Environmental Science and Pollution Research

, Volume 24, Issue 14, pp 13121–13135 | Cite as

Simulating changes in cropping practices in conventional and glyphosate-resistant maize. II. Weed impacts on crop production and biodiversity

  • Nathalie Colbach
  • Henri Darmency
  • Alice Fernier
  • Sylvie Granger
  • Valérie Le Corre
  • Antoine Messéan
Research Article


Overreliance on the same herbicide mode of action leads to the spread of resistant weeds, which cancels the advantages of herbicide-tolerant (HT) crops. Here, the objective was to quantify, with simulations, the impact of glyphosate-resistant (GR) weeds on crop production and weed-related wild biodiversity in HT maize-based cropping systems differing in terms of management practices. We (1) simulated current conventional and probable HT cropping systems in two European regions, Aquitaine and Catalonia, with the weed dynamics model FlorSys; (2) quantified how much the presence of GR weeds contributed to weed impacts on crop production and biodiversity; (3) determined the effect of cultural practices on the impact of GR weeds and (4) identified which species traits most influence weed-impact indicators. The simulation study showed that during the analysed 28 years, the advent of glyphosate resistance had little effect on plant biodiversity. Glyphosate-susceptible populations and species were replaced by GR ones. Including GR weeds only affected functional biodiversity (food offer for birds, bees and carabids) and weed harmfulness when weed effect was initially low; when weed effect was initially high, including GR weeds had little effect. The GR effect also depended on cultural practices, e.g. GR weeds were most detrimental for species equitability when maize was sown late. Species traits most harmful for crop production and most beneficial for biodiversity were identified, using RLQ analyses. None of the species presenting these traits belonged to a family for which glyphosate resistance was reported. An advice table was built; the effects of cultural practices on crop production and biodiversity were synthesized, explained, quantified and ranked, and the optimal choices for each management technique were identified.


GM crop Model Weed Glyphosate resistance Cropping system Biodiversity Yield gap Harmfulness Agroecology 


Herbicide-tolerant (HT) crops, particularly those tolerant to glyphosate, are grown on large acreages in some regions of the world (James 2013). Though these varieties simplify weed management (Bonny 2016; Brookes and Barfoot 2009; Shaner 2000), the overreliance on glyphosate has led to the development of resistant weeds (Bonny 2016; Heap 2016; Powles 2008). In contrast to the widespread annual use of glyphosate in HT maize and HT soybean rotations in the USA, the acreage of HT maize in Europe is almost nil today, and maize is often rotated with other crops that are not treated with glyphosate. However, glyphosate is frequently used during summer fallow, and cases of weed resistance to glyphosate have already been detected in arable fields with annual crops (Collavo and Sattin 2014). Moreover, the introduction of HT crops into the cropping systems may lead to other changes in cultural practices which can also favour the evolution of herbicide resistance, e.g. simplified rotations and/or simplified or no tillage (Beckie 2009; Boerboom 1999; Chauvel et al. 2009; Colbach et al. 2016b; Colbach et al. 2017; Friesen et al. 2000; Lievin et al. 2013; Moss and Clarke 1994; Moss et al. 2007).

This shift to herbicide-resistant biotypes potentially not only increases weed harmfulness for crop production (e.g. yield loss, harvest contamination, field infestion, Mezière et al. 2015b) but can also impair biodiversity. Indeed, weed flora is a major part of wild plant biodiversity in arable lands and provides habitat and food resources to a range of animals in agricultural landscapes, among which pollinators (Bretagnolle and Gaba 2015) or crop auxiliaries (Taylor et al. 2006). The management of glyphosate-tolerant crops can also lead to shifts to different weed species, which has already been analysed in recent studies (Bigler and Albajes 2011; Bürger et al. 2015; Heard et al. 2003). However, the particular effect of herbicide resistance on weed-related functional biodiversity (e.g. weeds as food resources) has yet to be investigated.

Because of long-term effects and the multiplicity of the implicated factors, herbicide resistance is increasingly investigated via modelling and simulations (Cavan et al. 2000; Colbach et al. 2016b; Gressel and Segel 1990; Maxwell et al. 1990; Neve et al. 2003; Renton et al. 2014). These models are, however, limited to a single species and neglect the impact of weeds on crop production and biodiversity. Therefore, in the companion paper (Colbach et al. 2017), we included a simple herbicide resistance submodel in the existing multispecific FlorSys model. FlorSys quantifies the effects of cropping systems and weather on multiannual weed dynamics and a series of indicators reflecting weed harmfulness for crop production and weed contribution to wild plant biodiversity and functional biodiversity (Colbach et al. 2014a). Weed harmfulness is defined here as any negative effect for the farmer, in terms in production amount and quality, work load and reputation.

Here, the objective was to evaluate, with simulations, the impact of glyphosate-resistant (GR) weeds, on crop production and biodiversity in cropping systems including HT maize. The tested cropping systems consisted of current conventional situations and future systems including HT maize with on-crop glyphosate applications as well as other probable changes in cultural practices (e.g. simplified rotation and tillage). These cropping systems were identified in a previous simulation study from surveys and expert opinion in two large maize-growing European regions, Aquitaine in South-Western France and Catalonia in North-Eastern Spain (Bürger et al. 2015). In this previous study, we already analysed the impact of weed flora (consisting mainly of winter and spring species) on production and biodiversity in these cropping systems. Here, we continued the analysis, working with an extended weed flora consisting of additional and more diverse species and discriminating glyphosate-susceptible and glyphosate-resistant populations. We analysed (1) the relationship between the presence of GR weeds and weed impact on crop production and biodiversity, (2) how this impact was affected by cultural practices and (3) whether species traits other than glyphosate resistance influenced weed impacts. Ultimately, the goal is to assess whether cropping systems including HT crops are sustainable in the long term, in the event of herbicide resistance.

Material and methods

The virtual field FlorSys

FlorSys is a virtual field on which cropping systems can be experimented and a large range of crop, weed and environmental measures estimated, with a series of indicators translating weed contribution to biodiversity and weed harmfulness for crop production (hence weed-impact indicators, section “Assessing weed impacts on crop production and biodiversity” below). The structure of FlorSys is presented in detail in previous papers (Colbach et al. 2014b; Colbach et al. 2014c; Gardarin et al. 2012; Mezière et al. 2015b; Munier-Jolain et al. 2014; Munier-Jolain et al. 2013) and the main aspects are described in the companion paper (Colbach et al. 2017).

The input variables of FlorSys consist of (1) a description of the simulated field (daily weather, latitude and soil characteristics), (2) all the simulated cultural operations in the field and (3) the initial weed seed bank. These input variables influence the annual life cycle which applies to annual weeds and crops, with a daily time-step. FlorSys parameters (section A in supplementary material online) are currently available for 25 frequent weed species covering the main ecological niches of temperate European cropping systems and including the species frequently found in European maize crops: Abutilon theophrasti (EPPO code ABUTH), Alopecurus myosuroides (ALOMY), Amaranthus retroflexus (AMARE), Ambrosia artemisiifolia (AMBEL), Avena fatua (AVEFA), Capsella bursa-pastoris (CAPBP), Chenopodium album (CHEAL), Datura stramonium (DATST), Digitaria sanguinalis (DIGSA), Echinochloa crus-galli (ECHCG), Galium aparine (GALAP), Geranium dissectum (GERDI), Matricaria perforata (MATIN), Mercurialis annua (MERAN), Panicum miliaceum (PANMI), Poa annua (POAAN), Polygonum aviculare (POLAV), Fallopia convolvulvus (POLCO), Polygonum maculosa (previously P. persicaria, POLPE), Senecio vulgaris (SENVU), Sonchus asper (SONAS), Solanum nigrum (SOLNI), Stellaria media (STEME), Veronica hederifolia (VERHE) and Veronica persica (VERPE).

FlorSys was evaluated with independent field data, showing that daily species densities and, particularly, densities averaged over the years were generally well predicted and ranked in the model’s original region, i.e. Burgundy (Colbach et al. 2016a). At southern latitudes, weeds flower too early, and a corrective patch was used here to improve the prediction of weed flowering dates.

Mutation and glyphosate resistance

The details of how glyphosate resistance was modelled are given in the companion paper (Colbach et al. 2017) and are summarized here. Although several studies show that glyphosate resistance in weeds can depend on several genes (Sammons and Gaines 2014), glyphosate resistance is assumed here to depend on a single gene. The combination of a wild allele W and a mutant, resistant allele R at one unique locus leads to three genotypes: susceptible (WW), semi-dominant resistant (WR) and resistant (RR). These differ in terms of (1) plant mortality after glyphosate (0.30 and 0.10 for WR and RR populations, respectively, at 3 l/ha) and (2) reproductive fitness (seed production of WR and RR plants is 0.90 and 0.75 of glyphosate-susceptible seed production in the absence of herbicides, respectively).

Twelve species considered in FlorSys can potentially develop resistance to glyphosate in the model (ALOMY, AMARE, AMBEL, AVEFA, CHEAL, DIGSA, ECHCG, PANMI, POAAN, MATIN, SENVU and SONAS). These species belong to families for which glyphosate resistance was reported (see references in Colbach et al. 2017). Each time ovules or pollen are produced, a tiny proportion (10−6) mutate from glyphosate-susceptible to resistant. Outcrossing species are pollinated with the pollen of all flowering plants of the same species; selfing species self-pollinate.

The verisimilitude of the simulated effects of cultural practices on weed densities and the advent of glyphosate resistance has already been discussed in the companion paper, stressing that pleiotropic effects, cross-resistance with other herbicides and gene flow from neighbouring fields could modify the conclusions based on the simulations (Colbach et al. 2017). The companion paper also demonstrated, via a sensitivity analysis, that the values chosen for the model parameters driving glyphosate resistance (for which few data are available) would not notably affect simulation outcome.

Assessing weed impacts on crop production and biodiversity

The weed densities simulated by FlorSys are translated into a set of annual indicators depicting the weed flora impact on crop production and biodiversity (Mezière et al. 2015b; see section A.4 online). Four indicators reflect the weed harmfulness for crop production, discriminating direct (crop yield loss, harvest pollution, i.e. weed seeds and debris harvested with the crop seeds), technical (harvesting problems due to green weed biomass blocking the combine) and sociological harmfulness (field infestation by weed biomass during crop growth). The latter reflects the farmer’s worry of being considered incompetent by his peers, irrespective of any weed effect on crop production.

Weed-mediated biodiversity indicators comprise two indicators of wild plant biodiversity, i.e. (1) species richness (the number of weed species) and (2) species equitability (Pielou’s index). Three other indicators assess functional biodiversity, i.e. weed contribution to feed other organisms in the agro-ecosystems, considering the seasons of activity and food shortage: (3) weed seeds on soil surface in autumn and winter to feed field birds, (4) lipid-rich seeds on soil surface in summer to feed carabids and (5) weed flowers in spring and summer to feed domestic bees.

Cropping systems are always assessed over several years as farmers are usually not only worried about the effect of the current year’s weed plants on crop production but also about the future effect of the many seeds produced by this year’s weeds (Mezière et al. 2015b).

Simulation plan

The simulation plan was described in detail by Bürger et al. (2015) and again more superficially in the companion paper (Colbach et al. 2017). Only a short summary is given here, more details can be found in section B of supplementary material online. Simulations were carried out for Aquitaine in South-Western France and Catalonia in North-Eastern Spain. Typical maize-based cropping systems, as well as probable systems with HT maize, were simulated. To decorrelate the effects of the changes in cultural practices (e.g. no till) accompanying HT maize from those of glyphosate treatments, the HT scenarios were also simulated with conventional herbicide programmes, resulting in a total of 45 scenarios. The tested systems also differed in terms of crop diversity in the rotation, tillage frequency and intensity, sowing dates, etc. Each scenario was simulated over 28 years and was repeated with 10 different weather series consisting of 29 randomly chosen weather years, using the same 10 series for each scenario. The initial seed bank was identical for all simulations and consisted of the 25 species currently included in FlorSys. Relative species abundance was based on regional flora assessments (Colbach et al. 2016a), and initial GR proportions resulted from preliminary simulations (Colbach et al. 2017) and ranged from 10−6 to 10−5 for the potentially mutating species.

The same simulation plan (45 scenarios × 10 weather repetitions) was then repeated without GR weeds, starting with only glyphosate-susceptible populations and applying a zero mutation rate for all species. The comparison of the output produced by the two simulation series allowed us to assess how much weed impact was due to GR weeds.

Analysed outputs and statistics

Statistical analyses were run with version 9.4 of SAS and version 3.3.0 of R (R Development Core Team, 2016). In the first step, potential trade-offs between the annual weed-impact indicators were analysed to evaluate, for instance, whether weed harmfulness increased with increasing contribution to biodiversity (which makes reconciling production and biodiversity difficult), whether all biodiversity contributions increased or all harmfulness criteria decreased in the same conditions (which simplifies management). To do this, Pearson correlations (PROC CORR of SAS) and principal component analysis (R library FactoMineR) were calculated for the 45 scenarios, 10 weather repetitions and simulated 28 years. These analyses were carried out separately for results from simulations with and without GR weeds. Then, data from simulations with and without GR weeds were pooled, and the differences in indicator values in simulations with vs. without GR weeds were analysed as a function of weather repetition, region, indicator values in GR-free simulations and the interaction between the latter two, with linear regressions using PROC GLM of SAS.

In the next step, only the last eight simulated years were used, when differences between simulations with and without GR weeds were highest. In the second step, analyses of variance were run for each region (Aquitaine or Catalonia) with PROC GLM of SAS to explain indicator values as a function of time since simulation onset (as qualitative variable), presence of GR weeds, cropping system as well as interaction between the latter two as fixed effects and weather repetition as block effect. Least-square means of weed-impact indicators were compared for cropping systems, using least-significant difference tests. For each cropping system, the differences in indicators from simulations with vs. without GR weeds were compared to zero, using t tests.

In the third step, indicator values averaged over the last eight simulated years were analysed with linear models using PROC GLMSELECT of SAS as a function of the same synthetic cropping system descriptors (e.g. average number of superficial operations per year and proportion of winter crops in the rotation) as those used in the companion paper (Colbach et al. 2017). Further variables were added here to account for the particularities of the biodiversity indicators (e.g. mouldboard ploughing during the carabid-activity period), resulting in a total of 17 descriptors. These descriptors as well as region were used as independent variables and were added sequentially (forward selection) to the linear model in order to optimize the Schwarz Bayesian information criterion. The final model was chosen among the successive models as the one with the lowest predicted residual sum of square with cross validation.

Finally, RLQ analyses were used to identify pertinent relationships between indicator variables (averaged over the last 8 years and the 10 repetitions) and species traits, using the R library ade4 (Chessel et al. 2004). The RLQ analysis was initially developed to analyse correlations between cultural practices (R matrix) and species traits (L matrix) via weed species densities (L matrix). Here, the practices were replaced by indicator variables. Only trait-indicator relationships significant at p = 0.05 were considered.

As in the companion paper (Colbach et al. 2017), we used population densities instead of species densities, considering WW, WR and RR populations of a given species as three distinct species that differed in two traits, i.e. glyphosate resistance and population fitness cost for resistance. Instead of 25 species, we thus worked with 49 populations. The same species traits as in the companion paper were used, i.e. traits related to seed bank processes, plant growth, morphological plasticity and glyphosate resistance. In total, 21 traits were used in the analysis (section A.2.1 of supplementary material online).


Weed and GR weed impact over time

When there were no GR weeds, weed harmfulness and contribution to biodiversity usually took a few years of increase or decrease before stabilizing at values depending on the weed-impact indicator and the cropping system. For instance, bird-food offer dropped from a score of 7.9 at simulation onset to 5.2 during the first year in the unploughed, early-sown maize monoculture and then oscillated between 5.1 and 5.3 during the rest of the simulation (Fig. 1a). If the field was tilled with a mouldboard plough, the indicator stabilized at 5.5 to 5.7 (Fig. 1b). In some cases, the initial decrease or increase took 10 years (e.g. decrease in species richness in untilled maize monoculture, Fig. 1c) or more (e.g. the increase in field infestation in ploughed monoculture had not yet reached an equilibrium after 25 years, Fig. 1d).
Fig. 1

Diversity in the response of weed impact on crop production and biodiversity when including glyphosate resistant (GR) weeds in the simulations (red circles) compared to simulations without glyphosate-resistant weeds (black triangles). a Fast initial decrease and no effect of GR weeds (example of bird food offer in unploughed field). b Fast initial decrease and later huge increase with GR weeds (example of bird food offer in ploughed field). c Slow initial decrease and later small decrease with GR weeds (example of species richness in untilled field). d Very slow initial increase and later huge increase with GR weeds (example of field infestation in ploughed field). All are examples of early sown HT maize monoculture in Aquitaine and means of ten weather repetitions

When GR weeds were included in the simulations, there usually was no difference in weed impact during the ten first simulated years, compared to the simulations without GR weeds (Fig. 1). Later, the impact of GR weeds greatly differed among cropping systems. In some systems, there was no real difference in weed impact (Fig. 1a). In a few situations, including GR weeds in the simulations increased weed harmfulness tremendously after a few years, e.g. field infestation increased to a maximum of 0.7 vs. 0.3t /ha in GR-free simulations (Fig. 1d). The same could happen for biodiversity, e.g. with GR weeds, bird offer rose to more than 7 vs. less than 5.8 in GR-free simulations (Fig. 1b). GR weeds could also deteriorate biodiversity, e.g. species richness decreased by two species when including GR weeds (Fig. 1c).

Correlations between weed-impact indicators

The principal component analysis between annual weed-impact indicator values in simulations with GR weeds identified three groups (Fig. 2): (1) weed harmfulness indicators, (2) species richness and functional biodiversity indicators (i.e. weed-based food offer indicators) and (3) species equitability, Fig. 2. There was no change when simulations were run without GR weeds (section C.1.2 online). Weed harmfulness indicators were all highly correlated (Pearson correlation coefficient >0.91, p < 0.0001, section C.1.1 online). Biodiversity indicators were less correlated, with carabid food and bee food the most correlated (0.86, p < 0.0001). Because of the high correlation among harmfulness indicators, only crop yield loss was considered in the following sections. The other results can be found in section C of supplementary material online. Bee food results are also only presented online.
Fig. 2

Principal component analysis of annual weed-impact indicators simulated with FlorSys with a 10−6 mutation rate in Aquitaine and Catalonia, with 21–24 cropping system scenarios per region and 10 weather repetitions per scenario. Biodiversity indicators are in green and harmfulness for crop production in red

Indicator values in simulations with vs. without GR weeds were also highly correlated, with Pearson correlation coefficients ranging from 0.57 each for bird food and species equitability to 0.90 for carabid food and 0.91 for bee food offer (p < 0.0001, section C.1.3 online). Whatever the weed-impact indicator, values in simulations including GR weeds were higher than in GR-free simulations when indicator values were low in GR-free simulations (positive intercept values in Table 1). For instance, when species richness was close to zero in GR-free simulations, there were 3.8 and 5.4 additional species in GR-including simulations in Aquitaine and Catalonia, respectively. Generally, the difference in biodiversity between simulations with vs. without GR weeds was highest in Catalonia (largest intercept values) and the difference in harmfulness was largest in Aquitaine.
Table 1

Linear regressions of difference of annual weed-impact indicator values in simulations with vs. without glyphosate-resistant (GR) weeds as a function of indicator values from GR-free simulations, region, interaction between both as well as interaction between region and weather repetition with PROC GLM of SAS

Weed-impact indicator


Regression parameter






Wild plant biodiversity

 Species richness










 Species equitability










Functional biodiversity

 Bird food










 Carabid food










 Bee food










Harmfulness for crop production

 Crop yield loss










 Harvest pollution










 Harvesting problems










 Field infestation










Region × weather repetition was also significant at p = 0.05. Intercept values and regression parameters followed by the same letters for a given indicator were not significantly different at p = 0.05

The more indicator values increased in GR-free simulations, the more the additional effect due to GR weeds decreased (i.e. negative regression parameter in Table 1). The decrease in additional biodiversity was strongest in Catalonia (most negative regression parameter) and the decrease for harmfulness was largest in Aquitaine. When indicator values were large in GR-free simulations, including GR weeds decreased weed impact. For instance, when species richness was high (e.g. 22 species) in simulations without GR weeds, there were four (3.84–0.34 × 22) and two species (5.45–0.36 × 22) less in respectively Aquitaine and Catalonia after including GR weeds.

In conclusion, GR weeds increased weed impact when this impact was initially low; they decreased the impact when it was initially high. This tendency was valid for all indicators (i.e. significant intercept and regression values in Table 1) but only explained a small part of the difference between simulations with vs. without GR weeds. Indeed, the total explained variability (R2) of the linear models of Table 1 was quite low even though interactions with region and the effect of weather repetitions were included.

Weed impact in cropping systems

To make the effect of GR weeds more visible, only the eight last simulated years were analysed here. Whatever the region and the analysed weed-impact indicator, the effects of weather repetition and time since simulation onset were either not significant or negligible (partial R2 close to zero in Table 2). The largest effect was due to the cropping system (largest partial R2). In each region, variations were smallest for species richness which varied two- to threefold (e.g. from 10 species to nearly 19 in Catalonia, lines 14 and 8 in Table 2—B) and biggest for crop yield loss, which increased from −0.11% in the late-sown maize monoculture (line 8) to 83% in the unploughed diverse rotation in Catalonia (line 3). Surprisingly, weeds sometimes increased crop yield, e.g. in the early-sown unploughed maize monoculture in Aquitaine (negative yield loss in line 10 in Table 2—A). There, weeds delayed maize emergence by 2–5 days compared to simulations excluding weeds, which protected the crop from late frost in several weather repetitions (section C.4 online).
Table 2

Effect of weed flora and glyphosate-resistant (GR) weeds on biodiversity and crop production in HT maize cropping systems during the last eight simulated years evaluated with analyses of variance of weed impact indicators as a function of simulated cropping system, presence vs. absence of GR weeds, time and weather repetition. Mean indicator values (Mean) per cropping system are coloured from red (lowest value of a given column) to green (highest value) for biodiversity indicators and vice versa for harmfulness indicators; values of a given column followed by the same letter are not significantly different at p = 0.05. Differences (Diff) between simulations with and without GR weeds are coloured in red for deterioration (decreased biodiversity or improved harmfulness) and green for improvement (vice versa); empty cells show non-significant differences at p = 0.05

When looking in detail at the tested cropping systems, it appeared that switching from conventional maize varieties and herbicides to HT maize and glyphosate had no big effect on weed-impact indicators (lines 2 vs. 1 in Table 2). Other scenarios showed much bigger changes. For instance, both biodiversity (except species equitability) and harmfulness increased tremendously in no-till systems, irrespective of the region, the glyphosate frequency or the sowing dates (lines 11–13 vs. 9). Conversely, introducing an additional crop (barley catch crop in Aquitaine, triticale cash crop in Catalonia) before maize decreased biodiversity (except species equitability) and harmfulness (lines 14–15 vs. 6). In Catalonia, the deleterious effect of the additional crop on biodiversity was attenuated or even cancelled out if tillage instead of glyphosate was used to clean the field after the additional crop (line 15 vs. 6).

The effect was less clear for other scenarios. For instance, unploughed scenarios performed differently, depending on the region, the rotation and the indicator. In Aquitaine, with the exception of species equitability, biodiversity deteriorated in the unploughed vs. ploughed diverse rotation (line 3 vs. 2 in Table 2—A) and maize monoculture (9 vs. 6) but improved in the unploughed vs. ploughed maize/wheat rotation (5 vs. 4). The effect on harmfulness was similar: decrease in the diverse rotation, no effect in maize monoculture, large increase in maize/wheat.

Including GR weeds in the simulations had little consistent effect. The impact of GR weeds depended very much on the cropping system (only the interaction between the two factors was significant in Table 2). It was negligible for most indicators (partial R2 close to zero) and only significant in a few cropping systems. In Aquitaine, early-sown maize monoculture was the scenario where including GR weeds had the biggest effect (line 7 in Table 2—A), with an additional 14% of yield loss, adding more than two species to species richness, increasing trophic resources by +1 point, and decreasing species equitability by 0.17. In other scenarios, the presence of GR weeds decreased yield loss (e.g. diverse rotation, lines 1 and 2) or biodiversity (e.g. untilled, early-sown maize monoculture with two glyphosate sprayings, line 13). In Catalonia, including GR weeds changed weed-impact indicators more often, frequently decreasing biodiversity (particularly in maize monocultures, e.g. lines 6,7, 9, 12 in Table 2—B) and, more rarely, yield loss (e.g. conventional diverse rotation, line 1).

Effect of cultural practices

The variability in effects of changes in cultural practices (e.g. no-plough) was not only due to interactions with rotation or region but also to smaller modifications in cultural practices that accompanied the analysed major changes (e.g. increase in tillage frequency and/or herbicide applications). These hidden modifications were taken into account in the regression analyses of weed impact indicators as a function of cultural practices in Table 3. Generally, biodiversity indicators were lower in Aquitaine than in Catalonia (negative regression parameter of line 3 in Table 3), except species equitability which was larger. Weed harmfulness was also lower in Aquitaine than in Catalonia.
Table 3

Effect of cultural practices, in interaction with the presence of glyphosate-resistant (GR) weeds, on weed-impact indicators averaged over the last 8 years simulated with FlorSys in Aquitaine and Catalonia. Regression parameters estimated with linear models using forward selection and cross-validation. All variables kept in the final model were significant at p = 0.05. Empty cells show non-significant effects

$Variable tested in interaction with another (presence or absence of additional crop, tillage or ploughing)

§5 and 95% percentiles of indicator values

Ten cultural practices influenced crop yield loss. Late maize sowing increased weed harmfulness (line 5 in Table 3), though a delay of nearly four weeks would be necessary to result in and additional yield loss of 10% (i.e. dividing 10 by the regression parameter 0.391 = 25.5 days). Adding cover or cash crops (lines 7 and 8) that lasted less than 5.5 months (=84.5/15.21) increased harmfulness whereas each additional month of cover subtracted 15% yield loss. Each additional superficial tillage operation removed 10% yield loss (line 9). Tillage was more effective if the first operation was delayed relative to previous crop harvest (line 17), with an additional 1% yield-loss reduction for every 10 days delay. Mouldboard ploughing reduced harmfulness even more, particularly when carried out before winter crops (lines 12 and 13). The later the field was ploughed relative to previous harvest, the more yield loss was reduced (line 20) because more time was left for superficial tillage. Glyphosate treatments reduced harmfulness (lines 22 and 23), particularly in-crop applications which subtracted 14% yield loss. Harmfulness though increased when herbicides were delayed (line 21).

The effect of cultural practices on biodiversity indicators mostly depended on the same factors than their effect on harmfulness indicators, but with varying effects. For instance, late maize sowing increased species richness and carabid food but decreased species equitability and bird food (line 5). Generally, tillage decreased biodiversity (lines 9, 16) though the effect greatly depended on timing (e.g. lines 16 vs. 14 and lines 17, 19 and 20). For instance, early post-harvest tillage was better for carabid food but worse for bird food (line 17). Indeed, late tillage leaves seeds longer on soil surface during the critical bird-feeding period (Oct. to March); early tillage leaves less time for seeds to be exposed to rain before burial, which makes seeds more dormant and thus persistent, delaying weed emergence and seed production until the next carabid-feeding period (April–Sept.). In contrast to harmfulness, fallow glyphosate applications decreased food-offer indicators more than in-crop glyphosate (lines 22 and 23). The effect on plant biodiversity was different: species richness and species equitability increased when the broad-spectrum glyphosate was used respectively during fallow and in crops instead of selective products. Generally, the later herbicides were applied to crops, the better for plant biodiversity and carabid-food offer (line 21).

Few of these effects depended on the presence of GR weeds in the simulations, i.e. only three interactions between cultural practices and GR-weed presence were significant in Table 3 and all three concerned plant biodiversity. Late maize sowing decreased species equitability more when GR weeds were included in the simulations (line 6) whereas the summer ploughing was less deleterious when including GR weeds (line 15). Delaying the first post-harvest tillage was more beneficial for species richness in simulations with vs. without GR weeds (line 18).

Which species and traits determine weed impact?

Similarly to weed-impact indicators, total weed densities greatly varied among cropping systems, from less than 0.01 plants/m2 averaged over all simulated years and weather repetitions in the unploughed early-sown maize monoculture, to 3467 plants/m2 in the untilled monoculture in Aquitaine (section C.3.1). Differences between simulations with vs. without GR weeds varied comparatively little, with densities divided by 4 in the Catalonia early-sown maize monoculture and multiplied by more than 11 in the Aquitaine equivalent. Differences between simulations with vs. without GR weeds also depended on weed species. In Aquitaine, the densities of most weed species decreased when including GR weeds, with decrease ranging from a factor 2 (G. aparine) to 7 (D. sanguinalis) in simulations with vs. without GR weeds. Two species were more abundant when including GR weeds, i.e. C. album and S. asper which increased sixfold (section C.3.2 online). In Catalonia, only S. asper was more abundant in GR-including simulations (+10%); all other species decreased in simulations when GR weeds were included, with C. album (−34%) and G. aparine (−49%) decreasing the least and D. sanguinalis the most (−97%). The species whose densities increased most when including GR weeds where those with a high seed area/mass ratio (regression parameter = 0.42, p = 0.001) and a large specific plant width (0.00165, p = 0.0007, R2 = 0.36, section C.4 online), i.e. species with fast-germinating seeds and mostly investing biomass into plant width.

Species traits most pertinent for understanding weed-impact indicators are described by Pearson correlation coefficients determined via an RLQ analysis (Table 4). The two traits that we are most interested in are population fitness cost for resistance and glyphosate resistance. The latter slightly reduced species equitability (Pearson correlation coefficient of −0.16 in line 22 in Table 4) whereas the effect of fitness cost was not significant (results not shown). Weed impact on crop production and biodiversity almost entirely depended on other species traits, none of which were correlated to glyphosate resistance in the 25 species used in the present simulations, except monocot status (Fig. 3)
Table 4

Relationships between weed species traits or characteristics and weed-impact indicators averaged over the last eight simulated years in simulations with and without GR weeds, identified by fourth-corner analyses preceded by the RLQ analysis. Pearson correlation coefficients r between indicators and traits, and tests of the null hypothesis that species are distributed independently of their preferences for scenarios and of their traits (highest p values of permutation models permuting scenarios or species). Only species traits significantly correlated to at least one indicator at p = 0.05 are listed here

§A positive shading sensitivity parameter indicates that the associated species parameter increases with shading intensity

$Specific plant height (or width) is plant height (or width) divided by plant biomass

&Ratio of mature seed biomass vs total above-ground biomass

Fig. 3.

Principal component analysis of species traits of the 25 weed species included in FlorSys. Red boxes show traits highly correlated to crop yield loss (based on the results of Table 4) and the potential of glyphosate resistance of species. SLA is specific leaf area, HM and WM specific plant height and widths, LBR leaf biomass ratio, and shadeTRAIT is the sensitivity of a species trait to shading

The species that were most harmful for crop production in the simulations were efficient in producing large leaf areas (positive correlation with specific leaf area in line 12 in Table 4) without investing too much biomass (negative correlation with leaf biomass ratio in line 13); they were often climbing species (line 14), potentially tall (line 15) and invested more biomass into seed production (line 21). When shaded by neighbouring plants (shading sensitivity parameters, lines 17–20), the most harmful species were those that did not reduce their leaf area (negative correlation with shading sensitivity of specific leaf area in 18) or plant width (line 19). The effect of emergence-related traits depended on the analysed harmfulness indicators. Crop yield loss was increased by species with a low pre-emergent seedling loss (i.e. dicotyledonous vs. monocotyledonous species, line 1) and a large potential emergence season (line 10), i.e. species that can emerge in winter and spring crops. Harvest pollution, which is an issue in wheat but less so in maize (at least with the here simulated cropping systems and weed species), increased with species emerging in late autumn and early winter (lines 6–7), i.e. after winter wheat was sown, and decreased with those emerging in late spring (line 9), i.e. after maize sowing.

The effect of species traits on biodiversity indicators depended on the analysed indicator. Species equitability responded similarly to harmfulness. Species richness was increased by fast-emerging species (positive correlation with seed lipid content and seed area/mass ratio, lines 3–4) that were able to increase their leaf biomass when shaded by neighbouring plants (shading sensitivity of leaf biomass ratio, line 17). The response of food-offer indicators such as bird-food offer usually were the opposite of the harmfulness response, with a particular detrimental effects of traits leading to seed disappearance at the onset of the feeding the period. For instance, bird food offer (which is crucial during October to March) was greatly decreased if the species emergence season included December to February (lines 5, 7) but increased if the species temperature was high (line 11), thus hampering winter germination.


The present paper (1) quantified how much the presence of GR weeds contributed to weed impacts on crop production and biodiversity, (2) determined the effect of cultural practices on the impact of GR weeds and (3) identified which species traits most influence weed-impact indicators and whether these are included in GR weeds.

Implications for managing glyphosate resistance

The consequences of glyphosate-tolerant maize and of the accompanying changes in cultural practices for weed impacts on biodiversity and crop production have already been discussed in a previous paper (Bürger et al. 2015). Here, we will focus on the impact of glyphosate resistance in weeds.

The first major result of the present study was the demonstration that glyphosate resistance only rarely affected the variables of crop production and biodiversity that we studied here, during the 28 years after switching to HT maize, in the particular case of the maize-based cropping systems tested here and with herbicide-resistance model used here (see the following section for a discussion of the limits of the approach). The biggest effects were due to cultural practices and region. This is consistent with field surveys reporting that in-crop glyphosate treatments had no effect (Schwartz et al. 2015) or improved (Young et al. 2013) plant biodiversity which mostly depended on geographical location (Schwartz et al. 2015; Young et al. 2013). Here, including GR weeds in the simulations increased weed impact only when this impact was originally low; when weed impact was already high, the GR weeds had no more effect or weed impact was lower than when disregarding GR weeds. This applied even when GR densities were quite high as in no-till systems (see results in Colbach et al. 2017). In the systems tested here, GR populations tended to replace glyphosate-susceptible populations and/or species without increasing weed densities, particularly when the habitat-carrying capacity was reached as in the no-till systems.

The second key result was the identification of the weed species traits that were responsible for the biggest crop yield loss in the tested cropping systems, i.e. (1) traits that allow species to avoid preventive tillage-based techniques via higher seed persistence (e.g. smaller seed area/mass) when these techniques are applied, (2) traits that determine plant ability to compete with crop plants for light by maximizing space occupation and shade avoidance and (3) traits that optimize conversion of light into seed production via a high specific leaf area and harvest index (part of above-ground plant biomass attributed to seed production). The analysis of the correlation among species traits showed that none of the potentially GR species simulated here present these traits. If a species would, however, combine glyphosate resistance with the traits that make weeds more competitive toward crops, crop production would probably be much more affected by GR weeds than in the present simulations.

The same conclusions apply for weed-related functional biodiversity, i.e. including GR weeds only has an occasional and small effect. This remains true even in those cases where plant biodiversity is affected. In other words, the weed community composition changes but the trait composition remains similar. Again, this conclusion only applies with the weed community used here where none of the species combined glyphosate resistance with traits that are beneficial or detrimental for functional biodiversity.

Consequently, weed management should not focus only on resistant weeds as such, but on species presenting the trait combinations that are most harmful for crop production and/or beneficial for biodiversity. These are synthesized in Table 5. This advice is consistent with the conclusions of the companion paper (Colbach et al. 2017) which, in common with many papers on herbicide resistance (Colbach et al. 2016b; Moss and Clarke 1994; Renton and Flower 2015; Vencill et al. 2012; WRAG 2015), concludes that the practices for avoiding herbicide-resistant weeds are the same as those best for controlling weeds as such, and that many of these practices are also efficient for controlling herbicide-resistant weeds. The novelty of our study was to detect the traits of the weed species that should be targeted, depending on the cropping system objective in terms of ecosystem services, and to identify the practices targeting these different traits.
Table 5

Synthetic advice table for managing weed impact in maize-based cropping systems in Aquitaine and Catalonia, based on the present results (in bold) and the companion paper (in italic; Colbach et al. 2017)

Cropping system component

Advice in order to

Control harmfulness

Promote biodiversity (species richness, trophic resources)

% of winter crops



Cash crop sowing date


Maize sowing date

Do not delay

Delay except for carabid food

Additional crop before maize

≥6 months but leave time for tillage

≥6 months

No till



Superficial tillage





Before weeds become big,during non dormancy of most harmful species


 1st post-harvest tillage


Delay except for carabid food

 Last pre-sowing tillage

With sowing


Mouldboard plough


Summer only

 Before maize



 Before winter crops




Highest if short-living and superficially emerging seeds



Leaves the most competitive species



Delay to leave time for surface seeds to germinate



Efficient (as long as no resistance),better in crops than during fallow

Fallow spraying for species richness, none otherwise


Selects glyphosate-resistant weeds if no till


Timing of herbicides

Do not delay

Delay except for bird food

Target weed traits

Voluminous plants$andhigh efficiency#,even when shaded&,generalist§in diverse rotation and autumn-emerging if many winter crops in rotation

Small seeds, no emergence in late autumn/early winter, slowly growing plants, large plant area and leaf area when shaded

$ Climbing, potentially tall

& Do not reduce plant width and specific leaf area when shaded

# High specific leaf area with low biomass ratio and high harvest index

§ Long potential emergence season

Limits of the present results

The limits of the glyphosate-resistance submodel and the simulation plan have already been discussed in the companion paper (Colbach et al. 2017). Here, we will focus on weed harmfulness and contribution to biodiversity.

We already started to discuss the limits of the FlorSys model for predicting weed impact on biodiversity and crop production in previous simulation studies (Bürger et al. 2015; Mezière et al. 2015a). The comparison of the present results based on 25 weed species with our previous study testing the same cropping systems with only 16 species (Bürger et al. 2015) shows the limits of the present approach. The same tendencies were observed and similar conclusions were reached in both studies but the amplitude of the effects changed. In the present study, rotation was less efficient to reduce weed harmfulness, and biodiversity was less affected by changes in cultural practices. This is consistent with the use of additional species here, leading to a larger range of species traits and covering more diverse niches (e.g. summer-emerging weed species) in the simulated fields. Further simulation studies with more diverse cropping systems and weed floras that differ in terms of species numbers and diversity will be necessary to draw more generic conclusions on the effects of cultural practices, glyphosate resistance and weed impact on crop production and biodiversity.

None of the cropping systems tested here reconciled low weed harmfulness with high biodiversity contribution, and several biodiversity indicators were correlated to harmfulness indicators. Moreover, all harmfulness indicators were highly correlated, indicating that the cropping systems tailored to target a given harmfulness issue would also control all other harmfulness issues (or vice versa). This was not true in another simulation study investigating more diverse cropping systems in two other French regions (Mezière et al. 2015a). These authors found cropping systems that reconciled crop production and biodiversity, and they observed a diversity of biodiversity and harmfulness profiles (e.g. systems with high yield loss and low harvest contamination). This was not surprising insofar as these authors worked with diverse and longer rotations (including multiannual grasslands) and more varied management techniques, including mechanical weeding.

Implications for other types of herbicide resistance

The adoption of stacked GM crops tolerant to both glyphosate and a synthetic auxin (2,4-D or dicamba) will dramatically increase the use of dicamba and 2,4-D. The risk that new resistances evolve or already evolved resistances to these herbicides expand is high. Indeed, resistance has currently been reported in several tens of species (Heap 2016). As was recently discovered, resistance to synthetic auxins can be caused by mutations within auxin transporter genes (Goggin et al. 2016). The genetic mechanism of resistance is thus similar to the one considered in our model for glyphosate. Moreover, as glyphosate, dicamba is applied post-emergence. Consequently, the simulations presented here could be extended to model the evolution of resistance to synthetic auxins, alone or after a first evolution of resistance to glyphosate, or for modelling the simultaneous evolution of both resistances.


The present simulation study with maize-based cropping systems showed that glyphosate resistance did not notably change the impact of weeds on biodiversity and crop production during the first 28 years after introducing HT crops and that weed impact almost entirely depended on cultural practices. This though only applies as long as no weed species combines glyphosate resistance with the traits that were shown to cause the greatest yield or biodiversity losses. The novel conclusions drawn here were made possible by combining three approaches, i.e. a weed dynamics model driven by cultural practices and weather, a genetic mutation and heredity model, and a series of indicators of weed impact on crop production and biodiversity. As a result, a generic advice table was proposed, on how to optimize cultural practices depending on the planned production and biodiversity objectives, and in which conditions their application is needed. This advice applies to maize-based systems; further simulations are necessary to establish generic rules and/or advice specific to different regions and cropping-system types.


This project is supported by INRA, the European project AMIGA (Assessing and Monitoring Impacts of Genetically modified plants on Agro-ecosystems, FP7-KBBE-2011-5-CP-CSA), the French project CoSAC (ANR-14-CE18-0007) and the research programme “Assessing and reducing environmental risks from plant protection products” funded by the French Ministries in charge of Ecology and Agriculture.

Supplementary material

11356_2017_8796_MOESM1_ESM.pdf (1.5 mb)
ESM 1(PDF 1555 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Nathalie Colbach
    • 1
    • 2
  • Henri Darmency
    • 1
  • Alice Fernier
    • 1
  • Sylvie Granger
    • 1
  • Valérie Le Corre
    • 1
  • Antoine Messéan
    • 3
  1. 1.Agroécologie, AgroSup Dijon, INRA, University Bourgogne Franche-ComtéDijonFrance
  2. 2.INRA, UMR1347 AgroécologieDijonFrance
  3. 3.Eco-Innov, INRAThiverval-GrignonFrance

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