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Article

Quantity and Quality of Narrow-Leaved Ash (Fraxinus angustifolia Vahl) Wood Forest Products in Relation to Tree Crown Defoliation

by
Branko Ursić
1,
Željko Zečić
2 and
Dinko Vusić
1,*
1
Faculty of Forestry and Wood Technology, University of Zagreb, Svetošimusnka cesta 23, 10 000 Zagreb, Croatia
2
Aleja Pomoraca 17, 10 000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Forests 2025, 16(1), 147; https://doi.org/10.3390/f16010147
Submission received: 25 December 2024 / Revised: 9 January 2025 / Accepted: 11 January 2025 / Published: 14 January 2025
(This article belongs to the Special Issue Recent Advances in Nondestructive Evaluation of Wood: In-Forest Wood Quality Assessments—2nd Edition)
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Abstract

:
Forest stands are developing in changeable climate conditions that influence stand health and consequently assortment quality. Narrow-leaved ash is strongly affected by dieback because of new fungal diseases. The main aim of this study was to determine the quantity and quality of produced wood assortments in dieback-affected narrow-leaved ash stands. Based on the study results, the average tree value increased with tree diameter and partially decreased with tree crown defoliation degree. The healthy (crown defoliated up to 25%) and 3A (crown defoliated from 61 to 80%) trees had significantly higher average tree values (EUR/m3) compared to the significantly defoliated 3B trees (crown defoliated from 81 to 99%) and dead trees (100% defoliated crown). The influence of stand age and share of narrow-leaved ash in stand volume were confirmed as factors influencing the average tree value. Wood chips quality remained the same regardless of tree crown defoliation degree. Based on the significance influence of the tree crown defoliation degree on the average tree value, current assortment tables should be expanded in order to achieve more accurate expected values.

1. Introduction

Timber quality on the stand level is mostly influenced by site conditions, competition intensity, light availability, genetics, and disturbances [1,2]; however, on the tree scale, it is influenced by crown development, branch size and location, knot size, type and placement, stem shape, and taper [3]. Forest management can shape the structure of the stand in order to achieve higher total tree utilization and quality of produced assortments [4]; however, during the stand rotation (which is usually over 60 years in natural forests), various natural influences can appear, such as windbreaks, snowbreaks or, for example, the appearance of new diseases, which can significantly change the conditions in which stands grow. Narrow-leaved ash is tree species that in Croatia, but also in the rest of Europe, has started to dieback significantly due to the influence of climate change and new biotic pests. It is the second most common ash species in Europe [5], and it is the fifth most common deciduous tree species and represents 3.19% of the total growing stock in Croatia [6]. Narrow-leaved ash stands are ecologically and economically extremely important in lowland floodplain forests [7], so the death of individual trees and entire stands in the last three decades represents a serious problem. The beginning of ash dieback was recorded for common ash in 1992 in Poland, and by 2023, ash dieback was recorded in 26 European countries [5,8]. In Croatia, the beginning of common ash dieback was recorded in 2009, while the beginning of narrow-leaved ash dieback was recorded in 2011 [9]. Given that the main cause of death is the fungal disease Hymenoscyphus fraxineus (T. Kowalski) Baral, Queloz & Hosoya [10,11], which causes rot inside the tree, a decrease in the quality and value of the produced wood assortments is expected.
Models for determining and projecting growth, yield, tree size, tree, and stand volume have been very well researched, while verified models for predicting the quality of wood assortments are still lacking [12,13]. In addition to knowing the total harvesting volume, information on the expected assortment structure is very valuable information in harvesting plans, since the profitability of planned work depends on total harvested value, which is calculated in harvesting plans based on expected gross volume, expected assortment structure, and unit price of assortments. Information on expected assortment structure is a strong benefit for forest owners (who can adjust management plans to increase quality) and users of wood forest products (to facilitate more efficient trade and use) [12], and it could be valuable information in harvesting productivity calculation [14]. The quality and quantity of produced wood assortments correlate with the tree species, tree quality, and tree dimensions [15]. Wood quality, from a technical perspective is defined as combination of intrinsic characteristics, key attributes, and factors influencing its suitability for a specific use [12,16]. Tree quality and assortment structure can be investigated in three main ways: by assessing tree quality in standing trees, by measuring produced wood forest products, and after sawmill processing, with the latter being the most accurate [17].
Previous assortment structure research has been focused on researching the impact of basic trees’ features: tree diameter at breast height, tree height, height of the first live branch, and presence of wood defects [17,18,19,20,21]. The influence of stand attributes, including site classes and stand age [22], phytocenosis [23], stand type (pure or mixed stands) [24], felling type (thinning, preparatory cuts, seeding cuts, and final cut) [25,26], tree crown defoliation degree and tree health condition [27,28,29,30,31,32], and different bucking and grading rules [33,34], on the probability of the occurrence of assortment classes was also investigated. The final result of the mentioned research resulted in the assortment structure for observed tree species and area, emphasizing the issue of assortment structure determination, but also questioning the use of unique assortment tables.
The reliability of calculated harvesting plans depends on the suitability of the input data. In Croatian forestry, single-entry volume tables and assortment tables could be made based on key stand and tree attributes that can be easily estimated during field work, e.g., tree vigor, visible fungal infection, cracks, etc. [35]. Tree crown defoliation is a very important indicator of forest and tree health [8] and could be an indicator of reduced wood quality and value due to the increased presence of wood defects [31,32].
In Croatia, the research related to the narrow-leaved ash wood products quality [18,36] was conducted before ash stands were affected by dieback. The main goal of this research is to determine quantity and quality of narrow-leaved ash trees affected by dieback, as well as value of produced wood assortments, considering main influence factors (tree diameter, tree height, stand age, and phytocenosis) and the influence of tree crown defoliation. The main research hypothesis is that net tree volume, tree quality, and value as well as wood chips quality are negatively influenced by increasing the tree crown defoliation degree.

2. Materials and Methods

2.1. Research Sites

The research was conducted in Croatian floodplain forests in which ash dieback was present. Eight sites were chosen where pedunculate oak (Quercus robur L.) and narrow-leaved ash (Fraxinus angustifolia Vahl) were the dominant tree species. Sites were chosen according to the harvesting plan made by »Croatian Forests« Ltd. Zagreb, Croatia, a state company that manages 73% of the Croatian forest area. Site data are shown in Table 1.

2.2. Sample Preparation

Tree marking for felling on each site was done by authorized »Croatian Forests« Ltd. employees following management plan prescriptions. Then, sample trees were chosen considering diameter at breast height and tree crown defoliation degree. Two perpendicular diameters (DBH) were measured (0.1 cm) at breast height (1.30 m from the ground), and tree crown defoliation degree was estimated according to the ICP guidelines for visual assessment of crown condition and damaging agents [37]. Original tree defoliation degree classes were modified, and trees were classified into four following categories: healthy tree (H) if crown defoliation was less than 25%, 3A tree (3A) if crown defoliation was 61%–80%, 3B tree (3B) if crown defoliation was 81%–99%, and dead tree (D) if crown was 100% defoliated (Figure 1).
After sample trees were chosen, felling, bucking, and log measurement were done. Tree bucking was done according to the commonly used standard in »Croatian Forests« Ltd., which classified assortments in classes regarding the possibility of their end use in the wood industry. Regarding the minimal log dimensions and allowed wood defects, produced logs were classified as veneer logs [38], saw logs (first and second quality class) [39], and fuel wood [40].

2.3. Logs Measurements

Log length (accuracy of 0.01 m) and two mid-length diameters (accuracy of 0.1 cm) were measured for each produced log. The length of the notch cut was additionally measured for the butt log as well as the unprocessed crown length for each tree. The quality class were assigned regarding the HRN [38,39,40] and HRN EN [41] standards. The fuel wood logs were marked regarding tree crown defoliation degree. Data recording was done using UMTPlus Max 20.1.12 software installed on a Huawei P30 smartphone, in which the original interface was customized for recording dimensions, wood defects, and the quality class of produced logs.

2.4. Wood Chips Quality Determination

To determine the influence of tree crown defoliation degree on wood chips quality, samples were divided into four categories:
  • WCH–wood chips produced from fuel wood that was produced from H trees;
  • WC3A–wood chips produced from fuel wood that was produced from 3A trees;
  • WC3B–wood chips produced from fuel wood that was produced from 3B trees;
  • WCD–wood chips produced from fuel wood that was produced from D trees.
Sampling was done during the chipping process according to the HRN EN ISO 18135:2017 [42]. The chipper model and mesh size that were used in the research are shown in Table A1. Samples were packed in airtight plastic bags and delivered to the laboratory on the same day, and moisture content analysis was started by measuring the mass of the test portions. After moisture content analysis, samples were prepared on the cutting mill to reduce the particle size to less than 1.0 mm, and general analysis samples were taken according to the HRN EN ISO 14780:2017 [43] and used for ash, carbon, sulfur, nitrogen, and hydrogen content determination as well as for calorific value determination. The rest of the delivered sample mass was used to determine particle size distribution. The standards and laboratory equipment used are listed in Table A2.

2.5. Data Processing

Data processing started by transforming data recorded with UMTPlus Max 20.1.12 software and creating a database in MS Excel 2412 software. Gross volume was calculated based on measured dimensions for each produced log. Then, measured dimensions were rounded according to the HRN and EN standards to calculate log net volume. The value of produced logs was calculated based on the »Croatian Forests« Ltd. pricelist [44]. The calculated gross and net volume and value for individual logs were summed on the tree level. The average tree value (EUR/m3) was calculated by dividing the total tree value by the tree net volume. Trees with a defoliated crown over 60% (3A and 3B trees) must be marked for felling according to the regulation on tree marking, classification of forest products, consignment note, and forest order [45]. The expected gross and net volume and expected average tree value of each DBH class were calculated using management plan prescribed single-entry volume tables, assortment tables, and the »Croatian Forests« Ltd. pricelist (fco. forest road) [44], and the research determined number of trees in each DBH class. In Croatian forestry, assortment tables are divided into three types: (a) assortment tables for thinning; (b) assortment tables for regeneration felling; and (c) assortment tables for dead trees. The assortment tables for thinning and regeneration felling were used to calculate the expected assortment volume from healthy trees, and assortment tables for dead trees were used to calculate the expected assortment volume from 3A, 3B, and dead trees. The percentage deviation (Δ€/m3, %) of average defoliated tree value (EUR/m3) was calculated for each DBH class based on the recovered average tree value of H trees.
Statistical analyses were performed using TIBCO Statistica 14.0.0.15 software. Multiple linear regression analyses were used to determine the influence of tree height and tree DBH on gross and net tree volume for each site and each tree crown defoliation degree. A nonparametric test (Kolmogorov–Smirnov test) was used to compare expected and actual gross volume. A t-test was used to compare average expected and actual tree values. Factorial ANOVA was used to determine the influence of tree crown defoliation degree and DBH class on actual average tree value. Analysis of variance was used to test the influence of tree crown defoliation degree, stand age, share of narrow-leaved ash in stand volume, and phytocenosis on average tree value, as well as the effect of tree crown defoliation degree on wood chips quality. Scheffe and unequal N HSD post-hoc tests were used to determine homogenous groups. Statistical significance was accepted at α < 0.05.

3. Results

In total, 351 narrow-leaved ash trees were sampled, and sample data (site, crown defoliation degree, and diameter class) are shown in Table 2. Since only salvage felling was performed on sites C and E, H trees were not sampled, and additionally 3A trees on site C were not sampled because of the low number of remaining trees. On sites A, B, and D a combination of salvage felling and thinning were performed. On site F, the final cut was performed.

3.1. Gross and Net Volume

Multiple regression parameters showed a significant influence of tree diameter on actual gross and actual net tree volume for all sites. On sites B and D, actual gross and actual net tree volume were influenced additionally by tree height (Table A3 and Table A4). Regarding tree crown defoliation degree, actual gross and actual net tree volume depended only on tree diameter (Table A5 and Table A6).
Compared to the actual gross volume, expected gross volume was lower at all sites, with differences ranging from an acceptable 2.36% for site C to unacceptable 45.78% for site F. Since variance homogeneity was not demonstrated (F = 23.53; p < 0.05), the results of the nonparametric Kolmogorov–Smirnov test are shown in Table 3. The difference between the expected and actual harvesting gross volume was statistically significant for sites B, D, and F.

3.2. Assortment Structure and Average Tree Value

The actual assortment structures for all sites regarding tree crown defoliation degree and HRN and HRN EN standard grading rules are shown in Appendix B (Figure A1 and Figure A2).
Since the value of produced logs depends on their quality and the price of each quality class, to avoid the influence of differences in the expected and actual net log’s volume, differences in total tree value were not tested. Instead, the average expected and actual value (EUR/m3) was tested (Table 4). Statistically significant differences between expected and actual average tree value were found on sites A and F.

3.2.1. Influence of Tree Crown Defoliation Degree on Actual Average Tree Value

The average DBH class value, regarding tree crown defoliation degree (Figure 2), was the same in 17.5 and 22.5 DBH classes since only fuel wood could be produced because the minimum log dimensions for veneer and saw log could not be achieved (minimum under bark diameter for the II quality of saw logs is 25 cm). The average value of 3A trees decreased up to the DBH class of 47.5, with the exception of DBH class 37.5, where the average value increased by 5.0%). A slight increase in average value was noticed in DBH classes 47.5 (0.9%) and 52.5 (1.3%), while a significant average value increase of 48.5% was noted in DBH class 62.5. For 3B trees, the average value slightly increased in DBH class 27.5 (1.0%), with a significant increase of 16.7% in DBH class 37.5. The average value of 3B trees decreased in DBH class 32.5, as well as in DBH classes 42.5 to 62.5. The average value of D trees decreased in all DBH classes in which veneer and/or saw logs could be produced.
The ANOVA test results showed statistically significant influences of DBH (F = 46.3; p < 0.01) and tree crown defoliation on average tree value (F = 23.7; p < 0.01) (Table A7). The average tree value up to DBH class 32.5 was not significantly influenced by tree crown defoliation degree (Figure 3). The highest average tree value and the highest deviation regarding tree crown defoliation degree (from 183.2 EUR/m3 for 3A trees to 59.76 EUR/m3 for D trees) were found in DBH class 62.5. The results of the unequal N HSD variance homogeneity test due to its complexity are shown in Table A8.

3.2.2. Influence of Site Characteristics on Average Tree Value

In order to clarify the influence of tree crown defoliation degree on average tree value, additional analyses were done to determine the influence of stand age, narrow-leaved ash share, and phytocenosis on average tree value (Figure 4). The ANOVA test results showed that average tree value is significantly influenced by tree crown defoliation degree (Table A9, Table A10 and Table A11) and additionally by stand age (F = 5.05; p < 0.05) and narrow-leaved ash share in stand volume (F = 19.97; p < 0.05). The influence of phytocenosis was not confirmed on researched sites (F = 2.13; p = 0.15).

3.3. Wood Chips Quality

In total, 57 wood chips samples were taken. The descriptive statistics of determined wood chips properties are shown in Table 5. Tree crown defoliation degree showed a statistically significant influence only on moisture content (Table A12), resulting with the most favorable moisture content of WCD. The highest net calorific value was found in WCD, but without a statistically significant difference compared to the wood chips produced from other tree crown defoliation degrees.

4. Discussion

This study was driven by a lack of knowledge about narrow-leaved ash assortment structure in the currently accelerated changes observed in forest ecosystems of lowland floodplain forests. The presented study results offer valuable insight into narrow-leaved ash wood quantity and quality changes influenced by tree crown defoliation degree.
The initial hypothesis that quantity and quality and consequently average tree value, as well as wood chips quality decrease in the order of H trees > 3A trees > 3B trees > D trees was partially confirmed since in some cases 3A and 3B trees had higher average values. On the other hand, some speculations that single-entry volume tables and assortment tables in Croatia cannot predict harvesting volume and value to the fullest extent due to the changing climate conditions and the current ash dieback situation were confirmed.
Namely, the differences between the expected and actual gross volume, according to previous research, are mostly within ±2.5% [46], and site C only showed deviation in the suggested range (2.36%). The deviation between expected and actual gross volume on sites A (8.21%) and E (4.93%) could be partially justified by different calculation methods between expected and actual gross volume. The expected gross volume was calculated based on the number of trees in each DBH class multiplied by volume stated in the management plan prescribed single-entry volume table, which were constructed by adding a determined volume of 1.0–2.0 m long tree segments. On the other hand, the 45.78% deviation in gross volume (site F) cannot be justified even by the low number of sampled trees and the already known poor single-tree volume prediction by single-entry volume tables [47]. Due to their higher accuracy in final felling [48], the application of double-entry volume tables should be considered. Significant differences between expected and actual gross volume are not only a problem in Croatia. Moreover, this problem has already been addressed in other countries [46,49]. For example, in the Czech Republic, the average determined deviation was 8.8% for stands under 50 years old (with deviation in gross volume ranging from −79.1% to 97.0%) to 11.0% for stands over 50 years old (with deviation in gross volume ranging from −56.8% to 181.0%) [46].
In Croatian forestry, assortments tables are constructed based on tree species, type of cut (thinning or regeneration cuts), and tree health (healthy or dead). Discrepancies between actual and expected assortment structures in Croatia were thoroughly researched in beach stands regarding numerous influencing factors [25,26]. A theoretical overestimation was found for birch stands, where the recovered sawlog outcome was −24.32 percentage points on average, and the same researchers found a theoretical overestimation of up to 60.96 percentage points for aspen [50].
Tree crown defoliation is factor that is mostly researched to estimate the influence of different types of stressors (e.g., climate change and air pollution) on tree health [51], improve tree mortality models [52], and improve growth rate models [31], while research related to its influence on assortment quality is still lacking. The importance of constructing assortment tables using tree crown defoliation degree (as one of the influencing factors) to increase their accuracy is supported by results of this study. The decrease in the average tree value was significantly influenced by high tree crown defoliation (3B and D trees), and additionally by already known influencing factors, e.g., stand age [50]. Similar findings of reduced tree value caused by tree crown defoliation in Croatia were found in fir stands [28,30] and oak stands [29], indicating a general problem in assortment structure estimations caused by tree crown defoliation as well as reduced utilized tree volume and value.
The quality of wood chips has become very important since the European Union has ambitious targets in reducing greenhouse gas emissions by using renewable energy sources. Based on the study results related to the determined wood chips properties, tree crown defoliation degree did not influence the raw material quality from which tested wood chips were produced, with the exception of total moisture content. The reason for the quite similar wood chips properties (except moisture content) among all tree crown defoliation degrees relies upon the fact that wood chips were produced from tree parts that were classified as fuel wood mainly due to the dimensions, but also because of the poor quality due to the presence of wood defects (e.g., wood decay). Wood chips produced from dead trees will be more favorable when used immediately after trees are felled due to the lower moisture content since significant differences in other determined properties did not occur.

5. Conclusions

The average tree value shows a strong positive correlation with DBH class, but a negative correlation with tree crown defoliation degree. Defoliated trees that were still alive had higher average values compared to the dead trees, indicating the necessity of expanding current assortment tables to include tree crown defoliation degree as one of easily estimated parameter that could help to increase assortment table accuracy.
Since roundwood scaling in Croatian forestry is performed by authorized employees, and data of produced assortments are digitally recorded (length, diameter, and quality class), current software should be used to record produced assortments based on tree level. Additionally, if tree crown defoliation degree was estimated during tree marking and if the quantity and quality of produced assortments were added to each marked tree, a database could be created that would enable the modeling of local assortment tables.

Author Contributions

Conceptualization, B.U., D.V. and Ž.Z.; methodology, B.U., D.V. and Ž.Z.; validation, D.V., Ž.Z. and B.U.; formal analysis, B.U. and D.V.; investigation, B.U.; writing—original draft preparation, B.U. and D.V.; writing—review and editing, D.V., Ž.Z. and B.U.; visualization, B.U.; supervision, D.V. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the Ministry of Agriculture, Forestry and Fisheries of the Republic of Croatia with funds for the compensation for the use of non-market forest functions (OKFŠ) within the Project Conservation of Narrow-leaved ash stands (Fraxinus angustifolia Vahl) in the Republic of Croatia with an emphasis on harmful biotic factors.

Data Availability Statement

The original data set is not publicly available, but it can be requested from the authors.

Acknowledgments

The authors thank the company »Croatian Forests« Ltd. for assistance in organizing and conducting field harvesting trials.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Materials and Methods

Table A1. Chipper type and mesh size.
Table A1. Chipper type and mesh size.
SiteChipperMesh Size, mm
ARudnick & Enners, Rudnick & Enners Maschinen- und Anlagenbau GmbH, Alpenrod, Germany50 × 50
BAlbach Diamant 2000, Albach Maschinenbau AG, Menning, Germany80 × 80
DJenz, Jenz GmbH Maschinen- und Fahrzeugbau, Petershagen, Germany50 × 50
GAlbach Diamant 2000, Albach Maschinenbau AG, Menning, Germany80 × 80
HAlbach Diamant 2000, Albach Maschinenbau AG, Menning, Germany80 × 80
Table A2. Standards and laboratory equipment used for wood chip quality determination.
Table A2. Standards and laboratory equipment used for wood chip quality determination.
Researched FeaturesUsed StandardUsed Equipment
Moisture contentHRN EN ISO 18134-2–Solid biofuels–Determination of moisture content–Part 2: Total moisture–Simplified method [53]Heating chambers Binder FD 115 and Binder FD 250, Binder GmbH, Tuttlingen, Germany;
Balance Kern 440-49A, KERN & SOHN GmbH, Balingen-Frommern, Germany
Sample preparationHRN EN ISO 14780 Solid biofuels–Sample preparation [43]Retsch SM 300 cutting mill;
Mash size 0.5 mm, Retsch GmbH, Haan, Germany
Ash contentHRN EN ISO 18122–Solid biofuels–Determination of ash content [54]Furnace Nabertherm P330, Nabertherm GmbH, Lilienthal, Germany;
Analytical balance Mettler Toledo XA204, Mettler-Toledo AG, Greifensee, Switzerland
Moisture in general analysis sampleHRN EN ISO 18134-3:2015 Solid biofuels–Determination of moisture content–Oven dry method–Part 3: Moisture in general analysis sample [55]Heating chamber Binder FD 115, Binder GmbH, Tuttlingen, Germany;
Analytical balance Mettler toledo XA204, Mettler-Toledo AG, Greifensee, Switzerland
Carbon, hydrogen, nitrogen, and sulfur contentHRN EN ISO 16948–Solid biofuels–Determination of total content of carbon, hydrogen, and nitrogen [56]
HRN EN ISO 16994–Solid biofuels–Determination of total content of sulfur and chlorine [57]		Elementar Vario Macro Cube CHNS configuration, Elementar Analysensysteme GmbH, Langenselbold, Germany;
Analytical balance Mettler toledo XA204. Mettler-Toledo AG, Greifensee, Switzerland;
Reference material: wood chips of Scots pine (Pinus sylvestris L), Institut für Bioenergie GmbH, Vienna, Austria
Calorific valueHRN EN ISO 18125–Solid biofuels–Determination of calorific value [58]Calorimeter IKA C1/10;
Analytical balance Mettler toledo XA204;
IKA pelletized benzoic acid, IKA-Werke GmbH & Co. KG, Staufen, Germany
Particle size distributionHRN EN ISO 17827-1–Solid biofuels–Determination of particle size distribution for uncompressed fuels–Part 1: Oscillating screen method using sieves with apertures of 3.15 mm and above [59]Horizontal sieve shaker Retsch AS 400;
Sieve sizes 3.15, 8.0, 16.0, 31.5, 45.0, and 63.0 mm, Retsch GmbH, Haan, Germany;
Balance Kern 440-49A, KERN & SOHN GmbH, Balingen-Frommern, Germany

Appendix B. Results

Table A3. Multiple regression parameters of gross volume across different sites.
Table A3. Multiple regression parameters of gross volume across different sites.
SiteParameterb*SE of b*bSE of btpM R2
AIntercept −1.117710.112834−9.90584<0.0000010.9306
DBH0.9416540.0468820.064270.00320020.08576<0.000001
TH0.0279570.0468820.003960.0066450.596340.552351
BIntercept −4.218490.380217−11.0950<0.0000010.8916
DBH0.7738890.0487490.097450.00613915.8750<0.000001
TH0.2216300.0487490.072480.0159424.54640.000017
CIntercept −1.089470.133049−8.18848<0.0000010.9510
DBH0.9424450.0639880.065270.00443214.72857<0.000001
TH0.0395070.0639880.005150.0083490.617420.540741
DIntercept −0.9277160.105213−8.81750<0.0000010.9098
DBH0.8150660.0730650.0478430.00428911.15530<0.000001
TH0.1639760.0730650.0151570.0067542.244240.029185
EIntercept −4.691490.680992−6.88920<0.0000010.8681
DBH0.9092800.0616340.149210.01011414.75278<0.000001
TH0.0511020.0616340.017730.0213840.829120.411725
FIntercept −8.199911.879386−4.363080.0005570.7942
DBH0.8332250.1202200.184460.0266146.930840.000005
TH0.1798580.1202200.065970.0440981.496080.155376
SE—standard error; M—multiple; DBH—diameter at breast height (cm); TH—tree height (m).
Table A4. Multiple regression parameters of net volume across different sites.
Table A4. Multiple regression parameters of net volume across different sites.
SiteParameterb*SE of b*bSE of btpM R2
AIntercept −0.9673350.094949−10.1879<0.0000010.9353
DBH0.9423010.0452690.0560440.00269220.8156<0.000001
TH0.0301060.0452690.0037190.0055920.66500.507616
BIntercept −3.174310.342634−9.26445<0.0000010.8687
DBH0.7872940.0536500.081180.00553214.67471<0.000001
TH0.1902310.0536500.050940.0143663.545800.000618
CIntercept −0.8636270.096154−8.98168<0.0000010.9574
DBH0.9044080.0596460.0485640.00320315.16283<0.000001
TH0.0884830.0596460.0089510.0060341.483460.146420
DIntercept −0.8106620.082790−9.79181<0.0000010.9152
DBH0.7595000.0708370.0361840.00337510.72177<0.000001
TH0.2298730.0708370.0172450.0053143.245080.002076
EIntercept −3.148350.751614−4.188780.0001410.7200
DBH0.8289580.0897940.103050.0111639.23177<0.000001
TH0.0446470.0897940.011730.0236010.497220.621630
FIntercept −5.286521.072518−4.929080.0001820.8490
DBH0.8665850.1029740.127820.0151888.41557<0.000001
TH0.1736610.1029740.042440.0251661.686450.112390
SE—standard error; M—multiple; DBH—diameter at breast height (cm); TH—tree height (m).
Table A5. Multiple regression parameters of gross volume across different tree crown defoliation degrees.
Table A5. Multiple regression parameters of gross volume across different tree crown defoliation degrees.
TCDDParameterb*SE of b*bSE of btpM R2
HIntercept −1.449040.398053−3.640330.0005310.9108
DBH0.0788520.104850.00803613.047420.0000000.078852
TH0.078852−0.022750.021119−1.077150.2852770.078852
3AIntercept −2.004020.314772−6.36658<0.0000010.9180
DBH0.9758560.0623140.108980.00695915.66019<0.000001
TH−0.0211450.062314−0.005650.016653−0.339330.735351
3BIntercept −2.283820.409504−5.57704<0.0000010.8947
DBH0.9445960.0696520.112980.00833113.56169<0.000001
TH0.0014360.0696520.000470.0225640.020610.983593
DIntercept −1.686960.292228−5.77277<0.0000010.9002
DBH0.9700220.0638090.097340.00640315.20196<0.000001
TH−0.0248470.063809−0.006430.016505−0.389390.697878
TCDD—tree crown defoliation degree; SE—standard error; M—multiple; DBH—diameter at breast height (cm); TH—tree height (m); H—tree with crown defoliation less than 25%; 3A—tree with crown defoliation 61%–80%; 3B—tree with crown defoliation 81%–99%; D—tree with 100% defoliated crown.
Table A6. Multiple regression parameters of net volume across different tree crown defoliation degrees.
Table A6. Multiple regression parameters of net volume across different tree crown defoliation degrees.
TCDDParameterb*SE of b*bSE of btpM R2
HIntercept −1.200840.315718−3.803520.0003110.9184
DBH1.0316630.0753890.087220.00637413.68455<0.000001
TH−0.0835940.075389−0.018570.016750−1.108830.271467
3AIntercept −1.525890.243495−6.26661<0.0000010.9193
DBH0.9705610.0618260.084510.00538315.69831<0.000001
TH−0.0140070.061826−0.002920.012882−0.226550.821416
3BIntercept −1.903910.281147−6.77194<0.0000010.9038
DBH0.8637460.0665600.074230.00572012.97703<0.000001
TH0.0962830.0665600.022410.0154921.446570.150940
DIntercept −1.311350.218090−6.01287<0.0000010.9016
DBH0.9353700.0633600.070550.00477914.76289<0.000001
TH0.0164490.0633600.003200.0123170.259610.795739
TCDD—tree crown defoliation degree; SE—standard error; M—multiple; DBH—diameter at breast height (cm); TH—tree height (m); H—tree with crown defoliation less than 25%; 3A—tree with crown defoliation 61%–80%; 3B—tree with crown defoliation 81%–99%; D—tree with 100% defoliated crown.
Table A7. ANOVA table of the influence of tree crown defoliation degree on average tree value.
Table A7. ANOVA table of the influence of tree crown defoliation degree on average tree value.
EffectSSDFMSFp
Intercept904,304.61904,304.63170.515<0.000001
DBH118,848.6913,205.446.298<0.000001
TCDD20,258.736752.923.676<0.000001
DBH × TCDD26,849.427994.43.486<0.000001
Error88,704.4311285.2
DBH—diameter at breast height (cm); TCDD—tree crown defoliation degree.
Table A8. Results of unequal N HSD post-hoc variance homogeneity test.
Table A8. Results of unequal N HSD post-hoc variance homogeneity test.
TCDDDBHMean, €/m3abcdefghijk
H17.535.57****
3A17.535.57****
3B17.535.57****
D17.535.57****
H22.535.57****
3A22.535.57****
3B22.535.57****
D22.535.57****
3A27.536.21**** ****
D27.536.46**** ****
H27.537.02**** ********
3B27.537.39**** ********
D42.538.53**** ********
D32.538.91**** ********
D37.541.61****************
3A32.541.98****************
3B32.543.56**************** ****
H32.545.70**************** ********
D47.546.97**************** ********
H37.548.63****************************
3B42.549.75**************** ********
3A37.551.05**************************** ****
D52.552.10**************************** ****
3A42.552.27**************************** ****
3B47.553.22************************************
3B37.556.76************************************
D62.559.76****************************************
H42.561.55************************************
3B52.570.61 **** ********************
H47.571.05 ************************************
3A47.571.69 **** ****************************
3B57.581.16 **** ************
D57.584.85 ************************
H52.586.62 **** ****************
3B62.586.92 **** ****************
3A52.587.71 **** ****
H57.592.16 ************
3A57.5103.49 ****
H62.5123.38********************************************
3A62.5183.25 ****
TCDD—tree crown defoliation degree; **** Homogenous groups; H—tree with crown defoliation less than 25%; 3A—tree with crown defoliation 61%–80%; 3B—tree with crown defoliation 81%–99%; D—tree with 100% defoliated crown.
Table A9. ANOVA table of the influence of stand age on average tree value.
Table A9. ANOVA table of the influence of stand age on average tree value.
EffectSSDFMSFp
Intercept199,421.11199,421.12041.504<0.000001
Stand age493.51493.55.0520.027062
TCDD2939.83979.910.0320.000009
Stand age × TCDD398.93133.01.3610.259824
Error8693.88997.7
TCDD—tree crown defoliation degree.
Table A10. ANOVA table of the influence of narrow-leaved ash share on average tree value.
Table A10. ANOVA table of the influence of narrow-leaved ash share on average tree value.
EffectSSDFMSFp
Intercept90,715.92190,715.921417.659<0.000001
Ash share1277.5811277.5819.9650.000055
TCDD486.681486.687.6060.008442
Ash share × TCDD34.57134.570.5400.466217
Error2815.564463.99
TCDD—tree crown defoliation degree.
Table A11. ANOVA table of the influence of phytocenosis on average tree value.
Table A11. ANOVA table of the influence of phytocenosis on average tree value.
EffectSSDFMSFp
Intercept140,398.41140,398.4577.4725<0.000001
Phytocenosis517.81517.82.12980.152078
TCDD2570.521285.25.28630.009067
Phytocenosis × TCDD340.72170.40.70070.502069
Error9968.241243.1
TCDD—tree crown defoliation degree.
Table A12. Analysis of variance of wood chip properties.
Table A12. Analysis of variance of wood chip properties.
VariableSS EffectDF EffectMS EffectSS ErrorDF ErrorMS ErrorFp
Mar54.75357318.25119230.961534.357764.1882070.009843
Adr0.0601230.020042.242530.042290.4738470.701809
d5026.1684238.722812055.2005338.777360.2249460.878636
C0.1263430.042118.526530.160870.2617770.852608
H0.7570230.2523416.275530.307070.8217630.487680
N0.0032330.001080.077530.001460.7389290.533538
S0.0000030.000000.011530.000200.0061870.999322
O0.9340130.3113413.122530.247591.2574510.298391
Qp, net, d0.0355730.011861.081530.020390.5815130.629740
Mar—moisture content in mass as received; Adr—ash content in mass of dry basis; d50—particle size distribution median; C—carbon content in mass of dry basis; H—hydrogen content in mass of dry basis; N—nitrogen content in mass of dry basis; S—sulfur content in mass of dry basis; O—oxygen content in mass of dry basis; Qp, net, d—net calorific value.
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Figure A1. Assortment structure according to the HRN standard grades (VL—veneer logs; SLI—saw logs of the first quality class; SLII—saw logs of the second quality class; FW—fuel wood; W—waste) of (a) H trees; (b) 3A trees; (c) 3B trees; and (d) D trees.
Figure A1. Assortment structure according to the HRN standard grades (VL—veneer logs; SLI—saw logs of the first quality class; SLII—saw logs of the second quality class; FW—fuel wood; W—waste) of (a) H trees; (b) 3A trees; (c) 3B trees; and (d) D trees.
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Figure A2. Assortment structure according HRN EN standard grades (A–D quality classes according to the HRN EN 1316-3:1999 [41]; FW—fuel wood; W—waste) of (a) H trees; (b) 3A trees; (c) 3B trees; and (d) D trees.
Figure A2. Assortment structure according HRN EN standard grades (A–D quality classes according to the HRN EN 1316-3:1999 [41]; FW—fuel wood; W—waste) of (a) H trees; (b) 3A trees; (c) 3B trees; and (d) D trees.
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Figure 1. Crown defoliation degree classes: (a) H–tree with crown defoliation less than 25%; (b) 3A–tree with crown defoliation 61%–80%; (c) 3B–tree with crown defoliation 81%–99%; and (d) D–tree with 100% defoliated crown.
Figure 1. Crown defoliation degree classes: (a) H–tree with crown defoliation less than 25%; (b) 3A–tree with crown defoliation 61%–80%; (c) 3B–tree with crown defoliation 81%–99%; and (d) D–tree with 100% defoliated crown.
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Figure 2. Percentage deviation in average tree value of defoliated trees from the average H tree value (EUR/m3).
Figure 2. Percentage deviation in average tree value of defoliated trees from the average H tree value (EUR/m3).
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Figure 3. Influence tree crown defoliation degree on average tree value.
Figure 3. Influence tree crown defoliation degree on average tree value.
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Figure 4. Influence of (a) stand age; (b) narrow-leaved ash share in stand volume; and (c) phytocenosis on average tree value; different letters denote significant differences at the 5% level.
Figure 4. Influence of (a) stand age; (b) narrow-leaved ash share in stand volume; and (c) phytocenosis on average tree value; different letters denote significant differences at the 5% level.
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Table 1. Site data.
Table 1. Site data.
SiteManagement Unit;
Subcompartment		CoordinatesPhytocenosisResearched
Feature		AgeAsh Share, %
ARadinje; 20cφ 45°08′35″ N;
λ 17°36′05″ E		Genisto elatae-Quercetum roboris Horvat 1938Assortment structure and wood chips quality6531.05
BRadinje; 10bφ 45°08′49″ N;
λ 17°34′44″ E		Genisto elatae-Quercetum roboris Horvat 1938Assortment structure and wood chips quality9548.57
CTrstenik; 10aφ 45°12′48″ N;
λ 18°29′32″ E		Genisto elatae Quercetum roboris caricetosum remotae Horvat 1938Assortment structure7079.20
DKusare; 6aφ 45°07′10″ N;
λ 18°36′17″ E		Genisto elatae-Quercetum roboris Horvat 1938Assortment structure and wood chips quality6243.86
EČesma; 101c, 99a, 99cφ 45°49′52″ N;
λ 16°37′22″ E		Carpino betuli-Quercetum roboris
typicum Rauš 1975		Assortment structure11639.50
FTuropoljski lug; 88aφ 45°37′58″ N;
λ 16°11′43″ E		Carpino betuli-Quercetum roboris fagetosum Rauš 1975Assortment structure13814.00
GRadinje; 9cφ 45°08′46″ N;
λ 17°34′15″ E		Genisto elatae-Quercetum roboris Horvat 1938Wood chips quality10786.31
HGrede kamare; 13aφ 45°15′53″ N;
λ 16°58′42″ E		Leucojo-Fraxinetum angustifoliae Glavač 1959Wood chips quality38100.00
Table 2. Number of trees sampled in diameter at breast height class, site, and tree crown defoliation degree.
Table 2. Number of trees sampled in diameter at breast height class, site, and tree crown defoliation degree.
SiteABCDEF
TCDDH3A3BDH3A3BD3BDH3A3BD3A3BDH3A3BD
DBH, cm
17.54444 444444
22.54444 444444
27.54 b4 b4 b4 b 4 b4 b4445
32.55 a, b4 a, b4 a, b4 a, b4 a4 a4a4 a4 b4 b32
37.54 a, b4 a, b4 a, b4 a, b4 a4 a4a4 a4 b4 b 2
42.54 a4 a4 a4 a4 a4 a4a4 a 51
47.52 44 c4 c4 c 1 c7 c4 c121
52.5 44 c4 c4 c 2 c5 c4 c111
57.5 4434 35 1221
62.5 33122
Total2724242424242324202015141213625144761
TCDD—tree crown defoliation degree; a data set used to test the influence of stand age on average tree value; b data set used to test the influence of narrow-leaved ash share on average tree value; c data set used to test the influence of phytocenosis on average tree value.
Table 3. Kolmogorov –Smirnov test results for expected and actual gross volume.
Table 3. Kolmogorov –Smirnov test results for expected and actual gross volume.
SiteNMean AMean EMNDMPDpSD ASD EDifference, %
A990.9060.837−0.06060.1313>0.100.6080.5378.21
B952.4652.040−0.02110.2316<0.025 *1.0650.84120.82 *
C400.8240.805−0.12500.1250>0.100.4480.4532.36
D540.5500.477−0.09260.2963<0.025 *0.2760.22715.38 *
E453.4613.298−0.13330.1778>0.101.0660.9434.93
F184.3052.9530.00000.555556<0.01 *1.3670.68545.78 *
* Significant difference; N—number of observations; A—actual gross volume; E—expected gross volume; MND—minimum negative difference; MPD—maximum positive difference; SD—standard deviation.
Table 4. The t-test parameters for average expected and actual tree value.
Table 4. The t-test parameters for average expected and actual tree value.
SiteMean AMean Et-ValuedfpSD ASD EF–Ratio Variancesp
Variances
A40.3450343.40048−2.392771960.017666 *9.1328918.8329781.0690600.741648
B58.9777855.953841.0791471880.28190524.3124112.444103.817062<0.000001
C43.4073540.056511.834057780.07045910.587384.6288405.2315810.000001
D37.0100636.981640.0399461060.9682114.2776733.0048112.0266600.011295
E58.1519356.023270.745544880.45793017.870816.8901676.727120<0.000001
F124.307969.762365.005913340.000017 *41.5078918.038625.2948630.001584
* Significant difference; A—actual average tree value; E—expected average tree value; SD—standard deviation.
Table 5. Descriptive statistics of wood chips properties.
Table 5. Descriptive statistics of wood chips properties.
VariableTCDDNMeanMinimumMaximumSD
Mar, %WCH1531.19 a27.7033.601.75
WC3A1531.12 a28.8033.101.17
WC3B1230.83 ab26.7032.902.08
WCD1528.85 b23.7032.602.95
Adr, %WCH151.431.131.850.22
WC3A151.491.211.800.18
WC3B121.481.261.670.14
WCD151.411.072.090.25
d50, cmWCH1516.408.0025.006.13
WC3A1517.008.0024.006.04
WC3B1218.338.0024.005.97
WCD1516.938.0028.006.70
C, %WCH1549.3948.7049.830.36
WC3A1549.5148.8049.950.38
WC3B1249.4448.8050.020.44
WCD1549.4848.6050.000.43
H, %WCH155.6075.0246.3830.504
WC3A155.5905.1386.4830.473
WC3B125.8765.1606.7870.694
WCD155.5845.0306.3360.554
N, %WCH150.1570.0900.2600.043
WC3A150.1380.0900.1630.027
WC3B120.1410.1000.1800.025
WCD150.1480.1000.2770.050
S, %WCH150.013<0.002 UCR0.0450.013
WC3A150.013<0.002 UCR0.0380.013
WC3B120.014<0.002 UCR0.0460.018
WCD150.014<0.002 UCR0.0380.013
O, %WCH1543.4042.4444.010.46
WC3A1543.2642.4043.850.49
WC3B1243.0642.3343.920.48
WCD1543.3642.2744.000.55
Qp, net, d, MJ/kgWCH1518.4318.2118.610.12
WC3A1518.4518.2818.590.10
WC3B1218.4118.1018.560.17
WCD1518.4818.2018.750.17
Different letters denote significant differences at the 5% level; UCR—under calibration range; Mar—moisture content in mass as received; Adr—ash content in mass of dry basis; d50—particle size distribution median; C—carbon content in mass of dry basis; H—hydrogen content in mass of dry basis; N—nitrogen content in mass of dry basis; S—sulfur content in mass of dry basis; O—oxygen content in mass of dry basis; Qp, net, d—net calorific value; WCH—wood chips produced from fuel wood that was produced from H trees; WC3A—wood chips produced from fuel wood that was produced from 3A trees; WC3B—wood chips produced from fuel wood that was produced from 3B trees; WCD—wood chips produced from fuel wood that was produced from D trees.
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MDPI and ACS Style

Ursić, B.; Zečić, Ž.; Vusić, D. Quantity and Quality of Narrow-Leaved Ash (Fraxinus angustifolia Vahl) Wood Forest Products in Relation to Tree Crown Defoliation. Forests 2025, 16, 147. https://doi.org/10.3390/f16010147

AMA Style

Ursić B, Zečić Ž, Vusić D. Quantity and Quality of Narrow-Leaved Ash (Fraxinus angustifolia Vahl) Wood Forest Products in Relation to Tree Crown Defoliation. Forests. 2025; 16(1):147. https://doi.org/10.3390/f16010147

Chicago/Turabian Style

Ursić, Branko, Željko Zečić, and Dinko Vusić. 2025. "Quantity and Quality of Narrow-Leaved Ash (Fraxinus angustifolia Vahl) Wood Forest Products in Relation to Tree Crown Defoliation" Forests 16, no. 1: 147. https://doi.org/10.3390/f16010147

APA Style

Ursić, B., Zečić, Ž., & Vusić, D. (2025). Quantity and Quality of Narrow-Leaved Ash (Fraxinus angustifolia Vahl) Wood Forest Products in Relation to Tree Crown Defoliation. Forests, 16(1), 147. https://doi.org/10.3390/f16010147

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