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Integrated Weed Management systems allow reduced reliance on herbicides and long-term weed control
Agriculture, Ecosystems and Environment 132 (2009) 237–242
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Integrated Weed Management systems allow reduced reliance on herbicides and long-term weed control R. Chikowo a, V. Faloya b, S. Petit a, N.M. Munier-Jolain a,* a b
INRA, UMR1210 Biologie et Gestion des Adventices, F-21000 Dijon, France INRA, UE115 Ferme expe´rimentale de Dijon-Epoisses, F-21000 Dijon, France
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 July 2008 Received in revised form 9 April 2009 Accepted 10 April 2009 Available online 7 May 2009
Current concerns about the environmental impacts of pesticide use in agriculture require the investigation of novel cropping systems that would reduce their reliance on pesticides. In this paper, we report on an experiment carried out over 6 years to assess the performance of four cropping systems based on Integrated Weed Management (IWM) as compared to a reference standard system. Systems differed in crop rotations, soil tillage, mechanical and chemical weeding and crop management. Fewer herbicides were applied in IWM-based systems as compared to the reference, resulting in a lower environmental impact. Over the 6 years, we detected no significant increase in the density of winter or summer annual broad-leaved, and of grassy weeds in IWM systems, with one exception that is discussed. These results indicate that the combination of various IWM techniques allow both the long-term control of arable weeds and a significantly reduced reliance on herbicides. ß 2009 Elsevier B.V. All rights reserved.
Keywords: System experiment Environmental impact Soil tillage Crop rotation Cumulative effect
1. Introduction In Western Europe, weed management on conventional farms is primarily accomplished through the use of herbicides. This has been very effective for weed control in major crops, greatly reducing yield losses and stabilizing potential weed infestations at low and constant levels. Herbicides have therefore facilitated the simplification of cropping systems, the expansion of monocultures and the adoption of reduced tillage systems (Buhler et al., 2000). However, the use of herbicides as the sole weed management tool is seriously being questioned by farmers because of herbicide costs and technical problems related to resistance of weed populations to one or several herbicides (Richeter et al., 2002; Mace et al., 2007). In addition, there is an increasing concern about the environmental impacts of herbicide residues into surface- and ground-water. Therefore, one of the main challenges is to develop Integrated Weed Management (IWM) systems that limit weed infestations with a low reliance on herbicides, preferably without side effects on the productivity and the overall system economic performance. The main guidelines for IWM result from the current knowledge on the influence of cropping systems on weed demography. In addition to crop rotation, IWM recommends adapted soil tillage to manage the
depth of the seed bank (Munier-Jolain et al., 2004), stale seedbed and adapted sowing dates (Rasmussen, 2004), adapted sowing densities and row widths (Olsen et al., 2005), competitive cultivars (Lemerle et al., 2001), and mechanical weeding. This paper reports on a cropping system experiment that was set up to evaluate the efficiency of IWM principles. We compared five cropping systems that were designed to meet different economic and environmental criteria and therefore characterised by contrasted levels of herbicide use. The objective was to assess whether or not any of the combinations of IWM principles tested in the cropping systems would be efficient enough to control weeds in the long term. The main criterion for assessing the performance of IWM regarding long-term weed control was the temporal variation in weed infestation over the course of the experiment. Weed management in a given field was considered successful if no increases in the overall weed density, nor in the density of each weed species (or group of species) separately, were observed. The issues of economic profitability and of other environmental impacts such as global warming, eutrophication and biodiversity are not addressed in this paper. 2. Materials and methods 2.1. Experimental design
* Corresponding author at: INRA - UMR Biologie et Gestion des Adventices, 17, rue Sully, BP 86510, 21065 Dijon Cedex, France. Tel.: +33 3 80 69 30 35; fax: +33 3 80 69 32 22. E-mail address: [email protected] (N.M. Munier-Jolain). 0167-8809/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2009.04.009
The experiment was located at the INRA experimental farm in Dijon, eastern France (478200 N, 5820 E), in a region with a semicontinental climate. For each of the five investigated cropping
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systems, a set of decision rules was defined as a function of the objectives and management options. Each set of decision rules was applied in two fields, one in the eastern part of the experimental farm (designated Area A), and one in the western part about 1 km away (designated Area D). Soil in the A fields was a calcareous clayey soil, with clay content of 35% and 0.9 m depth, while soil in the D fields had 50% clay content and a shallow depth of only 0.5 m. The area of the 10 fields (2 per cropping system) ranged between 1.1 ha and 1.8 ha, which was large enough for the use of normalsized field equipment for soil tillage and crop management. Before the beginning of the experiment, the cropping systems in the 10 fields were composed of crop sequences with winter cereals, mainly wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), and summer crops, mainly soybean (Glycine max L.) and sunflower (Helianthus annuus L.). The weed flora was dominated by spring-emerging broad-leaved species (Polygonum aviculare L., P. persicaria L., Fallopia convolvulus L., Solanum nigrum L., Amaranthus retroflexus L.) but also included autumn–winter emerging weeds (Veronica persica Poir., V. hederifolia L.) and a few species emerging in nearly all seasons (Capsella bursa-pastoris L.). In addition to those species, Alopecurus myosuroides Huds. was sown in each field on a 100 m2 area immediately before sowing the first experimental crop, to compensate for the under-representation of grassy weeds as compared to the regional weed flora. The first cropping system (S1) was a standard reference, designed to maximize financial returns, and emphasized the use of chemical herbicides for weed control. Mouldboard ploughing was carried out each year during summer, and herbicides were chosen following the recommendations of extension services. Systems S2, S3, S4 and S5 were IWM systems in which the use of herbicides was expected to be reduced gradually compared to S1. S2 was designed to reduce labour requirement and excluded time consuming operations such as mouldboard ploughing, rotary harrowing and mechanical weeding. S3 allowed for ploughing and other soil tillage operations when necessary for weed seed bank management, but excluded mechanical weeding, considered as potentially too time consuming for some farmers, and/or difficult to implement in farms that do not have the proper equipment. In contrast, S4 was a typical IWM system which relied partly on mechanical weeding. This system included sugar beet (Beta vulgaris L.) in the crop rotation to test the consequences of growing this crop, which typically requires high amounts of herbicides, under a lower-herbicide regime thanks to on-rows band spraying combined with between-row mechanical weeding. Finally, S5 excluded the use of any herbicides, and relied only on other physical and cultural means to contain weed infestations. A detailed description of the cropping systems is given in Table 1. 2.2. Environmental impact assessment The reliance on herbicides of the five cropping systems was estimated by calculating for each field the amounts of active ingredients applied during the first 6 years (i.e. two crop rotations in S1 and one rotation in the four IWM-based systems). The environmental impacts of herbicides were estimated using the indicator Ipest (Van der Werf and Zimmer, 1998). Ipest was calculated for each herbicide application and ranged potentially from 0 (no estimated impact on the environment) to 1 for treatments with a very strong impact. Average annual values of (i) the number of herbicide applications, (ii) the amounts of herbicide active ingredients applied, and (iii) the cumulative Ipest indicator, were compared among the systems. Because the data did not support the assumptions for ANOVAs (variances were not equal, being nil for S5), the effects of the systems were analysed using a permutation method that did not require any assumption about
the data distributions: the Fisher’s F-statistic was calculated and compared to the distribution of 10,000 Fisher’s F calculated for 10,000 random distributions of the values among the systems. Sugar beet, which was grown only in S4, introduced a bias in the comparison of systems, because the indicators of herbicide use were very high in this crop as compared to all the other crops, as expected. Therefore, in S4, the indicators were calculated both over the 6 years of the experiment and over 5 years, excluding the year in which sugar beet was grown. 2.3. Assessment of weed abundance In each field, weed species were identified and their abundance estimated using five density classes (<1, 1–3, 3–20, 20–50 and 50– 500 individuals m2). This was repeated two to four times a year, the number of assessments depending mainly on the timing of the field operations. For the first 2 years of the experiment, each field was divided into eight zones (approximately 0.25 ha each). The abundance of each species was estimated through visual assessments in each zone, and then averaged to provide an estimate of the species density at the field level. For the following 4 years, abundance of individual weed species in each field was geo-referenced with a global positioning system (GPS) in 30–200 16-m2 locations (mean number of approximately 90 locations per species). Density values were used for weed mapping, with ordinary kriging using Geostatistical Analyst software (Johnston et al., 2001). Mean field densities were then estimated by averaging the interpolated densities of each species on a 2 m 2 m grid. For a given species, the number of geo-referenced locations was varied as a function of the abundance and homogeneity, increasing the density of locations within and around dense patches to improve the precision of weed mapping. 2.4. Effects of cropping systems Weed species were classified into three groups, namely ‘winter annual broad-leaved’, ‘summer annual broad-leaved’ and ‘grassy weeds’. Winter annual broad-leaved species were expected to be favoured by crop sequences with 100% winter crops such as in S1, summer annual broad-leaved species were expected to be favoured by crop sequences including spring and summer crops such as in IWM-based systems, and grassy weeds (mainly A. myosuroides) were expected to be favoured both by winter crop sequences and by reduced tillage (S2). Mean weed densities were transformed [H(x + 1)] and systems were compared using t-tests (P < 0.05). Separate analyses were performed on data collected before and after weed control operations. Density before weed control provided information on the potential infestation related to the seed bank, while density after weed control provided information on the weed infestation actually competing with the crop during the main part of the crop cycle. The evolution of weed density throughout the 6 year experiment was estimated by a Pearson’s correlation coefficient between year and weed density (either before or after weed control) (Proc CORR of SAS v8 software, SAS Institute Inc., 2007). For the field S4-A, Pearson’s correlations were only computed after weed control, because too few data were available before weed control. 3. Results 3.1. Environmental impact assessment As could be expected, the mean annual number of herbicide treatments was lower in the IWM systems than in the standard S1
Table 1 Crop sequences, soil tillage, crop sowing periods and weed control programs on the 10 fields. Crops written in parenthesis were grown as cover crops. Numbers in parenthesis are the numbers of mechanical weeding operations and of shallow cultivations (SC). In IWM systems, herbicides were occasionally applied on weed patches only. MP = Mouldboard ploughing. No
Cropping system
S1
Standard
S3
IWM, reduced tillage
IWM, no mechanical weeding
Soil tillage
Harvest 2001
Soil tillage
Harvest 2002
Soil tillage
Harvest 2003
Soil tillage
Harvest 2004
Soil tillage
Harvest 2005
Soil tillage
Harvest 2006
D
MP +SC(3)
MP +SC(3)
Oilseed rape End August 3 herbicides W-Wheat Early October 3 herbicides
MP +SC(2)
W-Wheat Early October 3 herbicides W-Barley Early October 4 herbicides
MP +SC(1)
W-Barley Early October 2 herbicides Oilseed rape End August 4 herbicides
MP +SC(2)
MP +SC(2)
W-Barley Early October 1 herbicide Oilseed rape End August 3 herbicides
MP +SC(1)
A
W-Wheat Early October 3 herbicides W-Wheat Early October 3 herbicides
Oilseed rape End August 2 herbicides W-Wheat Early October 3 herbicides
D
SC(3)
A
SC(3)
D
SC(2) MP
S4
S5
IWM, mechanical weeding
IWM, no herbicide
A
+SC(3)
D
SC(3)
A
SC(2)
D
SC(2)
A
MP +SC(4)
W-Wheat End October 1 herbicide W-Wheat End October 1 herbicide
MP +SC(2)
SC(3)
SC(4)
W-Barley Early October 0 herbicide (Oat) Soybean End April 3 herbicides
W-Wheat End October 2 herbicides (Mustard) Soybean End April 3 herbicides
MP +SC(2)
SC(2)
End October 2 herbicides
W-Wheat End October 1 herbicide
MP +SC(2)
W-Wheat
MP
Early August 1 herbicide harrow (1)
+SC(3)
Oilseed rape Early August 1 herbicide, harrow (2) (Mustard) S-Barley Mid March 0 herbicide, harrow (1)
W-Wheat End October Harrow (1) W-Barley End October Harrow (1)
MP +SC(3) MP +SC(3)
Oilseed rape Early August 1 herbicide W-Wheat
W-Barley Mid October Harrow (3) Oilseed rape Early August Harrow, hoe (2)
MP +SC(1)
SC(9)
SC(2)
SC(5) MP +SC(4)
SC(5)
SC(3)
SC(4)
SC(4)
Soybean End April 2 herbicides W-Wheat End October 1 herbicide
MP +SC(2)
SC(3)
SC(1)
W-Wheat End October 2 herbicides Oilseed rape Early August 1 herbicide
W-Wheat End October 1 herbicide (Phacelia) Mustard Mid March 2 herbicides
MP +SC(3)
SC(3)
End October 2 herbicides
W-Wheat End October 0 herbicide, harrow (4) Oilseed rape
MP +SC(6)
Sugar Beet End March 4 banded herb. hoe (4) W-Wheat
Early August 1 herbicide, harrow (1)
SC(5)
Oilseed rape Early August Hoe (3) W-Wheat End October Harrow (3)
MP +SC(4) MP +SC(4)
(Oat) Soybean End April 1 herbicide W-Wheat
MP +SC(1)
SC(1)
SC(4)
Oilseed rape Early August 2 herbicides Wheat End October 3 herbicides
MP +SC(1)
SC(2)
SC(3)
Triticale End October 2 herbicides S-Barley Mid March 2 herbicides
S-Barley Mid March 1 herbicide Oilseed rape
MP +SC(3)
S-Oats Mid March 1 herbicide Triticale
Early August 1 herbicide
SC(3)
End October 2 herbicides
SC(2)
S-Barley Mid March 1 herbicide
MP +SC(4)
W-Faba bean End October 2 herbicides
MP
Sugar Beet
End October 2 herbicides, harrow (1)
+SC(4)
End March 1 herbicide, 3 banded herb. hoe (3)
W-Wheat End October Harrow (3) W-Barley Mid October Harrow (3)
MP +SC(5)
Sunflower Mid April Harrow (3) S-Faba bean Early March Harrow (3)
SC(2) MP +SC(1)
MP +SC(3)
Triticale SC(3)
SC(2)
SC(2)
End October 2 herbicides
R. Chikowo et al. / Agriculture, Ecosystems and Environment 132 (2009) 237–242
S2
Area
Triticale End October Harrow (3) Triticale End October Harrow (3)
239
0.71 (1.1) a 2.8 (1.1) b 0.19 (0.3) a 0.42 (0.2) a 0.57 (0.2) a 0.95 (1.2) a 7.3 (1.7) b 1.1 (0.5) a 2.1 (0.3) ab 4.2 (5.2) ab 0.97 (0.03) a 4.5 (5.9) a 3.6 (2.0) a 4.7 (3.8) a 2.7 (1.1) a (1 10 ) a 7.6 (11.2) a 1.7 (2.6) a 13.4 (15.7) a 1.1 (0.5) a
After WC Before WC After WC
4
a
WC: weed control.
8 9 9 5 10
11 16 16 14 16
7.9 16.1 3.8 19.3 5.7
(10.0) ab (9.2) b (1.1) a (9.9) ab (6.3) a
2.2 8.1 5.5 7.0 3.7
(1.5) (7.0) (4.1) (4.1) (0.6)
a a a a a
6.5 (9.0) b 0.74 (0.9) ab 1.7 (2.1) ab 3.6 (6.1) ab 0.05 (0.06) a
0.32 0.36 0.75 0.99 0.07
(0. 1) ab (0.5) ab (0.5) ab (0.7) b (0.07) a
2 10
4
S1 S2 S3 S4 S5
(Table 2). When sugar beet crops in S4 were excluded from the analysis, the differences among systems were significant, and the mean number of treatments in the typical IWM-based S4 was below 1, therefore indicating that some crops were not treated at all. On the contrary, sugar beets required more than four applications per year, thus increasing the overall number of treatments over the 6 years in S4 up to a number close to levels observed in S2 and S3. The mean yearly amount of herbicide active ingredients in S1 was also far higher than in IWM fields, with a significant effect of the cropping system (Table 2). As compared to the standard S1, the amount of active ingredients was reduced by 77%, 71% and 89% in S2, S3 and S4, respectively. It was reduced by 92% in S4 over the 5 years without sugar beet. The reduction in the amount of active ingredients applied in IWM systems was partly due to the reduction in the number of applications, but also to the chosen herbicides that required lower amounts of active ingredients per treatment. The mean yearly Ipest values associated with herbicides were lower in S2, S3 and S4 than in S1 (Table 2). The system effect was significant when the sugar beet was excluded. The high Ipest in sugar beet was mainly related to the number of treatments required in this crop. Contrary to common practice in this region, herbicides with high potential impact, such as metamitron, were not used in IWM sugar beet in this experiment, and the mixed active ingredients were applied only on the crop rows (about one third of the total field area). Thus, the potential environmental impact of sugar beet was reduced compared to usual sugar beet management. When the sugar beet crop was excluded from the analysis, the mean yearly Ipest value in S4 was 87% lower than the standard S1. The mean Ipest values of individual sprayings were 0.22 and 0.35 in IWM systems and the standard system, respectively (data not shown). Considering that the Ipest indicator accounted for the risks of pesticide contamination in ground- and surface-water and in the air, and for the sensitivity of a range of non-target organisms, the lower Ipest values in IWM indicated that these systems succeeded in reducing the potential environmental impact of herbicides.
Summer annual broad-leaved
8.7 0.14
Before WC
Effect of the system (systems: S2, S3 and S4* excluding sugar beet) Fisher’s F 3.2 4.7 P-value 0.20 0.068
After WC
Effect of the system (systems: S1, S2, S3 and S4* excluding sugar beet) Fisher’s F 9.7 38.8 12.2 P-value 0.026 0.011 0.021
Winter annual broad-leaved
9.2 0.078
Before WC
Effect of the system (all years, systems: S1, S2, S3 and S4) Fisher’s F 5.8 43.2 P-value 0.12 0.008
0.79 0.31 0.32 0.27 0.10 1.11 0
After WC
2496 571 722 271 187 696 0
Total weed
2.25 1.27 1.33 1.29 0.74 4.07 0
Cumulative Ipest
Before WC
Active ingredients (g a.i. ha1)
After WC
Number of treatments
Number of observations
S1 S2 S3 S4 S4* S4** (sugar beet) S5
Mean annual values
Before WCa
Cropping system
Cropping systems
Table 2 Mean annual (i) number of herbicide applications, (ii) total amount of active ingredients applied, (iii) cumulative Ipest indicator, for five cropping systems over the 6 years of the experiment (S4* = S4 without sugar beet; S4** = S4 for sugar beet only). S5 was excluded from the bootstrap statistical analysis as it differed from the others by design (no herbicide application).
Annual grassy weeds
R. Chikowo et al. / Agriculture, Ecosystems and Environment 132 (2009) 237–242 Table 3 Mean weed densities (plants m2, back-transformed values) in the five cropping systems before and after weed control (WC) over the 6 years of the experiment. Within each column, mean values followed by the same letter are not different according to LSD (P < 0.05). Values in parenthesis are SE.
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241
Table 4 Pearson correlations between year and weed density during the cropping season, either before or after weed control (WC) in each field. Nb and Na are the numbers of observations over the 6 years, before and after weed control, respectively. Cropping systems
Area
Nb
Na
Total weed
Winter annual broad-leaved
Summer annual broad-leaved
Annual grassy weeds
Before WCa
After WC
Before WC
After WC
Before WC
After WC
Before WC
After WC
S1
A D
4 4
5 6
0.91 0.6
0.2 0.3
0.87 0.8
0.32 0.20
0.91 0.77
0.32 0.30
0.25 0.5
0.25 0.06
S2
A D
4 5
8 8
0.78 0.77
0.09 0.03
0.82 0.13
0.64 0.15
0.81 0.69
0.08 0.08
0.38 0.82
0.26 0.09
S3
A D
5 4
8 8
0.22 0.99**
0.27 0.33
0.24 0.14
0.02 0.28
0.55 1**
S4
A D
2 3
8 6
ncb 0.86
0.08 0.45
nc 0.99
0.13 0.10
nc 0.88
0.05 0.63
S5
A D
5 5
7 9
0.14 0.29
0.06 0.32
0.39 0.72
0.22 0.50
0.07 0.29
0.009 0.25
0.25 0.08
0.56 0.65
0.4 0.54
nc 0.98
0.15 0.43
0.12 0.29
0.17 0.009
*P < 0.05. a WC: weed control. b nc: not calculated (too few data). ** P < 0.01.
3.2. Effects of cropping systems Mean weed densities were highly variable among the two replicates of a given cropping system (Table 3), probably because of differences in the initial seed banks. Overall weed density averaged for the 6 years of the experiment was not significantly higher in IWM cropping systems than in S1. Before weed control, mean weed densities were highest in S2 and S4, intermediate in S1 and lowest in S3 and S5. However, weed densities tended to be lower in S1 than in IWM systems after weed control, indicating that the control of emerged weeds was more effective in the standard system. Mean densities of winter annual broad-leaved species before weed control tended to be lower in IWM cropping systems compared to S1, and the difference was significant between S1 and S5 (Table 3). Part of this effect is due to the fact that S1 was the only system with 100% winter crops grown over the 6 years. Indeed, few winter annual species were likely to emerge between crop sowing and weed control in the years of spring–summer crops in IWM cropping systems. However, part of the difference may also be due to an effect of IWM management options that reduced the density of winter annual broad-leaved seedling emergence even in winter crops. After weed control, the mean density of winter annual broad-leaved species in S1 was reduced to a level not significantly different from densities in IWM systems, including S5. Mean densities of summer annual broad-leaved species tended to be higher both before and after weed control in IWM cropping systems than in S1, but the differences were not significant. Higher densities of summer annuals were expected in IWM-based cropping systems because crop sequences in S1 did not include any spring–summer crops, in contrast to diversified crop sequences in IWM. The group of annual grassy weed species was mainly composed of A. myosuroides. In S2, the mean density of annual grasses over the 6 years both before and after weed control was higher than in the other cropping systems. This result is in accordance with previous pluriannual experiments that demonstrated that noninversion soil tillage favoured grassy weed species (e.g. Cardina et al., 1991; Chauvel et al., 2001; Gerowitt, 2003). Overall, 76 coefficients of Pearson’s correlation were computed between year and weed density for the 10 fields and 4 weed groups (namely the total weed species, winter annual broad-leaved, summer annual broad-leaved and annual grassy weeds) before and after weed control (Table 4). Among those coefficients, two-thirds
were negative (51) and one-third were positive (25) values, but most correlations were not significant, indicating that weed densities did not increase or decrease over the course of the study. The only two significant correlations were negative ones, corresponding to the field S3-D, where the total weed density decreased before weed control, partly in relation to the decline in the emergence of summer annual broad-leaved species. No correlation coefficient indicated any significant trend for any increase in density for any weed group or field. Although not significant, the correlation coefficients tended to be high for annual grassy weeds in S2, and were close to significance before weed control in the field S2-D (P = 0.09). A. myosuroides, the dominant annual grassy weed species, showed a marked cyclic demography in this system with no deep tillage. The population rapidly increased during years of winter crops and rapidly decreased during years with spring sown crops, due to the repetition of shallow tillages triggering the germination of many cohorts during the autumn. In the field S2-D, the study ended with three consecutive years of winter crop. The end of the study was therefore favourable to winter grassy species which might explain the high positive Pearson’s correlation obtained for this species group in this field. 4. Discussion In this experiment, a high variability of weed densities was observed within systems among the experimental replicates, probably due to differences in the initial seed bank. The weed flora observed in the 2 years before the beginning of the experiment and in the first year just after the sowing of the first crop in 2000 was very field specific, although most fields were grown with the same crops. However, the IWM systems were successful in (i) reducing the reliance on herbicides, (ii) reducing the potential impact of herbicides on the environment assessed through the Ipest indicator, and (iii) controlling weed infestations on a long-term basis. All the indicators were consistent with satisfactory long-term weed control in each of the eight fields managed according to IWM principles. After weed control, the densities of remaining weeds were low enough to avoid any yield loss exceeding 1% (Wilson and Wright, 1990), with only a few exceptions (A. myosuroides in the S2-D field in the winter barley harvested in 2002 and the triticale harvested in 2006, and in the S2-A field in the oilseed rape harvested in 2004). Higher densities were observed only between
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growing seasons, before weed control, or at the end of the crop cycle. The effective regulation of weed densities for an ‘integratedflexible’ system was previously attributed to a number of interventions, among which was a well conceived and expanded crop sequence that included a cover crop and some mechanical weeding (Liebman and Dyck, 1993; Jordan et al., 1997; Doucet et al., 1999; Gerowitt, 2003). Similar effects of cover crops and of crop species diversity in the rotation on weed density have been noted in organic crop rotations (Pimentel et al., 2005; Rasmussen et al., 2006; Smith et al., 2008). In our experiment, cover crops were not often grown, as the weed management strategies included mostly bare soils during the inter-crop periods in order to implement the false seedbed technique. However, the combination of a variety of measures in IWM also resulted in increased system complexity, which might hamper their adoption by farmers (Bastiaans et al., 2008). For example, IWM might introduce bottlenecks in the labour organisation at the farm level, because both mechanical weeding and the false seedbed preparation are time-consuming techniques, and delaying the sowing of winter cereals on large areas at the farm scale may be a poor decision because the weather might become less favourable for sowing in the late autumn, leaving only few days (if any) available for field operations. The economical profitability is also likely to be affected in such modified systems. The study presented here is the first step toward the evaluation of the sustainability of the tested IWM-based cropping systems. The indicator of environmental impact used in this study addresses only the impacts related with the release of herbicides in the environment. Other factors such as energy input, greenhouse-gas emissions, soil erosion or biodiversity also need to be assessed for a full appraisal of the environmental impacts of IWM. Such issues will be considered in further studies in order to provide a comprehensive evaluation of these cropping systems. Acknowledgements We thank the experimental farm (INRA-Dijon-Epoisses) for their very profitable collaboration. M. Bourlier, P. Chamoy, P. Farcy, C. Martin, D. Meunier, G. Louviot, M. Penning and F. Strbik provided their indispensable technical assistance. T. Castel and J.M. Brayer helped for achieving the geostatistical procedures and F. Dessaint provided helpful advices for the statistical analyses. The work was supported by the Re´gion Bourgogne and the French Ministe`re de l’Ame´nagement du Territoire et de l’Environnement. The authors
thank R. Gunton and anonymous referees and editors for their contributions to the improvements of the manuscript. References Bastiaans, L., Paolini, R., Baumann, D.T., 2008. Focus on ecological weed management: what is hindering adoption? Weed Res. 48, 481–491. Buhler, D.D., Liebman, M., Obrycki, J., 2000. Theoretical and practical challenges to an IPM approach to weed management. Weed Sci. 48, 274–280. Cardina, J., Regnier, E., Harrison, K., 1991. Long-term tillage effects of seed banks in three Ohio soils. Weed Sci. 39, 186–194. Chauvel, B., Guillemin, J.P., Colbach, N., Gasquez, J., 2001. Evaluation of cropping systems for management of herbicide-resistant populations of blackgrass, Alopecurus myosuroides Huds. Crop Prot. 19, 127–137. Doucet, C., Weaver, S.E., Hamill, A.S., Zhang, J., 1999. Separating the effects of crop rotation from weed management on weed density and diversity. Weed Sci. 47, 729–735. Gerowitt, B., 2003. Development and control of weeds in arable farming systems. Agric. Ecosyst. Environ. 48, 247–254. Johnston, K., ver Hoef, J.M., Krivoruchko, K., Lucas, N., 2001. Using ArcGis Geostatistical Analyst. ESRI, Redlands, CA, USA. Jordan, V.W.L., Hutcheon, J.A., Donaldson, G.V., Farmer, D.P., 1997. Research into and development of integrated farming systems for less-intensive arable crop production: experimental progress, 1989–1994 and commercial implementation. Agric. Ecosyst. Environ. 64, 141–148. Lemerle, D., Gill, G.S., Murphy, C.E., Walker, S.R., Cousens, R.D., Mokhtari, S., Peltzer, S.J., Coleman, R., Luckett, D.J., 2001. Genetic improvement and agronomy for enhanced wheat competitiveness with weeds. Aust. J. Agric. Res. 52, 527–548. Liebman, M., Dyck, E., 1993. Crop rotations and intercropping strategies for weed management. Ecol. Appl. 3, 92–122. Mace, K., Morlon, P., Munier-Jolain, N.M., Quere, L., 2007. Time scales as a factor in decision-making by French farmers on weed management in annual crops. Agric. Syst. 93, 115–142. Munier-Jolain, N.M., Chauvel, B., Gasquez, J., 2004. Strate´gies de protection inte´gre´e contre les adventices des cultures: le retour de l’agronomie. In: Regnault-Roger, C. (Ed.), Enjeux Phytosanitaires pour l’Agriculture et l’Environnement du XXIe`me Sie`cle. Lavoisier, Paris, pp. 411–430. Olsen, J., Kristensen, L., Weiner, J., Griepentrog, H.W., 2005. Increased density and spatial uniformity increase weed suppression by spring wheat. Weed Res. 45, 316–321. Pimentel, D., Hepperly, P., Hanson, J., Douds, D., Seidel, R., 2005. Environmental, energetic, and economic comparisons of organic and conventional farming systems. Bioscience 55, 573–582. Rasmussen, I.A., 2004. The effect of sowing date, stale seedbed, row width and mechanical weed control on weeds and yields of organic winter wheat. Weed Res. 44, 12–20. Rasmussen, I.A., Askegaard, M., Olesen, J.E., Kristensen, K., 2006. Effects on weeds of management in newly converted organic crop rotations in Denmark. Agric. Ecosyst. Environ. 113, 184–195. Richeter, O., Zwerger, P., Bottcher, U., 2002. Modelling spatio-temporal dynamics of herbicide resistance. Weed Res. 42, 52–64. Smith, R.G., Gross, K.L., Robertson, G.P., 2008. Effects of crop diversity on agroecosystem function: crop yield response. Ecosystem 11, 355–366. Van der Werf, H., Zimmer, C., 1998. An indicator of pesticide environmental impact based on a fuzzy expert system. Chemosphere 36, 2225–2249. Wilson, B.J., Wright, K.J., 1990. Predicting the growth and competitive effects of annual weeds in wheat. Weed Res. 30, 201–211.
Contents lists available at ScienceDirect
Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee
Integrated Weed Management systems allow reduced reliance on herbicides and long-term weed control R. Chikowo a, V. Faloya b, S. Petit a, N.M. Munier-Jolain a,* a b
INRA, UMR1210 Biologie et Gestion des Adventices, F-21000 Dijon, France INRA, UE115 Ferme expe´rimentale de Dijon-Epoisses, F-21000 Dijon, France
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 July 2008 Received in revised form 9 April 2009 Accepted 10 April 2009 Available online 7 May 2009
Current concerns about the environmental impacts of pesticide use in agriculture require the investigation of novel cropping systems that would reduce their reliance on pesticides. In this paper, we report on an experiment carried out over 6 years to assess the performance of four cropping systems based on Integrated Weed Management (IWM) as compared to a reference standard system. Systems differed in crop rotations, soil tillage, mechanical and chemical weeding and crop management. Fewer herbicides were applied in IWM-based systems as compared to the reference, resulting in a lower environmental impact. Over the 6 years, we detected no significant increase in the density of winter or summer annual broad-leaved, and of grassy weeds in IWM systems, with one exception that is discussed. These results indicate that the combination of various IWM techniques allow both the long-term control of arable weeds and a significantly reduced reliance on herbicides. ß 2009 Elsevier B.V. All rights reserved.
Keywords: System experiment Environmental impact Soil tillage Crop rotation Cumulative effect
1. Introduction In Western Europe, weed management on conventional farms is primarily accomplished through the use of herbicides. This has been very effective for weed control in major crops, greatly reducing yield losses and stabilizing potential weed infestations at low and constant levels. Herbicides have therefore facilitated the simplification of cropping systems, the expansion of monocultures and the adoption of reduced tillage systems (Buhler et al., 2000). However, the use of herbicides as the sole weed management tool is seriously being questioned by farmers because of herbicide costs and technical problems related to resistance of weed populations to one or several herbicides (Richeter et al., 2002; Mace et al., 2007). In addition, there is an increasing concern about the environmental impacts of herbicide residues into surface- and ground-water. Therefore, one of the main challenges is to develop Integrated Weed Management (IWM) systems that limit weed infestations with a low reliance on herbicides, preferably without side effects on the productivity and the overall system economic performance. The main guidelines for IWM result from the current knowledge on the influence of cropping systems on weed demography. In addition to crop rotation, IWM recommends adapted soil tillage to manage the
depth of the seed bank (Munier-Jolain et al., 2004), stale seedbed and adapted sowing dates (Rasmussen, 2004), adapted sowing densities and row widths (Olsen et al., 2005), competitive cultivars (Lemerle et al., 2001), and mechanical weeding. This paper reports on a cropping system experiment that was set up to evaluate the efficiency of IWM principles. We compared five cropping systems that were designed to meet different economic and environmental criteria and therefore characterised by contrasted levels of herbicide use. The objective was to assess whether or not any of the combinations of IWM principles tested in the cropping systems would be efficient enough to control weeds in the long term. The main criterion for assessing the performance of IWM regarding long-term weed control was the temporal variation in weed infestation over the course of the experiment. Weed management in a given field was considered successful if no increases in the overall weed density, nor in the density of each weed species (or group of species) separately, were observed. The issues of economic profitability and of other environmental impacts such as global warming, eutrophication and biodiversity are not addressed in this paper. 2. Materials and methods 2.1. Experimental design
* Corresponding author at: INRA - UMR Biologie et Gestion des Adventices, 17, rue Sully, BP 86510, 21065 Dijon Cedex, France. Tel.: +33 3 80 69 30 35; fax: +33 3 80 69 32 22. E-mail address: [email protected] (N.M. Munier-Jolain). 0167-8809/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2009.04.009
The experiment was located at the INRA experimental farm in Dijon, eastern France (478200 N, 5820 E), in a region with a semicontinental climate. For each of the five investigated cropping
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systems, a set of decision rules was defined as a function of the objectives and management options. Each set of decision rules was applied in two fields, one in the eastern part of the experimental farm (designated Area A), and one in the western part about 1 km away (designated Area D). Soil in the A fields was a calcareous clayey soil, with clay content of 35% and 0.9 m depth, while soil in the D fields had 50% clay content and a shallow depth of only 0.5 m. The area of the 10 fields (2 per cropping system) ranged between 1.1 ha and 1.8 ha, which was large enough for the use of normalsized field equipment for soil tillage and crop management. Before the beginning of the experiment, the cropping systems in the 10 fields were composed of crop sequences with winter cereals, mainly wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), and summer crops, mainly soybean (Glycine max L.) and sunflower (Helianthus annuus L.). The weed flora was dominated by spring-emerging broad-leaved species (Polygonum aviculare L., P. persicaria L., Fallopia convolvulus L., Solanum nigrum L., Amaranthus retroflexus L.) but also included autumn–winter emerging weeds (Veronica persica Poir., V. hederifolia L.) and a few species emerging in nearly all seasons (Capsella bursa-pastoris L.). In addition to those species, Alopecurus myosuroides Huds. was sown in each field on a 100 m2 area immediately before sowing the first experimental crop, to compensate for the under-representation of grassy weeds as compared to the regional weed flora. The first cropping system (S1) was a standard reference, designed to maximize financial returns, and emphasized the use of chemical herbicides for weed control. Mouldboard ploughing was carried out each year during summer, and herbicides were chosen following the recommendations of extension services. Systems S2, S3, S4 and S5 were IWM systems in which the use of herbicides was expected to be reduced gradually compared to S1. S2 was designed to reduce labour requirement and excluded time consuming operations such as mouldboard ploughing, rotary harrowing and mechanical weeding. S3 allowed for ploughing and other soil tillage operations when necessary for weed seed bank management, but excluded mechanical weeding, considered as potentially too time consuming for some farmers, and/or difficult to implement in farms that do not have the proper equipment. In contrast, S4 was a typical IWM system which relied partly on mechanical weeding. This system included sugar beet (Beta vulgaris L.) in the crop rotation to test the consequences of growing this crop, which typically requires high amounts of herbicides, under a lower-herbicide regime thanks to on-rows band spraying combined with between-row mechanical weeding. Finally, S5 excluded the use of any herbicides, and relied only on other physical and cultural means to contain weed infestations. A detailed description of the cropping systems is given in Table 1. 2.2. Environmental impact assessment The reliance on herbicides of the five cropping systems was estimated by calculating for each field the amounts of active ingredients applied during the first 6 years (i.e. two crop rotations in S1 and one rotation in the four IWM-based systems). The environmental impacts of herbicides were estimated using the indicator Ipest (Van der Werf and Zimmer, 1998). Ipest was calculated for each herbicide application and ranged potentially from 0 (no estimated impact on the environment) to 1 for treatments with a very strong impact. Average annual values of (i) the number of herbicide applications, (ii) the amounts of herbicide active ingredients applied, and (iii) the cumulative Ipest indicator, were compared among the systems. Because the data did not support the assumptions for ANOVAs (variances were not equal, being nil for S5), the effects of the systems were analysed using a permutation method that did not require any assumption about
the data distributions: the Fisher’s F-statistic was calculated and compared to the distribution of 10,000 Fisher’s F calculated for 10,000 random distributions of the values among the systems. Sugar beet, which was grown only in S4, introduced a bias in the comparison of systems, because the indicators of herbicide use were very high in this crop as compared to all the other crops, as expected. Therefore, in S4, the indicators were calculated both over the 6 years of the experiment and over 5 years, excluding the year in which sugar beet was grown. 2.3. Assessment of weed abundance In each field, weed species were identified and their abundance estimated using five density classes (<1, 1–3, 3–20, 20–50 and 50– 500 individuals m2). This was repeated two to four times a year, the number of assessments depending mainly on the timing of the field operations. For the first 2 years of the experiment, each field was divided into eight zones (approximately 0.25 ha each). The abundance of each species was estimated through visual assessments in each zone, and then averaged to provide an estimate of the species density at the field level. For the following 4 years, abundance of individual weed species in each field was geo-referenced with a global positioning system (GPS) in 30–200 16-m2 locations (mean number of approximately 90 locations per species). Density values were used for weed mapping, with ordinary kriging using Geostatistical Analyst software (Johnston et al., 2001). Mean field densities were then estimated by averaging the interpolated densities of each species on a 2 m 2 m grid. For a given species, the number of geo-referenced locations was varied as a function of the abundance and homogeneity, increasing the density of locations within and around dense patches to improve the precision of weed mapping. 2.4. Effects of cropping systems Weed species were classified into three groups, namely ‘winter annual broad-leaved’, ‘summer annual broad-leaved’ and ‘grassy weeds’. Winter annual broad-leaved species were expected to be favoured by crop sequences with 100% winter crops such as in S1, summer annual broad-leaved species were expected to be favoured by crop sequences including spring and summer crops such as in IWM-based systems, and grassy weeds (mainly A. myosuroides) were expected to be favoured both by winter crop sequences and by reduced tillage (S2). Mean weed densities were transformed [H(x + 1)] and systems were compared using t-tests (P < 0.05). Separate analyses were performed on data collected before and after weed control operations. Density before weed control provided information on the potential infestation related to the seed bank, while density after weed control provided information on the weed infestation actually competing with the crop during the main part of the crop cycle. The evolution of weed density throughout the 6 year experiment was estimated by a Pearson’s correlation coefficient between year and weed density (either before or after weed control) (Proc CORR of SAS v8 software, SAS Institute Inc., 2007). For the field S4-A, Pearson’s correlations were only computed after weed control, because too few data were available before weed control. 3. Results 3.1. Environmental impact assessment As could be expected, the mean annual number of herbicide treatments was lower in the IWM systems than in the standard S1
Table 1 Crop sequences, soil tillage, crop sowing periods and weed control programs on the 10 fields. Crops written in parenthesis were grown as cover crops. Numbers in parenthesis are the numbers of mechanical weeding operations and of shallow cultivations (SC). In IWM systems, herbicides were occasionally applied on weed patches only. MP = Mouldboard ploughing. No
Cropping system
S1
Standard
S3
IWM, reduced tillage
IWM, no mechanical weeding
Soil tillage
Harvest 2001
Soil tillage
Harvest 2002
Soil tillage
Harvest 2003
Soil tillage
Harvest 2004
Soil tillage
Harvest 2005
Soil tillage
Harvest 2006
D
MP +SC(3)
MP +SC(3)
Oilseed rape End August 3 herbicides W-Wheat Early October 3 herbicides
MP +SC(2)
W-Wheat Early October 3 herbicides W-Barley Early October 4 herbicides
MP +SC(1)
W-Barley Early October 2 herbicides Oilseed rape End August 4 herbicides
MP +SC(2)
MP +SC(2)
W-Barley Early October 1 herbicide Oilseed rape End August 3 herbicides
MP +SC(1)
A
W-Wheat Early October 3 herbicides W-Wheat Early October 3 herbicides
Oilseed rape End August 2 herbicides W-Wheat Early October 3 herbicides
D
SC(3)
A
SC(3)
D
SC(2) MP
S4
S5
IWM, mechanical weeding
IWM, no herbicide
A
+SC(3)
D
SC(3)
A
SC(2)
D
SC(2)
A
MP +SC(4)
W-Wheat End October 1 herbicide W-Wheat End October 1 herbicide
MP +SC(2)
SC(3)
SC(4)
W-Barley Early October 0 herbicide (Oat) Soybean End April 3 herbicides
W-Wheat End October 2 herbicides (Mustard) Soybean End April 3 herbicides
MP +SC(2)
SC(2)
End October 2 herbicides
W-Wheat End October 1 herbicide
MP +SC(2)
W-Wheat
MP
Early August 1 herbicide harrow (1)
+SC(3)
Oilseed rape Early August 1 herbicide, harrow (2) (Mustard) S-Barley Mid March 0 herbicide, harrow (1)
W-Wheat End October Harrow (1) W-Barley End October Harrow (1)
MP +SC(3) MP +SC(3)
Oilseed rape Early August 1 herbicide W-Wheat
W-Barley Mid October Harrow (3) Oilseed rape Early August Harrow, hoe (2)
MP +SC(1)
SC(9)
SC(2)
SC(5) MP +SC(4)
SC(5)
SC(3)
SC(4)
SC(4)
Soybean End April 2 herbicides W-Wheat End October 1 herbicide
MP +SC(2)
SC(3)
SC(1)
W-Wheat End October 2 herbicides Oilseed rape Early August 1 herbicide
W-Wheat End October 1 herbicide (Phacelia) Mustard Mid March 2 herbicides
MP +SC(3)
SC(3)
End October 2 herbicides
W-Wheat End October 0 herbicide, harrow (4) Oilseed rape
MP +SC(6)
Sugar Beet End March 4 banded herb. hoe (4) W-Wheat
Early August 1 herbicide, harrow (1)
SC(5)
Oilseed rape Early August Hoe (3) W-Wheat End October Harrow (3)
MP +SC(4) MP +SC(4)
(Oat) Soybean End April 1 herbicide W-Wheat
MP +SC(1)
SC(1)
SC(4)
Oilseed rape Early August 2 herbicides Wheat End October 3 herbicides
MP +SC(1)
SC(2)
SC(3)
Triticale End October 2 herbicides S-Barley Mid March 2 herbicides
S-Barley Mid March 1 herbicide Oilseed rape
MP +SC(3)
S-Oats Mid March 1 herbicide Triticale
Early August 1 herbicide
SC(3)
End October 2 herbicides
SC(2)
S-Barley Mid March 1 herbicide
MP +SC(4)
W-Faba bean End October 2 herbicides
MP
Sugar Beet
End October 2 herbicides, harrow (1)
+SC(4)
End March 1 herbicide, 3 banded herb. hoe (3)
W-Wheat End October Harrow (3) W-Barley Mid October Harrow (3)
MP +SC(5)
Sunflower Mid April Harrow (3) S-Faba bean Early March Harrow (3)
SC(2) MP +SC(1)
MP +SC(3)
Triticale SC(3)
SC(2)
SC(2)
End October 2 herbicides
R. Chikowo et al. / Agriculture, Ecosystems and Environment 132 (2009) 237–242
S2
Area
Triticale End October Harrow (3) Triticale End October Harrow (3)
239
0.71 (1.1) a 2.8 (1.1) b 0.19 (0.3) a 0.42 (0.2) a 0.57 (0.2) a 0.95 (1.2) a 7.3 (1.7) b 1.1 (0.5) a 2.1 (0.3) ab 4.2 (5.2) ab 0.97 (0.03) a 4.5 (5.9) a 3.6 (2.0) a 4.7 (3.8) a 2.7 (1.1) a (1 10 ) a 7.6 (11.2) a 1.7 (2.6) a 13.4 (15.7) a 1.1 (0.5) a
After WC Before WC After WC
4
a
WC: weed control.
8 9 9 5 10
11 16 16 14 16
7.9 16.1 3.8 19.3 5.7
(10.0) ab (9.2) b (1.1) a (9.9) ab (6.3) a
2.2 8.1 5.5 7.0 3.7
(1.5) (7.0) (4.1) (4.1) (0.6)
a a a a a
6.5 (9.0) b 0.74 (0.9) ab 1.7 (2.1) ab 3.6 (6.1) ab 0.05 (0.06) a
0.32 0.36 0.75 0.99 0.07
(0. 1) ab (0.5) ab (0.5) ab (0.7) b (0.07) a
2 10
4
S1 S2 S3 S4 S5
(Table 2). When sugar beet crops in S4 were excluded from the analysis, the differences among systems were significant, and the mean number of treatments in the typical IWM-based S4 was below 1, therefore indicating that some crops were not treated at all. On the contrary, sugar beets required more than four applications per year, thus increasing the overall number of treatments over the 6 years in S4 up to a number close to levels observed in S2 and S3. The mean yearly amount of herbicide active ingredients in S1 was also far higher than in IWM fields, with a significant effect of the cropping system (Table 2). As compared to the standard S1, the amount of active ingredients was reduced by 77%, 71% and 89% in S2, S3 and S4, respectively. It was reduced by 92% in S4 over the 5 years without sugar beet. The reduction in the amount of active ingredients applied in IWM systems was partly due to the reduction in the number of applications, but also to the chosen herbicides that required lower amounts of active ingredients per treatment. The mean yearly Ipest values associated with herbicides were lower in S2, S3 and S4 than in S1 (Table 2). The system effect was significant when the sugar beet was excluded. The high Ipest in sugar beet was mainly related to the number of treatments required in this crop. Contrary to common practice in this region, herbicides with high potential impact, such as metamitron, were not used in IWM sugar beet in this experiment, and the mixed active ingredients were applied only on the crop rows (about one third of the total field area). Thus, the potential environmental impact of sugar beet was reduced compared to usual sugar beet management. When the sugar beet crop was excluded from the analysis, the mean yearly Ipest value in S4 was 87% lower than the standard S1. The mean Ipest values of individual sprayings were 0.22 and 0.35 in IWM systems and the standard system, respectively (data not shown). Considering that the Ipest indicator accounted for the risks of pesticide contamination in ground- and surface-water and in the air, and for the sensitivity of a range of non-target organisms, the lower Ipest values in IWM indicated that these systems succeeded in reducing the potential environmental impact of herbicides.
Summer annual broad-leaved
8.7 0.14
Before WC
Effect of the system (systems: S2, S3 and S4* excluding sugar beet) Fisher’s F 3.2 4.7 P-value 0.20 0.068
After WC
Effect of the system (systems: S1, S2, S3 and S4* excluding sugar beet) Fisher’s F 9.7 38.8 12.2 P-value 0.026 0.011 0.021
Winter annual broad-leaved
9.2 0.078
Before WC
Effect of the system (all years, systems: S1, S2, S3 and S4) Fisher’s F 5.8 43.2 P-value 0.12 0.008
0.79 0.31 0.32 0.27 0.10 1.11 0
After WC
2496 571 722 271 187 696 0
Total weed
2.25 1.27 1.33 1.29 0.74 4.07 0
Cumulative Ipest
Before WC
Active ingredients (g a.i. ha1)
After WC
Number of treatments
Number of observations
S1 S2 S3 S4 S4* S4** (sugar beet) S5
Mean annual values
Before WCa
Cropping system
Cropping systems
Table 2 Mean annual (i) number of herbicide applications, (ii) total amount of active ingredients applied, (iii) cumulative Ipest indicator, for five cropping systems over the 6 years of the experiment (S4* = S4 without sugar beet; S4** = S4 for sugar beet only). S5 was excluded from the bootstrap statistical analysis as it differed from the others by design (no herbicide application).
Annual grassy weeds
R. Chikowo et al. / Agriculture, Ecosystems and Environment 132 (2009) 237–242 Table 3 Mean weed densities (plants m2, back-transformed values) in the five cropping systems before and after weed control (WC) over the 6 years of the experiment. Within each column, mean values followed by the same letter are not different according to LSD (P < 0.05). Values in parenthesis are SE.
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R. Chikowo et al. / Agriculture, Ecosystems and Environment 132 (2009) 237–242
241
Table 4 Pearson correlations between year and weed density during the cropping season, either before or after weed control (WC) in each field. Nb and Na are the numbers of observations over the 6 years, before and after weed control, respectively. Cropping systems
Area
Nb
Na
Total weed
Winter annual broad-leaved
Summer annual broad-leaved
Annual grassy weeds
Before WCa
After WC
Before WC
After WC
Before WC
After WC
Before WC
After WC
S1
A D
4 4
5 6
0.91 0.6
0.2 0.3
0.87 0.8
0.32 0.20
0.91 0.77
0.32 0.30
0.25 0.5
0.25 0.06
S2
A D
4 5
8 8
0.78 0.77
0.09 0.03
0.82 0.13
0.64 0.15
0.81 0.69
0.08 0.08
0.38 0.82
0.26 0.09
S3
A D
5 4
8 8
0.22 0.99**
0.27 0.33
0.24 0.14
0.02 0.28
0.55 1**
S4
A D
2 3
8 6
ncb 0.86
0.08 0.45
nc 0.99
0.13 0.10
nc 0.88
0.05 0.63
S5
A D
5 5
7 9
0.14 0.29
0.06 0.32
0.39 0.72
0.22 0.50
0.07 0.29
0.009 0.25
0.25 0.08
0.56 0.65
0.4 0.54
nc 0.98
0.15 0.43
0.12 0.29
0.17 0.009
*P < 0.05. a WC: weed control. b nc: not calculated (too few data). ** P < 0.01.
3.2. Effects of cropping systems Mean weed densities were highly variable among the two replicates of a given cropping system (Table 3), probably because of differences in the initial seed banks. Overall weed density averaged for the 6 years of the experiment was not significantly higher in IWM cropping systems than in S1. Before weed control, mean weed densities were highest in S2 and S4, intermediate in S1 and lowest in S3 and S5. However, weed densities tended to be lower in S1 than in IWM systems after weed control, indicating that the control of emerged weeds was more effective in the standard system. Mean densities of winter annual broad-leaved species before weed control tended to be lower in IWM cropping systems compared to S1, and the difference was significant between S1 and S5 (Table 3). Part of this effect is due to the fact that S1 was the only system with 100% winter crops grown over the 6 years. Indeed, few winter annual species were likely to emerge between crop sowing and weed control in the years of spring–summer crops in IWM cropping systems. However, part of the difference may also be due to an effect of IWM management options that reduced the density of winter annual broad-leaved seedling emergence even in winter crops. After weed control, the mean density of winter annual broad-leaved species in S1 was reduced to a level not significantly different from densities in IWM systems, including S5. Mean densities of summer annual broad-leaved species tended to be higher both before and after weed control in IWM cropping systems than in S1, but the differences were not significant. Higher densities of summer annuals were expected in IWM-based cropping systems because crop sequences in S1 did not include any spring–summer crops, in contrast to diversified crop sequences in IWM. The group of annual grassy weed species was mainly composed of A. myosuroides. In S2, the mean density of annual grasses over the 6 years both before and after weed control was higher than in the other cropping systems. This result is in accordance with previous pluriannual experiments that demonstrated that noninversion soil tillage favoured grassy weed species (e.g. Cardina et al., 1991; Chauvel et al., 2001; Gerowitt, 2003). Overall, 76 coefficients of Pearson’s correlation were computed between year and weed density for the 10 fields and 4 weed groups (namely the total weed species, winter annual broad-leaved, summer annual broad-leaved and annual grassy weeds) before and after weed control (Table 4). Among those coefficients, two-thirds
were negative (51) and one-third were positive (25) values, but most correlations were not significant, indicating that weed densities did not increase or decrease over the course of the study. The only two significant correlations were negative ones, corresponding to the field S3-D, where the total weed density decreased before weed control, partly in relation to the decline in the emergence of summer annual broad-leaved species. No correlation coefficient indicated any significant trend for any increase in density for any weed group or field. Although not significant, the correlation coefficients tended to be high for annual grassy weeds in S2, and were close to significance before weed control in the field S2-D (P = 0.09). A. myosuroides, the dominant annual grassy weed species, showed a marked cyclic demography in this system with no deep tillage. The population rapidly increased during years of winter crops and rapidly decreased during years with spring sown crops, due to the repetition of shallow tillages triggering the germination of many cohorts during the autumn. In the field S2-D, the study ended with three consecutive years of winter crop. The end of the study was therefore favourable to winter grassy species which might explain the high positive Pearson’s correlation obtained for this species group in this field. 4. Discussion In this experiment, a high variability of weed densities was observed within systems among the experimental replicates, probably due to differences in the initial seed bank. The weed flora observed in the 2 years before the beginning of the experiment and in the first year just after the sowing of the first crop in 2000 was very field specific, although most fields were grown with the same crops. However, the IWM systems were successful in (i) reducing the reliance on herbicides, (ii) reducing the potential impact of herbicides on the environment assessed through the Ipest indicator, and (iii) controlling weed infestations on a long-term basis. All the indicators were consistent with satisfactory long-term weed control in each of the eight fields managed according to IWM principles. After weed control, the densities of remaining weeds were low enough to avoid any yield loss exceeding 1% (Wilson and Wright, 1990), with only a few exceptions (A. myosuroides in the S2-D field in the winter barley harvested in 2002 and the triticale harvested in 2006, and in the S2-A field in the oilseed rape harvested in 2004). Higher densities were observed only between
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growing seasons, before weed control, or at the end of the crop cycle. The effective regulation of weed densities for an ‘integratedflexible’ system was previously attributed to a number of interventions, among which was a well conceived and expanded crop sequence that included a cover crop and some mechanical weeding (Liebman and Dyck, 1993; Jordan et al., 1997; Doucet et al., 1999; Gerowitt, 2003). Similar effects of cover crops and of crop species diversity in the rotation on weed density have been noted in organic crop rotations (Pimentel et al., 2005; Rasmussen et al., 2006; Smith et al., 2008). In our experiment, cover crops were not often grown, as the weed management strategies included mostly bare soils during the inter-crop periods in order to implement the false seedbed technique. However, the combination of a variety of measures in IWM also resulted in increased system complexity, which might hamper their adoption by farmers (Bastiaans et al., 2008). For example, IWM might introduce bottlenecks in the labour organisation at the farm level, because both mechanical weeding and the false seedbed preparation are time-consuming techniques, and delaying the sowing of winter cereals on large areas at the farm scale may be a poor decision because the weather might become less favourable for sowing in the late autumn, leaving only few days (if any) available for field operations. The economical profitability is also likely to be affected in such modified systems. The study presented here is the first step toward the evaluation of the sustainability of the tested IWM-based cropping systems. The indicator of environmental impact used in this study addresses only the impacts related with the release of herbicides in the environment. Other factors such as energy input, greenhouse-gas emissions, soil erosion or biodiversity also need to be assessed for a full appraisal of the environmental impacts of IWM. Such issues will be considered in further studies in order to provide a comprehensive evaluation of these cropping systems. Acknowledgements We thank the experimental farm (INRA-Dijon-Epoisses) for their very profitable collaboration. M. Bourlier, P. Chamoy, P. Farcy, C. Martin, D. Meunier, G. Louviot, M. Penning and F. Strbik provided their indispensable technical assistance. T. Castel and J.M. Brayer helped for achieving the geostatistical procedures and F. Dessaint provided helpful advices for the statistical analyses. The work was supported by the Re´gion Bourgogne and the French Ministe`re de l’Ame´nagement du Territoire et de l’Environnement. The authors
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