Does Partial Organic Fertilization Maintain Physiological and Biometric Performance in Apple Trees?

Abstract

The MOPLUS project, funded by the Portuguese Recovery and Resilience Plan (PRR), aims to enhance soil organic matter, soil structure, and water retention in apple orchards located in the “Maçã de Alcobaça” Protected Geographical Indication area through organic fertilization based on locally available livestock effluents, thereby reducing reliance on synthetic fertilizers under Mediterranean climatic conditions. This study evaluated the physiological and biometric responses of apple trees subjected to four fertilization strategies (M1–M4) in three commercial ‘Gala’ orchards in central Portugal over three growing seasons (2023–2025). Measurements included leaf functional traits, gas exchange, chlorophyll fluorescence, spectral indices, vegetative growth, fruit production per tree and mean fruit weight. Interannual climatic variability and orchard-specific conditions were the dominant drivers of tree response, while fertilization effects were smaller and mainly expressed through interactions with year and orchard. When analyzed within the same orchard, fertilization strategies M2 and particularly M3 maintained physiological performance, vegetative growth, and fruit production per tree at levels comparable to full mineral fertilization. Among treatments, M3 showed the most consistent responses across sites and years, indicating that partial mineral substitution with pig slurry can sustain tree functioning while maintaining or enhancing fruit production per tree. The most restrictive strategy (M4) occasionally showed reduced photosynthetic performance under specific orchard–year combinations, suggesting a threshold effect associated with stronger mineral reduction, but without evidence of generalized physiological stress. Overall, these findings demonstrate that partial substitution of mineral fertilizers by organic amendments—especially pig slurry (M3) and, to a lesser extent, composted cattle manure (M2)—is agronomically viable, allowing apple tree performance and productivity to be maintained while enhancing system resilience under Mediterranean climatic variability. These results also provide practical decision support for site-adapted fertilization management in commercial drip-irrigated apple orchards, supporting reduced mineral fertilizer dependence without compromising productivity.

1. Introduction

The escalating costs of mineral fertilizers, driven by global economic instability and recurrent energy crises, have posed substantial challenges for orchard management across Europe [1,2]. At the same time, excessive use of synthetic nutrients has led to soil degradation, nutrient leaching, contamination of surface and groundwater, greenhouse gas emissions, and shifts in soil microbial communities, which are increasingly evident [3,4,5]. In response, the European Union has introduced regulatory measures targeting a 20% reduction in synthetic fertilizer use by 2030, prompting stakeholders to explore innovative nutrient management strategies that balance productivity, cost-efficiency, and environmental stewardship [6,7]. In parallel, evolving consumer expectations increasingly demand fruit production systems—such as apple orchards—that ensure high-quality yields while minimizing environmental impacts [8].

Alternative fertilization strategies have therefore gained attention. These include slow-release mineral formulations, enhanced-efficiency fertilizers, cover crops and green manures, biofertilizers, and organic amendments derived from livestock or agro-industrial by-products [9,10,11,12,13,14,15]. These approaches enhance soil fertility and structure, stimulate microbial activity, improve nutrient cycling, and reduce the carbon footprint compared with conventional mineral fertilization [16,17,18]. Beyond nutrient supply, organic inputs support long-term soil health by increasing water retention, reducing erosion risk, and fostering resilient orchard ecosystems capable of adapting to climate variability [19,20]. Their integrated adoption aligns with principles of circular agriculture and regenerative soil management, reconciling productivity, economic feasibility, and environmental sustainability [21,22].

Among these alternatives, livestock-derived organic amendments have emerged as particularly promising. They supply essential macronutrients, increase soil organic matter and microbial diversity, promote nutrient cycling, and facilitate integration of plant and animal production systems [23,24,25]. Such amendments can partially or completely replace mineral fertilizers, reducing production costs while enhancing soil health and potentially improving tree physiological performance and fruit quality [26,27]. However, under commercial orchard conditions, multi-year evidence integrating tree physiological functioning, vegetative growth, and yield responses to organic fertilization remains limited, particularly in perennial fruit systems exposed to increasing climatic variability. Evaluating apple tree responses to organic fertilization—including photosynthetic performance, chlorophyll fluorescence, and growth dynamics—is therefore essential for defining management practices that sustain yield and fruit quality while meeting environmental and regulatory objectives [28,29,30].

This study was conducted within the framework of the MOPLUS project, which aimed to identify and validate sustainable nutrient management practices in the Alcobaça region [31]. Field trials were conducted in three commercial ‘Gala’ apple orchards, where mineral fertilizers were partially replaced with organic amendments derived from locally available livestock effluents. The experimental design allowed the evaluation of tree responses under real production conditions, with particular emphasis on management practices related to soil structure and water retention, organic amendment application and incorporation methods, inter-row management for soil regeneration, and monitoring of soil organic matter and nutrient dynamics. Complementary activities included the assessment of tree physiological performance and fruit quality, as well as knowledge transfer through living labs and technical training for local producers.

Against this background, the present study aimed to disentangle the relative contributions of fertilization strategy, orchard-specific conditions, and interannual climatic variability to apple tree physiological performance, vegetative growth, and fruit development. By combining agronomic, environmental, and physiological approaches, this multi-year study provides an integrated assessment of how organic fertilization influences leaf functional traits, photosynthetic performance, vegetative growth, and fruit development in high-value apple orchards. The results support evidence-based fertilization strategies that maintain productivity, enhance soil health, reduce reliance on synthetic inputs, and provide solutions for the circular use of animal organic residues, thereby reducing the risks of ecosystem disruption. More broadly, they contribute to discussions on sustainable intensification and adaptive orchard management, helping growers balance economic viability with environmental responsibility under increasingly variable climatic and regulatory conditions.

2. Materials and Methods

2.1. Study Sites and Experimental Design

This study was conducted in three drip-irrigated commercial apple orchards (Malus domestica Mill., cv. Gala, with approximately 10% pollinators—Granny Smith and Malus floribunda varieties), chosen for their different soil textures, which are managed by members of grower organizations involved in the project. The orchards are located within the municipality of Alcobaça (district of Leiria), in the Central region of Portugal (Oeste sub-region): Frubaça (FR) (39°35′18″ N, 8°59′21″ W), CARSAG (CAR) (39°30′29″ N, 9°06′22″ W), and Campotec (CT) (39°30′55″ N, 9°00′53″ W) (Figure 1 and Figure 2).

Figure 1. (a) Regional location of the experimental apple orchards in Alcobaça, Portugal; (b) detailed view of the study area. Municipal boundaries are shown in orange, and the Alcobaça municipality is highlighted in yellow (Google Earth, November 2025).

Figure 2. Location and delineation of the experimental plots within the municipality of Alcobaça, Portugal: (a) FR (Frubaça), (b) CT (Campotec), and (c) CAR (CARSAG) orchards (Google Earth, November 2025).

The study covered three consecutive growing seasons (2023–2025). According to the Köppen–Geiger climate classification [32], the region exhibits a Mediterranean climate with mild, dry summers (Csb), characterized by wet winters and temperate summers. Long-term climatic normals indicate a mean annual precipitation of approximately 696 mm, concentrated mainly between October and March, and a mean annual air temperature of 15.4 °C, with average maximum and minimum temperatures of 18.9 °C and 12.5 °C, respectively [33]. The proximity to the Atlantic Ocean and local topography contribute to microclimatic variability within the study area, which is reflected in site-specific differences among orchards.

Soil properties differed among sites, as determined by laboratory analyses, and represent a key source of spatial heterogeneity. The CAR orchard (Figure 2c) is characterized by sandy loam soil (85.1% sand, 5.9% silt, 9.1% clay), offering balanced drainage and moderate water-holding capacity. The FR orchard (Figure 2a) presents loamy sand soil (84.1% sand, 4.9% silt, 11.1% clay), with high drainage and comparatively lower water retention. In contrast, the CT orchard (Figure 2b) exhibits sandy clay loam soil (67.1% sand, 10.9% silt, 22.1% clay), which provides greater water and nutrient retention capacity. These textural differences are expected to influence soil water dynamics and tree physiological responses under varying climatic conditions.

All orchards were managed under drip irrigation to maintain optimal soil moisture, with irrigation scheduling guided by soil moisture sensor data obtained from either Sentek EnviroSCAN soil moisture profiling probes (Sentek Technologies, Stepney, SA, Australia) or multi-depth capacitance probes (EnviroPro^®, ICT International Pty Ltd., Armidale, NSW, Australia), and a permanent living mulch cover was maintained between rows. Soil maintenance in the inter-row zones is performed using mechanical methods, whereas in-row zones are managed through targeted herbicide applications. Trees were trained under a spindle system and managed following standard commercial practices. Key structural and management characteristics of the orchards are summarized in Table 1.

Table 1. Main characteristics of the orchards included in the study.

Orchard Code	Orchard GO *	Year of Plantation	Clone	Rootstock	Soil	Spacing (m)	Training System	Area (ha)	Weed Control
FR	Frubaça	2015	Galinette	M9 T337	Sandy loam	0.8 × 3.8	Spindle	3.86	Herbicide (row)
CAR	CARSAG	2018	Redlum	M9 T337	Loamy sand	0.6 × 3.2	Spindle	1.99	Herbicide (row)
CT	Campotec	2017	Venus Fengal	M9 T337	Sandy clay loam	0.9 × 3.3	Spindle	0.65	Herbicide (row)

* GO: Grower Organization.

2.2. Fertilization Treatments

Each experimental orchard included four fertilization treatments, arranged in a randomized complete block design with three replicates per treatment. The four fertilization treatments were defined to represent a realistic gradient of mineral N replacement by locally available organic amendments under commercial orchard conditions. Each replicate consisted of five tagged apple trees, which were selected within the experimental plot to represent the average canopy size and vigor and were not necessarily spatially contiguous. In total, 60 trees were monitored per orchard (4 treatments × 3 replicates × 5 trees).

The fertilization strategies were designed to evaluate contrasting proportions of inorganic and organic nutrient sources while maintaining comparable total nutrient inputs. The four treatments differed in the relative contribution of mineral fertilizers, composted cattle manure, and pig slurry, as well as in their application zone, timing, and management practices (Table 2). Mineral fertilization was applied through both topdressing and fertigation using commercial inorganic fertilizers, including Amicote^® (0–26–0), Nitromagnésio 27 (27–0–0 + Mg), NPK 18–18–18, NPK 15–5–30, 13–0–46, Solusop^® 52 (0–0–52), and monoammonium phosphate (MAP; 12–61–0). Fertilizer type, timing, and application rates varied slightly between orchards and years according to local fertilization plans.

Table 2. Fertilization treatments applied in the experimental orchards, including fertilizer type and proportion, application zone, application method, timing, and complementary management actions.

Treatment	Materials Used	App. Zone	App. Method	Timing	Complementary Actions
M1	100% Inorg	R	BC + FGS	BB + FGS + PH	–
M2	75% Inorg	R	BC + FGS	BB + FGS + PH	–
	25% M	R	BC	BB	–
	12.5% S	IR	BC	PH	IM
M3	75% Inorg	R	BC + FGS	BB + FGS + PH	–
	25% S	R	BC	BB	INC
	12.5% S	IR	BC	PH	IM
M4	50% Inorg	R	BC + FGS	BB + FGS + PH	–
	25% M	R	BC	BB	–
	37.5% S	R + IR	BC	PH	INC (R) + IM (IR)

Abbreviations: Inorg, inorganic fertilizer; M, composted cattle manure; S, pig slurry; R, tree row; IR, inter-row; BC, broadcast application; FGS, fertigation during the growing season; BB, budbreak; PH, post-harvest; IM, immediate mowing; INC, soil incorporation.

Treatment M1 consisted exclusively of inorganic fertilization (100% Inorg), applied along the tree row by broadcast application combined with fertigation during the growing season (FGS), including applications at budbreak (BB) and post-harvest (PH).

Treatment M2 combined 75% inorganic fertilization, applied as in M1, with 25% composted cattle manure applied to the tree row at budbreak, and 12.5% pig slurry applied to the inter-row at post-harvest, followed by immediate mowing (IM) of the living mulch.

Treatment M3 followed the same inorganic fertilization strategy as M2 but replaced composted manure with 25% pig slurry applied to the tree row at budbreak and incorporated into the soil (INC). An additional 12.5% of pig slurry was applied to the inter-row at post-harvest, followed by immediate mowing (IM).

Treatment M4 reduced the inorganic fraction to 50%, applied as in M1, and combined it with 25% composted cattle manure applied to the tree row at budbreak. The remaining 37.5% of pig slurry was applied at post-harvest, distributed between the tree row and inter-row, with soil incorporation in the tree row (INC) and immediate mowing (IM) of the living mulch in the inter-row.

The organic fertilizers used in the study comprised composted cattle manure and pig slurry, representative of locally available organic amendments. Organic amendments were sourced locally in each orchard; therefore, their composition was not identical across sites and may vary between years. Detailed compositional characterization of the organic fertilizers is being addressed in a dedicated companion manuscript currently in preparation. Although fertilization treatments are expressed as relative proportions, the corresponding total nitrogen inputs were within the range typically recommended for ‘Gala’ apple orchards in the region (approximately 70–90 kg N ha⁻¹ year⁻¹), adjusted annually based on leaf and soil nutrient analyses.

2.3. Meteorological Data and Environmental Conditions

Meteorological data were obtained from automatic weather stations located in orchards adjacent to the experimental sites, at distances ranging from approximately 150 m to 2 km from the monitored plots. For each site, the following variables were recorded: precipitation; mean, minimum, and maximum air temperature; relative humidity; solar radiation; wind speed; and reference evapotranspiration (ET₀).

ET₀ was used as an integrative indicator of atmospheric evaporative demand, incorporating the combined effects of air temperature, relative humidity, solar radiation, and wind speed. Daily ET₀ was calculated using the FAO-56 Penman–Monteith equation for a reference grass surface [34]:

$${\mathrm{E}\mathrm{T}}_0= {\displaystyle \frac{0.408·∆·\left({\mathrm{R}}_{\mathrm{n}}-\mathrm{G}\right)+\mathsf{\gamma }·\frac{900}{\mathrm{T}+273} · {\mathrm{u}}_2 · ({\mathrm{e}}_{\mathrm{s}}-{\mathrm{e}}_{\mathrm{a}})}{\Delta +\mathsf{\gamma }·(1+0.34·{\mathrm{u}}_2)}}$$

(1)

where ET₀ is the reference evapotranspiration (mm day⁻¹), R_n is the net radiation at the crop surface (MJ m⁻² d ⁻¹), G is the soil heat flux density (MJ m⁻² d ⁻¹), T is mean daily air temperature at 2 m height (°C), u₂ is the wind speed at 2 m height (m s⁻¹), e_s is the saturation vapor pressure (kPa), e_a is the actual vapor pressure (kPa), Δ is the slope of the vapor pressure curve (kPa °C⁻¹), and γ is the psychrometric constant (kPa °C⁻¹).

Daily meteorological data were aggregated on a quarterly basis (Q1: January–March; Q2: April–June; Q3: July–September; Q4: October–December) to characterize seasonal climatic patterns and facilitate interannual and inter-site comparisons. Quarterly and annual summaries were used to describe the environmental conditions prevailing during the study period (2023–2025) at the three orchards.

2.4. Biometric Measurements

Biometric measurements were conducted to monitor vegetative growth and canopy development of apple trees from 2023 to 2025. In each experimental orchard, fifteen trees per fertilization treatment (M1–M4) were permanently tagged and monitored, resulting in a total of 60 trees per orchard. Trees were selected within each experimental plot to represent average canopy size and vigor. In cases where tagged trees were removed due to phytosanitary management (e.g., fire blight), they were replaced by nearby trees within the same treatment and plot, and this was accounted for in the interpretation of growth increments.

All tagged trees were included in the biometric assessment. However, due to logistical constraints and the nature of specific measurements, different subsets of trees were used for variables, as described below.

Biometric measurements were carried out twice per year, corresponding to a spring–early summer assessment and a late autumn assessment, to quantify seasonal growth increments. In 2023, initial measurements were conducted on 1 June at FR, 6 June at CT, and 14 June at CAR, while final measurements were performed on 15 November 2023 at all sites. In 2024, first measurements were carried out in March (21 March at CT and CAR, and 25 March at FR), with final measurements conducted in November 2024 at all orchards. In 2025, initial measurements were taken on 29 May at CT and CAR, and on 6 June at FR. Final measurements were performed on 2 December 2025 at CT and on 3 December 2025 at the remaining orchards.

Trunk diameter was measured on all tagged trees at 20 cm above the grafting point using a digital caliper (Calibit, Horticultural Knowledge, Italy) (Figure 3a), with measurements taken at three equidistant positions around the trunk. The average of the three diameters (D₁, D₂, D₃) was used to calculate the trunk cross-sectional area (TCSA, cm²) according to:

$$\mathrm{TCSA} = \mathsf{\pi } \times {\left({\displaystyle \frac{D_1 + D_2 + D_3}{3 \times 2}}\right)}^2$$

(2)

where D₁, D₂, and D₃ are trunk diameters (cm) measured at 20 cm above the graft union, and π is the mathematical constant (≈3.1416).

Figure 3. Overview of field and laboratory measurements. (a) Trunk diameter measurement with a digital caliper; (b) leaf disk sampling for SLA determination; (c) CCI measurement using MC-100; (d) leaf gas exchange measurements using IRGA; (e) leaf reflectance indices acquired with PolyPen; and (f) chlorophyll fluorescence measurements using PEA.

Tree height (TH) and canopy dimensions were assessed on subsets of ten trees per treatment, selected from the tagged population. Total tree height (H_total) and height to the base of the canopy (H_βase) were measured using a graduated measuring rod (GR 500 Professional, Bosch, Stuttgart, Germany). Canopy width was measured using a flexible measuring tape at three vertical positions: at the base (L_βase), at mid-height (L_mid), and at the upper canopy level (L_top), measured 50 cm under the canopy apex. The methodology has been frequently applied to capture differences in trees exhibiting a cone-shaped crown with a broad base, as observed in this region as a consequence of the training and pruning techniques employed.

Canopy volume (CV, m³) was estimated using an empirical geometric approach based on the mean canopy width and canopy height, as previously described in [10]. Canopy volume was calculated as follows:

$$\mathrm{CV} = (\left({\displaystyle \frac{L_{\mathsf{\beta }\mathrm{ase}} + L_{\mathrm{mid}} + L_{\mathrm{top}}}{3 \times 2}}\right) ÷ 100) \times (H_{\mathrm{total}}-H_{\mathsf{\beta }\mathrm{ase}})$$

(3)

where L_βase, L_mid, and L_top are canopy widths (cm), and H_total and H_βase, are measured in meters.

Vegetative growth was quantified using annual growth increments, calculated as the difference between final and initial measurements within each year for trunk cross-sectional area (ΔTCSA) and canopy volume (ΔCV), which were used as integrative indicators of vegetative development.

Because ΔTCSA relies on the continuity of individual trees across measurement dates, this variable was interpreted with caution in situations involving tree replacement or replanting associated with phytosanitary management (e.g., removal of trees affected by fire blight), which may disrupt trunk growth continuity. In most cases, only the infected tree was removed; however, in a few situations neighboring trees were also removed for phytosanitary reasons. Therefore, growth increment analyses (ΔTCSA and ΔCV) were restricted to trees whose surrounding conditions remained unchanged between measurement dates, minimizing potential bias due to altered competition.

2.5. Physiological Measurements

Physiological measurements were conducted to characterize leaf functional traits, photosynthetic performance, and optical properties of apple trees under the different fertilization treatments. All physiological assessments were performed on the same sampling dates within each year, ensuring full temporal coherence among variables, including specific leaf area (SLA), chlorophyll content index (CCI), gas exchange, leaf reflectance indices, and chlorophyll fluorescence.

Two sampling campaigns were carried out annually, corresponding to late spring/early summer and mid-summer. Sampling dates were orchard-specific but consistent across all physiological variables. At the FR orchard, leaf sampling was conducted on 1 June and 3 August 2023, 6 June and 8 August 2024, and 16 June and 13 August 2025. At CT, measurements were performed on 6 June and 9 August 2023, 7 June and 9 August 2024, and 20 June and 8 August 2025. At CAR, sampling took place on 14 June and 1 August 2023, 28 May and 1 August 2024, and 24 June and 1 August 2025.

All measurements were carried out on fully expanded, healthy, sun-exposed leaves located in the mid-portion of current-season shoots. For each orchard and sampling date, five trees per fertilization treatment were assessed, and tree means were used for all statistical analyses.

2.5.1. Specific Leaf Area (SLA)

SLA was determined from leaf samples collected during the same two annual sampling campaigns described above. For each fertilization treatment, 50 leaves were collected per campaign. Leaves were selected from the middle third of the canopy and transported in sealed containers and kept shaded at ambient temperature, minimizing moisture loss prior to processing.

Using a metal punch with a 14 mm diameter, two disks were extracted from each leaf blade, avoiding major veins (Figure 3b). Disk area was calculated as follows:

$$\mathrm{Area} \mathrm{of} \mathrm{each} \mathrm{disk} = \mathsf{\pi } \times {\left({\displaystyle \frac{d}{2}}\right)}^2 = \mathsf{\pi } \times {\left(0.7 \mathrm{cm}\right)}^2 = 1.54 {\mathrm{cm}}^2$$

(4)

For each treatment, five Petri dishes (replicates) were prepared, each containing 20 leaf disks. Fresh mass was measured using an analytical balance (Kern ALJ 160-4AM; precision 0.1 mg; Kern and Sohn GmbH, Balingen, Germany). Samples were oven-dried (WTC Binder 7200, Typ E 115; Tuttlingen, Germany) at 70 °C for 48 h and weighed immediately after removal. SLA was calculated following Reich et al. [35] as follows:

$$SLA\left({\displaystyle \frac{{cm}^2}{g}}\right)=\pi \times {\left({\displaystyle \frac{disc diameter}{2}}\right)}^2 \times number of discs \times {\displaystyle \frac{1}{Dry weight discs}}$$

(5)

2.5.2. Chlorophyll Content Index (CCI)

Chlorophyll content index (CCI) was measured using a portable chlorophyll concentration meter (MC-100, Apogee Instruments, Logan, UT, USA) (Figure 3c), which estimates chlorophyll content based on the ratio of leaf transmittance at 653 nm and 931 nm wavelengths [36].

Measurements were conducted during the same sampling dates used for SLA and all other physiological variables. Sampling dates were orchard-specific but consistent across variables and years (2023–2025). At each measurement date, five trees per treatment were assessed, and multiple leaves per tree were measured to obtain representative tree means.

2.5.3. Gas Exchange Parameters

Leaf gas exchange was measured on the same dates as CCI assessments using a portable infrared gas analyzer (IRGA; LCproT, ADC BioScientific Ltd., Hoddesdon, UK) equipped with a 6.35 cm² leaf chamber (Figure 3d). Measurements were performed on 10 trees per treatment, with one fully expanded leaf measured per tree.

The following parameters were recorded: net photosynthetic rate (A_n, µmol CO₂ m⁻² s⁻¹), stomatal conductance (g_s, mol H₂O m⁻² s⁻¹), transpiration rate (E, mmol H₂O m⁻² s⁻¹), and intercellular CO₂ concentration (Cᵢ, µmol mol⁻¹). Instantaneous water-use efficiency (WUE, µmol CO₂ mmol⁻¹ H₂O) was calculated automatically by the IRGA and analyzed at the annual scale.

Measurements were conducted in the morning (09:30–13:00 h) under stable clear-sky conditions to minimize diurnal variability in vapor pressure deficit and avoid afternoon stomatal depression, which is common in Mediterranean orchards during the warm season. Although chamber conditions were controlled, this time window was selected to ensure comparability among trees, orchards, and years under non-stressful and physiologically stable conditions. Chamber conditions were standardized as follows: CO₂ concentration ≈ 400 µmol mol⁻¹, photosynthetically active radiation (PAR) 1500 µmol m⁻² s⁻¹, leaf chamber temperature 25 °C, and airflow 200 µmol s⁻¹.

2.5.4. Leaf Reflectance Indices

Leaf reflectance measurements were performed on fully expanded, sun-exposed leaves collected from previously marked apple trees within each fertilization treatment, following the same sampling dates described in Section 2.5. Measurements were conducted using a PolyPen RP 410 UVIS spectroradiometer (Photon Systems Instruments, Drásov, Czech Republic), operating over a spectral range of 380–790 nm (Figure 3e).

The instrument is equipped with an integrated xenon light source and a non-destructive leaf clip, ensuring consistent illumination and measurement geometry. Reflectance spectra were recorded on one leaf per sampled tree, and the PolyPen automatically calculated 25 vegetation indices based on the acquired spectra, including indices related to canopy structure, chlorophyll content, carotenoids, and stress responses (e.g., NDVI, SRI, MCARI, OSAVI, SIPI, GM1, GM2, ARI1, ARI2, CRI1, CRI2).

Before each measurement session, the instrument was calibrated using a Spectralon^® white reference panel according to the manufacturer’s instructions. Reflectance data and the corresponding vegetation indices were stored in the device’s internal memory and later exported via USB for further processing.

2.5.5. Chlorophyll Fluorescence

Chlorophyll fluorescence was assessed using a HandyPEA Plant Efficiency Analyzer (Hansatech Instruments Ltd., Norfolk, UK) on the same sampling dates as the other physiological measurements (Figure 3f). Leaves were dark-adapted for 20 min and then exposed to a saturating light pulse of 3500 µmol photons m⁻² s⁻¹.

A subset of parameters derived from the OJIP fluorescence transient was selected to avoid redundancy and improve interpretability, including the maximum quantum yield of PSII (Fv/Fm), the performance index on an absorption basis (PI_abs), absorption per reaction centerrr (ABS/RC), energy dissipation per reaction centre (DIo/RC), and electron transport per reaction centre (ETo/RC), providing complementary information on PSII efficiency, energy partitioning, and electron transport capacity [37,38,39].

2.6. Fruit Production and Mean Fruit Weight Assessment

Fruit production was assessed at harvest on the same tagged trees used for biometric and physiological measurements. At each orchard, fruits from tagged trees within each fertilization treatment were harvested according to the commercial harvest schedule, either in a single harvest or in successive harvest rounds when applicable.

Fruits collected from the tagged trees were kept separate from the remaining orchard production. For each fertilization treatment and orchard, total fruit fresh weight (kg tree⁻¹) and fruit number were recorded at the tree level to characterize per-tree production and fruit load. Due to differences in planting density, orchard structure, and tree replacement among sites, yield was not extrapolated to a per-area basis. Mean fruit weight (g) was subsequently calculated as the ratio between total fruit fresh weight and total fruit number and used as an indicator of fruit size and performance rather than overall productivity.

2.7. Statistical Analysis

All statistical analyses were conducted using JASP software (version 0.95.4, University of Amsterdam, The Netherlands). Statistical significance was set at p < 0.05.

SLA, CCI, gas exchange parameters, chlorophyll fluorescence variables, PolyPen-derived spectral indices, biometric traits, and mean fruit weight were analyzed using three-way analysis of variance (ANOVA), with Year, Orchard, and Treatment (M1–M4) as fixed factors. Two- and three-way interaction terms were included to evaluate context-dependent responses across years and orchard conditions.

Normality of residuals was assessed using the Shapiro–Wilk test, and homogeneity of variances using Levene’s test. No data transformations were applied prior to statistical analyses. When the assumption of homoscedasticity was violated, Welch’s ANOVA was applied.

Games–Howell post hoc tests were used for pairwise comparisons when significant effects were detected, as this procedure does not assume equal variances or equal sample sizes. For the descriptive comparisons within each year × orchard subset reported in Table 3, Tukey’s HSD test (p < 0.05) was applied to assign letter groupings among treatments. ANOVA summary results are reported in the Appendix A (Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8, Table A9, Table A10 and Table A11) to support the interpretation of main effects and interactions while avoiding over-fragmentation of the Results section. No additional family-wise error correction (e.g., Bonferroni) was applied, as the Games–Howell procedure provides robust control of Type I error under heteroscedasticity and unequal sample sizes.

Table 3. Mean fruit production (±SE) per tree (kg tree⁻¹) by year, orchard, and fertilization treatment. Within each year and orchard, different letters indicate significant differences among fertilization treatments according to Tukey’s HSD test (p < 0.05). Comparisons across years or orchards are not implied.

Year	Orchard	Treatment	Fruit Production (kg Tree−1)
2023	CAR	M1	5.31 ± 0.36 b
	CAR	M2	3.48 ± 0.40 a
	CAR	M3	4.30 ± 0.49 ab
	CAR	M4	3.44 ± 0.35 a
	CT	M1	15.93 ± 1.32 a
	CT	M2	15.79 ± 1.35 a
	CT	M3	20.01 ± 1.63 a
	CT	M4	17.62 ± 1.09 a
	FR	M1	14.94 ± 1.22 a
	FR	M2	15.21 ± 0.69 a
	FR	M3	13.59 ± 0.63 a
	FR	M4	12.93 ± 0.88 a
2024	CAR	M1	3.55 ± 0.36 a
	CAR	M2	3.76 ± 0.39 a
	CAR	M3	3.70 ± 0.37 a
	CAR	M4	4.14 ± 0.41 a
	CT	M1	31.71 ± 2.87 a
	CT	M2	25.06 ± 2.31 ab
	CT	M3	25.53 ± 2.20 ab
	CT	M4	17.63 ± 1.71 a
	FR	M1	13.42 ± 0.71 a
	FR	M2	13.41 ± 0.92 a
	FR	M3	16.02 ± 0.83 a
	FR	M4	14.41 ± 0.82 a
2025	CAR	M1	7.75 ± 1.22 a
	CAR	M2	8.19 ± 0.68 a
	CAR	M3	11.11 ± 1.14 a
	CAR	M4	8.15 ± 0.85 a
	CT	M1	7.33 ± 0.82 a
	CT	M2	7.92 ± 0.92 a
	CT	M3	10.72 ± 1.07 a
	CT	M4	8.18 ± 0.83 a
	FR	M1	12.74 ± 0.81 a
	FR	M2	12.98 ± 0.39 a
	FR	M3	12.85 ± 0.96 a
	FR	M4	13.94 ± 1.11 a

Effect sizes were reported as partial eta squared (η²p) to quantify the magnitude of main and interaction effects. Effect size magnitude was interpreted using partial eta squared (η²p), with values of approximately 0.01, 0.06, and ≥0.14 indicating small, moderate, and large effects, respectively.

To reduce redundancy among the 25 PolyPen-derived vegetation indices, Principal Component Analysis (PCA) was performed separately for spring and summer datasets. Sampling adequacy was evaluated using the Kaiser–Meyer–Olkin (KMO) test, and inter-variable correlations were assessed using Bartlett’s test of sphericity. Components with eigenvalues greater than 1 were retained, and promax rotation was applied. Scores of the retained rotated components (RCs) were subsequently analyzed using three-way ANOVA (Year × Orchard × Treatment), applying Welch’s ANOVA and Games–Howell post hoc tests when variance homogeneity was violated.

3. Results

3.1. Meteorological Conditions During the Study Period

Meteorological conditions varied markedly among years and orchards, reflecting both interannual climatic variability and site-specific environmental gradients, particularly associated with distance from the Atlantic coast (Figure 4 and Figure 5). To capture seasonal dynamics relevant to apple tree phenology and physiology, climatic data were analyzed on a quarterly basis (Q1–Q4).

Figure 4. Quarterly precipitation (bars) and reference evapotranspiration (ET₀; lines) at the three experimental orchards (CT, FR, and CAR) during 2023, 2024, and 2025. Bars represent total precipitation accumulated per quarter, and lines represent mean quarterly ET₀. Data are presented by quarter (Q1–Q4).

Figure 5. Quarterly means air temperature and relative humidity recorded at the three experimental orchards (CT, FR, and CAR) during 2023, 2024, and 2025. Solid lines represent mean air temperature, dashed lines represent relative humidity, and line colors correspond to the different orchards (CT, FR, and CAR). Data are presented by quarter (Q1–Q4).

At Campotec (CT), precipitation followed a pronounced seasonal pattern, with wetter conditions during Q1 and Q4 and dry springs and summers (Q2–Q3). This inland site exhibited greater thermal amplitude, with mean air temperature increasing from Q1 to Q3 and summer maxima approaching 27–28 °C, resulting in elevated reference evapotranspiration (ET₀) during the warmest quarters.

The Frubaça (FR) orchard showed intermediate climatic characteristics. Precipitation was more variable than at CT, with occasional summer rainfall events, particularly in 2023. Summer air temperatures were slightly higher on average than at CT, while relative humidity declined during Q2–Q3, leading to consistently high summer ET₀.

In contrast, CARSAG (CAR) displayed climatic features typical of a coastal environment, characterized by milder temperatures, reduced thermal amplitude, and persistently higher wind speeds. Relative humidity remained higher and more stable throughout the year, and the combined influence of maritime air masses moderated temperature extremes and shaped a distinct evaporative regime compared with the more inland orchards.

Overall, the quarterly analysis revealed a clear spatial climatic gradient, ranging from a more continental inland site (CT), through an intermediate environment (FR), to a maritime-influenced coastal site (CAR). These contrasting environmental conditions provide a robust framework for interpreting orchard-specific physiological, biometric, and yield responses presented in subsequent sections.

3.2. Physiological Parameters

3.2.1. Specific Leaf Area

(a)

Spring

SLA measured in spring differed markedly among years and orchards (Figure 6). In 2023, the highest SLA values were observed at CT, whereas CAR consistently exhibited lower values across all fertilization treatments, with FR showing intermediate responses. A generalized reduction in SLA occurred in 2024 across all orchards, followed by a pronounced increase in 2025, particularly at CT, where the highest SLA values were recorded. These patterns indicate a dominant influence of interannual climatic variability and orchard-specific conditions on spring SLA, with fertilization treatments playing a secondary and context-dependent role.

Figure 6. Mean SLA (±SE) across orchards and fertilization treatments in spring over 2023–2025.

Three-way ANOVA revealed significant main effects of Year and Orchard on spring SLA, whereas Treatment did not show a significant main effect when considered independently (Appendix A.1, Table A1). However, significant interaction terms—particularly Year × Orchard and higher-order interactions involving Treatment—indicated that SLA responses to fertilization treatments were contingent on orchard-specific conditions and interannual climatic variability.

(b)

Summer

A similar pattern was observed for SLA measured in summer (Figure 7). SLA values varied significantly among years and orchards, with CT consistently presenting higher values and CAR lower values, while FR remained intermediate. SLA increased markedly in 2025 across all orchards, with the highest values recorded at CT, particularly under treatments M2–M4.

Figure 7. Mean SLA (±SE) across orchards and fertilization treatments in summer over 2023–2025.

Three-way ANOVA confirmed significant effects of Year and Orchard on summer SLA, while Treatment showed no consistent main effect (Appendix A.1, Table A1). Higher-order interactions revealed that treatment-related differences emerged only under specific orchard–year combinations, reinforcing the secondary and context-dependent role of fertilization modality relative to dominant climatic and site effects. However, M3 consistently exhibited higher SLA values in CT across all analyzed years and in CAR in 2025, while M4 showed higher SLA values in CT and FR in 2025, indicating that treatments based on pig slurry application rather than mineral fertilization were associated with more favorable SLA values.

3.2.2. Chlorophyll Content Index

(a)

Spring

CCI measured in spring differed significantly among orchards and years (Figure 8). FR consistently exhibited the highest CCI values, CT the lowest, and CAR intermediate values. Across years, a general decline in CCI was observed at CT in 2025, whereas FR maintained relatively stable and high CCI values throughout the study period.

Figure 8. Mean CCI (±SE) across orchards and fertilization treatments in spring over 2023–2025.

Three-way ANOVA indicated significant effects of Year and Orchard on spring CCI, while Treatment did not show a significant main effect (Appendix A.1, Table A2). Significant interaction terms, particularly the Year × Orchard interaction, indicated that interannual CCI dynamics differed among orchards, whereas treatment-related differences were expressed only under specific orchard conditions.

(b)

Summer

A comparable spatial pattern was observed for CCI measured in summer (Figure 9), with FR presenting the highest values, CT the lowest, and CAR intermediate responses. Interannual variation was evident, with a marked reduction in CCI at CT in 2025, while CAR and FR showed year-dependent trends.

Figure 9. Mean CCI (±SE) across orchards and fertilization treatments in summer over 2023–2025.

Three-way ANOVA confirmed significant main effects of Year and Orchard on summer CCI, whereas Treatment again showed no consistent main effect (Appendix A.1, Table A2). Treatment effects emerged only through a weak Orchard × Treatment interaction, indicating a limited and context-dependent influence of fertilization treatments relative to dominant site and climatic effects. CCI values were consistently higher in CT across all years in M3 and M4, suggesting enhanced leaf chlorophyll content and, consequently, improved photosynthetic capacity under this treatment.

3.2.3. Measurement of Gas Exchange Parameters

(a)

Spring

In spring, gas exchange parameters were predominantly influenced by interannual variability and orchard-specific conditions, with fertilization effects being secondary and parameter dependent (Appendix A.2, Table A3).

Intercellular CO₂ concentration (Cᵢ) and transpiration rate (E) showed strong and significant effects of Year and Orchard, together with pronounced Year × Orchard interactions, indicating contrasting interannual trajectories among orchards. These patterns reflect the dominant role of environmental conditions in shaping leaf-level gas exchange during spring. Minor Orchard × Treatment and Orchard × Treatment × Year interactions were also detected for E; however, their effect sizes were small to moderate, indicating a limited and context-dependent influence of fertilization on spring transpiration dynamics.

Stomatal conductance (g_s) was significantly affected by Treatment, Year, and Orchard. However, treatment-related effects were small compared with the influence of environmental drivers, indicating a secondary and context-dependent role of fertilization.

Net photosynthetic rate (A_n) was significantly influenced by Orchard and Treatment, with a strong Year × Orchard interaction, indicating that interannual variability modulated orchard-specific photosynthetic performance, while fertilization effects remained comparatively limited (Figure 10). The consistently lower performance of M4 across the majority of years and orchards warrants particular attention.

Figure 10. Mean net photosynthetic rate (A_n ± SE) across orchards and fertilization treatments in spring over 2023–2025.

Overall, spring gas exchange dynamics were primarily environmentally driven, with fertilization effects acting as secondary modifiers under specific orchard–year conditions.

(b)

Summer

During summer, gas exchange parameters exhibited strong and coherent responses to temporal and spatial factors, with Year and Orchard emerging as the dominant sources of variability across all measured variables (Appendix A.2, Table A3).

Cᵢ varied significantly among years and orchards, with a strong Year × Orchard interaction, reflecting the combined influence of interannual climatic variability and site-specific conditions on internal CO₂ dynamics.

E was strongly controlled by Year, together with a pronounced Year × Orchard interaction. In addition, Treatment and Year × Treatment effects were detected, indicating that fertilization modulated transpiration responses depending on climatic conditions. These results highlight the dominant role of climate and local microclimate in driving evaporative demand during summer.

g_s was significantly affected by Year, Orchard, and Treatment, with strong Year × Orchard interactions and weaker Orchard × Treatment effects, indicating that stomatal regulation was primarily structured by interannual and orchard-related environmental variability, with a secondary but statistically detectable modulation by fertilization treatment.

A_n was mainly controlled by Year and Orchard, with additional significant effects of Treatment and Orchard × Treatment, indicating that fertilization influenced photosynthesis in an orchard-dependent manner during summer (Figure 11).

Figure 11. Mean net photosynthetic rate (A_n ± SE) across orchards and fertilization treatments in summer over 2023–2025.

Collectively, summer gas exchange responses confirmed the dominant role of environmental drivers, while fertilization effects were secondary and expressed mainly through context-dependent interactions, without consistent treatment-specific trends across orchards or years. Lower values were observed for M2 and M4 in CT in 2023 and 2025, with M4 consistently showing reduced values in CAR in 2024 and 2025.

3.2.4. Water-Use Efficiency

Leaf-level WUE exhibited strong interannual and orchard-level variability (Appendix A.2, Table A4). Year exerted the strongest influence on WUE, while Orchard also contributed significantly.

In contrast, fertilization treatments did not show a significant main effect, and its associated effect size was negligible. Interaction terms indicated that treatment-related differences in WUE emerged only under specific site conditions.

Overall, WUE was primarily structured by climatic variability and orchard-specific environmental drivers.

3.2.5. Measurement of Leaf Reflectance Indices

(a)

Spring

In spring, PCA of leaf reflectance indices showed very good sampling adequacy (KMO = 0.86). Four rotated components (RC1–RC4) with eigenvalues >1 were retained, explaining 89.1% of the total variance (Appendix A.3, Table A6). Promax rotation was applied.

RC1, representing overall canopy greenness and structural vigor (e.g., NDVI, RDVI, OSAVI, MCARI1), was strongly driven by interannual variability and orchard-specific conditions. Orchard-related differences were minor in 2023 but became increasingly pronounced in 2024 and 2025, indicating increasing differentiation among orchards over time. Fertilization treatments did not exert a consistent main effect, emerging only under specific orchard–year combinations. To visually illustrate the dominant spring spectral gradient captured by RC1, RDVI—the index with the highest loading in this component—was selected as a representative example (Figure 12).

Figure 12. Mean RDVI (±SE) across orchards and years in spring over 2023–2025, calculated from individual observations and pooled across fertilization treatments. RDVI was selected as a representative index of RC1 based on its highest loading in the spring PCA.

RC2, dominated by pigment ratio and structural indices, reflected strong environmental gradients associated with both year and orchard. This component was largely independent of fertilization treatments, indicating that spring pigment balance was primarily regulated by climatic variability and site characteristics rather than nutrient inputs.

RC3 captured stress- and efficiency-related spectral responses and was overwhelmingly structured by interannual variability, with orchard conditions modulating the magnitude of responses. While orchard-level differences were consistent, fertilization effects were expressed only through interactions, highlighting the context-dependent nature of physiological stress responses.

RC4, associated mainly with anthocyanin-related indices, explained a smaller proportion of variance, and reflected secondary pigment dynamics. Its variability was driven predominantly by year and orchard, with treatment effects remaining marginal and restricted to interaction terms.

Overall, spring leaf reflectance variability was dominated by interannual climatic forcing, with orchard-specific conditions acting as secondary modulators. Fertilization effects were limited and expressed primarily through context-dependent interactions rather than stable main effects.

(b)

Summer

In summer, PCA of leaf reflectance indices showed very good sampling adequacy (KMO = 0.85). Five rotated components (RC1–RC5) were retained, together explaining 87.9% of the total variance (Appendix A.3, Table A7). As in spring, Promax rotation was applied.

RC1, integrating indices related to canopy greenness, structure, and pigment balance (e.g., SIPI, TCARI, NDVI, OSAVI), was strongly controlled by interannual variability and orchard-specific conditions, indicating pronounced temporal and spatial variability in summer spectral responses. Treatment effects were not expressed as consistent main effects but emerged through interactions under specific environmental contexts. To visually illustrate the dominant summer spectral gradient captured by RC1, SIPI was selected as a representative example (Figure 13).

Figure 13. Mean SIPI (±SE) across orchards and years in summer over 2023–2025, calculated from individual observations and pooled across fertilization treatments. SIPI was selected as a representative index of RC1 based on its highest loading in the summer PCA.

RC2 reflected broad temporal and spatial gradients in canopy spectral behavior, with strong differentiation among years and orchards and negligible sensitivity to fertilization treatments.

RC3 exhibited orchard-dependent interannual trajectories, with fertilization effects emerging only through interactions with year and orchard, indicating that stress-related spectral responses during summer were highly context dependent.

RC4 was overwhelmingly dominated by interannual variability, with year explaining the largest proportion of variance among all components. Orchard and treatment effects were secondary and manifested mainly through higher-order interactions.

RC5 captured finer-scale spectral variability and showed significant effects of year, orchard, and treatment, together with multiple interaction terms, reflecting subtle but structured differences in leaf reflectance associated with specific orchard–year–treatment combinations.

Overall, summer leaf reflectance variability was primarily governed by interannual climatic conditions and orchard location, while fertilization effects remained secondary and highly context dependent.

3.2.6. Chlorophyll Fluorescence Parameters

(a)

Spring

In spring, chlorophyll fluorescence parameters were primarily driven by interannual variability and orchard-specific conditions, whereas management treatments exerted limited influence (Appendix A.2, Table A5; Figure 14).

Figure 14. Mean maximum quantum efficiency of PSII (Fv/Fm; ± SE) across orchards and years in spring over 2023–2025, calculated from individual observations and pooled across fertilization treatments.

The maximum quantum efficiency of PSII (Fv/Fm) showed significant effects of Year and Orchard, with higher values in 2025 compared with 2023–2024 and consistently lower values in the coastal orchard (CAR). A significant Year × Orchard interaction indicated that interannual trajectories differed among orchards, with the strongest increases observed at FR.

The performance index on an absorption basis (PI_abs) was significantly affected by Year, Orchard, and Treatment, as well as by a Year × Orchard interaction. PI_abs showed a marked decline in 2025, particularly in inland orchards, while treatment effects were limited and mainly driven by contrasts between M1 and M4.

Energy flux parameters at the reaction center level (ABS/RC and DIo/RC) were significantly influenced by Year and Orchard, with higher ABS/RC and lower DIo/RC values in 2025, indicating seasonal adjustments in energy absorption and dissipation. Significant Year × Orchard interactions further highlight orchard-dependent interannual responses. No consistent main effects of treatments were detected for these parameters.

(b)

Summer

During summer, chlorophyll fluorescence responses were dominated by interannual variability, with orchard-specific conditions exerting secondary context-dependent influences, while fertilization treatment effects remained small and context dependent (Appendix A.2, Table A5; Figure 15).

Figure 15. Mean maximum quantum efficiency of PSII (Fv/Fm; ± SE) across orchards and years in summer over 2023–2025, calculated from individual observations and pooled across fertilization treatments.

Fv/Fm exhibited strong interannual variability, with a pronounced reduction in 2024 followed by partial recovery in 2025, consistent across orchards. Orchard-related differences were moderate, and fertilization treatment effects were small and expressed in a context-dependent manner, mainly associated with contrasts between M1 and M4. A significant Year × Orchard interaction indicated site-specific seasonal dynamics in PSII efficiency.

PI_abs was overwhelmingly controlled by Year, accounting for the largest proportion of variance, with lower values in 2024 and partial recovery in 2025. Orchard and treatment effects were statistically significant but small, and no higher-order interactions involving treatments were detected.

Energy flux parameters TRo/RC and ABS/RC showed strong Year effects, with progressive decreases from 2023 to 2025, consistent with increasing summer stress. Orchard effects were weaker but significant, whereas treatment effects were minor and mainly reflected lower ABS/RC values under M4. Interaction effects were limited and did not show consistent patterns across sites.

Overall, PEA-derived chlorophyll fluorescence parameters indicate that seasonal and interannual environmental variability, together with orchard-specific conditions, were the primary drivers of PSII performance during summer, while fertilization treatment effects were secondary and largely expressed through specific parameters or isolated interactions.

3.3. Biometric Parameters

Tree growth parameters were primarily governed by interannual variability and orchard-specific conditions, whereas treatment exerted a minor or negligible influence (Appendix A.4, Table A9).

Canopy volumetric growth was overwhelmingly affected by Year, which explained the largest proportion of variance (η²p = 0.869), indicating that interannual climatic variability was the dominant driver of vegetative development. Volumetric growth was significantly higher in 2025 compared with both 2023 and 2024, while no differences were observed between 2023 and 2024. Orchard location also exerted a strong effect (η²p = 0.327), with CT showing consistently higher volumetric increments than CAR and FR, reflecting more favorable site conditions for vegetative expansion.

In contrast, treatment showed only a small effect on canopy volumetry (η²p = 0.031), indicating that management-related differences were minor relative to the dominant influence of year and orchard. Although several higher-order interactions reached statistical significance, their associated effect sizes were small and did not indicate consistent biological trends; therefore, these effects were not examined in further detail.

The increment in trunk cross-sectional area (ΔTCSA) followed a similar pattern. Year exerted a strong influence (η²p = 0.599), confirming a pronounced interannual growth effect consistent with canopy volumetry results, with trunk growth being significantly greater in 2025. Orchard also had a strong effect (η²p = 0.481), with CT exhibiting higher increments than CAR and FR. However, ΔTCSA was interpreted with caution for the final year of the study, as tree replacement events disrupted the continuity of trunk growth measurements, and this variable was therefore not used for graphical representation.

3.4. Fruit Production and Mean Fruit Weight

Fruit production was assessed at harvest on the same tagged trees used for biometric and physiological measurements. At each orchard, fruits from tagged trees within each fertilization treatment were harvested according to the commercial harvest schedule, either in a single harvest or in successive harvest rounds when applicable. Fruits collected from the tagged trees were kept separate from the remaining orchard production.

Fruit production was first evaluated on a per-tree basis (kg tree⁻¹), using total fruit fresh weight recorded for each tagged tree (Table 3). This approach allowed differences in fruit load among treatments, orchards, and years to be assessed, while avoiding extrapolation to yield per unit area due to differences in planting density, orchard structure, and tree replacement among sites.

Across the three growing seasons, fruit production per tree varied markedly among years and orchards, reflecting strong interannual climatic variability and orchard-specific conditions. Higher per-tree production was generally observed in CT, whereas CAR consistently exhibited lower values, reflecting differences in orchard structure and tree vigor. Interannual variability was also evident, with production levels differing substantially between years across all orchards.

In addition to these dominant effects, fertilization treatment exerted a statistically significant influence on fruit production per tree in the global analysis (Appendix A.5, Table A10), although with a markedly smaller effect size than Year and Orchard. Post hoc comparisons derived from the overall model indicated a higher mean fruit production per tree under treatment M3 relative to M2 and M4, and a positive trend compared to the fully mineral treatment (M1). However, when examined within individual year × orchard combinations (Table 3), treatment effects were generally weak or absent, with significant differences detected only in specific site–year contexts. The presence of a significant Treatment × Year × Orchard interaction further confirms that fertilization effects were context dependent, varying according to site-specific and interannual conditions rather than following a consistent ranking across orchards.

Following the analysis of fruit production per tree, mean fruit weight was examined as a complementary indicator of fruit size and potential market valorization, acknowledging its strong dependence on crop load and tree-level functional performance.

Mean fruit weight was significantly affected by Year, Orchard, and Treatment (Figure 16; Appendix A.5, Table A11), with Year explaining the largest proportion of variance (η²p = 0.242), indicating strong interannual control on fruit size. Mean fruit weight increased from 2023 to 2024 and declined markedly in 2025, a pattern consistently observed across all orchards.

Figure 16. Mean fruit weight (g) as affected by fertilization treatment (M1–M4) across three growing seasons (2023–2025) in the three experimental orchards: CARSAG (CAR), Campotec (CT), and Frubaça (FR).

Orchard location also exerted a significant effect (η²p = 0.084), with fruits harvested in CAR consistently showing lower mean weights than those from CT and FR. Treatment had a statistically significant but relatively small effect (η²p = 0.037), with M1 tending to produce lighter fruits relative to M2 and M4. A significant Treatment × Orchard interaction (η²p = 0.084) indicated that fertilization effects on fruit size were orchard dependent, while the significant Year × Orchard interaction (η²p = 0.123) reflected non-uniform interannual variability across sites. In contrast, interactions involving Treatment × Year and the three-way interaction were not significant, indicating that treatment effects on fruit size were relatively stable across years.

Overall, fruit production per tree was primarily controlled by interannual climatic variability and orchard-specific conditions, while fertilization treatment exerted a statistically significant but smaller effect. Treatment-related differences were mainly expressed under specific orchard–year combinations, with M3 generally showing higher fruit production per tree relative to M2 and M4. Mean fruit weight reflected these differences in fruit load, confirming its role as a complementary indicator of fruit size and potential market value rather than overall productivity.

4. Discussion

4.1. Dominant Role of Interannual Climatic Variability on Leaf Functional Traits and Physiology

Across all physiological, spectral, and biometric variables, interannual climatic variability emerged as the dominant driver of apple tree responses, consistently outweighing the effects of fertilization treatments. This pattern was particularly evident for SLA, gas exchange parameters, chlorophyll fluorescence, and leaf reflectance indices, in both spring and summer measurements.

Specific leaf area responded strongly to Year and Orchard in both seasons. In spring, SLA was significantly affected by year (F = 42.90, p < 0.001, η²p = 0.326) and orchard (F = 60.08, p < 0.001, η²p = 0.404), while treatment had no significant main effect. A similar pattern was observed in summer, with large effects of year (F = 27.44, p < 0.001, η²p = 0.258) and orchard (F = 54.69, p < 0.001, η²p = 0.439). The marked increase in SLA observed in 2025, particularly at the inland orchard (CT), coincided with higher spring–summer temperatures and radiation combined with pronounced summer drought, suggesting the development of thinner, more expansive leaves under conditions favoring rapid canopy deployment despite increasing atmospheric demand [40,41].

However, when comparisons are made within the same orchard, agronomically relevant treatment-related patterns emerge. Treatment M3 consistently exhibited higher SLA values in CT across all analyzed years and in CAR in 2025, while M4 also showed higher SLA values in CT and FR in 2025. These results indicate that fertilization strategies based on pig slurry, alone or combined with composted manure, were able to sustain or even enhance leaf structural development relative to the fully mineral treatment.

In addition, CCI values were consistently higher in CT across all years under M3 and M4, reflecting increased leaf chlorophyll content and suggesting that organic-based fertilization strategies maintained adequate nitrogen availability to support photosynthetic pigment synthesis and leaf functional performance.

Gas exchange parameters measured with IRGA further confirmed the predominance of environmental control. Intercellular CO₂ concentration (cᵢ) and transpiration rate (E) showed large and highly significant effects of both year (cᵢ: F = 57.57, η²p = 0.144; E: F = 64.30, η²p = 0.158) and orchard (cᵢ: F = 114.78, η²p = 0.251; E: F = 121.40, η²p = 0.262), with strong year × orchard interactions. These results indicate that stomatal behavior and carbon assimilation were primarily regulated by atmospheric demand and site-specific microclimatic conditions rather than by fertilization regime [42,43].

Within orchards, treatment M3 consistently sustained net photosynthetic rates comparable to the fully mineral treatment, indicating that partial substitution of mineral fertilization did not constrain carbon assimilation. In contrast, the lower A_n values occasionally observed under M4—particularly in CT during 2023 and 2025 and in CAR during 2024 and 2025—suggest that stronger reductions in mineral fertilization may approach a threshold beyond which nitrogen availability becomes limiting under certain site and climatic conditions. Importantly, these reductions were context dependent and did not translate into generalized physiological stress, as indicated by stable Fv/Fm values. Nitrogen is a major constituent of chlorophyll and plays a central role in the formation of photosynthetic pigments. Deficiency in nitrogen has been shown to decrease chlorophyll content and CO₂ assimilation in leaves, ultimately impairing light harvesting and photosynthetic electron transport efficiency under N-limiting conditions [44].

Chlorophyll fluorescence parameters derived from the OJIP transient showed a comparable structure. In summer, year explained a very large proportion of variance for PIabs (F = 117.62, p < 0.001, η²p = 0.421), TRo/RC (F = 104.63, η²p = 0.392), and ABS/RC (F = 104.61, η²p = 0.392). Despite this strong seasonal forcing, Fv/Fm values remained within non-stressful ranges, indicating functional acclimation of PSII rather than chronic photoinhibition, even under hot and dry summer conditions [45,46,47].

4.2. Orchard-Specific Modulation by Soil Properties and Microclimate

Although climate was the primary driver, orchard location consistently modulated the magnitude and direction of physiological responses. The inland orchard CT, characterized by higher thermal amplitude, higher solar radiation, and a sandy clay loam soil with greater water and nutrient retention, showed higher SLA, greater canopy development, and stronger vegetative growth. This was reflected in very large effects of year (ΔVolumetry: F = 1666.41, η²p = 0.869) and orchard (F = 122.17, η²p = 0.327) on canopy growth.

In contrast, the coastal orchard CAR, under persistent wind exposure, higher relative humidity, and moderated temperatures, exhibited more conservative physiological behavior, including lower transpiration rates and distinct chlorophyll fluorescence and reflectance signatures. FR, with loamy sand soil and intermediate climatic exposure, consistently displayed intermediate responses across most variables. These orchard-level contrasts highlight the importance of soil texture and microclimate as filters through which interannual climatic variability is expressed at the tree level. It should be noted that the orchard factor integrates not only soil texture and microclimatic conditions, but also differences in clone, tree age, and planting density, which should be considered when interpreting site-specific effects [48,49,50,51,52].

In addition to abiotic constraints, orchard-specific differences in biotic stress likely contributed to the observed patterns. In particular, the higher incidence of fire blight (Erwinia amylovora) symptoms observed at the CAR orchard represents an additional source of stress that may have constrained vegetative vigor and carbon allocation processes. Although disease incidence was not quantitatively assessed, its uneven spatial occurrence likely acted as an additional site-specific stressor and should therefore be considered a confounding but ecologically realistic factor. This biotic pressure likely exacerbated limitations on fruit growth at this site. However, lower mean fruit weight at CAR should be interpreted in the context of its generally lower fruit production per tree, indicating that reduced fruit size reflects the combined influence of biotic stress, orchard structure, and fruit load rather than stress effects alone. Although disease severity was not quantitatively assessed, its uneven spatial occurrence should be considered when interpreting orchard effects and year × orchard interactions [53,54,55].

4.3. Fertilization Treatments: Maintaining Productivity and Physiological Stability Under Reduced Mineral Inputs

Although fertilization treatments exerted smaller effects than interannual climatic variability and orchard-specific conditions, their agronomic relevance becomes evident when comparisons are conducted within the same orchard. Under these conditions, fertilization strategies M2 and M3 consistently maintained physiological performance, vegetative growth, and fruit development at levels comparable to the fully mineral treatment, demonstrating that partial substitution of mineral fertilizers by organic amendments can sustain orchard performance without physiological or productive penalties.

Among the tested strategies, M3 showed the most consistent responses across orchards and years, supporting the hypothesis that pig slurry provides a more readily available nutrient supply than solid organic amendments. This faster nutrient availability likely contributed to the maintenance of leaf physiological functioning and fruit development under reduced mineral fertilization, in line with previous observations from long-term orchard trials [56,57,58,59,60].

Leaf-level water-use efficiency was primarily structured by year and orchard, while treatments showed no significant main effect. This indicates that organic-based fertilization strategies did not compromise the balance between carbon assimilation and water loss, even under years of higher evaporative demand, reinforcing their suitability under Mediterranean climatic conditions [61,62,63]. Where treatment-related differences occurred, they were expressed mainly under specific orchard–year combinations.

Mean fruit weight provided an integrative perspective linking physiological performance, vegetative growth, and environmental constraints, particularly when interpreted in conjunction with fruit production per tree. Given that fruit size is strongly influenced by crop load, variations in mean fruit weight largely reflect the combined effects of assimilate availability and sink competition rather than productivity per se.

From a grower-oriented perspective, the absence of systematic reductions in fruit production per tree under M2 and M3 is particularly relevant. Despite interannual variability, these treatments maintained fruit load and mean fruit weight comparable to the fully mineral strategy, indicating that partial mineral substitution does not compromise economically relevant yield components. This finding is consistent with recent evidence showing that fertilization management influences not only fruit size but also quality-related traits and volatile profiles in ‘Gala’ apples, without compromising market-relevant attributes under reduced mineral inputs [64].

In addition to maintaining fruit size, fruit production per tree provides a complementary functional perspective on fertilization effects. Despite the dominant influence of year and orchard, fertilization treatment exerted a statistically significant effect on fruit production per tree, with M3 consistently showing higher values than M2 and M4 and a positive trend relative to full mineral fertilization. Importantly, this increased fruit load under M3 was not associated with reductions in photosynthetic performance, chlorophyll status, or PSII efficiency, indicating that higher reproductive output did not result from physiological overexertion. Instead, these results suggest an improved balance between carbon assimilation and sink demand at the tree level, reinforcing the agronomic relevance of M3 as a strategy that sustains both physiological functioning and productive expression under reduced mineral inputs.

Orchard-specific patterns further modulated these responses. Fruits harvested at CAR consistently exhibited lower mean weights than those from CT and FR, reflecting the combined influence of coastal microclimate, soil characteristics, and higher biotic stress pressure, in combination with its generally lower fruit production per tree. Treatments exerted a statistically significant but small effect on mean fruit weight, with reduced mineral inputs combined with organic amendments maintaining fruit size at levels comparable to the fully mineral treatment. Treatment effects were dependent on orchard conditions, reinforcing the context-dependent nature of management responses [65].

Finally, although the most restrictive strategy (M4) occasionally showed lower physiological performance under specific orchard–year combinations, these effects were not systematic and did not translate into generalized yield penalties. This suggests that M4 represents a practical lower boundary for mineral input reduction, highlighting the importance of site-adapted fertilization strategies rather than uniform prescriptions.

4.4. Soil Organic Matter Dynamics and Delayed Treatment Effects

The generally weak main effects of treatments are consistent with the delayed and cumulative nature of organic amendments. Composted manure and slurry require time for mineralization, microbial processing, and incorporation into soil aggregates before translating into detectable changes in plant physiology [66,67,68,69]. This interpretation is supported by the recurrent significance of higher-order interactions involving year and orchard, particularly for sensitive parameters such as g_s, A_n, and several PCA-derived reflectance components.

Soil texture likely modulated the expression of these delayed effects. In CT, the higher intrinsic water-holding capacity may buffer short-term stress but also limit the immediate physiological impact of additional organic inputs. In contrast, the coarser-textured soils of FR and CAR may benefit more progressively from organic amendments through improvements in soil structure and moisture buffering, particularly under dry summer conditions [70,71].

The permanent living mulch present across all orchards likely contributed to baseline improvements in soil moisture conservation and surface microclimate, acting as a stabilizing background factor rather than a primary source of treatment differentiation [9,72]. This delayed-response framework further supports the observed stability of M2 and M3, which appear to balance short-term nutrient availability with longer-term improvements in soil functioning.

4.5. Implications for Sustainable Orchard Management

Overall, this study demonstrates that apple tree physiological performance, vegetative growth, and fruit growth were primarily governed by interannual climatic variability and orchard-specific abiotic and biotic conditions, while fertilization treatments exerted a secondary and context-dependent influence. Across all measured variables—specific leaf area, gas exchange, chlorophyll fluorescence, leaf reflectance indices, biometric growth, and mean fruit weight—Year and Orchard consistently explained a substantially larger proportion of variability than Treatment strategy [73].

From a management perspective, these results support the feasibility of reducing synthetic fertilizer inputs while maintaining physiological stability and fruit size, provided that soil characteristics, local climate, and orchard-specific stress factors are taken into account. Under scenarios of increasing climatic variability, strategies that enhance soil functioning and system resilience—rather than maximizing short-term inputs—are likely to be more effective for sustaining apple orchard performance [1,52].

To facilitate interpretation across the large number of measured variables, a synthesis of dominant drivers based on effect size magnitude is provided in Table 4. Across all functional domains, interannual climatic variability and orchard-specific conditions consistently outweighed fertilization treatment effects, which emerged mainly through higher-order interactions [74,75].

Table 4. Relative importance of Year, Orchard, Treatment, and their interactions across functional domains.

Functional Domain	Year	Orchard	Treatment	Interactions (Year × Orchard/Treatment)
Specific leaf area (SLA)	●●●	●●●	○	●●●
Chlorophyll content (CCI)	●	●●●	○	●●
Gas exchange (IRGA)	●●●	●●●	●	●●●
Water-use efficiency (WUE)	●●	●●	○	●
Chlorophyll fluorescence (OJIP)	●●●	●●	●	●●
Leaf reflectance (PCA components)	●●●	●●●	●	●●●
Vegetative growth (ΔVol, ΔTCSA)	●●●	●●●	●	●●
Mean fruit weight (g)	●●●	●●	●	●●
Fruit production (kg tree−1)	●●●	●●●	○	●●

Dominant effects were classified based on the magnitude and consistency of partial eta-squared (η²p) values across variables within each domain. ●●● = large and dominant effect (η²p ≥ 0.14, recurrent); ●● = moderate effect (η²p ≈ 0.06–0.14 or large but variable); ● = small or context-dependent effect (η²p < 0.06 or sporadic); ○ = negligible or non-significant effect.

5. Conclusions

This study provides clear evidence that apple tree performance is primarily driven by interannual climatic variability and orchard-specific conditions, while fertilization treatments play a secondary and context-dependent role. Across physiological, spectral, biometric, and fruit growth–related variables, environmental forcing consistently outweighed management effects, highlighting the central role of climate–soil interactions in regulating tree functioning in Mediterranean apple orchards.

Crucially, fertilization strategies integrating organic amendments with reduced mineral inputs—particularly M3 and, to a slightly lesser extent, M2—maintained physiological performance, vegetative growth, tree production and fruit size at levels comparable to full mineral fertilization. These results provide robust field-based evidence that partial replacement of mineral fertilizers by organic inputs is agronomically viable in commercial apple orchards, without inducing physiological stress or yield penalties.

While the most restrictive strategy (M4) occasionally showed reduced photosynthetic performance under specific orchard–year combinations, these effects were not systematic and did not compromise overall tree functioning. This indicates that M4 represents a practical lower boundary for mineral input reduction, emphasizing the importance of site-adapted implementation rather than uniform fertilization prescriptions.

The generally weak main effects of fertilization treatments are consistent with the delayed and cumulative nature of organic amendments, whose benefits depend on progressive mineralization, microbial activity, and improvements in soil structure. The recurrent significance of higher-order interactions involving Year and Orchard underscores the need to interpret fertilization responses within a multi-year and site-adapted framework, particularly under increasing climatic variability.

Several limitations should be acknowledged. The experimental timeframe may not fully capture long-term changes in soil organic matter pools and nutrient cycling associated with organic amendments. In addition, soil biological indicators were not directly assessed, limiting mechanistic insight into microbial-mediated processes underlying delayed treatment responses. Finally, physiological measurements were conducted at the leaf level and may not fully represent whole-canopy or whole-orchard carbon and water dynamics.

Future research should therefore adopt longer-term experimental designs integrating soil biological indicators, continuous soil moisture monitoring, and canopy-scale physiological assessments. Linking leaf-level measurements with remote sensing approaches and ecosystem-scale carbon and water fluxes would further improve understanding of orchard resilience under increasing climatic uncertainty.

Overall, these results support the adoption of site-adapted fertilization strategies that promote the use of organic amendments as partial substitutes for mineral fertilizers, prioritizing soil functioning and long-term system resilience over short-term input maximization, and reinforcing the role of integrated soil and nutrient management in sustainable perennial fruit production systems.

Appendix A.1. Leaf Structural Traits

Table A1. Three-way ANOVA results for specific leaf area (SLA) measured in spring and summer.

Season	Effect	F-Value	p-Value	η2p	Effect Size
Spring	Year	42.90	<0.001	0.326	Large
	Orchard	60.08	<0.001	0.404	Large
	Treatment	0.74	0.531	0.012	Negligible
	Treatment × Year	5.89	<0.001	0.197	Large
	Orchard × Year	12.19	<0.001	0.253	Large
	Orchard × Treatment	3.79	0.002	0.137	Moderate-Large
	Orchard × Treatment × Year	3.01	<0.001	0.200	Large
Summer	Year	27.44	<0.001	0.258	Large
	Orchard	54.69	<0.001	0.439	Large
	Treatment	2.42	0.071	0.040	Small
	Orchard × Year	17.54	<0.001	0.328	Large
	Treatment × Year	5.44	<0.001	0.185	Moderate-Large
	Orchard × Treatment	3.36	0.004	0.123	Moderate
	Orchard × Treatment × Year	2.23	0.013	0.156	Moderate-Large

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p). Despite violations of homogeneity of variances (Levene’s test), ANOVA results are reported for consistency across parameters; post hoc comparisons were adjusted accordingly. Similar effect sizes across seasons reflect consistent variance partitioning across models.

Table A1. Three-way ANOVA results for specific leaf area (SLA) measured in spring and summer.

Season	Effect	F-Value	p-Value	η2p	Effect Size
Spring	Year	42.90	<0.001	0.326	Large
	Orchard	60.08	<0.001	0.404	Large
	Treatment	0.74	0.531	0.012	Negligible
	Treatment × Year	5.89	<0.001	0.197	Large
	Orchard × Year	12.19	<0.001	0.253	Large
	Orchard × Treatment	3.79	0.002	0.137	Moderate-Large
	Orchard × Treatment × Year	3.01	<0.001	0.200	Large
Summer	Year	27.44	<0.001	0.258	Large
	Orchard	54.69	<0.001	0.439	Large
	Treatment	2.42	0.071	0.040	Small
	Orchard × Year	17.54	<0.001	0.328	Large
	Treatment × Year	5.44	<0.001	0.185	Moderate-Large
	Orchard × Treatment	3.36	0.004	0.123	Moderate
	Orchard × Treatment × Year	2.23	0.013	0.156	Moderate-Large

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p). Despite violations of homogeneity of variances (Levene’s test), ANOVA results are reported for consistency across parameters; post hoc comparisons were adjusted accordingly. Similar effect sizes across seasons reflect consistent variance partitioning across models.

Table A2. Three-way ANOVA results for chlorophyll content index (CCI) measured in spring and summer.

Season	Effect	F-Value	p-Value	η2p	Effect Size
Spring	Year	9.48	<0.001	0.036	Small
	Orchard	83.76	<0.001	0.249	Large
	Treatment	0.53	0.660	0.003	Negligible
	Orchard × Year	45.14	<0.001	0.264	Large
	Treatment × Year	1.22	0.296	0.014	Negligible
	Orchard × Treatment	2.57	0.018	0.030	Small
	Orchard × Treatment × Year	1.14	0.329	0.026	Small
Summer	Year	20.87	<0.001	0.076	Moderate
	Orchard	144.35	<0.001	0.364	Large
	Treatment	1.66	0.174	0.010	Negligible
	Orchard × Year	19.17	<0.001	0.132	Moderate-Large
	Treatment × Year	0.19	0.980	0.002	Negligible
	Orchard × Treatment	4.30	<0.001	0.049	Small
	Orchard × Treatment × Year	1.11	0.347	0.026	Small

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A2. Three-way ANOVA results for chlorophyll content index (CCI) measured in spring and summer.

Season	Effect	F-Value	p-Value	η2p	Effect Size
Spring	Year	9.48	<0.001	0.036	Small
	Orchard	83.76	<0.001	0.249	Large
	Treatment	0.53	0.660	0.003	Negligible
	Orchard × Year	45.14	<0.001	0.264	Large
	Treatment × Year	1.22	0.296	0.014	Negligible
	Orchard × Treatment	2.57	0.018	0.030	Small
	Orchard × Treatment × Year	1.14	0.329	0.026	Small
Summer	Year	20.87	<0.001	0.076	Moderate
	Orchard	144.35	<0.001	0.364	Large
	Treatment	1.66	0.174	0.010	Negligible
	Orchard × Year	19.17	<0.001	0.132	Moderate-Large
	Treatment × Year	0.19	0.980	0.002	Negligible
	Orchard × Treatment	4.30	<0.001	0.049	Small
	Orchard × Treatment × Year	1.11	0.347	0.026	Small

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Appendix A.2. Gas Exchange, Water-Use Efficiency and Chlorophyll Fluorescence

For chlorophyll fluorescence parameters, full factorial ANOVA models were evaluated. For clarity, Table A3 and Table A5 reports only the statistically relevant main effects supporting the interpretations discussed in the main text.

Table A3. Three-way ANOVA results for gas exchange parameters measured in spring and summer.

Season	Parameter	Effect	F-Value	p-Value	η2p	Effect Size
Spring	Cᵢ	Year	159.78	<0.001	0.497	Large
		Orchard	181.24	<0.001	0.528	Large
		Treatment	2.10	0.100	0.019	Negligible
		Orchard × Year	137.04	<0.001	0.629	Large
		Orchard × Treatment	1.88	0.085	0.034	Small
		Orchard × Treatment × Year	1.11	0.354	0.039	Small
	E	Year	193.43	<0.001	0.544	Large
		Orchard	225.21	<0.001	0.582	Large
		Orchard × Year	155.60	<0.001	0.658	Large
		Orchard × Treatment	2.41	0.027	0.043	Small
		Orchard × Treatment × Year	2.21	0.011	0.076	Moderate
	gs	Treatment	12.61	<0.001	0.105	Small
		Year	14.13	<0.001	0.080	Moderate
		Orchard	38.00	<0.001	0.190	Moderate–Large
	An	Orchard	26.33	<0.001	0.140	Moderate
		Treatment	9.59	<0.001	0.082	Small
		Orchard × Year	32.15	<0.001	0.284	Large
Summer	Cᵢ	Year	12.36	<0.001	0.071	Moderate
		Orchard	51.54	<0.001	0.241	Large
		Orchard × Year	23.37	<0.001	0.224	Large
	E	Year	28.26	<0.001	0.149	Large
		Treatment	9.24	<0.001	0.079	Moderate
		Orchard × Year	53.23	<0.001	0.397	Large
		Treatment × Year	3.46	0.003	0.060	Moderate
	gs	Year	91.92	<0.001	0.362	Large
		Orchard	51.87	<0.001	0.243	Large
		Treatment	5.38	0.001	0.047	Small
		Orchard × Year	22.79	<0.001	0.220	Large
		Orchard × Treatment	2.17	0.045	0.039	Small
	An	Year	32.66	<0.001	0.168	Large
		Orchard	20.35	<0.001	0.112	Moderate–Large
		Treatment	4.11	0.007	0.037	Small
		Orchard × Year	13.47	<0.001	0.143	Large
		Orchard × Treatment	4.05	<0.001	0.070	Moderate

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A3. Three-way ANOVA results for gas exchange parameters measured in spring and summer.

Season	Parameter	Effect	F-Value	p-Value	η2p	Effect Size
Spring	Cᵢ	Year	159.78	<0.001	0.497	Large
		Orchard	181.24	<0.001	0.528	Large
		Treatment	2.10	0.100	0.019	Negligible
		Orchard × Year	137.04	<0.001	0.629	Large
		Orchard × Treatment	1.88	0.085	0.034	Small
		Orchard × Treatment × Year	1.11	0.354	0.039	Small
	E	Year	193.43	<0.001	0.544	Large
		Orchard	225.21	<0.001	0.582	Large
		Orchard × Year	155.60	<0.001	0.658	Large
		Orchard × Treatment	2.41	0.027	0.043	Small
		Orchard × Treatment × Year	2.21	0.011	0.076	Moderate
	gs	Treatment	12.61	<0.001	0.105	Small
		Year	14.13	<0.001	0.080	Moderate
		Orchard	38.00	<0.001	0.190	Moderate–Large
	An	Orchard	26.33	<0.001	0.140	Moderate
		Treatment	9.59	<0.001	0.082	Small
		Orchard × Year	32.15	<0.001	0.284	Large
Summer	Cᵢ	Year	12.36	<0.001	0.071	Moderate
		Orchard	51.54	<0.001	0.241	Large
		Orchard × Year	23.37	<0.001	0.224	Large
	E	Year	28.26	<0.001	0.149	Large
		Treatment	9.24	<0.001	0.079	Moderate
		Orchard × Year	53.23	<0.001	0.397	Large
		Treatment × Year	3.46	0.003	0.060	Moderate
	gs	Year	91.92	<0.001	0.362	Large
		Orchard	51.87	<0.001	0.243	Large
		Treatment	5.38	0.001	0.047	Small
		Orchard × Year	22.79	<0.001	0.220	Large
		Orchard × Treatment	2.17	0.045	0.039	Small
	An	Year	32.66	<0.001	0.168	Large
		Orchard	20.35	<0.001	0.112	Moderate–Large
		Treatment	4.11	0.007	0.037	Small
		Orchard × Year	13.47	<0.001	0.143	Large
		Orchard × Treatment	4.05	<0.001	0.070	Moderate

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A4. Three-way ANOVA results for water-use efficiency (WUE = A/E).

Effect	F-Value	p-Value	η2p	Effect Size
Year	50.17	<0.001	0.067	Moderate
Orchard	30.74	<0.001	0.042	Moderate
Treatment	0.59	0.619	0.001	Small
Orchard × Year	13.28	<0.001	0.037	Small-Moderate
Treatment × Year	0.21	0.975	<0.001	Small
Orchard × Treatment	2.44	0.024	0.010	Small
Orchard × Treatment × Year	0.50	0.917	0.004	Negligible

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A4. Three-way ANOVA results for water-use efficiency (WUE = A/E).

Effect	F-Value	p-Value	η2p	Effect Size
Year	50.17	<0.001	0.067	Moderate
Orchard	30.74	<0.001	0.042	Moderate
Treatment	0.59	0.619	0.001	Small
Orchard × Year	13.28	<0.001	0.037	Small-Moderate
Treatment × Year	0.21	0.975	<0.001	Small
Orchard × Treatment	2.44	0.024	0.010	Small
Orchard × Treatment × Year	0.50	0.917	0.004	Negligible

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A5. Three-way ANOVA results for chlorophyll fluorescence parameters measured in spring and summer.

Season	Parameter	Effect	F-Value	p-Value	η2p	Effect Size
Spring	Fv/Fm	Year	25.35	<0.001	0.136	Moderate
		Orchard	22.23	<0.001	0.121	Moderate
	PIabs	Year	11.05	<0.001	0.064	Moderate
		Orchard	41.22	<0.001	0.203	Large
	ABS/RC	Year	11.23	<0.001	0.065	Moderate
	DIo/RC	Year	10.69	<0.001	0.062	Moderate
Summer	Fv/Fm	Year	57.91	<0.001	0.263	Large
	PIabs	Year	117.62	<0.001	0.421	Large
	TRo/RC	Year	104.63	<0.001	0.392	Large
	ABS/RC	Year	104.61	<0.001	0.392	Large

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A5. Three-way ANOVA results for chlorophyll fluorescence parameters measured in spring and summer.

Season	Parameter	Effect	F-Value	p-Value	η2p	Effect Size
Spring	Fv/Fm	Year	25.35	<0.001	0.136	Moderate
		Orchard	22.23	<0.001	0.121	Moderate
	PIabs	Year	11.05	<0.001	0.064	Moderate
		Orchard	41.22	<0.001	0.203	Large
	ABS/RC	Year	11.23	<0.001	0.065	Moderate
	DIo/RC	Year	10.69	<0.001	0.062	Moderate
Summer	Fv/Fm	Year	57.91	<0.001	0.263	Large
	PIabs	Year	117.62	<0.001	0.421	Large
	TRo/RC	Year	104.63	<0.001	0.392	Large
	ABS/RC	Year	104.61	<0.001	0.392	Large

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Appendix A.3. Leaf Reflectance Indices

For the ANOVA performed on PCA response components (RCs), full factorial models including all main effects and interactions were evaluated. For clarity, Table A8 reports only the statistically relevant effects supporting the interpretations discussed in the main text.

Table A6. Rotated component loadings from PCA of vegetation indices (spring). Only loadings ≥ 0.40 are shown; strong loadings (≥0.70) are bold. Promax rotation applied.

Component	Variance Explained (%)	Cumulative (%)	Representative Indices (Loadings ≥ 0.40)
RC1	31.10	31.10	RDVI 1.06, MCARI1 0.96, TVI 0.94, OSAVI 0.94, SIPI 0.89, Lic1 0.74, SR 0.70, NDVI 0.70, GM2 0.44, GM1 0.43, Ctr2 −0.51
RC2	26.50	57.60	GM1 0.98, ZMI 0.96, GM2 0.85, TCARI 0.84, MCARI −0.81, G −0.81, CRI1 −0.65, Ctr1 −0.63, CRI2 −0.53, Ctr1 −0.52
RC3	19.40	77.00	SPRI 0.89, NPCI −0.88, Lic2 0.84, NPQI 0.71, PRI 0.66, Ctr1 −0.59
RC4	12.10	89.10	ARI2 0.94, ARI1 0.91, CRI2 0.61

Table A6. Rotated component loadings from PCA of vegetation indices (spring). Only loadings ≥ 0.40 are shown; strong loadings (≥0.70) are bold. Promax rotation applied.

Component	Variance Explained (%)	Cumulative (%)	Representative Indices (Loadings ≥ 0.40)
RC1	31.10	31.10	RDVI 1.06, MCARI1 0.96, TVI 0.94, OSAVI 0.94, SIPI 0.89, Lic1 0.74, SR 0.70, NDVI 0.70, GM2 0.44, GM1 0.43, Ctr2 −0.51
RC2	26.50	57.60	GM1 0.98, ZMI 0.96, GM2 0.85, TCARI 0.84, MCARI −0.81, G −0.81, CRI1 −0.65, Ctr1 −0.63, CRI2 −0.53, Ctr1 −0.52
RC3	19.40	77.00	SPRI 0.89, NPCI −0.88, Lic2 0.84, NPQI 0.71, PRI 0.66, Ctr1 −0.59
RC4	12.10	89.10	ARI2 0.94, ARI1 0.91, CRI2 0.61

Table A7. Rotated component loadings from PCA of vegetation indices (summer). Only loadings ≥ 0.40 are shown; strong loadings (≥0.70) are in bold. Promax rotation applied.

Component	Variance Explained (%)	Cumulative (%)	Representative Indices (Loadings ≥ 0.40)
RC1	38.80	38.80	GM1 1.05, GM2 0.98, TCARI 0.94, ZMI 0.93, SIPI 0.93, SR 0.92, NDVI 0.92, Lic1 0.87, Ctr2 −0.82, OSAVI 0.80, RDVI 0.63, MCARI −0.50, CRI2 0.44, CRI1 0.43
RC2	13.80	52.60	G 0.92, CRI1 0.84, MCARI 0.81, CRI2 0.78, Ctr1 0.50
RC3	12.00	64.60	MCARI1 1.00, TVI 0.94, RDVI 0.74
RC4	11.70	76.20	ARI2 0.92, ARI1 0.91, PRI 0.71, G −0.42
RC5	11.70	87.90	Lic2 0.88, NPCI −0.80, SPRI 0.80, NPQI 0.95, Ctr1 −0.67

Table A7. Rotated component loadings from PCA of vegetation indices (summer). Only loadings ≥ 0.40 are shown; strong loadings (≥0.70) are in bold. Promax rotation applied.

Component	Variance Explained (%)	Cumulative (%)	Representative Indices (Loadings ≥ 0.40)
RC1	38.80	38.80	GM1 1.05, GM2 0.98, TCARI 0.94, ZMI 0.93, SIPI 0.93, SR 0.92, NDVI 0.92, Lic1 0.87, Ctr2 −0.82, OSAVI 0.80, RDVI 0.63, MCARI −0.50, CRI2 0.44, CRI1 0.43
RC2	13.80	52.60	G 0.92, CRI1 0.84, MCARI 0.81, CRI2 0.78, Ctr1 0.50
RC3	12.00	64.60	MCARI1 1.00, TVI 0.94, RDVI 0.74
RC4	11.70	76.20	ARI2 0.92, ARI1 0.91, PRI 0.71, G −0.42
RC5	11.70	87.90	Lic2 0.88, NPCI −0.80, SPRI 0.80, NPQI 0.95, Ctr1 −0.67

Table A8. Three-way ANOVA results for PCA response components (RCs) measured in spring and summer.

Season	RC	Effect	F-Value	p-Value	η2p	Effect Size
Spring	RC1	Year	614.10	<0.001	0.709	Large
		Orchard	45.93	<0.001	0.154	Large
		Treatment	4.02	0.008	0.023	Small
	RC2	Year	60.15	<0.001	0.193	Large
		Orchard	5.84	0.003	0.023	Small
	RC3	Year	1898.44	<0.001	0.883	Large
		Orchard	86.37	<0.001	0.255	Large
	RC4	Year	1102.81	<0.001	0.814	Large
		Orchard	45.01	<0.001	0.152	Large
Summer	RC1	Year	490.66	<0.001	0.661	Large
		Orchard	200.34	<0.001	0.443	Large
	RC2	Year	143.58	<0.001	0.363	Large
		Orchard	54.43	<0.001	0.178	Large
	RC3	Orchard	18.63	<0.001	0.069	Moderate
	RC4	Year	1407.31	<0.001	0.848	Large
	RC5	Year	128.94	<0.001	0.339	Large
		Orchard	43.69	<0.001	0.148	Large
		Treatment	15.39	<0.001	0.084	Moderate

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A8. Three-way ANOVA results for PCA response components (RCs) measured in spring and summer.

Season	RC	Effect	F-Value	p-Value	η2p	Effect Size
Spring	RC1	Year	614.10	<0.001	0.709	Large
		Orchard	45.93	<0.001	0.154	Large
		Treatment	4.02	0.008	0.023	Small
	RC2	Year	60.15	<0.001	0.193	Large
		Orchard	5.84	0.003	0.023	Small
	RC3	Year	1898.44	<0.001	0.883	Large
		Orchard	86.37	<0.001	0.255	Large
	RC4	Year	1102.81	<0.001	0.814	Large
		Orchard	45.01	<0.001	0.152	Large
Summer	RC1	Year	490.66	<0.001	0.661	Large
		Orchard	200.34	<0.001	0.443	Large
	RC2	Year	143.58	<0.001	0.363	Large
		Orchard	54.43	<0.001	0.178	Large
	RC3	Orchard	18.63	<0.001	0.069	Moderate
	RC4	Year	1407.31	<0.001	0.848	Large
	RC5	Year	128.94	<0.001	0.339	Large
		Orchard	43.69	<0.001	0.148	Large
		Treatment	15.39	<0.001	0.084	Moderate

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Appendix A.4. Tree Growth Parameters

Table A9. Three-way ANOVA results for tree growth parameters.

Parameter	Effect	F-Value	p-Value	η2p	Effect Size
ΔVolumetry	Year	1666.41	<0.001	0.869	Large
	Orchard	122.17	<0.001	0.327	Large
	Treatment	5.33	0.001	0.031	Small
	Orchard × Treatment	4.64	<0.001	0.053	Small
	Treatment × Year	5.30	<0.001	0.060	Moderate
	Orchard × Year	121.98	<0.001	0.493	Large
	Orchard × Treatment × Year	4.60	<0.001	0.099	Moderate
ΔTCSA	Year	607.70	<0.001	0.599	Large
	Orchard	232.47	<0.001	0.481	Large
	Treatment	3.77	0.011	0.022	Small
	Year × Treatment	1.62	0.139	0.019	Small
	Year × Orchard	40.16	<0.001	0.242	Large
	Orchard × Treatment	2.28	0.035	0.027	Small
	Orchard × Treatment × Year	0.71	0.744	0.017	Small

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A9. Three-way ANOVA results for tree growth parameters.

Parameter	Effect	F-Value	p-Value	η2p	Effect Size
ΔVolumetry	Year	1666.41	<0.001	0.869	Large
	Orchard	122.17	<0.001	0.327	Large
	Treatment	5.33	0.001	0.031	Small
	Orchard × Treatment	4.64	<0.001	0.053	Small
	Treatment × Year	5.30	<0.001	0.060	Moderate
	Orchard × Year	121.98	<0.001	0.493	Large
	Orchard × Treatment × Year	4.60	<0.001	0.099	Moderate
ΔTCSA	Year	607.70	<0.001	0.599	Large
	Orchard	232.47	<0.001	0.481	Large
	Treatment	3.77	0.011	0.022	Small
	Year × Treatment	1.62	0.139	0.019	Small
	Year × Orchard	40.16	<0.001	0.242	Large
	Orchard × Treatment	2.28	0.035	0.027	Small
	Orchard × Treatment × Year	0.71	0.744	0.017	Small

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Appendix A.5. Yield Components

Table A10. Three-way ANOVA results for fruit production per tree (kg tree⁻¹).

Effect	F-Value	p-Value	η2p	Effect Size
Year	80.64	<0.001	0.242	Large
Orchard	23.14	<0.001	0.084	Moderate
Treatment	6.44	<0.001	0.037	Small
Orchard × Treatment	7.75	<0.001	0.084	Moderate
Treatment × Year	1.71	0.116	0.020	Small
Orchard × Year	17.65	<0.001	0.123	Large
Orchard × Treatment × Year	1.41	0.159	0.032	Small

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A10. Three-way ANOVA results for fruit production per tree (kg tree⁻¹).

Effect	F-Value	p-Value	η2p	Effect Size
Year	80.64	<0.001	0.242	Large
Orchard	23.14	<0.001	0.084	Moderate
Treatment	6.44	<0.001	0.037	Small
Orchard × Treatment	7.75	<0.001	0.084	Moderate
Treatment × Year	1.71	0.116	0.020	Small
Orchard × Year	17.65	<0.001	0.123	Large
Orchard × Treatment × Year	1.41	0.159	0.032	Small

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A11. Three-way ANOVA results for mean fruit weight (g).

Effect	F-Value	p-Value	η2p	Effect Size
Year	80.64	<0.001	0.242	Large
Orchard	23.14	<0.001	0.084	Moderate
Treatment	6.44	<0.001	0.037	Small
Orchard × Treatment	7.75	<0.001	0.084	Moderate
Treatment × Year	1.71	0.116	0.020	Small
Orchard × Year	17.65	<0.001	0.123	Large
Orchard × Treatment × Year	1.41	0.159	0.032	Small

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).

Table A11. Three-way ANOVA results for mean fruit weight (g).

Effect	F-Value	p-Value	η2p	Effect Size
Year	80.64	<0.001	0.242	Large
Orchard	23.14	<0.001	0.084	Moderate
Treatment	6.44	<0.001	0.037	Small
Orchard × Treatment	7.75	<0.001	0.084	Moderate
Treatment × Year	1.71	0.116	0.020	Small
Orchard × Year	17.65	<0.001	0.123	Large
Orchard × Treatment × Year	1.41	0.159	0.032	Small

Note: Type III sums of squares were used. Effect sizes are expressed as partial eta squared (η²p).