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Local and neighbourhood effects of organic and conventional wheat management on aphids, weeds, and foliar diseases
Agriculture, Ecosystems and Environment 161 (2012) 121–129
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Local and neighbourhood effects of organic and conventional wheat management on aphids, weeds, and foliar diseases Marie Gosme a,b,∗ , Maguie de Villemandy a,b , Mathieu Bazot a,b , Marie-Hélène Jeuffroy a,b a b
INRA, UMR211 Agronomie, F-78850 Thiverval-Grignon, France AgroParisTech, UMR211 Agronomie, F-78850 Thiverval-Grignon, France
a r t i c l e
i n f o
Article history: Received 30 January 2012 Received in revised form 9 July 2012 Accepted 12 July 2012 Available online 19 August 2012 Keywords: Organic farming Conventional farming Pests Between-field interaction
a b s t r a c t The area under organic farming is increasing in many countries. The effect of a significant increase in the proportion of organic agriculture on pest (sensu lato) populations at the landscape scale is unknown and will depend on both the production of propagules in organic fields and the risk of pest dispersal between fields. In this study, we observed the dynamics of four foliar diseases, aphids, and weeds in 216 wheat fields over 2 years in northern France. We used the survey data to estimate the local effect of how a field was managed (organic or conventional) and the presence or absence of adjacent organic fields (neighbourhood effect) on pest abundance in that field. Because conventional and organic may be considered extremes along a continuum of management practices, a large survey was undertaken of management practices to ensure that the fields were classified according to the actual cropping practices. The presence or absence of organic certification was determined to be the only relevant criterion for classifying cropping practices. The results of proportional odds mixed models showed that some pests responded to local crop management: leaf blotch incidence and aphid density were significantly lower while weed diversity and abundance were higher in organic fields. Only aphids and leaf blotch responded to the neighbourhood effect: the presence of organic fields in the neighbourhood decreased the number of aphids in both organic and conventional fields and decreased leaf blotch incidence but only in conventional fields. These results indicate that the increase in organic acreage in landscapes will not increase pest problems in the short term under the conditions of the study (low disease pressure). © 2012 Elsevier B.V. All rights reserved.
1. Introduction Organic farming is often proposed as a solution to the environmental problems and public health threats posed by intensive agriculture. However, the share of agricultural land cropped under organic farming is low: less than 1% worldwide and only 1.9% in Europe (FIBL and IFOAM, 2011). Several countries have promoted organic farming by providing subsidies to farmers for conversion, by funding research and extension in organic agriculture (U.S. Government, 2008), and sometimes by setting quantitative goals in terms of agricultural area that should be farmed organically (INRA, 2011). As a result, organic acreage has increased dramatically over the last decade: global acreage increased by 238% between 1999 and 2009, from less than 11,000,000 to more than 37,000,000 ha (FIBL and IFOAM, 2011), and this increase is expected to continue in the future. Concerns have been raised regarding the threats to food safety and food security that could appear if organic farming has a large
∗ Corresponding author. Tel.: +33 1 30 81 52 26; fax: +33 1 30 81 54 25. E-mail address: [email protected] (M. Gosme). 0167-8809/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agee.2012.07.009
share in agricultural land, not only because organic fields produce lower yields but also because they could harbour more pests that might then spread to conventional fields. For example, a mathematical model showed that there is a critical threshold of organic fields in a region above which pathogen outbreaks are more likely to occur, spread into all organic fields, and force conventional farmers to use more pesticides (Adl et al., 2011). This model, however, is based on important assumptions, in particular that pathogen growth is unchecked in organic fields. Another theoretical study has shown that the effect of the share of agricultural land under organic management on disease level and crop production at the regional scale depends critically on the amount of inoculum produced by organic fields (Gosme et al., 2010). Therefore, the effect of an increase in organic farming on pest populations (sensu lato, i.e., insect pests but also diseases, weeds, and other pests) cannot be predicted without precise data on the effect of organic farming on the different diseases, pests, and natural enemies (Anonymous, 2001). Data on the relative abundance and/or diversity of several taxonomic groups in organic vs. conventional fields is steadily increasing in the literature, but results are highly inconsistent among studies. Furthermore, response to organic management is expected to depend on taxonomic group as
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well as trophic level: a meta-analysis found that birds, predatory insects, soil organisms, and plants benefited from organic farming, whereas non-predatory insects and pests did not (Anonymous, 2001). One explanation that has been proposed for the inconsistencies between studies is the effect of the landscape surrounding the observed fields on biodiversity and especially on the diversity and abundance of natural enemies. In intensively managed landscapes, the species pool is so depleted that natural enemies are unavailable for colonization of organic fields. In highly complex landscapes (i.e., those containing many well-connected semi-natural habitats), in contrast, biodiversity remains high whether crops are managed conventionally or organically. As a result, the positive effect of organic farming on biodiversity is maximum in landscapes of low to medium complexity (Tscharntke et al., 2005). For these reasons, landscape complexity has been considered in recent comparisons of biodiversity or biological control in organic vs. conventional fields (Östman et al., 2001; Clough et al., 2005; Schmidt et al., 2005; Gabriel et al., 2006; Rundlöf et al., 2008b; Winqvist et al., 2011). Landscape complexity in these studies is usually estimated as the percentage of arable crops (or semi-natural habitats) or through an index of diversity based on different land use categories. To our knowledge, however, research has not been conducted on the effect of the proportion of organic farming in the landscape on diseases and pest populations (but see Rundlöf et al. (2008a) on butterflies, Gabriel et al. (2010) on the biodiversity of several taxonomic groups, and Holzschuh et al. (2008) on pollinators). Thus new studies are needed to determine whether the presence of organic fields in the landscape affects pest populations in conventional and other organic fields. Studies comparing organic and conventional fields can also be weakened by the fact that although the organic certification proves that no synthetic fertilizer and chemicals have been used, even organic farms can differ in management intensity (i.e., they can differ in the quantities of external inputs), and “conventional” farms can range from those that are intensively managed to others that have low-input or that are effectively organic but not certified. The objective of this study was to determine whether the presence of organic fields affected aerial diseases, weeds, and aphids in nearby organic and conventional wheat fields in our study region (the Seine River basin in France). Accomplishing this objective required determining whether the designations of “organic” and “conventional” were meaningful for these fields and whether subgroups of conventional farmers should be recognized. 2. Materials and methods The approach taken in this paper differs from that usually followed in the literature: instead of selecting pairs of fields in different landscapes and describing landscape only in terms of land use, we focused on only one landscape but precisely described cropping practices in all the fields of the landscape. The chosen study area is not representative of the whole agricultural region in terms of acreage of organic farming (see below), but it is representative in terms of soil, topography, and farm size. The survey comprised two parts: first, an exhaustive survey of cropping practices of all the crops in the study area, and second, a survey of key pests in a subset of the wheat fields in the same area. 2.1. Study area The study area was approximately 50 km west of Paris, France (between 48◦ 52 N and 48◦ 58 N and 1◦ 32 E and 1◦ 41 E). This zone falls within the Seine basin, in the “Drouais” agricultural region, which is mainly characterized by intensive production of cereals
and oil/protein crops (mean wheat yields in 2009 and 2010 were 8.4 and 7.6 t/ha, respectively, vs. 7.7 and 7.3 t/ha nationwide mean [source: Agreste]). The rotations in this region are based on wheat, pea, and oilseed rape (Mignolet et al., 2007). Because organic farms in this region are scarce (less than 1% of Utilized Agricultural Area or UAA in 2009), the study area was chosen to include as many certified organic fields as possible: it covered 62 km2 and encompassed the fields of three organic farmers. The study area was composed of 74.4% UAA (97.7% of UAA was arable land, 11.3% of UAA was cultivated in organic farming), 15.4% semi-natural areas (woods and abandoned land), and 7.2% urban area (small villages with detached houses and gardens), i.e., landscape complexity was low to medium. The topography of the study area was flat, with altitudes ranging from 130 to 150 m asl, except where a river (50 m asl) crossed the study area. Sizes of organic and conventional farms and fields were comparable: the surveyed conventional farms ranged from 30 to 305 ha (mean 147, median 143), and the three organic farms were 133, 146, and 310 ha. Field size ranged from 0.38 to 30.80 (mean 4.67, median 3.34) in conventional farms and from 0.52 to 24.35 (mean 6.9, median 4.83) in organic farms. The landscape in the region is open-field with few hedgerows, and fields are generally adjacent to each other except when separated by roads. 2.2. Crop management survey To confirm that the classification of fields into the two categories (organic/conventional) was relevant in our study area (i.e., to confirm that there were consistent differences between organic and conventional management and a lack of subdivisions among conventional farmers), we collected information on the cropping practices for all fields in the area. We contacted by mail, and phone when possible, all 163 farmers who had at least one field in the counties where the study area was located (but most of them had fields outside the study area). Finally, 54 farmers (67.8% of the UAA in the study area) were involved in the study, including all three farmers with the organic certification (their farms had been certified organic for more than 8 years). Farmers were interviewed individually to collect information on the farm and on all of their fields in the study area: the collected information included farm area, location of each field on an aerial photograph, and crop succession over the previous 5 years in each field. Each farmer was later interviewed at least once per year in 2009 and 2010 to complete the information on cropping practices: the collected information included number of ploughings and other soil tillage operations, the cultivars sown, sowing density, date of sowing, and dates and doses of pesticide and fertilizer applications (see Table 1 for a summary of the variables and their short names). Thus, information was collected on 613 fields, representing a total of 1082 management sequences over both years (the number of sequences was less than 1226 because some farmers did not participate in the second year of the study). Information on the disease resistance of the wheat varieties was obtained from the descriptions of varieties published by Arvalis-Institut du Végétal (2009). 2.3. Pest population survey A total of 216 wheat fields were surveyed in 2009 and 2010 (Table 2). These included all of the organic wheat fields in the study zone and a sample of conventional wheat fields; the latter were chosen to provide a range of landscape situations (presence or absence of organic fields immediately adjacent to them). The fields were classified (see results below) as organic or conventional (“field type”) and as having or not having at least one organic field immediately adjacent to them (“neighbourhood type”). Because of rotations, most fields surveyed in 2009 could not be studied in 2010
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Table 1 Main variables observed in the study and used in the analysis. Short name
Description
Unit Proportion of fields
mecha
Whether or not wheat was grown in the field the previous year Whether or not the field was ploughed Number of soil tillage operation apart from ploughing Sowing date Sowing density Cultivar resistance to brown rust, yellow rust, powdery mildew and leaf blotch, respectively (wheat only) Treatment frequency index of fungicides (dose applied per ha in one year divided by the standard approved dose found at http://e-phy.agriculture.gouv.fr/) Same for herbicides and insecticides total rate of nitrogen (in the case of organic farmers, this was estimated based on the amount of organic fertilizer and N content of the compost and manure that they used Number of mechanical weeding operations
Variables in pest survey APH WA WD BR YR LB PM
Aphid abundance Weed abundance Weed diversity Tiller incidence of brown rust Tiller incidence of yellow rust Tiller incidence of leaf blotch Tiller incidence of powdery mildew
Variables in crop management survey prev plo til sow dst BRR, YRR, PMR, LBR TFIF
TFIH and TFII N
because they were no longer cropped in wheat, but 11 fields were studied in both 2009 and 2010. Observations were made six times each year on a diagonal transect across each field (not necessarily at the same place each time). Each transect consisted of eight 50 × 50 cm quadrats placed every 15–30 m, depending on the size of the field. In each quadrat, the number of weeds per genus were counted, except for genera in the Poaceae family, which were pooled together. Ten wheat stems were randomly chosen in each quadrat, and the aphids (all species pooled but the most abundant was Metopolophium dirhodum [Walker]) on the leaves or ear were counted. All living leaves of the same 10 stems were observed, and the presence/absence of the following four aerial diseases was recorded for each stem: yellow rust, caused by Puccinia striiformis Westendorp f. sp. tritici; brown rust, caused by Puccinia recondita Roberge f. sp. recondita; leaf blotch, caused by Mycosphaerella graminicola (Fuckel) Schöter; and powdery mildew, caused by Erysiphe graminis DC. f. sp. avenae. These observations were then summed in each field at each observation date to obtain aphid abundance, weed abundance, weed diversity (number of weed genera encountered in the field), and incidence of the four diseases (proportion of infected stems). In 2009, the six observations were made every 3 weeks from the end of February to the end of June; in the 2009/2010 cropping season, the first observation was made at the end of November and then every 3 weeks from March to June.
Table 2 Number of wheat fields sampled in 2009 and 2010 by field type and neighbourhood type for the pest population survey. Neighbourhood type Year
Field type
At least 1 organic
All conventional
2009
Organic Conventional
24 32
4 69
2010
Organic Conventional
12 35
4 36
Proportion of fields Julian day kg/ha 0–9
kg/ha
Individuals/tiller Individuals/m2 genera/field
2.4. Statistical analysis Crop management data were analysed by hierarchical clustering to determine how fields should be grouped for the analysis of pest populations. In particular, we wanted to determine whether cropping systems are adequately categorized as organic and conventional, i.e., whether organic and conventional cropping practices (including the diversity of crops planted) are actually different and whether there are important subgroups among conventional practices. Agglomerative nesting (function agnes of the “cluster” package for R [Maechler et al., 2005]) was performed using Manhattan distance (after standardisation of the data when variables did not have comparable scales). Three analyses were performed: (i) a classification of farmers based on the proportion of each crop in their cropping plans over 6 years (only for farmers whose crop rotations were known for more than 10 fields so that the proportions were meaningful); (ii) a classification of farmers based on their average cropping practices (averaged over all of their fields planted with a given crop, regardless of the year) on each of the following crops: oilseed rape, wheat, faba bean, maize, barley, and pea (only for farmers whose cropping practices were known for more than 10 fields); and finally, (iii) a classification of wheat fields based on their cropping practices, which indicated whether results based on average cropping practices adequately reflected the diversity of cropping practices. Wilcoxon–Mann-Whitney tests were performed on each wheat cropping practice to determine whether there was a difference between organic and conventional management for the given variable. To determine whether neighbourhood effect and pesticide use were confused in the analysis of pest populations, a linear mixed model was used on the treatment frequency indices (separately for insecticides, herbicides, fungicides and molluscicides) for conventional fields; this analysis would indicate whether fields that were located adjacent to organic fields received different doses of pesticides than fields surrounded only by conventional fields. Crop was used as a random effect, and only crops that were planted in both types of neighbourhood (i.e., adjacent to organic fields and conventional field) were used; these included wheat, oilseed rape, faba bean, maize, barley, and pea. The test was performed by
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Fig. 1. Agglomerative hierarchical clustering trees of farmers and fields. (A) Farmers (n = 24) clustered according to the proportion of each crop in their cropping plans over 6 years. (B) Farmers (n = 39) clustered according to their mean cropping practices on oilseed rape, wheat, faba bean, maize, barley, and pea. (C) Winter wheat fields (n = 544) clustered according to cropping practices; lines in the lower part of the graph join each field with its owner; fields in black are organic fields, and fields in grey are conventional fields. Farmers 4, 6, and 17 were organic farmers, but farmer 4 had three fields that were not managed organically.
comparing the model including neighbourhood type as a fixed effect and the model with only the random effect. Pests data were first visualized by plotting the means of the variables as a function of time and by linear discriminant analysis (LDA, function candisc in package cansdisc for R [Friendly and Fox, 2011]), with the categories OO (organic field in organic neighbourhood), OC (organic field in conventional neighbourhood), CO (conventional field in organic neighbourhood), and CC (conventional field in conventional neighbourhood). To test for the effect of field type (organic vs. conventional) and neighbourhood type (at least one adjacent field organic vs. all adjacent fields conventional), proportional odds mixed models (function clmm of the “ordinal” package for R [Christensen, 2010]) were fitted on weed abundance and diversity, incidence of each of the diseases, and aphid abundance. The data were treated as ordinal data because none of the standard transformations or link functions provided the normality of residuals required by linear or generalized linear models. Each variable was transformed into categories: the first category was always 0, and then non-zero values were divided into deciles, except for weed abundance for which the first five categories were combined (see below). The fixed effects were field type, neighbourhood type, and their interaction, and the random effects were field and date nested within year (except for aphids, which were present only during the last observation in each year). In a first step, the interaction was tested in the complete model. If no significant interaction was detected, the model was rewritten without the interaction term to test the main effects (the Wald test and a likelihood ratio test were used to compare the model with and without this effect). If there was a significant interaction, the effect of neighbourhood type was tested separately for each type of field, and
the effect of field type was tested separately for each type of neighbourhood. The proportional odds assumption for field type and neighbourhood type was checked visually (by verifying that the lines for the empirical logit of the different categories were parallel) as well as with a likelihood ratio test that compared the proportional odds model (POM) with a partial proportional odds model (PPOM) with the given factor as a nominal effect and with only field as a random variable (because PPOM with several random factors is not yet available in R). The assumption was fulfilled except initially for weed abundance, which had no observations in the first four categories in organic fields. When the first five categories were combined for weed abundance, the proportional odds test was no longer significant for the neighbourhood type effect; because it remained significant (p = 0.002) for the field type effect, field type was kept as a nominal effect in a PPOM. 3. Results 3.1. Classification of crop management The most frequent crops in the study area were winter wheat (50.7% of the surveyed area), winter oilseed rape (15.2%), and maize (9.5%). Of the 1082 management sequences, 1042 had complete information and were used in the analysis (917 conventional, 125 organic) (Table 3). The hierarchical classification of farmers and fields showed a clear distinction between organic and conventional farmers/fields, for both cropping plan (Fig. 1A) and cropping practices (Fig. 1B and C): the highest node always distinguished between all conventional farmers/fields on one side and all organic farmers/fields on the other side. Furthermore, the internode branches were always
M. Gosme et al. / Agriculture, Ecosystems and Environment 161 (2012) 121–129 Table 3 Number of fields in the crop management survey for each crop, year, and type of management. 2010a
2009 Crop
Conventional
Organic
Conventional
Organic
Winter wheat Winter oilseed rape Maize Winter barley Spring faba bean Pea Winter triticale Winter faba bean Winter oat Sunflower Spring barley Spring oat Alfalfa Clover Fodder beet Spelt Total
259 93 43 42 23 11 1 1 1 0 1 3 1 0 1 1 481
27 0 5 0 7 0 11 4 2 0 0 0 0 0 0 0 56
238 82 29 25 28 18 0 3 4 5 2 0 2 0 0 0 436
20 0 13 0 8 0 16 5 2 1 2 0 0 2 0 0 69
a The total number of fields differed between 2009 and 2010 because some conventional farmers who participated in 2009 did not participate in 2010, and because one of the organic farmers in 2009 provided complete information on cropping practices only for wheat.
longer between the highest node within a management system and the top of the tree than between lower nodes. Organic farmers grew less wheat than conventional farmers (organic and conventional farmers grew wheat in 35% and 57% of their fields, respectively), and their crops were more diversified: the Shannon index computed for the number of fields under different crops for each farmer was significantly higher for organic farmers than for conventional
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farmers (t = −3.965, df = 22, p-value <0.001). In contrast, dissimilarity in cropping practices was greater among conventional farmers than among organic farmers (probably due to our limited sample of organic farmers). Despite this diversity, no clear group appeared among conventional farmers: internodes were always short, indicating that conventional farmers were distributed evenly along a gradient of crop management. Consequently, fields were classified as organic or conventional (hereafter referred to as field type effect) and as having or not having at least one organic field in their immediate neighbourhood (hereafter referred to as neighbourhood type effect) in the analysis of pest populations. Fig. 2 presents beanplots of the cropping practices of organic and conventional farmers for wheat fields. Ploughing and other tillage methods before sowing were performed less frequently for conventional wheat than organic wheat (89% of organic wheat fields were ploughed vs. 51% of conventional wheat fields; the median number of other tillage methods was 3 for organic wheat fields vs. 2 for conventional fields). The mean sowing date was 20 days later in organic than in conventional fields. The sowing density was higher in organic fields. The total rate of nitrogen application was lower and more variable in organic fields. Levels of disease resistance were similar for varieties planted in conventional vs. organic fields, except that resistance to brown rust was higher in varieties used in organic fields. Finally, no pesticides were used in organic farming, while the treatment frequency indices in conventional farming were on average 2.0, 1.6, and 0.4 for fungicides, herbicides, and insecticides, respectively. The comparison of pesticides use in conventional fields between neighbourhood types showed that the treatment frequency indices for fungicides, herbicides, and insecticides were not significantly different between neighbourhood types (p > 0.05) but that the
Fig. 2. Beanplots of cropping practices (see Table 1 for the meaning of the variables) of the 544 conventional (C) and organic (O) wheat fields. The width of the “bean” indicates the density of the distribution of the values, the black line indicates the means of each group, and the dotted line indicates the overall mean. Significant differences between organic and conventional are indicated by a star in the plot title. For variables plo and prev, the bean represents the distribution of the mean for each farmer.
org, org neigh org, conv neigh conv, org neigh conv, conv neigh
1.5
2.0
1.0
1.5
0.5
1.0
0.0
0.5 0.0
aphids per stem
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
4
4
6
6
8
8
10 12 14
10 12 14
0
0
50 100
50 100
200
200
300
300
1
0.0
0.0
0.1
0.1
0.2
0.2
0.3
0.3
0.4
0.4
0.0
0.0
0.2
0.2
0.4
0.4
0.6
0.6
0.8
0.8
0
0
2
2
weed density (/m2)
Both 2009 and 2010 were characterized by a cold winter and dry spring, which led to a very low levels of leaf blotch and powdery mildew and almost no rust (disease incidence was less than 0.1% for both brown rust and yellow rust). For this reason, rusts were not analysed. Aphids were present only on the last observation date, i.e., at the end of June (Fig. 3). Fifty genera of weeds were identified; weed diversity tended to increase from the beginning of spring until summer in both years because of the appearance of spring-germinating weeds. Weed density was highly variable among fields, ranging from 0 to more than 700 individuals per m2 . Mean weed density peaked in April in both years (reflecting peaks in Poaceae, Sinapis, and Polygonum), and was also high before winter in 2010 (no observations were made before winter in 2009). The number and diversity of weeds were higher in organic than in conventional fields throughout the season in both 2009 and 2010. Leaf blotch incidence remained variable throughout the season in 2009; in 2010, it was maximal in March and then nearly zero by June. Mean leaf blotch incidence was higher in conventional than in organic fields in both years. Finally, powdery mildew temporal patterns differed in organic vs. conventional fields in 2009 and 2010. In 2009, powdery mildew incidence decreased between February and March and then increased until June in conventional fields but started to decrease again in May in organic fields. In 2010, powdery mildew incidence was higher in conventional fields than in organic fields before winter, was higher in organic than in conventional fields in spring, and then dropped to low levels in both types of fields in early May before increasing again from May to June. In both years, the first discriminant function of the discriminant analysis (Fig. 4), which separated organic fields (on the right) and conventional fields (on the left), explained a very high percentage of the variability (96.3% in 2009 and 97.6% in 2010) among the four classes (organic field in an organic or conventional neighbourhood and conventional field in an organic or conventional neighbourhood) and was positively correlated with weed density and negatively correlated with leaf blotch incidence. The second axis, which explained a small percentage of the variability between classes, separated organic neighbourhood (at the top) and conventional neighbourhoods (at the bottom) in 2009, while in 2010, the difference between organic and conventional neighbourhood was evident only in organic fields. This second axis was correlated with powdery mildew incidence in 2009 and with weed abundance in 2010. Thus, field type explained much of the variation in weeds and to a lesser extent leaf blotch incidence but neighbourhood type explained little of the variation in pest variables. These results were confirmed by the POMM (Table 4). For aphid abundance, there was no significant interaction between the effects of field type and neighbourhood type (p-value of the likelihood ratio test = 0.61) but there was a significant effect of both field type and neighbourhood type, with more aphids in conventional than in organic fields and more aphids in neighbourhoods with only conventional fields than in neighbourhoods with at least one organic field. For weed abundance, the interaction between the local and neighbourhood effect was not significant (p = 0.86), the effect of neighbourhood type was not significant (p = 0.22), the effect of field type was significant (p < 0.001 according to the likelihood ratio test between the models with and without field type effect), and the odds ratio of being in a higher rather than in a lower abundance class was always higher for organic fields, whatever the abundance
weed richness
3.2. Pest population dynamics
leaf blotch incidence
treatment frequency index for molluscicides was significantly higher in fields with at least one organic neighbour (Chi-sq = 6.34, df = 1, p = 0.012), especially for oilseed rape fields (which means either that slug infestations were higher or that farmers feared they might be higher).
2.0
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powdery mildew incidence
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Observation
Observation
Fig. 3. Population dynamics of wheat pests (aphids, weeds, leaf blotch, and powdery mildew) in organic and conventional fields having, or not having, at least one organic field in the neighbourhood on six observation dates in 2009 (left side) and 2010 (right side). Error bars = ± standard error of the mean.
classes. The same pattern was observed for weed diversity in that there was no significant interaction (p = 0.11) or significant effect of neighbourhood type but there was a significant effect of field type in that diversity was significantly higher in organic than in conventional fields. For leaf blotch incidence, there was a significant interaction between field type and neighbourhood type (p = 0.02). The field type effect was significant in both neighbourhood types, with more disease in conventional fields, but the neighbourhood
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Table 4 Estimates of the fixed effects of the proportional odds mixed models for wheat pests (number of aphids per stem, number of weed genera, weed density, leaf blotch incidence, and powdery mildew incidence). Variable
Subseta
Effectb
Aphid abundance
All data
Conventional field Conventional neighbourhood
Weed abundance
All data
Conventional field Conventional neighbourhood
Weed diversity
All data
Conventional field Conventional neighbourhood
−4.49 −0.51
Leaf blotch incidence
Organic fields Conventional fields Organic neighbourhoods Conventional neighbourhoods
Conventional neighbourhood Conventional neighbourhood Conventional field Conventional field
Powdery mildew incidence
All data
Conventional field Conventional neighbourhood
Estimatec 0.96 0.99 [−5.46, −3.71] −0.53
Std. Error 0.331 0.27
z value 2.913 3.673
Pr(>|z|) 0.004 <0.001
[−9.468, −6.237] −1.222
<0.001 0.222
0.401 0.313
−11.196 −1.62
<0.001 0.105
−1.24 0.52 2.07 3.62
0.681 0.251 0.367 0.541
−1.82 2.052 5.642 6.691
0.069 0.04 <0.001 <0.001
−0.06 −0.36
0.26 0.216
−0.216 −1.664
0.829 0.096
[0.553, 0.723 0.43
a If the interaction between field type and neighbourhood type was significant, the effect of neighbourhood type was tested separately on organic and conventional fields, and the effect of the field type was tested separately on the two types of neighbourhoods. b Conventional field: effect of conventional management vs. organic management of the field itself. Conventional neighbourhood: effect of all the neighbouring fields being conventional vs. at least one being organic field. Effects in bold are significant at ˛ = 0.05. c The sign of the estimate indicates the direction of the effect: if positive, conventional fields or neighbourhoods have more attacks; if negative, conventional fields or neighbourhoods are healthier.
type effect was significant only in conventional fields, with more disease in conventional neighbourhoods. Field type and neighbourhood type had no significant effect on powdery mildew. 4. Discussion In this study, the effect of farm management (cropping plan and cropping practices) on several pests (sensu lato) of wheat was examined for the field in question and for neighbouring fields. Among the studied farms and fields, the only clear categories with respect to cropping plan and cropping practices exactly mapped with organic certification. The differences between cropping practices in organic and conventional wheat fields (Fig. 2) corresponded to the expected differences. Weed control being the most problematic issue in organic wheat management, sowing dates are delayed in organic fields to allow an increased number of soil cultivations before planting. Conventional farmers, in contrast, tend to reduce ploughing because of time constraints, fuel costs, and reliance on herbicides. Organic farmers also use more diverse crops (including weed-suppressing crops such as fodder bean and winter as well as spring crops) than conventional farmers to control weed. In addition to permitting increased cultivation for weed control, late sowing in organic fields reduces the risk of insect damage and disease during autumn. A higher sowing density in organic than in conventional fields helps compensate for crop losses due to seedling diseases (seed rots, seedling blights, and/or root rots). Organic fields rely on organic fertilisers (compost and hen droppings in our case) rather than on synthetic fertilizers and so receive a lower amount of nitrogen because these fertilizers are costly and have a low N content. Finally, no pesticides were used in organic fields because synthetic pesticides are forbidden in organic farming. Cropping practices observed in the fields studied here are representative of the agricultural practices in cereal-based systems of northern France. The greater abundance and diversity of weeds in organic than in conventional fields was expected because weeds are recognized as the main problem in organic wheat cropping (Roschewitz et al., 2005a; Gabriel et al., 2006; Gibson et al., 2007; Casagrande, 2008). This is because herbicides are not used (Rundlöf et al., 2010) and because cultural practices (mainly soil tillage) provide only partial control. Concerning the effect of crop management on aphids, published results have been variable: some studies reported fewer aphids in
organic than in conventional wheat or barley fields (Östman et al., 2001; Roschewitz et al., 2005b), but others have found the opposite (Macfadyen et al., 2009; Gabriel et al., 2010). This discrepancy is probably due to the numerous factors influencing aphid population dynamics. The variation in numbers of aphids can partly be explained by predation, which is typically higher in organic than in conventional farms (Östman et al., 2001) and is increased by plant diversity including the presence of weeds (Andow, 1991). Aphid abundance is also affected by the use of insecticides on conventional farms (Macfadyen et al., 2009), nutrient availability in the plant (Garratt et al., 2010) and colonisation from surrounding habitats (Thies et al., 2005). The fewer aphids in organic than in conventional fields in the current study may therefore be explained by a lower plant nutrient content, higher predation/parasitism, and higher plant diversity in the organic than in the conventional fields. Although quantity of applied nitrogen is not necessarily a good indicator of nutrient availability in organic fields because soil fertility is maintained by legume crops and other means, the number of aphids per stem was not related to the quantity of nitrogen applied in the organic fields in the current study, indicating that differences in aphid numbers probably did not result from differences in plant nutrient content. There is also no consensus in the literature regarding the effect of organic management on diseases but leaf rust and leaf blotch (VanBruggen, 1995) and powdery mildew and yellow rust (Hannukkala and Tapio, 1990) are often less severe with organic than with conventional management. Although genetic resistance can greatly affect disease development, it cannot explain the lower level of leaf blotch in the organic than in the conventional fields in the current study because the varieties were less resistant to leaf blotch in the organic than in the conventional fields (p < 0.001, mean varietal resistance in conventional fields = 5.4, mean resistance in organic fields = 4.9, on a 0–9 resistance scale). Another possible explanation for the lower level of leaf blotch in our organic fields could be differences in plant nutrition: N fertilization has been shown to increase leaf blotch severity (Simón et al., 2003) and other diseases (Neumann et al., 2004; Cooper et al., 2006). The relationship between plant nutrition and disease in organically grown crops, however, is poorly understood (Walters and Bingham, 2007). Sowing date (in conjunction with earliness) can also influence disease dynamics, and late sowing has been shown to decrease leaf blotch severity (Shaw and Royle, 1993).
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2
PM
0
+ + CC
WD
++ OC WA
LB
−2
OO
APH conv org convnei orgnei
−4
Can2 (3.4%)
CO
−4
−2
0
2
4
2
Can1 (96.3%)
0 −2
WD
LB WA
−4
Can2 (1.8%)
OC
+CO + PMOO ++
APH CC
−6
conv org convnei orgnei −2
0
2
4
Can1 (97.6%) Fig. 4. Linear discriminant analysis of the pest population data in each year for weed abundance (WA), weed diversity (WR), leaf blotch incidence (LB), powdery mildew incidence (PM), and aphid abundance (APH). Black symbols: organic fields, grey symbols: conventional fields; circles: at least one organic field in the neighbourhood, triangles: all adjacent fields conventional. Categories used for the discriminant analysis were: OO (organic field in organic neighbourhood), OC (organic field in conventional neighbourhood), CO (conventional field in organic neighbourhood), and CC (conventional field in conventional neighbourhood).
In our study, the presence of at least one organic field in the neighbourhood of a field decreased aphid abundance in that field. This could be explained by a lower “spill over” of aphids: conventional fields, which have higher numbers of aphids, would act as sources of aphids that would then migrate to neighbouring fields, so a lower proportion of conventional fields in the neighbourhood would result in a lower immigration of aphids. It could also be caused by the biological control of aphids by natural enemies coming from organic fields (not only wheat). “Landscape” effects on natural enemies have often been observed although the studied landscape variable was usually the percentage of semi-natural habitats around the field or farm (Chaplin-Kramer et al., 2011). For example, a study on aphids and their parasitoids showed that aphid parasitism decreased with increasing percentage of arable land in the landscape 500 m and 1 km around the observed fields,
and that an increase in the percentage of arable reduced the rate of population increase of aphids between two observation dates, although initial aphid populations were also higher in landscapes rich in semi-natural habitats (Thies et al., 2005). In our case, the number of fields where aphids were present on more than one observation date (and thus where the population growth rate could be computed) was too low to allow the detection of any possible effect of management type in the field or in neighbouring fields on population growth rate (results not shown). The type of neighbourhood significantly affected leaf blotch but only in conventional fields; in organic fields, the effect was not significant and the trend was reversed. This weakens any conclusions that could be drawn regarding how the presence of organic farming in the vicinity of a field affects leaf blotch in that field. In conventional fields, the lower leaf blotch incidence in organic neighbourhoods than in conventional neighbourhoods could be caused by a lower inoculum load because of the lower disease level in organic fields. The absence of an effect of neighbourhood type on weeds is surprising, given the strong difference in weed abundance and diversity between organic and conventional fields. This lack of landscape effect, which was not due to a too recent change in management because the organic fields had been grown organically for more than 8 years previous to the study, is consistent with previously published results: weeds have been shown to respond weakly to factors beyond the field scale (Gaba et al., 2010; Marshall, 2009). In other words the weeds in organic fields do not seem to disperse to conventional fields. Furthermore, they can provide ecosystem services (e.g., they provide food for pollinators and natural enemies) at a scale larger than the field (Petit et al., 2011). Consequently, the increase in the acreage under organic farming should not cause an increase in weed problems in neighbouring conventional fields and could even provide ecosystem services to those fields. 5. Conclusion Our results provide no evidence that pest inoculum (sensu lato) will multiply in organic fields and then spread to neighbouring conventional fields, forcing conventional farmers to use even more pesticides and threatening organic farming sustainability. The use of fungicides, insecticides, and herbicides was not significantly higher in conventional fields adjacent to organic fields compared to conventional fields surrounded only by conventional fields. Organic fields did not necessarily support more pests than conventional fields, and the only neighbourhood effects that were significant were beneficial (i.e., they were detrimental to pest populations). Because these results were obtained over only 2 years and in years with low disease levels and concerned only wheat crops in one agricultural region that contained a moderate acreage of organic farming, our conclusions cannot be extrapolated to all situations, e.g., other crops or climatic conditions more favourable to diseases. In the future, the use of spatially explicit models calibrated with experimental data over a range of production systems and environmental conditions could allow researchers to make realistic predictions concerning the effect of a change in production systems (e.g., widespread adoption of organic farming) on pest populations. Acknowledgements We thank all the farmers who participated in the survey and allowed us to collect data in their fields in the counties of Boinviliers, Boissets, Dammartin-en-Serve, Favrieux, Flacourt, Flins-Neuve-Eglise, Fontenay-Mauvoisin, Le MesnilSimon, Le Tertre-Saint-Denis, Longnes, Ménerville, Mondreville,
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Perdreauville, Rosay, Saint Ouen-Marchefroy, Soindres, Tilly, Vert, and Villette. We also thank Thierry Doré for helpful comments on the manuscript. References Adl, S., Iron, D., Kolokolnikov, T., 2011. A threshold area ratio of organic to conventional agriculture causes recurrent pathogen outbreaks in organic agriculture. Sci. Total Environ. 409, 2192–2197. Andow, D.A., 1991. Vegetational diversity and arthropod population response. Annu. Rev. Entomol. 36, 561–586. Anonymous, 2001. Review on the possible interactions of pests, diseases and weeds in cereals grown in organic and conventional agriculture (No. OF0194). Ministry of Agriculture, Fisheries and Food, U.K. Arvalis-Institut du Végétal, 2009. Varieˇıteˇıs de bleˇı tendre: choisir ses varieˇıteˇıs. Arvalis Infos, 4–7. Casagrande, M., 2008. Evaluation précoce des performances du blé biologique (rendement et teneur en protéines): une approche combinée de diagnostic agronomique, de modélisation à l’aide d’indicateurs de nuisibilité et d’études des pratiques dans les exploitations agricoles. (Ph.D. thesis). Chaplin-Kramer, R., O’Rourke, M.E., Blitzer, E.J., Kremen, C., 2011. A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932. Christensen, R.H.B., 2010. ordinal -Regression models for ordinal data. R package version 2011. 09-14. Clough, Y., Kruess, A., Kleijn, D., Tscharntke, T., 2005. Spider diversity in cereal fields: comparing factors at local, landscape and regional scales. J. Biogeogr. 32, 2007–2014. Cooper, J.M., Schmidt, C.S., Wilkinson, A., Lueck, L., Hall, C.M., Shotton, P.N., Leifert, C., 2006. Effect of organic, low-input and conventional production systems on yield and quality of winter wheat. In: Delivering Sustainability Within Profitable Farming Systems–Is It Possible? Aspects of Applied Biology, pp. 121–126. FIBL, IFOAM, 2011. Organic Agricultural Land (In-Conversion and Fully Converted) By Region 1999–2009 [WWW Document]. URL http://www.organicworld.net/statistics-data-tables-excel.html#c6167. Friendly, M., Fox, J., 2011. candisc: Generalized Canonical Discriminant Analysis. R package version 0. 5-21. Gaba, S., Chauvel, B., Dessaint, F., Bretagnolle, V., Petit, S., 2010. Weed species richness in winter wheat increases with landscape heterogeneity. Agric. Ecosyst. Environ. 138, 318–323. Gabriel, D., Roschewitz, I., Tscharntke, T., Thies, C., 2006. Beta diversity at different spatial scales: plant communities in organic and conventional agriculture. Ecol. App. 16, 2011–2021. Gabriel, D., Sait, S.M., Hodgson, J.A., Schmutz, U., Kunin, W.E., Benton, T.G., 2010. Scale matters: the impact of organic farming on biodiversity at different spatial scales. Ecol. Lett. 13, 858–869. Garratt, M.P.D., Wright, D.J., Leather, S.R., 2010. The effects of organic and conventional fertilizers on cereal aphids and their natural enemies. Agric. For. Entomol. 12, 307–318. Gibson, R.H., Pearce, S., Morris, R.J., Symondson, W.O.C., Memmot, J., 2007. Plant diversity and land use under organic and conventional agriculture: a whole-farm approach. J. Appl. Ecol. 44, 792–803. Gosme, M., Suffert, F., Jeuffroy, M.-H., 2010. Intensive versus low-input cropping systems: what is the optimal partitioning of agricultural area in order to reduce pesticide use while maintaining productivity? Agric. Syst. 103, 110–116. Hannukkala, A.O., Tapio, E., 1990. Conventional and organic cropping systems at Suitia. 5. Cereal diseases. J. Agric. Sci. Finland 62, 339–347.
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Holzschuh, A., Steffan-Dewenter, I., Tscharntke, T., 2008. Agricultural landscapes with organic crops support higher pollinator diversity. OIKOS 117, 354–361. INRA, 2011. Ecophyto R&D [WWW Document]. URL http://www.international. inra.fr/the institute/advanced studies/ecophyto r d. Macfadyen, S., Gibson, R., Raso, L., Sint, D., Traugott, M., Memmott, J., 2009. Parasitoid control of aphids in organic and conventional farming systems. Agric. Ecosyst. Environ. 133, 14–18. Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., 2005. Cluster Analysis Basics and Extensions. R package version 1.14.2. Marshall, E.J.P., 2009. The impact of landscape structure and sown grass margin strips on weed assemblages in arable crops and their boundaries. Weed Res. 49, 107–115. Mignolet, C., Schott, C., Benoît, M., 2007. Spatial dynamics of farming practices in the Seine basin: methods for agronomic approaches on a regional scale. Sci. Total Environ. 375, 13–32. Neumann, S., Paveley, N.D., Beed, F.D., Sylvester-Bradley, R., 2004. Nitrogen per unit leaf area affects the upper asymptote of Puccinia striiformis f.sp tritici epidemics in winter wheat. Plant Pathol. 53, 725–732. Östman, Ö., Ekbom, B., Bengtsson, J., 2001. Landscape heterogeneity and farming practice influence biological control. Basic Appl. Ecol. 2, 365–371. Petit, S., Boursault, A., Le Guilloux, M., Munier-Jolain, N., Reboud, X., 2011. Weeds in agricultural landscapes. A review. Agronomy for Sustainable Development 31, 309–317. Roschewitz, I., Gabriel, D., Tscharntke, T., Thies, C., 2005a. The effects of landscape complexity on arable weed species diversity in organic and conventional farming. J. Appl. Ecol. 42, 873–882. Roschewitz, I., Hücker, M., Tscharntke, T., Thies, C., 2005b. The influence of landscape context and farming practices on parasitism of cereal aphids. Agric. Ecosyst. Environ. 108, 218–227. Rundlöf, M., Bengtsson, J., Smith, H.G., 2008a. Local and landscape effects of organic farming on butterfly species richness and abundance. J. Appl. Ecol. 45, 813–820. Rundlöf, M., Edlund, M., Smith, H.G., 2010. Organic farming at local and landscape scales benefits plant diversity. Ecography 33, 514–522. Rundlöf, M., Nilsson, H., Smith, H.G., 2008b. Interacting effects of farming practice and landscape context on bumble bees. Biol. Conserv. 141, 417–426. Schmidt, M.H., Roschewitz, I., Thies, C., Tscharntke, T., 2005. Differential effects of landscape and management on diversity and density of ground-dwelling farmland spiders. J. Appl. Ecol. 42, 281–287. Shaw, M.W., Royle, D.J., 1993. Factors determining the severity of epidemics of Mycosphaerella graminicola (Septoria tritici) on winter wheat in the UK. Plant Pathol. 42, 882–899. Simón, M.R., Cordo, C.A., Perelló, A.E., Struik, P.C., 2003. Influence of nitrogen supply on the susceptibility of wheat to Septoria tritici. J. Phytopathol. 151, 283–289. Thies, C., Roschewitz, I., Tscharntke, T., 2005. The landscape context of cereal aphidparasitoid interactions. Proc. R. Soc. London, Ser. B – Biol. Sci. 272, 203–221. Tscharntke, T., Klein, A.M., Kruess, A., Steffan-Dewenter, I., Thies, C., 2005. Landscape perspectives on agricultural intensification and biodiversity – ecosystem service management. Ecol. Lett. 8, 857–874. U.S. Government, 2008. Food, Conservation, and Energy Act of 2008. An act to provide for the continuation of agricultural programs through fiscal year 2012 and for other purposes. Public Law. 110–234. VanBruggen, A.H.C., 1995. Plant disease severity in high-input compared to reducedinput and organic farm systems. Plant Dis. 79, 976–984. Walters, D.R., Bingham, I.J., 2007. Influence of nutrition on disease development caused by fungal pathogens: implications for plant disease control. Ann. Appl. Biol. 151, 307–324. Winqvist, C., Bengtsson, J., Aavik, T., Berendse, F., Clement, L.W., Eggers, S., Fischer, C., Flohre, A., Geiger, F., Liira, J., Paert, T., Thies, C., Tscharntke, T., Weisser, W.W., Bommarco, R., 2011. Mixed effects of organic farming and landscape complexity on farmland biodiversity and biological control potential across Europe. J. Appl. Ecol. 48, 570–579.
Contents lists available at SciVerse ScienceDirect
Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee
Local and neighbourhood effects of organic and conventional wheat management on aphids, weeds, and foliar diseases Marie Gosme a,b,∗ , Maguie de Villemandy a,b , Mathieu Bazot a,b , Marie-Hélène Jeuffroy a,b a b
INRA, UMR211 Agronomie, F-78850 Thiverval-Grignon, France AgroParisTech, UMR211 Agronomie, F-78850 Thiverval-Grignon, France
a r t i c l e
i n f o
Article history: Received 30 January 2012 Received in revised form 9 July 2012 Accepted 12 July 2012 Available online 19 August 2012 Keywords: Organic farming Conventional farming Pests Between-field interaction
a b s t r a c t The area under organic farming is increasing in many countries. The effect of a significant increase in the proportion of organic agriculture on pest (sensu lato) populations at the landscape scale is unknown and will depend on both the production of propagules in organic fields and the risk of pest dispersal between fields. In this study, we observed the dynamics of four foliar diseases, aphids, and weeds in 216 wheat fields over 2 years in northern France. We used the survey data to estimate the local effect of how a field was managed (organic or conventional) and the presence or absence of adjacent organic fields (neighbourhood effect) on pest abundance in that field. Because conventional and organic may be considered extremes along a continuum of management practices, a large survey was undertaken of management practices to ensure that the fields were classified according to the actual cropping practices. The presence or absence of organic certification was determined to be the only relevant criterion for classifying cropping practices. The results of proportional odds mixed models showed that some pests responded to local crop management: leaf blotch incidence and aphid density were significantly lower while weed diversity and abundance were higher in organic fields. Only aphids and leaf blotch responded to the neighbourhood effect: the presence of organic fields in the neighbourhood decreased the number of aphids in both organic and conventional fields and decreased leaf blotch incidence but only in conventional fields. These results indicate that the increase in organic acreage in landscapes will not increase pest problems in the short term under the conditions of the study (low disease pressure). © 2012 Elsevier B.V. All rights reserved.
1. Introduction Organic farming is often proposed as a solution to the environmental problems and public health threats posed by intensive agriculture. However, the share of agricultural land cropped under organic farming is low: less than 1% worldwide and only 1.9% in Europe (FIBL and IFOAM, 2011). Several countries have promoted organic farming by providing subsidies to farmers for conversion, by funding research and extension in organic agriculture (U.S. Government, 2008), and sometimes by setting quantitative goals in terms of agricultural area that should be farmed organically (INRA, 2011). As a result, organic acreage has increased dramatically over the last decade: global acreage increased by 238% between 1999 and 2009, from less than 11,000,000 to more than 37,000,000 ha (FIBL and IFOAM, 2011), and this increase is expected to continue in the future. Concerns have been raised regarding the threats to food safety and food security that could appear if organic farming has a large
∗ Corresponding author. Tel.: +33 1 30 81 52 26; fax: +33 1 30 81 54 25. E-mail address: [email protected] (M. Gosme). 0167-8809/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agee.2012.07.009
share in agricultural land, not only because organic fields produce lower yields but also because they could harbour more pests that might then spread to conventional fields. For example, a mathematical model showed that there is a critical threshold of organic fields in a region above which pathogen outbreaks are more likely to occur, spread into all organic fields, and force conventional farmers to use more pesticides (Adl et al., 2011). This model, however, is based on important assumptions, in particular that pathogen growth is unchecked in organic fields. Another theoretical study has shown that the effect of the share of agricultural land under organic management on disease level and crop production at the regional scale depends critically on the amount of inoculum produced by organic fields (Gosme et al., 2010). Therefore, the effect of an increase in organic farming on pest populations (sensu lato, i.e., insect pests but also diseases, weeds, and other pests) cannot be predicted without precise data on the effect of organic farming on the different diseases, pests, and natural enemies (Anonymous, 2001). Data on the relative abundance and/or diversity of several taxonomic groups in organic vs. conventional fields is steadily increasing in the literature, but results are highly inconsistent among studies. Furthermore, response to organic management is expected to depend on taxonomic group as
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well as trophic level: a meta-analysis found that birds, predatory insects, soil organisms, and plants benefited from organic farming, whereas non-predatory insects and pests did not (Anonymous, 2001). One explanation that has been proposed for the inconsistencies between studies is the effect of the landscape surrounding the observed fields on biodiversity and especially on the diversity and abundance of natural enemies. In intensively managed landscapes, the species pool is so depleted that natural enemies are unavailable for colonization of organic fields. In highly complex landscapes (i.e., those containing many well-connected semi-natural habitats), in contrast, biodiversity remains high whether crops are managed conventionally or organically. As a result, the positive effect of organic farming on biodiversity is maximum in landscapes of low to medium complexity (Tscharntke et al., 2005). For these reasons, landscape complexity has been considered in recent comparisons of biodiversity or biological control in organic vs. conventional fields (Östman et al., 2001; Clough et al., 2005; Schmidt et al., 2005; Gabriel et al., 2006; Rundlöf et al., 2008b; Winqvist et al., 2011). Landscape complexity in these studies is usually estimated as the percentage of arable crops (or semi-natural habitats) or through an index of diversity based on different land use categories. To our knowledge, however, research has not been conducted on the effect of the proportion of organic farming in the landscape on diseases and pest populations (but see Rundlöf et al. (2008a) on butterflies, Gabriel et al. (2010) on the biodiversity of several taxonomic groups, and Holzschuh et al. (2008) on pollinators). Thus new studies are needed to determine whether the presence of organic fields in the landscape affects pest populations in conventional and other organic fields. Studies comparing organic and conventional fields can also be weakened by the fact that although the organic certification proves that no synthetic fertilizer and chemicals have been used, even organic farms can differ in management intensity (i.e., they can differ in the quantities of external inputs), and “conventional” farms can range from those that are intensively managed to others that have low-input or that are effectively organic but not certified. The objective of this study was to determine whether the presence of organic fields affected aerial diseases, weeds, and aphids in nearby organic and conventional wheat fields in our study region (the Seine River basin in France). Accomplishing this objective required determining whether the designations of “organic” and “conventional” were meaningful for these fields and whether subgroups of conventional farmers should be recognized. 2. Materials and methods The approach taken in this paper differs from that usually followed in the literature: instead of selecting pairs of fields in different landscapes and describing landscape only in terms of land use, we focused on only one landscape but precisely described cropping practices in all the fields of the landscape. The chosen study area is not representative of the whole agricultural region in terms of acreage of organic farming (see below), but it is representative in terms of soil, topography, and farm size. The survey comprised two parts: first, an exhaustive survey of cropping practices of all the crops in the study area, and second, a survey of key pests in a subset of the wheat fields in the same area. 2.1. Study area The study area was approximately 50 km west of Paris, France (between 48◦ 52 N and 48◦ 58 N and 1◦ 32 E and 1◦ 41 E). This zone falls within the Seine basin, in the “Drouais” agricultural region, which is mainly characterized by intensive production of cereals
and oil/protein crops (mean wheat yields in 2009 and 2010 were 8.4 and 7.6 t/ha, respectively, vs. 7.7 and 7.3 t/ha nationwide mean [source: Agreste]). The rotations in this region are based on wheat, pea, and oilseed rape (Mignolet et al., 2007). Because organic farms in this region are scarce (less than 1% of Utilized Agricultural Area or UAA in 2009), the study area was chosen to include as many certified organic fields as possible: it covered 62 km2 and encompassed the fields of three organic farmers. The study area was composed of 74.4% UAA (97.7% of UAA was arable land, 11.3% of UAA was cultivated in organic farming), 15.4% semi-natural areas (woods and abandoned land), and 7.2% urban area (small villages with detached houses and gardens), i.e., landscape complexity was low to medium. The topography of the study area was flat, with altitudes ranging from 130 to 150 m asl, except where a river (50 m asl) crossed the study area. Sizes of organic and conventional farms and fields were comparable: the surveyed conventional farms ranged from 30 to 305 ha (mean 147, median 143), and the three organic farms were 133, 146, and 310 ha. Field size ranged from 0.38 to 30.80 (mean 4.67, median 3.34) in conventional farms and from 0.52 to 24.35 (mean 6.9, median 4.83) in organic farms. The landscape in the region is open-field with few hedgerows, and fields are generally adjacent to each other except when separated by roads. 2.2. Crop management survey To confirm that the classification of fields into the two categories (organic/conventional) was relevant in our study area (i.e., to confirm that there were consistent differences between organic and conventional management and a lack of subdivisions among conventional farmers), we collected information on the cropping practices for all fields in the area. We contacted by mail, and phone when possible, all 163 farmers who had at least one field in the counties where the study area was located (but most of them had fields outside the study area). Finally, 54 farmers (67.8% of the UAA in the study area) were involved in the study, including all three farmers with the organic certification (their farms had been certified organic for more than 8 years). Farmers were interviewed individually to collect information on the farm and on all of their fields in the study area: the collected information included farm area, location of each field on an aerial photograph, and crop succession over the previous 5 years in each field. Each farmer was later interviewed at least once per year in 2009 and 2010 to complete the information on cropping practices: the collected information included number of ploughings and other soil tillage operations, the cultivars sown, sowing density, date of sowing, and dates and doses of pesticide and fertilizer applications (see Table 1 for a summary of the variables and their short names). Thus, information was collected on 613 fields, representing a total of 1082 management sequences over both years (the number of sequences was less than 1226 because some farmers did not participate in the second year of the study). Information on the disease resistance of the wheat varieties was obtained from the descriptions of varieties published by Arvalis-Institut du Végétal (2009). 2.3. Pest population survey A total of 216 wheat fields were surveyed in 2009 and 2010 (Table 2). These included all of the organic wheat fields in the study zone and a sample of conventional wheat fields; the latter were chosen to provide a range of landscape situations (presence or absence of organic fields immediately adjacent to them). The fields were classified (see results below) as organic or conventional (“field type”) and as having or not having at least one organic field immediately adjacent to them (“neighbourhood type”). Because of rotations, most fields surveyed in 2009 could not be studied in 2010
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Table 1 Main variables observed in the study and used in the analysis. Short name
Description
Unit Proportion of fields
mecha
Whether or not wheat was grown in the field the previous year Whether or not the field was ploughed Number of soil tillage operation apart from ploughing Sowing date Sowing density Cultivar resistance to brown rust, yellow rust, powdery mildew and leaf blotch, respectively (wheat only) Treatment frequency index of fungicides (dose applied per ha in one year divided by the standard approved dose found at http://e-phy.agriculture.gouv.fr/) Same for herbicides and insecticides total rate of nitrogen (in the case of organic farmers, this was estimated based on the amount of organic fertilizer and N content of the compost and manure that they used Number of mechanical weeding operations
Variables in pest survey APH WA WD BR YR LB PM
Aphid abundance Weed abundance Weed diversity Tiller incidence of brown rust Tiller incidence of yellow rust Tiller incidence of leaf blotch Tiller incidence of powdery mildew
Variables in crop management survey prev plo til sow dst BRR, YRR, PMR, LBR TFIF
TFIH and TFII N
because they were no longer cropped in wheat, but 11 fields were studied in both 2009 and 2010. Observations were made six times each year on a diagonal transect across each field (not necessarily at the same place each time). Each transect consisted of eight 50 × 50 cm quadrats placed every 15–30 m, depending on the size of the field. In each quadrat, the number of weeds per genus were counted, except for genera in the Poaceae family, which were pooled together. Ten wheat stems were randomly chosen in each quadrat, and the aphids (all species pooled but the most abundant was Metopolophium dirhodum [Walker]) on the leaves or ear were counted. All living leaves of the same 10 stems were observed, and the presence/absence of the following four aerial diseases was recorded for each stem: yellow rust, caused by Puccinia striiformis Westendorp f. sp. tritici; brown rust, caused by Puccinia recondita Roberge f. sp. recondita; leaf blotch, caused by Mycosphaerella graminicola (Fuckel) Schöter; and powdery mildew, caused by Erysiphe graminis DC. f. sp. avenae. These observations were then summed in each field at each observation date to obtain aphid abundance, weed abundance, weed diversity (number of weed genera encountered in the field), and incidence of the four diseases (proportion of infected stems). In 2009, the six observations were made every 3 weeks from the end of February to the end of June; in the 2009/2010 cropping season, the first observation was made at the end of November and then every 3 weeks from March to June.
Table 2 Number of wheat fields sampled in 2009 and 2010 by field type and neighbourhood type for the pest population survey. Neighbourhood type Year
Field type
At least 1 organic
All conventional
2009
Organic Conventional
24 32
4 69
2010
Organic Conventional
12 35
4 36
Proportion of fields Julian day kg/ha 0–9
kg/ha
Individuals/tiller Individuals/m2 genera/field
2.4. Statistical analysis Crop management data were analysed by hierarchical clustering to determine how fields should be grouped for the analysis of pest populations. In particular, we wanted to determine whether cropping systems are adequately categorized as organic and conventional, i.e., whether organic and conventional cropping practices (including the diversity of crops planted) are actually different and whether there are important subgroups among conventional practices. Agglomerative nesting (function agnes of the “cluster” package for R [Maechler et al., 2005]) was performed using Manhattan distance (after standardisation of the data when variables did not have comparable scales). Three analyses were performed: (i) a classification of farmers based on the proportion of each crop in their cropping plans over 6 years (only for farmers whose crop rotations were known for more than 10 fields so that the proportions were meaningful); (ii) a classification of farmers based on their average cropping practices (averaged over all of their fields planted with a given crop, regardless of the year) on each of the following crops: oilseed rape, wheat, faba bean, maize, barley, and pea (only for farmers whose cropping practices were known for more than 10 fields); and finally, (iii) a classification of wheat fields based on their cropping practices, which indicated whether results based on average cropping practices adequately reflected the diversity of cropping practices. Wilcoxon–Mann-Whitney tests were performed on each wheat cropping practice to determine whether there was a difference between organic and conventional management for the given variable. To determine whether neighbourhood effect and pesticide use were confused in the analysis of pest populations, a linear mixed model was used on the treatment frequency indices (separately for insecticides, herbicides, fungicides and molluscicides) for conventional fields; this analysis would indicate whether fields that were located adjacent to organic fields received different doses of pesticides than fields surrounded only by conventional fields. Crop was used as a random effect, and only crops that were planted in both types of neighbourhood (i.e., adjacent to organic fields and conventional field) were used; these included wheat, oilseed rape, faba bean, maize, barley, and pea. The test was performed by
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Fig. 1. Agglomerative hierarchical clustering trees of farmers and fields. (A) Farmers (n = 24) clustered according to the proportion of each crop in their cropping plans over 6 years. (B) Farmers (n = 39) clustered according to their mean cropping practices on oilseed rape, wheat, faba bean, maize, barley, and pea. (C) Winter wheat fields (n = 544) clustered according to cropping practices; lines in the lower part of the graph join each field with its owner; fields in black are organic fields, and fields in grey are conventional fields. Farmers 4, 6, and 17 were organic farmers, but farmer 4 had three fields that were not managed organically.
comparing the model including neighbourhood type as a fixed effect and the model with only the random effect. Pests data were first visualized by plotting the means of the variables as a function of time and by linear discriminant analysis (LDA, function candisc in package cansdisc for R [Friendly and Fox, 2011]), with the categories OO (organic field in organic neighbourhood), OC (organic field in conventional neighbourhood), CO (conventional field in organic neighbourhood), and CC (conventional field in conventional neighbourhood). To test for the effect of field type (organic vs. conventional) and neighbourhood type (at least one adjacent field organic vs. all adjacent fields conventional), proportional odds mixed models (function clmm of the “ordinal” package for R [Christensen, 2010]) were fitted on weed abundance and diversity, incidence of each of the diseases, and aphid abundance. The data were treated as ordinal data because none of the standard transformations or link functions provided the normality of residuals required by linear or generalized linear models. Each variable was transformed into categories: the first category was always 0, and then non-zero values were divided into deciles, except for weed abundance for which the first five categories were combined (see below). The fixed effects were field type, neighbourhood type, and their interaction, and the random effects were field and date nested within year (except for aphids, which were present only during the last observation in each year). In a first step, the interaction was tested in the complete model. If no significant interaction was detected, the model was rewritten without the interaction term to test the main effects (the Wald test and a likelihood ratio test were used to compare the model with and without this effect). If there was a significant interaction, the effect of neighbourhood type was tested separately for each type of field, and
the effect of field type was tested separately for each type of neighbourhood. The proportional odds assumption for field type and neighbourhood type was checked visually (by verifying that the lines for the empirical logit of the different categories were parallel) as well as with a likelihood ratio test that compared the proportional odds model (POM) with a partial proportional odds model (PPOM) with the given factor as a nominal effect and with only field as a random variable (because PPOM with several random factors is not yet available in R). The assumption was fulfilled except initially for weed abundance, which had no observations in the first four categories in organic fields. When the first five categories were combined for weed abundance, the proportional odds test was no longer significant for the neighbourhood type effect; because it remained significant (p = 0.002) for the field type effect, field type was kept as a nominal effect in a PPOM. 3. Results 3.1. Classification of crop management The most frequent crops in the study area were winter wheat (50.7% of the surveyed area), winter oilseed rape (15.2%), and maize (9.5%). Of the 1082 management sequences, 1042 had complete information and were used in the analysis (917 conventional, 125 organic) (Table 3). The hierarchical classification of farmers and fields showed a clear distinction between organic and conventional farmers/fields, for both cropping plan (Fig. 1A) and cropping practices (Fig. 1B and C): the highest node always distinguished between all conventional farmers/fields on one side and all organic farmers/fields on the other side. Furthermore, the internode branches were always
M. Gosme et al. / Agriculture, Ecosystems and Environment 161 (2012) 121–129 Table 3 Number of fields in the crop management survey for each crop, year, and type of management. 2010a
2009 Crop
Conventional
Organic
Conventional
Organic
Winter wheat Winter oilseed rape Maize Winter barley Spring faba bean Pea Winter triticale Winter faba bean Winter oat Sunflower Spring barley Spring oat Alfalfa Clover Fodder beet Spelt Total
259 93 43 42 23 11 1 1 1 0 1 3 1 0 1 1 481
27 0 5 0 7 0 11 4 2 0 0 0 0 0 0 0 56
238 82 29 25 28 18 0 3 4 5 2 0 2 0 0 0 436
20 0 13 0 8 0 16 5 2 1 2 0 0 2 0 0 69
a The total number of fields differed between 2009 and 2010 because some conventional farmers who participated in 2009 did not participate in 2010, and because one of the organic farmers in 2009 provided complete information on cropping practices only for wheat.
longer between the highest node within a management system and the top of the tree than between lower nodes. Organic farmers grew less wheat than conventional farmers (organic and conventional farmers grew wheat in 35% and 57% of their fields, respectively), and their crops were more diversified: the Shannon index computed for the number of fields under different crops for each farmer was significantly higher for organic farmers than for conventional
125
farmers (t = −3.965, df = 22, p-value <0.001). In contrast, dissimilarity in cropping practices was greater among conventional farmers than among organic farmers (probably due to our limited sample of organic farmers). Despite this diversity, no clear group appeared among conventional farmers: internodes were always short, indicating that conventional farmers were distributed evenly along a gradient of crop management. Consequently, fields were classified as organic or conventional (hereafter referred to as field type effect) and as having or not having at least one organic field in their immediate neighbourhood (hereafter referred to as neighbourhood type effect) in the analysis of pest populations. Fig. 2 presents beanplots of the cropping practices of organic and conventional farmers for wheat fields. Ploughing and other tillage methods before sowing were performed less frequently for conventional wheat than organic wheat (89% of organic wheat fields were ploughed vs. 51% of conventional wheat fields; the median number of other tillage methods was 3 for organic wheat fields vs. 2 for conventional fields). The mean sowing date was 20 days later in organic than in conventional fields. The sowing density was higher in organic fields. The total rate of nitrogen application was lower and more variable in organic fields. Levels of disease resistance were similar for varieties planted in conventional vs. organic fields, except that resistance to brown rust was higher in varieties used in organic fields. Finally, no pesticides were used in organic farming, while the treatment frequency indices in conventional farming were on average 2.0, 1.6, and 0.4 for fungicides, herbicides, and insecticides, respectively. The comparison of pesticides use in conventional fields between neighbourhood types showed that the treatment frequency indices for fungicides, herbicides, and insecticides were not significantly different between neighbourhood types (p > 0.05) but that the
Fig. 2. Beanplots of cropping practices (see Table 1 for the meaning of the variables) of the 544 conventional (C) and organic (O) wheat fields. The width of the “bean” indicates the density of the distribution of the values, the black line indicates the means of each group, and the dotted line indicates the overall mean. Significant differences between organic and conventional are indicated by a star in the plot title. For variables plo and prev, the bean represents the distribution of the mean for each farmer.
org, org neigh org, conv neigh conv, org neigh conv, conv neigh
1.5
2.0
1.0
1.5
0.5
1.0
0.0
0.5 0.0
aphids per stem
2
3
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6
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6
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6
1
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3
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10 12 14
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0
0
50 100
50 100
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1
0.0
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0.1
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0.0
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0.2
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0.6
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0.8
0
0
2
2
weed density (/m2)
Both 2009 and 2010 were characterized by a cold winter and dry spring, which led to a very low levels of leaf blotch and powdery mildew and almost no rust (disease incidence was less than 0.1% for both brown rust and yellow rust). For this reason, rusts were not analysed. Aphids were present only on the last observation date, i.e., at the end of June (Fig. 3). Fifty genera of weeds were identified; weed diversity tended to increase from the beginning of spring until summer in both years because of the appearance of spring-germinating weeds. Weed density was highly variable among fields, ranging from 0 to more than 700 individuals per m2 . Mean weed density peaked in April in both years (reflecting peaks in Poaceae, Sinapis, and Polygonum), and was also high before winter in 2010 (no observations were made before winter in 2009). The number and diversity of weeds were higher in organic than in conventional fields throughout the season in both 2009 and 2010. Leaf blotch incidence remained variable throughout the season in 2009; in 2010, it was maximal in March and then nearly zero by June. Mean leaf blotch incidence was higher in conventional than in organic fields in both years. Finally, powdery mildew temporal patterns differed in organic vs. conventional fields in 2009 and 2010. In 2009, powdery mildew incidence decreased between February and March and then increased until June in conventional fields but started to decrease again in May in organic fields. In 2010, powdery mildew incidence was higher in conventional fields than in organic fields before winter, was higher in organic than in conventional fields in spring, and then dropped to low levels in both types of fields in early May before increasing again from May to June. In both years, the first discriminant function of the discriminant analysis (Fig. 4), which separated organic fields (on the right) and conventional fields (on the left), explained a very high percentage of the variability (96.3% in 2009 and 97.6% in 2010) among the four classes (organic field in an organic or conventional neighbourhood and conventional field in an organic or conventional neighbourhood) and was positively correlated with weed density and negatively correlated with leaf blotch incidence. The second axis, which explained a small percentage of the variability between classes, separated organic neighbourhood (at the top) and conventional neighbourhoods (at the bottom) in 2009, while in 2010, the difference between organic and conventional neighbourhood was evident only in organic fields. This second axis was correlated with powdery mildew incidence in 2009 and with weed abundance in 2010. Thus, field type explained much of the variation in weeds and to a lesser extent leaf blotch incidence but neighbourhood type explained little of the variation in pest variables. These results were confirmed by the POMM (Table 4). For aphid abundance, there was no significant interaction between the effects of field type and neighbourhood type (p-value of the likelihood ratio test = 0.61) but there was a significant effect of both field type and neighbourhood type, with more aphids in conventional than in organic fields and more aphids in neighbourhoods with only conventional fields than in neighbourhoods with at least one organic field. For weed abundance, the interaction between the local and neighbourhood effect was not significant (p = 0.86), the effect of neighbourhood type was not significant (p = 0.22), the effect of field type was significant (p < 0.001 according to the likelihood ratio test between the models with and without field type effect), and the odds ratio of being in a higher rather than in a lower abundance class was always higher for organic fields, whatever the abundance
weed richness
3.2. Pest population dynamics
leaf blotch incidence
treatment frequency index for molluscicides was significantly higher in fields with at least one organic neighbour (Chi-sq = 6.34, df = 1, p = 0.012), especially for oilseed rape fields (which means either that slug infestations were higher or that farmers feared they might be higher).
2.0
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powdery mildew incidence
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Observation
Observation
Fig. 3. Population dynamics of wheat pests (aphids, weeds, leaf blotch, and powdery mildew) in organic and conventional fields having, or not having, at least one organic field in the neighbourhood on six observation dates in 2009 (left side) and 2010 (right side). Error bars = ± standard error of the mean.
classes. The same pattern was observed for weed diversity in that there was no significant interaction (p = 0.11) or significant effect of neighbourhood type but there was a significant effect of field type in that diversity was significantly higher in organic than in conventional fields. For leaf blotch incidence, there was a significant interaction between field type and neighbourhood type (p = 0.02). The field type effect was significant in both neighbourhood types, with more disease in conventional fields, but the neighbourhood
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Table 4 Estimates of the fixed effects of the proportional odds mixed models for wheat pests (number of aphids per stem, number of weed genera, weed density, leaf blotch incidence, and powdery mildew incidence). Variable
Subseta
Effectb
Aphid abundance
All data
Conventional field Conventional neighbourhood
Weed abundance
All data
Conventional field Conventional neighbourhood
Weed diversity
All data
Conventional field Conventional neighbourhood
−4.49 −0.51
Leaf blotch incidence
Organic fields Conventional fields Organic neighbourhoods Conventional neighbourhoods
Conventional neighbourhood Conventional neighbourhood Conventional field Conventional field
Powdery mildew incidence
All data
Conventional field Conventional neighbourhood
Estimatec 0.96 0.99 [−5.46, −3.71] −0.53
Std. Error 0.331 0.27
z value 2.913 3.673
Pr(>|z|) 0.004 <0.001
[−9.468, −6.237] −1.222
<0.001 0.222
0.401 0.313
−11.196 −1.62
<0.001 0.105
−1.24 0.52 2.07 3.62
0.681 0.251 0.367 0.541
−1.82 2.052 5.642 6.691
0.069 0.04 <0.001 <0.001
−0.06 −0.36
0.26 0.216
−0.216 −1.664
0.829 0.096
[0.553, 0.723 0.43
a If the interaction between field type and neighbourhood type was significant, the effect of neighbourhood type was tested separately on organic and conventional fields, and the effect of the field type was tested separately on the two types of neighbourhoods. b Conventional field: effect of conventional management vs. organic management of the field itself. Conventional neighbourhood: effect of all the neighbouring fields being conventional vs. at least one being organic field. Effects in bold are significant at ˛ = 0.05. c The sign of the estimate indicates the direction of the effect: if positive, conventional fields or neighbourhoods have more attacks; if negative, conventional fields or neighbourhoods are healthier.
type effect was significant only in conventional fields, with more disease in conventional neighbourhoods. Field type and neighbourhood type had no significant effect on powdery mildew. 4. Discussion In this study, the effect of farm management (cropping plan and cropping practices) on several pests (sensu lato) of wheat was examined for the field in question and for neighbouring fields. Among the studied farms and fields, the only clear categories with respect to cropping plan and cropping practices exactly mapped with organic certification. The differences between cropping practices in organic and conventional wheat fields (Fig. 2) corresponded to the expected differences. Weed control being the most problematic issue in organic wheat management, sowing dates are delayed in organic fields to allow an increased number of soil cultivations before planting. Conventional farmers, in contrast, tend to reduce ploughing because of time constraints, fuel costs, and reliance on herbicides. Organic farmers also use more diverse crops (including weed-suppressing crops such as fodder bean and winter as well as spring crops) than conventional farmers to control weed. In addition to permitting increased cultivation for weed control, late sowing in organic fields reduces the risk of insect damage and disease during autumn. A higher sowing density in organic than in conventional fields helps compensate for crop losses due to seedling diseases (seed rots, seedling blights, and/or root rots). Organic fields rely on organic fertilisers (compost and hen droppings in our case) rather than on synthetic fertilizers and so receive a lower amount of nitrogen because these fertilizers are costly and have a low N content. Finally, no pesticides were used in organic fields because synthetic pesticides are forbidden in organic farming. Cropping practices observed in the fields studied here are representative of the agricultural practices in cereal-based systems of northern France. The greater abundance and diversity of weeds in organic than in conventional fields was expected because weeds are recognized as the main problem in organic wheat cropping (Roschewitz et al., 2005a; Gabriel et al., 2006; Gibson et al., 2007; Casagrande, 2008). This is because herbicides are not used (Rundlöf et al., 2010) and because cultural practices (mainly soil tillage) provide only partial control. Concerning the effect of crop management on aphids, published results have been variable: some studies reported fewer aphids in
organic than in conventional wheat or barley fields (Östman et al., 2001; Roschewitz et al., 2005b), but others have found the opposite (Macfadyen et al., 2009; Gabriel et al., 2010). This discrepancy is probably due to the numerous factors influencing aphid population dynamics. The variation in numbers of aphids can partly be explained by predation, which is typically higher in organic than in conventional farms (Östman et al., 2001) and is increased by plant diversity including the presence of weeds (Andow, 1991). Aphid abundance is also affected by the use of insecticides on conventional farms (Macfadyen et al., 2009), nutrient availability in the plant (Garratt et al., 2010) and colonisation from surrounding habitats (Thies et al., 2005). The fewer aphids in organic than in conventional fields in the current study may therefore be explained by a lower plant nutrient content, higher predation/parasitism, and higher plant diversity in the organic than in the conventional fields. Although quantity of applied nitrogen is not necessarily a good indicator of nutrient availability in organic fields because soil fertility is maintained by legume crops and other means, the number of aphids per stem was not related to the quantity of nitrogen applied in the organic fields in the current study, indicating that differences in aphid numbers probably did not result from differences in plant nutrient content. There is also no consensus in the literature regarding the effect of organic management on diseases but leaf rust and leaf blotch (VanBruggen, 1995) and powdery mildew and yellow rust (Hannukkala and Tapio, 1990) are often less severe with organic than with conventional management. Although genetic resistance can greatly affect disease development, it cannot explain the lower level of leaf blotch in the organic than in the conventional fields in the current study because the varieties were less resistant to leaf blotch in the organic than in the conventional fields (p < 0.001, mean varietal resistance in conventional fields = 5.4, mean resistance in organic fields = 4.9, on a 0–9 resistance scale). Another possible explanation for the lower level of leaf blotch in our organic fields could be differences in plant nutrition: N fertilization has been shown to increase leaf blotch severity (Simón et al., 2003) and other diseases (Neumann et al., 2004; Cooper et al., 2006). The relationship between plant nutrition and disease in organically grown crops, however, is poorly understood (Walters and Bingham, 2007). Sowing date (in conjunction with earliness) can also influence disease dynamics, and late sowing has been shown to decrease leaf blotch severity (Shaw and Royle, 1993).
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PM
0
+ + CC
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−2
OO
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−4
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−4
−2
0
2
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2
Can1 (96.3%)
0 −2
WD
LB WA
−4
Can2 (1.8%)
OC
+CO + PMOO ++
APH CC
−6
conv org convnei orgnei −2
0
2
4
Can1 (97.6%) Fig. 4. Linear discriminant analysis of the pest population data in each year for weed abundance (WA), weed diversity (WR), leaf blotch incidence (LB), powdery mildew incidence (PM), and aphid abundance (APH). Black symbols: organic fields, grey symbols: conventional fields; circles: at least one organic field in the neighbourhood, triangles: all adjacent fields conventional. Categories used for the discriminant analysis were: OO (organic field in organic neighbourhood), OC (organic field in conventional neighbourhood), CO (conventional field in organic neighbourhood), and CC (conventional field in conventional neighbourhood).
In our study, the presence of at least one organic field in the neighbourhood of a field decreased aphid abundance in that field. This could be explained by a lower “spill over” of aphids: conventional fields, which have higher numbers of aphids, would act as sources of aphids that would then migrate to neighbouring fields, so a lower proportion of conventional fields in the neighbourhood would result in a lower immigration of aphids. It could also be caused by the biological control of aphids by natural enemies coming from organic fields (not only wheat). “Landscape” effects on natural enemies have often been observed although the studied landscape variable was usually the percentage of semi-natural habitats around the field or farm (Chaplin-Kramer et al., 2011). For example, a study on aphids and their parasitoids showed that aphid parasitism decreased with increasing percentage of arable land in the landscape 500 m and 1 km around the observed fields,
and that an increase in the percentage of arable reduced the rate of population increase of aphids between two observation dates, although initial aphid populations were also higher in landscapes rich in semi-natural habitats (Thies et al., 2005). In our case, the number of fields where aphids were present on more than one observation date (and thus where the population growth rate could be computed) was too low to allow the detection of any possible effect of management type in the field or in neighbouring fields on population growth rate (results not shown). The type of neighbourhood significantly affected leaf blotch but only in conventional fields; in organic fields, the effect was not significant and the trend was reversed. This weakens any conclusions that could be drawn regarding how the presence of organic farming in the vicinity of a field affects leaf blotch in that field. In conventional fields, the lower leaf blotch incidence in organic neighbourhoods than in conventional neighbourhoods could be caused by a lower inoculum load because of the lower disease level in organic fields. The absence of an effect of neighbourhood type on weeds is surprising, given the strong difference in weed abundance and diversity between organic and conventional fields. This lack of landscape effect, which was not due to a too recent change in management because the organic fields had been grown organically for more than 8 years previous to the study, is consistent with previously published results: weeds have been shown to respond weakly to factors beyond the field scale (Gaba et al., 2010; Marshall, 2009). In other words the weeds in organic fields do not seem to disperse to conventional fields. Furthermore, they can provide ecosystem services (e.g., they provide food for pollinators and natural enemies) at a scale larger than the field (Petit et al., 2011). Consequently, the increase in the acreage under organic farming should not cause an increase in weed problems in neighbouring conventional fields and could even provide ecosystem services to those fields. 5. Conclusion Our results provide no evidence that pest inoculum (sensu lato) will multiply in organic fields and then spread to neighbouring conventional fields, forcing conventional farmers to use even more pesticides and threatening organic farming sustainability. The use of fungicides, insecticides, and herbicides was not significantly higher in conventional fields adjacent to organic fields compared to conventional fields surrounded only by conventional fields. Organic fields did not necessarily support more pests than conventional fields, and the only neighbourhood effects that were significant were beneficial (i.e., they were detrimental to pest populations). Because these results were obtained over only 2 years and in years with low disease levels and concerned only wheat crops in one agricultural region that contained a moderate acreage of organic farming, our conclusions cannot be extrapolated to all situations, e.g., other crops or climatic conditions more favourable to diseases. In the future, the use of spatially explicit models calibrated with experimental data over a range of production systems and environmental conditions could allow researchers to make realistic predictions concerning the effect of a change in production systems (e.g., widespread adoption of organic farming) on pest populations. Acknowledgements We thank all the farmers who participated in the survey and allowed us to collect data in their fields in the counties of Boinviliers, Boissets, Dammartin-en-Serve, Favrieux, Flacourt, Flins-Neuve-Eglise, Fontenay-Mauvoisin, Le MesnilSimon, Le Tertre-Saint-Denis, Longnes, Ménerville, Mondreville,
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