10.5061/DRYAD.W3R2280R5
Barbu, Corentin
0000-0001-6869-5345
National Research Institute for Agriculture, Food and Environment
Delaune, Thomas
National Research Institute for Agriculture, Food and Environment
Ouattara, Malick S.
National Research Institute for Agriculture, Food and Environment
Ballot, Remy
National Research Institute for Agriculture, Food and Environment
Sausse, Christophe
Terres Univia
Felix, Irène
Arvalis - Institut du Végétal
Maupas, Fabienne
Institut Technique de la Betterave (ITB)
Chen, Mathilde
National Research Institute for Agriculture, Food and Environment
Morison, Muriel
National Research Institute for Agriculture, Food and Environment
Makowski, David
National Research Institute for Agriculture, Food and Environment
Landscape drivers of pests and pathogens abundance in arable crops
Dryad
dataset
2021
crop pests
crop protection
crop pathogens
FOS: Agricultural sciences
Agence Nationale de la Recherche
https://ror.org/00rbzpz17
ANR-11-LABX-0034
Agence Nationale de la Recherche
https://ror.org/00rbzpz17
ANR-001368-P00004321
GIS GCHP2E*
2015-2016
GIS GCHP2E
2015-2016
2021-07-11T00:00:00Z
2021-07-11T00:00:00Z
en
https://doi.org/10.1101/641555
35404210 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Farmers’ use of fungicides and insecticides constitutes a major threat to
biodiversity that is also endangering agriculture itself. Landscapes could
be designed to take advantage of the dependencies of pests, pathogens, and
their natural enemies on elements of the landscape. Yet the complexity of
the interactions makes it difficult to establish general rules. In our
study, we sought to characterize the impact of the landscape on pest and
pathogen prevalence, taking into account both crop and semi-natural areas.
We drew on a nine-year national survey of 30 major pests and pathogens of
arable crops, distributed throughout the latitudes of metropolitan France.
We performed binomial LASSO generalized linear regressions on the pest and
pathogen prevalence as a function of the landscape composition in a total
of 39,880 field × year × pest observation series. We observed a strong
disequilibrium between the number of pests or pathogens favored (15) and
disadvantaged (2) by the area of their host crop in the landscape during
the previous growing season. The impact of the host crop area during the
ongoing growing season was different on pests than on fungal pathogens:
the density of most pathogens increased (11 of 17, and no decreases) while
the density of a small majority of pests decreased (7 of 13, and 4
increases). We also found that woodlands, scrublands, hedgerows, and
grasslands did not have a consistent effect on the studied spectrum of
pests and pathogens. Although overall the estimated effect of the
landscape is small compared to the effect of the climate, a territorial
coordination that generally favors crop diversity but excludes a crop at
risk in a given year might prove useful in reducing pesticide use.
Relevant extracts from the methods of the paper: Pests and pathogens data
Since 2008, the French epidemiological services record and centralize
observational data of crop pests and pathogens from arable field
monitoring. In this study, we made use of two epidemiological information
subsystems: Vigicultures ® (Sine et al. 2010) and VIGIBET (ITB – Sugar
Beet Research Institute), that covered 17 of the 22 former French
administrative regions including approximately two-thirds of its territory
over the 2009-2017 period. From these two databases, we extracted
information for 30 pests or pathogens on six crops (winter wheat, winter
barley, corn, oilseed rape, sugar beet, potato). We eliminated from the
data the observations for which the reported crop didn’t match the crop
indicated in the RPG data, considered here as the gold standard as they
are tax data and have been successfully used to train automated detection
of crops based on satellite imagery (Inglada et al. 2017). Depending on
the crop, this could affect 5 to 30% of the field x year combinations in
the database. Many of these observations also had little to no
observations of pests or pathogens. We understand them as monitoring
points entered by mistake and never really monitored. Data were collected
each year during the cropping season from weekly monitoring of
georeferenced fields by technicians from various organizations and trained
farmers (Table SI.1.). A different set of fields was monitored each year,
freely chosen by the technicians performing the surveillance. It was
requested that the fields be far enough apart to reflect the diversity of
the territory for which the technicians are responsible, but practical
access considerations are also taken into account. Possible issues with
repeated measurements and auto-correlation in the data are discussed in
Supplementary Materials SI.7. All fields were conventional farming fields.
The head of the observation network informed us that some observations
were performed in non-treated spots but we could not account for the crop
protection practices because the information was often missing in the
database. In each field, several observation types assessing the state of
crop epidemics were retrieved through standardized protocols for each
monitored pest and pathogen (e.g. damage severity scale on the plant for
pathogens, relative or absolute organism abundance observed on the plant
or in traps, amount of plants with symptoms, etc.). Not all the
observation types were reported in equal numbers. Here we kept for each
organism considered only the observation type with the highest number of
field x year observed to maximize the spatiotemporal extent of each pest
or pathogen information. We also expected these widely used observation
types to be relevant to describe the risk linked to the organisms as they
are originally used to motivate pesticide applications. In total, data for
13 pests of winter wheat, corn, and oilseed rape, and 17 pathogens of
winter wheat, winter barley, oilseed rape, sugar beet, and potatoes were
analyzed. Detailed information on the pests and pathogens studied,
observation periods, and observation types we used can be found in tables
SI.1 and SI.2. Landscape composition data The delimitation of all French
agricultural fields subsidized within the framework of the European Common
Agricultural Policy is provided through the "Registre Parcellaire
Graphique" (RPG). For annual crops, it is reputed to be nearly
exhaustive. The geometry of the fields is described by farmers based on
the aerial photographs of the BD Ortho®, a departmental orthophotography
of 50 cm resolution provided by the French National Institute for
Geographic and Forestry Information (IGN) (ASP et IGN 2019; Font 2018).
From 2006 to 2014, fields were described by islets, a group of contiguous
fields, but 80% of them had only one type of crop. In each islet, the
detailed areas were given by crop types (28 crop types for 329 crops
registered). Here we used six of them: winter wheat, oilseed rape, winter
barley, corn (including both silage and grain corn), other industrial
crops (mainly and considered here to be beet), and flowering vegetables
(mainly and considered here to be potatoes). From 2015 to 2017, the
description of crops in the RPG was available by species (not crop type)
and by field (not islet) and we used this more precise information. The
semi-natural components considered were woods, grasslands, scrublands, and
hedgerows. The RPG provided us with grassland delineations for the year of
the observation (temporary and perennial grasslands are not distinguished
here). The BD TOPO® (vegetation layer version 2.2 2017), a vector map with
a resolution of 1 m (IGN 2016) drawn from the BD ORTHO® by the French
National Institute for Geographic and Forestry Information, provided us
with the geometry of the other components: woods, hedgerows, and
scrublands, considered to be stable over the studied years. From this
database, we grouped as “woodlands” the broadleaved, coniferous, and mixed
woodlands, with closed or open canopy. Variables preparation and control
variables Pest and pathogen abundance measurements were not normally
distributed, often rounded informally, and sometimes distributed into
categories. Also, the number of observations of a given pest or pathogen
varied by field and year. As a result, we simplified the data into two
counts per field and year: the count of observations above and under the
median of the observations for all fields and years (SI.2.). For half of
the organisms, only presence-absence data were available (SI.2) we then
used the counts of observations with or without the pest or pathogen among
the observations of the year in a given field. In both cases (with/without
or above/under median), the two counts have by construction, a binomial
distribution and describe the risk of being above a threshold (presence or
median), hereafter referred to as the risk. We quantified the landscape
composition by measuring the area (m2) of semi-natural components and of
the pest or pathogen host crop around each observation in buffers with
radii of 200 m, 1 km, 5 km, and 10 km. As the abundance of a crop in the
landscape could be correlated with its recurrence in the rotation at the
field level, the field level rotation effect could be attributed by the
regression models to landscape variables. To avoid such confusions we
explicitly considered two crop rotation variables: the time elapsed in the
observed field since 1) the host crop or 2) grassland, were cultivated. As
only 2 years of RPG data were available before the first observations of
pests and pathogens, we simplified these variables to three values: 1, 2,
and 3 years or more. We discarded the points when the host crop or the
grassland was not alone in the islet the last time it appeared. To account
for the potential effect of annual weather and the heterogeneity of crop
management in different sub regions, we added two variables to the pool of
variables: first, a categorical variable by year and region based on a
supra-regional zonation of agroclimatic conditions (SI.3, Figure SI.1b)
aggregating French départements (Lorgeou et al. 2012) and second, a
sub-regional zonation of homogeneous farming systems (SI.3, Figure SI.1a),
as defined by the French technical institute for cereals Arvalis, Institut
du Végétal, (Arvalis 2011).
This dataset has one line per observation site - year - pest. In columns
are 1) the observations of the pests (number observations above the
threshold in the year, total number of observations (positive or negative)
of the pest in the year. 2) the landscape variables: for each landscape
component and buffer (200m, 1km, 5km, 10km), the square meters covered by
the landscape component 3) the control variables: region, year,
homogeneous practice small region.