Organic Farming | 2016 | Volume 2 | Issue 1 | Pages 23–27
Can the Adoption of Organic Farming Be Predicted by
Biogeographic Factors? A French Case Study
*, Anja Vieweger
and A. M
Animal and Plant Health Unit, European Food Safety Authority (EFSA), Parma, Italy
Organic Research Centre, Elm Farm, Hamstead Marshall, Newbury, UK
Centro de Investiga
ao em Biodiversidade e Recursos Gen
eticos (CIBIO), InBIO Research Network in Biodiversity and
Evolutionary Biology, University of
* Corresponding author: E-Mail: mar[email protected]; Tel.: +39 521036775
The positions and opinions presented in this article are those of the authors alone and are not intended to represent the
views or scientiﬁc works of EFSA.
Submitted: 13 January 2016 | In revised form: 21 April 2016 | Accepted: 9 June 2016 |
Published: 29 June 2016
Organic farming adoption is on the rise in many countries, due to the increased awareness of
farmers, citizens, governments and other stakeholders of its more sustainable nature. Various studies
have investigated the socio-economic drivers (e.g., consumer demand, support measures, agricultural
policies) of organic farming adoption, but less attention has been paid to whether biogeographic factors
could also be associated with variation in rates of organically managed farms in certain regions within
countries. We investigate whether biogeographic factors are associated with variation in the proportion of
land under organic farming in French departments. The proportion of land under organic farming increased
with decreasing latitude and increasing department area. Non-signiﬁcant factors were number of plant
taxa, proportion of Natura 2000 protected areas, connectivity, longitude, altitude and department population.
These results were robust to controlling for spatial autocorrelation. Larger and southern French departments
tend to have a greater adoption of organic farming, possibly because of the more extensive nature of
agriculture in such regions. Biogeographic factors have been relatively neglected in investigations of the
drivers of organic farming adoption, but may have an important explanatory value.
biodiversity; France, human population; land sharing; macroecology; organic farming; plant
species richness; protected areas; spatial autocorrelation; sustainable development
Organic farming is on the rise globally [
]. Between 2001
and 2011, agricultural land under organic management
increased from nearly 16 to over 37 million hectares world-
]. This trend is also reﬂected in the market for organ-
ically grown produce; during the same decade, the global
organic market grew by 170%, with sales reaching nearly
63 billion US$ in 2011 [
]. In 2011, France (3.8 billion Eu-
ros) was the second largest market for organic products in
Europe (21.5 billion Euros) .
Organic farming aims to reconnect agriculture with na-
2016 by the authors; licensee Librello, Switzerland. This open access article was published
under a Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
ture and biodiversity, making use of natural systems and
cycles and reducing external inputs (for example, growing
a wider variety of crops and using natural ways to reduce
pest populations, e.g. rotations). Industrial agriculture is
currently one of the greatest threats to biodiversity [
Several studies have shown that organic farming beneﬁts
biodiversity, because of its less intensive cultivation prac-
tices [8–14] (see also ).
So far, studies investigating factors driving organic farm-
ing adoption have focused on socio-economic factors (e.g.,
agricultural policies, consumer demand, support measures
and networks) [
]. However, given the connection of
organic farming with nature, it could also be expected that
the presence of organic farming co-varies with regional vari-
ation in biodiversity, as was shown at the landscape level
]. Given that large-scale variation in biodiversity is in turn
associated with biogeographic factors such as latitude, area
and human population [
], it is reasonable to expect
that also regional patterns in organic farming adoption will
tend to be associated with such biogeographic factors.
This study aims to test this hypothesis by using data
on organic farming adoption from French departments, to-
gether with some key biogeographic factors. Our main
question was: is organic farming more likely to be adopted
in regions with an already higher presence of biodiversity?
More generally, can biogeographic factors help predict pat-
terns in organic farming adoption across regions?
2. Material and Methods
Data on the proportion of agricultural land under or-
ganic farming (2008) for the 96 French metropolitan de-
partments (with exception of Paris: data not available)
were obtained from the website for sustainable develop-
ment of the French government (http://www.statistiques.
developpement-durable.gouv.fr/). From the same website,
data for each French department were obtained on land-
scape connectivity (average size of natural habitats; 2006),
the proportion of Natura 2000 protected areas (2009), the
total area and human population (2011). Altitude was
obtained from [
] as a raster map at ca. 1 km
tion, and averaged at French departments using the zonal
statistics plugin of QGIS 2.6 [
] and an administrative
map downloaded from the EDIT Geoplatform [
2000 data are indicators of recent efforts to achieve na-
ture protection and may not be representative of historic
or overall actions to protect nature, as the Natura 2000
reserve selection focused on regions with low presence
of already available protection (i.e. National and Regional
parks). Data on the number of vascular plant taxa (including
subspecies) recorded for each department were obtained
in 2012 from the Tela Botanica website (http://www.tela-
botanica.org/page:chorologie?format=html). Given the rel-
atively low number of data points, we avoided including
an excessive number of explanatory variables; further bio-
geographic factors could be considered in future analyses,
including distance from the sea and road density.
Multivariate models were run in Spatial Analysis for
Macroecology (SAM) [
]. Given that spatial autocorre-
lation can reduce the effective degrees of freedom, thus
leading to potentially misleading P-values [
], the analysis
was performed both without (Linear Regression Model) and
with controlling (Spatial Autoregression, Generalized Least
Squares, with a Gaussian Model for the residual spatial
component) for spatial autocorrelation [
]. All variables
(apart from latitude and longitude) were log-transformed
prior to analysis so as to better approach a normal distri-
bution. Non-signiﬁcant variables (at p
0.05) were kept
in the models to demonstrate that they were not signiﬁcant
predictors. The signiﬁcance of the signiﬁcant factors was
robust against model reduction. We did not observe strong
collinearity (correlation coefﬁcient
0.70) among the ex-
planatory variables, with the only exception of connectivity
and plant biodiversity (correlation coefﬁcient = 0.75).
Without controlling for spatial autocorrelation, the propor-
tion of organic farming in French departments increased
signiﬁcantly with decreasing latitude and increasing depart-
ment area. There was no signiﬁcant association with plant
biodiversity, proportion of protected areas, connectivity, lon-
gitude, altitude and human population size (Table 1).
All these results were confirmed when controlling for spa-
tial autocorrelation, although with slightly different P-values
and parameter estimates (Table 2). On its own, latitude ex-
plains about 40% of the variation among French departments
in their proportion of organic farming (Figure 1). Department
area on its own explains about 17% of the variation in pro-
portion of organic farming, but this is largely due to a few
data points, i.e. some small departments in the Ile-de-France
area with very low proportion of organic farming.
Results of a General Linear Model for the proportion of agricultural land under organic farming in French
Departments (2008) as a function of plant biodiversity, landscape connectivity, proportion of Natura 2000 protected areas,
latitude, longitude, altitude, human population size and department area. The number of data points is 95, the adjusted
of the model 0.50, and the intercept 1.457 (s.e. = 2.074).
N of plant taxa Landscape connectivity % Natura 2000 Latitude Longitude Altitude Human population Area
parameter estimate 0.264 0.047 0.113 -0.084 0.003 0.038 0.056 0.327
s.e. 0.486 0.101 0.09 0.024 0.017 0.172 0.132 0.139
P-value 0.59 0.64 0.21 <0.001 0.87 0.82 0.67 0.02
Results of a Generalized Least Squares model controlling for spatial autocorrelation, for the proportion of agricul-
tural land under organic farming in French Departments (2008) as a function of plant biodiversity, landscape connectivity,
proportion of Natura 2000 protected areas, latitude, longitude, altitude, human population size and department area. The
number of data points is 95, the Akaike Criterion Indicator of the model 59.9, and the intercept 1.457 (s.e. = 1.972).
N of plant taxa Landscape connectivity % Natura 2000 Latitude Longitude Altitude Human population Area (km
Parameter estimate 0.264 0.047 0.113 -0.084 0.003 0.038 0.056 0.327
s.e. 0.462 0.096 0.086 0.023 0.016 0.164 0.125 0.133
P-value 0.57 0.63 0.19 <0.001 0.87 0.81 0.65 0.02
Several studies have shown the essential role of agro-
ecological approaches, and particularly organic agriculture,
for sustainable development [
]. Ecological intensiﬁ-
cation enables an improvement of productivity while at the
same time reducing adverse effects on the environment
]. Not only the adoption of organic farming practices,
but also research on organic farming has expanded consid-
erably over the last years [
]. This study provides
evidence that biogeographic factors can be associated with
patterns in organic farming adoption across regions.
There are three main results of this analysis. First, there
are no substantial differences between models of the pro-
portion of organic farming in French departments (i) taking
spatial autocorrelation into account, and (ii) not taking it
into account. The results of models taking into account
spatial correlation are more conservative and should be
trusted more than those without taking it into account, but
in this case there are only slight differences in parameter
estimates and P-values.
Second, this result does not imply that there is no spatial
autocorrelation in the examined variables. For example, the
investigated response variable (the proportion of cultivated
land under organic farming) was signiﬁcantly spatially auto-
correlated at short distances, as shown by an analysis of
Moran’s I (Figure 2). This result is in agreement with previ-
ous reports of spatial aggregation and neighbouring effects
in the adoption of organic farming within countries [42–45].
To some extent, such spatial aggregation might be due to
the underlying spatial autocorrelation of biogeographic fac-
tors associated with variation in organic farming adoption.
However, there is also an important role of neighbouring
effects of e.g. social networks and farmer communities in
explaining the spatial aggregation of organic farming.
A third result of this study (which holds when controlling for
spatial autocorrelation) is the latitudinal gradient from North
to South in French adoption of organic farming. Farmers in
the South of France might have switched more easily to or-
ganic cultivation because of the larger variety of crops they
can cultivate in their climatic and environmental settings. In
addition, it could be easier to switch to organic cultivation in
viticulture (which is typical in Southern France) than for other
crops. Moreover, due to a mix of climatic, edaphic, historical
and cultural reasons, Southern French departments tend to be
located in regions of less intensive agriculture, thus facilitating
the adoption of less intensive agricultural practices . This
finding is in agreement with previous analyses in England,
Germany, the USA and Sweden, which found that organic
farming was more likely to occur in marginal areas, where
the loss of production due to organic conversion is relatively
] and in regions with more heterogeneous land-
], thus likely to harbour greater plant biodiversity
]. However, we did not observe a significant association
of organic farming adoption with plant biodiversity. It is also
possible that the observed latitudinal gradient in organic farm-
ing adoption correlates with other socio-economic factors (e.g.
personal beliefs, levels of political and financial support, public
perceptions, type of crops) that were not considered here.
40 42 44 46 48 50 52
% organic agriculture
Latitudinal gradient of the proportion of agri-
cultural land under organic farming (logarithmically trans-
formed) in French departments (2008; n = 95,
−0.125x + 6.079, R
= 0.40, p < 0.001).
Moran’s I for the proportion of agricultural land un-
der organic farming (logarithmically transformed) in French
Interestingly, although there is generally little variation
among French departments in area, this was a signiﬁcant
factor in our analysis, due to the few very small departments
(mainly located in the Paris area) having surprisingly low
organic farming adoption rates.
Further research should investigate whether biogeo-
graphic factors are still significant determinants of organic
farming adoption when including a large suite of socio-
economic explanatory variables. Econometric and socio-
economic models of organic farming adoption may benefit
from including data on large-scale biogeographic factors. It
would also be interesting to use a finer spatial resolution of the
data used here, to test for any scale-dependence in the rela-
tive importance of biogeographic and socio-economic factors
as explanatory variables for organic farming adoption rates.
Most research on regional patterns of organic farming
has focused on socio-economic and cultural factors,
from policy support to agglomeration effects and from
the philosophy of farmers to the development of mar-
kets for organic produce and organic seed [40,51–59].
Whilst these factors are undoubtedly important, this
study builds on evidence obtained at the landscape
level on the role of environmental factors in shaping
organic farming adoption [
] and suggests that bio-
geographic variables may play a contributing role in
how widespread organic farming is becoming across
Many thanks to V. Chable, O. Holdenrieder, D. McKey
and S. Vos for insights and discussions, and to T.
oring, T. Matoni and anonymous reviewers for helpful
comments on a previous draft. AMB is supported by
ao para a Ci
encia e a Tecnologia through ‘FCT
Investigator’ contract IF/00266/2013 and exploratory
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