Agro-Physiological and Pomological Characterization of Plum Trees in Ex-Situ Collections: Evaluation of Their Genetic Potential in the Saïss Plain

Abstract

The aim of this research is to assess the genetic potential of plum cultivars in an ex situ collection at the National Institute for Agronomic Research in Meknes, Morocco, under the conditions of the Saïss plain. This is an essential preliminary stage in the study of varietal adaptation to climate change. Twenty-eight cultivars will be analyzed, using agro-morphological, physiological, and pomological descriptors. This characterization was based on measuring the genetic diversity of all the cultivars, production traits (yield, average weight, and fruit size), vegetative traits (leaf area, number of leaves per fruit, and shoot leaf load), physiological traits (stomatal traits, leaf proline content, cuticular wax, chlorophyll a and b) and the measurement of certain chemical and biochemical quality components of the fruit. The study of plum cultivars revealed significant variability in agronomic, vegetative, physiological, and pomological traits. Analysis using the Unweighted Pair Group Method enabled us to classify them into two main groups for all the characteristics assessed. This study will identify the cultivars best adapted to Moroccan conditions, thus meeting scientific, agronomic, and environmental objectives. Furthermore, the results of this research will have a significant impact on the management of the plum collection, ensuring not only the preservation of genetic diversity and the sustainability of the species, but also contributing to the promotion of sustainable agricultural practices. This will help optimize resource use, reduce environmental impact, and enhance crop resilience to climate change while supporting breeding programs.

1. Introduction

In Morocco, arboriculture plays a key role in the national economy, with the Rosaceae family standing out due to its remarkable diversity of species. This family is divided into two main groups: pome fruit trees (apple, pear, and quince) and stone fruit trees (almond, apricot, peach, cherry, and plum) [1]. Plum orchards in Morocco are primarily based on Japanese varieties (Prunus salicina Lindl.), commonly referred to as Japanese plums. These include cultivars such as ‘Golden Japan’, ‘Santa Rosa’, ‘Formosa’, ‘Methley’, ‘Red Beauty’, ‘Angelino’, and ‘Black Amber’. These varieties are characterized by their high water content and sensitivity to handling and transportation. They bloom between February and March, and are harvested from June to July [2]. European varieties (Prunus domestica L.), which originate from a natural hybridization between Prunus spinosa L. and Prunus cerasifera L., are also present. Key cultivars include ‘Stanley’ and ‘Prune d’Ente’. Although less widely cultivated, their production is steadily expanding due to the growth of processing units. These varieties bloom from March to April, and are harvested from August to September [3,4]. Primarily adapted to temperate climates, plums are grown in various parts of the world, from harsh winters to the subtropical conditions of the Mediterranean basin [5].

Plums are highly nutritious fruits known for their significant health benefits. They exhibit medicinal properties, including anti-hemorrhagic, laxative, and anti-constipation effects [6]. Regular consumption of plums can aid in preventing and managing various health conditions, such as circulatory disorders, certain cancers, digestive issues, diabetes, cardiovascular diseases, and obesity. Plums are characterized by their low fat content and richness in carbs, minerals, vitamins, phenolic acids, and flavonoids, offering remarkable antioxidant properties [7]. They are particularly high in potassium while being low in sodium. The antioxidants they contain play a key role in neutralizing free radicals, thus protecting cells from damage [8]. Research has also highlighted the strong antioxidant potential of their bioactive compounds, such as polyphenols, flavonoids, anthocyanins, and vitamin C [9]. Moreover, the combination of sugar content, acidity, and volatile compounds gives plums a complex aromatic profile, unique to each variety. The quality assessment of plums includes a detailed assessing their optical appearance and firmness [10]. This approach not only helps identify preferences of consumers, but also as provides valuable insights to growers and producers, enabling them to optimize cultivation practices and post-harvest management methods [11].

The phenotypic and molecular characteristics of plum cultivars grown in Morocco, such as vegetative growth, assessment of thermal requirements, and measurement of fruit taste quality, have led to the development of various classifications [12]. These classifications are fundamental in diversity assessment models, as they help identify traits contributing to this diversity and evaluate the degree of similarity or divergence among genotypes [13]. The evaluation of genetic diversity within a collection of plum cultivars is a crucial method for the description and identification of plum varieties, as well as for optimizing breeding and improvement programs [14]. It is important to stress that in Morocco, no research program has yet been launched to explore the genetic potential of plum trees and the adaptation traits of introduced varieties to climate change. In fact, the ability of varieties to maintain stable production, despite the conditions generated by these changes, is a promising area for improvement. The study aims to examine the agro-physiological and pomological characterization of an ex situ collection of Moroccan plum trees in the Saïss plain. By measuring various agronomic, physiological, and vegetative traits, as well as analyzing fruit quality, this research aims to develop effective tools for the preservation, management, and optimal use of genetic material. This approach will contribute to addressing the challenges related to improving fruit production in Morocco.

2. Materials and Methods

2.1. Plant Material and Experimental Conditions

The study site is situated in the experimental field of the National Institute for Agronomic Research (INRA) at Ain Taoujdate, 30 km from the city of Meknes in the Saïs plain at an altitude of around 550 m (33°56′ E. 5°13′ N. 499 m). This area is characterized by clay, limestone, and alluvial soils. The minimum temperature occurs in January (2.8 °C), and the maximum in July (37 °C). The annual amount of chill recorded is 540 h of temperature below 7 °C and annual rainfall is around 440 mm. The present work was carried out during 2022 on an ex situ plum collection consisting of 16 local cultivars and 12 introduced cultivars aged 16 years (

Table 1. International and national plum cultivars examined in this research.

National Cultivars	Species	International Cultivars	Species
INRA-PR34	Prunus salisina Lindl.	Friar	Prunus salisina Lindl.
INRA-PR35	Prunus salisina Lindl.	Singlobe	Prunus salisina Lindl.
INRA-PR36	Prunus salisina Lindl.	Monglobe	Prunus salisina Lindl.
INRA-PR37	Prunus salisina Lindl.	Golden Japan	Prunus salisina Lindl.
INRA-PR38	Prunus salisina Lindl.	Santa Rosa	Prunus salisina Lindl.
INRA-PR39	Prunus salisina Lindl.	Methley	Prunus salisina Lindl.
INRA-PR40	Prunus salisina Lindl.	Fortune	Prunus salisina Lindl.
INRA-PR41	Prunus salisina Lindl.	Angelino	Prunus salisina Lindl.
INRA-PR42	Prunus salisina Lindl.	Black Amber	Prunus salisina Lindl.
INRA-PR43	Prunus salisina Lindl.	Stanley	Prunus domestica L.
INRA-PR44	Prunus salisina Lindl.	Prune d’Ente	Prunus domestica L.
INRA-PR45	Prunus salisina Lindl.
INRA-PR46	Prunus salisina Lindl.
INRA-PR47	Prunus salisina Lindl.
INRA-PR48	Prunus salisina Lindl.
INRA-PR49	Prunus salisina Lindl.
Timhdit	Prunus salisina Lindl.

). These cultivars are grafted onto ‘Myrobolan’ rootstock and planted at a spacing of 5 × 5 m, with 10 trees per cultivar. From flowering in February, a drip irrigation system was used to irrigate the entire collection with a total volume of water of around 2000 m³/h, until the ripening stage in early October. NPK was added as fertilizer in quantities of 160 g N, 80 g P₂O₅ and 190 g K₂O per tree. The entire collection underwent the same horticultural management practices, including weed elimination and tree thinning with the aim of homogenizing all the cultivars, as well as pest control.

2.2. Fruit Yield and Dimensions

During the fruit harvesting period, damaged fruit was eliminated, while healthy samples were kept and subjected to pomological characterization. Five fruit samples were taken at random from five different trees for each cultivar, and the average weight of the fruit was determined using a precision balance. The fruit yield was calculated as the product of the average fruit weight and the number of fruits counted per tree. Fruit dimensions were determined by measuring length, width, height, and core weight. Measurements were taken using a numerical caliper on five fruits chosen at random from each tree.

2.3. Vegetative Growth Traits

During the fruit development stage, two well-developed, two-year-old shoots (10 cm) bearing several shoots were taken at random from the same side of five trees for all cultivars. Immediately after sampling, all of the branches were placed in jute bags moistened with water to maintain their turgidity. They were then taken to the laboratory to determine vegetative and physiological traits in accordance with [15]. The shoot leaf load was determined by counting the leaves on the two branches sampled, i.e., five replicates for each cultivar. The number of leaves per fruit was determined by counting the leaves surrounding the fruit, i.e., five randomly selected fruits from each tree, i.e., five replicates per cultivar.

To measure leaf area, five fully developed leaves were selected. It was determined by measuring the length (L) and width (l) of the leaf, according to the formula cited by Karimi et al. [16],

LA = π × L × l.

2.4. Physiological Traits

Physiological traits, i.e., stomatal traits and leaf proline and chlorophyll content, were determined on the leaves of the branches sampled above, i.e., five replicates for each cultivar. According to Gitz and Baker [17], for each branch, two of the most developed leaves were observed, on which the stomatal density (SD), stomatal length (SL), stomatal width (SW), stomatal area (SA), and stomatal area index (SAI) were determined. As well as leaf proline content using the Monneveux and Nemmar [18] method. Cuticular wax (CW) following the Marcell and Beattie [19] protocol. Leaf chlorophyll a and b content (Chla and Chlb) using the formula of Singh and Billore [20]. Stomatal conductance (SC) was measured in the field using a portable porometer, taking leaves from trees of each cultivar. Five leaves per tree were chosen from different positions on all four sides of the tree (20 leaves per tree) to ensure a representative sample. Stomatal conductance is expressed in mmol m⁻² s⁻¹.

2.5. Fruit Quality Traits

In the laboratory, 5 g of freeze-dried and crushed fruits were placed in 10 mL of 80% ethanol to obtain ethanolic extracts. The Total Soluble Solids (TSS) was analyzed on the aqueous extract using a refractometer (Atago PAL-1, (Atago Co., Ltd., Tokyo, Japan)). The pH of the plums was obtained by dipping the pH meter electrode into 5 g of plum pulp mixed in 50 mL of tap water and shaking vigorously. Titratable acidity (TA) was measured using the experimental technique developed by Lichou [21]. The maturity index (MI) is derived from the equation MI = TSS/TA [17]. Firmness was measured with a PCE-PTR 200 N penetrometer (PCE Instruments, Horb am Neckar, Germany). The Soluble Sugar Content (SSC) was obtained by the Dubois et al. [22] method, and Amino Acid Content (AAC) was obtained following the Yemm and Coocking [23] method. Similarly, Total Polyphenol Compounds (TPC) was measured using the method of Singleton and Rossi [24] and Total Antioxidant Capacity (TAC) has been established by the methodology of Brand-Williams et al. [25]. For each traits analyzed, five measurements were taken for each cultivar.

2.6. Statistical Analysis

SPSS v22 was selected to assist in the data analysis. ANOVA has been carried out to determine significant distinction for every cultivar among the traits analyzed. The Student–Newman and Keuls (SNK) test was applied to the mean values of the traits. Principal component analysis (PCA) was performed with the correlation matrices, on the basis of the mean of all the normalized traits. Correlation coefficient and significance level have been calculated by correlation of Pearson of mean values in identifying the relationship among the traits analyzed.

3. Results and Discussion

3.1. Fruit Yield and Dimensions

The characterization of 28 plum cultivars in collection at the National Institute for Agronomic Research of Meknes to determine the genetic diversity of all the cultivars was based on the measurement of production, vegetative growth, and physiological traits and certain chemical and biochemical quality components of the fruit. Significant differences in fruit yield, average weight, and dimensions were observed between the cultivars studied (

Table 2. Fruit yield and dimensions of fruit tested plum cultivars.

Cultivars	FY (kg tree−1)	FW (g)	FL (mm)	FWd (mm)	FH (mm)	CW (g)
INRA-PR34	59.27 ± 19.17 d	28.12 ± 1.61 ab	18.28 ± 0.85 a	17.73 ± 0.66 a	15.31 ± 0.66 a	0.90 ± 0.03 a
INRA-PR35	13.82 ± 0.54 a	60.12 ± 0.44 ghi	41.31 ± 0.85 de	41.59 ± 0.34 e	42.30 ± 0.34 fg	1.48 ± 0.04 a
INRA-PR36	19.05 ± 7.36 ab	38.33 ± 0.04 bcde	44.55 ± 1.37 ghi	43.02 ± 0.05 ef	41.03 ± 0.05 defg	0.90 ± 0.01 a
INRA-PR37	41.00 ± 25.58 c	36.51 ± 0.64 abcd	45.97 ± 0.14 i	45.57 ± 0.18 fgh	42.71 ± 0.18 g	1.54 ± 0.11 a
INRA-PR38	8.95 ± 1.51 a	58.70 ± 0.5 fghi	41.92 ± 0.14 de	42.04 ± 0.20 e	38.29 ± 0.20 d	1.40 ± 0.01 a
INRA-PR39	24.51 ± 1.18 ab	57.74 ± 2.13 fghi	51.74 ± 1.34 k	48.66 ± 0.45 i	49.18 ± 0.45 i	1.36 ± 1.90 b
INRA-PR40	21.52 ± 0.69 a	86.58 ± 0.59 j	42.93 ± 0.29 efg	41.16 ± 0.54 e	39.41 ± 0.54 def	1.22 ± 0.08 a
INRA-PR41	30.08 ± 6.73 b	40.30 ± 0.40 bcde	42.43 ± 0.28 de	41.86 ± 1.37 e	38.39 ± 1.37 d	0.79 ± 0.07 a
INRA-PR42	9.18 ± 0.16 a	41.38 ± 1.1 bcde	41.12 ± 1.17 de	38.95 ± 1.24 d	38.74 ± 1.24 de	1.09 ± 0.03 a
INRA-PR43	26.04 ± 1.01 ab	52.25 ± 2.62 efgh	44.10 ± 0.21 fg	43.75 ± 0.68 efg	45.33 ± 0.68 h	1.32 ± 0.04 a
INRA-PR44	10.45 ± 1.15 a	62.95 ± 0.20 hi	41.71 ± 0.31 de	41.66 ± 1 e	38.98 ± 1 de	0.83 ± 0.03 a
INRA-PR45	42.38 ± 1.22 c	92.42 ± 0.25 j	42.72 ± 1.85 ef	42.33 ± 0.04 e	38.57 ± 0.04 d	1.24 ± 0.07 a
INRA-PR46	17.41 ± 3.71 a	63.32 ± 0.40 hi	46.03 ± 2.01 i	45.84 ± 1.16 gh	44.07 ± 1.16 gh	1.33 ± 0.19 a
INRA-PR47	17.74 ± 6.31 ab	48.54 ± 0.28 defgh	44.24 ± 0.33 fgh	42.45 ± 3.14 e	41.64 ± 3.14 efg	1.06 ± 0.04 a
INRA-PR48	11.76 ± 0.67 a	49.13 ± 2.63 defgh	44.54 ± 0.98 ghi	43.16 ± 1.01 ef	42.27 ± 1.01 fg	1.26 ± 5.31 c
INRA-PR49	34.71 ± 1.09 ab	67.74 ± 2.15 i	49.10 ± 1.61 j	44.24 ± 1.31 efgh	43.28 ± 1.31 gh	1.29 ± 0.20 a
Stanley	14.00 ± 0.82 a	45.94 ± 2.77 cdefg	42.77 ± 1.44 ef	37.63 ± 2.97 d	52.69 ± 2.97 j	1.40 ± 6.43 d
Prune d’Ente	16.76 ± 1.7 a	47.14 defgh	40.11 ± 0.54 de	42.46 ± 2.98 e	39.78 ± 1.86 de	0.93 ± 2.56 a
Friar	47.62 ± 0.44 c	68.27 ± 2.00 i	51.66 ± 1.65 k	43.33 ± 1.52 a	48.66 ± 0.78 i	1.63 ± 5.10 c
Fortune	47.53 ± 4.3 c	34.35 ± 0.98 abcd	41.62 ± 2.76 d	42.34 ± 2.87 e	38.15 ± 2.65 de	0.94 ± 5.12 a
Methley	31.65 ± 8 ab	25.33 ± 1.43 a	18.93 ± 3.87 a	16.99 ± 3.09 a	18.15 ± 3.44 a	0.86 ± 0.76 a
Santa Rosa	21.76 ± 1.50 ab	24.30 ± 1.30 a	19.13 ± 0.01 a	17.84 ± 0.78 a	17.09 ± 0.29 a	0.77 ± 0.03 a
Angelino	19.24 ± 0.48 ab	44.92 ± 1.37 cdef	31.57 ± 1.15 c	27.15 ± 1.53 c	24.84 ± 0.36 c	1.37 ± 0.34 a
Black Amber	40.47 ± 0.20 c	36.65 ± 0.94 abcd	40.82 ± 0.63 d	42.09 ± 0.29 e	39.05 ± 0.53 de	0.93 ± 0.04 a
Golden Japan	41.47 ± 5.21 c	45.83 ± 0.57 abc	34.12 ± 1.51 de	29.44 ± 0.36 e	30.20 ± 0.06 efg	1.15 ± 0.06 a
Monglobe	13.65 ± 0.9 a	59.83 ± 3.86 fghi	46.39 ± 3.21 hi	45.48 ± 1.76 h	52.71 ± 3.23 i	1.86 ± 0.12 c
Singlobe	11.76 ± 1.41 a	57.53 ± 5.96 fghi	45.79 ± 1.79 hi	46.38 ± 0.33 h	50.21 ± 1.44 i	1.36 ± 0.33 c
Timhdit	5.42 ± 0.46 a	40.00 ± 1 bcde	24.81 ± 0.4 b	23.05 ± 0.53 b	22.70 ± 0.02 b	0.69 ± 0.02 a

Means labeled with the same letter are statistically distinct at a significance level of p ≤ 0.05, as determined by the SNK test. FY: fruit yield; FW: fruit weight; FL: fruit length; FWd: fruit width; FH: fruit height; CW: core weight.

). Cultivar yields ranged from 5.42 to 59.27 kg tree⁻¹. The highest yield was recorded by the ‘INRAPR34’ cultivar, with an average value of 59.27 kg tree⁻¹. The lowest values were obtained by the cultivars ‘Timhdit’, ‘INRA-PR38’, and ‘INRA-PR42’ with mean values of 5.42, 8.95, and 9.18 kg tree⁻¹, respectively. The average fruit weight of the cultivars ranged from 24.30 g to 92.42 g. The highest average weight was recorded by the ‘INRA-PR40’ and ‘INRA-PR45’ cultivars, with average values of 86.58 and 92.42 g, respectively. The lowest values were recorded by the cultivars ‘Santa Rosa’ and ‘INRA-PR34’, with mean values of 24.30 and 28.12 g, respectively. Fruit length of the cultivars ranged from 18.28 mm to 51.74 mm. The highest lengths were recorded by the cultivars ‘INRA-PR49’, ‘Friar’, and ‘INRA-PR39’, where the mean values were 49.10, 51.60, and 51.74 mm, respectively. The lowest values were recorded by the cultivars ‘INRA-PR34’, ‘Methley’, and ‘Santa Rosa’, with mean values of 18.28, 18.93, and 19.13 mm, respectively. The fruit widths of the cultivars varied between 16.99 mm and 48.66 mm. The highest widths were recorded by the cultivars ‘INRA-PR37’, ‘INRA-PR46’, ‘Singlobe’, and ‘INRA-PR 39’, with mean values of 45.57, 45.84, 46.38, and 48.66 mm, respectively. The lowest values were recorded for the cultivars ‘Methley’ and ‘INRA-PR34’, with mean values of 16.99 and 17.73 mm, respectively. Fruit height of the cultivars ranged from 15.31 mm to 52.71 mm. The highest heights were recorded by the genotypes ‘Singlobe’, ‘Stanley’, and ‘Monglobe’ with mean values of 50.21, 52.69, and 52.71 mm, respectively. The lowest values were recorded by the cultivars ‘INRA-PR34’ and ‘Santa Rosa’, with mean values of 15.31 and 17.09 mm, respectively. The core weight of the fruits of the cultivars varied between 0.69 g and 1.86 g. The highest core weight was recorded by the cultivar ‘Monglobe’, with an average value of 1.86 g. The lowest value was recorded by the cultivar ‘Timhdit’, with an average of 0.69 g. The results obtained are in line with several previous studies on plums, including those by Ait Bella et al. [26] and Gitea et al. [27], which point out that yield variations, particularly in terms of tree load and dimensions traits, can be influenced by several factors. These factors include the varietal effect, linked to the high genetic diversity of the cultivars studied, as well as exogenous and endogenous elements, such as the adaptation of cultivars to climatic and edaphic conditions, or the ripening period (early or late cultivars) [28].

3.2. Vegetative Growth Traits

Significant differences in the vegetative traits studied were observed between the cultivars studied (

Table 3. Vegetative traits of plum cultivars studied.

Cultivars	SLL	NLF	LA (cm2)
INRA-PR34	12.00 ± 4.5 ab	11.50 ± 3 a	57.16 ± 1.02 ef
INRA-PR35	16.00 ± 1 ab	9.00 ± 2 a	42.71 ± 0.55 abcdef
INRA-PR36	11.50 ± 0.5 ab	14.50 ± 3.5 ab	28.98 ± 0.85 ab
INRA-PR37	8.00 ± 0.12 a	12.00 ± 0.54 a	40.61 ± 0.50 abcdef
INRA-PR38	12.67 ± 2 ab	9.00 ± 1 a	28.74 ± 0.20 ab
INRA-PR39	10.00 ± 0.5 ab	11.00 ± 1 a	34.93 ± 0.31 abcd
INRA-PR40	14.00 ± 1.5 ab	17.17 ± 1.5 ab	28.29 ± 0.24 ab
INRA-PR41	16.17 ± 1 ab	16.17 ± 0.5 ab	34.22 ± 0.80 abc
INRA-PR42	18.83 ± 2 b	22.67 ± 2.5 b	27.61 ± 0.50 ab
INRA-PR43	14.50 ± 1 ab	17.67 ± 0.5 ab	22.31 ± 0.25 a
INRA-PR44	11.83 ± 1 ab	15.00 ± 0.23 ab	25.66 ± 0.13 ab
INRA-PR45	15.67 ± 1.15 ab	14.67 ± 1.5 ab	46.50 ± 0.08 bcdef
INRA-PR46	14.17 ± 0.5 ab	9.33 ± 1.5 a	59.04 ± 0.54 f
INRA-PR47	11.50 ± 0.5 ab	10.17 ± 1 a	54.59 ± 0.32 def
INRA-PR48	7.00 ± 0.5 a	14.17 ± 0.21 ab	45.25 ± 0.25 bcdef
INRA-PR49	8.33 ± 3 a	13.17 ± 2 ab	38.30 ± 0.10 abcde
Stanley	9.00 ± 0.43 a	14.00 ± 0.5 ab	51.46 ± 0.30 cdef
Prune d’Ente	8.90 ± 1 a	14.00 ± 1 ab	53.66 ± 0.18 cdef
Friar	9.50 ± 1.5 ab	13.33 ± 2 ab	44.98 ± 0.12 bcdef
Fortune	10.76 ± 0.5 ab	14.63 ± 1 ab	45.78 ± 0.26 bcdef
Methley	13.53 ± 1 ab	12.56 ± 0.5 a	44.75 ± 0.20 bcdef
Santa Rosa	10.33 ± 1 ab	11.50 ± 1.5 a	43.55 ± 0.19 bcdef
Angelino	8.83 ± 0.5 a	12.00 ± 2 a	49.38 ± 0.10 cdef
Black Amber	11.33 ± 1 ab	14.83 ± 2 ab	36.50 ± 0.29 abcd
Golden Japan	9.56 ± 1 ab	12.28 ± 0.5 ab	45.97 ± 0.44 ab
Monglobe	13.57 ± 0.5 ab	15.57 ± 1.5 ab	28.21 ± 0.22 ab
Singlobe	12.50 ± 0.5 ab	14.50 ± 0.5 ab	27.51 ± 0.42 ab
Timhdit	12.50 ± 0.5 ab	16.50 ± 1.5 ab	26.46 ± 0.35 ab

Means labeled with the same letter are statistically distinct at a significance level of p ≤ 0.05, as determined by the SNK test. SLL: shoot leaf load; NLF: number of leaves per fruit; LA: leaf area.

). The shoot leaf loads of cultivar ranged from 7.00 to 18.83 leaves. The highest value was recorded by the cultivar ‘INRA-PR42’ where the average was 18.83 leaves per shoot. The lowest values were recorded by the cultivars ‘INRA-PR48’, ‘INRA-PR37’, ‘INRA-PR49’, and ‘Stanley’, with averages of 7.00, 8.00, 8.33, and 9.00 leaves per shoot, respectively. The number of leaves per fruit of the cultivars varied from 9.00 to 22.67 leaves. The highest value was recorded by the cultivar ‘INRA-PR42’ where the average was 22.67 leaves per fruit. The lowest values were recorded by the cultivars ‘INRA-PR35’, ‘INRA-PR38’, ‘INRA-PR46’, ‘INRA-PR47’, ‘INRA-PR39’, ‘Santa Rosa’, and ‘Angelino’, with averages of 9.00, 9.33, 10.17, 11.00, 11.50, and 12.00 leaves per fruit, respectively. The leaf area of the cultivars varied from 22.3 to 59.04 cm². The highest value was recorded by the cultivar ‘INRA PR46’ with an average of 59.04 cm², respectively. The lowest value was 22.3 cm² for the cultivar ‘INRAPR43’. The variations in vegetative traits observed in the different plum cultivars studied are in line with the findings of several previous studies on plum, including those by Minin et al. [29] and Selka et al. [4]. These studies highlighted significant variability in vegetative traits, attributable to the adaptation of plant material to edapho-climatic conditions. These differences are also influenced by factors such as agro-climatic requirements, in particular chilling and heat requirements. Optimum satisfaction of these requirements favors efficient dormancy breaking and successful bud burst in early cultivars, leading to harmonious vegetative growth. On the other hand, late-ripening cultivars, which require higher levels of chilling and heat, may experience growth imbalance in the event of insufficient thermal conditions. This phenomenon is particularly marked in areas with mild winters, such as the Saïss region, and results in an alteration in growth traits, adversely affecting flowering, fruit set and, consequently, the final yield of the trees. According to Gitea et al. [27], the cultivar ‘Methley’, known for its high thermal requirements and adaptation to cold areas, showed shorter dormancy in the Saïss region. Variations in temperature during this period compromised its vegetative growth, unlike the cultivars ‘Black Amber’ and ‘Fortune’, whose moderate thermal requirements enabled more favorable vegetative development.

3.3. Physiological Traits

Significant differences between the cultivars studied for the physiological traits were revealed (

Table 4. Stomatal traits of plum cultivars studied.

Cultivars	SD (Stomata mm−2)	SL (µm)	SW (µm)	SA (µm2)	SAI (µm2 mm−2)
INRA-PR34	347.00 ± 1 f	5.42 ± 0.13 abc	4.21 ± 0.02 cde	17.89 ± 0.32 bcde	1071.68 ± 42.55 bcd
INRA-PR35	355.00 ± 2 g	4.07 ± 0.04 a	3.54 ± 0.09 abcd	11.31 ± 0.43 ab	792.90 ± 59.53 abc
INRA-PR36	345.00 ± 1 ef	4.69 ± 0.62 abc	4.00 ± 0.78 bcde	14.96 ± 4.86 abcd	861.87 ± 298.08 abc
INRA-PR37	343.00 ± 1 de	4.52 ± 0.31 abc	3.59 ± 0.47 abcde	12.66 ± 0.82 abcd	694.13 ± 61.19 abc
INRA-PR38	354.00 ± 2 g	4.31 ± 0.04 ab	3.47 ± 0.11 abcd	11.72 ± 0.52 abc	806.99 ± 65.88 abc
INRA-PR39	341.00 ± 1 d	4.64 ± 0.23 abc	3.49 ± 0.04 abcd	12.73 ± 0.83 abcd	664.25 ± 27.14 ab
INRA-PR40	334.00 ± 2 c	4.64 ± 0.23 abc	3.92 ± 0.38 bcde	14.34 ± 2.13 abcd	617.57 ± 55.72 ab
INRA-PR41	343.00 ± 1 de	3.68 ± 0.57 a	2.87 ± 0.23 a	8.23 ± 0.60 a	450.35 ± 22.86 a
INRA-PR42	328.00 ± 1 a	4.55 ± 0.23 abc	3.85 ± 0.31 bcde	13.78 ± 1.83 abcd	490.05 ± 47.80 a
INRA-PR43	329.00 ± 0.54 a	4.43 ± 0.40 abc	3.23 ± 0.50 ab	11.33 ± 2.77 ab	418.48 ± 102.63 a
INRA-PR44	365.00 ± 1 i	7.20 ± 0.69 def	5.38 ± 0.35 f	30.56 ± 4.96 g	2526.05 ± 371.73 f
INRA-PR45	368.00 ± 1 j	6.96 ± 0.07 def	3.83 ± 0.04 bcde	20.92 ± 0.04 de	1811.85 ± 22.67 e
INRA-PR46	335.00 ± 0.21 c	7.51 ± 0.04 ef	5.38 ± 0.35 f	31.74 ± 1.90 g	1415.05 ± 85.12 de
INRA-PR47	361.00 ± 1 h	9.33 ± 0.14 g	7.44 ± 0.26 g	54.47 ± 1.08 h	4233.89 ± 154.00 g
INRA-PR48	336.00 ± 1 c	5.41 ± 0.62 abc	3.78 ± 0.04 abcde	16.06 ± 2.04 abcd	738.18 ± 114.44 abc
INRA-PR49	356.00 ± 1 g	5.07 ± 0.09 abc	4.02 ± 0.19 bcde	15.99 ± 0.46 abcd	1140.44 ± 12.48 cd
Stanley	336.00 ± 1 c	4.62 ± 0.11 abc	3.64 ± 0.23 abcde	13.17 ± 0.52 abcd	604.20 ± 40.86 ab
Prune d’Ente	376.00 ± 1 c	4.87 ± 0.28 abc	3.83 ± 0.28 abcde	13.37 ± 2.28 abcd	600.84 ± 105.45 ab
Friar	336.00 ± 1 c	7.99 ± 0.09 f	5.45 ± 0.09 f	34.21 ± 0.19 g	1568.65 ± 34.84 e
Fortune	330.00 ± 2 c	8.39 ± 0.28 f	5.88 ± 0.09 f	34.65 ± 0.33 g	1500.01 ± 7.34 e
Methley	359.40 ± 1 g	6.47 ± 0.52 bcd	6.74 ± 0.21 f	23.80 ± 3.81 ef	1694.73 ± 227.04 e
Santa Rosa	363.00 ± 2 hi	6.08 ± 0.33 cde	4.28 ± 0.02 de	20.42 ± 1.01 cde	1637.31 ± 29.22 e
Angelino	347.00 ± 2 f	7.46 ± 0.52 ef	5.26 ± 0.19 f	30.89 ± 3.29 g	1843.94 ± 118.58 e
Black Amber	356.00 ± 1 g	5.98 ± 0.38 bcd	5.07 ± 0.28 f	23.75 ± 0.18 ef	1694.73 ± 42.92 e
Golden Japan	348.67 ± 3 ef	7.18 ± 0.47 ef	5.00 ± 0.14 e	28.51 ± 0.83 fg	1683.30 ± 82.63 e
Monglobe	387.00 ± 1 ab	4.98 ± 0.57 abc	3.87 ± 0.23 ab	11.76 ± 2.32 ab	387.78 ± 169.46 a
Singlobe	326.00 ± 1 a	4.40 ± 0.31 abc	3.13 ± 0.52 ab	10.85 ± 0.74 ab	359.38 ± 60.47 a
Timhdit	344.00 ± 2 ef	4.83 ± 0.78 abc	3.25 ± 0.28 abc	12.49 ± 4.71 abcd	702.78 ± 420.32 ab

Means labeled with the same letter are statistically distinct at a significance level of p ≤ 0.05, as determined by the SNK test. SD: stomatal density; SL: stomatal length; SW: stomatal width; SA: stomatal area; SAI: stomatal area index.

and

Table 5. Stomatal conductance, leaf proline content, cuticular wax, leaf chlorophyll content of plum cultivars studied.

Cultivars	SC (mmol m−2 s−1)	LPC (g L−1)	CW (g Kg−1)	Chlor a (mg L−1)	Chlor b (mg L−1)
INRA-PR34	4.39 ± 0.54 efg	0.38 ± 0.03 de	16.84 ± 1.40 h	4.80 ± 0.22 cde	10.58 ± 0.26 b
INRA-PR35	3.16 ± 0.38 abcd	0.21 ± 0.01 ab	5.24 ± 1.51 ab	7.54 ± 0.39 g	11.63 ± 0.15 c
INRA-PR36	3.42 ± 0.20 abcd	0.22 ± 0.01 ab	13.06 ± 1.54 g	5.51 ± 0.24 e	12.29 ± 0.75 c
INRA-PR37	4.62 ± 0.41 fg	0.34 ± 0.06 cde	17.39 ± 1.19 h	3.87 ± 0.16 ab	22.71 ± 1.00 i
INRA-PR38	2.65 ± 0.21 a	0.32 ± 0.02 cd	5.96 ± 1.03 abc	8.47 ± 0.17 h	17.26 ± 0.10 e
INRA-PR39	3.54 ± 0.50 abcde	0.41 ± 0.02 de	12.09 ± 1.15 fg	8.19 ± 0.10 h	13.63 ± 0.36 d
INRA-PR40	2.96 ± 0.45 abcd	0.26 ± 0.03 bc	6.61 ± 1.29 abcd	8.66 ± 0.50 h	20.19 ± 0.13 h
INRA-PR41	3.29 ± 0.32 abcd	0.54 ± 0.05 g	10.43 ± 1.70 defg	5.21 ± 0.24 de	18.44 ± 0.18 fg
INRA-PR42	3.06 ± 0.12 abcd	0.32 ± 0.04 cd	9.25 ± 1.42 bcdef	4.75 ± 0.28 cd	11.55 ± 0.38 c
INRA-PR43	3.89 ± 0.20 defg	0.23 ± 0.03 ab	8.87 ± 1.01 bcdef	6.37 ± 0.34 f	13.72 ± 0.35 d
INRA-PR44	3.08 ± 0.11 abcd	0.18 ± 0.03 ab	7.56 ± 2.42 abcde	4.52 ± 0.40 bcd	24.19 ± 0.76 j
INRA-PR45	3.48 ± 0.44 abcde	0.37 ± 0.04 de	6.28 ± 0.91 abc	4.89 ± 0.11 cde	11.70 ± 0.17 c
INRA-PR46	2.90 ± 0.20 abcd	0.43 ± 0.01 ef	10.39 ± 1.33 defg	5.52 ± 0.60 e	12.58 ± 0.15 cd
INRA-PR47	3.19 ± 0.03 abcd	0.54 ± 0.05 g	11.54 ± 2.08 efg	7.30 ± 0.13 g	9.48 ± 0.45 a
INRA-PR48	2.69 ± 0.26 ab	0.34 ± 0.05 cde	3.76 ± 0.84 a	3.49 ± 0.33 a	12.69 ± 0.21 cd
INRA-PR49	3.79 ± 0.40 cdef	0.27 ± 0.05 bc	8.88 ± 1.27 bcdef	6.27 ± 0.08 f	17.55 ± 0.36 ef
Stanley	3.32 ± 0.38 abcd	0.40 ± 0.02 de	5.70 ± 1.05 abc	4.50 ± 0.31 bcd	18.53 ± 0.09 fg
Prune d’Ente	3.22 ± 0.17 abcd	0.45 ± 0.04 de	5.30 ± 1.21 abc	4.70 ± 0.12 bcd	17.53 ± 0.22 fg
Friar	2.63 ± 0.29 a	0.27 ± 0.02 bc	5.14 ± 1.60 ab	7.41 ± 0.40 g	18.46 ± 0.44 fg
Fortune	2.33 ± 0.15 a	025 ± 0.03 bc	5.34 ± 1.66 ab	7.61 ± 0.16 g	19.16 ± 0.30 fg
Methley	3.55 ± 0.25 bcdef	0.52 ± 0.06 fg	8.15 ± 2.37 bcdef	8.60 ± 0.46 h	11.31 ± 0.16 ed
Santa Rosa	4.70 ± 0.50 g	0.52 ± 0.01 g	17.06 ± 2.80 h	6.55 ± 0.27 f	22.59 ± 0.73 i
Angelino	3.45 ± 0.48 abcde	0.15 ± 0.02 a	7.56 ± 1.10 abcde	6.33 ± 0.24 f	12.66 ± 0.20 cd
Black Amber	3.75 ± 0.09 bcdef	0.50 ± 0.05 fg	8.75 ± 1.34 bcdef	8.40 ± 0.26 h	12.61 ± 0.28 ed
Golden Japan	3.59 ± 0.67 defg	0.31 ± 0.01 de	9.92 ± 1.05 fg	6.76 ± 0.35 g	17.91 ± 0.27 d
Monglobe	2.58 ± 0.13 abc	0.55 ± 0.01 g	8.36 ± 1.38 bcdef	4.14 ± 0.21 bc	10.48 ± 0.63 c
Singlobe	2.78 ± 0.39 abc	0.53 ± 0.06 g	8.46 ± 0.99 bcdef	4.34 ± 0.19 bc	11.88 ± 0.64 c
Timhdit	3.06 ± 0.07 abcd	0.49 ± 0.01 fg	9.62 ± 1.04 cdefg	4.98 ± 0.10 cde	19.13 ± 1.15 g

Means labeled with the same letter are statistically distinct at a significance level of p ≤ 0.05, as determined by the SNK test. SC: stomatal conductance; LPC: leaf proline content; CW: cuticular wax; Chlor a: Chlorophyll a; Chlor b: Chlorophyll b.

). The stomatal density of the cultivars varied from 326.00 to 387.00 stomata mm⁻². The highest density was recorded by the cultivar ‘Monglobe’ with an average value of 387.00 stomata mm⁻². The lowest values were observed in the cultivars ‘Singlobe’, ‘INRA-PR42’, and ‘INRA-PR43’ with mean values of 326.00, 328.00, and 329.00 stomata mm⁻². The length of the stomata of the cultivars varied from 3.68 to 9.33 μm. The greatest length was recorded by the cultivar ‘INRA-PR47’ with an average of 9.33 μm. The lowest values were observed in cultivars ‘INRA-PR41’ and ‘INRA-PR35’ with mean values of 3.68 and 4.07 μm, respectively. The width of the stomata of the cultivars varied from 2.87 to 7.44 μm. The highest width was recorded by the cultivar ‘INRA-PR47’ with an average of 7.44 μm. The lowest value was observed in the cultivar ‘INRA-PR41’ with an average value of 2.87 μm. The stomatal area of the cultivars varied from 8.23 to 54.47 μm². The highest stomatal area was recorded by the cultivar ‘INRA-PR47’ with an average value of 54.47 μm². The lowest value was observed in the cultivar ‘INRA-PR41’ with an average value of 8.23 μm². The stomatal surface index of the cultivars ranged from 359.38 to 4233.89 μm² mm⁻². The highest value was recorded by the ‘INRA-PR47’ genotype with an average value of 4233.89 μm² mm⁻². The lowest values were observed in the cultivars ‘Singlobe’, ‘Monglobe’, ‘INRA-PR43’, ‘INRA-PR41’, and ‘INRA-PR42’ with mean values of 359.38, 387.78, 418.48, 450.35, and 490.05 μm² mm⁻², respectively. The stomatal conductance of the cultivars ranged from 2.33 to 4. 70 μmol m⁻² s⁻¹. The highest values were recorded by the cultivars ‘INRA-PR37’ and ‘Santa Rosa’ with mean values of 4.62 and 4.70 μmol m⁻² s⁻¹, respectively. The lowest values were observed in the cultivars ‘Fortune’, ‘Friar’, and ‘INRA-PR38’ with mean values of 2.33, 2.63 and 2.65 μmol m⁻² s⁻¹, respectively. The leaf proline content of the cultivars varied from 0.15 to 0.55 g L⁻¹. The highest values were recorded by the cultivars ‘Santa Rosa’, ‘Singlobe’, ‘INRA-PR41’, and ‘INRA-PR47’ and ‘Monglobe’, with mean values of 0.52, 0.53, 0.54, and 0.55 g L⁻¹, respectively. The lowest value was observed in the cultivar ‘Angelino’ with an average value of 0.15 g L⁻¹. The cuticular wax varied from 3.76 to 17.39 g kg⁻¹ leaf matter. The highest values were recorded by cultivars ‘INRA-PR34’, ‘Santa Rosa’, and ‘INRA-PR37’ with mean values of 16.84, 17.06, and 17.39 g kg⁻¹, respectively. The lowest value was observed in the cultivar ‘INRA-PR48’, with an average value of 3.76 g kg⁻¹. The leaf chlorophyll a content of the cultivars varied from 3.49 to 8.66 mg L⁻¹. The highest values were recorded by the genotypes ‘INRA-PR39’, ‘Black Amber’, ‘INRA-PR 38’, ‘Methley’, and ‘INRA-PR40’ where the averages were 8.19, 8.40, 8.47, 8.60, and 8.66 mg L⁻¹, respectively. The lowest value was recorded in the cultivars ‘INRA-PR48’ with an average of 3.49 mg L⁻¹. The leaf chlorophyll b content of the cultivars varied from 9.48 to 24.19 mg L⁻¹. The highest value was recorded by the cultivar ‘INRA-PR 44’ with an average of 24.19 mg L⁻¹. The lowest value was recorded in the cultivar ‘INRA-PR 47’ with an average of 9.48 mg L⁻¹. The variations observed between cultivars for these traits have been extensively studied in the plum literature, notably by Jiménez et al. [30], who attributed these differences to the specific adaptation of each cultivar to edapho-climatic conditions. This adaptation makes it possible to limit water loss through transpiration by regulating the opening and closing of stomata, while increasing resistance to abiotic factors thanks to increased leaf proline and cuticular wax content [31]. Stomata closure, a complex mechanism influenced by various internal and external factors, is known to be faster in small stomata. This suggests that varieties with small stomata may display greater tolerance to drought. The diversity of the variations observed highlights the richness of the structural and functional traits of the leaves, which determine the efficiency of water use. It could reflect a wide range of plasticity in olive species in the face of abiotic stresses, particularly drought [32]. However, these traits are strongly influenced by factors such as the genetic variability of cultivars and environmental conditions. This physiological response mechanism, although crucial, remains complex, and many aspects governing it are still poorly understood [33].

3.4. Fruit Quality Traits

3.4.1. Analysis of the Chemical Properties

Significant differences between the cultivars studied for plum chemical quality traits were revealed (

Table 6. Analysis of the chemical properties of plum cultivars studied.

Cultivars	F (N mm−2)	TSS	pH	TA (% of Citric Acid)	MI
INRA-PR34	15.30 ± 0.69 d	15.76 ± 1.02 cdefg	4.60 ± 0.02 cdef	5.00 ± 0.98 abc	3.42 ± 0.23 bcde
INRA-PR35	18.90 ± 0.29 e	18.66 ± 1.33 i	4.29 ± 0.08 bc	5.28 ± 0.26 abc	4.35 ± 0.31 i
INRA-PR36	27.55 ± 0.44 f	14.65 ± 0.65 abc	4.62 ± 0.12 cdef	5.32 ± 0.45 abc	3.17 ± 0.22 abc
INRA-PR37	14.85 ± 1.35 d	16.80 ± 0.43 defgh	4.34 ± 0.01 bcd	3.94 ± 0.81 a	3.87 ± 0.01 efgh
INRA-PR38	11.60 ± 0.09 c	16.85 ± 0.04 defgh	4.85 ± 0.01 efgh	4.68 ± 0.33 abc	3.47 ± 0.01 bcdef
INRA-PR39	12.30 ± 0.2 c	16.33 ± 0.15 defgh	4.64 ± 0.17 cdef	4.88 ± 0.09 abc	3.51 ± 0.16 cdefg
INRA-PR40	26.85 ± 0.85 f	17.75 ± 0.35 hi	4.36 ± 0.10 bcd	4.96 ± 0.11 abc	4.06 ± 0.17 h
INRA-PR41	43.95 ± 2.75 h	15.60 ± 0.3 cdef	3.28 ± 0.03 a	5.31 ± 0.55 abc	4.74 ± 0.04 j
INRA-PR42	16.95 ± 0.25 de	17.00 ± 0.3 efgh	4.41 ± 0.12 cde	4.51 ± 0.22 abc	3.85 ± 0.17 efgh
INRA-PR43	10.50 ± 1.6 c	20.10 ± 0.09 j	5.20 ± 0.03 hi	5.05 ± 0.73 abc	3.86 ± 0.01 efgh
INRA-PR44	3.60 ± 0.1 a	17.70 ± 0.19 hi	5.12 ± 0.01 ghi	4.31 ± 0.81 ab	3.45 ± 0.03 bcdef
INRA-PR45	16.60 ± 3.09 de	17.40 ± 0.6 ghi	4.44 ± 0.23 cde	5.94 ± 0.4 c	3.93 ± 0.33 fg
INRA-PR46	6.40 ± 0.89 b	13.40 ± 0.2 a	4.65 ± 0.14 cdef	4.40 ± 0.40 ab	2.88 ± 0.12 a
INRA-PR47	32.05 ± 1.85 g	15.40 ± 0.09 cde	4.00 ± 0.17 b	5.31 ± 0.44 abc	3.84 ± 0.14 efgh
INRA-PR48	27.31 ± 1.45 f	16.37 ± 0.34 defgh	5.08 ± 0.30 ghi	4.37 ± 0.25 ab	3.22 ± 0.13 abc
INRA-PR49	34.55 ± 1.35 h	16.30 ± 0.39 defgh	5.00 ± 0.17 ghi	5.81 ± 0.05 bc	3.25 ± 0.03 abcd
Stanley	24.47 ± 1.15 f	17.22 ± 0.10 fghi	4.44 ± 0.23 cde	4.44 ± 0.11 abc	3.88 ± 0.21 fgh
Prune d’Ente	25.72 ± 1.54 f	16.92 ± 0.43 defgh	4.14 ± 1.65 cde	4.34 ± 1.75 abc	3.76 ± 1.54 fgh
Friar	18.76 ± 0.67 e	15.56 ± 0.3 cdef	4.22 ± 0.10 cde	5.23 ± 0.11 abc	3.68 ± 0.14 defgh
Fortune	17.16 ± 0.54 e	14.96 ± 2.54 bcd	3.98 ± 0.54 bc	4.93 ± 0.65 abc	3.18 ± 0.12 abcd
Methley	26.15 ± 1.43 f	20.25 ± 1.43 j	4.68 ± 1.43 cdef	3.94 ± 1.54 a	5.05 ± 1.54 i
Santa Rosa	17.5 ± 1.90 de	14.63 ± 1.02 abc	4.84 ± 0.14 efgh	4.31 ± 0.55 ab	3.01 ± 0.14 ab
Angelino	19.4 ± 1.1 e	15.63 ± 1.02 cdef	4.77 ± 0.28 defg	4.25 ± 0.55 a	3.27 ± 0.17 abcd
Black Amber	25.75 ± 1.05 f	20.15 ± 0.35 j	4.24 ± 0.02 bc	3.75 ± 0.97 a	4.75 ± 0.11 j
Golden Japan	15.35 ± 1.94 d	13.80 ± 0.6 ab	4.44 ± 0.01 cde	5.95 ± 0.38 c	3.10 ± 0.14 abc
Monglobe	17.17 ± 1.03 e	15.99 ± 0.43 bcd	5.22 ± 1.54 i	4.99 ± 0.75 abc	2.93 ± 0.43 a
Singlobe	18.87 ± 0.77 e	16.69 ± 0.40 defgh	5.32 ± 0.28 i	5.28 ± 0.49 abc	3.13 ± 0.16 abc
Timhdit	4.36 ± 0.05 ab	15.10 ± 1.5 bcd	4.44 ± 0.23 cde	4.20 ± 0.44 a	3.39 ± 0.16 bcd

Means labeled with the same letter are statistically distinct at a significance level of p ≤ 0.05, as determined by the SNK test. TSS: total soluble solids; F: firmness; TA: titratable acidity; MI: maturity index.

). Fruit firmness varied from 3.60 to 43.95 N mm⁻². The highest value was recorded in cultivar ‘INRA-PR41’ with an average of 43.95 N mm⁻², while the lowest value was observed in cultivar ‘INRA-PR44’ with an average of 3.60 N mm⁻². The °Brix level of plums in all cultivars varied from 13.40 to 20.25 °Brix level. The highest values were recorded in the cultivars ‘INRA-PR43’, ‘Black Amber’, and ‘Methley’ with average values of 20.10, 20.15, and 20.25 °Brix level, while the lowest values were observed in the cultivar ‘INRA-PR46’ with an average of 13.40 °Brix level. Fruit pH ranged from 3.28 to 5.32. The highest values were recorded in the cultivars ‘Monglobe’ and ‘Singlobe’ with mean values of 5.22 and 5.32, while the lowest value was observed in the cultivar ‘INRA-PR41’ with a mean of 3.28. The titratable acidity of the fruit ranged from 3.75% to 5.95% of citric acid. The highest values were recorded in the cultivars ‘INRA-PR45’ and ‘Golden Japan’ with mean values of 5.94% and 5.95% of citric acid. The lowest values were recorded in the cultivars ‘Black Amber’, ‘INRA-PR37’, ‘Timhdit’, and ‘Angelino’ with mean values of 3.75%, 3.94%, 4.20%, and 4.25% of citric acid, respectively. The maturity index (TSS/TA) of the fruit ranged from 2.88 to 5.05. The highest values were recorded in cultivars ‘Black Amber’ and ‘Methley’ with mean values of 4.75 and 5.05, respectively. The lowest values were recorded in cultivars ‘INRA-PR46’ and ‘Monglobe’ with mean values of 2.88 and 2.93, respectively. Our results on the chemical quality of plums are comparable to those reported by Ozzengin et al. [3] and Liao et al. [34], who observed °Brix level values ranging from 15.66 to 20.53, titratable acidity (TA) ranging from 4.9% to 5.3% (expressed as % of citric acid), pH ranging from 7 to 7.8, and firmness ranging from 68.48 to 61.43 N mm⁻². However, these results differ significantly from those obtained in our study. The observed variations in the chemical traits of plums can be attributed to various factors, including varietal differences and sugar content, which change considerably during ripening, depending on the cultivars [35]. These changes also influence titratable acidity, which varies depending on the type of fruit and its degree of maturity. During the early stages of development, organic acids, such as malic acid, accumulate, while they decrease in the later stages of ripening, probably due to increased metabolism and enhanced synthesis of sugars or secondary compounds in ripe fruits. The accumulation of organic acids is genetically regulated, leading to marked differences not only between species but also between cultivars [36]. These variations directly influence the pH, firmness, and ultimately the maturity index of harvested fruits [34,37].

3.4.2. Analysis of the Biochemical Properties

Total polyphenol compounds in the fruit ranged from 0.56 to 0.84 g GAE L⁻¹ (

Table 7. Analysis of the biochemical properties of plum cultivars studied.

Cultivars	TPC (g GAE L−1)	SSC (mg GE L−1)	AAC (g GlyE L−1)	TAC (%)
INRA-PR34	0.81 ± 0.02 def	331.19 ± 3.92 abcd	3.40 ± 0.10 a	68.26 ± 2.20 cd
INRA-PR35	0.82 ± 0.01 ef	380.20 ± 15.92 de	4.16 ± 0.20 c	66.66 ± 0.61 cd
INRA-PR36	0.76 ± 0.01 cdef	315.25 ± 11.46 abcd	3.36 ± 0.17 a	52.66 ± 1.22 b
INRA-PR37	0.72 ± 0.01 cd	374.25 ± 15.80 de	3.52 ± 0.20 a	73.60 ± 0.69 d
INRA-PR38	0.77 ± 0.03 cdef	334.87 ± 28.35 abcd	3.46 ± 0.36 a	73.60 ± 0.69 d
INRA-PR39	0.73 ± 0.02 cde	309.18 ± 42.15 abcd	3.51 ± 0.16 a	75.46 ± 1.97 d
INRA-PR40	0.81 ± 0.02 def	356.88 ± 19.93 bcde	4.01 ± 0.04 bc	55.2 ± 2.11 bc
INRA-PR41	0.73 ± 0.08 cd	314.29 ± 10.90 abcd	3.41 ± 0.16 a	26.26 ± 3.33 a
INRA-PR42	0.56 ± 0.02 a	370.80 ± 13.93 cde	4.23 ± 0.39 c	69.86 ± 1.00 bcd
INRA-PR43	0.84 ± 0.07 f	413.99 ± 21.31 e	4.24 ± 0.20 c	63.33 ± 0.46 d
INRA-PR44	0.77 ± 0.05 cdef	371.28 ± 18.51 cde	4.02 ± 0.10 bc	73.46 ± 0.61 d
INRA-PR45	0.72 ± 0.01 cd	291.10 ± 38.35 abc	3.24 ± 0.16 a	69.60 ± 1.05 d
INRA-PR46	0.62 ± 0.01 b	276.23 ± 29.21 ab	3.02 ± 0.07 a	71.46 ± 0.23 d
INRA-PR47	0.71 ± 0.01 cd	318.34 ± 23.83 abcd	3.16 ± 0.25 a	62.66 ± 0.83 bcd
INRA-PR48	0.76 ± 0.01 cdef	346.41 ± 37.48 bcde	3.34 ± 0.29 a	70.54 ± 1.6 d
INRA-PR49	0.74 ± 0.01 cde	331.66 ± 24.58 abcd	3.30 ± 0.29 a	72.40 ± 0.39 d
Stanley	0.76 ± 0.02 cdef	342.49 ± 10.24 bcd	3.44 ± 0.32 a	65.86 ± 1.28 cd
Prune d’Ente	0.78 ± 0.65 cdef	365.19 ± 12.54 cde	3.74 ± 0.42 a	61.96 ± 1.54 bcd
Friar	0.72 ± 0.01 cd	302.99 ± 30.49 abcd	3.28 ± 0.20 a	63.20 ± 0.80 bcd
Fortune	0.70 ± 0.12 cd	312.49 ± 13.65 abcd	3.54 ± 0.32 a	61.76 ± 2.32 bcd
Methley	0.72 ± 0.21 cd	321.24 ± 13.65 abcd	3.94 ± 0.23 a	62.11 ± 3.54 bcd
Santa Rosa	0.78 ± 0.04 cde	300.97 ± 8.80 abcd	3.26 ± 0.18 a	69.06 ± 0.83 d
Angelino	0.74 ± 0.01 cde	300.14 ± 35.31 abcd	3.33 ± 0.23 a	66.13 ± 1.80 cd
Black Amber	0.74 ± 0.01 cde	327.74 ± 21.33 abcd	3.66 ± 0.10 a	68.41 ± 23.23 cd
Golden Japan	0.63 ± 0.01 b	261.24 ± 37.13 a	3.19 ± 0.11 ab	71.33 ± 1.61 d
Monglobe	0.79 ± 0.12 cde	321.39 ± 21.645 abcd	3.65 ± 0.12 a	69.63 ± 1.23 d
Singlobe	0.75 ± 0.01 cde	314.29 ± 39.79 abcd	3.38 ± 0.15 a	67.06 ± 1.40 cd
Timhdit	0.71 ± 0.01 c	279.80 ± 45.56 ab	3.42 ± 0.16 a	70.34 ± 1.2 d

Means labeled with the same letter are statistically distinct at a significance level of p ≤ 0.05, as determined by the SNK test. TPC: total polyphenol compounds; SSC: soluble sugar content; AAC: amino acid content; TAC: total antioxidant capacity.

). The highest value was recorded in cultivar ‘INRA-PR43’ with an average value of 0.84 g GAE L⁻¹. The lowest value was recorded in the cultivar ‘INRA-PR42’ with an average of 0.56 g GAE L⁻¹. Soluble sugar content of fruit varied from 261.24 to 413.99 mg GE L⁻¹. The highest value was recorded in cultivar ‘INRA-PR43’, with an average value of 413.99 mg GE L⁻¹. The lowest value was recorded in the cultivar ‘Golden Japan’, with an average of 261.24 mg GE L⁻¹. Amino acid content of the fruits varied from 3.02 to 4.16 g GlyE L⁻¹). The highest value was recorded in the cultivar ‘INRA-PR35’ with an average value of 4.16 g GlyE L⁻¹. The lowest value was recorded in the cultivar ‘INRA-PR46’ with an average of 261.24 g GlyE L⁻¹. Total antioxidant capacity of the fruit ranged from 26.26 to 75.46%. The highest value was recorded in cultivar ‘INRA-PR39’, with an average value of 75.46%. The lowest value was recorded in cultivar ‘INRA-PR41’, with an average of 26.26%. Our results on the chemical quality of prunes are consistent with several studies, including those of Rusu et al. [11]. These works reporting SSC values include between 418 and 459 mg GE.L⁻¹, TPC ranging from 3.89 to 7.76 g GAE L⁻¹, and TAC varying between 85% and 97.07%, levels significantly higher than those obtained in our study. This variability reflects significant differences between the cultivars studied, probably attributable to the genetic heterogeneity of the plants. This genetic diversity influences the biosynthesis of primary (sugars and amino acids) and secondary (phenolic compounds) metabolites, thus generating notable gaps in total phenolic and antioxidant activities during commercial ripening [11]. In addition to the predominant role of genotype, other factors influencing these disparities include climate, soil characteristics, geographical location, cultural practices, and harvest period. The latter is particularly influential in the context of the ex situ plum collection of the NIAR estate in Aïn Taoujdate. The cultivars have differentiated thermal requirements, linked to their precocity (early or late cultivars), which leads to variations in the duration of maturation and, consequently, in the harvest periods [38].

3.5. Principal Component Analysis

This study utilized principal component analysis (PCA) to pinpoint the traits that most effectively differentiate between the plum cultivars examined, based on the mean values of the recorded characteristics (

Table 8. Proportion of variance accounted for by the first four principal components of the PCA, based on the average values of measured traits in plum cultivars.

	Components
	1	2	3	4	5	6	7	8
FY	−0.132	−0.552	−0.625	0.311	0.193	−0.186	0.242	0.070
FW	0.390	0.615	−0.258	0.181	0.350	−0.345	0.229	−0.074
FL	0.488	0.562	0.149	0.166	0.254	−0.380	0.202	0.045
FWd	0.539	0.690	0.023	0.144	0.188	−0.203	0.216	0.041
FH	0.296	0.701	−0.445	0.025	−0.123	0.349	0.051	−0.019
CW	0.296	0.699	−0.445	0.025	−0.123	0.349	0.051	−0.019
Firmness	0.180	0.033	0.075	0.771	0.095	0.163	−0.044	−0.123
TSS	0.674	−0.209	0.229	−0.232	0.413	0.250	0.144	0.242
pH	0.095	0.139	−0.392	−0.756	0.057	−0.164	−0.236	−0.008
TA	0.022	0.214	0.154	0.402	0.190	−0.473	−0.495	−0.078
MI	0.452	−0.263	0.452	0.445	0.251	0.332	0.268	0.165
TPC	0.319	−0.315	−0.257	−0.093	0.577	0.358	−0.321	−0.234
SSC	0.716	−0.308	0.027	−0.287	0.258	0.100	0.203	0.140
AAC	0.686	−0.361	0.366	−0.377	0.067	0.007	0.152	0.062
TAC	−0.215	0.158	−0.224	−0.736	0.117	−0.165	0.104	0.299
NLF	0.324	−0.079	0.475	−0.154	−0.591	0.022	0.255	−0.075
SLL	0.199	−0.311	0.713	0.101	−0.238	−0.182	−0.223	0.148
LA	−0.410	0.081	−0.362	0.262	0.151	0.350	−0.238	0.424
SD	−0.515	−0.246	0.170	−0.073	0.420	0.189	−0.210	−0.077
SL	−0.821	0.305	0.295	−0.180	0.054	0.023	0.129	−0.071
SW	−0.703	0.356	0.318	−0.119	0.251	0.068	0.266	0.044
SA	−0.784	0.350	0.345	−0.099	0.185	0.051	0.216	−0.012
SAI	−0.785	0.166	0.376	−0.103	0.308	0.145	0.154	−0.015
SC	−0.239	−0.660	−0.443	0.097	0.196	−0.230	0.256	0.074
LPC	−0.345	0.026	−0.080	0.473	−0.227	0.225	0.243	0.163
CW	−0.425	−0.545	−0.410	0.193	−0.056	−0.352	0.327	0.028
Chlor a	−0.002	0.016	0.384	0.087	0.472	−0.199	−0.044	−0.205
Chlor b	0.064	−0.249	−0.164	−0.165	−0.019	0.199	0.308	−0.722
% of variance	21.57	15.41	12.33	10.76	7.52	6.16	5.28	3.95
% cumulative	21.57	36.98	49.32	60.08	67.60	73.77	79.05	83.00

Eigenvectors above 0.7 are indicated in bold.

). Traits with principal component loadings exceeding 0.7 were considered to have significant contributions. The total variance of over 60.08% is explained by four components. The first component explains 21.57% of the total variance. It was positively correlated with Soluble sugar content (r = 0.716), and negatively correlated with stomatal length (r = 0.821), stomatal width (r = 0.703), stomatal area (r = 0.784) and stomatal area index (r = 0.785). The second component represents 15.41% of the total inertia and is mainly positively correlated with fruit width (r = 0.701). The third component represents 12.33% of the total inertia and is mainly negatively correlated with shoot leaf load (r = 0.713). The fourth component represents 10.76% of the total inertia and is mainly positively correlated with firmness (r = 0.771), and negatively correlated with pH (r = 0.756) and total antioxidant capacity (r = 0.736). When a principal component loading exceeds 0.7, the most critical traits for distinguishing cultivars are those previously identified, showing strong correlations with the four principal components analyzed in the PCA.

3.6. Correlation

To gain deeper insights into the relationships among the measured traits of the studied plum cultivars, Pearson’s coefficient was used to perform a bivariate correlation analysis based on the mean values of all variables. Significant correlations at the 0.05 or 0.01 levels are presented in

Table 9. Matrix of correlation coefficients showing the mean values of the traits included in the search.

	FY	FW	FL	FWd	FH	CW	F	TSS	pH	AT	IM	TPC	SSC	AAC	TAC	NLF	SLL	LA	SD	SL	SW	SA	SAI	SC	LPC	CW	Chlor a	Chlor b
FY	1
FW	0.052	1
FL	−0.277	0.765 **	1
FWd	−0.338	0.798 **	0.313	1
FH	−0.195	0.723 **	0.246	0.529	1	0.900 **
CW	−0.195	0.723 **	0.246	0.529	0.011	1
F	0.142	0.172	0.172	0.164	0.085	0.085	1
TSS	−0.090	0.115	0.248	0.253	0.011	0.011	0.004	1
pH	−0.087	0.111	−0.017	0.016	0.200	0.200	−0.478	0.127	1
TA	−0.051	0.294	0.282	0.254	−0.025	−0.025	0.322	−0.172	−0.105	1
MI	0.008	0.011	0.211	0.180	−0.130	−0.130	0.418	0.646 **	−0.667 **	−0.043	1
TPC	0.220	−0.017	−0.167	−0.122	0.056	0.056	0.101	0.476	0.248	0.015	0.182	1
SSC	0.075	0.108	0.207	0.219	−0.038	−0.038	0.006	0.746 **	0.198	−0.208	0.402	0.453	1
AAC	−0.183	−0.055	0.114	0.106	−0.187	−0.187	−0.175	0.738 **	0.101	−0.170	0.460	0.277	0.824 **
TAC	−0.049	0.029	−0.044	−0.016	0.098	0.098	−0.671 **	0.064	0.617 **	−0.256	−0.478	−0.151	0.007	−0.017	1
NLF	−0.353	−0.219	−0.006	0.010	−0.016	−0.016	0.085	0.217	−0.068	−0.115	0.205	−0.348	0.221	0.471	−0.106	1
SLL	−0.302	−0.316	0.025	−0.091	−0.486	−0.486 *	−0.012	0.211	−0.312	0.261	0.392	−0.204	0.101	0.420	−0.246	0.444	1
LA	0.257	−0.100	−0.260	−0.172	0.131	0.131	0.095	−0.291	−0.170	−0.039	−0.090	0.028	−0.267	−0.238	0.053	−0.576 **	−0.319	1
SD	0.055	−0.357	−0.270	−0.391	−0.377	−0.377	−0.048	−0.007	0.003	0.089	0.000	0.200	−0.258	−0.239	0.191	−0.186	0.121	0.014	1
SL	−0.249	−0.197	−0.222	−0.235	−0.143	−0.143	−0.276	−0.455	−0.083	0.031	−0.319	−0.392	−0.395	−0.463	0.262	−0.068	−0.086	0.051	0.328	1
SW	−0.205	−0.009	−0.072	−0.087	−0.097	−0.097	−0.072	−0.306	−0.059	−0.061	−0.214	−0.286	−0.362	−0.354	0.202	−0.071	−0.090	0.037	0.333	0.828 **	1
SA	−0.239	−0.099	−0.121	−0.137	−0.133	−0.133	−0.134	−0.399	−0.131	0.018	−0.235	−0.346	−0.474	−0.420	0.185	−0.086	−0.082	0.049	0.343	0.945 **	0.954 **	1
SAI	−0.180	−0.237	−0.184	−0.237	−0.253	−0.253	−0.089	−0.266	−0.117	0.032	−0.136	−0.182	−0.391	−0.358	0.185	−0.081	−0.012	0.012	0.624 **	0.863 **	0.894 **	0.933 **	1
SC	0.848 **	−0.194	−0.398	−0.426	−0.372	−0.372	−0.016	−0.012	−0.011	−0.059	−0.016	0.160	0.047	−0.064	0.123	−0.233	−0.203	0.100	0.135	−0.055	−0.054	−0.081	−0.014	1
LPC	0.171	−0.155	−0.126	−0.090	0.127	0.127	0.193	−0.243	−0.333	−0.081	0.106	−0.250	−0.440	−0.448	−0.154	−0.058	−0.012	0.157	0.165	0.151	0.161	0.176	0.222	0.097	1
CW	0.801 **	−0.197	−0.370	−0.448	−0.386	−0.386	−0.078	−0.389	−0.108	−0.100	−0.216	−0.081	−0.155	−0.235	0.011	−0.220	−0.167	0.081	0.058	0.011	0.092	0.042	0.064	0.821 **	0.315	1
Cr a	−0.143	0.151	0.130	0.064	−0.213	−0.213	0.070	0.155	−0.192	0.169	0.231	0.160	−0.104	0.102	0.001	−0.201	0.177	−0.151	0.006	0.057	0.155	0.119	0.077	0.096	−0.086	0.029	1
Cr b	0.156	−0.048	−0.131	−0.090	−0.021	−0.021	−0.132	−0.008	0.002	−0.328	0.003	0.197	0.141	0.104	−0.034	0.030	−0.164	−0.087	0.057	−0.038	−0.144	−0.117	−0.085	0.170	−0.025	0.135	−0.050	1

** Significant at the 0.01 level (two-tailed). * Significant at the 0.05 level (two-tailed).

. Fruit yield is positively correlated with stomatal conductance and cuticular wax, with correlation coefficients of 0.848 and 0.801, respectively. Similarly, fruit weight is also positively correlated with fruit dimensions, with correlation coefficients of 0.765, 0.798, 0.723, and 0.723, respectively. These correlations can be explained by the fact that the more the stomatal area increases, the more the stomatal conductance also increases, i.e., the greater the flow of CO₂ that enters, and consequently an increase in photosynthesis, which influences the synthesis of organic matter and therefore a high yield and a high fruit size, and therefore a good development of plums during the ripening period. Certain correlations were observed between the chemical and biochemical quality components of plums, notably total antioxidant capacity was positively correlated with pH and negatively with firmness, with correlation coefficients of 0.671 and 0.617, respectively. The TSS is positively correlated with soluble sugar content, amino acid content and maturity index with correlation coefficients of 0.646, 0.746, and 0.738, respectively. The latter (maturity index) is negatively correlated with pH with a correlation coefficient of 0.667. Soluble sugar content correlated positively with amino acid content with a correlation coefficient of 0.824. These correlations can be explained by the frequent link between the two traits mentioned (SSC and AAC), which reflects the use of amino acids as precursors in the biosynthesis of sugars. As sugars are the main soluble solids in fruit, this trend was confirmed as statistically significant. It had already been reported, particularly when evaluating a wide diversity of genotypes [39]. They confirmed the well-established inverse relationship between changes in pH and MI in the fruit, often associated with maturity and the use of amino acids as precursors in sugar biosynthesis. These variations, particularly those in maturity index and AAC, appear to be closely linked to the genetic potential of the cultivar under full irrigation, impacting both yield and fruit weight [39]. Stomatal traits were also positively correlated with each other. Stomatal area index is positively correlated with stomatal density, stomatal length, stomatal width and stomatal area with correlation coefficients of 0.624, 0.863, 0.894, and 0.933, respectively. This indicates that cultivars with high stomatal density also have large stomata, which gives them a large surface area for transpiration through the stomatal pores, and large stomatal surface area means large surface area for CO₂ uptake, i.e., stomatal conductance, which has a positive influence on photosynthesis, yield and fruit size. Based on these results, it appears possible to take into consideration only one of the parameters measured on the stomata when evaluating cultivars with regard to the potential of the transpiratory surface, which is correlated with a high number of leaves and a large leaf area of the plant. Correlation coefficients offer valuable insights into distinguishing measured traits that play a key role in evaluating and classifying the studied plum cultivars. They highlight relationships between variables, helping to identify traits with significant contributions to the differentiation and characterization of the cultivars.

3.7. Hierarchical Clustering of Genotypes and Characterization of the Cultivars

A multivariate analysis of the mean values for all the measured traits demonstrated considerable variation among the plum cultivars studied. To further investigate the relationships between these cultivars, a cluster analysis was conducted using the Unweighted Pair Group Method (UPGMA) and the Euclidean distance coefficient, which helped to reveal both the similarities and differences between them. The cultivars were divided into two main groups (Figure 1). Group C1 consisted of 27 cultivars, subdivided into two distinct and homogeneous subgroups (C1-1 and C1-2). The first subgroup (C1-1) contains 24 cultivars, characterized by average fruit yield (8.95 to 59.27 kg·tree⁻¹), good fruit dimensions, high vegetative growth and moderate synthesis of proline and cuticular wax, as well as good organoleptic fruit quality. The second sub-group (C1-2) consists of 3 cultivars (‘INRA-PR44’, ‘Timhdit’, and ‘Methley’), which are characterized by average fruit yield (5.42 to 31.65 kg·tree⁻¹), average fruit dimensions, moderate vegetative growth, moderate synthesis of proline and cuticular wax, high stomatal conductance and fairly average chemical and biochemical quality of the plums studied. The second group (C2) is made up of a single cultivar ‘INRAPR47’, which is characterized by more or less low fruit yield (17.74 kg·tree⁻¹), good fruit dimensions, moderate vegetative growth, moderate synthesis of proline and cuticular wax and average fruit taste quality. The significant variation observed in the cluster analysis of all the cultivars may be attributed to the fact that they are part of an ex situ collection grown under similar edapho-climatic conditions. To validate the hypothesis of a shared genetic foundation, further analysis of other physiological and biochemical traits of the fruit would be necessary. However, based on the measured traits, the resulting clustering is highly valuable for farmers as it highlights the key similarities and differences between the cultivars, which are crucial for selecting commercial varieties in arid zones. This grouping approach can also be applied in plum breeding programs to aid in cultivar development.

4. Conclusions

The studied plum cultivars demonstrated significant variability in agronomic, vegetative, physiological traits, and fruit quality components. This comparative analysis, the first of its kind on cultivars from the NIAR ex situ plum collection grown in Morocco, opens promising prospects for selecting varieties better suited to local conditions, while also contributing to the preservation of genetic diversity and the improvement of breeding programs. By identifying cultivars resilient to climate challenges, this research enhances the sustainability of Moroccan agricultural systems, offering tailored solutions that reduce environmental impacts and increase productivity. It also supports local farming communities by strengthening food security, autonomy, and promoting economically viable long-term agriculture. Although the study was conducted over a single year, which may raise questions about the stability of the results, it provides an initial characterization of the genetic potential of the studied cultivars. This approach is particularly valuable for assessing their drought tolerance, offering preliminary insights into their adaptive capacity. The significant differences observed in the analyzed traits give a first indication of their plasticity in response to environmental stresses and provide a foundation for identifying the most discriminating parameters, paving the way for more in-depth future studies.