in the sense that a stratified sample 

35

The  Ministry  of  Agriculture  and  Forestry 

of  all  New  Zealand  dairy  farmers  is  included. 

(MAF)  in  New  Zealand  is  running  a  farm 

However, more and more farmers decided to take 

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

part,  so  that  now  about  10%  of  all  farmers  are 

2.2.7. Ukraine

in  the  system  and  the  results  give  a  fairly  good 

impression of the sector´s economic development 

In  the  Ukraine,  the  so-called  50-sg  report 

(Shadbolt 2011).

(State  national  wide  survey)  is  a  survey  of  all 

agricultural  enterprises  that  exceed  certain  size 

The  high  degree  of  farmer  involvement  is 

limits: 200 ha; 50 cows, pigs, sheep (500 poultry); 

believed  to  be  a  decisive  factor  for  top-quality 

20  workers,  150.000  UAH  revenue.  In  total, 

farm data. In the New Zealand system, the farmers 

information  on  about  9.000  farms  is  collected. 

receive an intensive feedback, and in the framework 

The  data  collection  is  done  through  the  50-sg 

of  the  benchmarking  system  they  can  experience 

district-level  bodies,  using  a  standardized  data 

each  year  that  only  true  data  lead  to  meaningful 

format. Data on agricultural production and sales 

results. They learn to work with the results so that a 

are  collected, but the data cannot be used  for  a 

win-win situation is created: an advantage for each 

profound analysis of CoP (Slaston 2011).

individual farmer who is a analyzing his results, and 

an  advantage  for  the  whole  sector  if  the  data  set 

A data base for the analysis of CoP is created 

is  used  to  better  understand  the  pros  and  cons  of 

within two projects that are both operated by the 

different management practices.

Ukrainian Agribusiness Club (UCAB):

In  the  new  system,  the  data  belong  to  the 

farmer.  That  means  that  they  have  total  control 

and  can,  for  example,  decide  whether  their 

- 

- 

the AgriEfficiency project

the  agri  benchmark  project  (embedded  in 

data  may  be  taken  for  a  scientific  study.  Only 

the worldwide agri benchmark consortium)

aggregates  go  to  the  report  that  the  industry 

receives.  The  farmers  get  their  data  back,  and 

The  agri  benchmark  project  is  based  on 

in  addition  they  also  receive  a  benchmark. The 

the  concept  of  “typical”  farms,  following  the 

benchmark  covers  physical  and  financial  data 

standard operating procedure of the international 

but  no  data  on  liquidity  (“some  figures  are  not 

network led by the Thünen Institute in Germany 

`benchmarkable´”).

(see section 3). Only few farms from the Ukraine 

need to be included, but the amount of data per 

Much effort is invested to train the people who 

farm  (required  by  the  global  agri  benchmark 

are  collecting  the  data.  It  is  important  that  they 

consortium)  is  relatively  high.  Data  collection 

understand the purpose of the overall exercise and 

for  agri  benchmark  is  done  by  face-to-face  and 

follow standardized data collection procedures.

additional phone interviews.

The  combined  analysis  of  both  detailed  CoP 

Because  of  the  high  effort  that  is  necessary 

data for the dairy enterprise and whole-farm data with 

to  meet  the  data  collection  requirements  of  the 

regard  to  farm  asset  valuation  can  be  a  challenge. 

agri benchmark project, the UCAB has developed 

However,  it  is  necessary  to  cover  both  aspects  in 

a  less  ambitious  intra-national  farm  comparison 

order to fully understand the nature of modern dairy 

project  called  AgriEfficiency.  Here 

the  data 

economics.  On  an  average,  a  New  Zealand  dairy 

collection is done with the help of an email or fax 

farm currently makes a profit of 15%. Of this, about 

questionnaire.

11% is gain in farm value and only 4% comes from 

36

annual  dairy  production.  Traditional  CoP  analysis 

AgriEfficiency is currently collecting data on 

is  not  able  to  reflect  this  situation  properly.  Hence, 

hog production but no data on beef production, 

more attention should be given to the question how 

and  in  agri  benchmark  it  is  vice  versa.  Data  on 

wealth creation can be better included into a holistic 

poultry production are currently neither collected 

analysis. (Shadbolt 2011)

by AgriEfficiency nor by agri benchmark.

 
 
 
 
 
 
 
 
 
 
 
Given  the  special  situation  of  Ukrainian 

Framework Conditions, Goals, Requirements

agriculture  (history,  transformation,  economic 

crisis), it is still very difficult to obtain meaningful 

In  the  initial  stages  of  the  two  networks, 

figures 

from 

the 

farms.  There  are  several 

emphasis  has  been  put  on  the  question  what 

comparability  issues,  caused  by  different  agro-

the  core  goals  of  the  international  comparisons 

ecological  zones  (forest,  forest-steppe,  steppe), 

should  be  and  whether  alternative  options  were 

different 

production 

technology 

packages 

available to achieve these goals.

(traditional,  conservational,  minimum  tillage), 

and  accounting  policies.  Furthermore,  heavy 

These discussions have lead to the important 

short-term  price  fluctuations  and  differences  in 

result  that  many  goals  of  global  CoP  systems 

the  product  quality  (both  on  the  inputs  and  on 

can  be  achieved  without  conducting  CoP 

the output side) are limiting comparability.

comparisons:

According to Slaston (2011) main challenges 

- 

If,  for  example,  policy  makers  would  “only” 

besides comparability are: 

want some average CoP figures per product and 

nation,  it  would  be  sufficient  to  just  compare 

- 

incentives for the farmers (the system should 

product  prices  across  nations.  The  reason  is 

deliver  the  kind  of  feedback  that  they  need 

that  farmers  all  over  the  world  follow  the  rule 

for the development of their business)

“marginal cost should equal product price”.

- 

- 

data reliability

- 

If  policy  makers,  for  example,  are  “only” 

interested in past competitiveness of certain 

high  costs  of  data  collection,  especially 

agricultural sectors, it would – in many cases 

when  disaggregated  costs  for  different  farm 

–  be  sufficient  to  compare  how  different 

enterprises shall be computed

agricultural  sectors  have  gained  or  lost 

- 

trust  and  confidentiality 

(convince 

the 

farmers  that  they  can  trust  the  system  and 

Both  alternative  indicators  (product  prices; 

that data confidentiality is assured)

market  shares)  can  be  obtained  easily  and  at 

market shares.

2.3. Global comparisons

relatively  low  cost  from  various  sources,  so  that 

it  would  not  be  necessary  to  develop  expensive 

global networks for detailed CoP comparisons.

This  section  describes 

two  world-wide 

Considerations in agri benchmark and IFCN 

initiatives  (agri  benchmark;  IFCN)  which  have 

have lead to the result that there are mainly two 

been  developed 

for  CoP  comparisons  and 

reasons why regular CoP comparisons on a global 

analysis on a global scale.

level a really useful and necessary:

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

In 

the  first  sub-section, 

the  goals, 

the 

- 

to understand the reasons for CoP differences 

theoretical framework and the practical limitations 

between farms and/or regions

of global CoP comparisons are briefly addressed. 

This forms the basis to derive requirements, which 

- 

to 

assess 

future 

competitiveness 

of 

any global CoP comparison has to meet.

productions systems, farms and/or regions

37

Subsequently the concept, the current status 

These  two  goals  cannot  be  achieved  by 

and  some  selected  findings  of  the  two  networks 

analyzing  past  market  share  developments  or 

agri benchmark and IFCN are presented.

current product price statistics.

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

In the current situation, where liberalization, 

from  various  countries 

into  a  common 

globalization,  climate  change,  bioenergy  and 

understanding of what they really mean.

new  technologies  are  driving  world  agriculture 

towards  a  major  restructuring,  the  second  reason 

- 

(“assessing future competitiveness”) is a very strong 

The system is applicable to all world regions 
that are important for the global agricultural 

argument for conducting global CoP analysis.

development.  Many  of 

these  countries 

Given that the core target of agri benchmark 

infrastructure of farm bookkeeping systems.

do  not  have  a  government-administered 

and  IFCN  is  “assessing  future  competitiveness”, 

the  following  requirements  were  applied  for  the 

- 

global CoP analysis:

is  made  suitable 

The  concept 
the 
analysis  of  both  small-scale  and  large-scale 
agriculture.

for 

- 

The  focus  is  on  “cost  and  returns  per  ton 
of	 traded	 product”,  including  supply  chain 
costs  and  returns  (processing,  by-products, 

- 

The organisational concept has the potential 
for	
to  develop 

intra-national	 networks 

transport)  because  at  the  consumer  level 

according 

to 

the  procedures  developed 

different  products  or  competing  against 

by 

the 

international  network.  This 

is 

each  other.  If  possible,  non-marketable  cost 

desirable  because  in  the  starting  phase  any 

(social  and  environmental  impact  per  ton) 

CoP  comparison  system  will  be  far  from 

should also be included.

- 

In  contrast,  it  is  not	 necessary	 to	 focus	 on	
“intraregional	 differences	 in	 farm	 income”. 
It  is  clear  that  we  would  find  “income 

distribution  curves”  in  each  region.  For 

“statistically 

representative”.  The  more 

convincing  the  approach  is,  the  better  the 

chances of permanent growth of the system, 

so that in the course of a more representative 

picture of global farming can be drawn.

the  individual  farmer  it  is  necessary  to 

- 

understand  where  on  this  curve  the  own 

farm  is  located.  For  a  global  analysis  of 

international 

competitiveness,  however, 

The  cooperation  within  the  network  should 
be organized in a way that up-to-date	figures	
and  assessments  on  the  situation  of  the 
branches  can  be  derived.  This  is  important 

this extra knowledge about individual farm-

because  only  analytical  tools  with  up-to-

to-farm  differences  in  farm  income  is  too 

date  figures  can  provide  relevant  answers 

detailed and therefore not useable.

with  regard  to  rapidly  changing  framework 

- 

The agri benchmark and IFCN CoP tools must 
provide  detailed  information  on  production 
systems  and  cost  structure,  and  the  system 
should also facilitate a direct contact to the 
farmers	 whose  farms  have  “produced”  the 
figures. This is necessary to understand how 

conditions worldwide.

The  networks 

“agri  benchmark”  and 

“IFCN”  have  been  organized  according 

to 

these  principles.  The  participating  institutions 

are  cooperating  in  order  to  analyse  production 

systems,  CoP,  and  framework  conditions  for 

farms are affected and may react to changing 

typical farms around the world.

framework conditions.

38

- 

A  network  approach  is  applied  that  creates 
sufficient incentives	for	analysts from various 
countries  to  participate  year  after  year. This 

countries  are  actively  exchanging  data  on  CoP, 

and  they  are  jointly  analyzing  these  data  in 

annual workshops. All continents are represented. 

is  important  because  only  in  the  course  of 

In  the  IFCN  dairy  network,  institutions  from 

time  analysts  learn  to  “translate”  figures 

another  35  countries  are  sending  CoP  data  to 

Partners  from  25  (beef)  up  to  45  (dairy) 

 
 
 
 
 
 
 
 
 
 
 
the  coordination  centre  and  receiving  results 

(Germany).  It  is  now  a  private  company, 

back.  (Deblitz  2011,  Isermeyer  2011;  Shadbolt, 

closely connected to the University of Kiel. 

Ndambi and Hemme 2011; Zimmer 2011, 2011; 

de Carvalho and de Zen 2011)

The  coordination  of  the  other  networks 

(agri  benchmark)  is  carried  out  by  the  Thünen-

The  networks  for  cash  crops,  milk  and  beef 

Institute in Braunschweig, which is the successor 

can already look back on a history of more than 

of  FAL  and  one  of  the  federal  research  centres 

10 years. Since then, the cash crop network has 

of  Germany,  in  cooperation  with  the  German 

widened  the  scope  of  crops  considerably  and  is 

Agricultural 

Society 

(DLG), 

a  non-profit 

now covering 15 different crops. Two years ago, 

organization 

involved 

in  many 

international 

the beef network was extended to beef and sheep. 

activities.  In  the  beef  &  sheep  network,  the 

Agri  benchmark  networks  for  further  branches 

Australian  partner 

is  performing  a  major 

(pork & poultry, horticulture, eventually organics) 

coordination part for the sheep subdivision.

are going to be launched in 2012.

Network of partners

The 

three  networks  which  have  been 

developed  so  far  (beef  and  sheep,  cash  crop, 

dairy)  rely  on  different  financial  sources.  In  the 

IFCN  and  agri  benchmark  follow  similar 

agri  benchmark  beef  and  sheep  network,  the 

organizational  approaches,  which  rely  (a)  on 

financial  basis  is  relatively  small,  and  the  major 

partners in many countries worldwide and (b) on 

share  comes  from  the Thünen-Institute  and  from 

coordination  centres  that  have  been  established 

the  partners  in  the  participating  countries.  In 

in Germany.

the  agri  benchmark  cash  crop  network  and  in 

the  IFCN  dairy  network,  a  greater  share  of  the 

The most important elements of the networks 

financial  support  is  contributed  by  agribusiness 

are  the  partners. These  are  experts  (mainly  farm 

partners  (companies,  institutions)  or  by  project 

level-oriented  agricultural  economists) 

from 

finance 

(e.g.  EU,  FAO).  Compared 

to  agri 

institutions  (e.g.  universities,  extension  services, 

benchmark,  the  IFCN  has  become  a  somewhat 

farmers´ organizations, marketing agencies) in the 

more  commercial  organization,  with  a  higher 

participating  countries,  who  are  responsible  for 

direct involvement of the agribusiness companies.

delivery of data, crosscheck of results and supply 

of  further  information.  It  is  desired  to  include 

Typical farms, data collection, calculation of CoP 

experts who can contribute data and knowledge 

about  production  systems  and  economics  of  the 

A strong plus of agri benchmark and IFCN is 

branch in question.

that these comparisons are the only ones available 

on  a  worldwide  basis.  A  strong  limitation  is 

The partners participate on a voluntary basis. 

that  in  most  participating  countries  only  2  or  3 

They decide about the annual work program and 

typical  farms  per  branch  are  represented  in  the 

about  strategic  developments  of  the  networks. 

global  comparison.  This  means  that  the  figures 

The  networks  are  independent  from  political 

presented cannot represent the variety of farms in 

parties, governments or individual companies.

each country´s farming sector. In some countries, 

this  disadvantage  has  been  somewhat  reduced 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

When the project was launched in the late 

by establishing intra-national networks of typical 

90s,  the  coordination  centre  of  all  networks 

farms (see below).

was 

located 

in 

the  Federal  Agricultural 

39

Research  Centre 

(FAL) 

in  Braunschweig 

The  comparison  of  “typical  farms”  is  the 

(Germany).  In  2005,  the  coordination  centre 

core  of  the  concept.  Within  each  of  the  three 

of  the  IFCN  dairy  network  moved  to  Kiel 

networks,  the  methods  for  farm  selection,  data 

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

collection  and  CoP  analysis  follow  a  standard 

data  can  be  used  to  show  where  the  typical 

operating procedure (SOP), so that a high degree 

farm is sitting on the distribution curve. In other 

of  comparability  is  achieved.  The  methods  are 

countries it is necessary to rely on the judgement 

harmonized  according  to  common  standards 

of regional experts and/or farmer panels.

which have been developed and agreed upon by 

the network participants.

Until 

now, 

the 

assessment 

and 

documentation of the relative position of typical 

As  already  stated  on  the  example  of  FADN 

farms  (average,  above-average,  etc.)  has  only 

(see section 2.1) it is not so important whether the 

been  done  rudimentarily,  so  that  observers  may 

CoP analysis is based on method A or method B. 

misinterpret  the  meaning  of  typical-farm  figures 

Much more important is that

for the whole sector.

- 

the  same  methods  are  applied 

for  all 

For  some  projects,  the  farmer  panels  of  the 

farms  and  all  countries 

(international 

agri  benchmark  network  play  a  very  important 

harmonization),

role.  A  panel  consists  of  about  5  farmers  who 

are  operating  a  farm  that  is  similar  to  the  typical 

- 

the  experts  (a)  understand  the  methods, 

(model) farm. Such a panel is important to validate 

(b)  understand  the  results  of  their  country 

the data on the production system, and it is even 

and  the  other  countries,  (c)  are  able  to 

more  important  to  assess  how  the  typical  farm 

explain  differences  in  CoP  by  linking  them 

would  adjust  to  changing  framework  conditions. 

to 

location 

factors,  political 

framework 

On a global scale, it will not be possible to answer 

conditions, etc.

such  a  question  sufficiently  through  a  “model 

only”-approach (without panel discussion). 

The  “typical  farms”  concept  is  the  only 

realistic way to include the agricultural sectors in 

For  each  of  the  typical  farms,  data  are 

South America, Africa, Asia  or  Russia  which  are 

collected and analyzed according to standardized 

more  and  more  important  for  global  agriculture 

procedures. For the IFCN, this process consists of 

(Isermeyer 2011). 

10 steps, going from (1) collection and validation 

in  the  country,  (2)  check  once  data  arrive  at 

For example, in the agri benchmark beef and 

coordination centre, (3) Mail traffic ... and so on, 

sheep network, currently 75 typical beef finishing 

up to ... (10) Feedback on the Dairy Report.

farms  from  25  countries,  46  typical  cow-calf-

farms  from  21  countries,  and  12  typical  sheep 

The data are coming from the bookkeeping 

farms  from  12  countries  are  being  compared. 

of  real  farms  and  from  additional  sources 

This comparison includes all countries playing a 

supplied  by  the  panel  participants.  The  main 

major role in world beef production.

data  base  is  (a)  the  profit  and  loss  account 

In  each  country,  the  typical  farms  should 

enterprises.  If  possible,  farm-individual  data  on 

represent  the  major  production  systems  and 

input  prices  and  input  quantities  are  collected. 

regions.  Based  on  the  analysis  of  farm  structure 

Furthermore, additional information with regard 

within a country, the basic features of the typical 

to  the  production  system  is  compiled.  Hence, 

and  (b)  the  detailed  account  for  single  farm 

40

farms  are  defined.  In  addition  to  average-size 

the  total  data  base  for  a  typical  farm  reaches 

farms, one larger farm is also selected in order to 

far  beyond  the  normal  scope  of  bookkeeping 

better  understand  possible  future  developments 

systems. There are at least two plausi-checks for 

in  the  regions  (cutting  edge).  In  those  countries 

the data set of each typical farm, one during the 

where  data  on  all 

farms  are  available 

(or 

panel  session  and  at  least  one  more  during  the 

representative  sample  data  like  FADN)  these 

international comparison.

 
 
 
 
 
 
 
 
 
 
 
The calculation of CoP is done with the aid 

National networks and capacity building

of  farm  models  (TIPI-CAL,  TYPI-CROP).  These 

models start at the whole farm, so that the farm´s 

Until  now, 

the  number  of  countries 

profit  and  loss-account  from  bookkeeping  data 

participating 

in 

the  networks  has  grown 

can  be  used.  To  analyze  the  single  enterprises 

considerably,  but  in  most  countries  the  number 

(as  part  of  the  whole  farm),  either  allocation 

of typical farms has remained constant (at a low 

factors are used (judgement on “which part of a 

number of only 2 or 3 per branch).

whole farm cost component is attributable to the 

single  enterprise?”)  or  engineering-type  budgets 

There  are  two  possibilities  to  overcome  this 

are  produced  (based  on  additional  information 

problem:  First,  if  there  are  national  networks 

supplied  by  participating  farmers  and  advisors: 

that  follow  a  similar  concept  as  IFCN  /  agri 

“how  is  the  production  system  designed?”). The 

benchmark,  cooperation  with  these  networks 

models  are  generating  various  CoP  and  profit 

can established and interfaces for data exchange 

figures  (Shadbolt,  Ndambi  and  Hemme  2011, 

can be built. Second, if such a national network 

Zimmer 2011).

is not available, it can be attempted to convince 

national  stakeholders  (e.g.  agricultural  policy, 

Both 

IFCN  and  agri  benchmark  are 

farmers  association,  agribusiness)  to  establish 

summarizing  their  core  results  in  annual  reports 

intra-national  networks  according  to  the  global 

(Deblitz 2011; Zimmer 2010).

agri benchmark standard.

Further weak points and future challenges

Both  strategies  have  been  successfully 

Time series analysis

pursued  in  some  countries.  The  first  strategy 

(cooperation  with 

existing 

networks) 

is 

Over  the  years,  an  interesting  data  base  for 

working  in  Brazil,  in  the  US,  and  partly  also  in 

time  series  analysis  is  emerging.  For  some  of 

Argentina  where  a  private  consultancy  network 

the typical farms, there is already a 10 year data 

is  benchmarking  their  farms  against  the  global 

record available. These time series data can have 

agri benchmark community. The second strategy 

a high potential for further economic analysis.

(national  “multiplication”  of  the  global  concept) 

is  working  in  South Africa, Australia,  Colombia, 

However,  for  a  full  exploitation  of  this 

Indonesia, Sweden and Spain.

potential  it  will  be  necessary  to  consider  the 

updating  procedures  more  carefully.  Currently, 

A 

further  expansion  of  “intra-national 

input  and  output  prices  are  updated  annually, 

network  strategy”  would  have  the  positive  effect 

while farm technology and size are only updated 

that  actors  and  “multipliers”  in  the  agribusiness 

every  three  years.  For  a  meaningful  time  series 

(e.g.  farmers,  advisors,  researchers,  teachers) 

analysis, this update can be seen as a precondition 

would improve their “understanding” of regional 

and as a disturbing factor at the same time. 

agricultural  systems.  The  panel  process  forces 

scientist,  advisors  and  farmers  to  jointly  and 

As  structural  change  proceeds  in  reality,  it 

deeply  analyse  a  typical  farm  of  their  region.  If 

would not be correct to keep size and technology 

this  process  is  embedded  into  an  international 

stable.  However,  changing  the  technology  and 

network, all participants have the chance to assess 

size  every  three  years  is  also  a  deviation  from 

their  local  “typical  farm”  (CoP,  sustainability, 

reality and may lead to “jumps” in the time-series 

responsiveness)  in  a  global  context.  This  is 

analysis. Annual updates in technology and size 

made  possible  because  in  the  agri  benchmark 

would be costly, and they would also not reflect 

participants  exploit  the  same  definitions  within 

the real situation properly because real farms do 

an 

international 

technical 

terminology  and 

grow with certain “jumps”.

understanding.  This  “capacity  building”  aspect 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

41

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

plays  a  major  role  in  recent  considerations  to 

ability  for  assessing  CoP,  competitiveness,  and 

increasingly 

implement 

the  agri  benchmark 

responsiveness on the single-farm level. Yet, a full 

concept in developing countries.

understanding  of  international  competitiveness 

is  only  possible  if  the  analysis  goes  beyond  the 

Integrated	view	on	different	enterprises

single-farm level (see section 3.1).

For  various  reasons  (organizational,  financial, 

In  particular,  there  are  two  main  aspects 

personal),  the  agri  benchmark  networks  have 

beyond  the  single-farm  level  that  shall  be 

evolved  “branchwise”  in  the  last  decade.  The 

captured  in  agri  benchmark  and  IFCN:  (a) 

separate  development  was  beneficial 

for 

the 

downstream  activities  and 

(b) 

the 

impact 

development  of  the  single  international  network 

of 

intra-regional  structural  change  on 

the 

(quick  and  easy  decision  making).  Yet,  there  are 

competitiveness of certain branches (see below). 

some questions that cannot be sufficiently answered 

As  explained  in  the  final  section,  it  will  be 

if the networks continue to act separately.

impossible  to  capture  all  the  important  aspects 

“beyond the farm gate” by sound business-based 

Future  competitiveness  of  certain  branches 

CoP  figures.  Therefore  it  will  be  necessary  to 

in  certain  regions  is  finally  determined  by  the 

supplement  CoP  data  by  “expert  judgements”, 

question whether a certain branch yields a lower 

e.g.  expert  judgements  on  how  far  regional 

or  higher  net  profit  (land  rent  per  hectare)  than 

competitiveness  of  milk  production  in  a  small-

other  branches.  In  other  words:  Understanding 

scale  region  could  be  improved  by  structural 

competitiveness  requires  the  analysis  of  both 

change (Isermeyer 2011).

international  competitiveness 

(one  branch) 

and 

intra-regional  competitiveness 

(different 

Up  to  now,  agri  benchmark  and  the  IFCN 

branches).  Up  to  now  the  CoP  calculation 

have  addressed  this  isssues  only  to  a  little 

systems 

(including  agri  benchmark)  do  not 

extend.  However,  due  to  their  network  design 

sufficiently  capture  this  important  intra-regional 

(world-wide,  ongoing  cooperation  of  regional 

competition. They can only offer a very imperfect 

experts)  they  should  have  a  comparatively 

way by taking the cost component “land rent” as 

high  potential  to  produce  sound  judgements 

a proxy for the opportunity cost of land.

which are needed to supplement the plain CoP 

At  the  current  stage  of  development,  a 

calculations.

special  disadvantage  of  the  agri  benchmark  / 

This  leads  us  back  to  the  issue  of  “capacity 

IFCN networks (compared to integrated systems as 

building”  (see  above).  The  network´s  ability  to 

FADN or the Australian CoP anaylsis) is that – due 

assess  future  competitiveness  depends  on  the 

to the “branchwise” development of the networks 

ability of the partners 

–  there  is  hardly  any  data  exchange  between 

the  networks. Therefore,  at  the  moment  it  is  very 

- 

to understand the nature of agribusiness 

difficult to derive conclusions regarding the intra-

in  their  region  (CoP,  structural  change,  main 

regional  competition  of  different  branches.  This 

players)

argument  supports  the  idea  to  consider  a  certain 

re-integration of the different networks.

- 

to share this information with colleagues 

42

Towards  a  “data,  knowledge  and  judgement 

network”

from other regions (national, international)

- 

to  compare  their  own  assessments  and 

projections  with  real  development  and  to  draw 

The 

above  mentioned 

activities 

are 

conclusions for improvements of their judgements 

designed 

to 

further 

improve 

the  network´s 

in following years. 

 
 
 
 
 
 
 
 
 
 
 
2.4. Summary, Conclusions, 
Recommendations

All  these  examples  underline  that  in  a 

globalized  and  liberalized  economy  there  is  a 

great  demand  for  farm-based  CoP  because  the 

Data on CoP have no value per se. Such data 

corresponding  questions  can  only  be  answered 

are  collected  and  analyzed  for  certain  purposes, 

on the basis of a sound CoP analysis. Therefore, 

and therefore the evaluation of different concepts 

it  is  suggested  to  the  answer  the  initial  core 

must start with the question “Why are we interested 

question “Why are we interested?” as follows: 

in information on CoP on an international level?”

The  liberalization  of  agricultural  market 

harmonized comparison of CoP because we need 

policies and the globalization of the agribusiness 

to  assess  future  competitiveness  of  productions 

have created an environment where policymakers 

systems,  farms  and/or  regions.  In  particular,  we 

We  are  interested  in  an  internationally 

can  no  longer  afford  to  neglect  the  global 

want to understand how

dimension of their decisions. For example,

- 

if  policymakers  want  to  increase  bioenergy 

with other world regions,

use they should be able to assess where the 

additional biomass will/should be produced 

- 

competitiveness 

and 

sustainability 

of 

and  how  this  will  affect  competition  with 

production systems can be improved,

- 

certain  agricultural  branches  can  compete 

food and feed production in different parts of 

the world;

- 

if  policymakers  want  to  improve  animal 

- 

different  farm  types  in  different  regions  can 

(and will) react to new challenges,

welfare  it  is  necessary  to  analyse  how  the 

- 

different  farms  are  affected  by  (and  will 

food  industry  may  adjust  to  higher  local 

adjust to) agricultural policy measures.

standards and shift product sourcing to other 

world regions;

Agricultural  economists  have  developed 

comprehensive  agricultural 

sector  models 

- 

if  policymakers  want  to  reduce  greenhouse 

that  are  designed  to  (partly)  answer  some  of 

gas  emissions  from  agriculture  they  should 

these  questions,  too.  However,  these  models 

consider  options 

in  various  production 

work  on  a  highly  aggregated  level  and  on 

systems  and  world  regions  and  opt  for 

many  assumptions  about  supply  and  demand 

the  most  efficient  ones,  for  only  globally 

elasticity. Therefore it is important to supplement 

integrated  strategies  can  solve  the  global 

these  models  by  farm-based  knowledge. At  the 

climate problems.

same  time,  it  must  be  clear  that  farm-based 

approaches  cannot  replace  sector  and  trade 

Decision  makers 

in 

the  agribusiness 

models  because  market  interactions  cannot  be 

have  also  developed  an  increasing  interest  in 

modelled at the farm level.

a  better  understanding  of  production  system 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

economics  worldwide.  In  a  liberalized  world, 

In  different  regions  of  the  world,  different 

political  developments  as  well  as  technological 

concepts 

for 

farm-based  CoP  analysis  have 

breakthroughs  like  GMO,  milking  robots  or 

been  developed  and  implemented  over  the  last 

disease prevention measures can change regional 

decades. These  concepts  can  be  categorized  by 

supply  conditions  very  quickly,  and  it  is  highly 

different criteria, for example:

43

important 

for  entrepreneurs 

to  analyze 

the 

economic  consequences  of  developments  as 

- 

regional  coverage 

(world-wide;  EU-wide; 

early and clearly as possible.

national; regional)

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

- 

representativity 

(stratified  sample; 

farmer 

and the non-existing possibility to interview 

groups with voluntary participation)

the farmers in between are severely limiting 

- 

individuality (single farm data; farm averages; 

answers for the questions mentioned above. 

the  usability  of  the  system  to  produce 

typical farms) 

The  Agricultural  and  Food  Policy  Centre 

(AFPC) at Texas A&M University is operating 

- 

depth  of  the  data  (only  whole  farm  data; 

an  alternative 

(so-called) 

representative 

farm enterprise data (selected or all items); 

farm concept which is successfully used for 

farm-level  policy  impact  assessment  for  the 

- 

access  to  farmers  (delivery  of  bookkeeping 

US  congress.  This  system  is  based  on  the 

data; interviews; panel discussions).

concept  of  typical  (virtual)  farms  which  are 

The  results  of  CoP  calculations  depend  on 

of bookkeeping data and expert judgement.

put together by regional panels on the basis 

the  method.  Different  ways  of  data  collection 

and  data  calculation  lead  to  different  results. 

- 

Canada:  There  is  neither  a  unified  data 

Therefore it is not possible to just assemble CoP 

collection 

system  nor  a  harmonized 

data from different sources into one big data pool 

farm  data  set  which  could  be  used  for 

unless data collection and calculation follows an 

standardized  CoP  calculations. 

Instead 

internationally harmonized protocol.

there  are  different  sour ces  of  information 

available,  some  containing  very  detailed 

In order to check whether there are different 

data.  Most  schemes  are  operated  under  the 

CoP  data  bases  available  that  might  be  suitable 

responsibility of the provinces.

for a combined analysis, the situation in various 

countries has been briefly described in section 2. 

- 

Brazil:  CONAB,  a  national  agency  for  the 

The result can be summarized as follows:

dissemination  of  information,  offers  CoP 

- 

European  Union:  For  intra-EU-comparisons, 

systems  and  regions. The  data  are  collected 

data  for  many  commodities,  production 

the  Farm  Accountancy  Data  Network 

by  focus  groups  for  typical  (virtual)  farms. 

(FADN)  offers  a  harmonized  data  base. The 

The  experience  has 

shown 

that 

this 

great  advantage  of  this  data  base  is  that  it 

procedure  leads  to  a  higher-quality  data 

contains  data  of  a  large  number  of  farms 

(especially  on  labour  costs)  than  could  be 

(stratified  sample)  and  is  updated  annually. 

collected  by  other  data  collection  methods. 

The  main  limitation  is  that  this  data  base  is 

The  CONAB  data  base  is  not  statistically 

inappropriate 

for  calculating  single-farm 

representative.  CONAB  does  not  cover  the 

CoP, broken down by cost components and 

whole agricultural sector. Some commodities 

commodities.  The  reason  is  that  the  FADN 

are  analyzed  by  other  institutions,  and  they 

contains  only  whole  farm  data  that  are  not 

also apply the concept of typical farms based 

broken down to single enterprises.

on focus groups (panel discussions).

44

-  USA: The Agricultural Resource Management 

- 

Australia:  The  Australian  Bureau  of 

Survey 

(ARMS)  provides  CoP  data 

that 

Agricultural 

and  Resource 

Economics 

are  statistically  representative.  The  data 

and  Sciences 

(ABARES) 

is  operating  a 

collection procedure is specifically designed 

representative  survey  on  CoP.  The  survey 

for the calculation of CoP although there are 

is  not  covering  all  commodities;  about 

some open questions regarding the quality of 

75%  of 

total  agricultural  production 

is 

the data (especially on labour costs). The long 

included.  The  data  collection  system  (face-

time-interval  (update  only  every  4-8  years) 

to-face interviews) is specifically designed to 

 
 
 
 
 
 
 
 
 
 
 
calculate CoP. For some crops, the data base 

to  assess  the  current  and  future  situation  of 

contains  data  going  back  30  years  which 

productions systems, farms and/or regions?”

allows interesting time series analysis.

There are basically three different conceptual 

-  New  Zealand:  The  traditional  monitoring 

strategies that could be pursued:

system  with  a  relatively  strong  statistical 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

basis  is  currently  being  replaced  by  a 

(1)  Take the different data bases as they are, build 

modern  system  which  is  based  on  the 

some  interfaces,  and  compare  the  resulting 

principles of voluntary participation, strong 

CoP figures across nations and continents

farmer and advisor involvement, timeliness, 

and  high  usability  of  the  benchmarking 

(2)  Convince  the  administrations  of  various 

data  by 

the 

farmers.  Up-to-date  data 

countries around the world to agree upon the 

are  collected  annually  by 

face-to-face 

establishment  of  one  harmonized  concept 

interviews,  and  these  interviews  are  also 

(data collection, CoP calculation).

used  to  validate  the  data  for  the  previous 

period.  The  data  pool  is  not  statistically 

(3)  Continue 

the  development  of  global 

representative. About 10% of all farmers are 

networks  (IFCN,  agri  benchmark)  which 

now taking part in the system.

have  developed  internationally  harmonized 

standards for CoP calculation and work on a 

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

-  Ukraine: The medium- and large-scale farms 

stepwise evolution of their network concepts

are reporting data on agricultural production 

and  sales  to  the  district-level  bodies.  This 

The  advantages  and  disadvantages  of  these 

data  source,  however,  is  not  suitable  for  a 

three  strategic  options  can  be  summarized  as 

profound analysis of CoP. Detailed CoP data 

follows:

for  a  handful  of  typical  farms  are  collected 

by the Ukrainian Agribusiness Club (UCAB); 

Option 1: Build interfaces between existing data 

this is done within the framework of the glo-

bases

bal network agri benchmark. The UCAB has 

also launched a project called AgriEfficiency, 

The  advantage  of  this  strategy  is  low  cost 

a  national  extension  of  the  agri  benchmark 

because  one  can  use  existing  data  bases.  It  is 

project  that  aims  at  collecting  data  from 

probably  relatively  easy  to  convince  institutions 

Ukrainian farms with less effort.

to  provide  existing  data  for  an  international 

comparison  whereas  it  is  much  more  difficult 

This listing shows that very different concepts 

to  make  them  change  their  data  collection  and 

for  the  collection  of  farm-based  CoP  data  have 

calculation schemes.

been implemented.

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

In  view  of  the  fundamental  methodological 

In  view  of  the  extreme  methodological 

differences  between 

the  national  schemes, 

differences  on  the  international  level  one  might 

however, 

this 

strategy 

is  not  convincing. 

be  tempted  to  ask:  “Which  CoP  methodology  is 

Comparability  across  borders  is  severely  limited 

best?” This question, however, can lead onto the 

because (a) only the EU is collecting a statistically 

wrong  track  if  the  core  target  of  CoP  analysis 

representative data set for all agricultural sectors 

disappears  from  view.  The  core  target  is  to 

on an annual basis, while most of the other world 

understand  the  current  and  future  situation  of 

regions  have  no  statistically  representative  data 

45

productions  systems,  farms  and/or  regions  (see 

bases to offer, and because (b) the data in many 

above).  Therefore  the  right  question  is:  “Which 

overseas  locations  allow  an  exact  and  detailed 

concept  can  yield  the  most  meaningful  results 

calculation of CoP for each commodity whereas 

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

the  data  basis  for  farm-individual,  commodity-

analysis  would  always  show  that  the  large-

specific CoP data in the EU is quite poor.

scale  dairy  farms  have  lower  CoP  than  the 

small-scale  dairy  farms.  This  phenomenon 

International  comparisons  of  CoP  which 

can  be  explained  by  two  reasons:  (1)  High 

have  been  collected  on  the  basis  of  highly 

CoP  in  small-scale  farms  are  mainly  caused 

diverging procedures are probably as valuable as 

by  high  cost  for  family  labour  (often  more 

mere comparisons of product prices (see section 

than  50%  of  total  CoP),  and  for  these  costs 

3.1): One can learn that there are differences but 

both  the  physical  values  (number  of  hours) 

it remains unclear (a) why these differences exist, 

and the process (wage rate per hour) is often 

(b) how meaningful they are for competitiveness 

highly  disputable.  (2)  If  a  small  scale  region 

of  regional  branches  and  (c)  what  the  impact 

contains many similar farms (e.g. dairy farms) 

of  changing  framework  conditions  on  CoP  and 

neighbouring  each  other,  the  exit  of  a  few 

competitiveness will be.

small  high-cost  farms  leads  to  a  substantial 

improvement  of 

economic 

framework 

A number of important features that a global 

conditions for the neighbouring farms which 

scheme for the analysis of future competitiveness 

are  staying  in  business  (more  land  and  cow 

should provide (section 3.1) is experts knowledge 

barns  available,  leading  to  lower  CoP).  On 

for certain branches:

the contrary, in large-scale dairy regions with 

only a few big dairy farms left, a close-down 

-  Without  a  systematic  consideration  of 

of a dairy operation does not lead to improved 

expert  judgements,  it  is  hardly  possible  to 

conditions for milk production in the region.

include  the  downstream  activities  into  the 

analysis.  In  particular,  transport  cost  from 

At  first  sight,  one  might  think  that  these 

the  farms  to  metropolitan  areas  or  harbours 

challenges  could  perhaps  be  met  by  simply 

are  very  important  because  (a)  for  some 

including  further  enterprises  into  the  global 

remote  locations  these  costs  can  amount  to 

comparison  of  CoP  (e.g.  transport  firms;  food 

more  than  half  of  the  on-farm  CoP  and  (b) 

processing  firms;  small-scale  and  part 

time 

these  costs  are  varying  by  product  group. 

farms) and/or by  feeding these figures  into more 

Hence,  transport  cost  is  one  of  the  main 

sophisticated  farm-based  modelling  (e.g.  non-

drivers  for  regional  specialization  in  global 

linear  programming  for  the  analysis  of  farm 

agriculture.  Besides  this,  the  conditions  for 

adjustments; cellular automata for the analysis of 

food processing also need to be considered. 

structural change within regions).

Some 

locations  offer  better  conditions 

for  processing  firms  (e.g.  water  supply, 

However,  a  sober  assessment  of 

these 

regulations,  markets  for  by-products)  than 

options  leads  to  the  clear  result  that  they  would 

others,  and  this  can  be  a  crucial  for  the 

not  be  viable  on  a  global  level.  CoP  data  for 

competitiveness of a certain branch.

the  processing  industry  are  strictly  confidential. 

Finding  the  “correct”  wage  rate  of  part-time 

- 

Second, it is necessary to include the impact 

farmers 

is  practically 

impossible.  Running 

of  intra-regional  structural  change  on  the 

sophisticated  models  for  assessing  intra-regional 

competitiveness  of  certain  branches.  It  is 

structural change on a global level is unaffordable.

important  to  understand  that,  for  example,  a 

46

region with a high share of small-scale dairy 

Therefore  we  must  accept  that  it  will  be 

farms  can  have  a  higher  competitiveness  in 

impossible  to  capture  all  the  important  aspects 

the  global  dairy  sector  than  a  region  with 

“beyond the farm gate” by sound business-based 

some  large  scale-scale  dairy  producers  – 

CoP  figures.  We  should  measure  everything 

although  each  and  every  single-farm  CoP 

that  can  be  measured  (e.g.  there  is  still  a  lot  to 

 
 
 
 
 
 
 
 
 
 
 
achieve  in  the  fields  of  transport  cost  and  price 

branch  (e.g.  ARMS)  would  require  collecting  a 

margins)  but  many  other  aspects  can  only  be 

lot of additional data per farm, and as these data 

captured  by  “expert  judgement”  –  and  this 

are currently non-existent, they would have to be 

requires  international  networks  of  experts  who 

“produced” by the farms throughout the year.

can  provide  information  besides  CoP  and  are 

able  to  assess  the  importance  of  regional  CoP 

Yet  it  is  not  only  a  matter  of  costs  but  also 

data in relation to these extra information.

a matter of motivation for the farmers who must 

Option 2: Establish government-administered, 

and  judgement  of  the  workshop  participants, 

harmonized CoP schemes worldwide

it  is  important  that  the  farmers  develop  a 

deliver  the  data.  According  to  the  experience 

From a European, Australian or US perspective 

data.  An  enforcement  of  data  delivery  via  cross 

it  may  seem  natural  to  broaden  the  concept  of 

compliance  or  similar  schemes  (payment  for 

government-administered CoP schemes which are 

data; no payment for missing or wrong data) was 

geared to draw a statistically representative picture 

not regarded as a fruitful option. 

personal 

interest 

in  delivering  high-quality 

of a countries´ CoP situation (e.g. FADN or ARMS) 

to the worldwide arena.

The  reason  is  that  the  on-farm  assessment 

of  important  CoP  data  such  as  (a)  family  labour 

However,  even  in  a  “two-country-case”  it  is 

hours,  (b)  opportunity  costs  per  hour  or  (c) 

doubtful  whether  the  institutions  in  one  country 

opportunity  costs  of  land  remains  difficult.  It  is 

would be willing to replace their system in favour of 

unlikely  that  any  central  entity  or  government 

a  foreign  system. The  existing  concepts  (e.g.  in  the 

would  be  able  to  control  whether  a  farmer 

US and the EU) are totally different and each system 

delivers  reliable  data  on 

labour  hours  or 

is showing a strong “path dependency”. Due to the 

opportunity  costs  of  family  labour.  The  same 

pronounced differences, international harmonization 

holds for the opportunity costs of land. If it is our 

would  in  fact  result  in  a  complete  change  of  data 

goal  to  really  “understand”  the  competitiveness 

collection structures in almost all countries.

of  a  certain  crop  in  a  certain  region,  we  have 

to  go  deeper  into  the  nature  of  joint  production 

Secondly, and even more important, it has to 

(disease  break,  soil  fertility,  labour  scarcity,  risk 

be noted that in most countries worldwide there 

management,  etc.).  These  dynamics  determine 

is simply no farm data base available that would 

the  true  opportunity  costs  of  land  which  can  be 

enable  the  administration  to  draw  a  random 

used  for  the  respective  crop,  and  the  true  figure 

sample. And due to the substantial intra-national 

and where farm bookkeeping data offers limited 

differences in many countries any strategy aiming 

support  in  reflecting  these  figures.  It  can  only 

at  “statistical  representativity”  would  require 

be  analyzed  by  the  farmers  themselves  and/or  a 

a  data  collection  in  a  large  number  of  farms. 

production economist who fully understands the 

Hence,  a  strategy  to  “globalize”  concepts  like 

nature of joint production in this specific region.

FADN or ARMS would become very expensive.

This 

train  of 

thought  suggests 

to  base 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Additional  costs  would  probably  occur 

international CoP comparison schemes potentially 

not  only  in  developing  countries  but  also,  for 

on data stemming from voluntary advisory groups, 

example,  in  the  EU.  Most  of  the  smaller  farms 

despite the fact that the resulting data base is not 

in  the  EU  do  not  have  single-enterprise  data 

representative in a statistical sense, and to closely 

available  (broken  down  by  cost  component  and 

connect  CoP  analysis  for  selected  farms  (a)  with 

47

commodity  produced). The  implementation  of  a 

single farm modelling and (b) with the possibility 

global CoP analysis scheme that would really be 

to feed the results back to the farmers and advisors 

able to calculate sound single-farm CoP data per 

who delivered the data initially.

 
 
 
 
 
 
 
 
 
 
 
 
w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

48

Option 3: Continue and broaden the existing 

- 

The  direct  access  to  farmers  and  advisors 

global network activities

gives  a  better  possibility 

to  assess 

the 

farm-level  impact  of  modern  agricultural 

From  where  we  stand,  the  typical  farm 

policies  (e.g.  agri-environmental  measures, 

concept 

is  probably 

the  most  appropriate 

animal  welfare  programmes, 

traceability 

approach  currently  available  for  the  intended 

schemes). Such policy measures trigger farm 

purpose of CoP data collection and analysis on a 

adjustments  which  can  only  be  assessed  if 

global scale. The main advantages of this concept 

the  analysts  can  take  a  very  detailed  view 

can be summarized as follows:

on  production  methods  and  possible  farm 

adjustments. This is beyond the scope of the 

- 

In  most  countries  worldwide,  the  majority 

“normal”  data  set  that  is  collected  in  mass 

of the farms do not have enterprise budgets 

CoP-related inquiries.

for  single  commodities  available.  If  these 

farmers  are  suddenly  asked  in  a  face-to-

- 

The in-depth analysis of typical farms is also 

face  interview  to  “assess”  such  data,  the 

gaining  importance  as  the  farming  sector 

results  will  not  be  reliable.  Experience  has 

is  increasingly  embedded  in  some  kind  of 

show that a “panel process” where farmers, 

“contract farming” (Cunningham 2011, Miele 

advisors  and  scientists  can  jointly  discuss 

2011 a,b). In such an environment, classical 

the figures of one typical farm leads to much 

CoP  analysis  shows  substantial  farm-to-farm 

better results.

differences  which  are  only  attributable  to 

different contract arrangements. For example, 

- 

In  the  few  countries  where  single-farm  data 

some farms will show high costs for manure 

are collected for a random sample of farms, 

disposal,  others  have  given  this  task  to  the 

the analysis of this data base is often restricted 

contractor and will therefore show no manure 

by confidentiality arguments so that analysts 

disposal  costs  but  only  a  reduced  price  for 

can  only  work  with  “farm  group  averages” 

their  broilers.  The  resulting  CoP  differences 

instead  of  “individual  farms”.  A  statistical 

can  be  properly  understood  and  interpreted 

average of different farms, however, is often 

in  an  intensive  panel  discussion,  whereas 

an artificial construct that does not reveal the 

data analysis based on a mass inquiry might 

real farm situation and is not a useable basis 

lead to wrong conclusions.

for further modelling work. Hence, it is better 

to  work  with  a  “median  farm”  as  created 

- 

Finally, 

the 

increasing  differentiation  of 

by  the  agri  benchmark  panels,  instead  of  a 

product quality is also an argument in favour 

“statistical average farm“.

of  typical  farm  analysis.  If  commodities 

from  different  regions  have  different  quality 

- 

The  panel  process  is  forcing  the  analysts  to 

features,  the  €/kg  CoP  analysis  has  always 

really  “work”  with  the  data  (e.g.  checking 

the  risk  of  comparing  apples  and  pears. 

plausibility),  to  validate  their  assumptions 

This  challenge  cannot  be  met  by  increasing 

and  to  defend  their  results  (e.g.  on  farm 

the  number  of  farms  (mass  CoP-related 

reactions  to  changing  conditions)  in  the 

inquiries).  Instead,  a  global  network  of 

panel  workshops.  This  process  is  guiding 

experts is needed who rather “understand” a 

all 

participants 

towards 

a 

dynamic 

few typical farms and the connected supply 

understanding  of  the  farm  –  following  an 

chain.  It  is  paramount  that  these  build  up 

important  result  of  the  Brussels  workshop: 

knowledge and trust (a) to the entrepreneurs 

“CoP is not the final destination - it can only 

within  their  home  regions  and  (b)  to  their 

be the starting point of the analysis”.

international network partners.

 
 
 
 
 
 
 
 
 
 
 
The  principle  limitation  of  the  typical  farm 

in  their  country  and  in  other  world  regions.  In 

concept  is  that  the  results  are  not  representative 

this  way,  for  example,  the  new  intra-national 

in  a  statistical  sense,  because  they  are  not  based 

benchmarking  system  in  New  Zealand  could 

on  a  random  or  stratified  sample  of  the  total  farm 

be  linked  to  the  global  benchmarking  activities. 

population.  As  long  as  only  very  few  farms  per 

The same applies to the CoP comparison that the 

country  and  branch  are  included,  even  the  label 

European  Dairy  Farmers  (EDF)  are  conducting 

“typical” is rather a euphemism – particularly in large 

each  year 

for  more 

than  250  dairy 

farms 

countries with a high variation in farming systems.

throughout Europe (Wille-Sonk 2011).

A possible solution would be to combine the 

Second,  as  the  New  Zealand  example  is 

advantages  of  FADN-type  schemes  (representative 

demonstrating,  the  representativity  of  voluntary 

data  set)  and  agri  benchmark-type  schemes  (in-

benchmarking schemes can be steadily improved 

depth  analysis),  for  example  by  using  FADN-type 

if these schemes are attractive for all participants 

data  to  analyse  where  a  certain  “typical  farm”  is 

in  the  sector  (Shadbolt  2011).  Brazil  is  heading 

sitting on the distribution curve of all farms. Yet, as it 

into  the  same  direction,  with  a  couple  of 

looks from now, it is rather unlikely that other world 

institutions  competing  for  the  most  attractive 

regions would make an effort to implement FADN-

typical farm concept (see section 2.3).

type random sampling schemes on their territory. 

This  is  also  the  course  agri  benchmark  is 

Hence,  globally,  for  the  intended  purpose 

currently  adopting.  In  Brazil,  Colombia,  South 

of  international  CoP,  this  may  mean  further 

Africa,  Indonesia,  Ukraine,  Spain  and  Sweden 

development  and  possible  extension  of 

the 

the  national  partners  have  already  decided  to 

agri  benchmark  approach  on  its  own.  For  this 

either  establish  intra-national  networks  which 

direction, 

the  Brussels  workshop  yielded  a 

are multiplying the agri benchmark network on a 

number of suggestions.

national scale, or to create interfaces that facilitate 

comparisons  between  the  global  benchmarking 

First,  the  farmer´s  motivation  to  participate 

system and comparable national systems.

in  the  survey  should  mainly  stem  from  his  or  her 

interest in getting reliable benchmarking data back 

The  main  challenges  for  the  global  CoP 

–  and  not  from  the  chance  to  earn  a  little  extra 

comparison schemes based on typical farms can 

money (for the data) or from the necessity to comply 

be summarized as follows:

with new government demands. Highly motivated 

farmers  are  a  source  not  only  for  high-quality 

- 

Are  the  results  valuable  enough  to  keep 

data  but  also  for  profound  information  on  future 

farmers and advisors interested?

farm  development  under  changing  conditions.  Of 

course, a high degree of “involvement” of farmers 

- 

Are  quality  aspects  (product  differentiation, 

or advisors is not only beneficial. It also bears the 

timely  delivery,  sustainability  standards) 

risk that dominant panel members are distorting the 

adequately incorporated in the analysis? 

results if, for example, they replace facts or sound 

judgement by wishful thinking.

-  Will  it  be  possible  to  “re-integrate”  the 

disconnected  networks  so  that  whole  farm 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

To  keep  such  risks  in  reasonable  limits, 

issues  (e.g.  crop  rotation,  risk  management, 

it  might  be  a  rewarding  “system  extension” 

ecosystem services) are adequately captured?

to  draw  a  line  from  the  typical  farm  approach 

(based  on  panel  discussions)  to  individual  farm 

- 

Can  the  concept  be  successfully  extended 

benchmarking.  Individual  farmers  might  find  it 

to  sectors  like  pig  and  poultry  which  are 

attractive  to  compare  their  farm  to  typical  farms 

dominated by contract farming?

49

 
 
 
 
 
 
 
 
 
 
 
 
- 

Are convincing solutions available to capture 

For  this,  CoP  can  at  best  be  a  reasonable 

the special situation of smallholder farms in 

starting  point,  allowing  a  quick  comparison  of 

developing countries (household economics; 

different  farms,  production  systems,  regions  or 

market access)?

points  in  time.  As  CoP  cannot  be  a  reasonable 

ultimate  goal  of  the  exercise,  however,  it  is 

-  What  is  the  most  cost-efficient  way  to 

important  that  data  collection  schemes  create  a 

include transport cost into the analysis?

powerful  basis  for  the  more  advanced  analyses 

that is really needed. 

-  Will  it  be  possible  to  build  up  financial 

power,  sustainable  knowledge  and  trust  in 

As  it  looks  from  now,  enterprise-specific 

order to include downstream companies into 

information 

(production 

technology  and  cost) 

the analysis?

and  direct  communication  with  the  decision-

makers  (regional  farmers)  are  important  features 

This  listing  demonstrates  once  again  that 

of  a  promising  analytical  approach.  Given 

a  modern  discussion  about  CoP  calculation 

the  tremendous  variety  of  agricultural  systems 

and  CoP  data  collection  on  a  global  level 

worldwide  (e.g.  product  differentiation,  contract 

must  reach  far  beyond  the  pure  statistical 

farming, 

smallholder  households), 

such 

a 

and  computational  aspects  of  CoP.  The  real 

framework for in-depth analysis is necessary. On the 

challenge is to create an infrastructure which is 

other hand, such an approach will never be able to 

laying a solid foundation to understand current 

replace  statistical  approaches  that  are  aiming  at  a 

and future developments of farming.

full coverage of the whole farming population.

w
e
i
v
r
e
v
O

l
a
b
o
l
G
a

-

s
t
s
o
C
s
n
o
i
t
c
u
d
o
r
P

f
o

s
n
o
s
i
r
a
p
m
o
C
d
n
a

s
e
i
g
o
l
o
d
o
h
t
e
M

.
2

r
e
t
p
a
h
C

50

 
 
 
 
 
 
 
 
 
 
 
Chapter 3. Production Cost Calculations and 

Methodologies Used for Dairy, Beef, Pork 
and Poultry Industries 

Dr. Dan L. Cunningham

Professor of Poultry Science

Department of Poultry Science

University of Georgia, USA

3.1. Introduction

The  purpose  of  this  section  was  to  provide 

examples  of  production  cost  calculations  and 

methodologies  utilized  in  various  countries  for 

the  dairy,  beef,  pork  and  poultry  industries. The 

countries  providing  reports  were  Argentina, 

•	

•	

•	

•	

harvested  off  the  effective  milking  area  and 

no grazing off the effective milking area. 

System 2. 4-14% of total feed imported.

System 3. 10-20% of total feed imported to 
extend lactation and for dry cows.

System 4. 20-30% of total feed imported and 
used at both ends of lactation and for dry cows.

System  5.  More 
imported.  Feed  used  all  year,  throughout 

than  30% 

total 

feed 

Brazil,  New  Zealand  and  the  United  States. 

the  lactation  period  and  for  dry  cows.  Split 

These  countries  were  chosen  as  a  result  of 

calving is common to this system.

the  significance  of  the  various  commodities 

produced in these parts of the world. The reports 

In 06/07 and 07/08 there were no significant 

included 

information  on  reporting  agencies, 

differences  in  operating  expenses  per  kilogram 

methodologies,  sampling  procedures,  as  well  as 

of  milk-solids  produced  between  any  of  the 

cost and returns calculations. 

3.2. Commodity Reports

3.2.1. Dairy

systems.  In  06/07  operating  expenses  ranged 

from  $3.60-$3.80  per  kilogram  of  milk-solids 

produced and from $4.80-$5.00 in 07/08 for the 

five  systems.  However,  in  08/09,  system  5  costs 

were  significantly  higher  ($5.40)  in  kilograms  of 

milk-solids  produced  than  systems  3  ($4.80),  2 

($4.80), and 1 ($4.40). System 4 was significantly 

Shadbolt 

(2011a)  presented  DairyBase 

higher  ($5.00)  than  systems  2  and  1.  When 

production  cost  data  from  06/07  to  08/09  for 

costs of production (full economic costing) were 

five  dairy  farm  systems  in  New  Zealand.  The 

compared, systems 2, 3 and 4 (~$5.00) were all 

DairyBase  program  is  a  process  where  farm 

significantly less than system 1 ($5.20) in 06/07. 

business owners working with rural professionals 

In  07/08  system  4  was  significantly  less  ($6.60) 

in  New  Zealand  obtain  and  enter  data  into  a 

than systems 1, 2, and 3 (~$7.00). In 08/09 there 

data  base  reporting  system  for  management 

were  no  significant  differences  between  any  of 

and  comparison  purposes  (Shadbolt,  2011b). 

the systems ($6.60-$7.40). 

The  DairyBase  program  results  in  standard  farm 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

reports 

including  physical  data  summaries, 

When  return  on  assets  (RoA)  and  return  on 

key  performance 

indicators,  operating  profit 

equity (RoE) were compared for the five systems, 

calculations, financial detail and physical detail. 

in 2006, 2007 and 2008 there were no significant 

The five dairy farm systems compared were:

differences  between  the  systems.  These  results 

51

•	

System  1.  Self-	 Contained  –  No  imported 
feed, no supplement fed, except supplement 

to  achieve  the  higher  production  delivered  a 

consistent return per unit of capital. Conversely, in 

indicated  that  the  additional  capital  required 

 
 
 
 
 
 
 
 
 
 
 
 
.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

2008/09,  a  significant  deterioration  in  operating 

including  input  from  research  partners,  data 

efficiency  coupled  with  the  additional  assets 

checks  and  cross  checks,  discussions  of  results, 

required  per  hectare  resulted  in  a  significantly 

report  editing  and  final  feedback  on  the  report. 

worse  outcome  under  intensification.  Systems 

The  IFCN  dairy  report  results  in  a  number  of 

3, 4, and 5 all had significantly lower RoA than 

outputs  including  conferences  in  January,  June 

system 1. Systems 4 and 5 had significantly lower 

and  September  as  well  as  a  variety  of  public 

RoE than systems 1 and 2.

events throughout the year.

The 

results 

indicated 

that 

increasing 

The cost methodology used is the 2011 TIPI-

production  intensity  improved  cost  leadership 

CAL version 5.2. The TIPI-CAL calculation model 

in  average  and  favourable  markets  but  this 

is a whole farm production and accounting model 

advantage  disappeared  under  unfavourable  milk 

(excel  based)  incorporating  300-700  economic 

price  to  cost  ratios.  When  using  a  metric  that 

and physical variables per typical farm. Currently 

incorporates  opportunity  costs  of  capital,  the 

dairy  researchers  in  more  than  40  countries 

CoP per kilogram of milk-solids at worst doesn’t 

use  the TIPI-CAL  program.  Dairy  farm  costs  are 

change and, at best, reduces as systems intensify. 

determined from dairy related expenses (includes 

Nambi  (Shadbolt,  et.  al.,  2011)  reported  on 
dairy  global  production  costs  using  the  IFCN 

quota costs) and by partitioning out labour, land, 

machinery  and  building  costs  from  whole  farm 

costs for dairy production. Opportunity costs for 

concept. The  IFCN  is  a  global  dairy  network  of 

family labour, land owned and capital owned are 

researchers,  companies  and  other  stakeholders 

also accounted for.

who are active in the dairy chain. The IFCN has 

a  research  centre  with  approximately  10  dairy 

The  IFCN  2009  report  provides  detailed  CoP 

researchers  coordinating  the  network  process 

comparisons  for  the  86  countries  participating  in 

and  running  dairy  research  activities.  The  IFCN 

the  global  network. The  costs  of  milk  production 

is independent from third parties and committed 

ranged from as low as US$5/100 kg of milk for some 

to truth, science and reliable results. The mission 

of the African nations to as high as US$100/100 kg 

of the IFCN is to create a better understanding of 

of milk for some of the western European countries. 

milk  production  worldwide.  The  IFCN  network 

Comparisons of average size farms for costs of milk 

includes research partners in 86 counties and 80 

production  for  some  of  the  countries  in  the  IFCN 

different agribusinesses.

report  resulted  in  less  than  US$20/100  kg  of  milk 

in  African  countries,  US$20-30/100  kg  milk  for 

The  IFCN  methodology  identifies  a  typical 

Argentina, India and New Zealand, US$30-40/100 

dairy  farm  for  a  region  based  on  the  largest 

kg milk for Australia, the United States, China and 

number of dairy farms in terms of size, livestock 

Brazil,  US$50-60/100  kg  milk  for  Germany  and 

system,  labour  organization,  and  technology 

Canada  and  US$60-120  for  the  Scandinavian 

used.  The  main  objectives  are  to  represent 

countries. Time series analysis from 2000-2010 for 

major  milk  production  systems,  to  represent 

selected farms showed substantial increases in the 

a  significant  number  of  farms  in  the  area  and 

costs  of  milk  production  beginning  in  the  2005-

capture a large amount of milk production in the 

2006 time period with the highest costs recorded in 

area. Examples of typical dairy farms in the IFCN 

the 2008 period.

report  range  from  as  low  as  one  cow  and  380 

52

farms in the Ukraine to 351 cows and 996 farms 

The  following  were  identified  as  challenges 

in New Zealand.

and opportunities for the IFCN network:

The 

IFCN 

farm 

comparison  process 

incorporates  a  10  step  validation  process 

1) 

2) 

identification and validation of typical farms, 

timeliness versus accuracy, 

 
 
 
 
 
 
 
 
 
 
 
3)  network of learning, 

9) 

leveraging  the  data  and  the  skills  of  the 

4)  maintaining a common purpose, 

5)  academic vs. commercial model, 

network.

6)  disseminating  information  for  the  common 

3.2.2. Beef

understanding, 

7)  enhancing the matrix of knowledge, 

Carvalho  (Carvalho,  2011)  of  the  Centre 

8)  going beyond costs of production analysis, and 

for  Advanced  Studies  on  Applied  Economics 

Table 3.1: Beef Cost Structure Example - Alta Floresta Farm, Brazil

% Costs

4.58

0.21

9.29

0.80

6.85

41.7

27.2

9.5

Revenue

Administrative, tax & energy

Buildings maintenance

Machinery maintenance

Pasture maintenance

Vet and medicine

Hired labor

Mineral supplement

Animal purchase

Cash Costs

Depreciation

Buildings

Machinery

Service animals

Pasture

Pro-labor(family)

Depreciation Total

Cash Costs + Depreciation

Total Costs -TC 

Capital return-buildings

Capital return-machinery

Capital return-animals 

Capital return-pasture

Opportunity cost of land

Total

Total Costs-TC

Gross Revenue

Cash Costs

Depreciation Total

Cash Costs + Depreciation

Return of Capital

Total Costs-TC

Net Revenue

Total Net Revenue (R-CC-Depreciation)

Total Net Revenue (R-Total Costs) 

Source:	Carvalho	(2011).

Income

R$ 2,161.0

R$ 98.6

R$ 4,385.2

R$ 375.2

R$ 3,232.7

R$ 19,680.6

R$ 12,746.6

R$ 4,500.0

R$ 47,179.88

R$ 3,944.00

R$ 4,424.00

R$ 175.00

R$ 1,899.70

R$ 48,000.00

R$ 58,442.70

R$ 105,622.58

R$ 2,103.84

R$ -

R$ 19,317.02

R$ 341.95

R$ 16,204.32

R$ 39,623.84

R$ 145,246.42

R$ 150,706.44

R$ 47,179.88

R$ 58,442.70

R$ 105,622.58

R$ 39,623.84

R$ 145,246.42

R$ 103,526.56

R$ 45,083.86

R$ 5,460.02

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

53

 
 
 
 
 
 
 
 
 
 
 
 
(CEPEA),  University  of  Sao  Paulo,  reported  on 

cost structure analysis for a farm in Mato Grosso 

production  costs  of  beef  in  Brazil.  The  CEPEA 

state was provided (Table 3.1)

provides research and analysis for agribusinesses 

related  to  market  analysis,  price  evaluations, 

Information  on  increases  in  costs  of  beef 

international 

trade,  management 

strategies, 

production and beef prices from January, 2004 to 

environmental  and  social  aspects,  and  family 

April of 2011 in Brazil was provided (Figure 3.1). 

farms.  The  information  produced  is  used  by 

Cash costs plus depreciation expenses increased 

farmers, agribusiness agents and government. The 

by  96.6%  during  this  time  period  while  prices 

CEPEA generates information on production costs 

received for beef increased only 73.4%.

for animal and vegetable products in Brazil. The 

animal  products  include  beef  and  dairy  while 

Increases  in  individual  cash  costs  during 

the  vegetable  products  include  corn,  soybean, 

this  period  were:  labour  (127.09%),  minerals 

cotton,  wheat,  rice,  bean,  coffee,  oranges, 

(120.59), animal purchases (111.36%), fertilizers 

sugarcane, cassava, grape, potato and tomato.

(63.07%), forage seeds-pasture (241.38%), fuel + 

maintenance (75.13%),  medicine (41.0%),  feeds 

CEPEA  partners  with  the  National  Farmer’s 

(55.4%),  energy  (60.7%),  animal  reproduction 

Association  of  Brazil 

to  derive 

the  various 

(-8.09%),  building  maintenance  (19.68%),  and 

production costs. CEPEA uses a panel of farmers 

traceability  (65.7%).  Cash  costs,  depreciation 

to determine the definition of typical production 

and  beef  returns 

for 

typical  cow-calf  and 

systems  and  calculations  of  production  costs  for 

finishing  farms  in  the  various  regions  of  Brazil 

a  region.  CEPEA  then  produces  an  analysis  of 

were  presented.  For  some  regions,  beef  returns 

the  modal  property  (i.e.  the  most  representative 

exceeded cash and depreciation costs while cash 

of  the  region. Typical  farms  maps  are  produced 

and depreciation costs exceeded beef returns for 

identifying  the  locations  and  distribution  of  the 

others (Figures 3.2 and 3.3). 

farms  participating  in  the  analysis.  The  recent 

report  included  61  finishing  farms  in  13  states 

Bengtsson 

(2011) 

reported  on  beef 

and 56 cow-calf farms in 13 states. An example 

production  in  Argentina.  In  order  to  optimize 

Figure 3.1: Costs and Prices of Brazilian Beef (2004-2011)

.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

54

Source:	Carvalho	(2011).

 
 
 
 
 
 
 
 
 
 
 
Figure 3.2: Costs and Returns for Typical Brazilian Cow-Calf farm

Source:	Carvalho	(2011).

Figure 3.3: Costs, Depreciation and returns for a Typical Brazilian Farm

l

i

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Source:	Carvalho	(2011).

55

 
 
 
 
 
 
 
 
 
 
 
 
.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

56

Figure 3.4: Argentine Beef Consumption

Source:	Bengtsson	(2011).

Figure 3.5: Argentine Slaughter of female Livestock

Source:	Bengtsson	(2011).

production  in  each  property  to  its  full  potential, 

and  fattening,  milk  production,  forestry  and 

they  carefully  design  customized  productive 

irrigation. Argentina is a major player in the beef 

projects by getting involved in all operations and 

industry  maintaining  approximately  51  million 

activities  such  as  crop  production,  cattle  raising 

head.  About  16  million  head  are  slaughtered 

 
 
 
 
 
 
 
 
 
 
 
annually  resulting  in  some  3.3  million  tonnes 

forms  are  compulsory  sworn  statements 

of  beef  production.  Domestic  consumption 

that  are  completed  annually.  The  program 

(2.6  million  tons)  accounts  for  79%  of  the  beef 

was  originally  used  for  sanitation  control 

produced.  Annual  per  capita  consumption  has 

purposes but it currently intends to become a 

declined during the past 30 years from nearly 90 

reliable traceability system for all agricultural 

kg/capita in 1981 to 57 kg/capita in 2010 (Figure 

commodities produced in Argentina.

3.4). Nevertheless, Argentina remains one of the 

greatest per capita beef consuming countries.

The  Argentine  beef 

sector  has  been 

3.  National Institute of Agricultural Technology 
(INTA).  INTA  continuously  conducts  field 
tests  and  research  through  stations  located 

reconverting.  This  is  a  result  of  an  expansion  of 

around Argentina.

agriculture  due  to  technological  improvements 

that have enabled previously unfit land to be used 

The  AACREA  methodology  evaluates 

for  agriculture.  This  has  shifted  agriculture  into 

farms for gross income (cattle and crop sales), 

traditional  cattle  land  expelling  beef  producers  to 

commercial  costs  (sales  commissions,  sales 

more  marginal  lands.  Government  policies  have 

taxes,  freights  and  traceability  cost),  direct 

produced unstable conditions for producers forcing 

costs  (seeding,  chemical,  contractor,  feeding, 

them to increase slaughter of female cattle (+49%) 

sanitation,  health  and 

labour),  overhead 

beyond reposition quota to remain in business. This 

costs  (structure  costs  and  amortization)  and 

has resulted in a dramatic fall in cattle inventories 

opportunity  costs.  Beef  production,  both 

during 2009 and 2010 (Figure 3.5).

direct  and  commercial  costs,  are  allocated 

directly  to  production  using  market  values. 

In  order  to  benchmark  costs  nationwide 

Overhead  costs  include  local  and  net  worth 

there  are  several  institutions  in  Argentina  that 

taxes,  property 

taxes,  and  management. 

publish  reliable  regional  and  national  data  for 

Freight  costs  are  from  inside  the  farm  gate  to 

agricultural products. These include;

the  nearest  port.  Feeding  costs  include  hay, 

1.  Argentine  Association  of  Agricultural 

pasture 

amortization 

and  maintenance. 

supplementation,  corn  silage,  fodder  crops, 

Consortium 
for 
Agricultural 
Experimentation  (AACREA).  The  AACREA 
was  founded  in  1960  with  the  purpose  of 

improving  the  business  efficiency  of  the 

Opportunity 

costs 

include 

costs 

of 

maintenance  and  production  inventories  and 

opportunity costs for land rental.

farmer. It is a network of farmers and experts 

Argentina  is  traditionally  divided  into  five 

in 18 regions consisting of 205 groups, 2000 

productive regions with different climate and land 

producers  and  200  technical  individuals. 

characteristics. These  regions are  the subtropical 

The  AACREA  developed  an  agricultural 

moonsonic  (NOA),  subtropical  (NEA),  semi-arid 

management  tool  where  every  segment  of 

(CUYO),  humid 

(PAMPEANA),  and  dessertic 

the  farm  is  considered  as  a  different  profit 

(PATAGONICA).  Five  types  of  beef  production 

centre  which  should  be  profitable  for  itself 

systems coexist in Argentina with each system in 

carrying  its  own  costs  and  selling  goods 

each  area  having  a  different  cost  structure.  The 

or  services  to  the  other  segments  or  profit 

five production systems are:

centers at market costs.

2.  National  Organization  for  Animal  and 
(SENSA). 
SENA  conducts  strict  controls  on  animal 

Food  Sanitation  and  Quality 

1.  Breeding, 

2.  Rearing, 

3.  Breeding-Rearing, 

4.  Fattening, and 

and  food  sanitation  and  quality.  RENSPA 

5.  Complete Cycle. 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

57

 
 
 
 
 
 
 
 
 
 
 
 
.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

58

Figure 3.6: Costs Structure and Gross Margin for Breeding Farms in Argentina

Source:	Bengtsson	(2011).

Figure 3.7: Cost structure and Gross Margins for fattening Farms in Argentina

Source:	Bengtsson	(2011).

The  costs  structures  and  gross  margins  for 

based  solely  on  natural  grasslands  with  low 

breeding and fattening farm systems in Argentina 

margins  and  very  low  capital  intensity.  These 

are  summarized  in  Figures  3.6  &  3.7.  The 

systems  range  from  0.25  cows  per  hectare  to 

breeding  operations  are  very  extensive  systems 

almost  2.0  cows  per  hectare.  The  breakdown 

 
 
 
 
 
 
 
 
 
 
 
i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

of  costs  as  a  percentage  of  total  commercial 

production  into  marginal  zones.  Cow-calf 

and  direct  costs  was:  inventory  purchases  (-18), 

enterprises  have  been  forced  to  move  to 

commercial  costs  (-27),  labour  (-21),  feed  (-29) 

more marginal lands where breeding calves 

and  animal  sanitation  (-7). The  gross  margin  for 

is  the  only  choice.  This  same  factor  has 

this production system ranged from a low of U$D 

led  to  intensification  and  new  innovation 

-32/ha to a high of U$D 359/ha.

of  production  systems 

in  areas  which 

traditionally used very extensive practices.

The break-down of costs as a percentage of 

commercial  and  direct  costs  for  fattening  farms 

3)  This progressive migration to marginal lands 

were:  inventory  purchases  (-50),  commercial 

has  reconverted  beef  production  from  the 

costs (-20), labour (-8), animal and sanitation (-1), 

traditional extensive cattle breeding schemes 

and  feed  costs  (-19). The  gross  margin  for  these 

to modern mixed production systems based 

farms ranged from U$D 353/ha to U$D 699/ha.

on  pastures,  grain  supplementation  fodder 

crops and natural pastures.

Conclusions	for	the	Argentine	beef	report	were:

4)  Due  to  current  political  and  economic 

1)  Management and CoP allocation methodology 

situations  in  Argentina,  the  beef  sector  is 

developed by AACREA has proven to be reliable 

going through a stagnation period and slowly 

and  objective.  This  system  is  widely  used 

recovering 

from  erroneous  government 

throughout the Argentina farming sector and is 

policies.  In  spite  of  this,  the  productive 

currently used by farm management firms. The 

potential  will  tend  to  recover  slowly  with 

role  of  voluntary  participation  enabled  a  large 

new investments.

network of farmers from every region. The role 

of INTAs’ research and Senasas control and data 

3.2.3. Pork

base were also important factors.

2)  More profitable activities such as agriculture 

costs  in  Brazil. The  Embrapa  swine  and  poultry 

and  intensive  fattening  have  forced  beef 

team  is  composed  of  three  rural  economic 

Miele  (2011a)  reported  on  pork  production 

Table 3.2: Summary of Variable Costs and Calculations for Pork

Item

Feed

Genetics

Labor

Electricity

Medicines

Transportation  

Capital costs

Maintenance

Miscellaneous

Social security

Source:	Miele	(2011a).

Coefficient

Sows: kg/head/year 
Piglets and swine: kg/head

Sows and males 
Sperm dose

Price 

Declared market feed price X Calculated feed price 
based on ingredients market prices

Purchase and cull market price 
Market price

Number of persons with full dedication for the 
standard scale

Family: minimum wage
Hired: market wage + payroll charges

kWh/sow/year or month (N and F)

Sows: mL or dose/litter
Piglets and swine: mL or dose/head

Distance

6% p.a.

1% p.a. over investment

2.5-3.0% over VC (in discussion)

2.3% over producer gross income

Rural price

Market price

Market fees

Long term interest rate

59

 
 
 
 
 
 
 
 
 
 
 
 
Table 3.3: Sources of Information for Pork Costs in Brazil

Item

Investment

Labor

Energy

Maintenance

Depreciation

Technical Coefficients

Sows productivity and reproductive performance

Weight and age

FCR, feed formula 
Medicines prescriptions

Prices

Source:	Miele	(2011a).

Source

Panel with practitioners

Producers and slaughterhouses

Panel with practitioners and researchers
Farm management database

Panel with practitioners and researchers 
Slaughterhouses and panel

Panel with practitioners and researchers 
Embrapa’s Production Good Practices

Conab Regional Offices
Panel with practitioners
Swine producers associations
Slaughterhouses associations
Rural economics institutions
Cooperatives and industries

Figure 3.8: Cost and Price Evolution for Pork in Santa Catarina, Brazil

.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

Source:	Miele	(2011a).

60

researchers,  one  rural  economics  assistant,  and 

From  a  historical  perspective, 

swine 

49  swine  and  poultry  researchers  that  help  with 

production  costs  have  been  collected  for  the 

technical expertise. Embrapa works cooperatively 

south region (Santa Catarina) since the 1980s and 

with  Conab,  the  production  costs  division  of 

poultry production costs since the 1990s. Swine 

the  Ministry  of  Agriculture,  Livestock  and  Food 

and poultry production costs have been collected 

Supply  on  swine  and  poultry  production  costs. 

in  the  11  main  producer  states  in  cooperation 

Conab has regional offices in 11 states in Brazil.

with Conab since 2005. Related projects include 

 
 
 
 
 
 
 
 
 
 
 
Figure 3.9: Feed Price Evolution in Santa Catarina (2007-2010)

Source:	Miele	(2011a).

Figure 3.10: Inflation and Currency Exchange Rate (Brazil, 2007-2010)

Source:	Miele	(2011a).

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

the Swine and Poultry Information Centre, which 

percentage of the swine heard are the north-east 

conducts  production  costs  and  market  analysis, 

and north (24%), south-east (11%), south (48%), 

Competitiveness  and  Public  Policy  Analysis, 

and  central-west  (18%).  For  swine  slaughtered, 

which  conducts 

international  comparisons 

the  southern  region  had  the  highest  percentage 

(InterPig Network), impact of taxes and currency, 

(69%)  with  the  north-east  and  northern  region 

and 

impact  of 

technological  changes,  and 

slaughtering the lowest percentage (2%).

Contract Costs.

Swine  production 

in  Brazil 

is  divided 

in Brazil are the farrow to finish, farrow to nursery, 

into  four  major  regions.  The  regions  and  their 

farrow to wean, nursery, and finishing. The farrow 

Types of swine production systems evaluated 

61

 
 
 
 
 
 
 
 
 
 
 
 
.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

62

Table 3.4: Estimated Returns for Hogs in Iowa 

Estimated Returns for Farrowing and Finishing Hogs or Producing 
Weaned Pigs in Iowa1/

Addendum to M-1284c 
Cooperative Extension 
Service Iowa State 
University

Jun-10 Jul-10 Aug-10 Sep-10 Oct-10

Nov-10 Dec-10 Jan-11 Feb-11 Mar-11 Apr-11 May-11

Production Period

Jul-10 Aug-10 Sep-10 Oct-10 Nov-10

Dec-10 Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11

Jan-11 Feb-11 Mar-11 Apr-11 May-11

Jun-11

Jul-11 Aug-11 Sep-11 Oct-11 Nov-11 Dec-11

65.97
31.12
28.50

60.48
31.76
28.50

72.53
58.98
31.90
31.00
28.50
28.50
173.59 173.61 173.61 173.64
4.77
4.51
54.89
54.89
38.55
37.00

4.66
54.89
37.76

4.58
54.89
37.24

78.72
33.36
28.50
173.67
4.82
54.89
39.36

85.98
34.00
28.50
173.70
4.94
54.89
40.21

34.06
28.50

102.27 105.88 114.12 116.92
94.04
34.46
35.12
35.13
35.18
28.50
28.50
28.50
28.50
173.75 173.80 173.91 173.96 173.95
5.34
5.25
5.08
54.89
54.89
54.89
43.59
42.48
41.20

5.20
54.89
42.08

5.36
54.89
43.25

40.71

39.71

40.68

39.62

40.62

45.75

51.93

50.98

43.40

39.29

34.82

3.71

2.47

2.92

1.07

1.26

5.54

10.73

8.90

0.92

(3.96)

(8.77)

1.14

1.19

0.97

0.10

(0.53)

(0.57)

(0.60)

0.68

1.52

1.16

0.68

4.85

3.66

3.88

1.17

0.73

4.97

10.12

9.58

2.44

(2.81)

(8.08)

53.81
20.67
2.58
11.35
88.41

21.83
2.95
8.45

58.35
20.98
2.79
11.35
93.47

21.86
3.08
8.45

62.01
21.34
2.96
11.35
97.66

21.91
3.21
8.45

65.47
21.58
3.13
11.35
101.53

68.46
21.57
3.24
11.35
104.62

21.94
3.32
8.45

21.95
3.38
8.45

158.64 164.10 168.98 173.79

177.77

58.75

60.78

62.58

64.37

65.84

62.40

69.21
56.31
152.04 168.48 170.56 186.87

63.17

69.99
188.97

(6.60)

4.38

1.58

13.08

11.20

(0.13)

0.07

0.49

0.74

0.53

(6.73)

4.45

2.07

13.82

11.73

Farrowing Month:
Sales Month:
Sell 12# Feeder Pig
Sell 270# Market 
Hog
Cost of Producing 
12# Weaned Pigs:
Com
Soybean meal
Vitamin & mineral
Variable costs2/
Operating Interest
Fixed Costs
Cost per 12# pig
Sold as 12# feeder 
pig
Profit (loss) per 
head
Sow Value Change / 
Feeder Pig Sold
Total Profit (loss) 
per head
Cost of finishing 
12-270# pigs:
Feed costs:
Com costs
Soybean meal
Dried distiller grain
Vitamin & mineral
Total feed costs
Non-feed costs:
Variable costs3/
Operating Interest4/
Fixed costs
Average Market 
Hog. 270#:
Total Costs/head
Break-even price 
$/cwt.
Selling price, $/cwt.
Sales value
Profit (loss) per 
head
Sow Value Change / 
Hog Marketed
Total Profit (loss) 
per head
Source:	Ellis	(2011).

1/ Numbers are in dollars per head, unless otherwise noted.

2/	Variable	costs	per	pig	multiplied	by	9.2	pigs	per	litter.	Individual	costs	include:	labor	($7.76),	utilities	($1.54),	vet/med	($2.32),	
feed	delivery	($0.63),	manure	($1.00).

3/	Variable	growing	and	finishing	costs	per	pig	include	labor	(3.67),	utilities	($2.57),	vet/med	($2.40),	feed	delivery	($3.68),	manure	
($2.00),	production	cost	of	pigs	lost,	and	the	additional	cost	of	transporting	finished	hogs	instead	of	feeder	pigs.

 
 
 
 
 
 
 
 
 
 
 
to finish system has been evaluated in the south 

(Figure 3.9). The price of maize in January of 2007 

region of Brazil since 1980 and in the south-east, 

was approximately US$ 150 per ton but reached 

central-west,  and  north  east  regions  since  2005. 

levels above US$ 300 per ton in 2008 and 2009. 

The farrow to nursery system has been evaluated 

Soybean  meal  prices  in  January  of  2007  were 

in the south region since 2005 and the farrow to 

below  US$  300  per  ton  but  increased  to  more 

wean, nursery and finishing systems evaluated in 

than  US$  500  per  ton  in  2009. Total  feed  costs 

the  south  region  since  2010.  Farrow  to  nursery 

have increased from US$ 200 per ton in January 

and finishing systems have been evaluated in the 

of 2007 to more than US$ 300 per ton in 2011. 

central-west region since 2011.

Tracking of inflation and currency exchange rates 

during  this  same  period  shows  an  increase  in 

The methodology for determining costs uses 

inflation  of  about  25%  while  the  exchange  rate 

the following formula: 

for currency (R$/US$) has declined (Figure 3.10).

Total costs (TC) = Operational costs (OC) + 

The 

following  challenges 

for  analyzing 

Capital Costs (CC)

pork  production  costs  in  Brazil  were  outlined: 

1)  comparability  with  changes  in  production 

OC = Variable Costs (VC) +Depreciation (D)

systems  and  volatility  of  currency  and  exchange 

rates; 2) feed and medicine prices are inaccurate 

CC = Fixed capital (FC) + Working capital (WC) 

in some states; 3) discipline in schedule depends 

on making the results public in a comprehensible 

Calculations  utilize  average  capital,  straight 

and  easy  form  in  Brazil  and  abroad;  4)  all 

line depreciation and variable costs derived from 

prices  need  to  be  collected  and  reviewed  in 

technical  coefficients  x  market  prices. The  costs 

a  timely  manner,  and  5)  the  need  to  work  with 

evaluated  and  details  of  their  calculation  are 

representative farms on an annual basis.

summarized in Tables 3.2 and 3.3.

Ellis (2011) reported on hog production and 

Recent  costs  (2010)  on  a  percentage  basis 

cost  structure  in  Iowa.  Iowa  uses  a  production 

for  swine  farmers  were  feed  (71%),  labour 

cost  estimated  returns  program  as  a  barometer 

(7%),  energy,  maintenance  and  miscellaneous 

of profitability for the swine industry. Production 

(7%),  interest  costs  (4%),  depreciation  (5%),  vet 

costs  are  estimated  for  two  production  systems; 

and  medicines  (4%)  and  breeding  costs  (2%). 

1) farrow to finish and 2) wean to finish. In recent 

The  cost  composition  for  swine  producers  was 

years  variable  costs  have  become  more  volatile 

labour  (42%),  depreciation  (36%),  energy  (7%), 

primarily as a result of increased feed costs. Corn 

Miscellaneous  (7%),  maintenance  (6%),  and 

prices  have  increased  more  than  100%  while 

insurance (2 %). 

soybean prices have increased by 50%.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Regional differences in  total  costs for swine 

An example of a recent Iowa State University 

production  in  2010  ranged  from  a  low  of  US$ 

report  on  estimated  costs  for  hogs  produced  in 

0.99/kg live weight in the central-west region to 

Iowa was provided (Table 3.4). 

as high as US$ 1.59/kg live weight in the north-

east region. Tracking of variable costs and prices 

Total profit (loss) per head ranged from a low 

in  Santa  Catarina  since  January  of  2007  has 

of ($6.73) in June of 2010 to a high of $13.82 in 

shown increases over this period (Figure 3.8).

September of 2010.

63

The price of feed ingredients (i.e. maize and 

Fixed  costs  include  land  value  and  taxes, 

soybean meal) and swine feed from 2007 to 2011 

and  equipment  and  building  depreciation. 

has  increased  substantially  in  Santa  Catarina 

Building  depreciation  for  farrowing  barn  is 

 
 
 
 
 
 
 
 
 
 
 
 
.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

64

Figure 3.11. Estimated Returns for Hog Operations in Iowa ($/Head)

Source:	Ellis	(2011).

Table 3.5: Cost Percentages for Hogs in Iowa (2001-2011)

Item

Feed

Variable

Interest

Fixed

Source:	Ellis	(2011).

Jan. 2001

May 2011

45%

38%

3%

15%

67%

23%

2%

8%

Table 3.6: Cost Increases for Hogs in Iowa (2001-2011)

Item

Feed

Variable

Interest

Fixed

Source:	Ellis	(2011).

Jan. 2001

May 2011

% Change 

$43.86

$36.69

$3.05

$14.22

$119.42

$ 40.23

$ 3.89

14.22

172

10

28

0

calculated  at  $114/space/year,  nursery  at  $10/

and  medicine,  labour,  utilities  and  repairs. 

space/year  and  finishing  barn  at  $17/space/

Calculated  variable  costs  include  fuel,  interest 

year.  Assumed  variable  costs 

include  vet 

rates and death loss.

 
 
 
 
 
 
 
 
 
 
 
Estimated  returns  to  farrow  to  finish  hog 

Total Cost (TC) = Operational Costs (OC) + 

production  in  Iowa  from  January  2001  to  January 

Capital Costs

2011  were  presented  (Figure  3.11).  Estimated 

returns per head of hogs produced in Iowa during 

The variable costs and sources for poultry in 

this  ten  year  period  ranged  from  a  low  of  nearly 

Brazil are presented in Tables 3.8 and 3.9.

negative  $50  to  a  high  of  close  to  plus  $50. The 

estimates  demonstrate  a  great  deal  of  year  to  year 

The  costs  composition  on  a  percentage 

variability in hog returns in Iowa during this period.

basis  for  poultry  meat  production  in  Brazil  was 

The various cost items for hog production in 

and  assistance  (5%),  labour  (4%),  energy  (3%), 

Iowa were compared as a percentage of total costs 

depreciation (2%), and interest costs (2%). 

feed  (66%),  breeding  (17%),  transport,  loading 

for January of 2001 and may of 2011(Table 3.5).

When  costs  per  head  were  compared  from 

basis  for  contract  production  was  labour  (27%), 

January 2001 to May 2011, feed costs increased 

depreciation 

(26%),  energy 

(15%0,  poultry 

by  172%,  variable  costs  increased  by  10%  and 

litter  (12%),  loading  (10%),  maintenance  and 

interest costs increased by 28% (Table 3.6).

insurance (6%), and miscellaneous (4%).

The  cost  composition  on  a  percentage 

3.2.4. Poultry

Regional  differences  in  costs  to  produce 

poultry in Brazil are presented in Figure 3.12.

Miele 

(2011b) 

reported 

on 

poultry 

production  costs  in  Brazil.  Poultry  production 

The  central-west  region  had  the  lowest  cost 

costs evaluations are a team effort with Embrapa 

(US$  0.922/kg  live  weight)  with  the  north-east 

and Conab cooperation as presented in the swine 

showing the largest cost (US$ 1.229/kg live weight).

presentation  for  Brazil.  The  production  systems 

evaluated  for  poultry  and  regional  locations  are 

Maize  and  soybean  meal  price  evolution 

outlined in Table 3.7.

and inflation and currency rate changes in Brazil 

from  January  2007  to  November  of  2010  are 

The  formula  used  for  computing  total  cost 

presented  in  Figures  3.9  and  3.10  of  the  swine 

(TC) is the same as used for swine in Brazil:

section (page 67). The price of maize in January 

Table 3.7: Poultry Production Systems and regions in Brazil

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Period

1990 – 2004

2005 – 2011

Since 2010

Source:	Miele	(2011b).

Region

SC (South region)

South (3)
Southeast (3)
Central-West (3)
North-east  (2)

South (3)
Southeast (3)
Central-West (3)
North-east  (2)

Production system

Market

Manual feeding
Automatic feeding
Acclimatized (fan)

Manual feeding
Automatic feeding
Acclimatized (fan)

Conventional
Acclimatized (fan)
Acclimatized (exhausting)

Putting out contract

Putting out contract

Putting out contract

65

 
 
 
 
 
 
 
 
 
 
 
 
Table 3.8: Variable Cost details for Poultry in Brazil:

Item

Coefficient

Price

Feed

kg/head

Declared market feed price X
Calculated feed price based on ingredients market 
prices

Genetics

Chick

Market price

Labour

Number of persons with full time dedication for the 
standard scale

Family: minimum wage 
Hired: market wage  + payroll charges

Electricity

kWh/month or parcel

Rural price

Poultry litter

M³/parcel and n. of parcels reused

Market prices

Heating

Firewood M³/parcel

Medicines

In the feed

Loading

Service (heads or m2) 

Transport

Distance

Market price

Market prices

Market price

Market fees

Capital cost

6% p.a.

Long Term Interest Rate is an alternative

Maintenance

1% p.a. over investment

Miscellaneous

1,0 – 3,0 % over VC (in discussion)

Social security

2,3% over producer gross income

Source:	Miele	(2011b).

.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

of  2007  was  US$  150/t  but  reached  levels  of 

3)  discipline  is  needed  in  price  collection  and 

US$  200/t  in  2008  and  2009.  Soybean  meal 

making  the  result  public  in  Brazil  and  abroad, 

prices in January of 2007 were below US$ 300/t 

and 4) implementation of a project with selected 

but  increased  to  more  than  US$  500/t  in  2009. 

farmers  to  be  observed  by  Embrapa  and  its’ 

Tracking of inflation and currency exchange rates 

partners on an annual basis.

during this period showed an increase in inflation 

of 25% while the exchange rate for currency (R$/

Cunningham  (2011)  reported  on  broiler 

US$)  declined.  Poultry  feed  price  changes  from 

production  costs  in  the  United  States. The  state 

January 2007 to November 2010 are presented in 

of  Georgia  ranks  number  one  in  the  production 

Figure 3.13.

of  broilers 

in 

the  United  States  producing 

approximately 1.2 billion birds and 2.7 billion kg 

66

The 

following  challenges 

for  analyzing 

of  meat  each  year. The  economic  impact  of  the 

poultry  production  costs  in  Brazil  were  outlined 

broiler industry in Georgia is approximately $15 

as:  1)  comparability  with  changing  production 

billion  annually.  Broiler  production  is  supported 

systems  and  volatility  of  currency  exchange 

by  15,000  grow-out  houses  on  4,000  farms 

rates, 2) feed and medicine prices are inaccurate, 

within 23 integrated complexes.

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.9: Sources for Poultry Cost Analysis in Brazil

Item

Source

Technical coefficients

Investment (system and scale)
Labor
Poultry litter
Energy and heating
Maintenance
Depreciation

FCR, feed formula
Medicines prescription
Poultry litter
Weight and age

Prices

Panel with practitioners
Producers and slaughterhouses 
negotiations

Panel with practitioners and researchers
Embrapa’s Production Good Practices
Slaughterhouses and panel

Conab Regional Offices at 11 states
Panel with practitioners
Poultry Producers State Associations
Slaughterhouses State Associations
Rural Economics State Institutions
Cooperatives and industries

Source:	Miele	(2011b).

Figure 3.12: Regional Differences in Costs to produce Poultry in Brazil

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Source:	Miele	(2011b)

Commercial  broiler  production 

in 

the 

of chickens. This system has been in place in the 

US  employs  a  vertical  integration  system  of 

US for nearly 60 years and represents one of the 

production  where  companies  own  or  control 

most successful agricultural businesses there.

all  of  the  key  components  of  production. A  key 

67

component  of  the  vertical  integration  system  is 

Broiler  contracts  in  the  U.S.  are  structured 

contract  production  where  poultry  companies 

such that the contract growers provide the land, 

provide farmers service contracts for the growing 

labour, houses with equipment, utilities and litter. 

 
 
 
 
 
 
 
 
 
 
 
 
.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

Figure 3.13: Feed Price Evolution in Santa Catarina Brazil

Source:	Miele	(2011).

The poultry companies provide the chicks, feed, 

a  typical  four  house  broiler  production  farm  in 

medicines,  service  programs,  processing  and 

Georgia (Table 3.10).

marketing.  Under  the  contract  system,  growers 

are  paid  on  a  price  per  pound  of  live  weight 

Economic  information  related  to  broiler 

produced  (currently  ~  $0.055/lb.). The  contracts 

integrator  costs  and  returns  is  not  publically 

are competitive with growers with above average 

available  in  the  United  States.  Although  USDA 

performances  paid  more  than  the  base  pay  and 

collects  and  publishes  economic  analysis 

growers  with  below  average  performance  paid 

for  some  commodities,  they  do  not  provide 

less than the base rate.

The  Department  of  Poultry  Science, 

University  of  Georgia, 

regularly  publishes 

information  on  broiler  costs  and  returns  in 

Georgia 

(Cunningham  and  Fairchild,  2010). 

economic information on poultry integrator costs 

and  returns.  Poultry  companies  in  the  US  do, 
however,  participate  in  a  private  service  (Agri 
Stats  Inc.,  6510  Mutual  drive,  Fort  Wayne,  IN 
46825) that provides detailed economic analysis 
and  comparative  data  to  poultry  integrators. 

The  information  is  obtained  from  surveys  of 

The  confidential  information  is  used  extensively 

growers,  integrators,  construction  contractors, 

by  poultry  integrators  for  efficiency  analysis 

and  bankers.  The  grower  costs  are  based  on 

(economic  and  production  performances)  and 

an  average  of  40-45  farm  records  for  each 

comparative  standing  within  the  industry.  The 

publication. The  information  is  confidential  and 

information  in  these  reports  is  presented  in 

is  published  as  a  summary  report  only.  Access 

a  coded  fashion  such  that  the  identity  of  the 

to  this  information  relies  heavily  on  personal 

individual  companies  and  complex  operations 

68

relationships  developed  between  the  University 

is  not  discernable  to  other  participants.  The 

of Georgia and the poultry industry.

individual  companies  are  able  to  evaluate  their 

Costs  and  returns  numbers  for  the  most 

participating  in  the  service  and  to  address 

recent  report  (2009-2010)  where  provided  for 

management  and  performance  issues  based  on 

performances  relative  to  all  other  companies 

 
 
 
 
 
 
 
 
 
 
 
Table 3.10: Costs and Returns for Georgia Broiler Growers (4 Houses)

Fixed Investment

Tunnel house and equipment

Manure removal equipment

Manure storage

Total Investment

Grower Income

4 house capacity (#chicks)

Batches/ year

Bird weight (lb.)

Capacity marketed (%)

Contract payment ($/lb.)

Fuel bonus ($)

Litter (750 ton @ $12.00)

Total Gross Income

Variable Costs

Shavings

Electricity

Fuel

Repairs & miscellaneous 

Equipment replacement

Interest on capital

Total Variable Costs

Fixed Costs

Building depreciation (5%)

Equipment depreciation (6.6%)

Interest on investment (7.5%)

Insurance & taxes

Owners labour

Land

Total Fixed Costs

Grower Net Income

Gross income

Variable costs

Fixed costs

Net Income

Source:	Cunningham	(2011).

$870,000

$35,000

$8,000

$913,000

125,000

5.5

5.6

96

0.0545

2,400

8,640

$212,472

$4,800

$14,990

$24,900

$9,800

$4,074

$2,343

$60,907

$21,750

$31,548

$31,955

$9,850

$14,400

$600

$110,103

$212,472

$60,907

$110,103

$41,462

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

their rankings within these reports. These reports 

Although 

integrator  costs  and 

returns 

are instrumental in improving poultry producer’s 

information is not publically available in the US, 

competitiveness not only in the United States but 

recent  costs  and  returns  where  estimated  based 

from a global perspective as well.

on personal industry contacts (Table 3.11).

69

 
 
 
 
 
 
 
 
 
 
 
 
.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

Table 3.11: Estimates for Georgia Broiler Integrator Costs and Returns

Item

Live production Costs ($/lb.)

Chick Costs

Grower Costs

Feed Costs

Other

Total

RTC Costs ($/lb.)

Eviscerated

Processing

General

Total

Georgia Dock Price ($/lb.)

Profit/ (Loss) ($/lb.)

Source:	Cunningham	(2011).

2010

0.045

0.055

0.235

0.040

0.375

0.510

0.222

0.090

0.820

0.855

0.035

2011

0.045

0.055

0.325

0.042

0.467

0.635

0.230

0.090

0.955

0.868

(0.087)

Based  on  the  estimates  provided  for  the 

agencies used to estimate production costs vary 

US  broiler  industry  costs  and  returns,  most 

considerably from country to country. Variability 

US  integrators  are  expected  to  lose  money  in 

within  commodities  with  regard  to  production 

2011.  The  economic  conditions  are  similar  to 

systems,  farm  size,  regional  differences  and 

those  experienced  in  2008  where  feed  prices 

management programs complicate comparisons. 

contributed 

to  substantial 

losses 

for  many 

In  addition,  different  commodities  also  have 

producers.  Feed  prices,  as  a  component  of 

different business models (e.g. contract vs. non-

production costs now represent more than 70% of 

contract production). These variables raise issues 

live production costs and have increased by more 

with  regard  to  sampling  techniques,  sampling 

than  35%  during  the  last  half  of  2010  and  first 

sizes,  sampling  time  periods,  and  accounting 

half  of  2011.  Prior  to  2008  feed  costs  typically 

procedure  differences  between  countries  and 

ranged between 50-55% of live production costs.

commodities that make meaningful comparisons 

Challenges 

for 

future  poultry  economic 

problematic.

analysis in the US where provided as: 1) information 

Agencies 

involved 

in  collecting  and 

on  poultry  economics  not  readily  available  to 

analyzing cost and returns data across countries 

individuals  outside  the  industry,  2)  access  to 

included  governmental,  private,  academic, 

information  therefore  requires  development  of 

industry and farm owner groups. Depending on 

personal  relationships,  and  3)  many  production 

the  group  or  combination  of  groups  involved, 

variables  exists  at  the  farm  and  integrator  levels 

the  purpose  and  agenda  for  collecting  and 

requiring large sample size for reliability of analysis.

disseminating the information can be different. 

For  example,  governmental  and  academic 

groups  may  be  interested  in  providing  service 

70

3.3. Conclusions and Recommendations

and  education  to  all  producers  while  private 

groups may have proprietary interests reserved 

Based  on  the  reports  for  dairy,  beef,  pork 

for  clients. 

In  some  cases 

relevant  cost 

and  poultry  from  the  participating  countries 

information is not publically available or easily 

it  is  apparent  that  the  methodologies  and 

obtained.

 
 
 
 
 
 
 
 
 
 
 
Different 

commodities 

have 

different 

Cost  and  returns  analysis  for  broilers  in  Georgia 

business  models  that  make  cost  comparisons 

were  based  on  a  sample  of  approximately  40 

difficult.  Poultry  and  pork  producers  operate 

farms  every  two  years.  The  inherent  variability 

under  a  contract  system  in  some  countries  and 

of 

farms  between  countries,  regions  within 

as  individual  non-contract  producers  in  others. 

countries, reporting periods,  production models, 

The  economic 

factors 

for 

the 

two  systems 

and  management  systems,  makes  sampling  size 

are 

substantially  different  and  complicate 

critical and problematic to achieving meaningful 

comparisons for these commodities.

comparisons. The nature of these variables would 

likely  require  very  large  sampling  numbers  to 

The  methodologies  used  with  regard  to 

result  in  any  confidence  in  farm  comparisons 

sampling  vary  considerably  from  country  to 

between  countries.  In  addition,  accuracy  and 

country  and  represent  a  major  obstacle  in 

validation  of  information  collected  is  an  issue 

meaningful  cost  comparisons.  Definitions  of  a 

for  some  reports.  For  example,  Brazil  reported 

“typical  farm”  differ  substantially.  For  example, 

problems with inaccuracy for feed and medicine 

dairy  farms  in  the  IFCN  network  use  size  of  the 

prices in some states for pork and poultry analysis. 

farm  as  a  primary  method  for  defining  typical. 

The cost and returns reports for broiler production 

Typical  farms  for  IFCN  ranged  from  one  cow  in 

in  Georgia  rely  on  voluntary  participation  by 

the Ukraine to 351 cows in New Zealand. New 

farmers and personal relationships with producers 

Zealand uses five different production systems for 

without  systematic  validation  of  the  data.  The 

their  Dairybase  program  comparisons.  For  beef 

reports  in  Georgia  are  not  intended  to  be  a 

production,  typical  farms  were  defined  by  type 

definitive  statement  on  broiler  costs  but,  rather, 

of  production  with  Brazil  reporting  on  finishing 

a  general  guide  to  current  costs  and  returns  for 

farms  and  cow-calf  farms  whereas  Argentina 

farms there. 

reported  on  breeding,  rearing,  breeding-rearing, 

fattening and complete cycle production models. 

Accounting  methodologies  utilized  were 

For  pork,  Brazil  reported  on  farrow  to  finish, 

very similar among reports. All reported the basic 

farrow  to  nursery,  farrow  to  wean,  nursery  and 

components of cost analysis (total costs = variable 

finishing  production  systems.  For  the  United 

+  fixed  costs).  Some  reports  also  included 

States,  farrow  to  finish  and  wean  to  finish  were 

total  income  estimates  for  the  commodities. 

the two systems compared. The US comparisons 

Components  of  the  costs  analysis,  however, 

were  based  on  estimated  costs  rather  than 

varied  somewhat  between  reports.  For  example, 

sampling  of  farms.  For  poultry,  production  costs 

Brazil  reported  administrative,  tax  and  energy 

were  compared  for  four  regions  of  Brazil  while 

costs for beef producers while Argentina reported 

costs for broiler producers in the US were based 

sales commissions, export and sales taxes, freight 

on  a  sample  of  poultry  farms  in  the  state  of 

and traceability costs as part of their commercial 

Georgia.

costs.  For  labour  costs,  Brazil  reported  both 

hired  labour  and  family  labour  while  Argentina 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Sampling  size  and  reporting  period  also 

reported  hired  labour  and  management  costs. 

varied  from  country  to  country  and  commodity 

For  pork  production  Iowa  reported  on  costs 

to  commodity.  For  the  IFCN  network  sample 

of  production  at  the  farm  gate  while  Brazil 

size  for  dairy  ranged  from  as  few  as  10  farms 

included  transportation  costs  to  market.  Brazil 

in  Indonesia  to  as  many  as  1000  for  China.  For 

also  included  a  cost  for  social  security  that  was 

beef,  comparisons  in  Brazil  were  based  on  117 

not  included  in  the  Iowa  analysis.  For  poultry, 

farms  while Argentina  reported  that  some  2,000 

Brazil  included  costs  for  loading,  transportation 

71

producers participate in their analysis. Reporting 

and  social  security.  Due  to  differences  in  the 

periods  ranged  from  as  frequently  as  monthly 

production systems, these costs were not part of 

(US hogs) to as much as two years (US broilers). 

the Georgia poultry analysis. The Georgia broiler 

 
 
 
 
 
 
 
 
 
 
 
 
analysis  included  an  opportunity  cost  for  land 

•	 Validation of data

that was not included in the Brazil report. 

•	

The need for consistent reporting cost categories

The  variability  between  methodologies 

and  production  systems  makes  commodity 

Although  there  are  significant  challenges  to 

comparisons  between  countries  difficult.  The 

achieving  meaningful  international  comparisons 

following  is  a  summary  of  some  of  the  major 

it  may  be  possible  with  a  central  coordinating/

obstacles for meaningful comparisons:

directing group providing responsibility for a global 

The need for representative sample size

production  costs  for  dairy  is  an  example  of  the 

approach. The IFCN program for reporting on global 

feasibility of such a program. A central coordinating/

The need for accurate information

directing  group  could  standardize  methodologies, 

sampling 

size, 

reporting 

requirements  and 

The  need  for  discipline  and  timing  of  data 

command  the  discipline  necessary  to  produce 

•	

•	

•	

collection

meaningful 

results.  Participation  by  various 

countries  would  depend,  however,  on  available 

•	 Defining the ‘typical’ farm

resources and commitment to the project.

.
.
.
k
r
o
P
,
f
e
e
B

,
y
r
i
a
D

r
o
f
d
e
s
U
s
e
i
g
o
l
o
d
o
h
t
e
M
d
n
a

s
n
o
i
t
a
l
u
c
l
a
C

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
3

r
e
t
p
a
h
C

72

 
 
 
 
 
 
 
 
 
 
 
Chapter 4. Production Cost Methodologies for Cereals 

and Arable Crops

Dr. Jean-François Garnier

Crop Economist

Arvalis Institut du végétal

France

4.1. Introduction

calculated  with  rather  similar  methodological 

choices.  So,  it  is  important  to  start  by  clarifying 

the  purpose  of  the  study.  Then,  by  focusing  on 

the  various  methods  for  calculating  production 

costs,  the  chapter  will  aim  to  determine  the 

main  methodological  differences.  The  chapter 

will  also  discuss 

the  main  challenges  and 

opportunities  for  each  method  and  try  to  give 

The  CoP  is  an  economic  indicator  among 

some recommendations to improve international 

others 

(margins,  return  on 

investment,  etc.) 

comparisons of production costs.

that  evaluates  the  economic  performance  of 

agricultural  production.  It  is  a  useful  indicator 

The  analysis  and  interpretations  are  in  part 

for  comparing 

the  performance  of  different 

based  on  the  presentations  of  22  June,  session 

production  systems  within  the  same  farm  or 

6: Production cost for wheat in the United States 

between  farms,  between  regions  and  countries. 

(Lazarus,  2011);  Production  cost  calculations 

The  calculation  of  production  cost  of  crops  per 

in  Ukraine  (Slaston,  2011);  Prairie  Canadian 

tonne allows comparisons with output sale price 

Competitiveness and Dynamic Cost of Production 

and  farmers  to  set  marketing  thresholds  or  to 

(Schoney,  2011);  and  Global  Production  Cost 

estimate the sustainability of different production 

Calculation - Cereals (Zimmer, 2011).

systems.  In  addition,  more  detailed  analysis  of 

production  costs  can  determine  the  factors  of 

The  report  will  detail  the  following  studies: 

competitiveness  and  help  to  assess  the  impact 

for  the  United  States,  the  Agricultural  Resource 

of  context  changes  (market  prices,  input  prices, 

Management  Study  (ARMS)  method  from  the 

weather,  political  decisions,  etc.).  Moreover, 

USDA  Economic  Research  Service;  for  Ukraine, 

the  study  of  important  sets  of  production  costs 

a national method of production cost calculation: 

also  allows  the  assessment  of  competitiveness 

the  50-sg  report  method; 

for  Canada, 

the 

evolution over time.

method  of  study  of  the  performance  scale  (Top 

Management/Top  Win)  will  be  appointed  later 

There are many methods of calculating CoP 

in the Win-top method; and the method used in 

throughout the world. Methodological differences 

the agri benchmark cash crop project referred to 

in  data  collection  and  costs  calculation  could 

as  the  agri  benchmark  method.  Finally,  we  will 

make 

international 

comparisons 

difficult. 

analyse  other  studies  which  were  not  part  of 

This  report  is  based  on  the  presentations  and 

the  workshop  but  which  seem  interesting  and 

discussions  of  session  6  on  cereals  and  arable 

appropriate  to  include  in  the  analysis.  Indeed, 

crops of the workshop. It aims to achieve a state 

in  these  other  studies  there  are  some  other 

of  the  art  of  major  studies  on  the  subject  and 

interesting  approaches  and  methodological 

to  conduct  a  critical  analysis  of  the  differences 

points to complete the analysis: a French national 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

between  these  methods. This  chapter  will  begin 

production  cost  observational  study  based  on 

by  clarifying  the  objectives  of  different  studies 

data  from  National  Council  of  Rural  Economy 

73

and  by  defining  the  various  costs  of  production 

Centre (CNCER), referred to as the France Arvalis-

calculations  on    field  crops.  Indeed,  comparison 

Unigrains  observatory,  and  also  a  study  about 

results can be made if the costs of production are 

international  production  cost  comparison:  the 

 
 
 
 
 
 
 
 
 
 
 
 
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

international  Arvalis  observatory.  Moreover,  in 

may be noted that the agri benchmark and Arvalis 

order to widen the analysis, the chapter will study 

have  made  some  “at  port”  and  “at  end-user” 

methods  of  calculating  production  costs  from 

calculations.

the  FADN,  and  the  possibilities  of  international 

comparison  within  the  FACEPA  project  (referred 

4.2.2. Cash cost or Total cost?

to as in the following text).

4.2. Which type of production cost?

broad categories: cash costs or total costs.

There 

are  different  production 

cost 

calculations  which  can  be  simplified  into  two 

In order to make comparisons between crop 

The	“cash”	production	cost:

competitiveness studies, at first, it is necessary to 

agree  on  key  hypotheses  before  going  further  in 

Cash cost is a production cost that takes into 

the methods.

account  the  costs  disbursed  during  a  campaign 

(all  direct  expenses,  expenses  of  paid  labour, 

4.2.1. Perimeter of calculating the cost of 

loan  repayments,  rent,  etc.). This  “cash”  cost  of 

production 

production  determines  a  minimum  production 

cost.  If  the  crop  income  is  equal  to  the  cash 

In  the  crops  sector,  the  production  cost  is 

production  cost  there  is  no  deficit,  but  unpaid 

usually  calculated  per  tonne  of  product,  which 

expenses  such  as  depreciation,  family  labour, 

allows a comparison with market prices.

equity (capital, land) are not remunerated.

To  try  to  compare  methods  and  results 

The	“total”	production	cost:

between studies, defining the scope of production 

costs calculation is essential; i.e. at which level of 

A total production cost takes into account 

the supply chain are the calculations made.

all  charges  with  opportunity  costs  particularly 

for  family  labour  and  land.  Machinery  costs 

It  is  important  to  determine  whether  the 

take  into  account  all  the  charges  including 

costs  of  production  are  only  for  agricultural 

depreciation.  Depreciation  is  either  technical 

production  and  thus  calculate  a  cost  of  “farm 

(based  on  hours  of  equipment  use)  or 

gate” production or calculate the cost of “at port” 

accounting according to the methods. The CoP 

production or even “at end users “. For “at port” 

includes all expenses of the production process 

production  costs,  storage,  logistics  and  transport 

and  this  study  allows  the  assessment  of  the 

costs are added to the cost at the farm gate. “At 

sustainability of production.

port”  or  “at  end  user”  production  costs  are  very 

interesting when comparing competing products 

Figures 4.1, 4.2 and 4.3 give some illustration 

for export. For example, to the CoP calculated for 

of  the  different  costs  that  could  be  calculated, 

wheat from the Canadian Great Plains, we must 

and also different ways to present the results.

add  the  transport  of  wheat  across  2000  km  to 

export  ports.  Similarly,  depending  on  the  freight 

The  comparison  of  cash  cost  and  total 

cost,  the  competitiveness  for  the  production 

cost  enable  us  to  better  understand  some 

arriving  in  importing  countries  may  differ  from 

phenomena.  Indeed,  in  some  situations  when 

74

one exporter to another.

comparing  total  production  costs  to  income, 

the  total  costs  are  consistently  higher  than 

Most of the production costs in the different 

incomes  while  the  farms  are  efficient  in  the 

studies  are  calculated  at  “farm  gate”. Therefore, 

long-term. In this case, the comparison of cash 

this  chapter  will  be  limited  to  this  definition.  It 

costs helps us to better understand the situation 

 
 
 
 
 
 
 
 
 
Figure 4.1: Cost of production of ARVALIS UNIGRAINS French wheat CNCER sample (2000-2009)

Source:	ARVALIS	UNIGRAINS	French	Wheat	Farm	Sample	(CERFRANCE	data	2000-2009).

Figure 4.2: Cumulative distribution of U.S wheat farm at different cost levels

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Source:	USDA	Agricultural	Resource	Management	Study	(1998).

and  to  put  these  first  results  into  perspective. 

and  different  countries  with  very  different 

The  inclusion  of  family  labour  and  equity 

production  contexts.  Most  comparisons  of 

explains a major part of these differences.

international  production  costs  are  made 

75

However,  the  total  cost  of  production 

production  calculation.  Thereafter  we  will 

allows  comparisons  between  different  systems 

study the total production cost.

with  production  costs  approaching  a  total 

 
 
 
 
 
 
 
 
 
 
 
 
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

Figure  4.3:  Total  cost  ($/t)  and  gross  revenue  in  wheat  average  2008-2010  (Australia,  Canada, 

Ukraine, and UK)

Source:	Agri	benchmark	(2011).

4.3. Studies of competitiveness for 

different purposes

data  since  1978  for  wheat). The  purpose  of  the 

France  Arvalis-Unigrains  observatory  is  similarly 

to  follow  the  historical  trend  of  French  wheat 

With  the  production  costs  calculated,  the 

and  corn  production  costs.  It  may  be  noted  that 

target  sample  and  the  method  used  depend  on 

it is possible to perform average calculations also 

the  study  objectives,  but  also  on  the  available 

for the most efficient, or low-cost, 20%. The aim 

resources. In the cases studied in this report, the 

of  the  50-sg  report  method  (Ukraine)  is  to  have 

main objectives of the studies are different.

representative  production  costs  for  average  and 

large farms at national and regional levels.

If  the  objective  is  to  determine  an  average 

competitiveness,  with 

a 

distribution 

by 

The purpose of the production cost studies from 

production systems, and implement a monitoring 

the FADN is to study and compare competitiveness 

of  production  cost  evolution,  it  is  necessary  to 

between the Member States of the European Union. 

ensure the representativeness of the sample base. 

Having  long  datasets  of  the  evolution  of  costs,  so 

This is often the case when the goal is to evaluate 

to  measure  the  impact  of  the  general  context  and 

the  impact  of  public  policies  or  of  different 

agricultural policies, is its main priority.

economic  context  on  the  competitiveness  of 

agricultural production.

If  we  try  to  assess  the  competitiveness  by 

focusing on efficient farms in order to have an idea 

For  example,  the  objective  of  the  ARMS 

of the production potential, representativeness is 

76

method is to have an average annual production 

not crucial. Indeed the aims of such studies are to 

cost  per  crop  and 

for  each  United  States 

better understand the factors of competitiveness, 

department  of  Agriculture  (USDA)  agricultural 

i.e.  what  the  explanatory  factors  are  for  a 

region. There  is  also  a  desire  to  have  a  tool  for 

particular  farm  to  produce  at  a  given  level  of 

monitoring the evolution of production costs (e.g. 

competitiveness.

 
 
 
 
 
 
 
 
 
The  agri  benchmark  cash  crop  approach  is 

explain the different methodological choices that 

intended to examine the competitiveness and the 

we will try to explain later. Careless comparisons 

potential of different crop production regions. The 

of  results  and  conclusions  of  these  studies  may 

aim is also to explain the competitiveness factors: 

be  difficult  and  even  risky.  Without  attempting 

not only “Measuring figures but looking behind; 

exhaustiveness,  this  chapter  will  try  to  provide 

looking & understanding” (Isermeyer, 2011).

some understanding on the main methodological 

With the same idea, for the international Arvalis 

observatory,  CoP  is  one  indicator  amongst  others 

4.4.1. Data Collection 

to  assess  the  economic  performance  of  different 

differences observed.

production systems. Other technical and economic 

Target	farms	and	sampling	methods

indicators  are  calculated  (e.g.  labour  productivity, 

numbers  of  hours  worked/  ha,  number  of  horse 

Depending on the objective, the definition of 

power / ha etc.). The production cost is also used as a 

the target, and thus of the sample to assess within 

diagnostic tool to determine the room for manoeuvre 

the study, will be different.

to  evolve.  International  comparisons  aim  to  place 

France  in  the  worldwide  competition,  particularly 

If  the  goal  is  to  evaluate  an  average 

in  regards 

to  competitors  on  export  markets. 

production  cost  per  country,  per  region,  or  for 

Understanding  the  factors  of  competitiveness  and 

each  major  farming  system,  representativeness 

their evolution in different areas of grain production 

of  the  studied  sample  will  be  critical  (Methods 

worldwide is also an objective.

ARMS,  50-sg  report,  France  Arvalis-Unigrains 

Observatory, FADN). Nevertheless, if the goal is 

Studies  running  with  the  Win-tops  method 

to  evaluate  the  production  costs  of  performing 

(Canada) try to assess the competitiveness of efficient 

farms 

(concept  of  production  potential, 

farms with a diagnostic objective. Here, the CoP is 

anticipation of the future), or to characterise the 

not considered as an end in itself but as an indicator 

economic  impact  of  innovative  practices  (e.g. 

to  assess  the  production’s  sensitivity to  risk  and  the 

minimum  tillage,  low  input  system,  organic 

farms resilience. This is done through the utilisation 

farming etc.), then the representativeness is still 

of  the  Agent  Based  Simulation  Model  (ABSM)  in 

important  but  is  secondary  compared  to  the 

order to analyse the dynamic changes of farms and 

needs  of  specific  economic  and  technical  data 

to evaluate the impacts of agricultural policies.

for further analysis. 

Among  the  various  studies’  purposes,  it  is 

In  the  ARMS  method,  each  sampled  farm 

possible to distinguish two different goals: on one 

represents  a  defined  number  of  farms,  which 

hand, the desire to obtain representative average 

allow the data to be weighted and an estimation 

costs and outcomes in terms of evolution, and on 

for  the  whole  population  to  be  provided.  The 

the other the desire to use the CoP as a diagnostic 

definition  of  a  farm  is  a  business  generating 

tool, to go further in the economic and technical 

over  $  1,000  of  agricultural  products  per  year. 

analysis,  to  explain  the  performance  factors  and 

Furthermore,  the  target  population  includes  all 

to evaluate room for manoeuvre.

farms  operating  an  acre  or  more  of  the  studied 

product.  The  primary  sample  comes  from  the 

National  Agricultural  Statistic  Service  (NASS) 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

4.4. Different methods to estimate the 

statistical basis by selecting the farms that exhibit 

cost of production

certain  characteristics.  The  second  sampling  is 

an  area  framed  by  random  selection  of  selected 

77

The  various  studies  on  the  competitiveness 

agricultural land segments that are representative 

of  crops  have  different  objectives,  which  can 

of all land for each farm.

 
 
 
 
 
 
 
 
 
 
 
 
 
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

78

Table 4.1: Method to specify a typical Agri benchmark farm

Source:	Zimmer	(2011).

In  the  France Arvalis-Unigrains  observatory, 

performing  farms  according  to  local  experts  (in 

calculations  are  made  from  a  sample  of  4000 

the  top  10-20%,  and  farms  that  will  still  be  in 

farms from 14 departments (CNCER / UNIGRAINS 

place in ten years’ time).

collaboration).  These  farms  are  all  part  of  a 

“management  centre”,  have  a  cost  accounting 

Regarding  the Win-tops  method  in  Canada, 

system  and  are  specialised  in  crop  production 

target  farms  are  also  chosen  because  of  their 

(OTEX 13, 14, 81, 82). Different departments are 

efficiency and determined by an expert.

taken  into  account  and  their  weight  in  the  final 

production cost depends on their relative share of 

Data	available	for	the	analysis

national production.

The  data  available  for  analysis  is  different 

Concerning  the  50-sg  report  method,  farms 

between  various  methods  and  samples.  Some 

considered in the sample are those which include 

studies  which  focus  on  representativeness  are 

at  least  one  of  the  following  characteristics: 

directly based on the results of cost accounting (e.g. 

greater  than  200  ha  of  cropped  land  or  more 

France Arvalis/Unigrains Observatory); other studies 

than 20 employees or more than 150 000 hryvnia 

are  also  based  on  more  aggregated  data  derived 

(UAH) of agricultural products’ revenue.

from  accounts 

(FADN,  50-sg  report).  Studies 

developing  an  approach  with  “typical  farms” 

For 

the  agri  benchmark  method, 

the 

based  on  the  survey  or  panel’s  data  rebuild  the 

“typical  farm”  is  based  on  a  panel  of  farmers 

cost of production starting from the crop schedule 

and advisers. The partner is responsible for the 

(international Arvalis Observatory, agri benchmark). 

selection  and  representativeness  of  the  farms. 

Other  methods  combine  both;  they  use  data  from 

When  there  are  different  farms  in  a  country, 

direct surveys and from other databases (ARMS). 

some  are  performing  and  some  are 

the 

average. A process to select the typical farm is 

Data  available  for  calculations  and  analysis 

available  to  partners. Table  4.1  highlights  the 

are  also  different  from  one  study  to  another  for 

theoretical process.

various  cost  headings.  Basically,  there  is  data 

directly  observed  per  crop,  some  data  need 

In 

the 

international  Arvalis  observatory, 

to  be  recalculated  and  other  data  need  to  be 

“typical farms” in crop production are built from 

reallocated to the crop.

 
 
 
 
 
 
 
 
 
Data directly observed per crop:

crop  which  requires  the  use  of  an  econometric 

model to distribute all the cost between crops and 

This data is directly from the farmer account 

to calculate production costs. The FACEPA (Farm 

or  from  the  survey  response,  for  example  how 

Accountancy Cost Estimation and Policy Analysis 

much is paid for fertiliser by a farmer to produce 

of European Agriculture) project, of the European 

a crop? 

Community’s  Seventh  Framework  Programme, 

has  developed  econometric  tools  and  methods 

It is relatively straight forward for direct input 

to  measure  production  costs  for  agricultural 

but it could be more difficult for fuel for example 

commodities  in  EU  agriculture  using  the  FADN 

because it is used not only for a single crop but 

(Farm Accountancy Data Network) data.

for the entire farm.

Recalculated data:

A  benefit  of  having  direct  technical  and 

economic  data  specific  to  the  crop  (machinery 

used,  number  of  passages,  inputs  used,  prices 

For  some  data,  particularly  for  input  like 

and  doses  etc.)  is  to  be  able  to  conduct  more 

home-grown  seed  or  farm  manure,  a  cost  of 

specific  analysis  on  the  determinants  of  the 

opportunity could be used. So there is a valuation 

system competitiveness and to make simulations.

of the physical quantities with other data on input 

prices (i.e. national data base).

Updated data

Data	from	a	reallocation	of	total	expenses	to	crop:

The 

regularity  and  method  of  dataset 

updating 

is  essential.  While  some  methods 

To  calculate  a  cost  of  production  per  crop, 

(FADN, 50-sg report and France Arvalis-Unigrains 

the  whole-farm  costs  have  to  be  allocated  to 

Observatory) allow an annual update of all data 

specific  crops.  Therefore  there  is  a  different 

including structure, it is not the case for the ARMS 

allocation  scheme  according  to  the  different 

methods, or methods based on the “typical farm” 

methods.  Table  4.2  reports  an  example  of  the 

methods  used  by  the ARMS  method  to  estimate 

For  the  FADN  method  the  update  of  the 

the different items of the production cost.

database is made every year, but due to treatment 

delay,  the  most  recent  data  from  the  base  is  2 

The  data  available  in  the  different  studies 

years  old.  For  the Arvalis  Unigrains  observatory, 

enable  us  to  calculate  the  CoP  more  or  less 

the  analysis  delay  is  approximately  1  year  (one 

easily and accurately. The data used in the FADN 

harvest  before).  There  is  also  estimation  for  the 

studies  are  aggregated  at  farm  level  and  not  per 

current year.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Table 4.2: Approaches used to estimate commodity costs in US (Economic Research Service USDA)

Direct costing

Valuing input quantities

Indirect costing

Allocating whole-farm expenses

Purchased seed

Homegrown seed

Fuel, lube, & electric

General farm overhead

Fertilizer

Chemicals

Manure

Unpaid labor

Custom operations

Land

Hired labor

Operating interest

Purchased water

Ginning

Source:	Lazarus	(2011).

Repairs

Capital recovery

Taxes and insurance

79

 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

80

y
r
o
t
a
v
r
e
s
b
O

s
i
l

a
v
r
A

l

a
n
o
i
t
a
n
r
e
t
n

i

s
p
o
T
n
W

i

k
r
a
m
h
c
n
e
b

i
r
g
A

e
c
n
a
r
F

y
r
o
t
a
v
s
r
e
b
O

i

s
n
a
r
g
n
U
-
s
i
l

i

a
v
r
A

N
D
A
F

t
r
o
p
e
r
g
S
0
5

i

e
n
a
r
k
U

S
M
R
A

s
e
i
d
u
t
s

t
s
o
c
n
o
i
t
c
u
d
o
r
p
t
n
e
r
e
f
f
i
d
n

i
a
t
a
d
e
h
t

t
c
e
l
l
o
c
o
t
d
e
s
u
s
e
h
c
a
o
r
p
p
A

:
3
.
4
e
l
b
a
T

l

i

n
a
p
x
e

d
n
a

e
r
a
p
m
o
C

e
h
t

e
r
a
p
m
o
C

s
s
e
n
e
v
i
t
i
t
e
p
m
o
c

e
h
t

i

n
a
d
a
n
a
c

i

t
n
e
c
fi
f
e

f
o
P
o
C

s
s
e
n
e
v
i
t
i
t
e
p
m
o
c

d
n
a

t
a
e
h
w

r
o
f

i

e
d
w
d
l
r
o
w

,
c
i
t
s
o
n
g
a
d
,

i

m
r
a
f

l

i

n
a
p
x
e

d
n
a

i

e
d
w
d
l
r
o
w

i

n
a
m
e
h
t

n
o

s
u
c
o
f

n
r
o
c

i

g
n
h
c
a
e
t

s
s
e
n
e
v
i
t
i
t
e
p
m
o
c

e
h
t

r
e
t
r
o
p
x
e

s
r
o
t
c
a
f

h
c
n
e
r
F

e
h
t

f
o

n
o
i
t
u
o
v
E

l

P,
o
C
n
r
o
c

d
n
a

t
a
e
h
w

P
o
C
e
v
i
t
a
t
n
e
s
e
r
p
e
R

y
r
t
n
u
o
c

n
a
e
p
o
r
u
e

t
a

w
o

l

%
0
2

d
n
a
m
u
d
e
m

i

s
e
i
r
e
s
m
r
e
t

g
n
o

l

,
l
e
v
e

l

t
s
o
c

n
o
i
t
u
o
v
e

l

t
a
P
o
C
e
v
i
t
a
t
n
e
s
e
r
p
e
R

l

a
n
o
g
e
r

i

d
n
a

l

a
n
o
i
t
a
n

l

e
v
e

l

P
o
C

l

a
u
n
n
a

e
g
a
r
e
v
A

A
D
S
U
y
b

d
n
a

p
o
r
c

y
b

i

,
n
o
g
e
r

l

a
r
u
t
l
u
c
i
r
g
a

n
o
i
t
u
o
v
e

l

s
e
v
i
t
c
e
j
b
o
n
a
M

i

m
o
r
f
”
m
r
a
f

l

i

a
c
p
y
T
“

s
m
r
a
f

i

g
n
m
r
o
f
r
e
p

n

i

t
r
e
p
x
e

o
t

i

g
n
d
r
o
c
c
a

e
h
t

n
i
(

a
e
r
a

y
d
u
t
s

e
h
t

)

%
0
1

p
o
t

m
r
a
f

i

t
n
e
c
fi
f
E

t
r
e
p
x
e

y
b

i

d
e
n
m
r
e
t
e
d

d
e
s
a
b

s
i
”
m
r
a
f

l

i

a
c
p
y
T
“

s
r
e
m
r
a
f

f
o

l

e
n
a
p

a

n
o

s
r
e
s
i
v
d
a

d
n
a

s
t
n
u
o
c
c
a

h
t
i

w
s
m
r
a
f

p
o
r
c

n

i

d
e
z
i
l

i

a
c
e
p
s

n
o
i
t
c
u
d
o
r
p

s
m
r
a
f

l

a
n
o
i
s
s
e
f
o
r
p

f
o

i

e
z
i
S
c
m
o
n
o
c
c
e

h
t
i

w

a
i
r
e
t
i
r
c

)

U
S
E
(
t
i
n
U

0
0
0
4

f
o

l

e
p
m
a
S

l

e
p
m
a
s

e
v
i
t
a
t
n
e
s
e
r
p
e
R

f
o

a
h

0
0
2
>
s
m
r
a
F

0
2
>

r
o

d
n
a

l

d
e
t
a
v
i
t
l
u
c

0
5
1
>

r
o

s
e
e
y
o
p
m
e

l

l

a
r
u
t
l
u
c
i
r
g
a

f
o
H
A
U
0
0
0

e
u
n
e
v
e
r

s
t
c
u
d
o
r
p

l

d
e
p
m
a
s
m
r
a
f

h
c
a
E

n
w
o
n
k

a

s
t
n
e
s
e
r
p
e
r

m
r
a
f
A

.
s
m
r
a
f

f
o

r
e
b
m
u
n

d
n
a
d
e
t
e
g
r
a
t

s
m
r
a
F

l

a
r
u
t
l
u
c
i
r
g
a

f
o

$
0
0
0
1
:

p
o
r
c

f
o

s
e
r
c
a

1
>
e
m
o
c
n

i

d
e
r
e
d
i
s
n
o
c

s
d
o
h
t
e
m
g
n

i
l

p
m
a
s

t
s
o
m
a

l

s
i

e
t
a
d
p
u

e
h
T

,
s
t
s
o
c

t
c
e
r
i
d

r
o
f

l

a
u
n
n
a

d
n
a

t
s
o
c

t
n
e
r

,
r
o
b
a

l

e
h
t

f
o

e
t
a
d
p
u

l

s
d
e
i
y

y
r
e
v
e

s
e
r
u
t
c
u
r
t
s

s
m
r
a
f

s
r
a
e
y

5
-
4

e
t
a
d
p
u

o
N

s
e
i
t
i
d
o
m
m
o
c

d
n
a

t
u
p
n

I

d
e
t
a
d
p
u

l

s
d
e
i
y

,
s
e
c
i
r
p

p
u

l

e
t
e
p
m
o
c
,
r
a
e
y

y
r
e
v
e

”
m
r
a
f

l

i

a
c
p
y
t
“
f
o

e
t
a
d

s
r
a
e
y
4

o
t
2

y
r
e
v
e

a
t
a
d

l
l

a

f
o

e
t
a
d
p
u

l

a
u
n
n
A

a
t
a
d

l
l

a

f
o

e
t
a
d
p
u

l

a
u
n
n
A

a
t
a
d

l
l

a

f
o

e
t
a
d
p
u

l

a
u
n
n
A

h
c
a
e

r
o
f

s
r
a
e
y

8
-
4

s
y
e
v
r
u
s

s
r
e
m
r
a
F

y
r
e
v
e

d
e
t
c
u
d
n
o
c

e
r
u
t
c
u
r
t
s

i

g
n
d
u
c
n

l

i

e
r
u
t
c
u
r
t
s

i

g
n
d
u
c
n

l

i

e
r
u
t
c
u
r
t
s

i

g
n
d
u
c
n

l

i

d
e
t
a
d
p
u
,
n
o
i
t
c
u
d
o
r
p

n
o

d
e
s
a
b

r
a
e
y

y
r
e
v
e

s
e
t
a
m

i
t
s
e

e
t
a
d
p
U

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Concerning  the ARMS  method,  estimates  of 

calculation  methods  of  crop  production  costs. 

production  costs  are  based  on  farmers’  surveys 

Once again, it does not aim to be exhaustive in 

conducted  every  4-8  years  for  each  crop  and 

the comparison of methods, but tries to highlight 

updated every year based on estimates of annual 

the main points of divergence.

prices,  surface  and  technical  innovations.  For 

a  given  crop,  during  the  campaigns  with  no 

Valuation	of	organic	fertilisers

survey,  the  changes  are  estimated  using  price 

index  and  other  indicators.  So  there  can  be 

The  valuation  of  organic  fertilisers  may  in 

discontinuities when the new survey data replace 

some  cases  be  significantly  different  between 

previous  estimates  (changes  regarding  structure, 

studies.

techniques, sampling methods, etc.).

For the majority of the studied methods, the 

In the agri benchmark method, input prices, 

cost  of  non-purchased  organic  fertilisers  is  not 

selling prices and changes in yields are updated 

included.  However,  in  the  ARMS  method,  costs 

every  year. With  statistics  this  gives  an  index  of 

of  non-purchased  organic  fertilisers  are  valued 

variability which can be applied to existing data. 

at the fertiliser prices. First the fertilisation values 

Depending on the speed of structural change and 

are  determined  and  then  calculated  with  the 

innovation  development,  a  complete  update  of 

market prices of fertiliser units.

the  “typical  farm”  is  required  (it  is  usually  done 

every two to four years).

In  the  agri  benchmark  calculation,  taking 

into  account  the  cost  of  non-purchased  organic 

The  update  for  the  International  Arvalis 

fertiliser  depends  on  the  geographic  area  where 

observatory 

is  almost  annual 

for  operating 

the farm is located. If the “typical farm” is located 

expenses,  labour,  rent  cost,  selling  prices  and 

in an area where organic fertilisers are in excess, 

yields.  The  update  of  the  farm  structures  is 

it  is  considered  as free.  But if  the  “typical  farm” 

conducted every 4-5 years.

is in a deficit area of organic fertiliser, there is an 

opportunity cost with a consideration of nutrient 

Changes in typical farms or irregular update of 

units at half the price of mineral units.

the farms’ structures involve a difficult tracking of 

the competitiveness evolution over time. Methods 

Machinery costs

with  a  representative  sample  and  an  annual 

update of all the expenses are better to study the 

Machinery  expenses  are  an 

important 

evolution of production costs over the longer term. 

part  of  total  expenses  in  the  arable  crop  sector. 

Moreover,  the  relative  stability  of  the  calculation 

Indeed,  machinery  and  labour  costs  are  often  a 

method  used  is  critical  for  studies  regarding 

major factor in the comparative competitiveness. 

evolution or relative competitiveness comparisons.

Machinery  costs  are  calculated  according  to 

different  methods  which  can  lead  to  bias  in  the 

Table 4.3 aims to summarise the information 

analysis.

concerning  the  objectives,  the  farms  targeted 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

in  the  sample  and  the  update  of  the  different 

The  ARMS  method  uses  a  capital  recovery 

methods.

method  to  estimate  asset  ownership  costs,  using 

replacement  prices 

for  machinery.  For  agri 

4.4.2. Main differing points in the calculation 

benchmark, the depreciation is linear and is also 

methods 

based on the repurchase price of the equipment.

81

The  following  section  attempts  to  address 

As far as the International Arvalis Observatory 

the  major  methodological  differences  between 

is  concerned,  machinery  costs  are  calculated 

 
 
 
 
 
 
 
 
 
 
 
 
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

with  a  nonlinear  “technical”  depreciation  based 

at  the  level  of  the  regional  agricultural  worker 

on replacement value and taking into account the 

wage  of  the  type  of  farm.  The  region  and 

use of materials and their durability.

database  definition  could  also  be  a  source  of 

For  the  Win-tops  method,  mechanisation 

charges can be calculated on replacement value 

In  the  50-sg  report  method,  labour  costs 

or based on the use of materials. Fuel charges and 

come  directly  from  the  accounting,  the  family 

repair charges are the actual one (if available).

labour cost is not taken into account.

differences between the methods.

For  methods  based  on  the  results  of  cost 

Land costs

accounting 

(FADN,  France  Arvalis-Unigrains 

Observatory)  mechanisation 

charges 

are 

The 

inclusion  of 

land  costs  may  also 

calculated from the depreciation available in cost 

be  a  source  of  bias  between  the  methods 

accounting. This raises the question of a possible 

of  calculation. 

Indeed, 

the 

land  enforcing 

overestimation  of  mechanisation  costs  due  to 

modes  (ownership,  renting,  different  forms  of 

depreciation strategies related to taxation.

sharecropping,  etc.)  sometimes  make 

these 

Cost	of	labour	

cost  assessments  complex.  In  order  to  make 

comparisons  between  different  systems, 

the 

opportunity costs are often calculated.

Calculations  of  labour  costs  may  also  be 

a  source  of  variation  in  the  different  methods 

Concerning  the  ARMS  method,  land  is 

results.  Particularly  the  estimation  of  family 

valued  according  to  the  average  cash  rental 

labour may differ significantly.

rate  for  land  producing  the  commodity  in  the 

particular area. Cost of land is valued at the land 

For  agri  benchmark,  an  opportunity  cost 

rent  price  from  the  farm  part  rented.  Otherwise, 

is  used  to  pay  family  labour  -  how  much  the 

the  state  average  is  used.  For  owned  land,  it  is 

person  would  receive  working  outside  the  farm. 

an  opportunity  cost  based  on  the  renting  cost. 

In  the  ARMS  calculation  method  too,  family 

However  in  some  countries  (e.g.  United  States, 

labour  is  an  opportunity  cost  assessed  in  terms 

Argentina),  rental  costs  are  regularly  negotiated 

of  unpaid  labour  hours  and  are  valued  using  an 

and  are  therefore  directly  related  to  the  sale 

estimate  of  the  wages  earned  off-farm  by  farm 

price of crops. This method raises the question of 

operators.  These  opportunity  costs  for  family 

the  link  between  income  and  cost  of  crop  land, 

labour  are  adjusted  for  age,  education  level 

especially  in  situations  of  fluctuating  prices.  In 

and  characteristics  of  the  labour  market.  The 

some  alternative  methods  of  calculation,  for 

calculation  is  derived  from  a  regression  in  two 

example  the  FINBIN  (farm  financial  data  base, 

steps:  first  the  probability  of  working  off-farm, 

Minnesota, United States) method the land cost is 

then income.

the real farmer cost which may account for much 

of the observed cost differences.

In  the  international  Arvalis  observatory, 

82

family  labour  is  valued  at  a  tractor  driver  cost 

For  agri  benchmark  and  Arvalis,  an 

(net  income)  in  the  area  concerned.  For  the 

opportunity  cost  for  owned  land  based  on  the 

France  Arvalis-Unigrain  Observatory,  a  number 

renting rate in the current zone is also used. For 

of  family  workers  in  farm  are  given  in  the 

FADN, the family land is remunerated at the level 

accountancy. The  number  of  hours  assigned  to 

of  the  regional  land  rent.  The  region  definition 

an active full-time is 1600 h/  year and the hourly 

could  be  another  source  of  bias.  Regarding  the 

cost is calculated in each region. Concerning the 

Win-tops  method,  a  cash  lease  equivalent  for 

FADN,  the  family  labour  force  is  remunerated 

farm land is used.

 
 
 
 
 
 
 
 
 
The  50  sg  method  does  not  take  land  in 

Allocation	of	fixed	costs	to	the	crop

property  into  account.  It  is  not  such  a  big  issue 

as  50  sg  is  calculated  only  for  medium  and  big 

Fixed costs are expenses that are not directly 

agricultural enterprises (they almost have no land 

related  to  one  crop  but  to  the  whole  farm. The 

in property - 99% is rented).

Interests and equity cost

allocation of these costs by crop is very important 

because  the  fixed  costs  (costs  of  mechanisation, 

labour  and  “overhead”)  represent  an  important 

part on crop production costs in agriculture.

When  calculating  the  total  CoP,  the  cost 

of  access  to  finance  is  a  factor  taken  into 

Therefore  it  is  clear  that  the  differences  in 

account  through  interests  on  loans  and  equity. 

distribution  of  fixed  costs  between  crops  can 

This  is  even  more  crucial  when  comparing  the 

lead to significant differences in the final results. 

competitiveness  of  different  countries  with  very 

In addition, as we shall see later, some expenses 

different economic contexts.

are allocated according to the income of crop in 

relation to total income. This may raise questions 

For  ARMS  method,  there  is  long-term 

in  the  event  of  large  variations  in  sale  prices 

interest  on  machinery,  buildings  and  short-

between crops. In particular, changes in costs per 

term  on  input,  fuel,  repairs  and  oil.  A  long-

hectare can be artificially induced in response to 

run  rate  of  return  to  farm  assets  out  of  current 

changes in relative crop price.

Income (10-year moving average) is used as the 

interest  rate  (1.23%  for  2001-2010).  Interest 

The labour cost allocation to specific crops in 

on  operating  inputs  is  based  on  the  6-month 

ARMS are based on the number of hours worked 

US Treasury  Bill  interest  rate  (“risk-free”  rate). 

per  a  given  crop.  A  similar  approach  is  applied 

In  the  same  way,  for  agri  benchmark,  there  is 

by  Win-tops;  only  costs  not  directly  traceable 

interest and equity on input (rates of short-term 

such  as  overheads,  farm  taxes,  insurance,  etc. 

loan and short-term deposit, less than one year) 

are allocated based on the share of each crop in 

and on machinery and buildings (rates of long-

the  gross  margin  (share  of  total  farm  operating 

term loan and long-term deposit). Similarly for 

margin, value of production less operating costs). 

FADN,  other  family  assets  are  remunerated 

The  cost  allocation  based  on  gross  margins 

with  the  interest  rate  of  10  years  National 

further  raise  questions  in  the  event  of  negative 

Treasury and long-term bonds.

gross margins.

France Arvalis-Unigrains Observatory, equity 

The cost of mechanisation in the International 

is  made  into  a  single  charge  including  input 

Arvalis Observatory are calculated by crop based 

machinery and buildings.

on  the  number  of  passes  and  hours  actually 

given  to  mechanisation.  The  labour  charges  are 

Regarding 

the 

International 

Arvalis 

distributed  30%  per  hectare  and  70%  depending 

Observatory,  interests  and  equity  are  only  on 

on  the  hours  in  field  worked  on  the  crop.  Other 

agricultural  repurchase  price  machinery.  These 

fixed costs are broken down per hectare. 

costs are neglected for inputs and buildings and 

not included in the analysis.

Mechanisation  and  labour  charges  in  agri 

benchmark are allocated to crops by the percentage 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Table  4.4  shows 

the  main  differences 

of the total time of machines used in field. Overhead 

between  the  studied  methods  concerning  the 

costs related to the entire farm (buildings, insurance, 

valuation  of  organic  fertiliser,  machinery  cost, 

management  fees,  etc.)  are  assigned  according  to 

83

labour cost, land cost and equity and interest.

the share of each crop in the total income.

 
 
 
 
 
 
 
 
 
 
 
 
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

84

y
r
o
t
a
v
r
e
s
b
O

s
i
l

a
v
r
A

l

a
n
o
i
t
a
n
r
e
t
n

i

d
e
s
a
h
c
r
u
p

t
o
N

e
r
a

s
r
e
z
i
l
i
t
r
e
f

i

c
n
a
g
r
o

e
e
r
f

s
a

d
e
r
e
d
i
s
n
o
c

i

”
l
a
c
n
h
c
e
t
“
r
a
e
n

i
l

n
o
N

n
o

d
e
s
a
b

i

n
o
i
t
a
c
e
r
p
e
d

t
u
b

e
u
a
v

l

t
n
e
m
e
c
a
p
e
r

l

e
h
t

t
n
u
o
c
c
a

o
t
n

i

i

g
n
k
a
t

d
n
a

s
l
a
i
r
e
t
a
m

f
o

e
s
u

y
t
i
l
i

b
a
r
u
d

r
i
e
h
t

d
e
u
a
v

l

s
i

r
o
b
a

l

y
l
i

m
a
F

r
o
t
c
a
r
t

a

f
o

t
s
o
c

e
h
t

t
a

a
e
r
a

e
h
t

n

i

r
e
v
i
r
d

d
e
n
r
e
c
n
o
c

s
p
o
T
n
W

i

k
r
a
m
h
c
n
e
b

i
r
g
A

e
c
n
a
r
F

y
r
o
t
a
v
s
r
e
b
O

i

s
n
a
r
g
n
U
-
s
i
l

i

a
v
r
A

N
D
A
F

g
S
0
5

i

e
n
a
r
k
U

t
r
o
p
e
r

S
M
R
A

s
d
o
h
t
e
m

t
n
e
r
e
f
f
i
d
n

i

s
t
s
o
c
t
n
e
r
e
f
f
i
d
e
h
t
e
t
a
l
u
c
l
a
c
o
t
d
e
s
u
s
e
h
c
a
o
r
p
p
A

:
4
.
4
e
l
b
a
T

n
o
i
t
a
m
r
o
f
n

i

o
N

e
h
t

n
o

i

g
n
d
n
e
p
e
d

t
s
o
c

e
r
a

s
r
e
z
i
l
i
t
r
e
f

i

c
n
a
g
r
o

e
r
a

s
r
e
z
i
l
i
t
r
e
f

i

c
n
a
g
r
o

e
r
a

s
r
e
z
i
l
i
t
r
e
f

i

c
n
a
g
r
o

e
h
t

t
a

s
t
i
n
u

r
e
z
i
l
i
t
r
e
f

y
t
i
n
u
t
r
o
p
p
o

n
a

r
o

e
e
r
F

d
e
s
a
h
c
r
u
p

t
o
N

d
e
s
a
h
c
r
u
p

t
o
N

d
e
s
a
h
c
r
u
p

t
o
N

e
h
t

f
o

n
o
i
t
a
u
a
V

l

a
e
r
a

e
e
r
f

s
a

d
e
r
e
d
i
s
n
o
c

e
e
r
f

s
a

d
e
r
e
d
i
s
n
o
c

e
e
r
f

s
a

d
e
r
e
d
i
s
n
o
c

s
e
c
i
r
p

t
e
k
r
a
m

c
i
n
a
g
r
o

f
o
n
o
i
t
a
u
a
V

l

s
r
e
z
i
l
i
t
r
e
f

r
o

e
u
a
v

l

t
n
e
m
e
c
a
p
e
R

l

f
o

e
s
u

e
h
t

n
o

d
e
s
a
b

s
l
a
i
r
e
t
a
m

i

n
o
i
t
a
c
e
r
p
e
d

r
a
e
n
L

i

e
h
t

n
o

d
e
s
a
b

l

e
b
a

l
i

a
v
a

i

n
o
i
t
a
c
e
r
p
e
D

l

e
b
a

l
i

a
v
a

i

n
o
i
t
a
c
e
r
p
e
D

l

e
b
a

l
i

a
v
a

i

n
o
i
t
a
c
e
r
p
e
D

y
r
e
v
o
c
e
r

l

a
t
i
p
a
C

g
n
i
s
u

d
o
h
t
e
m

e
h
t

f
o

e
c
i
r
p

e
s
a
h
c
r
u
p
e
r

g
n
i
t
n
u
o
c
c
a

t
s
o
c

n

i

g
n
i
t
n
u
o
c
c
a

t
s
o
c

n

i

g
n
i
t
n
u
o
c
c
a

t
s
o
c

n

i

r
o
f

s
e
c
i
r
p

t
n
e
m
e
c
a
p
e
r

l

t
n
e
m
p
u
q
e

i

i

y
r
e
n
h
c
a
m

t
s
o
c

i

y
r
e
n
h
c
a
M

n
o
i
t
a
m
r
o
f
n

i

o
N

s
i

t
s
o
c

y
t
i
n
u
t
r
o
p
p
o

n
A

r
o
b
a

l

y
l
i

m
a
f

r
o
f

d
e
s
u

s
i

r
e
k
r
o
w
y
l
i

m
a
f

f
o

b
N

e
m

i
t
-
l
l

u
f

e
v
i
t
c
a

n
a

n
e
v
i
g

e
h
t

r
a
e
  y

/

h

0
0
6
1
s
i

s
i

t
s
o
c

y
l
r
u
o
h

l

a
n
o
g
e
r

i

l

d
e
t
a
u
c
a
c

l

e
c
r
o
f

r
o
b
a

l

y
l
i

m
a
F

e
h
t

t
a

d
e
t
a
r
e
n
u
m
e
r

l

i

a
n
o
g
e
R
e
h
t

f
o

l

e
v
e

l

s
e
g
a
W

l

a
r
u
t
l
u
c
i
r
g
A

f
o

e
p
y
t

e
h
t

f
o

r
e
k
r
o
w

m
r
a
f

t
n
u
o
c
c
a

o
t
n

i

n
e
k
a
t

y
l
t
c
e
r
i
d

r
o
b
a

l

f
o

s
t
s
o
C

t
o
n

s
i

t
s
o
c

r
o
b
a

l

y
l
i

m
a
f

r
o
b
a

l

y
l
i

m
a
f

r
o
f

d
e
s
u

,
g
n
i
t
n
u
o
c
c
a
m
o
r
f

s
i

t
s
o
c

y
t
i
n
u
t
r
o
p
p
o

n
A

n

i

r
o
b
a

l

f
o

t
s
o
C

y
l
i

m
a
f

l

r
a
u
c
i
t
r
a
p

r
o
b
a

l

r
o
f

t
s
o
c

y
t
i
n
u
t
r
o
p
p
O

n
o

d
e
s
a
b

d
e
n
w
o

d
n
a

l

n

i

g
n
i
t
n
e
r

f
o

t
s
o
c

e
h
t

e
n
o
z

t
n
e
r
r
u
c

e
h
t

l

t
n
e
a
v
i
u
q
e

e
s
a
e

l

h
s
a
C

d
n
a

l

m
r
a
f

r
o
f

r
o
f

t
s
o
c

y
t
i
n
u
t
r
o
p
p
O

n
o

d
e
s
a
b

d
e
n
w
o

d
n
a

l

n

i

g
n
i
t
n
e
r

f
o

t
s
o
c

e
h
t

e
n
o
z

t
n
e
r
r
u
c

e
h
t

t
a

d
e
u
a
v

l

s
i

d
n
a

l

f
o

t
s
o
C

m
o
r
f

e
c
i
r
p

t
n
e
r

d
n
a

l

e
h
t

d
e
t
n
e
r

t
r
a
p
m
r
a
f

e
h
t

s
i

d
n
a

l

y
l
i

m
a
F

e
h
t

t
a

d
e
t
a
r
e
n
u
m
e
r

l

a
n
o
g
e
r

i

e
h
t

f
o

l

e
v
e

l

t
n
e
r

d
n
a

l

e
k
a
t

t
’
n
s
i

d
n
a

l

y
t
r
e
p
o
r
P

)
d
e
t
n
e
r

%
9
9
(

t
n
u
o
c
c
a

n

i

d
e
u
a
v

l

s
i

d
n
a

l

f
o

t
s
o
C

e
c
i
r
p

t
n
e
r

d
n
a

l

e
h
t

t
a

t
r
a
p
m
r
a
f

e
h
t

m
o
r
f

e
h
t

e
s
i
w
r
e
h
t
o
,
d
e
t
n
e
r

d
e
s
u

s
i

e
g
a
r
e
v
a

e
t
a
t
s

t
s
o
c
d
n
a
L

y
l
n
o

y
t
i
u
q
e

d
n
a

t
s
e
r
e
t
n

I

t
n
e
m
p
u
q
e

i

n
o

n
o
i
t
a
m
r
o
f
n

i

o
N

d
n
a

t
s
e
r
e
t
n

I

m
r
e
t
-
g
n
o
L

i

,
y
r
e
n
h
c
a
m
n
o

y
t
i
u
q
e

m
r
e
t

t
r
o
h
s
,
s
g
n
d

i

l
i

u
b

t
u
p
n

i

n
o

i

e
n
h
c
a
m

t
u
p
n

i

d
e
d
u
c
n

l

i

e
h
t

h
t
i

w
d
e
t
a
r
e
n
u
m
e
r

s
e
g
r
a
h
c

l

e
g
n
i
s
A

s
t
e
s
s
a

y
l
i

m
a
f

r
e
h
t
O

g
n
d

i

l
i

u
b

d
n
a

s
r
a
e
y

0
1
f
o

e
t
a
r

t
s
e
r
e
t
n

i

d
n
a

t
s
e
r
e
t
n

i

e
c
n
a
n
F

i

g
n
i
t
n
u
o
c
c
a
m
o
r
f

y
t
i
u
q
e

d
n
a

t
s
e
r
e
t
n

I

m
r
e
t
-
g
n
o
L

i

,
y
r
e
n
h
c
a
m
n
o

y
t
i
u
q
e

m
r
e
t

t
r
o
h
s
,
s
g
n
d

i

l
i

u
b

t
u
p
n

i

n
o

y
t
i
u
q
e
d
n
a
s
t
s
e
r
e
t
n
I

t
s
o
c

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
France  Arvalis-Unigrains  Observatory:  the 

of  marketing  and  storage.  Therefore  income 

distribution  of  overhead  and  labour  costs  is 

is  calculated  using  a  harvest-period  price  (an 

made  in  proportion  to  the  crop  income.  The 

average  price  by  state  and  month  of  harvest).  In 

mechanisation  charges  are  spread  per  hectare 

reality  farmers  are  delaying  the  sale  by  storing 

unless  part  of  the  industrial  crops  (sugar  beet  + 

their  products  to  sell  at  a  higher  price  than 

potato) is greater than 10% or for potato is greater 

the  sum  price  at  harvest  and  storage  cost  over 

than  5%  of  the  total  area.  In  this  case,  costs  of 

transport.  It  means  that  revenues  calculated  are 

mechanisation  are  allocated  based  on  the  part 

slightly underestimated relative to actual income 

of the crop income considered in relation to the 

received by farmers. 

total income.

The  agri  benchmark  selling  prices  are 

The allocation key in the 50 sg report method 

estimated by the partner of the average prices for 

is original. Indeed, production overhead costs are 

the typical farm at the data collection time.

allocated in proportion to the direct expenses of 

the  crop  from  the  direct  costs  of  all  crops. This 

The  international  Arvalis  observatory  uses 

method of allocation of costs to specific crops is 

selling  prices  from  the  same  marketing  year. 

interesting  because  it  is  not  related  to  the  crop 

Attention  is  paid  to  the  differentiation  of  selling 

sale  price.  General  overhead  costs,  interest  and 

prices according to wheat quality. The quality of 

return on equity are allocated based on the share 

wheat  grains  considered  are  CRWS  and  HRW 

of each crop income.

standards  for  Canada  and  the  United  States, 

Class 4 for Russia and Ukraine, and Trigo pan for 

With  respect  to  the  FADN,  the  allocation 

Argentina, etc.

of  all  costs  categories  to  specific  crops  largely 

depends  on  the  approach  undertaken  which 

The  other  methods  used  the  real  farmer 

often  is  based  on  econometric  estimation.  The 

prices present in the account.

application  of  the  econometric  model  might  be 

questionable  in  the  context  of  fluctuating  sales 

Table  4.5  shows 

the  main  differences 

prices and purchase input prices.

concerning  the  cost  allocation  to  crop,  and 

Commodities selling prices 

selling prices.

Selling  prices  used 

for  comparison 

to 

4.4.3. Challenges and opportunities of the 

production  costs  in  studies  may  be  different  (e.g. 

different methods

price  at  harvest  or  after  the  end  of  the  marketing 

season),  which  also  induces  bias  in  the  analysis. 

During  the  presentations  and  discussions, 

The  pricing  period  taken  into  account  may  differ 

the  workshop  participants  highlighted  ways 

either by method choice (e.g. harvest crop prices) 

of  improvement  and  future  challenges  for  the 

or because of producers’ trade policy (e.g. storage). 

different  methods  towards  calculating  CoP  and 

This  is  especially  important  in  the  current  price 

their  comparisons.  Some  concerns  are  common 

volatility  context.  In  addition,  sales  prices  and 

to different studies and others are more specific. 

production  costs  can  vary  depending  on  quality, 

For example, a major concern is the difficulty to 

so  it  is  also  important  to  carefully  determine  and 

obtain information directly from farmers. Farmers 

communicate the quality considered.

are  increasingly  less  willing  to  share  their  data 

In  the  ARMS  method,  the  production  costs 

required  to  collect  and  analyse  data.  This  can 

take  all  costs  incurred  in  the  production  of 

lead  to  delays  in  the  analysis,  in  particular  to 

each  product  into  account,  excluding  the  costs 

update detailed studies.

freely.  Further  difficulty  is  the  cost  and  time 

l

i

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

85

 
 
 
 
 
 
 
 
 
 
 
 
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

86

y
r
o
t
a
v
r
e
s
b
O

s
i
l

a
v
r
A

l

a
n
o
i
t
a
n
r
e
t
n

i

s
p
o
T
n
W

i

k
r
a
m
h
c
n
e
b

i
r
g
A

e
c
n
a
r
F

y
r
o
t
a
v
s
r
e
b
O

i

s
n
a
r
g
n
U
-
s
i
l

i

a
v
r
A

N
D
A
F

g
S
0
5

i

e
n
a
r
k
U

t
r
o
p
e
r

S
M
R
A

p
o
r
c

y
b

l

d
e
t
a
u
c
a
C

l

f
o

r
e
b
m
u
n

e
h
t

n
o

d
e
s
a
b

s
e
s
s
a
p

r
e
p
%
0
3

d
e
t
u
b
i
r
t
s
i
D

%
0
7
d
n
a

e
r
a
t
c
e
h

d
e
s
a
b

s
i

e
s
u

t
c
e
r
i
D

e
s
u

l

d
e
fi

n

i

l

a
u
t
c
a

n
o

s
r
u
o
h

e
h
t

n
o

i

g
n
d
n
e
p
e
d

r
o
f

i

n
g
r
a
m
s
s
o
r
g

)
2

r
o

e
h
t

n
o

d
e
k
r
o
w
d
e
fi

l

n

i

p
o
r
c

s
e
d
u
c
n
i
(

l

e
s
u

t
c
e
r
i
d
n

i

)
e
s
u

t
c
e
r
i
d
n

i

f
o

s
r
u
o
h

k
r
o
w

f
o

e
m

i
t

f
o

e
r
a
h
S

n

i

p
o
r
c

h
c
a
e

f
o

e
r
a
h
S

i

g
n
d
u
c
n

l

i

s
e
g
r
a
h
c

l
l

A

n

i

t
s
o
c

t
c
e
r
i
d

f
o

e
r
a
h
S

p
o
r
c

y
b

s
r
u
o
h

b
N

t
s
o
c

r
o
b
a

l

f
o

n
o
i
t
a
c
o

l
l

A

e
h
t

n

i

p
o
r
c

h
c
a
e

n
o

k
r
o
w

f
o

e
m

i
t

l

a
t
o
t

h
t
i

w
p
o
r
c

y
b

d
e
t
a
c
o

l
l

a

l

e
d
o
m
c
i
r
t
e
m
n
o
c
e

e
m
o
c
n

i

l

a
t
o
t

e
r
a

t
s
o
c

t
c
e
r
i
d

t
s
o
c

t
c
e
r
i
d

l

a
t
o
t

e
h
t

i

y
r
e
n
h
c
a
m
e
h
T

r
e
p

d
a
e
r
p
s

e
r
a

s
e
g
r
a
h
c

e
r
e
h
t

s
s
e
n
u

l

e
r
a
t
c
e
h

f
o

t
r
a
p

t
n
a
c
fi
n
g
i
s

i

a

s
i

>
-
-

s
p
o
r
c

l

a
i
r
t
s
u
d
n

i

e
h
t

n
o

d
e
s
a
b

d
e
t
a
c
o

l
l

a

n

i

p
o
r
c

h
c
a
e

f
o

e
r
a
h
s

e
m
o
c
n

i

l

a
t
o
t

e
h
t

e
r
a
t
c
e
h

y
b

t
s
o
C

i

y
r
e
n
h
c
a
m

f
o

n
o
i
t
a
c
o

l
l

A

t
s
o
c

a
h

y
b

t
s
o
C

n

i

p
o
r
c

h
c
a
e

f
o

e
r
a
h
s

i

n
g
r
a
m
s
s
o
r
g

l

a
t
o
t

e
h
t

n

i

p
o
r
c

h
c
a
e

f
o

e
r
a
h
S

n

i

p
o
r
c

h
c
a
e

f
o

e
r
a
h
S

e
m
o
c
n

i

l

a
t
o
t

e
h
t

e
m
o
c
n

i

l

a
t
o
t

e
h
t

n

i

p
o
r
c

h
c
a
e

f
o

e
r
a
h
S

e
m
o
c
n

i

l

a
t
o
t

e
h
t

n

i

p
o
r
c

h
c
a
e

f
o

e
r
a
h
S

i

n
g
r
a
m
s
s
o
r
g

l

a
t
o
t

e
h
t

d
a
e
h
r
e
v
o

f
o

n
o
i
t
a
c
o

l
l

A

t
s
o
c

s
e
c
i
r
p

e
g
a
r
e
v
A

s
e
c
i
r
p

r
e
m
r
a
F

e
c
i
r
p

e
g
a
r
e
v
A

s
e
c
i
r
p

s
r
e
m
r
a
F

s
e
c
i
r
p

r
e
m
r
a
F

s
e
c
i
r
p

r
e
m
r
a
F

d
o
i
r
e
p
-
t
s
e
v
r
a
h

l

i

a
n
o
g
e
R

g
n

i
l
l

e
s

s
e
i
t
i
d
o
m
m
o
C

e
c
i
r
p

s
e
c
i
r
p

d
o
h
t
e
m

t
n
e
r
e
f
f
i
d
n

i

s
e
c
i
r
p
g
n

i
l
l
e
s
e
t
a
m

i
t
s
e
o
t
d
n
a
s
t
s
o
c
t
n
e
r
e
f
f
i
d
e
h
t
e
t
a
c
o
l
l
a
o
t
d
e
s
u
s
e
h
c
a
o
r
p
p
A

:
5
.
4
e
l
b
a
T

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Methods  based  on  comparisons  of  “typical 

According  to  Dick  Schoney,  calculations  of 

farms”  like  the  agri  benchmark  cash  crop  or 

CoP  studies  are  “one  shot”  and  static,  and  are 

international Arvalis observatory have to evaluate 

primarily  used  as  a  diagnostic  tool. They  do  not 

and develop the representativeness of their data, 

take  sufficient  account  of  the  notion  of  risk  and 

for  example,  in  backing  the  “typical  farm”  with 

change. The study of production costs alone does 

statistics.  But  complete  agricultural  statistics  are 

not  determine  the  resilience  of  systems. Also,  in 

not available in all the analysed countries and the 

his  view,  other  points  are  to  be  developed  such 

statistics are not always usable in relation to the 

as  better  taking  into  account  of  the  quality  of 

sample chosen.

products  or  transport  costs.  Similarly,  we  must 

seek to better integrate the rotational effects into 

For  the  ARMS  method,  William  Lazarus 

production  costs  and  improve  the  allocation  of 

suggested  some  points  of  the  method  which 

fixed costs to crop. Works on the environmental 

could  possibly  be 

improved.  For  example, 

externalities  of  production,  and  the  economic 

compared  with  other  sources,  the  costs  of 

impacts of environmental measures, also require 

pesticides 

are 

probably 

underestimated. 

development. 

Moreover, 

the  estimation 

for 

family 

labour 

can  be  reviewed  (regression  from  1988  data). 

Concerning  mechanisation,  works  on  the  use 

4.5. International comparisons of crop 

and depreciation life of equipment are required. 

production costs

There  are  also  recent  interesting  works  to  better 

consider the costs of maintenance and repair.

4.5.1. Main challenges 

For 

the  agri  benchmark,  an  area 

for 

The purpose of the next section is to highlight 

improvement is to reach the real selling price and 

some  important  points  to  keep  in  mind  when 

it appear to depend on the time of data collection. 

comparing  international  costs  of  production, 

If we want the data to be as up to date as possible, 

in  particular  in  relation  to  the  main  points  of 

selling prices are often an estimation. Indeed, during 

discussion at the workshop.

the  period  of  data  collection  the  sale  campaign 

of  commodities  is  not  complete  so  the  final  sale 

Exchange rate

price is unknown. Yelto Zimmer opined the need to 

improve the consideration of the opportunity family 

When 

carrying 

out 

international 

labour  especially  for  small-sized  farms,  as  well  as 

the costs for organic fertilisers.

comparisons  of  production  costs,  attention 
needs  to  be  paid  to  exchange  rates.  In  most 

cases  the  comparisons  are  expressed  in  $/t  or 

Roman  Slaston  focused  on  the  difficulty  in 

in  €/t.  The  exchange  rates  are  often  averaged 

distinguishing  technologies  (e.g.  tillage,  no  till, 

for the year. Recent changes in exchange rates, 

direct seeding, etc.) used on farms in the results 

particularly  following  the  financial  crisis,  have 

of the 50 sg report. Similarly, the previous crop 

influenced  crop  competitiveness,  in  particular 

effect  is  not  sufficiently  taken  into  account. 

for  export. 

In  addition, 

the  variability  of 

The  level  of  detail  is  often  too  low  for  relevant 

exchange  rates  during  a  production  campaign 

analysis  and  values    are  sometimes  aberrant 

can  be  very  high  and  can  affect  the  costs 

compared  to  reality.  Data  on  pesticides  are 

of  imported  inputs  and  the  selling  prices  of 

missing.  In  addition,  the  different  accounting 

products.  This  is  particularly  true  for  Ukraine 

policies can cause problems later in the analysis. 

in 2009-2010, where fertilisers were purchased 

According  to  Slaston,  a  better  definition  of  the 

at  high  prices  and  selling  prices  were  low. 

quality  of  inputs  and  outputs  would  also  help 

An  average  exchange  rate  for  the  year  cannot 

refine the analysis.

account for this phenomenon.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

87

 
 
 
 
 
 
 
 
 
 
 
 
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

Representativeness

CoP. This can involve many hectares in different 

countries (Australia, Brazil, Russia, Ukraine, etc.), 

The  issue  of  representativeness  and  the 

and it could be difficult for the studies based on 

choice of “typical farm” as in the agri benchmark 

“typical farms” to take this into account.

studies, or the international Arvalis observatory, is 

a central point. The link between statistics and a 

If these marginal lands are cultivated over a long 

“typical farm” has to be questioned.

period,  they  are  theoretically  captured  by  the  farm 

structure updates. Moreover, it is possible to add new 

It  is  possible  to  move  the  characteristics  of 

“typical  farms”  to  reflect  this  reality.  However  this 

the  “typical  farm”  closer  to  the  local  statistics 

requires a multiplication of “typical farms” to study 

particularly  in  terms  of  cropping  patterns,  yields 

and therefore more work time. It must also be borne 

and  structures.  This  is  the  first  step  to  connect 

in mind that this reality is not instantaneous.

the  “typical  farm”  to  the  agricultural  statistics 

available in different countries.

The	rotation	effects	

Farms’ structural evolution 

Farmers  grow  different  crops  in  rotation 

and  this  is  for  various  agronomic  reasons  (fight 

How  does  a  “typical  farm”  evolve  over 

against  pests  and  diseases  specific  to  certain 

the  years  in  terms  of  structure  and  innovation? 

crops,  weed  management,  nitrogen  effect,  etc.), 

In the case of agri benchmark, “typical farms” are 

or work organisation (spread out the peak work at 

updated in terms of structure every 2-4 years. This 

harvesting or seeding) but also to manage climatic 

captures  the  changes  in  structures,  crop  rotation 

risks and selling price risks on different crops. All 

and  development  of  innovative  practices  and 

these  rotation  effects  are  important  for  farmers, 

technologies.  However,  this  could  imply  some 

but are not sufficiently taken into account by crop 

important changes in production costs following the 

production cost approaches. Similarly, phosphorus 

update of the farm structure and makes the study of 

and  potash  fertilisation  is  often  performed  in  the 

long production cost series difficult. The analysis is 

rotation  or  at  least  for  several  crops,  but  with 

true to a lesser extent for the ARMS/USDA method. 

the  methods  of  calculating  CoP  there  is  a  risk 

For example if the objective is to study the impact of 

of  affecting  all  the  charges  on  a  single  crop. The 

rising energy costs on  the evolution  of production 

allocation  to  the  crop  of  some  regulatory  aspect 

costs,  methods  based  on  a  representative  sample 

like cover crop also poses questions.

will certainly be more appropriate.

Cultivation	 of	 marginal	 lands	 and	 impacts	 on	

effect on the following crop. A wheat with a pulse 

Some crops, such as pulses, have a positive 

production costs

as  the  previous  crop  does  not  often  obtain  the 

same yield or the same operating expenses above 

The  high  selling  crop  prices,  and  resulting 

as  a  wheat  with  another  wheat  as  the  previous 

attractive  margins,  are  a  strong  incentive  for 

crop.  Indeed,  different  approaches  to  calculate 

farmers  to  cultivate  marginal  lands.  These  new 

costs of production take such issues into account 

areas  for  cultivation  are  of  low  productive 

poorly.  It  must  be  noted  that  it  is  possible  to 

potential  and  are  only  profitable  to  grow  in 

separate  crops  according 

to 

their  previous 

favourable  economic  contexts.  This  may  be  the 

cultural with the approaches of a “typical farm”. 

88

case for example in Australia, where lands in areas 

receiving  less  precipitation  are  cropped  when 

Reliability	of	data	in	changing	economic	contexts

profitability  allows.  Crops  on  these  marginal 

lands will therefore have a higher production cost 

The  consideration  of  risk,  and  particularly 

which  will  mechanically  increase  the  average 

of  the  variability  of  production  costs  in  general, 

 
 
 
 
 
 
 
 
 
is  not  sufficiently  developed  in  production  cost 

a  restatement  in  order  to  have  a  common  result 

comparisons. Dispersion and variability of results 

presentation  format  may  be  useful  to  facilitate 

in a single year, or over time, are generally not well 

comparisons. 

made   in the results of competitiveness studies. In 

particular, yields, and therefore production costs, 

Table  4.6  gives  an  example  of  a  reporting 

can  be  variable  in  some  countries  especially  in 

format for ARMS, the agri benchmark and Arvalis 

marginal  production  areas.  Also  the  quality  of 

international  observatory.  We  can  see  that  in 

obtained products can be variable depending on 

order  to  do  an  accurate  comparison  between 

the year in some areas.

the  results  of  the  three  methods,  we  need  to 

reorganize  the  different  items.  In  particular  fuel, 

In  addition,  recent  economic  contexts  of 

repairs  and  contract  operation  are  sometimes 

agricultural  production  are  fluctuating.  Indeed, 

counted as direct costs or as machinery costs. The 

prices of products, but also the purchase prices of 

equity  and  interest  cost  are  not  always  included 

inputs (fertilisers and oil-related energy costs), are 

in the same way.

highly variable in recent years. 

4.5.2. How can we compare results from 

different methods?

Ensuring that the target is equivalent

Having  a  precise  definition  of  the  “typical 

farm”  studied,  or  of  the  core  sample  of  the 

Ensuring	the	equivalence	of	the	charges	included	

calculation,  is  crucial  in  order  to  compare  the 

in the calculation

results of different studies. For example, in some 

countries  the  crop  production  is  clearly  divided 

The  results  of  different  studies  of  crop 

into  two  categories  between  efficient  farms, 

production costs can be compared if it has been 

competitive regarding the global market, and the 

previously  verified  that  the  charges  considered 

smaller farms in near self-sufficiency or orientated 

and  the  methods  to  calculate  and  allocate  them 

to the domestic market. In this case, it is illusory 

by crop are close. This is particularly important for 

to  compare  the  cost  of  wheat  production  of  the 

costs  of  irrigation  and  drying  mainly  for  corn  or 

country’s  average  with  the  production  cost  of  a 

labour charges (including social security charges 

high performing farm.

for  managers)  or  crop  insurance.  Indeed,  these 

charges, for example, are often treated differently 

In order to compare the production costs of 

and may explain some of the differences in results 

a  crop,  we  must  also  try  to  identify  the  quality 

across studies. 

aspect (e.g. protein content of wheat) because the 

sale  price  as  well  as  the  CoP  may  be  different, 

Another  point  of  attention 

for  costs 

indeed the dynamic of the crop and yield could 

allocation, mainly land, labour or mechanisation, 

be different. The accuracy of the product quality 

is when two crops are grown in the same year on 

in the study’s results may be relevant.

the same parcel which is very common in South 

America.

Furthermore, it must also be checked that 

the  studied  areas  are  comparable.  Indeed, 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Comparing  the  cost  item  by  item,  in  order 

some  countries  have  very  different  soil  and 

to  go  further  in  the  analysis  of  competitiveness 

climatic  conditions,  and 

thus  yields  and 

factors, can be complex because the definition of 

production  costs  are  very  heterogeneous 

cost item in the various studies could be different. 

within 

the  same  country  or  region.  For 

Therefore  if  the  desire  is  to  make  detailed 

example,  in  Russia  the  production  potentials 

89

comparisons, it is necessary to have the different 

are  very  different  between  the  central  black 

cost items and their method of calculation. Here, 

earth  region,  Siberia  and  the  Kuban  region 

 
 
 
 
 
 
 
 
 
 
 
 
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

Table 4.6: Example results presentation in various posts of charges for 3 different methods

ARMS Method 

Agri Benchmark Method

International Arvalis 
Observatory Method

Operating cost: 
Seeds
Fertiliser 
Chemicals 
Custom operations: Contract 
operations Technical services 
Commercial drying

Direct costs: 
Seeds 
Fertiliser 
Plant protection 

Direct costs:
Seeds
Fertiliser
Pesticides
Other pesticides

Direct costs

Fuel, Lube and electricity 
Repairs 

Crop insurance

Crop insurance 

Purchased irrigation water 

Water if irrigated crop

Water energy if irrigated crop

Interest on operating capital  

Other variable cost : Interest 
and equity cost on input

Allocated Overhead: 
Capital recovery 

Operating cost: 
Diesel
Contractor cost
Machinery: depreciation 
maintenance, repairs

Fixed cost: 
Equipment (depreciation, 
repairs, fuel, subcontracted 
work, interest)

Fixed cost

Paid and unpaid labour:
Hired labour  
Family labour (opp.cost)

Hired labour 
Family labour  (opp.cost)

Employee: wages +social 
insurance
 Family labour +social security 
payment

Taxes and insurance
General farm overhead

Buildings
Depreciation
Overhead (Miscellaneous)

Other fixed costs:
Insurance, phone, advice etc.
Capital equity on equipment

Opportunity cost of land, = 
rental rate: 

 Land 

Land rental cost

(south).  Therefore,  we  must  pay  particular 

Ensuring	that	the	period	of	analysis	is	the	same

attention  to  the  samples  in  terms  of  soil 

90

and  climatic  zones.  The  choice  of  typical 

The  comparison  has  to  be  based  on  the  same 

farm  in  different  areas  can  lead  to  different 

analysis period. In the case of comparing the average 

conclusions for the same country. It is possible 

CoP,  the  analysis  period  has  to  be  the  same  (e.g. 

to compare farms with different potentials, but 

average  of  the  same  3  years,  5  years).  In  case  of 

it  must  be  clear  and  precise  whether  this  is  a 

comparing the CoP each year, it must be the same 

main or a marginal production area.

year  of  grains  marketing  on  which  the  analysis  is 

 
 
 
 
 
 
 
 
 
performed. This  issue  may  seem  trivial  but  can  be 

or  euros  can  hide  the  changes  in  CoP  linked  to 

quite complicated especially in case of international 

production  systems  or  regional  contexts.  It  is 

comparisons  with  countries 

in 

the  Southern 

important to have the data in local currency to be 

Hemisphere  where  the  production  seasons  are 

able  to  subsequently  perform  calculations  with 

shifted. The double-cropping, common in Brazil and 

different exchange rates. Especially when studying 

Argentina, increases the confusion, and complexity, 

the  evolution  of  production  costs  in  a  currency 

as  the  same  crop,  corn  in  Brazil  for  example,  can 

different from the country of study, attention must 

be  grown  in  two  distinct  periods  of  the  year  with 

be  paid  to  changes  in  relative  exchange  rates. 

different conducts and returns. Given the strong intra-

Why  not  also  make  these  calculations  with  an 

annual fluctuations in input prices and commodities 

average exchange rate (e.g. average of 3 years) to 

selling  prices,  ill-defined  periods  of  analysis  for 

smooth  out  variations  related  to  external  factors 

annual comparisons can be misleading.

and  focus  on  competitive  factors  more  related 

Preferring	 comparisons	 of	 average	 production	

think about different presentations as production 

costs or annual production costs depends on the 

costs with an index presentation.

to  the  production  system?  Here,  one  could  also 

objectives

Comparing  item  by  item,  with  different 

In  some  countries,  the  variation  in  inter-

charges contained in each item, can be a source 

annual  yield  is  high,  e.g.  recurrent  drought  in 

of error in analysis. A common presentation mode 

Australia,  so  it  is  risky  to  make  comparisons 

of the results with the same expense and the same 

of  competitiveness  in  a  single  year.  Indeed, 

costs  in  each  item  would  simplify  comparisons. 

the  drought  year  with  yields  divided  by  two 

Indeed,  it  could  be  possible  to  work  initially  on 

is  not  representative  of  the  competitiveness  of 

the detailed items and then to make groups using 

Australian grain production.

the same nomenclature.

Therefore comparisons of average production 

costs over several years, or at least with average 

4.6. Conclusions and Recommendations

yields,  allow  a  more  accurate  assessment  of 

competitiveness and a more rational explanation 

We  have  seen  with  this  overview  of  the 

of the competitiveness factors.

methods  to  calculate  crop  production  costs, 

that  the  studies  present  different  objectives,  and 

However, 

strong 

inter-annual 

yields 

thus  different  methods  to  collect  and  process 

variation is also a characteristic of some soil and 

data. This  makes  international  comparisons  with 

climate  areas.  It  is  also  important  to  highlight 

different methods difficult or even risky. However, 

this  variability  in  comparisons  of  international 

some  recommendations  can  be  made  in  order 

competitiveness 

in  one  way  or  another. 

to  be  able  to  better  compare  the  results  from 

A  comparison  of  the  history  of  the  annual 

different  studies.  Indeed  to  ensure  an  equivalent 

production costs may enhance this variability.

targeted sample, same period of analysis, similar 

How	 can	 we	 present	 the	 results	 of	 international	

mainly  same  costs  taken  into  account,  is  very 

methods  of  calculation  and  allocation  and, 

comparisons?

important  to  perform  reliable  comparisons.  The 

result  presentation  is  also  a  critical  point  (items, 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

The  issue  of  presentation  of  the  results, 

exchange rates, etc.).

especially  when 

taking  exchange  rates 

into 

91

account,  is  a  recurrent  issue  and  does  not  seem 

On  an  international  level,  and  simplifying 

to have a ready solution. The effects of exchange 

voluntarily,  there  are  two  different  methods  to 

rates when presenting production costs in dollars 

calculate  and  compare  production  costs:  the 

 
 
 
 
 
 
 
 
 
 
 
 
s
p
o
r
C
e
l
b
a
r

A
d
n
a

s
l
a
e
r
e
C

r
o
f

s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

.
4

r
e
t
p
a
h
C

methods  based  on  large  representative  samples 

the  sample,  the  methods  and  the  presentation 

and the method based on a “typical farm”. These 

are  different.  It  is  complicated  to  compare  with 

methods are complementary because they do not 

precision  such  different  data.  It  seems  easier  to 

deal with the same questions. Methods based on 

use  the  same  method  to  compare  international 

large  sample  with  account  results  are  methods 

competitiveness.

that  achieve  a  degree  of  representativeness 

important  for  impact  measures  and  as  support 

Agri 

benchmark 

and 

the 

Arvalis 

for  political  decisions.  Nevertheless, 

these 

international observatory both employ a constant 

methods cannot be reactive (2, 3 years late), are 

methodology, although different from each other, 

not  very  precise  on  some  charges  and  on  their 

to calculate and compare the production cost in 

explanation  of  the  crop  competitiveness.  Also, 

different  countries.  It  allows  easier  international 

in order to more precisely analyse the factors of 

comparisons 

than 

to  compare  results 

from 

competitiveness, and the ways for improvement, 

different  methods.  The  FACEPA  project  working 

it  is  necessary  to  have  more  technical  data  to 

with  FADN  data  allows  comparisons  within 

explain what lies “behind” the data. Indeed, the 

Europe but there is no such data available in other 

assessment  of  competitiveness  is  important,  but 

countries  (e.g.  there  are  few  reliable  statistics 

the  precise  explanations  of  these  competitive 

from Ukraine or Argentina). 

factors are also.

Moreover,  to  assess  the  international  farms’ 

Which 

factors 

impact 

the  most  on 

sustainability, some qualitative information is also 

competitiveness? 

Is 

it  crop  yield,  work 

important. The expertise of partners in the case of 

organisation,  costs  of  labour,  input  costs?  What 

a  network  such  as  the  agri  benchmark  may  be 

is  the  impact  on  competitiveness  of  a  specific 

important to evaluate the context of production, 

technical  change? The  methods  of  analysis  with 

the  long  term  fertility,  the  risk  of  weeds,  the 

“typical  farm”  of  the  agri  benchmark  network, 

qualification of the workforce and the margins of 

the international Arvalis Observatory or the win-

progress at short to medium term. 

tops method are more responsive to these issues 

but  the  representativeness  of  the  data  remains  a 

For 

international  crop  competitiveness 

genuine concern. 

comparison,  connection  between 

the 

two 

methods  would  certainly  permit  constructive 

On 

the  other  hand, 

the  representative’s 

interaction  at  a  European  level.  For  example, 

approaches 

to  calculate  costs  of  production 

an  estimation  of  the  representativeness  of  the 

permit the study of time series and longer trends. 

typical  farm,  based  on  FADN  data,  on  simple 

This  is  possible,  provided  that  the  sample  and 

criteria such as the farm structure, crop rotation, 

the  methodology  are  constant. The  observatories 

yields,  etc.,  could  be  useful  for  the  typical  farm 

based  on  the  “typical  farm”  do  not  have  the 

methods. On the other hand the methods based 

same  range  of  results.  For  agri  benchmark  or  the 

on the “typical farm” would probably bring more 

Arvalis  International  observatory,  the  changes 

field  data  like  technical  crop  schedule,  average 

in  calculation  methods  and  the  definition  of  the 

doses  of  input,  etc.,  that  would  probably  refine 

typical  farm  are  too  frequent  in  recent  years  to 

the  analysis  and  interpretations  of  international 

have an evolution of a long set of production costs.

comparison. 

92

Some  methods  exposed 

in 

this  report 

In  addition, 

there  are  demands 

for 

(ARMS,  win-tops,  50  sg  report,  France  Arvalis-

agricultural  production  performance  evaluation 

Unigrains  observatory)  are  interesting  to  study 

beyond  just  economic  performance.  Indeed, 

for the method and perspective but are not really 

how  can  we  better  consider  the  externalities  of 

relevant  for  international  comparison  because 

agricultural  production,  in  particular  the  social 

 
 
 
 
 
 
 
 
 
and environmental costs and benefits? The multi-

technical 

information 

such 

as 

technical 

criteria  analysis  of  production  systems  with 

procedures and input doses, or even more specific 

simple  indicators  such  as  energy  consumption, 

information  on  soil  and  climate  conditions,  are 

greenhouse  gas  emissions  or  the  efficiency  of 

needed.  Here,  the  study  methods  based  on  the 

nitrogen  for  example,  can  help  to  provide  some 

“typical farm” may be well-positioned to expand 

answers. To perform the evaluation of agronomic 

the  valuation  of  performance  by  switching  to 

and  environmental  sustainability,  some  detailed 

multi-criteria analysis.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

93

 
 
 
 
 
 
 
 
 
 
 
 
Chapter 5. Horizontal  Technical  Issues  on  Production 

Cost Methodologies

Prof. Dr. Ashok K. Mishra

assistance program programs like input subsidies. 

W. H. Alexander Professor of Agricultural 

Last, but not the least, international comparisons 

Economics

could be useful information in making investment 

Department of Agricultural Economics and 

decisions.

Agribusiness

Louisiana State University

Baton Rouge, LA 70803, USA

5.1. Introduction and Background

The International panel presented analysis from 

different countries regarding calculation of costs of 

production  as  it  relates  to:  (1)  farm  heterogeneity 

and  exchange  rates—presented  by Yves  Surry;  (2) 

inputs  and  policy  linkages—presented  by  Robert 

Koroluk;  (3)  climate  adjusted  productivity  and 

Production  cost  is  a  powerful  indicator 

economies of scale—presented by Maxwell Foster; 

and  has  implications  on  location  of  production 

(4) opportunity cost of family labour in the United 

process,  movement  of  resources,  income  of 

farming  enterprise  and  farm  households,  and 

States—presented by Ashok Mishra; (5) family farm 
diversity  and  opportunity  cost  of  family  labour  in 

poverty  status  within  the  population.  However, 

Brazil  presented  by  Wellington  Teixeira;  and  (6) 

there  is  heterogeneity  in  CoP.  This  may  be  due 

to;  scale  of  operation  (large  vs.  small),  labour 

production  costs  and  farming  systems—presented 
by Folkhard Isermeyer.5

intensity,  soil  productivity,  and  technology.  For 

the  last  two  decades  agricultural  productivity 

has slowed down and to some extent economists 

agree that perhaps productivity improvement has 

not taken place.

5.2. Production Allocation Costs, Farm 
Heterogeneity and Exchange Rates

The  first  presentation  in  this  session  was 

With an increasing emphasis on international 

done by Surry (2011). He presented a theoretical 

trade  and  globalization  of  agriculture,  there  are 

basis  of  considering  the  impact  of  inflation  and 

several  reasons  for  undertaking  international 

exchange  rates  on  CoP.  Surry  strongly  believes 

comparisons  of  commodity  CoP.  First,  involves 

that  exchange  rates  and  inflation  should  be 

intercountry  competitiveness 

studies.  There 

taken  into  consideration  when  comparing  CoP 

might be interest in knowing whether producers 

of  agricultural  commodities  across  countries. 

are competitive not only in the domestic market 

Choosing  the  appropriate  exchange  rate  and 

but  also  in  the  international  market  (such  as 

adjusting  for  inflation  are  problems  common 

importing  country).  One  would  also  want  to 

to  all  intercountry  CoP  estimates  because  all 

know  what  kind  of  competition  they  would  be 

estimates have to be denominated in a common 

facing  in  terms  of  their  product  competing  in 

currency  at  one  point  in  time  in  order  to  make 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

the  world  market.  Second,  assessment  of  how 

changes in trade policies affecting tariffs, quotas, 

and  domestic  support  policies  might  affect  the 

competitive position of producers. Third, counties 

might  want  to  compare  costs  of  production  of 

the  commodity  in  question  in  the  other  country 

and how these costs are affected by government 

5  Although  the  session  at  the  workshop  discussed  several 
topics  that  would  be  of  interest  to  researchers  and 
policymakers,  when  comparing  cost  of  production 
between  countries,  it  is  essential  to  point  out  that  due 
to  time  and  resource  constraints  some  issues  were  left 
out.  These  include  method  and  data  requirements  to 
consistently  estimate,  among  various  countries,  overhead 
costs and opportunity cost of land. 

95

 
 
 
 
 
 
 
 
 
 
 
 
s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

n
o

s
e
u
s
s
I

l
a
c
i
n
h
c
e
T
l
a
t
n
o
z
i
r
o
H

.
5

r
e
t
p
a
h
C

96

accurate comparisons. Choosing the appropriate 

a  minimum  CoP  for  a  given  technology.  Also, 

exchange rate to use in converting local currency 

one  should  be  willing  to  talk  about  hybrid  cost 

costs  to  US  dollars  can  be  a  difficult  task.  The 

functions and multi-functionality of agriculture. 

dollar  is  the  usual  currency  of  comparison.  Its 

value has varied considerably against other major 

currencies, especially in recent years. This raises 

5.3. Inputs and Policy Inter-Linkages

the question of not only the appropriate exchange 

rate  to  use,  but  also  the  appropriate  year(s)  in 

The presentation by Koroluk (2011) discussed 

which  to  select  the  exchange  rate.  There  are 

the issue of inputs and policy inter-linkages. His 

several methods to consider exchange rates. First, 

presentation was particularly relevant to Canadian 

in  countries  with  reasonably  stable  exchange 

agriculture.  Expenditures  on  inputs  can  have  a 

rates,  an  average  or  mean  exchange  rate  over  a 

direct impact on government payment programs. 

number of years adjusted by a ratio of domestic 

For  example,  several  business  risk  management 

deflators  can  be  used  to  test  whether  or  not  an 

programs  (BRM)  in  Canada  have  elements  of 

exchange  rate  for  a  given  year  is  appropriate. 

inputs  costs  in  payment  calculations.  Koroluk 

Second,  with  unstable  currencies,  one  can  use 

(2011)  discusses  the  issue  of  costs  of  adopting 

the  World  Bank  measure  of  degree  of  over-  or 

new  practices  and 

technologies  and  agri-

undervaluation  of  specific  currencies.  Economic 

environmental  and  food  safety  policies.  Other 

studies can discount domestic costs by the degree 

horizontal  policy  issues  addressed  by  Koroluk 

of  over-  or  undervaluation  amount  when  the 

(2011) that are linked to production costs include 

official  exchange  rate  is  used  to  convert  local 

farm  taxation  policies  and  policies  for  ongoing 

currency to dollars.

research and development. Koroluk (2011) points 

out  that  government  payment  to  producers, 

Similarly,  inflation  distorts  CoP  estimates 

from  several  major  business  risk  management 

in  several  ways.  It  tends  to  bias  nominal  net 

programs,  in  Canada  are  based  on  some  form 

income upward because of the time lag between 

of margin calculation. Specifically, Farm Income 

production  costs  and  receipt  of  sales  revenue, 

Stabilization  Insurance  (ASRA), AgriStability  and 

to  increase  investment  in  assets  which  hold 

AgriInvest  are  the  principle  programs  in  Canada 

value,  to  create  economic  uncertainty  which 

that  are  based  on  gross  margin  calculations. 

discourages  long-term  investment,  and  to  cause 

Generally,  national  programs  focus  on  whole 

loss  of  value  against  foreign  currencies.  There 

farm  margins,  while  some  provincial  programs 

are  several  methods  to  deal  with  inflation.  First, 

are based on the CoP of individual sectors.

an effort should be made to adjust all prices and 

values to a common point in time. Second, inter-

The  margin-based  programs 

typically 

seasonal  adjustments  (e.g.  across  years)  can  be 

include expenditures on direct inputs as eligible 

accomplished  by  uniformly  adjusting  all  prices 

expenses,  and  don’t  require  the  allocation  of 

and  values  in  the  CoP  to  the  desired  point  in 

fixed costs and joint costs.

time  using  the  country’s  annual  and/or  monthly 

price indexes. Third, if the country’s price indexes 

Canada  is  a  price  taker  in  most  major 

are unavailable or unreliable, linkage to a third-

input  markets  (seed,  fuels,  nitrogen,  phosphate, 

country’s  price  index  or  currency  exchange  rate 

pesticides). By design, BRM programs in Canada 

can be used for inter-seasonal comparisons.

provide  support  to  farmers  during  periods  of 

short-term  input  cost  increases,  as  payments 

Finally,  Surry  (2011)  discussed,  at  the  farm 

are  based  on  average  margin  calculated  over  a 

level,  the  importance  of  cost  allocation  and 

predetermined reference period. Therefore, input 

farm  heterogeneity.  He  recommended  that  the 

costs  have  increased  as  a  result  of  increased 

European  Commission  should  come  up  with 

program  payments  to  farmers.  Further,  several 

 
 
 
 
 
 
 
 
business  risk  management  programs  in  Canada 

5.4. Climate-adjusted Productivity and 

have  elements  of 

input  costs 

in  payment 

Economies of Scale

calculations.  Structural  adjustment, 

though, 

removes  the  impacts  of  input  cost  changes  via 

From the Australian perspective Foster (2011) 

increases in farm size (consolidation) or changing 

presented issue concerns regarding climate change 

product mix.

and agricultural productivity. Foster presented the 

value  of  Australian  agriculture  by  climate  zone 

Koroluk  (2011)  then  discusses  the  costs 

and  found  that  tropical  agriculture  has  the  least 

of  adopting  new 

technologies 

that  affect 

production.  However,  farmers  in  tropical  climate 

environmental 

and 

food 

safety  policies. 

are likely to specialize in beef cattle, sheep, dairy 

Specifically,  nutrient  management  with 

the 

cattle, fruits and nuts. Foster (2011) points out that 

objective  of  eliminating  excessive  nutrients, 

58%  of  Australian  agricultural  production,  worth 

source  water  protection  and  improvements  to 

about  $40  billion  US  dollars,  is  exported.  The 

water  quality,  including:  1)  efficient  nutrient 

major  agricultural  commodities  that  are  exported 

use;  2)  input  use  efficiency  and  environmental 

(50%  or  more)  are:  cotton,  sugar,  sheep  meat, 

loading; 3) GPS and precision farming; 4) tracking 

wool, wheat, and beef.

and tracing systems; 5) minor use pesticides and 

grower use pesticides; and 6) energy efficiency.

The  data  used  in  the  analysis  of  income, 

balance  sheet,  and  CoP  at  the  national  level  is 

Finally,  Koroluk 

(2011)  presents  other 

collected  through  surveys  that  are  administered 

horizontal  policy  issues.  For  example,  tax  system 

by  the  Australian  Bureau  of  Agricultural  and 

considerations: (1) accelerated write-off of manure 

Resource  Economics  and  Sciences.  This  is  a 

storage facilities; (2) tax exemptions for farm use of 

detailed  dataset  that  can  be  used  to  calculate 

fuel; (3) ongoing public research and development; 

many  dimensions  of  agricultural  production, 

(4)  enhancing  productivity  of  land,  labour  and 

including  labour  and  other  input  costs.  These 

capital; and (5) use of no-till resulted in substantial 

data are used to construct farm financial balance 

reductions  in  production  costs  of  Prairie  grains 

sheet.  Table  5.1  shows  the  exact  calculation  of 

and  oilseeds.  These  horizontal  policy  issues  are 

farm income and balance sheet.

linked  to  production  costs  and  ultimately  affect 

government  program  payments  and  business  risk 

Foster  (2011)  then  delved  into  agricultural 

management programs in Canada.

productivity  and  reports  that  climate  adjusted 

Table 5.1: Calcuation of farm income and balance sheet, Australia

less

less

less

plus

divided by

Source:	Foster	(2011).

Farm cash receipts

Farm cash costs

Farm cash income

Imputed value of owner/family labour

Depreciation

Build up in trading stocks

Farm business profit

Total farm capital

Rate of return on capital at full equity

Excluding capital appreciation

Including capital appreciation

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

97

 
 
 
 
 
 
 
 
 
 
 
 
s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

n
o

s
e
u
s
s
I

l
a
c
i
n
h
c
e
T
l
a
t
n
o
z
i
r
o
H

.
5

r
e
t
p
a
h
C

Figure 5.1: Climate-adjusted productivity, Australia

Source:	Foster	(2011).

total factor productivity is lower compared to the 

conducted  about  every  4-8  years 

for  each 

regular  total  factor  productivity  for  specialized 

commodity and updated each year with estimates 

croppers  and  mixed  crop-livestock 

farms 

of annual price, acreage, and production changes. 

(see  Figure  5.1).  Technical  change  (TC)  –  the 

This essentially fixes the technology that underlies 

availability of new technologies and knowledge—

the accounts to that used in the survey year.

was  about  1.5  per  cent  for  1978  to  2007-08, 

but  slowing  more  recently.  Technical  efficiency 

Commodity-specific  surveys  as  part  of  the 

change  (TEC)  –  further  adoption  of  existing 

annual  Agricultural  Resource  Management 

technologies was about 0.3 per cent over whole 

Survey  (ARMS)  have  been  used  to  collect  the 

period,  meaning  the  gap  between  leading  edge 

data  since  1996.  Data  in  prior  years  were 

farms  and  average  farms  is  increasing.  Finally, 

collected  as  part  of  the  annual  Farm  Costs 

Foster  reported  that  scale  and  mix  efficiency 

and  Returns  Survey  (FCRS).  ARMS  survey  data 

(SME)  changes  were  about  0.3  per  cent  over 

collected  every  4-8  years  for  each  commodity 

the  whole  period.  Finally,  Foster  reported  that 

on  a  rotating  basis.  Estimates  are  updated 

the  opportunity  cost  of  unpaid  family  labour  in 

between  surveys  according  to  price  changes 

Australia is calculated via a federal off-farm wage 

Estimates  are  historical,  or  “after  the  fact”,  and 

rate that is dictated by the government.

not  projections.  Estimates 

include  all  costs 

contributed by landlords and farm operators, and 

contractors  and  contractees.  Estimates  are  made 

5.5. Opportunity Cost of Family Labour 

at  the  farm-level  in  order  to  summarize  data  for 

in the United States

research  and  industry  outlook.  The  theoretical 

basis and accounting methods used for the most 

98

The  United  States  has  clearly  presented  and 

recent estimates of commodity costs and returns 

advocated  the  correct  methodology  to  calculate 

conform  to  standards  recommended  by  the 

CoP  of  commodities,  especially  for  commodities 

American  Agricultural  Economics  Association 

that  are  eligible  for  government  payments.  The 

(AAEA)  Task  Force  on  Commodity  Costs  and 

annual  estimates  are  based  on  producer  surveys 

Returns.  In  addition,  accounts  published  in  this 

 
 
 
 
 
 
 
 
Table 5.2: Approaches used to estimate commodity costs in the US

Direct costing

Value input quantities

Indirect costing

Allocating farm expenses

Crop commodities

Purchased seed

Home-grown feed

Fuel, lube, and electric

General farm overhead

Fertilizer

Chemicals

Manure

Unpaid labor

Repairs

Capital recovery

Taxes and insurance

Custom operation

Land

Hired labor

Operating interest

Purchased water

Ginning

Purchased feed

Feeder animals

Vet medicine

Livestock commodities

Home-grown feed

Capital recovery

General farm overhead

Grazing fee

Unpaid labor

Taxes and insurance

Bedding and litter

Land

Operating interest

Marketing

Custom service

Fuel, lube, and electric

Repairs

Source:	Economic	Research	Service,	U.S.	Department	of	Agriculture	(2011).

format  are  presented  using  ERS  Farm  Resource 

The presentation by Mishra (2011) focused on 

Regions,  which  provide  a  consistent  regional 

the calculation of opportunity cost of family labour. 

delineation  across 

the  commodities.  Table 

He pointed out that off-farm labour supply literature 

5.2  presents  the  approaches  used  to  estimate 

has  improved  modelling  of  human  time  use  and 

commodity costs in the US.

valuation  of  farm  household  members. Total  time 

is defined as the sum of farm, off-farm, and leisure 

Labour  is  one  of  the  important  inputs  in 

time. Husband’s and wife’s time is treated as being 

agricultural  production.  How  it  is  measured 

heterogeneous because they possess different skill. 

and  valued  is  important  for  establishing  the 

Therefore, opportunity cost (OC) of time allocated 

marginal  cost  (MC)  of  agricultural  commodities; 

to farm work is the maximum of the value of a unit 

specifically, labour’s share of the CoP.

time  allocated  to  off-farm  work  or  leisure.  For  off-

Until 1999—value to unpaid farm labour was 

wage rate (OFWR) and OFWR is also the price of 

farm  to  occur  the  OC  must  be  equal  to  off-farm 

estimated  as  hours  worked  on  farm  multiplied 

unpaid family labour.

by  average  wage  rate  for  hired  farm  labour 

(Morehart,  Shapouri,  and  Dismukes,  1992). 

Therefore,  the  opportunity  cost  of  unpaid 

Another method “current family living expenses” 

labour  (farmer  and  spouse,  in  the  case  of  US) 

has  also  been  used  by  economists  in  California 

is  the  value  unpaid  farm  labour  at  the  off-farm 

(Klonsky,  1992;  McGrann,  1991).  However,  this 

opportunity  wage  of  farm  workers—if  less  than 

type  of  method  to  calculate  the  value  of  unpaid 

16 years, use minimum wage, otherwise calculate 

family  labor  is  only  possible  in  small  surveys 

the  off-farm  wage,  which  is  calculated  via  the 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

and special cases. In the agricultural productivity 

following labour supply model (see Ahearn El-Osta, 

literature—unpaid  labour  is  valued  at  the  wage 

1991).  Specifically,  Off-farm  wage  =  f  (Age,  Age, 

99

and  salary  (W&S)  for  “similar  skilled”  W&S 

Education,  Region).  Using  this  approach  operators 

workers  in  US  agriculture  (controls  for  gender, 

working off-farm, using 2010 Agricultural Resource 

age, education, and occupation).

Management  Survey  (ARMS)  calculated  the  off-

 
 
 
 
 
 
 
 
 
 
 
 
s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

n
o

s
e
u
s
s
I

l
a
c
i
n
h
c
e
T
l
a
t
n
o
z
i
r
o
H

.
5

r
e
t
p
a
h
C

Table 5.3: Opportunity cost of Unpaid and Hired Labour for Dairy Farms, by Farm Size, in US 

Item

Fewer than 50 cows

50-99 cows

100-199 cows

200 or more cows

Dollars per hundred weight

Unpaid labour

Hired labour

Source:	Mishra	(2011).

13.42

0.79

7.45

1.75

4.84

2.25

1.15

3.21

Table 5.4: Opportunity cost of Unpaid and Hired Labour for Dairy Farms, by region, in US

Item

Northeast region of US

Upper Midwest region of the US West region of the US

Dollars per hundred weight

Unpaid labour

Hired labour

Source:	Mishra	(2011).

10.75

1.60

6.76

1.61

1.64

3.28

farm and farm wage of farm operator as; (1) off-farm 

total  farm  household  income.  More  than  4  million 

wage = $21.15 / hour and (2) farm wage = $9.15 

family farms are involved in agriculture. About 12.3 

/  hour.  However,  the  opportunity  cost  for  unpaid 

million people are linked to family farms (74.4% of 

labour in specialized farming like corn production 

employed persons in agriculture), with an average of 

may  be  much  higher.  For  example,  unpaid  labour 

2.6 people employed in agriculture. The non-family 

cost  for  corn  producer  in  1996  (Corn  survey  of 

farming employed 4.2 million people, equivalent to 

ARMS)  was  $29  per  acre  and  increased  to  about 

25.6% of the agricultural workforce. Family farm has 

$39  per  acres  in  2010.  Similarly,  Mishra  reported 

a  social  importance  and  should  be  maintained  as 

that  opportunity  cost  of  unpaid  labour  varied  by 

such. Farms are very diverse in their output (such as 

enterprise  and  region  of  production.  For  example, 

cassava, beans, corn, coffee, rice, and poultry). More 

opportunity cost for unpaid workers on dairy farms 

than 16% of soybeans are produced by family farms. 

ranges  from  $13.42/hundred  weight  milk  sold  for 

Companhia  Nacional  de Abastecimento  (Conab)  is 

small  farms  (50  or  less  cows)  to  $1.15/hundred 

responsible for collecting data related to farm costs 

weight  milk  (see  Table  5.3)  sold  for  large  farms 

and  returns,  including  variable  CoP,  average  salary 

(200  cow  or  more  cows).  On  the  other  hand,  we 

of temporary employee, and opportunity cost of land 

also  observe  regional  variation  in  the  opportunity 

(Teixeira,  2011).  In  Brazil,  there  has  been  a  strong 

cost for unpaid workers on dairy farms (Table 5.4). 

trend  of  migration  of  young  people  from  farms  to 

For  example,  opportunity  cost  for  unpaid  workers 

cities  in  search  of  employment  in  industries  where 

on dairy farms ranges from $10.75/hundred weight 

they can find jobs with different working conditions. 

milk sold for dairy farm workers in the Northeast of 

In this case, the opportunity cost of family labour is 

US to $1.64/hundred weight milk sold for worker in 

fundamental  and  must  be  measured  to  reflect  the 

the Western US.

reality of the opportunities that farmer’s face, in the 

region  where  farms  are  located.  In  such  cases  off-

farm  wages  must  be  taken  into  account  to  reflect 

5.6. Family Farm Diversity and 

opportunity cost of family labour.

Opportunity Cost of Family Labour 
in Brazil

100

In  the  case  of  Brazil,  where  farming  sector 

Systems

plays  an  important  role  in  the  livelihoods  of  many 

people,  farmers  and  their  families  rely  on  income 

Isermeyer (2011) described that comparison 

from farming. Farm income is a major component in 

of  farm  income  is  not  a  good  measure  of 

5.7. Production Costs and Farming 

 
 
 
 
 
 
 
 
international  competitiveness.  One  needs 

to 

The  European  Commission  uses  the  FADN 

compare  total  CoP  unit  of  output.  If  one  is  just 

(Farm  Accountancy  Data  Network)  database  to 

interested  in  comparing  CoP  across  regions 

calculate  opportunity  costs  of  unpaid  labour  and 

and  countries  then  one  could  simply  compare 

land.  FADN  contains  information  on  output  and 

product  prices.  However,  product  price  may 

subsidies per enterprise; however, as regards costs, it 

deviate in the short-run and they may be distorted 

provides only information referring to the farm as a 

by policy instruments. Moreover, for the analysis 

whole. Family labour cost is calculated on the basis 

of  differences  of  CoP  it  is  important  to  have  a 

of the wages the farm owner would have to pay if he 

good  breakdown  of  cost  components  including 

were to hire employees to do the work carried out by 

opportunity  costs. 

Isermeyer 

(2011)  pointed 

family members. This cost is estimated as the average 

out  that  there  is  an  urgent  need  to  analyse  and 

regional  wage  per  hour  obtained  in  the  FADN 

prescribe  a  consistent  methodology 

for 

the 

database multiplied by the number of hours worked 

estimation of opportunity cost of land and labour. 

by  family  workers  on  the  farm.  However,  it  should 

Opportunity  cost  of  unpaid  labour  could  be  the 

be  mentioned  that  the  wage  recorded  in  FADN 

payment that the farmer has to make to a person 

corresponds to the gross wage, plus the social security 

who  replaces  the  farmer  when  the  farmer  is  on 

costs. Moreover, it is commonly acknowledged that 

vacation.  Wage  rate  for  skilled  worker  is  also 

the hours worked by family members are sometimes 

used  as  a  proxy  for  opportunity  cost  of  unpaid 

overestimated. Thus,  the  method  uses  a  maximum 

family labour.

of 3000  hours  per Annual Work Unit  (this  is  equal 

to 8.2 hours a day, 365 days a year and corresponds 

It  should  be  pointed  out  that  small  scale 

more or less to the time farmers can spend milking 

farms  are  often  competitive  although  high  CoP 

cows on a farm). 

are  calculated  for  them. This  indicates  that  real 

opportunity costs for land or labour are probably 

lower than those assumed in the calculations.

5.8. Conclusions and Recommendations 

Agri  Benchmark  is  an  organization6  based 
in  Germany  that  coordinates  data  collection, 

analysing  cost  and 

returns  and  presenting 

The session on International Panel Discussion-
Horizontal  Issues,  prove  to  be  an  excellent 
platform to discuss various issues related CoP and 

results  which  is  homogenously  applied  for  cash 

methodologies  on  various  topics  and  countries. 

crops, beef, sheep, pork and poultry farms in all 

With increased globalization warrants comparison 

participating countries. Agri Benchmark defines a 
“typical farm7” and then contacts farm operators 
who  operate  a  “typical  farm”  and  collects  full 

of costs of production of agricultural commodities. 

Economists  and  agricultural  economists  in  this 

session  presented  their  work  on  various  topics 

sets  of  economic  and  physical  farm  data  on 

related  to  CoP  and  its  consistency  in  order  to 

production  systems,  quantities,  and  prices  of 

compare  them  across  countries.  For  example, 

inputs.  Most  of  the  agri  benchmark  farm-level 

Surry concluded that inflation and exchange rates 

calculations  are  done  on  a  per  unit  basis,  for 

tend  to  bias  the  true  CoP  and  hence  economists 

example per kg beef produced, per ton of wheat 

and  policymakers  need  to  be  mindful  when 

produced. 

comparing CoP of agricultural commodities across 

nations.  In  the  case  of  Canada  horizontal  policy 

issues that are linked to production costs include, 

farm  taxation  policies  and  policies  for  ongoing 

6  Visit  http://www.agribenchmark.org/  for  more  information 

research  and  development.  Generally,  national 

on this organization. 

7  Typical  farm  has  several  advantages  over  individual  farm 
data and average farm data surveys (FADN). These include 
consistency of data sets, quantity structure, data availability, 
timeliness, data confidentiality and cost of data collection.

programs in Canada focus on whole farm margins, 

while some provincial programs are based on the 

CoP of individual sectors.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

101

 
 
 
 
 
 
 
 
 
 
 
 
s
e
i
g
o
l
o
d
o
h
t
e
M

t
s
o
C
n
o
i
t
c
u
d
o
r
P

n
o

s
e
u
s
s
I

l
a
c
i
n
h
c
e
T
l
a
t
n
o
z
i
r
o
H

.
5

r
e
t
p
a
h
C

The  Australian  Bureau  of  Agricultural  and 

of  production  that  is  easily  transportable  and 

Resource Economics and Sciences collects farm-

abundantly  clear  as  to  how  to  calculate  costs 

level data to estimate and analyse income, balance 

across  farm  type,  commodities,  farming  region, 

sheet, and CoP at the national level. Agricultural 

and  country;  (3)  unit  of  data  collection—farm-

productivity  differs  with  climate.  Specifically, 

level, typical farm, regional data, aggregate data, 

climate adjusted total factor productivity is lower 

regional  data;  and  (4)  unit  of  analysis—such  as 

compared to the regular total factor productivity 

per  arable  acres,  per  unit  of  output.  In  all  these 

for specialized croppers and mixed crop-livestock 

presentations  a  common  thread  was  farm-level 

farms. American agriculture uses farm-level data 

data  being  used  for  analysis.  If  that  is  the  case, 

that is collected every year and is used for various 

then  we  can  have  a  common  methodology  to 

purposes, including calculation of CoP of various 

calculate  CoP  of  agricultural  commodities  and 

commodities.  The  USDA  follows  a  set  formula, 

compare  them  across  countries.  Here,  the  main 

as  recommended  by  the  American  Agricultural 

challenges  in  accomplishing  these  ideas  maybe 

Economics  Association  Taskforce  on  Cost  of 

in  terms  of  resource  constraints,  specifically,  the 

Production,  for  estimating  various  components 

willingness  to  do  and  resource  allocation—both 

of  the  income  statement,  balance  sheet,  cost  of 

budgetary and personnel.

production, including opportunity cost of unpaid 

family  labour.  The  opportunity  cost  of  unpaid 

However, 

presenters 

and  workshop 

labour  (farmer  and  spouse,  in  the  case  of  US) 

participants  argued  that  collection  of  farm-level 

is  the  value  unpaid  farm  labour  at  the  off-farm 

data  was  too  expensive,  time  consuming,  and 

opportunity  wage  of  farm  workers—if  less  than 

many  countries  do  not  have  the  infrastructure 

16 years, use minimum wage.

and  the  budget  to  collect  such  data  on  a  regular 

basis. The session concluded with discussion of a 

Brazil 

is  becoming  a  major 

force 

in 

“typical  farm”.  Specifically,  Isermeyer  introduced 

production  of  agricultural  commodities.  The 

this  concept.  Agri  benchmark  is  an  organization 

Brazilian farming sector plays an important role in 

based in Germany that coordinates data collection 

the livelihoods of many people, farmers and their 

form  “typical  farms”,  analysing  cost  and  returns 

families  rely  on  income  from  farming. A  central 

and  presenting  results  which  is  homogenously 

agency  is  responsible  for  collecting  data  related 

applied  for  cash  crops,  beef,  sheep,  pork  and 

to  farm  costs  and  returns,  including  variable 

poultry  farms  in  all  participating  countries.  Most 

CoP, average salary of temporary employee, and 

of the agri benchmark farm-level calculations are 

opportunity  cost  of  land.  However,  Brazilians 

done on a per unit basis, for example per kg beef 

recognize  the  fact  that  off-farm  employment  is 

produced, per ton of wheat produced.

becoming  attractive  to  farm  families  and  many 

are  working  off  the  farm  and  surmise  that  off-

There are several recommendations that can 

farm wages must be taken into account to reflect 

be gleaned from these presentations. First, farm-

opportunity cost of family labour.

level  data  is  the  best  option  to  compare  costs 

of  production  across  countries.  Second,  these 

While 

the  workshop  provided  a  good 

costs  should  be  adjusted  frequently  to  adjust 

understanding  of  various  aspects  of  costs 

for  government  subsidies  and  other  structural 

of  production, 

it 

is  abundantly  clear 

that 

changes  in  agriculture.  Secondly,  any  policy 

international comparisons of CoP needs to have a 

that  affects  use  of  inputs  should  also  be  noted 

102

good foundation,. where we have the following: 

and  adjusted  for  in  the  final  CoP.  Third,  CoP 

(1)  a  common  definition  of  a  farm;  (2)  common 

comparison should be adjusted for inflation and 

methodologies  to  calculate  various  aspects  of 

exchange  rates  when  comparing  costs  across 

CoP,  income  statement,  and  balance  sheet. 

countries.  Fourth,  CoP  should  be  calculated 

These  items  can  then  be  used  to  calculate  costs 

on  a  per  unit  basis,  for  example  per  kg  beef 

 
 
 
 
 
 
 
 
produced,  per  ton  of  wheat  produced.  This 
option  of  collecting  data  and  taking  a  “typical 
farm”  into  consideration  can  prove  to  be  useful 
in developing costs of production for agricultural 

opportunity  cost  of  unpaid  labor  is  the  implicit 

compensation  for  unpaid  farm  labour  is  based 

on  the  opportunity  cost  of  off-farm  work,  or  the 

return  available  in  the  next  best  alternative  use 

commodities  and  comparing  these  costs  across 

of  this  labour  time  and  effort.  All  adult  unpaid 

countries. It is an alternative that is cheaper and 

farm labour (and salaried labour with ownership 

can  be  pursued  in  the  future  if  counties  and 

claims) should be valued at its opportunity cost, 

organization choose to do so. Finally, one has to 

defined  to  be  the  maximum  value  for  non-farm 

be  cognizant  about  several  other  factors  when 

uses.  However,  this  method  would  require  a 

comparing  costs  of  production  across  various 

survey data, and a detailed data on demographics, 

countries. These  include:  (1)  farm  heterogeneity 

local  labour  markets,  and  other  socio-economic 

(size); (2) diversified farm enterprises; (3) climate-

variables. Consequently, although this methods is 

adjusted  productivity;  and  (4)  data  requirements 

economically sound, it would require additional 

to derive an opportunity cost of unpaid labour.

resources in terms of time and money. In light of 

this,  one  can  use  alternative  methods  that  may 

Finally,  one  of 

the  most  controversial 

be  cheaper,  readily  available,  and  consistent 

issues  facing  economists  is  valuation  of  unpaid 

across  countries.  These  include:  (1)  hired  farm 

farm  labour.  Though  unpaid  farm  labour  does 

worker  wage  rate;  (2)  skilled  worker  wage  rate; 

not  generally  receive  a  wage,  it  does  have  an 

(3)  replacement  worker  wage  rate;  and  (4) 

economic  cost.  The  best  method  to  obtain 

governments can set off-farm wage rates.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

103

 
 
 
 
 
 
 
 
 
 
 
 
Chapter 6. Conclusions and Recommendations 

Stephen Langrell, Pavel Ciaian and

However,  their  main  disadvantage  is  that  they  are 

Sergio Gomez y Paloma

based on small and non-representative samples and 

Institute for Prospective Technological Studies 

cover only a restricted set of commodities.

(IPTS) 

Joint Research Centre (JRC)

European Commission

6.1. Introduction

Most countries conduct their own collection 

of  data  on  production  costs  as  part  of  national 

agricultural  data  gathering  exercise.  However, 

methodological  approaches  vary  strongly 

in 

terms  of  collection  approaches,  type  of  data 

collected,  disaggregation  of  cost  items,  data 

Previous chapters provided a comprehensive 

processing  and  cost  calculation  methodology, 

comparison of methodologies and approaches for 

hence  it  is  problematic  to  use  them  for  inter-

CoP data collection, processing and comparative 

country comparison.

analyses  as  applied  across  countries  and  at 

international level. Gathering production costs is 

Key 

issues  which  pose  problems  of 

a complex process encompassing issues spanning 

from  data  collection  design  to  selection  and 

development  of  methodologies  for  processing 

and  analysis.  The  development  and  application 

of  a  standard  methodology  across  countries 

for  this  purpose  is  far  from  being  solved  and 

needs  further  methodological  and  conceptual 

comparability of cost data across-countries using 
available statistical sources include:

•	 The variation in data collection method.

•	 The variation in the type of data available at 
country level in terms of quality and types of 

work.  In  this  chapter  we  attempt  to  provide  key 

cost-related information collected.

summarising issues related to CoP methodologies 

and outline potential recommendations on global 

comparison of production costs. The analyses are 

based  on  previous  chapters’  contents  reflecting 

the workshop presentations and discussions. 

•	 Differences in definition of cost items

•	 Differences in cost calculation methodologies. 

•	 Variation in sampling strategy(ies)

6.2. Key Methodological Challenges for 

global comparison of production costs

•	 Identification of an appropriate methodology 
for allocation of common inputs (e.g. family 

labour,  depreciation,  overheads)  to  specific 

Conducting  robust  comparative  analysis  of 

commodities.

production costs across agricultural commodities and 

across  countries  requires  availability  of  data  which 

•	 Identification 

of 

an 

appropriate 

apply  similar  data  collection  approaches  and  cost 

methodology for accounting for opportunity 

calculation  methodologies.  Few  statistical  sources 

costs  of  own  inputs  (farm  owned  labour, 

satisfy these requirements. Notable exceptions are the 

land and capital).

agri benchmark and the IFCN. Both databases cover 

all major world trading regions and apply a common 

methodology for costs identification and calculation. 

•	 The allocation of cost items to joint products 
(e.g. primary products versus by-products).

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

105

 
 
 
 
 
 
 
 
 
 
 
 
s
n
o
i
t
a
d
n
e
m
m
o
c
e
R
d
n
a

s
n
o
i
s
u
l
c
n
o
C

.
6

r
e
t
p
a
h
C

•	 Variation of costs by structural effects (farms 

(and  will)  react  to  new  challenges,  and  how 

size, technology, climatic zone, etc)

different  farms  are  affected  by  (and  will  adjust 

•	 Data update and timing of their availability

to)  agricultural  policy  measures 

(Chapter 

2).  Garnier  (Chapter  4)  and  Mishra  (Chapter 

5)  pointed 

to  several  challenges 

that  need 

•	 Other  issues:  exchange  rate,  weather  effect 
(unit  production  costs  vary  strongly  year  to 

to  be  taken  into  account  when  conducting 

international  comparison  of  CoP  such  as 

year,  and  region  by  region,  due  to  climate 

selection  of  appropriate  exchange  rates  and  the 

variation); quality differences in input factors; 

issue of inflation, suitability of approach in terms 

valuation  of  input  factors  for  which  there 

of  sample  representativeness  versus  the  choice 

are  no  market  transaction  (e.g.  opportunity 

of  “typical  farm”  approach,  farms’  structural 

costs of own labour, land and capital); crop 

evolution, 

farm  heterogeneity,  variability  of 

rotation  effects  on  production  costs;  inputs 

economic  context,  comparability  of  methods  in 

and policy interlinkages

6.3. Recommendations

terms  of  cost  item  definition,  period  of  analysis, 

inter-annual  variation 

in  climatic  condition, 

inputs  and  policy  inter-linkages,  and  accounting 

for opportunity cost of unpaid labour, etc.

The 

liberalisation  of  agricultural  market 

Indeed,  in  different  regions  of  the  world, 

policies,  and 

the  ever 

increasing  global 

very different concepts for the collection of farm-

dimension  of  agribusiness,  present  clear  and 

based  CoP  analysis  have  been  developed  and 

complex challenges to policy makers with respect 

implemented  over  the  last  number  of  decades, 

to  ensuring  the  competitiveness  and  viability 

categorized  by  different  criteria,  including  (but 

of  their  various  production  operations  across 

not  exclusively  limited  to):  regional  coverage 

various  policy  arenas,  in  particular  with  respect 

(world-wide; 

EU-wide;  national; 

regional), 

to addressing future competiveness of production 

representativeness 

(stratified 

sample; 

farmer 

systems,  farms,  and/or  regions.  Here,  the  broad 

groups  with  voluntary  participation),  unit  of 

consensus  view  of  expert  participants  at  this 

analysis (single farm data; farm averages; typical 

workshop  re-iterated  fundamental  differences  in 

farms), depth of the data (whole farm data; farm 

concepts  and  approaches  to  CoP  calculations 

enterprise  data),  and  data  collection  method 

from across the world (typically regionally tailored 

(delivery  of  book  keeping  data; 

interviews; 

to  meet  particular  specifics  for  a  wide  range  of 

panel  discussions).  Further, 

intra-commodity 

agricultural  commodities)  towards  international 

variability  with  regard  to  production  systems, 

comparisons of CoP as both incompatible and of 

farm  size,  regional  differences  and  management 

considerable  difficulty  to  integrate  and  interpret 

programs,  as  well  as  different  business  models 

in  a  meaningful  and 

informative  way 

(see 

(e.g.  contract  vs.  non-contract  production) 

Chapter  2,  for  further  considerations).  Isermeyer 

complicate comparisons, and further raise issues 

highlighted 

several 

factors 

that  make  an 

with  regards  sampling  size(s),  technique,  timing 

internationally  harmonized  comparison  of  CoP 

and accounting procedures. 

useful  and  relevant  as  it  may  satisfy  a  number 

106

of  fundamental  questions  such  as  to  better 

Of  the  different  concepts  and  approaches 

understand future competitiveness of production 

currently  deployed  regionally,  all  are  effectively 

systems, 

including  how  certain  agricultural 

based either on large representative samples (e.g. 

branches can compete with other world regions, 

how  competitiveness  and 

sustainability  of 

production  systems  can  be 

improved,  how 

FADN, ARMS) or the “typical farm”approach (e.g. 
agri  benchmark,  IFCN,  Conab).  According  to 
Garnier (Chapter 4) the difference in objectives of 

different  farm  types  in  different  regions  can 

national and global data collection systems, and 

 
 
 
 
differences  in  their  use,  largely  determines  the 

as  Asia,  South  America,  Russia  and  Africa. 

methodology  in  general,  and  sampling  strategy, 

Important weaknesses of the agri benchmark and 

in particular. If the goal is to evaluate an average 

the  IFCN  is  low  representativeness  of  collected 

production  cost  per  country,  per  region,  or  for 

data, inability to capture adequately variation of 

each major farming systems, representativeness of 

farming systems within regions, and coverage of 

the studied sample will be critical (e.g. ARMS, 50-

limited  number  of  commodities.  Improvements 

sg  report,  France  Arvalis-Unigrains  Observatory, 

need  to  go  in  this  direction,  by  building  cost 

FADN).  However,  if  the  goal  is  to  evaluate  the 

values  from  better  designed  samples  and  taking 

production  costs  of  performing  farms  or  to 

more commodities on board. A second limitation 

characterise  the  economic  impact  of  innovative 

of  this  approach  is  that  involvement  of  experts/

practices (e.g. minimum tillage, low input system, 

advisors 

introduces  certain  subjectivity  and 

organic  farming,  etc.),  then  representativeness  is 

personal perceptions in the whole data collection 

still important but is secondary compared to the 

process.  The  approach  relays  predominantly 

needs  of  having  detailed  and  specific  economic 

on  expert  judgments  and/or  the  opinion  of 

and technical data on technology, farm practices, 

farmer  panels  deciding  all  aspects  related  to 

and timing of activities through the season, etc.

CoP  analysis,  from  typical  farm  selection  to 

assigning  CoP  values  to  each  cost  category  and 

Conducting  robust  comparative  analysis  of 

activity.  Finally,  this  approach  is  not  well  suited 

production costs across agricultural commodities, 

to capture farm structural changes (adjustment in 

and across countries, requires availability of data 

technology, farm size, etc). Any structural change 

which  apply  similar  data  collection  approaches 

is  accounted  for  through  exogenous  adjustment 

and  cost  calculation  methodologies.  Few 

of  typical  farm  in  regular  intervals.  Although  it 

statistical  sources  satisfy  these  requirements. 

is  desirable  to  adjust  the  typical  farm  approach 

The  agri  benchmark  and  the  IFCN  based  on  the 

to  reflect  actual  farm  structure,  it  may  pose  a 

“typical farm” approach are the only data sources 

problem of comparability of CoP data over time 

currently  available  for  international  comparison 

due to the fact that characteristics of typical farms 

of  production  costs.  They  apply  a  common 

change over time.

methodology 

for  costs 

identification  and 

calculation  across  all  covered  countries.  They 

Most countries conduct their own collection 

can  be  applied  without  further  methodological 

of  data  on  production  costs  as  part  of  national 

adjustments to compare production costs among 

agricultural  data  gathering  exercises.  However, 

available commodities and regions.

methodological  approaches  vary  strongly 

in 

terms  of  collection  approaches,  type  of  data 

These  approaches  are  based  on  networks 

collected,  disaggregation  of  cost  items,  data 

of  experts,  advisors,  panel  of  farmers,  and 

processing,  and  cost  calculation  methodology, 

statisticians located in different parts of the world 

hence  it  is  problematic  to  use  them  for  inter-

who  collect  and  process  data  locally. The  main 

country  comparison(s).  The  application  of 

advantage is that they can be flexibly designated 

national  sources  for  international  comparison 

to  focus  specifically  on  topics  of  interest  to  this 

would  require  further  data  processing  and/or 

aspect  of  agricultural  production,  including  the 

harmonization  of  methodologies.  This  could  be 

development  and  implementation  of  standard 

potentially  achieved  (following  the  analysis  of 

methodology.  The  typical  farm  method  used 

Chapters 2 to 5) at three levels:

by  this  approaches  is  a  relatively  inexpensive 

methodology 

from  an 

implementation  point 

of  view,  with  an  advantage  of  application 

•	 Minimalistic  harmonization:  Exploit 

the 

existing  databases  available  and  harmonise 

on  a  regular  basis  on  a  wider  regional  scale, 

methodologies  without 

altering 

the 

particularly  in  less  resourceful  countries  such 

current  system  of  data  collection  and  type 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

107

 
 
 
 
 
 
 
 
 
 
 
 
s
n
o
i
t
a
d
n
e
m
m
o
c
e
R
d
n
a

s
n
o
i
s
u
l
c
n
o
C

.
6

r
e
t
p
a
h
C

108

of  data  collected.  This  approach  would 

countries. 

In  principle 

this  would 

lead 

lead  to  harmonization  of  certain  aspects 

to  a  redesign  of  whole  national  systems 

of  methodologies  such  as  structuring  of 

starting from harmonization of primary data 

cost  categories,  harmonization  of  certain 

collection  method  (e.g.  sampling  strategy) 

cost  items  not  connected  directly  to  data 

to  harmonization  of  costs  calculation 

collection  process  and  which  are 

less 

methodologies.  This  approach  would  lead 

demanding in terms of resource requirements 

to  full  comparability  of  cost  data  across 

(e.g. accounting for opportunity costs of own 

countries.  However,  main  constrain  might 

inputs,  etc.).  This  approach  wiould  lead  to 

be  low  interest  of  countries  to  join  these 

limited  harmonization  of  methodologies 

schemes as the existing systems would need 

across-countries  and  may  allow  conducting 

to  be  replaced  by  new  harmonised  system. 

comparison  only  for  selected  type  of  cost 

The  exiting  national  systems  are  developed 

categories  (e.g.  variable  costs).  Note  that 

to  address  multiple  policy  objectives  and 

with this approach the possibility to compare 

are not solely design to deliver only the CoP 

CoP data across countries will still remain an 

data. Redesigning the national system for the 

issue of concern. A large part of heterogeneity 

purpose to improve the CoP data collection 

in  cost  values  will  still  be  driven  by 

may  thus  conflict  with  the  delivery  of  data 

differences  in  underlining  methodologies 

for  addressing  other  policy  objectives  at 

(e.g. by differences in sampling strategy, cost 

national  level  hence  making  this  option 

allocation  methodologies,  the  level  detail 

highly unrealistic.

CoP  calculation)  which  is  specific  to  each 

country’s data collection system. 

The 

successfulness 

of 

implementing 

•	 Partial harmonization: This approach proposes 
to  harmonise  the  type  of  data  collected 

harmonization  of  national  methodologies  would 

require  cooperation  among  national  authorities, 

with an increasing level of cooperation depending 

and  cost  calculation  methodology,  while 

on  the  level  and  degree  of  actual  harmonization 

keeping  current  systems  of  data  collection 

undertaken.  One  of  the  main  limitations  of  the 

(e.g.  sampling  strategy)  applied  at  national 

harmonization approach is that in many countries 

level  unchanged.  In  principle  this  approach 

farm data collection systems may not be available, 

would  require  extension  or  adjustment  of 

nor sufficient financial resources that would enable 

the  current  national  systems  in  terms  of 

their participation in the scheme. As a result, many 

questionnaire  design 

(e.g.  harmonization 

important  global  players  might  be  unintentionally 

of  the  type  of  cost  data  collected)  and  cost 

omitted  unless  an  alternative  solution  is  found.  A 

calculation methodology (e.g. harmonization 

key challenge for this type of global data collection 

of  the  method  to  account  for  opportunity 

system,  where  many  stakeholders  are  involved, 

costs). The partial harmonization would lead 

suffers  from  a  challenge  of  coordination,  not 

to  significant  improvement  in  comparability 

least  complex  processing  and  validation  of  the 

of  cost  data  across-countries  because  many 

final  datasets  and  databases.  Indeed,  experience 

methodological 

differences  would 

be 

from  national  systems  shows  that  this  complexity 

removed  or  considerably  reduced.  However, 

may  lead  to  important  delays  in  finalization  and 

the  actual  level  of  comparability  would 

publication of CoP of such sets. Additionally, similar 

depend on what aspect of the cost calculation 

to other survey based collection systems, issues of 

methodology will be actually harmonized.

confidentiality may prevent full exploitation of rich 

global farm-level datasets that might be collected. 

•	 Full harmonization: Application of common 
for  data  collection  and 

methodology 

Irrespective  of,  and  addition 

to,  such 

calculation of cost values in all participating 

conceptual  and  methodological  discrepancies, 

 
 
 
 
include  a  range  of  horizontal 

issues 

that 

international exchange rates (which bias the true 

further  complicate  meaningful 

integration 

costs  of  production)  should  not  be  overlooked 

and  interpretation  of  international  production 

as  a  considerable  complicating  factor  in  CoP 

cost  estimates  (Chapter  5).  In  addition  to  the 

comparison(s).  Again,  from  the  workshop,  a 

general  considerations  of  farm  heterogeneity 

broad  consensus  existed  for  the  (1)  common 

(size),  diversified 

farm  enterprises,  climate-

definition  of  a  “typical’  farm”,  (2)  common 

adjusted  productivity,  and  data  requirements 

methodology(ies)  to  calculate  various  aspects 

to  derive  opportunity  costs  of  unpaid  labour, 

of  CoP,  (3)  definition  and  agreement  on  the 

other  regional  issues  of  importance  for  the 

unit of data collection (farm-level, typical farm, 

consideration  of  international  comparisons  of 

regional  data,  aggregate  data,  regional  data), 

CoP include farm taxation policies, agricultural 

and (4) unit of analysis (e.g. per unit of output). 

subsidies,  ongoing  R&D  taxes  and  accounting 

However,  it  remains  unclear  how  realistic,  or 

for  environmental  externalities.  Further, 

the 

probable,  such  criteria  could  be  effectively 

bias of asymmetrical inflation and fluctuation of 

satisfied at an international level. 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

109

 
 
 
 
 
 
 
 
 
 
 
 
References

AAEA  (2000)  Commodity  Costs  and  Returns  Estimation  Handbook,  A  Report  of  the  AAEA  Task  Force 

on  Commodity  Costs  and  Returns.  ftp://ftp-fc.sc.egov.usda.gov/Economics/care/AAEA/PDF/AAEA%20

Handbook.pdf

Ahearn, M., and H. El-Osta. 1991. Estimating the Opportunity Cost of Unpaid Farm Labor for U.S. Farm 

Operators.  Technical  Bulletin  No.  1848.  Economic  Research  Service,  U.S.  Department  of  Agriculture, 

Washington DC. 

ARMS 1998: http://www.ers.usda.gov/publications/sb974-5/sb974-5.pdf

Bengtsson,  C.  2011.  “Sustainability  and  Production  Costs  in  the  Global  Framing  Sector:  Comparative 

Analysis and Methodologies.” Paper presented at the workshop on “Sustainability and Production Costs 

in the Global Farming Sector: Comparative Analysis and Methodologies” European Commission, Brussels 

21-22 June 2011.

Carvalho, T.  B.  2011.  “Production  Costs  of  Beef  in  Brazil:  Methodologies  and  Comparative  Analysis.” 

Paper presented at the workshop on “Sustainability and Production Costs in the Global Farming Sector: 

Comparative Analysis and Methodologies” European Commission, Brussels 21-22 June 2011. 

Cesaro,  L.,  S.  Marongiu,  F. Arfini,  M.  Donati,  and  M.G.  Capelli  (2008).  “Cost  of  production.  Definition 

and Concept.” FACEPA Deliverable D1.1. 2, http://www2.ekon.slu.se/facepa/documents/Deliverable_D1-

1-2_LEI.pdf

Cunningham, D. 2011. “Georgia Broiler Production Costs and Returns.” Paper presented at the workshop 

on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative  Analysis  and 

Methodologies” European Commission, Brussels 21-22 June 2011.

Cunningham,  D.,  and  B.  Fairchild,  2010.  2009-2010  Broiler  Production  Systems  in  Georgia:  Costs  and 
Returns Analysis. Extension Bulletin # 1240 (poultry.uga.edu) Department of Poultry Science, University of 
Georgia.

Deblitz, C. (ed.) 2011. Beef and Sheep report 2011. Thünen-Institute, Braunschweig.

Desbois, D. 2006. “Méthodologie d’estimation des coûts de production agricole: comparaison de deux 
méthodes sur la base du RICA” Revue MODULAD 45-72.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Ellis, S. 2011. “Iowa Hog production Cost Structure.” Paper presented at the workshop on “Sustainability 

and Production Costs in the Global Farming Sector: Comparative Analysis and Methodologies” European 

Commission, Brussels 21-22 June 2011.

111

ERS Commodity cost and returns: methods http://www.ers.usda.gov/Data/CostsAndReturns/methods.htm, 

Accessed 1st September, 2011

 
 
 
 
 
 
 
 
 
 
 
 
s
e
c
n
e
r
e
f
e
R

ERS  commodity  cost  and  returns:  Data  http://www.ers.usda.gov/Data/CostsAndReturns,  Accessed  1st 

September, 2011

European  Commission.  2009.  Production  Costs  and  Margins  of  Pig  Fattening  Farms  –  2008  report. 

European Commission, Unit L3 D (2009), Brussels.

FACEPA  (2011).  Farm  Accountancy  Cost  Estimation  and  Policy  Analysis  of  European  Agriculture,  June 

2011 http://www2.ekon.slu.se/facepa/

Foster,  M.  2011.  “Agricultural  Production  Costs  and  Productivity,  Australia.”  Paper  presented  at  the 

workshop  on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative Analysis 

and Methodologies” European Commission, Brussels 21 - 22 June 2011.

Foster,  M.  2011.  “Climate-Adjusted  Productivity  and  Economies  of  Scale.”  Paper  presented  at  the 

workshop  on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative Analysis 

and Methodologies” European Commission, Brussels 21-22 June 2011.

Isermeyer.  F.  2011.  “Global  Comparison  of  Production  Cost”  Paper  presented  at  the  workshop 

on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative  Analysis  and 

Methodologies” European Commission, Brussels 21 - 22 June 2011.

Isermeyer, F. 2011. “Farming Systems and Opportunity Costs of Land.” Paper presented at the workshop 

on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative  Analysis  and 

Methodologies” European Commission, Brussels 21-22 June 2011.

Klonsky, K. 1992. “Results of a National Survey on Data and Methods,” in M.C. Ahearn and U. Vasavada, 
Costs	and	Return	for	Agricultural	Commodities. Bouler, CO: Westview Press, 192, pp: 147-164.

Koroluk,  R.  2011.  “Cost  of  Production  Methodologies  as  applied  in  Canada”.  Paper  presented  at  the 

workshop  on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative Analysis 

and Methodologies” European Commission, Brussels 21 - 22 June 2011.

Koroluk, R. 2011. “Input and Policy Inter-Linkages.” Paper presented at the workshop on “Sustainability 

and Production Costs in the Global Farming Sector: Comparative Analysis and Methodologies” European 

Commission, Brussels 21-22 June 2011.

Lazarus. W. 2011. “Production cost for wheat in the USA” Paper presented at the workshop on “Sustainability 

and Production Costs in the Global Farming Sector: Comparative Analysis and Methodologies” European 

Commission, Brussels 21 - 22 June 2011.

Lazarus.  W.  2011.  “Production  Cost  Methodologies  in  the  USA”  Paper  presented  at  the  workshop 

on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative  Analysis  and 

112

Methodologies” European Commission, Brussels 21 - 22 June 2011.

McGrann,  J.,  R.  Jones.,  and  D.  McCorkle.  1991.  The  Standardized  Cow-Calf  Enterprise  and  Financial 

Performance Analysis System.” College Station, TX: Texas A&M University.

Miele, M. 2011a. “Pork production Costs in Brazil.” Paper presented at the workshop on “Sustainability 

and Production Costs in the Global Farming Sector: Comparative Analysis and Methodologies” European 

Commission, Brussels 21-22 June 2011.

Miele, M. 2011b. “Poultry Production Costs in Brazil.” Paper presented at the workshop on “Sustainability 

and Production Costs in the Global Farming Sector: Comparative Analysis and Methodologies” European 

Commission, Brussels 21-22 June 2011.

Mishra,  A.  2011.  “Opportunity  Costs  of  Family  Labour  in  the  U.S.”  Paper  presented  at  the  workshop 

on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative  Analysis  and 

Methodologies” European Commission, Brussels 21-22 June 2011.

Morehart, M. J., H. Shapouri and R. Dismukes. 1992. Major	Statistical	Series	of	the	U.S.	Department	of	
Agriculture	Volume	 12:	 Costs	 of	 Production.	 Washington  DC:  Economic  Research  Service,  Agriculture 
Handbook No. 671.

Richardson, J.W., Outlaw, J., Knapek, G.M., Raulston, J.M., Herbst, B.K., Anderson, D.P. and S.L. Klose. 

2011.  “Representative  Farms  Economic  Outlook  for  the  January  FAPRI/AFPC  Baseline.”  AFPC  Briefing 

Paper 11-1, College Station, Texas.

Schoney. R. 2011. “Prairie Canadian Competitiveness and Dynamic Cost of Production” Paper presented 

at  the  workshop  on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative 

Analysis and Methodologies” European Commission, Brussels 21 - 22 June 2011.

Shadbolt,  N.  2011a.  “Production  Cost  Calculations  in  New  Zealand:  Dairy.”  Paper  presented  at  the 

workshop  on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative Analysis 

and Methodologies” European Commission, Brussels 21-22 June 2011.

Shadbolt,  N.  2011,b.  “Methodologies  and  Comparisons-  A  global  Overview:  Methodologies  as  Applied  in 

New Zealand” Paper presented at the workshop on “Sustainability and production Costs in the Global Farming 

Sector: Comparative Analysis and Methodologies” European Commission, Brussels 21-22 June 2011.

Shadbolt, N., A. Ndambi, and T. Hemme. 2011. “Dairy-Global Production Cost Calculations using the IFCN 

Concept.” Paper presented at the workshop on “Sustainability and Production Costs in the Global farming 

Sector: Comparative Analysis and Methodologies” European Commission, Brussels 21-22 June 2011.

Slaston.  R.  2011.  “Production  Cost  calculation  in  Ukraine”  Paper  presented  at  the  workshop  on 

“Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative  Analysis  and 

Methodologies” European Commission, Brussels 21 - 22 June 2011.

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Slaston.  R.  2011.  “Methodologies  and  comparisons  of  Cost  of  Production:  Ukraine”  Paper  presented  at 

the workshop on “Sustainability and Production Costs in the Global Farming Sector: Comparative Analysis 

and Methodologies” European Commission, Brussels 21 - 22 June 2011.

113

Surry, Y. 2011. “Production Allocation Costs, Farm Heterogeneity, and Exchange Rates.” Paper presented at 

the workshop on “Sustainability and Production Costs in the Global Farming Sector: Comparative Analysis 

and Methodologies” European Commission, Brussels 21-22 June 2011.

 
 
 
 
 
 
 
 
 
 
 
 
s
e
c
n
e
r
e
f
e
R

Teixeira, W. 2011. “Opportunity Costs of Family Labour.” Paper presented at the workshop on “Sustainability 

and Production Costs in the Global Farming Sector: Comparative Analysis and Methodologies” European 

Commission, Brussels 21-22 June 2011.

Teixeira, W.S.  2011.  “Methodology  Production  Costs  CONAB  Brazil.”  Paper  presented  at  the  workshop 

on  “Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative  Analysis  and 

Methodologies” European Commission, Brussels 21 - 22 June 2011.

U.S.  Department  of Agriculture.  2002.  Increased  Funding  to  Strengthen  USDA´s Agricultural  Resources 

Management  Survey  (ARMS).  http://www.ers.usda.gov/features/arms/armsplash.htm.  Accessed  October 

31, 2011.

U.S.  Department  of  Agriculture.  2011.  Economic  Research  Service.  Commodity  Costs  and  Returns: 

Methods. http://www.ers.usda.gov/data/CostsAndReturns/methods.htm, Accessed September 7, 2011.

U.S. Department of Agriculture. 2011. Agricultural Resources Management Survey (ARMS). http://www.

ers.usda.gov/briefing/ARMS. Accessed October 31, 2011.

Vard. T. 2011. “Methodologies as applied in the EU” Paper presented at the workshop on “Sustainability 

and Production Costs in the Global Farming Sector: Comparative Analysis and Methodologies” European 

Commission, Brussels 21 - 22 June 2011.

Wille-Sonk, 

S. 

2011. 

EDF  Cost 

of 

Production  Comparison 

2011. 

http://www.dairy- 

farmer.net/uploads/media/05_Steffi.pdf. Accessed October 31, 2011.

Zimmer, Y. (ed.) 2010. Agri benchmark Cash Crop report 2010. Thünen-Institute, Braunschweig.

Zimmer.  Y.  2011.  “Global  Production  Cost  Calculation  -  Cereals”  Paper  presented  at  the  workshop 

on  „Sustainability  and  Production  Costs  in  the  Global  Farming  Sector:  Comparative  Analysis  and 

Methodologies“ European Commission, Brussels 21 - 22 June 2011. 

114

Annex

Programme/agenda

Day I – 21 June 2011

09:00 – 10:00

Session 1: Setting the scene

Chair: John Bensted-Smith, JRC-IPTS

09:00 – 09:15

Welcome. Background of workshop

John Bensted-Smith, JRC-IPTS

09:15 – 09:30

Policy Background

09:30 – 09:50

Production cost structure(s) – implications for 
sustainability and food security 

09:50 – 10:00

WOC 2011 synthesis report

Tassos Haniotis, DG AGRI, European 
Commission

Pekka Pesonen, COPA-COGECA 

Hubertus Gay, DG-AGRI, European 
Commission

10:00 – 10:30

Coffee break

10:30 – 12:30

Session 2: Methodologies and comparisons – a global 
overview 

Chair: Jacques Delincé, JRC-IPTS

10:30 – 10:40

Introduction from the chair & Setting the scene

10:40 – 11:00

Methodologies on data collections and processing in the EU

Thierry Vard, DG – AGRI, European 
Commission

11:00 – 11:20

Methodologies as applied in the USA

Bill Lazarus, University of Minnesota, USA 

11:20 – 11:40

Methodologies as applied in Brazil

11:40 – 12:00

Methodologies as applied in Canada

12:00 – 12:30

Open discussion

12:30 – 14:00

Networking lunch

Wellington Silva Teixeira, Companhia 
Nacional de Abastecimento (Conab), Brazil

Robert Koroluk, Agriculture and Agri-Food 
Canada

14:00 – 15:45

Session 3: Methodologies and comparisons – a global 
overview - continuation

Chair: Sergio Gomez y Paloma, JRC-IPTS

14:00 – 14:20

Methodologies as applied in Australia

Max Foster, ABARES, Australia 

14:20 – 14:40

Methodologies as applied in New Zealand 

Nicola Shadbolt, Massey University, New 
Zealand

14:40 – 15:00

Methodologies as applied in Russia/Ukraine

Roman Slaston, Ukrainian Agribusiness Club

15:00 – 15:20

Global comparison of production costs 

Folkhard Isermeyer, Thünen-Institute, 
Germany

15:20 – 15:50

Open discussion

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

115

 
 
 
 
 
 
 
 
 
 
 
 
 
x
e
n
n
A

15:50 – 16:00 

Coffee break

16:00 – 18:00

Session 4: International panel discussion - horizontal 
technical Issues

Chair: Ashok Mishra, Louisiana State 
University, USA

16:00 – 16:05

Introduction from the chair and Setting the scene

16:05 – 16:15

International panellists:

16:15 – 16:25

Yves Surry, Swedish University of Agricultural Sciences

16:25 – 16:35

Robert Koroluk, Agriculture and Agri-Food Canada

16:35 – 16:45

Max Foster, ABARES, Australia

16:45 – 16:55

Wellington Silva Teixeira, Companhia Nacional de 
Abastecimento (Conab), Brazil

16:55 – 17:05

Ashok Mishra, Louisiana State University, USA

17:05 – 18:00

Folkhard Isermeyer, Thünen-Institute, Germany

Open discussion

18:00

End of Day I

116

09:00 – 12:30

Session 5: Animal Products

Chair: Stuart Platt, DEFRA, UK

Day II - 22 June 2011

09:00 – 09:05

Introduction from chair & setting the scene

Dairy

09:05 – 09:25

Production cost calculations in New Zealand 

Nicola Shadbolt, Massey University, New Zealand

09:25 – 09:45

Global production cost calculations

Asaah Ndambi, International Farm Comparison 
Network, Germany

Beef

09:45 – 10:05

Production cost calculations in Brazil

10:05 – 10:25

Production cost calculations in Argentina 

10:25 – 10:40

COFFEE

Pork

Thiago Bernardino de Carvalho, Centro de 
Estudos Avançados em Economia Aplicada, Brazil

Christian Bengtsson, Hillock Capital 
Management, Argentina

10:40 – 11:00

Production cost calculations in Brazil

Marcello Miele, Empresa Brasileira de Pesquisa 
Agropecuária (Embrapa), Brazil

11:00 – 11:20

Production cost calculations in USA

Shane Ellis, Iowa State University, USA

Poultry

11:20 – 11:40

Production cost calculations in Brazil

Wellington Silva Teixeira, Companhia Nacional de 
Abastecimento (Conab), Brazil

11:40 – 12:00

Production cost calculations in USA

Dan Cunningham, University of Georgia, USA

12:00 – 12:30

Open discussion

12:30 - 14:00

Networking lunch

14:00 –16:00

Session 6: Cereals and arable crops

Chair: Jean-François Garnier, ARVALIS, France 

14:00 – 14:05

Introduction from the chair & Setting the scene

14:05 – 14:25

Production cost calculations in USA

Bill Lazarus, University of Minnesota, USA 

14:25 – 14:45

Production cost calculations in Ukraine and Russia 

Roman Slaston, Ukrainian Agribusiness Club

14:45 – 15:05

Production cost calculations in Canada

Richard Schoney, University of Saskatchewan, 
Canada

15:05 – 15:25

Global production cost calculations

Yelto Zimmer, Thünen-Institute, Germany

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

15:25 – 16:00

Open discussion

117

 
 
 
 
 
 
 
 
 
 
 
 
x
e
n
n
A

16:00 – 16:15

Coffee break

16:15 – 17:00

Session 7: Global comparisons - open discussion and 
general considerations

Chair: John Bensted-Smith, JRC-IPTS

16:15 – 16:45

General Discussion

16:45 – 17:00

Concluding remarks

17:00

End of Workshop

All participants

JRC-IPTS and DG AGRI

List of participants – contact details

Mr Alexander Bartovic

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 03/116

Brussels 1040

Belgium

Telephone: +32 2 2962807

Fax: +32 2 2965991

E-mail: alexander.bartovic@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Mr Christian Bengtsson

Finance Department 

Hillock Capital Management

Cerrito 1070

Ciudad autónoma de Buenos Aires

1010 Argentina

Telephone: +54 11 5199 06 32

Fax: 

E-mail: cbengtsson@hillockcapital.com

Website: www.hillockcapital.com

Mr John Bensted-Smith

118

Director Institute for Prospective Technological Studies (IPTS)

European Commission-Joint Research Centre (JRC)

Edificio EXPO

E-41092 Seville

Spain

 
Telephone: +34 95 44 8 8273

Fax: +34 95 44 8 8274

E-mail: john.bensted-smith@ec.europa.eu

Website: http//www.jrc.es/home/index.html

Mr Thiago Bernardino de Carvalho

Department: Department of Economics, Administration and Sociology 

CEPEA/ESALQ/USP

Padua Dias Avenue, 11

Piracicaba

Brazil, 13418-900

Telephone: +55 19 3429 8802 

Fax: +55 19 3429 8829

E-mail: tbcarval@cepea.org.br

Website: http://www.cepea.esalq.usp.br/english/

Ms Florence Buchholzer

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 08/225A

Brussels 1040

Belgium

Telephone: +32 2 2956924

Fax: +32 2 2959236

E-mail: florence.buchholzer@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Dr Pavel Ciaian

Agriculture and Life Sciences in the Economy Unit

Institute for Prospective Technological Studies (IPTS)

European Commission-Joint Research Centre (JRC)

Edificio EXPO

E-41092 Seville

Spain

Telephone: +34 95 4488429

Fax: +34 95 4488434

E-mail: pavel.ciaian@ec.europa.eu

Website: http//www.jrc.es/home/index.html 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

119

 
 
 
 
 
 
 
 
 
 
 
 
 
x
e
n
n
A

Dan L. Cunningham

Department of Poultry Science

University of Georgia

Athens, Georgia 30602

USA

Telephone: +1 706 542 1325

Fax: +1 706 542 8383

E-mail: dcungham@uga.edu

Website: http//www.poultry.uga.edu

Mr Alberto D’Avino 

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 08/223A

Brussels 1040

Belgium

Telephone: +32 2 2966557

Fax: +32 2 2959236

E-mail: alberto.davino@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Dr Jacques Delince

Agriculture and Life Sciences in the Economy Unit

Institute for Prospective Technological Studies (IPTS)

European Commission-Joint Research Centre (JRC)

Edificio EXPO

E-41092 Seville

Spain

Telephone: +34 95 44 88320

Fax: +34 95 44 88434

E-mail: jacques.delince@ec.europa.eu

Website: http//www.jrc.es/home/index.html

Mr Shane Ellis

Department of Economics

475 Heady Hall

Iowa State University

Ames, Iowa  50011-1070

USA

120

  
Telephone: +1 515 294-8030 

Fax: +1 515 2941700

E-mail: shanee@iastate.edu

Website: http://www2.econ.iastate.edu/prosci/ellis/

Mr Max Foster

Agriculture and Food

ABARES (Australian Bureau of Agricultural Resource Economics and Sciences)

GPO Box 1563

Canberra ACT 2615

Australia

Telephone: +61 2 6272 2095

Fax: +61 2 6272 2104

Email: max.foster@abares.gov.au

Website: www.abares.gov.au

Ms Livia Galita

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 05/019

Brussels 1040

Belgium

Telephone: +32 2 2958453 

Fax: +32 2 2993527

E-mail: livia.galita@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Mr Tomas Garcia Azcarate

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 07/044

Brussels 1040

Belgium

Telephone: +32 2 2953317

Fax: +32 2 2971536

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

E-mail: tomas.garcia-azcarate@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

121

 
 
 
 
 
 
 
 
 
 
 
 
x
e
n
n
A

Mr Jean-François Garnier

Economics and Farming System Department

ARVALIS Institut du Végétal

Adresse  Station Expérimentale

Boigneville 91720 

France

Telephone: +33 (0)1 64 99 22 75

Fax: +33 (0)1 64 99 30 39 

E-mail: jf.garnier@arvalisinstitutduvegetal.fr

Website: http://www.arvalisinstitutduvegetal.fr

Dr Hubertus Gay

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 05/025

Brussels 1040

Belgium

Telephone: +32 2 2956730

Fax: +32 2 2958453 

E-mail: hubertus.gay@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Dr Sergio Gomez y Paloma

Agriculture and Life Sciences in the Economy Unit

Institute for Prospective Technological Studies (IPTS)

European Commission-Joint Research Centre (JRC)

Edificio EXPO

E-41092 Seville

Spain

Telephone: +34 95 44 88358

Fax: +34 95 44 88434

E-mail: sergio.gomez-y-paloma@ec.europa.eu

Website: http//www.jrc.es/home/index.html

Mr Tassos Haniotis

Director

Directorate L

Directorate General – Agriculture and Rural Development

122

European Commission

BRU-L130 07/100

Brussels 1040

Belgium

Telephone: +32 2 2991381

Fax: +32 2 2987186

E-mail: anastassios.haniotis@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Prof Dr Folkhard Isermeyer

President

Johann Heinrich von Thünen – Institute

Bundesallee 50

38116 Braunschweig

Germany

Telephone: +49 531 596 1001

Fax: +49 531 596 1099

E-mail: folkhard.isermeyer@vti.bund.de 

Website: www.vti.bund.de

Mr Robert Koroluk

Chief, Farm Income and Inputs Section

Research and Analysis Directorate, Strategic Policy Branch

Agriculture and Agri-Food Canada

1341 Baseline Road, Tower 7

Ottawa, Ontario

K1A 0C5

Canada

Telephone: +1 613 773 2428

Fax: +1 613 773 2499

E-mail: robert.koroluk@agr.gc.ca

Website: www.agr.gc.ca

Dr Stephen Langrell

Agriculture and Life Sciences in the Economy Unit

Institute for Prospective Technological Studies (IPTS)

European Commission-Joint Research Centre (JRC)

Edificio EXPO

E-41092 Seville

Spain

Telephone: +34 95 4488256

Fax: +34 95 4488434

E-mail: stephen.langrell@ec.europa.eu

Website: http//www.jrc.es/home/index.html 

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

123

 
 
 
 
 
 
 
 
 
 
 
 
 
x
e
n
n
A

Ms Elsa Laval 

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 03/132A

Brussels 1040

Belgium

Telephone: +32 2 2980839

Fax: +32 2 2965991

E-mail: elsa.laval@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

William F. Lazarus, PhD

Department of Applied Economics

University of Minnesota-Twin Cities Campus

249 Ruttan Hall, 1994 Buford Avenue

St. Paul

Minnesota, 55108

USA

Telephone: +1 612 625-8150

Fax: +1 612 625-6245

E-mail: wlazarus@umn.edu

Website:http://faculty.apec.umn.edu/wlazarus/

Mr Pierluigi Londero

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 08/233

Brussels 1040

Belgium

Telephone: +32 2 2991255

Fax: +32 2 2959236  

E-mail: pierluigi.londero@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Mr Leo Maier 

Directorate General – Agriculture and Rural Development

124

European Commission

BRU-L130 08/029

Brussels 1040

Belgium

 
 
Telephone: +32 2 2998195

Fax: +32 2 2921735 

E-mail: leo.maier@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Mr Marcelo Miele

Embrapa Swine and Poultry

Br 153, Km 110

Caixa Postal 21

CEP 89700-000 Concórdia-SC-Brasil

Telephone: +55 (49)3441 043

Fax: +55 (49)3441 0497

E-mail: marcelo@cnpsa.embrapa.br

Website: http://www.cnpsa.embrapa.br

Prof Ashok K. Mishra

Deptarment of Agricultural Economics and Agribusiness

LSU AgCenter

211 Martin D. Woodin Hall

Baton Rouge

Louisiana 70803

USA

Telephone: +1 225 578 0262

Fax: +1 225 578 2716

E-mail: amishra@agcenter.lsu.edu; amishra@lsu.edu

Website: http://www.lsuagcenter.com/en/communications/authors/AMishra.htm

Dr Asaah Ndambi

Farm comparison and sustainability team

International Farm Comparison Network- Dairy Research Center

Schauernburger Str. 116

24118 Kiel

Germany

Telephone: +49 431 5606 254

Fax: +49 431 5606 262

E-mail: ndambi.asaah@ifcndairy.org

Website: www.ifcndairy.org

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

125

 
 
 
 
 
 
 
 
 
 
 
 
x
e
n
n
A

Mr Dominik Olewinski

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 06/065 

Brussels 1040

Belgium

Telephone: +32 2 2995682
Fax: +32 2 2971536 
E-mail: dominik.olewinski@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Mr Pekka Pesonen

Secretary General

COPA-COGECA

Rue de Trèves 61

1040 Brussels

Belgium

Tel: +32 (0)2 287 27 22

Fax: +32 (0)2 287 27 00

E-mail: pekka.pesonen@copa-cogeca.eu

Website: http://www.copa-cogeca.be/Main.aspx?page=HomePage&lang=en

Mr Stuart Platt

Deputy Director for Farming and Food Statistics

Department for Environment, Food and Rural Affairs (Defra)

3rd floor Foss House

Kings Pool

1-2, Peasholme Green

York

YO1 7PX

UK

Telephone: +44 1904 455054

Fax: +44 1904 455065

Email: stuart.platt@defra.gsi.gov.uk

Website: www.defra.gov.uk

126

Richard Schoney, PhD

Dept of Bioresource Policy, Business & Economics

University of Saskatchewan

3D24 Agriculture Building 

51 Campus Drive 

Saskatoon, SK, S7N 5A8

Canada

Telephone: +1 (306) 966 4018

Fax: +1 (306) 966 8413

E-mail: dick.schoney@.usask.ca

Website: http://agbio.usask.ca/index.php?page=bioresource-policy

Mr Willi Schulz-Greve 

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 08/233

Brussels 1040

Belgium

Telephone: +32 2 2960945

Fax: +32 2 2921735 

E-mail: willi.schulz-greve@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Prof Nicola M Shadbolt

IFNHH

Riddet 11 Building, Room 2.58

Massey University 

Private Bag 11222 

Palmerston North 4442

New Zealand 

Telephone: +64 63569099 ext.81412

Fax: +64 63505657

E-mail: N.M.Shadbolt@massey.ac.nz

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

Website: 

http://www.massey.ac.nz/massey/learning/departments/institute-food-nutrition-human-health/

agricommerce/contributing-staff/nicola-shadbolt/en/nicola-shadbolt_home.cfm

127

 
 
 
 
 
 
 
 
 
 
 
 
x
e
n
n
A

Mr Wellington Silva Teixeira

Companhia Nacional de Abastecimento (Conab)

SGAS 901 Bloco ‘A’ Lote 69 – Asa Sul

Cep: 70.390-010

Brasilia - DF

Brazil

Telephone: +61 3312 6247

Fax: +61 3321 2029

E-mail: wellington.teixeira@conab.gov.br

Website: http://www.conab.gov.br/

Mr Roman Slaston

Association Ukrainian Agribusiness Club

20A Velyka Zhytomyrska

Kyiv

01034

Telephone: +380442014950

Fax: +380442014951

E-mail: slaston@agribusiness.kiev.ua

Website: www.agribusiness.kiev.ua

Prof Yves Surry 

Department of Economics 

Box 7013

750 07 Uppsala

Sweden

Telephone:  +018 67 17 95 

Fax: +01867 35 02

E-mail: yves.surry@slu.se 

Website: http://www.slu.se/ekonomi

Mr Thierry Vard

Directorate General – Agriculture and Rural Development

European Commission

BRU-L130 03/124

Brussels 1040

Belgium

128

Telephone: +32 2 2958423

Fax: +32 2 2965991 

E-mail: thierry.vard@ec.europa.eu

Website: http://ec.europa.eu/agriculture/index_en.htm

Dr Yelto Zimmer

Institute of Farm Economics

von Thünen Institute

Federal Research Institute for Rural Areas, Forestry & Fishery

Bundesallee 50

38116 Braunschweig

Germany

Telephone: +49 531 596 5155

Fax: +49 531 596 5199

E-mail: yelto.zimmer@vti.bund.de

Website: www.agribenchmark.org

i

l

s
e
g
o
o
d
o
h
t
e
M
d
n
a

l

s
i
s
y
a
n
A
e
v
i
t
a
r
a
p
m
o
C

:
r
o
t
c
e
S
g
n
m
r
a
F

i

l

l

a
b
o
G
e
h
t
n

i

s
t
s
o
C
n
o
i
t
c
u
d
o
r
P
d
n
a

y
t
i
l
i

i

b
a
n
a
t
s
u
S

129