Exploring the Biometric Traits and Potential of Radiata Pine (Pinus radiata D. Don) as a Non-Native Species for Sustainable Forest Systems in Portugal

Abstract

This chapter aims to provide information on biometric traits of radiata pine (Pinus radiata D. Don) outside its natural range, considering as a case study the use of the species in Portugal. The specific objectives of this study are: i) characterising the species; ii) its management; iii) its provisioning potential. To achieve the latter, data on the biometric characteristics of radiata pine trees in Portugal was compiled and analyzed. Briefly, the approach followed employs an equation developed to predict the stem volume of individual trees, which is then coupled with the inherent wood basic density to provide oven-dried biomass estimates. The volume equation demonstrated a noticeable goodness-of-fit (R² = 0.994 and standard error of the residuals = 0.026 m³) across the entire range of diameters within the dataset (ranging from 7.5 to 45 cm). Additionally, a proposed wood density value of 460 kg/m³ is put forth as a representative value for the species. The tree stem biomass (and sequestered carbon) is then generalized to the stand unit. The results show that the species compares favourably with maritime pine in terms of wood provisioning and usage, broadening the options of pine species to consider in Portugal for reforestation or afforestation programs.

1. Introduction

Provision is one of the categories of benefits provided by forests, with timber and fiber being the most common products in this category [1]. Given the current challenges faced by the timber industry and the impact of climate change, it is crucial to plan for the selection of appropriate forest species for afforestation and reforestation. Radiata pine, also known by the common names Monterey pine and insignis pine, has been widely used in timber plantations in many temperate regions due to its fast growth and wood properties. In Portugal, the interest in the species was translated into forestation programs, carried out in the 20th century, in different locations around the country, to study its adaptability. The interest was recently renewed in view of the threats of climate change and the pressure to obtain coniferous woody material for the Portuguese industry. The decrease of maritime pine (Pinus pinaster Ait.) forest area, in the last decades [2], heavily affected by fires and pests (the most expressive being nematode), and the expectations of changes in its distribution area due to climate change have motivated the use of other pine species. Among these, the radiata pine is of particular interest to the industry, namely due to the remarkable growth reported for the species in countries with a long tradition of managing the species, such as Chile [3].

To make an informed decision about utilizing this species, it is crucial to collect information about its characteristics and assess its potential for use in the wood supply service beyond its native range. This chapter provides information on radiata pine and being structured into four major parts. The first part covers the species’ distribution, biotic and abiotic disturbances, and the diversity and sustainability of its forest systems. The second part summarizes information on the management of the species, including silvicultural systems and practices. The third part focuses on the utilization of the species for provisioning, with a specific emphasis on its application in Portugal. Specifically, the case study delves into three aspects: (i) assessing tree stem volume using volume equations; (ii) presenting information on the species’ wood properties; and (iii) generalizing the volume quantification for biomass assessment. This study employs biometric data obtained from felled Pinus radiata trees, encompassing measurements of tree diameter, height, volume, and bark thickness. This dataset was originally compiled and utilized in prior studies conducted by one of the authors [4, 5], serving as a foundational resource for formulating a volume equation tailored to this particular species. Subsequently, samples of wood cores were acquired from standing trees to facilitate an analysis of wood characteristics and contribute to biomass assessment.

2. Distribution and ecology of Pinus radiata

Pinus radiata D. Don is an evergreen conifer that belongs to the Pinaceae family and Pinus genus. This species has several common names, namely radiata pine, Monterey pine, and insignis pine [6]. Its natural distribution is in California [6], in the coastal zone (Figure 1) from the parallel 35°30’ N to the parallel 37° N, corresponding to an area of about 200 km long, 10 km wide, and an elevation less or equal to 300 m [8]. The actual area of distribution is about 67,020 ha, in 5 patches, four inland with a total area of 58,270 ha (with patches of 13,450, 31,002, 6358, and 7461 ha, respectively, from north to south) and one in the Guadalupe island (circa 8750 ha) [7]. This species in their natural range from a conservation point of view is considered vulnerable and endangered status [6].

Figure 1.

Natural distribution of radiata pine (data source: [7]).

Radiata pine has been introduced in Australia, New Zealand, Spain, Argentina, Chile, Uruguay, Kenya, South Africa, Portugal [5, 8], United Kingdom, France [9], and Italy [10]. The largest areas are found in New Zealand, Chile, and Australia [11]. From these countries, the largest areas of plantations are located in New Zealand and Australia [6], with a total area of about 4 million hectares.

In its area of natural distribution stands of radiata pine can be pure or mixed with several conifers and/or broadleaved species [8]. Pinus radiata has a relatively short lifespan (circa 50 years), with a diameter at breast height reaching 60–120 m and a height of 30–50 m. Its crown presents high variability from conical with strong epinastic control and strong annual height growth (that can reach 1–2 m) to a flat and shorter length [10, 11]. Its branch longevity is large, resulting in larger crowns when compared to Pinus pinaster. Natural pruning is not frequent, and branches, live and dead, remain in the tree for many years, thus artificial pruning is prescribed to produce knot-free stems. In general, the stems are straight, but have some tendency to fork, due likely to pest attacks; and to develop a curvature in the stem, which results in reaction wood and thus wood with less interesting technological properties [11]. It is a species semi-shade tolerant though it develops better in full sunlight [8]. Its root system is superficial, sometimes without a pivot root, but well developed laterally up to a soil depth of 60 cm [10, 11], most of which up to 30 cm of soil depth [11]. Fruiting begins at 7–8 years old and becomes abundant at 15–20 years old. Fruit-full development is reached in the second year, cones are serotinous, opening after hot weather or fire, usually in the spring after fruit-full development, and have numerous seeds [6, 10, 11]. The cones after hot weather or fire open their scales releasing part of their seeds and close the scales shortly after. As cones have a high number of viable seeds and are maintained in the trees for many years, it enables several seed rains, which favor its regeneration [11]. It is a very fast-growing species [9].

This species develops in a wide range of temperatures from −5–41°C, with a mean annual temperature between 12 and 14°C, mean annual temperatures of 9–11°C in the winter and 16–18°C in the summer [8, 11]. Prefers mean annual precipitations between 380 mm and 890 mm, concentrated in the winter (an average of 300–510 mm) and less than 50 mm in the remaining months [8]. The lack of precipitation in July and August is balanced by mist precipitation, due to high air relative humidity (about 60–70%) and fogs [8, 10]. Radiata pine resistance to strong maritime winds is good [8, 11], but is sensitive to frost [10, 11]. It prefers deep well-drained soils and tolerates acidic soils but is sensitive to high zinc content [8], heavy and compact soils as well as soils with drainage problems [11].

3. Silviculture and management

In its natural range of distribution low-density stands and gaps in the canopy created by silvicultural practices have, in general, high number of seedlings and saplings. This regeneration is originated by the multiple seed rains, which are the result of the species traits. The cones are kept in the tree for many years. They open the scales after hot weather or fire releasing the seeds and closing the scales shortly after, which allows the trees to storage enough seeds for the regeneration of the stands. Moreover, the semi-shade tolerance during the young stages of development enables the seedlings and saplings to survive and grow under the canopy in semi-light environments. For example, it can live under the canopy of Quercus agrifolia for many years. Furthermore, due to the fast growth rates of the regeneration (during the first 15 years), it can outcompete other species [11].

Outside its natural range, most stands result from artificial regeneration (frequently plantation), resulting in pure even-aged stands, at regular spacing and with plantation densities ranging from 815 stems/ha to 1666 stems/ha and rotations between 25 and 35 years [11, 12].

For Portugal, pure even-aged stands with a plantation initial density of 1666 trees/ha with short rotation (25–30 years) and 2 or 3 thinnings from below at 8–10 years old, 15–18 years old, and circa 20 years old, respectively for the first, second and third thinning were recommended. Natural pruning occurs at higher densities, yet at low densities, it may be needed. Control of spontaneous vegetation is required. The trees react to fertilization with an increase in growth. It has some sensitivity to processionary attacks, which might decrease its growth [12].

For Spain, two models of silviculture have been found for Galicia [11]. One model considers the plantation with an initial density of 833 stems/ha and a rotation of 25 years. The other model considers the plantation with an initial density of 1142 stems/ha and a rotation of 35 years. In both models three thinnings are prescribed, the first non-commercial and the latter two commercial. The latter two are thinning from below, the intensity moderate to heavy and the periodicity of about 5 years. Two prunings are considered at the first and second thinning. It also considers the control of spontaneous vegetation with a periodicity of about 5–6 years [11].

Silvicultural management depends on the site quality and the management aims. In Chile [3], for high-productivity sites, it is considered an initial density of 1000 to 1100 trees/ha and 25 years of rotation length. There are three pruning operations in a subset of 500–600 trees (2.1 m—5 yr., 3.6 m—6 yr., and 5.5 m—7 yr) and two thinning operations (at 5 and 9 yr), aiming to reduce density to around 500 trees/ha. In low-productivity sites, the initial density is similar (1000 to 1100 trees/ha), with a lower rotation length (21 to 24 years). There is no prescription for intervention, other than a phytosanitary pruning. The final density is around 800 trees/ha assuming an expected average mortality of 20% of the trees.

4. Wood traits, tree volume, and biomass

Understanding the variability of the wood characteristics of a species not only allows the understanding of the development conditions of the trees but also to evaluate the quality of the wood produced by it and to infer its most appropriate applications. Wood density is a straightforward measure of the amount of woody material in a given volume. It can be quickly and accurately determined, and is often highly heritable with significant variability, making it an ideal target for genetic modification. Additionally, wood density is closely linked to numerous important properties and technological features that are essential to the production and utilization of forest products. As such, it is the most informative index for understanding the fundamental characteristics of wood [13, 14, 15, 16, 17].

Wood density can be expressed in multiple ways, including anhydrous density, saturated density, density at 12% moisture, and basic density, with the latter being one of the most referred to in the literature. Expressed by the ratio between the anhydrous weight of the wood and its saturated volume, it indicates the amount of mass present in a given volume of wood without considering the presence of water. Although it does not represent any real situation from the wood utilization point of view, it is of great value in studying the woody variation of trees. Besides being relatively fast and accurate, its determination is perfectly feasible in irregularly shaped and small samples, requiring minimal equipment. Basic wood density also has the added advantage of providing information that can be used to estimate the biomass of wood in dry weight, given that its volume is known (e.g., [18, 19]).

To determine the volume of a tree, indirect methods are commonly used. These methods involve equations based on the allometric model or other mathematical relationships, which typically estimate stem volume (v) based on the diameter measured at 1.30 m above ground level (d) or that diameter combined with the total height of the tree (h) (see, [20, 21], for further information on this topic). Biomass can be evaluated by destructive methods or estimated by equations that are similar to those used for modeling volume. For the species Pinus radiata, a number of volume and biomass models were proposed (see, [22, 23, 24, 25, 26, 27, 28]). For an overview of available models for the species, with reference to the location they were developed for, see the GlobAllomeTree database (http://www.globallometree.org/; [29]). Biomass can also be estimated by combining volume estimates with wood density, as mentioned. This method is especially useful when stem volume data or a predictive volume model is available and the goal is to obtain wood biomass estimates from volume. From the search carried out, and to the best knowledge of the authors, there are no studies on Pinus radiata that follow this approach.

5. Assessing wood traits, tree volume, and biomass: a case study in Portugal

5.1 Material and methods

The case study joins information on individual tree characteristics collected in seven forest stands of radiate pine across mainland Portugal (Odemira, Montejunto, Lavos, Leirosa, Aveiro, Marão and Cabreira) and processed in previous studies authored by the co-author R. Costa [4, 5], with additional measurements taken in 2022 and 2023, in tree additional sites (Moimenta da Beira, Vila Real and Malcata). The former data support the study’s analysis of the tree’s volume while the second data collection was specifically intended for targeting the evaluation of wood characteristics in radiate pine trees. Figure 2 shows the localization of the mentioned sampled radiate pine in Portugal used in the case study.

Figure 2.

Locations in mainland Portugal where radiate pine data was collected.

Figure 3 provides an overview of the materials, activities, and techniques used in the case study to develop tree volume equations, assess wood characteristics, and estimate the stem biomass of radiate pine. The combination of both methods and data sets allowed for the non-destructive estimate of the biomass of the species’ stem which can be extended to quantifying the carbon stored in this component. Yet, auxiliary calculations might be needed. When determining the volume of trees using volume equations, the volume usually includes the stem and the bark. To estimate the volume of wood, the portion of the volume that corresponds to the bark must be excluded. To do this, bark thickness is assessed in a sample set of trees, followed by the assessment of the bark reduction factor (k) for the species. This factor is then combined with information on the volume of the stem with bark to estimate the volume of wood in the stem (v_u). This methodology was proposed in Meyer [30] and can be found in forest measurement literature (e.g., [19, 31]). The main procedures used for the case study are described, including data characterization to assess the bark reduction factor and equations to quantify k and v_u.

Figure 3.

Schematic diagram of material and methodological preparation and development techniques used in the study.

This section is structured into separate subsections to favor the presentation of the distinct data sets and methodologies followed. Subsequent subsections encapsulate both the presentation and discussion of the results.

5.1.1 Volume equation development

Pinus radiata tree data from seven locations in mainland Portugal available from previous studies and processed by one of the co-authors [4, 5] served as a foundation for formulating a volume equation tailored to this particular tree species. The data was obtained from 120 trees that were cut down during thinning from below performed in five sites (Odemira, Montejunto, Lavos, Leirosa, Aveiro,) and from 43 standing trees in sites where felling was not possible (Marão and Cabreira). The sites are shown in Figure 2.

The data collected encompassed measurements of tree diameter at breast height over bark (d, cm) and total tree height (h, m). The stems of the felled trees were measured at 0.1 m and 1.30 m above ground for diameter and over 2 m length sections along the stem. The diameters were measured with a caliper with cross measurements and the length with a tape. The volume of each section was then computed using analytical formulae (e.g., the cylinder formula for the portion corresponding to the stump, the cone formula for the upper part of the trunk, and Smalian’s formula for the intermediate logs). The volume of the stem (v, m³) was obtained by summing the volumes of all portions from the base up to stem height (h) in accordance with the rigorous cubage method [19]. The diameter variable was measured to the nearest mm and the length of the sections was recorded to the nearest cm. The volume of standing trees was assessed using a Bitterlich relascope, by identifying the formal height and applying the Pressler-Bitterlich cubage method [5, 19, 32].

Table 1 provides a summary of the dataset, while Figure 4 displays scatter plots depicting the relationship between tree diameter at breast height over bark and total tree height with stem volume for the VOLUME data set.

Variable	Min	Average	Max	Standard deviation	CV (%)
d (cm)	7.5	23.2	44.9	11.4	49
h (m)	6.5	17.5	28.5	6.1	35
v (m^3)	0.012	0.481	1.723	0.481	100

Table 1.

Characterization of the data set VOLUME (n = 163 obs.).

Figure 4.

Graphical representation of the observations used in the volume modeling (n = 163 obs.). Scatter plots of stem volume in relation to the variables diameter at breast height (a) and tree total height (b).

Upon analyzing the observations, tests were conducted to produce a volume equation for the species. The tests included common formulations of volume equations found in literature, such as the ones mentioned in ([33], p. 8), or in other reference books (e.g., [19] or [20]). The considered potential regressors were diameter and height, which were found to be clearly associated with volume, as shown in Figure 4. To address potential heteroscedasticity, the logarithm of the volume was utilized as the response variable instead of using the original variable volume. The exogenous variables tree diameter and height, as well as their transformations and interactions, were tested. The models were fitted with ordinary least squares (OLS) regression.

Statistical analysis was conducted to evaluate the goodness-of-fit of the estimated models using the coefficient of determination ($R^2$), and the Root Mean Square Error (RMSE) to identify departures to the ordinary least squares assumptions. The Spearman’s rank correlation test was applied to detect the presence of heteroscedasticity and the variance inflation factor ($\mathit{VIF}$) was evaluated to assess multicollinearity [34, 35]. The accuracy of the models was also assessed using the Furnival index ($\mathit{FI}$) [36], regarded as an average standard deviation of the residuals transformed into units of volume. The model development was performed using the JMP® software (JMP®, Version 17.2.0. SAS Institute Inc., Cary, NC, 1989–2023), and the model among the ones essayed with the best performance, based on the fitting criteria ($R^2,\mathit{\text{RMSE}},\text{and}\mathit{FI}\text{statistics}$) and OLS assumptions were ultimately chosen. The chosen fitted model was adjusted by incorporating the correction term (exp(MSE/2)) suggested by [37] to account for the transformation bias resulting from transforming the logarithm of volume values back into volume estimates as a final step.

5.1.2 Wood basic density and radial growth assessment

The material used to study the basic density and radial growth (width of growth rings) of radiata pine refers to samples of wood cores collected in three stands located in Moimenta da Beira, Vila Real, and Malcata (Figure 2), with ages of approximately 12, 40, and 50 years, respectively. At each of these sites, a selection of 3–6 radiata pines was made. The selection process involved choosing trees encompassing the observed diameter classes within a 5 cm range, in each stand, with upright, healthy stems, and excluding those that displayed possible formation of reaction wood.

A radial sample from cambium to pith was extracted from each tree with an increment borer (Pressler auger) about 5 mm thick, taken at 1.30 m above ground level and as perpendicular as possible to the tree vertical axis. The diameter over bark at breast height and total height values of the sampled trees are provided in Table 2.

Variable	Min	Average	Max	Standard deviation	CV (%)
d (cm)	13.7	28.8	50.0	11.6	40
h (m)	10.0	16.3	21.5	3.6	22

Table 2.

Characterization of the data set WOOD (n = 15 obs.).

Each core sample properly identified was prepared for laboratory evaluation, with placement in a support structure followed by sanding. The width of each growth ring was measured (using a magnifying glass with a micrometric displacement table with an accuracy of 0.001 mm) and then sectioned into specimens consisting of three growth rings. The basic density was calculated by the ratio between the anhydrous weight and the saturated volume of each wood specimen. The saturated volume of the specimens was determined by the method of impulsion in distilled water of the previously saturated samples (Archimedes’ principle), with the aid of an analytical balance with an accuracy of ±0.0001 g. The anhydrous weight was quantified after the specimens were placed in an oven at 100 ± 3°C until constant weight stabilization. The analyses were carried out at the Forest Products Laboratory of the University of Trás-os-Montes and Alto Douro.

5.1.3 Bark thickness, bark reduction coefficient, and stem volume under bark evaluations

The thickness of the bark (B) was evaluated on the sample radiata pine trees mentioned in subsections 5.1.1 and 5.1.2, for a total of 178 trees, by measuring two opposite sides of the trunk with a thickness gauge that has an accuracy of 1 mm. Measuring the double thickness of the bark (2B) allows the diameter under bark (du) to be evaluated from the diameter over bark (d) using the expression: du = d – 2B.

Table 3 presents a summary of the diameter with and without bark and bark thickness values measured at 1.30 m (variables d, du, and B, respectively), which constitutes the BARK subset of data (Figure 3).

Variable	Min	Average	Max	Standard deviation	CV (%)
d (cm)	7.5	17.7	50.0	9.4	53
du (cm)	7.0	16.0	45.0	8.1	51
2B (cm)	0.2	1.7	6.4	1.5	87

Table 3.

Characterization of the data set BARK (n = 178 obs.).

From the information on d and du, the bark reduction factor (k) is determined. This factor is calculated following Meyer [30] as the ratio between the sum of the diameters of the stem with bark and without bark (Eq. (1)):

$$k=\frac{{\sum }_{i=1}^n{d}_u}{{\sum }_{i=1}^{n}d}$$E1

According to the same reference [30], the volume of the stem without bark can be approximated by the relationship shown in Eq. (2), where k is the bark reduction factor calculated through Eq. (1).

$$v_u=k^{2}v$$E2

5.2 Results and discussion

5.2.1 Stem volume over and under bark

After the fitting procedures on the VOLUME subset of data, the model that has shown overall goodness-of-fit statistics is the equation that considers as an explanatory variable the product of the square diameter and height (d²h). Results of the estimation are provided in Table 4.

Model	${\beta }_0\left(s_{{\beta }_0}\right)$	${\beta }_1\left(s_{{\beta }_1}\right)$	R^2	RMSE	FI (m^3)	FI (%)
$lnv={\beta }_0+{\beta }_{1}ln\left(d^{2}h\right)$	$-10.2318\left(0.0558\right)$	$0.9928\left(0.0063\right)$	0.994	0.013	0.026	5.4

Table 4.

Parameters of the selected volume equation and goodness-of-fit statistics (n = 163 obs.).

Analysis of the residuals did not show departures to normality. Additionally, as the model considers a single regressor, there is no multicollinearity (VIF = 1). The Spearman’s rank correlation test did not evidence the presence of heteroscedasticity ($\rho =-0.1182,$p-value$=0.1328)$. After some adjustments including applying the Baskerville factor, $exp\left({0.013}^2/2\right)$, to the selected model, Eq. (3) is obtained. This equation can be utilized to approximate the total stem volume over the bark of radiata pine in Portugal.

$$v=3.6242\times {10}^{-5}{d}^{1.9856}{h}^{0.9928}$$E3

The proposed equation (Eq. (3)) for stem volume estimation over bark, adequately describes the volume pattern observed for the range of diameter and height values measured, as shown in Figure 5 (continuous line). The graph in Figure 5 depicts a trend line (dotted line) for volume values derived from the volume equation utilized for the maritime pine species observed in Portugal’s national forest inventory, NFI6 [2]. The trend line is provided for comparison and assumes that both pines have a similar height-diameter relationship, which needs to be tested. If this assumption holds true, it can be inferred that radiata pine produces lower volume in the stem than maritime pine, although it tends to compare favorably with the latter, namely for trees up to the 30 cm diameter class (calculated mean difference of estimated values circa 0.020 m³, based on n = 108 obs.).

Figure 5.

Graphical representation of the data set (ο) and the fitted line for volume estimation of radiata pine over bark (Eq. (3), continuous line). Estimates of stem volume over bark for maritime pine (NFI6, [2] dotted line).

Determining the volume of a tree stem without bark (v_u) based on its volume with bark (v) can be easily accomplished by using the bark reduction factor (k). Estimation of the bark reduction factor for radiata pine through Eq. (1) yield, k = 0.9055, around 0.91.

To estimate the volume of the stem without bark (v_u), the bark reduction factor (k) is applied in Eq. (3), as previously shown (Eq. (2)). Thus, the equation proposed for stem volume estimation, excluding bark, is:

$$v_u=2.972\times {10}^{-5}{d}^{1.9856}{h}^{0.9928}$$E4

Considering the bark coefficient, the estimated value of k (a. 0.91) is within the range of values mentioned in [31] (0.87–0.4) for this variable, which can vary with the species, the age, and site factors. Marques et al. ([21] and cited references herein) point out limitations with the expedited methods (Eqs. 1 and 2) for determining bark amount and volume under the bark. These methods assume a constant bark reduction factor along the trunk, and the linearity may not apply to all diameters. Additionally, bark thickness vary due to factors such as growing conditions and tree age/size. The case study did not explicitly analyze the impact of growing conditions and age. However, the variation was assessed based on tree size. It was found that there was a slight decrease in k values from the smallest to the largest diameter classes (0.93–0.90). This study’s broader geographical scope, the large sample size used, and the short range of variation provide confidence in the method’s reliability and supports the use of an average value of k = 0.91.

For maritime pine, Duarte [38] presents values of the bark coefficient ranging from 0.65 to 0.95, with average values around 0.81. Based on the sample data used in the study, Duarte [38] mentions that the average proportion of bark in comparison to the total volume of the stem is 30%. When comparing the bark coefficient values of maritime pine reported by Duarte [38] with the values obtained in the case study for radiata pine (k circa 0.81 and 0.91, respectively), radiata pine has a greater wood volume and less bark than maritime pine for the same amount of stem volume over bark.

5.2.2 Wood density

Table 5 shows the values of basic density and ring width, per tree, in each of the three sampled sites (Moimenta da Beira, Vila Real, and Malcata). From the values, it can be observed that Pinus radiata trees in Moimenta da Beira have an average wood density of 361 kg/m³. The average value per tree ranges from 350 to 369 kg/m³. These trees are still young, with only 8 to 9 rings at 1.30 m level, and are mainly composed of juvenile wood, which explains the relatively low wood density.

	Basic density (kg/m^3)			Ring width (mm)
Tree	Moimenta da Beira	V. Real	Malcata	Moimenta da Beira	V. Real	Malcata
1	369	513	431	11.0	3.6	3.1
2	350	464	438	10.0	4.4	2.2
3	365	453	539	9.7	1.8	2.7
4		461	444		2.0	1.9
5		493	503		3.5	1.7
6		436	434		2.7	3.0
Average	361	470	465	10.2	3.0	2.4
Standard deviation	10.0	28.1	45.2	0.7	1.0	0.6
CV %	2.8	6.0	9.7	6.7	33.7	24.1

Table 5.

Values of basic density and ring width, by site and tree.

In contrast, the trees in Vila Real and Malcata stands are much older (around 40 to 50 years old) and are composed of both juvenile and adult wood, yielding an average wood density value of 470 kg/m³. The average value per tree ranges from 431 to 539 kg/m³. These values are similar to those obtained for Pinus pinaster wood by Fonseca and Lousada [39] in three stands in the north of Portugal aged between 35 to 55 years old, which presented an average value of 489 kg/m³ (ranging from 383 to 528 kg/m³ between trees), as well as by Louzada [40] in two stands around 80 years old in Gerês (426 kg/m³) and Marinha Grande (479 kg/m³).

As P. radiata revealed wood density values very similar to P. pinaster (the most used coniferous species in Portugal), we can therefore conclude that these two species produce wood with very identical characteristics. Hence, whenever necessary, P. radiata can be used as a substitute for P. pinaster, being suitable for use in carpentry, framing, furniture, veneer, laminate, and chipboard, as well as with identical carbon-sequestration capacity.

Regarding the ring width, the trees in Moimenta da Beira have a considerably higher radial growth (ring width) than the other two sites (Moimenta da Beira = 10.2 mm; Vila Real = 3.0 mm and Malcata = 2.4 mm) since they are exclusively composed of juvenile wood. In comparison, Fonseca and Lousada [39] reports an average value of 3.9 mm for ring width in adult P. pinaster trees, while Louzada [40] reports 2.5 and 1.9 mm for the stands in Gerês and Marinha Grande, respectively.

5.2.3 Biomass and carbon

Estimating the biomass of the stem is straightforward when an equation is available that estimates the volume of the wood (Eq. (4)), complemented by the value of its basic density. Based on the analyses performed for the case study, the recommended value for wood basic density is 460 kg/m³.

The biomass of wood in dry weight (b, kg) is calculated by multiplying the volume of wood (v_u, m³) by the average wood basic density. In the alternative, the basic density value is applied directly in Eq. (4). Thus, the equation proposed for dry biomass estimation of stem wood, is:

$$b=0.01367d^{1.9856}{h}^{0.9928}$$E5

To the best of the authors’ knowledge, Eq. (5) is the first model that is available for estimating the stem biomass of Pinus radiata trees in Portugal. The method used involved combining the stem volume data with wood density to convert volume measurements into mass measurements. This approach has not been previously applied to the studied species, according to the literature. Allometric equations have been proposed to estimate radiata pine biomass (e.g. [22, 23, 24, 27, 28]). However, these equations were developed using data from other regions and might result in biased estimations in Portugal.

Montero et al. [24] found that 78.5% of the dry matter in this particular species is located in the above-ground portion of the tree with 83% of this dry matter in the stem (including the bark but free of branches). These figures allow to consider that roughly 2/3 of the biomass of the tree is in the stem.

For evaluating the carbon content in this component, a conversion factor of 0.5 (or a more specific value if known) can be used, assuming that 50% of the dry biomass is composed of carbon. To estimate the carbon stored in the stem component, the estimates are adjusted using a coefficient that takes into account the ratio between the molecular weight of carbon dioxide and the atomic weight of carbon (44/12). This calculation gives the amount of carbon stored in CO₂ equivalent (CO₂e) (Eq. (6)).

$$\mathit{CO}2e=0.02506d^{1.9856}{h}^{0.9928}$$E6

The relationships derived from tree variables and wood parameters (Eqs. (3) to (5)) form the basis to assess the supply capacity of species in afforestation programs, filling a national knowledge gap.

6. Conclusions

The results obtained in the case study permit the estimation of tree stem volume and dry biomass of radiata pine, which is of the utmost importance for characterizing the wood provisioning service potential and carbon quantification for the species. The information complements previous studies about radiata pine in Portugal and enables meaningful comparisons with other pine species, thereby supporting informed decision-making in forest and timber management planning. The results of this study reveal that radiata pine exhibits wood properties and offers provisioning services that are comparable to those of maritime pine. These findings widen the range of pine species that can be considered for national reforestation or afforestation programs.

When making decisions about which species to use, it is important to consider edapho-climatic factors, as they have a significant impact on the success and productivity of the selected species. To refine decision-making, future analyses must include a comparative study of the growth patterns of various pine species under identical conditions. This comprehensive understanding will enable more informed decisions in forestry management, ensuring that the chosen species can successfully adapt to their specific environments.

Acknowledgments

Thanks are due to the International Union of Forest Research Organizations (IUFRO), namely Division 1 (Silviculture), unit 1.01.10 Ecology and Silviculture of Pine, and Centro PINUS association, for promoting fruitful discussions on the silviculture and management of pine forests that have contributed to the decision of writing this document. Carlos Fernandes from CIFAP-UTAD is thanked for his valuable contribution to data collection for the WOOD dataset.

Conflict of interest

The authors declare no conflict of interest.

Funding

This work was partially funded by the FCT – Fundação para a Ciência e a Tecnologia) to Forest Research Centre (CEF), within project UIDB/00239/2020, to Center for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), under project UIDB/04033/2020, and to MED – Mediterranean Institute for Agriculture, Environment and Development under the Project UIDB/05183/2020.