Seasonal resin production in Pinus pinaster Ait. plantations: Dendrometric and meteorological influences

Abstract

The relationship between dendrometric and meteorological parameters and resin production in Pinus pinaster plantations was studied using data from 90 trees collected between June and October. Resin production was measured every 15 days over a five-month period to explore how environmental factors influence resin production rates. The correlation between diameter at breast height (DBH) ranging from 20 cm to 49 cm and total and average resin production was examined, with the goal to optimize resin harvesting practices and to understand the ecological significance of resin in these plantations. The bi-monthly resin production was tested using the open wound tapping method over a five-month period beginning in June. Through regression models, significant seasonal variability in resin production was observed. Specifically, higher resin yields were recorded in June (354 g) and lower yields in October (53.5 g). The impact of DBH, tree height, basal area, and volume on resin yield were also assessed. Descriptive statistics, correlation, and regression analyses elucidated the relationships between tree metrics, meteorological factors, and resin production. This study contributes new insights into how tree characteristics influence resin production and how this relationship is modulated by seasonal changes. Such findings can inform sustainable forest management practices and improve resin harvesting methods.

Full Article

Seasonal Resin Production in Pinus pinaster Ait. Plantations: Dendrometric and Meteorological Influences

İnci Caglayan *

The relationship between dendrometric and meteorological parameters and resin production in Pinus pinaster plantations was studied using data from 90 trees collected between June and October. Resin production was measured every 15 days over a five-month period to explore how environmental factors influence resin production rates. The correlation between diameter at breast height (DBH) ranging from 20 cm to 49 cm and total and average resin production was examined, with the goal to optimize resin harvesting practices and to understand the ecological significance of resin in these plantations. The bi-monthly resin production was tested using the open wound tapping method over a five-month period beginning in June. Through regression models, significant seasonal variability in resin production was observed. Specifically, higher resin yields were recorded in June (354 g) and lower yields in October (53.5 g). The impact of DBH, tree height, basal area, and volume on resin yield were also assessed. Descriptive statistics, correlation, and regression analyses elucidated the relationships between tree metrics, meteorological factors, and resin production. This study contributes new insights into how tree characteristics influence resin production and how this relationship is modulated by seasonal changes. Such findings can inform sustainable forest management practices and improve resin harvesting methods.

DOI: 10.15376/biores.20.1.548-565

Keywords: Pinus pinaster plantation; Non-timber forest product; Resin yield; Bark streak tapping

Contact information: Istanbul University-Cerrahpaşa, Faculty of Forestry, Istanbul, Türkiye; *Corresponding author: inciyaylaci@iuc.edu.tr

INTRODUCTION

Resin production from trees is a significant ecological and economic activity, particularly in forest ecosystems dominated by species such as Pinus pinaster Ait. (maritime pine) (Soliño et al. 2018; López-Álvarez et al. 2023b). Pines produce resin, a versatile raw material that, when fractionated, yields distinct components such as rosin and turpentine, each with unique industrial applications (da Silva Rodrigues-Corrêa et al. 2013). Rosin is widely used in adhesives, inks, and varnishes, while turpentine is valuable in the chemical and pharmaceutical industries (Sarria-Villa et al. 2021). Additionally, wood resin has potential applications in bioenergy and the development of biodegradable batteries, underscoring its significant untapped potential (Neis et al. 2019). This valuable forest product is used in various industries, including pharmaceuticals, adhesives, and varnishes (Neis et al. 2019; da Silva Júnior et al. 2020). Therefore, understanding the factors influencing resin production in Pinus pinaster is essential for enhancing resin yield and ensuring sustainable management of forest resources. Effective resin harvesting practices contribute to the economic viability of forest enterprises (Soliño et al. 2018).

Pinus pinaster is extensively studied within its natural range, particularly in Spain and Portugal, where significant research is ongoing (López-Álvarez et al. 2023a). Known for its resin-producing capabilities (Tadesse et al. 2002; López-Álvarez et al. 2023b), this species is also significant in Türkiye’s afforestation efforts, where it is preferred due to its adaptability and rapid growth rate. Pinus pinaster plantations in Türkiye contribute to timber production, soil stabilization, and coastal erosion prevention. Although not endemic to Türkiye, it has been successfully introduced and cultivated due to its resilience and economic value. Management practices have helped sustain resin production in Pinus pinaster forests despite global socioeconomic and climatic changes (Moreno-Fernández et al. 2021).

Resin production in Pinus pinaster is influenced by intrinsic factors, such as tree size (Moura et al. 2023) and health, and extrinsic factors, such as environmental conditions. Tree size, particularly diameter at breast height (DBH) (Yu et al. 2020; Garcia-Forner et al. 2021; Sabo et al. 2022), tree height (Rodríguez-García et al. 2014), and tree age (Zas et al. 2020a), are generally positively correlated with resin production in Pinus pinaster. As the DBH increases, resin yield also increases (Rodríguez-García et al. 2014; López-Álvarez et al. 2023a, Caglayan et al. 2024). Resin production slows tree growth, creating a trade-off between growth rate and tree size (Génova et al. 2014). However, this reduction in growth does not stop trees entirely; they adapt to the slower growth while maintaining their functions (Kopaczyk et al. 2023).

Additionally, seasonal changes can significantly affect resin production, with factors, such as temperature and precipitation, playing crucial roles. Meteorologic parameters, including temperature, wind speed, and precipitation, are known to impact physiological processes in trees, which in turn affect resin secretion (Caglayan et al. 2024). For instance, higher temperatures can enhance metabolic activities, leading to increased resin production. Conversely, wind speed might negatively impact resin yield by causing physical damage or increasing water loss through evapotranspiration. Studies in Spain by Rodríguez-García et al. (2015, 2016) and Zas et al. (2020) show positive correlations between Pinus pinaster growth and temperature. The influence of other climatic factors is also highlighted, such as precipitation, potential evapotranspiration, relative humidity, and available water deficit on resin production (López-Álvarez et al. 2023a).

Despite the extensive research on Pinus pinaster, the combined influence of dendrometric parameters (such as DBH) and meteorologic factors (including temperature and wind speed) on resin production remains poorly understood. There is a notable gap in comprehensive studies that integrate these variables to provide a holistic understanding of resin yield determinants in Pinus pinaster, especially in non-native plantations like those in Türkiye. Addressing this gap is crucial for understanding how these trees perform in different environmental contexts and optimizing resin harvesting practices globally.

This study aimed to provide a comprehensive evaluation of resin yield in Pinus pinaster plantations. Through conducting a detailed correlation and regression analysis, this research aims to provide insights into the optimal conditions for resin harvesting. The findings are expected to contribute to better management practices in Pinus pinaster plantations, ensuring that resin production is ecologically sustainable. Moreover, understanding the ecological significance of resin in forest ecosystems can help in developing strategies for forest conservation and management. Given that Pinus pinaster resin production ranges from 1.0 to 4 kg per tree (Palma et al. 2012), predicting resin yield accurately could have significant economic and ecological benefits.

This study investigated the bi-monthly resin production of Pinus pinaster plantations using the bark streak tapping method over a five-month period beginning in June. The study’s objectives were multifaceted. First, it sought to understand how tree size impacts resin yield by examining parameters such as DBH, tree height, basal area, and volume. Larger trees are generally expected to produce more resin due to their greater capacity for photosynthesis and resource allocation. Second, the study aims to explore the seasonal variability of resin production by analyzing data collected across different months, specifically June, July, August, September, and October. To achieve this, the study involved the analysis of data collected from 90 trees. In doing so, the impact of DBH, tree height, basal area, and volume on resin yield was assessed. Descriptive statistics, correlation, and regression analyses elucidate the relationships between dendrometric, meteorologic factors, and resin production. The author hypothesized that larger trees, exposed to higher temperatures and lower wind speeds, would exhibit greater resin production.

EXPERIMENTAL

Study Area

The Enez Forest Management Unit is located in the Marmara region of Türkiye. Geographically, it spans between 26°2’10” and 26°22’25” East longitude, and 40°35’20” and 40°46’40” North latitude (Fig. 1). The elevation within this area ranges from zero to 373 m above sea level, covering an area of 549 ha. In this region, the rotation age for Pinus pinaster is 30 years, with resin production permitted until three years before the final harvest.

Fig. 1. The study area, Enez forest management unit, Edirne, Türkiye

Pinus pinaster, commonly known as maritime pine, is native to the western Mediterranean region, including areas such as Spain, Portugal, and North Africa. Although not indigenous to Türkiye, it has been introduced there for plantation purposes due to its adaptability and rapid growth rate. Consequently, Pinus pinaster plantations have been established for various functions, including timber production, soil stabilization, and as windbreaks to protect against coastal erosion. The species’ ability to thrive in Mediterranean climates has facilitated its successful cultivation in regions outside its native range (Elvira-Recuenco et al. 2014). In Türkiye, these plantations contribute to afforestation efforts and commercial forestry enterprises, supporting both environmental and economic goals (GDF et al. 2013). Overall, the introduction and management of Pinus pinaster in Türkiye demonstrate its importance in achieving sustainable forestry and enhancing the region’s ecological stability.

Data Collection

Data were collected from 90 Pinus pinaster trees within the Enez Forest Management Unit. The study was conducted over a five-month period, from June to October, with resin production measurements taken at the end of the period using the bark streak tapping method. This method involves making incisions in the tree bark to facilitate resin flow, which is then collected and measured.

Figure 2a illustrates the study area where the resin tapping experiment was conducted. The plot shows a typical section of the forest used in the study, highlighting the environment and the distribution of trees subjected to the resin tapping process. Additionally, Fig. 2b shows a single tree with multiple resin collection bags attached. Throughout the five months, new wounds were made on the tree bark every 15 days, and resin was collected in plastic bags. The image displays 10 resin-filled bags, demonstrating the cumulative resin yield over the specified period.

Fig. 2. Sample plots (a) and plastic resin bags (b) from June to October

Resin Extraction Process

For this study, a detailed resin extraction process was employed on Pinus pinaster trees, involving several key steps. First, the bark was carefully removed using a scraper on the south-eastern side of the tree to one-third of its diameter without reaching the wood layer (Fig. 3a). Next, a 5-cm-wide wound was made, starting 20 cm above the base, and extending to one-third of the bark’s thickness (Fig. 3b). To enhance resin flow, approximately 0.5 g of acid paste was applied to the upper part of the wound (Fig. 3c). A commercial acid paste used for resin production from Pinus pinaster plantations in Türkiye was utilized. Acid-based pastes significantly increase resin yield (Lukmandaru et al. 2021), particularly the combination of sulfuric acid and Ethephon, which boosted oleoresin production by approximately 2 times. Similarly, stimulant pastes, such as salicylic acid, have also demonstrated effectiveness in enhancing resin yield (Lema et al. 2024), highlighting the value of these treatments in optimizing resin extraction. Following this, a transparent plastic bag was then attached beneath the wound using staples to collect the resin (Fig. 3d). In this study, new tappings were made every 15 days, and at the end of the study period, the resin collected in the bags representing 15-day intervals was weighed.

Fig. 3. Steps in resin extraction process: (a) bark removal, (b) wound opening, (c) acid application, and (d) attaching plastic bag

Statistical Analysis

Descriptive statistics were computed to summarize the dendrometric and meteorologic data. Correlation analysis was performed to examine the relationships between the parameters and resin yield. To address potential heteroskedasticity in the data, a Weighted Least Squares (WLS) regression analysis was conducted. The predictors included DBH, average temperature, and wind speed, while resin yield served as the dependent variable. Additionally, factor analysis was conducted to better understand the relationships among the parameters. All statistical analyses were performed using SPSS 29 (IBM Corp., Armonk, NY, USA).

Dendrometric data

Key descriptive statistics for the dendrometric parameters showed that DBH ranged from 20 cm to 49 cm, with a mean of 31.1 cm (Table 1). Tree height ranged between 12 m and 18 m, averaging 14.7 m. The basal area had a mean of 774 cm², and the tree volume averaged 0.55 m³. The average resin yield per tree per wound was 212 g.

Table 1. Descriptive Statistic for Dendrometric Parameters

Meteorological data

The provided data encompasses various environmental factors recorded at the Ipsala meteorological station in 2023, alongside measurements of average resin production from trees planted in June. The factors include monthly precipitation, average temperatures (including minimum and maximum values), wind speed, and relative humidity. Additionally, average resin production in grams is listed for each month from June to October (Table 2).

Table 2. Descriptive Statistic for Meteorological Parameters

Precipitation varied across the months, with the highest recorded in October (7.8 mm) and the lowest in August (0 mm). Average temperatures ranged from 18.2 °C in October to 26.8 °C in July. Minimum temperatures varied from 8 °C in October to 15.5 °C in August, while maximum temperatures ranged from 28 °C in October to 38.8 °C in August.

Resin production showed a corresponding decline, starting at 354 g in June and dropping to 53.5 g in October (Fig. 4). The highest resin production occurred in June, with average temperatures at 22.2 °C, while the lowest was in October, with average temperatures at 18.2 °C. Both minimum and maximum temperatures followed similar trends, with resin production decreasing as temperatures became extreme, either high or low. Wind speed was highest in August and September (5.8 m/s) and lowest in June and July (4.4 m/s). Resin production decreased as wind speed increased, particularly noticeable in August and September. Vapor pressure, measured in hPa, peaked in July at 20.1 hPa and was lowest in October at 14.5 hPa. Higher vapor pressure in July coincided with a high resin yield (313 g), while the lowest vapor pressure in October aligned with the lowest resin yield. Relative humidity was highest in October (71.7%) and lowest in July (59.9%). Higher relative humidity in October did not correspond with higher resin production, which was at its lowest. Conversely, the relatively lower humidity in June and July coincided with higher resin yields.

Fig. 4. Monthly meteorological overview for the study area

Factor Analysis

The communalities of the variables used in Principal Component Analysis (PCA) are shown in Table 3. Communalities indicate the proportion of each variable’s variance explained by the extracted components. High extraction values, close to 1.000, suggest that most of the variance in these variables is accounted for by the principal components. For example, “Average_Temperature” and “Wind_speed” have extraction values of 1.000 and 0.998, respectively, indicating that nearly all their variance is explained by the components.

Table 3. Communalities of Variables in Principal Component Analysis

Table 4 provides an overview of the total variance explained by the principal components extracted through PCA. Component 1 had an initial eigenvalue of 5.679, explaining 51.6% of the variance. After extraction, it retained the same eigenvalue and percentage of variance. With rotation, the total variance explained by Component 1 was 50.8%. Component 2 had an initial eigenvalue of 3.673, accounting for 33.4% of the variance, with the cumulative variance reaching 85.019%. After extraction and rotation, Component 2 explained 33.6% of the variance, contributing to a cumulative 84.436%. Component 3 started with an eigenvalue of 1.066, explaining 9.7% of the variance, and maintains this value post-extraction. With rotation, Component 3 explained 10.276% of the variance, bringing the cumulative total to 94.7%.

Table 4. Total Variance Explained by the Principal Components Extracted through PCA

The rotated component matrix (Table 5), obtained using Varimax (SPSS 29, IBM Corp., Armonk, NY, USA) with Kaiser normalization, presents the loadings of each variable on the three principal components extracted through PCA. Component 1 was strongly influenced by the vapor pressure (0.981), average temperature (0.999), humidity (-0.988), minimum temperature (0.957), maximum temperature (0.983), and precipitation (-0.865). Component 2 showed strong loadings for dendrometric parameters such as DBH (0.978), basal area (0.973), volume (0.987), and tree height (0.903). Component 3 was primarily influenced by wind speed (0.989). These rotated loadings help better interpret the structure of the data, showing that Component 1 was primarily associated with temperature and humidity-related variables, Component 2 with dendrometric parameters, and Component 3 with wind speed.

Table 5. Rotated Component Matrix

RESULTS

Dendrometric Data

The correlation analysis in Table 6 shows significant relationships between dendrometric parameters and resin yield. The DBH was strongly correlated with tree height (r = 0.799, p < 0.01), basal area (r = 0.993, p < 0.01), and volume (r = 0.959, p < 0.01). Tree height also showed strong correlations with basal area (r = 0.796, p < 0.01) and volume (r = 0.890, p < 0.01). Basal area was highly correlated with volume (r = 0.945, p < 0.01). Moreover, resin yield exhibited moderate positive correlations with DBH (r = 0.338, p < 0.01), tree height (r = 0.262, p < 0.01), basal area (r = 0.337, p < 0.01), and volume (r = 0.318, p < 0.01). These significant correlations indicate that larger trees, as measured by DBH, height, basal area, and volume, tended to yield more resin.

Table 6. Correlation of Dendrometric Variable with Average Resin

Meteorologic Data

There was no significant correlation between precipitation and resin yield (r = -0.085, p = 0.078), indicating that precipitation levels do not directly influence the amount of resin produced. However, precipitation showed strong negative correlations with minimum, average, and maximum temperatures (r = -0.780, -0.857, and -0.802, respectively), suggesting that wetter conditions are associated with lower temperatures. Precipitation was also positively correlated with humidity (r = 0.855) and negatively correlated with vapor pressure (r = -0.848), showing that high precipitation led to higher humidity and lower vapor pressure.

All temperature variables (minimum, average, and maximum) showed significant positive correlations with resin yield, especially average temperature (r = 0.309) and maximum temperature (r = 0.291). This suggests that higher temperatures promote resin production. Additionally, there was a strong interrelationship between minimum, average, and maximum temperatures, with very high correlations among them (ranging from 0.949 to 0.989), indicating that temperature variations are consistent across different metrics.

Wind speed had a moderate negative correlation with resin yield (r = -0.428), indicating that higher wind speeds reduced resin production. This could be because wind increases water loss from trees or affects resin flow dynamics.

Humidity was negatively correlated with resin yield (r = -0.234), meaning that higher humidity levels were associated with reduced resin production. This could be due to the effect of moisture levels on resin flow or tree physiology. Humidity also showed strong negative correlations with temperature variables and vapor pressure, which is consistent with the idea that humid conditions are typically cooler and associated with lower vapor pressure. Vapor pressure was positively correlated with resin yield (r = 0.342), suggesting that higher vapor pressure, which often accompanies higher temperatures, promoted greater resin production.

Table 7. Correlation of Meteorological Data

Results of Regression Analysis with Factor Scores

In the regression analysis, three variables were entered: the regression factor (REGR) scores for the three components identified in the PCA. These variables are labeled as REGR factor score 1 for analysis 1, REGR factor score 2 for analysis 1, and REGR factor score 3 for analysis 1. No variables were removed from the model. The method used for the regression analysis was the ‘Enter’ method, meaning that all the requested variables were included in the model. The dependent variable in this analysis was resin yield. The inclusion of factor scores derived from PCA allows for a simplified yet comprehensive model to assess the relationship between the components and resin yield. Through incorporating these factor scores, the model effectively captures the underlying patterns and relationships among the variables, providing a more robust understanding of how the identified components influence resin yield.

Regression Analysis Summary

The regression analysis indicated a moderate positive correlation between the predictors and resin yield, with an R value of 0.657 and an R² of 0.432, explaining 43.2% of the variance in resin yield. The model was statistically significant (F(3, 423) = 107.199, p < 0.001). The unstandardized coefficients for the predictors were as follows: 53.741 for REGR factor score 1 (t = 8.232, p < 0.001), 59.528 for REGR factor score 2 (t = 9.118, p < 0.001), and -85.291 for REGR factor score 3 (t = -13.065, p < 0.001). This suggests that the first two components positively influenced resin yield, while the third component had a negative impact.

Table 8. Summary of the Regression Analysis with Factor Scores Predicting Resin Yield