A Preliminary Model for Forestry Machinery Chain Selection and Calculation of Operating Costs for Wood Recovery ^†

Abstract

Selecting the most suitable machines to use for wood recovery is essential for computing the operating costs of the whole forestry machinery chain (FMC). Nevertheless, a generalized approach for selecting the most suitable FMC and quantifying the corresponding economic performances for wood recovery (i.e., harvesting and long-distance transport) is still missing. The primary aim of this study is to describe a decision support model, called FOREstry MAchinery chain selection (“FOREMA v1”), which is able to (i) select the most feasible FMC and (ii) calculate the costs (such as EUR∙h⁻¹; EUR∙t⁻¹ of dry matter, DM) of each operation (OP) comprising the FMC. The model is made up of three different modules (Ms): machinery chain selection (M1), machinery chain organization (M2), and cost calculation (M3). In M1, feasible FMCs are defined according to seven technical parameters that characterize the forest area. For each FMC, FOREMA v1 defines the sequence of OPs and the types of machines that can potentially be used. Once the characteristics of the area in which wood recovery occurs are processed, the user selects the types of machines to use according to the model’s suggestions. In M2 and M3, the user is supported in organizing the FMC (e.g., calculation of the required time, working productivity, and so on) and computing the operating costs. The secondary aim of this study is to discuss a case study focused on chips production for energy generation, providing empirical evidence on how FOREMA v1 works. The proposed model provides a systematic approach for the selection and optimization of the most suitable FMC to adopt for biomass recovery, thus supporting decision-making processes. The results showed that felling had the lowest cost per unit of time (63.7 EUR·h⁻¹) but the highest cost per unit of mass (35.4 EUR·t DM⁻¹) due to its longer working time and lower productivity. Loading and long-distance transport incurred the highest costs both per unit of time (223.5 EUR·h⁻¹) and per unit of mass (29.4 EUR·t DM⁻¹), attributed to the use of medium–small-sized trailers coupled with tractors operating at low speeds, leading to a high number of cycles. For the entire FMC the costs were equal to 147.3 EUR·h⁻¹ and 101.1 EUR·t DM⁻¹.

1. Introduction

Wood recovery refers to cutting and delivering trees in a safe, economical, and ecological way, through which standing trees are converted into merchantable assortments according to specific industrial or individual needs [1,2]. Wood recovery consists of several operations (OPs): (i) felling, (ii) delimbing, (iii) sectioning, (iv) extraction, (v) stacking, and (vi) loading and long-distance to the final users. If logging residues (e.g., branches, tops, stumps, small stems, and non-commercial wood parts that fall on the forest floor during felling) are comminuted into chips for energy generation, loading and long-distance transport is preceded by chipping [1,2].

For each OP, different pieces of equipment and implements can be used, ranging from manual to fully mechanized ones. Moreover, the sequence of OPs may vary according to the work organization and the characteristics of the supply chain. Machinery can successfully operate under a wide range of conditions, and the same OP can be performed using different types of machines [1,2]. For example, compared to a chainsaw, a harvester or feller buncher can increase work safety and reduce physical stress on the operators, as well as stand damage. However, negative environmental impacts can occur due to soil compaction. All such elements should be considered when evaluating the types of machines for use in silvicultural interventions [3].

In many cases, the selection of suitable machines is still based on the experience of forest workers and technicians; this can be good for short-term forest planning decisions but can lead to failures if changes in the long-term occur, such as variation in the harvested wood volume, the development of new machines and technologies, and road-network improvement [4]. Environmental, economic, and social parameters should also be considered, including (i) forest characteristics, (ii) the characteristics of roads, (iii) the extraction distance and soil conditions [5,6], (iv) the harvesting method and degree of mechanization [7], and (v) climate [8].

According to [2], the main parameters that affect the selection of machines in the short-term are (i) fuel cost, (ii) employee training, and (iii) machine productivity, whereas, in the long-term, the main parameters are (i) the degree of mechanization, (ii) supply-chain organization, (iii) forest road conditions, and (iv) wood assortment. The authors of [9] stated that the main parameters affecting wood recovery are (i) tree species, (ii) forest structure, (iii) wood quality (e.g., a stem of regular shape and without knots), (iv) tree volume, and (v) wood assortment. For ground-based harvesting systems, the parameters affecting the choice of machine can be classified into internal and external [7]. The former—which are under the control of the logging company—are mainly related to (i) machine energy consumption and (ii) operator training and skills, whereas the latter—which are not under control—include (i) government policies regarding taxation, (ii) existing infrastructure, and (iii) capital availability and interest rates. According to a robust literature review, the authors of [10] identified six key parameters that significantly influence the choice and performance of wood extraction: terrain slope, distance, soil bearing capacity, road density, terrain roughness, and extractable timber amount. These parameters not only affect operational productivity and economic viability but also have important environmental and social implications. According to that, the authors of [10] presented a first attempt of integration, within a Forest Management Plan (FMP), of a Geographic Information System (GIS) and an Analytic Hierarchy Process (AHP) approach for the selection of the most suitable harvesting chain, using a forest area of Central Italy as a case study.

Terrain slope and roughness directly impact on machine mobility, work safety, and soil disturbance, particularly in mountain environments. Similarly, extraction distance and road density influence not only productivity but also the degree of site disruption and operator safety. Soil bearing capacity is a critical constraint for ground-based systems, with low bearing capacity increasing the risk of soil compaction and operational hazards. Lastly, the extractable timber amount affects the cost-efficiency of interventions, with higher volumes generally improving system performance.

One of the main challenges for the EU forestry sector is increasing access to wood resources by making forestry OPs more productive and efficient [3], as they considerably affect the forest wood value chain [7]. For this purpose, a significant step is the selection of the most suitable machines to use. When developing a forest-wood-supply plan, decision support systems (DSSs) can be of practical assistance. The authors of [11] investigated the use of multicriteria decision support and GIS in choosing the optimal harvesting chain for predominantly selection-cutting forest management on the example of two Forest Management Units (FMU) in Northwest Bosnia and Herzegovina. The authors of [12] proposed a DSS to identify the best machines to use according to the ground slope, which involved computing the operating costs. The authors of [13] performed geographic information system (GIS) analysis considering three types of machines and wood biomass types to calculate the lowest costs for wood harvesting. The authors of [4] developed a GIS-based model to define the most suitable machine to use under different environmental conditions and stakeholder interests.

The authors of [14] evaluated the impact of forest-harvest intensity and transportation distance on biomass delivered costs within sustainable forest management in southeastern Canada. In Italy, the authors of [15] applied GIS-Based Software to improve the sustainability of a forwarding operation, whereas the authors of [16] investigated the application of two approaches using GIS technology implementation in forest-road-network planning in a mountain setting. The authors of [17] investigated the use of GIS for the multi-criteria evaluation of the environmentally friendly use of skidding technologies. The authors of [18] performed a literature review over the last two decades investigating the use of GIS applications in forest operations and road-network planning.

Building on these GIS-based approaches and technological advancements, several models have been developed to support decision-making in forest operations, addressing different aspects of forestry mechanization through a variety of digital solutions, ranging from an Microsoft-Office-Excel-based spreadsheet, Windows-based, and web-based tools.

For the Microsoft-Office-Excel-based spreadsheet, the “COST” model [19] is a good example. This model is relatively straightforward to use, and it gives a simultaneous view of the input parameters and resulting cost outputs for a given operation. On the other hand, no machine-selection algorithms are implemented, and the costs can be computed operation by operation; the model does not include a section/module for calculating the productivity based on working times and does not provide a holistic view of the selected machinery chain and its operational parameters.

Windows-based applications include “HeProMo” by the authors of [20], and “ECOCOST” by the authors of [21], whereas web-based solutions include “WoodChainManager” by the authors of [22].

The HeProMo allows for a high degree of flexibility by enabling the user to define the types of machinery to be used in the operation. This user-driven approach makes the model adaptable to various scenarios based on the specific machinery available. While the model offers flexibility, it requires the user to have significant technical knowledge to input the correct parameters. Moreover, the model does not automatically suggest the most suitable machinery based on the operational context, which can be a drawback for less experienced users.

The ECOCOST model is particularly useful for sustainability analysis and for assessing the broader economic performance of forestry operations. Like HeProMo, The ECOCOST model does not explicitly address the selection of the appropriate machinery chain. As a result, it may not provide a comprehensive solution for users looking to integrate machinery selection and cost analysis in a unique framework. Moreover, in ECOCOST, the working productivity is estimated but limited to the harvester and forwarder.

The Web-based tool WoodChainManager is made up of three modules intended for the assessment of the material costs of individual machines or the total costs of all selected machines in a forest-harvesting system. Users can test the impact of individual technologies on the total material costs of the harvesting system and can thus optimize operation processes. The tool has built-in algorithms which prevent the selection of an illogical harvesting system. The selected method for calculating costs for individual machines is simple but still reflects the state of the actual incurred costs. WoodChainManager offers cost calculations for a wide range of technologies, machines, and appurtenant attachments. The disadvantage is that the model still relies on the user to define which operations are carried out, assuming the user has prior knowledge of the machinery chain.

A more generalized model should simplify the selection process by automatically suggesting the necessary operations based on the type of product required by the user (e.g., beams/poles, firewood, or chips for energy production).

Considering all these factors, a generalized approach to selecting the most suitable FMC and quantifying the corresponding economic performances for wood recovery still seems to be missing. The primary aim of this study is to describe a decision support model, called FOREstry MAchinery chain selection (“FOREMA v1”), which supports the user in (i) selecting the most feasible FMC and (ii) calculating the operating costs (such as EUR∙h⁻¹ and EUR∙t⁻¹ of dry matter DM) associated with each OP composing the FMC.

The model is composed of three interconnected modules, and their logical structure and functioning are described in detail in Section 2. To make the functioning of the model and the obtainable results easier to understand, the secondary aim of the work is to present a case study. The plan was to carry out a silvicultural intervention in a coppice forest area, applying the fulltree harvesting method aimed at chips production for energy generation. The analyzed situation is hypothetical but, at the same time, one of the situations most representative of the Italian Alpine area. According to the analyzed situation, the OP sequence, as well as the type of machine that could potentially be used, is defined. Therefore, according to the selected machine models, the costs of each OP are computed. Finally, in the next section, a general discussion of the work is presented, highlighting the strengths and weaknesses of the proposed approach as well as the advantages deriving from the potential application of FOREMA v1 by forestry consortia and logging companies.

2. Materials and Methods

FOREMA v1 is an MS-Office-Excel-based model comprising three modules (Ms):

• M1—machinery chain selection;
• M2—machinery chain organization;
• M3—operating cost calculation.

2.1. M1—Machinery Chain Selection

M1 consists of two spreadsheets: (i) database (definition of forestry machinery chain) and (ii) user-friendly interface (Figure 1).

The database represents the supporting logic for the User-Friendly Interface and is made up of an algorithm that defines the temporal sequence of the Ops for a generic FMC and, for each of them, the types of machines that can potentially be used. Specifically, the database is made up of three steps:

• Step 1—definition of classification code;
• Step 2—definition of sequence of operations;
• Step 3—definition of types of usable machines and qualitative levels.

According to the suggestions of the database, the user selects the type of machine to use for each OP in the User-Friendly Interface.

2.1.1. Database “Definition of Forestry Machinery Chain”

Step 1—Definition of the Classification Code

The FMCs are identified by combining different categories that characterize seven technical parameters. These are, in turn, aggregated into three groups of limiting factors:

• Characteristics of the forest;
• Characteristics of the production system;
• Site-specific operating conditions.

The parameter related to “characteristics of the forest” is the management system. The parameters related to “characteristics of the production system” are as follows:

• Wood assortment;
• Harvesting method;
• Degree of mechanization;

Finally, the parameters related to “site-specific operating conditions” are as follows:

• Forest road transitability class;
• Forest accessibility class;
• Harvested mass (t∙ha⁻¹ DM).

Each parameter is sub-divided into different categories, each of which is associated with an alphanumeric code.

Parameter 1 (management system) is classified into two categories—(i) coppice and (ii) high forest—according to the method of tree regeneration.

The categories that characterize parameter 2 (wood assortment) are (i) firewood, (ii) beams/poles, and (iii) chips.

Parameter 3 (harvesting method) defines the form through which wood is delivered from the forest to the roadside, and its production depends on the assortment [7,11]. The categories that define this parameter are (i) cut-to-length (CTL), (ii) tree length (TL), and (iii) fulltree (FT). In the CTL method, felling, delimbing, debarking (if required), and sectioning into pre-determined lengths are performed inside the forest. The stem is sectioned into 1–7 m lengths, according to the final utilization of the material. This method generally leads to the incomplete exploitation of residues, as only the tops can be effectively recovered, while the branches generally remain at the felling site [2]. When the mass of the residues is sufficiently high to justify the increase in working time and production factors, an additional step can be carried out for branch removal; nevertheless, this step is quite complex and not always economically sustainable, especially in Alpine areas [23]. In the TL method, the felled tree is delimbed inside the forest and the stem is delivered to the roadside with a length equal to or greater than 7 m. Similar to the previous method, logging residues generally remain at the felling site or are extracted later [2].

In the FT method, both the stem and branches are delivered to the roadside, intermediate log yards, or landing zones, where they are further delimbed and processed. Only roots and stumps remain at the felling site. This harvesting method is generally applied when the forest floor must be cleared of all the residues, or when a high mass of material is available for energy-conversion processes [2].

Parameter 4 (degree of mechanization) essentially expresses the technological level of the machines and it is strictly linked to the productivity of the machines: as the technological level increases, the productivity increases as well. At the same time, as the technological level of the machines increases, the capital investments for purchasing and managing the machines also increase and the incidence of labor costs regarding the amount of processed wood decreases. The technological level is generally classified as follows [7]: (i) basic, (ii) intermediate, or (iii) fully mechanized. At the basic level, felling, delimbing, and sectioning are performed with chainsaws, whereas bunching, extraction, and long-distance transport are generally carried out using agricultural tractors of medium–low engine power, equipped with cages or winches. At the intermediate level, machines specifically designed for forestry works are used, such as (i) cable cranes; (ii) forestry tractors of medium–high engine power equipped with front or rear loaders and coupled with winches, forestry trailers, or chippers; and (iii) processors. Finally, the fully mechanized level is based on highly specialized (usually self-propelled) machines such as harvesters and forwarders. Starting from this classification, in FOREMA v1, this parameter is classified into two categories: (i) low and (ii) medium–high (combinations between intermediate and fully mechanized).

Parameter 5 (forest road transitability class) expresses the characteristics of forest roads and is essential for choosing both the type and dimensions of the long-distance transport machine. FOREMA is based on the transitability class (TC) classification introduced by the authors of [24], which has been widely applied in the Italian Alpine areas (

Table 1. Transitability classes according to [24]. Modified from [25].

TC	Types of Machines	Max Load lmax (t)	Min Width wmin (m)	Prevailing Slope sp (%) a	Max Slope smax (%)	Min Turning Radius tr (m)
I	Truck	25	3.5	≤10	12 (16) b	9
II	Tractors and trailers	20	2.5	≤12	14 (20)	8
III	Small tractors	10	2.0	≤14	16 (25)	6
IV	Small vehicles	4	1.8	>14	>16 (>25)	<6

^a Not overcome for at least 75%–80% along the whole road; ^b the first value refers to natural bottom, whereas the second value refers to stabilized bottom.

).

Starting from this classification, the same transitability classes as reported by the authors of [25,26] are used in FOREMA v1: (i) medium–low (combination between classes III and IV), and (ii) medium–high (combination between classes I and II).

Parameter 6 (forest-area accessibility class) expresses the ease of reaching each area. FOREMA v1 is based on the classification introduced by the authors of [27] and applied by the authors of [25,26] (

Table 2. Area accessibility. Striped backgrounds: insufficient accessibility. Modified from [25].

).

Starting from this classification, the same classes as reported by the authors of [25,26] were defined in FOREMA v1: (i) low, (ii) medium, and (iii) high. The accessibility class “insufficient” was not considered, as it was assumed that a machine is not used under this condition due to both technical and economic limitations.

Finally, for parameter 7 (harvested mass), two categories were defined: (i) ≤15 t∙ha⁻¹ DM and (ii) >15 t∙ha⁻¹ DM [28].

Table 3. Limiting factors, technical parameters, categories, and corresponding sub-codes. Legend: DM, dry matter.

Limiting Factor		Technical Parameter
No.	Name	No.	Name	Category	Sub-Code
1	Characteristics of the forest	1	Management system	Coppice	F1
				High forest	F2
2	Characteristics of the production system	2	Wood assortment	Firewood	A1
				Beams/poles	A2
				Chips	A3
		3	Harvesting method	Cut-to-length	M1
				Tree length	M2
				Fulltree	M3
		4	Degree of mechanization	Low	L1
				Medium–high	L2
3	Site-specific operating conditions	5	Forest road transitability class	Medium–high	T1
				Medium–low	T2
		6	Forest area accessibility class	Maximum (AC I)	AC1
				Medium–high (AC II)	AC2
				Low (AC III)	AC3
		7	Harvested mass	≤15 t∙ha−1 DM	H1
				>15 t∙ha−1 DM	H2

details the (i) limiting factors, (ii) technical parameters, (iii) categories that compose each parameter, and (iv) the corresponding sub-codes.

The above-described technical parameters, with their corresponding categories and sub-codes, are arranged in a hierarchical tree structure: starting from parameter 1, each category of each parameter includes all the categories of the subsequent ones, forming a cascading structure.

Combining the categories across all the parameters, 432 combinations were obtained. These combinations represent the total number of FMCs that can be theoretically applied under the assumption that all combinations among all categories are technically possible.

However, within this expansive theoretical space, only 44.4% (192 combinations) of FMCs are deemed practically applicable. For these feasible combinations, FOREMA v1 generates a unique classification code (CC) by combining the sub-codes associated with each category, thereby encapsulating the essential characteristics of each feasible FMC. Each CC represents a feasible FMC providing a streamlined and informative way to categorize and understand the diverse array of feasible operational configurations within the forestry mechanization sector.

For example, when combining the (i) management system → “Coppice” → F1, (ii) wood assortment → “Firewood” → A1, (iii) harvesting method → “Cut-to-Length” → M1, (iv) degree of mechanization → “Medium-high” → L2, (v) forest road transitability class → “Medium-low” → T1, (vi) forest area accessibility class → “High” → AC1, and (vii) harvested mass → “≤15 t∙ha⁻¹ DM” → H1, the generated CC is “F1A1M1L2T1AC1H1”.

On the contrary, when combining the (i) management system → “Coppice” → F1, (ii) wood assortment → “Chips” → A3, (iii) harvesting method → “Cut-to-Length” → M1, (iv) degree of mechanization → “Medium-high” → L2, (v) forest road transitability class → “Medium-high” → T1, (vi) forest area accessibility → “High” → AC1, and (vii) harvested mass → “≤15 t∙ha⁻¹ DM” → H1, the CC is not generated, as it is assumed that that the combination between the categories “Chips” and “Cut-to-length” is not possible.

Step 2—Definition of Sequence of Operations

In the following step, according to the harvesting method, FOREMA v1 defines the temporal sequence of the OPs by which the FMC is organized. For example, for the previously defined CC (F1A1M1L2T1AC1H1), the sequence of OPs is as follows: (i) felling, (ii) delimbing, (iii) sectioning, (iv) bunching, (v) extraction, and (vi) loading and long-distance transport. Overall, FOREMA v1 is based on seven different OPs (

Table 4. OP sequence according to the harvesting method, with reference to the Italian Alpine area.

OP	Harvesting Method
	Cut-to-Length	Tree Length	Fulltree
Felling	First	First	First
Delimbing	Second	Second	Fourth
Sectioning	Third	Fifth	Fifth
Bunching	Fourth	Third	Second
Extraction	Fifth	Fourth	Third
Chipping	-	-	Sixth
Loading and long-distance transport	Sixth	Sixth	Seventh

).

Step 3—Definition of Types of Usable Machines and Qualitative Levels

In this step, for each OP, the types of machines that can potentially be used are defined. To further support the user in the selection process, each type of machine is also classified at a qualitative level, as defined by an empirical value (low → −1, medium → 0, and high → 1; graphically corresponding to the colors red, yellow, and green, respectively) which specifies whether the use of that type of machine is recommended/not recommended and, if its use is recommended, expresses the ease of use of the specific type of machine under the defined site-specific operating conditions.

The concept of ease-of-use is essentially linked to the concepts of maneuverability and handling: if the maneuverability and handling are high (i.e., the machine can perform the work and move across the workplace under safe conditions), the ease-of-use is high; consequently, the empirical qualitative level is high, and vice versa.

Table 5. Types of forestry machines included in FOREMA v1 that can potentially be used. Legend: TR, tractor.

Machine Types
N.	Type	N.	Type	N.	Type
1	Chainsaw	8	Cable crane mds a	17	Tractor with cages
2	Feller buncher	9	Grapple skidder	18	Grapple tractor
3	Feller skidder	10	Skidder with winch	19	Tractor with dumper
4	Harvester 4-wheel drive	11	Traditional cable crane	20	Forwarder
5	Crawler harvester	12	Forvester	21	Lorry
6	Processor	15	Helicopter	22	Chipper
7	Tractor with winch	16	Tractor with trailer	23	Road train

^a Mobile drive station.

shows the types of machines that are included in the model and which can potentially be used.

Figure 2 reports a simplified scheme of the relationships between Steps 1 and 3 in M1.

2.1.2. User-Friendly Interface

This interface consists of the following steps:

• Step 1—selection of category for each parameter;
• Step 2—selection of types of usable machines and qualitative levels;
• Step 3—selected types of usable machines.

Step 1—Selection of Category for Each Parameter

In Step 1, the user defines the category for each technical parameter. Through the combination of sub-codes associated with each category, FOREMA v1 generates the corresponding CC, which is then searched for within the CC list in the database “Definition of forestry machinery chain” of the first spreadsheet.

If the generated CC does not exist in the database (i.e., the categories entered by the user do not correspond to a real FMC), an error message invites the user to change the previously selected categories. Otherwise, through a selection algorithm, FOREMA v1 defines the exact sequence of the OPs and the types of machines that can potentially be used, along with the corresponding qualitative levels.

Step 2—Selection of Types of Usable Machines and Qualitative Levels

In Step 2, the user can visualize the sequence of the OPs that comprise the FMC and the list of usable machine types. For each OP, the user can select only one machine type.

Step 3—Selected Types of Usable Machines

Step 3 represents the final output of M1, which shows the sequence of the OPs that compose the selected FMC and, for each of them, the selected types of usable machines with the corresponding qualitative levels. By clicking on the “reset” button, the sequence of OPs and the corresponding machine types are deleted, and the user can make a new selection. The user can select any type of machine among those proposed by the model, even a type characterized by a low qualitative level, as the selection is based on real machine availability at the logging company. The logical framework of the User-Friendly-Interface spreadsheet is shown in Figure 3.

2.2. M2—Machinery Chain Organization

In Module 2, the user must enter data related to the area under analysis and the OP previously defined. For the area, the following data are required:

• Cut area (ha);
• Wood species;
• Harvested wood volume yield (m³·ha⁻¹).

For each OP, the following data are required:

• Useful time (days);
• Number of working hours per employee per shift (h·employee⁻¹·shift⁻¹);
• Number of shifts per day (shifts·day⁻¹);
• Number of employees (1 machine/implement).

For felling, delimbing, and sectioning and chipping, the value of working productivity (expressed as m³·h⁻¹) related to a single machine/implement is also required as additional input data. For bunching, extraction, and loading and long-distance transport, the productivity value is calculated. For bunching and extraction, the following are calculated:

• Distance (m);
• Operator speed (m·s⁻¹);
• Rope winding speed (m·s⁻¹);
• Extraction distance (m);
• Machine speed (km·h⁻¹), forward (loaded) and backward (unloaded);
• Extracted wood volume per cycle (m³·cycle⁻¹);
• Required working time (1 machine/implement; h).

For loading and long-distance transport, the following are calculated:

• Distance (km);
• Machine speed (km·h⁻¹), forward (loaded) and backward (unloaded);
• Loaded wood volume per cycle (m³·cycle⁻¹);
• Volumetric filling coefficient (ratio between the volume occupied by the wood material and the useful loading volume of the implement used for long-distance transport; dimensionless);
• Required working time (1 machine/implement; h).

For both bunching and extraction, as well as loading and long-distance transport, the required working time is computed according to the methodology of the Comité International d’Organisation Scientifique du Travail en Agricolture (CIOSTA) [29]. This methodology is widely used for on-field agricultural operations and, for this work, was specifically adapted for forestry operations. According to this methodology, each OP is subdivided into different phases, each of which corresponds to a specific working time. The required working time is calculated as the sum of the times corresponding to each phase.

Before proceeding with the productivity calculation, the volume values (e.g., volume of wood to be cut) are converted into dry mass values using the specific wood basic density value (t·m⁻³ DM) [30]. Wood basic density expresses the ratio between the dry mass of wood (at 0% moisture content) and its fresh volume (with moisture content typically ≥30% on a wet basis). This value is expressed in terms of t DM·m⁻³ (tons of dry matter per cubic meter of fresh matter) [30].

The dry mass (t DM) is calculated by multiplying the volume of the wood (measured in cubic meters of fresh matter, or the value of the machine’s productivity, expressed as m³·h⁻¹) by the wood basic density value previously defined.

This conversion is particularly useful in contexts where the goal is to quantify the biomass for energy production, as in the case study presented in this work. In such cases, it is important to know the real amount of dry mass available for energy production, as the energy content of wood is largely determined by its dry mass. Moisture content significantly influences the calorific value of wood, so converting the fresh volume to dry mass provides a more accurate estimation of the energy potential (i.e., excluding the water content). This is important for making informed decisions about energy-resource management, especially for bioenergy applications [31].

For each OP, the output data defining the organization and mechanical performances of the FMC used for operating cost quantification are as follows:

• Useful time per employee (h·employee⁻¹);
• Required working times (1 machine/implement; h);
• No. of required machines and/or implements (−);
• Productivity (total, related to “n” machines and/or implements; t·h⁻¹ DM);
• No. of employees (total, related to “n” machines and/or implements; −);
• Required working time (total, related to “n” machines and/or implements working simultaneously; h).

2.3. M3—Operating Costs

For each OP, the total cost is computed as the sum of (i) fixed costs (FCs) and (ii) variable costs (VCs), expressed as follows: (i) per unit of time (EUR∙h⁻¹) and (ii) per unit of mass (EUR∙t⁻¹ DM).

FCs (i.e., financial depreciation and overheads) are computed separately for any machine or implement, by summing up the financial depreciation (EUR·y⁻¹) and overheads (EUR·y⁻¹), dividing the sum by the annual machine use (h·y⁻¹). Financial depreciation is calculated according to [32], using a salvage value (EUR) computed according to [33]. Overheads include insurance and taxes, supervision, management, administration, and garaging, and are quantified as a fraction of the purchase price (EUR) [33].

VCs include fuel, oil and lubricant, maintenance and repair, and labor. If tractors and implements are used, these costs are quantified as if they were related to the tractor–implement system, except for maintenance and repair, which is computed separately and added afterward. Fuel, oil, and lubricant costs (EUR·h⁻¹) are quantified according to the authors of [33]; these costs depend on the market prices—which must be defined as an input parameter by the user—and consumption (kg·h⁻¹). Fuel consumption is computed according to the following mechanical parameters: (i) the machine maximum engine power (kW), (ii) the machine specific fuel consumption (kg·kWh⁻¹) and (iii) the machine engine load (i.e., the ratio between the required engine power and maximum engine power, which is dimensionless). The load varies during the OP phases, as the required engine power varies according to the specific task that needs to be accomplished. Oil and lubricant (i.e., engine, transmission and hydraulic oil, grease, and filters) consumption is quantified according to the maximum engine power [33].

The repair and maintenance costs (EUR·h⁻¹) are quantified as a fraction of the depreciation [34]; finally, the labor costs (EUR·h⁻¹) are computed according to the basic wages plus overheads and fringe costs (i.e., insurance, social security, and welfare charges).

3. Case Study and Results

3.1. Machinery Chain Selection

It was assumed that a silvicultural intervention would be carried out on a coppice area of Fagus sylvatica L. (area: 5 ha; harvested wood volume yield: 20 m³·ha⁻¹; wood basic density value: 0.61 t·m⁻³ DM), applying fulltree chipping.

The low yield value was introduced as part of a conservative approach, with the aim of simulating a harvesting scenario designed to minimize ecological disturbance. This choice reflects the application of the precautionary principle, which is commonly adopted in sensitive, high-value, or ecologically fragile forest ecosystems. In such contexts the objective is not merely economic extraction but the careful balancing of forest use with long-term ecosystem health.

By assuming a low harvest intensity, it is possible to represent a situation where forest-regeneration capacity, soil stability, and biodiversity conservation are prioritized. Additionally, this case study was inspired by a real-world scenario in a mountainous area of Northern Italy, where current legal and ecological constraints significantly limit biomass extraction. These restrictions reflect both environmental sensitivity and policy considerations related to sustainable land use. By modeling such a constrained scenario, the aim was to generate insights into how the algorithm performs under non-ideal but realistic field conditions, and to lay the groundwork for future validation efforts.

Table 6. Technical parameters and categories selected with the model FOREMA v1.

Technical Parameter	Category
Management system	Coppice
Wood assortment	Chips
Harvesting method	Fulltree
Degree of mechanization	Medium–high
Forest road transitability class	Medium–high
Area accessibility class a	High
Harvested mass (t∙ha−1 DM)	≤15.0

^a Defined by assuming an average slope of 10% and a distance from the forest road of 250 m.

shows the technical parameters and categories selected in FOREMA v1, whereas

Table 7. Operations, machine types, and models. Legend: TR, tractor.

Operation		Types of Usable Machines	Selected Types of Usable Machine	Machine Models
N.	Name
1	Felling	Chainsaw, harvester 4WD, crawler harvester, feller buncher, feller skidder, forvester	Chainsaw	Stihl MS 660
2	Bunching and extraction	Grapple tractor, tractor with winch, grapple skidder, skidder with winch, forvester, forwarder, cable crane with mobile drive station, traditional cable crane	Tractor with winch	TR: Case IH Puma 145; winch: Krpan 4.5 E
3	Sectioning	Chainsaw, processor	Chainsaw	Stihl MS 660
4	Chipping	Chipper	Tractor + chipper	TR: John Deere 8270 R; chipper: Gandini 40–60 TTS
5	Loading and long-distance transport	Tractor with trailer, lorry, road train	Tractor with trailer	TR: Massey Ferguson 7626; trailer: Bossini R2A2005D

shows the sequence of OPs, the types of potentially usable machines, and the selected ones with the corresponding models.

3.2. Machinery-Chain Organization

For felling, the required time was computed as the ratio between the total harvested mass (61 t DM) and the productivity related to a single chainsaw (0.6 t·h⁻¹ DM). It was then assumed that OPs 2, 3, 4, and 5 occurred simultaneously. For chipping, productivity was expressed in terms of m³·h⁻¹ (stems and branches). Assuming a volume coefficient (i.e., the ratio between stem and branch volume and chips volume) of 3.3 [31], the productivity of chipping (for chips) was approximately assumed to be equal to 37.5 m³·h⁻¹, and the machine model was chosen accordingly from commercial catalogs.

Table 8. Input and output data related to bunching and extraction productivity.

Input Data	Unit of Measure	Value
Bunching distance	m	100.0
Employee speed	m·s−1	1.0
Speed of rope winding on the winch a	m·s−1	1.3
Extraction distance	m	250.0
Forward speed (loaded)	m·s−1	1.5
Return speed (unloaded)	m·s−1	2.0
Extracted wood volume per cycle b	m3·cycle−1	2.0
Output Data	Unit of Measure	Value
No. of cycle	-	50
Time for rope winding on the winch	s	80
Time for transfer (travel loaded)	s	167
Time of effective work (EF; rope winding + travel loaded)	h	3.4
Additional time (AT)
Machine/implement arrangement on field	s	900
Time of transfer (travel unloaded)	s	6250
On-field maintenance c	s	123
Delays (avoidable and unavoidable) d	s	0
Employee rest	s	0
Other times not included in the previous ones e	s	9250
Required time (EF + AT; 1 machine)	h	8.0
Productivity (1 machine)	t·h−1 DM	7.6

^a Stems of medium dimensions [35]. ^b Average value for forest winch [35]. ^c Assumed equal to 1% of EF. ^d Included times: (i) avoidable (e.g., idleness, bad organization of the work) and (ii) unavoidable (e.g., mechanical components breaking or employee personal needs) ^e Times for (i) reaching the felled trees (5000 s), (ii) rope hooking (3000 s), and (iii) rope unhooking (1250 s).

and

Table 9. Input and output data related to loading and long-distance transport productivity.

Input data
Transport distance	km	10.0
Forward speed (loaded)	km·h−1	25.0
Return speed (unloaded)	km·h−1	35.0
Loaded wood volume per cycle	m3	16.0
Volumetric filling coefficient	%	100
Output data
No. of cycle	-	19
Time of effective work (EF; travel loaded)	h	7.6
Additional time (AT)
Machine/implement arrangement on field	s	0.0
Time of transfer (travel unloaded)	s	19,481
On-field maintenance a	s	273
Delays (avoidable and unavoidable) b	s	0
Employee rest	s	0
Other times not included in the previous ones c	s	35,618
Required time (1 machine; EF + AT)	h	23.0
Productivity (1 machine)	t·h−1 DM	2.7

^{a, b} Defined as in Table 9. ^c Times for trailer: (i) filling (28,800 s) and (ii) emptying (6818 s).

show the main input and output data related to the productivity of bunching and extraction and loading and long-distance transport, respectively.

Table 10. Data related to the organization of the FMC. Legend: imp, implement.

Data	Unit	OPs and Machines
		1	2	3	4	5
		Chainsaw	Tractor with Winch	Chainsaw	Tractor with Chipper	Tractor with Trailer
Useful time	Days	5.0	1.0	1.0	1.0	1.0
No. of working hours per employee per shift	h·employee−1·shift−1	8.0	8.0	8.0	8.0	8.0
No. of shifts per day	Shifts·day−1	1.0	1.0	1.0	1.0	1.0
Useful time per employee	h·employee−1	40.0	8.0	8.0	8.0	8.0
Productivity (1 machine/imp.)	t·h−1 DM	0.6	7.6	1.2	7.6	2.7
Required working time (1 machine/imp.)	h	100.0	8.0	50.0	8.0	23.0
No. of required machines/imp.	-	3	1	6	1	3
Productivity (total)	t·h−1 DM	1.8	7.6	7.6	7.6	7.6
No. of employees (1 machine/imp.)	-	1	2	1	3	1
No. of employees (total)	-	3	2	6.0	3	3
Required working time (total)	h	33.3	8.0	8.0	8.0	8.0

summarizes the data related to the organization and mechanical performance of the FMC.

3.3. Operating Costs

Table 11. Data and parameters for operating cost calculation. Legend: TR, tractor.

Data/Parameters	Machines
	Chainsaw	TR	Winch	TR	Chipper	TR	Trailer
Model	Stihl MS 660	Case IH Puma 145	Krpan 4.5 E	John Deere 8270R	Gandini 40–60 TTS	Massey Ferguson 7626	Bossini R2A2005D
Max engine power (kW)	5.2	117.6	-	201.6	-	183.6	-
Purchase price (P; EUR)	1200	46,500	3300	82,000	25,000	75,000	15,000
Salvage value rate (EUR; % P)	20.0%	12.5%	10%	12.5%	20.0%	12.5%	18.0%
Interest rate (EUR; % P)	4.0%	5.0%	4.0%	5.0%	4.0%	5.0%	4.0%
Overhead rate (EUR; % P)	4.0%	4.0%	4.0%	4.0%	4.0%	4.0%	4.0%
Employee wage (EUR∙h−1)	15.0	15.0	15.0	15.0	15.0	15.0	15.0
Repair and maintenance rate (% D a)	100%	100%	100%	100%	100%	100%	100%
Fuel price (EUR∙dm−3)	1.5	1.5	-	1.5	-	1.5	-
Lubricant price (EUR∙dm−3)	4.0	4.0	-	4.0	-	4.0	-
Service life (h)	3175	12,000	6000	12,000	10,000	12,000	4000
Economic life (y)	3	12	8	12	10	12	12
Annual use (h∙y−1)	400	1000	250	1000	800	1000	250

^a Depreciation (EUR∙y⁻¹).

provides the main technical–economic data and parameters used to calculate the operating costs, while Figure 4 and Figure 5 show the operating costs (EUR·h⁻¹; EUR·t⁻¹ DM).

Figure 4 and Figure 5 show the costs per unit of time (EUR·h⁻¹) and per unit of mass (EUR·t DM⁻¹), respectively, of each OP that composes the FMC.

4. Discussion

4.1. Selecting Algorithm for Machinery-Chain Selection: Strengths and Weaknesses and Future Development

Wood recovery is an essential part of every forest-wood supply chain and, in the context of sustainable forest management, is a key issue through which human impacts on ecosystems can be reduced [36].

Planning occurs both at the macro-scale (e.g., landscape level), considering parameters such as the mass to be recovered and the forest areas, as well as at the micro-scale (e.g., stand level), for which other parameters such as accessibility and the characteristics of the area are essential [1]. Planning wood recovery is quite important, as it allows for integration between forest management and mechanization. Concerning this aim, one of the most important steps is to define the most appropriate machines to use for specific operations.

In the Italian Alps, wood is generally recovered using the FT method, both in coppices and in high forest areas; if the wood is not suitable for industrial purposes, all biomass is comminuted into chips for energy generation. As a traditional scheme, tree felling is mainly performed with chainsaws, bunching and extraction are carried out with tractors coupled winches or cable cranes, and long-distance transport involves the use of tractors and trailers for distances up to 15–20 km; otherwise it involves lorries or road trains. Identifying alternative machines and technologies for wood recovery is important from both strategic (long-term) and tactical (short-term) points of view, as it has important consequences on the sustainability of forest management [11].

In mountainous areas, where the landscape conditions (e.g., slope and distance from the forest roads) can change quite considerably at the small scale, wood-recovery planning is challenging. Several aspects need to be incorporated within the same model, and the results are derived from different compromises [11]. This is particularly important if the aim of the model is to support users with different levels of knowledge, priorities, objectives, and goals [37].

To address this issue, the FOREMA v1 model was developed to both select the most suitable FMC for wood recovery and compute the corresponding operating costs. To develop the model, a wide-ranging analysis of the literature was carried out to select the parameters that most influence forestry-machinery selection and the corresponding technical–operational performance. FOREMA v1 represents an innovation in the sector of forestry mechanization, as it provides a systematic approach for selecting the most suitable FMC based on several technical parameters, ensuring that the available options are well-defined while covering a wide range of possibilities. The real-world challenges faced by forestry companies are addressed by considering as best as possible the multiple aspects of forestry management.

In more detail, FOREMA v1 is based on a tailored selection of machinery based on the specific characteristics of the forest, production system, and site-specific working conditions, mitigating the risk of inaccurate cost generalization. This improves the accuracy of the selection process that supports decision making, as the choice of machines for the OPs that comprise the chain can be optimized.

The model can be used for strategic decision-making (e.g., by a forestry company that owns heterogeneous stands), empowering technicians to make informed strategic decisions regarding investment prioritization, equipment acquisition, and long-term planning. At this strategic level, detailed operational parameters (such as tree diameter and soil type, which are important at the operational level but were here excluded) are less critical. Moreover, especially in Italian Alpine forests, collecting these data is quite challenging. Indeed, focusing on the strategic level ensures that FOREMA v1 remains useful even when such detailed data are not available.

The ability to determine a dynamic sequence of OPs enhances the ability of the model to adapt to different scenarios, ensuring optimized wood-recovery processes. Moreover, the model involves user interaction in the machinery-selection process, allowing users to choose machine types based on the model’s suggestions. This interactive decision support feature is a key element of FOREMA v1, as the user expertise is incorporated into the decision-making process. FOREMA v1 does not provide information about the machine models used for the OPs; this information must be provided by the user when the costs are calculated, as this depends on the machine model’s availability at the logging company.

In this way, FOREMA v1 may effectively be used as a basis for (i) rationalizing work; (ii) improving the organization of activities, thereby ensuring a smooth and coordinated workflow; (iii) defining public grants and subsidies, thus helping local decision makers in allocating financial support to relevant stakeholders; and (iv) defining proper OP tariffs, thereby supporting logging companies and consortia in establishing fair and transparent prices for their services. This is particularly beneficial for forestry consortia and logging companies in negotiations and client relations.

At the same time, as the developed model could support users with different needs, some simplifications are needed. Therefore, some technical parameters originally included in FOREMA v1 were subsequently excluded, as the inclusion of the species would lead, in many cases, to the generation of several CCs (i.e., machinery chains) identical to each other, causing the structure of the model to become more complex without providing any additional useful information for the user. For example, in the current version of the FOREMA v1 model, the age characteristics of the forest area were not explicitly included. While the importance of age in shaping forest structure and its implications for operational planning is fully recognized, the decision was made based on the observation that, from a practical standpoint, to our knowledge, many harvesting systems are sufficiently adaptable to be applied across a range of silvicultural systems, provided that physical and technical site conditions (such as slope, terrain accessibility, and stand density) are compatible.

Including a categorical variable to distinguish between even-aged and uneven-aged systems would have increased the complexity of the model without necessarily providing a proportional improvement in accuracy, particularly given the regional variability in how these silvicultural systems are defined and implemented.

However, it is acknowledged that the integration of age characteristics—but also the average diameter of the trees and wood quality—could enhance the model’s ecological depth and operational relevance, and this will be considered in future developments and refinements of the tool.

Another factor not included in the current version of the model is the age of the forestry machines.

Although it is recognized that older machines may be more prone to breakdowns or may experience reduced fuel efficiency, it was considered that age alone is not always a reliable indicator of performance or operating costs. In practice, a machine’s condition can be influenced by several factors, such as the quality of maintenance, operator behaviour, working environment, and intensity of use. For this reason, excluding the machine age was a deliberate choice aimed at maintaining the model’s flexibility and applicability across diverse real-world contexts.

Nevertheless, this element will certainly be considered for future developments of the model, recognizing that this could improve its accuracy and performance predictions.

The effects on soil and forest ecosystems, such as soil compaction, erosion, as well as also changes in litter quantity and quality, were not thoroughly examined. For example, recent studies have shown that wood extraction can affect the soil for several years [38]. Under wet conditions, soils rich in clay and loam are generally highly susceptible to compaction, offering low traction for wheel-based machines; moreover, erosion can occur if the soil surface is not properly covered with branches or other logging residues. On the contrary, very dry sandy soils cause difficulty for wheel-based machines, often making it necessary to irrigate the soil surface to prevent the machines becoming stuck. Therefore, both clay/loam and sandy soils can lead to restrictions when selecting the most appropriate types of machines to use [39]. Moreover, it should be taken into consideration that wood recovery generally causes changes in litter quantity and quality, nutrient availability, and vegetation [40,41].

Despite the abovementioned limitations, the importance of the technical parameters on which the model is based has been confirmed by the authors of [42,43,44,45,46,47]. Moreover, the integration of three different modules allows for a seamless workflow, thus enhancing the usability and effectiveness of the model.

As one of the major steps in model development and application is its validation, incorporating a testing procedure will undoubtedly strengthen the scientific validity and practical applicability of FOREMA v1. This ensures the robustness and reliability of the model itself.

Nevertheless, during this initial stage of the research, the primary focus was on establishing the conceptual framework and operational principles of FOREMA v1. The aim was to provide a comprehensive description of the model, defining the theoretical underpinnings, including the selection algorithm, its key components, and functionalities, as well as discussing the practical applications through a case study.

Moreover, validation typically requires full access to extensive empirical data and resources, which were not readily available at this first stage of model development. By deferring validation to later stages, it will be possible to leverage ongoing research efforts to collect relevant data, carry out field tests, and improve the accuracy of the model based on practical feedback and insights provided by forestry technicians and stakeholders.

4.2. Case Study: Machinery-Chain Organization, Working Times, and Productivity

When defining the organization of the FMC, it was assumed that OPs 2, 3, 4, and 5 occurred simultaneously (i.e., the productivity of the OPs must be the same to ensure a constant flow of biomass), which requires perfect organization of the chain. With this organization, the “bottleneck” is represented by the chipping operation.

This assumption is reasonable for the Alpine area as, at the landing site, the space available for wood stocking is generally low and wood chipping should be carried out as soon as possible to avoid wood deterioration and market-price reduction.

For the first three parameters in Table 10 (i.e., from “useful time” to “n. of shift per day” were defined) the values were hypothesized and assumed according to recommendations from local forestry companies that carry out work using the same types of machines and under similar working conditions as those of the case study.

The comparison of productivity and operating costs values across areas/region/countries, where working and environmental conditions differ, is quite difficult, even if the machines used are similar. Therefore, only some general comparisons are reported, as a complete and exhaustive comparative analysis of the results is not possible.

For felling, the total required time was computed as the ratio between the total mass to be harvested (61 t DM) and the productivity related to a single chainsaw. Productivity is affected by several elements, such as (i) tree species and dimensions, (ii) terrain slope and the presence of obstacles, (iii) environmental conditions, and (iv) employee abilities [27,35,47]. In natural coppice stands, when the CTL method is applied for firewood production, small stem sizes, uncomfortable working positions, and the need to cut stems into manageable lengths lead to low productivity values (e.g., between 0.3 and 1.4 m³·h⁻¹·employee⁻¹) [47]. Indeed, when the FT method is adopted, directional felling with a chainsaw can increase productivity to between 1 and 4 m³·h⁻¹·employee⁻¹ [47].

The authors of [27] have reported general values of productivity for felling and processing in coppices according to species, wood characteristics, and the types of woodcuts (i.e., final cuts on mature stands, conversion to high forests, or thinning). For the final cuts, the values ranged between 2.5 and 4.0 m³·d⁻¹·employee⁻¹ for dirty coppices with poor physical characteristics and 8.0–15.0 m³·d⁻¹·employee⁻¹ for old coppices of Castanea sativa Mill., Fagus sylvatica L., and Quercus cerris L. For conversion to high forests or thinning, productivity values ranged between 2.0 and 3.0 m³·d⁻¹·employee⁻¹ for coppices with mixed species and 4.0–5.0 m³·d⁻¹·employee⁻¹ for old coppices of Castanea sativa Mill., Fagus sylvatica L., and Quercus cerris L.

The authors of [48] have performed regression analysis considering the elements affecting the productivity of felling with a chainsaw in traditional coppice stands. For a harvested wood volume yield of approximately 20 m³·ha⁻¹—like that considered in the case study in this research—the productivity was approximately 1 m³·h⁻¹·employee⁻¹. For this reason, and as specific measured data were not available for this case study, the productivity of felling with a chainsaw was defined as equal to the value reported by the authors of [48].

The main parameters affecting the productivity of wood extraction are the extraction distance and the average loaded volume per cycle [49]. The authors of [49], for example, performed three different tests in different locations in Central Italy to calculate the productivity of wood extraction in coppice stands where the FT method is applied. They found that the productivity values ranged between 1.5 and 7.9 m³·h⁻¹ with an average loaded volume of wood ranging between 1.1 and 3.0 m³·cycle⁻¹.

For the OPs of bunching and extraction as well as loading and long-distance transport, the CIOSTA methodology for calculating the working time was chosen to standardize the terminologies used for agricultural and forestry machinery (Forest Work Study Nomenclature of the International Union of Forestry Research Organizations, IUFRO; Ref. [50], thus making a comparison possible. Compared to the IUFRO methodology, when using CIOSTA, some of the times were differently named while others were further split; this is due to the accuracy of the calculation but could be easily aggregated.

Working times, productivity, and other operational parameters can vary significantly depending on the unique characteristics of the machines and the specific working conditions present in a forest environment. Even if factors such as soil type, weather conditions, tree species, and density, as well as the presence of underbrush and obstacles are not directly considered in the model, once the types of machines and the specific working conditions are defined, the user can adjust the working times and therefore productivity to better match the actual conditions of the workplace. This approach allows for a more effective management strategy that considers the inherent variability of forest environments.

The results indicated that felling was characterized by the lowest cost per unit of time (63.7 EUR·h⁻¹) but the highest per unit of mass (35.4 EUR·t DM⁻¹). This is essentially because the required working time of this OP is the highest compared with the other OPs, but its productivity is the lowest (33.3 h and 1.8 t·h⁻¹ DM, respectively).

At the same time, loading and long-distance transport were characterized by the highest cost per unit of time and, after felling, per unit of mass (223.5 EUR·h⁻¹ and 29.4 EUR·t DM⁻¹, respectively), as the use of medium–small-sized trailers coupled with tractors operating at low speeds was assumed and, therefore, characterized by a high number of cycles.

For the entire FMC, the cost per unit of time was 147.3 EUR·h⁻¹, whereas the cost per unit of mass was 101.1 EUR·h⁻¹.

For felling with chainsaws, the authors of [51] reported a cost per unit of time in Southern Italy equal to 25.1 EUR·h⁻¹·worker⁻¹, with a cost of labor (1 worker) of 21 EUR·h⁻¹. For a short rotation coppice of eucalyptus for firewood production, the authors of [4,52] reported a cost per unit of time of 17.4 EUR·h⁻¹·worker⁻¹, with a cost of labor of 15 EUR·h⁻¹. In [53], the cost per unit of time was 18.7 EUR·h⁻¹·worker⁻¹, with a cost of labor equal to 15 EUR·h⁻¹. The authors of [54] reported a cost per unit of time of 23.4 EUR·h⁻¹·worker⁻¹ (cost of labor of 15 EUR·h⁻¹). For bunching and extraction with one tractor and one winch, the authors of [51] reported a cost of 68.7 EUR·h⁻¹·worker⁻¹, whereas in this study, a cost equal to 102.7 EUR·h⁻¹·worker⁻¹ was obtained. In [52], the cost per unit of time was 40.9 EUR·h⁻¹·worker⁻¹, whereas the authors of [53] reported a cost per unit of time equal to 35.4 EUR·h⁻¹·worker⁻¹.

Given the different site-specific working conditions, machine characteristics and values of economic parameters, it becomes essential to carry out a thorough analysis that considers the local contexts, including the specific characteristics of the forest and working conditions, the available machinery, and the economic factors. Forests exhibit diverse characteristics, influencing the selection of machinery and the corresponding costs. Generalizing costs across different forest types can lead to inaccurate estimations. Regional or country-specific studies may be necessary to account for these variations and provide accurate and context-specific insights into the operating costs of recovery. Standardizing the comparison may require careful normalization or adjustment of cost metrics to account for these diverse factors.

To validate and improve the general structure of the model, experimental tests should be carried out to collect accurate information on the working conditions under which forestry machines are used as well as operating costs.

5. Conclusions

Determining the optimal production technology or their chain for specific natural and productive conditions of forest property is one of the most important and, at the same time, the most demanding tasks of forest managers.

This preliminary model analyzes the whole timber-harvesting chain, allowing for an assessment of the overall efficiency of the production process. A key feature of the model is its ability to suggest the most suitable machine types for each operation within the production chain by considering specific technical parameters of the forest area. This ensures that the proposed solution is tailored to local operating conditions.

Moreover, the model goes beyond merely identifying usable machines; it also calculates productivity and operating costs for each alternative, enabling forest managers to make informed decisions based on the balance between production efficiency and economic sustainability. To further facilitate the decision-making process, the model assigns to each machine type a qualitative level that defines the ease of use of the machine under the identified working conditions, helping managers select solutions not only based on technical performance but also on operational simplicity.

Additionally, as the model relies on parameters that can be easily obtained from local FMPs, it can be applied to any forest area where the required input data are made available, enhancing its potential for widespread adoption and further applications across different forestry contexts.