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Understanding beef-cattle farming management strategies by identifying
motivations behind farmers' priorities
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DOI: 10.1017/S175173111100231X · Source: PubMed
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Animal, page 1 of 9 & The Animal Consortium 2011
doi:10.1017/S175173111100231X
Understanding beef-cattle farming management strategies by
identifying motivations behind farmers’ priorities
M. A. Magne1-, M. Cerf2 and S. Ingrand1
1
INRA – UMR1273 Métafort, Equipe Select, F-63122 Saint-Genès Champanelle, France; 2INRA – UR SenS 1326, F-78850 Thivernal-Grignon, France
(Received 16 November 2010; Accepted 19 October 2011)
This study aimed to identify and better understand management strategies that help livestock farmers adapt to changes in their
production contexts, a fundamental challenge. A total of nine beef-cattle farmers were interviewed three times over 1 year to
discuss 13 dimensions of livestock farming (e.g. reproduction, feeding, sales, etc.). Characterisation of management strategies
rested on three main factors: (i) ranking of the dimensions according to the degree to which farmers desired to control them,
(ii) reasons for the ranking and (iii) management guidelines. Although farmers agreed upon the rank of certain dimensions, such
as herd management, they differed on that of others, such as sales and administration/regulations. Four motivation categories
were identified: risk, pleasure, efficiency and ability to control the dimension. Three management guidelines were identified,
which indicated that farmers managed for future survival of their farms at different scales (animal/herd v. whole-farm), involving
different resources (biological v. financial) and based on different animal categories (reproductive cows v. animals sold).
These results improve understanding of individual livestock farmers and their current management strategies by integrating
the motivations behind their strategies. For this reason, they constitute methodological elements that agricultural advisors
can use to provide relevant information to farmers while accommodating differences in farm management.
Keywords: management strategies, livestock farming system, agricultural advice, farmer motivation, farmer priorities
Implications
Our method of identifying and understanding livestock farming
management strategies enables inclusion of the meaning that
farmers attribute to management. It appears that the strategies
that farmers devise and develop result from a compromise
between activities they should do, activities they would like to
do and activities they do not accomplish. From an operational
viewpoint, this approach should help advisors visualise livestock
management strategies as envisaged by beef-cattle farmers, as
this is key in assisting farmers with change. Thus, the ranking
of livestock production dimensions according to the type of
mastery sought, categorisation of underlying motivations and
identification of management guidelines can be tools for advisors to identify farmers’ views, possible obstacles to change and
potential difficulties encountered in livestock management.
Introduction
It is well established that under similar production conditions,
technical and economic performance can differ considerably
Present address: Université de Toulouse/ENFA, UMR1248 AGIR, F-31326
Castanet Tolosan, France. E-mail: marie-angelina.magne@toulouse.inra.fr
among farmers (Rougoor et al., 1998; Hansson, 2007). The
human component of farming systems plays an important role
in management practices, and this fuller representation of
farmer behaviour and motivation is crucial for understanding
differences in management practices and performances across
farms (Solano et al., 2006). In particular, it plays an important
role in determining whom farmers approach for information or
help and the technology they adopt (Solano et al., 2006). Thus,
for advisors who want to understand failures of some of their
services or technical messages among particular farmers, as
well as to design advice that accommodates differences in
farm management among farmers, it is essential to have tools
that illuminate farmers’ management strategies.
Considerable research has focussed on predicting the
influence of farmer and farm characteristics (usually biographical and structural, respectively) on technology adoption rates and use, farm management and performance
(Solano et al., 2006). Nevertheless, a more comprehensive
approach towards the human component of the farming
system is required, which includes farmers’ economic and
technical orientations, decision-making approaches and
information preferences (Solano et al., 2006). Such research
on livestock farming systems has been conducted by
1
Magne, Cerf and Ingrand
focussing on characterising and understanding the diversity of
farmers’ management strategies (Gibon et al., 1996; Girard
et al., 2001; Fiorelli et al., 2007) or farming styles (Van der
Ploeg, 1993; Brodt et al., 2006). Many of these studies have
developed classification systems to describe this diversity, but
such systems may not be practical for advisors who want to
identify an individual livestock farmer’s management strategies. Moreover, these studies usually deal with management
strategies to address a problem predefined by the researcher in
a particular dimension of livestock production (e.g. feeding,
reproduction, animal health). Although classification systems
provide better understanding of management strategies within
a specific dimension, they do not encompass all management
strategies and do not identify farmer priorities, which is all the
more crucial in a production context marked by uncertainty and
change. Indeed, farmers try less to reach a specific level of
quantitative performance than to control, as best they can, the
various dimensions of livestock farming to ensure their farms’
survival (Dedieu and Ingrand, 2010).
The objective of this study was to present an approach
that can be used to obtain a more comprehensive view of
beef-cattle farmers’ management strategies and to identify
and understand those of an individual farmer. We first present the research protocol we used. We then present how
beef-cattle farmers ranked the different dimensions of livestock farming they have to manage, the underlying motivations and the management guidelines, which structure their
ranking. We finally discuss these main results and how our
approach can be used for advisory work.
Material and methods
Analysis framework
Our research is based on three related key concepts and incorporates farming system management (Keating and McCown,
2001). A livestock farming management strategy enables
understanding of observed livestock farming operations (Hubert
et al., 1993). According to Rougoor et al. (1998), farm management is defined simply as ‘using what you have to get what
you want’. This definition stresses that setting priorities and
allocating resources accordingly is a basic condition for farmers
to perform effective farm management. A farmer, as a manager,
generates an overview of the dimensions he should address and
then chooses which is most valuable for investing material (e.g.
money, work) and immaterial (e.g. information, skills) resources.
An agricultural advisor may not consider the management
priorities, which a farmer establishes as efficient or sufficiently
relevant, and such differences in opinion can explain the
farmer’s refusal to adopt advisory recommendations.
On the basis of this framework, we identified three main
concepts to render operational the underlying definition of
‘the set of objectives and management guidelines’ characterising livestock management strategies according to
Gibon et al. (1996). These concepts are:
>
2
mastery farmers seek while managing their farms, which
refers to activities farmers can and want to do for their
>
>
farm to survive, rather than activities they plan to do to
optimise performances;
dimensions of livestock farming, the mastery of which
farmers identify as crucial for farm survival. Management
sciences suggest analysing the strategy of a business by
segmenting it into strategic activities. These are defined as
‘sub-assemblies of an organisation to which it is possible
to allocate or from which to withdraw resources
autonomously and which correspond to specific combinations of the key factors for success’ (Johnson et al., 2008).
Drawing upon this work, we segmented the livestock
farming system into dimensions (sub-assemblies) that
farmers decide need to be mastered to ensure farm survival.
To this extent, our approach is grounded in research on farm
resilience rather than in farm competitiveness. Therefore,
mastery refers to the use of physical (e.g. milking machinery)
and information (e.g. sire registers) resources, which achieve
the objectives that farmers have assigned to a specific
dimension.
ranking of dimensions according to the importance farmers
assign to their mastery, which refers to the priorities
established by farmers (Rougoor et al., 1998). Identification
of the dimensions, their ranking and the reasons for the
ranking constitute what Gibon et al. (1996) call the farming
management strategy, for example, a set of farming
objectives (motivations) and management guidelines
(which structure the dimensions on which decisions and
actions are made).
Farming system sample and data collection
The study was carried out from 2005 to 2006 in the traditional beef-cattle farming area in the Centre region of France.
Sampling was based on a two-step process (Figure 1). In the
first step, farmers completed surveys about ways they obtain
information from outside the farm. Surveys continued until we
obtained a sufficiently wide range of diversity in practices
(i.e. 30 surveys). A list of different management dimensions in
the beef-cattle farming system was discussed with the farmers
during this survey step, which enabled us to co-construct a set
of 13 dimensions: feeding (distribution), calving (taking care of
cows and calves), reproduction (batching cows and choosing
periods), genetics (choosing bulls according to the genetic level
target), replacement (choosing heifers), culling (choosing cows
to sell), animal health, feed stocks (for the wintering period),
grazing (management), fertilisation, administration/regulations,
accounting and sales. Cluster analysis of the survey data
revealed four distinct patterns of information-seeking behaviour (Cerf and Magne, 2007), which depended neither on
farm structure (i.e. production orientation, size) nor on the
location (departments). On the contrary, we postulated that
different information-seeking patterns were linked to different
farming strategies. Therefore, for the second step we subsampled nine of the 30 farmers, who served to represent these
patterns (Table 1). Two semi-directive interviews were carried
out with each to analyse the relations between informationseeking patterns and priorities for mastering the 13 previously
defined dimensions. During the first interview, farmers ranked
What do beef-cattle farmers seek to control and why?
Step 1
Thrity on-farm surveys followed by a
multivariate analysis, using a 9
columns x 30 lines table
(thirty farmers; Nine variables
describing how farmers mobilize
information from outside the farm)
"seed"
ep
1
St
: a farm
pattern 3 = a desire for self-reliance
pattern 1 = planned
mobilisation
of information
pattern 2 = piecemeal
gathering of information
as opportunities arose
Ste
p2
pattern 4 = a need for outside assistance
and reassurance about the choices made
Step 2
Nine farms sampled from each
of the four patterns for some
follow-up surveys (Table 1)
Figure 1 Diagram of the two-step methodology.
Table 1 Main characteristics of 9 out of 30 beef-cattle farms sampled, according to functional and structural variables (the four patterns of
acquisition of external information (from outside the farm) identified in the 30 farms are represented)
Farmera
C1
C9
A6
H5
C2
H6
A3
C4
H2
Pattern of acquisition of
external information
Organised and forward-planned
Organised and forward-planned
Organised and forward-planned
As opportunity arises
Become self-reliant
Become self-reliant
Get assistance
Get assistance
Get assistance
Number of
Farm
Production
workers size (ha)
system
1
3
1
1
2.5
1.5
3
1
1.25
70
140
83
87
175
145
225
60
86
CC
CC
F&B
F
F
F
F
F&B
F
Culling
rate (%)
Calving period
Breeding
method
Animal purchasers
17
15
25
17
22
9
30
35
19
Autumn
Winter
Winter
Autumn, winter
Autumn
All year round
Winter
Winter
Autumn, winter
Bull
Bull 1 AI
Bull 1 AI
Bull
Bull
Bull
Bull 1 AI
Bull 1 AI
Bull
Individual purchasers
One producer group
Mixed
One producer group
Individual purchasers
Mixed
Mixed
Mixed
Individual purchasers
CC 5 cow-calf system; F 5 fattening system; F&B 5 fattening and breeding; AI 5 artificial insemination.
a
A, C, H: Department of France (Allier, Cantal, Haute-Vienne).
the 13 dimensions in decreasing order of the importance they
attributed to their mastery (i.e. high rank 5 1, low rank 5 13)
and provided reasons for their rankings. The interviews were
recorded and lasted an average of 4 h. A second interview, 6
months later, provided the same information at a different point
in time (because priorities could change with the season) and, if
necessary, supplemented data from the first survey.
Data analysis
The recorded interviews were transcribed into fact sheets,
which identified reasons farmers gave to justify their rankings, along with the content, origin and medium of the
information the farmer used to control each dimension.
These fact sheets allowed preliminary structuring of the data
and initial analysis of variability in dimension rankings. The
structured data were then analysed to reveal relations
between rankings, motivations explaining these rankings
and guidelines structuring the management strategies as a
whole, which clarified their coherence to farmers.
Statistical analysis to describe variability in ranks. Distributions of the ranks of the 13 dimensions were described with
boxplots.
Data categorisation to identify motivations to control each
dimension. A method of data abstraction based on knowledge engineering was used to delineate farmer motivations
regarding the dimensions. This qualitative method is based
on the characterisation of observed variability by determining key categories that structure and synthesise it (Girard
3
Magne, Cerf and Ingrand
et al., 2001). We characterised variability in rankings by
defining categories of motivations based on farmers’ justifications for the importance they assigned to mastery of the
dimensions, which was related to two factors: reasons why
farmers considered mastery important (e.g. to counter a
threat to farm survival) and the magnitude of this importance (e.g. crucial v. minor). Motivations were grouped into
four categories:
>
>
>
>
risk to the farm’s survival if the dimension was not controlled.
pleasure experienced in the quest for mastery. For
example, some farmers ranked certain dimensions lower
because the quest for their mastery gives them little
satisfaction (or indeed, dissatisfaction).
efficiency, which represents farmers’ satisfaction about
the results obtained in a specific dimension considering
the resources devoted to it. For example, certain farmers
felt that they had low efficiency in technical dimensions,
although they ranked them higher.
ability to control mastery, which represents the degree to
which farmers perceive that they have the power to control
dimensions (v. their being driven by outside forces). For
example, some farmers ranked certain dimensions higher
because they felt that they could control them.
For each dimension, the importance of each of these four
categories to a farmer was described as high, medium or
low, allowing a dimension’s rank to be explained by one or
more of the motivation categories.
Multivariate analysis to identify management guidelines at
the system scale. Principal component analysis (PCA, using
SPAD 5.0) was used to explore potential relations among the
sets of 13 dimensions presented above. Each factorial axis
obtained, therefore, represented a ‘farming management
3rd quartile
Maximum
guideline’. Description and interpretation of the factorial
axes were based on analysis of the contributions of every
variable (i.e. the dimensions) to each axis. For each farmer,
the coherence of the whole strategy according to the priorities (which result from the combination of motivations)
given towards mastering the dimensions could then be
described by combinations of different farming management
guidelines.
The PCA was derived from a table containing 13 dimensions in columns and nine beef-cattle farmers in rows, with
the 117 values the dimension ranks. Ranks were then
translated to a [0; 100] range and inverted (e.g. rank 1
became rank 100) to make PCA results easier to read
(i.e. dimensions with higher original ranks (more importance)
lay further from the centre of the factorial plan).
Results
Variability in dimension rankings
Although farmers ranked the desire to master certain
dimensions similarly, ranks tended to vary greatly (Figure 2).
Dimensions were organised into three groups based on their
median ranks. The first group (median rank 5 3 to 5) included the dimensions’ reproduction, genetics, feeding, calving
and animal health, all of which are related to the technical
management of the herd. Their mastery is fundamental for
farmers as they influence the progress of the production
process (producing and rearing animals and keeping them
healthy). The second group (median rank 5 7 to 8) included
sales, replacement, administration/regulations, feed stocks,
grazing, fertilisation and culling, which are mainly concerned
with exploiting products and the forage system. Lastly,
accounting was classed separately as the least important to
control (median rank 5 12). According to farmers, no ‘action
Minimum
1rst quartile
Median
Rank (1 to 13) ascribed by the nine farmers
13
12
11
10
9
8
7
6
5
4
3
2
1
Repro
Gen
Feeding Calving Animal
Health
Sales
Replac
Adm/
Regul
Feed Grazing
stocks
Ferti
Culling Account
Dimensions of livestock farming (n=13)
Figure 2 Ranking of 13 dimensions of livestock farming by nine farmers according to the importance they attributed to their mastery and development.
Rank 1 equals highest importance; rank 13 equals lowest importance. Vertical lines separate dimensions by median-rank group. Repro 5 reproduction;
Gen 5 genetics; Replac 5 replacement; Adm/regul 5 administration/regulations; Ferti 5 fertilisation; Account 5 accounting.
4
What do beef-cattle farmers seek to control and why?
levers’ existed to control this dimension, which is considered
simply a requirement for farming activity.
The variability (interquartile range, IQR) in dimension
ranks showed that farmers did not always agree on the
importance of certain dimensions. However, some dimensions had relatively low variability (IQR 5 2) around high
(reproduction, feeding, animal health) or low (feed stocks,
grazing, fertilisation, accounting) median ranks. Variability
was greater (IQR 5 6) for genetics and calving (with high
median ranks) and replacement and culling (with lower
median ranks). Finally, variability was the greatest (IQR 5 9
to 11) for administration/regulations and sales.
Motivations to understand dimension rankings
The following two examples show how identifying and estimating the magnitude of farmers’ motivations for seeking to
control the farming system improved understanding of why
they ranked farming dimensions in the order that they did.
Administration/regulations rank influenced by ability to
control. Farmers C2 and C1 ranked the mastery of administration/regulations as the most and the least important,
respectively, but they reported the same levels of risk (high)
and pleasure (low; Table 2). Conversely, they differed in
regard to efficiency (high and medium for C2 and C1,
respectively) and especially ability to control (high and low
for C2 and C1, respectively). Analysis of the combination of
motivation levels suggests that the two farmers weighed the
two latter motivations differently. Hence, risks carried more
weight for farmer C1 than pleasure because he considered
the dimension as under his control, which explains the high
Table 2 Comparison of two beef-cattle farmers regarding the importance attributed to motivations behind their ranking of the mastery of
administration/regulations
Farmer
Rank
Risk
Pleasure
Efficiency
Ability to control
C2
C1
1
High
Low
High
High
13
High
Low
Medium
Low
rank he attributed. In contrast, lack of pleasure carried more
weight for farmer C2 than the risks, partly because he considered it a dimension out of his control, which explains the
low rank he ascribed. It is important to note that differences
between the two farmers did not depend on their production
orientations (fattening for C2 and cow–calf for C1), because
(i) administration/regulation requirements did not differ
a priori between them, and (ii) other farmers performing
fattening (e.g. H5) also ranked this domain low (12) and
reported the same motivation levels as farmer C1.
Genetics rank influenced by pleasure and efficiency. Farmers
C4 and C9 ranked mastery of genetics identically (8), but
the former used a greater variety of information sources
(in terms of medium, origin and content) than the latter
(Table 3). Analysis of the combination of motivation levels
explains these differences (Table 3). They assigned the same
levels to risks (medium) and ability to control (low, due to
biological variability), but differed with regard to the pleasure and efficiency associated with its mastery. Farmer C4,
who subscribed to performance testing and was registered in
the French Herd Book, used livestock performance indicators
and genealogical criteria to select bulls, plan matings, select
surrogate mothers for embryo transplants and evaluate his
herd’s genetic progress (high pleasure). Farmer C4 also perceived that he had more progress to make (medium efficiency). Conversely, farmer C9 used few performance
indicators to plan the genetics of his herd; matings depended
more on purchases of complete herds, which he expanded,
rather than on planned genetics (low pleasure). However,
farmer C9 was satisfied with the results he obtained considering the few resources he allocated (high efficiency). The
differences between the two farmers’ level of pleasure
and efficiency explain their different levels of information
seeking in genetics.
Guidelines for understanding relations among dimensions
The first three factorial axes of the PCA explained 74% of the
total variability of the data. Correlation analysis of the initial
variables (i.e. dimensions) with each of the axes (Table 4) along
with the coordinates and contributions of the nine farmers to
the factorial axes (Table 5) identified three management
guidelines (one per axis) to ensure the farm survival.
Table 3 Comparison of two farmers regarding their ranking of the mastery of genetics, the importance attributed to their motivations and their
information-seeking activity
Farmer
Rank
Risk
Pleasure
Efficiency
Ability to control
Information resources acquired
C4
8
Medium
High
Medium
Medium
Livestock technical performance indicators and
genealogical criteria for bull selection, planned matings,
selection of surrogate mother cows for embryo
transplantation, evaluation of genetic progress
C9
8
Medium
Low
High
Medium
Few livestock technical
performance indicators and no
genealogical data
5
Magne, Cerf and Ingrand
Table 4 Interpretation of the three factorial axes of the principal component analysis from their correlations with dimensions of livestock farming
Axis
1
Dimension
Correlation
Dimension
Correlation
Reproduction
0.77
Feed stocks
20.85
Replacement
Culling
Genetics
0.77
0.67
0.65
2
Grazing
0.70
Administration/regulations
Grazing
Fertilisation
Animal health
Sales
20.82
20.69
20.64
20.51
20.71
0.63
0.67
0.59
Accounting
20.70
3
Fertilisation
Calving
Sales
Genetics
20.65
Reproduction
20.50
Interpretation of the axis
The scale at which failure of farm survival is
defined: the herd v. the farm as a whole
The types of resources on which management is
based: biological (biological management) v.
financial (economic management)
The animal component which it is intended to
exploit: the products v. the breeding stock
Table 5 Analysis of the contributions of the nine farmers to the three factorial axes from the principal component analysis: identification of the
principal components that characterize them
Axis 1
Axis 2
Axis 3
Farmer
Coord
Contr
Coord
Contr
Coord
Contr
Principal components
A3
A6
C1
C2
1.31
2.33
3.29
21.17
3.8
12.0
24.0
3.0
1.44
20.74
0.83
22.70
6.9
1.9
2.4
27.0
20.88
0.87
0.57
22.40
5.2
5.1
2.2
39.0
C4
C9
0.84
23.11
1.5
21.6
21.87
2.99
12.3
31.6
0.97
0.61
6.3
2.0
H2
H5
H6
20.66
0.85
23.70
0.9
1.6
30.0
1.60
0.09
21.55
21.75
0.39
1.61
20.0
1.0
17.0
|
Mastery on the herd scale
Mastery on the herd scale
Management of financial resources 1
Exploitation of the breeding stock
Management of financial resources
Mastery on the system scale 1
Management of biological resources
Exploitation of the breeding stock
|
Mastery on the system scale 1
Exploitation of products
9.0
0.03
8.0
Coord 5 coordinates; Contr 5 contribution (%).
|: no structuring principal component.
The first factorial axis (explaining 38% of the variability)
represents the scale at which risk of failure and the farm’s
survival are perceived. Thus, it contrasts farmers (C9 and H6)
who perceived risks at the herd scale (mastering reproduction, genetics and replacement) with those (C1 and A6) who
perceived risks at the whole-farm scale (mastering feed
stocks, administration/regulations, grazing, fertilisation and
animal health). Farmers C9 and H6 mastered risk at the
whole-farm scale by seizing outside opportunities (notably
the optimisation of subsidies), which allowed them to optimise profitability and minimise forage and veterinary costs
(Table 5). Conversely, farmers C1 and A6 attempted to control risks at the herd scale by managing herd composition
and dynamics on the basis of their perceived ability to control dimensions and the importance they ascribe to dimensions of animal management and technical performance.
Thus, farmer C1 perceived that he possesses greater mastery
6
over biological (e.g. oestrus cycles, insemination) and biotechnical hazards (e.g. accidental loss of cows) within his
farm than over problems of external origin (e.g. administrative and regulatory reforms, extreme weather). Likewise,
farmer A6 focused on improving his herd’s reproduction and
genetics, claiming that his criteria for culling and replacements depended heavily on his policy of genetic improvement and the animals’ registration in the Herd Book.
The second axis (explaining 24% of the variability)
represents the farm resources on which farmers focus
attention and effort. Thus, it contrasts farmers (C9) who
aimed to master biological resources (e.g. grazing, fertilisation, calving) with those (C2 and C4) who aimed to master
financial resources (e.g. sales, accounting). Farmer C9 perceived himself as relatively inefficient in biological dimensions and preferred to concentrate on mastering them rather
than financial dimensions. In contrast, farmers C2 and C4
What do beef-cattle farmers seek to control and why?
structured their management strategy around a well-defined
sales strategy and accounts-based management, which
allowed them to evaluate production results in depth.
Indeed, farmer C2 asserted that mastering accounting consists of thinking about investments and commercial strategy.
These two farmers considered selling and sales strategies
integral parts of farm management, but they also believed
that this dimension demands extra attention and is difficult
to control, as it depends on factors outside the farm. They
believed that they efficiently mastered biological dimensions
and were satisfied with the results they obtained.
The third axis (explaining 12% of the variability) represents the animal component, which farmers seek to exploit
and improve to control the production process. Thus, it
contrasts farmers (C2 and H2) who based their reasoning on
exploitation of the cow herd (e.g. reproduction, genetics)
with those (H6) who based it on exploitation of herd products (e.g. sales). According to farmers C2 and H2, the
challenge at the herd scale was to control the dimensions
associated with the reproductive herd, as they affect the
continuity of the production process. Farmer C2 perceived
that he was not efficient enough in the mastery of reproduction and genetic dimensions, especially because he considered their control a reflection of his competence, although
these dimensions are subject to biological variability. Farmer
H2 claimed that the control of the reproductive herd
remained his greatest challenge because it constituted his
sole capital, as he rented the land. He aimed to develop the
herd’s genetic potential over the long term to generate as
much capital as possible for his retirement. Finally, planning
the matings and considering genetic selection were activities
he enjoyed, whereas farmer H6 attempted not only to exploit
the growth potential of products but also to obtain the best
price when he sold them. He stated, ‘What matters is to have
calves; all the cows, whatever their genetic potential, can
produce them’. Farmer H6 was mainly motivated by the
commercial side of farm management.
Discussion
Technical herd dimensions are central to farmers’
management strategies
Our study showed that for all beef-cattle farmers sampled,
three dimensions of technical herd management (i.e. feeding, reproduction and animal health) are priorities to control
to ensure their farms’ survival. These dimensions refer to the
three main functions of animals (as biological systems),
which provide the capacity to adapt to the farm environment
(Blanc et al., 2006). All farmers ranked the dimensions concerning forage management (harvesting for stocks and
grazing) as less important to control, perhaps because
(i) farmers were located in a grassland region, which may
have decreased their sensitivity towards the need to manage
grassland resources, and (ii) the study occurred in 2005,
before farmers had noticed effects of climate change
(despite looking for them for years). In contrast, there was
great variability in the importance that farmers attached to
the mastery of the administration/regulations and sales
dimensions, explained mainly by differences in the pleasure
of and ability to control them. Therefore, it is interesting to
discover that technical herd management remains the main
dimension that farmers seek to control. This result confirms
that in the face of transformation of the Common Agricultural
Policy payments of entitlements into single payments per farm,
mastering technical dimensions of livestock farming are ways
that farmers adapt and ensure their farms’ survival and viability
(Veysset et al., 2005). Thirty other surveys performed on beefcattle farming systems and 25 surveys on suckler sheep systems
have confirmed these technical leanings of beef-cattle and
sheep farmers (Ingrand et al., 2009).
Implications for researchers and agricultural advisors
The study’s findings indicate that greater consideration of
the human dimension in livestock farming systems is a major
challenge of future research on these systems (Gibon and
Hermansen, 2006). The originality of our approach renders
farmers’ management strategies visible by analysing their
rankings of dimensions of livestock farming and underlying
motivations, rather than practices linked to a preconceived
problem or a specific situation. Doing so identifies concepts
that make sense to farmers in their day-to-day management
(i.e. what they do, but also what they think they should do,
what they would like to do, what they do not attempt to
manage or the difficulties they encounter, etc.). Identifying
these elements enabled the understanding of ways in which
farmers allocate information resources to each management
dimension. In addition, comparing a representation of
farmers’ strategies with an external diagnosis of the farms’
performances should identify discrepancies, which could be
the keystone of the advisory process with farmers. This offers
opportunities for advisors to customise their services to
farmers better.
Our approach differs from studies that aim to characterise
farmers’ management strategies, as it focuses less on
defining at-risk types of strategies (or farming styles) than on
establishing criteria useful and practical for identifying and
understanding an individual farmer’s strategy. In fact, simply
showing that farmers’ strategies are not defined according to
technical, economic or more subjective motivations (Fiorelli
et al., 2007) is not original. In contrast, delineating several
categories of farmers’ motivations and showing ways in which
they can be combined and be influential enable a better
understanding of farmers’ management strategies. Finally,
contrary to methods whose results vary depending on the
farmers sampled, our approach’s results are assumed not to
be contingent on the individuals sampled, meaning that they
can be tested as guidelines for the whole population. The use
of ranking dimensions to identify and understand livestock
farming management strategies had already been tested on a
larger sample of beef-cattle and sheep farmers (Ingrand et al.,
2010), which validated the robustness of the analysis framework. The method can also be used in other farming systems,
with a redefinition of the farming dimensions concerned.
The dimensions of livestock farming we identified seem to be
7
Magne, Cerf and Ingrand
generic for livestock farming systems. On the contrary, they
have to be adapted in crop farming systems.
The study’s findings have not yet been put into practice by
livestock farming advisors. However, the ways we intend to
use them are based on numerous experiences and exchanges with agricultural advisors and farmers during interviews
aimed at updating advisory approaches to beef-cattle farming (Magne and Ingrand, 2004). A method in the form of
a card game is being tested to obtain farmers’ ranking of
dimensions. Each card corresponds to a dimension, and
farmers are asked to sort the cards according to the four
categories of motivations: the risks, pleasure, efficiency and
ability to control, which they perceive. The game can be
played with an individual or a group. This approach is currently being used with 30 beef-cattle farmers and 24 sheep
farmers in 2009 (Ingrand et al., 2009 and 2010). Initial
observations indicate that the greatest difference in ranking
between cattle and sheep farmers concerns the pleasure
motivation, with highly ranked dimensions focusing on animals
for sheep farmers (e.g. flock, reproduction, feeding), and on
resources for cattle farmers (e.g. feed stocks, grazing). Future
research would explore the use of this method in agricultural
advisory work; to do so, we have introduced other key elements
(farmers’ objectives, farmers’ criteria to characterise the situations they have to manage, farmers’ criteria to choose the
information resources to manage these situations) to better
understand the link between management strategies and the
information resources farmers use to succeed (Magne and Cerf,
2009; Magne et al., 2010). Ultimately, we believe that a
quantitative, rather than qualitative, characterisation of the
motivations underlying the rank farmers assign to mastering
dimensions of livestock farming could facilitate interpretation
for advisors.
Conclusion
A simple method of ranking by beef-cattle farmers of
13 livestock farming dimensions that they manage enabled a
more comprehensive identification and understanding of
their farming management strategies. Results demonstrated
that strategies emerge from a compromise between activities they must do, activities they would like to do and
activities they do not achieve. Three guidelines were identified, contrasting specific combinations of the dimensions on
which beef-cattle farmers focus: the animal/herd v. wholefarm scale, biological v. financial dimensions and reproductive cows v. animals sold. The study’s findings have
implications for research by better integrating the meaning,
which farmers attribute to livestock farming management.
Understanding strategies of livestock farming as envisioned
by farmers is a key step for agricultural advisors. The ranking
of management dimensions, identification of management
guidelines and combination of the categories of motivation
necessary to master each dimension constitute elements,
which could become operational tools for advisors who wish
to support a variety of management strategies. Further
research is necessary to validate our findings and to go further
8
in the operationalisation of the methodological elements we
propose here.
Acknowledgements
This work was carried out with the financial support of (i) ANR,
the French National Research Agency, under the Programme
Agriculture et Développement Durable, project Discotech (ANR05-PADD-04-01); (ii) the French Livestock Institute; and (iii) the
French Ministry of Research and Education.
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