Functional Trait-Based Responses of the Moroccan Menara Cultivar to Deficit Irrigation

Abstract

The olive tree (Olea europaea L.) is a keystone species in Mediterranean agroecosystems, where it plays a central economic and cultural role. However, the Mediterranean Basin is increasingly exposed to climate change, with rising temperatures and prolonged droughts threatening the long-term sustainability of olive cultivation. Understanding the adaptive responses of olive trees to water scarcity is critical for ensuring resilience in olive-based agroecosystems. This study investigates the functional responses of the Moroccan Menara olive cultivar under different controlled deficit irrigation (DI) strategies, namely regulated (RDI) and sustained (SDI) deficit irrigation. By analyzing key leaf functional and biochemical traits, we assessed how varying levels of water stress influence resource allocation and stress mitigation mechanisms. Under full irrigation (100% of crop water evapotranspiration) throughout the growing season and during sensitive growth periods, trees exhibited increased stomatal density, leaf area, and higher leaf carbon, nitrogen, and phosphorus contents, traits associated with enhanced growth and photosynthetic capacity. Meanwhile, under RDI treatments, with a 20% water reduction during sensitive periods and 40% during non-sensitive periods, Menara trees showed increased leaf tissue density and accumulation of polyphenols. SDI treatments, however, triggered higher concentrations of osmoprotectants (glycine, sugars, and proline), reduced stomatal density, and smaller leaf area associated with increased stomatal size. Principal component analysis revealed a major trade-off between growth-related and stress-protective traits, primarily driven by water availability during phenological growth stages. Notably, the strength of this trade-off was positively associated with olive fruit yield, underscoring the importance of strategically timed irrigation in balancing physiological resilience and productivity. These findings emphasize the crucial role of irrigation strategy in modulating functional responses of olive trees to water deficit, offering insights into optimizing water use under future climate scenarios.

1. Introduction

Widespread aridity and increasing drought, driven by ongoing climate change, are affecting multiple dimensions of life on Earth, including biodiversity and ecosystem functioning [1]. As global climate change accelerates, drought events are projected to become more frequent, prolonged, and severe [2,3]. The Mediterranean region, recognized as a climate change hotspot, is expected to experience a marked rise in temperatures coupled with declining precipitation, leading to more frequent heat waves and extended dry periods [4]. These climatic shifts are likely to have far-reaching consequences for Mediterranean ecosystems [5]. Evidence is already emerging of climate change-driven disruptions in key ecological processes, including altered phenology [6,7], reduced water availability [8], declining soil fertility and structure [9], as well as shifts in pest and disease dynamics [10,11]. Within this context, the increasing aridity manifested by reduced rainfall, declining soil moisture, and rising temperatures poses a significant threat to agricultural productivity [12], particularly for olive-based agroecosystems [13,14,15], which are deeply embedded in the ecological, economic, and cultural fabric of the Mediterranean societies [16].

The olive tree (Olea europaea L.) is a cornerstone species of Mediterranean agroecosystems, with over 1200 clonally propagated cultivars grown across the region [17], reflecting the remarkable genetic diversity of this emblematic species [18]. This diversity underpins the global production of olive oil, which exceeded 3 million tons in 2019 [19], of which approximately 98% originated from Mediterranean countries, positioning the olive tree among the most economically and culturally significant fruit crops in the region [16]. Recent research has considerably advanced our understanding of the physiological, biochemical, and morphological mechanisms underlying drought tolerance in olive trees. Olive trees exhibit a wide range of strategies, including stress tolerance and recovery, often supported by stress memory and priming mechanisms that enhance responses to recurrent drought events [20,21,22,23]. At the physiological level, drought tolerance is mediated through efficient stomatal regulation, reductions in leaf area and water potential, and the maintenance of photosynthetic activity under water-limited conditions [24,25,26]. In parallel, biochemical adjustments include the accumulation of osmoprotectants and antioxidant compounds, which mitigate oxidative damage and stabilize cellular metabolism under dehydration [27,28,29]. Morphological traits, such as deep and extensive root systems, waxy and trichome-rich leaf surfaces, and reduced stem growth, further contribute to water conservation and hydraulic resilience [30,31,32,33]. Collectively, these adaptive mechanisms illustrate the remarkable plasticity of olive trees. However, this strategic crop is increasingly threatened by climate change, as evidenced by a notable decline in olive yields in several major production areas in recent years [34,35,36]. In fact, rising temperatures have been shown to disrupt the phenological cycle of the olive tree, including flowering and fruit set timing [7,37], which directly affects productivity. Simultaneously, increasing aridity is challenging the resilience and adaptation of many olive cultivars by elevating the risk of xylem dysfunction and impairing critical ecophysiological processes such as sap conduction and water-use efficiency [20]. These challenges may compromise the long-term sustainability of olive-based agroecosystems and the livelihoods that depend on them [14,15]. In this context, the adoption of climate-smart agricultural practices, such as adaptive irrigation management [38,39] and promoting cultivars endowed with stress-tolerant traits and enhanced resource use [20,40,41], could mitigate the negative impacts of climate change on Mediterranean food resources from table olives and olive oil.

Although the olive tree has triggered extensive research on its genetic diversity, biogeography, and domestication history (Besnard et al. [18]), few studies have addressed its functional trait-based responses under stressful environmental conditions [20,41,42,43,44,45]. Plant functional responses are often inferred by examining variations in traits associated with growth forms and life history strategies [46,47] and their relationships with environmental gradients [48,49]. Functional traits are defined as any morphological, biochemical, structural, or phenological characteristics directly linked to an individual’s performance and serve as proxies for its capacity to acquire and utilize resources [49]. For instance, plants growing under arid and water-deficit conditions are expected to exhibit smaller leaves with higher tissue density [44,50], increased stomatal density [51], and greater concentrations of secondary metabolites [52,53]. These adjustments help reduce water loss through smaller leaf surface area and denser tissues, enhance structural resistance to desiccation, and enable better regulation of gas exchange [51,54]. Additionally, elevated levels of secondary metabolites offer protection against oxidative stress, UV radiation, and herbivory [53,55]. Collectively, these traits support a conservative resource-use strategy that enhances survival and persistence under harsh environmental conditions [46,56]. Conversely, under more favorable conditions, an increase in leaf area and higher concentrations of leaf nitrogen (N), carbon (C), and phosphorus (P) are often associated with improved growth and rapid resource acquisition [57,58]. Therefore, the plant functional trait framework provides a robust approach to explaining and predicting plant responses and adaptive strategies in the context of climate change [59,60,61].

Understanding the mechanisms that confer improved resistance and adaptation to climate change in high-value crop species, such as the olive tree, holds significant promise for the agricultural sector [34,41,62]. It is also essential to characterize how aridity and water deficit affect the variability and structure of functional traits to refine the screening and selection of olive cultivars best adapted to current and predicted stresses [20]. Accordingly, our study aims to improve understanding of the magnitude and variability of functional responses exhibited by olive cultivars under decreasing water availability, as imposed by different deficit irrigation (DI) treatments. We hypothesize that the Menara olive cultivar will exhibit distinct functional responses under regulated (RDI) and sustained (SDI) deficit irrigation. Specifically, sustained DI is expected to promote the accumulation of osmoprotectants and secondary metabolites, whereas regulated DI will favor resource acquisition traits and yield performance, depending on water availability. We selected the Menara cultivar as a model because it is among the most widely cultivated olive varieties in Morocco [63], directly derived and clonally propagated from Picholine marocaine, the country’s dominant cultivar [64]. The Menara is extensively used in large-scale olive agroecosystems [50] and is widely distributed across Morocco’s main olive-growing regions, similar to Picholine marocaine. In addition, its strong agronomic performance [25] makes Menara highly appreciated by Moroccan farmers. Altogether, these characteristics make the Menara cultivar a highly relevant candidate for evaluating functional responses to water deficit. Moreover, its use provides insights applicable to a large proportion of Moroccan olive orchards. Under DI, irrigation is applied at levels below the full crop water requirements (e.g., crop evapotranspiration), either throughout the entire growing season or independently of specific phenological stages [38,65]. This results in plants being exposed to a controlled level of water stress [66], providing a powerful experimental framework for characterizing olive tree functional responses to water limitation. In this study, we focus on a set of key leaf functional traits associated with resource acquisition and stress tolerance in the Moroccan Menara olive cultivar subjected to varying levels of deficit irrigation. Specifically, we address the following questions: (1) Do Menara trees subjected to different deficit irrigation treatments exhibit distinct functional responses? (2) If so, do these responses vary according to the level of the applied water stress? (3) Are these differences influenced by the phenological timing of water stress application? (4) What is the relationship between functional responses and the agronomic performance (e.g., fruit yield) in the Menara cultivar?

2. Materials and Methods

2.1. Experimental Site and Design

The study was carried out within the experimental domain of Saada (31.628 N; −8.146 W; 411 m a.s.l.) managed by the National Institute of Agricultural Research (INRA) of Marrakech, Morocco (Supplementary Figure S1). The experimental site features a Mediterranean climate, characterized by arid to semi-arid conditions with hot, dry summers, low annual rainfall, and irregular precipitation patterns, with most rainfall occurring between late November and March, with minimal precipitation during summer [38,67]. The study was conducted on an orchard of 12-year-old trees of the Menara cultivar, planted in a square-spaced scheme with an 8 m side length and a density of 156 trees ha⁻¹, irrigated through drip irrigation [38]. The trees were planted in a clay loam texture soil with a pH of 7.82 and an organic matter content of 2.3% and subjected to the same management practices. The studied Menara cv. was developed by INRA through clonal selection from the Picholine marocaine cultivar, which is the most dominant cultivar within traditional olive agroecosystems in Morocco [64,68]. The experimental design is based on different deficit irrigation treatments categorized into two main strategies: regulated deficit irrigation (RDI) and sustained deficit irrigation (SDI), encompassing six treatments, each applied to two different rows of nine trees per row. DI treatments were defined depending on the crop water evapotranspiration (ETc), the phenological stages of the olive tree, and the duration of irrigation (

Table 1. Deficit irrigation strategies and treatments applied according to phenological stages and crop water evapotranspiration (ETc) during 2022.

Strategy	Treatment	Phenological Growth Stages and Corresponding Dates	Water Irrigation Amount (mm) 1				Fruit Yield (kg/Tree) 1
			Total Water	SP1	NP	SP2
Control	T0	Full season (all phases)	304	88 (100%ETc)	110 (100%ETc)	106 (100%ETc)	114.4
Regulated Deficit Irrigation (RDI)	T1	SP1: Full flowering to the beginning of pit hardening (5 April–1 June) (58) 2 NP: From pit hardening to the beginning of oil synthesis (2 June–3 August) (63) 2 SP2: From the beginning of oil synthesis to harvest (4 August–3 November) (92) 2	246	88 (100%ETc)	77 (70%ETc)	106 (100%ETc)	123.3
	T2		235	88 (100%ETc)	66 (60%ETc)	106 (100%ETc)	112.5
	T3		212	70 (80%ETc)	77 (70%ETc)	84 (80%ETc)	99.6
	T4		201	70 (80%ETc)	66 (60%ETc)	84 (80%ETc)	88.3
Sustained Deficit Irrigation (SDI)	T5		196	62 (70%ETc)	77 (70%ETc)	74 (70%ETc)	53.5
	T6		168	53 (60%ETc)	66 (60%ETc)	63 (60%ETc)	55.8

ETc: crop water evapotranspiration. SP: sensitive period. NP: non-sensitive period. ¹ Data retrieved from Ibba et al. [38]. ² The duration of irrigation in days.

). Briefly, as detailed by Ibba et al. [38], phenological growth stages were based on the BBCH scale [69], including full flowering, pit hardening, and oil synthesis (Table 1). Hence, the irrigation requirement was defined based on the daily crop water evapotranspiration (ETc) and calculated as ETc = ET_o × Kc × Kr, where ET_o represents the daily reference evapotranspiration, Kc represents the crop coefficient, and Kr represents the reduction coefficient (Ibba et al. [38]). Accordingly, under the RDI strategy, and regarding phenological stages, four treatments (i.e., T1 to T4) were applied, ranging from 100% to 80% of ETc during sensitive periods (SP1 and SP2) and from 70% to 60% of ETc during non-sensitive periods (NP; Table 1). Meanwhile, SDI treatments (T5 and T6) received 70% and 60% of ETc, respectively, during the full season (Table 1). Accordingly, DI is applied throughout the entire growing season under SDI, while it depends on phenological stages under RDI treatments. Besides RDI and SDI, a control treatment (T0) was considered with 100% of ETc, implying full irrigation through the irrigation season during sensitive and non-sensitive periods. All treatments were carried out under identical environmental, plantation, and management conditions to ensure reliable comparison of responses.

2.2. Leaf Traits Sampling and Measurements

Following standardized protocols [70] and the methodology outlined by Kassout et al. [40,41,44,71] for the olive tree, twigs with fully expanded, mature, and healthy leaves were randomly selected from the sun-exposed side of the upper canopy of 35 sampled trees (five trees per treatment), then wrapped in moist paper towels [72], placed in sealed plastic bags, and stored in a cooled isothermal box. Sampling was carried out between 9 a.m. and 12 a.m. on the same day for each treatment during December of the year 2022. At the laboratory, leaf fresh mass (LFM, g) was measured using an electronic balance. and leaf area was determined by scanning the leaves and analyzing the images using ImageJ software V.1.53K [73]. After removing trichomes from the abaxial leaf surface, without damaging the leaf epidermis, and making impressions with clear nail polish, stomatal density (SD, no. mm⁻²), stomatal length (SL, μm), and stomatal width (SW, μm) were measured from four separate areas of the abaxial surface at 400× magnification using an Olympus BX43 microscope [44,74]. The leaves were then oven-dried at 70 °C for 72 h, and leaf dry mass (LDM, g) was then measured. Consequently, leaf dry matter content (LDMC, mg g⁻¹) was calculated as described by Kassout et al. [44] by dividing LDM by LFM. The stomatal size (SS, μm²) was calculated using the formula SS = SL × SW × π/4 [74]. Thirty replicates per leaf trait were taken from each of the 35 trees across seven treatments, resulting in a total of 1050 replicate leaves.

2.3. Biochemical Traits Determination

2.3.1. Total Soluble Sugar (SUC) Content

For the biochemical traits, a second subset of leaves was considered from the same sampled twigs of the five trees per treatment and pooled to form a composite sample. Three replicates for each trait were analyzed. The total soluble sugar (SUC) content in the leaves was measured following the method described by Eris et al. [75], with some modifications. A 0.1 g fresh leaf sample was placed in a glass test tube. The sugars were extracted by suspending the tissue in 5 mL of 95% (v/v) ethanol and incubating the mixture in a water bath at 85 °C for 1 h. After each extraction, the liquid phase was carefully separated from the tissue. This procedure was repeated four times with successive extraction durations of 1 h, 30 min, 15 min, and 15 min. The resulting ethanolic solutions were combined and evaporated to dryness at 55 °C. The extracted sugars were then dissolved in 1 mL of distilled water. The SUC content was determined using the anthrone reagent method [76], and the absorbance was measured at 620 nm using a spectrophotometer, with glucose solutions as standards, and the results were expressed in mg/g of fresh matter.

2.3.2. Proline Content (PRO)

The proline content (PRO) was measured according to the protocol described by Paquin et al. [77]. 100 mg of fresh material were added to 1.5 mL of a methanol–chloroform–water mixture, previously cooled to 0 °C. The mixture was centrifuged at 2500× g for 5 min, and the supernatant was collected. Subsequently, 0.25 mL of chloroform and 0.9 mL of distilled water were added, followed by stirring and incubation in the dark at 4 °C for 12 h. After incubation, 1 mL of the upper phase was collected and heated in a water bath at 100 °C for 45 min. Then, 0.33 mL of each acetic acid, distilled water, and ninhydrin were added, and the mixture was heated in a water bath at 100 °C for 1 h. Subsequently, 2 mL of toluene was added to the solutions, and the optical density of the upper phase was measured at 520 nm. A calibration curve was prepared using commercial pure proline, and the results were expressed as mg of proline per g of fresh matter.

2.3.3. Glycine Betaine Content (GLY)

The glycine betaine content (GLY) was measured using the methodology described by Grieve et al. [78]. 0.5 g of dry plant material was ground, then transferred into tubes, to which 20 mL of distilled water was added. The tubes were mechanically shaken for 48 h at 25 °C. After filtration, 1 mL of the extract was mixed with 1 mL of sulfuric acid, then stirred in an ice bath for 60 min. After incubation, 0.2 mL of a potassium tri-iodide solution was added to 0.5 mL of the extract. The incubation continued for 16 h at 4 °C, followed by centrifugation for 15 min at 8000× g at 0 °C. The supernatant was recovered, and 9 mL of dichloroethane was added. The optical density of the lower phase was then measured at 365 nm. A calibration range of commercial glycine betaine was used to determine the concentration, and the results were expressed in µmol g⁻¹ of dry matter.

2.3.4. Total Leaf Nitrogen Content (LNC)

In addition, a third subset of dried leaves (dried at 35 °C for 72 h) from pooled samples per treatment was ground into a fine powder. The leaf total nitrogen content determination (LNC) was carried out using the Kjeldahl method modified by Barbano et al. [79]. This method consists of three main steps: first, the digestion of 0.5 g of dry matter with concentrated sulfuric acid (H₂SO₄) to convert organic nitrogen into mineral nitrogen in the form of ammonium sulfate (NH₄)₂SO₄, followed by a distillation in which (NH₄)₂SO₄ is converted into ammonium hydroxide (NH₄OH) in the presence of sodium hydroxide (NaOH), and the released ammonia was captured in a boric acid solution (1N). Finally, the resulting solution is titrated using sulfuric acid at a normality of 0.001 N.

2.3.5. Total Leaf Phosphorus Content (LPC)

After the mineralization step during nitrogen determination, a volume of 0.5 mL of the filtrate was collected and placed in a test tube. Then, 2.5 mL of a reagent mixture consisting of sodium molybdate (2.5%) and hydrazine sulfate (0.15%), along with 2 mL of distilled water, was added. The resulting mixture was heated in a water bath at 95 °C for 10 min to allow the formation of the blue molybdenum complex. After cooling to room temperature and gentle agitation, spectrophotometric measurements were taken at a wavelength of 820 nm, following the method described by Oukaltouma et al. [80]. The total leaf phosphorus content (LPC) was then expressed in mg/g of dry matter using a calibration curve prepared with KH₂PO₄.

2.3.6. Total Leaf Carbon Content (LCC)

The total leaf carbon content (LCC) was analyzed using a TOC analyzer equipped with a solid sample module (Shimadzu 5050A with SSM-5000A; Shimadzu, Kyoto, Japan). The procedure was performed in accordance with ISO 10694 [81]. Samples were finely ground and passed through a 0.2 mm mesh sieve. A 10 mg sample was weighed into a ceramic crucible and placed into a catalytic combustion chamber heated to 900 °C, containing a mixture of cobalt oxide and platinum. In this chamber, carbon in the leaf samples was oxidized to CO₂ using a stream of oxygen. The generated CO₂ was transported by the carrier gas (O₂) to a non-dispersive infrared (NDIR) detector for measurement [82].

2.3.7. Total Polyphenol Content (TPC)

For the preparation of the methanolic extract used for determining total polyphenol content (TPC), 1 g of dried leaf powder was mixed with 20 mL of a methanol–water (1:9) solution following the extraction method described by Albu et al. [83]. The mixture was then subjected to sonication at 20 kHz and 50 °C for 45 min. After extraction, the solvent was removed by rotary vacuum evaporation. The resulting product was stored at −20 °C in the dark for future use. The total polyphenol content (TPC) was determined using the Folin–Ciocalteu method, as described by Tunç et al. [84] with modifications. A volume of 500 µL of extract was mixed with 2500 µL of Folin reagent (previously diluted 10-fold), followed by the addition of 2000 µL of 7.5% Na₂CO₃ and 5 mL of distilled water. The mixture was stirred and incubated in the dark for one hour at 20 °C. Absorbance was measured at 765 nm. Gallic acid was used as the standard, and the total phenolic content was expressed as milligrams of gallic acid equivalents (GAE) per gram of dry extract.

2.3.8. Total Flavonoid Content (TFC)

The total flavonoid content (TFC) was measured using the colorimetric method with aluminum chloride. An aliquot of 1 mL of the previously prepared methanolic extract or standard catechin solution was added to a 10 mL volumetric flask containing 4 mL of distilled water. Then, 0.3 mL of 5% sodium nitrite (NaNO₂) was added to the flask. After 5 min, 0.3 mL of 10% aluminum chloride (AlCl₃) was added. After 6 min, 2 mL of 1 M sodium hydroxide (NaOH) solution was added, and the total volume was adjusted to 10 mL with distilled water. The solution was thoroughly mixed, and the absorbance was measured relative to a reagent blank at 510 nm. The total flavonoid content was expressed as mg of catechin equivalents (CE) per g of dry weight [85]. Consequently, structural allocation and ecophysiological traits, along with chemical traits relating to resource capture and stress mitigation at the plant level, were measured and determined [44,53].

2.4. Statistical Analyses

To highlight trait variation across Menara cv. trees subjected to different irrigation treatments, descriptive statistics, including means, standard deviations (SDs) and ranges, were calculated using the full dataset. In addition, to quantify trait variability, the coefficient of variation (CV) was calculated as: CV (%) = (standard deviation_trait/mean_trait) × 100 [71]. The dlookr and gtsummary packages were used for data description [86,87]. To assess differences among different irrigation treatments, an analysis of variance (ANOVA) was performed on log₁₀-transformed data, followed by Tukey’s HSD test. The significance level was set at p = 0.05, and the agricolae package [88] was used for this analysis. Normality and homogeneity of variances were checked with the Shapiro–Wilk and Levene’s tests, respectively; when assumptions were violated, log₁₀-transformations were applied. To test for significant correlations among trait pairs, the Pearson correlation coefficient was calculated using the metan package [89]. Trait–trait correlation was calculated using the Pearson coefficient and using the cor() function in R. To explore and evaluate the structure, covariation and differences among traits of Menara cv., a principal component analysis (PCA) was conducted using the mean trait values of each treatment with data scaled to unit variance. The FactoMiner package [90] was used to perform PCA, and the Factoextra package was used for results visualization [91]. Then, the results from the PCA were used to evaluate how trait structure varies according to water irrigation and yield. To this end, linear regression model analysis was performed to test for relationships between mean PCA axis 1 and 2 scores and the total irrigation delivered for Menara trees, water amount during the sensitive and non-sensitive periods (e.g., SP1, SP2 and NP), and the total fruit yield of trees under each irrigation treatment. The lm() function in R was used to run the analysis. Based on the assumption that reduced irrigation increases the effective temperature stress experienced by the plant [92,93], analogous to reductions in transpirational cooling and increased exposure to environmental stress [94], a simulated climate stress index was constructed by adjusting the constant site-level mean temperature by observed irrigation levels, scaled to represent treatment-level variation in effective microclimate and soil moisture. Specifically, the annual mean temperature (T_MEAN) and its standard deviation (T_SD) were used as a proxy for natural climatic variability, and the following formula was calculated (Equation (1)) [95]:

Adjusted Temperature (AT) = T_MEAN + T_SD × (1 − Ri)

(1)

where Ri is the normalized irrigation level for treatment i, scaled between 0 and 1, reflecting increased water availability. T_MEAN and T_SD were calculated based on daily temperature observations of the year 2022 measured by a standard meteorological station (model iMETOS, Pessl Instruments) installed on the experimental site. Based on recorded data, the mean annual temperature (T_MEAN) was 20.5 °C and the standard deviation (T_SD) was 6.48 °C.

3. Results

3.1. Trait Variation Across Deficit Irrigation Treatments

Analysis of the complete dataset revealed that leaf traits varied significantly among Menara cultivar trees subjected to different levels of deficit irrigation. The studied traits showed significant differences in mean values (p < 0.001) and exhibited wide amplitude of variability across treatments (

Table 2. Distribution characteristics and summary of leaf functional traits among trees of Menara cultivar under DI treatments.

Trait Group	Traits (Units)	Mean	SD 2	Min	Max	CV%	ANOVA F
Structural allocation and ecophysiological	LDMC (mg g−1)	561.20	48.81	200.38	929.07	8.69	42.984 ***
	LA (cm2)	4.15	0.90	2.42	7.74	21.80	38.743 ***
	SS (μm)	80.49	22.70	20.09	144.52	28.21	142.68 ***
	SD 1 (no. per mm−2)	220.02	39.74	100	346.66	18.06	440.119 ***
Biochemical	LNC (mg/g dw)	19.73	1.17	17.64	21.98	5.96	18.170 ***
	LCC (mg/g dw)	143.75	8.89	134	159.1	6.18	76.098 ***
	LPC (mg/g dw)	1.23	0.31	0.77	1.64	25.25	59.568 ***
	TFC (mg CE/g dw)	4.01	1.17	2.48	5.34	29.38	505.257 ***
	TPC (mg GAE/g dw)	23.89	3.40	17.68	28.19	14.27	118.952 ***
	PRO (mg/g fw)	15.76	7.05	6.51	25.65	44.74	373.262 ***
	SUC (mg/g fw)	21.26	6.19	12.41	29.20	29.12	334.646 ***
	GLY (μmol/g dw)	1.48	0.395	0.92	2.36	26.76	30.616 ***

SUC, Total Soluble Sugar Content; PRO, Proline Content; GLY, Glycine betaine content; LNC, Total Leaf Nitrogen Content; LPC, Total Leaf Phosphorus Content; LCC, Total Leaf Carbon Content; TPC, Total Polyphenol Content; TFC, Total Flavonoid Content; SS, Stomatal Size; ¹ SD, Stomatal Density; LA, Leaf Area; LDMC, Leaf Dry Matter Content. ² SD: standard deviation. Min: minimum value. Max: maximum value. CV%: coefficient of variation. *** significant at p < 0.001.

). For instance, biochemical traits showed coefficient of variation (CV) ranging from 5.96% for LNC (Mean ± SD = 19.73 ± 1.17 mg/g dw) to 44.74% for PRO (15.76 ± 7.05 mg/g fw). Structural allocation and ecophysiological traits displayed CVs ranging from 8.69% for LDMC (561.20 ± 48.81 mg g⁻¹) to 28.21% for SS (80.49 ± 22.70 μm) (Table 2).

Regarding irrigation strategies, trees under control (T0) and regulated deficit irrigation (RDI) generally exhibited the highest values for LNC, LPC, LCC, LA, LDMC and SD (

Table 3. Descriptive statistics (Mean ± SD) of structural allocation and ecophysiological traits per treatment, one-way ANOVA and Tukey HSD tests among trees of Menara under deficit irrigation (RDI and SDI) and control treatments (T0).

Strategy	Treatment	LDMC	LA	SS	SD
Control	T0	562.70 ± 29.324 b	4.881 ± 0.882 a	70.129 ± 10.34 c	251.867 ± 33.153 a
RDI	T1 1	555.434 ± 41.821 b	4.878 ± 0.919 a	70.457 ± 12.952 c	249.466 ± 38.001 a
	T2	555.882 ± 49.994 b	4.327 ± 0.873 a.b	85.774 ± 11.057 a	232.177 ± 31.068 b
	T3	606.394 ± 27.173 a	4.102 ± 0.914 b.c	74.612 ± 11.850 b.c	215.066 ± 36.603 b.c
	T4	563.155 ± 38.647 b	3.646 ± 0.821 c	80.385 ± 9.555 a.b.c	220.355 ± 34.923 b.c
SDI	T5	545.468 ± 62.075 b	3.9263 ± 0.629 b.c	84.108 ± 11.192 b.c	209.955 ± 37.296 c.d
	T6	540.893 ± 35.439 b	4.051 ± 0.746 b.c	87.622 ± 12.8250 a	193.155 ± 35.464 d
	ANOVA F	42.984 ***	38.743 ***	142.68 ***	440.119 ***

¹ For treatment descriptions, see Table 1. *** significant at p < 0.001. Values with the same suffix are not statistically significantly different at p < 0.05 in Tukey HSD post hoc tests. SS, Stomatal Size; SD, Stomatal Density; LA, Leaf Area; LDMC, Leaf Dry Matter Content.

and

Table 4. Descriptive statistics (Mean ± SD) of biochemical traits per treatment, one-way ANOVA and Tukey HSD tests among trees of Menara under deficit irrigation (RDI and SDI) and control treatments (T0).

Strategy	Treatment	LNC	LCC	LPC	TFC
Control	T0	21.140 ± 0.779 a	157.367 ± 2.267 a	1.624 ± 0.019 a	2.574 ± 0.135 e
RDI	T1	20.902 ± 0.492 a,b	156.400 ± 2.500 a	1.554 ± 0.127 a,b	2.669 ± 0.124 e
	T2	20.665 ± 0.482 a,b	142.433 ± 1.115 b	1.381 ± 0.023 b,c	2.937 ± 0.008 d
	T3	19.730 ± 0.140 b,c	139.700 ± 0.200 b,c	1.323 ± 0.068 c	4.387 ± 0.116 c
	T4	18.890 ± 0.042 c,d	137.933 ± 0.550 b,c	1.064 ± 0.035 d	4.976 ± 0.079 b
SDI	T5	18.694 ± 0.424 c,d	135.600 ± 1.558 c	0.847 ± 0.098 e	5.278 ± 0.032 a
	T6	18.251 ± 0.542 d	136.833 ± 2.850 c	0.835 ± 0.060 e	5.242 ± 0.094 a,b
	ANOVA F	18.170 ***	76.098 ***	59.568 ***	505.257 ***
		TPC	PRO	SUC	GLY
Control	T0	18.190 ± 0.545 f	6.690 ± 0.171 e	12.842 ± 0.137 e	0.978 ± 0.044 e
RDI	T1	20.198 ± 0.258 e	6.985 ± 0.734 e	13.078 ± 0.804 e	1.111 ± 0.017 d,e
	T2	23.652 ± 0.532 d	11.285 ± 0.885 d	19.288 ± 0.934 d	1.324 ± 0.018 c,d,e
	T3	26.729 ± 0.914 a,b	18.578 ± 0.602 c	21.948 ± 0.518 c	1.429 ± 0.005 b,c,d
	T4	24.674 ± 0.716 c,d	19.312 ± 0.321 c	25.575 ± 0.103 b	1.601 ± 0.052 b,c
SDI	T5	28.129 ± 0.060 a	22.605 ± 0.873 b	27.668 ± 0.647 a	1.735 ± 0.064 b
	T6	25.678 ± 0.484 b,c	24.852 ± 0.711 a	28.476 ± 0.611 a	2.169 ± 0.318 a
	ANOVA F	118.952 ***	373.262 ***	334.646 ***	30.616 ***

*** significant at p < 0.001. Values with the same suffix are not statistically significantly different at p < 0.05 in Tukey HSD post hoc tests. LNC, Total Leaf Nitrogen Content; LPC, Total Leaf Phosphorus Content; LCC, Total Leaf Carbon Content; TFC, Total Flavonoid Content. SUC, Total Soluble Sugar Content; PRO, Proline Content; GLY, Glycine betaine content; TPC, Total Polyphenol Content.

) compared to those under sustained deficit irrigation (SDI). For instance, SD ranged from 193.155 ± 35.464 no. per mm⁻² (CV = 18.36%) in T6 to 251.867 ± 33.153 no. per mm⁻² (CV% = 13.16) in T0, while LPC varied from 0.835 ± 0.060 mg/g dw (CV = 7.18%) in T6 to 1.627 ± 0.019 mg/g dw in T0 (CV% = 1.16) (Figure 1; Table 3 and Table 4). In contrast, an opposite trend was observed for SUC, GLY, PRO, TPC, and TFC, which reached their highest values in trees under SDI treatment (e.g., T5 and T6) compared to the RDI treatment. For instance, PRO and GLY increased from 6.990 ± 0.171 mg/g fw (CV = 2.44%) and 0.978 ± 0.044 µmol/g dw (CV = 4.49%) in T0 to 24.852 ± 0.711 mg/g fw (CV = 2.89%) and 2.169 ± 0.318 µmol/g dw (CV = 14.66%) in T6, respectively (Figure 1). Within the RDI treatments, trees under T3 showed the highest values for LDMC (606.394 ± 27.173 mg g⁻¹; CV = 4.48%), and along with T4, they exhibited higher values for TFC, TPC, PRO, GLY and SUC but lower values for LNC, LPC, LA and SD compared to T1 and T2 (Table 3 and Table 4). Conversely, LA was lower under T3 and T4 relative to T1 and T2 (Table 3). According to Tukey’s HSD multiple comparison test, trees under the control (T0) and T1 (RDI) treatments did not differ significantly for most structural and ecophysiological traits, nor for biochemical traits, except for TPC, where T0 trees showed the lowest values (Table 4).

3.2. Correlations Among the Studied Traits

Correlation analysis revealed that Menara cv. traits were interrelated, with several significant associations observed among themes (Figure 2). Specifically, SD was positively correlated with LPC, LCC and LA, but negatively correlated with SS, TPC, SUC, GLY, PRO and TFC (Figure 2). LA was positively correlated with LCC and LPC and negatively correlated with TFC, TPC, SUC, and PRO. SS correlated positively with SUC and GLY and negatively with LPC and LCC. Furthermore, LNC, LPC, and LCC were found to be negatively correlated with GLY, SUC, TFC, PRO and TPC, while LPC showed a positive correlation with both LNC and LCC (Figure 2).

3.3. Structure and Covariation of Functional Traits Under Deficit Irrigation

Multivariate analysis confirmed that the studied leaf traits were interdependent, exhibiting distinct covariation patterns (Figure 3; Supplementary Table S1). The first principal component (PCA) axis explained 79.81% of the total variation in Menara leaf traits and was positively associated with SS, PRO, TFC, GLY, SUC, and TPC, while negatively associated with SD, LA, LPC, LNC, and LCC (Figure 3). The second PCA axis, accounting for 11.12% of the variation, was mainly and positively driven by LDMC (Table S4). Thus, PCA axis 1 effectively discriminated trees under T3, T4, T5, and T6 SDI treatments from those under T1, T2 and T0 (RDI) treatments, while PCA axis 2 distinguished trees under T3 and T4 from all other treatments (Figure 3).

Regression analyses revealed significant relationships between PCA covariation patterns and the irrigation regime as well as yield (Figure 4;

Table 5. Regression analysis results between principal component scores (PC1 and PC2) and water irrigation during phenological periods, fruit yield and adjusted temperature of the studied experimental site.

Variables	Regression Parameters	PC1 a	PC2 a
Total irrigation water amount	R2	0.922	0.067
	p-value	0.002	0.550
	Relationship	-	n.s.
	Estimates	−0.076	0.003
	Standard error	0.012	0.004
Water amount during SP1 b	R2	0.860	0.025
	p-value	0.002	0.737
	Relationship	-	n.s.
	Estimates	−0.222	0.007
	Standard error	0.038	0.021
Water amount during SP2 b	R2	0.860	0.026
	p-value	0.002	0.730
	Relationship	-	n.s.
	Estimates	−0.181	0.006
	Standard error	0.031	0.017
Water amount during NP b	R2	0.371	0.007
	p-value	0.005	0.756
	Relationship	-	n.s.
	Estimates	−0.135	0.003
	Standard error	0.028	0.011
Fruit yield	R2	0.824	0.039
	p-value	0.001	0.482
	Relationship	-	n.s.
	Estimates	−0.108	0.005
	Standard error	0.016	0.007
Adjusted temperature index (AT)	R2	0.852	0.048
	p-value	0.002	0.653
	Relationship	+	n.s.
	Estimates	1.443	−0.055
	Standard error	0.248	0.117

^a PCA scores were retrieved from the PCA on the studied leaf traits (See Supplementary Table S1). ^b See Table 1 for period descriptions. SP: sensitive period. NP: non-sensitive period. n.s.: non-significant relationship. Bold values reflect significant relationship. R²: Coefficient of determination.

). Mean PCA axis 1 scores were negatively correlated with total irrigation water amount (R² = 0.922, p < 0.01), irrigation during the sensitive phases SP1 and SP2 (R² = 0.860, p < 0.01), and irrigation during the non-sensitive phase (R² = 0.371, p = 0.005). A significant relationship was also found between PCA axis 1 and total fruit yield (R² = 0.886, p = 0.03), whereas no relationship was detected between PCA axis 2 scores and irrigation or yield (Figure 4; Table 5). Finally, a significant association was observed between PCA axis 1 scores and the adjusted temperature (AT) derived from the simulated climate index (R² = 0.852, p < 0.01; Table 5).

4. Discussion

4.1. Variability of Menara Functional Responses Under Deficit Irrigation

The results obtained in this study contribute to ongoing efforts to understand plant functional responses to environmental stress [96]. Moreover, they support the growing body of evidence highlighting the ecological relevance of intraspecific trait variability (ITV) within species [97,98,99,100]. Our findings revealed significant differences and a high magnitude of variability in the studied traits among Menara olive trees subjected to increasing levels of water deficit (Table 2). This trait variation reflects the phenotypic plasticity of the Menara cultivar under water limitation [101], suggesting flexible functional strategies in this emblematic Mediterranean tree [102]. For instance, leaf area (LA) and stomatal density (SD) showed marked differences and substantial variability across deficit irrigation (DI) treatments (Table 2; Figure 2). These two traits play a critical role in regulating leaf energy balance, photosynthesis, light interception, water use efficiency, and nutrient dynamics [50,51]. While gas exchange per unit area is largely controlled by stomatal density and conductance, total photosynthetic carbon gain also depends on the leaf area available for light interception and CO₂ assimilation. Thus, photosynthesis at the whole-plant level reflects a combined effect of leaf area (structural determinant of total photosynthetic surface) and stomatal density (anatomical regulator of CO₂ diffusion efficiency). This coordination ensures an optimal balance between carbon acquisition and water conservation under varying environmental conditions. In line with previous studies on both wild [71] and cultivated olive trees [40,41], our results confirm that functional trait expression varies significantly with environmental conditions, particularly increasing aridity. Additionally, biochemical traits such as total polyphenol content (TPC), leaf phosphorus content (LPC), proline (PRO) and total soluble sugars (SUC) exhibited considerable variability, further demonstrating phenotypic plasticity across DI treatments. This observation is consistent with previous findings on within-species variability in biochemical leaf traits [103,104,105]. Such variability may reflect underlying physiological mechanisms that mediate olive tree functioning and adaptive responses to water stress [52,53,106,107]. However, as a slow-growing species with substantial genetic variability, the olive tree is likely to exhibit phenotypic plasticity in response to micro-environmental heterogeneity, including differences in water availability and climatic fluctuations.

Our results showed that the studied traits are not independent, displaying significant correlation patterns (Figure 2), which highlight functional trade-offs at the intraspecific level in the Menara cultivar [57,108]. Under increased water stress, our results revealed a coordinated adjustment of leaf functional traits, characterized by a reduction in leaf area (LA) and stomatal density (SD), coupled with an increase in stomatal size (SS). The reduction in LA limits the transpiring surface, thereby decreasing total water loss and improving leaf water balance [50,109]. Simultaneously, lower SD reduces the total number of stomatal pores, while larger stomata ensure sufficient CO₂ diffusion during brief periods of favorable humidity or irrigation, maintaining minimal yet efficient photosynthetic activity [74,110]. This functional compromise between gas exchange and water conservation represents a key component of drought acclimation in Mediterranean woody species, including the olive tree [40,41,108]. Moreover, although a smaller leaf area may reduce light interception, olive trees compensate through adjustments in chlorophyll composition, particularly an increase in chlorophyll b concentration and a lower chlorophyll a/b ratio, which enhances light absorption efficiency under high irradiance and water stress [111,112]. Collectively, these coordinated trait adjustments enable the Menara cultivar to sustain physiological activity and photoprotection under prolonged water deficit, reflecting an ecological balance between productivity and survival.

The concurrent increase in LPC and LCC with LA and SD may be interpreted as a functional adjustment to sustain photosynthesis by maintaining Rubisco enzyme carboxylation capacity [113,114]. These coordinated trait shifts reflect a resource-acquisitive trait syndrome [57,115,116]. Additionally, our results revealed a negative relationship between SD and the accumulation of leaf polyphenols (TPC), flavonoids (TFC), sugars (SUC), proline (PRO), and glycine betaine (GLY) (Figure 2), suggesting a potential trade-off between growth-related and stress-protective traits [117]. Under favorable conditions, plants tend to allocate more resources to growth and reduce investment in the production of costly osmolytes (e.g., PRO, GLY) and secondary metabolites (e.g., TPC, TFC), which are typically synthesized in response to environmental stress [118]. Furthermore, the negative correlations observed between leaf nutrient contents (LCC, LNC, and LPC) and secondary osmoprotective metabolites (TFC, GLY, and PRO) demonstrate the existence of a trade-off between resource acquisition and stress protection in the Menara cultivar, consistent with the Growth–Differentiation Balance Hypothesis [117]. While this framework is well established, our results provide experimental evidence of how this balance operates under controlled deficit irrigation regimes. Under water-limited conditions, Menara trees progressively redirected resources from primary metabolism and growth towards the synthesis of protective compounds, such as proline and phenolics, which help maintain cellular homeostasis and oxidative stress tolerance. These findings demonstrate that the Menara cultivar exhibits a flexible resource allocation strategy, adjusting its physiological priorities in response to water availability. This cultivar-specific response suggests that Menara can maintain metabolic activity and protect cellular integrity during prolonged water stress, reflecting a shift toward stress protection rather than a simple reduction in productivity under limited resource conditions [119].

4.2. Trade-Offs Underlying Functional Strategies of Menara Under Deficit Irrigation

Regarding the DI strategies and treatments, the Menara cultivar exhibited differential responses depending on the level of irrigation applied (Figure 3), aligning with previous findings indicating that water availability is a key limiting factor influencing plant performance [109], including olive tree growth and productivity [14,38,41,43,120]. Along the first PCA axis (Figure 3), trees subjected to permanent water stress under the SDI strategy (T5 and T6) exhibited higher accumulation of osmoprotectant compounds (e.g., GLY, SUG, PRO) than trees under regulated deficit irrigation (RDI) treatments. This accumulation may represent a functional mechanism contributing to the maintenance of cellular homeostasis, stabilization of intracellular membranes and macromolecular structures, and mitigation of oxidative stress under prolonged water stress conditions [53,121,122,123]. These findings are consistent with studies showing that the accumulation of such compounds enhances plant drought tolerance [124,125]. For instance, previous studies reported increased proline content in Chemlali and Manzanilla olive cultivars under drought stress [126,127] and higher sugar accumulation in Meski and Picholine cultivars under restricted irrigation at 50% of ETc [128]. Similarly, total phenols and soluble carbohydrates increased in Zard, Amigdalolia, and Konservolia olive cultivars under water stress [27]. When 80% of ETc was delivered during sensitive periods (SP1 and SP2) for trees under the T3 and T4 RDI treatments, our results showed a notable rise in secondary metabolites (e.g., TFC, TPC) and higher tissue density (e.g., LDMC) along with increased accumulation of osmoprotectant compounds. Secondary metabolites are specialized compounds produced by plants to mitigate stress [52], playing critical roles in oxidative stress responses [129] and plant adaptation to high temperatures or nutrient-poor environments [130,131]. Increased accumulation of phenolic compounds under water stress has been well-documented in olive leaves, fruits, and oil [62,132,133,134,135]. Furthermore, the higher LDMC observed under T3 treatment after a reduction of 20% of ETc during SP1 and SP2 and 30% of ETc during the non-sensitive phase (NP) compared to the control treatment (T0) reflects the development of thicker, denser leaf tissues, indicative of efficient resource conservation strategies that balance photosynthesis and water-use efficiency under drought [57]; such adjustments were previously documented in wild olive under arid conditions in Morocco [71].

Collectively, these findings provide evidence of a coordinated stress tolerance strategy in the Menara cultivar under water-limited conditions. This response is expressed through investment of resources in denser leaves and in the production of secondary metabolites and osmoprotectants, which enhance resilience to abiotic stress. Nevertheless, such investments often entail a cost to growth and productivity [46,56]. Conversely, at the opposite end of the first PCA axis (Figure 3), trees receiving 100% of ETc during sensitive periods (SP1 and SP2) under the T0, T1 and T2 RDI treatments displayed a strategy focused on rapid resource acquisition and enhanced growth performance. This strategy was reflected in increased LA and SD, traits associated with water balance and maximized photosynthetic potential [41,50,71]. Thus, the observed higher LNC and LPC promote rapid resource acquisition and photosynthetic efficiency, supporting competitive strategies in resource-rich environments, while higher LCC underpins structural resilience and leaf longevity, traits linked to stress tolerance [57,116]. Together, these elemental and structural traits govern the balance among growth, stress tolerance, and performance, serving as critical markers of the functional responses of the Menara cultivar under varying water availability. Recent findings by Ibba et al. [38] further support this, showing that Menara trees subjected to full irrigation (100% ETc) outperformed those under reduced irrigation in terms of vegetative growth and fruit production.

The trade-off between growth-related traits and stress-protective traits (as reflected by the first PCA axis) in the Menara olive cultivar under deficit irrigation (DI) was strongly dependent on the amount of water delivered during sensitive phenological periods (Figure 4), while irrigation during non-sensitive periods had a lower effect on this trade-off (R² = 0.371, p < 0.01; Figure 4) compared to sensitive periods (R² = 0.860, p < 0.01). This suggests that water availability during critical developmental stages related to flowering or oil biosynthesis plays a pivotal role in modulating resource allocation strategies [136,137], aligning with the Growth–Differentiation Balance Hypothesis [117]. When sufficient water was supplied during sensitive periods, plants prioritized investment in growth and reproduction over stress defense mechanisms, resulting in higher fruit yields [38,120,138]. In contrast, water limitation during these stages promoted the accumulation of osmoprotectants and secondary metabolites at the expense of growth, ultimately reducing yield potential. Moreover, the positive relationship observed between the expression of the growth–stress protection trade-off and olive yield highlights the importance of targeted irrigation during key developmental stages to optimize resource use and maximize productivity [28].

Considering that lower irrigation may increase the effective temperature stress experienced by the plant [92,139], the positive relationship between PC1 (growth–stress protection trade-off) and the adjusted temperature index (Table 5) suggests that higher thermal stress shifts resource allocation toward protective mechanisms at the expense of growth in the Menara cultivar. Elevated temperatures likely amplify both oxidative and water stress [140,141], thereby promoting the accumulation of osmolytes and secondary metabolites [118], consistent with predictions of the Growth–Differentiation Balance Hypothesis. Consequently, temperature and water deficits act synergistically to shape the trade-off between growth and stress protection traits, influencing the functional responses and yield potential of the Menara olive cultivar under Mediterranean drought-prone conditions. The distinct responses observed under the two deficit irrigation strategies reflect their adaptation to specific climatic drought regimes. RDI, which applies full irrigation during sensitive phenological stages and moderate reductions during less critical phases, is best suited for intermittent or seasonal drought conditions. This strategy allows olive trees to recover between stress periods, thereby sustaining vegetative growth, fruit development, and oil yield. In contrast, SDI imposes a consistent water restriction throughout the season, promoting gradual physiological acclimation through enhanced osmotic adjustment, accumulation of protective metabolites, and improved long-term water-use efficiency. Consequently, SDI may be more appropriate for persistent or prolonged drought scenarios, where maintaining physiological stability and survival is more critical than maximizing productivity.

Overall, the obtained results emphasize the importance of integrated irrigation and temperature management strategies under climate change scenarios. However, it is important to note that the present study was conducted within a single growing season and therefore did not account for interannual variability in precipitation, temperature, or vapor pressure deficit (VPD), all of which can strongly influence olive tree physiology. Our experimental design intentionally focused on controlled deficit irrigation treatments to isolate their effects on functional traits, but this necessarily limits the extent to which our results capture the full range of climate-induced variability in olive functional response. Long-term, multi-year studies that integrate comprehensive climatic datasets (precipitation, temperature, VPD) will be essential to assess the stability of trait–performance relationships across contrasting environmental conditions.

4.3. Agronomic Implications of Deficit Irrigation

In light of the above findings, identifying irrigation strategies that optimize the balance between growth and stress tolerance is crucial for sustainable olive cultivation under Mediterranean conditions [142]. Among the tested DI treatments, T3 under the RDI strategy (80% ETc during SP and 70% during NP) emerged as the most promising compromise between productivity and physiological stability. This treatment maintained relatively high fruit yields, with only a moderate decline from 123.3 kg/tree under T1 (100% SP, 70% NP) to 99.6 kg/tree (Table 1), while promoting effective functional responses. These included the development of dense and long-lived foliage (high LDMC) and increased metabolite production. Together, these features reflect a balanced functional strategy that supports both resource-use efficiency and resilience to water stress. Such coordinated adjustments also suggest that moderate deficit irrigation during key phenological stages can effectively sustain yield productivity while enhancing the tree’s ability to cope with environmental stressors. While the T3 treatment demonstrated favorable outcomes in terms of leaf functional traits, physiological adjustments, and yield, it is important to recognize that these advantages were evaluated from a biological and functional perspective rather than an economic or ecological one. Future studies should integrate assessments of economic water-use efficiency, energy costs, and ecological trade-offs, including potential effects on soil health, carbon dynamics, and long-term orchard sustainability. A main limitation of this study lies in its restricted scope. First, trait measurements were conducted during a single growing season, which provided only a snapshot of functional performance. Given that the olive is a long-lived perennial species with pronounced interannual variability in physiology and yield, long-term monitoring would be necessary to evaluate the stability of trait–performance relationships under contrasting climatic conditions. Second, our work focused exclusively on the Menara cultivar. While this choice is justified by its agronomic importance and representativeness in Morocco, extending the analysis to multiple cultivars would help determine whether the observed functional responses are specific to Menara or broadly applicable across the olive germplasm.

4.4. Innovations and Perspectives for Sustainable Irrigation of Olive Orchards Under Mediterranean Climatic Conditions

Addressing water scarcity in semi-arid and arid regions requires innovative irrigation strategies that enhance water-use efficiency while sustaining agricultural productivity. In olive cultivation, these approaches aim to optimize water delivery in response to plant demand and environmental variability, thereby supporting both ecological sustainability and production resilience [143]. Precision irrigation systems, such as drip and subsurface drip irrigation, minimize evaporation and percolation losses by applying water directly to the root zone [143,144], enabling site-specific control of irrigation volume and frequency while maintaining productivity under limited water supply [145]. Their adoption in Mediterranean orchards has resulted in substantial water savings without major yield penalties [143]. The integration of smart irrigation technologies, including soil moisture and climate sensors, automated controllers, and weather-based scheduling, further enhances irrigation efficiency [145]. By providing real-time information, these systems help prevent both excessive watering and severe water stress. The use of Internet of Things (IoT) and Artificial Intelligence (AI) platforms enables data-driven and automated irrigation control, allowing precise prediction of crop water requirements and optimization of irrigation cycles to improve water productivity [146]. Remote sensing and GIS technologies complement these innovations by facilitating large-scale monitoring of soil moisture and vegetation status, allowing targeted spatial management of water resources [147,148]. Beyond technological advances, water reuse has gained global recognition as a sustainable approach to alleviate water scarcity and ensure long-term agricultural viability under climate change [149]. It has been successfully implemented in both water-limited and water-abundant regions for diverse purposes, including agriculture, industry, groundwater recharge, and urban landscaping [150]. In Mediterranean olive systems, the reuse of treated wastewater provides a valuable complementary resource when integrated with precision and smart irrigation systems [151]. Such integration ensures the safe and efficient use of reclaimed water while maintaining soil quality, mitigating salinity risks, and sustaining yield and oil quality. Looking ahead, the convergence of IoT- and AI-based irrigation control with treated wastewater reuse represents a critical step toward circular and resilient water management in semi-arid and arid agroecosystems. When combined with deficit irrigation strategies and nature-based solutions, including rainwater harvesting, soil–water conservation, and adaptive irrigation scheduling, these innovations can markedly improve the sustainability and climate resilience of Mediterranean olive cultivation.

5. Conclusions

This study was conducted on the Moroccan olive cultivar Menara under controlled field conditions, applying six deficit irrigation (DI) treatments based on varying fractions of crop evapotranspiration (ETc) during both sensitive and non-sensitive phenological stages. A comprehensive set of morphological (e.g., leaf area, stomatal density) and biochemical (e.g., osmolytes, phenolic compounds, and leaf nutrients) traits was analyzed to evaluate the functional and physiological responses of the cultivar to water deficit.

The results demonstrate that the Menara olive cultivar exhibits substantial intraspecific variability and functional plasticity in response to different deficit irrigation strategies. The observed leaf trait variation highlights the cultivar’s ability to adjust its resource allocation strategies according to water availability, particularly during sensitive phenological stages. A central finding is the expression of a trade-off between growth-related and stress-protective traits, modulated by both irrigation regime and thermal stress, in agreement with the Growth–Differentiation Balance Hypothesis. Under full irrigation during critical growth stages, Menara trees prioritized acquisitive traits, maximizing growth and yield. Conversely, water limitation during these periods shifted trait expression toward defense-oriented mechanisms, leading to increased accumulation of osmolytes and secondary metabolites but reduced productivity. These responses underscore the cultivar’s functional plasticity and provide a framework for designing adaptive irrigation schedules in Mediterranean orchards. Beyond confirming that irrigation enhances productivity, this study identifies the mechanistic and ecological bases underlying these improvements, offering practical insights for optimizing water-use efficiency and sustaining olive production under future climate scenarios. Importantly, the DI treatment combining 80% ETc during sensitive periods with 70% ETc during non-sensitive phases (T3) provided the most effective balance between physiological resilience and yield performance. Overall, the main conclusions can be summarized as follows:

• The Menara cultivar exhibited significant morphological and biochemical plasticity under deficit irrigation, particularly through reductions in leaf area and adjustments in stomatal density, reflecting plastic responses to limited water availability.
• Accumulation of osmolytes (e.g., proline, soluble sugars) and enhanced synthesis of phenolic compounds contributed to maintaining cellular homeostasis and protecting leaf tissues under water stress.
• Moderate deficit irrigation maintained a favorable leaf nutrient status, suggesting efficient resource allocation and metabolic regulation that support stress tolerance without compromising essential physiological functions.
• Severe water deficit treatments (lowest ETc fractions) caused marked declines in performance, indicating critical thresholds beyond which Menara’s compensatory mechanisms are no longer effective.
• Moderate deficit irrigation can optimize water use efficiency while preserving functional integrity, offering a viable strategy for sustainable olive cultivation under Mediterranean drought conditions.

Finally, these findings emphasize the importance of optimizing irrigation timing and intensity to ensure sustainable olive production in Mediterranean regions increasingly affected by drought and heat stress. Integrating functional trait analysis into irrigation planning represents a promising approach for enhancing the resilience and productivity of olive-based agroecosystems under future climate scenarios.