1.1 Investigation of statistical links between the motivations and
social and economic indicators
1.1.1 Principles of the approach
The investigation of links between farm socio-economic indicators and farmer motivations is a key element for
extending the analysis of the farmers interviews to a larger population than the interviewed ones. Such an
extension is required in order to use the results in a model involving a larger population than the interviewed
one.
In general, more or less complete socio-economic data bases of socio-economic data of the farm population are
available. If a link between some of these available socio-economic indicators exist, then it is possible to
associate to each socio-economic description of a farm, the likely motivations of the farmer.
One can expect that the statistical link between the indicators and the motivations will be weak, because it would
be really surprising to find a kind of determinism linking these two types of variables. It is very likely that the
motivations depend also strongly on different psychological and historical aspects on which no data are
available.
We investigated this statistical link using stepwise linear regressions, which choose the relevant variables in a
linear model.
Considering a set of socio-economic indicators (V1 ,V2 ,..., Vn ) , and a motivation M we investigate linear
models of the form :
M  1V1   2V2  ...   nVn  
Where
( 1 , 2 ,..., n ) and  are scalar.
The indicators used are presented in the table “indicator description” below. The availability of the indicators
depends on the region of study. It must be noticed that we investigated also indicators that were available from
our interviews, but not in the usual socio-economic data bases. The reason is that it was interesting anyway to
detect this type of links, even though we knew it was impossible to use them for the dynamic social model on a
whole population. In the dynamic model, more rough models would then be used.
The results presented below were obtained by a stepwise forward regression, which tests all possible linear
models with only one variable V, selects the best, and then all the models when adding an new variable, and so
on until the situation where adding a new variable does not bring enough improvement to the model (according
to an information criterion).
The accuracy of the model is given mainly by the R squared variable which expresses the percentage of variance
explained by the model. This gives us the indications on the remaining noise of the model which must be added
in the generation of farmer motivations.
This implies that some randomness is introduced into the determination of the motivation within the considered
generated population.
1.1.2 Comments on the results
The first results of statistical models are given in the tables ?? below. The indicators are presented horizontally,
and the motivations vertically. For each motivation, the results for the different zones of study are presented
together. In some cases, different populations of farms are considered for one zone of the study. This is the case
in Auvergne, where two different populations are considered (organic farming and “mesure locale” Haute vallée
de l’Allier). In the case of Breadalbane ESA, the different populations come from different availability of some
indicators.
The grey boxes signify that the indicator was not available for the considered sample. In the white boxes a sign
’+’ or ‘-‘ indicates that the considered variable has been found relevant by the stepwise regression, and the sign
corresponds to the sign of the parameter of the regression.
The first general comments are :
 Generally very few indicators are found relevant. 2 or 3 in general. Some are obviously related to non
relevant specificity of the sample.
 The squared R are low in general (very few are higher than 0.5).
 There are almost no common variable selected for all the different populations. Such a result would have
been a very good indication for the statistical robustness.
Moreover, the size of the samples and the sampling methods lead to be very careful in the exploitation of these
results.
France Auvergne
LANDSCAPE
0,52
1
0,23
2
0,28
0.46
3
Italie Lombardie
0,08
Italie - Friuli
0,11
France Auvergne
-
PRODUCER IDENTITY
-
France - Isère
+
-
2
0,11
0.39
3
Italie - Friuli
0,23
France Auvergne
0,15 pop1
FAMILY PATRIMONY
-
+
0,50
2
0,58
0.11
3
sample size
Agri-tourism
Load
28
33
+
38
27
+
-
-
1
28
29
+ -
+
0,58
Wk productivity index
Permanent grass %SFP
Nbre enfants
AA per cent
Rel anim
Technical neighbough
57
+ -
30
+
28
-
+
+
+
+
0,35 pop3
0,36
30
28
0,31 pop2
Italie - Friuli
37
27
pop3
0,17
0,28
33
+
1
28
28
pop1
0,21
Italie Lombardie
Geographic neighbourgh
+ +
+
+
+ +
+
+
+
0,24
France - Isère
Water quality
+
29
+
+
Italie Lombardie
UK Breadalbane
LOCAL
UNION
TECH GPS
ENV GPS
QUAL CERT
INTEN
Wast man
N rate
NBR LSTCK
SIZE
RENT LAND
OWN AA
N inorganic input
57
-
+
0,35 pop2
UK Breadalbane
SPECLS
+
0,34 pop3
France - Isère
SYST
-
0,06 pop1
0,54 pop2
UK Breadalbane
SUBS
DEBT
YIELD
AGRI INC
REVENUE/UTA
FGM
PATRIM
INC % FM
DIVERS
DEPEND/SAU
DEPEND
NON FAMWK
FAM WK
TOT FM LAB
SUCCS
AGE
recherche n°
R2
-
57
28
29
+
+
+
+ +
+
+ +
28
33
+
37
27
30
-
Table 3.39 : Results of the stepwise linear regression linking the social and economic indicators to the motivations.
28
France Auvergne
INCOME INCREASE
0,41
1
0,45
2
0,23
0.36
3
TECHNICAL MASTER
0,12
Italie - Friuli
0,19
France Auvergne
0,18 pop1
France - Isère
0,26
1
0,33
2
0,35
0.57
3
0,19
France Auvergne
0,41 pop1
1
0,39
2
0,45
0.47
3
sample size
Agri-tourism
Load
Wk productivity index
Permanent grass %SFP
Nbre enfants
AA per cent
Rel anim
Technical neighbough
Geographic neighbourgh
Water quality
LOCAL
UNION
TECH GPS
ENV GPS
QUAL CERT
57
+
+
+
+
-
28
-
+ +
- - -
33
+
36
27
+
-
-
+
+
30
28
+
57
28
- +
+
28
29
+
+
+
+
0,56
0,39
INTEN
28
+
0,32 pop3
Italie - Friuli
38
27
30
-
0,65 pop2
0,32
33
+
+
Italie - Friuli
Italie Lombardie
28
+
0,52
France - Isère
Wast man
-
-
Italie Lombardie
UK Breadalbane
N inorganic input
-
0,20 pop3
UK Breadalbane
N rate
NBR LSTCK
SIZE
RENT LAND
OWN AA
29
+
+
+
-
Italie Lombardie
57
28
+
0,68 pop2
NATURE PRESERVATION
SPECLS
SYST
DEBT
YIELD
AGRI INC
REVENUE/UTA
FGM
PATRIM
INC % FM
DIVERS
DEPEND/SAU
DEPEND
NON FAMWK
FAM WK
SUBS
-
-
0,29 pop3
France - Isère
TOT FM LAB
+
0,40 pop1
0,26 pop2
UK Breadalbane
SUCCS
AGE
R2
recherche n°
IMAGES project – Final report – version 1
29
+ +
+
+
+ +
+
-
28
33
+
-
-
-
-
36
27
-
Table 3.40 : Results of the stepwise linear regression linking the social and economic indicators to the motivations.
3
30
28
France Auvergne
-
0,35 pop1
0,54 pop2
INTENSITY
France - Isère
SOCIAL REWARD
EXTERNAL ASSESSMENT
Italie - Friuli
France Auvergne
0,10
1
0,11
2
0,11
0.41
3
sample size
Agri-tourism
Load
Wk productivity index
Permanent grass %SFP
Nbre enfants
AA per cent
Rel anim
Technical neighbough
Geographic neighbourgh
Water quality
LOCAL
UNION
TECH GPS
ENV GPS
QUAL CERT
INTEN
N inorganic input
Wast man
28
28
-
33
+
+
40
27
-
-
28
0,21 pop1
0,11
1
0,15
2
3
France - Isère
0,07
0.41
Italie - Friuli
0,11
France Auvergne
0,24 pop1
-
+
57
-
+
28
+
29
-
28
+
+
33
36
27
+
+
0,38 pop2
28
+
+
-
0,43
1
0,44
2
0,43
0.51
3
57
+
+
0,34 pop3
France - Isère
57
29
+
0,38 pop3
UK Breadalbane
+
-
0,29 pop2
UK Breadalbane
+
+
+
0,16 pop3
UK Breadalbane
N rate
NBR LSTCK
SIZE
RENT LAND
OWN AA
SPECLS
SYST
SUBS
DEBT
YIELD
AGRI INC
REVENUE/UTA
FGM
PATRIM
INC % FM
DIVERS
DEPEND/SAU
DEPEND
NON FAMWK
FAM WK
TOT FM LAB
SUCCS
AGE
R2
recherche n°
IMAGES project – Final report – version 1
29
+
+
+
+
28
28
33
+
+
Table 3.41 : Results of the stepwise linear regression linking the social and economic indicators to the motivations.
4
37
27
IMAGES project – Final report – version 1
5