Biomass production of 17 poplar clones in a short-rotation coppice culture on a waste disposal site and its relation to soil characteristics

Forest Ecology and Management 187 (2004) 295–309

Biomass production of 17 poplar clones in a short-rotation coppice culture on a waste disposal site and its relation to soil characteristics I. Laureysensa,*, J. Bogaertb, R. Blustc, R. Ceulemansa a

Department of Biology, University of Antwerp (UIA), Universiteitsplein 1, B-2610 Wilrijk, Belgium b Campus du Solbosch, Ecole Interfacultaire de Bioinge´nieurs, Universite´ Libre de Bruxelles, Avenue F.D. Roosevelt 50, B-1050 Bruxelles, Belgium c Department of Biology, University of Antwerp (RUCA), Groenenborgerlaan 171, B-2020 Antwerpen, Belgium Received 15 August 2002; received in revised form 17 June 2003; accepted 14 July 2003

Abstract This study describes the above ground biomass production of 17 poplar (Populus spp.) clones after a 4-year rotation in a shortrotation coppice culture. In addition, the link with soil characteristics was studied. In April 1996, an experimental field plantation with 10,000 cuttings ha
1 was established in Boom (province of Antwerp, Belgium) on a former waste disposal site. A randomised block design was used with three replicate plots (9 m  11:5 m). At the end of the establishment year, all plants were cut back to a height of 5 cm to create a coppice culture. At the end of the fourth year after coppicing, shoot diameters of all living and dead shoots were measured, and biomass production was estimated with an allometric power equation. A composite soil sample was taken for all plots, and pH, organic matter, water content, bulk density, content of nutrients, minerals and heavy metals were determined. Highest production was found for P. trichocarpa  P. deltoides hybrids Hazendans and Hoogvorst, P. trichocarpa clones Fritzi Pauley, Columbia River and Trichobel, and native P. nigra clone Wolterson with mean annual biomass production ranging between 8.0 and 11.4 Mg ha
1 per year. Lowest performance was observed for P. trichocarpa  P. deltoides hybrid Boelare, P. deltoides  P. trichocarpa hybrids IBW1, IBW2 and IBW3, and P. deltoides  P. nigra hybrids Gaver and Gibecq with a mean annual biomass production ranging between 2.8 and 4.7 Mg ha
1. Mean dead biomass accounted for less than 2% of total standing biomass for all clones. Some clones exhibited a uniform production across replicates, implying low susceptibility to soil heterogeneity; other clones showed a high inter-replicate variation. However, no cause for this interreplicate variation was identified. A cluster analysis enabled identification of two groups of plots with significant differences in soil characteristics and in biomass production. But a Spearman’s rank correlation test showed only a negative correlation between biomass production and plant available magnesium and potassium in the soil. A principal component analysis and multiple regression could not reveal an unambiguous impact of soil either, caused by the low variance in soil characteristics, the high genotypic variation and/or the impact of non-identified (environmental) factors. # 2003 Elsevier B.V. All rights reserved. Keywords: Populus spp.; Productivity; Soil properties; Site–growth relationships; Principal component analysis; Cluster analysis; Waste disposal site

* Corresponding author. Tel.: þ32-3-820-22-89; fax: þ32-3-820-22-71. E-mail address: [email protected] (I. Laureysens).

0378-1127/$ – see front matter # 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2003.07.005

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1. Introduction In Belgium, as in most other European countries, poplar (Populus spp.) has mainly been grown for the plywood and veneer industry (Stevens et al., 1992; Terrasson and Valadon, 1995). In America, on the other hand, this hardwood species has mainly been considered a resource for the paper and pulp industry, and has as such been grown as a short-rotation tree crop since the sixties (Dickmann and Stuart, 1983). However, in the light of the enhanced greenhouse effect and the depletion of fossil fuels, short-rotation tree cropping or short-rotation forestry (SRF) has gained more interest as a source of renewable energy, because of the possibility of carbon sequestration and the substitution of fossil fuels. Moreover, in comparison with traditional agriculture, SRF has several additional environmental benefits, such as a positive impact on biodiversity, nutrient capture, and carbon circulation in the soil– plant atmosphere system (Perttu, 1995). As an industrial product, maximal profits by achieving maximal biomass production and minimal costs are aimed at. The main costs are plant material, site establishment, and harvest. Maximum biomass production can be achieved by optimising genotype and/or cultural management (Ledin and Willebrand, 1996). Plant material is selected for high growth vigour, high biomass production and disease resistance. Cultural management generally includes site preparation, high planting density, irrigation, fertilisation, and short-rotation coppicing. Coppicing refers to the cutting of a tree at the base of its trunk to use the ability of the trees to regenerate from the cut stump, resulting in the emergence of new shoots from the stump and/or roots (Blake, 1983). Coppicing is frequently applied at the end of the establishment year to promote sprouting of many shoots per cutting, which is supposed to increase final biomass production (Dickmann and Stuart, 1983; Macpherson, 1995). In addition, the coppicing ability of the selected tree species can reduce plant material and establishment costs. Final biomass yields that can be achieved for poplar in optimal conditions (favourable climate, irrigation, fertilisation . . .) are in the order of 20–25 Mg ha
1 per year (Heilman and Stettler, 1985; Heilman et al., 1994; Scarascia-Mugnozza et al., 1997). In less intensive conditions, annual yields of 10–15 Mg ha
1 are more realistic (Cannell and Smith, 1980; Hansen, 1991).

However, there still are remarkable differences between small, experimental plots and larger field plantations, mainly due to edge effects (Zavitkovski, 1981; van Hecke et al., 1995) and soil heterogeneity (Hansen, 1991). Ceulemans et al. (1992) and Heilman et al. (1994) demonstrated the sensitivity of some poplar clones to microsite differences. To avoid site impact on productivity, two breeding strategies are possible: (1) breeding clones with a good biomass production over a wide range of soil conditions; or (2) breeding clones adapted to a specific site (e.g. exarable land or marginal land with unfavourable soil conditions). To achieve this second strategy, soil characteristics that influence biomass production should be identified. In our study, 17 clones were grown on heterogeneous soil conditions, providing the opportunity to study the relationship between biomass production and soil characteristics for a wide genotypic range. Therefore, the objectives of this paper are as follows. 1. To quantify the above ground biomass production of 17 poplar clones in a short-rotation coppice culture after a 4-year rotation. 2. To demonstrate the biomass production potential of these clones on a waste disposal site with nonoptimal soil conditions. 3. To study genotype  soil interactions. 2. Materials and methods 2.1. Experimental plantation and management regime In April 1996, a high-density experimental field plantation was established in the industrial zone of Boom near Antwerp (Belgium, 518050 N, 048220 E). The plantation is situated on an old clay pit, originating from the 1970s, which was filled with household waste and covered with a 2 m thick layer of sand, clay and rubble. The site is situated at ca. 5 m above sea level and has a temperate climate. The 4-year rotation period (1997–2000) of this study exhibited a mean temperature of 11 8C and a mean annual precipitation of 847 mm. Prior to planting, the area was levelled and cleared of large stones, plastic, metal and other debris. A rotor tiller was used in the winter of 1995–1996 for final pre-planting soil preparation. Seventeen clones,

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belonging to different species and interspecific hybrids, were tested: one P. trichocarpa T. & G.  P. balsamifera L. (T  B), six P. trichocarpa  P. deltoides Marsh. (T  D), three P. trichocarpa (T), three P. deltoides  P. nigra L. (D  N), three P. deltoides  P. trichocarpa (D  T) and one P. nigra (N) (Table 1). One plot was established with the remaining cuttings of different clones in a random, blind mixture, and will be referred to as the plot with a mixture of clones. All clones were planted as 25 cm long dormant, unrooted hardwood cuttings, after being soaked in water for 24 h. Cuttings were planted manually to a depth of 22 cm, leaving one or two buds above the soil surface. They were planted in a

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double row design with alternating inter-row distances of 0.75 and 1.50 m, and a spacing of 0.90 m within the rows, yielding an overall planting density of 10,000 cuttings ha
1. A randomised block design was used with three replicate plots (except clone Hoogvorst with six replicates, and clones IBW1 and Raspalje with two replicates) according to a protocol prescribed by the British Forestry Commission (Armstrong, 1997). Individual plot size was 9:0 m  11:5 m, containing 10 rows of 10 trees each. To avoid edge effects, each plot was considered as having a double border row, leaving 36 assessment trees in the centre of each plot (Zavitkovski, 1981). To promote an optimal establishment, the plantation was irrigated once in

Table 1 Gender, clone code number, parentage, parent code number, place of origin, latitude and longitude of the 17 clones in the experimental plantation of Boom (518050 N, 048220 E) Name

Gender

Clone code number

Parentage

Wolterson

F

1026

P. nigra

Columbia River Fritzi Pauley

M F

V.24 V.235

P. trichocarpa P. trichocarpa

Trichobel

M

S.724-101

Beaupre´

F

S.910-2

Boelare

F

S.910-8

Hazendans

F

69.039-4

Hoogvorst

F

69.038-6

Raspalje

F

S.910-10

Unal

M

S.910-1

IBW1 D  T

F

71.009-1

IBW2 D  T

F

71.009-2

IBW3 D  T

F

71.015-1

Gaver

M

S.688-22

Gibecq

M

S.688-30

Primo

M

S.682-59

Balsam Spire

F

TT32

P. trichocarpa  P. trichocarpa P. trichocarpa  P. deltoides P. trichocarpa  P. deltoides P. trichocarpa  P. deltoides P. trichocarpa  P. deltoides P. trichocarpa  P. deltoides P. trichocarpa  P. deltoides P. deltoides  P. trichocarpa P. deltoides  P. trichocarpa P. deltoides  P. trichocarpa P. deltoides  P. nigra P. deltoides  P. nigra P. deltoides  P. nigra P. trichocarpa  P. balsamifera

Parent code number

V.235  V.24 V.235  S.1-173 V.235  S.1-173 V.235  S.620-225 V.235  S.620-225 V.235  S.1-173 V.235  S.1-173 S.333-44  S3 S.333-44  S3 S.333-44  S3 S.71-3  Gibecq S.71-3  Gibecq S.9-2  Ghoy3 Hastata  Michauxii

Place of origin

Latitude

Longitude

Doesburg, The Netherlands (Ijsel River) Oregon Washington

518590 N

68060 E

458300 N 498–0 N

1228400 W 1228300 W

498–0 N 458300 N 498–0 N

1228300 W 1228400 W 1228300 W

498–0 N

1228300 W

498–0 N

1228300 W

498–0 N

1228300 W

498–0 N

1228300 W

498–0 N

1228300 W

498–0 N

1228300 W

498–0 N

1228300 W

498–0 N

1228300 W

518–0 N

048–0 E

518–0 N

048–0 E

518–0 N

048–0 E

Washington  Oregon Washington  (Iowa  Missouri) Washington  (Iowa  Missouri) Washington  Michigan Washington  Michigan Washington  (Iowa  Missouri) Washington  (Iowa  Missouri) Michigan  (Washington  Idaho) Michigan  (Washington  Idaho) Michigan  (Washington  Idaho) Illinois  Belgium (Flanders) Illinois  Belgium (Flanders) (Iowa  Ontario)  Belgium (Flanders)

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April and once in May 1996. In December 1996, all trees were cut back to a height of ca. 5 cm above soil level to create a coppice culture. Each tree produced between 2 and 10 shoots per stump. The trees that did not survive the establishment year were replaced in the spring of 1997 with new 25 cm long hardwood cuttings (40 cm for the clones with a mortality rate higher than 10%). These trees were included in the biomass production estimates. Mechanical weeding with a trimmer was done frequently during the 1996 and 1997 growing seasons. In February 1997, a 5 cm thick layer of mulch (ground woody debris) was applied to reduce weed growth. In June 1996 and June 1997, weeds in between the trees were treated with a mixture of glyphosate (at 3.2 kg ha
1) and oxadiazon (at 9.0 kg ha
1), using a spraying device with a hood-covered nozzle to minimise impact on the trees. No fertilisation or irrigation was applied after the establishment of the experiment. 2.2. Measurements and harvesting In January 2001, i.e. at the end of the fourth growing season after coppicing, shoot diameter, stool mortality

(Laureysens et al., 2003) and dry weight production were determined for all 17 clones. Shoot diameter was measured at 22 cm above soil level (Pontailler et al., 1997) for all living and dead shoots in the 52 plots, using a digital calliper (Mitutoyo, type CD-15DC, UK). For shoots thicker than 3 cm, diameter was measured in two perpendicular directions (Pontailler et al., 1997), and the mean value was used in further calculations. Basal area was calculated from the shoot diameter, and a frequency distribution of shoot basal area of all living shoots was calculated for each plot. Five leafless shoots per clone were harvested. The shoots were selected using the technique of the quantils of the total, so that the sampled shoots represented the total basal area and its variation for each clone, over the three replicates (Cerma´ k and Kucera, 1990; Cerma´ k et al., 1998). The selected shoots were cut at about 5 cm above soil level, and separated into stem and branches. To estimate the dry mass of the stem, a subsample was taken from the lower half of the stem (Telenius, 1997). Dry mass of stems and branches was determined after drying in a forced air oven at 105 8C until constant mass was reached. Allometric power equations were calculated for each clone to estimate

Table 2 Regression coefficients a and b of an allometric power equation between the diameter (mm) of a shoot and its dry mass (g) for 17 poplar clones at the end of a 4-year rotation in a short-rotation coppice culture (n ¼ 5) Parentage

Clone

a

b

Adjusted R2

S.E.

TB

Balsam Spire

0.072

2.633

0.993

0.059

TD

Beaupre´ Boelare Hazendans Hoogvorst Raspalje Unal

0.248 0.251 0.345 0.141 0.129 0.276

2.300 2.275 2.241 2.406 2.468 2.276

0.999 0.999 0.995 0.998 0.994 1

0.041 0.050 0.069 0.055 0.017 0.025

T

Columbia River Fritzi Pauley Trichobel

0.442 0.063 0.146

2.155 2.645 2.439

0.988 0.998 0.998

0.173 0.060 0.067

DN

Gaver Gibecq Primo

0.284 0.080 0.168

2.263 2.611 2.369

0.991 0.999 0.998

0.123 0.047 0.053

DT

IBW1 IBW2 IBW3

0.133 0.095 0.181

2.449 2.541 2.361

0.998 0.997 1

0.057 0.074 0.024

N

Wolterson MIX

0.130 0.182

2.487 2.373

0.994 0.991

0.122 0.122

The adjusted R2 and S.E. of the estimated are shown for every equation. T, P. trichocarpa; B, P. balsamifera; D, P. deltoides; N, P. nigra.

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dry mass of all living shoots as a function of shoot diameter using the relation: y ¼ axb

(1)

where y is the above ground shoot biomass (g), x the shoot diameter at 22 cm (mm), and a and b the regression coefficients. The regression coefficients are shown in Table 2. For the plot with a mixture of clones, all clones were pooled to calculate an allometric power equation. The resulting plot estimates were scaled up to produce dry mass per hectare per year. For 12 plots, dead shoots were harvested and weighted separately for each plot. In addition, their dry mass was estimated, using the allometric power equation of the living shoots (1). Next, a linear regression between the estimated and observed dry mass of the harvested dead shoots was used to estimate the dry mass of the remaining dead shoots: y ¼ 0:786x

(2)

where x is the plot dry mass of dead shoots estimated using Eq. (1) and y the corrected plot dry mass of dead shoots. 2.3. Soil sampling In June 2001, composite soil samples were systematically taken to a depth of 30 cm from 14 locations in each plot. These composite samples were dried at 60 8C, mixed and homogenised. Organic matter, pH (KCl), total organic nitrogen (N) and plant available phosphorus (P), calcium (Ca), magnesium (Mg), potassium (K) and sodium (Na) were determined by the Soil Service of Belgium (Leuven). Total organic N was determined with the Kjeldahl method, and organic matter by the Walkley and Black method. Plant available P, Ca, Mg, K and Na were extracted with ammonium lactate (Egner–Rhiem method); Ca and Mg in the extract were measured with atomic absorption spectophotometry, K and Na with atomic emission spectophotometry, and P with colorimetry. Silver (Ag), aluminium (Al), arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickle (Ni), lead (Pb) and zinc (Zn) were extracted from air-dry soil for 2 h with a 0.01 M calcium chloride (CaCl2) solution of 20 8C in a 1:10 extraction ratio (w/v) (Houba et al., 2000). The concentrations of Al, Cu, Fe and Mn in the centrifugate

299

were measured with inductively coupled plasma atomic emission spectrometry (ICP-AES); those of Ag, As, Cd, Co, Cr, Ni, Pb and Zn were measured with inductively coupled plasma mass spectrometry (ICP-MS) (De Wit and Blust, 1998). The concentrations of Ag and Pb were found below the detection limit (0.1 mg l
1) for more than 60% of the samples, and were therefore excluded from the results. Total soil content of Ag, Al, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were determined using a complete soil destruction procedure. After adding a mixture of sub-boiled nitric acid (Merck, Pro Analysis) and sub-boiled hydrofluoric acid (Merck, Suprapur), air-dry, lyophilized and homogenized soil samples were stepwise heated from 270 to 900 W in 30 min in a microwave oven (Milestone Ethos 900W, Analis). After cooling, 4% boric acid (Merck, Suprapur) was added and stepwise heated from 100 to 800 W in 15 min in a microwave oven (Tessier et al., 1979; Lander, 1987). Ag, Al, Co, Cr, Fe, Mn, Ni and Zn were measured with ICP-AES; As, Cd, Cu and Pb were measured with ICP-MS. In more than 30% of the soil samples, Ag content was found below the detection limit (1 mg l
1) and excluded from the analyses. In each plot, bulk density samples (100 cm3) were systematically taken at a depth of 15 cm at eight locations, and mean values were used in further calculations. Bulk density samples were dried at 60 8C. 2.4. Statistical analysis Since the soil showed heterogeneity among the plots, a hierarchical cluster analysis was performed to group the different plots based on their soil characteristics. The CaCl2 extracted metals were not included to avoid missing values. Agglomerative clustering methods were applied, which start with the individual plots and join them together with other individual plots (or groups of plots) into larger groups, until all plots are grouped in one big group (cluster). The degree of ‘‘matching’’ between each pair of plots was based on dissimilarity coefficients, i.e. Euclidean distance and squared Euclidean distance. Three clustering methods were performed: unweighted pair group (average linkage), Ward’s minimum variance, and farthest neighbour (complete linkage) (Causton, 1988; Ludwig and Reynolds, 1988). Single linkage was not performed, because of chaining (Sharma, 1996). The results of the different clustering methods were combined to

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determine the position of each individual plot. A Wilcoxon signed rank test was performed to investigate differences in soil characteristics, biomass production, and stool mortality between the soil clusters. Furthermore, a principal component analysis (PCA) was performed to synthesise soil data. PCA is a multivariate technique of partitioning a resemblance matrix, traditionally a variance–covariance or correlation matrix, into a set of orthogonal axes or components. There are as many components as there are original variables. Each component, in descending order of importance, represents a certain amount of variation in the matrix and is made up of highly correlated combinations of the original variables. The first components (denoted as the ‘principal’ components), upon which the plots will be positioned, represent the largest percentage of the total variation. By identifying plots that are the most similar (or dissimilar) based on their coordinate position, it is possible to search for the underlying factors that are responsible for the observed patterns (Causton, 1988; Ludwig and Reynolds, 1988; Randerson, 1993). Missing values for the CaCl2

extracted metals were filled in with the detection limit. Finally, a Spearman’s rank correlation and a stepwise multiple regression were used to select soil characteristics (principal components) that explained biomass production. All analyses were performed with the SAS statistical software package (SA System 6.12, SAS Institute Inc., Cary, NC). Clustering was performed with the cluster procedure, the Wilcoxon signed rank test with the npar1way procedure, the PCA with the princomp procedure, the Spearman’s correlation tests with the corr procedure and the multiple regression with the reg procedure (SAS Institute Inc., 1990). The level of significance was set to P ¼ 0:05 for all analyses.

3. Results 3.1. Above ground biomass production Clonal differences in mean annual above ground woody biomass production were considerable, with

Table 3 Annual above ground living woody biomass production and proportion (%) of dead biomass for 17 poplar clones at the end of a 4-year rotation in a short-rotation coppice culture Parentage

Clone

Biomass (Mg ha
1 per year) Mean (S.E.)

Range

Dead biomass (%) CV (%)

Mean (S.E.)

3.1–7.7

54

0.04 (0.02)

(1.4) (0.5) (2.9) (0.7) (0.1) (1.8)

3.1–7.4 3.1–4.7 5.6–14.3 8.7–12.8 5.8–5.9 4.7–10.2

41 25 44 17 1 47

0.80 0.59 1.71 0.70 0.16 0.49

TB

Balsam Spire

TD

Beaupre´ Boelare Hazendans Hoogvorst Raspalje Unal

T

Columbia River Fritzi Pauley Trichobel

8.0 (1.9) 8.3 (1.6) 8.5 (3.0)

5.7–11.7 6.0–11.4 3.4–13.7

41 34 61

0.37 (0.27) 0.30 (0.19) 0.82 (0.62)

DN

Gaver Gibecq Primo

4.7 (0.9) 2.2 (0.4) 5.2 (2.2)

2.9–5.6 1.6–3.0 2.2–9.4

32 33 72

0.10 (0.05) 0.04 (0.02) 0.69 (0.60)

DT

IBW1 IBW2 IBW3

3.7 (1.9) 2.8 (0.9) 3.6 (0.4)

1.8–5.5 1.1–3.8 2.9–4.2

72 54 17

1.60 (1.28) 0.32 (1.17) 0.32 (0.04)

N

Wolterson MIX

8.2 (0.8) 10.1

6.6–9.1 10.1

17 –

0.20 (0.07) 1.28 (
)

4.8 (1.5) 5.8 3.7 11.4 10.4 5.9 6.6

(0.30) (0.30) (0.96) (0.19) (0.02) (0.08)

For living biomass production, the mean (S.E.), the range and the coefficient of variation (CV) among replicates are presented; for dead biomass the mean (S.E.) are presented. T, P. trichocarpa; B, P. balsamifera; D, P. deltoides; N, P. nigra.

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annual means ranging from 2.2 Mg ha
1 for Gibecq to 11.4 Mg ha
1 for Hazendans (Table 3). The variation in mean above ground biomass production of clones within the P. trichocarpa  P. deltoides (T  D) parentage and within the P. deltoides  P. nigra (D  N) parentage, expressed as the coefficient of variation (CVpar), was 41 and 40%, respectively. Mean annual biomass production for the T  D hybrids ranged from 3.7 to 11.4 Mg ha
1; biomass production for the D  N hybrids ranged from 2.2 to 5.2 Mg ha
1 per year. The variation in biomass production for the P. trichocarpa (T) clones and the P. deltoides  P. trichocarpa (D  T) hybrids was low, with the CVpar being 3 and 15%, respectively. The T parentage biomass production ranged from 8.0 to 8.5 Mg ha
1 per year the D  T parentage biomass production ranged from 2.8 to 3.7 Mg ha
1 per year. Native clone Wolterson (N) achieved a relatively high biomass production of 8.2 Mg ha
1 per year; balsam poplar Balsam Spire obtained a mean biomass production of 4.8 Mg ha
1 per year. The plot with a mixture of clones yielded 10.1 Mg ha
1 per year. Besides high clonal variation in biomass production, high inter-replicate variation (quantified by the coefficient of variation) among the replicates was observed for some clones (Table 3). Primo and IBW1 had both a CV exceeding 70%. The CV of Raspalje, Wolterson, Hoogvorst and IBW3 was less than 20%. Lowest plot biomass production was obtained from IBW2 (1.1 Mg ha
1 per year), and highest plot performance was recorded for Hazendans (14.3 Mg ha
1). Dead shoots accounted for less than 4% of total standing biomass. The average proportion of dead biomass ranged from 0% for Balsam Spire (T  B) to 1.7% for Hazendans. In the plot with a mixture of clones, dead biomass accounted for 1.3% of total standing biomass (Table 3). 3.2. Soil analysis Tables 4 and 5 summarise the physico-chemical properties of the soil studied at the 52 plots. The soil was characterised by a high bulk density (heavy clay– loam soil) and high Ca-levels/high pH (Dickmann and Stuart, 1983). The nutrient and mineral content was extremely high in comparison with forest soils (Van den Berge et al., 1992), but moderate in comparison with agricultural soils for most elements. The levels of

301

Table 4 Soil characteristics of the experimental short-rotation coppice plantation of poplar in Boom

pH OM (%) N (mg/100 g) P (mg/100 g) K (mg/100 g) Mg (mg/100 g) Ca (mg/100 g) Na (mg/100 g) Water (%) bd (g/cm3)

Group 1

Group 2

7.7a (0) 1.3a (0) 122.7a (2.5) 14.5a (1.4) 36.1a (1.1) 40.2a (1.6) 1177.9a (31.5) 3.7a (0.2) 15.9a (0.4) 1.457a (0.020)

7.7a (0) 1.2b (0) 121.3a (2.0) 18.3a (2.8) 27.3b (0.9) 24.5b (1.4) 852.6b (61.7) 3.5a (0.3) 13.4b (0.3) 1.476a (0.020)

Mean (S.E.) soil pH (KCl), organic matter (OM), total nitrogen (N), and plant available phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca) and sodium (Na), water content and bulk density (bd) are presented for two clusters of replicate plots, i.e. groups 1 and 2. Values marked by different letters within the same row differ significantly at the P ¼ 0:05 level.

Mg and Ca were extremely high, even in comparison with agricultural soils (Cottenie et al., 1982). The site is slightly polluted with heavy metals (De Temmerman et al., 1982). A cluster analysis, based on soil data only, classified the plots into two main groups. This clustering was supported by a Wilcoxon signed rank test, showing one group of 29 plots (group 1) with significantly higher K, Mg and Ca levels, water content, total content of Al, Cd, Co, Cr, Fe, Mn, Pb and Zn in the soil (P < 0:001), with a significantly higher content of organic matter (OM), CaCl2 extracted Ni and Zn (P < 0:01), with a significantly higher total content of As and Cu in the soil (P < 0:05), and with a significantly lower CaCl2 extracted Mn (P < 0:01). In addition, a significantly lower biomass production was observed in this group (P < 0:05). Since there was no significant difference in stool mortality, the difference in biomass production between the two groups was caused by differences in biomass increment among replicates and not by the grouping of clones with high stool mortality in one cluster. Good performing clones Hazendans, Hoogvorst, Columbia River, Fritzi Pauley and Trichobel were present in 48% of the plots in the group with the higher biomass production (group 2), and in 24% of the plots in the other group. Bad performing clones Boelare, IBW1, IBW2, IBW3, Gaver, Gibecq and Primo were present in 30% of the plots in group 2, and in 45% of the plots in group 1.

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Table 5 Soil characteristics of the experimental short-rotation coppice plantation of poplar in Boom Group 1

Al As Cd Co Cr Cu Fe Mn Ni Pb Zn

Group 2

CaCl2 extracted

Total

0.917a 0.008a 0.004a 0.002a 0.011a 0.075a 0.909a 0.180a 0.080a – 0.054a

21.8c 6.5c 0.8c 9.9c 56.4c 27.1c 17.3c 157.9c 29.8c 52.3c 160.7c

(0.085) (0.001) (0.001) (0) (0.002) (0.004) (0.059) (0.015) (0.012) (0.006)

(0.8) (0.4) (0.1) (0.5) (1.2) (0.9) (0.5) (2.9) (1.0) (3.0) (12.7)

CaCl2 extracted

Total

0.761a (0.082) 0.007a (0.001) 0.006a (0.002) 0.002a (0) 0.008a (0.001) 0.080a (0.008) 0.852a (0.048) 0.343b (0.055) 0.034b (0.006) – 0.031b (0.005)

16.3d (0.7) 4.8d (0.4) 0.4d (0) 6.2d (0.5) 45.9d (2.0) 20.4d (2.1) 14.3d (0.4) 142.9d (4.1) 27.9c (0.9) 39.3d (6.6) 103.0d (5.8)

Mean (S.E.) calcium chloride (CaCl2) extracted and total soil aluminium (Al), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickle (Ni), lead (Pb) and zinc (Zn) are presented for two clusters of replicate plots, i.e. groups 1 and 2. All concentrations are expressed as mg/g, except for total Al and Fe content (mg/g). Values followed by different letters within the same row differ significantly at the P ¼ 0:05 level.

3.3. Biomass production and soil characteristics A Spearman’s rank correlation test showed a significant negative correlation between biomass production and plant available Mg (r ¼
0:3924, P < 0:005), and between biomass production and plant available K (r ¼
0:4160, P < 0:005). No other soil characteristics significantly influenced biomass production (data not shown). A multiple regression was necessary to determine the variation explained by Mg, K and the other soil characteristics. Since most of these characteristics were significantly intercorrelated, it would not be appropriate to perform a multiple regression on these redundant variables (multicollinearity). Therefore, a principal component analysis was performed to synthesise the 19 soil characteristics into a few principal components, composed as linear combinations of these characteristics, but being completely uncorrelated (orthogonal). First, a PCA was performed on the covariance matrix of all soil characteristics (excluding metals determined by soil destruction) and biomass production. Since Ca was numerically the largest in the unstandardised data set, it dominated the analysis, due to its large variance (80,177). The first component accounted for more than 99% of the total variation in the data set (total variance ¼ 80,643) and was almost completely determined by Ca (loading (l) of almost 1). Therefore, all other components explained only a

marginal proportion of the total variation. The second component (0.3% of total variation) was dominated by biomass (l ¼ 0:8893), but showed also a relatively ‘high’ loading of Mg (l ¼ 0:3625) and K (l ¼ 0:2329), supporting the Spearman’s rank correlation. The reduction of the 20 variables into one component, i.e. Ca, was rather meaningless, because solely based on variance. The nature and sizes of the correlation between variables are also important (Causton, 1988). Table 6 Results of the principal component analysis applied on the correlation matrix of 14 soil characteristics

Organic matter Phosphorus Magnesium Calcium Natrium Water Aluminium Cadmium Cobalt Chromium Copper Iron Manganese Zinc

PC1

PC2

PC3

PC4

0.2856
0.0667 0.3736 0.1832 0.0009 0.3183 0.2462 0.2508 0.3860 0.3726
0.1623 0.2373
0.0152 0.3811


0.2873 0.2822
0.1029 0.1156 0.4122* 0.0101
0.2524 0.3927 0.1574 0.1991 0.4078*
0.0659 0.4289* 0.0830

0.0563 0.0596
0.3493
0.4988*
0.4412*
0.2403 0.2258 0.2633 0.1282 0.0256 0.1693 0.3942 0.1895 0.1124


0.1162 0.0450 0.0865 0.4057* 0.1668
0.1314 0.4210* 0.0667
0.0441 0.2942 0.3944 0.4747*
0.2777
0.1977

The eigenvectors of the first four principal components (PC) are presented. Values 0.4 have been flagged by an asterisks.

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Therefore, all variables were standardised by performing a PCA on the correlation matrix of the variables. Variables with a CV below 10% (pH, N and bulk density) and variables that were strongly correlated with other variables (r > 0:8) (K and Ni) were omitted from the analysis to limit the number of principal components. The PCA with the 14 remaining variables showed that only the first four principal components explained individually more than 10% of total variation, and together explained only 66% of total variation. The eigenvector table (Table 6) illustrates that the principal components showed no ‘‘simple structure’’, i.e. a component should have high loadings for some variables, and near-zero loadings for the others (Hatcher and Stepanski, 1997). There were no variables with a particularly high loading (Hatcher and

303

Stepanski, 1997) on the first component (27%); Co, Mg, Cr and Zn were dominant. The second component (14%) was dominated by Mn, Na and Cu, the third component (14%) was dominated by Ca and Na, and the fourth component was dominated by Fe, Al and Ca. Na showed to be a relatively important element, since it had a relatively high loading on the second and the third component, while traditionally important soil characteristics as pH, organic matter, N and P were not present on the principal components. After standardisation, Ca appeared on the third and the fourth component. When multiple regression was used to link the 14 components to biomass production, only the first and the second component showed to be significant (P < 0:05). In Fig. 1, the 52 plots are shown in function of the first two principal components.

Fig. 1. Principal component ordination of 52 plots using principal component analysis on 14 soil characteristics and biomass production. The X- and Y-axes represent the first and the second principal component, respectively. Soil characteristics and biomass production are presented as vectors.

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Each soil characteristic is represented as a vector, and the angles between them correspond to the degree of correlation. Orthogonal variables (and components) are completely uncorrelated; variables with an angle of 1808 are entirely, but negatively, correlated. The plots showed no clear pattern, and the principal components were strongly correlated with multiple soil characteristics. 3.4. Clone–site interaction In Fig. 2, the relative inter-replicate difference in biomass production is shown as a function of the interreplicate dissimilarity in soil characteristics. Per clone, the difference in biomass production between each

pair of replicates was expressed as a percentage of the highest biomass production of the two replicates. Per clone, the relative absolute distance (with a range between 0 and 2) between each pair of replicates, based on 14 soil characteristics, was calculated to measure the inter-replicate variation in soil characteristics (Ludwig and Reynolds, 1988). The soil characteristics were first rescaled—the smallest value was expressed as a percentage of the highest value per pair of replicates—to prevent Ca from dominating the dissimilarity measures. Raspalje (T  D) and IBW1 (D  T) are not shown, since they had only two replicates. Interreplicate biomass variation ranged between 0 and 83%; inter-replicate soil dissimilarity ranged between 13 and 28%. The latter result suggests that the soil

Fig. 2. Relative inter-replicate difference in biomass production (%) in function of inter-replicate dissimilarity in soil characteristics per clone. Inter-replicate variation in soil characteristics is expressed as a relative absolute distance [0, 2]. Per clone, maximum inter-replicate variation in biomass production is indicated with a circle, maximum inter-replicate variation is indicated with a cross. BA, Balsam Spire; BE, Beaupre´ ; BO, Boelare; HA, Hazendans; HO, Hoogvorst; RA, Raspalje; UN, Unal; CO, Columbia River; FR, Fritzi Pauley; TR, Trichobel; GA, Gaver; GI, Gibecq; PR, Primo; I1, IBW1; I2, IBW2; I3, IBW3; WO, Wolterson.

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heterogeneity was rather low, since all replicates were at least 72% similar. For 1/3 of the presented clones, maximum variation in biomass was accompanied by maximum variation in soil characteristics, leaving 2/3 of the clones with a biomass production independent of the measured soil characteristics. Ten clones showed a maximum biomass difference of at least 50%; only four clones showed a maximum soil dissimilarity of more than 25%. Primo (D  N) and IBW2 (D  T) showed the highest biomass variation, but the soil variation of IBW2 (28%) was relatively high in comparison with Primo (21%). This demonstrates the difference in susceptibility to soil heterogeneity for both clones. Hoogvorst (T  D) and Wolterson (N) both had a low biomass variation (<30%), but their soil variation ranged between 13 and 25% (almost the entire range of soil variation). In addition, the maximum biomass variation was not accompanied by maximum soil variation, demonstrating the ‘‘stability’’ of the biomass production of clones Hoogvorst and Wolterson, and their insusceptibility to soil heterogeneity. Therefore, the X-axis can be interpreted as a measure of soil heterogeneity, and the Y-axis as a measure of ‘‘susceptibility to soil heterogeneity’’ or ‘‘stability’’ of biomass production.

4. Discussion In optimal environmental conditions (favourable climate, fertilisation, irrigation), annual yields of P. trichocarpa  P. deltoides (T  D) clones of 27.8– 35.2 Mg ha
1 and P. trichocarpa (T) clones of 16.3–27.5 Mg ha
1 after 4 years have been reported (Heilman and Stettler, 1985; Heilman and Xie, 1993; Heilman et al., 1994; Scarascia-Mugnozza et al., 1997). Other studies report annual 4-year rotation yields of 1.2–13.6 Mg ha
1 for various poplar species, depending on clone, soil, climate and management regime (Strong and Hansen, 1993; Armstrong and Johns, 1997; Beale and Heywood, 1997). The latter yields correspond more to the ‘‘working maximum’’ yields as suggested by Cannell and Smith (1980). The annual biomass production of the best performers in this study, i.e. the T  D clones Hoogvorst and Hazendans with a maximum of 14.3 and 12.8 Mg ha
1, respectively, and T clones Columbia River, Fritzi Pauley and Trichobel with a maximum between 11.4

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and 13.7 Mg ha
1, can be considered high, regarding the non-optimal soil conditions. The nutrient levels were sufficient, but the bulk density and pH/Ca-levels were too high for optimal poplar growth (Dickmann and Stuart, 1983). Dickmann and Stuart (1983) stated that poplars could grow almost everywhere, but perform up to their full potential only on the best sites. Our results support the overall performance growth, e.g. the possibility of growing poplar on marginal land. Bungart and Huttl (2001) already reported 4-year-rotation yields of 2.3–9.2 Mg ha
1 per year for poplar on marginal mining sites with low fertility. However, not all clones performed ‘well’ at our site. Especially the P. deltoides  P. trichocarpa (D  T) and P. deltoides  P. nigra (D  N) clones experienced high mortality during the establishment year, presumably due to bad rooting capacity in the heavy clay– loam soil at our site (Laureysens et al., 2003). Besides significant clonal differences in performance, relatively high inter-replicate variation was found for some of the clones. This replicate variation was also reported in other studies, even on ‘‘homogeneous’’ sites, illustrating the sensitivity of particular poplar clones to microsite differences (Ceulemans et al., 1992; Heilman et al., 1994). In our study, however, the soil was heterogeneous, providing the opportunity to study the relationship between site/soil characteristics and biomass production. A multiple regression and principal component analysis failed, however, to relate biomass production unambiguously to one or more soil characteristics. The main objective of a PCA is to reduce a large dataset into a limited number of components that together contain most of the total variation in the dataset. These components are preferably made up of a few soil characteristics that share the same conceptual meaning, e.g. a component composed of variables all representing soil texture or soil fertility. In this study, we were not able to reduce the number of principal components. Each component explained only a small proportion of the total variation across the 52 plots. Furthermore, the soil characteristics were present on several components and were not meaningfully grouped within a single component, making it hard to interpret the components. When these components were related to biomass production in a multiple regression analysis, only the first two components had a significant impact on biomass production. The first component was determined by almost all soil

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characteristics; the second component was dominated by sodium, cadmium, copper and manganese. These results suggest that no soil characteristic(s) responsible for the variation in biomass production between and within clones was (were) identified. There are several explanations possible for this failure of linking biomass production to soil characteristics: (1) the low variance for certain soil characteristics; (2) clonal differences; and (3) the impact of unidentified factors on biomass production. Some potentially important soil characteristics, e.g. total nitrogen and bulk density, showed a very low variation across replicates. A second problem was the considerable genetic variation in this study. Clones can differ significantly in their nutrient and water-use efficiency, and in their susceptibility to, e.g. drought, water logging and rust (Steenackers and Van Slycken, 1982; Dickmann and Stuart, 1983; Nelson et al., 1987; Blake and Tschaplinski, 1992; Ericssonn et al., 1992). In this study, D  N and D  T clones suffered from high cutting mortality, presumably because of the combination of poor rooting capacity with a heavy clay–loam soil (Laureysens et al., 2003). Other clones were apparently negligibly affected by the high bulk density, making it hard to detect a significant relation between biomass production and bulk density when all clones were pooled. But even within the D  N and D  T clones, no significant correlation between biomass production and bulk density was found, probably because of the low variation in bulk density across the plots. An individual correlation test between biomass and soil characteristics per clone was not possible because of the limited number of replicates. A third possibility is that we did not include certain soil characteristics that could have a significant impact on biomass production in the analyses, although we included all soil characteristics known to influence poplar biomass production. For intensive coppice stands of poplar, Ranger and Nys (1996) found a negative correlation between magnesium and biomass production, explained by the limited availability of this element in the calcareous soils of their study, or by a dilution effect as biomass production increased. For willow, Tahvanainen (1996) found a clear positive correlation of nitrogen content in the soil with biomass production. Stool mortality was positively correlated with manganese and negatively with potassium and organic matter. They concluded that willow needs

sufficient nitrogen and potassium in the soil for a good biomass production. In wet and low oxygenic soils, a high manganese content may result in toxicity effects, because of oxidation and reduction reactions in the soil. Hoffmann (1985) showed the long-lasting effect of permanent or stagnant water to be the main factor influencing growth of poplar, depending on clone. In other studies, soil bases calcium and magnesium, and the nutrients nitrogen and phosphorus showed to be the most limiting for plant growth (Gilmore, 1976; Bowersox and Ward, 1977). Ericssonn et al. (1992) showed nitrogen, phosphorus, sulphur, potassium, magnesium, iron and manganese to act as the most important growth limiting factors in short-rotation forests. In our study, only magnesium and potassium seemed to have a small influence on biomass production. In addition, Ceulemans et al. (1992) found significant inter-replicate variation on a fertile soil, thus the above mentioned elements were not likely to be responsible for the latter variation in biomass variation. Therefore, we conclude that other unidentified soil characteristics or combinations of soil characteristics might determine biomass production. This implies that it will be difficult or even impossible to find ‘the right clone for the right site’, since we still can not define all factors that influence biomass production. Our study clearly distinguishes two types of clones, i.e. ‘‘stable’’ and ‘‘unstable’’ clones. ‘‘Stable’’ clones Hoogvorst (T  D) and Wolterson (N) had a uniform performance across replicates, even when considerable variation in soil characteristics was evident across these replicates. Such clones should be the aim of the first breeding strategy, i.e. clones with a good overall performance, guaranteeing stable and predictable biomass production. ‘‘Unstable’’ clone Hazendans (T  D) achieved the experiment’s highest biomass production for two replicates, but performed very badly in a third replicate. This clone might perform extremely well under optimal soil conditions (e.g. exarable land), but shows a high susceptibility to soil heterogeneity. A second breeding strategy, i.e. selecting clones matched to specific sites, aims to select these clones with an extremely high biomass production on fertile land, or to select clones with the capacity of growing in poor environmental conditions (e.g. marginal or polluted land). However, to obtain good results with this second breeding strategy, we need to know all environmental factors that limit plant

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growth for every clone individually. Since biomass production is the product of soil conditions, light, temperature, water, disease resistance and cutting quality, a wide range of experimental settings should be set up for every clone. Even if we could identify per clone which soil conditions and climate are limiting for its growth, we might not be able to guarantee a high biomass production, since we can not control all environmental factors. Steenackers and Van Slycken (1979) showed the significant impact of temperature on clonal differences in growth. Growth increase was strongly correlated to a high weekly average temperature, but the response to these high temperatures was not the same for all clones. Good performing clones should not only have the capacity to profit maximally from favourable temperatures, but also have relatively high growth intensity in colder periods. The selection of highly specific clones will result in extremely high biomass production in optimal ‘selected’ environmental conditions, but will increase the susceptibility to unidentified and uncontrollable environmental factors, resulting in unpredictable biomass production. Therefore we suggest the use of an extensive mixture of clones with a good overall performance, as polyclonal mixtures have also been proposed as a non-chemical strategy for reducing pests and disease attacks (DeBell and Harrington, 1993; McCracken and Dawson, 1997; Hunter and Peacock, 2001).

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soil characteristics showed to have a significant and unambiguous impact on biomass production. Since all (environmental) factors limiting growth potential are not yet identified, we suggest the use of plantations with an extensive mixture of clones with a good overall performance.

Acknowledgements This study is being supported by a research contract with the Province of Antwerp. All plant materials were kindly provided by the Institute for Forestry and Game Management (Geraardsbergen) and by the Forest Research, Forestry Commission (UK). The project has been carried out in close co-operation with Eta-com B., supplying the grounds and part of the infrastructure, and with the logistic support of the city council of Boom. We gratefully acknowledge B. Assissi, T. and P. Laureysens for help with the harvest and the soil sampling, as well as M. Selens and D. Qadah for help with the soil extraction and destruction, and the heavy metal analysis. We are also grateful to P. Van Hecke for help with the statistical analysis, and to two anonymous referees for their valuable comments and suggestions on an earlier version of the manuscript.

References 5. Conclusion Certain poplar clones seem to grow rather well on a heavy clay–loam soil with high pH, even when slightly polluted with heavy metals. P. trichocarpa  P. deltoides clones Hazendans and Hoogvorst, P. trichocarpa clones Columbia River, Fritzi Pauley and Trichobel, and native P. nigra clone Wolterson achieved a mean annual above ground woody biomass production between 8.0 and 11.4 Mg ha
1. Two types of clones were distinguished, i.e. ‘‘stable’’ clones with a uniform biomass production across replicates and ‘‘unstable’’ clones with a biomass production susceptible to soil heterogeneity. Multivariate analyses (cluster analysis, principal component analysis and multiple regression) were performed to identify soil characteristics responsible for the inter-replicate variation in biomass production. None of the 19 studied

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