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Article

Influence of Organic Mulching Strategies on Apple Tree (Mallus domestica BORKH.) Development, Fruit Quality and Soil Enzyme Dynamics

by
Ioana Maria Borza
1,†,
Cristina Adriana Rosan
2,†,
Daniela Gitea
3,*,
Manuel Alexandru Gitea
1,*,
Alina Dora Samuel
4,
Carmen Violeta Iancu
2,
Ioana Larisa Bene
1,
Daniela Padilla-Contreras
5,6,7,8,9,
Cristian Gabriel Domuta
1 and
Simona Ioana Vicas
2
1
Department of Agriculture, Horticulture, Faculty of Environmental Protection, University of Oradea, 410048 Oradea, Romania
2
Department of Food Engineering, Faculty of Environmental Protection, University of Oradea, 410048 Oradea, Romania
3
Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, 410028 Oradea, Romania
4
Department of Biology, Faculty of Sciences, University of Oradea, 1 Universitatii St., 410087 Oradea, Romania
5
Master Program in Fruitculture, Faculty of Agricultural Sciences and Environment, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4811230, Chile
6
Scientific and Technological Bioresources Nucleus (BIOREN-UFRO), Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4811230, Chile
7
Laboratory of Physiology and Plant Nutrition for Fruit Trees, Faculty of Agricultural Sciences and Environment, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4811230, Chile
8
Laboratory of Soil Fertility, Faculty of Agricultural Sciences and Environment, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4811230, Chile
9
Doctoral Program in Science of Natural Resources, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4811230, Chile
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Agronomy 2025, 15(9), 2021; https://doi.org/10.3390/agronomy15092021
Submission received: 31 July 2025 / Revised: 11 August 2025 / Accepted: 21 August 2025 / Published: 22 August 2025

Abstract

Mulching is a sustainable agronomic practice that can improve soil quality and fruit characteristics in crops. This study investigated the influence of sheep wool mulch and a soil conditioner on growth, the accumulation of bioactive compounds, and soil enzymatic activity in apple orchards. A two-year field experiment (2023–2024) was conducted using three experimental methods: mulching with sheep wool (V2), application of a soil conditioner, corn starch-based polymer (V3), and a combination of sheep wool and corn starch-based polymer (V4) along with a control (V1). Tree growth parameters, fruit physicochemical properties, total phenolic and flavonoid content, and soil enzyme activities (dehydrogenase, catalase, phosphatase) were assessed. Data were analyzed using Principal Component Analysis (PCA) and Pearson’s correlation. PCA showed that the combined variant (V4) improved fruit size, weight, and bioactive compound content, while wool mulch alone (V2) was associated with higher fruit yield and better vegetative growth. Catalase activity correlated positively and consistently with bioactive compounds in both years, while phosphatase activity showed an intensified positive relationship in 2024. Dehydrogenase activity was negatively correlated with phenolic content in both seasons. Organic and integrated mulching practices can beneficially modulate both aboveground and belowground plant–soil interactions. The combined variant proved to be the most effective strategy, enhancing fruit nutritional quality and supporting sustainable apple orchard management.

1. Introduction

Agriculture is facing increasing challenges due to climate change, particularly water scarcity, shrinking agricultural land and the spread of diseases and pests, which threaten sustainability and food security [1,2,3,4,5].
Drought is a major global challenge, reducing crop yields and hindering global agricultural development due to the decrease in annual rainfall and its uneven distribution throughout the year, especially during the growing season. Evaporation of water at ground level intensifies soil drought, which must be addressed in the future by new technologies that help retain and deliver water to plant roots [6,7,8,9]. In addition to these challenges, there are also problems with weed control, which is usually achieved by mechanization or herbicides, but both approaches have disadvantages: mechanization accelerates soil erosion, while herbicides contaminate the environment, promote resistance and risks to human and animal health.
Sustainable management of orchard tree rows is essential not only for tree growth and fruit quality, but also for maintaining soil health and biodiversity. Effective management focuses on controlling orchard weeds, which compete strongly with fruit trees for water and nutrients. Due to their lower root density, fruit trees are generally weaker competitors than weeds. [10,11,12]. Traditional tillage as a weed control has been shown to negatively affect tree growth, fruit yield and quality, root development, and overall soil fertility. [13,14,15,16]. Mulching primarily aims to reduce evaporation and water erosion, regulate soil temperature, enhance soil moisture retention, and suppress weed growth. As a result, it supports plant development, increases crop yields, and reduces water consumption [17,18,19,20,21,22].
Mulch is defined as a protective layer of organic or inorganic material applied to the soil surface [23,24]. Organic mulches, derived from plant or animal materials, are effective in reducing nitrate leaching, improving soil physical properties, enhancing biological activity, regulating the nitrogen cycle, contributing organic matter, controlling temperature and moisture, and reducing erosion. However, their use in horticultural crop production remains limited at commercial scale due to high costs, labor requirements, and logistical challenges [25,26]. Straw mulching has been shown to reduce soil salinity and enhance key soil health indicators [27,28].
Organic mulches are highly effective in conserving soil moisture without hindering water infiltration or retention. Appropriate mulching can significantly reduce irrigation frequency and, in some cases, eliminate the need for irrigation altogether [25]. Research into the use of textile mill residues has been going on, but now this valuable natural material is taking on a whole new meaning: application in the form of mulch and compost (obtained from its degradation on the soil) has produced amazing results. It has thus been found that sheep’s wool applied to plant rows contributes significantly to improving the physico-chemical and microbiological qualities of the soil, the basis for increasing the plants’ resistance to various factors. Besides its beneficial action against weed growth and soil micro-organisms, wool has also been shown to be a very good fertilizer [29].
Although the agricultural use of sheep wool has been investigated in different cropping systems, its application in orchard management remains poorly documented. A recent literature [30] survey identified 90 indexed studies on wool use in plant production, with 61% focusing on horticultural crops such as tomato, lettuce, eggplant and pepper [31,32,33], and smaller numbers on field crops, ornamental plants, grasses, medicinal plants, and aromatic species [34,35,36]. In sharp contrast, fruit trees and shrubs were represented by only three experimental trials, involving olive trees [37], plum trees [38], and raspberries [39]. This underrepresentation is notable given the promising results reported in other crops, where raw or processed wool has been shown to improve soil moisture retention, enhance nutrient availability, and increase yields. For example, in horticultural species, wool-based products such as pellets, compost, and raw wool mulches have been associated with yield increases of 20–30%, improved soil health indicators, and reduced dependence on synthetic fertilizers [40]. The lack of equivalent studies in perennial fruit systems represents a significant research gap, especially in temperate climates where soil moisture conservation, nutrient cycling, and sustainable management practices are critical for productivity.
In our previous article, sustainable methods for improving the yield and quality of Stanley plums (Prunus domestica) under drought conditions were investigated [38]. The results showed that the use of sheep wool mulch combined with a corn starch-based soil improver significantly increased both productivity and fruit quality, compared to traditional practices [38].
Considering that each fruit species has distinct physiological characteristics, and that tree response to agronomic practices varies depending on species and cultivar, it is necessary to validate these technologies in other fruit systems. Apples (Malus domestica Borkh), being one of the most widely cultivated fruit crops globally [41], represent an important agronomic model for evaluating the effects of natural mulching materials on fruit yield and quality, as well as on soil properties.
The novelty of the present study lies in the combined application and assessment of sheep wool mulch and corn starch-based polymers in an apple orchard, aiming to analyze the impact on productivity, physicochemical properties of fruits, tree growth, and improvement of soil physicochemical parameters. To the best of our knowledge, this represents one of the first documented applications of this approach in apple cultivation.

2. Materials and Methods

2.1. Plant Material

The experiments were conducted in an apple orchard (Malus domestica Borkh) in 2023 and 2024. The orchard is located in Berechiu, Bihor County, Romania (Latitude: 46°57′ 55.464″ N, Longitude: 21°44′8.249″ E) and was established in the spring of 2013 with an area of 1.30 ha, within an intensive cultivation system. The apple crop is planted at a distance of 4 m between rows and 2 m between trees per row with a density of 1250 trees/hectare, with the base variety Florina and the pollinator varieties Liberty and Auriu de Bistrița. The experimental orchard was established on a wet phreatic chernozem soil, characterized by favorable physical and chemical properties that support perennial crop development. The soil features a uniform granulometric distribution and an undifferentiated profile texture, classified as medium clay. Its bioaccumulation horizon contains a moderate humus level (3.1%), while deeper layers show lower humus concentrations (<2%). The soil reaction ranges from neutral to slightly alkaline (pH 7.2–8.6), and the total porosity and bulk density indicate good aeration and water retention capacity—crucial for root development. Additionally, the soil is well-supplied with essential macronutrients, particularly phosphorus and potassium, which support optimal tree growth and fruit development. These characteristics provided a suitable basis for establishing the orchard and applying the experimental mulching and soil conditioning variants.
According to the description of Romanian soils provided by Berchez et al., [42] the main characteristics of the wet phreatic chernozem at the experimental site are presented in Table 1.

2.2. Mulching with Sheep Wool and Applying Corn Starch-Based Polymer

To amend the soil, researchers used ZEBA (UPL Benelux B.V., Breda, The Netherlands) and for mulching, sheep wool was chosen. The decision to use sheep wool as a mulch between rows was based on how readily it is accessible. Market demands impose particular quality criteria, and current economic reorientations make it impossible for sheep producers in the area to capitalize on this product. As it decomposes, the complex protein fibers of sheep wool—particularly the keratins—contribute nitrogen to the soil together with the 1–2 percent lipids and various mineral elements like potassium, magnesium, calcium, sodium, and iron [43]. In Romania, three main sheep breeds are raised for both wool and meat production: Țurcană, Țigaie, and Merinos. For the present experiment, the Țurcană breed was selected, as it accounts for approximately 80% of the national sheep population. This breed is characterized by lower-quality wool compared to the other two breeds, making it less suitable for use in the textile industry. However, the thick and evenly distributed fleece of Țurcană sheep facilitates its application as an organic mulch in agricultural systems. At the time of shearing, the sheep were four years old. Sheep wool is relatively resistant to microbial degradation, with biodegradation occurring only under hydrophilic conditions. The keratin in wool serves as a substrate for proteases, esterases, and lipases, while keratinolytic microorganisms and insects contribute to decomposition through the secretion of extracellular enzymes [44]. Wool degradation rates have practical implications for waste management strategies. According to research, wool can be efficiently converted into organic fertilizer through controlled hydrolysis processes, and the nitrogen-rich molecules generated during wool biodegradation have fertilizing value [45]. This makes it possible to use circular economy strategies in sheep farming. Depending on the degradation process of wool observed over time, the remaining material will be incorporated into the soil during routine orchard management operations, but not before 4–5 years after application as mulch.
The experimental design included four variants applied to 15 apple trees per variant, beginning on April 15, 2023 (Figure 1). Variant 1 (V1) served as the control group, where neither mulching nor soil conditioner was applied (Control). Variant 2 (V2) involved the application of sheep wool mulch alone. For Variants 3 (V3) and 4 (V4), a corn starch-based soil conditioner (ZEBA) was incorporated at a rate of 150 g per tree. In addition, Variant 4 (V4) combined sheep wool and corn starch-based polymer. Prior to applying the conditioner, soil loosening was performed using a modified scarifier equipped with a vat, ensuring uniform incorporation at a depth of 25 cm. The sheep wool mulch was then distributed around the base of the trees in V2 and V4, covering an area 80 cm wide and 20 cm thick along the tree row.
In the second year of the experiment (15 April 2024), the ZEBA conditioner was reapplied to V3 and V4 in the same quantity and manner, in accordance with the manufacturer’s recommendations. The soil improver, with a declared effectiveness of approximately five months, was applied to ensure uniformity across experimental conditions.

2.3. Production and Physico-Chemical Parameters of Fruit Quality

2.3.1. Fruit Production

Every year during the experiment, the fruits from each variant’s fifteen trees were carefully plucked and weighed using an electronic scale. The total fruit production was then presented as the average of the 15 trees’ yield in kilograms of fruit per tree, per hectare. During harvesting, the fruit was manually separated into two classes according to fruit size, measured in mm diameter, in accordance with the marketing standards for apples laid down in Regulation (EU) 2023/2429: Quality I fruit had a diameter of at least 60 mm, while Quality II fruit had a diameter of between 50 mm and 60 mm [46].

2.3.2. Fruit Quality Indices

An electronic caliper was used to measure the big diameter (D), small diameter (d), and height (h) of 25 fruit per analyzed sample. The average of these measurements was then used to calculate the fruit size in millimeters using the following Formula (1) [47]:
S i z e   o f   f r u i t   m m =   D + d + h 3
The average fruit weight was determined by individually weighing 50 fruits per sample using an electronic balance. The results were expressed as mean values in grams (g).
Fruit sugar content was determined using the iodometric method, based on the reduction in sugars in an alkaline solution of cupric salt. The resulting cuprous oxide was indirectly titrated with a sodium thiosulfate solution [48].
A refractometer (ABBE, model 2VAJ, Optika SRL, Ponteranica, Italy) was used to evaluate the soluble solid content (SSC) at 22 °C using a single drop of juice extract from each fruit. The results were shown as a percentage [49].
Titratable acidity was measured as an indicator of fruit maturity and sourness, both of which influence the appearance, flavor, and postharvest quality of the fruit. Fruit juice samples were titrated to a constant pH using 0.1 M NaOH in order to determine the concentration of titratable hydrogen ions. Results were expressed as percentage of malic acid equivalents [50].

2.3.3. Determination of Bioactive Compounds of Apple Fruits

Apple fruits were dried at 40 °C to avoid the degradation of bioactive compounds. The dried material was then macerated for several days in 70% (v/v) ethanol at a solid-to-solvent ratio of 1:10 (w/v). After filtration, the ethanol was removed using a rotary evaporator (Heidolph Instruments, Berlin, Germany), at a temperature of 50 °C, 200 rpm, for 25 min. and the resulting crude extract was subsequently used for the analyses described below.
The total phenol content (TPh) of apple fruits was determined using the Folin–Ciocalteu method, with some modifications. In short, 100 µL of aqueous apple extract was combined with 1700 µL of distilled water, 200 µL of freshly produced Folin–Ciocalteu reagent (diluted 1:10, v/v), and 1000 µL of 7.5% Na2CO3 solution. The mixture was then incubated in darkness at room temperature for 2 h. The absorbance was recorded at a wavelength of 765 nm using a Shimatzu miniUV–Vis spectrophotometer from Kyoto, Japan. Gallic acid is used as the reference, and the results are expressed as mg of gallic acid equivalents (GAE)/g of dry weight (dw) [38].
The total flavonoid content (FLAV) was measured using the colorimetric method of aluminum chloride. In a 10 mL volumetric flask, a 1 mL apple extract in water solution, 4 mL distilled water, and 0.3 mL a 5% NaNO2 solution were combined and allowed to react for five minutes. After that, the mixture was thoroughly mixed and then allowed a 6-min break before 0.3 mL of a 10% AlCl3 solution was added. Distilled water and 2 mL of a 1 M NaOH solution were combined to produce a final volume of 10 mL. A spectrophotometer (Shimatzu miniUV-Vis, Kyoto, Japan) was used to determine the absorbance at 510 nm after a 15-min break. The results were expressed in mg Quercetin equivalents (QE) / g dw [38].

2.4. Tree Growth Measurements and Determinations

The trunk cross-sectional area (TCSA) was calculated at a height of 30 cm above ground level. Measurements were performed on 15 apple trees per variant. The trunk diameter was measured using a digital caliper, and TCSA was computed using Formula (2) [51]:
TCSA = π·D2/4
where D represents the diameter in centimeters. The results were expressed in cm2 [51,52].
The total number of annual shoots was counted for each of the 15 trees per variant to calculate the average. The total length of annual shoots was measured for the same trees, and the average was used to determine the mean annual shoot growth.
Chlorophyll content was determined using a SPAD 502PLUS Digital Chlorophyll Meter (Konica Minolta, Osaka, Japan) during the experiment. The measurements were taken on the same set of 6 leaves from 15 different trees every month on the 10th. These leaves represented different parts of the tree’s crown, so a total of 6 leaves crown, 3 leaves from each side (right and left as follows: 2 leaves from the bottom, middle, and top of the crown). The average of these readings was then used to make the determinations [53].

2.5. Soil Enzymatic Analyses

On October 10 2023 and 2024, soil was sampled from the 0–20-, 20–40-, 60–80- and 80–100 cm depths. After removing any plant material, the samples used for enzymological analysis. In order to assess the dehydrogenases (DEH) activity, the samples were incubated with 2,3,5-triphenyltetrazolium chloride and the absorbance of triphenylformazane (TPF) at 546 nm was measured, following the steps outlined in the Casida technique. The findings were reported mg TPF g−1 24 h−1 [54,55].
The Johnson and Temple technique was used to determine the catalase (CAT) activity, with a substrate of 0.3% hydrogen peroxide solution. Titration with 0.02 M KMnO4 in an acidic environment was used to measure the residual H2O2 [54,56].
Soil acid phosphatase (AcP) and alkaline phosphatase (AlP) activity was evaluated by detecting the amount of p-nitrophenol (pNP) produced following incubation at 37 °C for 1 h at a pH of approximately 6.5 and pH of about 11.0, respectively [54,57].

2.6. Statistical and Correlation Analysis

After the experimental data was centralized in Microsoft Excel, all statistical analyses were conducted using JASP software, version 0.18 (JASP Team, Amsterdam, The Netherlands). To evaluate differences among the four applied variants (V1 to V4) a one-way analysis of variance (ANOVA) was applied. When significant differences were detected, Tukey’s honest significant difference (HSD) post hoc test was used for pairwise comparisons between variants. Data were presented as the average ± the standard deviation (sd), and a p-value less than 0.05 was used to determine statistical significance. Different superscript characters were used to indicate differences among means. In addition, Pearson’s correlation analysis was conducted to assess linear relationships between fruit productivity, physicochemical parameters of the fruits, and tree growth indicators. We used the following criteria to determine the correlation strength: very strong (|r| ≥ 0.80), strong (|r| = 0.60–0.79) and moderate (|r| = 0.40–0.59). The significance level was determined at p < 0.05. Principal Component Analysis (PCA) was performed in MATLAB R2024b using the averaged data from both years (2023 and 2024) for each working variant, in order to identify patterns among productivity, fruit quality, and tree growth characteristics.

3. Results

3.1. Fruit Yield

The fruit harvest was conducted on October 5, 2023, and October 15, 2024. The results regarding fruit yield for both years are summarized in Table 2, including the distribution of fruit across the defined quality categories.
In both years, the yield and quality of Florina apple variety fruits increased in all experimental mulching variants and/or with the application of soil conditioner (V2, V3, and V4) compared to the control treatment (V1) (Table 1). V4 achieved the highest yield increases, exceeding the control by 54.4% in 2023 and by approximately 64% in 2024. Wool mulching (V2) also produced substantial increases of approximately 41% in 2023 and 42% in 2024, while the soil conditioner (V3) led to more moderate but still significant improvements, with increases of approximately 9% and 21% in those years. Year-to-year comparisons revealed a notable increase in yield in both V3 and V4 (p < 0.05). The proportion of high-quality fruit followed a similar pattern, with V4 consistently recording the highest values compared to the control. All differences between variants were statistically significant in each year (p < 0.05).

3.2. Physical and Chemical Parameters of Fruit Quality

Regarding fruit size and fruit weight (Table 3) in both 2023 and 2024, values varied significantly depending on the variant applied. Statistically significant improvements (p < 0.05), compared to both the control and other experimental variants, were recorded for V4, which combines sheep wool mulching with soil conditioning. In 2024, fruits from the V4 reached an average size of 85.92 ± 0.34 mm and an average weight of 272.80 ± 0.6 g, confirming the superior qualitative potential of this combined approach.
The analysis of fruit morphological parameters revealed significant differences among the experimental variants in terms of both fruit size and average weight. Regardless of the harvest year, V4 (wool mulching + soil conditioner) consistently recorded the highest values for both traits, with fruits exceeding 83 mm in diameter and 267 g in weight per fruit. These values were significantly higher compared to the other variants (p < 0.05). The control variant (V1) consistently had the smallest fruits, with diameters below 70 mm and weights under 170 g, significantly lower than V2, V3, and V4 (p < 0.05). Moreover, a significant difference was observed between V2 and V3, with V2 outperforming V3 in both size and weight (p < 0.05). Representative images of Florina apple fruits from all four variants in 2024 are shown in Figure 2.
Table 4 summarizes the results of the evaluated chemical parameters, including total sugar content, soluble solids, and titratable acidity (TA).
Regarding total sugar content, V2 recorded the highest values in both 2023 (14.30 ± 0.16%) and 2024 (14.26 ± 0.1%), with statistically significant differences observed between variants in each year (p < 0.05). However, no significant year-to-year differences were noted within any individual variant for this parameter. A similar trend was observed for soluble solids content: V2 and V3 exhibited the highest values, such as 14.40 ± 0.24% in V2 during 2023, with significant differences between variants, but no significant variation across years (p > 0.05).
Titratable acidity showed significant year-to-year variation in variants V1, V2, and V4, where increases were observed in 2024, marked with an asterisk in Table 3 (p < 0.05). Among the treatments, V4 consistently displayed the highest acidity levels in both years.
As for the TSS/TA ratio, which reflects the taste balance of the fruit, variant V3 maintained the highest and most stable values, significantly exceeding those of the control (V1). Significant differences between years were recorded for variants V1, V2, and V4 (p < 0.05), indicating a decrease in this ratio for V4 and slight increases for V1 and V2.
The total polyphenol content of apple fruits subjected to sheep wool mulching, soil conditioner application, and the combination of the two treatments is shown in Figure 3A. The control sample (V1) displayed the lowest total polyphenol levels, with a modest increase from 2023 to 2024 that was not statistically significant (p = 0.057). In contrast, apples from V2, V3, and V4 showed a significant year-over-year increase (p < 0.05), with the highest values recorded in V4, reaching approximately 4.95 ± 0.05 mg GAE/g dw.
Regarding flavonoid content (Figure 3B), the control group (V1) again exhibited the lowest levels, with minimal change between years. A significant increase in flavonoid concentration was observed in V3 and V4 between 2023 and 2024 (p = 0.032 and p = 0.034, respectively). The application of sheep wool alone (V2) also resulted in higher flavonoid levels compared to the control, but the difference was not statistically significant (p = 0.102).
Overall, both polyphenol and flavonoid contents were positively influenced by the soil conditioner and mulching, especially when applied in combination (V4), suggesting an additive effect of the two treatments in enhancing the antioxidant profile of the apple fruits.

3.3. Measurements and Growth Assessments of the Trees

The analysis of vegetative growth parameters (TCSA, number and length of annual shoots, and chlorophyll content) in the Florina apple variety presented in Table 5 revealed significant differences between the experimental treatments during the two-year study period (2023–2024). Statistically significant differences were observed not only between the experimental variants but also between the two years in terms of annual shoot number, shoot length, and trunk cross-sectional area (TCSA). V4 consistently showed the highest values for most indicators, including the greatest number of shoots (178.20 ± 18.23), shoot length (113.16 ± 12.6 cm), and TCSA (65.08 ± 0.47 cm2) in 2024. These differences were statistically significant compared to the control and other variants (p < 0.05), as indicated by distinct lowercase letters and asterisks in the table.
Regarding year-to-year evolution, all variants showed a general increasing trend in both shoot number and length in 2024, with statistically significant differences observed in V1, V2, V3, and V4. For example, in V3, mean shoot length increased significantly from 95.20 cm (2023) to 98.40 cm (2024), and in V2 from 105.60 cm to 118.80 cm. Significant annual increases in TCSA were observed in V2, V3, and V4, with the greatest growth registered in V4. In contrast, SPAD index values (chlorophyll content) did not show significant changes between years, remaining stable across all variants (p > 0.05).

3.4. Soil Enzymatic Activity Profile

The enzymatic activity of dehydrogenase, catalase, and phosphatase across different soil depths (0–100 cm) for the years 2023 and 2024 is presented in Figure 4A–C. In Figure 4A, dehydrogenase activity exhibited a consistent decreasing trend with increasing soil depth for all treatments. The highest enzymatic activity was recorded in the 0–20 cm layer, particularly in variants V2 and V4. In contrast, the lowest activity was observed in variant V3. Overall, dehydrogenase activity in 2024 was slightly higher compared to 2023, especially in V2 and V4, suggesting enhanced microbial activity due to the application of sheep wool mulch. Catalase activity (Figure 4B) was relatively stable across the soil profile, with only slight reductions observed at deeper layers (60–100 cm), particularly in the control (V1). The highest catalase values were found in V4, followed by V2, indicating improved oxidative stress management when sheep wool mulch is applied. A modest increase in catalase activity was observed in 2024 compared to 2023 across all variants, most notably in the topsoil (0–40 cm). Phosphatase activity (Figure 4C) decreased sharply with depth, with the highest activity localized in the upper soil layers (0–40 cm). Treatments V2 and V4 consistently recorded the highest phosphatase levels, suggesting enhanced phosphorus mineralization under these conditions. In all variants, phosphatase activity was higher in 2024, indicating the cumulative effects of sheep mulch application on microbial functionality.
The weighted average values of soil enzyme activities are presented in Table 6. V2 showed the highest dehydrogenase activity in both years monitored, significantly different from V3 (p < 0.05), indicating improved microbial oxidative metabolism in this variant. V4 showed the highest catalase activity in both years, with statistically significant differences between all variants (p < 0.05), suggesting improved aeration and redox potential in the amended soils. In terms of phosphatase activity, the highest values were recorded in V2 and V4, significantly higher than those in the control (V1), in 2023 and 2024. A statistically significant increase in phosphatase activity was observed between 2023 and 2024 for variants V2 and V3 (p < 0.05, paired t-test).

3.5. Correlation Analysis

Correlation matrices revealed both consistent patterns and year-specific differences. The intensity and direction of linear relationships between quantitative variables were assessed using Pearson’s correlation coefficient, which ranges from –1 to +1. Values close to +1 indicate a strong positive correlation, while those near –1 reflect a strong negative correlation. In contrast, values around 0 suggest no linear relationship between the variables. In the correlation heatmap, color shading was used to visually emphasize the strength and sign of the correlations. Weak correlations (r values between 0.00 and ±0.30) were represented by light blue or pale-yellow shades. Moderate correlations (r between ±0.31 and ±0.70) appeared in green or orange hues. Strong correlations, with r values ranging from ±0.71 to ±1.00, were displayed in intense colors—deep red for strong positive associations and dark blue for strong negative ones. This visual gradient in the heatmap facilitated rapid identification of the most significant correlations among the analyzed variables, enhancing the interpretability of the data.
As shown in Figure 5, Pearson’s correlation analysis for the year 2023 revealed several very strong positive relationships (r > 0.9) among the studied variables. A perfect correlation (r = 1.00) was observed between fruit production and branch length, indicating a direct link between vegetative growth and yield. Likewise, fruit production was strongly correlated with branch number (r = 0.98), while fruit size and fruit weight also displayed a very strong correlation (r = 0.96). A near-perfect correlation was noted between branch number and branch length (r = 0.99), suggesting structural interdependence. Additionally, chlorophyll content was highly associated with fruit weight (r = 0.95) and catalase activity (r = 0.90), reflecting the connection between physiological status and enzymatic activity. Among soil enzymes, dehydrogenase and phosphatase activities exhibited a very strong correlation (r = 0.98), suggesting synergistic microbial processes in the soil.
In 2024, Pearson’s correlation analysis (Figure 6) revealed multiple strong positive linear relationships among physiological and enzymatic parameters. Fruit production was highly correlated with fruit size (r = 0.97), fruit weight (r = 0.98), and branch number (r = 0.99), indicating that increased yield was closely associated with vegetative vigor and fruit growth. Trunk cross-sectional area (TCSA) also showed strong correlations with branch number (r = 0.93) and branch length (r = 0.91), reflecting the connection between stem robustness and canopy development. Enzymatic activity relationships were particularly prominent: dehydrogenase activity exhibited very strong positive correlations with both catalase (r = 0.98) and phosphatase (r = 0.98), suggesting synergistic microbial activity in the soil. Additionally, chlorophyll content strongly correlated with fruit weight (r = 0.95), branch length (r = 0.97), and catalase activity (r = 0.86), underscoring the role of photosynthetic capacity in plant productivity and soil biochemical function.
In both years, strong positive correlations (r > 0.90) were consistently observed between fruit production, fruit size, and fruit weight, suggesting a stable yield-growth relationship across time. Similarly, branch number and branch length remained highly correlated in both years (r > 0.95), indicating consistency in vegetative growth patterns. However, certain correlations intensified in 2024. For instance, the association between chlorophyll content and fruit weight strengthened from r = 0.89 in 2023 to r = 0.95 in 2024, implying a more pronounced role of photosynthetic capacity in yield under the conditions of 2024. Additionally, correlations among enzymatic activities—particularly between dehydrogenase and catalase (r = 0.69 → 0.98) and dehydrogenase and phosphatase (r = 0.98 in both years)—revealed greater biochemical synchrony in 2024. Notably, negative or weak correlations were more evident in 2023. For example, total sugar content exhibited weak or even slightly negative correlations with other traits (e.g., r = −0.10 with fruit size), while in 2024, this trend was diminished, with more balanced or slightly positive associations. This may reflect environmental or physiological stabilization influencing sugar accumulation. In contrast, 2024 showed fewer negative associations, and overall correlation strengths appeared higher, as indicated by more intense color gradients in the heatmap (Figure 6). The enhanced correlations, particularly involving enzymatic indicators, suggest improved soil biochemical balance and tighter integration between plant physiological and microbial processes during the second year.
The correlation analysis showed that dehydrogenase activity maintained moderate negative associations with both total phenolic (r = −0.37 in 2023, r = −0.32 in 2024) and total flavonoid content (r = −0.43 in 2023, r = −0.42 in 2024). This consistent trend suggests that enhanced oxidative microbial activity in the soil, as indicated by dehydrogenase, may not directly support the accumulation of antioxidant compounds in apple fruits, and may even be inversely related to secondary metabolism. By contrast, phosphatase activity showed a substantial increase in positive correlation with phenolic compounds in 2024, indicating a stronger role of phosphorus mobilization in supporting antioxidant biosynthesis under certain seasonal conditions.

3.6. Principal Component Analysis

Principal Component Analysis (Figure 7) was performed to explore the relationships among apple tree growth variables (branch number and length, TCSA, chlorophylls, fruit production and size), fruit physicochemical traits (total sugar, soluble solids, acidity), polyphenols/antioxidant indicators (total phenols and flavonoids content), and soil enzymatic activities (dehydrogenase, catalase and phosphatase) under four mulching treatments over two years (2023 and 2024, respectively). The first two principal components (PC1 and PC2) accounted for 81.3% of the total variance (PC1: 64.9%, PC2: 16.4%), providing a clear separation between experimental variants. PC1 showed strong positive loadings for trunk cross-sectional area (TCSA), branch length, branch number, fruit weight, fruit size, fruit production, and catalase and phosphatase activities, whereas negative loadings were observed for total phenol (mg GAE/g dw), total flavonoids (mg QE/g dw), and fruit acidity. PC2 was mainly associated with total soluble solids, total sugars, and dehydrogenase activity. In the PCA biplot, variant V4 was distinctly located in the lower right quadrant, characterized by higher contents of polyphenols, flavonoids, and fruit acidity. Variant V2 was positioned in the upper right quadrant and associated with enhanced sugar accumulation and higher soil enzymatic activity. Conversely, V1 and V3 were clustered on the left side of the plot, displaying overall lower performance in terms of both physiological and antioxidant parameters.

4. Discussion

By applying innovative technologies such as sheep wool mulching, the use of a polymer-based soil conditioner, and the combination of both methods, our study introduced unconventional solutions for conserving soil moisture in orchard systems. These practices, which have not been widely used in fruit growing so far, offer viable alternatives for reducing water stress caused by increasingly severe climate change and limited access to irrigation sources.
The results obtained in the study indicate a positive impact of the experimental variants on fruit production in the Florina apple variety. In 2023, all experimental variants (V2–V4) recorded higher yields compared to the control variant (V1), and the differences were statistically significant (p < 0.05). The highest production increases were recorded in V2 and V4, confirming the effectiveness of wool mulching in conserving water and creating a favorable microclimate in the root zone. In 2024, the upward trend in production continued and even intensified, especially in variants V3 and V4. These significant increases compared to the previous year (p < 0.05) suggest that the positive effects of applying the experimental variants can also be cumulative, especially when mulching and soil conditioner are used in combination. Variant V4 continued to be the most efficient, reaching 52.36 ± 0.84 tons/ha, which recommends it as an optimal solution in apple cultivation technology under water stress conditions.
During the two years of study, a clear influence of the applied technologies on the size and weight of the fruits was observed. In both years, variant V4 recorded the highest values for both average fruit size (83.79 ± 0.75 mm in 2023 and 85.92 ± 0.34 mm in 2024) and fruit weight (267.20 ± 2.0 g in 2023 and 272.80 ± 0.6 g in 2024), highlighting a beneficial synergistic effect of sheep wool mulching and soil conditioner application. Compared to the V1 control, all three experimental variants (V2, V3, and V4) showed statistically significant differences in fruit size and weight (p < 0.05). In particular, variant V2 contributed significantly to increasing fruit size compared to the control, suggesting that this simple technique can improve the water and nutritional supply to trees. The data also show that fruit size and weight were slightly higher in 2024 than in 2023, which may reflect a progressive adaptation of the trees to the experimental conditions. This trend supports the idea that the beneficial effects of sustainable practices may become more evident over time.
The levels of total sugars, soluble solids, and titratable acidity represent key parameters in determining fruit quality. Fruit acidity plays a critical role in shaping the organoleptic profile, particularly influencing the perceived taste. The sourness of fruit is primarily attributed to the presence of organic acids such as malic, citric, and tartaric acids, while sweetness is largely determined by the concentration of soluble solids. Evaluating soluble solids content in conjunction with titratable acidity provides a reliable basis for assessing both the sensory and commercial quality of fruits. Moreover, the sugar-to-acidity ratio is considered a defining factor in the overall flavor and taste balance of fruit products, contributing significantly to consumer preference and marketability [58].
The chemical composition of the fruits, expressed by total sugar content, soluble substances, and titratable acidity, showed significant differences between the experimental variants and between years. In 2023, the highest total sugar levels were recorded in variants V2 and V3 (14.30 ± 0.16% and 13.83 ± 0.12%), and the lowest values were observed in V1 and V4. The same trend continued in 2024, with a slight increase in V2 (14.26 ± 0.1%) and V3 (13.88 ± 0.11%). In terms of soluble substance content, statistically significant differences (p < 0.05) indicate a clear influence of the experimental variants applied. Variant V2 stood out in both seasons with high values, indicating an optimal balance between sugar accumulation and water stress reduction. Titratable acidity varied significantly between variants. In 2023, the highest acidity was observed in V4 (0.51 ± 0.2%), which also led to the lowest TSS/TA ratio (26.47), while V2 and V3 recorded more balanced values of the ratio (40.00 and 39.71), suggesting a more pleasant taste for consumption. The same relationship was maintained in 2024, when V3 had the highest TSS/TA ratio (39.86), associated with a balanced and pleasant taste.
The application of sheep wool mulch, soil conditioner, and their combination had a positive impact on the polyphenol and flavonoid content in apple fruits. The control variant (V1) recorded the lowest values, with no significant differences between years. In contrast, V3 and especially V4 showed significant increases from one year to the next (p < 0.05), indicating that the experimental variants applied stimulated the accumulation of antioxidant compounds. The combination of the two technologies (V4) proved to be the most effective in improving the nutritional quality of the fruit.
The vegetative growth of the trees was significantly influenced, especially in the case of variant V4, which recorded the highest values for all parameters analyzed in both years. The number of annual branches, their length, chlorophyll level (SPAD), and trunk cross-sectional area (TCSA) increased significantly compared to the control variant (V1), suggesting an improvement in the vigor and physiological condition of the trees. In 2024, the values of these indicators were generally higher than in 2023, indicating a cumulative effect of the applied variants. The results confirm the potential of these technologies to stimulate vegetative growth and, implicitly, tree productivity.
Soil enzyme activities, specifically dehydrogenase, catalase, and phosphatase, are widely used as indicators of soil health, fertility, and biological activity. Determining the activity of these enzymes helps assess the impact of environmental factors, contaminants, and management practices on soil quality and ecosystem functioning [59,60].
The results obtained in the two years of study show that the application of sheep wool (V2), soil conditioner (V3), and a combination of both (V4) had a positive influence on enzyme activity levels compared to the control variant (V1). In 2023, the sheep wool variant (V2) showed high values of dehydrogenase activity (9.33 mg TPF/g soil/24 h), suggesting an intensification of microbial respiration. This trend continued in 2024, with an increase in the value to 9.75 mg TPF/g soil/24 h. Catalase activity recorded the highest values in the combined variant (V4), reaching 9.93 mg H2O2/g soil/24 h in 2024, indicating better antioxidant protection at the soil level. For phosphatase activity, which is associated with phosphorus mineralization, high values were noted in V4 (2.99 mg phenol/g soil/24 h in 2024), signaling an increased potential for phosphorus mobilization in the soil. Enzyme activities were also generally higher in the surface layers (0–20 cm), gradually decreasing with depth, reflecting the presence of most microbial activities in the active root zone.
Mulching, especially with organic materials, is a proven strategy to enhance soil enzyme activities, improve nutrient cycling, and support soil health. The benefits are most pronounced with long-term, organic mulching, making it a valuable practice for sustainable agriculture and soil management [61].
In both years, strong and consistent positive correlations (r > 0.90) were observed between fruit production, size, and weight, indicating a stable relationship between yield and growth. Branch number and length also remained strongly correlated (r > 0.95). In 2024, several associations became stronger—for example, chlorophyll content was more closely correlated with fruit weight (r = 0.95), and enzymatic activities such as dehydrogenase and catalase showed increased synchronization (r = 0.98). Compared to 2023, fewer negative or weak correlations were found in 2024, suggesting an improved physiological balance between soil microbial processes and trees.
The PCA revealed a clear separation among the experimental variants and years, highlighting the significant impact of treatments on the evaluated traits. The PCA results suggest that the combination of wool and polymer mulch (V4) had a synergistic effect on secondary metabolism activation, as evidenced by increased levels of phenolic compounds and flavonoids in the fruit. These antioxidants play a central role in plant stress response and fruit quality enhancement. The increased acidity further supports the idea of enhanced organic acid synthesis under the combined treatment (V4). In contrast, the wool mulch alone (V2) primarily stimulated sugar metabolism and soil microbial activity, possibly due to the gradual mineralization of organic nitrogen and stimulation of enzymatic pathways. The PCA results revealed that catalase and dehydrogenase activities were strongly associated with PC1, alongside tree growth and fruit production traits. This suggests that aerobic microbial activity and oxidative stress mitigation in soil played a central role in promoting plant vigor and fruit development. These enzymatic activities are often stimulated by organic inputs, such as wool mulch or its combination with polymers, which improve soil aeration and microbial dynamics. On the other hand, phosphatase activity was primarily associated with PC2, together with fruit soluble solids and total sugar content. This pattern indicates that organic phosphorus cycling, likely enhanced under wool-based treatments, contributed more to nutrient remobilization and carbohydrate accumulation in fruits than to vegetative growth. Therefore, the differentiation along PC1 and PC2 reflects a dual influence of mulching on both soil enzymatic pathways and fruit quality formation, with variant V4 supporting overall biomass and antioxidant accumulation, while V2 favored sugar metabolism and phosphorus availability. The control (V1) and polymer-only treatment (V3) showed limited capacity to enhance either antioxidant activity or soil biological processes, reinforcing the importance of organic inputs in sustainable orchard management.
The results of our study on Florina apples confirm the effectiveness of the experimental water retention variants, previously tested on Stanley plums [38]. The combined variant (V4) with sheep’s wool and soil conditioner yielded the best results in terms of fruit production and quality, similar to those reported for plums. Sheep wool (V2) proved to be an effective and affordable solution, improving water retention in the soil and supporting the physiological balance of the trees. These techniques can be recommended for fruit crops under water stress conditions [38].
Our findings are consistent with previous research highlighting the multifaceted impact of wool mulch on water use efficiency and soil biological activity. Compared to rapidly decomposing mulches such as straw, wool decomposes more slowly and may require specific hydrophilic conditions for optimal biological impact. These observations corroborate our results, particularly the improved enzymatic responses and fruit quality observed in the wool mulch + soil conditioner treatment, supporting the idea that integrated approaches can improve both soil water retention and microbial functioning [62].

5. Conclusions

The PCA revealed that the mulching system significantly influences both fruit quality and soil enzymatic dynamics. The combination of sheep wool mulching and the application of corn starch-based soil conditioner (V4) proved to be the most effective strategy for promoting the accumulation of bioactive compounds in apple fruits, while supporting the overall vitality of the trees. These findings highlight the potential of integrated mulching practices to improve both productivity and nutritional quality in apple orchards. Although the study clearly demonstrates an improvement in soil water availability during periods of drought in the growing season, further research is needed to validate these conclusions for other fruit tree and fruit shrub species While V4 produced the highest values in terms of fruit weight and size, as well as bioactive compound content, the results obtained for V2 confirm its viability as a practical solution for orchard systems. This is particularly relevant given the low cost of implementation, as sheep’s wool is a readily available material with limited commercial value.

Author Contributions

Conceptualization, I.M.B., M.A.G., D.G. and S.I.V.; methodology, D.G. and S.I.V.; software, I.L.B. and D.P.-C.; validation, C.A.R., D.G. and S.I.V.; formal analysis, D.G., C.G.D. and S.I.V.; investigation, A.D.S., C.V.I., I.L.B. and D.P.-C.; resources, M.A.G.; data curation, I.M.B., C.G.D. and C.V.I.; writing—original draft preparation, I.M.B., M.A.G., D.G. and S.I.V.; writing—review and editing, I.M.B., M.A.G., D.G., C.A.R. and S.I.V.; project administration, M.A.G.; funding acquisition, M.A.G. All authors have read and agreed to the published version of the manuscript.

Funding

The research has been funded by the University of Oradea, within the Grants Competition “Scientific Research of Excellence Related to Priority Areas with Capitalization through Technology Transfer: INO—TRANSFER—UO—2nd Edition”, Project No. 237/20226.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

Acknowledgments

Thanks to the University of Oradea for the material used in the preparation of this article. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AcPSoil acid phosphatase
AlPAlkaline phosphatase activity
ANOVAOne-way analysis of variance
CatCatalase activity
DEHDehydrogenase activity
FLAVTotal flavonoid content
HSDHonest Significant Difference
PCPrincipal Component Analysis
pNPp-nitrophenol
SSCSoluble solid content
TATitratable acidity
TCSATrunk cross-sectional area
TPhTotal phenol content
V1Control group
V2Application of sheep wool
V3Application of a soil conditioner, corn starch-based polymer
V4A combination of sheep wool and corn starch-based polymer

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Figure 1. Florina variety fruits; (A) V1, control; (B) V2, mulching with sheep wool; (C) V3, soil conditioner based on corn starch; (D) V4, mulching with sheep wool + soil conditioner; In the images, the labels indicate the description corresponding to each experimental variant.
Figure 1. Florina variety fruits; (A) V1, control; (B) V2, mulching with sheep wool; (C) V3, soil conditioner based on corn starch; (D) V4, mulching with sheep wool + soil conditioner; In the images, the labels indicate the description corresponding to each experimental variant.
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Figure 2. Fruits of the Florina cultivar; (A), V1—control, (B), V2—sheep wool mulch, (C), V3—applying corn starch-based polymer, (D), V4—sheep wool mulch + corn starch-based polymer.
Figure 2. Fruits of the Florina cultivar; (A), V1—control, (B), V2—sheep wool mulch, (C), V3—applying corn starch-based polymer, (D), V4—sheep wool mulch + corn starch-based polymer.
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Figure 3. Total phenol content (A) and total flavonoid content (B) of apple fruits with different treatments of mulching soil. The results represent the means ± SD (n = 3). Different lowercase letters indicate significant differences between experimental variants within each year (2023 and 2024) (p < 0.05). Asterisks in the bar charts indicate statistically significant differences between 2023 and 2024 (p < 0.05). V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner. GAE—equivalent gallic acid, QE—equivalent quercetin.
Figure 3. Total phenol content (A) and total flavonoid content (B) of apple fruits with different treatments of mulching soil. The results represent the means ± SD (n = 3). Different lowercase letters indicate significant differences between experimental variants within each year (2023 and 2024) (p < 0.05). Asterisks in the bar charts indicate statistically significant differences between 2023 and 2024 (p < 0.05). V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner. GAE—equivalent gallic acid, QE—equivalent quercetin.
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Figure 4. Enzymatic activity in soil according to the experimental variants: (A) dehydrogenase activity (mg TPF/g soil/24 h), (B) catalase activity (units/g soil/2 h), (C) phosphatase activity (units/g soil/2 h); V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner, in the years 2023 and 2024, at depths of 0–100 cm. Dashed lines represent values from 2024.
Figure 4. Enzymatic activity in soil according to the experimental variants: (A) dehydrogenase activity (mg TPF/g soil/24 h), (B) catalase activity (units/g soil/2 h), (C) phosphatase activity (units/g soil/2 h); V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner, in the years 2023 and 2024, at depths of 0–100 cm. Dashed lines represent values from 2024.
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Figure 5. Pearson’s correlation analysis of fruit production and quality indicators of Florina apples in 2023, following the application of sheep wool mulching, corn starch-based polymers, and their combination. In the correlation matrix, strong correlations (|r| > 0.7) are highlighted in dark red, indicating robust linear associations between the measured parameters.
Figure 5. Pearson’s correlation analysis of fruit production and quality indicators of Florina apples in 2023, following the application of sheep wool mulching, corn starch-based polymers, and their combination. In the correlation matrix, strong correlations (|r| > 0.7) are highlighted in dark red, indicating robust linear associations between the measured parameters.
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Figure 6. Pearson’s correlation analysis of fruit production and quality indicators of Florina apples in 2024, following the application of sheep wool mulching, corn starch-based polymers, and their combination. In the correlation matrix, strong correlations (|r| > 0.7) are highlighted in dark red, indicating robust linear associations between the measured parameters.
Figure 6. Pearson’s correlation analysis of fruit production and quality indicators of Florina apples in 2024, following the application of sheep wool mulching, corn starch-based polymers, and their combination. In the correlation matrix, strong correlations (|r| > 0.7) are highlighted in dark red, indicating robust linear associations between the measured parameters.
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Figure 7. Principal component analysis biplot of apple tree growth parameters, fruit physicochemical traits, total phenolic and flavonoid content, and soil enzymatic activities. Samples: V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner, across 2023 and 2024.
Figure 7. Principal component analysis biplot of apple tree growth parameters, fruit physicochemical traits, total phenolic and flavonoid content, and soil enzymatic activities. Samples: V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner, across 2023 and 2024.
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Table 1. The physico-chemical characteristics of the wet phreatic chernozem.
Table 1. The physico-chemical characteristics of the wet phreatic chernozem.
HorizonApABBtCBCg
Depth (cm)0–2929–4545–6868–110110–151
Coarse sand (2–0.2 mm)0.80.20.20.10.4
Fine sand (0.2–0.02 mm)53.254.150.853.855.2
Silt (0.02–0.002 mm)18.72.022.020.224.8
Clay (<0.002 mm)23.724.726.925.919.6
TextureLLLLLLLLSF
pH7.27.47.68.28.6
Humus3.12.81.60.4-
Carbonates1.21.44.85.613.1
Phosphorus pentoxide (ppm)10596---
Potassium oxide (ppm)350310302287116
Bulk density (g/cm3)1.221.231.341.381.42
Total porosity (%)54.154.349.648.146.8
Soil horizon abbreviations: Ap—surface plough layer; AB—transition horizon between Ap and Bt; Bt—subsurface horizon with clay illuviation (argillic horizon); CB—transition horizon between Bt and Cg; Cg—gleyed parent material horizon influenced by groundwater.
Table 2. Fruit production and quality distribution in Florina cultivar.
Table 2. Fruit production and quality distribution in Florina cultivar.
SamplesFruit Production
kg/Tree
Fruit Production
tons/ha
Quality I
%
Quality II
%
2023
V124.24 ± 0.71 a30.30 ± 0.90 a87.6412.36
V234.16 ± 0.66 c42.70 ± 0.76 c92.127.88
V326.41 ± 0.45 b33.01 ± 0.56 b89.4310.57
V437.42 ± 0.66 d46.77 ± 0.82 d94.175.83
2024
V125.44 ± 0.4 a31.80 ± 1 a86.1213.88
V236.25 ± 0.3 c45.31 ± 0.5 c94.165.84
V330.56 ± 0.6 b,*38.20 ± 0.1 b,*90.279.73
V441.89 ± 1.3 d,*52.36 ± 0.8 d,*95.304.70
The results represent the means +/− SD (n = 15). Different lowercase letters within a column indicate significant differences between samples/year (p < 0.05); * significant difference between years (2023 vs. 2024, p < 0.05, t-test); V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner.
Table 3. Fruit quality indices of Florina cultivar in relation to the method applied.
Table 3. Fruit quality indices of Florina cultivar in relation to the method applied.
SamplesSize
(mm)
Weight
(g)
2023
V169.54 ± 0.43 a,*169.20 ± 1.9 a
V277.54 ± 1.04 b223.20 ± 1.6 c
V373.15 ± 0.80 a188.40 ± 1.5 b
V483.79 ± 0.75 c267.20 ± 2.0 d
2024
V168.15 ± 0.16 a166.70 ± 0.1 a
V278.11 ± 0.60 b226.90 ± 0.3 c
V373.85 ± 0.52 b186.70 ± 0.5 b
V485.92 ± 0.34 c272.80 ± 0.6 d
The results represent the means +/− SD (n = 15). Different lowercase letters within a column indicate significant differences between samples (p < 0.05); * significant difference between years (2023 vs. 2024, p < 0.05, t-test); V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner.
Table 4. Total sugar, soluble solids content, titratable acidity and TSS/TA ratio.
Table 4. Total sugar, soluble solids content, titratable acidity and TSS/TA ratio.
SamplesTotal Sugar
%
Soluble Solids Content
%
Titratable Acidity
% Acid Malic
TSS/TA
2023
V113.14 ± 0.06 a13.50 ± 0.09 a0.38 ± 0.1 b35.53 ± 0.17 b
V214.30 ± 0.16 b14.40 ± 0.24 c0.36 ± 0.2 a40 ± 0.2 c
V313.83 ± 0.12 b13.90 ± 0.03 b0.35 ± 0.1 a39.71 ± 0.25 c
V413.04 ± 0.06 a13.50 ± 0.09 a0.51 ± 0.2 c26.47 ± 0.3 a
2024
V113.12 ± 0.06 a13.40 ± 0.09 a0.36 ± 0.1 a,*37.22 ± 0.17 b
V214.26 ± 0.1 a14.30 ± 0.2 b0.39 ± 0.2 b,*36.67 ± 0.2 b,*
V313.88 ± 0.11 a13.95 ± 0.1 b0.35 ± 0.1 b39.86 ± 0.25 c
V413.20 ± 0.06 a13.45 ± 0.2 a0.45 ± 0.2 a,*29.89 ± 0.25 a,*
The results represent the means +/− SD (n = 15). Different lowercase letters within a column indicate significant differences between samples (p < 0.05); * significant difference between years (2023 vs. 2024, p < 0.05, t-test); V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner.
Table 5. Results of tree growth determinations for the Florina cultivar.
Table 5. Results of tree growth determinations for the Florina cultivar.
SamplesThe Number of
Annual Grown Branches
The Length of the
Annual Branch Growths
(cm)
Chlorophylls
SPAD
TCSA
(cm2)
2023
V1126.81 ± 31.23 a96.20 ± 24.18 a69.60 ± 2.44 a55 ± 0.25 a
V2142.86 ± 26.93 b118.80 ± 22.52 b86.20 ± 2.47 c61.9 ± 0.25 c
V3125.81 ± 31.13 a98.40 ± 18.53 a73.40 ± 0.90 b57.61 ± 0.42 b
V4151.45 ± 24.80 b135.80 ± 27.83 c93.80 ± 1.51 d64.50 ± 0.1 d
2024
V1140.49 ± 36.50 a90.40 ± 16.35 a,*63.06 ± 1.06 a53.91 ± 1.42 a
V2167.23 ± 33.23 b,c,*105.60 ± 15.34 b,*80.58 ± 1.20 c62.46 ± 0.45 c
V3153.92 ± 37.61 a,b95 ± 20.02 a,*70.14 ± 4.42 b57.32 ± 0.74 b,*
V4178.20 ± 18.23 c,*113 ± 16.12 c,*87.18 ± 1.82 d65.08 ± 0.47 c
The results represent the means +/− SD (n = 15). Different lowercase letters within a column indicate significant differences between samples (p < 0.05); * significant difference between years (2023 vs. 2024, p < 0.05, t-test); V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner.
Table 6. Weighted average values of soil dehydrogenase, catalase, and phosphatase activities.
Table 6. Weighted average values of soil dehydrogenase, catalase, and phosphatase activities.
PlotDehydrogenase Activity
(mg TPF/g Soil/24 h)
Catalase Activity
(mg H2O2/g Soil/24 h)
Phosphatase Activity
(mg Phenol/g Soil/24 h)
2023
V18.45 ± 0.21 b6.93 ± 0.18 a1.96 ± 0.03 c
V29.33 ± 0.21 c8.52 ± 0.16 b2.69 ± 0.02 c
V36.00 ± 0.14 a8.48 ± 0.16 c2.11 ± 0.01 b
V48.64 ± 0.15 b9.57 ± 0.13 c2.65 ± 0.02 a
2024
V18.36 ± 0.12 b6.90 ± 0.12 a2.02 ± 0.01 a
V29.75 ± 0.15 c8.60 ± 0.15 b2.85 ± 0.02 b,*
V35.86 ± 0.11 a8.34 ± 0.11 b2.12 ± 0.01 c,*
V48.93 ± 0.12 b,c9.93 ± 0.13 c2.99 ± 0.02 c
The results represent the means +/− SD (n = 3). Different lowercase letters within a column indicate significant differences between samples (p < 0.05); * significant difference between years (2023 vs. 2024, p < 0.05, t-test); V1, control; V2, mulching with sheep wool; V3, soil conditioner based on corn starch; V4, mulching with sheep wool + soil conditioner.
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Borza, I.M.; Rosan, C.A.; Gitea, D.; Gitea, M.A.; Samuel, A.D.; Iancu, C.V.; Bene, I.L.; Padilla-Contreras, D.; Domuta, C.G.; Vicas, S.I. Influence of Organic Mulching Strategies on Apple Tree (Mallus domestica BORKH.) Development, Fruit Quality and Soil Enzyme Dynamics. Agronomy 2025, 15, 2021. https://doi.org/10.3390/agronomy15092021

AMA Style

Borza IM, Rosan CA, Gitea D, Gitea MA, Samuel AD, Iancu CV, Bene IL, Padilla-Contreras D, Domuta CG, Vicas SI. Influence of Organic Mulching Strategies on Apple Tree (Mallus domestica BORKH.) Development, Fruit Quality and Soil Enzyme Dynamics. Agronomy. 2025; 15(9):2021. https://doi.org/10.3390/agronomy15092021

Chicago/Turabian Style

Borza, Ioana Maria, Cristina Adriana Rosan, Daniela Gitea, Manuel Alexandru Gitea, Alina Dora Samuel, Carmen Violeta Iancu, Ioana Larisa Bene, Daniela Padilla-Contreras, Cristian Gabriel Domuta, and Simona Ioana Vicas. 2025. "Influence of Organic Mulching Strategies on Apple Tree (Mallus domestica BORKH.) Development, Fruit Quality and Soil Enzyme Dynamics" Agronomy 15, no. 9: 2021. https://doi.org/10.3390/agronomy15092021

APA Style

Borza, I. M., Rosan, C. A., Gitea, D., Gitea, M. A., Samuel, A. D., Iancu, C. V., Bene, I. L., Padilla-Contreras, D., Domuta, C. G., & Vicas, S. I. (2025). Influence of Organic Mulching Strategies on Apple Tree (Mallus domestica BORKH.) Development, Fruit Quality and Soil Enzyme Dynamics. Agronomy, 15(9), 2021. https://doi.org/10.3390/agronomy15092021

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