*Article* **Physiological Performance and Biomass Growth of Different Black Locust Origins Growing on a Post-Mining Reclamation Site in Eastern Germany**

**Christian A. Lange 1,\*, Dirk Knoche <sup>1</sup> , Robin Hanschke <sup>1</sup> , Sonja Löffler <sup>2</sup> and Volker Schneck <sup>3</sup>**


**Abstract:** Black Locust/Robinia can play an important role in land reclamation due to its pronounced nitrogen fixation capability, fast initial growth and relative high drought tolerance. Hence, we set up a trial to test 12 Black Locust clones and three provenances growing on sandy overburden material within the open cast lignite mine *Welzow-Süd* (South Brandenburg) in March 2014. Since then, biomass growth of the Black Locust trees was examined and physiological performance was studied on several occasions using chlorophyll a fluorescence and Dualex® measuring technique. Plant physiological measurements revealed differences in photosynthetic vitality (PIABS), although the PIABS values followed a similar pattern and sequences across the plot. While the genotypes *Fra3* and *Roy* show the highest photosynthetic vitality, the clones *Rog* and *Rob* display the lowest PIABS mean values. Chlorophyll and phenol content as well as the nutrition supply of the test trees vary depending on their origin and site conditions. The annual biomass growth rate corresponds to photosynthetic vitality and both depend on weather conditions during the growing season. After six years, the growing biomass amounts to 14.7 Mg d.m. ha−<sup>1</sup> for clone *Rob* and 44.8 Mg d.m. ha−<sup>1</sup> for clone *Fra3*, i.e., 2.5 to 7.5 Mg d.m. ha−<sup>1</sup> year−<sup>1</sup> . Our data demonstrate a good correlation between biophysical parameters and biomass growth. We, thus, infer that physiological measuring methods can be combined to strengthen predictions regarding the physiological performance of Black Locust origins.

**Keywords:** *Robinia pseudoacacia* L.; photosynthetic vitality; chlorophyll and phenol content; nutrition supply; dry matter yield; land reclamation

#### **1. Introduction**

Black Locust (*Robinia pseudoacacia* L.)—once introduced to central Europe on the strength of its remarkable flowering—has established itself as a common tree species. Since the time of its introduction, the prolific Robinia has demonstrated its remarkable ability to spread [1,2], though it often remains neglected by silviculture. Current estimates suggest that Black Locust covers 34,000 hectares of German forest cultivation area, with over two thirds of this area situated in the north-east German lowlands [3]. Black Locust is considered a fast-growing tree species and stands out due to its superior wood characteristics which make it suitable for wide-ranging and high-quality usage. Furthermore, in times of climate change, Robinia is gaining in importance by virtue of its outstanding tolerance to drought and heat [4] as well as its good adaptability to climate change. Hence, Black Locust is predestined to contribute to the sustainable productivity of forests, even during critical weather situations such as drought and frost. In addition, Robinia plays an important role in the rehabilitation/reclamation of nutrient-poor lignite mining and

**Citation:** Lange, C.A.; Knoche, D.; Hanschke, R.; Löffler, S.; Schneck, V. Physiological Performance and Biomass Growth of Different Black Locust Origins Growing on a Post-Mining Reclamation Site in Eastern Germany. *Forests* **2022**, *13*, 315. https://doi.org/10.3390/ f13020315

Academic Editor: Dirk Landgraf

Received: 13 January 2022 Accepted: 11 February 2022 Published: 15 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

abandoned land on account of its ability to fix atmospheric nitrogen. Black Locust stands currently cover 4.9% (i.e., 102 hectares) of the total reclamation area managed by the lignite mining company LEAG.

The declared aim of breeding Black Locust genotypes is to provide vital plus trees prior to material utilisation. However, to date, Robinia breeding initiatives have predominantly focused on external characteristics such as the stem form and improving the biomass growth performance. Nonetheless, criteria such as plants' physiological performance and tolerance to abiotic stressors are becoming increasingly decisive for the successful establishment of forest stands as well as for short rotation coppices in agriculture. Despite this, surprisingly little is known about the physiological performance of the Black Locust clones and the Black Locust provenances available on the market, and the research on cultivating Robinia genotypes which meet both criteria—high biomass growth as well as promising stress tolerance—is still in its infancy [5]. There is evidence showing significant genetic variation with regard to drought tolerance [6–9].

Within the framework of the FastWOOD subprojects 6 and 7, we investigated biomass growth as well as individual physiological performance of different Black Locust origins (i.e., clones and provenances). Our goal was to determine specific reaction patterns of promising origins to abiotic stressors. We used so-called biomarkers as major indicators to identify and classify Robinia clones and provenances with regard to their climate adaptability [8,10]. We relied on an experimental blend consisting of plot experiments [10] as well as field trials [8] to test selected Black Locust origins. In this context, we applied physiological marker techniques to reveal the individual adaptive capacity of the Robinia clones and provenances to drought, nutrient deficiencies and late frost.

Given that Robinia clones and provenances differ in their physiological performance and stress tolerance, insights into a genotype's suitability for cultivation in short coppice rotations and on forest land are valuable. The earlier a measurement procedure can deliver reliable results predicting the physiological performance, the faster research into relevant tree breeding can progress. Hence, our hypothesis is that there is a relationship between biophysical parameters and biomass growth which can be used to deliver physiological performance predictions of different Robinia origins at an early stage.

#### **2. Materials and Methods**

#### *2.1. Experimental Site*

Our test plot (51◦3604900 N, 14◦1400500 E) is situated in the northern part of *Welzow-Süd* open-cast lignite mining site, in the south of the Federal State of Brandenburg (Germany), Figure 1. Our experimental plot covers 0.4 hectares in total. We also run a weather station approximately 5 km east of our experimental plot which records standard meteorological data (i.e., precipitation, temperature, etc.).

**Figure 1.** Map and aerial image showing our test plot in the north of lignite surface mine *Welzow-Süd*, photo taken on 12 April 2018.

#### *2.2. Soil Conditions*

The test area, situated in the northern part of the lignite mining pit *Welzow-Süd*, has been undergoing rehabilitation since 2012. Investigations on local dump soil substrate by Hanschke [11] revealed the prevalence of two different dumped materials:


Note that the evaluation of the post-mining soil substrate by Hanschke [11] was carried out prior to lime application on our test site. Both dump substrates units are characterised by low pH value, low nutrition level and low water holding capacity [11], Table 1.

**Table 1.** Soil chemical composition of two identified mining substrates [11] related to the Robinia clonal test plot.


Ctot: total carbon content; Ntot: total nitrogen content; Stot: total sulphur content; C/N: C/N ratio, total carbon content divided by total nitrogen content.

#### *2.3. Test Design*

The trial was designed to investigate 12 selected clones of Black Locust along with 3 provenances (Table 2). The original plus trees of the clones (ortets) were selected on the basis of their outstanding stem form and growth performance in 1990 [12]. The trial was established with one-year-old plants from tissue culture (clones) and provenances in spring 2014. All plants were cut after planting 5–7 cm above ground. Potential competitive shoots were removed during the first growing season. A complete randomised subplot design was used with 12 replications and 9 trees per plot. The spacing was 1.5 m by 1.5 m.

**Table 2.** Experimental set up for testing different Black Locust clones and provenances. The background colour is highlighting the 10 test units actually investigated in the study (10 of 15 in total).


However, in order to reduce costs and delay we only examine 7 clones and the 3 provenances in this study (grey shaded in Table 2). Five of the investigated clones have their origins in the Federal State of Brandenburg (*Rob*, *Rog*, *Romy*, *Row* and *Roy*, respectively) and two in the Federal State of Hesse (*Fra3*, *Lan*).


According to the principle described in [8], we picked five out of nine test plants per patch for biophysical measurement (Figure 2).

**Figure 2.** Test design, comprising 4 (B, F, G, L) out of a total of 12 test subplots. Per test unit, 5 out of 9 Robinia test plants were used in our investigations (see [8]).

#### *2.4. Chlorophyll a Fluorescence Measurements and JIP-Test*

A portable Plant Efficiency Analyser (Pocket PEA, Hansatech, King's Lynn, UK) was used to non-invasively measure chlorophyll fluorescence on the leaves' surface of Black Locust test plants in order to assess their physiological status (i.e., vitality). Lange et al. [13] successfully employed this methodology to determine plant physiological effects of different soil ameliorants applied to young sessile oaks (*Quercus petraea* (Matt.) Liebl.) growing on a uranium tailings dump in *Schlema* (Ore Mountains, Germany). Before taking the actual measurement, leaves of the test plants were dark-adapted for at least one hour before the chlorophyll a fluorescence measurements were performed. The fast phase fluorescence transients were quantified by means of the JIP-test [14,15] and using the Biolyzer software [15]. The JIP-test, developed and tested under both laboratory and practical conditions, is well accepted amongst experts to detect, describe and quantify the dynamic capacities of the photosynthetic sample. It has been widely and successfully used for the investigation of photosystem II behaviour in various photosynthetic organisms under different stress conditions and enables the study of synergistic and antagonistic effects of different co-stressors [10,13,16–18].

In order to quantify and compare the individual physiological performance of the Robinia test trees both under normal and stress conditions we chose to use the Performance Index (PIABS) as the JIP-test parameter. PIABS is a multiparametric expression which incorporates the independent parameters contributing to photosynthesis, namely absorption (RC/ABS), the quantum efficiency of trapping (ϕPo/(1 − ϕPo)) and efficiency of conversion of trapped excitation energy to electron transport (ψo/(1 − ψo)), see Strasser et al. [19]. The Performance index (PIABS) is presented below on absorption basis, Equation (1) [19]:

$$\text{PI}\_{\text{ABS}} = \frac{\gamma \text{RC}}{1 - \gamma \text{RC}} \cdot \frac{\text{\textdegree Po}}{1 - \text{\textdegree Po}} \cdot \frac{\text{\textdegree \textbullet}}{1 - \text{\textdegree \textbullet}} = \frac{\text{RC}}{\text{ABS}} \cdot \frac{\text{\textdegree Po}}{1 - \text{\textdegree Po}} \cdot \frac{\text{\textdegree \textbullet}}{1 - \text{\textdegree \textbullet}} \tag{1}$$

where γRC is the fraction of reaction center chlorophylls relative to the total chlorophyll: γRC = ChlRC/Chltotal. Since Chltot = Chlantenna + ChlRC, we get: γRC/(1 − γRC ) = ChlRC/Chlantenna = RC/ABS.

#### *2.5. Dualex® Scientific+TM for Determining Chlorophyll and Flavonols in Leaves*

According to the manufacturer's specification, the Dualex® Scientific+TM system was developed on the basis of research conducted by the CNRS (Centre national de la recherche scientifique, Paris, France) and Force-A (University of Paris-Sud, Orsay, France). Using a photometric measurement principle, the Dualex® is able to perform a non-destructive and rapid measurement of the chlorophyll content in leaves as well as flavonol and anthocyanin contents in the epidermis with sufficient accuracy and in real time [20]. There is some evidence that polyphenols, especially antioxidative flavonols such as anthocyanin, are reliable indicators for plants' vitality. Under abiotic stress and/or as a result of nutrition deficiency, biosynthesis of chlorophyll decreases whereas the production of secondary plant substances such as flavonols increases [21]. The Nitrogen Balance Index (NBI), calculated from the ratio of chlorophyll content and flavonol concentration, indicates the nitrogen supply status of the tested plant [22,23]. Ultimately, an efficient and field-suited optical sensor is available to screen large datasets of leaf samples in a relatively short period of time.

#### *2.6. Biophysical Measurements*

Throughout the growing seasons in 2015 and 2016, we carried out in vivo chlorophyll a fluorescence and Dualex® measurements (usually five measurements per tree) on five out of nine test plants from seven different Robinia clones and three provenances located in the subplots B, F, G and L (Figure 2). Altogether, physiological test results were collected for 200 Black Locust trees over seven measuring dates.

#### *2.7. Plants' Leaf Analysis*

After sampling at the beginning of August 2015 and 2016, the leaf tissue was analysed to determine the (N, P, K, Ca, Mg and S) content. Leaves were dried at 80 ◦C for 48 h and then finely ground using a vibrating sample mill. The total nitrogen content was derived by combustion according to the Dumas principle using an element analyser. The other major elements (K, Ca, Mg, P, S) were measured with an inductively coupled plasma atomic emission spectrometer (ICP-AES). Prior to ICP-AES measurement, dried leaf powder was digested in a microwave pressure digestion system with HNO3. Determination methods are listed in Table 3.



#### *2.8. Biomass Growth Measurements*

Towards the end of the particular growing season, we recorded plant height and diameter at breast height (i.e., 1.3 m above ground level) for 200 test specimens. Subsequently, we calculated the mean annual height and diameter growth of the clones and provenances. In addition, we estimated the individual biomass growth rate (aboveground annual woody biomass) using an allometric equation [24–26]. The biometric investigations focusing on 265 Black Locust test trees in the joint project FastWOOD resulted in the allometric equation (Equation (2)) which is tailored toward young Robinia forests on reclamation sites [27,28].

$$\text{BM}\_{\text{dry matter}}\,\left[\text{kg}\right] = 0.00059909 \cdot \text{d}\_{1.3} \,\,^{2.356}\tag{2}$$

#### *2.9. Quality Assessment*

Parallel to growth measurements, the stem form and crown formation were also evaluated. The number of trees with multiple stems, forks and/or ramicorns was assessed as well as trees with stem and bark injuries, and branch and crown fractures.

#### **3. Results and Discussions**

#### *3.1. Weather Conditions from 2014–2019*

Weather recordings from the time period 2014–2019 presented in Table 4 help to characterise the growth conditions for our Robinia test plants.

**Table 4.** Monthly sum of rainfall [mm] and average air temperature [◦C] throughout the years 2014–2019. Data recorded at FIB weather station *Welzow*, Brandenburg, Germany. The background colour is a stylistic instruments.


As shown in Table 4, the total precipitation was considerably lower in 2018 and 2019 than in the years from 2014 to 2017, especially during the vegetation period (Apr–Oct). Indeed, the average precipitation during the vegetation period in 2018 and 2019 was, respectively, 219 mm (−53.5%) and 200 mm (−49.0%) lower than the 2014–2017 average. Compared to the average air temperature in the years 2014–2017, the mean air temperature also increased by 2◦ K (growth year 2018) and by 1◦ K (2019). Therefore, in 2018, the young Black Locust trees were faced with the most extreme conditions since their planting in March 2014.

#### *3.2. Chlorophyll a Fluorescence and Photosynthetic Vitality*

Results determined from the measurements taken during growth season 2015 and 2016 are presented in the following section. PIABS, a significant JIP-test parameter which enables the quantification of plants' vitality status at a specific time point, will be addressed in greater detail. PIABS average values vary across the Black Locust test units, but follow similar sequences and patterns over time (Figures 3 and 4). After starting on a relative low

level in June of both of the test years, average PIABS values of all the test trees were found to have increased on subsequent occasions.

**Figure 3.** PIABS mean values and standard deviation of Black Locust clones and provenances during the growth season 2015 at clonal test plot *Welzow*.

**Figure 4.** PIABS mean values and standard deviation of Black Locust clones and provenances during the growth season 2016 at clonal test plot *Welzow*.

As shown, individual PIABS mean values of each particular Robinia clone and provenance remain more or less the same in their relationship to each other, independent of measuring date and time. The highest average PIABS values were observed in the test clones *Fra3* and *Roy*. Test trees of *Rog* and *Rob* clones show the lowest PIABS mean values, indicating a weak photosynthetic vitality and low physiological performance.

#### *3.3. Chlorophyll and Phenol Content*

In addition to the chlorophyll a fluorescence described above, we also performed Dualex® measurements on exactly the same Robinia leaves. The results of these measurements enable us to check for correlations between parameters originating from different measuring methods. Table 5 contains average values of chlorophyll, flavonol and anthocyanin as well as the above-mentioned NBI collected over 7 measuring dates in 2015 and 2016.

**Table 5.** Mean values of Dualex® parameter determined on Robinia leaves of different clones and provenances at clonal test plot *Welzow* in 2015 and 2016.


When comparing the data collected in 2015 and in 2016, it becomes apparent that both the mean chlorophyll content as well as average flavonol and anthocyanin concentrations were higher in 2015, although the average NBI was found to be lower in 2015. The increase in the plant's own secondary substances in the leaves during growing season 2015 and the decrease in NBI can likely be attributed to the weather conditions (relatively dry and warm August 2015, Table 4).

#### *3.4. Plant Nutrition*

As mentioned above, the NBI indicates the nitrogen supply status of the test plant. The nutrient supply of test trees belonging to Robinia clones and provenances *Row*, *Roy*, *Fra3*, *Lan*, *Kis* and *Cuc* is therefore discussed in the next section (Table 6).


**Table 6.** Results of the determination of plant nutrients in the leaf tissue of Robinia test trees taken on 10 August 2015 and 10 August 2016 at clonal test plot *Welzow*. The font colour and italics are stylistic instruments.

The data presented in Table 6 indicate that with a few exceptions, the uptake of nitrogen, phosphor, potassium and sulphur in Robinia leaves was slightly higher during the growing season in 2016 than in 2015. In contrast, calcium and magnesium contents were found to have decreased from 2015 to 2016, regardless of plant origin. Test trees of *Kis* and *Roy* displayed the highest N and P concentrations (valid for 2015), whereas *Fra3* and *Row* exhibited the highest Ca and Mg contents in general.

Bearing in mind that the growing season in 2015 was warmer and drier than in 2016 (Table 4), it is highly likely that drought, monitored in late summer 2015, limited nitrogen fixation and resulted in lower N and P content in the Robinia leaf tissue.

Our measured leaf nutrient concentrations correspond closely with previously published data, e.g., [29,30]. Heinsdorf [30] investigated nutrient uptake and nutrient supply of a seven-year-old Black Locust stand growing on a former open cast lignite mining site in East Germany and reported the follow leaf analysis data (mean values): Ntot 3.64%, Nsoluable 0.92%, P 0.18%, K 1.19% and Mg 0.16%. Our findings also showed a K-to-Caantagonism, as reported by Heinsdorf [30], where high K concentrations in Robinia leaves correspond to low Ca concentrations (Figure 5).

Furthermore, we examined our data for a systematic relationship between Dualex® derived NBI values and Ntot content as well as NBI and the P concentration, respectively (Figure 6).

**Figure 6.** Regression analysis between NBI and N content (**left**) and between NBI and P concentration (**right**) in Robinia leaves.

The regression analyses show a good correlation between the NBI, determined using the Dualex®, and Ntot content in Robinia leaves as well as NBI and P concentration, respectively (Figure 6). Hence, Dualex® measurements offer a more cost-effective and time-efficient method for determining the nutrient status of plants.

#### *3.5. Biomass Growth*

As already mentioned in the previous section, PIABS mean values vary depending on the origin of the plant sampled. Generally, it can be stated that higher PIABS values, indicating greater efficiency in primary photosynthetic processes, are likely to result in greater plant growth. Clear and significant differences can be observed between the clones and provenances with regard to the mean annual height growth (Figure 7).

**Figure 7.** Plant height growth through the years 2014–2019 of ten Robinia test units. Note that one bar usually comprised 20 Robinia test trees per origin and per year.

Clones *Fra3* and *Row* demonstrated the best height growth (mean values) after six years of growth (Ø 772 cm and Ø 738 cm, respectively). In contrast, the clone *Rob* has the lowest performance (Ø 474 cm).

After the first growing season, Robinia test trees gained a height increment of 144 cm (averaged over all trees). Despite the relatively dry year that followed (2015), the trees nonetheless produced 110 cm growth on average. To date, even the 2018 and 2019 growing seasons, namely the driest and hottest vegetation periods, resulted in a minimum average height increment of 59 cm (2018) and 30 cm (2019).

Furthermore, we examined biomass formation during the growth periods of 2014 to 2019 (Figure 8). Annual biomass growth rates vary depending on the weather conditions during the growing season and corresponding to origin. After six years, the mean biomass yield averaged over all tested Robinia clones and provenances was 28.20 Mg d.m. ha−<sup>1</sup> , whereas the mean annual increment was 4.70 Mg d.m. ha−<sup>1</sup> year−<sup>1</sup> .

**Figure 8.** Biomass growth [Mg dry matter ha−<sup>1</sup> ] through the years 2014–2019 of different Robinia test units; data given in small boxes represent individual average increment of biomass [Mg dry matter ha−<sup>1</sup> ] in 6 years of time.

Similar to their performance in height growth, the clones *Fra3* and *Row* achieved the highest mean biomass growth values (7.47 Mg d.m. ha−<sup>1</sup> year−<sup>1</sup> and 5.79 Mg d.m. ha−<sup>1</sup> year−<sup>1</sup> , respectively) whilst the low-performing clone *Rob* yielded only 2.45 Mg d.m. ha−<sup>1</sup> year−<sup>1</sup> . The differences in biomass yield between the test units were statistically significant.

#### *3.6. Quality Assessment*

A quality assessment of all the test trees revealed a frequent occurrence of crooked stem forms and unfavourable crown formation. Altogether, 66.1% of all trees tested showed multiple stem formation with varying severity between individuals. Only 30% of the test trees belonging to clone *Romy* were found to have forks, whereas more than two thirds (83.3%) of the plants of the provenance *Kis* exhibited forks. These findings raise the question of whether it is possible to generate a sufficient number of straight-boled trunks for material utilisation.

#### **4. Discussion**

Our results show that the drought, monitored in late summer 2015, is very likely to have caused limitations of nitrogen fixation and resulted in lower N and P content in the Robinia leaves' tissue (Table 6). Such findings have been confirmed by other authors. Mantovani et al. [31], for example, studied carbon allocation, nodulation, and biological nitrogen fixation of two-year-old Black Locust (*Robinia pseudoacacia* L.) saplings under soil water limitation. The authors used stable isotopic composition of C (δ 13C) and N (δ 15N) of the leaves to investigate adverse effects of drought as well as to identify the portion N accrued from the atmosphere by biological nitrogen fixation. They also found that drought stress significantly reduces total aboveground biomass production of the test plants as well as increases the nodule biomass of Black Locust in order to maintain biological nitrogen fixation and counteract the lower soil nitrogen availability.

Regarding adaptation to climate change, Mantovani et al. [32] showed that Black Locust plants can adapt to prolonged drought conditions by lessening water loss through both reduced transpiration and leaf size. However, under well-watered conditions, Robinia does not regulate its transpiration. It, therefore, cannot be considered a water-saving tree species. Veste and Kriebitzsch [7] carried out pot experiments in order to evaluate the growth and ecophysiological performance of Black Locust under drought stress. They demonstrated that when Black Locust is exposed to drought, it drastically reduces leaf area in order to minimise transpiration. Moreover, their test plants showed different adaptations and a high plasticity of the ecophysiological processes to cope with long-term drought stress and high temperatures, which also enables them to grow in drier regions [7]. Bhusal et al. [33] have shown that drought resistance is indicated by leaf mass per area, photosynthetic rate, leaf water potential and further factors. While drought resistance was not concretely explored in this study, it opens avenues to combine our results with investigations into drought response of Black Locust origins in the future.

Furthermore, Seserman et al. [34] pointed out that tree yields in Black Locust short rotation coppices were positively impacted by air temperature increase and negatively by decreasing precipitation.

The reduction in biomass growth of Black Locust during the drought years (Figure 8) is in accordance with previous studies [34,35]. Hence, Mantovani et al. [35] investigated spatial and temporal variation of drought impact on Black Locust's water status and growth. They conducted their study at two different sites: one site with fertile agricultural soil (site 1) and a reclaimed post-mining site with heterogeneous unstructured soil (site 2). They found that stem growth was drastically reduced during a period of summer drought, particularly in the post-mining area, as a result of the adverse edaphic conditions (below the critical pre-dawn water potential value of −0.5 MPa). However, the trees could cope with the extreme soil and weather conditions in the post-mining site without perishing.

Our biomass growth rates are in range with average annual values gained from other sites in the Lusatian post-mining area reported by Knoche et al. [28]. The latter reported biomass yields after six growth years for study site *Drebkau 1* = 37.7 Mg d.m. ha−<sup>1</sup> (Ø 6.3 Mg d.m. ha−<sup>1</sup> year−<sup>1</sup> ) and for site *Drebkau 2* = 40.7 Mg d.m. ha−<sup>1</sup> (Ø 6.8 Mg d.m. ha−1 year−<sup>1</sup> ) as well as 19.3 Mg d.m. ha−<sup>1</sup> (Ø 3.2 Mg d.m. ha−<sup>1</sup> year−<sup>1</sup> ) for site *Senftenberg*.

To give a comprehensive evaluation of all clones and provenances studied, plant physiological performance, biomass growth and quality data were individually evaluated and ranked (Table 7). Note that parameters given in the table are not weighted and deep balanced but may nonetheless assist in identifying the best Robinia plant material for land users in post-mining areas.


**Table 7.** Ranking of major plant-physiological, biomass growth and quality parameters of ten Black Locust clones and provenances. The background colour is a stylistic instrument.

#### **5. Conclusions**

Our study reveals pronounced differences in the physiological performance, biomass growth and stem quality of the Black Locust clones and provenances studied. Under the challenging climatic and edaphic conditions of our test site—considering photosynthetic vitality, chlorophyll and phenol content, nutrition state, biomass growth and stem quality—Robinia genotypes *Rowena, Fra3* and *Romy* as well as the Brandenburg provenance *Schöneiche* show the most promise with regard to growth performance, especially for cultivation in short coppice rotation and on forest land.

Biophysical measurements using the Pocket PEA and Dualex®, especially when combined and used in parallel, are reliable indicators for detecting abiotic stress already in an early stage. We were able to show that while mean PIABS values vary across Black Locust test units, they still follow similar sequences/patterns. We found correlations between biophysical parameters resulting from chlorophyll a fluorescence and Dualex® measurements. Correlation analysis revealed good accordance of Dualex®-derived NBI (Nitrogen Balance Index) and Ntot content as well as P content, detected in Robinia leaves. Hence, using Dualex® measurements can provide insights into the nutrient status (especially nitrogen) of plants in a cost and time efficient manner.

We conclude that biophysical measurements have the potential to shorten otherwise long-lasting research plans in tree breeding. In addition, biophysical measurements enable the early assessment of the physiological performance and stress tolerance of different Robinia clones and provenances.

**Author Contributions:** Conceptualization, C.A.L., D.K. and V.S.; methodology, C.A.L. and S.L.; validation, D.K., C.A.L. and V.S.; formal analysis, C.A.L., R.H. and S.L.; investigation, D.K., C.A.L. and V.S.; resources, V.S., S.L. and C.A.L.; data curation, C.A.L., S.L. and R.H.; writing—original draft preparation, C.A.L.; writing—review and editing, D.K., R.H., C.A.L. and V.S.; visualization, C.A.L., S.L. and R.H.; supervision, D.K.; project administration, D.K.; funding acquisition, V.S. and D.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was kindly funded by the German Fachagentur für Nachwachsende Rohstoffe with support by the Federal Ministry of Food and Agriculture (joint project FastWOOD, Project No. 22001014). We would also like to thank the mining company LEAG for financial support of perennial data acquisition.

**Data Availability Statement:** The raw data sets generated during the time period 2014–2019 are available from the corresponding author on reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**


## *Article* **Soil Carbon Modelling in** *Salix* **Biomass Plantations: Variety Determines Carbon Sequestration and Climate Impacts**

**Saurav Kalita <sup>1</sup> , Hanna Karlsson Potter <sup>1</sup> , Martin Weih <sup>2</sup> , Christel Baum 3,\* , Åke Nordberg <sup>1</sup> and Per-Anders Hansson <sup>1</sup>**


**Abstract:** Short-rotation coppice (SRC) *Salix* plantations have the potential to provide fast-growing biomass feedstock with significant soil and climate mitigation benefits. *Salix* varieties exhibit significant variation in their physiological traits, growth patterns and soil ecology—but the effects of these variations have rarely been studied from a systems perspective. This study analyses the influence of variety on soil organic carbon (SOC) dynamics and climate impacts from *Salix* cultivation for heat production for a Swedish site with specific conditions. Soil carbon modelling was combined with a life cycle assessment (LCA) approach to quantify SOC sequestration and climate impacts over a 50-year period. The analysis used data from a Swedish field trial of six *Salix* varieties grown under fertilized and unfertilized treatments on Vertic Cambisols during 2001–2018. The *Salix* systems were compared with a reference case where heat is produced from natural gas and green fallow was the land use alternative. Climate impacts were determined using time-dependent LCA methodology—on a land-use (per hectare) and delivered energy unit (per MJheat) basis. All *Salix* varieties and treatments increased SOC, but the magnitude depended on the variety. Fertilization led to lower carbon sequestration than the equivalent unfertilized case. There was no clear relationship between biomass yield and SOC increase. In comparison with reference cases, all *Salix* varieties had significant potential for climate change mitigation. From a land-use perspective, high yield was the most important determining factor, followed by SOC sequestration, therefore high-yielding fertilized varieties such as 'Tordis', 'Tora' and 'Björn' performed best. On an energy-delivered basis, SOC sequestration potential was the determining factor for the climate change mitigation effect, with unfertilized 'Jorr' and 'Loden' outperforming the other varieties. These results show that *Salix* variety has a strong influence on SOC sequestration potential, biomass yield, growth pattern, response to fertilization and, ultimately, climate impact.

**Keywords:** biomass production; life cycle assessment; climate impact; soil organic carbon; *Salix*; willow; short rotation coppice; genotypic difference

#### **1. Introduction**

It has been established that the current atmospheric concentrations of three major greenhouse gases (GHGs)—carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), are at the highest levels estimated for the past 800,000 years [1]. Most of this increase has happened post 1750, which was the beginning of the Industrial Revolution. The most alarming trend is that the decadal rate of increase in atmospheric CO<sup>2</sup> was highest in 2002–2011 since direct measurements began in 1958 [2]. There is consensus among the scientific community that the principal cause of this rapid increase is use of fossil fuels and

**Citation:** Kalita, S.; Karlsson Potter, H.; Weih, M.; Baum, C.; Nordberg, Å.; Hansson, P.-A. Soil Carbon Modelling in *Salix* Biomass Plantations: Variety Determines Carbon Sequestration and Climate Impacts. *Forests* **2021**, *12*, 1529. https://doi.org/10.3390/ f12111529

Academic Editor: Dirk Landgraf

Received: 20 September 2021 Accepted: 4 November 2021 Published: 6 November 2021

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land use change associated with the start of the Industrial Age. The increased atmospheric concentration of GHGs has enhanced radiative forcing, leading to higher average global temperatures and climate change.

Countries and organizations worldwide have set certain regulations and targets to limit the increase in average global temperatures to avoid the negative impacts of climate change. The European Commission has set targets to cut GHG emissions by at least 40% below 1990 levels and to increase renewable energy share to at least 32% by 2030 [3]. The long-term strategy is to reach a climate-neutral EU by 2050 [4]. Sweden has made an ambitious commitment to phase out all GHG emissions completely by 2045 [5,6]. The climate crisis induced by increased GHG emissions has led to a quest for different strategies to mitigate the problem. Bioenergy from sustainable biomass can be part of a viable climate mitigation strategy by replacing fossil fuels for heat and electricity generation. At the global scale biomass accounted for 9% of renewable electricity generation and 96% of renewable heat generation in 2018 [7].

The cultivation of plant species such as *Salix* (willow) and *Populus* (poplar) in shortrotation coppice (SRC) systems has emerged as an interesting approach to sustainably produce renewable biomass [8,9]. *Salix* SRC systems are characterized by short growth cycles of 2–5 years, after which the stems are harvested, and shoots regrow from the stumps left in the soil [10]. SRC plantations can have a positive effect on soil organic carbon (SOC) sequestration, because of the addition of large amounts of root and leaf litter to the soil, which are better incorporated into the soil due to minimal soil disturbance compared with annual crops [11]. *Salix* propagates easily via cuttings and is well suited to growth in temperate and Arctic climatological conditions. Commercial plantations of *Salix* are gaining interest worldwide for use as a biomass crop, with the largest cultivated areas (as of 2015) in China and Argentina, followed by North America and Europe [12]. There is high interest in European countries such as Sweden, where commercial *Salix* plantations were established in the 1990s, with policies proposed to increase energy crop cultivation to 40,000 hectares by 2030 [13].

In Sweden, the area under SRC plantations reached a peak of about 18,000 hectares in the mid-1990s, which decreased to about 12,000 hectares by 2015 [14,15]. This was attributed to a combination of factors such as poor management, inefficient policy and low prices—which meant that the practical results did not meet the high expectations [16,17]. New varieties and better management practices adapted to Swedish conditions have emerged in the past two decades. These, combined with the ambitious Swedish emission reduction targets, make SRC *Salix* an interesting prospect for biomass feedstock in the Swedish context.

The SOC sequestration potential of SRC plantations is gaining attention among researchers for its climate mitigation effects. Multiple studies [18–22] have found that SRC *Salix* systems sequester more carbon than conventional cropping systems. However, the SOC sequestration of *Salix* established on grasslands is more uncertain and can be lower [23,24]. The magnitude and potential for SOC change depend on previous land use, soil and climate conditions [18,24,25]. This, combined with the different soil profile depths considered in different studies leads to variation in reported SOC stock change rates. Long-term field data, and especially those on belowground biomass production rates, are necessary to validate and improve the accuracy of SOC sequestration estimates for SRC *Salix* plantations under different growth and soil conditions.

Biomass for bioenergy utilization can be considered carbon neutral as CO<sup>2</sup> emitted from its conversion phase is recaptured by new growth. However, there is a need to assess the climate impact in a system perspective including changes in SOC and land use, and impacts from site preparation, production of inputs, machinery operations, transports and energy conversion. Quantification of the potential effects and impacts of biomass use over spatial and temporal horizons is needed to ensure its sustainability.

There are several tools for environmental impact evaluation, and one of the most commonly used is Life Cycle Assessment (LCA). LCA is a well-established and standardized tool for estimation of potential environmental impacts from a product or service over its whole lifespan. The LCA methodology was originally designed for industrial processes and products but has been expanded in recent decades to evaluate and compare agricultural, forestry and bioenergy processes and products [26,27]. In the context of bioenergy production system evaluation, LCA helps by expanding the perspective beyond the production system itself. This is important as the environmental consequences of a bioenergy production system frequently depend more on the impacts on other parts of the value chain than on the production system itself. Thus, the broad system perspective makes LCA a suitable tool for planning of bioenergy systems and policymaking, especially in the context of the potential effects of bioenergy production systems on climate change mitigation. However, when modelling large and often complicated systems in LCA studies, parts of the data are often more uncertain and some subjective aspects may be handled in order to reach the broad system perspectives [28,29]. These limitations are not unique to LCA, and similar problems occur even in other methods for environmental systems analysis. The decisions on data quality requirements play an important role in the results of the assessment. Ambitions about completeness of data must be balanced against availability of resources and workload. These are intrinsic and accepted aspects of LCA studies, as long as the relevance, data quality and relevant major assumptions are appropriately described [30]. The LCA methodology is constantly evolving as understanding of climate and environmental impacts develops.

The most common climate impact metric used in LCA is global warming potential (GWP100), which is based on radiative forcing and captures the integrated impacts over a single time horizon of 100 years [31]. It does not capture the effect of timing and persistence of GHG fluxes and temporal changes in SOC [32]. It does not represent the actual impacts on ecosystems such as temperature change, sea level change or biodiversity loss.

Using a time-dependent method can counter this by expressing the climate metric as a function of time. Several studies have developed such alternative methods and applied them in LCA to capture the emissions and fluxes of carbon flows between the atmosphere, biomass and soil [17,32–34]. An absolute time-dependent climate metric such as the absolute global temperature change potential (∆*Ts*) developed by Ericsson et al. [35] represents the impact on global mean surface temperature from emission or removal of a GHG at a particular point in time. This can aid in better understanding of climate impacts of bioenergy as biomass systems capture and emit carbon at different points in time. Several LCA studies have assessed *Salix* cultivation for bioenergy utilization [17,32,33,36–40]. However, studies looking at the magnitude of impact of differences between *Salix* varieties on the overall bioenergy system are rare.

Differences between *Salix* varieties can have a significant impact on physiological traits, biomass quality, growth patterns and soil ecology. Weih and Nordh [41] showed that key traits and shoot biomass production are variety-specific and that there is a need to account for these variety differences at the field level. Adegbidi et al. [42] found that biomass production, nutrient use efficiency and nutrient removal are strongly influenced by variety in *Salix* plantations. Cunniff et al. [43] observed significant differences in allocation between aboveground and belowground biomass in different varieties and at different locations in the UK. Data from *Salix* field trials in Sweden have demonstrated that the effects of fertilization on soil ecology are also affected by variety [44]. *Salix* varieties have been found to differ significantly in their response to fertilization and in carbon storage potential in shoots and soil [44].

Despite of the many plant-and field-scale reports indicating significant impacts of *Salix* varieties on plant traits that potentially affect their environmental performance, there is a lack of systems-scale research (such as LCAs) accounting for these differences. Thus, there is a need to address the differences between *Salix* varieties regarding the impact on soil carbon sequestration and climate impact when assessing bioenergy systems in a life cycle perspective

This study aimed to analyze the effects of *Salix* variety and fertilization treatment on SOC dynamics, and subsequent effects on climate impacts of *Salix* cultivation for bioenergy on a commercial scale, with a 50-year time horizon. A field trial established in 2001 is the source of the harvest and SOC data for the selected *Salix* varieties in this study [44,45]. Unfortunately, root biomass data over time from field-grown trees were not available from the trials used here, and we therefore used indirect methods to estimate root biomass allocation over time from published reports using pot and lysimeter experiments, in which root biomass can be assessed more easily. Other data are either taken from literature and studies on *Salix* systems where available, or based on assumptions derived from other biomass systems.

Specific objectives of the study were to:


It is expected that quantification of the magnitude of varietal effects will highlight the importance of their inclusion in systems analysis studies of bioenergy. The intention was to provide a basis for comparison of *Salix* varieties in terms of energy and climate performance, which can aid in the consideration of optimal *Salix* variety selection for a particular purpose, e.g., maximized carbon sequestration potential.

We believe that studies like this investigation will motivate the need for varietyand location-specific root and belowground data to make realistic, accurate and detailed assessments of the environmental performance of bioenergy systems.

#### **2. Materials and Methods**

The effect of *Salix* variety on the climate impact and energy performance of a *Salix*biomass production system under Swedish conditions (Uppsala region) was analyzed using LCA methodology. Two functional units (FU) of 1 MJ of heat and 1 hectare of land were chosen to describe the two different functions of the system—generation of heat and use of land as a resource for mitigating climate impacts. The energy FU compares the relative impact of using the *Salix* varieties as an energy source, while the land FU unit compares the different impacts from a land use perspective considering land as a restricted resource.

The climate impact calculation considers three major GHGs (CO2, N2O and CH4) and is expressed in terms of two metrics—global warming potential (GWP100) and a timedependent climate impact (∆*Ts*) as defined in [35], with a one-year time step. The flux of carbon in the soil due to addition and decomposition of biomass was modelled with the carbon model ICBM developed by Andrén and Kätterer [46]. Annual net flux of the selected GHGs was estimated for each source and sink, and the associated emission impulses were based on the timing of the emissions.

#### *2.1. Plant Material, Field Trial and Data Collection*

The analysis was based on data collected from a field trial during the period 2001−2017 at Pustnäs, near Uppsala in central Sweden by Weih and Nordh [45]. The following six commercial *Salix* varieties were part of the study: 'Björn' (*Salix schwerinii* E. Wolf. × *S. viminalis* L.), 'Gudrun' (*S. burjatica* Nasarow × *S. dasyclados* Wimm.), 'Jorr' (*S. viminalis*), 'Loden' (*S. dasyclados*), 'Tora' (*S. schwerinii* × *S. viminalis*) and 'Tordis' ((*S. schwerinii* × *S. viminalis*) × *S. viminalis*). There were two experimental treatments—fertilized (approx. 100 kg N, 14 kg P, 47 kg K ha−<sup>1</sup> yr−<sup>1</sup> ) and unfertilized. Plots were 6.75 m × 7.00 m in size and contained 84 plants each, corresponding to a planting density of about 18,000 plants per hectare. Each variety and treatment had four replicate plots. The dominating soil type was a vertic cambisol with a sandy loam as topsoil (0–20 cm soil depth) with 66% sand, 16% silt and 18% clay. Initial SOC content at 0–10 cm soil depth was 11.1 g kg−<sup>1</sup> , with a bulk density of 1.3 g cm−<sup>3</sup> . Further details of the field trial can be found in Weih and Nordh [45].

After establishment of the plantation in 2001, the plantation was managed in three-year cutting cycles with shoots harvested during winter in 2004, 2007, 2010, 2013 and 2016. Mean air temperature during the growing season (April to October) in the years relevant to this study was 12.5 ◦C, and the corresponding mean annual precipitation sum was 841 mm [44].

For the present analysis, the average yield for the first harvest (2004) and for subsequent harvests (average value for 2007–2016 harvests) were calculated. The first yield after planting is usually lower, as the plant root system is still establishing. The shoot growth and biomass yield figures after the field measurement period (post-2017) were assumed to follow the average values calculated from the field trial data. Table 1 presents the average harvest values from the field study used as input to the modelling work.


**Table 1.** Average harvested biomass yield (dry weight, DW) and standard deviation (SD) of the six commercial *Salix* varieties grown under two fertilization regimes in central Sweden from 2001 to 2018. F0 and F+ refer to the unfertilized and fertilized treatment, respectively.

The field site was ploughed shortly before planting of the *Salix* stem cuttings. The soil in each plot was sampled (five replicates per plot) with a soil corer (3 cm diameter), to a depth of 10 cm in spring 2001 and to a depth of 20 cm in 2018. The initial soil sampling was performed prior to laying out the plots. The field site is characterized by a flat surface without relief-promoted erosion, which contributed to the lack of significant differences in soil properties between the different plots. An additional follow-up soil sampling in 2002 showed no significant differences in the bulk density and SOC content among the plots. As such, the ploughing did not cause a measurable difference between the first (2001) and second year (2002). The SOC content in the 0–10 cm layer was recorded and is reported by Baum et al. [44], who provide full details of the soil sampling and analysis procedures.

As the plough depth was about 25 cm during the year of establishment of the field trial, the topsoil (0–20 cm soil depth) was assumed to be homogenous and to have similar characteristics. Hence, the initial SOC stock in the 10–20 cm soil layer in 2001 was assumed similar to that in the 0–10 cm soil layer. The bulk density in 2018 had not changed significantly from the initial value of 1.3 g cm−<sup>3</sup> , which can be expected as consequence of combined lack of loosening by tillage under the perennial crops, but improved aeration of the soil by increased SOC content. The SOC stock in the 10–20 cm soil layer from 2018 was analyzed following the same methodology as was described by Baum et al. [44] for the 0–10 cm soil layer. The resulting SOC stocks in the 0–10 cm, 10–20 cm and 0–20 cm layers in 2001 and 2018 are displayed in Table 2. The reduction in SOC content in the 10–20 cm layer for some of the *Salix* varieties is not unexpected under SRC as evidenced by similar results reported by Kahle et al. [47].

**Table 2.** Soil organic carbon stock (Mg ha−<sup>1</sup> ) in the 0–10 and 10–20 cm soil layers measured in field trials on six *Salix* varieties at Pustnäs, Sweden, in 2001 (pre-establishment) and in 2018. F0 and F+ refer to the unfertilized and fertilized treatments respectively.


#### *2.2. System Boundaries*

The system studied comprised the steps from preparation of the field site for *Salix* cultivation to production of heat in a boiler in a heating plant (Figure 1). Energy flows and emissions from field operations, production of inputs, biomass transportation and thermochemical conversion were included within the system boundaries. Downstream losses and emissions after production of heat and ash at the incineration plant were considered as outside the system boundaries. Belowground changes and biomass inputs (from leaf, stumps, fine roots and coarse roots) to 20 cm depth were within the system boundaries as the SOC values from the field studies were determined with accuracy within the 0–20 cm soil layer. Highest litter input from fine roots and leaf litter are within this soil profile [48]. As the SOC changes in the sub-20-cm-profile are not part of the study system, a higher total carbon sequestration in the complete soil profile can be assumed.

**Figure 1.** System boundaries (dotted lines) showing the processes considered within the study. Greenhouse gas and energy fluxes associated with the processes within the system boundaries were included in the analysis. **Figure 1.** System boundaries (dotted lines) showing the processes considered within the study. Greenhouse gas and energy fluxes associated with the processes within the system boundaries were included in the analysis.

#### *2.3. Field Operations and Management 2.3. Field Operations and Management*

The SRC *Salix* system followed a typical three-year cutting cycle, with the *Salix* harvested and chipped on-site at the end of every third growth cycle. The *Salix* then regrew from the stumps left in the field. According to current practical recommendations [49], one rotation period was assumed to last 25 years, after which the stumps would be broken up and removed and a new rotation would be established with new cuttings. The study period for the system was set to 50 years, which resulted in two rotation cycles. Technologies and management practices were assumed unchanged during this period. The data and assumptions used to calculate energy and emissions associated with the The SRC *Salix* system followed a typical three-year cutting cycle, with the *Salix* harvested and chipped on-site at the end of every third growth cycle. The *Salix* then regrew from the stumps left in the field. According to current practical recommendations [49], one rotation period was assumed to last 25 years, after which the stumps would be broken up and removed and a new rotation would be established with new cuttings. The study period for the system was set to 50 years, which resulted in two rotation cycles. Technologies and management practices were assumed unchanged during this period. The data and assumptions used to calculate energy and emissions associated with the production of inputs and processes can be found in the Supplementary Material (Tables S6 and S7).

production of inputs and processes can be found in the Supplementary Material (Tables S6 and S7). The harvest period for SRC systems is usually during winter months because the biomass is drier, the plant is dormant and the hard frozen soil provides a higher The harvest period for SRC systems is usually during winter months because the biomass is drier, the plant is dormant and the hard frozen soil provides a higher machinery carrying capacity [49,50]. It was assumed that the conventional method of harvesting and direct chipping was followed. Thereafter, the chips were transported to a heating plant for production of heat. The average road transportation distance was set as 40 km in this study.

#### machinery carrying capacity [49,50]. It was assumed that the conventional method of harvesting and direct chipping was followed. Thereafter, the chips were transported to a *2.4. Thermochemical Conversion*

*2.4. Thermochemical Conversion* 

heating plant for production of heat. The average road transportation distance was set as 40 km in this study. The higher heating value (HHV) of the *Salix* chips was considered to be 19.9 GJ/Mg DM (dry and ash-free), based on which the lower heating value (LHV) adjusted for moisture content was calculated [51,52]. The average storage period of the chips was 30 days, during

DM (dry and ash-free), based on which the lower heating value (LHV) adjusted for moisture content was calculated [51,52]. The average storage period of the chips was 30 days, during which 3% dry matter loss occurred. The heating plant produces heat from which 3% dry matter loss occurred. The heating plant produces heat from biomass incineration and is equipped with flue gas condensation, which raises the overall efficiency. The energy efficiency for heat and flue gas condensation is 84% and 10% respectively (LHV basis), which gives an overall energy efficiency of 94%. The ash produced from biomass incineration was assumed to be transported by road for an average distance of 100 km. Calculation of ash quantities was on ash content of 3% in the *Salix* biomass [53]. The downstream processing and end-use of the ash were deemed outside the system boundaries.

#### *2.5. Reference System*

The reference energy system in this study was a fossil fuel-based energy generation system. A natural-gas-powered incineration plant supplied heat equivalent to the amount generated in the same year from the SRC *Salix* system. The alternative land use scenario was green fallow. The modelled SOC increase and use of fossil fuel for topping the land annually were included in the LCA. Assumptions concerning emissions and energy modelling are included in the Supplementary Material (Table S8).

#### *2.6. Energy Performance Indicator*

Energy performance was quantified by the indicator energy ratio (*ER*), which is the ratio between the delivered usable energy (thermal energy in this case) and the total primary energy input to the system [54,55]:

$$ER = \frac{Delivered\ energy\ (E\_{out})}{Energy\ Inputs\ (E\_{in})} \tag{1}$$

The delivered energy (*Eout*) is the energy produced (as heat) from the heating plant. Energy inputs (*Ein*) is the sum of all primary energy inputs associated with field processes and management, machinery operation, and production of inputs (fertilizers, pesticides and cuttings). *Ein* excludes the energy contained in the *Salix* biomass produced by cultivation.

This means that the losses in the thermochemical conversion process are excluded, but they indirectly reduce the delivered energy (*Eout*). The ER metric is dimensionless and describes the useful energy produced per unit of energy consumed.

#### *2.7. Mineral Fertiliser*

Addition of nitrogen in the form of mineral fertilizers and biomass entering the soil lead to direct and indirect emissions of N2O. The amount of fertilizer was set according to the levels used in the field studies, where all fertilized treatment plots received 100 kg N, 14 kg P, 47 kg K per hectare annually, excluding the year of establishment [44].

The direct (*N*2*Odirect*) and indirect (*N*2*Oindirect*) emissions were calculated as:

$$N\_2O\_{direct} = EF\_N \cdot \left(N\_{applied} + N\_{litter} + N\_{roots}\right) \cdot \frac{44}{28} \tag{2}$$

$$\mathrm{N\_2O\_{induced}} = \mathrm{N\_{applied}} \cdot \left(\mathrm{F\_A \cdot EF\_D} + \mathrm{N\_{leadhed}} \cdot EF\_L\right) \cdot \frac{44}{28} \tag{3}$$

where *Napplied* is the nitrogen applied by mineral fertilizer, *Nlitter* and *Nroots* is the nitrogen contained in aboveground litter and roots respectively, and *Nleached* is the nitrogen lost by leaching. *EFN*, *EF<sup>D</sup>* and *EF<sup>L</sup>* are emission factors for direct emissions from applied nitrogen, indirect emissions from volatilization and re-deposition, and leaching respectively. *F<sup>A</sup>* represents the fraction of applied nitrogen emitted as ammonia. The fraction <sup>44</sup> 28 converts nitrogen to N2O. The emissions are calculated using default parameter values from IPCC [56], and are presented in Table A1, Appendix A. The same methodology was followed to calculate emissions from the fallow reference case.

N2O emissions from biomass residues were based on the nitrogen content in *Salix* leaf litter reported for the selected varieties by Weih and Nordh [41] (details in Table S4 in Supplementary Material) and for stems as 0.43% (of total solids) [41]. Root nitrogen content was calculated from a dataset by Manzoni et al [57]. The estimated mean nitrogen content of roots from plants with low and high fertilization was 0.83% and 1.76% (of total solids) respectively (Table S5 in Supplementary Material). There are few studies on nitrogen content between different plant components, especially among different *Salix* varieties.

#### *2.8. Soil Carbon*

Soil carbon balances were calculated using the regional Introductory Carbon Balance Model (ICBMr) [46,58]. While the field trials provide measured SOC change for the first 17 years, the soil carbon modelling was used to estimate the SOC sequestration over the study period of 50 years. The model calculates the carbon flux based on variable annual inputs and regional differences. The ICBM model compartmentalizes the soil carbon into two pools, a young pool (Y) and an old pool (O), and the dynamics are governed by five parameters (*i*, *ky*, *ko*, *h* and *re*). The annual carbon input, denoted *i*, enters the young pool primarily in the form of leaf litter and dead roots. Both the young and old carbon pools undergo decomposition according to first-order kinetics as determined by decay constants *k<sup>y</sup>* and *ko*, respectively. The humification coefficient *h* denotes the fraction of the young pool that enters the old pool, while the remainder returns to the atmosphere as CO<sup>2</sup> emissions. The variable *r<sup>e</sup>* represents the effect of external factors (mostly climatic and edaphic) on the decomposition rates. The initial calibration of the model was carried out using data from the Ultuna long-term field trial [59]. The ICBM parameters from the long-term trials are the basis of the parameters used in our study for SOC modelling as the long-term field trials are in the same region as our study.

The humification factor (*h*) varies depending on biomass quality and studies have indicated that roots can contribute more to SOC than aboveground residues [60]. *Salix* fine roots specifically have been shown to have higher turnover rates [48]. Therefore, the model was modified to represent the two different input biomass types—aboveground inputs (*ia*) and belowground inputs (*i<sup>b</sup>* ), with separate humification coefficients (*h<sup>a</sup>* and *h<sup>b</sup>* ). Hence, there were two parallel young pools, a young pool representing the aboveground biomass input (*Ya*) and a young pool representing the belowground input (*Y<sup>b</sup>* ). Equations (4) and (5) were used to calculate the SOC stock with an annual time step:

$$Y\_{[a,b]}(t) = \left(Y\_{[a,b]\_{t-1}} + i\_{[a,b]\_{t-1}}\right) \* \exp^{-k\_y r\_\varepsilon} \tag{4}$$

$$\begin{split} \mathcal{O}(t) &= \left( O\_{l-1} - \left( \frac{h\_{\mathrm{d}} k\_{\mathrm{y}}}{\left( k\_{\mathrm{o}} - k\_{\mathrm{y}} \right)} \left( Y\_{\mathrm{d}\_{l-1}} + i\_{\mathrm{d}\_{l-1}} \right) + \frac{h\_{\mathrm{b}} k\_{\mathrm{y}}}{\left( k\_{\mathrm{o}} - k\_{\mathrm{y}} \right)} \left( Y\_{\mathrm{b}\_{l-1}} + i\_{\mathrm{b}\_{l-1}} \right) \right) \right) \cdot \exp^{-k\_{\mathrm{o}} r\_{\mathrm{c}}} \\ &+ \left( \frac{h\_{\mathrm{d}} k\_{\mathrm{y}}}{\left( k\_{\mathrm{o}} - k\_{\mathrm{y}} \right)} \left( Y\_{\mathrm{d}\_{l-1}} + i\_{\mathrm{d}\_{l-1}} \right) + \frac{h\_{\mathrm{b}} k\_{\mathrm{y}}}{\left( k\_{\mathrm{o}} - k\_{\mathrm{y}} \right)} \left( Y\_{\mathrm{b}\_{l-1}} + i\_{\mathrm{b}\_{l-1}} \right) \right) \cdot \exp^{-k\_{\mathrm{y}} r\_{\mathrm{c}}} \end{split} \tag{5}$$

The aboveground input, *ia*, consists of the leaf litter. The belowground input, *i<sup>b</sup>* , consists of the yearly fine root turnover and the accumulated coarse roots and stumps broken up and added to the soil after each 25-year rotation. The sum of the young and old pools represents the total SOC content at the specific point in time. Based on Kätterer et al. [60], *h<sup>b</sup>* was assumed to be 2.3 times the value of *ha*. The parameters were estimated from previous SOC studies [17,33,35,38] on *Salix* using the same methodology. The parameter details of the ICBM model are included in supplementary material (Tables S1 and S2).

#### *2.9. Biomass Production Allocation*

The standing biomass in *Salix* plants was divided into two major pools, aboveground and underground. The aboveground pool consisted of the stems (*S*) and leaves (*L*), while the underground pool consisted of the fine roots (*F*) and coarse roots (*C*). The stump material was included in the coarse root pool. The biomass growth allocation for these pools in a 3-year growing cycle are included in the Supplementary Material (Table S3). The ratio of 3-year accumulated net primary production (NPP) of aboveground biomass to belowground biomass, denoted as *η* was calculated as:

$$\eta = \frac{\mathcal{S} + L}{F + \mathcal{C}} = \frac{(1 + a)\mathcal{S}}{(1 + b)F} \tag{6}$$

where *S*, *L*, *F* and *C* are the net production of stems, leaves, fine roots and coarse roots (including stumps), respectively over the 3-year cutting cycle period, *a* is the ratio of leaves to stems and *b* is the ratio of coarse roots to fine roots.

The differences in growth patterns between the various *Salix* varieties and treatments can be expected to lead to variation in values of *η* between them. Thus, varying the ratio *η* would lead to different input parameters (*i<sup>a</sup>* and *i<sup>b</sup>* ), resulting in different SOC values calculated by the ICBM model. This would lead to differences in biomass input between the varieties and variations in SOC accumulation. The ratios *a* and *b* were determined from lysimeter studies on *Salix* growth by Rytter [61] to be 0.244 and 0.238, respectively, and are considered to remain unchanged between the different *Salix* varieties. Introduction of the factor *η* was an attempt to represent the impact of genetic differences between *Salix* varieties on plant growth and biomass allocation.

Rytter and Hansson [62] found that around 70% of total fine root biomass lies in the upper 20 cm of the soil profile. Based on this, annual root biomass input in the 0–20 cm soil layer was set to 70% of annual root NPP. For the equivalent green fallow reference case, the root biomass was 60% of the root NPP in the 0–20 cm layer [63].

The ICBM model was used to calculate the SOC change in the 0–20 cm soil layer for the 17-year period. The above-to-below ground accumulation ratio (*η*) was adjusted until the calculated SOC values from the ICBM model matched the measured SOC values from the field trials for all six varieties and treatments. The *η* values obtained by this method are presented in Table 3.

**Table 3.** Ratio of aboveground to belowground biomass accumulation (*η*) over 3-years for the different *Salix* varieties and treatments obtained from optimization of the ICBM soil carbon model with field-based soil organic carbon measurements. F0 and F+ refer to the unfertilized and fertilized treatments respectively.


#### *2.10. Climate Impact*

In the normalized GWP<sup>100</sup> metric, the cumulative warming potential of a GHG emission is represented relative to that of CO<sup>2</sup> for a 100-year period [64] and expressed in CO2-equivalents. The emissions of CO2, CH<sup>4</sup> and N2O are multiplied by their respective characterization factors and summed to arrive at the total GWP100. While this is a simplified and popular metric for representation of climate impacts, GWP<sup>100</sup> does not capture the effects of timing of the emissions and their absolute impacts on the ecosystem [30,54].

Absolute global temperature change potential (AGTP), also referred to as ∆*Ts*, is a metric that takes into account the timing of emissions and represents the climate impact as a change in temporal global mean surface temperature [65]. Using an absolute metric like AGTP displays the climate impact from a GHG emission as change in temperature (∆*Ts*), which approaches the actual physical effect on global temperature but increases uncertainty. This time-dependent LCA methodology, developed by Ericsson et al. [35], was used here as a climate impact indicator in addition to GWP100.

Emission of a GHG at a particular point in time leads to a change in its atmospheric concentration which affects the radiative forcing (RF). This leads to a change in the energy balance on Earth, which results in an increase or decrease in temperature represented as ∆*Ts* [35,56]. GHGs vary in their radiative efficiency and atmospheric residence time, e.g.,

N2O and CH<sup>4</sup> have atmospheric residence times of 12.1 and 12.4 years, respectively, while CO<sup>2</sup> stays in the atmosphere until it is absorbed by the ocean or biosphere [66]. The lifetime of CO<sup>2</sup> is modelled based on the Bern carbon cycle. The temperature response of a GHG (AGTP*x*) is defined as:

$$AGTP\_x(H) = \int\_0^H RF\_x(t)R\_T(H-t)dt \left(\mathbf{K \, kg\_{gas}}^{-1}\right) \tag{7}$$

which represents the complex interaction between radiative forcing (*RF*) and the temperature response function (*RT*) caused by a unit change in RF due to a pulse emission of a GHG '*x*' at a specific time interval (*t*), and '*H*' is the timeframe of the study. The parameter *R<sup>T</sup>* captures the change in temperature due to the change in RF because of emission or uptake of a GHG (*x*) from the atmosphere at time interval (*t*). Integrating over the studied period '*H'* gives the temperature response for a particular GHG (AGTP*x*) in terms of K kggas −1 . The overall temperature response (∆*Ts*, measured in K) is the summation of the AGTP of the individual GHG emissions over the study timeframe '*H*'. A detailed explanation of the methodology is given in Ericsson et al. [35].

The time-dependent climate impact methodology requires the creation of an inventory of GHG emissions and uptakes distributed over time of the study. Individual temperature responses of each emission are calculated from this inventory. The total system response (∆*Ts*) is obtained by summing the individual responses and can be plotted as the change in temperature over time.

#### *2.11. Sensitivity Analysis*

Even with accurate data collection and standardized methods, uncertainties are unavoidable due to the multiple assumptions and variability involved in modelling and LCA approaches. Sensitivity analysis makes it possible to understand how different factors influence the final results of the analysis [67].

The setting of the system boundary to 20 cm of soil depth is a source of uncertainty. This is a type of parameter uncertainty and model uncertainty, as change in depth of soil profile changes the system boundaries of the model and related parameters such as SOC values and inputs from BGB. To assess how a greater soil profile would influence the SOC modelling and climate impacts from the different *Salix* varieties, a one-at-a-time sensitivity analysis was performed. The system boundary was adjusted to include a soil depth of 25 cm and related parameter of below ground input (*i<sup>b</sup>* ) and initial and final SOC values were changed, while other parameters in the analysis remained constant. The average plough depth of 20–25 cm was the motivation for limiting the soil profile depth, as the subsoil characteristics at the site (both before and after establishment of *Salix*) were not known.

In soil carbon modelling, the average SOC stock in the 20–25 cm layer was estimated to be half of the stock in the 10–20 cm layer for each of the varieties described in the previous sections. The root biomass input for *Salix* and the reference fallow case was 80% and 65% of the annual belowground NPP, respectively based on studies of root distribution for *Salix* [48] and grasses [63,68]. The root distribution is subject to variability due to factors such as soil and climate, and hence is a potential source of uncertainty.

#### **3. Results**

#### *3.1. Energy Use and Efficiency*

Regarding energy performance, the fertilized treatments of varieties 'Tordis', 'Björn', 'Tora' and 'Jorr' performed best in the ambient conditions, with ERs (GJout GJin −1 ) of 28.2, 26.5, 25.1 and 24.7, respectively (Table 4). Among the unfertilized varieties, 'Tordis' and 'Björn' gave the best energy performance, with ERs of 47.7 and 48.2, respectively. Average annual net heat output varied from 69 to 234 GJ ha−<sup>1</sup> year−<sup>1</sup> between the different *Salix* varieties and treatments. Fertilized 'Tordis' had the highest primary energy input of all the varieties as it had the highest yield levels, leading to high biomass and heat output

(234 GJ ha−<sup>1</sup> year−<sup>1</sup> ). Fertilization of 'Gudrun' and 'Loden' did not lead to major improvement in their yield over the unfertilized treatment, which resulted in relatively poor energy performance of the fertilized treatment of these two varieties. Among the unfertilized treatments, the variety 'Björn' had the highest annual heat output, 150 GJ ha−<sup>1</sup> year−<sup>1</sup> . The energy output from the heating plant is directly proportional to the biomass yield, which was higher when the plots were fertilized. Hence, the energy outputs in the form of heat were consistently higher for the fertilized treatment compared with the unfertilized treatment. The primary energy input for the fertilized treatment of each variety was about 2.5–4.6 higher than in the equivalent non-fertilized treatment. Consequently, ERs for the unfertilized cases was much higher than in the fertilized cases.

**Table 4.** Primary energy input, heat output, energy in biomass and energy ratio for the six SRC *Salix* varieties in fertilized and unfertilized treatments during two rotation periods (years 0–50). F0 and F+ refer to the unfertilized and fertilized treatments, respectively.


The contribution of the individual cultivation, transportation and handling processes to the total primary energy input over the study period are described in Table 5. The primary energy associated with pesticides, field preparation, production and planting of seedlings, and stump removal were the same for all six *Salix* varieties and treatments, as these processes are independent of variety type and fertilization. These are presented on a per hectare basis. The processes of harvesting, chipping, forwarding and transportation are directly proportional to the amount of shoot biomass produced, and hence are presented on basis of per GJ of energy in biomass. Production and spreading of fertilizers were the greatest contributor to primary energy input for fertilized cases, while it was zero for non-fertilized cases.

**Table 5.** Primary energy inputs by process category associated with the bioenergy system of six *Salix* varieties in fertilized and unfertilized treatments over the 50-year study period.


<sup>a</sup> Processes which are equal for all varieties. <sup>b</sup> Primary energy associated with fertilization is zero for the unfertilized treatment. <sup>c</sup> These processes are proportional to the amount of biomass produced in the field.

#### *3.2. Soil Organic Carbon*

Soil carbon modelling results showed that all varieties and treatments led to an increase in SOC over the initial level in the topsoil (0–20 cm) during the study period (50 years) consisting of two rotation periods (Table 6). The SOC stock calculated by the ICBM model at the end of both the first rotation period (after 25 years) and second rotation period (after 50 years) are shown in Table 6. Fertilized 'Loden' and 'Björn' showed the lowest net increase in SOC during the 50-year period, 15.8 and 13.3 Mg ha−<sup>1</sup> , respectively. These values were only slightly greater than the SOC increase for the fallow reference case (9.5 Mg C ha−<sup>1</sup> ).

**Table 6.** Initial, total and net soil organic carbon increase in the 0–20 cm soil layer after two rotation periods (50 years), as calculated by the ICBM soil carbon model. F0 and F+ refer to the unfertilized and fertilized treatments, respectively.


The carbon modelling results also showed that the unfertilized treatment for each variety was able to sequester about 1.6 to 3.3 times more SOC than the fertilized case, except for 'Tora'. Both treatments of 'Tora' led to a similar increase in SOC stock in the topsoil.

The low-yielding variety 'Jorr' showed the greatest potential for net carbon sequestration, capturing 73.6 Mg C ha−<sup>1</sup> and 44.9 Mg C ha−<sup>1</sup> over 50 years in the unfertilized and fertilized treatments, respectively. The variety 'Gudrun' had similar biomass yields for both the fertilized and unfertilized treatments (Table 1), but net SOC increase in the unfertilized case was almost double that in the fertilized case. 'Björn' had high biomass yields, but the SOC increase was at the lower end of the spectrum. Thus, no clear correlation between biomass yield and net SOC increase was established. These results indicate that the impacts on SOC are variety-specific, and that fertilization in general leads to lower net SOC increase.

#### *3.3. Time-Dependent Climate Impact*

3.3.1. Impact Per Hectare of Land (Including Substitution Effects)

All *Salix* varieties and treatments gave a negative temperature response (∆*Ts*) over the study period, which equated to a lowering of the global mean temperature when substituting reference fossil energy (natural gas) and reference land use (fallow) (Figure 2). There was great variation in temperature response between the varieties, from <sup>−</sup>2.15 <sup>×</sup> <sup>10</sup>−<sup>10</sup> K ha−<sup>1</sup> for fertilized 'Loden' to <sup>−</sup>5.99 <sup>×</sup> <sup>10</sup>−<sup>10</sup> K ha−<sup>1</sup> for fertilized 'Tordis'. Fertilized 'Tordis', 'Björn', 'Tora' and 'Jorr' had the greatest negative ∆Ts per hectare of land, which is explained by the high levels of yield combined with an increase in SOC stocks. These cases represent the best use of land area under the study conditions for climate change mitigation.

mitigation.

fertilization led to a lower climate mitigation potential.

**Figure 2.** Time-dependent temperature response of the *Salix* SRC systems with substitution effects included. The vertical dashed line represents the end of the first rotation and start of the second (at 25 years). F0 and F+ refer to the unfertilized and fertilized treatments, respectively. **Figure 2.** Time-dependent temperature response of the *Salix* SRC systems with substitution effects included. The vertical dashed line represents the end of the first rotation and start of the second (at 25 years). F0 and F+ refer to the unfertilized and fertilized treatments, respectively.

3.3.2. Impact Per Unit of Heat Output (Including Substitution Effects) A different picture emerges when the climate impacts from all cases were expressed based on their function of delivering energy services (per MJheat) and replacing fossilgenerated heat (Figure 3). Unfertilized 'Jorr' showed the greatest climate mitigation effect (−5.11∙× 10−15 K MJ−1), while fertilized 'Björn' (−2.39∙× 10−15 K MJ−1) had the lowest. The nonfertilized varieties showed a greater negative temperature response (per MJheat) than the fertilized varieties. This can be attributed to the higher primary energy demand for the Although the unfertilized treatment of each variety had greater CO<sup>2</sup> sequestration potential, the increase in biomass output achieved by fertilization led to higher replacement of fossil energy. As a result, fertilized cases had lower ∆*Ts* values. 'Loden' and 'Gudrun' were exceptions, as their fertilized cases showed a greater temperature response than the unfertilized cases. These two varieties gained little to no improvement in their yield from fertilization, so the additional energy and material input through fertilization led to a lower climate mitigation potential.

is explained by the high levels of yield combined with an increase in SOC stocks. These cases represent the best use of land area under the study conditions for climate change

Although the unfertilized treatment of each variety had greater CO2 sequestration potential, the increase in biomass output achieved by fertilization led to higher replacement of fossil energy. As a result, fertilized cases had lower Δ*Ts* values. 'Loden' and 'Gudrun' were exceptions, as their fertilized cases showed a greater temperature response than the unfertilized cases. These two varieties gained little to no improvement in their yield from fertilization, so the additional energy and material input through

#### fertilized treatments, combined with the greater SOC increase for the unfertilized cases. 3.3.2. Impact Per Unit of Heat Output (Including Substitution Effects)

The unfertilized cases were more favorable for climate change mitigation on comparing when the climate impacts per unit of energy delivered (MJheat) by the biomass systems. This is relevant when comparing energy generation systems and land is not a restricted resource. Unfertilized 'Jorr' and 'Loden' were the best-performing varieties in terms of potential for temperature reduction per unit of energy, although they had the lowest biomass yield. Fertilized 'Loden', 'Gudrun' and 'Björn' had the lowest temperature A different picture emerges when the climate impacts from all cases were expressed based on their function of delivering energy services (per MJheat) and replacing fossilgenerated heat (Figure 3). Unfertilized 'Jorr' showed the greatest climate mitigation effect (−5.11 <sup>×</sup> <sup>10</sup>−<sup>15</sup> K MJ−<sup>1</sup> ), while fertilized 'Björn' (−2.39 <sup>×</sup> <sup>10</sup>−<sup>15</sup> K MJ−<sup>1</sup> ) had the lowest. The non-fertilized varieties showed a greater negative temperature response (per MJheat) than the fertilized varieties. This can be attributed to the higher primary energy demand for the fertilized treatments, combined with the greater SOC increase for the unfertilized cases.

The unfertilized cases were more favorable for climate change mitigation on comparing when the climate impacts per unit of energy delivered (MJheat) by the biomass systems. This is relevant when comparing energy generation systems and land is not a restricted resource. Unfertilized 'Jorr' and 'Loden' were the best-performing varieties in terms of potential for temperature reduction per unit of energy, although they had the lowest biomass yield. Fertilized 'Loden', 'Gudrun' and 'Björn' had the lowest temperature decrease (∆*Ts* per MJheat) over the study period. Those cases also had the lowest SOC increase over the study period.

**Figure 3.** Temperature response per MJ of heat for the *Salix* SRC systems, with substitution effects included. The vertical dashed line represents the end of the first rotation and start of the second (at 25 years). F0 and F+ refer to the unfertilized and fertilized treatments, respectively. **Figure 3.** Temperature response per MJ of heat for the *Salix* SRC systems, with substitution effects included. The vertical dashed line represents the end of the first rotation and start of the second (at 25 years). F0 and F+ refer to the unfertilized and fertilized treatments, respectively.

#### *3.4. Global Warming Potential 3.4. Global Warming Potential*

increase over the study period.

The life cycle impact assessment of the different varieties under the two fertilization regimes showed varying climate impacts. A negative value of the GWP100 metric means that there is a net reduction of atmospheric GHG concentration, leading to a climate mitigation effect. In absolute terms (not including the effect of substituting the reference case), unfertilized 'Jorr' had the lowest GWP100 (−333 Mg CO2-eq. ha−1), while fertilized The life cycle impact assessment of the different varieties under the two fertilization regimes showed varying climate impacts. A negative value of the GWP<sup>100</sup> metric means that there is a net reduction of atmospheric GHG concentration, leading to a climate mitigation effect. In absolute terms (not including the effect of substituting the reference case), unfertilized 'Jorr' had the lowest GWP<sup>100</sup> (−333 Mg CO2-eq. ha−<sup>1</sup> ), while fertilized 'Björn' had the highest total GWP<sup>100</sup> (30 Mg CO2-eq. ha−<sup>1</sup> ) (Table 7).

decrease (Δ*Ts* per MJheat) over the study period. Those cases also had the lowest SOC

'Björn' had the highest total GWP100 (30 Mg CO2-eq. ha−1) (Table 7). Among the fertilized varieties, 'Björn' and 'Loden' were the worst performing in terms of climate mitigation effects per hectare over 50 years. These varieties had the lowest increase in SOC among the fertilized varieties, which contributed to their poorer climate performance. Fertilized 'Tora' and 'Jorr', which had the highest increase in SOC among fertilized varieties, showed the greatest reduction in GWP, indicating the importance of Among the fertilized varieties, 'Björn' and 'Loden' were the worst performing in terms of climate mitigation effects per hectare over 50 years. These varieties had the lowest increase in SOC among the fertilized varieties, which contributed to their poorer climate performance. Fertilized 'Tora' and 'Jorr', which had the highest increase in SOC among fertilized varieties, showed the greatest reduction in GWP, indicating the importance of soil carbon sequestration for achieving a climate change-mitigating effect.

soil carbon sequestration for achieving a climate change-mitigating effect. Considering the effects of substitution of a natural gas-based reference system for the SRC *Salix*, all varieties showed a climate-mitigating effect during the study period. The magnitude of the mitigation effect ranged from −312 Mg CO2-eq.ha−1 for fertilized 'Loden' to −858 Mg CO2-eq. ha−1 for fertilized 'Tordis'. On considering the substitution effects, the yield level influenced GWP. High yields contributed to a greater climate mitigation effect, as seen for fertilized 'Tordis', 'Björn', 'Jorr' and 'Tora'. This is a result of avoided Considering the effects of substitution of a natural gas-based reference system for the SRC *Salix*, all varieties showed a climate-mitigating effect during the study period. The magnitude of the mitigation effect ranged from <sup>−</sup>312 Mg CO2-eq.ha−<sup>1</sup> for fertilized 'Loden' to <sup>−</sup>858 Mg CO2-eq. ha−<sup>1</sup> for fertilized 'Tordis'. On considering the substitution effects, the yield level influenced GWP. High yields contributed to a greater climate mitigation effect, as seen for fertilized 'Tordis', 'Björn', 'Jorr' and 'Tora'. This is a result of avoided equivalent emissions from heat produced in the fossil reference system.

equivalent emissions from heat produced in the fossil reference system.

**Table 7.** Global warming potential (GWP100) for the *Salix* cropping systems and fossil-powered reference system and effect of substitution when *Salix* was assumed to replace the reference system. The GWP is expressed in both Mg CO<sup>2</sup> -eq per hectare and g CO<sup>2</sup> -eq per MJ of heat during the 50-year study period. F0 and F+ refer to the unfertilized and fertilized treatments, respectively. A positive value indicates emissions to atmosphere, and a negative value indicates reduction.


<sup>a</sup> Climate impact of SRC *Salix* system without substitution effect. <sup>b</sup> Climate impact of reference system—heat from natural gas and green fallow land use. <sup>c</sup> Climate impact of SRC *Salix* system including substitution effects of reference system.

> From the perspective of heat delivered with substitution effects, fertilized 'Jorr' had the highest climate mitigation effect, −192 g CO2-eq.MJheat <sup>−</sup><sup>1</sup> produced, while fertilized 'Gudrun' was at the other end of the spectrum, with −74 g CO2-eq.MJheat <sup>−</sup><sup>1</sup> produced.

The contribution of the *Salix* production chain emissions, SOC sequestration and substitution effects to the overall net GWP<sup>100</sup> per hectare for the different *Salix* varieties are presented in Figure 4. The production chain leads to GHG emissions while SOC sequestration and substitution effects remove or replace GHG emissions. Emissions from the production chain (field operations, transportation, fertilizer and soil emissions) are higher for fertilized varieties due to fertilizer production and greater soil N2O emissions. The substitution effects are the main contributor to the overall negative GWP<sup>100</sup> for all *Salix* varieties, except for unfertilized Loden and Jorr. These two varieties showed a greater potential of SOC sequestration relative to harvest yields in comparison to the other *Salix* varieties. Alternatively fertilized Gudrun and Loden have a higher GWP<sup>100</sup> compared to their unfertilized counterparts due to relatively lower improvement in yield. *Forests* **2021**, *12*, 1529 17 of 27

**Figure 4.** Contribution of the *Salix* production chain, SOC sequestration, substitution effects (from replacing green fallow and fossil energy) to the net GWP100 per hectare of each of the *Salix* bioenergy system. **Figure 4.** Contribution of the *Salix* production chain, SOC sequestration, substitution effects (from replacing green fallow and fossil energy) to the net GWP<sup>100</sup> per hectare of each of the *Salix* bioenergy system.

**Table 8.** Sensitivity analysis of soil organic carbon (SOC) sequestration and global warming potential (GWP100) for the six *Salix* varieties in the fertilized and unfertilized treatments, when soil depth considered was increased from 20 to 25 cm. F0

 **(Mg ha<sup>−</sup>1) (Mg ha−1 yr−1) (Mg ha−1) (g MJ−1) (Mg ha−1) (Mg ha−1 yr−1) (Mg ha−1) (g MJ−1)**  Björn F0 37.2 0.74 −143 −19 40.1 0.80 −100 −13 Björn F+ 13.3 0.27 30 3 10.8 0.22 58 6 Gudrun F0 50.5 1.01 −220 −44 64.1 1.28 −193 −38 Gudrun F+ 20.9 0.42 −31 −6 19.7 0.39 9 2 Jorr F0 73.6 1.47 −333 −97 82.0 1.64 −258 −75 Jorr F+ 44.9 0.90 −114 −13 45.7 0.91 −55 −6 Loden F0 51.5 1.03 −231 −68 57.1 1.14 −175 −51

**0–20 cm Soil Layer 0–25 cm Soil Layer** 

**Net SOC Increase** 

**Annual SOC** 

**Uptake GWP100 GWP100**

from considering a soil depth of 25 cm, compared with the base case of 20 cm, are shown in Table 8. Generally, a deeper soil layer gave a greater net SOC increase within the system boundary, leading to a lower climate impact. Fertilized 'Björn', 'Gudrun' and 'Loden' were exceptions to this, as the net SOC increase in the 0–25 cm layer was smaller than in the 0–20 cm layer. Consequently, the climate impacts for these three cases were also

Fertilized 'Björn', 'Gudrun' and 'Loden' showed the lowest SOC increase in field measurements from 2001–2018, which led to lower SOC sequestration rates. On considering a deeper soil layer, the starting SOC level prior to *Salix* establishment was also higher. In absolute terms, the final SOC stock was greater with a deeper soil layer, but the net increase was lower for these three cases when compared with a shallower (20 cm) layer. Thus, a lower sequestration rate combined with a greater initial SOC level led to a smaller SOC increase for these fertilized varieties with increased soil depth. Overall, the changes in SOC stock and climate impacts were not highly influenced by considering a

*3.5. Sensitivity Analysis* 

deeper soil layer of 25 cm.

**Uptake GWP100 GWP100**

and F+ refer to the unfertilized and fertilized treatments, respectively.

**Annual SOC** 

**Variety and Treatment** 

**Net SOC Increase** 

greater.

#### *3.5. Sensitivity Analysis*

The sensitivity analysis results for net SOC increase and climate impacts (GWP100) from considering a soil depth of 25 cm, compared with the base case of 20 cm, are shown in Table 8. Generally, a deeper soil layer gave a greater net SOC increase within the system boundary, leading to a lower climate impact. Fertilized 'Björn', 'Gudrun' and 'Loden' were exceptions to this, as the net SOC increase in the 0–25 cm layer was smaller than in the 0–20 cm layer. Consequently, the climate impacts for these three cases were also greater.

**Table 8.** Sensitivity analysis of soil organic carbon (SOC) sequestration and global warming potential (GWP100) for the six *Salix* varieties in the fertilized and unfertilized treatments, when soil depth considered was increased from 20 to 25 cm. F0 and F+ refer to the unfertilized and fertilized treatments, respectively.


Fertilized 'Björn', 'Gudrun' and 'Loden' showed the lowest SOC increase in field measurements from 2001–2018, which led to lower SOC sequestration rates. On considering a deeper soil layer, the starting SOC level prior to *Salix* establishment was also higher. In absolute terms, the final SOC stock was greater with a deeper soil layer, but the net increase was lower for these three cases when compared with a shallower (20 cm) layer. Thus, a lower sequestration rate combined with a greater initial SOC level led to a smaller SOC increase for these fertilized varieties with increased soil depth. Overall, the changes in SOC stock and climate impacts were not highly influenced by considering a deeper soil layer of 25 cm.

#### **4. Discussion**

The analysis revealed that cultivation of the selected *Salix* varieties for bioenergy to substitute equivalent fossil fuels (under the given environmental and site conditions) can potentially mitigate climate change as it has a net cooling effect on global mean surface temperature over a 50-year time horizon. *Salix* variety had a major influence on the climate change mitigation potential. The *Salix* varieties in this study varied in some key factors derived from measured field data (SOC sequestration, biomass yield and response to fertilization) and these factors affected the overall climate impact between the different varieties. The major contribution to the climate mitigation effect comes from substitution of fossil fuels and SOC sequestration. While fossil fuel replacement is relatively easy to estimate using harvest yields, estimation of SOC change over time is complicated as it is subject to various environmental conditions and uncertainties.

The flue gas condensation technology assumed in the incineration plant with heat recovery gives high energy efficiency, leading to a greater output of energy delivered which puts the energy ratio in the higher range. This is a common technology in Swedish power plants [64], although it might not be common in other countries. The conversion efficiency

of the thermochemical processes selected in a study determines the amount of useful energy production from the system and its subsequent ER. The ER in this study was within the range 16.1–28.2 for fertilized *Salix* varieties and 43.2–48.2 for non-fertilized varieties. Values of ER reported in the literature range from 16 to 79 in energy performance analysis studies [69–76], which are indicative of different methods and assumptions considered in individual studies.

The results in the present study indicate that *Salix* variety and fertilization regime strongly affect the NPP distribution between aboveground and belowground biomass. The ratio of NPP of annual aboveground biomass (AGB) to belowground biomass (BGB) in our study was estimated at 0.4–1.8 for unfertilized treatments and 1.9–8.0 for fertilized treatments (Table 3). The estimation of these values is based on the well-established conception that variety and fertilization influence the production of BGB relative to AGB, which leads to variation in SOC change.

Data on AGB and BGB production and allocation for *Salix* from some studies are presented in Table 9. Heinsoo et al. [77] reported large differences in the magnitude of the ratio between AGB and fine root production for fertilized and control plots in an Estonian *Salix* plantation with two species (*S. viminalis* and *S. dasyclados*). This study reported a significant reduction in annual production of fine root biomass under fertilization, while AGB production was greatly improved. The AGB to BGB production ratio for *S. viminalis* was 1.04–2.07 for lysimeter-grown *Salix* in sandy and clayey soils [61]. Rytter [78] found significant differences in biomass allocation to fine-roots between N-limited and unlimited growing conditions (*for S. viminalis*) but no change in annual turnover rates of fine roots. These studies support the idea that fertilization can lead to lower BGB production that leads to very different AGB to BGB ratios between unfertilized and fertilized treatments.


**Table 9.** Aboveground to Belowground biomass production and allocation ratios of *Salix* varieties reported under different environmental conditions.

> Pacaldo et al. [79] reported biomass allocation for a single *Salix* variety (*S. dasyclados*) from two locations with different plantation ages and soil conditions; based on their data, the AGB to BGB allocation ratio was 0.32–0.61. The ratio of annual production of AGB to BGB in our study falls within the range of values reported for *Salix* in different studies, but these figures need to be validated by further studies on belowground biomass to increase accuracy in soil carbon modelling estimates.

*Salix* roots are characterized by high growth and mortality rates [80] and are not bound by seasonal patterns, with some growth and decay observed even during winter [61]. This indicates that root production is relatively higher under a non-fertilized regime, which combined with unchanged turnover rates would lead to higher belowground biomass input to the soil compared with a fertilized treatment, which can lead to greater SOC stocks.

The few previous studies on how biomass growth and allocation differ between *Salix* varieties [43,81,82] have shown that variety and growing environment can have significant impacts on biomass allocation and growth patterns. Cunniff et al. [43] found that belowground allocation differed up to 10% between *Salix* varieties and up to 94% between locations. Furthermore, a study by Gregory et al. [81] found significant differences in root density between *Salix* varieties, especially in the upper layers.

There is a scarcity of data on belowground biomass allocation and its variation between *Salix* varieties and environmental conditions. Only a few studies measured the production and turnover of roots (especially fine roots) as these analyses are time-consuming, laborintensive and expensive [83]. Furthermore, the estimation of root growth and number can greatly vary due to the measurement method used [84]. A study including two *Salix* varieties [85] has also shown differences in decomposition rates of fine root litters, which further stresses the need for variety focused studies. This makes it difficult and complicated to compare data on aboveground to belowground biomass accumulation from different sources, as variations can occur owing to multiple factors. This is a source of variability in determining especially belowground biomass growth and its contribution to SOC sequestration. There is need for further research and standardization of methods to enable comparisons and calibration of soil carbon models to make more reliable long-term predictions.

In spite of the uncertainties regarding the variety-related input variables for soil carbon modelling, this investigation provides useful insights into the expected variety-related SOC changes over a longer period of time and based on measured data of above ground biomass and soil SOC over an 18-year period. While these uncertainties might affect all investigated varieties in a similar way, they are likely to result mostly in an uncertain absolute magnitude of SOC after a certain period of time, whereas the variety-specific pattern of SOC change is expected to be more robust. Thus, we believe that the use of *Salix* variety-specific data from the field study in this analysis is a clear improvement over previous studies dealing with SOC modelling in *Salix*. The scaling and extrapolation of soil carbon models is a challenge due to lack of long-term data and the complexity of SOC sequestration mechanisms. Despite the challenges, such approaches with assumed data are a necessary part of making sustainable management decisions. The accuracy of the models and their predictions can be constantly adjusted by feedback of new measured data and advancing knowledge of SOC.

The carbon modelling based on measured SOC levels from the measured field trial data, showed that non-fertilization led to a greater increase in SOC compared with fertilization of the same variety under the same soil conditions. A relationship between shoot biomass yield and increase in SOC was expected from other studies, but was not seen in our study, as greater yield did not correlate with more CO<sup>2</sup> being sequestered in the soil. For example, unfertilized 'Jorr' had one of the lowest shoot biomass yields among all varieties investigated here, but showed the highest increase in SOC in the top 20 cm soil layer; while fertilized 'Björn', with high biomass output, had one of the lowest increases in SOC stocks. This result questions the common assumption of higher shoot biomass yield leading to a greater increase in SOC due to higher production of leaf and root litter. While greater shoot biomass may lead to increased leaf litter production, root litter production might show a differential pattern. Interestingly, Pappas et al. [86] found that in boreal forests, aboveground biomass growth is decoupled from the carbon input to the ecosystem, highlighting the significance of belowground carbon inputs independent from aboveground growth. Also, Khan et al. [87] conclude that N-fertilization increases harvests for crops but can have a negative effect on SOC sequestration.

The SOC accumulation rate in our study was 0.24–1.29 Mg ha−<sup>1</sup> yr−<sup>1</sup> for the 0–20 cm soil layer over 50 years. Direct comparisons of SOC changes reported in different studies are difficult, because of variations in initial soil conditions, study period, growing conditions, methodology and depth of soil profile considered in the study. The test site had a clay content of 18%. This clay content promotes long-term carbon sequestration by stabilization of SOC against decomposition [88]. SOC sequestration rates of 1.44–2.27 Mg ha−<sup>1</sup> yr−<sup>1</sup> for the top 30 cm soil layer have been reported for two *Salix* varieties during a 6-year study period in the UK [81]. Other recent studies have recorded SOC sequestration rates of 1 Mg ha−<sup>1</sup> yr−<sup>1</sup> in the upper 10 cm in Italy [89] and high levels of 6.7–10.2 Mg ha−<sup>1</sup> yr−<sup>1</sup> in the upper 60 cm in Belgium [90]. In a meta-analysis by Agostini et al. [18], SOC accumulation rates in the range <sup>−</sup>0.06 to 3.57 Mg ha−<sup>1</sup> yr−<sup>1</sup> Mg ha−<sup>1</sup> yr−<sup>1</sup> were found for *Salix*. However, the studies in the meta-analysis varied greatly in methodology, soil conditions and length of study period, impeding comparisons. Greater accumulation rates have been reported for *Salix* grown on former arable land compared with grassland [91]. The amount and rate of SOC change are highly dependent on the previous land use, which consequently plays a major role in the climate impact. In any case, the annual SOC accumulation rates in our study clearly fall within the range reported from other sources. However, there is a need for further investigation of root production, turnover and decay based on soil types, plant variety, and nutrient regimes because the soil carbon change has an important effect in determining the climate impacts and should therefore be included in systems studies

From a land use perspective, the climate impact was governed by the *Salix* biomass yield. Higher biomass yields contributed to a greater replacement of fossil energy, thereby contributing to a greater cooling effect. Exceptions to this were the varieties 'Gudrun' and 'Loden', which showed almost no improvement in yield from fertilization. Thus, for optimum climate mitigation per unit land area, a high-yielding variety needs to be selected. However, on comparing the varieties from the functional unit of energy output (per MJ of energy output), the SOC sequestration potential played the major role in determining the climate impact. In this regard, the unfertilized varieties with good yields and SOC sequestration potential offered greater potential cooling effects. Hence the basis of comparison (land use or energy output) also plays an important role in the interpretation of climate impact results.

A literature review by Djomo et al. [54] reported that LCAs of short-rotation bioenergy crops often use very different system boundaries, impact indicators and conditions, which makes comparisons between studies difficult. All scenarios analyzed in the present study showed a GWP reduction potential of 95 to 237% compared with the fossil reference system (Table 7). This is much higher than the 90–99% reduction potential presented in the review by Djomo et al. [54], but only one study in that review had considered the effects of soil carbon sequestration. The high yield levels for unfertilized *Salix* varieties in the present study, combined with SOC sequestration, explain the much higher GWP reduction potential estimated in our study. However, the soil is not an endless C sink and increasing temperatures under climate change will accelerate the degradation of SOC, thereby reducing the size of the sink. Thus, the SOC sequestration potential is expected to decrease over time because of climate change. It is difficult to predict technological change during a long period, so in this study the systems were assumed to remain static during the 50-year period. Assuming a constant level of cultivation of *Salix* at the same location, the cooling effect from an increasing SOC pool will eventually decline, but the warming effect due to GHG emissions from the production system will continue to increase over time. The major sources of emissions are production of fertilizers and N2O soil emissions. From a longer time perspective, these emissions will be of uppermost importance in improving the climate performance of *Salix* production systems.

Default IPCC values for calculation of nitrogen leaching from mineral fertilizers were used in this study, due to lack of site-specific data. *Salix* has been shown to have lower nitrogen leaching rates than other crops [40,92], and thus the default values used here might be on the higher side for *Salix* cultivation. In the field trials, all fertilized plots were enriched with the same quantity of mineral fertilizer, which might be higher or lower than the optimal fertilization level of the plant. Fertilization studies can help to determine the optimum fertilization by variety, which will greatly influence the emissions and energy input of the fertilization phase and the AGB to BGB production ratio.

The scarcity of complete data that are site- and variety-specific for all aspects of the *Salix* bioenergy production and decomposition poses some limitations. The SOC changes and climate impacts from one study should not be directly extrapolated to other cases as there are several factors (such as environmental conditions and previous land use) which can lead to different results. The results of this study stress the importance of accounting for variety and fertilization effects when estimating SOC changes and climate impacts of *Salix* bioenergy systems. As such, these effects should not be ignored in planning for bioenergy systems of the future. There is potential to develop varieties with high levels of both shoot and root biomass with efficient fertilizer utilization, which would give a greater climate mitigation benefit.

#### **5. Conclusions**

Soil carbon modelling based on Swedish field trial data showed that all *Salix* varieties tested can potentially increase the SOC stock in the soil over a period of 50 years under given soil conditions of vertic cambisols. *Salix* variety and fertilization treatment determined the magnitude of CO<sup>2</sup> sequestration. No clear relationship was found between biomass yield and SOC sequestration potential across the varieties and soil type used in this study, which indicates that belowground biomass accumulation and decomposition should not be directly estimated from shoot yield alone. High production and turnover rate of fine roots was estimated to be the major contributor to SOC inputs by *Salix*. Fertilization led to an increase in biomass yield (and therefore energy output), but a decrease in SOC sequestration potential, across all varieties.

The fertilized 'Björn' biomass systems showed a warming effect on the climate (positive GWP) without inclusion of substitution effects from replacing a natural gas-based reference case. However, all varieties and treatments showed the potential to mitigate climate change (negative GWP and ∆*Ts*) on inclusion of substitution effects. High-yielding *Salix* varieties had the greatest potential to mitigate climate change when looking from a land-use perspective. When comparing per energy unit, the SOC sequestration effects become more prominent in determining the overall magnitude of the climate change mitigation potential of the different *Salix* varieties. System analysis approaches like LCA should incorporate SOC effects, which can significantly affect the climate impacts of biomass cultivation systems, as seen here for six *Salix* varieties.

Initial soil conditions are very important for biomass productivity because they influence the amount of leaf and root litter produced, which in turn influence the SOC accumulation rate. Hence, previous land use needs careful consideration when evaluating climate impacts. Results in previous studies, combined with our findings, show that there is some uncertainty about SOC sequestration rates, which makes it important to research belowground biomass production, including varietal and location effects.

The results from this study highlight the effects of variety on SOC sequestration, biomass yield, response to fertilization and, ultimately, climate impact. This shows the importance of selecting the appropriate variety of *Salix* and management practices based on the desired outcome from the bioenergy system.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/f12111529/s1, Table S1: Parameters used to model SOC changes in ICBM. Table S2: Initial values of aboveground (Ya) and belowground (Y<sup>b</sup> ) young pool, and old pool (O) used in the ICBM calculation. Table S3: Values used to calculate the biomass allocation between the different pools (stems, leaves, fine roots and coarse roots) at stages of growth as a percentage of their 3-year net primary production. Table S4: The nitrogen content in leaf litter was calculated according to the abscission leaf N content by variety and fertilization as reported by Weih and Nordh, 2002. Table S5: The nitrogen (N) content of roots was calculated from the dataset by Manzoni et al., 2021. Table S6:

Energy input and emissions associated with production of pesticides, cutting, fertilizer and fossil fuels. Table S7: Data used to estimate emissions and energy usage for operations in the biomass procurement chain. Table S8: Data used to model emissions and energy for the reference case.

**Author Contributions:** Conceptualization, S.K., H.K.P., M.W., C.B., Å.N. and P.-A.H.; methodology, S.K.; formal analysis, S.K.; investigation, C.B.; data curation, M.W. and C.B.; writing—original draft preparation, S.K.; writing—review and editing, S.K., H.K.P., M.W., C.B., Å.N. and P.-A.H.; supervision, P.-A.H. and M.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received funding from Formas (The Swedish Research Council for Environment, Agricultural sciences and Spatial Planning) for the OPTUS and MixForChange projects under grants no 942-2016-31 and 2020-02339. Part of the soil analyses was funded by Deutsche Forschungsgesellschaft (DFG), project no. BA 1494/9-1.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available the supplementary material and within the article. If required, further relevant details are available from the corresponding author on request.

**Acknowledgments:** We are grateful to Niclas Ericsson and Torun Hammar for their work on soil carbon modelling and climate impacts of *Salix*, and their help in adapting those methods for this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** Default parameters used in Equations (2) and (3) to calculate N2O emissions as described in IPCC 2019 [56].


#### **References**


### *Article* **Potential Areas in Poland for Forestry Plantation**

**Piotr Boruszewski <sup>1</sup> , Agnieszka Laskowska <sup>1</sup> , Agnieszka Jankowska 1,\*, Marcin Klisz <sup>2</sup> and Marcin Mionskowski <sup>2</sup>**


**Abstract:** Plantations have many advantages when compared to natural or semi-natural forests, such as shortening production cycles, the production of wood with specific characteristics, and near-market production concentrations. The intensive development of this form of industrial wood production is practiced all over the world. The wood industry in Poland struggles in recent years, with a large shortage of wood. The deficit of wood has been accumulated for several years and is steadily increasing. One of the possibilities to change this trend can be development of fast-growing trees plantations. The main aim of this study was to determine the potential of land in Poland, which could be used for the cultivation of fast-growing trees plantations. The analyses took into account the area and marginal agricultural land. The potential plantation land areas were determined for poplar cultivar "Hybrid 275" and European larch (*Larix decidua* Mill.). The results show a possibility to generate a considerable area that can be developed into plantations of fast-growing trees in Poland. According to the analyses carried out for the purpose of this study, with only 5% use of the sown area and 5% use of forest lands, as well as the boscage (wooded land and bushy land), it is possible to obtain approximately 0.6 MM ha of land for fast-growing tree plantations. In the case of planting 50% of these lands with larch and 50% with poplar, and if a 50% capacity of the plantation is assumed, it will be possible to obtain nearly 6 MM m<sup>3</sup> of wood per year.

**Keywords:** capacity; European larch; fast-growing trees; plantations; plantation area; poplar cultivar "Hybrid 275"; sown area

#### **1. Introduction**

According to the European Panel Federation (EPF), in recent years, there has been rapid growth in the development of European wood sectors. In 2019, in Poland, 11.7 MM m<sup>3</sup> of wood-based panels were produced, which made an 18% share in the UE market; therefore, Poland has become the second (after Germany)-largest manufacturer in Europe [1]. The strategic value of wood in Poland is confirmed by the fact that the industry based on the processing of this raw material is one of the pillars of the Polish economy. The market share of the forestry-based industry in GDP (gross domestic product) is ca. 1.7% and is higher than in the EU (about 1%) [2]. Despite that, the wood-based panels sector still faces serious challenges, especially a limited availability of raw wood. Since 2012, a shortage of wood has been observed for the wood-based panels industry in Poland, reaching 20%, and the trend is maintained [3]. Raw material shortages are now a widespread problem in the world. It is estimated that the wood deficiency would reach 200 MM m<sup>3</sup> and 300 MM m<sup>3</sup> in 2025 and 2030, respectively [4]. This is an apparent confirmation of the thesis about the strategic importance of wood as a raw material [5].

One of the possibilities to reduce the shortage of raw material can be creating an outer wood production ecosystem or a sub-ecosystem within forest ecosystems consisting of plantations of fast-growing trees. Already, over one-third of the global wood production comes from plantations [6]. There are different directions of using wood from plantations.

**Citation:** Boruszewski, P.; Laskowska, A.; Jankowska, A.; Klisz, M.; Mionskowski, M. Potential Areas in Poland for Forestry Plantation. *Forests* **2021**, *12*, 1360. https:// doi.org/10.3390/f12101360

Academic Editors: Dirk Landgraf and Timothy A. Martin

Received: 15 July 2021 Accepted: 3 October 2021 Published: 7 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Initially, the leading direction was the establishment of plantations with the possibility of obtaining wood mainly for energy purposes. There are many local, national and international initiatives to support the development of renewable energy sources [7]. In the past decades, the most important tree species grown in European short-rotation coppices intended as a renewable energy source were willow, poplar and, to a lesser extent, aspen and robinia [8,9]. European commercial willow plantations were mainly located in Sweden, the UK, Poland and Germany [7,8,10,11]. In Poland, they covered a small area of approx. 6800 ha in 2008 [8] and approx. 5515 ha in 2011 [12]. A steady decrease of willow plantations in recent years in Sweden was observed too [13]. It should be noted that, in Poland, willow wood from plantation crops was grown mainly for energy purposes [12].

The growing deficit of wood, mainly in the wood-based panels sector, forced a change in the policy of establishing and running fast-growing tree plantations. Species intended for this type of purpose should be characterized by appropriate dimensional and qualitative characteristics [14]. Moreover, it was found that, in Poland, among the species of fastgrowing trees, the best prognoses are: poplar (mainly the cultivar "Hybrid 275") and European larch (*Larix decidua* Mill.) [15,16]. When relatively dense spacing (3 × 3 m) is used, a very high annual increment is obtained—in the case of larch, up to 14 m<sup>3</sup> <sup>×</sup> ha−<sup>1</sup> , and poplars even up to 25 m<sup>3</sup> <sup>×</sup> ha−1—while, usually, it is 14–20 m<sup>3</sup> <sup>×</sup> ha−<sup>1</sup> [16].

Warmbier et al., [17] pointed to the limited possibilities of using willow wood for the production of wood-based panels. Willow wood can be mainly dedicated for use only for core layers of particleboards, and replacing pine particles 25–30% with willow particles in the core layer of three-layer particleboards allows obtaining materials with properties that meet the requirements of the appropriate standards [18,19]. The wood of willow clones has an approx. 25% higher density than the wood of poplar clones. The use of wood of willow clones for the production of wood-based materials (especially with lower densities) is recommended to a lesser extent. This is due to the technological aspects and the anticipation of obtaining lower-strength parameters of boards made of higher-density wood, because, during pressing, particles from woods of higher density are less susceptible to compression [20–25]. In turn, the usefulness of plantation poplar and larch wood for the production of wood-based panels was confirmed by the published research results [26,27]. This was also evidenced by the implementation of technology and production of this type of material on an industrial scale in Europe (production based on plantation poplar wood).

Plantation development is a very important aspect for the Polish wood industry, especially for the manufacturers of wood-based panels. One of the most important facts justifying the establishment of plantations is the European context of strategic value of wood in the industry. According to the European Commission, wood is also considered an important source of raw materials for emerging bio-based industries [28]. Similar solutions to those already put in place by one of the leading manufacturers of wood pulp and paper can be successfully adopted by the manufacturers of wood-based panels who require large quantities of medium-size wood. Apart from the large corporations, State Forests National Forest Holding (SFN FH)—the main manager of Polish forests, as well as one of the leading manufacturers of wood—-should undertake the production of commercial wood from plantations, which would be in compliance with the global tendency to ease the pressure on natural forests. Plantations of fast-growing trees managed only by SFN FH utilize a small area that currently amounts to 2547.94 ha (equivalent of 0.03% of total forest land) [29].

The formation of forests on former agricultural land can be the result of a natural secondary progression. Recently, particularly in Europe, that is the result of intentional afforestation. This was the effect of promotion by the European Parliament and of the Council the Regulation (EU) No 1305/2013 [28]. The Regulation established a community aid scheme for forestry measures in agriculture, with the general aim of transforming agricultural lands into forested areas. Parallel to this, due to the increase in the world's demand on wood, extensive research on harvesting wood from fast-growing trees plantations is being carried out [30]. The current trends concern mainly wood harvested from genetically modified trees (GMO) [31–34]. As a result of the insufficient recognition of genetically mod-

ified materials, ethical aspects and legislation shortages, the wide utilization of wood from genetically modified trees in Europe is presently ineffective [35–38]. Insufficient knowledge in the area of genetically modified species, especially in the utilitarian context, imposes the development of new research directions. Experienced foresters are exceptionally helpful in the breeding of fast-growing trees. The advantages of plantations are not only high productivity but, also, the availability and easy access to highly concentrated and deployed resource of raw materials for the wood industry [39–42].

Plantations of fast-growing trees on agricultural lands have a positive effect on the structure of the soil due to the lack of regular heavy agrotechnical treatment. In a longer perspective, the content of carbon and nitrogen in the soil will increase. This is an additional argument for locating plantations of fast-growing trees on low-class lands for a period of time and subsequently returning to cultivating agricultural crops after resources from the plantation are collected. The periodical usage of agricultural land for plantations of fast-growing trees facilitates the regeneration of overexploited land by improving the soil fertility and, consequently, boosts crop growth. This is due to the factors such as [16]: (I) by penetrating the ground, tree roots crumble the lower layer of soil, (II) the intensity of mineral fertilizing is decreased thanks to plants using nutrients that return into the soil with falling leaves, (III) the organic matter from the forest litter increases the humus layer, and (IV) plantations protect soil against excessive evaporation and erosion.

Agroforestry as a form of plantation of fast-growing trees is widely known and used in Western Europe, Asia and South America. Plantations as such are established on spaces larger than standard plantations, where agricultural and meadow plants are primarily cultivated within the first few years [43]. It is beneficial, as the crop residues left in the rows on the fields additionally fertilize the soil. Moreover, agroforestry may be a link between agricultural plant cultivation and tree plantations. It is estimated that the average annual gain from a poplar plantation in Poland is comparable to that from the production of oil-yielding rape or wheat. Moreover, the production cycles become shorter thanks to advancements in science and technology [44]. Fast-growing trees on agricultural land can achieve high biomass yields with a relatively low input of nitrogen fertilizer and are regarded as efficient nitrogen users [8,45]. Dimitrou and Rutz [7] indicated a number of other, nonproduction benefits resulting from the cultivation of short rotation woody crops (SRC or SRWC). The authors report that this type of coppices helps to improve the water quality; enhance biodiversity; provide ecosystem services, i.e., hunting, beekeeping, water supply and fire protection; mitigate animal diseases between farms; prevent erosion; reduce artificial input materials, i.e., fertilizers and pesticides, and mitigate climate change due to carbon storage.

Recently, in Poland, there has been a noticeable increase of the fallowing and setaside process on agricultural land where the conditions for agricultural productions are unfavorable. Such land accounts for approximately 10% of the agricultural land [46]. This is caused mainly by a low availability of agricultural production areas, which results from unfavorable natural environment and soil quality [47]. Such land is located in areas where a combination of factors such as unfavorable conditions for agricultural production and unfavorable landscape features (eroded land, steppe formation, rocky ground and moorland) occur. The usefulness of agricultural land is also limited by the anthropogenic influence (land degraded and devastated due to careless human activity). The vicinity of factories, express roads and motorways is an additional factor that adds to the negative agricultural conditions. Agricultural land with unfavorable conditions for agricultural production as the above can be a potential area for plantations of fast-growing trees.

Collecting and processing data on forestry plantations is a common practice introduced in many countries around the world [48–50]. However, due to the lack of unambiguous criteria for classifying certain areas into the category of "forest plantation", it is difficult. The data should be derived from statistically designed inventories of forest plantations or statistics for planted areas reported by planting agencies or appearing in national reports. However, it often comes from many sources, e.g., nursery production, seedling distribution, estimates derived from agencies, industries and nongovernmental organizations participating in planting programs [51]. For this reason, each study in this area should be treated as a valuable source of information.

The main purpose of the study was to provide suggestions on the potential possibilities of meeting the demand for raw materials, which is considered to be the main problem of the wood industry. As part of the work, the areas available for the establishment of plantations of fast-growing trees were determined, taking into account the potential to generate wood resources as raw materials for the production of wood-based panels. The knowledge in this area is crucial to meet the needs of the wood industry— more, the woodbased panels industry in Poland is highly competitive. The potential plantation land areas were determined for poplar cultivar "Hybrid 275" and European larch. This work is an original study in terms of the definition of potential areas in Poland for forestry plantation.

#### **2. Materials and Methods**

In the first part of the work, the current data on the area and stock (taking into account age classes) of fast-growing trees: poplar cultivar "Hybrid 275" and European larch (*Larix decidua* Mill.) were presented. The data were prepared based on the State Forests Information System (SILP) [29]. The results of the study were presented in the form of maps prepared on the basis of Central Codification Information (COT) obtained from the Department of State Forestry Informatics (ZILP). The COT consists of SILP subsystem tables, excluding the planning system. The selections were made as follows:


The second stage of the research was carried out on the basis of data from the Central Statistical Office of Poland [46,52], and the Agency for Restructuring and Modernisation of Agriculture (ARMA) [53] consisted of an analysis of the structure of agricultural areas in Poland and the average area of agricultural holdings with a division into voivodeships. The adopted size of the assumptions resulted from the willingness to present the possibilities of developing agricultural land with a small and, at the same time, rational use of these areas without significantly limiting their area and importance. According to Zabielski [44], a single plantation should have a minimum area of 5 ha. A plantation area cannot be too small if all the cultivation and maintenance are mechanized with the use of special equipment to be cost-effective. Baum et al., [8] reported that the more diverse the surrounding landscape, the more species are able to be established in the plantation. Smaller plantations with longer-edged habitats facilitate species immigration from the surroundings better than larger plantations. Small plantations may increase the regional diversity.

For the purpose of this stage, the following were assumed:

	- not less than 5% and not more than 10% of the sown areas, permanent crop areas and forest land and
	- not less than 5% and not more than 30% of the areas of fallow land, wasteland and other land.

In the third stage, after indicating a potential area for plantation, the available amounts of alternative raw materials for the wood industry were determined based on the following assumptions:


Such extensive research dealing with the potential area for forest plantation has never been undertaken before. In the study, the potential areas in Poland for forestry plantations with the available amounts of raw materials were linked.

#### **3. Results and Discussion**

*3.1. The Area and Stock of Raw Material from Plantation of Poplar Cultivar "Hybrid 275" and Larch (Larix decidua Mill.) in Poland—Current State*

The data obtained from the State Forest Information System were imposed on the cartograms presented in Figure 1. The species dominating on the plantations of fastgrowing trees in Poland are poplar and larch, which reach up to 80.29% of the overall plantation area [29]. In general, Poland is characterized by a low share of the poplar and larch plantations in the total forest areas (0.026%). The plantations with poplar as the dominant species or poplar single-species plantations (in the first stage of tree stands) are cultivated on 1156.05 ha, while plantations with larch as the dominant species or larch single-species plantations (in the first stage of tree stands) cover 889.7 ha. The highest share of the larch plantation is in Radom RDLP, which is reflected by the natural distribution of this species in Poland, excluding mountainous areas, while poplar plantations occupy the largest area in RDLP Gda ´nsk and Lublin, which means in the regions with the suitable soils conditions for this species. A nearly complete lack of fourth age class plantations seems to be understandable due to the purpose of the plantations of fast-growing trees characterized by short production cycles. In turn, the lack of plantations in mountain areas is associated with limiting climatic conditions. The total stock on plantations of fast-growing poplar trees (taking into account all age classes) was estimated at about 280,000 m<sup>3</sup> , of which most are in the third age class, i.e., 190,000 m<sup>3</sup> , whereas the total stock on plantations of fast-growing larch trees (taking into account all age classes) was estimated at about 160,000 m<sup>3</sup> , of which most are in the second age class, i.e., 120,000 m<sup>3</sup> . In 2015, the annual shortage of raw wood only for the wood-based composites industry in Poland, reached the level of around 7.7–11.4 MM m<sup>3</sup> [54]. On the basis of the presented data, it can be clearly stated that the acreage of fast-growing trees (poplar and larch) in Poland is insufficient to eliminate the deficit of wood to a large extent. Therefore, there is a need to increase plantation areas in Poland.

*Poland* 

**Figure 1.** Arrangement of fast-growing tree plantations: (**A**,**B)** larch and (**C**,**D**) poplar in the regional LP Directorates. The carto diagram of the surface and stock (thickness) broken down into age classes (bars) and the cartogram of the total surface (bronze shades, **A**,**C**) and thicknesses (shades of green, **B**,**D**). In the RDLP without the colours and bars, there is a lack of fast-growing tree plantations. The sizes of the bars are linear. The symbols of the regional names of the LPs are explained in Abbreviations. In addition to the legend: On the left, cartographic explanations (colours RDLP: shades of brown-surface and green-stock). On the right-hand side, explanations for the carto diagrams (the colours of the bars and their sizes). Abbreviations: RDLP-Regional Directorates of the State Forests; BLK-RDLP in Białystok; GDK-RDLP in Gdańsk; KKW-RDLP in Kraków; KSO-RDLP in Krosno; KWE-RDLP in Katowice; LBN-RDLP in Lublin; LDZ-RDLP in Łódź; OTN-RDLP in Olsztyn; PLA-RDLP in Piła; PZN-RDLP in Poznań; RDM-RDLP in Radom; SZK-RDLP in Szczecinek; SZN-RDLP in Szczecin; TRN-RDLP in Toruń; WCW-RDLP in Wrocław; WRA-RDLP in Warszawa; ZGA-RDLP **Figure 1.** Arrangement of fast-growing tree plantations: (**A**,**B)** larch and (**C**,**D**) poplar in the regional LP Directorates. The carto diagram of the surface and stock (thickness) broken down into age classes (bars) and the cartogram of the total surface (bronze shades, **A**,**C**) and thicknesses (shades of green, **B**,**D**). In the RDLP without the colours and bars, there is a lack of fast-growing tree plantations. The sizes of the bars are linear. The symbols of the regional names of the LPs are explained in Abbreviations. In addition to the legend: On the left, cartographic explanations (colours RDLP: shades of brown-surface and green-stock). On the right-hand side, explanations for the carto diagrams (the colours of the bars and their sizes). Abbreviations: RDLP-Regional Directorates of the State Forests; BLK-RDLP in Białystok; GDK-RDLP in Gda´nsk; KKW-RDLP in Kraków; KSO-RDLP in Krosno; KWE-RDLP in Katowice; LBN-RDLP in Lublin; LDZ-RDLP in Łód´z; OTN-RDLP in Olsztyn; PLA-RDLP in Piła; PZN-RDLP in Pozna´n; RDM-RDLP in Radom; SZK-RDLP in Szczecinek; SZN-RDLP in Szczecin; TRN-RDLP in Toru ´n; WCW-RDLP in Wrocław; WRA-RDLP in Warszawa; ZGA-RDLP in Zielona Góra.

#### in Zielona Góra. *3.2. The Assessment of the Potential Area of Land for Plantations of Fast-Growing Trees in Poland*

*3.2. The Assessment of the Potential Area of Land for Plantations of Fast-Growing Trees in*  According to the Statistical Yearbook of Agriculture of the Central Statistical Office of Poland [46], in 2016, there were 14.5 MM ha of agricultural land, nearly 99.1% of which was in good agricultural condition. Of the above-mentioned land, plantations may be established on sown areas or fallow lands. Additionally areas used for permanent crops and permanent meadows can be partially prepared for plantations. A potential area for the plantations of fast-growing trees may also partially consist of forest land, as well as of woody and bushy land. The total area of this type of land was 944,031 ha in 2016 (Table According to the Statistical Yearbook of Agriculture of the Central Statistical Office of Poland [46], in 2016, there were 14.5 MM ha of agricultural land, nearly 99.1% of which was in good agricultural condition. Of the above-mentioned land, plantations may be established on sown areas or fallow lands. Additionally areas used for permanent crops and permanent meadows can be partially prepared for plantations. A potential area for the plantations of fast-growing trees may also partially consist of forest land, as well as of woody and bushy land. The total area of this type of land was 944,031 ha in 2016 (Table 1). The data concerning the agricultural land and the number of agricultural farms did not include agricultural landowners who do not perform agricultural activity and owners of

less than 1 ha of agricultural land who perform agricultural activities on a small scale [46]. Taking into consideration the geodesic area of the country, its utilization and the above assumptions, the potential area of land for plantations of fast-growing trees was shown in Table 1. Assuming only 5% use of the sown area, it is theoretically possible to obtain, respectively, 531,999 ha of land for plantations of fast-growing trees. If the utilized area constitutes 5% of the sown area and 5% of the fallow land, permanent meadows, forest land and woody and bushy land and wasteland and other land, a potential area for plantations of fast-growing trees may even reach almost 0.77 MM ha. If the target assumptions are reached, i.e., the land allotted to plantations of fast-growing trees constitutes 10% of the sown area, permanent meadows and forest land and 30% of the fallow land, uncultivated and other land and a potential area for plantations may reach approximately 1.52 MM ha. Xu and Mola-Yudego [13] analyzed the evolution and location of fast-growing plantations in Sweden for 30 years (for the period 1986–2017). The authors pointed out that willow tends to be planted on higher-productivity agricultural areas and poplar on less-productive lands. On this basis, it can be assumed that establishing poplar plantations on less fertile soils is an opportunity to change the management of the available land. According to the National Agricultural Census, since 2010, 300,000 farmers did not declare agricultural activity on their own farms, which theoretically constituted 447,000 hectares of uncultivated land potentially available for plantations [52]. However, these data do not fully reflect the situation in this kind of agricultural farm. Some of the agricultural areas (of the agricultural farms) were leased by the owners of other agricultural farms without an official contract. According to the data collected for the National Agricultural Census [52], the highest number of agricultural farms that do not carry out agricultural activities was in Sl ˛askie ´ Voivodeship (31.7%), Lubuskie Voivodeship (24.4%) and Małopolskie Voivodeship (20.4%), while the lowest number of such holdings was in Lubelskie Voivodeship (8.0%).


**Table 1.** The potential area of land for the plantation of fast-growing trees in Poland [46].

According to the adopted assumptions, the minimum area for plantation should be 5 ha [44]. An analysis of the data presented in Table 2 leads to a conclusion that agricultural farms meeting the expectations of an average area lower than 5 ha are located in Małopolskie and Podkarpackie Voivodeships. An observation can be made that Małopolskie Voivodeship characterizes the smallest average area of agricultural land per farm, i.e., 4.16 ha [53]. This is due to the fact that only one out of five agricultural farms in this voivodeship does not carry out any agricultural activities; it can be assumed that this is a region where small-sized areas are concentrated. Upon the merger of such lands, they will form an area large enough to be used for the plantation of fast-growing trees. This direction is in line with foreign trends. The need for the cultivation of different tree species

in small-scale units is indicated [55]. In Sweden, there is a trend towards preferring smaller plantations (below 1 ha) versus large ones (above 10 ha) [13].


**Table 2.** Average area of land in an agricultural farm in 2020 in Poland [53].

\* Poland is divided administratively into 16 voivodeships.

It is clear that plantations located on agricultural lands are the most efficient-speaking in terms of production [16]. However, as such locations are limited and utilized for more demanding crops, it is necessary in Poland to reach for low-quality, devastated and degraded soil. Such land is collectively referred to as marginal land. These areas at a low cost (cost of soil type and quality verification) can be adopted and serve as plantations of fast-growing trees. Marginal lands are lands currently in agricultural use or classified in the agricultural land records as not suitable for the production of healthy food, due to unfavorable environmental and anthropogenic conditions, are qualified for a different use form. In Poland, the area of marginal lands reaches 2.3 MM ha, which is equivalent to 16% of the agricultural lands. Approximately 1.7-MM ha (90%) of those lands are very light, dry and barren sandy areas. The marginal lands include the unfertile parts of agricultural lands where production is not cost-effective due to unfavorable environmental conditions and erosion. These lands are located in Małopolskie Voivodeship and Podkarpackie Voivodeship and amount to 370,000 hectares. The lands of various quality class but chemically polluted account for 140,000 hectares, and degraded or mechanically transformed lands that lack humus accumulate to 50,000 hectares. Furthermore, the marginal lands include lands with unfavorable environmental territorial conditions. This group includes cultivated areas difficult to access or difficult to cultivate mechanically [56].

As an illustration of a recultivation process of devastated soil in Poland, a forestation of a part of a dumping ground in an open pit sulfur mine in Piaseczno near Tarnobrzeg City can be set as an example [57]. Between 1967 and 1969, black locust was planted in a space of 1.2 m × 0.6 m on the slopes with unfavorable soil and untreatable agrotechnical (average slope of 60%) ravines. The tree stands were not fertilized or cut. In 2009, the 42-year-old tree stands were tested. It was calculated that the annual stand increment was 5.9 m<sup>3</sup> <sup>×</sup> ha−<sup>1</sup> on the north slope, 4.8 m<sup>3</sup> <sup>×</sup> ha−<sup>1</sup> on the southeast and 3.6 m<sup>3</sup> <sup>×</sup> ha−<sup>1</sup> on the south slope. These numbers are not high; however, the single purpose of planting black locust was, first and foremost, soil reclamation. The wood produced was a byproduct of the process. On the other hand, in such extremely unfavorable soil, the average annual increment of 4.8 m<sup>3</sup> <sup>×</sup> ha−<sup>1</sup> can be considered as a relatively high value, compared to the black locust average production capacity of 7.06 m<sup>3</sup> <sup>×</sup> ha−<sup>1</sup> per year [57]. Hybrid poplar 275 is a cultivar with low requirements for soil quality conditions capable of adapting to soils of different types. In Poland, there is a long tradition of growing poplar cultivar "Hybrid 275" (as a clone obtained from the *S*. *Tacamahaca* species cross *P. maximowiczii* × *P. trichocarpa*) [42,58]. Therefore, it can be treated as the most appropriate mean in the process of the reclamation of lands offering annual increments higher than black locust. Moreover, poplar has the ability to accumulate substantial amounts of cadmium, zinc, lead and copper. Thus, in the areas polluted with heavy metals, poplar may be used for bioremediation of the environment [59].

The predicted values are vectors of probabilities of transitions to alternative land uses and the transition of land uses and forest management type conditional on the biophysical and socioeconomic factors. Regardless of the data presented, it may have some limitations. The potential of plantation crops of fast-growing trees in agriculture depends on the availability of the lands, climate conditions and water supply, as well as the quality of soils [12]. However, plantations can contribute to the enhancement of biodiversity in intensively used landscapes with low habitat heterogeneity [55]. In addition, some recommendations have been already developed to further improve the habitat function of fast-growing tree plantations and to increase their contributions to farmland biodiversity, especially to plant species diversity (phytodiversity). This includes the cultivation of different tree species in small-scale units; the sectional harvesting of trees in order to establish a mosaic of different growth stages side by side and the integration of accompanying structures such as headlands, clearings or rides to provide additional open habitat elements. The knowledge in this area can be used to promote phytodiversity in agricultural landscapes, as they contain relatively high species numbers (of mainly common and adaptable species) and support distinct plant communities that differ from other farmland habitats.

The presented results are related with Polish lands. Due to the fact that the Polish wood sector is important for all the European region, the information in that area is necessary. In the other regions of Europe (or the world), due to different forms of land and forest ownership, as well as regulations, there are no applications to obtain the results. The specificities of the regions require analyses at the regional level. For example, research determining potential areas for the establishment of commercial forest plantations were performed for Mexico for Tabebuia rosea (Bertol.) DC. by using geographic information systems [60]. A land suitability evaluation was evaluated for two forest plantations, including oak (*Quercus robur* L.) and pine (*Pinus sylvestris* L.) in the northeast of Iran [61]. The study area involved an area of about 394 km<sup>2</sup> with a total mainstream length of 35 km. The relatively small area allowed for the assessment of the potential area for trees plantation, taking into account the climate, soil and terrain data. Similar research was conducted for the southern part of the USA [62]. Land resources potentially available for pine plantations were determined using matrices of land and forest-type changes conditional on the biophysical and socioeconomic factors and applying them to the available land and forest resources to forecast the dynamics of pine plantations [62].

#### *3.3. The Assessment of Potentially Available Quantities of Alternative Raw Material for the Wood Industry*

The estimated increment of poplar and larch on sown and forest lands is shown in Table 3. The calculations assume 5% use of the sown lands (i.e., 531,999 ha) and 5% of the forest land (i.e., 47,202 ha) and 100% coverage of the land with the poplar plantation, with the average increase in the tree stand thickness for the poplar about 25 m<sup>3</sup> <sup>×</sup> ha−<sup>1</sup> . In the case of 50% capacity of the poplar plantation, the annual wood production is estimated as 7 MM m<sup>3</sup> . In the case of planting 50% of the land with larch and 50% with poplar, an assumed 50% capacity of the plantation should produce 5.63 MM m<sup>3</sup> of wood per year.

From the data presented in Table 3, it was concluded that, when plantations cover 5% of sown areas and 5% of forest areas, the annual increase in large-sized wood may be from 8.11 MM m<sup>3</sup> (100% coverage with larch) to 14.48 MM m<sup>3</sup> (100% coverage with poplar). Considering the fact that, in 2015, the annual shortage of wood only for the wood-based panels industry reached the level of around 7.7–11.4 MM m<sup>3</sup> [33] by using the indicated areas, it will be possible to significantly reduce the deficit of raw wood material in Poland.


**Table 3.** Data on the potential wood base from sown land and forest land.

Lindegaard et al., [63] indicated that, with the intensification of the activities aimed at increasing the plantation areas of rapidly growing trees in Europe so far, each region has developed a number of recommendations for policymakers, public authorities and government agencies to support the development, production and use of biomass obtained from these types of crops for applications for energy and industrial purposes. It was pointed out that each region has many similarities regarding the restrictions on establishing and running fast-growing tree plantations. There is a need to educate farmers and policymakers about the multifunctional benefits of fast-growing tree plantations. In order to develop the market for planting fast-growing trees, more financial support is needed from regional and/or national authorities. Introducing targeted subsidies as an encouragement for growers could solve the problem of the lack of local supply chains. Haughton et al., [64] stated that promoting the plantation of fast-growing trees on agricultural land is important to increasing the landscape diversity and improving the ecosystem functions. Overall, fast-growing tree plantations are viewed positively by farmers to a limited extent, unless they receive the same benefits, subsidies and support that are offered to renewable energy providers. The relevant issue in this aspect also supports bureaucratic procedures at the regional and national levels. The role of scientists is also crucial in ensuring that there is clear and concrete evidence that planting fast-growing trees produces a range of environmental and socioeconomic benefits.

#### **4. Conclusions**

A constant supply of raw wood for the Polish market is a crucial matter for the Polish wood industry. Establishing fast-growing tree plantations would make it possible to obtain a supplementary source of lignocellulosic raw materials for wood-based panels production. Unlike traditional forestry based on natural or semi-natural forests, plantations offer a number of advantages, such as relatively short production cycles, as well as a possibility of producing raw material that meet specific requirements (for particular clients). Such wood may be produced in large quantities only thanks to plantations of vegetatively reproduced progeny from properly selected species. Moreover, the production of plantation woody raw materials may be located near end customers. Such and many other benefits have led to a noticeable worldwide development of this form of commercial wood production. In order to maintain a leading position in the Polish industry for the production of wood-based panels in the world, Poland must implement deep changes not only in the process of plantations creation but, also, in the legislation area, emphasizing how strategic creating a secondary source of raw materials is.

The analyzed data evidenced that the research conducted in Poland for several decades showing that the species with the highest productivity in plantations are poplar (mainly the cultivar "Hybrid 275") and, also, European larch (less popular in other European countries). Based on the presented research results, it can be concluded that, using only 5% of the

sown area or 5% of the forest land and woody and bushy land, it is theoretically possible to obtain, respectively, 531,999 or 47,202 hectares of land for plantations of fast-growing trees. In the case of planting 50% of these lands with larch and 50% with poplar, at a 50% capacity of the plantation, it will be possible to obtain nearly 6 MM m<sup>3</sup> of wood per year. When the land allotted to plantations of fast-growing trees constitutes 10% of the sown area, permanent meadows and forest land and 30% fallow land, uncultivated and other land, the potential area for plantations may reach approximately 1.52 MM ha. It was concluded that, using an estimated plantation area covering only 5% of the sown areas and 5% of the forest areas, the annual increase in large-sized wood may be from 8.11 MM m<sup>3</sup> (100% coverage with larch) to 14.48 MM m<sup>3</sup> (100% coverage with poplar).

From a practical point of view, based on the obtained results, it can be said that, in Poland, plantations can be established on sown lands; forest lands and, partially, on areas under cultivation, permanent meadows and on fallow and idle lands. Plantations can be established by large companies through land leases, by farmers on their own lands and, also, by SFN FH on lands transformed by agricultural activity placed at their disposal under the provisions of the National Programme for the Augmentation of Forest Cover. Taking that into consideration, another conclusion can be made that such research plays a part in ensuring there is clear and concrete evidence in the field of the environmental and socioeconomic benefits of the cultivation of fast-growing tree plantations. Such a quantity of potential of the raw material makes it possible to eliminate the wood deficit existing in Poland as a crucial global producer of wood-based materials.

**Author Contributions:** Conceptualization, P.B.; methodology, P.B.; formal analysis, A.J. and A.L.; investigation, A.J., A.L., M.K. and M.M.; resources A.J., A.L., M.K. and M.M.; data curation, P.B., A.J., A.L., M.K. and M.M.; writing—original draft preparation, A.J. and A.L.; writing—review and editing, P.B., A.J. and A.L.; visualization, M.K. and M.M.; supervision, A.J. and A.L.; project administration, P.B. and funding acquisition, P.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was carried out under development project No. LIDER/002/406/L-4/NCBR/2013, financed by The National Centre for Research and Development.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Felix Zitzmann \* and Michael Rode**

Institute of Environmental Planning, Leibniz Universität Hannover, Herrenhäuser Str. 2, 30419 Hannover, Germany; rode@umwelt.uni-hannover.de

**\*** Correspondence: zitzmann@umwelt.uni-hannover.de; Tel.: +49-511-762-19506

**Abstract:** In recent years, the impact of short-rotation coppice (SRC) on biodiversity has been a regular subject of research and ecological guidelines have been developed to make biomass cultivation on SRC more compatible with biodiversity concerns. However, since these guidelines are only implemented voluntarily by farmers, there are barely any SRC that are managed according to ecological guidelines. Consequently, knowledge about their importance for farmland biodiversity and about the impact of different measures for increasing biodiversity remains scarce. Therefore, three experimental SRC, which are managed according to ecological guidelines and thus include stands of different tree species (varieties of poplar (*Populus*) and willow (*Salix*), rowan (*Sorbus aucuparia*), silver birch (*Betula pendula*)) and different growth-stages within the same site, were investigated with regard to their importance as habitat for vascular plants. Species numbers and species composition were compared with the following habitat types: afforestations (AFO), young (HE-Y) and old hedges (HE-O), field margins (FM) and arable land (AL). Furthermore, different stand types (i.e., stands with different tree species and growth-stages, headlands, clearings) within these SRC were surveyed and compared. Species numbers of SRC were similar to HE-Y, AFO and FM and significantly higher than in AL and HE-O. The composition of plant communities in SRC differed considerably from the other farmland habitats, especially from AL, HE-O and FM. Within the SRC, most stand types had similar species numbers. Only the non-harvested poplar stands were particularly species-poor. Harvesting led to increased species numbers. This increase was significant for the poplar stands but only moderate for the willow stands. With regard to their species composition, the different stand types differed considerably in many cases. We conclude that SRC, which are managed according to ecological guidelines, can be an additional measure to promote phytodiversity in agricultural landscapes as they contain relatively high species numbers (of mainly common and adaptable species) and support distinct plant communities that differ from other farmland habitats. Therefore, measures such as the cultivation of different tree species or sectional harvesting could be offered as agri-environmental schemes to further increase the ecological sustainability of biomass production on SRC.

**Keywords:** woody biomass crops; bioenergy; biodiversity; species richness; flora; vascular plants

#### **1. Introduction**

The decline of biodiversity is progressing rapidly. Thereby, this negative development is particularly evident in agricultural landscapes and even species that were formerly common and widespread are now affected [1–6]. A major reason for the decline in biodiversity is an increase in intensive agricultural use [7], for example, through the increasing cultivation of biomass crops [8–14]. However, biomass cultivation also offers opportunities to promote farmland biodiversity, since extensively managed perennial biomass crops, such as short-rotation coppice (SRC), can provide new habitats for wildlife and plants in agricultural landscapes [15–18]. SRC are biomass crops that consist of fast-growing trees (mostly cultivated varieties of poplar (*Populus*) or willow (*Salix*)), which are harvested in short cycles in order to use their wood for energy purposes [19].

**Citation:** Zitzmann, F.; Rode, M. Short-Rotation Coppice Managed According to Ecological Guidelines—What Are the Benefits for Phytodiversity?. *Forests* **2021**, *12*, 646. https://doi.org/10.3390/ f12050646

Academic Editor: Dirk Landgraf

Received: 28 April 2021 Accepted: 18 May 2021 Published: 19 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

In previous studies on the biodiversity of these woody crops, their importance as habitat for vascular plants was a frequent subject of investigation [20,21]. Thereby, SRC were found to be more species-rich than conventional arable crops [22,23] and clearly differed from arable land and forests with regard to their plant species composition [24–26]. Studies on the development of plant communities within SRC have shown dynamic changes with increasing age of the plantation and within rotation cycles [27–30] as well as differences in species numbers and species composition between plantations with different tree species or varieties [23,31,32].

From these results, it was concluded that SRC can contribute to the enhancement of biodiversity in intensively used landscapes with low habitat heterogeneity [33]. In addition, recommendations were developed to further improve the habitat function of SRC and to increase their contribution to farmland biodiversity, especially to plant species diversity ("phytodiversity"). The proposed measures aim to increase the structural diversity of these woody biomass crops. This includes the cultivation of different tree species in small-scale units, sectional harvesting of trees in order to establish a mosaic of different growth-stages side by side and the integration of accompanying structures such as headlands, clearings or rides to provide additional open habitat elements within SRC [cf. [15,27,34]]. Currently, these measures can only be implemented by farmers on a voluntary basis and there is no regular financial compensation (e.g., agri-environmental schemes) for the associated management effort or yield losses [cf. [35]]. Therefore, SRC managed according to ecological guidelines are quite rare. Consequently, there are no studies available that evaluate their importance as habitats for vascular plants and that examine and directly compare the effects of the different measures on one and the same plantation (i.e., on the same site with similar conditions with regard to plantation age, land use history or adjacent habitats, which are all factors that can have a strong influence on the composition of the recent vegetation of SRC [27,30,36]).

Therefore, in this study, we want to determine the importance of appropriately managed SRC as habitat for vascular plants on three experimental SRC, which are managed according to the ecological guidelines mentioned above. Our aim is to evaluate how these SRC perform in comparison to other farmland habitats with regard to their plant species diversity and whether they can provide an additional value for phytodiversity in an intensively used agricultural landscape. Furthermore, we want to evaluate the effects of the different measures implemented within the investigated SRC. For this purpose, we want to compare different stand types (i.e., stands with different tree species and growth-stages, headlands, clearings) within our SRC study sites with regard to their species numbers and species composition.

#### **2. Materials and Methods**

#### *2.1. Study Area and Study Sites*

Investigations were carried out in the municipality of Schapen (Emsland district, Lower Saxony) in north-western Germany. The landscape in this rural region is dominated by intensive agriculture (approx. 70%, predominantly arable land) and contains a relatively low (14%) proportion of woodland [37]. The study area is located 30–40 m above sea level. Mean annual precipitation accounts for 800 mm and mean annual temperature for 10 ◦C (long-term recordings from 1981–2010, Climate station Lingen, [38]). The region is dominated by sandy soils such as Podzols, Gleyic Podzols and, in areas closer to the groundwater, Gleysols, which developed from glacial sand deposits [39].

In addition to SRC managed according to ecological guidelines, five other farmland habitat types were selected for the study. In addition to arable land (previous land use), this includes a range of typical farmland habitats (afforestations, young and old hedges, field margins), which are regularly implemented as measures to increase biodiversity in agricultural landscapes (e.g., as agri-environmental schemes or greening measures in the context of the Common Agricultural Policy of the European Union). Therefore, the study fo-

cuses on the six following habitat types: short-rotation coppice (SRC), afforestations (AFO), young (HE-Y) and old hedges (HE-O), field margins (FM) and arable land (AL) (Table 1).


**Table 1.** Surveyed habitat types and number of surveyed sites, stands and plots per type.

The three SRC sites, each about 2 ha in size, were established in spring 2011 and 2012, respectively, and were thus in their 8th or 9th growing season after establishment at the time of the study (June 2019). These small-scale and structurally diverse plantations are experimental sites which were established and are managed according to ecological guidelines [cf. [15,27,34]] in order to increase their contribution to farmland biodiversity. Therefore, different tree species and varieties were cultivated within the same site (see Figure 1) and are managed without any use of fertilisers or pesticides. In addition to varieties of poplar (Max 3, Hybride 275) and willow (Inger, Tordis), some native tree species (rowan (*Sorbus aucuparia*) and silver birch (*Betula pendula*)) were also planted. The trees were each planted in species-specific stands of 20 m width. Within the poplar and willow stands, harvesting was carried out in sections, so that three different growth-stages existed side by side at the time of the study: stands that were not harvested so far and stands that had been harvested in February 2018 or in February 2019, respectively, i.e., regrowth in the first or second growing season after harvesting (Table 2, Figure 1). *Forests* **2021**, *12*, 646 4 of 21

**Figure 1.** Aerial view on one of the three surveyed experimental SRC sites (site no. 3). The stands with different varieties of poplar and willow (in different growth-stages) and with different native tree species as well as the accompanying structures headland and clearing are clearly visible (Recording date: June 2019). See Table 2 for abbreviations of the SRC stand types. We selected three sites of each habitat type SRC, AFO and HE-Y for the study, as **Figure 1.** Aerial view on one of the three surveyed experimental SRC sites (site no. 3). The stands with different varieties of poplar and willow (in different growth-stages) and with different native tree species as well as the accompanying structures headland and clearing are clearly visible (Recording date: June 2019). See Table 2 for abbreviations of the SRC stand types.

only few (AFO) or no (SRC, HE-Y) other sites were available in the region and a random selection was therefore not possible. For the habitat types AL, HE-O and FM, the six study sites per type were randomly selected. Therefore, one site each for AL, HE-O and

In addition to the six different habitat types, various stand types within the SRC were investigated (Table 2, Figure 1). The SRC stand types included stands with different native tree species (BEP, SOA) and different tree varieties (PMAX, P275, WING, WTOR) typically grown on SRC in various growth-stages (-nh, -rg1, -rg2) as well as accompanying structures such as headlands (HEAD) and clearings (CLEAR, areas > 500 m2 where trees failed to establish). The average size of the individual stands within the SRC was 1510 ± 720 m2 (Range: 400–3850 m2). Each stand type was present once per site. Certain stand types were absent on individual SRC sites. Therefore, the number of replications per stand type was two or three (Table 2). Since the afforestations also contained different structures within the sites (besides tree stands there were also rides, clearings and margins), different stands were also investigated within the individual sites (Table 1).

around each of the three AFO sites.


**Table 2.** Characteristics of the surveyed SRC stand types.

The three afforestations (AFO) were established at the end of 2012. At the time of the study, they were in their 7th growing season since establishment. Hence, they were similar in age to the SRC. Within the afforestations, the deciduous tree species *Quercus robur*, *Q. petraea, Betula pendula*, *Fagus sylvatica, Carpinus betulus* and *Acer pseudoplatanus* were planted in varying proportions per site. The habitat type young hedge (HE-Y) included three hedge plantings of about 8 m width, also planted at the end of 2012, which directly bordered on the three AFO. Old hedges (HE-O) were mature hedgerows that had already been established decades or centuries ago in order to separate different fields from each other. The old hedges studied were characterised by shrubs and trees that had last been coppiced at least 10 to max. 50 years ago. The width of the hedges ranged from 4 to 10 m. Due to the intensive agricultural use of the adjacent fields, the hedges did not have any fringes, but bordered directly on intensively used arable land. Field margins (FM) were 1–3 m wide strips of herbaceous vegetation between two adjacent arable fields or at the edge of a field between the field and a track. Arable land (AL) comprised arable fields cultivated with maize (*Zea mays*), since maize represents the most commonly cultivated crop in the region.

We selected three sites of each habitat type SRC, AFO and HE-Y for the study, as only few (AFO) or no (SRC, HE-Y) other sites were available in the region and a random selection was therefore not possible. For the habitat types AL, HE-O and FM, the six study sites per type were randomly selected. Therefore, one site each for AL, HE-O and FM was randomly selected within a radius of 500 m around each of the three SRC and around each of the three AFO sites.

In addition to the six different habitat types, various stand types within the SRC were investigated (Table 2, Figure 1). The SRC stand types included stands with different native tree species (BEP, SOA) and different tree varieties (PMAX, P275, WING, WTOR) typically grown on SRC in various growth-stages (-nh, -rg1, -rg2) as well as accompanying structures such as headlands (HEAD) and clearings (CLEAR, areas > 500 m<sup>2</sup> where trees failed to establish). The average size of the individual stands within the SRC was 1510 <sup>±</sup> 720 m<sup>2</sup> (Range: 400–3850 m<sup>2</sup> ). Each stand type was present once per site. Certain stand types were absent on individual SRC sites. Therefore, the number of replications per stand type was two or three (Table 2). Since the afforestations also contained different structures within

the sites (besides tree stands there were also rides, clearings and margins), different stands were also investigated within the individual sites (Table 1).

The selection of the study sites resulted in six spatial clusters, each containing several sites (located within a radius of 500 m around each of the three SRC and AFO sites) or stands (located within each SRC and AFO site) that were spatially aggregated. For the statistical analysis, these clusters were considered as a fixed effect in the model (see data preparation and statistical analysis). Sites of SRC and AFO were located between 1 and 5.5 km apart.

#### *2.2. Survey of the Flora*

Surveys were carried out in June 2019. For each site (AL, HE-O, HE-Y and FM) and for each stand within the SRC and AFO sites, the flora was recorded using 15 randomly distributed quadratic sample plots (hereafter "plots"), each with a size of 1 m<sup>2</sup> (1 × 1 m). In order to minimize edge effects, the edge-zones were excluded from the surveys as far as possible and the 15 plots were distributed within the sites/stands at a minimum distance of 2 m from the edge. For HE-O and FM, it was not possible to exclude the edge-zones from the surveys, since these linear habitats were too narrow. Since the sites of arable land (AL) were fairly large, a 20 m wide area (corresponding to the width of the individual tree stands on the SRC) on a randomly selected side of the field was demarcated for the surveys.

All vascular plant species growing within each sample plot (of 1 m<sup>2</sup> ) were identified according to Jäger [40] and noted (presence/absence; no recording of the cover of individual species). The scientific nomenclature also followed Jäger [40]. Cultivated non-native plant species (e.g., varieties of poplar or willow, *Zea mays*) were excluded from sampling. Due to the high number of stands studied, especially on the SRC, but also on the AFO, there were major differences with regard to the total number of plots sampled per habitat type (Table 1).

#### *2.3. Data Preparation and Statistical Analysis*

The different habitat types as well as the different stand types within the SRC were compared with regard to their species numbers at (i) plot-level and at (ii) stand-level. Therefore, the species numbers were log-transformed before analysis (loge(y + 1), y = species number) to account for samples with no species detections. For the log-transformed data, a linear mixed effect model (LMM) was fitted to account for the unbalanced hierarchical sampling design. Spatial clusters (i.e., sites of different habitat types clustered within a 500 m radius around each SRC and AFO site, see section study area and study sites), habitat types and stand types were included as fixed effects. Two random effects were included: the variance between sites, and the variance between stands nested within sites. The residuals variance was thus the variance between plots nested within stands. For the number of species per site/stand (i.e., per 15 m<sup>2</sup> ) a simplified mixed model was fitted with the spatial clusters (see above), habitat types and stand types as fixed effects, and variance between sites as random effect. Assessment of normality assumption of the residuals was performed using R package 'hnp' [41]: the empirical distribution of residuals was symmetric, with no indication of skewedness or heterogeneity of variance and no indication of extreme values. Based on the fitted LMM, all pairwise comparisons of means between habitat types and between stand types were computed (analogously to the Tukey test). Significant differences at the 5%-level (*p* < 0.05) are shown in graphs by compact letter display. The statistical analysis was performed in R [42], using packages 'lme4' [43] for fitting mixed models, package 'lmerTest' [44] for ANOVA tables of the fixed effects, package 'emmeans' [45] for pairwise comparisons and compact letter display and 'ggplot' [46] for graphs.

To compare species numbers of SRC and AFO at site-level, despite different numbers of stands and plots sampled per site (Table 1), the species numbers for 15 and 45 plots were estimated for each of the three sites of these two habitat types by a sample-based rarefaction [cf. [47]] using the software BioDiversity Pro [48]. Therefore, samples of the dataset of each site (from plots of all stand types occurring there) were pooled in random order and the number of random sorts to perform on each pass was set to 50. For the three SRC sites, this estimation was also performed for 135 plots, since this was the minimum number of sampled plots per site (see Table 1).

Species composition of the habitat types and stand types was compared with regard to the habitat preferences of the occurring plant species. Therefore, the detected species were assigned to one of the following categories according to Oberdorfer [49]: (a) species of arable land or short-lived ruderal habitats, (r) species of persistent ruderal habitats, (g) grassland species, (h) species of heathland or nutrient-poor/dry grasslands, (w) species of woodlands incl. herbaceous vegetation of woodland margins, shrubs or hedges and (x) indifferent, not stated or species of other habitats. For the comparison of habitat types and stand types, all sites, stands and plots of the respective type were combined. For the comparison of the stand types, different varieties of the same genus (poplar or willow) were combined. For all comparisons, each species was weighted with its frequency, i.e., the number of plots in which the species was detected in the respective habitat type or stand type.

To compare the species composition of the different habitat types a detrended correspondence analysis (DCA) was applied using R package 'vegan' [50] for analysis and 'gplots' [51] for graphs. All stands of the different habitat types, respectively, stand types were included in this analysis. The species within the individual stands/sites were weighted with their frequency (i.e., occurrence in *n*/15 plots per stand/site). The default options were used, including detrending by 26 segments, non-linear re-scaling of axes with 4 iterations and no downweighting of rare species.

#### **3. Results**

#### *3.1. Comparison of Habitat Types*

#### 3.1.1. Species Numbers

A total of 182 species were found across all surveyed habitat types. The majority of these species are widespread and common, no species classified as threatened in Germany were detected [52]. At the regional level (Lower Saxony), two of the species found are classified as threatened [53]: *Malus sylvestris* and *Ulmus minor*, the former was recorded on the AFO and the latter on some HE-O sites.

The highest number of species was recorded on SRC, with a total of 123 and the lowest on AL, with a total of 18. AFO ranked between SRC and AL, with a total of 108 species. HE-Y and FM both had 68 and HE-O had 41 species (Table 3). However, these species numbers are not directly comparable due to the different numbers of sites and stands surveyed per habitat type and thus different numbers of sampled plots (Table 1). Therefore, species numbers per stand (=15 m<sup>2</sup> ) and species numbers per plot (=1 m<sup>2</sup> ) were used for comparisons.

HE-Y was the most species-rich habitat type in terms of the number of species recorded per plot and per stand, followed by AFO, SRC and FM (Figure 2, Table 3). There were no differences between these four habitat types in terms of species numbers at either plot- or stand-level. AL had the lowest number of species at both levels and differed significantly from all other habitat types. HE-O took an intermediate position between the four speciesrich habitat types and AL and differed significantly from them in terms of species number per plot. With regard to the species numbers per stand, HE-O differed significantly from AFO, HE-Y and AL. Like AFO and HE-Y, SRC and FM also had higher species numbers per stand than HE-O, but the differences were not significant.


**Table 3.** Number of species (overall and separated by species of different habitat preferences) per habitat type (total number and mean ± SD per site/stand and per plot). Values with no consistent letter indicate significant differences (*p* < 0.05).

<sup>1</sup> Abbreviations for habitat preferences: a = species of arable land or short-lived ruderal habitats, r = species of persistent ruderal habitats, g = grassland species, w = species of woodland, shrubs or hedges (incl. vegetation of woodland margins).

**Figure 2.** Species numbers of the different habitat types at plot-level (**A**) and at stand-level (**B**). Types with no consistent letter indicate significant differences (*p* < 0.05). Median values are presented as horizontal orange lines, mean values as orange diamonds. Unfilled circles show the data of the single plots or stands sampled per type. In each boxplot, the boundaries of the box are the 25th and 75th percentiles and the whiskers represent the lowest and largest values no further than 1.5 times away from the 25th and 75th percentiles. **Figure 2.** Species numbers of the different habitat types at plot-level (**A**) and at stand-level (**B**). Types with no consistent letter indicate significant differences (*p* < 0.05). Median values are presented as horizontal orange lines, mean values as orange diamonds. Unfilled circles show the data of the single plots or stands sampled per type. In each boxplot, the boundaries of the box are the 25th and 75th percentiles and the whiskers represent the lowest and largest values no further than 1.5 times away from the 25th and 75th percentiles.

Since several stands per site were investigated for SRC and AFO, a comparison of species numbers for a larger number of plots was possible. Therefore, a sample-based rarefaction was used to estimate the species number per site for a given number of sampled plots. Estimated species numbers for the sites of both habitat types were consistent, ranging from 35–43 species for 15 plots and 52–59 species for 45 plots (Figure 3). The SRC Since several stands per site were investigated for SRC and AFO, a comparison of species numbers for a larger number of plots was possible. Therefore, a sample-based rarefaction was used to estimate the species number per site for a given number of sampled plots. Estimated species numbers for the sites of both habitat types were consistent, ranging from 35–43 species for 15 plots and 52–59 species for 45 plots (Figure 3). The SRC sites also had similar estimated species numbers (74–80 species) per site for 135 plots.

sites also had similar estimated species numbers (74–80 species) per site for 135 plots.

80.3

**Figure 3.** Estimated number of species for the different short-rotation coppice (SRC) and afforestation (AFO) sites for a given number of sampled plots (based on *sample-based rarefaction*, see data

3.1.2. Species Composition and Species Numbers with Regard to Habitat Preferences

Figure 4 shows the species composition within the habitat types with regard to the habitat preferences of the detected species weighted with their frequencies. AL was dominated by species of arable land or short-lived ruderal habitats, while HE-O was dominated by species of woody habitats. The highest proportion of grassland species (almost 30%) was found in FM. SRC had high proportions of species from arable land or short-lived ruderal habitats (45%) and from persistent ruderal habitats (35%). With re-

**SRC\_1 SRC\_2 SRC\_3 AFO\_1 AFO\_2 AFO\_3**

15 Plots 45 Plots 135 Plots

<sup>35</sup> 37.3

53.4 52

74 76.2

<sup>37</sup> 39.7 41.7 43.3

preparation and statistical analysis).

Estimated no. of species

ther than 1.5 times away from the 25th and 75th percentiles.

Since several stands per site were investigated for SRC and AFO, a comparison of species numbers for a larger number of plots was possible. Therefore, a sample-based rarefaction was used to estimate the species number per site for a given number of sampled plots. Estimated species numbers for the sites of both habitat types were consistent, ranging from 35–43 species for 15 plots and 52–59 species for 45 plots (Figure 3). The SRC sites also had similar estimated species numbers (74–80 species) per site for 135 plots.

**Figure 2.** Species numbers of the different habitat types at plot-level (**A**) and at stand-level (**B**). Types with no consistent letter indicate significant differences (*p* < 0.05). Median values are presented as horizontal orange lines, mean values as orange diamonds. Unfilled circles show the data of the single plots or stands sampled per type. In each boxplot, the boundaries of the box are the 25th and 75th percentiles and the whiskers represent the lowest and largest values no fur-

**Figure 3.** Estimated number of species for the different short-rotation coppice (SRC) and afforestation (AFO) sites for a given number of sampled plots (based on *sample-based rarefaction*, see data preparation and statistical analysis). **Figure 3.** Estimated number of species for the different short-rotation coppice (SRC) and afforestation (AFO) sites for a given number of sampled plots (based on *sample-based rarefaction*, see data preparation and statistical analysis).

#### 3.1.2. Species Composition and Species Numbers with Regard to Habitat Preferences 3.1.2. Species Composition and Species Numbers with Regard to Habitat Preferences

Figure 4 shows the species composition within the habitat types with regard to the habitat preferences of the detected species weighted with their frequencies. AL was dominated by species of arable land or short-lived ruderal habitats, while HE-O was dominated by species of woody habitats. The highest proportion of grassland species (almost 30%) was found in FM. SRC had high proportions of species from arable land or short-lived ruderal habitats (45%) and from persistent ruderal habitats (35%). With re-Figure 4 shows the species composition within the habitat types with regard to the habitat preferences of the detected species weighted with their frequencies. AL was dominated by species of arable land or short-lived ruderal habitats, while HE-O was dominated by species of woody habitats. The highest proportion of grassland species (almost 30%) was found in FM. SRC had high proportions of species from arable land or short-lived ruderal habitats (45%) and from persistent ruderal habitats (35%). With regard to the arable species, although the frequency proportion was lower for SRC than for AL, considerably more arable species were detected on the SRC than on AL due to the significantly higher total species number (Table 3). Compared to FM, HE-Y and AFO, the proportion of arable species in SRC was higher but the proportion of species from persistent ruderal habitats was lower. *Forests* **2021**, *12*, 646 9 of 21 gard to the arable species, although the frequency proportion was lower for SRC than for AL, considerably more arable species were detected on the SRC than on AL due to the significantly higher total species number (Table 3). Compared to FM, HE-Y and AFO, the proportion of arable species in SRC was higher but the proportion of species from persistent ruderal habitats was lower.

**Figure 4.** Proportions of plant species of different habitat preferences in the different habitat types (see Table 1 for abbreviations) weighted with their frequencies. Data of all sites, stands and plots included per type. Abbreviations for habitat preferences: a = species of arable land or short-lived ruderal habitats, r = species of persistent ruderal habitats, g = grassland species, h = species of heathland or nutrient-poor/dry grasslands, w = species of woodland, shrubs or hedges (incl. vegetation of woodland margins), x = indifferent, not stated or other habitat. **Figure 4.** Proportions of plant species of different habitat preferences in the different habitat types (see Table 1 for abbreviations) weighted with their frequencies. Data of all sites, stands and plots included per type. Abbreviations for habitat preferences: a = species of arable land or short-lived ruderal habitats, r = species of persistent ruderal habitats, g = grassland species, h = species of heathland or nutrient-poor/dry grasslands, w = species of woodland, shrubs or hedges (incl. vegetation of woodland margins), x = indifferent, not stated or other habitat.

In addition to Figure 4, Table 3 shows the mean numbers of species of different habitat preferences at site/stand-level and at plot-level. In comparison to the other habitat types, SRC had the highest number of species of arable land and short-lived ruderal habitats, both at site/stand-level and at plot-level. The number of species of persistent In addition to Figure 4, Table 3 shows the mean numbers of species of different habitat preferences at site/stand-level and at plot-level. In comparison to the other habitat types, SRC had the highest number of species of arable land and short-lived ruderal habitats, both

ruderal habitats was particularly high in AFO and HE-Y. In SRC, the number of species

significant. In contrast, HE-O and AL had significantly lower numbers of species of persistent ruderal habitats than SRC, AFO, FM and HE-Y. The number of grassland species was highest in FM at both site- and plot-level. Their number was significantly higher there than in SRC. Species of woodland, shrubs or hedges were particularly frequent in HE-O and HE-Y. Both hedge types differed significantly from SRC with regard to the

On the DCA graph, the stands are separated into four different groups (Figure 5), which are, however, very heterogeneous and widely scattered. AL, HE-O and FM are each separated from a large group consisting of SRC, AFO and HE-Y. Within the latter group, a certain separation into two groups, SRC and AFO/HE-Y, is visible, but there are also smooth overlaps between both groups, respectively, the three habitat types. In addition, one FM site and single AFO and SRC stands were fairly similar. These stands were

The considerable dispersion within all groups illustrates that the individual sites or stands of the respective habitat types did not show homogeneous plant communities and sometimes differed significantly with regard to their qualitative (occurring species) and quantitative (frequency of the species) species composition. For SRC and AFO/HE-Y, there were somewhat higher similarities between individual stands, but also considera-

number of species of this category.

3.1.3. Similarity of Plant Communities

a margin on a AFO site and a clearing on a SRC site.

ble differences between others (in detail see Section 3.2.3).

at site/stand-level and at plot-level. The number of species of persistent ruderal habitats was particularly high in AFO and HE-Y. In SRC, the number of species of this category was slightly lower, but the differences with AFO and HE-Y were not significant. In contrast, HE-O and AL had significantly lower numbers of species of persistent ruderal habitats than SRC, AFO, FM and HE-Y. The number of grassland species was highest in FM at both site- and plot-level. Their number was significantly higher there than in SRC. Species of woodland, shrubs or hedges were particularly frequent in HE-O and HE-Y. Both hedge types differed significantly from SRC with regard to the number of species of this category.

#### 3.1.3. Similarity of Plant Communities

On the DCA graph, the stands are separated into four different groups (Figure 5), which are, however, very heterogeneous and widely scattered. AL, HE-O and FM are each separated from a large group consisting of SRC, AFO and HE-Y. Within the latter group, a certain separation into two groups, SRC and AFO/HE-Y, is visible, but there are also smooth overlaps between both groups, respectively, the three habitat types. In addition, one FM site and single AFO and SRC stands were fairly similar. These stands were a margin on a AFO site and a clearing on a SRC site. *Forests* **2021**, *12*, 646 10 of 21

**Figure 5.** DCA ordination of the individual stands/sites of the different habitat types (see Table 1 for abbreviations). Eigenvalue axis 1: 0.4266, axis 2: 0.3283, length of axis 1: 4.9152, axis 2: 3.0447. **Figure 5.** DCA ordination of the individual stands/sites of the different habitat types (see Table 1 for abbreviations). Eigenvalue axis 1: 0.4266, axis 2: 0.3283, length of axis 1: 4.9152, axis 2: 3.0447.

*3.2. Comparison of SRC Stand Types*  3.2.1. Species Numbers When comparing the different stand types within the SRC, the number of species at plot-level did not differ significantly in most cases (Figure 6). The lowest species numbers were found in the non-harvested poplar stands. Stands with the poplar variety Hybride 275 (P275-nh) were particularly species-poor and differed significantly from all other The considerable dispersion within all groups illustrates that the individual sites or stands of the respective habitat types did not show homogeneous plant communities and sometimes differed significantly with regard to their qualitative (occurring species) and quantitative (frequency of the species) species composition. For SRC and AFO/HE-Y, there were somewhat higher similarities between individual stands, but also considerable differences between others (in detail see Section 3.2.3).

stand types. For all poplar and willow stands, an increase in species numbers was observed after harvesting. This was particularly evident for the poplar stands. Here, non-harvested stands (nh) mostly differed significantly from stands in the first and second growing season after harvesting (rg-1, rg-2). Notably, PMAX-rg1 showed the highest number of species of all investigated stand types. For the willows, however, differences

nificant. Stand types with native tree species (SOA, BEP) as well as accompanying structures (HEAD, CLEAR) showed average species numbers. Species numbers at stand-level (Figure A1 in Appendix A) followed the trend of species numbers at plot-level. Since only two or three individual stands were surveyed per stand type, a

statistical comparison at this level was not applicable.

#### *3.2. Comparison of SRC Stand Types*

#### 3.2.1. Species Numbers

When comparing the different stand types within the SRC, the number of species at plot-level did not differ significantly in most cases (Figure 6). The lowest species numbers were found in the non-harvested poplar stands. Stands with the poplar variety Hybride 275 (P275-nh) were particularly species-poor and differed significantly from all other stand types. For all poplar and willow stands, an increase in species numbers was observed after harvesting. This was particularly evident for the poplar stands. Here, non-harvested stands (nh) mostly differed significantly from stands in the first and second growing season after harvesting (rg-1, rg-2). Notably, PMAX-rg1 showed the highest number of species of all investigated stand types. For the willows, however, differences between the harvested and the non-harvested stands were less pronounced and not significant. Stand types with native tree species (SOA, BEP) as well as accompanying structures (HEAD, CLEAR) showed average species numbers. Species numbers at stand-level (Figure A1 in Appendix A) followed the trend of species numbers at plot-level. Since only two or three individual stands were surveyed per stand type, a statistical comparison at this level was not applicable. *Forests* **2021**, *12*, 646 11 of 21

**Figure 6.** Species numbers of the different SRC stand types at plot-level. Types with no consistent letter indicate significant differences (*p* < 0.05). Median values are presented as horizontal lines, mean values as orange diamonds. Unfilled circles show the data of the single plots sampled per type. In each boxplot, the boundaries of the box are the 25th and 75th percentiles and the whiskers represent the lowest and largest values no further than 1.5 times away from the 25th and 75th percentiles. See Table 2 for abbreviations of stand types. **Figure 6.** Species numbers of the different SRC stand types at plot-level. Types with no consistent letter indicate significant differences (*p* < 0.05). Median values are presented as horizontal lines, mean values as orange diamonds. Unfilled circles show the data of the single plots sampled per type. In each boxplot, the boundaries of the box are the 25th and 75th percentiles and the whiskers represent the lowest and largest values no further than 1.5 times away from the 25th and 75th percentiles. See Table 2 for abbreviations of stand types.

#### 3.2.2. Species Composition and Species Numbers with Regard to Habitat Preferences 3.2.2. Species Composition and Species Numbers with Regard to Habitat Preferences

Figure 7 shows the species composition within the different SRC stand types with regard to the habitat preferences of the detected species weighted with their frequencies. Willow and poplar stands (varieties of the same genus were combined for this analysis) had particularly high proportions (45–70%) of species from arable land or short-lived ruderal habitats, both when harvested (-rg1, -rg2) and when not harvested (-nh). The highest proportions (almost 70%) were found in the poplar stands in the first growing season after harvest (P-rg1). Species of persistent ruderal habitats had particularly high Figure <sup>7</sup> shows the species composition within the different SRC stand types withregard to the habitat preferences of the detected species weighted with their frequencies. Willow and poplar stands (varieties of the same genus were combined for this analysis) had particularly high proportions (45–70%) of species from arable land or short-lived ruderal habitats, both when harvested (-rg1, -rg2) and when not harvested (-nh). The highest proportions (almost 70%) were found in the poplar stands in the first growing season after harvest (P-rg1). Species of persistent ruderal habitats had particularly high

proportions (50–60%) in SOA, BEP and CLEAR, while grassland species (almost 35%) were most frequently in HEAD. Species of woody habitats had low proportions overall,

lishment of the SRC (SOA, BEP, W-nh, P-nh).

proportions (50–60%) in SOA, BEP and CLEAR, while grassland species (almost 35%) were most frequently in HEAD. Species of woody habitats had low proportions overall, but were slightly more frequent in stands that had not been harvested since the establishment of the SRC (SOA, BEP, W-nh, P-nh). *Forests* **2021**, *12*, 646 12 of 21

**Figure 7.** Proportions of plant species of different habitat preferences within the SRC stand types weighted with their frequencies (see Table 2 for abbreviations of stand types; different poplar (P) and willow (W) varieties are combined). Abbreviations for habitat preferences: a = species of arable land or short-lived ruderal habitats, r = species of persistent ruderal habitats, g = grassland species, h = species of heathland or nutrient-poor/dry grasslands, w = species of woodland, shrubs or hedges (incl. vegetation of woodland margins), x = indifferent, not stated or other habitat. **Figure 7.** Proportions of plant species of different habitat preferences within the SRC stand types weighted with their frequencies (see Table 2 for abbreviations of stand types; different poplar (P) and willow (W) varieties are combined). Abbreviations for habitat preferences: a = species of arable land or short-lived ruderal habitats, r = species of persistent ruderal habitats, g = grassland species, h = species of heathland or nutrient-poor/dry grasslands, w = species of woodland, shrubs or hedges (incl. vegetation of woodland margins), x = indifferent, not stated or other habitat.

In addition to Figure 7, Table A1 shows the mean numbers of species of different habitat preferences within the different SRC stand types at plot-level. Particularly high numbers of arable species were found within the different willow (WING, WTOR) and poplar (P275, PMAX) stands in the first (-rg1) and second (-rg2) growing season after harvesting. In contrast, BEP, SOA and CLEAR had particularly low numbers of arable species. Species of persistent ruderal habitats were found with similar numbers per plot in most of the surveyed stand types. Only the non-harvested poplar stands (P275-nh, PMAX-nh) had comparatively low numbers of species belonging to this category. Grassland species were also recorded with similar numbers per plot in most of the stand types. HEAD had the highest number of grassland species per plot while P275-nh and PMAX-nh had the lowest. The number of woodland species was generally low in all stand types and there were no significant differences between the different types. In addition to Figure 7, Table A1 shows the mean numbers of species of different habitat preferences within the different SRC stand types at plot-level. Particularly high numbers of arable species were found within the different willow (WING, WTOR) and poplar (P275, PMAX) stands in the first (-rg1) and second (-rg2) growing season after harvesting. In contrast, BEP, SOA and CLEAR had particularly low numbers of arable species. Species of persistent ruderal habitats were found with similar numbers per plot in most of the surveyed stand types. Only the non-harvested poplar stands (P275-nh, PMAXnh) had comparatively low numbers of species belonging to this category. Grassland species were also recorded with similar numbers per plot in most of the stand types. HEAD had the highest number of grassland species per plot while P275-nh and PMAX-nh had the lowest. The number of woodland species was generally low in all stand types and there were no significant differences between the different types.

#### 3.2.3. Similarity of Plant Communities 3.2.3. Similarity of Plant Communities

The DCA graph (Figure 8) shows a wide dispersion of the different SRC stands both within and between the sites. Stands within the same site were therefore not clearly separated into distinct groups, instead there were overlaps between the three sites and their individual stands. These overlaps indicate some similarities with regard to the development of the flora and the species composition on the three surveyed SRC sites (as complexes consisting of different stand types). The DCA graph (Figure 8) shows a wide dispersion of the different SRC stands both within and between the sites. Stands within the same site were therefore not clearly separated into distinct groups, instead there were overlaps between the three sites and their individual stands. These overlaps indicate some similarities with regard to the development of the flora and the species composition on the three surveyed SRC sites (as complexes consisting of different stand types).

In terms of qualitative (occurring species) and quantitative (frequency of the species) species composition there were, in some cases, somewhat greater similarities between stands from different sites, but in other cases there were also clear differences. Overall, the graph of the DCA shows a heterogeneous result in which no clear patterns of similarity between certain stands or stand types are discernable. On the one hand, it is not clearly discernible that stands of the same stand type (on different sites) were regularly

within the same site with regard to their species composition. This was most evident for SOA and BEP on all three sites. In other cases, however, there were also greater similari-

stands of different varieties and growth-stages.

**Figure 8.** DCA ordination of the individual stands of the different SRC stand types. See Table 2 for abbreviations of SRC stand types. Eigenvalue axis 1: 0.3243, axis 2: 0.2540, length of axis 1: 2.3706, axis 2: 2.1698. **Figure 8.** DCA ordination of the individual stands of the different SRC stand types. See Table 2 for abbreviations of SRC stand types. Eigenvalue axis 1: 0.3243, axis 2: 0.2540, length of axis 1: 2.3706, axis 2: 2.1698.

**4. Discussion**  In accordance with previous studies on phytodiversity of SRC [22,23,26,31], we also found significantly higher species numbers compared to conventional arable fields with annual crops. Species of arable land and short-lived ruderal habitats still had high proportions on the surveyed SRC in the 8th and 9th year after establishment and were particularly frequent in comparison to species of other habitat preferences. A reason for this is the landscape context (high proportion of arable land) and the previous agricultural use of the SRC sites. Both factors can influence the species composition of SRC for a long time after its establishment [27,30,36]. On the other hand, the high proportion of arable species can also be explained by the sectional harvesting, since it repeatedly creates suitable conditions for these light-demanding species in certain areas of the plantations [cf. [31,34]]. Especially within the harvested stands, high numbers and frequency proportions of arable species (such as *Aphanes australis, Myosotis arvensis, Spergula arvensis* or In terms of qualitative (occurring species) and quantitative (frequency of the species) species composition there were, in some cases, somewhat greater similarities between stands from different sites, but in other cases there were also clear differences. Overall, the graph of the DCA shows a heterogeneous result in which no clear patterns of similarity between certain stands or stand types are discernable. On the one hand, it is not clearly discernible that stands of the same stand type (on different sites) were regularly very similar. At the same time, stands (of different stand types) within the same sites were not necessarily very similar. Some stands differed considerably from other stands within the same site with regard to their species composition. This was most evident for SOA and BEP on all three sites. In other cases, however, there were also greater similarities between different stands within the same site. This was particularly noticeable at sites 2 and 3. For example, on site 3 there was a fairly high similarity between the willow stands of different varieties and growth-stages.

ties between different stands within the same site. This was particularly noticeable at sites 2 and 3. For example, on site 3 there was a fairly high similarity between the willow

#### *Veronica arvensis*) were found. Many of these species were absent or less frequent in the **4. Discussion**

surveyed arable fields with maize cultivation. Therefore, recently harvested (as well as recently established) SRC can provide a substitute habitat for common arable species that do not find suitable conditions on intensively used conventional arable fields anymore. However, SRC are not expected to have any potential to promote rare or threatened arable species, since the shading from trees and competition with the accompanying perennial herbaceous vegetation does not provide favorable conditions for highly specialized and low-competitive arable species [23,31,54,55]. In order to promote rare and threatened arable species, specific measures within arable fields are required [56,57], and SRC do not offer an alternative to these. In accordance with previous studies on phytodiversity of SRC [22,23,26,31], we also found significantly higher species numbers compared to conventional arable fields with annual crops. Species of arable land and short-lived ruderal habitats still had high proportions on the surveyed SRC in the 8th and 9th year after establishment and were particularly frequent in comparison to species of other habitat preferences. A reason for this is the landscape context (high proportion of arable land) and the previous agricultural use of the SRC sites. Both factors can influence the species composition of SRC for a long time after its establishment [27,30,36]. On the other hand, the high proportion of arable species can also be explained by the sectional harvesting, since it repeatedly creates suitable conditions for these light-demanding species in certain areas of the plantations [cf. [31,34]]. Especially within the harvested stands, high numbers and frequency proportions of arable species

(such as *Aphanes australis, Myosotis arvensis, Spergula arvensis* or *Veronica arvensis*) were found. Many of these species were absent or less frequent in the surveyed arable fields with maize cultivation. Therefore, recently harvested (as well as recently established) SRC can provide a substitute habitat for common arable species that do not find suitable conditions on intensively used conventional arable fields anymore. However, SRC are not expected to have any potential to promote rare or threatened arable species, since the shading from trees and competition with the accompanying perennial herbaceous vegetation does not provide favorable conditions for highly specialized and low-competitive arable species [23,31,54,55]. In order to promote rare and threatened arable species, specific measures within arable fields are required [56,57], and SRC do not offer an alternative to these.

In comparison to AFO, HE-Y and FM, the surveyed SRC had similar species numbers. In comparison to HE-O, the species numbers of SRC were even higher. At the same time, species composition of SRC was quite different from the other habitat types, in particular from HE-O, FM and AL. Even though many species found on the SRC also occurred in the other habitat types, the combination of these species on the SRC was different from the other habitat types in qualitative (species inventory) and quantitative (species frequencies) terms. Furthermore, the SRC also contained some species that were not found in the other surveyed habitats. Therefore, the SRC formed distinct plant communities that differed from the other farmland habitat types (cf. similar findings of Baum et al. [26]). Similarity of the surveyed SRC with AFO and HE-Y was highest in comparison to the other habitat types. This is not a surprising result, as these habitats had a similar age and vegetation structure as the SRC and were also established on arable land. However, during succession, the similarity of AFO and HE-Y with SRC will further decrease. This is already indicated by the higher numbers and proportions of perennial species and the lower numbers and proportions of short-lived species in AFO and HE-Y (Table 3, Figure 4). Despite their similar species numbers, SRC are not an immediate substitute for other habitats of agricultural landscapes, since they have their own distinct plant communities. Instead, SRC are novel habitats that can increase the habitat diversity of a landscape [24,26,33] and complement the range of existing agricultural crops and the range of regularly implemented measures to promote farmland biodiversity such as afforestations, hedges or field margins. Thus, SRC can contribute to phytodiversity at the landscape-level, especially in intensively used agricultural landscapes with low habitat heterogeneity [cf. [25,33]].

A major result of our surveys of different stands within the SRC is that different stand types, either with different tree species or in different growth-stages, can significantly increase plant species diversity within a plantation. This confirms recommendations [cf. [15,27,34]] for implementing appropriate measures to promote phytodiversity on SRC. The positive effect of such a mosaic is shown by the fact that the species composition of different stand types within the same site differed considerably in several cases. Different stand types create heterogeneous conditions (e.g., light availability, intensity of disturbance), providing habitats for species with different habitat requirements within the same site [34,58]. Regular disturbance by harvesting was particularly beneficial for phytodiversity at stand-level. This was particularly evident for the poplar stands, where species numbers strongly increased after harvesting. For the willow stands, species numbers also increased after harvesting, but more moderately. In contrast, stands with native tree species (SOA, BEP) as well as headlands (HEAD) and clearings (CLEAR) contained fairly average species numbers. SOA and BEP had not been disturbed by harvesting so far. In addition, these stand types were either characterized by numerous tree failures (BEP) or tree growth was significantly lower (SOA) than for the poplar and willow clones. Due to the high light availability and the lack of any disturbance, these stand types, as well as HEAD and CLEAR, were often dominated by highly competitive species (e.g., *Elymus repens*, *Festuca rubra*) of persistent ruderal habitats or grasslands (similar observations were made by Glaser and Schmidt [23] within gaps of SRC, where trees failed to grow), while species of arable land or short-lived ruderal habitats had lower numbers and proportions

(Figure 7, Table A1). However, despite their more average species numbers, these stand types were also important for the phytodiversity of the entire plantation, as they differed considerably from the poplar and willow stands in terms of their species composition (see Figures 7 and 8). In addition, stands with native tree species and/or accompanying structures are important components of SRC for other species groups such as ground beetles [59], large and medium-sized mammals [60], breeding birds [61,62] or birds in the winter season [63] and are therefore important features of SRC with regard to overall biodiversity. Furthermore, plant species richness in non-wooded accompanying structures of SRC such as headlands, clearings/gaps and rides can be further increased by additional measures, e.g., by establishing species-rich fringe vegetation by seeding of native seed mixtures (see Kiehl et al. [64] for general recommendations).

In addition to the partly considerable differences between harvested and non-harvested stands within the SRC (especially within the poplar stands), the non-harvested poplar and willow stands also differed considerably with regard to their species numbers and species composition. These differences can essentially be explained by the specific local conditions that exist in SRC stands with different tree species. Specific characteristics (e.g., leaf shape and size) of different tree species and varieties can influence the environmental conditions within the stands; large-leafed poplar clones lead to much greater shading than narrow-leafed willow clones at similar planting densities [31]. The resulting low species numbers in the non-harvested poplar stands, however, lead to a significant increase in species numbers directly after harvesting, as many plant species are able to establish spontaneously in these almost vegetation-free stands [cf. [31,54]]. In the willow stands, on the other hand, the increase in species numbers after harvest was much more moderate. This was due to the fact, that there still existed a comparatively species-rich and dense herb layer before harvest which meant that there was less space for additional species to establish spontaneously. Based on these findings, it can be concluded that stand types that differ significantly in terms of their structural characteristics (e.g., poplar and willow stands) are particularly beneficial for the phytodiversity of the entire plantation (as a mosaic of different stands) [cf. 34]. In contrast, if only different clones of the same tree genus are cultivated within a SRC, the increase in phytodiversity is likely to be less pronounced since the habitat conditions in stands of the same genera are more similar [cf. [31]].

A finding that seems unusual at first sight is the high frequency of species from arable land and short-lived ruderal habitats within the non-harvested willow and poplar stands (Figure 7). However, it must be taken into account that these were, especially in the case of the poplar stands, only a few frequently occurring species (in P-nh especially *Chenopodium album* and *Stellaria media*), which colonized the stands at the beginning of the vegetation period (April–June), but which had already disappeared in summer when the canopy closed. Similar findings of seasonal changes on flora are reported by Gustafsson [28] for SRC in Sweden and by Heilmann et al. [31] for SRC in Germany.

In addition to the positive contribution of a mosaic of different stand types to the phytodiversity of the SRC at the site-level, the small size of the individual stands is likely to have had a beneficial effect on phytodiversity at stand-level, since edge-zones of SRC are usually more species-rich than central areas [22,25,32,54,65]. To promote phytodiversity, SRC should either be small-scale and established in an elongated and rectangular rather than a square shape, or larger plantations should be managed in a small-scale manner (like on the investigated SRC sites) in order to increase the proportion of edge-zones and improve immigration opportunities for plants [34].

Basically, the plant communities of the surveyed SRC and all other investigated habitat types were dominated by widespread, common and non-threatened plant species. In our study region, the presence of species of conservation concern was not expected, since the establishment potential for these species is limited due to many decades of intensive agricultural use [66–68]. However, in many other studies on SRC in different regions of Germany and in other European countries [22–26,29,33,54,55,69], these woody crops did not prove to be a habitat of major importance for rare or threatened plant species. Instead,

they supported adaptable, mostly competitive, widespread and common plant species. The few threatened or rare species found on SRC so far were mostly light-demanding pioneer species that were present in the first few years after establishment and disappeared as the SRC matured [34,70]. Therefore, SRC cannot be expected to be a suitable measure to promote rare, threatened or specialized plant species, even when managed according to ecological guidelines.

#### **5. Conclusions**

Small-scale and structurally diverse SRC which are managed according to ecological guidelines provide suitable habitats for a variety of different plant species due to their diverse habitat conditions within the same site. They have similar species numbers as hedges and afforestations of the same age or narrow field margins. Due to their specific habitat characteristics, they form distinct plant communities that differ considerable from other farmland habitats. Therefore, they can increase habitat diversity in intensively used agricultural landscapes and thus be an additional tool to promote farmland phytodiversity [cf. [33]]. However, the species that benefit from their establishment are mostly adaptable, widespread and common species of no conservation concern [cf. [34]]. On the investigated SRC, it was confirmed that measures such as harvesting in sections or cultivation of different tree species in small-scale units within the same plantation, are particularly effective in promoting phytodiversity of these woody biomass crops. Therefore, these measures could be offered as agri-environmental schemes in order to compensate for the associated effort or yield reduction and to further increase the ecological sustainability of biomass production on SRC [cf. [71,72]].

**Author Contributions:** Conceptualization, F.Z.; methodology, F.Z. and M.R.; formal analysis, F.Z.; investigation, F.Z.; data curation, F.Z.; writing original draft preparation, F.Z.; writing review and editing, F.Z. and M.R.; visualization, F.Z.; supervision, M.R.; project administration, M.R.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Lower Saxony Ministry of Food, Agriculture and Consumer Protection [Niedersächsisches Ministerium für Ernährung, Landwirtschaft und Verbraucherschutz], grant number 105.2-3234/1-13-4. The publication of this article was funded by the Open Access Fund of Leibniz University Hannover.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The raw-datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

**Acknowledgments:** We are grateful to the Lower Saxony Ministry of Food, Agriculture and Consumer Protection for the funding of our research. Furthermore, we thank K. Grobe, C. Meiser, J. Nöhren and G. Wienrich for their assistance in field work and L. von Falkenhayn for proofreading the English manuscript. We are also grateful to S. Budig, F. Schaarschmidt and A. Grobe for advice on the statistical analysis and for their help with some statistical analyses using the programme R. The publication of this article was funded by the Open Access Fund of Leibniz University Hannover.

**Conflicts of Interest:** The authors declare no conflict of interest. The funding organisation had no role in the design, execution, interpretation or writing of the study.

#### **Appendix A**

*Forests* **2021**, *12*, 646 17 of 21

**Appendix A** 

**Figure A1.** Species numbers of the different stand types at stand-level. Filled circles show the data of the single stands sampled per stand type (with *n* <sup>=</sup> 2 or 3 replications per type). See Table 2 for abbreviations of stand types. **Figure A1.** Species numbers of the different stand types at stand-level. Filled circles show the data of the single stands sampled per stand type (with *n* = 2 or 3 replications per type). See Table 2 for abbreviations of stand types.



1 Abbreviations for habitat preferences: a = species of arable land or short-lived ruderal habitats, r = species of persistent ruderal habitats, g = grassland species, w = species of woodland, shrubs or hedges (incl. vegetation of woodland margins).

#### **References**


## *Article* **Effects of Leaf Loss by Artificial Defoliation on the Growth of Different Poplar and Willow Varieties**

**Christiane E. Helbig 1,\* , Michael G. Müller <sup>1</sup> and Dirk Landgraf <sup>2</sup>**


**Abstract:** The cultivation of fast-growing tree species in short rotation coppices has gained popularity in Germany in recent years. The resilience of these coppices to phyllophagous pest organisms is crucial for their profitable management, since the loss of a single annual increment can lead to uncompensable economic losses. To study the effects of leaf loss on the growth of poplar and willow varieties that are frequently cultivated under local conditions, three sample short rotation coppices including five poplar (*Populus* spp.) and three willow (*Salix* spp.) varieties were established in a randomized block design with four artificial defoliation variants and, on one site, with three different variants regarding the number of defoliation treatments. After up to three defoliation treatments within two growing seasons, the results show negative effects of leaf loss on the height growth and the fresh weight of the aboveground biomass of plants. Our data also suggests a lasting effect of defoliation on plant growth and re-growth after the end of the treatment. In general, defoliation had a greater impact on the growth of poplars than on willows. We conclude that even minor leaf loss can have an impact on plant growth but that the actual effects of defoliation clearly depend on the site, tree species, and variety as well as the extent and number of defoliations, which determine the ability of plants for compensatory growth.

**Keywords:** short rotation coppices; poplars; willows; feeding simulation; defoliation; herbivory

#### **1. Introduction**

Several global developments, such as the depletion of fossil fuels, the increasing demand for wood products, and the striving for climate protection, have been the reason for an increasing importance of the cultivation of fast-growing tree species in short rotation coppices on agricultural land in Germany. Short rotation coppices are defined as highdensity plantations with rotation times between 2 and 20 years [1,2]. Poplars (*Populus* spp.) and willows (*Salix* spp.), which are characterized by a very fast juvenile growth, a great resprouting ability, and an easy propagation, have proven to be particularly suitable and are widely used for this kind of land use [3–7].

As typical monocultures with a high plant density, a low genetic diversity, and a distinctive spatial homogeneity, short rotation coppices generally hold a high risk for the occurrence of plant diseases and the outbreaks of pest organisms [8–13]. In addition, poplars and willows are naturally associated with an exceptionally high number of insect species in comparison to other tree species [14–17]. Accordingly, many studies have reported a large number of pest insects in poplar and willow short rotation coppices with a particular emphasis on phyllophagous species, which find ideal living conditions in these plantations [18–22]. The feeding activities of their larvae and/or adults cause a loss of leaf area but only in rare cases lead to the death of plants [23]. That is, in most cases, no lasting impact of leaf feeding can be directly seen. Several studies have shown, however, that the natural or artificial reduction of the leaf area of plants can already lead to a reduction of

**Citation:** Helbig, C.E.; Müller, M.G.; Landgraf, D. Effects of Leaf Loss by Artificial Defoliation on the Growth of Different Poplar and Willow Varieties. *Forests* **2021**, *12*, 1224. https://doi.org/10.3390/f12091224

Academic Editor: Daniele Castagneri

Received: 28 July 2021 Accepted: 3 September 2021 Published: 8 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

biomass yield after a relatively short period of time, and can still be detected after several months or years without this kind of damage [24,25]. Negative consequences of a loss of leaf area by insect feeding have also been documented on several other parameters of plant fitness, such as seed production [26–28]. For these reasons, the resilience of plants to biotic pest organisms is one of the most crucial preconditions for a large-scale, reliable, and profitable cultivation of fast-growing tree species on agricultural land, in particular because the loss of a single annual increment can lead to economic losses that may not be compensated within the short rotation times [23]. With regard to the strong economic focus of short rotation coppices, the actual effects of plant damage by insects on the yield of the coppice is a crucial aspect for their cultivation and management, for example when deciding for or against the use of insecticides.

A common procedure to study the effects of leaf area loss on the growth and yield of plants is the simulation of leaf feeding of phyllophagous pest insects by artificial defoliation [24,29–32]. The main advantage of this procedure is the ability to precisely control and modify the extent, number, and timing of defoliation, whereas the difference in the duration between natural and artificial defoliation as well as the potential lack of herbivore-induced plant volatiles due to a merely mechanical damage are disadvantageous [30,33,34]. Since the reaction of plants to herbivory is a very complex process that is not only determined by the actual leaf loss, studies comparing artificial and natural defoliation often show differences in the reaction of plants to these procedures [30]. Chen et al. (2002), for example, documented a greater reduction in plant height, height increment, and root to shoot ratio by an artificial defoliation of three-year-old Douglas fir seedlings in comparison to a natural defoliation, whereas the natural process had a greater impact on the diameter growth [29]. In contrast, Coyle et al. (2002) reported greater effects of natural feeding by *Chrysomela scripta* (Coleoptera: Chrysomelidae) on poplars than by artificial defoliation [35]. Nevertheless, many studies came to the conclusion that artificial defoliations are generally suitable to demonstrate the effects of natural defoliations [25,31,36–42].

The aims of this study were to transfer the approaches of existing studies, which were mainly carried out on potted plants [29,31,32,38–40], into the field, where the competition between plants is not excluded, and to examine the short-term and long-term effects of different extents and frequencies of leaf loss under local conditions on those poplar and willow varieties that are mainly planted in Germany.

#### **2. Materials and Methods**

#### *2.1. Sites, Plant Material, and Experimental Design*

For the leaf feeding simulation experiment sample short rotation coppices with a size of about 0.2 ha were established on one site in the federal state of Saxony [Obercarsdorf (50◦51035.000 N 13◦39010.500 E), 400 m a. s. l., 8.2 ◦C mean annual temperature, 786 mm average annual precipitation, typical cambisol, former pasture] and two sites in the south of the federal state of Brandenburg [Großthiemig (51◦23034.500 N 13◦40035.800 E), 94 m a. s. l., 8.6 ◦C mean annual temperature, 561 mm average annual precipitation, sandy gleyic cambisol, tree nursery land and Schönheide (51◦34039.400 N 14◦30017.600 E), 120 m a. s. l., 9.6 ◦C mean annual temperature, 568 mm average annual precipitation, slightly-loamy sandy cambisol, former grassland] [43].

Each of the three coppices consisted of a poplar area planted in double rows with a distance of 0.75 m within the double rows, 1.50 m between double rows, and 1.00 m between plants within the rows (8888 plants ha−<sup>1</sup> ), and a willow area planted with the same row spacing and a distance of 0.70 m between plants within rows (12,698 plants ha−<sup>1</sup> ) (Figure 1).

of every variety.

insects as accurately as possible (Figure 1).

Max 4, Muhle Larsen) and three willow varieties (Sven, Tora, Tordis), respectively [32,44], and four different defoliation treatment variants (0%, 25%, 50%, 75% leaf loss) that were based on Reichenbacker et al. (1996) [41]. The cuttings were obtained from the Research Institute for Post-Mining Landscapes (FIB) in Finsterwalde (Max 1–4), Lantmännen Agroenergi AB (Sven, Tora, Tordis), and P&P Tree Nursery in Großthiemig (Androscoggin, Muhle Larsen) and planted manually. In order to avoid micro-spatial differences among treatments, varieties were planted with four consecutive plants representing the four treatments (Figure 1). Furthermore, the poplar and the willow area of each sample short rotation coppice were bordered with one double row of the other species on each side and two plants of the other species on each beginning and end of rows to reduce potential edge effects. On each of the three locations, the four defoliation treatments were represented with 32 plants per variety, that is, there were 32 plots with four plants each

In accordance with Powers et al. (2006), the simulation of leaf feeding was carried out as a reduction of leaf area on every single leaf instead of relating the intended proportion of defoliation to the total plant leaf mass [32]. Leaves were cut with paper scissors across the midvein, similar to Peacock et al. (2002) [44], to simulate the feeding of phyllophagous

**Figure 1.** Planting design of the sample short rotation coppices (**above**) and design of leaf clipping of the different defoliation treatments (**below**). **Figure 1.** Planting design of the sample short rotation coppices (**above**) and design of leaf clipping of the different defoliation treatments (**below**).

*2.2. Experimental Process and Data Recording*  The three sample short rotation coppices were established at the end of March (Großthiemig, Schönheide) and beginning of April 2007 (Obercarsdorf) after a standard soil preparation including ploughing, tilling, and, except for the organic farming site in The experiment was set up as randomized block design according to Powers et al. (2006) and Peacock et al. (2002) with five poplar varieties (Androscoggin, Max 1, Max 3, Max 4, Muhle Larsen) and three willow varieties (Sven, Tora, Tordis), respectively [32,44], and four different defoliation treatment variants (0%, 25%, 50%, 75% leaf loss) that were based on Reichenbacker et al. (1996) [41]. The cuttings were obtained from the Research Institute for Post-Mining Landscapes (FIB) in Finsterwalde (Max 1–4), Lantmännen Agroenergi AB (Sven, Tora, Tordis), and P&P Tree Nursery in Großthiemig (Androscoggin, Muhle Larsen) and planted manually. In order to avoid micro-spatial differences among treatments, varieties were planted with four consecutive plants representing the four treatments (Figure 1). Furthermore, the poplar and the willow area of each sample short rotation coppice were bordered with one double row of the other species on each side and two plants of the other species on each beginning and end of rows to reduce potential edge effects. On each of the three locations, the four defoliation treatments were represented with 32 plants per variety, that is, there were 32 plots with four plants each of every variety.

In accordance with Powers et al. (2006), the simulation of leaf feeding was carried out as a reduction of leaf area on every single leaf instead of relating the intended proportion of defoliation to the total plant leaf mass [32]. Leaves were cut with paper scissors across the midvein, similar to Peacock et al. (2002) [44], to simulate the feeding of phyllophagous insects as accurately as possible (Figure 1).

#### *2.2. Experimental Process and Data Recording*

The three sample short rotation coppices were established at the end of March (Großthiemig, Schönheide) and beginning of April 2007 (Obercarsdorf) after a standard soil preparation including ploughing, tilling, and, except for the organic farming site in

Obercarsdorf, the application of pre-emergent herbicides was carried out by the site owners. The first defoliation treatment took place four months after planting in July (Großthiemig, Schönheide) and August 2007 (Obercarsdorf). The willow varieties in Schönheide were excluded from the first defoliation due to their weak growth. Extensive browsing made it necessary to fence the willow area in Großthiemig in July 2007 and the whole coppice in Schönheide in May 2008. The second defoliation was carried out in June 2008 and the third defoliation in August 2008. During these two treatments, only a part of the plants in Großthiemig were defoliated so that at the end of the experiment the site held plants treated once, twice, or three times.

Data recordings on all three study sites took place directly prior to the first defoliation treatments in July/August 2007 and in December 2007. Recorded parameters were plant height, number of shoots, and plant damage (verbal description of damage and its potential abiotic or biotic causal factor). After a final data recording in Großthiemig in February 2009, the short rotation coppice was harvested manually in March 2009 to determine the fresh and dry weight of the aboveground biomass. Due to organizational reasons, it was not possible to determine the weight of the individual plants. Instead, the total weight of all plants per variety, defoliation variant, and number of defoliations was recorded. Determination of dry weight was only carried out on samples: four plots without plant losses were chosen for all poplars and those willows that were defoliated once, whereas for the willows that were defoliated twice or three times, dry weight was determined for all plants that were defoliated 0% or 75%. To study a potential long-term effect of defoliation on the re-growth of plants, additional data recordings in Großthiemig took place in June 2009, after which the resprouting shoots were reduced to the highest shoot per stool, and in September 2009. The short rotation coppices in Obercarsdorf and Schönheide were not harvested after the end of the defoliation treatments. Their final data recording took place in early April 2009 prior to bud burst. An additional data recording to study the potential long-term effect of defoliation on plant growth was carried out in early April 2010.

#### *2.3. Data Analysis*

All analyses were carried out using IBM SPSS Statistics 27.0 [45]. The significance level for all statistical tests was set at α = 0.05. Data were analyzed for normality using the Shapiro–Wilk test and for homogeneity of variances using the Levene test. Based on the results, data was either further analyzed using parametric or non-parametric tests. For parametric tests, the *t* test (TT) or Welch test was used to compare the mean values of two independent samples or an analysis of variance (ANOVA, AN) with Tukey or Tukey-Kramer post hoc tests in case of multiple samples. For non-parametric tests, the Mann–Whitney U test (MU) or the Kruskall–Wallis test with Dunn-Bonferroni post hoc tests (KW) were used to analyze differences in the central tendencies of bivariate or multivariate datasets. Relevant *p* values of statistical analyses are either included in the text or in tables. In addition, statistical tests are denoted using the abbreviations stated above. Statistical analyses regarding plant height are based on the main shoot of each plant, which is the highest one. Plants that showed significant damage by browsing, insects, or other biotic and abiotic factors were excluded from analyses.

#### **3. Results**

#### *3.1. Plant Growth Directly Prior and after the First Defoliation Treatment*

In summer 2007, four months after the establishment of the short rotation coppices and directly prior to the start of the first defoliation treatment, plant losses on the study site in Großthiemig (Ø 5.2%) were considerably lower than on the sites in Obercarsdorf (Ø 17.0%) and Schönheide (Ø 17.3%). Differences in site conditions and management are assumed to be the reason for this difference in plant survival. Optimal mechanical and chemical soil preparation, low ground vegetation cover, and a potentially better nutrient supply on the tree nursery site in Großthiemig facilitated a high plant survival rate and fast growth. In contrast, on the sites in Obercarsdorf and Schönheide, which had previously been used

as pasture and grassland, a full ground vegetation cover quickly re-developed despite the mechanical (Obercarsdorf) or mechanical and chemical (Schönheide) soil preparation, leading to greater plant losses in the newly established short rotation coppices on these two sites. In spite of these site-related differences, the trends regarding plant survival on tree genus and variety level were the same on all three study sites (Table A1 in Appendix A). Willows showed a slightly higher number of surviving plants than poplars. Within poplars, an average of 14.2% more plants of the three Max varieties survived in comparison to Androscoggin and Muhle Larsen, whereas within willows, an average of 6.7% more plants of the Tordis variety survived in comparison to Sven and Tora.

Clear differences among the three study sites were also visible with regard to plant heights and reflected the different site conditions similarly to the data on plant survival (Figure A1 in Appendix A). All eight varieties reached a significantly greater height in Großthiemig than in Obercarsdorf and Schönheide (KW: *p* = 0.000 for all pairwise comparisons). Despite the differences in site conditions, the height growth trends of the individual varieties are very similar on all three study sites and indicate a certain genetic fixation of height growth among varieties.

After the first defoliation treatment in July/August 2007, from which all willows in Schönheide were excluded due to their weak growth, plant heights were recorded again in December 2007 (Figure A2 in Appendix A). In general, data do not show a statistically significant effect of the four defoliation variants, except for Muhle Larsen in Großthiemig (AN: *p* = 0.032) and Obercarsdorf (*p* = 0.010). In Großthiemig, post hoc tests reveal significantly greater heights of undefoliated in comparison to 75% defoliated plants (*p* = 0.046) and in Obercarsdorf significantly greater heights of 25% defoliated in comparison to 50% defoliated plants (*p* = 0.017).

Although only very few statistically significant differences were detected among the four defoliation variants after the first treatment, the direct comparison of heights between 75% defoliated and undefoliated plants shows height losses for all poplar varieties on all three study sites with the only exception of Max 3 in Obercarsdorf (Table 1). In general, leaf loss had a greater impact on the growth of Androscoggin, Max 3 and Muhle Larsen than on Max 1 and Max 4. In contrast, data on all willow varieties in Großthiemig and on Sven and Tordis in Obercarsdorf indicate a positive effect of defoliation on their height growth. Height reductions due to defoliation were only visible for all willow varieties in Schönheide and for Tora in Obercarsdorf. However, statistical analyses again resulted in very few significant differences in the height between 75% defoliated and undefoliated plants: on the variety level for Muhle Larsen in Großthiemig (TT: *p* = 0.010) and on the tree genus level for poplars in Großthiemig (TT: *p* = 0.005) and Schönheide (*p* = 0.007).


**Table 1.** Height differences of 75% defoliated and undefoliated plants (∆ 75-0) in December 2007 after the first defoliation treatment (bold values indicate mean values of all varieties of a tree genus, \* statistically significant difference according to *t* test).

#### *3.2. Plant Growth after the Last Defoliation*

After the first defoliation treatment in July/August 2007, two more treatments were carried out in June and in August 2008, and data was recorded again in early spring 2009, prior to the start of the growing season. In Großthiemig, only a part of the plants were defoliated during the treatments in 2008, so that data from this site cannot only be grouped by variety and defoliation variant but also by number of defoliation treatments. With the mere regard to the number of defoliation treatments on the genus level, a different reaction of poplars and willows to the increasing number of defoliation treatments was recorded when considering the average of all defoliation variants. While there is no statistically significant difference in the plant height of poplars defoliated once (Ø 176.6 cm) and twice (Ø 177.9 cm), the poplars treated three times (Ø 164.9 cm) had a significantly reduced plant height in comparison to both, with a mean height reduction of 7%. Willows, in contrast, showed again a promotion of plant growth by defoliation. Plants defoliated twice had a significantly greater height (Ø 277.3 cm) than plants defoliated once (Ø 259.6 cm). However, defoliation carried out three times led to a significant height loss (Ø 239.3 cm) in comparison to plants defoliated once and twice, with a mean height reduction of 11%.

Taking into account not only the number of defoliation treatments but also the variety and defoliation variant, trends show a decreasing height with increasing leaf loss in several cases, in particular for poplar varieties, even though statistically significant differences on the group level only exist in the five cases marked with an asterisk (Figure 2). The *p* values for the pairwise comparisons of defoliation variants on tree genus level and those comparisons with at least one significant value on the variety level show an increasing number of statistically significant differences with an increasing number of defoliation treatments (Table 2). These especially occur when comparing 75% defoliated and undefoliated plants but also in parts among the three variants that included leaf loss. It can be noted that defoliation particularly led to significant differences in plant height among defoliation variants for the Muhle Larsen and Tora varieties.

**Table 2.** *p* values of pairwise comparisons of plant heights among defoliation variants (DVs) for both tree genera (green) as well as for all varieties with at least one significant value (orange) on the study site in Großthiemig with regard to the number of defoliation treatments prior to the start of the growing season in 2009 via Tukey HSD test (bold values indicate statistically significant differences).


**Figure 2.** Height of poplar and willow varieties on the study site in Großthiemig with regard to the number of defoliation treatments prior to the start of the growing season in 2009 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA). **Figure 2.** Height of poplar and willow varieties on the study site in Großthiemig with regard to the number of defoliation treatments prior to the start of the growing season in 2009 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA).

Despite the three defoliation treatments, analyses resulted in no statistically significant differences in plant heights among defoliation variants in Obercarsdorf, whereas in Schönheide all poplar varieties did show significant differences in plant heights with decreasing heights at increasing leaf loss (Figure 3). Data on the pairwise comparisons bet-ween defoliation variants generally show a *p* value decrease with an increasing difference in leaf loss (Table 3).

**Figure 3.** Height of poplar and willow varieties on the study sites in Obercarsdorf and Schönheide after two and/or three defoliation treatments prior to the start of the growing season in 2009 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA). **Figure 3.** Height of poplar and willow varieties on the study sites in Obercarsdorf and Schönheide after two and/or three defoliation treatments prior to the start of the growing season in 2009 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA).

**Table 3.** *p* values of pairwise comparisons of plant heights among defoliation variants (DVs) for both tree genera (green) as well as for all varieties with at least one significant value (orange) on the study sites in Obercarsdorf and Schönheide with regard to the number of defoliation treatments prior to the start of the growing season in 2009 via Tukey HSD test (bold values indicate statistically significant differences). **Genus/Variety Level Variety Level Obercarsdorf (3 defoliation treatments) Willows DV 0% 25% 50% 75% Poplars 0%** — 0.995 0.905 0.752 no case of pairwise comparisons among defoliation variants with at least one significant **25%** 0.827 — 0.981 0.914 **50% 0.043** 0.261 — 0.992 When only comparing the heights of those plants with the greatest leaf loss, that is 75%, with undefoliated plants, height reductions of up to 42% are visible, with only a few exceptions for willow varieties (Table 4). The data shows again that the effects of defoliation on plant height depend on the site, tree genus, variety, and frequency of defoliation. Generally, height reduction increased with increasing defoliation frequency, and defoliation had a greater impact on poplar than on willow varieties. On average for all three study sites, for poplars, Max 1 had the least height reduction with 11% and Muhle Larsen the greatest with 25%. For willows, Tora was most impacted by defoliation with a height reduction of 18%, whereas Tordis and Sven showed a reduction of 5% on average. Statistically significant differences were detected for all poplar varieties (TT: *p* = 0.001–0.005) as well as the Tora variety (*p* = 0.017). On the tree genus level, a statistically significant effect was only existent for poplars (*p* = 0.000), although the *p* value for willows (0.051) came very close to a significance.

**Schönheide (poplars: 3 defoliation treatments, willows: 2 defoliation treatments)** 

 **DV 0% 25% 50% 75% DV 0% 25% 50% 75%** 

**50% 0.000 0.013** — 0.998 **50% 0.029** 0.930 —

**25%** 0.261 — 0.905 0.832 **25%** 0.154 —

**Androscoggin** 

**75% 0.000 0.000** 0.100 — **75% 0.004** 0.457 0.769 —  **Max 3 Muhle Larsen DV 0% 25% 50% 75% DV 0% 25% 50% 75%** 

**0%** —

value

**75%** 0.057 0.317 0.999 —

**0%** — 0.989 0.747 0.644

 **Willows** 

**Poplars** 

statistically significant differences). **Genus/Variety Level Variety Level Obercarsdorf (3 defoliation treatments) Willows DV 0% 25% 50% 75% Poplars 0%** — 0.995 0.905 0.752 no case of pairwise comparisons among defoliation variants with at least one significant value **25%** 0.827 — 0.981 0.914 **50% 0.043** 0.261 — 0.992 **75%** 0.057 0.317 0.999 — **Schönheide (poplars: 3 defoliation treatments, willows: 2 defoliation treatments) Willows DV 0% 25% 50% 75% DV 0% 25% 50% 75% Poplars 0%** — 0.989 0.747 0.644 **Androscoggin 0%** — **25%** 0.261 — 0.905 0.832 **25%** 0.154 — **50% 0.000 0.013** — 0.998 **50% 0.029** 0.930 — **75% 0.000 0.000** 0.100 — **75% 0.004** 0.457 0.769 — **Max 3 Muhle Larsen DV 0% 25% 50% 75% DV 0% 25% 50% 75% Max 1 0%** — 0.805 0.133 **0.004 Max 4 0%** — 0.960 0.084 **0.004 25%** 0.979 — 0.586 0.060 **25%** 0.941 — 0.262 **0.026 50%** 0.241 0.451 — 0.571 **50%** 0.125 0.352 — 0.761 **75% 0.028** 0.077 0.780 — **75% 0.011 0.050** 0.773 —

**Table 3.** *p* values of pairwise comparisons of plant heights among defoliation variants (DVs) for both tree genera (green) as well as for all varieties with at least one significant value (orange) on the study sites in Obercarsdorf and Schönheide with regard to the number of defoliation treatments prior to the start of the growing season in 2009 via Tukey HSD test (bold values indicate

**Table 4.** Height differences between 75% defoliated and undefoliated plants (∆ 75-0) prior to the start of the growing season in 2009 after a one-time, two-time, or three-time defoliation treatment (\* statistically significant difference according to t or Welch test).


### *3.3. Plant Growth after the End of the Defoliation Treatments*

#### 3.3.1. Harvest and Growth in Großthiemig

After the last data recording in February 2009, the short rotation coppice in Großthiemig was completely harvested manually to determine the weight of the aboveground biomass. However, it was not possible to determine the fresh weight of each individual plant. Instead, it was determined as the total weight of plants per variety, defoliation variant, and number of defoliation treatments. The options for statistical analyses are therefore limited and the sample size is very low. Not taking into account the number of defoliation treatments provides a sample size of n = 3, at which no statistically significant differences among defoliation variants are visible on the variety level (AN: *p* = 0.079–0.996), whereas on the tree genus level, undefoliated poplars had significantly greater fresh weights than 75%

defoliated poplars (*p* = 0.045) (Figure 4). With a *p* value of 0.052, the comparison between 25% and 75% is very close to a significant difference. Looking merely at the statistical comparison of undefoliated and 75% defoliated plants instead of analyzing the differences among all four defoliation variants, significantly greater weights for undefoliated plants were computed for Muhle Larsen (TT: *p* = 0.014) as well as for the total of all poplar varieties (*p* = 0.011). comparison between 25% and 75% is very close to a significant difference. Looking merely at the statistical comparison of undefoliated and 75% defoliated plants instead of analyzing the differences among all four defoliation variants, significantly greater weights for undefoliated plants were computed for Muhle Larsen (TT: *p* = 0.014) as well as for the total of all poplar varieties (*p* = 0.011).

*Forests* **2021**, *12*, x FOR PEER REVIEW 11 of 26

**Figure 4.** Fresh weight of aboveground biomass of plants on the study site in Großthiemig in February 2009 (n per variety and defoliation variant = 3 by not taking into account the number of defoliations). **Figure 4.** Fresh weight of aboveground biomass of plants on the study site in Großthiemig in February 2009 (n per variety and defoliation variant = 3 by not taking into account the number of defoliations).

Despite a relatively low number of statistically significant differences, looking at absolute numbers and not considering the number of defoliation treatments, all plants that experienced leaf loss had lower fresh weights than undefoliated plants (Table 5). Poplars that had 75% of their foliage removed reached a 25% lower fresh weight than undefoliated plants, and poplars that had 50 or 25% of their foliage showed a fresh weight reduction of 9% or 1% in comparison to undefoliated plants. The corresponding values for willows are 5%, 3%, and 5%, meaning defoliation-induced fresh weight reductions were lower than for poplars. Moreover, willows in total had a 65% greater fresh weight than poplars. An effect of the number of defoliation treatments on the fresh weight of poplars (AN: *p* = 0.001) and willows (*p* = 0.000) is also visible when looking at the total of all plants. For both tree genera, all pairwise comparisons result in significant differences, with the one exception of poplars defoliated one time compared to those defoliated three times. Despite a relatively low number of statistically significant differences, looking at absolute numbers and not considering the number of defoliation treatments, all plants that experienced leaf loss had lower fresh weights than undefoliated plants (Table 5). Poplars that had 75% of their foliage removed reached a 25% lower fresh weight than undefoliated plants, and poplars that had 50 or 25% of their foliage showed a fresh weight reduction of 9% or 1% in comparison to undefoliated plants. The corresponding values for willows are 5%, 3%, and 5%, meaning defoliation-induced fresh weight reductions were lower than for poplars. Moreover, willows in total had a 65% greater fresh weight than poplars. An effect of the number of defoliation treatments on the fresh weight of poplars (AN: *p* = 0.001) and willows (*p* = 0.000) is also visible when looking at the total of all plants. For both tree genera, all pairwise comparisons result in significant differences, with the one exception of poplars defoliated one time compared to those defoliated three times.

**Table 5.** Mean fresh weight [g] of aboveground biomass of plants on the study site in Großthiemig with regard to the number of defoliation treatments in February 2009 (DV = defoliation variant, different letters indicate statistically significant differences). **Table 5.** Mean fresh weight [g] of aboveground biomass of plants on the study site in Großthiemig with regard to the number of defoliation treatments in February 2009 (DV = defoliation variant, different letters indicate statistically significant differences).


**Total 313.0 (a) 389.2 (b) 286.3 (a)** 329.5 **777.7 (a) 483.7 (b) 369.3 (c)** 543.6 A detailed description of the dry weight data is omitted, since in hindsight we cannot completely rule out an error during data recording and analyses. One plausible result of these data is a statistically significant difference of the dry weight between 75% defoliated A detailed description of the dry weight data is omitted, since in hindsight we cannot completely rule out an error during data recording and analyses. One plausible result of these data is a statistically significant difference of the dry weight between 75% defoliated and undefoliated plants of Muhle Larsen (TT: *p* = 0.010), which was also computed for its fresh weight.

and undefoliated plants of Muhle Larsen (TT: *p* = 0.010), which was also computed for its

75% 265.3 313.3 235.5 **271.4 (b)** 805.3 483.9 312.8 **534.0 (a)** 

fresh weight.

In June 2009, that is, three months after the harvest of the short rotation coppice in Großthiemig, the height of resprouting shoots and number of shoots per stool were recorded. Statistical analysis on the variety level only resulted in significant differences among defoliation variants for two groups (AN: Muhle Larsen/two defoliation treatments: *p* = 0.024, Max 3/total: *p* = 0.025) (Table 6). In contrast, on the tree genus level, significant differences in the number of shoots among the four defoliation variants were detected for poplars that had been defoliated twice (*p* = 0.007) and three times (*p* = 0.023), as well as in total (*p* = 0.002). In all three cases, undefoliated plants had a significantly greater number of shoots than 50% defoliated (*p* = 0.009–0.047) and 75% defoliated plants (*p* = 0.004–0.035). No effect of defoliations on the number of resprouting shoots after a harvest were recorded for willows.

**Table 6.** Mean number of resprouting shoots per stool on the study site in Großthiemig with regard to the number of defoliation treatments in June 2009 after the harvest in March 2009 with regard to the defoliation variant in 2007 and 2008.


Even though poplars on average had an 80 cm lower height than willows in June 2009, they reached a 10 cm greater height in September 2009. A statistical analysis of height data resulted in significant differences for the two-time defoliated plants of Max 1 (AN: *p* = 0.040) and the three-time defoliated plants of Max 3 (*p* = 0.002) and Tora (*p* = 0.026) in June, while in September, significant height differences were computed for the three-time defoliated plants of Androscoggin (*p* = 0.027) and Tora (*p* = 0.047) (Figures A3 and A4 in Appendix A). When looking at pairwise comparisons, an effect of the number of defoliation treatments is visible again (Table 7). The more often plants were defoliated, the more often significant effects on plant heights were recorded.


**Table 7.** *p* values of pairwise comparisons of plant heights among former defoliation variants (DVs) for both tree genera (green) as well as for all varieties with at least one significant value (orange) on the study site in Großthiemig in June and in September 2009 after a harvest in March 2009 with regard to the number of defoliation treatments in 2007 and 2008 via Tukey HSD test (bold values indicate statistically significant differences).

Merely comparing undefoliated plants with plants with the greatest leaf loss, statistically significant differences only exist for plants that had been defoliated three times and when looking at the total of plants (Tables 8 and 9). In general, height differences increase with an increasing number of defoliation treatments on the tree genus level, whereas this trend is hardly visible on the variety level. It is noticeable that the impact of defoliations on poplars is slightly greater than on willows, and that most of the significant height differences that were recorded in June still persisted in September. Furthermore, looking at the absolute figures of three-time defoliated plants, all varieties in June and all varieties with the exception of Max 4 in September showed reduced heights for 75% defoliated compared to undefoliated plants. Absolute height differences of undefoliated and 75% defoliated plants increased greatly between June and September.


**Table 8.** Height differences of 75% defoliated and undefoliated plants (∆ 75-0) on the study site in Großthiemig in June 2009 after a harvest in March 2009 with regard to the number of defoliation treatments in 2007 and 2008 (\* statistically significant difference according to the *t* test).

**Table 9.** Height differences of 75% defoliated and undefoliated plants (∆ 75-0) on the study site in Großthiemig in September 2009 after a harvest in March 2009 with regard to the number of defoliation treatments (\* statistically significant difference according to the *t* test).


#### 3.3.2. Growth in Obercarsdorf and Schönheide

After the last defoliation treatment in August 2008 and the final recording of height growth in April 2009, plants in the short rotation coppices in Obercarsdorf and Schönheide were measured again in April 2010 to study a potential long-term effect of defoliations. By this time a mechanical ground vegetation removal had led to plant losses and considerably lower plant numbers in Obercarsdorf than in Schönheide. Statistical analysis only resulted in significant height differences among defoliation variants for Max 1 (AN: *p* = 0.039), Max 3 (*p* = 0.007), and Muhle Larsen (0.023) in Schönheide (Figure 5). No such differences were computed for the plants in Obercarsdorf. Pairwise comparisons generally still show a decrease of *p* values with increasing difference in leaf loss (Table 10). Despite the statistically significant result for Muhle Larsen on the study site in Schönheide on the group level, no significant differences were detected with pairwise comparisons. However, comparison of plant heights between undefoliated and 25% defoliated plants with 75% defoliated plants are relatively close to a significant result (*p* = 0.055/0.056).

**Figure 5.** Height of poplar and willow varieties on the study sites in Obercarsdorf and Schönheide prior to the start of the growing season in April 2010 with regard to the defoliation variant in 2007 and 2008 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA) **Figure 5.** Height of poplar and willow varieties on the study sites in Obercarsdorf and Schönheide prior to the start of the growing season in April 2010 with regard to the defoliation variant in 2007 and 2008 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA).

**Table 10.** *p* values of pairwise comparisons of plant heights among former defoliation variants (DVs) for both tree genera (green) as well as for all varieties with at least one significant value (orange) on the study sites in Obercarsdorf and Schönheide prior to the start of the growing season in April 2010 via Tukey HSD test (bold values indicate statistically significant differences). **Table 10.** *p* values of pairwise comparisons of plant heights among former defoliation variants (DVs) for both tree genera (green) as well as for all varieties with at least one significant value (orange) on the study sites in Obercarsdorf and Schönheide prior to the start of the growing season in April 2010 via Tukey HSD test (bold values indicate statistically significant differences).


**75% 0.000 0.000** 0.376 — **75% 0.028** 0.501 0.890 — Comparing merely undefoliated and 75% defoliated plants results in considerably more significant height differences than analyzing differences among all four defoliation variants (Table 11). Significant differences were now also computed for the short rotation Comparing merely undefoliated and 75% defoliated plants results in considerably more significant height differences than analyzing differences among all four defoliation variants (Table 11). Significant differences were now also computed for the short rotation coppice in Obercarsdorf, where plants that had 75% of their foliage removed in 2007 and 2008 reached an almost one-third lower height in spring 2010 than undefoliated plants.

Looking at the average of both study sites, a significant effect of previous defoliation treatments on plant height was still visible after a year without such a treatment for the poplar varieties Max 3 and Max 4 as well as the willow varieties Sven and Tora. While the height reduction percentages of the five poplar varieties are relatively close to each other with 14 to 22%, the very low reduction of about 1% for Tordis in comparison to a reduction of about 17% for Sven and Tora is noticeable for the three willow varieties.

**Table 11.** Height differences between 75% defoliated and undefoliated plants (∆ 75-0) on the study sites in Obercarsdorf and Schönheide prior to the start of the growing season in April 2010 (\* significant difference according to the *t* test or Welch test).


#### **4. Discussion**

The results of this four-year study clearly show negative effects of defoliation on the height, fresh weight, and number of resprouting shoots of poplar and willow varieties, and that these effects explicitly depend on the site, tree species, and variety as well as the extent and number of defoliations. Several other studies on poplars and willows have also provided evidence for a plant growth reduction caused by defoliation and, in accordance with this study, for an increasing reduction of different growth parameters with an increasing extent of defoliation [25,41,44]. However, studies differ with regard to the minimum extent of leaf loss from which a significant effect on plant growth has to be expected. While some studies have already detected significant effects at 10–25% leaf loss [32,39,42], others have not recorded notable effects at defoliation levels of 40 and 50% but only starting from 75% [37,46]. In this study, significant effects were mainly detected at a defoliation level of 75% as well, but in several cases also at a level of 50%, in particular with plants that had been defoliated three times within two growing seasons. Only rarely did we record significant effects on plant height at a defoliation level of 25%. These exclusively occurred with the Tora variety, even though Bell et al. (2006) found the least effects of defoliation on this variety [24]. Anttonen et al. (2002) concluded that there is no consistently valid threshold value for negative effects of defoliation on plant growth but that instead it varies depending on the particular growth parameter [36].

When comparing the results of this study with literature, it has to be taken into account that other studies were often based on a different number of defoliation treatments per growing season and on different overall experiment durations. The maximum of three defoliation treatments within two growing seasons in this study lies below the number of treatments in many other studies that included two treatments within one growing season [24,42,44,47]. Moreover, Kendall et al. (1998) conclude that even two defoliation treatments within one growing season is not enough to simulate the natural defoliation by leaf beetles, which lasts for a longer period of time within the growing season [48]. This is why the defoliation treatment in some studies with poplars and willows was carried out four or five times within one growing season [25,32,41]. Therefore, we assume that

a higher number of defoliation treatments in this study, for example two times instead of one time in the first growing season, would have led to a better reproduction of the natural defoliation on these sites and would have resulted in even greater effects on the plant growth parameters and a higher number of statistically significant differences between defoliation variants. This conclusion is also confirmed by comparing the growth parameters of the plants in Großthiemig defoliated once, twice, or three times among each another. However, we deliberately refrained from a second defoliation treatment in the first growing season to ensure the survival of plants despite the partly unfavorable site and climatic conditions, and guarantee the general feasibility of this study. An extension of the defoliation treatments to a third growing season was not possible, amongst others due to the great heights and leaf masses of plants.

Nevertheless, the data from this study in Saxony and Brandenburg are well in line with the results of similar studies. On the three study sites, the mean height reduction of poplars that had been defoliated three times to an extent of 75% was between 17 and 28%, and the maximum height reduction between 27 and 42%, in comparison to the plants that had not been defoliated, whereas the same defoliation treatment had a lower effect on willows and resulted in an average reduction of about 10%, with maximum values ranging from 22–26%. Correspondingly, Gao et al. (1985), Tucker et al. (2004), and Bassman et al. (1982) recorded height reductions between 20 and 31% for poplars with a defoliation level of 75% [37,42,46], and Kendall et al. (1998) determined a 15% height reduction for willows with a defoliation level of 70% [48]. With regard to the aboveground biomass, Reichenbacker et al. (1996) documented a reduction of 33% for poplars with a defoliation level of 75% in comparison to the zero variant [41], and Bell et al. (2006) and Kendall et al. (1998) a reduction of 31% and 36–72% for willows with a defoliation level of 70 and 75%, respectively [24,48]. The biomass reduction between 32 and 39% caused by severe defoliation by the leaf beetle *Phratora vulgatissima* on *Salix viminalis* lies in a similar range [49]. In comparison, the results of the fresh weight determination in this study on the plants at Großthiemig show considerably lower reduction values, at least partially. A reduction of 25% was recorded for all poplars, and 21% when merely considering the poplars that had been defoliated three times, whereas the corresponding values for willows are 5 and 18%. Reasons for these differences to other studies may be the lower age and heights of the plants in this study, which are associated with lower diameters so that height differences have a less pronounced effect on the biomass yield.

Our data further shows that the actual effects of defoliation on plants depend on numerous factors. The reaction of plants to herbivory varies according to the prevailing conditions, which can result in different growth losses at similar defoliation levels [49,50]. Since the total size of the photosynthetically active leaf area determines the yield production of plants [51], it is generally assumed that the reduction of leaf area by phyllophagous insects or leaf-infecting fungi reduces plant growth due to a reduction of the photosynthetic capacity [52–54]. However, under certain circumstances, leaf losses can be adjusted by compensatory growth, but the ability for it depends on several abiotic and biotic factors. Regarding abiotic factors, site conditions, such as the availability of soil water, the soil nutrient, and heavy metal content, play an important role [47,49,50]. Each deviation from the site optimum causes stress [55], which negatively affects the compensatory growth of plants [47]. The effect of site conditions is also well reflected in the results of this study. In particular with regard to the poplars, which have somewhat higher nutrient requirements compared to willows [56], a greater height reduction was recorded on the two study sites with rather unfavorable conditions, Obercarsdorf and Schönheide, in comparison to the site at Großthiemig, where conditions were more favorable for plant growth. Correspondingly, only the fertilized three-year-old birch plants in a study were able to fully compensate a 25% defoliation, whereas this defoliation resulted in a significant biomass yield reduction of unfertilized plants [36]. In another study, no effect of a medium-level defoliation of poplars by *Clostera inclusa* (Lepidopotera: Notodontidae) was only detected on the one study site with excellent conditions [57]. Besides the general growth conditions, the time of

defoliation also influences its effects on plant growth. The earlier a defoliation takes place, the better plants are able to recover, that is, an early defoliation promotes the chances for compensatory growth [39,50,58]. The greatest impact on plant growth was recorded when poplars were defoliated during the most productive growth period between the beginning and the middle of summer [25]. The time of defoliation was no target parameter of this study, but the first and last defoliation treatment in July 2007 and August 2008 lay within the period mentioned by Larsson (1983) [25]. Only the defoliation treatment carried out between the beginning and middle of June 2008 was prior to this period and may have been balanced out more easily by compensatory growth.

Regarding biotic factors that have an influence on the compensatory growth of plants and therefore the effects of defoliation, tree species and variety play an especially important role. In a study with 11 willow varieties, significant differences in the reduction of plant height and biomass production caused by defoliation were documented [44]. On a few varieties, growth was not reduced but instead increased in comparison to the undefoliated plants, similar to the findings of another study on willows [38]. In contrast, the study by Bell et al. (2006) showed a negative reaction to simulated defoliation for all five willow varieties included, yet to a different extent [24]. In this study too, noticeable differences in the reaction to defoliation were recorded among varieties. While trends were relatively similar on all three study sites after the first defoliation treatment, there was no longer a consistent reaction of the individual varieties among sites after the third and last treatment. We noticed, however, that overall defoliation had a greater impact on the growth of poplar than on willow varieties. Comparing the heights of 75% defoliated plants to that of undefoliated ones, the promotion of plant growth by defoliation described by the two studies mentioned above [38,44] almost exclusively occurred with willow varieties, and on all three willow varieties included in this study. In particular with regard to the data recorded after the first defoliation treatment, an influence of the site conditions is visible as well. While 75% defoliated plants of all three willow varieties had a greater height as the undefoliated plants on the site with the most favorable conditions, those at the site with the least favorable conditions showed a reduced height growth. One reason for the generally better ability of willows for compensatory growth in comparison to poplars may be their superior regeneration capacity [5,59], which does not only apply after harvests but apparently also after defoliations. In contrast, only a single case of an increased growth of 75% defoliated plants compared to undefoliated plants occurred with poplars, namely with the Max 3 variety at the site in Obercarsdorf after the first defoliation treatment. Overall, the statement that faster growing poplar varieties suffer from greater height reductions by defoliations than slower growing varieties [46] was not confirmed by the data of this study. For example, plants of the Muhle Larsen variety often had significantly lower heights than other varieties but nevertheless showed rather great defoliation-induced height reductions.

The data of this study also suggests a lasting effect of defoliation events on the height growth of poplars and willows, and even on the number and height of resprouting shoots of plants that were harvested after those events. Accordingly, yield losses caused by artificial defoliation of willows during the first three-year growth period still persisted after the second three-year growth period without defoliation treatments [24]. Defoliation-induced reductions of root growth and drought tolerance are assumed to be some of the reasons for these long-term effects [41,60].

#### **5. Conclusions**

The results of this study confirm the literature stating that even minor leaf loss can have an impact on plant growth in short rotation coppices, which may also last. However, the actual effects of defoliation on a plant depend on numerous external and internal factors, which determine the ability of the plant for compensatory growth. In some cases, leaf loss can be fully compensated or, in single cases, even overcompensated. According to the results from Saxony and Brandenburg, this particularly applies to willow varieties.

Due to the illustrated complexity of the reaction of plants to leaf loss, an exact quantification of potential growth losses with regard to the extent of leaf loss as well as a specification of threshold values that, for example, indicate when control measures against phyllophagous pest insects in short rotation coppices are advisable, are hardly possible. In general, our data indicates that poplars are more susceptible to defoliation than willows, meaning control measures need to be applied earlier, and that defoliation levels above 50% often lead to significant growth reductions. Willows seem to be more resilient, which makes the need for interventions in these coppices less probable. However, the fact that the susceptibility to defoliation increases with decreasing site quality and with an increasing number of defoliation events applies to both tree genera. Ultimately, only the regular survey of plant growth and damage on site and their comparison to coppices on similar sites can help to assess the effects of existing leaf loss on the growth of the plants and the ability of the coppice for compensatory growth. In some cases, it can be profitable to already initiate countermeasures at moderate leaf loss. This particularly applies to situations when the survival of plants in newly established plantations is at risk due to leaf loss. If available, we also recommend considering the predictions of prognosis models on the weather-dependent population growth of the main insect pests during the decision process for or against control measures.

**Author Contributions:** Conceptualization, M.G.M., C.E.H. and D.L.; methodology, C.E.H.; software, C.E.H.; validation, M.G.M., C.E.H. and D.L.; formal analysis, C.E.H.; investigation, C.E.H.; resources, M.G.M. and C.E.H.; data curation, C.E.H.; writing—original draft preparation, C.E.H.; writing—review and editing, C.E.H. and D.L.; visualization, C.E.H. and D.L.; supervision, M.G.M.; project administration, M.G.M. and C.E.H.; funding acquisition, M.G.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was carried out within the AGROWOOD project funded by the German Federal Ministry of Education and Research (BMBF) (grant number 0330710 A).

**Acknowledgments:** We sincerely thank the Research Institute for Post-Mining Landscapes in Finsterwalde and the P&P Tree Nursery in Großthiemig for providing poplar cuttings at no charge, and the LTS GmbH in Groß Luja, the organic farm Biohof Böhme in Obercarsdorf and the P&P Tree Nursery in Großthiemig for providing the sites and the soil preparation for our sample coppices at no charge.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Appendix A Appendix A**

**Table A1.** Mean percentage of surviving plants on the three study sites in July/August 2007 four months after the establishment of the short rotation coppices (bold values indicate mean values of all varieties of a tree genus, different capital letters indicate statistically significant differences between tree genera, different small letters indicate statistically significant differences between varieties within tree genera). **Table A1.** Mean percentage of surviving plants on the three study sites in July/August 2007 four months after the establishment of the short rotation coppices (bold values indicate mean values of all varieties of a tree genus, different capital letters indicate statistically significant differences between tree genera, different small letters indicate statistically significant differences between varieties within tree genera).


**Figure A1.** Height of poplar and willow varieties on the three study sites in summer 2007 prior to the start of the defoliation treatments (n = range of number of plants per variety). **Figure A1.** Height of poplar and willow varieties on the three study sites in summer 2007 prior to the start of the defoliation treatments (n = range of number of plants per variety).

**Figure A2.** Height of poplar and willow varieties on the three study sites in December 2007 after the first defoliation treatment in July/August 2007, with the exception of the willow varieties in Schönheide (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA). **Figure A2.** Height of poplar and willow varieties on the three study sites in December 2007 after the first defoliation treatment in July/August 2007, with the exception of the willow varieties in Schönheide (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA).

**Figure A3.** Height of poplar and willow varieties on the study site in Großthiemig in June 2009 after a harvest of the short rotation coppice in March 2009 with regard to the number of defoliation treatments and the defoliation treatment variant in 2007 and 2008 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA). **Figure A3.** Height of poplar and willow varieties on the study site in Großthiemig in June 2009 after a harvest of the shortrotation coppice in March 2009 with regard to the number of defoliation treatments and the defoliation treatment variant in 2007 and 2008 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA).

**Figure A4.** Height of poplar and willow varieties on the study site in Großthiemig in September 2009 after a harvest of the short rotation coppice in March 2009 with regard to the number of defoliation treatments and the defoliation treatment variant in 2007 and 2008 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA). **Figure A4.** Height of poplar and willow varieties on the study site in Großthiemig in September 2009 after a harvest of the short rotation coppice in March 2009 with regard to the number of defoliation treatments and the defoliation treatmentvariant in 2007 and 2008 (n = range of number of plants per defoliation variant, \* statistically significant difference according to ANOVA).

#### **References References**

*Engl.* **2005**, *56*, 3–9.


3. Ball, J.P.; Carle, J.; del Lungo, A. Contribution of poplars and willows to sustainable forestry and rural development. *Unasylva* 

