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Article

Influence of Mulching and Planting Density on Agronomic and Economic Traits of Melissa officinalis L.

by
Stefan V. Gordanić
,
Dragoja Radanović
,
Miloš Rajković
,
Milan Lukić
,
Ana Dragumilo
*,
Snežana Mrđan
,
Petar Batinić
,
Natalija Čutović
,
Sara Mikić
,
Željana Prijić
and
Tatjana Marković
Institute for Medicinal Plants Research “Dr. Josif Pančić”, Belgrade, Tadeuša Košćuška 1, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(8), 866; https://doi.org/10.3390/horticulturae11080866
Submission received: 18 June 2025 / Revised: 17 July 2025 / Accepted: 18 July 2025 / Published: 22 July 2025
(This article belongs to the Special Issue Conventional and Organic Weed Management in Horticultural Production)

Abstract

Melissa officinalis L. (Lamiaceae) is a perennial plant species widely used in the pharmaceutical and food industries, particularly valued for its sedative properties. This study investigates the impact of synthetic mulch film and planting density as two experimental factors on agronomic performance, raw material quality, and economic efficiency in lemon balm production. The experiment was conducted at three locations in Serbia (L1: Bačko Novo Selo, L2: Bavanište, L3: Vilandrica) from 2022 to 2024, using two planting densities on synthetic mulch film (F1: 8.3 plants m−2; F2: 11.4 plants m−2) and a control treatment without mulch (C). The synthetic mulch film used was a synthetic black polypropylene film (Agritela Black, 90 g/m2), uniformly applied in strips across the cultivation area, covering approximately 78% of the soil surface. The results showed consistent increases in morphological parameters and yield across the years. Plant height in F1 and F2 treatments ranged from 65 to 75 cm, while in the control it reached up to 50 cm (2022–2024). Fresh biomass yield varied from 13.4 g per plant (C) to 378.08 g per plant (F2), and dry biomass yield from 60.3 g (C) to 125.4 g (F2). The highest essential oil content was observed in F2 (1.2% in 2022), while the control remained at 0.8%. The F2 treatment achieved complete weed suppression throughout the experiment without the use of herbicides, demonstrating both agronomic and ecological advantages. Economic evaluation revealed that F2 generated the highest cumulative profit (€142,164.5) compared to the control (€65,555.3). Despite higher initial investment, F2 had the most favorable cost–benefit ratio in the long term. This study highlights the crucial influence of mulching and planting density on optimizing lemon balm production across diverse climatic and soil conditions, while also underscoring the importance of sustainable, non-chemical weed management strategies in lemon balm cultivation.

1. Introduction

Melissa officinalis L. is a perennial herbaceous plant from the Lamiaceae family, with global applications due to its medicinal, aromatic, and melliferous properties. In traditional medicine, M. officinalis is used to alleviate symptoms of stress, anxiety, insomnia, and digestive disorders, while its essential oil is increasingly employed in the pharmaceutical, cosmetic, and food industries [1]. Its distinctive lemon-like aroma results from the presence of bioactive compounds such as linalool, citronellal, and geraniol, which contribute to its sedative and calming effects [2]. The World Health Organization (WHO) and the European Commission on Phytotherapy have officially recognized M. officinalis as an effective natural remedy for functional disorders of the nervous and digestive systems, including insomnia, anxiety, and dyspepsia [3]. Beyond its pharmacological importance, M. officinalis is highly valued in apiculture, where it is regarded as one of the most significant nectar-producing plants due to its high nectar yield, which attracts bees and other pollinators, thereby contributing to biodiversity conservation and improving honey production [4]. Moreover, its essential oil is appreciated in the perfume, cosmetic, and liqueur industries, including in products such as Benedictine and Chartreuse [5].
In recent decades, increasing market demand has stimulated efforts to improve agronomic practices aimed at enhancing both the yield and quality of M. officinalis. Although it is relatively resilient, its successful cultivation—similar to that of other medicinal plant species—requires specific agronomic conditions [6], including appropriate soil type, adequate fertilization, timely harvesting, and effective protection from weeds and diseases [2]. The reduction in yield and alteration of essential oil composition caused by weed competition can be effectively mitigated through the application of synthetic mulch films in medicinal plant cultivation [7]. Although previous studies have examined various mulch types (organic, synthetic, and biodegradable), biodegradable mulches were not considered in this experiment due to their potential to alter soil pH, bind nutrients, and reduce fertilizer availability during degradation [8]. To ensure chemical stability, a synthetic mulch film was chosen over biodegradable alternatives. The increasing demand for high-quality raw plant material necessitates the use of non-chemical weed control methods in the cultivation of medicinal and aromatic plants [9]. One such method is the application of various mulches, which have been shown to improve the yield of related perennial medicinal species within the same family [10]. By covering soil surfaces, synthetic mulch films can fully suppress weed growth, increase soil moisture retention, regulate soil temperature, and reduce erosion [11]. In addition, mulch application may reduce the need for herbicides and pesticides, while enhancing the economic viability of cultivation [10,11,12]. Soil type variation across different regions is also a key factor influencing plant growth parameters and nutritional composition [13,14]. However, to date, only a limited number of contemporary agroecological studies have focused on evaluating the impact of mulching and planting density on the yield, essential oil quality, and economic sustainability of M. officinalis production. Despite growing commercial interest in this species, there is still a lack of research specifically tailored to distinct agroecological conditions—particularly regarding on agronomic performance and economic viability. The agroecological conditions of different Serbian regions, characterized by significant climatic variability and heterogeneous soil types, present challenges to the sustainable and uniform cultivation of medicinal plants. Therefore, this study investigates cultivation strategies adapted to these regional constraints by providing site-specific data to support improved agricultural practices. The study is based on the concept that the growth dynamics, yield performance, and essential oil content of Melissa officinalis L. are significantly affected by the interaction between planting density and synthetic mulch film. Accordingly, the study evaluates agronomic outcomes across multiple growing seasons and examines the economic feasibility of the tested cultivation approaches.
The aim of this study is to evaluate the application of mulching in M. officinalis cultivation, with particular focus on its effects on plant growth and development, dry leaf yield, herb yield, essential oil production, and economic aspects of cultivation, including the potential to increase productivity and reduce production costs. Particular emphasis was given to synthetic mulch film, selected for its chemical stability, long-term durability, and proven efficacy in suppressing weeds without chemicals. It is expected that the results of this study will contribute to the improvement of cultivation practices for M. officinalis, enabling more efficient and ecologically sustainable commercial production of this important medicinal species.

2. Materials and Methods

2.1. Plant Material

2.1.1. Seeds and Seedlings

The propagation material of M. officinalis (seeds) was obtained from the collection of the Institute for Medicinal Plants, Dr. Josif Pančić, Belgrade, Serbia, with a 1000-seed weight of 0.43 g and a germination rate of 72%. Seedling production began at the end of September 2021 by sowing 1200 seeds in transplant trays (60 cm × 40 cm × 10 cm; length × width × height) (60 × 40 × 10 cm) at a depth of 0.5 cm, using the “Cultivo I SF” (Klasmann-Deilmann GmbH, Geeste, Germany) substrate. The trays were placed in a polyethylene tent (grow box) with controlled humidity (50–60%), temperature (20–25 °C), and lighting (12-h photoperiod). In the last ten days of October 2021 (38 days after sowing), the seedlings were transplanted into Styrofoam seedling containers (160 holes, square-shaped pattern), and by the end of November, they were hardened for three weeks. In mid-December 2021 (95 days after sowing), the seedlings were transplanted into polyethylene containers (28 cells, ø 8 cm) and moved to a tunnel greenhouse with semi-controlled conditions (humidity 70–80%, temperature 5–15 °C). The production was completed at the beginning of April 2022 (192 days after sowing), ensuring uniform, high-quality seedlings (Figure 1).

2.1.2. Site Description and Experimental Design

The field experiment was conducted from April 2022 to 2024 at three agroecologically distinct sites in Serbia (Figure 2): Bačko Novo Selo (L1; 45°17′45.0” N, 19°08′42.0” E), Bavanište (L2; 44°49′44.3” N, 20°52′20.8” E), and Vilandrica (L3; 43°12′10.0” N, 21°58′11.0” E). Uniformly developed seedlings were used to establish the experimental plots, which were laid out according to a randomized complete block design, comprising four replications per treatment at each location and in each year. Individual replicate plots covered an area of 10 m2. Weed management was performed exclusively by non-chemical methods, primarily through mulching. Based on prior research on medicinal plant cultivation, synthetic mulch film was selected as the primary mulching material due to its proven effectiveness in weed suppression [10]. The experimental design included three treatments: F1, involving synthetic mulch film with a planting density of 8.3 plants m−2; F2, synthetic mulch film with a density planting of 11.4 plants m−2; and C, the control treatment without mulch, with a lower planting density of 4.76 plants m−2 (Figures S1–S3). The synthetic mulch used in this study was a black polypropylene fabric (Agritela Black, 90 g m−2; thickness 0.45 mm), hereafter referred to as synthetic mulch (abbreviation of Agritela Black). This material is characterized by its ultraviolet (UV) stability, permeability to air and water, and proven efficacy in non-chemical weed suppression. When calculating the economic evaluation of production, it was assumed that the synthetic mulch film was applied in strips, covering approximately 78% of the cultivated area (equivalent to 7800 m2 per hectare), while the remaining 22% (2200 m2 per hectare) was covered between mulch films with organic mulch. This organic mulch was composed exclusively of finely chopped and dried wheat straw, applied in a uniform layer approximately 5–7 cm thick. Initial fertilization on the experimental plots was conducted using an organic pelleted fertilizer (Fertor; NPK 4.5:2.7:2.3 + 1.1% Mg + 9.3% Ca, with micronutrients: Fe, Mn, B, Zn, Cu). The fertilizer was applied in two stages—prior to primary soil tillage and immediately before transplanting—at a total rate of 250 g m−2, based on both soil analysis results (Table 1) and the manufacturer’s recommendations. The applied fertilization strategy corresponds to a moderate nutrient input level, aligning with previous findings by Németh-Zámborné et al. [15], who reported optimal growth and essential oil yield in M. officinalis under balanced mineral nutrition (approximately 150 kg ha−1 N, 22 kg ha−1 P, and 149 kg ha−1 K). Data on agroecological conditions, including climate and soil, are presented graphically (Figure 3) and in tabular form (Table 1). Climatic data were collected using data loggers, while soil data were obtained through agrochemical analyses.
The graphs (Figure 3) present climatic parameters—relative humidity (RH), precipitation, and temperature—at the field trial locations (L1, L2, and L3) over a three-year period, highlighting notable differences among them. Locations L2 and L3 received higher total precipitation, whereas L1 experienced lower precipitation, likely influencing the water availability for plants. Correspondingly, seasonal variations in RH were observed, with lower summer values predominantly at L1, while RH at L2 and L3 remained relatively stable. Temperatures were higher at L2 and L3, potentially leading to heat stress in plants, whereas L1 recorded milder temperatures, more favorable for growth. Harvest timing was adjusted according to the specific environmental conditions of each site, with L1 and L2 offering more optimal conditions for plant growth, while L3 exhibited limitations due to less favorable climatic factors.
The chemical and mechanical properties of the soil at the locations (L1, L2, and L3) differ significantly in terms of their agroecological potential. Location L1 has a neutral to slightly alkaline reaction (pH 7.92 in H2O) and a moderate humus content (2.5%) and nitrogen level (0.20%), while phosphorus (108.7 mg/100 g) and potassium (41.04 mg/100 g) are at moderate levels. The mechanical composition is dominated by fine sand (66.97%), which ensures good permeability and moisture retention. The location L2 is slightly alkaline (pH 8.02 in H2O), with a slightly higher humus content (2.6%) and nitrogen level (0.21%), while phosphorus (150.3 mg/100 g) and potassium (27.19 mg/100 g) are at satisfactory levels. The mechanical composition shows a high proportion of fine sand (50.48%) and silt (29.28%), which allows for better moisture retention but reduces aeration. Location L3 has a slightly acidic reaction (pH 6.88 in H2O), lower humus content (2.4%), and the highest nitrogen level (0.26%), while phosphorus (0.89 mg/100 g) and potassium (9.5 mg/100 g) are at very low levels, necessitating additional fertilization. The mechanical composition is dominated by sand (21.37% coarse, 35.35% fine), with a lower moisture retention capacity. Overall, L2 exhibits the highest potential for agricultural production, while L3 has the least favorable agrotechnical potential.

2.1.3. Harvest Time

At location L1, harvesting was conducted in all three years (2022–2024) following the same schedule. In the first year (2022), the first harvest occurred in late May to early June, the second in late July, and the third in early September. This schedule was repeated in the second (2023) and third year (2024). At location L2, harvesting was likewise performed three times per year during each of the three years (2022, 2023, and 2024), adhering to the same timeline: late May–early June, late July, and early September. These three years were selected due to the consistent and vigorous plant growth observed at both L1 and L2, despite the perennial nature of the species. At location L3, harvesting was conducted over two years. In 2022, three harvests were performed (late May/early June, late July, and early September). In 2023, only one harvest was conducted (in July), while in 2024, no harvests took place due to unfavorable edaphic conditions at this site (Figure 3, Table 1).

2.1.4. Plant Analysis

During the three-year experiment, plant height was measured immediately before each harvesting phase (Figure 3), from ground level to the highest point of the plant. For each plot, ten plants were sampled to ensure the representativeness of the growth analysis. The fresh herb yield was determined after the removal of unwanted factors or foreign impurities that could affect the measurements. The remaining plants in the plots were manually cut with scissors at 7 cm above ground, and the yield was immediately measured to obtain the total yield per plot. This fresh yield was then converted to yield per m2 and per hectare (ha−1) for comparison between plots and years. To determine the dry herb yield, a 500 g sample of fresh biomass from each plot was dried at 30 °C for 72 h. After drying, the samples were weighed. The leaves were then manually separated from the stems and weighed separately (Figure S4). Based on the mass of dry leaves and fresh biomass, the dry leaf-to-fresh biomass ratio was calculated, allowing for the estimation of dry leaf yield per m2 and per hectare. The essential oil content was determined by hydro-distillation using a Clevenger-type (VELP Scientifica, Usmate Velate, Italy) apparatus. For each treatment, 200 g of fresh plant material was collected at each harvest during the growing seasons of 2022–2024. The samples were immediately subjected to a 2 h hydro-distillation procedure in accordance with the official protocol described in the European Pharmacopoeia [16].

2.1.5. Economical Evaluation

For the assessment of the economic feasibility of applying the mulching technique, a cost–benefit analysis (CBA) was employed, enabling the quantification of profitability through an analysis of the relationship between revenues (Bt) and costs (Ct) of M. officinalis raw material. The key formula used for calculating the Net Present Value (NPV) was the following:
N P V = t = 1 n [ ( B t C t ) / ( 1 + r ) ]
N V P = t = 0 n B t C t ( 1 + r ) t
NPV—Net Present Value (economic feasibility); ∑—Sum (aggregation of all periods); t—Period (year of analysis); n—Total number of years analyzed; Bt—Benefits in year t (revenues, savings); Ct—Costs in year t (investments, maintenance); r—Discount rate (time value of money); (1 + r)t—Discount factor (adjusting future values). The validity of the economic analysis was evaluated based on the Net Present Value (NPV). A positive NPV (NPV > 0) indicated that the application of synthetic mulch film was economically justified, whereas a non-positive NPV (NPV ≤ 0) signified that the technique was not economically viable. This approach to evaluating economic feasibility is supported by previous research on the cost-effectiveness of implementing agrotechnical measures in agriculture and forestry [17,18,19,20]. The economic feasibility evaluation was carried out by comparing the costs of machine services based on the price list of the Cooperative Union of Vojvodina for the year 2024, with part of the data sourced from the production sector of the Institute for Medicinal Plants, Dr. Josif Pančić, in Belgrade, whose production units are located in Pančevo. All values were recalculated according to the official exchange rate of the National Bank of Serbia as of 31 March 2025, which amounted to 117.2106 dinars per 1 euro. Sensitivity analysis was conducted by varying the initial investment and essential oil market price by ±20%, in accordance with the framework proposed by Pannell [21], to assess the robustness of the Net Present Value (NPV) under different economic conditions.

2.1.6. Statistical Analysis

The analyzed parameters were examined using one-way analysis of variance (ANOVA), while significant differences between groups were determined by Duncan’s multiple range test. The relationship between yield, morphological traits of M. officinalis populations, and agroecological habitat conditions was assessed using Pearson’s correlation and principal component analysis (PCA). Data analysis was conducted using XLSTAT v2023.5 (Addinsoft, Paris, France) [22], SPSS v22.0 (IBM Corp., Armonk, NY, USA) [23] and OriginPro v9.0 (OriginLab Corporation, Northampton, MA, USA) [24] software packages. Statistical significance was determined at the level of p < 0.05.

3. Results and Discussion

The results of this study were validated through the analysis of the morphological characteristics of M. officinalis, key yield parameters, and the agroeconomic calculation of the produced raw material—leaves. Special emphasis was placed on the impact of different cultivation treatments on the biometric traits of the plants, biomass distribution, and overall productivity.

3.1. Morphological Characteristics

3.1.1. Plant Height

The applied treatments (F1, F2, C) and locations (L1, L2, L3) significantly influence the height of M. officinalis plants depending on the analyzed years (2022–2024). Plant height ranged from 34.65 to 63.2 cm (Figure 4). A similar variability was observed in an earlier study [25] where plant height ranged from 41.61 to 55.79 cm at one location and from 10.58 to 29.61 cm at another, which can be attributed to the agroecological conditions of the habitat [26]. Furthermore, reported plant heights of M. officinalis ranged from 27.70 to 48.06 cm by different plant densities and nitrogen doses [27], while Farahani et al. [28] found plant heights to range from 45.92 to 65.32 cm under water deficit stress conditions, which was the case in the first year on L2 location in our study. Kacar et al. [29] demonstrated different plant densities on an average plant height of 57.3 cm, which partially aligns with the results of this study. In the first two years of the experiment (2022 and 2023), the F1 and F2 treatments showed superior results, as the plants had the greatest height at all locations (Figure 4). Among these two treatments, F2 frequently stood out with slightly higher values compared to F1, suggesting a positive effect of density on stimulating plant growth. The control treatment (C) had lower height values in experimental years. However, in 2024, a significant change occurred—the control treatment (C) reached the maximum height at some locations compared to the F1 and F2 treatments. This phenomenon may indicate long-term soil depletion effects after multiple years of treatment application, which can reduce their effectiveness, especially in cases where plants were grown at higher densities (F1 and F2). A similar outcome was reported in the study by Massoud et al. [30] where a smaller spacing (40 cm) resulted in taller plants (42.08 and 43.89 cm) compared to a wider spacing (60 cm), which produced shorter plants (37.50 and 39.46 cm) in both harvest periods of the first season. The results of the second season showed the same trend, and it is suggested that reducing the spacing between plants may increase their height due to competition for light, whereas wider spacing promotes branching and influences yield and the stem-to-leaf ratio, consistent with the findings of Saglam et al. [31] and Harshavardhan et al. [32] for this species.

3.1.2. Stem-to-Leaf Biomass Ratio

The analysis shows that the proportion of leaf biomass was dominant over the stem biomass during this study (Figure S4). This trend is well known, as the plant transports the produced assimilates to the leaves, which is a result of agro-ecophysiological conditions and growth physiology. However, different treatments influenced the intensity of leaf growth and the stem-to-leaf ratio. The application of black polyethylene mulch, including the synthetic mulch film used in this study, has been shown to enhance the stem-to-leaf biomass ratio, primarily by reducing interspecific competition through effective weed suppression [9,33]. These factors may contribute to the increased accumulation of biomass in the leaves, which aligns with the obtained results. Additionally, research indicates that mulching with black film often leads to improved photosynthetic activity and more efficient use of resources, directly contributing to the increase in leaf biomass [34]. Accordingly, it is possible that a similar effect occurred in this study. On the other hand, the treatment without mulch (C) was likely exposed to higher evaporation and increased competition with weeds, which could negatively impact overall biomass accumulation. Nevertheless, even under such conditions, M. officinalis maintained a dominant proportion of leaves, confirming its adaptability and ability to optimize resources in various cultivation conditions. These results are consistent with previous studies that suggest medicinal plants often favor leaf development to optimize photosynthetic efficiency and secondary metabolite synthesis, even when exposed to stress factors [35]. It is important to note that black polyethylene film can also affect the soil microclimate by increasing temperature during the early growth stages, which may stimulate leaf development [36]. This effect may explain the higher leaf biomass values in the F1 and F2 treatments compared to the control. However, if the temperature under the mulch becomes too high, it could have had a negative impact on growth, which could potentially explain differences between treatments F1 and F2 at different locations. Overall, mulching with black polyethylene film in the F1 and F2 treatments positively influenced leaf biomass dominance, most likely through improved growth conditions and more efficient exploitation of agroecological conditions.

3.2. Yield Parameters

3.2.1. Fresh and Dry Herb Yield per Plant

The applied treatment, year of study, and location significantly influenced herb yield per plant (Figure 5 and Figure 6). The fresh herb yields per plant ranged from 13.4 g to 378.08 g, showing considerable variability that also affected the dry herb yield. According to available data [37], these differences can be attributed to various agrotechnical practices and agroecological conditions. By comparing the results presented in Figure 4 and Figure 5, it is apparent that the highest average plant height was recorded in 2022, particularly under the F2 treatment, whereas the maximum fresh biomass per plant was achieved in 2024, especially at the L2 location. This discrepancy indicates that greater plant height does not necessarily correspond to higher biomass accumulation. Instead, other factors—such as leaf density, branching pattern, and efficiency of resource allocation—may play a decisive role in determining the final herb yield. The yield analysis per plant further shows that mulching treatments (F1 and F2) resulted in significantly higher biomass values compared to the control (C), which is in line with previous studies [33,34], highlighting the positive effect of mulching on microclimatic conditions and biomass accumulation. The F2 treatment generally exhibited the best results, suggesting that the combination of denser planting and mulching further improved growth conditions. Conversely, the control treatment (C) consistently demonstrated the lowest yield values, confirming that plants grown without protection from weed competition and without microclimate regulation have a diminished capacity for growth and development. These findings are consistent with the results of Harshavardhan et al. [32], who reported that appropriate agrotechnical measures can significantly enhance the yield potential of M. officinalis.
The locations significantly influence fresh and dry biomass yields. The highest yields were recorded at location L2, while the lowest yields were observed at location L3. Ecological factors lead to differences, including soil type, moisture content, and microclimatic conditions. Similar outcomes have been observed in earlier studies, highlighting the importance of soil characteristics and nutrient availability for the growth of medicinal plants [29,38]. The lowest yields at location L3 suggest potential limiting factors, such as lower soil fertility, unfavorable water regime, or increased exposure to stress factors [39,40]. In general, location L2 showed the best results, which may indicate more optimal agroecological conditions, such as higher soil fertility, or a more favorable moisture regime. On the other hand, location L3 consistently had lower yields, suggesting that the conditions at this location were less favorable for M. officinalis growth. In 2022 and 2023, yields were relatively stable, while in 2024, an increase was observed in certain treatments, particularly in the F2 group at location L2 (Figure 5 and Figure 6). This outcome may be due to the cumulative effects of treatments and agroecological conditions, with plants gradually increasing their productivity under optimal conditions [31,41]. However, in the final months of 2024, a slight decline in yields was observed in some treatments, which, according to Seidler-Lozykowska et al. [42] and Neocleous et al. [43], could indicate over-exploitation of soil nutrients or a physiological response of the plants to successive harvests.
Comparing the fresh and dry herb ratio by treatments, the average water content in fresh herbs ranged from approximately 50% to 80%, with variations depending on the applied treatment and year of study. The lowest water content (48.69%) was recorded in control at location L3 during 2022, while the highest water content (73.17%) was recorded in treatment F1 at location L2 in the same year. A similar outcome was observed by Saglam et al. [31]. Overall, the results indicate that the use of black polyethylene mulch significantly improved the growth conditions for M. officinalis, likely by reducing competition with weeds and enhancing the microclimatic conditions in the root zone. Treatment F2 was the most efficient, while variations between locations and years of study confirmed the impact of agroecological factors on plant productivity.

3.2.2. Yield Parameters per Unit Area

The analysis of dry herb yield of M. officinalis per square meter from 2022 to 2024 at three locations revealed significant differences between the treatments, as well as between the locations themselves (Figure 7). Treatment F2 emerged as the most efficient, while the control showed the lowest yields, indicating the positive effect of mulching on biomass accumulation, as noted by Dragumilo et al. [10]. The highest yield was recorded in 2022 at location L2 in F2 (341.01 g m−2), while the lowest yield was recorded at L3 in C (80.87 g m−2). In 2023, yields increased across all locations, with L2 showing the highest values in F2 (418.85 g m−2) once again. In 2024, L2 recorded the maximum yield in F2 (750.32 g m−2), while L1 showed a sharp increase in the same treatment (840.10 g m−2). In contrast, L3 consistently had the lowest yield values across all years, with pronounced variations in C. These results confirm the significant impact of agrotechnical measures, particularly the use of mulch, on the yield of M. officinalis, with F2 proving to be the most effective. Additionally, the type of soil and microclimatic conditions at the locations played a crucial role in the yield variations, L2 being the most productive location, as reflected in other examined parameters.
The analysis of dry leaf yield of M. officinalis per square meter from 2022 to 2024, based on data from three locations, showed an advantage of treatments F1 and F2 over C (Figure 8). At L1, in 2022, F2 (191.66 g m−2), and F1 (122.07 g m−2) achieved significantly higher yields compared to C (23.96 g m−2). During 2023 and 2024, there was a moderate decline in yields for all treatments, with F1 and F2 still being more productive than C. A similar trend was observed at L2, where in 2023, the yields of F2 (588.67 g m−2) and F1 (358.02 g m−2) exceeded those of C (282.19 g m−2). In 2024, although F1 and F2 still had higher yields than C, a significant reduction was noted. Favorable agroecological conditions at L2 allowed for better results with the application of mulch and increased planting density. At L3, yields were lower throughout all years, but in 2023, F1 (263.24 g m−2) and F2 (430.99 g m−2) showed moderate values. However, in 2024, there was a significant decrease, indicating unfavorable climatic conditions.
The use of mulch film with different densities showed significant influences on the yield of dry leaf of M. officinalis per hectare during the study period (Figure 8). The highest yield was recorded in F2 in 2022 at L2, reaching approximately 10,500 kg ha−1. In the following years, yields decreased in comparison to C, and by 2024, at the same location, it amounted to about 8000 kg ha−1. The obtained results significantly exceed those from the study by Kacar et al. [29] where M. officinalis was grown at lower densities (50 cm × 30 cm, 50 cm × 40 cm, and 50 cm × 50 cm) with much lower yields (2650, 1510.7, and 1220 kg ha−1). Similar findings were reported in other studies [44,45] where yield ranged from 15 to 30 t ha−1 and 30 to 50 t ha−1, depending on planting density (60,000–125,000 plants ha−1). The yield of M. officinalis varies significantly depending on ecological conditions, as in previous research [46] where the highest yields observed on fertile soils with moderate climatic conditions ranged from 6.45 to 10.94 t ha−1. Our results show that F2 at L2 exceeded this range, indicating additional benefits from the application of mulch and optimization of planting density. Mihajlov et al. [47] emphasize that the average yield of dry leaf M. officinalis in commercial production is around 6775 kg ha−1, which aligns with the results of C in our study. However, mulch treatments at L2 achieved significantly higher values, while yields at L1 and L3 were often in the lower range.
These results confirm that the use of mulch contributes to an increase in yield, as well as in other studies with medicinal plants [7,10,11] where mulching increased the yields by 20–40% by reducing stress due to moisture loss and competition with weeds. Said-Al Ahl et al. [48] investigated the impact of planting density and irrigation on the yield of Origanum vulgare, a plant with similar ecological requirements to M. officinalis, and found that denser planting can increase yields by 15–30%, but only with adequate resources, particularly water and nutrients. In our study, F2 achieved the highest yields in most cases (Figure 9), confirming that increased planting density can be beneficial, but only under optimal agroecological conditions (e.g., at L2). At locations with less favorable conditions (L3), the yield of F2 did not always significantly surpass F1, suggesting that excessive density may lead to increased competition between plants when resources are limited (Figure 9). The yield decline in 2023 and 2024 can be explained by climatic factors, which have already been identified as key in the variability of medicinal plant yields [49]. Notably, the lowest yields were observed at L3, which may be a result of drier conditions and unfavorable soil structure (Figure 9). Similar trends were noted in studies analyzing the production of medicinal plants in different agroecological conditions, where stress factors such as moisture and temperatures led to reduced yields [50].
The analysis of essential oil yields indicates significant variations in production, influenced by different treatments, soil, and climatic factors [12,33,51]. At L1, F2 achieved the highest essential oil yield in 2022 (2.58%), while F1 and C had lower values (1.58% and 0.46%, respectively) (Figure 10). In 2023, there was a significant decrease in production, with yields of 0.11% (F2) and 0.13% (F1 and C). In 2024, there was a recovery in yields, with values of 2.56% (F2) and 1.25% (F1), while C reached 1.75%. At L2, exceptionally high yields were recorded in 2022, with F1 and F2 both achieving identical results of 5.59%. However, in 2023, there was a significant drop, with yields of 0.15% (F1) and 0.14% (F2 and C). In 2024, yields partially recovered, reaching 1.96% (F1), 1.33% (F2), and 1.65% (C), which confirms the impact of climatic factors on production variability. At L3, yields were generally lower, but F2 achieved a relatively high yield of 4.65% in 2022, while C reached 0.92%. In 2023, all treatments experienced a decline to 0.12%, which can be linked to unfavorable climatic conditions (Figure 10). Variations between treatments, according to Dragumilo et al. [10], can primarily be attributed to the impact of mulching, while plant density had a lesser effect [29]. Although F1 and F2 provided the best results in 2022, 2023 showed a significant decline in production, confirming the influence of climatic factors. At L2, yield reached the highest yields in 2022, also showing significant fluctuations in subsequent years.
Given the pronounced variability of yield parameters between locations and treatments, additional correlations and principal component analysis (PCA) were conducted to gain a comprehensive insight into the optimization of production conditions and to identify treatments that enable stable and high-yielding raw material production (Figure 11 and Figure 12).

3.2.3. Correlation and Principal Component Analysis of Soil Chemical Properties, Climatic Factors, and M. officinalis Yield

This is consistent with previous research emphasizing the importance of agroecological factors in biomass accumulation and secondary metabolite synthesis in plant species from the Lamiaceae family [26,52]. The correlation analysis between soil chemical properties, climatic factors, and the yield of M. officinalis provides valuable insights into the impact of agroecological parameters on the growth and quality of this species (Figure 11). Several statistically significant correlations were observed, indicating the complex interaction between edaphic and climatic variables across treatments and years. Soil pH (both pH-H2O and pH-KCl) showed a strong positive correlation with humus content (r = 0.907 and r = 0.910, respectively), P2O5 (r = 0.981), and K2O (r = 0.861), suggesting that more alkaline soils are associated with better nutrient availability and organic matter retention. Conversely, a strong negative correlation was observed between pH and total nitrogen (r = −0.970), indicating that more acidic soils may enhance nitrogen mineralization, corroborating findings by Neina [53].
Soil texture components such as coarse sand and clay displayed an inverse relationship (r = −0.995), confirming contrasting effects on drainage and nutrient retention. Clay content showed strong positive correlations with total nitrogen (r = 0.794) and silt (r = 0.750), supporting the hypothesis that clay-rich soils provide a more favorable medium for nutrient accumulation and plant growth, as described by Plante [54]. Climatic factors also played a significant role. Precipitation in 2022 negatively correlated with soil pH (r = −0.619), while precipitation in 2024 positively correlated with humus content (r = 0.713), suggesting that increased moisture in later years promoted organic matter formation. Temperature parameters across all years were negatively correlated with both total nitrogen and clay (r from −0.734 to −0.958), indicating that higher temperatures may accelerate mineralization and reduce soil fertility. These findings are consistent with the study by Nina [53], which examined the effects of climate change on soil chemical composition. Relative air humidity (RVV) showed a strong negative correlation with temperature (r = −0.972), confirming that warmer years were also drier, potentially increasing plant stress. Dry matter yield in all treatments (F1, F2, and C) negatively correlated with precipitation in 2022 and 2023 (r from −0.671 to −0.757), implying that excessive rainfall reduced biomass accumulation. These findings align with previous studies showing yield suppression in medicinal crops under overly wet conditions [53]. Essential oil yield exhibited treatment-specific correlation patterns. In F1, essential oil content positively correlated with soil pH and K2O (r = 0.322 to 0.453), indicating the beneficial role of alkaline conditions and potassium in secondary metabolite synthesis. F2 showed similar trends, whereas in C, essential oil content was most strongly correlated with relative humidity (r = 0.795), highlighting the importance of moderate and stable climatic conditions for essential oil productivity. Collectively, these results underscore the importance of balanced agroecological conditions—particularly soil chemistry and relative humidity—in maximizing both biomass and essential oil yield [26,45,48,49,50,51,52,53,54].
Overall, the correlation matrix highlights several key factors affecting the growth, yield, and quality of M. officinalis. To provide a clearer and more precise interpretation of these relationships, a principal component analysis (PCA) was conducted (Figure 12).
Principal component analysis (PCA) enabled the identification of key agroecological factors influencing the yield and quality of M. officinalis L. (Figure 12), across diverse locations (L1, L2, and L3). The first two principal components (PC1 and PC2) explained 38.4% and 24.7% of the total variance, respectively. Based on the results, the locations were clearly differentiated along the PC1 axis, with L1 characterized by higher P2O5 content and more alkaline pH values (H2O and KCl), in contrast to L3. L2 occupies a central position in the PCA space and shows the closest association with dry leaf biomass yield (kg ha−1) and essential oil content (%), indicating that these variables have high positive loadings on PC1. Variables such as humus content, precipitation in 2022 and 2023, and clay percentage contributed positively to PC2, while average temperature in 2024, silt content, and essential oil yield from F1 negatively influenced this component. These associations suggest that the agroecological conditions at L2 facilitate optimal growth and the accumulation of secondary metabolites. The organic matter content (humus) and precipitation levels in 2022 and 2023 showed a positive correlation with yield, whereas the average temperature in 2024 had a negative effect on essential oil productivity, likely due to the increased degradation of secondary metabolites. These findings indicate that moderate soil pH, adequate humus, and optimal precipitation distribution can contribute to increased yield and improved lemon balm quality [33,55,56,57]. This is consistent with previous research highlighting the importance of agroecological factors in biomass accumulation and secondary metabolite synthesis in plant species from the Lamiaceae family [58,59,60,61].

3.3. Economic Evaluation of Production

Seedling production is a complex process in which costs directly impact efficiency and profitability (Table 2, Table 3, Table 4 and Table 5). The total costs for seedling production for research purposes (€10,368) amount to €3517.32, resulting in a cost of €0.34 per seedling, which is crucial for evaluating economic viability [62]. The main costs include inputs such as seeds and the substrate “Cultivo I SF,” whose quality affects the germination and health of seedlings, improving plant yield and quality [63]. Growth materials, such as transplant trays, Styrofoam, and plastic containers, provide optimal growing conditions, especially in controlled systems such as greenhouses [62]. Additionally, investments in climate control, water, and light influence costs and productivity [63]. Labor is a significant portion of the costs, where quality training contributes to efficiency and reduces errors [62].
The cost analysis of production per hectare for F1, F2, and C in open fields shows significant differences in cost distribution throughout the study period. Among the evaluated treatments, the highest total cost was observed for F2 (€81,962.10), followed by F1 (€65,088.60), whereas C incurred the lowest expenditure (€25,956.20). These cost differentials primarily reflect variations in input requirements, including planting material, synthetic mulch film, organic mulch, labor, irrigation, and fertilization (Table 2).
The higher costs associated with F1 and F2 are attributable to the application of synthetic mulch film and organic mulch, whereas the lower overall investment in C reflects the absence of these inputs. The highest cost for planting material was observed in F2 (€38,760.00), which is about €10,000 more than F1 (€28,200.00) and more than double compared to C (€16,184.00), potentially contributing to higher yields and better plant quality [64], as also evident in this study. Labor costs are highest in F2 (€1529.30), followed by F1 (€1274.40), while C has minimal labor costs (€169.90). A similar trend was observed for irrigation and fertilization costs—F2 (€1019.50), F1 (€849.60), and C (€254.90), confirming the higher intensity of agronomic care in F2 [65]. Processing costs are also highest in F2 (€18,274.40), followed by F1 (€15,019.50), with significantly lower costs in C (€339.80). The total costs in the first year follow the same pattern—F2 (€11,002.50) and F1 (€9685.60) have higher initial investments compared to C (€1690.70). These data highlight the need to analyze the economic viability of treatments, where the cost–benefit ratio plays a crucial role in commercial production. Although F1 and F2 are more expensive, they may provide greater long-term sustainability and quality of production, while C is more suitable for producers with limited budgets or where the availability of human resources is higher.
The economic viability analysis of different treatments (Table 3) reveals significant differences in benefits per hectare over the three-year period (2022–2024). The results clearly indicate the superiority of F2, which achieved the highest cumulative benefit value (€142,164.5), while C had the lowest value (€65,555.3), and F1 occupied the intermediate position with a total benefit of €91,135.3. These data confirm previous studies [66,67], which emphasize the need for more intensive agronomic measures to increase productivity and the economic profitability of production. Accordingly, the economic profitability of F1, F2, and C in open-field conditions shows significant differences in benefits per hectare throughout the three-year period (2022–2024). F2 achieved the highest cumulative benefit (€142,164.5), while C had the lowest value (€65,555.3), and F1 ranked in the middle with a total benefit of €91,135.3.
The prices of production components are taken from the records of the 64 Cooperative Alliance of Vojvodina [68]. During 2022, the highest benefits were recorded at L2 for all treatments, with F2 reaching a maximum of €18,405.6, while L3 showed the greatest relative advantage for F2 (€25,586.2) compared to F1 (€15,673.6) and C (€6619.7). These data indicate favorable agroecological conditions at L2 and L3 with the application of intensive agronomic measures. In 2023, the benefit of F2 significantly increased at L1 (€19,787.4) and L2 (€27,207.7), while L3 recorded a decline for all treatments.
The lowest benefit during this period was recorded at C (€1059.8), which may indicate a negative impact of weeds and soil depletion, aligning with the previous research [69]. The year 2024 shows a decrease in benefits for all treatments, with the balance at L3 reaching zero, indicating the depletion of soil resources or other limiting factors. However, F2 remains the most profitable treatment, confirming its effectiveness in optimizing production. These findings are consistent with Deshmukh et al. [70], who emphasize the potential of regenerative practices such as crop rotation and tailored agronomic measures to maintain productivity at the L3 site.
The overall results of the study confirm the economic advantage of F2, generating 1.56 times the benefit of C and F1 (Table 4). However, the gradual decrease in benefits over the years suggests the need for strategies for sustainable land and resource management. Further research should focus on optimizing agronomic measures, adjusting irrigation practices, and implementing precision agriculture [71].
The analysis of costs, benefits, and net benefits for the three factors (F1, F2, and C) reveals significant differences in the efficiency of each factor (Table 5). Factor F2 has the highest costs (€81,962.1) but also achieves the highest benefit (€142,164.50). This factor also generates the highest net benefit (€60,202.40), indicating that despite higher costs, F2 provides the best ratio between invested resources and achieved benefits. This factor can be considered the most profitable, especially for growing perennial crops. Factor C, although having the lowest costs (€25,956.2), generates a smaller benefit (€65,555.30). However, its net benefit (€39,599.10) relative to the costs is still favorable. This factor may be attractive in situations where labor is available and initial investments need to be minimized, but its lower return compared to F2 indicates that it is not the most optimal in terms of benefits. Factor F1 has medium values for costs (€65,088.6) and benefits (€91,135.30), but generates a lower net benefit (€26,046.70) compared to F2. This suggests that while factor F1 provides solid returns, it is not as efficient as the other factors, particularly F2, which provides higher benefits. In general, although factor C has the lowest costs, it does not provide enough benefits to be optimal in all cases, especially when there is a lack of human resources. Factor F2, despite higher initial investments, provides the best return relative to costs, making it the most profitable factor. Factor F1 can be a suitable choice when moderate investment is sought for seed production, especially for plants with a larger habitus, but in this case, it is less efficient compared to F2. The results of this study suggest that the use of mulch in M. officinalis production demonstrates significant economic profitability by reducing cultivation costs, labor, and pesticide usage. Mulch improves soil quality, retains moisture, and reduces the occurrence of weeds, diseases, and pests, resulting in higher yields. Research confirms that the use of mulch contributes to long-term economic benefits and soil sustainability, justifying the initial investments [34,72].

3.4. Sensitivity Analysis of Economic Parameters

To evaluate the robustness of the economic analysis and account for potential regional variability in investment costs and market conditions, a sensitivity analysis was conducted following the framework proposed by Pannell [21]. Two critical input variables were varied by ±20% initial investment costs and the market price of Melissa officinalis essential oil. The outcomes of this analysis are summarized in Table 6, which presents the impact of these variations on the net present value (NPV).
The results indicate that a 20% increase in initial investment costs leads to a 27.1% reduction in NPV, although the production system remains economically viable. In contrast, a 20% decrease in essential oil price brings the NPV close to the breakeven point, underscoring the sensitivity of profitability to market fluctuations. Conversely, a 20% increase in product price results in a substantial 47% improvement in NPV, confirming that the economic outcome is most sensitive to price changes. These findings demonstrate that the F2 treatment maintains profitability across different economic scenarios; however, market volatility should be carefully considered when planning large-scale implementation. Incorporating sensitivity analysis into economic evaluations strengthens the robustness and broader applicability of the study’s conclusions.

4. Conclusions

The research on the impact of mulch on M. officinalis production under different agroecological conditions in the Republic of Serbia demonstrates significant ecological and economic benefits of applying mulch in agronomic practices. The application of synthetic mulch film with different planting densities resulted in complete weed suppression, increased yields, and improved morphological characteristics of plants compared to control. The highest yield of leaf was in F2 with plant density 11.4 plants m−2, in all analyzed years, with the maximum being 10.500 kg ha−1 in 2022.
The use of mulch in Melissa officinalis production suppressed weeds via non-chemical means, increasing plant height and fresh and dry herb yields, resulting in a higher total of dry leaf per m2 and essential oil yields. Economic evaluation showed that F2 offers the best cost–benefit ratio, despite higher initial investments. Based on cumulative profit over the three-year period, mulching has proven to be a sustainable and economically beneficial practice for perennial plant production.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae11080866/s1, Figure S1. Plant arrangement in treatment F1 with four replications per location. Figure S2. Plant arrangement in treatment F2 with four replications per location. Figure S3: Plant arrangement in the control treatment (C) with four replications. Figure S4. Proportion of leaf and stem biomass across treatments and study years.

Author Contributions

Conceptualization, D.R., S.V.G. and M.R.; methodology, D.R., T.M. and M.R.; research: S.V.G. and S.M. (Snežana Mrđan); software, P.B. and N.Č.; validation, M.R., A.D. and T.M.; formal analysis, S.V.G., A.D., S.M. (Snežana Mrđan), P.B. and N.Č.; investigation S.V.G.; resources, T.M., M.L. and M.R.; data curation, S.V.G. and M.R.; writing—original draft preparation, S.V.G., A.D. and S.M. (Sara Mikić); writing—review and editing, Ž.P., M.L. and T.M.; visualization, S.V.G. and M.R.; supervision, M.L. and T.M.; project administration, M.R. and M.L.; funding acquisition, M.L. and T.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by joint funding from the Ministry of Science and Technology of the Republic of Serbia (Grants: 451-03-136/2025-03/200003).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Melissa officinalis L. seedling.
Figure 1. Melissa officinalis L. seedling.
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Figure 2. Locations of field trials (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica).
Figure 2. Locations of field trials (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica).
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Figure 3. Climatic parameters at the experimental sites during the vegetative season: relative humidity (RH), total precipitation (mm), and mean air temperature (°C) for three locations—L1: Bačko Novo Selo, L2: Bavanište, and L3: Vilandrica.
Figure 3. Climatic parameters at the experimental sites during the vegetative season: relative humidity (RH), total precipitation (mm), and mean air temperature (°C) for three locations—L1: Bačko Novo Selo, L2: Bavanište, and L3: Vilandrica.
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Figure 4. The average plant height of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each fragmentation class according to Duncan’s new multiple-range test (DMRT), with different letters showing significant differences and the same letters indicating no significant differences.
Figure 4. The average plant height of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each fragmentation class according to Duncan’s new multiple-range test (DMRT), with different letters showing significant differences and the same letters indicating no significant differences.
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Figure 5. The average fresh herb yield per plant of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each fragmentation class according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
Figure 5. The average fresh herb yield per plant of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each fragmentation class according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
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Figure 6. The average dry herb yield per plant of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each fragmentation class according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
Figure 6. The average dry herb yield per plant of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each fragmentation class according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
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Figure 7. The average dry herb yield per square meter of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each fragmentation class according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
Figure 7. The average dry herb yield per square meter of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each fragmentation class according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
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Figure 8. The average yield of dry leaf per square meter of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each treatment group according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
Figure 8. The average yield of dry leaf per square meter of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each treatment group according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
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Figure 9. The average total leaf yield per year of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each treatment group according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
Figure 9. The average total leaf yield per year of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each treatment group according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
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Figure 10. The average yield of essential oil per year of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each treatment group according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
Figure 10. The average yield of essential oil per year of Melissa officinalis L. under different planting densities and mulch treatments (F1, F2, C) over the multi-year study period at diverse locations (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica). Lowercase letters indicate significant differences (p < 0.05) between each treatment group according to Duncan’s new multiple-range test (DMRT), with different letters indicating significant differences and the same letters indicating no significant differences.
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Figure 11. Pearson correlation analysis based on correlation coefficients of Melissa officinalis L. essential oil yield traits related to treatments, locations, and multi-year production variability.
Figure 11. Pearson correlation analysis based on correlation coefficients of Melissa officinalis L. essential oil yield traits related to treatments, locations, and multi-year production variability.
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Figure 12. PCA analysis of morphological, agroecological, and yield of Melissa officinalis L.: key variables and habitat relationships.
Figure 12. PCA analysis of morphological, agroecological, and yield of Melissa officinalis L.: key variables and habitat relationships.
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Table 1. Basic chemical properties and mechanical composition of the surface soil layer (0–25 cm) at the experimental sites (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica).
Table 1. Basic chemical properties and mechanical composition of the surface soil layer (0–25 cm) at the experimental sites (L1—Bačko Novo Selo; L2—Bavanište; L3—Vilandrica).
LocalityChemical Properties
L1pHHummus (%)Total N (%)Al-mg/100 g
H2OKClP2O5K2O
7.927.242.50.20108.741.04
Mechanical Composition
Coarse Sand (%)
2–0.2 mm
Fine Sand (%)
0.2–0.02 mm
Silt (%)
0.02–0.002 mm
Clay (%)
<0.002 mm
1.9966.9717.9613.08
L2Chemical Properties
pHHummus (%)Total N (%)Al-mg/100 g
H2OKClP2O5K2O
8.027.422.60.21150.327.19
Mechanical Composition
Coarse Sand (%)
2–0.2 mm
Fine Sand (%)
0.2–0.02 mm
Silt (%)
0.02–0.002 mm
Clay (%)
<0.002 mm
2.4850.4829.2817.76
L3Chemical Properties
pHHummus (%)Total N (%)Al-mg/100 g
H2OKClP2O5K2O
6.885.602.40.260.899.5
Mechanical Composition
Coarse Sand (%)
2–0.2 mm
Fine Sand (%)
0.2–0.02 mm
Silt (%)
0.02–0.002 mm
Clay (%)
<0.002 mm
21.3735.3524.0019.28
Table 2. Calculation of seedling production.
Table 2. Calculation of seedling production.
Calculation of Production Seedling
Production ComponentsCost (€)
Seeds51.00
Substrate “Cultivo I SF”203.88
Pecking boxes163.08
Styrofoam seedling containers (160 cell)305.88
Polyethylene containers (28 cells, ø 8 cm)1019.52
Grow box and greenhouse conditions509.76
Labor force1019.52
3517.32
Total number of seedlings produced = 10,368
Table 3. Calculation of production in the open field after planting per hectare.
Table 3. Calculation of production in the open field after planting per hectare.
Calculation of Production per ha on Different Treatments in the Open Field After Planting
Production ComponentsCost per Treatment (€)
F1F2C
Seedlings 28,200.038,760.016,184.0
Synthetic mulch film 4248.14248.1-
Organic mulch373.8373.8-
Labor force1274.41529.3169.9
Irrigation and fertilization849.61019.5254.9
Processing costs (during, manipulation)15,019.518,274.4339.8
Total first year9685.611,002.51690.7
Total for 3 years5437.66754.51316.9
65,088.681,962.125,956.2
Table 4. Benefit calculation of leaf production.
Table 4. Benefit calculation of leaf production.
Calculation of Benefit per ha on Different Treatments in the Open Field After Planting
YearLocalityBenefit per Treatment (€)
F1F2C
LeafLeafLeaf
2022L18104.211,110.13835.0
L217,663.218,405.617,009.2
L315,673.625,586.26619.7
2023L110,776.319,787.45470.7
L215,172.327,207.712,097.1
L33512.45687.31059.8
2024L19277.015,472.47015.8
L210,956.318,907.812,448.0
L3000
91,135.3142,164.565,555.3
Table 5. Cost–benefit analysis.
Table 5. Cost–benefit analysis.
ParameterF1F2C
Cost65,088.6081,962.1025,956.20
Benefit91,135.30142,164.5065,555.30
Net Benefit26,046.7060,202.4039,599.10
Table 6. Sensitivity analysis of NPV in response to changes in investment cost and product price.
Table 6. Sensitivity analysis of NPV in response to changes in investment cost and product price.
ScenarioInvestment CostEO PriceNPV (€)
Baseline100%100%60,202.40
−20% Cost80%100%76,527.70
+20% Cost120%100%43,877.10
−20% EO Price100%80%31,870.60
+20% EO Price100%120%88,534.20
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Gordanić, S.V.; Radanović, D.; Rajković, M.; Lukić, M.; Dragumilo, A.; Mrđan, S.; Batinić, P.; Čutović, N.; Mikić, S.; Prijić, Ž.; et al. Influence of Mulching and Planting Density on Agronomic and Economic Traits of Melissa officinalis L. Horticulturae 2025, 11, 866. https://doi.org/10.3390/horticulturae11080866

AMA Style

Gordanić SV, Radanović D, Rajković M, Lukić M, Dragumilo A, Mrđan S, Batinić P, Čutović N, Mikić S, Prijić Ž, et al. Influence of Mulching and Planting Density on Agronomic and Economic Traits of Melissa officinalis L. Horticulturae. 2025; 11(8):866. https://doi.org/10.3390/horticulturae11080866

Chicago/Turabian Style

Gordanić, Stefan V., Dragoja Radanović, Miloš Rajković, Milan Lukić, Ana Dragumilo, Snežana Mrđan, Petar Batinić, Natalija Čutović, Sara Mikić, Željana Prijić, and et al. 2025. "Influence of Mulching and Planting Density on Agronomic and Economic Traits of Melissa officinalis L." Horticulturae 11, no. 8: 866. https://doi.org/10.3390/horticulturae11080866

APA Style

Gordanić, S. V., Radanović, D., Rajković, M., Lukić, M., Dragumilo, A., Mrđan, S., Batinić, P., Čutović, N., Mikić, S., Prijić, Ž., & Marković, T. (2025). Influence of Mulching and Planting Density on Agronomic and Economic Traits of Melissa officinalis L. Horticulturae, 11(8), 866. https://doi.org/10.3390/horticulturae11080866

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