Spatio-temporal dynamics of Sorghum halepense in poplar short-rotation coppice under several vegetation management systems

Forest Ecology and Management 379 (2016) 37–49

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Spatio-temporal dynamics of Sorghum halepense in poplar short-rotation coppice under several vegetation management systems Carolina San Martín ⇑, Dionisio Andújar, Cesar Fernández-Quintanilla, José Dorado Institute of Agricultural Sciences, CSIC, Serrano 115b, 28006 Madrid, Spain

a r t i c l e

i n f o

Article history: Received 7 June 2016 Received in revised form 30 July 2016 Accepted 1 August 2016 Available online 8 August 2016 Keywords: Poplar coppice Sorghum halepense Johsongrass Weed-poplar competition Weed shifting

a b s t r a c t Weeds are one of the most important factors reducing productivity in poplar short-rotation coppices. Sorghum halepense is a very harmful perennial weed affecting irrigated crops preceding poplar plantations. The main goal of this study is to assess the potential hazards of residual S. halepense patches in newly established poplar crops and the effects of different vegetation control treatments. Our study objectives were: to describe the spatio-temporal dynamics of this perennial weed during the first rotation coppice of poplar in the presence of different vegetation management strategies and to assess the effect that S. halepense has on poplar growth. Experiments were conducted in an experimental field in central Spain and were repeated over time. S. halepense patches were artificially established in a random block design with four replications. Four types of vegetation management were performed: mechanical (field cultivator and rototiller) and chemical (glyphosate and fluazifop-p-butyl). Patches were monitored throughout all the experiments and poplar productivity parameters (height and biomass) were recorded. Monitoring indicated patch displacement towards crop rows, probably due to higher soil moisture levels in this irrigated area, and a reduction in patch density in the central inter-row area owing to different management strategies. However, displacement was barely detected lengthwise, as was originally expected, at least in mechanical management. Mardia’s test (circular statistics) indicated no significant differences in patch displacement between mechanical and chemical strategies. Regarding poplar growth, the mixed linear model indicated no consistent significant differences in terms of vegetation management, differences depending more on the proximity of S. halepense patches. This study has demonstrated that while S. halepense has very limited capacity to spread spatially in poplar short-rotation coppice, its proximity to crop rows may cause important damage in poplar productivity, especially during the first growing season. Therefore, proper management of this weed species is strongly recommended during this period. Ó 2016 Elsevier B.V. All rights reserved.

1. Introduction Use of energy crops for lignocellulosic biomass production has gained importance in recent years due to their potential as a renewable energy source. In this respect, European law has established a minimum share of energy from renewable sources out of gross final energy consumption by 2020. Two million hectares under short-rotation woody crops are needed to meet these energy targets (European Biomass Association, 2010). In Spain, the 2011–2020 Renewable Energy Plan set the target of 20% of total primary energy needs to be met by renewable sources and about 10% of these by bioenergy (IDAE, 2016).

⇑ Corresponding author. E-mail address: [email protected] (C. San Martín). http://dx.doi.org/10.1016/j.foreco.2016.08.001 0378-1127/Ó 2016 Elsevier B.V. All rights reserved.

Poplar (Populus spp.) is one of the species most widely used as an energy crop in southern Europe due to its yield, density and short-rotation coppices (Sixto et al., 2007; Sevine et al., 2009). Its economic viability is constrained by high production costs, mainly related to tree harvest and transport to power plants. There are also significant costs arising from the establishment of plantations including tilling, planting and effective weed control. Weeds are one of the main obstacles to poplar production in short rotation coppices and their importance has recently been highlighted by studies. Kauter et al. (2003) and Kabba et al. (2011) found that competition for soil nutrients between poplar and Taraxacum officinale Weber and Elytrigia repens (L.) Desv. ex Nevski significantly stunted crop growth. There are also references to the sensitivity of poplar (and other energy crops) to weed competition during the first year after planting (Albertsson et al., 2014; Broeckx et al., 2012; Buhler et al., 1998; Otto et al., 2010; Shock et al., 2002;

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Sixto et al., 2001) and immediately after plantation establishment (Buhler et al., 1998). Marino and Gross (1998) found that weed competition affects poplar dendrometric characteristics such as plant height, basal diameter and branch length. According to Clay and Dixon (1997), poplars failed to recover after intense weed competition, not only in the first growing season but also in regrowth after first harvest, perennial grasses being particularly aggressive (Henkel-Johnson et al., 2016). Weed control during the first year of plantation is typically based on the pre-emergence application of oxyfluorfen on crop rows and intensive tillage in the inter-row area (Sixto et al., 2007). This management system has a relatively high economic cost, is energy intensive and is often ineffective. Although some herbicides have shown promising weed control results for biomass crops (Vasic et al., 2015), none have been registered in Spain for this purpose. In recent years, a number of studies have focused on mechanical weed control, either as a stand-alone practice (Desrochers and Sigouin, 2014) or in combination with herbicides (Henkel-Johnson et al., 2016; Morhart et al., 2013). Poplars require substantial amounts of water which is a limiting factor in low rainfall areas such as the Mediterranean (Sixto et al., 2007). Therefore, poplar crops in Southern Europe are mostly found on irrigated lands previously used for corn (Otto et al., 2010). Therefore, and although no flora inventories have been conducted to date on Spanish crops, poplar plantations are most likely affected by the same weed communities as corn (personal communication). Sorghum halepense (L.) Pers. is a very aggressive C4 perennial grass found in the Mediterranean and in tropical and subtropical areas around the world (Holm, 1969; McWhorter, 1989). As it is a very common weed affecting corn crops in Spain (Andújar et al., 2011), this weed species will most likely be found in poplar plantations where corn was formerly grown. Previous studies on S. halepense in corn fields have shown that this weed expands in the same direction of field traffic, with little or no displacement in other directions. Apparently, tillage was the main dispersal mechanism of S. halepense rhizomes, with seeds playing only a minor role (Andújar et al., 2012). The spatial growth pattern of this weed in tilled corn is characterized by a compact spread of the original patch and the appearance of small patches at various distances from the primary source, which tend to merge with one another and also with the original source indicating an infiltration-type invasion strategy (Andújar et al., 2012). The dynamics of S. halepense in poplar crops grown in shortrotation coppices is likely to be somewhat different than that observed for corn. Since poplar inter-rows (3 m) are much wider than those of corn (0.75 m), crop competition is expected to be lower during the first growing season (the most critical for crop establishment). However, poplar shade may be a critical factor for S. halepense growth in subsequent years. Fortier et al. (2011) found that shade is an important barrier affecting shadeintolerant species in hybrid poplar rows. Poplar can also serve as a physical barrier to S. halepense expansion due to its dense root system (Friend et al., 1991). Also, in dry-land drip-irrigated fields, the central inter-row area is more subject to water stress and competition due to this factor than areas closer to the crop row (Pinno and Bélanger, 2009). The overall aim of this study was to assess the potential hazards presented by residual S. halepense patches (from previous crops) in newly established poplars crops and the effects of different vegetation control treatments. To that end, the study intends to (1) describe the spatio-temporal dynamics of this perennial weed during the first rotation coppice of poplar in the presence of different management strategies and (2) to assess the effect of S. halepense on poplar growth. The hypothesis was that due to the predominantly vegetative reproduction of this species, expansion of initial

patches would be limited. Also, chemical management was expected to be more efficient than its mechanical counterpart in controlling weed spread. Lastly, owing to the competitiveness of S. halepense (Bendixen, 1986; Ghosheh et al., 1996; Mueller et al., 1993; Vasilakoglou et al., 2005) and the slow growth of poplars in the first growing season, weed patches were also expected to have a strong influence on crop growth. Although the results of this study were intended to be applicable primarily to S. halepense infested irrigated plantations under semi-arid climates, we have tried to derive more general implications for poplar shortrotation coppices under different growing conditions and other perennial grasses infestations. 2. Methods 2.1. Experimental site Experiments were conducted from 2012 to 2015 at La Poveda Research Station in Arganda del Rey, Madrid, Spain (40.31°N, 3.49°W). This farm is located in the Jarama river basin and is flat. Soil has a sandy-loam texture with 39% sand, 47% silt, 14% clay, and 1.4% organic matter, N 0.75 g/kg, CaCO3, 34 g/kg and a pH of 8.1. The climate is Mediterranean Continental with cold winters and hot summers (mean daily temperature 13.5 °C), and an annual cumulative rainfall 400 mm. The experiment was performed on an area free of S. halepense where corn (2008–2010) and barley (2011) had previously been grown. Various annual broad-leaved weeds typical of corn crops (Chenopodium album L., Datura spp.) present in the plots were controlled by the management treatments tested. To take the effect of different initial conditions into consideration, two identical side-by-side experiments were set up, one in 2012 (EX-2012) and the other in 2013 (EX-2013), poplar plantations being established on 19 April 2012 and 9 April 2013, respectively. The poplar clone used was ‘‘I-214” (P. deltoides
P. nigra), one of the most popular hybrid clones used in two- or three-year short-rotation coppices in Spain (Sixto et al., 2007). Cuttings measuring 20 cm in length obtained from a commercial nursery were stored at 4 °C until planting time. Prior to planting, land was prepared with two passes of a double disk plough and an application of 4 L/ha Roundup Plus (450 ga.i./L glyphosate). Both plantations were also treated with 2.2 L/ha Goal (24 ga.i./L oxyfluorfen) applied immediately after planting. Poplars rows were planted three metres apart with 50 cm spacing within each row resulting in a density of 6666 plants/ha. Both experiments were drip irrigated, adjusting irrigation volume to crop needs. Irrigation schedules ranged from 6 to 8 h/month (30 m3/ha/h) in April, May and October to 44–52 h/month (30 m3/ha/h) in July and August. 2.2. Experimental design In order to study the dynamics of S. halepense, weed patches were artificially established using rhizomes obtained from an infested area nearby. Rhizomes were cut into 2-node pieces (fragments with 2 buds), simulating the fragmentation effect caused by soil tillage during soil seedbed preparation. The same density (50 pieces) was replicated in each patch (2 m
2 m). Patches were located in the middle of the inter-row area (separated by 0.5 m from crop rows) and were established immediately after poplar plantation. We used a randomized block design with four replications and four management strategies. The sampling area within each plot included two 13.5 m poplar rows adjacent to S. halepense patches (Fig. 1). The sampling area of contiguous plots was separated by a buffer inter-row in order to prevent interference of management strategy on neighboring patches from different plots. Although no

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4m

Block 4

13.5 m 3m

Plot width

4m

4.75 m

2m

2m

Block 3

Tillage direction

6.75 m

Sampled trees

Block 2 Fig. 1. Diagram showing the details of block 3 of the experimental design. The pattern fill in the boxes indicates the vegetation management strategy: gray (glyphosate), black (rototiller), sloping lines (cultivator), and white (fluazifop-p-butyl).

using ArcGISÒ 10.1 software (ESRI, 2011). Once the patch image was drawn, the exact position of each S. halepense stem in each patch was marked with the same software, thus establishing the exact coordinates of every weed stem within the patch.

control treatments were applied on S. halepense patches during the first growing season to allow a proper establishment, in subsequent growing seasons, four different management strategies were assessed in the inter-row area: (1) tillage with a 2-m field cultivator; (2) tillage with a 1.5 m rototiller; (3) 4 L/ha Roundup Plus (450 g a.i./L glyphosate); and (4) 1.4 L/ha Fusilade (13% W/W Fluazifop-p-butyl). Mechanical operations were always done in the same direction. Due to the narrow width of the rototiller, two parallel passes were made getting as close as possible to the crop row. Herbicide application was with an ATV plot sprayer with a 2.5-m boom and lateral protective hoods to prevent herbicide drift to poplar leaves, using 300 L of water per hectare at 200 kPa pressure with flat fan nozzles. All management treatments were performed in mid May, when S. halepense patches were well established but not too big for an effective weed control.

Non-destructive sampling (tree height) was performed annually at the end of the growing season (December-January). Destructive sampling (cutting poplars at 5 cm above ground) was only done at the end of the first rotation cycle. Both operations considered poplars located adjacent to the original S. halepense patches (10 plants, 5 on each side of the patch) and poplars unaffected by this weed, i.e. separated by at least 2 m from original patches (20 plants, 5 near each corner on both sides of the patch).

2.3. Sorghum halepense sampling

2.5. Data analysis: Sorghum halepense

Patches were geo-referenced by GPS during three growing seasons for EX-2012 (2013–2015) and during two seasons for EX-2013 (2014–2015). Monitoring was performed twice per season: in late May just before mechanical operations and herbicide treatments, and in late October at the end of the growing season. Hence, EX-2012 was monitored six times and EX-2013 four times. In the first sampling, we geo-referenced several strategic points in each patch with DGPS. These strategic points coincided with the four corners of the original patches which were also marked with coloured stakes for future sampling. At each sampling period, a 0.5 m
0.5 m geo-referenced grid was established in the original patches and in neighbouring areas using these marked points as a reference and we counted the number of S. halepense stems in each cell of the grid. In the October sampling we also determined S. halepense biomass per cell, cutting all stems at ground level and oven drying these samples at 80 °C for 48 h before weighing. In addition, we obtained digital images of all the individual cells (all measuring 0.5 m
0.5 m) from a 1.5 m height using a D70 Nikon digital camera, for all sampling periods. A unique total geo-referenced patch image was built by merging all these images

Different types of information were obtained from geopositioning data. In order to describe the width-wise distribution of S. halepense, we calculated the number of plants present at different intervals (10 cm strips) parallel to crop rows, both within and outside the inter-row area. Similarly, we calculated the number of plants at different intervals (20 cm strips) perpendicular to crop rows from the original patch centre to describe the lengthwise distribution of plants in each patch. This information was obtained using ArcGIS 10.1 software. Due to the non-parametric nature of these data we adjusted them to loess regression (locally weighted least squares regression). This analysis uses more local data to estimate the dependent variable, employing a smoother bandwidth (a) that uses a ‘nearest neighbours’ method to smooth the curve. A bandwidth of a means that we want to use a
100% of the data for each local fit. Therefore, bandwidth is a key parameter in this type of regression. A large a may result in over-smoothing or could miss important features in the data, while a small a could result in a fit with high noise. This parameter should be adjusted manually, visualizing the trends in each treatment using data from the four replications (patches) for each sampling period and

2.4. Poplar sampling

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experiment. For that we used the loess.smooth function, part of the ‘‘modreg” package of the R version 3.0.2 software (R Development Core Team, 2013). The distance and angle of all S. halepense stem positions with regard to the centre point of the initial patch was calculated with ArcGIS 10.1. From this information we performed circular statistics (Batschelet, 1981) using ORIANA 4.00 statistical software (Kovach, 2011). In circular statistics, data has no true zero and any designation of low or high values is arbitrary and consequently parameters are calculated in another way. For example, while a linear mean P ¼ angle results in a a=N, in circular statistics it would be   P sin a 1 1 a ¼ tan
P cos a . We calculated the mean of the entire set N of angles within a patch. It consists of a direction (the mean angle) and the length of the mean vector (r), which is an indication of the spread of the samples vectors. The closer the resulting vector is to 1, the more focused the data set is around the mean direction. The resultant vector was also associated with Rao’s test to check for data uniformity in each patch. The null hypothesis is that data are uniformly distributed. However, if the p-value is less than 0.05 we reject the null hypothesis and assume that data follow a preferential direction. We also calculated the Grand Mean Vector (GM), or mean of means, i.e. the mean of all patches in which we

performed the same treatment. This is calculated in a similar way to the mean of each variable, the latter being used as the data rather than the original angles. Some tests are associated with these ‘second order statistics’. This is the case of Moore’s modified Reyleigh test, a non-parametric test in which the null hypothesis is that population means are uniformly distributed around the circle meaning that rejection of the null hypothesis would indicate that patch means of the same treatment follow the same direction. We also performed a non-parametric test, Mardia’s two sample test, to detect the differences between the individual means and the grand mean for sample pairs. The null hypothesis is that the two samples come from the same population while the alternative hypothesis is that populations differ in some way (distribution, mean direction, or some other parameter). Because this test needs a minimum number of inputs, we could only use it to compare chemical and mechanical treatments. All this was calculated for each sampling date and for each experiment (EX-2012 and EX-2013). 2.6. Data analysis: Poplar We analysed data for the first growing season (non-destructive poplar sampling), the most critical period for poplar survival, and

Cultivator EXP-2012

EXP-2013

Fig. 2. Loess regression curves describing on the right the width-wise distribution (number of plants at 10 cm intervals from crop rows) and on the left the length-wise distribution (plants at 20 cm intervals from original patch centre), for S. halepense patches in the cultivator treatment in EX-2012 and EX-2013. Crop rows and original patch centres are represented by vertical lines, respectively.

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the last year of short-rotation coppice (destructive poplar sampling). We studied the relationship between tree height and dry biomass, respectively, and the independent variables performing a linear mixed model analysis. The fixed independent variables were weed presence (i.e. whether or not the poplar plant was adjacent to an S. halepense patch), type of vegetation management and patch side; random variables included the patch and block. SPSS 22.0 statistical software (SPSS, 2013) was used. 3. Results 3.1. Effects of vegetation management on S. halepense patch dynamics Although the demographic characteristics of S. halepense patches at the beginning of the two experiments were different, evolution during the experiments was similar (Figs. 2–5). In the experiment started in 2012 (EX-2012) S. halepense density was lower and plants were more spread length-wise than in the experiment started in 2013 (EX-2013). The dynamics of S. halepense patches varied depending on the experiment and vegetation management strategy used. In EX-2012, both chemical treatments

(glyphosate and fluazifop-p-butyl) substantially reduced weed populations (Figs. 4 and 5). After three years of herbicide application, S. halepense density at patch centres was nearly 50% lower than at the start and two out of the four glyphosate patches nearly disappeared altogether. In contrast, in the two mechanical treatments (cultivator and rototiller) S. halepense densities almost doubled during the same period (Figs. 2 and 3). In EX-2013, all of the treatments reduced S. halepense densities by nearly the same margin in one year, the fluazifop-p-butyl herbicide treatment being the only one that reduced the weed population by less than 50% (Fig. 5). In length-wise distribution (operation direction), distribution of S. halepense around the original patch was observed in all the cases with practically no displacement during the experimental period. Regarding width-wise distribution of S. halepense, in both experiments and in all treatments we observed bimodal distribution with a reduction in weed density at the centre of the patches (Figs. 2–5). In EX-2012 the differences between inter-row centres and the area close to the crop row were more pronounced in mechanical than in chemical management treatments. This trend was not observed in EX-2013. Although some S. halepense plants

Rototiller EXP-2012

EXP-2013

Fig. 3. Loess regression curves describing on the right the width-wise distribution (number of plants at 10 cm intervals from crop rows) and on the left the length-wise distribution (plants at 20 cm intervals from original patch centre), for S. halepense patches in the rototiller treatment in EX-2012 and EX-2013. Crop rows and original patch centres are represented by vertical lines, respectively.

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Glyphosate EXP-2012

EXP-2013

Fig. 4. Loess regression curves describing on the right the width-wise distribution (number of plants at 10 cm intervals from crop rows) and on the left the length-wise distribution (plants at 20 cm intervals from original patch centre), for S. halepense patches in the glyphosate treatment in EX-2012 and EX-2013. Crop rows and original patch centres are represented by vertical lines, respectively.

managed to cross the two poplar rows and invade adjacent areas, practically all the plants remained confined in their original inter-row locations. According to circular statistics (Rao’s test) practically all patches were unevenly distributed. Nearly all individual U values (for all treatments and sampling dates) were significant (p < 0.01) (Appendices A and B). This was true even on the first sampling date before any control treatment was applied. A clear distinction could be drawn in practically all patches between the centre and the two sides (next to the crop row). This was probably due to the soil moisture gradient depending on the proximity of the dripirrigation lines. Rao’s test could not be performed for some patches due to the limited number of points available, especially in EX-2012. However, the results of Moore’s test indicate significant differences between the four patches from the same treatment, especially in glyphosate and rototiller treatments (Appendices C and D). In general, the differences between chemical and mechanical treatments were negligible. Clear trends emerged when the spatial distribution of S. halepense plants in each treatment (average of the four patches) was considered at the end of the experiment. In all cases the Grand Mean Vectors (GMs) went in two nearly opposite directions lead-

ing to the poplar rows (Fig. 6 and Table 1 for the last year). This trend was particularly clear in the two mechanical treatments, with the strongest vector leading to the right row (see r values in Table 1). Despite the absence of significant differences based on Mardia’s tests, weed densities tended to be lower and more uniformly distributed in the two chemical treatments compared to the mechanical ones. 3.2. Effect of Sorghum halepense and vegetation management on poplar growth Presence of S. halepense patches had a significant effect on the height and biomass of adjacent poplar plants, reducing poplar height by between 5.1% and 17.5% during the first year and poplar dry weight by between 3.3 and 17.1% in the last year (Table 2). The negative effect of this weed was greater in EX-2013 than in EX2012, the latter with less density. As we expected, none of the experiments showed differences attributable to the vegetation management strategy employed during the first growing season since no weed management was performed. No consistent results were observed in the last year of the rotation, the lowest poplar dry weight being found in

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Fluazifop-p-butil EXP-2012

EXP-2013

Fig. 5. Loess regression curves describing on the right the width-wise distribution (number of plants at 10 cm intervals from crop rows) and on the left the length-wise distribution (plants at 20 cm intervals from original patch centre), for S. halepense patches in the fluazifop-p-butyl treatment in EX-2012 and EX-2013. Crop rows and original patch centres are represented by vertical lines, respectively.

4. Discussion

parts and reaching the underground storage organs thus preventing vegetative reproduction. In contrast, cultivator only destroys partially the aerial parts and breaks underground structures but does not kill the plants. Rototilling is more effective in destroying aerial parts but cuts underground rhizomes into small pieces that can grow into new plants.

4.1. Efficacy of vegetation management strategies

4.2. Dynamics of Sorghum halepense

Sorghum halepense is typically present in corn and other irrigated crops in Central Spain. We expected this weed to expand and become a serious problem in poplar short rotation coppices established in these fields. However, our results have shown that initial S. halepense patches generally remained in their original location and became less dense in experimental plots managed with mechanical tools or with chemical herbicides. These results are partially due to the competitiveness of high-density poplar stands (Fortier et al., 2011) and to the direct effect of control treatments. In general, herbicides such as glyphosate and fluazifopp-butyl are more effective in controlling perennial grasses than mechanical tools (Coll et al., 2007; Henkel-Johnson et al., 2016). These herbicides can translocate through plant, killing the aerial

In the absence of soil tillage and crop competition, Horowitz (1973) found that S. halepense had no preferential direction of expansion, established patches developing in a circular pattern. Spatial studies conducted with S. halepense in corn have shown a clear pattern of expansion of this weed, i.e. elongated patches in the same direction of field traffic and little or no displacement in other directions; in addition, small patches were initiated at various distances from the primary source (Andújar et al., 2012). In our case, initial square patches tended to segregate into two lateral patches close to the poplar rows. This could be due to different factors. One of these is the varying efficacy of control operations. Although these operations are performed as close as possible to

EXP-2012 in plots sprayed with the herbicide fluazifop-p-butyl, while in EXP-2013 the lowest weight was obtained with the cultivator treatment. Finally, no influence of the patch side on poplar growth was found.

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EX-2012

EX-2013

Cultivator

Rototiller

Glyphosate

Fluazifopp-butil

Fig. 6. Orientation of S. halepense stems with respect to the original patch centre at the end of the experimental period in EX-2012 and EX-2013. Circular histograms represent the number of stems according to their position in 10° bars in a grey colour scheme depending on the patch. Arrows represent the mean for all stems in each patch (both sides) and bold arrows represent the Gran Mean for all patches under the same treatment.

crop rows, there is inevitably a strip that goes untreated which is usually wider in mechanical treatments due to the difficulty of moving large machines through a dense poplar stand. In our experiment, the width of this strip was approximately 1 m in the cultivator treatment and 0.5 m in the treatments with herbicides or the two rototiller passes.

Another factor is higher soil moisture close to the drip irrigation lines. Water is a critical factor in poplar-weed competition (Bergante et al., 2010; Pinno and Bélanger, 2009). Although it is difficult to distinguish below-ground competition for water and nutrients (Nambiar and Sands, 1993; Radersma et al., 2005), Carmean (1996) believed that poplar productivity is more

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Table 1 Grand Mean and r values for mechanical and chemical treatments on each side of the inter-row in EX-2012 and EX-2013 at the end of the experimental period (2015) and statistical value of Mardia’s test (U) between treatment types. Physical treatments Left

EX-2012 EX-2013

Chemical treatments Right

Left

Right

r

GM

r

GM

r

GM

r

U

p

U

p

286.230 265.905

0.838 0.754

69.821 89.978

0.731 0.832

270.415 265.153

0.915 0.869

64.247 82.325

0.924 0.845

– 0.163

– 0.1 > p > 0.05

0.100 0.108

0.5 > p > 0.2 0.5 > p > 0.2

EX-2012

Treatment Cultivator Rototiller Glyphosate Fluazifop-pbutil

Left

GM

Table 2 Log transformed data of tree height (cm) and dry biomass of poplars (g) (mean values) depending on the presence of S. halepense patches and on inter-row treatment. Results are shown for two experiments (EX-2012 and EX-2013) for the first and third year in the crop cycle.

Patch presence No Yes

Mardia’s test Right

EX-2013

2012 (height)

2014 (biomass)

2013 (height)

2015 (biomass)

4.978a* 4.726b

3.048a 2.948b

4.392a 3.625b

2.630a 2.180b

4.926a 4.857a 4.848a 4.776a

3.02ab 3.046a 2.981ab 2.945b

3.616a 4.221a 3.908a 4.289a

2.279b 2.495a 2.461ab 2.396ab

* Values in the same column, considering patch presence or treatment, followed by the same letter are not significantly different (p = 0.05).

dependent on competition for water than for nutrients. This process depends on the root structure of the competing species (Nambiar and Sands, 1993). Poplar root systems are highly extended as result of rapid growth and may also compete in the inter-row area (Friend et al., 1991). Although no similar information is available on the root system of S. halepense, Lehmann et al. (1998) have shown that S. bicolor in competition with Acacia saligna developed shallower roots than when not having to compete with trees. A third factor is poplar rows. Although these rows were crossed by some S. halepense plants, in general they acted as a barrier to the expansion of weed patches. Competition for light may be one of the reasons for this, particularly after the establishment year (Fortier et al., 2011). Also, poplar plants are able to create a root network in the soil that interferes with the development of S. halepense rhizomes (Friend et al., 1991). This type of spatial pattern was not observed in corn (Andújar et al., 2011) probably due to the lower density of that crop’s root system owing to the annual displacement of corn row location and soil tillage. Sorghum halepense patches did not spread in a length-wise direction. This was to be expected in patches sprayed with herbicides considering the knock-down effect of these treatments and the lack of soil displacement. However, the lack of movement in mechanically controlled patches was quite surprising. Here the suppression effect was less pronounced and these tools (particularly the cultivator) displaced soil (and rhizomes) significant distances (Andújar et al., 2011).

4.3. Poplar growth Poplars are very sensitive to weed interference, especially in the first growing season after planting (Otto et al., 2010). Our results show that interference from S. halepense patches caused significant reductions in poplar growth. This result is not surprising considering that this weed is highly competitive with other crops (Ghosheh

et al., 1996). Early season weed competition is more damaging to young trees than later season weeds (Davison and Bailey, 1980). Weed interference is particularly important during early establishment in poplar plantations. Clay and Dixon (1996) reported that weed competition in this type of short rotation crops was usually most severe from April to June when weeds grow the fastest. In our case, S. halepenseis is a vigorous perennial grass that grows very early in the spring and establishes dense patches before June. Previous studies have already shown that perennial grasses are especially detrimental to poplar growth due to the fact that rhizomes create an extensive root system that is very effective in capturing nutrients and water (Henkel-Johnson et al., 2016). Weed interference can be caused by competition for resources or by allelopathic interactions. Allelopathy is one of the most common interactions in mixed-species forest plantations (Jose et al., 2006). It has been reported that rhizomes of S. halepense produce allelopathic compounds that inhibit growth (germination, development, distribution and/or reproduction) (Ghosheh et al., 1996). However, allelopathic effects are difficult to prove due to the difficulties in isolating them from environmental factors (Jose et al., 2006). In our study, a large number of dead poplar plants were found in the vicinity of S. halepense patches in the first year of EX-2013. Despite not having clear evidence, most of these plants presented symptoms compatible with possible allelopathic effects, e.g. blackish and then dried leaves before dying. In addition, although initial planting densities of S. halepense rhizomes were the same in EX-2012 and EX-2013, the patches were much denser in EX-2013. This resulted in greater reductions in poplar dry weights and, possibly, higher poplar mortality. Significant differences were found in the estimated dry weights of poplars between the different vegetation management strategies, higher weights being associated with rototiller treatment. This difference is not easy to explain considering that S. halepense densities in this treatment were similar to or higher than those found in the other treatments. One possible explanation is the improved soil aeration produced by rototilling and the subsequent beneficial effects on poplar root growth and degradation of allelopathic compounds. Previous studies have shown that weed control treatments have a major impact on poplar yields in short-rotation coppices (Buhler et al., 1998; Cañellas et al., 2012; Coll et al., 2007; Henkel-Johnson et al., 2016). However, chemical treatments seemed to have better effects on poplar growth than mechanical ones (Coll et al., 2007; Henkel-Johnson et al., 2016). 4.4. Management implications These findings may be applicable not only to irrigated plantations infested with S. halepense but also to other perennial grasses growing under different conditions. In our particular case, maintaining weed control treatments for two years gradually reduced weed populations and hindered patch spread. However, poplar biomass was seriously affected by this early interference. Studies conducted under similar climatic and cropping conditions indicated that weed control during the establishment year is much more effective at reducing losses than weed

46

C. San Martín et al. / Forest Ecology and Management 379 (2016) 37–49

control during any of the following years (Sixto et al., 2001). Otto et al. (2010), working in Northern Italy, indicated that a special attention should be given to weed control during the first year of the plantation in order to avoid yield losses. All these studies were conducted in 2–3 years rotation coppices, under intensive cropping systems (high density, irrigation, high soil fertility) and under favourable climatic conditions. However, many hybrid poplar plantations in northern countries might have rotation periods of 10 years or more. Plantations with longer rotations typically have wide spacing between trees where weeds can become established and compete for below-ground resources for several years before canopy closure (even if they are satisfactory controlled during the first year). In this regard, Buhler et al. (1998), in their review paper, recommended maintaining weed control for several years. The observed displacement of S. halepense patches towards the poplar rows was partially associated to the lack of effect of mechanical or chemical operations in the row area and to the higher moisture in that area due to drip irrigation. Although this last factor is only applicable to irrigated plantations, the first one is valid for much broader conditions. In many poplar plantations there is a relatively wide strip that goes untreated due to the difficulty of moving large machines through a dense poplar stand. Band spraying this area with herbicides may be hazardous due to the potential drift to the crop canopy. Fortunately, the strong poplar competition and the mulching effect created by poplar leaves accumulated in this area minimize this problem. The relatively low lengthwise spread of S. halepense spread in spite of the use of various mechanical control operations indicate that the role of cultivation in the dispersal of this weed is not as important as previously shown in corn crops (Andújar et al., 2012). This conclusion can also be applied to other rhizomatous grass species common in poplar crops such as Elitrigia repens or Poa pratensis (Henkel-Johnson et al., 2016).

4.5. Conclusions While S. halepense is one of the weeds that does the most damage to a number of irrigated crops (e.g. corn), the results of this study show that it has a very limited capacity to spread spatially in poplar short-rotation coppice as the original patches generally remained contained to their location and decreased in density, especially under chemical treatment. However, the presence of weed patches adjacent to poplar crop rows, mainly during the first growing season, can cause a high degree of competition in addition to symptoms consistent with allelopathic effects. Consequently, it is strongly recommended to properly manage this weed species in areas where there is competition during the first growing season. Regarding the spatio-temporal dynamics of S. halepense in poplar short-rotation coppice, weed patches did not spread length-wise, i.e. parallel to crop rows, but rather width-wise heading for the drip-irrigation lines, regardless of the vegetation management strategy employed. Apparent lack of length-wise dispersion when mechanical control methods were used was a surprising result, explaining why S. halepense is not as serious a threat to poplar as it is to other irrigated crops.

Acknowledgements This work was financially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the Project AGL2011-25243 and the F.P.I. Grant BES-2012-055222 granted to Carolina San Martín. Invaluable field assistance was provided by David Campos, José M. Martín and the entire La Poveda staff.

Appendix A. Statistical value (U) of Rao’s test for all sampling times and patches in EX-2012. pre = sampling before treatment; post = sampling after treatment.

Treatment Cultivator

Rototiller

Patch

Side

a

2013 pre

2013 post

2014 pre

2014 post

2015 pre

*

*

*

*

*

2015 post

4

Right Left

169.69

288.707 239.119*

265.010 254.121*

270.700 275.378*

267.316 256.438*

265.647* 298.016*

11

Right Left

140.824

232.199* 249.608*

231.202* 261.133*

238.588* 250.086*

238.323* 249.377*

266.698* 273.037*

15

Right Left

181.757*

238.943* 255.718*

241.998* 274.741*

246.730* 244.482*

235.703* 249.093*

243.463* 256.037*

16

Right Left

135.679

232.561* 241.988*

241.246* 249.709*

250.482* 266.687*

245.262* 236.243*

245.768* 256.843*

1

Right Left

164.316*

256.950* 262.228*

265.975* 264.869*

266.142* 272.288*

257.742* 255.485*

291.702* 276.763*

5

Right Left

153.193*

268.235* –

261.851* 295.059*

256.286* –

261.876* 222.729*

259.836* –

6

Right Left

167.617*

294.853* 191.791*

277.184* 263.778*

266.490* 277.961*

254.244* 271.064*

265.235* 282.061*

11

Right Left

156.073*

237.565* #

239.349* 268.729*

238.562* 291.966*

224.822* 259.427*

240.628* 323.849*

47

C. San Martín et al. / Forest Ecology and Management 379 (2016) 37–49

Appendix A (continued) Treatment Glyphosate

Fluazifop-p-butil

Patch

a

Side

2013 pre

2013 post

*

2014 pre

2014 post

2015 pre *

2015 post

2

Right Left

157.278

# 275.174*

# 279.354*

# 275.854*

229.050 286.320*

310.312* 292.855*

8

Right Left

171.699*

– –

– 297.181*

– –

– #

– –

12

Right Left

150.995*

219.142* 252.679*

280.426* 267.350*

– #

# #

– –

14

Right Left

145.736*

268.798* 248.370*

255.599* 257.413*

251.655* #

252.364* 213.152*

272.923* 292.477*

3

Right Left

166.327*

270.411* 284.754*

287.361* 290.356*

300.796* 271.996*

276.307* 271.344*

285.467* 270.781*

7

Right Left

178.746*

320.296* #

309.331* #

319.870* #

308.305* 264.648*

304.651* –

9

Right Left

165.118*

197.324* #

241.120* 274.940*

# 255.328*

310.532* 283.105*

– 304.573*

13

Right Left

157.94*

280.501* #

282.488* 274.339*

308.828* #

292.124* –

316.852* –

# Few point to perform the analysis. – No points to perform the analysis. a Note that in ‘‘2013 pre” there is no difference between left and right side because sampling was performed before any treatment or patch split. * p < 0.01.

Appendix B. Statistical value (U) of Rao’s test for all sampling times and patches in EX-2013. pre = sampling before treatment; post = sampling after treatment. Treatment Cultivator

Rototiller

Glyphosate

Patch

Side

a

2014 pre

2014 post

2015 pre

*

*

*

2015 post

4

Right Left

145.907

248.398 246.299*

251.879 236.988*

277.468* 267.454*

11

Right Left

152.014*

311.054* –

295.933* 231.518*

311.940* –

15

Right Left

146.221*

249.247* 242.590*

232.478* 229.979*

246.390* 269.187*

16

Right Left

149.964*

260.238* 271.072*

246.954* 231.445*

272.394* 308.406*

1

Right Left

151.928*

277.779* 245.676*

235.222* 232.833*

281.317* 264.108*

5

Right Left

143.520*

277.127* 245.302*

238.474* 246.766*

284.253* 266.332*

6

Right Left

157.365*

251.389* 292.917*

219.292* 234.765*

284.120* 289.879*

11

Right Left

146.405*

262.956* 308.336*

260.645* 248.025*

275.736* 293.662*

2

Right Left

148.944*

259.134* 295.067*

248.252* 261.650*

283.516* 326.024*

8

Right Left

155.827*

245.906* 251.284*

265.075* 242.583*

284.090* 313.783*

12

Right Left

165.153*

286.150* 295.587*

265.747* 255.937*

301.016* 284.783*

14

Right Left

145.764*

297.276* 291.308*

267.061* 271.988*

295.360* 284.372*

(continued on next page)

48

C. San Martín et al. / Forest Ecology and Management 379 (2016) 37–49

Appendix B (continued) Treatment

Patch

Side

a

Fluazifop-p-butil

3

Right Left

7

2014 pre

2014 post

2015 pre

2015 post

151.779*

243.306* 234.397*

244.261* 237.110*

260.636* 259.913*

Right Left

141.639*

242.818* 241.588*

241.673* 230.294*

288.913* 291.009*

9

Right Left

149.897*

259.138* 252.165*

238.633* 224.623*

259.364* 267.852*

13

Right Left

145.135*

253.893* 253.574*

242.066* 247.196*

273.323* 286.995*

# Few point to perform the analysis. – No points to perform the analysis. a Note that in ‘‘2014 pre” there is no difference between left and right side because sampling was performed before any treatment or patch split. * p < 0.01.

Appendix C. Statistical value (R) of Moore’s test for all sampling times in EX-2012. pre = sampling before treatment; post = sampling after treatment Treatment

Side

2013 pre

2013 post

2014 pre

2014 post

2015 pre

2015 post

R

p

R

p

R

p

R

p

R

p

R

p

Cultivator

Right Left

0.967

0.5> p > 0.1

1.249 1.207

<0.001 <0.001

1.245 1.153

<0.001 <0.025

1.249 1.21

<0.001 <0.01

1.248 1.201

<0.001 <0.01

1.249 1.216

<0.001 <0.005

Rototiller

Right

0.755

0.5 > p > 0.1

1.211

<0.01

1.085

1.19

<0.025

1.154

<0.025

1.155

<0.025

1.049

0.1 > p > 0.05

0.987

0.1 > p > 0.05 0.5 > p > 0.1

1.068

0.1 > p > 0.05

1.093

<0.05

1.068

0.1 > p > 0.05

1.041

0.1 > p > 0.05 0.1 > p > 0.05

1.068

1.051

<0.05

1.05

1.09

0.1 > p > 0.05 0.1 > p > 0.05

1.097

1.199

0.1 > p > 0.05 <0.01

1.214

<0.005

1.06

0.1 > p > 0.05 <0.025

Left Glyphosate

Right

0.796

0.5 > p > 0.1

Left Fluazifop-p-butil

Right

1.085 1.038

0.1 > p > 0.05

Left

1.15

<0.025

1.097

<0.05

1.141

<0.05

1.135

<0.05

0.995

1.183

<0.025

1.093

<0.05

1.2

<0.01

1.093

0.1 > p > 0.05

1.059

0.5 > p > 0.1 <0.05

Appendix D. Statistical value (R) of Moore’s test for all sampling times in EX-2013. pre = sampling before treatment; post = sampling after treatment Treatment

Side

2014 pre

2014 post

2015 pre

R

p

R

p

R

p

2015 post R

p

Cultivator

Right Left

1.024

0.1 > p > 0.05

1.173 0.88

<0.025 0.5 > p > 0.1

1.227 1.067

<0.005 0.1 > p > 0.05

1.238 1.134

<0.005 <0.025

Rototiller

Right Left

1.072

0.1 > p > 0.05

1.173 1.169

<0.025 <0.025

1.233 1.244

<0.005 <0.001

1.23 1.229

<0.005 <0.005

Glyphosate

Right Left

0.796

0.5 > p > 0.1

1.244 1.199

<0.001 <0.01

1.23 1.221

<0.005 <0.005

1.188 1.217

<0.025 <0.005

Fluazifop-p-butil

Right Left

0.236

0.95 > p > 0.9

1.228 1.209

<0.005 <0.01

1.179 1.228

<0.025 <0.005

1.068 1.234

0.1 > p > 0.05 <0.005

C. San Martín et al. / Forest Ecology and Management 379 (2016) 37–49

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