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Article

Glyphosate as a Tool for the Incorporation of New Herbicide Options in Integrated Weed Management in Maize: A Weed Dynamics Evaluation

by
Iñigo Loureiro
*,
Inés Santin-Montanyá
,
María-Concepción Escorial
,
Esteban García-Ruiz
,
Guillermo Cobos
,
Ismael Sánchez-Ramos
,
Susana Pascual
,
Manuel González-Núñez
and
María-Cristina Chueca
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Plant Protection Department, Ctra. La Coruña Km. 7.5, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Agronomy 2019, 9(12), 876; https://doi.org/10.3390/agronomy9120876
Submission received: 18 October 2019 / Revised: 3 December 2019 / Accepted: 9 December 2019 / Published: 11 December 2019
(This article belongs to the Special Issue Herbicide Resistance in Weed Management)
Browse Figures
Versions Notes

Abstract

:
A farm-scale investigation was conducted to evaluate the potential impact of integrating glyphosate into different weed management programs when cultivating herbicide-tolerant maize in central Spain from 2012 to 2014. The weed management programs were (1) a conventional weed management with pre- and post-emergent herbicide applications, (2) a weed management program in which the number and total amount of conventional herbicides applied were reduced, and (3) three weed management programs that comprised either two post-emergent applications of the herbicide glyphosate, or only one glyphosate application combined with pre- and/or post-emergent herbicides. Weed density throughout each cropping season was greater in those weed management programs that did not include a pre-emergence application of herbicides than those that did. Moreover, none of the weed management programs affected the richness and species diversity of the weeds or reduced yields. Although the impact of the different programs was similar in terms of weed species diversity, the composition of the weed community differed and this effect must be considered when providing agroecosystem services. Our results indicate that glyphosate-tolerant maize provides an additional tool that allows integrated weed control of the weed populations without reducing yields.

1. Introduction

Weeds are competitive threats to crop production. For maize, the yield loss due to weeds is 32%, and this loss is bigger than that due to insects (18%) and pathogens (15%) [1]. The European Union (EU) is the world′s main user of plant protection products (PPP), with 31% of the global market volume [2,3]. Herbicides are broadly applied in intensive farming practices for weed control and are used in more than 90% of the areas where maize is produced [4]. Despite their success in increasing crop productivity, this widespread use of PPPs has raised agricultural, environmental, and ecological concerns [5] and increased calls for sustainable pest management. The European Commission Directive 2009/128/EC established a framework to achieve a sustainable use of pesticides and to reduce the risks and impacts of pesticide use on human health and the environment [6]. For complying with the Directive, each member state of the EU is required to prepare an action plan for implementing an integrated pest management (IPM) program, which should include measures to promote and support low pesticide-input pest management. The potential of an IPM program is huge, as evidenced by the results of 62 international projects in which PPP use was reduced. When PPPs were reduced by 60%, the crop yield increased by 40% [7].
Genetically modified (GM) crops, either insect-resistant (IR), herbicide-tolerant (HT), or those that bear both stacked events, comprise a novel generation of tools for plant protection management. Maize, engineered with genes from Bacillus thuringiensis (Bt) bacteria that express insecticide toxins, was one of the first GM crops to be approved for use in the EU where it was cultivated on 136,000 ha in 2017, most of which was in Spain (124,000 ha) [8]. Bt maize cultivation has been found to reduce pesticide requirements [9] without affecting biodiversity [10,11] and with low risk of evolved insecticide resistance [12,13]. As biodiversity can boost ecosystem productivity, growing Bt maize also assists to sustain the productivity of ecosystems [14,15]. HT crops, particularly glyphosate-tolerant (GT) crops, became popular with farmers after their introduction because they could achieve broad weed control with the spraying of one type of herbicide. The cultivation of GT crops also initially facilitated the adoption of more sustainable agronomic practices, such as no-till farming and conservation agriculture. As a result, soil degradation, labor requirements, and costs were reduced with concomitant increases in crop yield, but continued use has led to the need for both increased herbicide use and further management measures like increased tilling that may offset the initial improvements [16]. In addition, the dependence on just one herbicide has lessened other herbicide uses, leading to changes in the flora, and triggering the evolution of weeds that are resistant to herbicides [17,18,19]. In next-generation HT crops, resistance traits for glyphosate are being stacked with resistance to herbicides with differing modes of action, which may slow the evolution of resistance, although increased selection pressure from herbicide mixtures may also lead to more cases of metabolic herbicide resistance [20,21]. New gene-editing techniques have the potential to accelerate plant breeding and scientists have modified genes such as EPSPS (5-enolpyruvylshikimate-3-phosphate synthase) and ALS (acetolactate synthase) to produce glyphosate and ALS-inhibitor herbicide-tolerant crops, respectively [22]. These crops, although non-transgenic, are intended to be used together with the complementary herbicides and the potential indirect risks associated to GM crops, as indirect effects on biodiversity resulting from the changes in weed management and the development of herbicide-resistant weeds [21,23] will also be relevant to future herbicide-tolerant genome-edited crops.
Weeds are crucial in agroecosystems because they help maintain biodiversity, which provides valuable ecological services in agricultural production, such as being an alternative host for pests and beneficial arthropods when their preferred host is absent [24,25,26,27,28,29,30,31]. There is a renewed interest in using post-emergent herbicides with no or little soil activity because they allow the persistence of weed populations below a given economic threshold. Thus, the GT maize cultivation and the post-emergent application of glyphosate could help to maintain weed abundance for specific periods when included in an integrated weed management (IWM) program. The use of glyphosate, applied alone or combined with commercially existing residual herbicides, can offer a feasible, flexible, and cost-effective alternative to a conventionally used herbicide program [32]. However, the composition and structure of a weed community can be altered by herbicides [33]. The potential changes in vegetation composition due to the use of glyphosate must be evaluated and compared with those due to the use of conventional pre- and post-emergent herbicides [34]. The presence or absence of weed species in maize fields can reduce or increase pest attacks or the incidence of beneficial species, such as insects, nematodes, and microorganisms [35]. The United Kingdom government has invested considerable resources in farm-scale evaluations (FSEs) to evaluate the potential environmental effects of Genetically Modified Herbicide-Tolerant (GMHT) crops. Specifically, these studies have collected much information on the effects of such crops on weeds and herbivores, detritivores, parasitoids, and many of their predators in agrosystems which are susceptible to weed community changes following the adoption of new herbicide regimes [36,37].
An unmet challenge in IWM is the design and deployment of programs for efficient weed management that reduce herbicide dependency and minimize their detrimental effects [38,39]. The role of the HT crops in an IWM program was reviewed by Lamichhane et al. [40] and it was concluded that effective weed management without herbicide applications in conventional intensive farming systems is not feasible because herbicides are the main component in any IWM program. Additionally, the benefits of weed suppression and/or the maintenance of weed diversity in maize production need to be carefully assessed for each IWM program.
Hence, the aim of this investigation is to determine weed density, richness, and diversity when maize is cultivated under different IWM programs. It is envisaged that the results of this investigation will assist in optimising weed control in maize fields while maintaining an appropriate population of weed species for supporting biodiversity.

2. Materials and Methods

2.1. Plant Material

Maize varieties DKC6451YG (Bt maize event MON810; that expresses the Cry1Ab protein) and the non-Bt near-isogenic line DKC6450 (both from Monsanto Agricultura, Madrid, España), which have an FAO maturity class index of 700, were used in the investigation. A GT maize variety was not used in the investigation because this variety is not authorised for planting in the EU.

2.2. Experimental Site and Field Layout

The field trial was carried out in 2012, 2013, and 2014 on a 6-hectare commercial maize field located in Seseña, Toledo, Spain (40°05′54″ N, 3°37′22″ W, 495 m above sea leve (a.s.l)) in the Tajo River basin. The field had been cultivated with conventional maize for at least the last ten years. The field’s soil has been classified as a typical Calcaric Fluvisol, with a pH of 8.4 and contains 2.1% of organic matter. The location’s climate is a semi-arid Mediterranean climate (BSk Köppen climate classification) characterised by wet and mild autumns and winters and by long, dry and hot summers (annual average temperature being 14.6 °C, with a summer and winter average temperature of 24 and 5.5 °C, respectively). The mean annual precipitation is of 366 mm, distributed mainly from October to May and is practically non-existent during the summer cropping season with the consequent summer hydric stress.
Table 1 summarizes the field operations in 2012, 2013, and 2014. All field operations, except weed management, were carried out following the practices of local farmers under real field conditions, which can be modified each year according to the requirements of the crop. The seedbed was prepared for sowing each year by disk harrowing, subsoiling, and turning with a rotavator. Maize seeds were annually planted in May at 3 cm depth in 75 cm apart rows using a precision seeder at 80,000 seeds ha−1. The seeding was carried out in May the three years of the trial. The field was fertilized with an initial basal NPK (8–15–15) fertilizer and a later nitrogen top dress, according to local agronomic practices, which can be modified each year according to the requirements of the crop. Since this region has low and variable rainfall, the maize fields were flood irrigated from June to August every 10–14 days.
The experimental design of the study is totally randomised. Forty plots of 600 m2 (20 × 30 m) were each seeded with 26 rows of maize, 25 with Bt seed, the other 15 with non-Bt seed. A 5-meter buffer strip of bare soil separated the plots. The experiment comprised five different weed management programs for controlling weeds in fields cultivated with GT maize: a conventional weed management program (Conv; the reference program), a weed management program which reduced the amount and the number of herbicide treatments (HR; herbicide-reduced), and three different programs which incorporated glyphosate. Each weed management program was replicated five times. The Conv program was the standard herbicide treatment that was done by local farmers according to the regional practices for maize production. This program comprised an initial application of two residual herbicides, S-metolachlor and terbuthylazine, to control a wide range of grasses and broad-leaved weeds followed by treatment with two post-emergent herbicides, nicosulfuron and mesotrione, to control grass weeds, especially Sorghum halepense (L.) Pers. The HR program was a post-emergence strategy that reduced the number of treatments and the doses of the herbicides which were applied in the Conv program. The Pre + Gly weed management program comprised an initial application of two commonly used pre-emergent (Pre-)herbicides, S-metolachlor and terbuthylazine, plus a glyphosate (Gly) application in post-emergence. The HR + Gly weed management program comprised the HR program and a glyphosate application in post-emergence implemented to improve the efficacy in controlling potentially difficult weeds. Under the 2 Gly weed management program, two post-emergent applications with glyphosate were performed. Table 2 displays the different weed management programs, the types and number of herbicide treatments, the application times, and the herbicide dosages that were used in the study. The Conv, HR and Pre + Gly programs were implemented on plots in which non-Bt and Bt maize seeds were planted. The herbicides in the HR + Gly and 2 Gly programs were applied only to plots in which Bt maize seeds were planted. The use of glyphosate on non-GT maize would mimic the use of an herbicide on HT maize, whereas its use on Bt maize would mimic the use of a herbicide on a stacked HT-IR maize. The same programs were always implemented on the same plots. In 2012, there was a problem with the layout of the trial and data could be provided for only the HR, HR + Gly and 2 Gly programs.
All herbicide applications, except those with glyphosate, were carried out with a tractor-mounted sprayer with flat fan nozzles, which were calibrated for spraying 350 L of herbicide solution ha−1. The glyphosate treatments were applied inter-row to the entire area of the experimental plots using electric backpack sprayers, which were calibrated to spray the same volume, with a preventive hood to prevent damage to glyphosate-susceptible maize and other crops in close proximity due to potential spray drift of glyphosate.
Temperature (°C) and rainfall (mm) data for the months of May and June, when the maize was sown, the weeds emerged, and the herbicide treatments were done, were obtained from the Aranjuez weather station. This station is located 5 km from the field trial, 40°2′31″ N, 3°37′53″ W, 487 m a.s.l, SiAR network [41]. For the historical period of 2004–2017, a 17.6 °C mean May temperature and a 41 mm average precipitation were recorded, while these values were 23.0 °C and 19 mm in June.

2.3. Data Collection

All plots were assessed for weed densities (plants m−2) at five sampling times: an initial “S0” assessment was made just after the maize was sown; an “S1” assessment was made just before the first application of glyphosate; an “S2” assessment was made 2–3 weeks following the first application of glyphosate; an “S3” assessment was made 2–3 weeks following the second application of glyphosate; and an “S4” assessment was made when the crop was physiologically mature. These assessments, which correspond to approximately 0, 30, 50, 70 and 120 days after sowing (DAS), were done to determine the effect of each management program on the weeds. The count of each weed species was done in twelve 0.25 m2 quadrats (3 m2) in the 6th, 13th and 20th row of each plot and 6, 12, 18 and 24 m from the plot’s edge. The position of the quadrats was fixed. The mean frequency for each weed species during each season was determined as the percentage of quadrats in each plot in which the species was present at all the sampling times.
Weed-species community structure changes were measured by determining weed density, the species richness (S) and the Shannon-Wiener diversity index (H′) for each plot in the five weed management programs. The H′ index of diversity was computed as H′ = −∑ pi·ln pi, with pi being the relative abundance of each species.
Maize yield was estimated in the years 2013 and 2014, according to Rozier et al. [42]. Ten ears from 10 plants in the middle of each plot were randomly manually collected at harvest and their total fresh weight was recorded in the field. Ears were shelled in the laboratory using a handheld maize sheller and the grain samples were weighed after placing them in an oven at 80 °C for 48 h. The average yield per plant (kg) was obtained as the total bulk weight divided by 10. The number of maize plants per row was also counted for three rows to estimate the plant density of each plot (plants m−2). Total dry grain yield (kg ha−1) per plot was estimated by the multiplication of the average yield of maize plants by the number of plants per plot.

2.4. Data Analysis

Weed density data, log (x + 1) transformed to normalise the distribution, from those plots sown with Bt and non-Bt maize that have received the same weed management programs throughout the three years of field experiment, were subjected to a two-way analysis of variance (ANOVA) to compare the effects of seed variety on weed density for each year. Since maize seed variety did not significantly influence weed density, linear mixed-effects models (LMM) were used to examine the effects on weed density of the five weed management programs in those experimental plots cultivated only with Bt maize in 2013 and 2014. For these analyses, the year and treatment were the fixed factors and the sampling date was the repeated measure factor. Interactions between the factors were included in the models. The best covariance structure for the repeated measures data was selected according to the lowest value of Akaike and Schwarz’s Bayesian information criteria [43,44]. The models were fitted using a restricted maximum likelihood method. Data on weed density data (counts for all quadrats within a plot) were first pooled and then transformed by log (x + 1) prior to the analysis for normality. Pairwise comparisons of the means of weed density among the weed management programs were made using the least significance difference (LSD) test when statistical significance was found. Data on weed species richness and diversity were square-root transformed, pooled across sampling times within plots, and tested by a two-way ANOVA (year and treatment). A two-way ANOVA was also done to examine the relationship between yields and the weed management programs. Pairwise comparisons of the means of weed species richness (S) and diversity (H′) among the weed management programs were made using the Student-Newman-Keuls test. Data for 2012 were analyzed separately (LMM for weed density data and one-way ANOVA for diversity indices). An analysis of major components (PCA) to determine associations between weed species and weed management programs was conducted. For this analysis, weed densities in five replicates per herbicide management system during 2013–2014 were used. All statistical analyses were done using a computerized statistical software package (SPSS, version 13.0, SPSS Inc., Chicago, IL, USA). Data are displayed as mean ± standard deviation and statistical significance was set at 5% for all tests.

3. Results

Figure 1 displays the average daily mean temperatures and total rainfalls during May and June in 2012, 2013 and 2014 for the region where maize was sown, weed germination started, and herbicide treatments were done in this investigation. May 2012 was hotter and drier than normal, with 21.2 °C mean temperature and 25 mm of precipitation. In June 2012, the mean temperature was 24 °C and only 0.61 mm of rain were recorded. May 2013 was much cooler than May 2012: the mean temperature was 14.6 °C and 52 mm of rain fell on the days just after the maize sowing. June 2013 was also cooler than June 2012: the mean temperature was 21.3 °C, and 1.2 mm rain fell. The mean temperatures and rainfalls for May and June 2014 were similar to the historical recordings for these months in the area: 16.4 °C and 4.7 mm and 22.1 °C and 14 mm, respectively.

3.1. Weed Species Composition

Across all weed surveys, 24 annual and five perennial species were recorded. The annual species were Abutilon theophrasti Medik., Amaranthus spp. (A. retroflexus L. and A. blitoides S. Watson), Chenopodium album L., Datura spp. (D. stramonium L. and D. ferox L.), Digitaria sanguinalis (L.) Scop., Echinochloa crus-galli (L.) Beauv., Fallopia convolvulus (L.) A. Löve, Kochia scoparia (L.) Schrad., Lactuca serriola L., Lamium amplexicaule L., Polygonum aviculare L., Polygonum persicaria L., Portulaca oleracea L., Salsola kali, L., Setaria spp. (S. adhaerens (Forssk) Chiov., S. verticillata (L.) Beauv. and S. viridis (L.) Beauv.), Solanum nigrum L., Sonchus spp. (S. asper (L.) Hill and S. oleraceous L.), Urtica urens L., and Xanthium strumarium L. The five perennial species were Cyperus rotundus L., Convolvulus arvensis L., Malva sylvestris L., Medicago sativa L. and S. halepense. The dicotyledonous species (22) were more abundant than the monocotyledonous species (7).
In our field trial, we found that the application of herbicides in pre- and post-emergence, linked to a maize monoculture whose duration is more than ten years, has led to the presence of several dominant weed species. The most frequent species, averaged over the three years in descending order, were those of the genus Setaria (25% ± 6%), E. crus-galli (23% ± 8%), D. sanguinalis (16% ± 6%), S. halepense (12% ± 6%), A. theophrasti (10% ± 5%), Datura spp. (mainly D. stramonium, 8% ± 2%), S. nigrum (5% ± 2%) and Amaranthus spp. (mainly A. retroflexus, 4% ± 2%). Other weed species were present in low densities.

3.2. Weed Species Abundance, Richness, and Diversity

The ANOVA conducted to compare the effects of seed variety on weed density showed that maize seed variety did not significantly affect weed density (F1, 288 = 2.66, P = 0.10). For this reason, and to avoid an unbalanced ANOVA model analysis, the effect of the weed management programs were compared only on weed density data from plots sown with the Bt maize variety, and the 2012 results are presented separately.
In 2012, the weed community analysis was done only for the HR, HR + Gly and 2 Gly management programs. The mean total weed density and richness (S) in the HR + Gly program were significantly higher (31.3 ± 10.6 plants m−2 and 6.5 ± 0.6 species) than those in the HR and 2 Gly programs, in which no significant differences were detected between total weed density and richness (Table 3). The weed density of the monocotyledonous species was higher compared to the dicotyledonous, averaging 79% ± 7% of the total weed density among the weed management programs. The Shannon-Wiener diversity indices were not significantly different among the HR, HR + Gly and 2 Gly management programs, but the species richness was significantly higher in the HR + Gly treatment (Table 3).
Figure 2 displays the effects of the five weed management programs on the mean total weed density in the Bt maize plots in 2013 and 2014. Weed density in 2014 was significantly higher than that in 2013 (F1, 40 = 49.66, p < 0.001). A significant “year-by-treatment” interaction was detected (F4, 40 = 8.10, p < 0.001). Significant differences in the total mean weed density also existed among weed management programs (F4, 40 = 10.96, p < 0.001). Weed densities in the Conv or Pre + Gly programs, in which a pre-emergent herbicide was applied, were lower than those in the HR, HR + Gly or 2 Gly programs, in which the herbicides were applied post-emergence. Among weed management programs, the weed density in monocotyledonous species averaged 79% ± 10% and 74% ± 11% of the total weed density in 2013 and 2014, respectively. In 2013 and 2014, the weed densities of monocotyledonous species in those programs with applications in pre-emergence were significantly lower than those with only post-emergence applications (F1, 40 = 48.15, p < 0.001). Three statistically different groups were formed: one which comprised those weed management programs that included pre-emergence applications (Conv and Pre + Gly), a second group which comprised those programs which included glyphosate (2 Gly and HR + Gly), and a third group which comprised the HR program, which had the highest density of monocotyledonous weeds. However, no significant differences were found in the dicotyledonous density among weed management programs (F1, 40 = 1.818, p = 0.144), only from year to year, with the highest weed density recorded in 2013 (F1, 40 = 68.18, p < 0.001).
Figure 3 displays the effects of the five weed management programs on the mean total weed density when assessed at the S0, S1, S2, S3 and S4 sampling times in 2013 and 2014. In 2013, the highest weed densities were recorded at 50 DAS (S2 sampling time) in the HR + Gly program and at 70 DAS (S3 sampling time) in the HR program. In 2014, the highest weed densities were recorded at 30 DAS in all weed management programs. Weed density at the five sampling times in those management programs that did not include an application of a pre-emergent herbicide was greater than those in the other management programs.
Significant differences were found in the abundance of the main weed species among management programs (Table 4). The densities of the main weed species increased when there were no applications of a pre-emergent herbicide. This higher density can be clearly seen for the monocotyledonous Setaria spp. and E. crus-galli in the HR, HR + Gly and 2 Gly programs (44.2–62.1 and 16.8–28.8 plants m−2, respectively) compared to those programs in which a pre-emergent herbicide was applied (8.6–11.0 plants m−2 and 5.6–5.9 plants m−2, respectively). The density for the dicotyledonous Amaranthus spp. in those programs in which no herbicide was applied in pre-emergence was higher than that in those programs in which this herbicide was used (3.9–16.2 plants m−2 versus 0.2–0.4 plants m−2; p < 0.05). Interestingly, the differences in the density of A. theophrasti in programs with and without an application of a pre-emergent herbicide were not so evident.
The relationship between weed species abundance and the weed management programs was examined by PCA (Figure 4). The results of this analysis revealed a positive correlation between monocotyledonous weed species, such as Setaria spp., E. crus-galli or D. sanguinalis, and the HR, HR + Gly and 2 Gly management programs. The analysis also showed a positive correlation for the abundance of A. theophrasti in the Pre + Gly and Conv programs. No significant relationships between the abundance of other species, such as S. halepense, S. nigrum or Datura spp. and the weed management programs were detected (Figure 4 and Table 4).
The impact that the weed management programs have on species richness and Shannon-Wiener diversity indices of weeds is shown in Table 5 for 2013 and 2014. The values for these two parameters in 2013 were higher than those in 2014 (p < 0.001). However, the weed management programs did not affect species richness (F4, 40 = 2.23, p = 0.08) and the diversity indices (F4, 40 = 2.11, p = 0.09).
The influence of the weed density on the estimated yield of Bt maize under the different weed management programs and years was investigated. The maize yield was significantly higher in 2014 than in 2013 (F1, 40 = 26.15, p < 0.007), but there was a year–weed management interaction (F4, 40 = 2.77, p = 0.04). The effect of the different weed management programs on yield was analyzed separately for each year. In 2013, the yield varied from 10,439 ± 709 kg ha−1 in the HR program to 12,117 ± 1264 kg ha−1 in the Pre + Gly program, and the five weed management programs did not significantly influence maize yields (F4,20 = 1.76, p = 0.18). In 2014, the yield varied from 12,473 ± 556 kg ha−1 in the HR + Gly program to 14,387 ± 550 kg ha−1 in the 2 Gly program, the yield in the HR + Gly program being significantly lower than those in the other four programs (F4, 20 = 7.11, p < 0.001).

4. Discussion

Local preceding farming practices are reflected in our maize field. A few weed species dominate the weed community, and this characterization is similar to that in other commercial maize fields in the region [45] and in maize fields in temperate regions [32,46].
Four of the five weed management programs, which we implemented in our study, comprised two herbicide treatments and all comprised at least one application of a post-emergent herbicide. Two programs included a pre-emergent herbicide treatment and glyphosate was used in post-emergence in three programs. The Conv weed management program used as the reference program was very effective in maintaining the plots almost weed-free. The mean total weed density was low because the residual herbicide eliminated weeds as they germinated and emerged. Additionally, this program relied on the application of the most common combination of pre-emergent selective herbicides, namely a mixture of a triazine, such as terbuthylazine, for dicotyledonous weed control, and a chloroacetanilide, such as S-metalochlor, for controlling monotyledonous weeds.
We found a similar level of weed control in the Conv and Pre + Gly programs: the use of a residual herbicide in pre-emergence to remove weeds at the early stages of growth followed by treatment with the non-selective glyphosate in post-emergence resulted in very low weed densities throughout the cropping season. Carey and Kells [47] claimed that some weeds could be left for a longer period between the rows of the crop with no loss of yield if they are timely controlled in post-emergence. As was previously reported in the FSEs for maize [48], we found that when the post-emergent herbicide glyphosate was applied, as in HR + Gly and 2 Gly programs, it was not as effective in controlling weeds as the conventionally used pre-emergent herbicides, which are mostly triazines [48]. Moreover, the HR program allowed a reduction in both the dosages of the herbicides used and in the number of applications in comparison to the Conv weed management program.
We found that the responses of some weed species differed in the weed management programs. The monotyledonous weeds, D. sanguinalis, E. crus galli, Setaria spp., and the dicotyledoneous A. retroflexus, were more abundant in those plots that were treated in post-emergence only than those which were treated in pre-emergence. Additionally, the results of the PCA suggested an association between weed management programs and some weed species. Our results show that Setaria spp. and E. crus-galli were controlled by S-metolachlor, which is highly effective against these monotyledonous weeds when applied in pre-emergence. However, their abundance increased by seven and three orders of magnitude, respectively, in the 2 Gly weed management program. We also found that repeated application of herbicides only in post-emergence could result in a weed shift toward these two late-emerging grasses. Furthermore, A. theophrasti, a weed that germinates throughout the season, was present with an aggregated distribution in some plots in the Pre + Gly and HR + Gly programs. This finding is in agreement with that of Dieleman and Mortensen [49], who reported that the patches of A. theophrasti are very stable in their location. When A. theophrasti was present in those plots in which the Pre + Gly program was applied, effective weed control was difficult using conventional pre-emergent herbicides, such as terbutilazine, due to their non-selectivity [50]. When A. theophrasti was present in those plots in which the HR + Gly program was applied, it was probably too late for post-emergence control using glyphosate [51]. The presence of A. theophrasti patches in those plots where the HR + Gly program was implemented, which also led to a higher weed density in 2012 in HR + Gly plots compared to HR or 2 Gly (Table 3), probably reduced the maize yields in 2014 because of late post-emergent weed control using glyphosate in 2012 and 2013. The weeds found in a field are an expression of the existing seedbank The study of effects of the different weed management programs on the germinable weed seedbank is ongoing and the results will contribute to display their effects on the seedbank, which is of utmost importance in IWM [52].
Nevertheless, our data support the notion that a single herbicide treatment, such as that done in the HR program, can be effective in controlling weeds in maize. Other authors have reported that lowering the herbicide dosages in herbicide applications during the maize cropping season does not affect yield [39,53]. It has been also reported that the application of glyphosate in GT maize cultivation, alone or in combination with pre-emergent herbicides, can provide a good weed control without affecting crop yield [32]. Since we found that the weed management programs did not affected the maize yield, any program that lowers the herbicide loads can be environmentally favourable because they increase weed abundance, thereby facilitating environmentally sustainable maize cultivation. The environmental benefits include reduced soil and water contamination by the herbicides, and increased abundance of other organisms, which in turn would sustain and even boost biological diversity [30]. Additionally, such programs economically benefit the farmer because their production costs are lowered, thereby increasing their profits. We must also take into account that the spray application technique used determines the efficacy and efficiency of the spray application, so different application techniques (e.g., backpack sprayer vs. tractor mounted boom sprayer) could lead to a different result.
An IWM program may potentially change the species diversity of weeds [54]. The indices of diversity found in this study were in line with the range reviewed by Vasileiadis et al. [39] for maize cultivation in different regions. Although we found that the different weed management programs have similar effects on the species diversity, the weed community composition was different. According to Bàrberi et al. [55], the alteration in the composition of a weed community changes the resources availability and influences the capacity of plant communities to provide ecosystem services. For example, it may lead to changes in the food supply, refuges or reproduction places to natural enemies of crop pests and/or pollinators [55,56,57]. When considering provision of agroecosystem services, interestingly, none of the abundant weed species following the application of only post-emergent herbicides can support insect pollinators [55]. Burger et al. [58], in a simulation study, showed that local conditions could influence the effects of GT maize on weeds and associated biodiversity. Furthermore, they asserted that the effects were due more to simplification of the cropping-system features of GT maize varieties than to the herbicide, glyphosate.
Before implementing a weed management program, it is important to consider any potential risks associated with the program and the temporal and spatial scales on which it will be used [59]. A reduction in herbicide doses, as well as the continued use of only one non-selective herbicide, has the potential to alter weed abundance and population composition when cultivating a GT crop [48,60]. The extensive cultivation of glyphosate-resistant crops constitutes a very strong selection pressure that favours the evolution of glyphosate-resistant weeds and the shifts in weed flora towards those that are more difficult to control with glyphosate [61,62]. When these pressures lead to the prevalence of a single or few weed species, they can also adversely affect the agro-ecosystem.
The weed dynamic assessment conducted in this study for the different weed management programs provide a scientific basis for the development of IWM programs for adoption at a national or regional level. Each weed management option can alter weed communities in different ways and the trophic levels of other communities, which include insects, birds and large animals in an agroecosystem [63,64], and these effects must be evaluated in the complex web of interactions between weeds and the other organisms that occur in the agroecosystem.

5. Conclusions

The conventional weed management program used as the reference program for weed control in maize maintained the plots almost weed-free. The weed management programs with an herbicide application in pre-emergence were more effective in lowering weed density than those with a post-emergence application, which allowed weed populations to remain in the field for a longer period. The HR program allowed a reduction in amount of herbicide applied without affecting the maize yield. Although the different weed management programs have similar effects on the species diversity, the weed community composition was different.

Author Contributions

Conceptualization, M.-C.C., I.L., I.S.-M. and M.-C.E.; data curation, I.L. and M.-C.E.; formal analysis, I.L. and I.S.-R.; investigation, I.L., I.S.-M., M.-C.E., E.G.-R., G.C., I.S.-R., S.P., M.G.-N. and M.-C.C.; methodology, M.-C.C., I.L. and M.-C.E.; project administration, M.-C.C.; resources, M.-C.C. and M.-C.E.; supervision, M.-C.C. and M.G.-N.; validation, M.-C.E. and I.L.; writing—original draft preparation, I.L. and M.-C.C.; writing—review and editing, I.L., I.S.-M., M.-C.E., E.G.-R., G.C., I.S.-R., S.P., M.G.-N. and M.-C.C.

Funding

This is the publication No. 35 produced within the framework of the project Assessing and Monitoring the Impacts of Genetically Modified Plants on Agro-ecosystems (AMIGA), funded by the European Commission in the Framework Programme 7. THEME [KBBE.2011.3.5-01].

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Oerke, E.C.; Dehne, H.W. Safeguarding production-losses in major crops and the role of crop protection. Crop. Prot. 2004, 23, 275–285. [Google Scholar] [CrossRef]
  2. Eurostat. The use of plant protection products in the European Union. Data 1992–2003. 2007. Available online: http://ec.europa.eu/eurostat/en/web/products-statistical-books/-/KS-76-06-669 (accessed on 17 September 2019).
  3. Birch, A.N.E.; Begg, G.S.; Squire, G.R. How agro-ecological research helps to address food security issues under new IPM and pesticide reduction policies for global crop production systems. J. Exp. Bot. 2011, 62, 3251–3261. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Meissle, M.; Mouron, P.; Musa, T.; Bigler, F.; Pons, X.; Vasileiadis, V.P.; Otto, S.; Antichi, D.; Kiss, J.; Pálinkás, Z.; et al. Pests, pesticide use and alternative options in European maize production: Current status and future prospects. J. Appl. Entomol. 2010, 134, 357–375. [Google Scholar] [CrossRef]
  5. Popp, J.; Pető, K.; Nagy, J. Pesticide productivity and food security. A review. Agron. Sust. Dev. 2013, 33, 243–255. [Google Scholar] [CrossRef]
  6. Directive 2009/128/EC of the European Parliament and of the Council Establishing a Framework for Community Action to Achieve the Sustainable Use of Pesticides. 2009. Available online: https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex%3A32009L0128 (accessed on 17 September 2019).
  7. Pretty, J. Sustainability in agriculture: Recent progress and emergent challenges. Sustainability in agriculture. Issues Env. Sci. Technol. 2005, 21, 1–15. [Google Scholar]
  8. James, C. Global Status of Commercialized Biotech/GM Crops: 2016; No. 53; ISAAA: Ithaca, NY, USA, 2017. [Google Scholar]
  9. Brookes, G.; Barefoot, P. Global impact of biotech crops environmental effects, 1996–2010. GM Crops Food 2012, 3, 1–9. [Google Scholar]
  10. Cortet, J.; Griffiths, B.S.; Bohanec, M.; Demsar, D.; Andersen, M.N.; Caul, S.; Birch, A.N.E.; Pernin, C.; Tabone, E.; de Vaufleury, A.; et al. Evaluation of effects of transgenic Bt maize on microarthropods in a European multi-site experiment. Pedobiologia 2007, 51, 207–218. [Google Scholar] [CrossRef]
  11. Romeis, J.; Bartsch, D.; Bigler, F.; Candolfi, M.P.; Gielkens, M.M.C.; Hartley, S.E.; Hellmich, R.L.; Huesing, J.E.; Jepson, P.C.; Layton, R.; et al. Assessment of risk of insect-resistant transgenic crops to non-target arthropods. Nat. Biotechnol. 2008, 26, 203–208. [Google Scholar] [CrossRef] [Green Version]
  12. Farinós, G.P.; Hernández-Crespo, P.; Ortego, F.; Castañera, P. Monitoring of Sesamia nonagrioides resistance to MON 810 maize in the European Union: Lessons from a long-term harmonized plan. Pest. Manag. Sci. 2018, 74, 557–568. [Google Scholar] [CrossRef]
  13. Camargo, A.M.; Andow, D.A.; Castañera, P.; Farinós, G.P. First detection of a Sesamia nonagrioides resistance allele to Bt maize in Europe. Sci. Rep. 2018, 8, 3977. [Google Scholar] [CrossRef]
  14. Pilgrim, E.S.; Macleod, C.J.A.; Blackwell, M.S.A.; Bol, R.; Hogan, D.V.; Chadwick, D.R.; Cardenas, L.; Misselbrook, T.H.; Haygarth, P.M.; Brazier, R.E.; et al. Interactions among agricultural production and other ecosystem services delivered from European grasslands. Adv. Agron. 2010, 109, 117–154. [Google Scholar]
  15. Duffy, J.E.; Godwin, C.M.; Cardinale, B.J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 2017, 549, 261–264. [Google Scholar] [CrossRef] [PubMed]
  16. Benbrook, C.M. Trends in glyphosate herbicide use in the United States and globally. Environ. Sci. Eur. 2016, 28, 3. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Johnson, W.G.; Davis, V.M.; Kruger, G.R.; Weller, S.C. Influence of glyphosate-resistant cropping systems on weed species shifts and glyphosate-resistant weed populations. Eur. J. Agron. 2009, 31, 162–172. [Google Scholar] [CrossRef]
  18. Bonny, S. Genetically modified herbicide-tolerant crops, weeds, and herbicides: Overview and impact. Environ. Manag. 2016, 57, 31–48. [Google Scholar] [CrossRef]
  19. García-Ruiz, E.; Loureiro, I.; Farinós, G.P.; Gómez, P.; Gutiérrez, E.; Sánchez, F.J.; Escorial, M.C.; Ortego, F.; Chueca, M.C.; Castañera, P. Weeds and ground-dwelling predators’ response to two different weed management systems in glyphosate-tolerant cotton: A farm-scale study. PLoS ONE 2018, 13, e0191408. [Google Scholar] [CrossRef] [Green Version]
  20. Gage, K.L.; Krausz, R.F.; Walters, S.A. Emerging challenges for weed management in herbicide-resistant crops. Agriculture 2019, 9, 8. [Google Scholar] [CrossRef] [Green Version]
  21. Beckie, H.J.; Ashworth, M.B.; Flower, K.C. Herbicide resistance management: Recent developments and trends. Plants 2019, 8, 161. [Google Scholar] [CrossRef] [Green Version]
  22. Seedek, K.E.M.; Mahas, A.; Mahfouz, M. Plant genome engineering for targeted improvement of crop traits. Front. Plant. Sci. 2019, 10. [Google Scholar] [CrossRef] [Green Version]
  23. Schütte, G.; Eckerstorfer, M.; Rastelli, V.; Reichenbecher, W.; Restrepo-Vassalli, S.; Ruohonen-Lehto, M.; Saucy, A.-G.W.; Mertens, M. Herbicide resistance and biodiversity: Agronomic and environmental aspects of genetically modified herbicide-resistant plants. Environ. Sci. Eur. 2017, 29. [Google Scholar] [CrossRef] [Green Version]
  24. Risch, S.J. Agricultural ecology and insect outbreaks. In Insect Outbreaks; Barbos, A.P., Schultz, J.C., Eds.; Academic Press: New York, NY, USA, 1987; pp. 217–238. [Google Scholar]
  25. Van Emden, H.F. Plant diversity and natural enemy efficiency in agroecosystems. In Critical Issues in Biological Control; Mackauer, M., Ehler, L.E., Roland, J., Eds.; Intercept: Andover, Hants, UK, 1990; pp. 63–80. [Google Scholar]
  26. Marshall, E.J.P.; Brown, V.K.; Boatman, N.D.; Lutman, P.J.W.; Squire, G.R.; Ward, L.K. The role of weeds in supporting biological diversity within crop fields. Weed Res. 2003, 43, 77–89. [Google Scholar] [CrossRef] [Green Version]
  27. Storkey, J.; Westbury, D.B. Managing arable weeds for biodiversity. Pest. Manag. Sci. 2007, 63, 517–523. [Google Scholar] [CrossRef] [PubMed]
  28. Fried, G.; Petit, S.; Dessaint, F.; Reboud, X. Arable weed decline in Northern France: Crop edges as refugia for weed conservation? Biol. Conserv. 2009, 142, 238–243. [Google Scholar] [CrossRef]
  29. Haddad, N.M.; Crutsinger, G.M.; Gross, K.; Haarstad, J.; Tilman, D. Plant diversity and the stability of foodwebs. Ecol. Lett. 2011, 14, 42–46. [Google Scholar] [CrossRef]
  30. Isbell, F.; Calcagno, V.; Hector, A.; Connolly, J.; Harpole, W.S.; Reich, P.B.; Scherer-Lorenzen, M.; Schmid, B.; Tilman, D.; van Ruijven, J.; et al. High plant diversity is needed to maintain ecosystem services. Nature 2011, 477, 199–202. [Google Scholar] [CrossRef]
  31. Petit, S.; Boursault, A.; Le Guilloux, M.; Munier-Jolain, N.; Reboud, X. Weeds in agricultural landscapes: A review. Agron Sustain. Dev. 2011, 31, 309–317. [Google Scholar] [CrossRef] [Green Version]
  32. Dewar, A.M. Weed control in glyphosate-tolerant maize in Europe. Pest. Manag. Sci. 2009, 65, 1047–1058. [Google Scholar] [CrossRef]
  33. Derksen, D.; Thomas, A.; Lafond, G.; Loeppky, H.; Swanton, C. Impact of post-emergence herbicides on weed community diversity within conservation-tillage systems. Weed Res. 1995, 35, 311–320. [Google Scholar] [CrossRef]
  34. Svobodová, Z.; Skokova-Habustova, O.; Holec, Z.; Bohac, J.; Jursik, M.; Soukup, J. Split application of glyphosate herbicide in herbicide-tolerant maize provides efficient weed control and favors beneficial epigeic arthropods. Agric. Ecosyst. Environ. 2018, 251, 171–179. [Google Scholar] [CrossRef]
  35. Liebman, M.; Mohler, C.L.; Staver, C.P. Ecological Management of Agricultural Weeds; Cambridge University Press: Cambridge, UK, 2001. [Google Scholar]
  36. Hawes, J.; Haughton, A.J.; Osborne, J.L.; Roy, D.B.; Clark, S.J.; Perry, J.N.; Rothery, P.; Bohan, D.A.; Brooks, D.R.; Champion, G.T.; et al. Responses of plants and invertebrate trophic groups to contrasting herbicide regimes in the farm scale evaluations of genetically modified herbicide-tolerant crops. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2003, 358, 1899–1913. [Google Scholar]
  37. Birch, A.N.E.; Begg, G.S. The future central role of IPM in EU crop protection: How can ecological research be put into practice? In Proceedings of the Crop Protection in Northern Britain 2010, Dundee, UK, 27–28 February 2018. [Google Scholar]
  38. Arpaia, S.; Messéan, A.; Birch, A.N.E.; Hokannen, H.; Härtel, S.; van Loon, J.; Lovei, G.; Park, J.; Spreafico, H.; Squire, G.R.; et al. Assessing and monitoring impacts of genetically modified plants on agro-ecosystems: The approach of AMIGA Project. Entomologia 2014, 2, 79–86. [Google Scholar] [CrossRef]
  39. Vasileiadis, V.P.; Otto, S.; van Dijk, W.; Urek, G.; Leskovšek, R.; Verschwele, A. On-farm evaluation of integrated weed management tools for maize production in three different agro-environments in Europe: Agronomic efficacy, herbicide use reduction, and economic sustainability. Eur. J. Agron. 2015, 63, 71–78. [Google Scholar] [CrossRef]
  40. Lamichhane, J.R.; Devos, Y.; Beckie, H.J.; Owen, M.D.K.; Tillie, P.; Messéan, A.; Kudsk, P. Integrated weed management systems with herbicide-tolerant crops in the European Union: Lessons learnt from home and abroad. Crit. Rev. Biotechnol. 2017, 37, 459–475. [Google Scholar] [CrossRef] [Green Version]
  41. SiAR. Agroclimatic Information System for Irrigation Network of the Spanish Ministry of Agriculture and Fisheries, Food and Environment (MAPAMA). Available online: http://eportal.mapama.gob.es/websiar/Inicio.aspx (accessed on 17 September 2019).
  42. Rozier, C.; Hamzaoui, J.; Lemoine, D.; Czarnes, S.; Legendre, L. Field-based assessment of the mechanism of maize yield enhancement by Azospirillum lipoferum CRT1. Sci. Rep. 2017, 7, 7416. [Google Scholar] [CrossRef] [Green Version]
  43. Littell, R.C.; Henry, P.R.; Ammerman, C.B. Statistical analysis of repeated measures data using SAS procedures. J. Anim. Sci. 1998, 76, 1216–1231. [Google Scholar] [CrossRef] [Green Version]
  44. Wang, Z.; Goonewardene, L.A. The use of MIXED models in the analysis of animal experiments with repeated measures data. Can. J. Anim. Sci. 2004, 84, 1–11. [Google Scholar] [CrossRef]
  45. San Martín, C.; Andújar, D.; Fernández-Quintanilla, C.; Dorado, J. Spatial distribution patterns of weed communities in corn fields of central Spain. Weed Sci. 2015, 63, 936–945. [Google Scholar] [CrossRef]
  46. Jensen, P.K.; Bibard, V.; Czembor, E.; Dumitru, S.; Foucart, G.; Froud-Williams, R.J.; Jensen, J.E.; Saavedra, M.; Sattin, M.; Soukup, J.; et al. Survey of weeds in maize crops in Europe. Aarhus University. 2011. [cited 2018 April 4]. Available online: https://www.researchgate.net/publication/232775702_survey_of_weeds_in_maize_cops_in_europe (accessed on 17 September 2019).
  47. Carey, J.B.; Kells, J.J. Timing of total postemergence herbicide applications to maximize weed control and corn (Zea mays) yield. Weed Technol. 1995, 9, 356–361. [Google Scholar] [CrossRef]
  48. Heard, M.S.; Hawes, C.; Champion, G.T.; Clark, S.J.; Firbank, L.G.; Haughton, A.J.; Parish, A.M.; Perry, J.N.; Rothery, P.; Scott, R.J.; et al. Weeds in fields with contrasting conventional and genetically modified herbicide-tolerant crops. 1. Effects on abundance and diversity. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2003, 358, 1819–1832. [Google Scholar]
  49. Dieleman, J.A.; Mortensen, D.A. Characterizing the spatial pattern of Abutilon theophrasti seedling patches. Weed Res. 1999, 39, 455–467. [Google Scholar] [CrossRef]
  50. Armel, G.R.; Wilson, H.P.; Richardson, R.J.; Hines, T.E. Mesotrione alone and in mixture with glyphosate in glyphosate-resistant corn (Zea mays). Weed Technol. 2003, 17, 680–685. [Google Scholar] [CrossRef]
  51. Hartzler, R.G.; Battles, B.A. Reduced fitness of velvetleaf (Abutilon theophrasti) surviving glyphosate. Weed Technol. 2001, 15, 492–496. [Google Scholar] [CrossRef]
  52. Schwartz-Lazaro, L.M.; Copes, J.T. A review of the soil seedbank from a weed scientist’s perspective. Agronomy 2019, 9, 7. [Google Scholar] [CrossRef] [Green Version]
  53. Champion, G.T.; May, M.J.; Bennett, S.; Brooks, D.R.; Clark, S.J.; Daniels, R.E.; Firbank, L.G.; Haughton, A.J.; Hawes, C.; Heard, M.S.; et al. Crop management and agronomic context of the Farm Scale Evaluations of genetically modified herbicide-tolerant crops. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2003, 358, 1801–1818. [Google Scholar]
  54. Clements, D.; Weise, S.; Swanton, C. Integrated weed management and weed species diversity. Phytoprotection 1994, 75, 1–18. [Google Scholar] [CrossRef] [Green Version]
  55. Bàrberi, P.; Bocci, G.; Carlesi, S.; Armengot, L.; Blanco-Moreno, J.M.; Sans, F.X. Linking species traits to agroecosystem services: A functional analysis of weed communities. Weed Res. 2018, 58, 76–88. [Google Scholar] [CrossRef]
  56. Storkey, J.; Brooks, D.; Haughton, A.; Hawes, C.; Smith, B.M.; Holland, J.M. Using functional traits to quantify the value of plant communities to invertebrate ecosystem service providers in arable landscapes. J. Ecol. 2013, 101, 38–46. [Google Scholar] [CrossRef] [Green Version]
  57. Armengot, L.; Blanco-Moreno, J.M.; Bàrberi, P.; Bocci, G.; Carlesi, S.; Aendekerk, R.; Berner, A.; Celette, F.; Grosse, M.; Huiting, H.; et al. Tillage as a driver of change in weed communities: A functional perspective. Agric. Ecosyst. Environ. 2016, 222, 276–285. [Google Scholar] [CrossRef]
  58. Burger, J.; Darmency, H.; Granger, S.; Guyot, S.H.M.; Messéan, A.; Colbach, N. Simulation study of the impact of changed cropping practices in conventional and GM maize on weeds and associated biodiversity. Agric. Syst. 2015, 137, 51–63. [Google Scholar] [CrossRef]
  59. Gabriel, D.; Sait, S.M.; Hodgson, J.A.; Schmutz, U.; Kunin, W.E.; Benton, T.G. Scale matters: The impact of organic farming on biodiversity at different spatial scales. Ecol. Lett. 2010, 13, 858–869. [Google Scholar] [CrossRef]
  60. Waltz, E. Glyphosate resistance threatens Roundup hegemony. Nat. Biotechnol. 2010, 28, 537–538. [Google Scholar] [CrossRef] [PubMed]
  61. Owen, M.D.K. Weed species shifts in glyphosate-resistant crops. Pest. Manag. Sci. 2008, 64, 377–387. [Google Scholar] [CrossRef] [PubMed]
  62. Powles, S.B. Evolved glyphosate-resistant weeds around the world: Lessons to be learnt. Pest. Manag. Sci. 2008, 64, 360–365. [Google Scholar] [CrossRef] [PubMed]
  63. Bàrberi, P.; Burgio, G.; Dinelli, G.; Moonen, A.C.; Otto, S.; Vazzana, C.; Zanin, G. Functional biodiversity in the agricultural landscape: Relationships between weeds and arthropod fauna. Weed Res. 2010, 50, 388–401. [Google Scholar] [CrossRef]
  64. Brooks, D.R.; Storkey, J.; Clark, S.J.; Firbank, L.G.; Petit, S.; Woiwod, I.P. Trophic links between functional groups of arable plants and beetles are stable at a national scale. J. Anim. Ecol. 2012, 81, 4–13. [Google Scholar] [CrossRef] [Green Version]
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Figure 1. Daily meteorological conditions at the experimental site in Seseña, Toledo, Spain. Lines represent the mean temperatures (°C), and bars represent the rainfall (mm) during the months of May and June in 2012 (grey), 2013 (black, continuous) and 2014 (black, discontinuous).
Figure 1. Daily meteorological conditions at the experimental site in Seseña, Toledo, Spain. Lines represent the mean temperatures (°C), and bars represent the rainfall (mm) during the months of May and June in 2012 (grey), 2013 (black, continuous) and 2014 (black, discontinuous).
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Figure 2. The effect of the five weed management programs on the mean total weed density in the Bt maize plots in 2013 and 2014. Data are displayed as the mean ± standard deviation of five replications. Different letters (a and b)indicate statistical differences (p < 0.05) among weed management programs according to the results of the least significant distance (LSD) test after investigating the effects of the weed management programs using linear mixed-effects models (LMM).
Figure 2. The effect of the five weed management programs on the mean total weed density in the Bt maize plots in 2013 and 2014. Data are displayed as the mean ± standard deviation of five replications. Different letters (a and b)indicate statistical differences (p < 0.05) among weed management programs according to the results of the least significant distance (LSD) test after investigating the effects of the weed management programs using linear mixed-effects models (LMM).
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Figure 3. The effect of the weed management programs on the mean total weed density in the Bt maize plots in 2013 and 2014. Data are displayed as the mean ± standard deviation of five replications. The black lines represent weed management programs, which included the application of pre-emergent herbicides. The grey lines represent weed management programs with post-emergent herbicides. Arrows represent the timing of the herbicidal treatments. PRE-Conv indicates the application of the pre-emergent herbicide in the Conv weed management program; POST-G1/HR indicates the first application of glyphosate in the Pre + Gly and 2 Gly weed management programs and the application of the HR treatment. POST-C/POST-G2 indicates the application time of the post-emergent herbicide in the Conv weed management program and the second application of glyphosate in the 2 Gly weed management program.
Figure 3. The effect of the weed management programs on the mean total weed density in the Bt maize plots in 2013 and 2014. Data are displayed as the mean ± standard deviation of five replications. The black lines represent weed management programs, which included the application of pre-emergent herbicides. The grey lines represent weed management programs with post-emergent herbicides. Arrows represent the timing of the herbicidal treatments. PRE-Conv indicates the application of the pre-emergent herbicide in the Conv weed management program; POST-G1/HR indicates the first application of glyphosate in the Pre + Gly and 2 Gly weed management programs and the application of the HR treatment. POST-C/POST-G2 indicates the application time of the post-emergent herbicide in the Conv weed management program and the second application of glyphosate in the 2 Gly weed management program.
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Figure 4. Principal components analysis (PCA) of weed species and the five different weed management programs. Dicotyledonous weed species (empty symbols): Abutilon theophrasti, Amaranthus spp., Datura spp. and Solanum nigrum. Monocotyledoneous weed species (full symbols): Digitaria sanguinalis, Echinochloas crus-galli, Sorghum halepense and Setaria spp. Species with low frequencies of occurrence (<5%) are not shown.
Figure 4. Principal components analysis (PCA) of weed species and the five different weed management programs. Dicotyledonous weed species (empty symbols): Abutilon theophrasti, Amaranthus spp., Datura spp. and Solanum nigrum. Monocotyledoneous weed species (full symbols): Digitaria sanguinalis, Echinochloas crus-galli, Sorghum halepense and Setaria spp. Species with low frequencies of occurrence (<5%) are not shown.
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Table
Table 1. Agronomic information for each of the three maize cropping years (2012–2014) in Seseña, Toledo, Spain.
Table 1. Agronomic information for each of the three maize cropping years (2012–2014) in Seseña, Toledo, Spain.
Field OperationsYear
201220132014
Soil preparationDisk harrowing24 and 27 January20 March6 and 10 March
Subsoiling31 January9 April14 March
Rotovating14 February16 April29 March
Sowing date 11 May9 May19 May
Maize seed variety DKC6450 DKC6451YGDKC6450
DKC6451YG		DKC6450 DKC6451YG
Planting density 80,000 seeds ha−180,000 seeds ha−180,000 seeds ha−1
Row width 0.75 m0.75 m0.75 m
FertilizationBasal dressing
(NPK)		13 February
8–15–15, 300 kg ha−1		15 April
8–15–15, 300 kg ha−1		27 March
8–15–15, 300 kg ha−1
Topdressing8 June
Urea-46, 300 kg ha−1		15 June
CAN 27% *, 500 kg ha−1		1 July
Urea-46, 600 kg ha−1
First irrigation 9 June2 June17 June
Plant height 2.60 m2.70 m2.75 m
Flowering 24 July3 August7 August
Harvest 5 December 9 January 30 December
* CAN: calcium ammonium nitrate.
Table
Table 2. Weed management programs, herbicides, application times, and dosages.
Table 2. Weed management programs, herbicides, application times, and dosages.
Weed
Management
Program		Herbicide
Treatments
(N°)		Application Time
(Maize Growth Stage)		Herbicide
Active Ingredients		Rate
(g a.i.ha−1)
Conventional
(Conv.)		2PRES-metolachlor 31.25% +
terbuthylazine 18.75%		1250
750
POST
(6–8 leaf stage)		nicosulfuron 6% +
mesotrione 10%		39
100
Herbicide
Reduced (HR)		1POST
(6–8 leaf stage)		S-metolachlor 31.25% +
terbuthylazine 18.75% +
nicosulfuron 6% +
mesotrione 10%		938
562
45
50
Pre + Gly2PRES-metolachlor 31.25% +
terbuthylazine 18.75%		1250
750
POST
(6–8 leaf stage)		glyphosate 36%1080
HR + Gly2POST
(4–6 leaf stage)		S-metholachlor 31.25% +
terbuthylazine 18.75% +
nicosulfuron 6% +
mesotrione 10%		938
562
45
50
POST
(8–10 leaf stage)		glyphosate 36%1080
2 Gly2POST
(4–6 leaf stage)		glyphosate 36%1080
POST
(8–10 leaf stage)		glyphosate 36%1080
Weed management programs: Conv: conventional; HR: herbicide-reduced; Pre + Gly: initial application of a commonly used pre-emergent herbicide followed by a post-emergent application of glyphosate; HR + Gly: HR program followed by a post-emergent application of glyphosate; and 2 Gly: two post-emergent applications of glyphosate. PRE = pre-emergence, POST = post-emergence.
Table
Table 3. The effect of the weed management programs on the mean abundance (plants m−2), species richness (S), and the Shannon–Wiener (H’) diversity index in Bt maize in 2012.
Table 3. The effect of the weed management programs on the mean abundance (plants m−2), species richness (S), and the Shannon–Wiener (H’) diversity index in Bt maize in 2012.
WeedWeed Management ProgramTreatment (T)
(F2,14 (P))
Bt/HRBt/HR + GlyBt/2 Gly
Abundance
(plants m−2)		9.7 ± 4.131.3 ± 10.64.9 ± 3.0119.26 *
(0.00)
Species Richness (S)3.9 ± 0.36.5 ± 0.63.9 ± 0.923.06 *
(0.00)
Shannon-Wiener
diversity index (H′)		0.9 ± 0.11.1 ± 0.21.0 ± 0.21.81
(0.21)
Data are presented as mean ± standard deviation. Means were compared by a one-way ANOVA and the calculated F value for the treatment (T = weed management program). * p < 0.05 and indicates statistical differences between the weed management programs. Herbicide-reduced (HR) weed management program, HR + Gly (glyphosate) weed management program and 2 Gly weed management program.
Table
Table 4. The effect of the five weed management programs on the density of the main weed species (plants m−2) in a 6-hectare commercial Bt maize field located in Seseña, Toledo, Spain in 2013 and 2014.
Table 4. The effect of the five weed management programs on the density of the main weed species (plants m−2) in a 6-hectare commercial Bt maize field located in Seseña, Toledo, Spain in 2013 and 2014.
Weed
Management
Program		Plant Density
DicotyledonousMonocotyledonous
Abutilon
theophrasti		Amaranthus
spp.		Datura
spp.		Solanum
nigrum		Digitaria
sanguinalis		Echinochloa
crus-galli		Setaria spp.Sorghum
halepense
Conv2.8 ± 4.2 a0.2 ± 0.3 a0.6 ± 0.50.4 ± 0.43.6 ± 5.0 a5.6 ± 5.0 a8.6 ± 5.9 a1.9 ± 2.0
Pre + Gly10.9 ± 19.8 ab0.4 ± 0.5 a1.4 ± 1.40.8 ± 1.71.7 ± 2.2 a5.9 ± 5.3 a11.0 ± 7.6 a2.3 ± 3.1
HR1.2 ± 0.8 a5.4 ± 7.6 b1.6 ± 1.31.5 ± 1.424.5 ± 11.6 d16.8 ± 9.2 b62.1 ± 40.5 b4.6 ± 3.3
HR + Gly12.8 ± 11.9 b3.9 ± 3.8 b1.4 ± 0.92.3 ± 2.16.4 ± 4.4 b28.8 ± 22.0 b44.2 ± 40.0 b4.9 ± 3.8
2 Gly4.1 ± 6.3 ab16.2 ± 18.5 b2.1 ± 2.70.8 ± 1.211.3 ± 6.6 c18.4 ± 15.2 b61.9 ± 63.7 b2.7 ± 2.7
Y (F1,40 (P))2.42
(0.12)		11.46 *
(0.00)		0.89
(0.35)		1.04
(0.31)		14.26 *
(0.00)		4.11 *
(0.04)		16.83 *
(0.00)		2.15
(0.15)
T (F4,40 (P))4.00 *
(0.01)		10.09 *
(0.01)		1.02
(0.41)		2.59
(0.05)		29.201 *
(0.00)		9.31 *
(0.00)		18.37 *
(0.00)		1.53
(0.21)
Y × T
(F4,40 (P))		0.35
(0.84)		2.51
(0.06)		1.14
(0.96)		0.74
(0.57)		3.43 *
(0.01)		0.91
(0.46)		6.03 *
(0.00)		2.35
(0.07)
Data were pooled across sampling dates and years within plots and are presented as mean ± standard deviation. Linear mixed-effects models were conducted to investigate the interaction effects of the weed management programs (T, treatments) and years (Y). Different letters indicate statistical differences at * p < 0.05 among weed management programs.
Table
Table 5. The effect of the five weed management programs on species richness (S) and the Shannon-Wiener diversity index (H’) of the weeds in a 6-hectare commercial Bt maize field located in Seseña, Toledo, Spain in 2013 and 2014.
Table 5. The effect of the five weed management programs on species richness (S) and the Shannon-Wiener diversity index (H’) of the weeds in a 6-hectare commercial Bt maize field located in Seseña, Toledo, Spain in 2013 and 2014.
YearWeed Management ProgramYear (Y)
(F1,40 (P))		Treatment
(T)
(F4,40 (P))		Y × T
(F4,40 (P))
ConvPre + GlyHRHR + Gly2 Gly
Species
Richness
(S)		20134.8 ± 0.94.7 ± 1.55.0 ± 1.55.1 ± 0.35.1 ± 0.735.19 *
(0.00)		2.23
(0.08)		1.21
(0.32)
20142.9 ± 0.63.2 ± 1.23.5 ± 0.33.1 ± 0.34.6 ± 0.4
Diversity
index
(H′)		20131.0 ± 0.11.1 ± 0.40.9 ± 0.11.0 ± 0.21.0 ± 0.236.04 *
(0.00)		2.11
(0.09)		1.53
(0.21)
20140.7 ± 0.10.7 ± 0.30.6 ± 0.10.5 ± 0.10.9 ± 0.2
Data were pooled across sampling dates within plots and are presented as mean ± standard deviation. Means were compared by a two-way ANOVA and the F calculated for the two factors, year (Y) and treatment (T = weed management program), and their interaction. * p < 0.05.

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Loureiro, I.; Santin-Montanyá, I.; Escorial, M.-C.; García-Ruiz, E.; Cobos, G.; Sánchez-Ramos, I.; Pascual, S.; González-Núñez, M.; Chueca, M.-C. Glyphosate as a Tool for the Incorporation of New Herbicide Options in Integrated Weed Management in Maize: A Weed Dynamics Evaluation. Agronomy 2019, 9, 876. https://doi.org/10.3390/agronomy9120876

AMA Style

Loureiro I, Santin-Montanyá I, Escorial M-C, García-Ruiz E, Cobos G, Sánchez-Ramos I, Pascual S, González-Núñez M, Chueca M-C. Glyphosate as a Tool for the Incorporation of New Herbicide Options in Integrated Weed Management in Maize: A Weed Dynamics Evaluation. Agronomy. 2019; 9(12):876. https://doi.org/10.3390/agronomy9120876

Chicago/Turabian Style

Loureiro, Iñigo, Inés Santin-Montanyá, María-Concepción Escorial, Esteban García-Ruiz, Guillermo Cobos, Ismael Sánchez-Ramos, Susana Pascual, Manuel González-Núñez, and María-Cristina Chueca. 2019. "Glyphosate as a Tool for the Incorporation of New Herbicide Options in Integrated Weed Management in Maize: A Weed Dynamics Evaluation" Agronomy 9, no. 12: 876. https://doi.org/10.3390/agronomy9120876

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