Next Article in Journal
Slightly Acidic Electrolyzed Water Improves the Postharvest Quality of Litchi Fruit by Regulating the Phenylpropane Pathway
Previous Article in Journal
Effects of Packaging Constraints on Vibration Damage of ‘Huangguan’ Pear During Simulated Transport
Previous Article in Special Issue
Yield and Fruit Quality of Loquat Trees as a Result of Flower Bud Thinning
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Horticulturae
Volume 11
Issue 7
10.3390/horticulturae11070750
horticulturae-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Agronomic Performance and Fruit Quality of Fresh Fig Varieties Trained in Espaliers Under a High Planting Density

by
Antonio Jesús Galán
,
María Guadalupe Domínguez
,
Manuel Pérez-López
,
Ana Isabel Galván
,
Fernando Pérez-Gragera
and
Margarita López-Corrales
*
Área de Fruticultura Mediterránea, Instituto de Investigación Finca La Orden-Valdesequera (LA ORDEN-CICYTEX), Junta de Extremadura, A.V. Km 372, 06187 Guadajira, Badajoz, Spain
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(7), 750; https://doi.org/10.3390/horticulturae11070750
Submission received: 22 May 2025 / Revised: 20 June 2025 / Accepted: 27 June 2025 / Published: 1 July 2025
(This article belongs to the Special Issue Subtropical Fruits: Sustainable Management, Propagation Techniques and Seedling Production)
Browse Figures
Versions Notes

Abstract

Traditional rainfed fig orchards intended for fresh consumption tend to have low yields and cultural practices difficulties due to wide plant spacing and large canopies. This study investigates whether the espalier training system, commonly employed in other fruit species, can be applied to fig cultivation to improve productivity and fruit quality under high-density irrigated plantations. For the first time, four fig varieties (‘San Antonio’, ‘Dalmatie’, ‘Albacor’, and ‘De Rey’) were evaluated in a high-density system (625 trees/ha) using espalier training over four consecutive years (2018–2021) in southwestern Spain. Among the varieties, ‘Dalmatie’ demonstrated the highest suitability to the system, combining low vegetative vigour with superior yield performance, reaching a cumulative yield of 103.15 kg/tree and yield efficiency of 1.94 kg/cm2. ‘San Antonio’ was the earliest to ripen and exhibited the longest harvest duration (81 days), enabling early and extended market availability. In terms of fruit quality, ‘Albacor’ stood out for its high total soluble solids content (24.97 °Brix), while ‘De Rey’ exhibited the best sugar–acid balance, with a maturity index of 384.58. The present work demonstrates that intensive fig cultivation on espalier structures offers an innovative alternative to traditional systems, thereby enhancing orchard efficiency, management, and fruit quality.

Graphical Abstract

1. Introduction

The fig tree (Ficus carica L.) belongs to the Moraceae family and encompasses about 1400 species grouped into 40 genera [1]. The fig is a fruit widely accepted by consumers due to its high health benefits and favourable organoleptic characteristics. It is rich in nutrients and biological compounds, which convert it into a new functional food [2].
At the global level, fig production has increased over the past decade, reaching a maximum of 1,304,849 tons [3]. Spain, accounting for 3% of global production, ranks 8th with a production of 39,650 tons and 69,737 ha cultivated, being the leading producer within the European Union, with 58% of its national production concentrated in the southwest, in the Autonomous Community of Extremadura [4].
Traditionally, in this region, fig trees have been cultivated under rainfed conditions, using wide planting spacing for 8 × 8 m or 10 × 10 m and large-canopied trees [5], resulting in limited yields primarily destined for dried fig production. In recent years, however, the need to diversify fruit production, combined with growing domestic and international demand for figs, has led to the introduction of new cultivation systems characterized by more intensive planting densities and supported by irrigation [6,7]. Consequently, the adoption of these techniques has led to an increase in plantations dedicated to the production of fresh figs [8]. Currently, more than 4000 ha of fig trees in Extremadura (Spain) are irrigated, with new plantations typically established under high-density (~500 plants/ha) [8], super high-density (~1000 plants/ha) [6], or even greenhouse production systems in other countries [9]. In intensive fresh fig production, planting distances of 5 × 4 m or 5 × 5 m are used, with trees trained into a vase shape with three or four main branches [10]. In this system, figs are manually harvested at optimal ripeness directly from the tree; therefore, the trees are trained from 50 cm above the ground and maintained at a limited height of approximately 2.20 m to ensure accessibility for pickers [11]. The super high-density system used in fig tree cultivation, with planting distances of 5 × 2 m or 3 × 2.5 m, allows for a higher number of trees per hectare and enables two possible training systems: hedgerow and espalier.
One of the espalier training methods is the horizontal arm palmette, also known as the Ferraguti palmette. In this system, trees are arranged along a vertical axis from which multiple primary branches are trained on steel wires, creating ‘levels’ at different heights. Ultimately, the trees end up joining together, forming a vegetative wall [12]. This training method has been successfully implemented in other fruit trees, such as the pear tree [13] and mango tree [14], due to the advantages it offers of reduced crop costs and a lower application of phytosanitary products, as well as better insolation and aeration of the canopy [12]. An interesting feature of espalier is the possibility of manipulating leaf distribution and enhancing light penetration within the canopy, improving the efficient use of available light resources [15].
The implementation of the espalier system entails higher initial costs, mainly due to the need for posts, wires, and specialised labour for structural formation, which may pose challenges for growers and generate doubts about these systems, which differ from conventional ones [15]. Nevertheless, these investments are increasingly justified by their long-term agronomic and economic advantages. In fruit trees, 2D training systems offer a clear series of benefits, like the lower proportion of structural wood in favour of fruiting organs, fruits positioned closer to the trunk (ensuring better water and nutrient supply), improved productivity, earlier entry into production, and higher-quality fruits, which can lead to better market prices [16,17]. These gains lead to a reduction in production costs, harvesting time, and the mechanization of certain agricultural labours [17]. Regarding pest management, the espalier system facilitates the early detection and uniform application of agrochemicals [18].
The use of espalier training systems in fig cultivation presents a promising alternative for enhancing fresh fig production. Although no information is currently available regarding its application in this species, evidence from other fruit species suggests potential benefits. In mango, high-density espalier systems have led to substantial yield improvements ~50,000 kg/ha by increasing light interception and optimising canopy volume compared to conventional low-density orchards [15]. Similarly, in apples, the use of espalier has resulted in greater fruit weight, higher yield per tree, and improved yield efficiency, attributed to better light distribution in the canopy [19]. By enhancing canopy structure, espalier training could improve productivity, facilitate fruit accessibility, and contribute to the production of higher-quality figs compared to the traditional vase-shaped system.
In this context, the main objective of the present study is to assess the agronomic performance and fruit quality of four fig varieties cultivated under an intensive espalier training system in southwestern Spain. For fig producers, this approach offers a practical way to modernise the crop, maximise production, reduce labour dependency, and meet the quality requirements demanded by fresh fruit markets.

2. Materials and Methods

2.1. Plant Material

Four fig tree varieties (Ficus carica L.), De Rey, Dalmatie, Albacor, and San Antonio, were used in this study. The plant material was propagated from cuttings obtained from the national germplasm bank of fig trees located at the “Finca La Orden–Valdesequera” research centre, part of the Scientific and Technological Research Centre of Extremadura (CICYTEX) (Government of Extremadura, Spain) (latitude 38°85′19″ N, longitude −6°68′28″ W, Guadajira, Badajoz, Spain). These varieties were selected based on their high commercial value, postharvest suitability for fresh consumption, capacity to cover the entire maturity calendar, and differences in fruit morphology [20]. In addition, they exhibit distinct different architectural and canopy characteristics. According to López-Corrales et al. [20], ‘Albacor’ and ‘San Antonio’ present medium vigour and branching density; ‘De Rey’ combines medium vigour with low branching density; and ‘Dalmatie’ shows low vigour with moderate branching density.
The field trial was established in 2016 following a randomized complete block design with three blocks (Figure 1). Each block included three single-tree replications per variety. Trees were planted at a spacing of 4 × 4 m, achieving a planting density of 625 trees/ha, with rows oriented from north–south to maximize sunlight exposure. This planting distance was chosen to suit the espalier structure, allowing a 2 m horizontal arm extension between trees and ensuring 4 m between rows for machinery access during orchard management and harvesting operations, considering that annual shoot growth is approximately 1 m on each side. The orchard was located at an altitude of 190 m above sea level and was equipped with a drip irrigation system providing an annual volume of 3000 m3/ha, applied between June and September. The experimental field receives an average annual precipitation of 448 mm, with mean temperatures ranging from 16 to 22 °C, average minimum temperatures fluctuating between 6 and 9 °C, and average maximum temperatures reaching 23 to 25 °C [21]. According to the USDA Soil Taxonomy, the soil type is classified as loam to clay-loam, belonging to the Alfisol Order, with high agricultural productivity. The soil pH is at 6.8. The main crop was harvested at the commercial ripening stage and immediately transported to the laboratory for further analysis. Vegetative and fruit quality traits were evaluated over four consecutive years (2018–2021).

2.2. Evaluation of Agronomic Parameters

Before establishing the trial, posts and wires for the training system were installed; along the planting line, 2.5 m wooden posts were placed, with 12 m between posts. Three rows of wires were fixed to these posts at 75 cm, 150 cm, and 200 cm above the ground. Training pruning began during the winter, with the fig trees being topped at the height of the first wire (75 cm). After budbreak, three branches were selected: two were guided horizontally by bending them to a 90° angle, while the third branch remained vertical (Figure 2). This process was repeated for the upper levels, except that the apical growth of the third level was removed, forming an espalier with horizontal arms.
Production pruning consisted of removing all shoots that did not grow vertically above the horizontal branches and cutting them back to 2 or 3 buds. In cases where branches lacked shoots, cuts were made close to the base of the buds to encourage sprouting [12]. Cuts were made perpendicular to the branch axis to allow the sap to drip and facilitate wound closure. Finally, the topping was performed by selecting the best-positioned buds for vertical growth, with copper-based products applied to the cuts to prevent fungal attacks. Other techniques employed included painting the trunks, with water-based plastic paint containing copper, up to the first wire to protect the bark from solar radiation, as fig wood is soft and highly sensitive to sun damage. Additionally, nets were also placed over the posts during the harvest period to deter bird attacks, such as from the European starling (Sturnus unicolor Temminck) and the azure-winged magpie (Cyanopica cooki Bonaparte) (Figure 3).
Harvesting was conducted manually with scissors from late July to mid-October, depending on the variety. Specifically, figs were harvested by variety and block three times a week during the first month and then twice a week until the end of the harvest period.

2.2.1. Yield, Ripening Period, and Cumulative Yield

Yield was measured by weighing the total production for each variety, year, and block. Results were expressed in kg/ tree. Along with this parameter, the start and end dates of harvesting were determined to establish the ripening period for each variety.
The cumulative yield (kg/tree) was obtained by summing the annual productions (kg/tree) over the studied period (2018–2021). This parameter indicates the total production in kg/tree from the onset of production until the final year of evaluation.

2.2.2. Trunk Cross-Section Area and Yield Efficiency

The trunk cross-sectional area (TCSA), related to vegetative growth, was measured with a measuring tape 20 cm above the ground. To ensure greater accuracy, measurements were taken in February 2021, coinciding with winter dormancy period. The TCSA (cm2) was calculated using the following formula: TCSA = P2/4π, where P is the trunk perimeter. This value was based on the average perimeter of the 9 trees corresponding to the three replications per cultivar.
Yield efficiency (kg/cm2) was determined by correlating cumulative yield (kg/tree) and TCSA (cm2). A higher value indicates greater production per unit of trunk surface area.

2.3. Evaluation of Quality Parameters

The figs were hand-harvested two to three times per week throughout the fruiting season at the commercial maturity stage based on skin colour and firmness. On each harvest date, ten fruits were randomly selected per block from the three trees within the block, resulting in a total of 30 fruits per variety per harvest. Fruit selection was based on uniform external appearance, appropriate ripeness, and absence of visible defects in order to reflect the commercial quality of the overall batch. Approximately 25 harvests were conducted per season, yielding around 750 fruits per variety, evaluated each year. These samples were used to determine the physicochemical quality parameters over four years (2018–2021). Subsequently, the following physicochemical parameters were determined:

2.3.1. Fruit Weight and Width

Fruit weight (g) was determined using KERN 572-39 precision balance (Kern & Sohn GmbH, Balingen, Germany), while fruit width (mm) was measured using a Mitutoyo CD-15DAXR digital micrometer (Mitutoyo Corporation, Kawasaki, Japan).

2.3.2. Firmness

Firmness was measured on both sides of the 10 fruits per block and harvest using a TA. XT2i Texture Analyzer (Stable Micro Systems, Godalming, UK) connected to a computer. Force was applied to achieve 6% deformation, with the force distance set at 40 mm. The slope was determined in the linear region of the force–deformation curve and the results were expressed in N/mm.

2.3.3. Colour Measurement

The following colour coordinates were determined: lightness (L*), redness (a*, red ± green) and yellowness (b*, yellow ± blue) using a CM600D tristimulus colorimeter (Minolta, Tokyo, Japan). Additionally, the chroma or saturation index (C*), calculated as C = √ (a*2 + b*2), and hue angle (h*), calculated as h* = arctangent (b*/a*), were determined on the skin of 10 fruits on opposite sides for each block and harvest.

2.3.4. Total Soluble Solids

The total soluble solids (TSS) content was measured for each block and harvest date using an RM40 digital refractometer (Mettler Toledo, Madrid, Spain). For this purpose, 10 fruits were homogenized, and three measures were taken. Results are expressed in °Brix.

2.3.5. Titratable Acidity, pH and Maturation Index

Titratable acidity (TA), expressed as grams of citric acid per 100 g of fruit, and pH were determined using the Mettler Toledo T50 automatic titrator (Mettler Toledo, Greifensee, Switzerland). For each cultivar, three samples of 5 g of homogenate were dissolved in 50 mL of distilled water. An acid-base titration was subsequently performed to calculate the volume of sodium hydroxide required to neutralize the fruit’s acidity and reach a pH of 8.1.
The maturation index (MI) was calculated as the ratio between TSS and TA (MI = TSS/TA).

2.3.6. Statistical Analysis

The statistical analysis was conducted using SPSS software, version 21.0 (IBM Corp., Armonk, NY, USA). A two-way analysis of variance (ANOVA) was performed to evaluate the effects of year, variety, and their interaction (year × variety) on yield components and fruit quality traits. For each dependent variable, partial eta squared (η2) was reported as a measure of effect size. When significant differences were detected (p < 0.05), mean comparisons were performed using Tukey’s HSD post-hoc test. In cases of marginal significance, additional post-hoc tests were applied to clarify group separations.

3. Results and Discussion

3.1. Agronomic Parameters

3.1.1. Yield and Ripening Period

The ripening calendar for the varieties studied during the period from 2018 to 2021 was established to range from 23 July to 11 October (Figure 4). The earliest variety was ‘San Antonio’, with harvests beginning in the second half of July, followed by ‘Dalmatie’, ‘Albacor’, and ‘De Rey’. ‘San Antonio’ also had the widest harvest period, with an average duration of 81 days, compared to ‘De Rey’, which only lasted 60 days. All varieties finished maturing their figs in the second week of October, coinciding with the onset of lower autumn temperatures, and the first autumn rainfall started that degrade the fig quality.
If we compare this calendar with the one obtained by Pereira [22] for fig trees cultivated intensively and using the vase training system, the varieties San Antonio, De Rey, and Albacor begin fig ripening later. This could be explained by the high intensity of pruning the trees undergo to complete their training on the espalier much higher than with the vase system, which delays the start of maturation since it requires more time for new shoots after the winter break [12]. In the case of ‘Dalmatie’, a variety with limited vigour, the level of pruning for the espaliers system was lower than in the other varieties studied, so the start of fig maturation was similar to that of the vase training system. In Japan, for the ‘Masui Dauphine’ fig variety established in a straight-line training with short pruning, they also observed a delay in fig maturation along with discoloration of the figs [23]. To solve these issues, the authors developed a new pruning method (renewal long pruning), whose long pruning allowed for early sprouting of the shoots and a tendency for early ripening of the figs. Since the first figs are highly valued by consumers and fetch higher prices in the markets, this espalier training system requires a mixed branch pruning technique for the fig trees. The weaker shoots (between 0.5 cm and 1 cm in diameter) will be left unpruned, while the more vigorous ones (over 1 cm in diameter) will be pruned to 2–3 buds. The first will produce the earliest figs, while the latter will yield the later figs due to a delay in bud sprouting.
The two-way ANOVA (Table 1) revealed that the year, variety, and the interaction between both (year × variety) had statistically significant effects (p < 0.05) on nearly all the measured parameters, with varying degrees of effect sizes (partial η2).
As shown in Table 1, annual yield was strongly influenced by both the year and the variety. However, the year effect was slightly greater, which can be explained by the fact that the varieties were evaluated during the first years of establishment of the orchard (2nd to 5th green) when annual yields tend to increase progressively as the trees reach their full production capacity (Figure 5). In addition, fig yield and quality are highly sensitive to edaphoclimatic conditions, particularly those related to climate and soil. The highest quality figs are usually produced in the Mediterranean and other dry warm, temperate climates, where temperature and low humidity favour ripening and sugar accumulation. Although fig trees are tolerant to a wide range of soil types, including heavy clays, loams, and sandy soils, they require well-drained topsoil to perform optimally [24]. In this study, all experimental plots were randomly planted in soils with similar characteristics and exposed to identical climatic conditions to minimise the influence of these factors.
With respect to yield results (Figure 5), all varieties begun fig production in the second year (2018), with significant differences between varieties observed from the third year onwards (2019).
For the studied period 2018–2021, ‘Dalmatie’ achieved the highest values in the fifth green (2021) (45.75 ± 8.18 kg/tree), showing significant differences compared to the other three varieties. ‘Albacor’ (27.53 ± 4.58 kg/tree) and ‘San Antonio’ (21.21 ± 6.34 kg/tree) had similar values, while ‘De Rey’ was the least productive in the fifth green (2021) (14.21 ± 1.13 kg/tree).
Compared to the values obtained by Pereira [22], with a planting distance of 5 × 4 m and a density of 500 plants/ha, ‘Albacor’ (synonym of ‘Colar Elche’) achieved a higher production in kg/tree in its fifth green (2021), while those of ‘San Antonio’ and ‘De Rey’ were lower. However, when extrapolating the production from kg/tree to kg/ha, all three varieties grown on espalier systems showed higher values due to the greater planting density (625 trees/ha). These differences were especially pronounced in ‘Albacor,’ with a difference of about 5600 kg/ha in the fifth green (2021) between the espalier training system and the vase system. Specifically, these annual productions obtained in the fifth green (2021) on espalier represent average fig yields of around 13,200 kg/ha for ‘San Antonio’, 28,600 kg/ha for ‘Dalmatie,’ approximately 8900 kg/ha for ‘De Rey’, and about 17,200 kg/ha for ‘Albacor.’ These increases in annual yield obtained in the espalier system could justify the additional cost associated with the higher number of plants per hectare, as well as the posts and wires required for setting up the plantation.

3.1.2. Cumulative Yield, Trunk Cross-Section Area, and Yield Efficiency

Regarding the cumulative yield (kg/tree) for the study period 2018–2021 (Figure 6), ‘Dalmatie’ was the variety that achieved significantly the highest values (103.16 ± 12.95 kg/tree), followed by ‘Albacor’ (62.15 ± 4.14 kg/tree), while ‘San Antonio’ (49.36 ± 10.40 kg/tree) and ‘De Rey’ (34.48 ± 3.14 kg/tree) had lower values. These productions were much higher (with a difference of 10 tons) compared to those obtained by Pereira et al. [8] in the fifth green for ‘Albacor,’ with trees grown in vase training on intensive systems (500 trees/ha). In contrast, for ‘San Antonio,’ the cumulative yield in the fifth green harvest in vase training was about 5 tons higher.
Chithiraichelvan et al. [25], in their study with different tree spacing distances for the Indian varieties Poona and Deanna in a vase training system, concluded that the higher the tree density per hectare, the higher the cumulative yield. These results would validate the espalier training system as an alternative to achieving higher productions in the varieties Dalmatie and Albacor.
TCSA or increased TCSA is widely used as an allometric indicator of overall tree vigour in fruit crops [26], but it may not fully capture the complexity of vegetative growth when used in isolation [27]. Tree vigour is a multifactorial trait influenced not only by genotype and environmental factors (such as soil and climate) but also by management practices and training systems. For example, in apple orchards, Nesme et al. [28] proposed combining TCSA with additional morphological variables, such as the number of water sprouts on the trunk and the length of annual shoots at the base of fruiting branches, to better classify orchard vigour. In this study, based on the results obtained from the TCSA ‘Albacor’ and ‘De Rey’ were the most vigorous, with very similar values, followed closely by ‘San Antonio’ with 72.05 ± 3.19 cm2, and finally, ‘Dalmatie’ with 53.05 ± 14.14 cm2, respectively. For the espalier training system, ‘Dalmatie’, due to its lower vigour, would be the variety best suited to its requirements, as it allows for better coexistence among a higher number of trees per hectare. On the other hand, in the fifth green harvest, all varieties, except for ‘Dalmatie,’ have occupied all available space in the rows, so that variety could be established in a more intensive tree spacing, such as 4 × 3 m.
The yield efficiency (kg/cm2) was notably high in Dalmatie (1.94 ± 0.25 kg/cm2), followed by ‘Albacor’ (0.83 ± 0.11 kg/cm2) and ‘San Antonio’ (0.69 ± 0.14 kg/cm2) with similar values, although ‘De Rey’ had the lowest value (0.46 ± 0.07 kg/cm2). Therefore, ‘Dalmatie’ can be considered the most productive variety for figs (Figure 6).

3.2. Quality Parameters

3.2.1. Weight, Width, and Firmness

The quality parameters, such as weight, firmness, skin colour, TSS, and TA were affected by the variety, ripening stage, and the interaction between them. During the study period (2018–2021), Dalmatie was the variety with the highest average weight (110.65 ± 15.79 g) in its fifth green (2021), followed by ‘Albacor’ (65.09 ± 7.73 g), while ‘De Rey’ and ‘San Antonio’ exhibited lower weight of 49.44 ± 5.37 g and 46.99 ± 8.16 g, respectively (Figure 7). This parameter evolved similarly across different greens for ‘Dalmatie’ and ‘De Rey’, which showed significantly higher weights in the fifth green than earlier greens. The results for ‘Dalmatie’ are comparable to those obtained by Şimșek [29] for the ‘KZTP-32222’ accession cultivated in the Mardin region (Turkey). Compared with the results obtained by Pereira et al. [8] in fig trees cultivated in a vase system training, the average weight of the ‘San Antonio’ variety was lower (54 g versus 46.99 ± 8.16 g). Furthermore, the results for Albacor’ were superior (49.6 g versus 65.09 ± 7.73 g). In general, the average weight obtained can be influenced by other factors, such as the number of fruits per shoot and their size. Larger shoots with a higher number of fruits tend to produce smaller fruits [30].
The calibre, or fruit width (mm), reached the highest value in ‘Dalmatie’ (58.23 ± 3.61 mm), with ‘San Antonio’ being the variety with the greatest homogeneity, showing similar values across the four greens during the study period (Figure 7). Compared with the results obtained by Küden et al. [31], with 22 accessions from Mediterranean basin and Southeastern Antolian (Turkey) with a tree spacing distance of 5 × 4 m, ‘Albacor’ shows similar values to ‘63 IN 14’ and ‘San Antonio’ shows similar values to ‘63 IN 15’. The values for ‘Dalmatie’ are close to those obtained by Şimșek [29] for ‘47-00-6’ accession, also in Southeastern Anatolia. Fruit size, particularly weight and width, represents a critical quality attribute in the commercial valuation of fresh figs, as larger fruits are generally associated with higher economic returns for producers. In addition, uniformity in these traits facilitates the standardization of postharvest handling and the optimization of packaging formats. In the context of the Spanish fresh fig market, consumer demand typically favours fruits with calibres ranging from 30 to 36 mm, marketed in boxes containing approximately 2.5 to 3.0 kg, with strict requirements for homogeneity in size, colour, and the absence of external defects [32].
Firmness (N/mm) is an indispensable parameter in preserving fresh figs. It has also been studied as a reference for detecting internal damages produced during commercialization and transport [33]. Their average values oscillated without significant differences, ranging from 1.73 ± 1.73 N/mm in ‘Dalmatie’ to 0.97 ± 0.19 N/mm in ‘San Antonio’, respectively. The only year with significant differences was 2020, when ‘Dalmatie’ (0.94 ± 0.16 N/mm) and ‘San Antonio’ (1.12 ± 0.38 N/mm) showed lower values than ‘Albacor’ (1.49 ± 0.35 N/mm) and ‘De Rey’ (1.49 ± 0.33 N/mm). This variation can be attributed to factors such as exceptional climatic conditions, including two heatwaves in the summer months [34] or the variation in ripening stages during the harvest in the same season. There is no significant difference between the different years for the same variety. The values are similar to those obtained by Pereira et al. [32] for ‘San Antonio’, ‘Colar Elche’ (synonym of ‘Albacor’) and for ‘Mission’, a synonym in California [35].

3.2.2. Colour

The skin and flesh colour are intrinsic to the variety (Figure 8). Although there is a considerable variability in these traits [36], they can be influenced by other factors, such as temperature [37], ripening state, and caprification [22]. For the skin colour, L* values ranged between 30.05 and 63.89, C* values between 3.18 and 51.91, and h* values between 30.01 and 254.43 with significant differences between most varieties and studied seasons (Figure 8a). ‘De Rey’, ‘San Antonio’, and ‘Albacor’ showed L*, C*, and h* values associated with a purple colour, with a darker tone angle for ‘Albacor’ (with h* values ranging from 166.51 to 254.43, making it closer to blue).
The L*, C*, and h* values in ‘San Antonio’, and ‘Albacor’ are consistent with those obtained by Pereira et al. [8]. ‘Dalmatie’, with a green–yellow colour, showed the greatest luminosity (L*), with values ranging from 59.06 to 63.89, similar to the values obtained by Gozlecki [38] for A112, A24, and A67 accessions in Alanya (Turkey). L* is crucial at a commercial level, according to a survey conducted by Knezović et al. [39], as it affects customer satisfaction and the likelihood of repeat purchases [40].
Regarding flesh colour, (Figure 8b), the values fluctuate between 43.38 and 54.12 for L*, 21.21 and 28.58 for C*, and 38.24 and 65.07 for h*. These figures correspond to an amber colour in ‘San Antonio’, reddish amber in ‘De Rey’ (h* between 0 –90, closer to 90 or yellow), pink in ‘Albacor’ (h* between 0–90, closer to 0 or red), and red colour in ‘Dalmatie’ (h between 0–90 with values closer to 0 or red). Compared to Pereira et al. [8], the results obtained for the pulp colour of ‘San Antonio’ and ‘Colar Elche’ are similar.

3.2.3. Total Soluble Solids, Titratable Acidity, pH, and Maturation Index

TSS and TA are parameters that directly influence the quality of the fruit [41,42] with an inversely proportional relationship. As several days pass after the fruit is stored, sugar levels increase while acidity levels decrease [43]. pH levels are inversely correlated with acidity levels [41]. Higher levels of TSS and MI (TSS/TA) in the fruit promote better acceptance by the consumer [42,44].
All the variables mentioned are affected by temperature, which is critical for conserving fresh figs. Low temperatures during the postharvest period slow down metabolism, delaying maturation and causing a loss of quality [45].
In the first year of study (2018), the highest total solid soluble (TSS) values were recorded, with ‘Albacor’ leading at 24.97 ± 3.13 °Brix, followed by ‘De Rey’ at 24.78 ± 1.47 °Brix. Both were significantly higher than ‘San Antonio’ (20.82 °Brix) and ‘Dalmatie’ (19.76 ± 1.47 °Brix) (Figure 9).
The Albacor and De Rey varieties are classified as having medium sugar content, while ‘San Antonio’ and ‘Dalmatie’, with values below 21 °Brix, are considered to have low sugar content. Between the years referring to the same variety, ‘Albacor’ showed lower values over time. This fact could be related to the lower initial productions during the tree training or variability in the ripening state of the analysed fruits. The results are similar to those reported by Çalișkan and Polat [46] for varieties and accessions analysed in Turkey.
On the other hand, ‘San Antonio’ and ‘Albacor’ reached higher values than those reported by Pereira et al. [22] for trees trained in an intensive vase system, with ‘Albacor’ leading at 24.7 °Brix compared to 18.6 °Brix in San Antonio. This data could be explained by the greater insolation of the fruits provided by the espalier training system. Fruits in the shade tend to have a smaller size and poorer taste quality, including lower sugar content and higher acidity [12]. Regarding apples, Musacchi and Greene [47] affirmed that sun interception and canopy distribution should be optimized to achieve the maximum high-quality fruit production.
Regarding titratable acidity (TA), the highest values were obtained for the Albacor variety (0.226 ± 0.04 g citric acid /100 g fruit) in 2019, while ‘De Rey’, showed only 0.066 ± 0.01 g citric acid/100 g fruit in 2021 (Figure 9). Significant differences were observed between varieties within the same year, and between different years for the same variety. For San Antonio and Albacor, the values were higher than those reported by Pereira et al. [8].
The maturity index (MI) was significantly higher in the De Rey variety, mainly in 2021 (384.58 ± 69.29), when it stood out compared to the other years of study. In contrast, ‘Albacor’ showed the lowest values (102.54 ± 15.31) in 2019. Although ‘San Antonio’ did not show significant differences across the years of study, it was the variety with the greatest homogeneity in its values, ranging between 221.88 ± 35.83 and 194.24 ± 32.56. The results obtained by Pereira et al. [8] were similar for the San Antonio and Albacor varieties.

4. Conclusions

All four fig varieties evaluated, Dalmatie, Albacor, San Antonio, and De Rey, exhibited strong adaptability to the espalier training system. Among them, ‘Dalmatie’ stood out by achieving the highest annual and cumulative yields, the greatest yield efficiency, and exhibiting the lowest vegetative vigour, which is advantageous in high-density plantations. ‘San Antonio’ was the earliest to ripen and had the longest harvest period, while ‘Albacor’ stood out for its superior in total soluble solids and acidity, and ‘De Rey’ achieved the highest maturity index, indicating a favourable balance between sweetness and acidity. While this study did not include a comparison with other training methods or planting densities, the findings suggest that the espalier system may provide meaningful benefits in high-density fig orchards, particularly in improving yield, fruit quality, harvesting efficiency, and orchard management. In addition, the 2D structure of tree canopies could facilitate the development of robotic harvesters based on the detection of ripe figs. Similarly, pre-pruning could be performed mechanically, followed by a small manual pass to ensure fruit quality.
Based on the results of this study, several practical recommendations can be made. The Dalmatie variety is ideal for espalier systems in intensive systems where it is essential to control vigour and obtain high productivity. For growers targeting early-season markets and aiming to lengthen the harvest period, ‘San Antonio’ appears to be a strategic choice. Finally, Albacor and De Rey are excellent options for those looking to prioritise fruit quality, thanks to their attractive profiles in terms of sugar content, acidity, and overall flavour balance.

Author Contributions

Conceptualisation, A.J.G. and M.L.-C.; methodology, F.P.-G. and M.L.-C.; validation, A.J.G. and M.L.-C.; formal analysis, M.P.-L. and A.I.G.; investigation, A.J.G., M.G.D., M.P.-L. and A.I.G.; resources, M.G.D. and M.L.-C.; data curation, M.G.D. and M.P.-L.; writing—original draft preparation, A.J.G., M.G.D., M.P.-L. and M.L.-C.; writing—review and editing, A.J.G. and M.L.-C.; visualisation, A.J.G. and M.P.-L.; supervision, F.P.-G. and M.L.-C.; project administration, M.L.-C.; funding acquisition, M.L.-C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the European Regional Development Fund (ERDF) through the Operational Programme FEDER Extremadura 2021–2027, project ADAPFRUIT, and by the project FRUCITEX, funded by the Operational Programme FEDER Extremadura 2014–2020.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Watson, L.; Dallwitz, M. The Families of Flowering Plants: Descriptions, Illustrations, Identification, and Information Retrieval. 1992. Available online: https://www.scirp.org/reference/referencespapers?referenceid=2844233 (accessed on 3 May 2025).
  2. Yang, Q.; Liu, Y.; Guo, Y.; Jiang, Y.; Wen, L.; Yang, B. New Insights of Fig (Ficus carica L.) as a Potential Function Food. Trends Food Sci. Technol. 2023, 140, 104146. [Google Scholar] [CrossRef]
  3. FAOSTAT. Available online: https://www.fao.org/faostat/es/#home (accessed on 3 May 2025).
  4. Superficies y Producciones Anuales de Cultivos. Available online: https://www.mapa.gob.es/es/estadistica/temas/estadisticas-agrarias/agricultura/superficies-producciones-anuales-cultivos/ (accessed on 3 May 2025).
  5. López-Corrales, M.; Pérez, F.; Manuel, G.; Serradilla, M.J.; Pereira, C. Estructura Varietal Del Cultivo Del Cultivo de La Higuera En Extremadura. In La Agricultura y la Ganadería Extremeñas en 2021; Coleto, J.M., Muslera, E., Pulido, F., Eds.; Caja Badajoz: Badajoz, Spain, 2012; pp. 121–130. [Google Scholar]
  6. Galván, A.I.; Serradilla, M.J.; Córdoba, M.G.; Domínguez, G.; Galán, A.J.; López-Corrales, M. Implementation of Super High-Density Systems and Suspended Harvesting Meshes for Dried Fig Production: Effects on Agronomic Behaviour and Fruit Quality. Sci. Hortic. 2021, 281, 109918. [Google Scholar] [CrossRef]
  7. Galán, A.J.; Martín, A.; Torres-Vila, L.M.; Ruiz-Moyano, S.; Galván, A.I.; Serradilla, M.J.; López-Corrales, M. Quantification and Identification of Damage Caused by Pests and Fungi in Dried Figs from Orchards with Different Levels of Agronomic Management in the Main Production Areas of Extremadura (SW Spain). Crop. Prot. 2023, 172, 106334. [Google Scholar] [CrossRef]
  8. Pereira, C.; Serradilla, M.J.; Martín, A.; Villalobos, M.d.C.; Pérez-Gragera, F.; López-Corrales, M. Agronomic Behaviour and Quality of Six Fig Cultivars for Fresh Consumption. Sci. Hortic. 2015, 185, 121–128. [Google Scholar] [CrossRef]
  9. Batlles-delaFuente, A.; Belmonte-Ureña, L.J.; Duque-Acevedo, M.; Camacho-Ferre, F. A Profitable Alternative for the Spanish Southeast: The Case of Production of Figs in Greenhouses. Agronomy 2022, 12, 2577. [Google Scholar] [CrossRef]
  10. Jafari, M.; López-Corrales, M.; Galán, A.J.; Galván, A.I.; Hosomi, A.; Nogata, H.; Ikegami, H.; Balas, F.; Serradilla, M.J.; Garza-Alonso, C.A.; et al. Orchard Establishment and Management. In The fig: Botany, Production and Uses; Sarkhosh, A., Yavari, A., Ferguson, L., Eds.; CABI: Wallingford, UK, 2022; pp. 184–230. [Google Scholar]
  11. Maia De Sousa, R.M. Fig Culture Techniques. Acta Hortic. 2003, 605, 99–101. [Google Scholar] [CrossRef]
  12. Urbina Vallejo, V. Mòdul 10. Monografías de Fruticultura—N.o 11. Poda y Formación de Los Frutales; Urbina Vallejo, V., Ed.; Escuela Técnica Superior de Ingeniería Agraria, Universidad de Lleida: LLeida, Spain, 2017; ISBN 978-84-697-7927-9. [Google Scholar]
  13. Musacchi, S.; Iglesias, I.; Neri, D. Training Systems and Sustainable Orchard Management for European Pear (Pyrus communis L.) in the Mediterranean Area: A Review. Agronomy 2021, 11, 1765. [Google Scholar] [CrossRef]
  14. Ibell, P.T.; Normand, F.; Wright, C.L.; Mahmud, K.; Bally, I.S.E. The Effects of Planting Density, Training System and Cultivar on Vegetative Growth and Fruit Production in Young Mango (Mangifera indica L.) Trees. Horticulturae 2024, 10, 937. [Google Scholar] [CrossRef]
  15. Mahmud, K.P.; Ibell, P.T.; Wright, C.L.; Monks, D.; Bally, I. High-Density Espalier Trained Mangoes Make Better Use of Light. Agronomy 2023, 13, 2557. [Google Scholar] [CrossRef]
  16. Iglesias, I. Costes de Producción, Sistemas de Formación y Mecanización En Frutales, Con Especial Referencia al Melocotonero. Rev. Frutic. 2019, 69, 50–59. [Google Scholar]
  17. Iglesias, I. Sistemas de Plantación En Frutales Adaptados a La Mecanización, Eficiencia y Efectos En Los Costes de Producción. In Proceedings of the 7° Congreso Internacional de Frutos Rojos, Huelva, Spain, 22–23 June 2022. [Google Scholar]
  18. Schöneberg, T.; English, L.A.; Popp, J.; Hamby, K.A. Impact of Modified Caneberry Trellis Systems on Microclimate and Habitat Suitability for Drosophila suzukii (Diptera: Drosophilidae). J. Econ. Entomol. 2022, 115, 943–954. [Google Scholar] [CrossRef] [PubMed]
  19. Mir, J.I.; Ahmed, N.; Singh, D.B.; Sharma, O.C.; Raja, W.H.; Shafi, W.; Zaffer, S.; Jan, S.; Kirmani, S.N. Canopy Management to Improve Fruit Quality of Coe Red Fuji, Granny Smith and Spartan Varieties of Apple (malus Domestica). Indian J. Agric. Sci. 2020, 90, 1397–1401. [Google Scholar] [CrossRef]
  20. López-Corrales, M.; Gil-Torralvo, M.; Pérez-Gragera, F.; Cortés-Prieto, J.; Serradilla-Sánchez, M.; Chomé-Fuster, P.M. Variedades de Higuera: Descripción y Registro de Variedades; Ministerio de Medio Ambiente, Medio Rural y Marino: Madrid, Spain, 2011. [Google Scholar]
  21. Red de Asesoramiento Al Regante De Extremadura. (REDAREX). Available online: https://redarexplus.juntaex.es/RedarexPlus/ (accessed on 3 May 2025).
  22. Pereira, C. Comportamiento Agronómico y Estudio Del Punto Óptimo de Maduración Nutricional y Funcional de Variedades de Higuera Interesantes Para Consumo Fresco. Available online: http://hdl.handle.net/10662/4702 (accessed on 3 May 2025).
  23. Hosomi, A.; Isobe, T.; Miwa, Y. Shoot Growth and Fruit Production of the “Masui Dauphine” Variety of Fig (Ficus carica L.) Undergoing Renewal Long Pruning. JARQ 2015, 49, 391–397. [Google Scholar] [CrossRef]
  24. Polat, A.A.; Sarkhosh, A. Environmental Requirements and Site Selection. In The Fig: Botany, Production and Uses; Sarkhosh, A., Yavari, A., Ferguson, L., Eds.; CABI: Wallingford, UK, 2022; pp. 171–183. ISBN 978-1-78924-288-1. [Google Scholar]
  25. Chithiraichelvan, R.; Kurian, R.M.; Awachare, C.M.; Laxman, R.H. Performance of Fig (Ficus carica L.) Under Different Planting Densities. Int. J. Curr. Microbiol. Appl. Sci. 2017, 6, 2603–2610. [Google Scholar] [CrossRef]
  26. Pearce, S.C. Studies in the Measurement of Apple Trees. I. The Use of Trunk Girths to Estimate Tree Size. Annu. Rep. East Malling Res. Stn. 1951 1952, A35, 101–107. [Google Scholar]
  27. Basile, B.; DeJong, T.M. Control of Fruit Tree Vigor Induced by Dwarfing Rootstocks. In Horticultural Reviews; Warrington, I., Ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2018; Volume 46, pp. 39–97. ISBN 9781119521082. [Google Scholar]
  28. Nesme, T.; Plenet, D.; Hucbourg, B.; Fandos, G.; Lauri, P.E. A Set of Vegetative Morphological Variables to Objectively Estimate Apple (Malus × Domestica) Tree Orchard Vigour. Sci. Hortic. 2005, 106, 76–90. [Google Scholar] [CrossRef]
  29. Şimşek, M. Fruit Performances of the Selected Fig Types in Turkey. Afr. J. Agric. Res. 2009, 4, 1260–1267. [Google Scholar]
  30. Hosomi, A. Relationship Between Shoot Growth and Fruit Production of “Masui Dauphine” and “Houraishi” Fig Trees at Various Plant Densities. Hort. J. 2020, 89, 375–383. [Google Scholar] [CrossRef]
  31. Küden, A.B.; Bayazit, S.; Çömlekcioglu, S. Morphological and Pomological Characteristics of Fig Genotypes Selected from Mediterranean and South East Anatolia Regions. Acta Hortic. 2008, 798, 95–102. [Google Scholar] [CrossRef]
  32. Pereira, C.; Corrales, M.L.; Martín, A.; Del, M.; Villalobos, C.; De Guía Córdoba, M.; Serradilla, M.J. Physicochemical and Nutritional Characterization of Brebas for Fresh Consumption from Nine Fig Varieties (Ficus carica L.) Grown in Extremadura (Spain). J. Food Qual. 2017, 2017, 6302109. [Google Scholar] [CrossRef]
  33. Çakmak, B.; Alayunt, F.N.; Akdeniz, R.C.; Aksoy, U.; Can, H.Z. Assessment of the Quality Losses of Fresh Fig Fruits during Transportation. J. Agric. Sci. 2010, 16, 180–193. [Google Scholar]
  34. Agencia Estatal de Meteorología (AEMET) Las Olas de Calor Se Han Duplicado En La Última Década. Available online: https://www.aemet.es/es/noticias/2020/09/olas_de_calor_duplicadas_esta_ultima_decada (accessed on 3 May 2025).
  35. Crisosto, C.H.; Bremer, V.; Ferguson, L.; Crisosto, G.M. Evaluating Quality Attributes of Four Fresh Fig (Ficus carica L.) Cultivars Harvested at Two Maturity Stages. HortScience 2010, 45, 707–710. [Google Scholar] [CrossRef]
  36. Ercisli, S. A Short Review of the Fruit Germplasm Resources of Turkey. Genet. Resour. Crop Evol. 2004, 51, 419–435. [Google Scholar] [CrossRef]
  37. Lin-Wang, K.; Micheletti, D.; Palmer, J.; Volz, R.; Lozano, L.; Espley, R.; Hellens, R.P.; Chagnè, D.; Rowan, D.D.; Troggio, M.; et al. High Temperature Reduces Apple Fruit Colour via Modulation of the Anthocyanin Regulatory Complex. Plant Cell Environ. 2011, 34, 1176–1190. [Google Scholar] [CrossRef] [PubMed]
  38. Gozlekci, S. Pomological Traits Of Fig (Ficus carica L.) Genotypes Collected In The West Mediterranean Region In Turkey. J. Anim. Plant Sci. 2011, 21, 646–652. [Google Scholar]
  39. Knezović, Z.; Šaravanja, P.; Mićić, N. Survey on Customer Satisfaction with Fresh and Dried Figs Supply in Mostar. Agro-Knowl. J. 2017, 18, 47–56. [Google Scholar] [CrossRef]
  40. Donovan, R.J.; Rossiter, J.R.; Marcoolyn, G.; Nesdale, A. Store Atmosphere and Purchasing Behavior. J. Retail. 1994, 70, 283–294. [Google Scholar] [CrossRef]
  41. Polat, A.A.; Çalışkan, O. Correlations among Important Fruit Quality and Plant Characteristics of Some Fig Genotypes. J. Life Sci. 2017, 11, 141–144. [Google Scholar] [CrossRef]
  42. Crisosto, H.; Ferguson, L.; Bremer, V.; Stover, E.; Colelli, G. 7—Fig (Ficus carica L.). In Postharvest Biology and Technology of Tropical and Subtropical Fruits; Woodhead Publishing: Cambridge, UK, 2011; pp. 134–158, 159e–160e. [Google Scholar] [CrossRef]
  43. Ingrassia, M.; Chironi, S.; Allegra, A.; Sortino, G. Consumer Preferences for Fig Fruit (Ficus carica L.) Quality Attributes and Postharvest Storage at Low Temperature by in-Store Survey and Focus Group. Acta Hortic. 2017, 1173, 383–388. [Google Scholar] [CrossRef]
  44. Bremer, V.; Norton, M.; Einhorn, T.; Crisosto, C. Use of Preharvest Ethephon Applications to Reduce Breba Crop Load. In Proceedings of the 35th Annual Meeting of the Plant Growth Regulation Society of America, San Francisco, CA, USA, 3–7 August 2008; Fidelibus, M., Ed.; Plant Growth Regulation Society of America: Asheville, NC, USA, 2008. [Google Scholar]
  45. Ramezanian, A.; Amiri, A.; Crisosto, C.H. Postharvest Physiology and Physiological Disorders of Fresh Fig Fruits. In The Fig: Botany, Production and Uses; Sarkhosh, A., Yavari, A., Ferguson, L., Eds.; CABI: Wallingford, UK, 2022; pp. 384–397. [Google Scholar]
  46. Çalişkan, O.; Polat, A.A. Fruit Characteristics of Fig Cultivars and Genotypes Grown in Turkey. Sci. Hortic. 2008, 115, 360–367. [Google Scholar] [CrossRef]
  47. Musacchi, S. Innovations in Apple Tree Cultivation to Manage Crop Load and Ripening Duane Greene, University of Massachusetts, USA. In Achieving Sustainable Cultivation of Apples; Evans, K., Ed.; Burleigh Dodds Science Publishing: London, UK, 2017; pp. 219–262. ISBN 9781351114233. [Google Scholar]
Horticulturae 11 00750 g001
Figure 1. Experimental design of the fig tree plantation.
Figure 1. Experimental design of the fig tree plantation.
Horticulturae 11 00750 g001
Horticulturae 11 00750 g002
Figure 2. Detail of an espalier-trained fig tree (5th green) in winter dormancy after pruning.
Figure 2. Detail of an espalier-trained fig tree (5th green) in winter dormancy after pruning.
Horticulturae 11 00750 g002
Horticulturae 11 00750 g003
Figure 3. Espalier-trained fig tree (4th green) during the fruiting period.
Figure 3. Espalier-trained fig tree (4th green) during the fruiting period.
Horticulturae 11 00750 g003
Horticulturae 11 00750 g004
Figure 4. Ripening calendar of the four fig varieties evaluated in this study (2018–2021): ‘San Antonio’ (blue), ‘Albacor’ (green), ‘De Rey’ (red), and ‘Dalmatie’ (orange).
Figure 4. Ripening calendar of the four fig varieties evaluated in this study (2018–2021): ‘San Antonio’ (blue), ‘Albacor’ (green), ‘De Rey’ (red), and ‘Dalmatie’ (orange).
Horticulturae 11 00750 g004
Horticulturae 11 00750 g005
Figure 5. Mean ± SD (error bars) of annual yield (kg/tree) of four studied varieties over the studied period (2018–2021). a, b, c Indicate significant differences (p ≤ 0.05) between varieties with the same year. 1, 2, 3 Indicate significant differences (p ≤ 0.05) between years with the same variety.
Figure 5. Mean ± SD (error bars) of annual yield (kg/tree) of four studied varieties over the studied period (2018–2021). a, b, c Indicate significant differences (p ≤ 0.05) between varieties with the same year. 1, 2, 3 Indicate significant differences (p ≤ 0.05) between years with the same variety.
Horticulturae 11 00750 g005
Horticulturae 11 00750 g006
Figure 6. Mean ± SD (error bars) of cumulative yield (kg/tree) and TCSA (cm2), shown on the left y-axis, and yield efficiency (kg/cm2), shown on the right y-axis, for four studied fig varieties over the studied period (2018–2021).
Figure 6. Mean ± SD (error bars) of cumulative yield (kg/tree) and TCSA (cm2), shown on the left y-axis, and yield efficiency (kg/cm2), shown on the right y-axis, for four studied fig varieties over the studied period (2018–2021).
Horticulturae 11 00750 g006
Horticulturae 11 00750 g007
Figure 7. Mean ± SD (error bars) of weight (g), width (mm), and firmness (N/mm) of figs from the four studied varieties over the studied period (2018–2021). a, b, c Indicate significant differences (p ≤ 0.05) between varieties with the same year. 1, 2, 3 Indicate significant differences (p ≤ 0.05) between years with the same variety.
Figure 7. Mean ± SD (error bars) of weight (g), width (mm), and firmness (N/mm) of figs from the four studied varieties over the studied period (2018–2021). a, b, c Indicate significant differences (p ≤ 0.05) between varieties with the same year. 1, 2, 3 Indicate significant differences (p ≤ 0.05) between years with the same variety.
Horticulturae 11 00750 g007
Horticulturae 11 00750 g008
Figure 8. Mean values ± SD (error bars) of (a) skin and (b) flesh colour parameters (L*, C*, and h*) of fresh figs from four varieties evaluated over four growing seasons (2018–2021). a, b, c Indicate significant differences (p ≤ 0.05) between varieties with the same year. 1, 2, 3 Indicate significant differences (p ≤ 0.05) between years with the same variety.
Figure 8. Mean values ± SD (error bars) of (a) skin and (b) flesh colour parameters (L*, C*, and h*) of fresh figs from four varieties evaluated over four growing seasons (2018–2021). a, b, c Indicate significant differences (p ≤ 0.05) between varieties with the same year. 1, 2, 3 Indicate significant differences (p ≤ 0.05) between years with the same variety.
Horticulturae 11 00750 g008
Horticulturae 11 00750 g009
Figure 9. Mean ± SD (error bars) of TSS (°Brix), pH, TA (citric acid/100 g), and MI (TSS/TA) of figs from the four studied varieties over the studied period (2018–2021). a, b, c Indicate significant differences p < 0.05 between varieties with the same year. 1, 2, 3, 4 Indicate significant differences p < 0.05 between years with the same variety.
Figure 9. Mean ± SD (error bars) of TSS (°Brix), pH, TA (citric acid/100 g), and MI (TSS/TA) of figs from the four studied varieties over the studied period (2018–2021). a, b, c Indicate significant differences p < 0.05 between varieties with the same year. 1, 2, 3, 4 Indicate significant differences p < 0.05 between years with the same variety.
Horticulturae 11 00750 g009
Table 1. Two-way ANOVA results showing the effects of the year, variety, and their interaction on annual yield and fruit quality traits of fig. Partial η2 values are included as a measure of effect size.
Table 1. Two-way ANOVA results showing the effects of the year, variety, and their interaction on annual yield and fruit quality traits of fig. Partial η2 values are included as a measure of effect size.
Dependent VariableSource of
Variation		dfFpPartial η2
Annual
yield		Year365.9<0.0010.86
Variety334.7<0.0010.76
Year × Variety93.30.0060.48
WeightYear363.0<0.0010.17
Variety3481.7<0.0010.60
Year × Variety94.5<0.0010.04
WidthYear396.3<0.0010.23
Variety3294.1<0.0010.48
Year × Variety94.3<0.0010.04
FirmnessYear30.50.6970.01
Variety32.80.0430.04
Year × Variety92.60.0070.10
TSSYear314.2<0.0010.17
Variety376.0<0.0010.52
Year × Variety95.7<0.0010.20
pHYear312.2<0.0010.15
Variety318.6<0.0010.21
Year × Variety92.10.0340.08
TAYear327.8<0.0010.29
Variety339.0<0.0010.36
Year × Variety94.3<0.0010.16
MIYear322.3<0.0010.25
Variety337.1<0.0010.36
Year × Variety99.1<0.0010.29
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Galán, A.J.; Domínguez, M.G.; Pérez-López, M.; Galván, A.I.; Pérez-Gragera, F.; López-Corrales, M. Agronomic Performance and Fruit Quality of Fresh Fig Varieties Trained in Espaliers Under a High Planting Density. Horticulturae 2025, 11, 750. https://doi.org/10.3390/horticulturae11070750

AMA Style

Galán AJ, Domínguez MG, Pérez-López M, Galván AI, Pérez-Gragera F, López-Corrales M. Agronomic Performance and Fruit Quality of Fresh Fig Varieties Trained in Espaliers Under a High Planting Density. Horticulturae. 2025; 11(7):750. https://doi.org/10.3390/horticulturae11070750

Chicago/Turabian Style

Galán, Antonio Jesús, María Guadalupe Domínguez, Manuel Pérez-López, Ana Isabel Galván, Fernando Pérez-Gragera, and Margarita López-Corrales. 2025. "Agronomic Performance and Fruit Quality of Fresh Fig Varieties Trained in Espaliers Under a High Planting Density" Horticulturae 11, no. 7: 750. https://doi.org/10.3390/horticulturae11070750

APA Style

Galán, A. J., Domínguez, M. G., Pérez-López, M., Galván, A. I., Pérez-Gragera, F., & López-Corrales, M. (2025). Agronomic Performance and Fruit Quality of Fresh Fig Varieties Trained in Espaliers Under a High Planting Density. Horticulturae, 11(7), 750. https://doi.org/10.3390/horticulturae11070750

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop