Socioeconomic Structure and Analysis of the Demand for Wood Raw Materials in the Poplar Wood-Processing Companies of the Sakarya and Kocaeli Provinces in Turkey

Journal of Sustainable Forestry

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Socioeconomic structure and analysis of the demand for wood raw materials in the poplar wood-processing companies of the Sakarya and Kocaeli provinces in Turkey

Selda Karakaya, İsmet Daşdemir & Mehmet Ercan

To cite this article: Selda Karakaya, İsmet Daşdemir & Mehmet Ercan (2017): Socioeconomic structure and analysis of the demand for wood raw materials in the poplar wood-processing companies of the Sakarya and Kocaeli provinces in Turkey, Journal of Sustainable Forestry, DOI: 10.1080/10549811.2017.1333912 To link to this article: http://dx.doi.org/10.1080/10549811.2017.1333912

Accepted author version posted online: 01 Jun 2017. Published online: 01 Jun 2017.

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Download by: [Bartin Universitesi] Date: 04 July 2017, At: 04:52 JOURNAL OF SUSTAINABLE FORESTRY https://doi.org/10.1080/10549811.2017.1333912

Socioeconomic structure and analysis of the demand for wood raw materials in the poplar wood-processing companies of the Sakarya and Kocaeli provinces in Turkey Selda Karakayaa, İsmet Daşdemirb, and Mehmet Ercanc aSakarya Regional Directorate of Forestry, Sakarya, Turkey; bBartın University Faculty of Forestry, Department of Forest Engineering, Bartın, Turkey; cPoplar and Fast Growing Forest Trees Research Institute, Kocaeli, Turkey

ABSTRACT KEYWORDS This study investigates the socioeconomic structure and factors Kocaeli; poplar affecting the demand for raw materials of the poplar wood-proces- wood-processing sing industrial companies within 11 districts of the Sakarya and companies; Sakarya; Kocaeli provinces. A face-to-face survey was conducted in 53 current socioeconomic characteristics; structure of companies. The data were evaluated using percentage methods and – demand and factors the Kruskal Wallis H-Test. The data on the raw material demand of affecting demand; Turkey the companies were also evaluated using statistical (factor and regression) analyses. The most important factors affecting the structure of the demand for raw materials were determined as follows: (a) the scale or size of company, (b) the demand level for products, (c) production form of the company, (d) price of raw materials, (e) external environmental conditions, (f) raw material types and fea-

tures, (g) seasons, and (h) the procurement conditions for raw materials. Also, the variables influencing the change in the raw material demand were determined to be: (a) the principle activity area of the company, (b) its amount of capital, (c) the price of raw materials, and (d) the production as well as profit levels of the company. Economic, technical, and managerial proposals were developed to advise the companies on how to operate more efficiently and profitably.

Introduction The consumption of wood raw materials has increased in parallel to industrialization and a growing global population. Moreover, rapid population growth and technological development will continue to cause an increase in the demand for wood, both in terms of the quantity and the variety of forest products, in almost every country of the world (Brooks, 1997; Lyke & Brooks, 1995). As the need for forest resources continues to increase, use of these resources in a carefully planned manner becomes more important. In order to reduce the pressure on and prevent destruction of natural forests, industrial plantations have been used as they provide more eﬃciency per unit area. Considering the rotation period of industrial poplar (Populus deltoides, Populus x euramericana) plantations (≈10–15 yrs), these systems may help to rapidly reduce the wood raw material supply

CONTACT İ smet Daşdemir [email protected] Bartın University Faculty of Forestry, Department of Forest Engineering, Turkey. © 2017 Taylor & Francis 2 S. KARAKAYA ET AL. deficit. Furthermore, poplar plantations can also reduce the pressure on natural forests and therefore promote sustainable forestry. Forests cover almost 4 billion ha of land area across the globe. Twenty-five percent of this area is in Europe, 22% in South America, 17% in North America, 16% in Africa, 14% in Asia, and 5% in Oceania. Approximately 27% of the total world land area is covered with forests, and 55% of the forests are in developing countries while 45% are in developed countries. The net forest loss in the world totaled 61,865,000 ha from 2000 to 2010 (FAO, 2015, 2016). The socioeconomic benefits from forests are mostly derived from the consumption of forest goods and services. Also, the formal forestry sector employs some 13.2 million people across the world, and at least another 41 million people are employed informally via the sector. Forest products also contribute to providing shelter for at least 1.3 billion people, or roughly 18% of the world’s population (FAO, 2014). On that note, the world population as of the time of this writing is 7,349 billion, 83% of which live in less developed and developing regions (UNFPA, 2015). It is estimated that the world’s population will be 7.5 billion by 2020 and 8.2 billion by 2030. At the same time, industrial roundwood production of the world is projected to increase to 2,166 million m3 in 2020 and 2,457 million m3 in 2030; industrial roundwood consumption, however, it is projected to increase to 2,165 million m3 in 2020 and 2,436 million m3 in 2030 (FAO, 2009). According to this, the industrial round wood production estimates are slightly higher than the consumption estimates. Due to the swiftly increasing demand, it is important to consider afforestation with fast growing tree species (such as poplar) and to examine the production as well as the demand structure of poplar wood-processing industrial companies to meet increasing consumption. It should also be noted that the poplar industry

makes positive contributions to rural economies, social development, forest resources, and natural environments (Zhi-Guang, Ming-Xiu, Guang-Hui, & Ding-Guo, 2005), thus providing multiple benefits other than solely wood products. In Turkey as of 2015, forest area totals 22.3 million ha, covering almost 29% of the country’s land area. Fifty-seven percent of the country’s forests is classified as productive for quality wood raw material production. From these forests, the growing stock in Turkey totals 1.61 billion m3 with an annual increment of nearly 46 million m3 (GDF, 2015a). The amount of wood production from state forests (industrial wood and firewood) is about 19 million m3 per year (GDF, 2015b). The total wood production from private forests, most of which is poplar, is estimated to be 4.8 million m3 per year (GDF, 2013). The annual wood consumption in Turkey, however, is nearly 29 million m3, and the 5.2 million m3 per year deficit is overcome through imports (GDF, 2015c). It was recently estimated that demand for industrial wood in Turkey would increase to 18.4 million m3 by 2020 and 23 million m3 by 2030 (Yıldırım, 2012). To ensure the wood deficit in Turkey does not continue to grow, it is necessary to establish industrial forests with fast growing species such as poplar to meet the growing demand. Although there is still no reliable inventory information available regarding poplar plantations in the country, nearly 4 million m3 of poplar wood is produced from 160 thousand ha of poplar plantations (Anonymous, 1995; Birler, 1995). Almost all of the production takes the form of irrigated agricultural lands managed by private individuals (Karakaya, 2010; Koçer, 1999). In order to determine the domestic market demand for forest products, a study was conducted by the General Directorate of Forestry (GDF). It was determined that raw JOURNAL OF SUSTAINABLE FORESTRY 3 material processing capacity of forest product processing companies reached up to 25 million m3. However, only 55% of the available capacity was used, with the amount of processed raw materials totaling nearly 14 million m3 (Kaplan, 2006). Recently, there have been significant investments and capacity increases for forest product processing companies, demonstrating the sector’s dynamic nature and potential. The inadequate supply from the existing sources vis-a-vis the dynamic nature of the industry and the uncertainty of future imports suggests the potential effective use of industrial plantations with fast- growing tree species, especially poplar species (Populus deltoides, Populus x euramericana), in Turkey. Most forest products are considered intermediate goods and are not directly sold to end users but instead to commercial large-scale buyers including timber companies, packaging manufacturers, and construction contractors (İlter & Ok, 2012). Therefore, demand for wood raw material (input) from companies depends on consumer preferences, raw material efficiency, cost, and the indirect substitution possibilities; this circumstance can be described as indirect demand, dependent demand,orderivative demand. Thus, proper investigation of derivative demand for poplar wood-processing companies and subsequent insights into the factors affecting the demand for raw material are needed to achieve sustainable forest management (Duerr, 1993; Kotler, 2002; Nautiyal, 1988; Palo, Uusivuori, & Mery, 2001).

Economic theory of factor demand and the structure of firms In general, the consumer demand of a goods (Q) is estimated as a function of its price ƒ (i.e., Q = (P)). However, the demand function becomes a multivariable structure when other factors affecting demand (e.g., prices of substitution and complementary goods, income, preferences, expectations, etc.) are taken into consideration (Brooks, Baudin, & Schwarzbauer, 1995;Daşdemir, 2014; Kangas & Baudin, 2003;Nautiyal,1988). Demand on producer companies for raw material or input is different from consumer demand. Since procedure firms supply the intermediate goods to other firms, or the final goods to consumers after processing the raw materials, they add value within the production/ consumption chain. In short, the demand for firms’ production factors (input) is based on their production of other goods or services. In other words, as demand for goods and services increases, demand for production factors also increases. As such, firms’ factor demand is considered indirect demand, dependent demand, or derivative demand. Aside from the consumer demand for goods and services, the production factor (input) demand for firmsdependsonthedemandlevelofgoodsofthefactorused,theefficiency of factor, its share in the cost of goods, and the substitution possibilities of the factor with other factors. Therefore, the demand function form can be written as follows: DEMAND ¼ Dx¼ f Px;Ps;Dg;Ef ;Fs;Of (1) where Dx shows demand quantity for factor X, Px is the price of the X factor, Ps is the price of substituting it with alternative inputs, Dg is the demand quantity of goods that ffi factors become, Ef is the e ciency of factor X, Fs is the factor share in the cost of goods, and Of equals other factors. Under perfect competition, there are many sellers and buyers, firms produce a homogeneous commodity, firms and buyers possess perfect information, and all are 4 S. KARAKAYA ET AL. freetoenterorleavethemarket,whileallresourcesareperfectlymobile.Atypical outcome of perfect competition is that the marginal revenue is equal to the marginal cost resulting in zero profits under equilibrium. An imperfect competition, of course, occurs in real-life situations, because the conditions of perfect competition do not always hold in the market due for a variety of reasons (market failures) (Daşdemir & Lise, 2007). The imperfect competition includes oligopolisitic (few firms) and mono- polistic (one firm) competition. Hence, the selling price is influenced by the features of thebuyersandgoodssupplied,andsometimesbythedemandfortheotherfactors related to imperfect competition (Ibid.). Under the assumption of the perfect competition in the factor markets, an explanation of how a firm might influence the demand of factors, or its factor employment decision, is given in Figure 1 (Daşdemir, 2014; Geray, 1998; Klemperer, 1996; Nautiyal, 1988). Assuming that all other conditions are constant (ceteris paribus), the price of a factor fi (Pf) in the market is constant for the production rm, and the factor supply curve of the firm is parallel to the X-axis. fi fi To ensure pro t maximization, a production rm will compare the price of factor (Px) fi and its monetary marginal productivity (MP); as long as MP > Px, the rm will demand (i.e., employ or use) the factor. The producer will demand the factor up to the point (B) of MP = Px. This condition demonstrates when the marginal revenue of a factor is equal to fi its marginal cost. At point of B, ensuring the equality MP = Px, the rm will maximize fi pro ts and reach stable market equilibrium by employing up Q0 factor. In the case that fi MP < Px, the rm will face damage and not demand factors. Therefore, the condition mandating factor employment is the equality MP = Px. However, in the presence of

imperfect competition conditions, or when there are large number of variables, factors affecting the demand of the firm must be determined with multi-dimensional approaches and analyses. If the producer firm uses n number of inputs, the optimal factor employment decision is as follows:

MP MP MP 1 ¼ 2 ¼¼ n 1 (2) P1 P2 Pn

MP Px

MP=Px

B Px

0 Quantity Q0 of factor MP>Px

Figure 1. The price and marginal productivity of factor by its employed quantity. JOURNAL OF SUSTAINABLE FORESTRY 5

According to this formula, the firm will employ factor in such a way that the proportions of the marginal revenues of n number of factors to their prices are equal each other, and the result of the formula is 1 or more (optimal factor combination). The structure of the forest products industry and wood demand are as follows: the wood-processing industries demand wood raw materials according to their capacities, production generally takes the form of lumber, parquet, plywood, veneer, furniture, paper, fiber-chip, packaging, etc., and they then supply products to other manufacturers, organizational buyers, and/or consumers as intermediate or final goods (İlter & Ok, 2012). In other words, the raw material demand of the wood-processing companies is a derivative demand due to its dependence on the demand and production of other goods, and the company will reach stable market equilibrium by following the equality MP = Px as described in Figure 1. While the poplar wood-processing companies in underdeveloped and developing countries are usually small or medium-scale, use labor-intensive technology, and operate as traditional family-owned businesses, in Europe, the United States and most developed countries they are often organized as professional firms using large-scale, capital-intensive technology (Anonymous, 2010, 2013; Baudin, Flinkman, & Nordvall, 2005;INDUFOR,2013;Wan,2009; Zaman & Ahmad, 2012). As a result, forest product industries constitute an important sector that creates demands for the wood outputs of the forestry sector as raw materials then processes them to produce new intermediate goods and final goods, thus creating addedvalueandemploymenttosupportthecountry’s economic and sustainable development.

Scope and purpose Although there are some studies on the socioeconomic structure and the demand analysis of the wood-processing industrial companies in Turkey and the world (Akyüz, 1995; Aiyeloja, Oladele, & Ozoemena, 2014; Anonymous, 2013;Cındık, Serin, Akyüz, & Akyüz, 2002; Gültekin, Kayacan, & Ok, 2009; INDUFOR, 2013; Karayılmazlar, Çabuk, &Aşkın, 2006; Kun, Wenming, & Hashiramoto, 2007; Okan, 2001; Prestemon, Wear, & Foster, 2015; Wan, 2009; Zaman & Ahmad, 2012), no study has directly been conducted regarding poplar wood-processing industrial companies’ socioeconomic structure and demand analysis. This study thus focuses on the poplar wood-processing companies, which have a certain share in the country’s economy, in order to reveal the socioeconomic structures behind the companies in the provinces of Sakarya and Kocaeli and to determine the factors aﬀecting the demand/demand structure of poplar wood. Additionally, the study intends to detect potential problems and expectations experienced by the companies. As such, the poplar industry’s current status and demand structure are analyzed, and the study contributes to future goal implementation and the creation of policies to improve the sector.

Material and method Sakarya and Kocaeli provinces were selected for the study area as their lands are suitable for poplar growth via intensive wood-processing companies (Figure 2). The 6 S. KARAKAYA ET AL.

Figure 2. The location map of the study area (the Sakarya and Kocaeli provinces).

Table 1. Name of variables used in the study, theirs units and labels. No Variables Unit Labels 1 Amount of raw material demand of company m3/yr ARMDC 2 Education level of company owner – ELCO 3 Main activity area of company – MAAC 4 Legal structure of company – LSC 5 Activity form of company – AFC 6 Production form of company – PFC 7 Market level of company – MLC 8 Adequacy of external environmental factors -Energy – AEEFE 9 Adequacy of external environmental factors—Raw material – AEEFR 10 Annual working time of company Months AWTC 11 Demand level for poplar products – DLPP 12 Preferred poplar clones – PPC 13 Procurement sources of raw material of company – PSRMC 14 Price of raw material U.S.$/m3 PRM 15 Raw material cost for company U.S.$/m3 RMCC 16 Purchase form of raw material – PFRM 17 Payment form in purchasing raw material – PFPRM 18 High season for raw material demand – HSRMD 19 Production technology of company – PTC 20 Type of customers who demanded company’s products – TCDCP 21 Number of employees in company Person NEC 22 Amount of capital of company U.S.$ ACC 23 Weekly working hours of company Day WWHC 24 Amount of annual production of company m3 AAPC1 25 Amount of annual profit of company U.S.$ AAPC2 26 Demand distance of company for raw material km DDCRM Note. Kaiser–Meyer–Olkin (KMO) coefficient is 0.701; Cronbach Alpha value is 0.801 JOURNAL OF SUSTAINABLE FORESTRY 7 socioeconomic and raw-material demand structures, including the factors affecting the demand and the problems/expectations of the poplar wood-processing companies in eleven districts (Akyazı, Adapazarı, Karapürçek, Geyve, Taraklı, Hendek, Arifiye, Sapanca, Karasu, Kocaali, and Ferizli) of the Sakarya province and in three districts (Gölcük, Eşme, and Körfez) of the Kocaeli province were studied. In the study area, 53 poplar wood-processing companies were identified for analyses, 6 of which were located in Kocaeli and 47 of which were located in Sakarya. Using the full field sampling method (Kalıpsız, 1988), face-to-face interviews were conducted in all of the industrial companies between 2011 and 2013, and the collected data were used for the study (Karakaya, Daşdemir, & Ercan, 2015). Firstly, data related to specific socioeconomic characteristics of the poplar wood- processing companies (education, activity area, legal structure, number of employees, total/operational capacity, market level, etc.) were evaluated as a percentage, and the differences in the scale or quantity of the raw material demand were analyzed using the Kruskal–Wallis H-Test. This test is a one-way analysis of variance applied to nonpara- metric data to check for significant differences between two or more populations with unknown distributions. In order to be able to apply the test, firstly, the measures belong- ing to the k numbered groups are ordered from small to large, and each is given a sequence number. Then, the sequence numbers in each group are summed (Ti), and the H-value is calculated as follows:

Xk 2 ¼ 12 ð Ti Þ ð þ Þ H ð þ Þ 3 N 1 (3) N N 1 i¼1 ni where ni is the number of units in the i th group, and N is the total number of units in the groups. The differences were checked using a chi-square table as H-values demonstrate chi-square distributions (Kalıpsız, 1988). Differences were tested for certain features of the companies according to the scale or quantity of raw material demand reflected in the Kruskal–Wallis H-Test, and the different groups were determined with a Duncan test (Ibid.). Secondly, factor analysis was used to identify the most important factors affecting the demand structure of poplar wood-processing companies. Factor analysis (FA) is based on the correlations between the variables (X1,X2, ... Xj,...Xn) and is a sequence of many mathematical techniques that provides a more meaningful and summarized representation “ ” of the data over a reduced number of dimensions, termed factors (F1,F2,...Fm). There are multiple stages in factor analysis, including the selection and standardization of variables (Zj), the calculation of correlations between variables, the derivation of common factors (factor matrix) using a correlation matrix, and finally the rotation and interpretation of factors. Although there are many methods for factor derivation, it is usually based on the Principal Components method. According to this method, common factors are derived as follows:

Xj ! Zj ¼ aj1F1 þ aj2F2 þ ::: þ ajp Fp þ ::: þ ajmFm (4) where aj are factor loadings that indicate the correlation between the factor and the variable. Kaiser or Scree Test Criteria are used to determine the number of common factors. Orthogonal (varimax, quartimax, etc.) and oblique rotations (oblimax, quartimin etc.) are used to facilitate scientiﬁc identiﬁcation and interpretation of factors (Bennet & 8 S. KARAKAYA ET AL.

Bowers, 1977;Daşdemir, 1987; Harman, 1976; Mucuk, 1978). In this study, the Principal Components method was used for the factor derivation, the number of common factors was determined according to the Kaiser criterion, and the Varimax method was used rotating the factor matrix. Finally, the variables aﬀecting variations in a company’s raw material demand were evaluated using multiple regression analysis. The multiple linear regression model deter- mines how the independent variables (X1,X2, ...,Xj, ... Xn)aﬀect the dependent variable (Y) and explains its changes as follows:

Y ¼ a þ b1X1 þ b2X2 þ ...þ bjXj þ ...þ bnXn þ ε (5) ffi ff where a is a constant value, bj is the regression coe cient indicating the e ectiveness of Xj to Y, and ε is the coincidence error (Kalıpsız, 1988; Özdamar, 2002). Based on the research data, the 26 variables related to the raw material demand of the companies were identified (Table 1). The SPSS 22.0 software package program was used for the statistical analyses.

Results and discussion Socioeconomic structure of poplar wood-processing companies The socioeconomic structure of companies is discussed as follows:

(1) Age and education of the company owner: The average age of 53 company owners interviewed is 49. The age of company owners ranged from 28 to 64, all of which consisted of active population members of working age (between 15 and 65 ages). In terms of education, 36%, 30%, and 23% of the company owners had graduated from primary, secondary, and high school, respectively, while only 11% of the owners had university degrees. (2) Main activity area of company: The main activity for 41% of companies was the production of packing cases, 32% concentrated on lumber production, 21% on pallet production, and 6% on plywood production. 79% of the owners who specialized in vegetable-fruit-ﬁsh box production and lumber production had primary and secondary degrees, and 71% of the company owners who produced pallets and plywood had high school and university degrees. (3) Legal-ownership structure of companies: 2%, 21%, and 77% of poplar wood-processing companies were composed of anonymous companies, limited companies, and individual companies, respectively. “Large” scale companies were structured as corporations whereas “small” and “medium” scale companies were structured as private companies. Sixty-two percent of companies were established through the initiatives of the company owners, while 30% were acquired by heritage and 8% by way of purchase. Eighty-one percent of companies exhibited individual ownership, while 19% leased property. Similarly, in the Black Sea Region of Turkey the majority of small and medium sized forest products industry companies operated as private business (Akyüz, 1995, 2000;Cındık et al., 2002). However, on average 67% of the forest products industry companies in the JOURNAL OF SUSTAINABLE FORESTRY 9

Bartın and Düzce provinces were structured as anonymous and limited companies (Aytin, 2006;Daşdemir, 2003). (4) Number of employees in companies, age group, and educational level: A total of 532 people in 2010, 524 people in 2011, and 496 people in 2012 worked in 53 companies. The gender ratio of the employees was consistent through all 3 yrs; 91% were male, and 9% were female. 5% of women and 22% of men were composed of family members within the companies. Most of the employees were in the 25–34 age group, the smallest proportion was the 15–24 age group. The education of employees within the companies is very low. Based on the 3-yr average: 4%, 21%, 23%, and 52% of the employees had a university, high school, secondary school, and primary school degree, respectively. Taking into account the sum of primary school and secondary school graduates (75%), the majority of the employees are elementary school graduates. In similar research (Akyüz, 1995; Aiyeloja et al., 2014, 2000;Cındık & Akyüz, 1998;Cındık et al., 2002;Daşdemir, 2003; Karayılmazlar et al., 2006), the vast majority of employees in small and medium-sized forest products industry companies were identiﬁed as primary school graduates. These results also show that the companies largely employ unskilled labor, so the sector is thus important for economic development. (5) Type of activity, production form, and production standards of the company: 15% and 85% of all companies operated as factories and workshops, respectively. On the other hand, 70% of the wood-processing companies in Düzce were factories and 9% were workshops (Aytin, 2006) as they also process other woods besides poplar and they are “large” scale companies. 94% of companies operated on a per-

order basis only, 4% were only engaged in series production, and 2% were involved in both types production. Sixty-four percent of companies did not use any standards during production. Twenty-one percent of companies produced according to the speciﬁcations of the order received, and 15% used a more widely accepted standard (Turkey Standards Institute-TSE, ISO, etc.) during production. It was also determined that 87% of companies had no cooperation with other institutes during production, 7% had a dialogue with SMEDO (Small and Medium Enterprises Development Organization), 2% with a university, 2% with the Ministry of Agriculture, and 2% dealt with the Poplar Research Institute (PRI) in Turkey. (6) Daily-weekly-yearly working times of companies: The daily worktime of companies varied between 8–24 hrs; 14% of them operated 8 hrs per day, 17% operated 10 hrs per day, 28% operated 16 hrs per day, and 41% operated 24 hrs per day. Thirteen percent of companies operated 7 days per week, 43% operated 5 days per week, and 44% operated 6 days per week. Packing case companies generally worked between 1 and 6 months in a year. Fifty-one percent of companies worked continuously for 10–12 months of year. In this respect, the majority of small and medium sized forest products industry companies (78%) worked 6 days per week (Akyüz, 1995, 2000;Cındık et al., 2002; Karayılmazlar et al., 2006). (7) Customer types, distribution channels, and marketing methods: 44% of companies sold their products to ﬁshing businesses as well as fruit and vegetable producers. And while 44% of companies sold their products to construction, furniture, transport, automotive, and maritime industries and factories, only 11% sold 10 S. KARAKAYA ET AL.

their products solely to iron and steel, pipe, cable, and paint factories. Manufactured products were distributed directly from all companies, and personal selling-marketing methods are used. 68% of companies marketed their products at the regional level, 24% at the national level, and 8% at the provincial level. Seventy-ﬁve percent of companies sold their products in 1–3 month terms, 8% in advance, and 17% do both. The level of demand is high for lumber, pallets and plywood, while the level of demand is low for packing cases. It is important to note that the level of demand also depends on the production of fruits and vegetables, ﬁsh harvests, and the mobility of construction and furniture industries in that year. (8) Company scale and capacity utilization situations based on the quantity of raw material processing: 19% of companies are “very small-scale” companies (0–99 m3/ yr), 36% are considered “small-scale” (100–999 m3/yr), 23% “medium-scale” (1000–2999 m3/yr), 8% “medium-large scale” (3000–4999 m3/yr), 11% “large- scale” (5000–9999 m3/yr), 2% “very large-scale” (10,000–14,999 m3/yr), and 2% “giant-scale” company (>15,000 m3/yr). Accordingly, the majority (55%) of poplar wood-processing companies are “small” sized. The rates of idle capacity of companies were determined as follows: “very small” scale companies 84%, “small” scale companies 77%, “medium” sized companies 43%, “medium-large” sized companies 36%, “large” scale companies 40%, and “very large” scale companies 65% and “giant” sized companies 55%. In Turkey’s Zonguldak province, 40% of the companies processing primary forest products were “large” and “large-scale” companies (Daşdemir, 2003). Accordingly, raw material processing capacities of

companies processing primary forest products are generally larger than solely poplar industrial companies. (9) Company scale and capacity utilization based on product type: 41% of companies were “small” scale and, 32% “medium”, 21% “large”, and 6% “very large” scale businesses. “Small”, “medium”, “large”, and “very large” scale businesses worked with 62%, 36%, 60%, and 52% of idle capacity respectively. These ratios are higher than the capacity utilization ratios (14–24%) of the forest products industry in Turkey’s Trabzon province (Cındık & Akyüz, 1998). (10) Scale and types of companies by the number of employees: Using the assessment from the Turkish Statistical Institute (TSI), it was determined that 79% of companies are “micro-scale” (1–9 employees, averaging 5 people), 19% “small-scale” (10–49 employees, averaging 23 people), and 2% “medium-scale” (50–249 people, averaging 81 people). Therefore, 98% of companies were “micro-small” businesses according to the number of employees. “Small” scale packing case companies run with an average of ﬁve people, and “medium” scale lumber companies work with at least seven people, while “large” scale pallet companies work with at least 15 people, and “very large” scale plywood companies work with at least 42 people. Forest products processing industries were mostly “small” and “medium” sized business that employed 3–4 people (Cındık & Akyüz, 1998), with 80% employing less than 10 workers (Anonymous, 2010). (11) External environmental conditions of companies: All companies were well-located in terms of accessibility. Eighty-seven percent of them had a “good” energy conditions, 9% had “medium” energy conditions, and 4% had “poor” energy JOURNAL OF SUSTAINABLE FORESTRY 11

conditions. Forty-seven percent of them had “poor” conditions, 34% “medium” conditions, and 19% “good” conditions in terms of proximity to raw material sources of poplar wood according to the scoring of companies’ owners between 1 and 10 (1–3 being “poor”,4–6 “medium”,7–10 “good”). (12) Preferred poplar clones: 57% of businesses preferred the I-214 poplar clone, 7% the Samsun clone, 32% used both, and 4% used the Samsun clone with other poplar species. The I-214 poplar clone was often preferred by ﬁsh-vegetable-fruit case manufacturers, because its wood is lighter and easier to process. Another study on poplar growers (Karakaya, 2010) showed that 64% of companies preferred the Samsun clone, leading to contradicting data claims that should be investigated in future studies. (13) Procurement of raw material: 43% of businesses preferred their raw material delivered directly to the company, and 23% preferred to buy it as planted in the land. Eighty-three percent of companies provided poplar wood from a 0–100 km distance, while only 11% provided it between a 101–200 km distance. Sixty-ﬁve percent of companies provided poplar wood from individual producers, and 13% provided from both their own productions and individuals. A very small number (2%) of companies produced the raw materials themselves. Thirty-seven percent of companies buy poplar wood in advance, 25% in terms, and 38% do both. No companies stock the poplar wood raw material. Gavcar, Aytekin, and Şen (1999) similarly stated that 51% of the forest products industries in Turkey do not stock raw materials. Companies paid attention to price (30%), clones (22%), diameters (21%) and other elements (body uniformity, payment terms, distance etc.) (27%)

when they provided the poplar wood raw material. Poplar wood demand increases during the summer and autumn, and 81% of companies face problems during winter and spring in procuring poplar wood due to unfavorable land conditions. Therefore, the procurement of poplar wood is a very important factor for a company’s sustainability. (14) Problems in procuring raw materials and the proposed solutions: The majority of companies (79%) had problems in procuring poplar wood raw materials. It was determined that the most important three problems in this regard were: a lack of local poplar wood resources, high raw material prices, and a lack of poplar plantation policies on the part of the related forestry organization. Other problems encountered included unfavorable payment terms, a lack of raw material in expected quality and steadiness, problems in customs and shipping, and losing potential poplar plantation sites to agriculture. Similarly, 83% of forest product industry companies in Turkey had problems in procuring raw materials, and 61% of these problems deal with quality while 22% deal with price (Gavcar et al., 1999). In the province of Trabzon, the biggest problem (75%) while procuring raw materials was low quality and high price (Cındık & Akyüz, 1998). In order to solve these problems, companies recommended creating a policy for poplar plantation for the GDF and expediting poplar plantation activities through the suitable state forest enterprises (41%). Other recommendations included having the government support poplar growers to reduce price of poplar raw material (36%), giving a priority allocation of land for companies processing wood raw material (11%), and registering fast-growing new poplar clones as well as 12 S. KARAKAYA ET AL.

increasing scientific research (10%). Likewise, Zaman and Ahmad (2012) proposed that farmers and small-scale entrepreneurs should be supported in order to meet the demand for wood raw materials in Pakistan. (15) Problems in the production stage: 28% of the companies experienced financial problems at the production stage. The problems included a high cost of the energy used for production (24%), the inability to find skilled labor (23%), and a lack of stable and continuous poplar raw material in the desired quality/quantity (18%). Other issues that the companies faced at the production stage were the inadequate use of technology, continuous power outages, and the high price of nails used in packing case. (16) Problems in the marketing stage: 87% of the companies experienced some problems at the marketing stage, while 13% of companies did not experience any problems. Thirty-seven percent of companies faced problems with collecting money from selling products during the marketing phase. The most important problem encountered in the marketing phase of packing case companies was the inability to compete with foam/plastic/cardboard cases (21%). Additional problems during the marketing phase were the high product price (10%) depending on the high cost of raw materials and production (13%).

The problems faced by poplar wood-processing companies via the procurement of raw materials, finance, production, and marketing resemble common problems of SMEs (small and medium-sized enterprises) (Erden & Duru, 2010). The most important structural problems of the Turkish forest products industry appeared to be the ffi fi procurement of raw materials, the insu ciency of capital, and the lack of quali ed personnel, credit, and energy (Kurtoğlu, Koç, Erdinler, & Sofuoğlu, 2009). In addition, it was determined that 56% of the forest industry companies in Turkey’sBartın province had production and marketing problems such as high production costs, a low quality of final products, inadequate marketingnetworks,andpoorcompetitive power (Karayılmazlar et al., 2006).

(17) Capital, production, and profitability: 78% of the companies had “very small” and “small” scale capital sized (U.S.$ 667–333,333). The highest profit belonged to the plywood company in the Adapazarı Region, while the lowest profit belonged to the packing case company in the Geyve Region. The average rate of return of capital for the 53 companies was 65.9%. It was identified that the rates of return of capital by the activity areas of companies were 27.3% at packing case companies, 149% in lumber, 26.9% in pallet, and 21.7% in plywood companies. Three-year (2011– 2013) average quantities of raw material usage of companies, given the type of product produced, were analyzed. The results showed that “small” scale packing case companies process 5,783 m3 of raw materials, “medium” scale lumber companies process 45,252 m3, “large” scale palette companies process 42,939 m3, and “very large” scale plywood companies process 29,233 m3 of raw materials. It was found that the efficiency ratio for the 22 packing case companies was 0.87, while the ratio was 0.43 for the 17 lumber companies, 0.48 for the 11 palette companies, and 0.53 for the plywood companies. According to these results, it can be concluded that the small-scale packing case companies operate more efficiently. JOURNAL OF SUSTAINABLE FORESTRY 13

On the other hand, the forest products industries in Turkey’s Trabzon province work with 75% eﬃciency and 20% proﬁt(Cındık & Akyüz, 1998). (18) Future outlook and policies: Companies did not have sustainable and stable access to poplar wood raw material due to inadequate raw material sources, and they therefore turn to other tree species or are sometimes closed down. Likewise, the use of plastic, cardboard, and foam material in the packaging industry negatively impacts poplar wood packing case companies and has led to the closure of 50% of them. Given these negative impacts, the future expectations of the companies are likely to be negative. Forty-two percent of the companies at the “very small” and “small” scale stated that “there is no future in the sector”, while 30% stated “they are in limbo”, and 11% stated “the future of the industry is bright”. The main three strategic policies considered for the near future were to maintain current capacity (40%), to increase capacity (32%), and to expand market share (6%) respectively.

Testing diﬀerences of some features of companies depend on the company scale

The H0 hypotheses about whether some features (education, activity area, legal structure, mode of operation, market level, etc.) of the poplar industrial companies diﬀer according to the company scale or quantity of raw material demand that was tested, and the diﬀerences were determined (Table 2). These results suggest that the production form of companies, the adequacy of external environmental factors, a company’s preferred poplar clone(s), the procurement sources of raw material, the purchasing form of raw material, the form of payment when purchasing

raw material, the high season (i.e. summer) for raw material demand, and the demand distance for raw material were not significantly different according to the amount of raw material demand or the company scale. Similarly, in a study conducted in the Düzce province, it was found that the demand for raw material of the forestry industry (lime- stone cover, parquet and plate sub-sectors) generally demonstrated a fluctuating structure, were met from domestic sources (state forest enterprises), and were not different according to the sub-sectors and/or company scale (Gültekin et al., 2009). On the other hand, the education level of the company owner and the demand level for poplar products had significant differences at the company scale (p < 0.040; Alpha = 0.05). Accordingly, owners of “very large” scale companies tended to have a university degree, while owners of “very small”, “small”, “medium” and “large” scale companies tended to have primary and secondary education degrees. Likewise, the level of demand for products from “very large” scale companies was high, while the level of demand for products of “very small”, “small”, “medium”, and “large” scale companies was at a low and medium level. Similarly, the company’s main activity area, legal structure, activity form, market level, annual working time, type (sector) of customers who demanded products from the company, number of employees, weekly working hours, amount of capital, amount of annual production, and profit all had significant differences at the company scale (p < 0.003; Alpha = 0.01). Accordingly, the main activity area of “very large” scale companies was plywood production, while the main activity area of “very small” and “small” scale companies was packing case production, and for “medium” scale companies it was lumber and pallet

14 Table 2. Results of testing diﬀerences of some features of companies depend on company scale. Diﬀerential Control with Duncan Test (Standings in the group are in order of severity)

ff AL. ET KARAKAYA S. Kruskal-Wallis Di erent Groups and Specialties H-Test (Chi- Group Members (Scale) (1: Very Small, 2: Small, 3: Medium, 4: Medium-Large, 5: Features square value) No Large, 6: Very Large-Giant Description Mean N 1. Education of company 11.64* 1. 1, 2, 3, 4, 5 Primary, Secondary, High 2.10 51 owner School 2. 6 University 4.00 2 2. Main activity area of 25.43** 1. 1, 2 Packing Case 1.34 19 company 2. 3, 4 Lumber 2.25 16 3. 5, Palette 3.00 6 4. 6 Plywood 3.50 2 3. Legal structure of the 26.13** 1. 1, 2, 3, 4 Individual 1.19 45 company 2. 5, 6 Anonymous, Limited 2.58 8 4. Activity form of company 38.60** 1. 6, 5 Workshop 1.08 8 2. 3, 1, 2, 4 Factory 1.98 45 5. Production form of 8.49 – Operating the companies only order, series or both of them is not significantly All companies work order, 2.08 53 company different according to the company scale series or both of them 6. Market level of company 14.53** 1. 1, 2, 3,4 Provincial level, Regional 2.08 45 level 2. 5, 6 National, International 2.75 8 7. Adequacy of external 5.38 – Transport, energy and adequacy of raw material do not differ according to the All companies operate in – 53 environmental factors companies scale. They have similar conditions because all of these companies similar external environment locate in the same area conditions 8. Annual working time of 28.91** 1. 1 1–5 months 1.50 10 company 2. 2, 3 6–9 months 3.12 31 3. 4, 5, 6 10–12 months 4.00 12 9. Demand level for poplar 11.97* 1. 1 Low 1.30 10 products 2. 2, 5, 4, 3 Middle 2.22 41 3. 6 High 3.00 2 10. Preferred poplar clones 5.59 – Choice of poplar clone does not differ according to the amount of raw material Choice of poplar clones of 1.91 53 demand or company scale. However, the Samsun clone is generally preferred companies is the same 11. Purchase form of raw 9.57 – Purchase form of raw material does not differ significantly according to the All companies use the same 2.05 53 material amount of raw material demand or company scale purchase form 12. Procurement sources of 8.11 – Procurement sources of raw material does not differ significantly according to All companies use the same 3.58 53 raw material of company the amount of raw material demand or company scale supply sources 13. High season for raw 1.71 – High season for raw material demand does not differ significantly according to High season for demand is 4.03 53 material demand the amount of raw material demand or company scale the same and it generally is summer

14. Type (sector) of 20.38** 1. 1, 2 Fish, Vegetable, Fruit 2.32 29 customers who 2. 4, 6, 3, 5 Construction, Furniture, 3.81 24 demanded company’s Factory, Transportation products 15. Number of employees in 34.97** 1. 1, 2,4, 3 6 people 5.61 45 company 2. 5 25 people 25.17 6 3. 6 63 people 63.00 2 16. Weekly working hours of 18.69** 1. 1, 2 5 days 5.18 29 company 2. 5, 3 5.5 days 5.88 18 3. 6, 4 6 days 6.25 6 17. Demand distance of 6.29 – Demand distance of company does not differ significantly according to the Companies provide raw 134 53 company for raw material amount of raw material demand or company scale material from almost the same distance 18. Amount of capital of 26.66** 1. 1, 2, 4, 3 U.S.$ 122,651 122,651 45 company 2. 5 U.S.$ 1,155,555 1,155,555 6 3. 6 U.S.$ 2,666,667 2,666,667 2 ORA FSSANBEFORESTRY SUSTAINABLE OF JOURNAL 19. Amount of annual 48.48** 1. 1, 2 172 m3 172 29 production of company 2. 3 1491 m3 1491 12 3. 4 3066 m3 3066 4 4. 5 5470 m3 5470 6 5. 6 9650 m3 9650 2 20. Amount of annual profit 41.14** 1. 1, 2, 3, 4 U.S.$ 8,619 8,619 45 of company 2. 5 U.S.$ 111,259 111,259 6 3. 6 U.S.$ 459,300 459,300 2 Note. *Significant at the 0.05 level (p < 0.05); **significant at the 0.01 level (p < 0.01); Degrees of Freedom (DF): it is a missing of group number of raw material demand (or number of scale), so it is 5. 15 16 S. KARAKAYA ET AL. production. With respect to legal structure, while “very small”, “small”, and “medium” scale companies tended to be individual business, “large” and “very large” scale companies were well-organized as anonymous and limited companies. With respect to market level, “large” and “very large” sized companies market their products at the national and international level, while “very small”, “small”, and “medium” sized companies work at the province and regional level. While the products (generally packing cases) of “very small”, “small”, and “medium” sized companies were in demand by fish and vegetable (customer type) sectors, the products (lumber, palette, plywood) of “medium”, “large”, and “very large” sized companies were sought by construction, furniture, and transportation sectors. In a study conducted on this topic (Karayılmazlar et al., 2006), it was determined that the majority of forest industry companies (≈95%) sold their products to consumers, wholesalers, retailer, or private units directly at the provincial and regional levels in domestic markets. Annual operating time is realized according to firm size, as well. In general, “large” and “very large” sized companies operated for 10–12 months per year, while “very small”, “small” and “medium” sized companies operated for less than 9 months per year. Similarly, as the company size increased, so did the number of weekly working days. The number of workers in companies also differed according to the scale; “very small”, “small”, and “medium” sized companies maintained their activities with an average of six people, while “large” companies operated with an average of 63 people. As expected, the amount of company capital varied according to firm size. “Large” scale companies had an average capital of U.S.$ 1,155,555, and “very large” scale companies averaged U.S.$ 2,666,667, while “very small”, “small”, and “medium” scale companies had

an average capital of U.S.$ 122,651 (as of 2010). Furthermore, the average lifespan of “small” and “medium” scale forest products industry companies in the Bartın province was determined to be 16 yrs, and the average founding capital was $ 34,686 (Karayılmazlar et al., 2006). Likewise, while the annual production amount of “very small”, “small”, and “medium” sized companies varied from 172 to 3,066 m3, “large” scale companies produced 5,470 m3, and “very large” scale companies 9,650 m3. Similarly, while the average annual profitof“very small”, “small”, and “medium” sized companies was U.S.$ 8,619, large scale companies’ profit was U.S.$ 111,259 and very large scale companies’ profit was U.S.$ 459,300 (as of 2010). Consequently, as the company scale grows, so does the annual and weekly working time, number of employees, amount of capital, annual production, and profit.

Determination of factors affecting raw material demand of companies Factor analysis was used with the 26 variables (Table 1) to determine the most important factors affecting raw material demand of a company. Using the Principal Components method and a varimax rotation, the first 8 factors with an eigenvalue greater than 1 were derived (Kaiser criterion). About 78% of the demand variability, which was defined by the 26 variables, was explained by these eight main factors. At the end of the rotation, the percentage of variance explained by the factors were 24.02%, 10.97%, 8.93%, 8.63%, 8.10%, 5.72%, 5.72%, and 5.57% respectively (Table 3). The rotated component matrix obtained in the analysis was provided in Table 4. The derived factors were identified and interpreted based on the factor loadings in this rotated component matrix. In order to JOURNAL OF SUSTAINABLE FORESTRY 17

Table 3. Factors derived and variance explained by factor analysis. Initial eigenvalues Rotation sums of squared loadings Factors Total % of variance Cumulative % Total % of variance Cumulative % 1 8.36 32.16 32.16 6.25 24.02 24.02 2 2.67 10.26 42.42 2.85 10.97 34.99 3 2.04 7.84 50.26 2.32 8.93 43.91 4 1.88 7.24 57.49 2.24 8.63 52.54 5 1.58 6.07 63.56 2.11 8.10 60.64 6 1.35 5.19 68.74 1.49 5.72 66.36 7 1.26 4.84 73.58 1.49 5.72 72.08 8 1.06 4.07 77.65 1.45 5.57 77.65 Note. The bold number represents total variance explained by 8 factors.

Table 4. Rotated component matrix and factors affecting structure of poplar wood demand. Factors (Components) Variables Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 NEC 0.90 ARMDC 0.90 ACC 0.89 AAPC2 0.86 AAPC1 0.85 AFC −0.79 LSC 0.63 MLC AWTC 0.84 DLPP 0.82 MAAC 0.65 WWHC 0.53 TCDCP 0.53 PFC 0.86 DDCRM 0.75 PSRMC 0.62 PRM 0.90 RMCC 0.86 AEEFE −0.83 AEEFR 0.51 PTC PPC 0.91 HSRMD 0.79 ELCO PFRM 0.79 PFPRM −0.62 % of variance 24.02 10.97 8.93 8.63 8.10 5.72 5.72 5.57 Note. *Significant at the 0.05 level (p < 0.05); *Significant at the 0.01 level (p < 0.01).

scientifically facilitate identification, the factor loadings greater than 0.5 absolute value were taken into account (Bennet & Bowers, 1977;Daşdemir, 1996) and the factor loadings less than 0.5 were removed from Table 4. Factor 1, as shown in Tables 3 and 4, is the most important factor and explains more than 26% of total variance. It consists of the NEC, ARMDC, ACC, AAPC2, AAPC1, and LSC variables with significantly positive factor loadings, and the AFC variable with a significantly negative factor loading. These variables are related to the amount of raw material demand, the number of employees, the amount of capital and production, profitability, legal structure, and the activity form of company. Upon examining the 18 S. KARAKAYA ET AL. common features of these variables, the factor becomes apparent that it describes the capacity, scale, and/or size of a company. Therefore, this factor was interpreted as the “scale” or “size” of the company. Factor 2 consists of the AWTC, DLPP, MAAC, WWHC, and TCDCP variables all with positive factor loadings. A high demand level for the poplar products of the company indicates that the annual and weekly operating time of the company should be higher and the company’ scale should be larger in order to meet the demand. Namely, the annual operating time and the company’s size are a function of demand (derived demand) for the products of the company, and thus Factor 2 was identified as the “demand level for products” of the company. Factor 3 consists of the PFC, DDCRM, and PSRMC variables all with positive factor loadings. The production form (series, order, or both) of the company, the sources of raw material, and the demand distance of the company impact the structure of the demand of the company. In a serial production form, the company provides raw material from long distances to meet its need. For these reasons, Factor 3 was identified as the “production form of company”. Factor 4 consists of the PRM and RMCC variables both having positive factor loadings. These variables are related to the price of poplar wood raw material and its cost for the company. These are the most important variables affecting the demand level of raw materials. Therefore, this factor was interpreted as the “price of raw material”. Factor 5 consisted of the AEEFE variable with a negative factor loading and the AEEFR variable with a positive factor loading. The properties of the energy and especially the raw ff materials in the external environment in which the company is located a ect the structure and characteristics of raw materials of the company. Therefore, this factor was identified as the “external environmental conditions”. In fact, Aytin (2006) also found that that a similar factor accounted for 21% of the variance in describing production variables (and similarly for the raw material procurement). Factor 6 is composed of only the PPC variable. Because the preferred tree species and clones affect the monetary value paid for raw material, the costs, and the profitability of company, it also affects the structure of demand. Because the unit volume weight of the Samsun clone is higher than that of the I-214 clone, its price is higher and it is less preferred. Therefore, this factor was referred to as the “raw material type and features”. Factor 7, consisting of the HSRMD variable, shows that raw material demand of company increases according to the seasons and generally rise in winter months. Therefore, this factor was identified as the “seasons”. Factor 8 consists of the PFRM and PFPRM variables having respectively positive and negative factor loadings. According to this result, purchasing preference (standing or delivered to business) of raw material and payment form (cash or futures) have an impact on the demand structure of a company. The delivery of raw materials to a business, and the company’s lack of deferred payment, facilitates an increase in the amount of company demand for raw materials. Hence, this factor was identified as the “procurement conditions for raw material”. Based on the explications of the factor analysis, the most important factors aff ecting the raw material demand structure of poplar wood-processing companies in the Sakarya and Kocaeli provinces, as well as the variables representing these factors with their weights, are JOURNAL OF SUSTAINABLE FORESTRY 19

Table 5. Factors affecting demand structure of raw material of companies, their indicators and weights. Factor Weight of factor Indicator variable of Weight of no. Name of factor (%) factor variable 1 Scale or size of company 24.02 NEC 0.90 2 Demand level for products of company 10.97 AWTC 0.84 3 Production form of company 8.93 PFC 0.86 4 Price of raw material 8.63 PRM 0.90 5 External environmental conditions 8.10 AEEFE −0.83 6 Raw material type and features 5.72 PPC 0.91 7 Seasons 5.72 HSRMD 0.79 8 Procurement conditions for raw 5.57 PFRM 0.79 material Total 77.65 given in Table 5. According to the results of factor analysis, approximately 78% of the variability in demand structure of raw material of companies depends on these first eight factors having financial, economic, technological, physical, environmental, social, and resource-use features. These factors, which affect the demand structure and determine its characteristics, indirectly affect both the wood production and the success of poplar growers (Karakaya, 2010) as well as the wood sale prices of state forest enterprises supplying wood raw materials (Daşdemir & Lise, 2007).

Explaining variation in raw material demand of companies In order to explain the variation in raw material demand of poplar wood-processing companies, the ARMDC was considered a dependent variable and the other variables were

Table 6. Results of multiple regression analysis for explaining variation in raw material demand. Dependent variable Independent variables Coefficients Std. error t Sig. R2,F,R2 ARMDC (Constant) −784.62 1394.02 −0.56 0.58 R2 = 0,995 ELCO 63.58 75.18 0.85 0.41 F = 235,06**R2 ¼ 0:99 MAAC* 269.96 112.46 2.40 0.02 LSC −260.50 152.20 −1.71 0.10 AFC −44.17 295.61 −0.15 0.88 PFC 259.63 404.11 0.64 0.53 MLC −15.78 155.69 −0.10 0.92 AEEFE −194.58 168.56 −1.15 0.26 AEEFR −22.63 88.15 −0.26 0.80 AWTC −0.33 99.28 0.00 1.00 DLPP −107.16 112.32 −0.95 0.35 PPC −4.12 80.06 −0.05 0.96 PSRMC −68.08 51.33 −1.33 0.20 PFRM 45.26 84.64 0.53 0.60 PFPRM 78.07 74.11 1.05 0.30 HSRMD −21.83 33.68 −0.65 0.52 PTC 7.82 69.71 0.11 0.91 TCDCP −104.00 73.32 −1.42 0.17 NEC 6.31 26.76 0.24 0.82 ACC* 0.00 0.00 2.10 0.05 WWHC 17.31 102.65 0.17 0.87 AAPC1** 1.04 0.06 17.59 0.00 AAPC2** 0.01 0.00 3.23 0.00 DDCRM 0.04 0.93 0.05 0.96 PRM* 20.14 9.90 2.04 0.05 RMCC −7.55 7.35 −1.03 0.31 Note. *Significant at the 0.05 level (p < 0.05); **Significant at the 0.01 level (p < 0.01). 20 S. KARAKAYA ET AL. considered independent variables. The effects of the independent variables on the ARMDC were examined using multiple regression analysis (Table 6). The success level of the regression model is 95.5% (R2 = 0.995). Therefore, 99.5% of the variation in the demand depends on the independent variables used in the regression model. However, not all of them have the same level of significance. Namely, while the MAAC, ACC, and PRM variables achieve significance with an alpha of 0.05, while the AAPC1 and AAPC2 variables achieve significance with an alpha of 0.01. According to these results, as the company’s scale grows and the amount of capital, production, and profit of the company also increase, the price of raw material decreases alongside the amount of raw material demand, while the company scale (actual capacity) increases.

Conclusions and recommendations Some recommendations and general conclusions of the evaluations made in the scope of this study conducted on poplar wood-processing companies are provided below: All company owners consist of active population at working age. Sixty-six percent of them graduated from primary school. The owners of “small” and “medium” sized companies graduated from primary and secondary school, while the owners of “very large” sized companies graduated from university. 75% of employees in poplar wood-processing companies are elementary (primary and secondary) school graduates. Therefore, the poplar wood sector is an important area for unskilled labor. The majority (77%) of company ownership is by individuals, and a very small portion “ ” (23%) of them is managed in a company structure. Often they follow a traditional family business structure where ownership is transferred from father to son. The main activity of the companies (73%) is chest and lumber production, these companies generally being “small” and “medium” sized enterprises (SMEs) in the form of workshops. Therefore, the problems (ﬁnding credit, marketing, the use of advanced technology, etc.) in SMEs and the precautions to be taken for SMEs are also valid for poplar wood-processing companies. The majority of companies prefer to the I-214 poplar clone as wood raw material due to its wood being lighter and easier to process. Therefore, the State should encourage poplar producers to plant with the I-214 poplar clone. Companies most frequently (83%) procure wood raw material from a 0–100 km distance. Thus, increasing poplar wood production in a 0–100 km radius area is important. Sixty-eight percent of the companies market their products at the regional level, and 24% of them at the national (Turkey) level. In this context, the companies must have an organized structure to carry out regional marketing analyses and the establishment of distribution systems. Focus should be given to poplar plantations in suitable ecosystems and habitats within regions where demand for wood raw material is high. Considering the demand and production form, production from orders should be improved and productions for customer satisfaction should be given importance. Due to the high demand for poplar raw material in spring and summer months, raw material procurement planning should be carried out well in these seasons. JOURNAL OF SUSTAINABLE FORESTRY 21

Due to companies being frequently structured in the form of “small”, “medium”,or “large” scale companies, these company sizes should be given importance. Demand for the products of “large” scale companies is high, while demand for the products of “very small”, “small”, “medium”, and “large” scale companies is low-medium. A company’s main activity area, legal structure, activity form, market level, annual working time, number of employees, and weekly working hours are significantly different according to the company scale or demand level for raw material. The more the company size increases, the more the weekly and annual operating time and number of employees increases. In general, “large” and “very large” companies operate over a period of 10–12 months while “very small”, “small”, and “medium” sized businesses operate for less than 9 months in year. According to the annual amount of raw materials processed, 55% of companies are “very small” and “small” sized, operating with an average of 57% idle capacity, which indicates that the poplar plantations in Turkey are inadequate to meet the needs of the poplar sector. Therefore, further research on poplar cultivation should be carried out on suitable sites in Turkey. Approximately 78% of the structure of raw material demand for poplar wood companies in the Sakarya and Kocaeli provinces depends on the scale of the company, the demand level for the company’s products, the production form of company, the price of raw material, the external environmental conditions, the seasons, and the features and procurement conditions of raw material. These factors are related to financial, economical, technological, physical, environmental, and social situations as well as the use of resources. Taking into account these factors in management, planning, production, and marketing activities will ensure an efficient and effective functioning of poplar wood-processing industrial companies.

Variations in the amount of raw material demand of poplar wood-processing companies are mostly explained by the company ’s main activity area, the level of operating capital, production form, and the level of profit. Accordingly, the profitability and productivity of the companies on a “large” scale often result in capital and production quantities that are usually higher than other company scales. To close the wood supply deficit and meet the raw material needs of poplar wood-processing companies at an appropriate time scale, quantity, and price in Turkey, there is a need to establish plantations with fast growing species in short rotations on productive sites within the forestry sector. For this purpose, the poplar industry should be developed as it supplies inputs to many other sectors, including packaging, paper, fiber-chips, and furniture industries. To ensure a positive future for the poplar sector and to solve its raw material problems, the GDF should include poplar in its plantation program, and these plantations must be implemented in a strategic way throughout the country. Also, the GDF must produce policies that support and develop private poplar plantations. Training seminars should be organized in addition to visual publications on national and local TV channels to improve the poplar producers and industrialists’ knowledge and experiences.

Funding

This study was funded by the IZT-379 (5309)/2011-2015) project entitled “Demand Forecast and Socioeconomic Structure in Firms Using Poplar Wood in the Sakarya and Kocaeli Provinces” supported by the GDF in Turkey. Hence, we would like to thank the managers and employees of the GDF. 22 S. KARAKAYA ET AL.

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