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Article

Consumables Usage and Carbon Dioxide Emissions in Logging Operations

by
Dariusz Pszenny
and
Tadeusz Moskalik
*
Department of Forest Utilization, Institute of Forest Sciences, Warsaw University of Life Sciences–SGGW, 159 Nowoursynowska Street, 02-776 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Forests 2025, 16(7), 1197; https://doi.org/10.3390/f16071197
Submission received: 8 June 2025 / Revised: 16 July 2025 / Accepted: 18 July 2025 / Published: 20 July 2025
(This article belongs to the Section Forest Operations and Engineering)
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Abstract

In this study, we comprehensively analyzed material consumption (fuel, hydraulic oil, lubricants, and AdBlue fluid) and estimated carbon dioxide emissions during logging operations. This study was carried out in the northeastern part of Poland. Four harvesters and four forwarders representing two manufacturers (John Deere-Deere & Co., Moline, USA, and Komatsu Forest AB, Umeå, Sweden) were analyzed to compare their operational efficiency and constructional influences on overall operating costs. Due to differences in engine emission standards, approximate greenhouse gas emissions were estimated. The results indicate that harvesters equipped with Stage V engines have lower fuel consumption, while large forwarders use more consumables than small ones per hour and cubic meter of harvested and extracted timber. A strong positive correlation was observed between total machine time and fuel consumption (r = 0.81), as well as between machine time and total volume of timber harvested (r = 0.72). Older and larger machines showed about 40% higher combustion per unit of wood processed. Newer machines meeting higher emission standards (Stage V) generally achieved lower CO2 and other GHG emissions compared to older models. Machines with Stage V engines emitted about 2.07 kg CO2 per processing of 1 m3 of wood, while machines with older engine types emitted as much as 4.35 kg CO2 per 1 m3—roughly half as much. These differences are even more pronounced in the context of nitrogen oxide (NOx) emissions: the estimated NOx emissions for the older engine types were as high as ~85 g per m3, while those for Stage V engines were only about 5 g per m3 of harvested wood. Continuing the study would need to expand the number of machines analyzed, as well as acquire more detailed performance data on individual operators. A tool that could make this possible would be fleet monitoring services offered by the manufacturers of the surveyed harvesters and forwards, such as Smart Forestry or Timber Manager.

1. Introduction

In recent decades, mechanization has become a cornerstone in the improvement in forestry operations, substantially enhancing logging efficiency and operator safety [1,2]. The widespread adoption of multi-operational machines, such as harvesters and forwarders, has streamlined the felling and extraction processes while reducing the workforce size and physical labor intensity [3,4,5].
The choice of dominant technological processes for wood harvesting in a given country is determined by two factors: the share of areas with an upslope gradient and the share of soils with a low bearing capacity in relation to the total area of forest land [6]. In addition, a country’s economic development level is fundamental, affecting labor costs. At high labor costs, solutions characterized by a high degree of work mechanization are used more often, through which relatively high labor productivity is achieved [7].
However, there is significant variation between countries due to the fear of unemployment and the degree of investment opportunities. The pace of investment in forestry mechanization also depends on the regional availability of skilled labor [8].
Under European conditions, the highest levels of mechanization are observed in countries such as Sweden and Norway (at 95%), Ireland (at 98%), and Germany (at 65%) [9]. There has also been a noticeable increase in the use of fully mechanized harvesting systems in Eastern Europe [10,11]. In 2021, the level of fully mechanized logging in Poland was 46.2%, almost double that in 2016 [12]. The number of harvesters in Poland has increased from 15 in 2004 to ca. 1100 machines at present. It is assumed that the number of working forwarders is ca. 1.5 times greater than the number of harvesters.
Mechanized operations in the forest sector are associated with significant environmental impacts, particularly concerning soil disturbance [13,14,15,16,17,18]. While improving productivity and safety, forest machines rely on diesel-powered machinery that contributes to greenhouse gas emissions and air pollution. The internal combustion engines of harvesters and forwarders emit substantial amounts of carbon dioxide (CO2)—a major greenhouse gas—along with noxious exhaust constituents such as nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxide (CO), and particulate matter (PM) [1,19]. In response, the European Union introduced Stage V emission standards, regulating the permissible levels of NOx, PM, HC, and CO emissions from internal combustion engines, including those in forestry machinery [20,21,22,23].
Emission levels from forestry machinery can vary widely depending on the work type, equipment size, and engine technology. Thinning operations (partial harvests) tend to consume more fuel per unit of extracted wood than clear-cuts, due to smaller tree sizes and more maneuvering, which leads to higher CO2 emissions per cubic meter harvested. Likewise, machine characteristics influence efficiency and emissions. Larger machines with high-power engines generally burn more fuel overall than smaller ones performing the same task—especially if the larger machine’s capacity is underutilized—resulting in higher per-unit emissions in some conditions. On the other hand, advances in engine design and control can improve efficiency: newer engines built to stricter standards often achieve lower fuel consumption per unit output than older-generation engines. For example, Stage V-compliant harvesters with smaller displacement engines have been shown to consume less fuel per hour and per cubic meter of timber than older Stage III machines of higher power. This indicates that downsizing and modernizing engines, when matched appropriately to the workload, can yield both fuel savings and emission reductions.
Additionally, when planning forestry operations, the costs associated with consumables—fuel, hydraulic oil, lubricants, and AdBlue fluid (for machines equipped with SCR systems)—must be carefully considered [24,25].
The aim of this study was to compare eight machines (four harvesters and four forwarders) operated by the State Forest Transport and Logistics Company in Giżycko (SFT&LC), focusing on the average monthly usage of consumable materials and estimated CO2 emissions.

2. Materials and Methods

This study is based on detailed operational records provided by the SFT&LC in Giżycko, which manages logging machinery for multiple forest districts in northeastern Poland. The dataset covers the period from 1 January 2022 to 31 December 2023 and includes eight machines: four harvesters (Table 1) and four forwarders (Table 2) manufactured by John Deere (Deere & Co., Moline, IL, USA) and Komatsu Forest AB (Umeå, Sweden). These machines span a range of engine sizes, emission standards, and capacities, providing a representative sample of contemporary cut-to-length (CTL) technology
The analyzed parameters included productive machine hours (PMH), fuel consumption (L/m3 and L/h), timber volume processed (m3), hydraulic oil consumption (L), AdBlue usage (L), and grease consumption (kg), based on operational reports from SFT&LC. All machines were deployed for thinning, clear-cut, and partial-cut operations, with a similar distribution of tasks. The double-shift working system was implemented, and different operators managed each machine during the study period.
From the information obtained from SFT&LC employees managing the machines, the machine utilization rate averaged 0.85. The distances of the machines’ travels, unfortunately, could not be measured, as the machines are not equipped with GPS receivers. However, after consulting with employees responsible for work planning and machine management, information was obtained that the average transport distance of wood was about 400 m.
CO2 emissions were estimated at 2.68 kg per liter of diesel fuel consumed, based on values from the literature [21,26,27]. The approximate emissions of other gases (NOx, PM, and HC) were calculated by considering diesel’s energy value (9.94 kWh/L) and an average internal combustion engine efficiency of 40% [28,29,30,31].
Statistical analyses were conducted using RStudio. The normality of the data distribution was verified using the Shapiro–Wilk test. On this basis, appropriate statistical methods were selected: for normal data, parametric tests (ANOVA) were used, and for non-normal data, non-parametric tests (Kruskal–Wallis) were used. A significance level of α = 0.05 was adopted for all statistical tests. If significant differences were detected, post hoc tests were used: the Tukey test for ANOVA analyses and the Dunn test for Kruskal–Wallis analyses. Statistical tests were applied separately for harvesters and forwarders. Dependent variables included fuel consumption, volume of timber harvested, operating time, consumable consumption, and efficiency. The independent variable was machinery. All statistical analyses were performed in the RStudio environment (version 2025.05.1+513).
For the emissions of other greenhouse gases, such as NOx, PM, and HC, we could only make estimates due to the fact that STAGE standards describe emissions expressed in grams per kWh [20,21,22]. To determine approximate values, the energy value of diesel—9.94 kWh [20,21]—must be multiplied by the efficiency of the engine. The efficiency of internal combustion engines is on average 40% [30,31]. The actual efficiency depends on the engine model or the RPM at which it runs. Assuming an engine efficiency value of 40%, we obtained a value of 3.98 kWh/L generated by an engine burning one liter of diesel. These values are approximate, and a specialized exhaust fume analysis device would have to be used to determine the exact emissions.
By encompassing energy inputs (fuel, AdBlue), maintenance inputs (oils, lubricants), and outputs (productivity in m3 and hours), the analysis covers the full spectrum of machine performance. It enables the identification of trade-offs—for example, a larger machine may harvest timber faster but at the expense of higher fuel and AdBlue consumption per hour—and helps pinpoint which machine configurations offer the best balance of operational cost-effectiveness and low environmental impact. The use of real-world data from a State Forests logging operation ensures that the findings are grounded in practical, operational reality. In particular, the SFT&LC dataset captures the variability of everyday forestry work (including different terrain, stand conditions, and silvicultural treatments), thereby enhancing the generalizability of the results. All data were quality-checked and aggregated on a monthly basis for analysis.

3. Results

3.1. Analysis of Normal Distributions

Among the harvesters, parameters such as machine hours, fuel consumption, and timber volume processed displayed normal distributions. However, hydraulic oil consumption, AdBlue fluid consumption, grease consumption, fuel consumption per cubic meter of timber, and AdBlue consumption per horsepower did not conform to standard distribution patterns.
For the forwarders, machine hours, fuel consumption, and timber volume processed followed normal distributions. Non-normal distributions were found for hydraulic oil, AdBlue fluid, grease usage, fuel consumption per machine hour, timber processed per hour, fuel consumption per cubic meter, and AdBlue usage per horsepower.

3.2. Correlation Analysis

The correlation analysis revealed a strong positive correlation between machine hours and fuel consumption (r = 0.81) and between machine hours and timber volume processed (r = 0.72).
A moderate positive correlation was observed between machine power (kW) and AdBlue (r = 0.53).
A strong negative correlation was found between fuel consumption per cubic meter of timber and total volume processed (r = −0.72), as well as total fuel consumption (r = −0.63) (Figure 1).

3.3. Harvester Efficiency Analysis

3.3.1. Machine Hours Worked

The ANOVA showed statistically significant differences in machine hours among the harvesters (p < 0.0000). The post hoc analysis indicated substantial distinctions, particularly between the 2018 JD1270G harvester and the older JD1270G and Komatsu 901XC models (Figure 2). The 2018 JD1270G harvester logged the fewest hours. Overall, newer Stage V machines tended to operate more consistently, reducing idle periods and boosting availability.

3.3.2. Timber Volume Harvested

The ANOVA revealed significant differences in harvested timber volumes among the harvesters (p < 0.0000), with the post hoc tests highlighting variations primarily between the John Deere and Komatsu models. The 2019 Komatsu 901 outperformed the 2018 JD1270G by over 15%, highlighting the efficiency advantage of smaller Stage V engines under clear-cut conditions.

3.3.3. Timber Harvesting Efficiency

A modest but significant difference was found in timber harvesting efficiency (p = 0.0227), with K901_19 slightly ahead of JD1270G_18 by 0.2 m3/h. Other pairwise comparisons were not significant, indicating broadly similar efficiency across machines. Operator rotation likely homogenized performance (Figure 3).

3.3.4. Fuel Consumption per Cubic Meter

Fuel use per m3 varied significantly (p < 0.0001): the Komatsu units required only 0.77–0.90 L/m3, while the John Deere machines used 1.35–1.62 L/m3. This 40% reduction underscores the benefit of matching engine size to workload (Figure 4).

3.4. Analysis of Forwarder Performance

3.4.1. Hours Worked

The forwarder operating hours differed significantly (p = 0.0028), driven by JD1910G_18’s higher idle time. Komatsu 855_22 showed the most balanced utilization, suggesting better adaptation to mixed thinning tasks. Engine size influenced delays more than terrain (Figure 2).

3.4.2. Amount of Transported Wood

Transport volumes varied (p < 0.0001); JD1510E_16 moved 20% less timber monthly than K855_19. JD1910G_18 carried the largest loads but ran fewer cycles, offsetting its payload advantage (Figure 3). Smaller forwarders proved more efficient in frequent short hauls.

3.4.3. Fuel Consumption per Unit Time

Hourly fuel use differed markedly (p < 0.0001), with JD1910G_18 consuming up to 1.2 L/h more than the Komatsu models. Dunn’s test confirmed this gap against all other units. A high hourly burn reduced its cost-effectiveness despite its larger capacity (Figure 4).

3.4.4. Forwarding Efficiency per Unit of Time

No significant difference emerged in forwarding efficiency per unit of time (p = 0.0667), indicating similar m3/h across machines. Payload capacity had little effect under thinning conditions. Terrain and operator skills likely dominated extraction rates.

Fuel Consumption per Unit of Wood Skidded

Fuel per m3 varied significantly (p < 0.0001): JD1910G_18 used 1.19 L/m3, while K855_19 needed only 0.67 L/m3. The largest forwarder’s weight penalty increased per unit fuel use by nearly 80%. Smaller machines thus offer better energy efficiency in mixed stands.

3.5. Consumption of Other Materials

Significant statistical differences were found between the tested harvesters in the consumption of AdBlue fluid (p = 0.01208). The post hoc test showed significant differences between all machines, except for the Komatsu harvesters. The analysis of hydraulic oil consumption showed no statistically significant differences between the machines tested (p = 0.1822). Lubricant consumption also showed no statistically significant differences (p = 0.07). The significant variation in AdBlue consumption among the machines is most likely attributable to differences in the design and calibration of their selective catalytic reduction (SCR) systems, which govern urea injection rates and exhaust after-treatment efficiency (Figure 5). In contrast, hydraulic oil and grease usage did not correlate with hours worked, indicating that these consumables are depleted through maintenance events or operator behavior (e.g., the frequency of component lubrication) rather than continuous engine operation.

3.6. CO2 and Other Greenhouse Gas Emissions

The results show considerable heterogeneity in the emission profile of the machines tested. There is a clear trend of carbon dioxide (CO2) emissions increasing with increasing fuel consumption per m3. The machines with higher fuel consumption generally had higher GHG emissions. The machines differed in their GHG emissions in terms of fuel consumed per energy generated. Newer machines in the higher STAGE standard had lower emissions in most parameters (Table 3).

4. Discussion

This study revealed pronounced differences in work efficiency among eight forestry machines evaluated, encompassing both harvesters and forwarders. The harvesters differed significantly in hours worked and timber volume processed, which likely reflects variations in their technical performance. Similar trends were noted by Liu et al. [30] and Drahanský et al. [32], who identified machine age as a key determinant of harvester productivity. In our analysis, the 2018 John Deere 1270G recorded the fewest operating hours, whereas the 2016 John Deere 1270G exhibited comparable hours and volumes to the Komatsu models, suggesting that factors beyond engine size—such as maintenance history or deployment strategy—may also play important roles.
Among the forwarders, statistically significant differences in hours worked were limited to the comparison between the largest engine (JD 1910G_18) and the newest Komatsu 855_22, indicating that engine size alone does not guarantee higher utilization under mixed thinning and clear-cut conditions. Despite its greater theoretical payload, JD 1910G did not outperform smaller machines in terms of timber transported per hour. This finding concurs with that of Ghaffariyan [33] and Proto et al. [34], who reported that larger forwarders achieve peak efficiency on clear-cut sites with long extraction distances, whereas smaller, more maneuverable units excel in thinning operations.
Harvesting efficiency per unit time was broadly similar across machines, implying that terrain characteristics (e.g., slope, soil bearing capacity, and the presence of watercourses) and operator skill may override differences in design and engine power [35,36,37,38]. Operator rotation in our dataset may have further attenuated machine-specific performance, as cognitive and technical proficiency can markedly influence cycle times and cut precision [5,39,40,41,42]. Likewise, the complexity of the cut—clear-cut versus partial thinning—affects productivity, with clear-cutting generally yielding higher output due to its more uniform workload [43,44,45].
Fuel consumption patterns mirrored engine displacement: the Komatsu harvesters, with smaller-capacity Stage V engines, consumed less fuel per machine hour and per cubic meter of wood than the larger Stage 3B John Deere units. This aligns with established relationships between engine size and fuel use [46,47,48,49], and it underscores the potential for energy savings through right-sizing machinery to the silvicultural task. In Polish forestry, where partial cuts are increasingly common, the adoption of smaller CTL (cut-to-length) machines—potentially including compact designs from Vimek AB (Vindeln Sweden) or Malwa Forest AB (Skene Sweden)—could enhance both economic and environmental performance.
Fuel consumption per cubic meter also drove CO2 emissions: machines with higher fuel use emitted proportionally more greenhouse gases. This highlights the dual benefit of selecting Stage V-compliant, lower-capacity engines—not only do they reduce operating costs (fuel can account for 20%–35% of total machine expenses [50,51,52]), but they also mitigate carbon footprints. As EU Stage V standards tighten allowable NOx, PM, and HC emissions, fleet modernization offers a viable pathway to align forestry operations with low-impact management goals [13,19,53,54].
The absence of significant differences in extraction efficiency (m3 per hour) among the forwarders suggests that a larger payload capacity may be neutralized by suboptimal site conditions or operational patterns. Eriksson et al. [55] demonstrated that machine weight increases fuel consumption, corroborating our observation that the JD 1910G’s higher weight did not confer productivity gains and indeed increased fuel use per unit of wood. Optimal machine selection must therefore consider site-specific factors—stand density, extraction distance, and ground firmness—to avoid efficiency losses.
The variability in AdBlue consumption among the machines likely stems from distinct SCR (selective catalytic reduction) system designs and calibration strategies [56,57,58,59]. In contrast, grease and hydraulic oil usage showed no clear correlation with operating hours, reflecting their linkage to intermittent maintenance events or operator practices rather than continuous wear [10,60,61].
In this study, we relied on indirect estimates of non-CO2 emissions based on stage emission standards and assumed engine efficiency; direct on-site exhaust measurements would refine greenhouse gas inventories. Additionally, operator rotation and mixed harvesting regimes introduced heterogeneity that future work could address by controlling for operator experience and focusing separately on clear-cut versus thinning operations. Investigations of emerging electro-hybrid drivetrains under real-world conditions would further inform strategies to decarbonize mechanized forestry.

5. Conclusions

The results emphasize that matching machine specifications—such as engine capacity, emissions rating, and size—is key to optimizing both productivity and environmental performance in modern forestry.
A strategic fleet renewal toward smaller Stage V-compliant harvesters and forwarders can bring significant benefits in terms of fuel efficiency and emission reduction, supporting the requirements of sustainable forest management. The results show that Stage V-compliant harvesters and forwarders with smaller capacity engines consume less fuel per machine-hour and per m3 of timber than larger machines with earlier engines. Appropriate machine sizing for logging tasks increases both energy efficiency and profitability. However, it is worth mentioning that engine size alone does not guarantee higher machine productivity. Terrain conditions and type of operation (thinning vs. clear-cutting) are also critical.
Fuel consumption per unit of wood is directly correlated with CO2 and other greenhouse gas emissions, highlighting the environmental benefits of Stage V-compliant engines. Older generation engines featured higher GHG emissions due to less restrictive emission standards. Upgrading a fleet of machines to smaller Stage V-compliant machines can reduce operating costs as well as positively impact the reduction of carbon dioxide emissions to minimize environmental damage.

Author Contributions

Conceptualization, D.P. and T.M.; methodology, D.P.; software, D.P.; validation, D.P.; formal analysis, D.P.; investigation, D.P.; resources, D.P.; data curation, D.P.; writing—original draft preparation, D.P.; writing—review and editing, D.P. and T.M.; visualization, D.P.; supervision, T.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Correlation diagram between the factors studied.
Figure 1. Correlation diagram between the factors studied.
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Figure 2. Monthly working time of each machine during the study period. The vertical line inside each box indicates the median value. The edges of the box represent the first (Q1) and third quartiles (Q3). The horizontal lines refer to the outer values. The dots represent statistical outliers.
Figure 2. Monthly working time of each machine during the study period. The vertical line inside each box indicates the median value. The edges of the box represent the first (Q1) and third quartiles (Q3). The horizontal lines refer to the outer values. The dots represent statistical outliers.
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Figure 3. Monthly fuel consumption [L] and processed wood [m3] during the analyzed period. The horizontal line inside each box indicates the median value. The edges of the box represent the first (Q1) and third quartiles (Q3). The vertical lines refer to the outer values. The dots represent statistical outliers.
Figure 3. Monthly fuel consumption [L] and processed wood [m3] during the analyzed period. The horizontal line inside each box indicates the median value. The edges of the box represent the first (Q1) and third quartiles (Q3). The vertical lines refer to the outer values. The dots represent statistical outliers.
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Figure 4. Comparison of fuel consumption by machine. black color—harvesters; gray color—forwarders. Bars represent mean values; error bars denote standard deviations.
Figure 4. Comparison of fuel consumption by machine. black color—harvesters; gray color—forwarders. Bars represent mean values; error bars denote standard deviations.
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Figure 5. Mean consumption of AdBlue [L], grease [Kg], and hydraulic oil [L] for machines studied.
Figure 5. Mean consumption of AdBlue [L], grease [Kg], and hydraulic oil [L] for machines studied.
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Table 1. Harvesters included in research.
Table 1. Harvesters included in research.
Machine IDJD1270G_16JD1270G_18K901_19K901_22
ManufacturerJohn DeereJohn DeereKomatsuKomatsu
Model 1270G1270G901901
Year of production 2016201820192022
Volume of engine9.0 L9.0 L6.6 L6.6 L
Rated power 200 kW200 kW170 kW170 kW
Engine torque1445 Nm/1300–1400 rpm1445 Nm/1300–1400 rpm950 Nm/1500 rpm950 Nm/1500 rpm
Tractive force 210 kN210 kN184 kN184 kN
Weight23,400 kg23,400 kg20,210 kg20,210 kg
CraneCH7: 10 mCH7: 10 m200H: 10 m200H: 10 m
Head/grappleH480CH480CC93C93
Gross lifting torque 197 kNm197 kNm198 kNm198 kNm
Table 2. Forwarders included in research.
Table 2. Forwarders included in research.
Machine IDJD1510E_16JD1910G_18K855_21 K855_22
ManufacturerJohn DeereJohn DeereKomatsuKomatsu
Model 1510E1910G855855
Year of production 2016201820212022
Volume of engine9.0 L6.8 L6.6 L6.6 L
Rated power 205 kW200 kW170 kW170 kW
Engine torque978 Nm/1200–1500 rpm1315 Nm/1400 rpm950 Nm/1500 rpm950 Nm/1500 rpm
Tractive force 185 kN230 kN187 kN187 kN
Nominal load capacity15,000 kg 19,000 kg14,000 kg14,000 kg
Weight16,330 kg19,500 kg16,500 kg16,500 kg
CraneCF7S: 10 mCF7S: 10 m145F: 10 m145F: 10 m
Head/grappleHSP 028HSP 050 G28 G28
Gross lifting torque 197 kNm210 kNm145 kNm145 kNm
Table 3. Estimated greenhouse gas emissions of investigated machines.
Table 3. Estimated greenhouse gas emissions of investigated machines.
MachineFuel Consumption per 1 m3kWh Generated CO2 [kg]CO [g]HC [g]NOx [g]PM [g]
JD1270G_161.355.373.6274.852.74983.030.27
JD1270G_181.626.464.3589.984.8884.840.64
K901_190.773.072.0742.802.324.890.18
K901_220.903.592.4250.062.725.720.21
JD1510E_160.793.122.1043.52N/DN/D2.49
JD1910G_181.194.723.1865.703.5761.940.47
K855_190.672.651.7836.872.004.210.16
K855_220.813.242.1845.132.455.160.19
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Pszenny, D.; Moskalik, T. Consumables Usage and Carbon Dioxide Emissions in Logging Operations. Forests 2025, 16, 1197. https://doi.org/10.3390/f16071197

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Pszenny D, Moskalik T. Consumables Usage and Carbon Dioxide Emissions in Logging Operations. Forests. 2025; 16(7):1197. https://doi.org/10.3390/f16071197

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Pszenny, Dariusz, and Tadeusz Moskalik. 2025. "Consumables Usage and Carbon Dioxide Emissions in Logging Operations" Forests 16, no. 7: 1197. https://doi.org/10.3390/f16071197

APA Style

Pszenny, D., & Moskalik, T. (2025). Consumables Usage and Carbon Dioxide Emissions in Logging Operations. Forests, 16(7), 1197. https://doi.org/10.3390/f16071197

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