Next Article in Journal
Inter-Specific Hybridization in Cotton (Gossypium hirsutum) for Crop Improvement
Previous Article in Journal
Efficient Separating Collected Seedlings Based on Precise Subdivision Air Streams
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 12
Issue 12
10.3390/agronomy12123157
agronomy-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 thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Different Suitability of Olive Cultivars Resistant to Xylella fastidiosa to the Super-Intensive Planting System

by
Salvatore Camposeo
1,*,
Anna Maria Stellacci
1,
Cristina Romero Trigueros
2,
Salem Alhajj Ali
1 and
Gaetano Alessandro Vivaldi
1
1
Department of Soil, Plant and Food Science, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy
2
Centro de Edafologia y Biologia Aplicada del Segura, CEBAS-CSIC, Campus Universitario de Espinardo, 30100 Murcia, Spain
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(12), 3157; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12123157
Submission received: 17 October 2022 / Revised: 29 November 2022 / Accepted: 7 December 2022 / Published: 13 December 2022
(This article belongs to the Section Horticultural and Floricultural Crops)
Browse Figures
Review Reports Versions Notes

Abstract

:
Until today, only Leccino and Fs-17 (=Favolosa®) olive cultivars proved resistant to Xylella fastidiosa subsp. pauca (Xfp) due to a low presence of bacteria in the xylem. Integrated disease management in olive growing areas threatened by the spread of Xfp is crucial to overcoming the environmental, economic and social crisis. Since the EU Decision allows for the plantation of resistant olive cultivars in infected areas, there is a need to define a suitable plantation system for these cultivars. The adoption of new planting systems, such as intensive and super-intensive (SHD), could compensate for the economic losses and restore the olive agroecosystem. The aim is to ascertain the suitability of the available Xfp-resistant cultivars to SHD planting systems that demonstrate the best economic and environmental sustainability. Hence, a five-year study was established in an experimental SHD olive orchard (Southern Italy) in order to analyse the main vegetative and productive traits of Leccino and Fs-17, together with four other Italian cultivars (Cipressino, Coratina, Frantoio and Urano), compared with the well-adapted cultivars to SHD orchards (Arbequina and Arbosana), by means of the von Bertalanffy function. The results indicated that cv. Fs-17 showed sufficient suitability for SHD planting systems, giving the best-accumulated yield despite some canopy growth limitations, whereas cv. Leccino did not show satisfactory results in terms of both vegetative and yield parameters, confirming its suitability for intensive planting systems. These results are useful for optimizing integrated resistance management in Xfp-infected areas by planting resistant host plants.

1. Introduction

The olive agroecosystem has long-lasting history and represents an invaluable local heritage for landscape, trade, and social traditions for the Italian people, especially in the Apulian region [1], where one-third of the country’s olive trees are grown with a wide diversity accounting for between one-third to one-half of national olive oil production [2]. However, this agroecosystem is currently under severe threat by the occurrence of the plant quarantine bacterium Xylella fastidiosa subsp. pauca (Xfp), which was first detected in the olive agroecosystem in late 2013 in the Salento (Apulia region, Southern Italy) area [3]. Xfp is associated with the OQDS (Olive Quick Decline Syndrome), a severe plant disease that causes massive leaves scorching followed by branches wilting and tree death [3]. As of today, the Italian outbreak of Xfp, concentrated mainly in the south region of Apulia, has spread over 100 km, killing millions of olive trees, and is still expanding [4]; up to 2019, a total of approximately 54,000 hectares of olive orchards have been killed or seriously damaged [5]. The most susceptible olive cultivars (Cellina di Nardò and Ogliarola salentina) are the most diffused ones in the infected areas [6], where OQDS is causing an economic, social and environmental emergency [7]. Moreover, Xfp has also demonstrated an aggressive polyphagous bacterium with a potential threat to Mediterranean and European agriculture [8], which makes it presently considered a phytosanitary priority for all EU countries [9].
Several efforts have been made in order to define the best strategy for controlling Xfp. Agrochemicals did not appear to be the right strategy [10,11]. Nonetheless, an integrated disease management approach is possible in order to limit the spread of the pathogen: first of all, the use of resistant or tolerant host plants [12,13]. Indeed, an important agronomic management strategy to limit Xfp damages is the identification of cultivars that show tolerance/resistance to the pathogen for their potential use in the field or in breeding programs [14]. The use of pathogen-resistant cultivars in crop production is currently the most economical and effective way to reduce losses caused by diseases, as well as the most studied approach [15]. Up to now, converging lines of scientific evidence indicate that cv. Leccino has some tolerance traits to Xfp, which develop milder symptoms due to the smaller size of the bacterial populations in its xylem tissues compared to those observed on susceptible cultivars [8,16]. In addition, more recent observations on olive trees under field conditions identified cv. Fs-17 (=Favolosa®) is an olive genotype with more resistance traits [17,18]. A lower bacterial titre of resistant cultivars may also exert a beneficial impact on epidemiology as it may result in a lower rate of Xfp transmission, helping the containment of the disease spread [8].
The EU Decision ((EC) N. 2017/2352) [19] makes it possible for the plantation of new olive cultivars in infected areas but only with the resistant cultivar (Leccino and Fs-17), as a useful strategy to overcome the severe economic and environmental losses due to Xfp infections. Thus, once the planting of these resistant olive cultivars in infected areas becomes possible, integrated disease management will be needed. However, under recent climate change scenarios, it is crucial that the new olive orchards, planted with resistant cultivars, are coupled with the implementation of the current new orchard design and with a sustainable management strategy that can ensure constant production and, at the same time, protect the environment [7,15,20].
Olive orchard intensification proceeded in two stages. First, in the 80s, planting density was increased from 100 trees ha−l in traditional orchards (low density, LD) to 400–600 trees ha−1 in intensive ones (medium density; MD) with lower vase-shaped canopies, adapted for efficient fruit harvesting by trunk shaker machines [21]. Later, a much bigger change was added in the middle 90s with the introduction of super high density (SHD) orchards (1500–2000 trees ha−1 [22]) with central leader trees in hedgerows to form continuous fruit-bearing canopies [23], fitting for over-the-row harvesting machines [24]. Compared to the MD system, several studies indicate that the SHD system represents a very encouraging advantage in terms of profitability through the reduction of production costs, thanks to full mechanisation from planting to harvesting, higher and more constant crop yield [23,25,26]. Mairech et al. [27] have conducted a spatial modelling analysis to investigate yield response to different olive orchards management systems around the Mediterranean and found that intensification from LD to SHD systems presented the greatest improvements in terms of productivity with an increase in yield ranging between 28 to 73%. The SHD olive orchard management system is spreading over 400,000 ha on five continents [28]. As a promising olive orchard management system, the yield performance of the SHD system is being studied, considering important agronomic aspects [22,24,29,30,31,32]. Furthermore, SHD systems showed higher environmental suitability compared to other ones. Indeed, these new planting systems represent now the best solution in terms not only of crop production [33,34] but also of ecosystem services: SHD orchards can (i) decrease both water and carbon footprint [35,36] and (ii) increase soil protection [37], therefore could be integrated into the “One Health” approach [38].
However, the success of the SHD olive management system depends on the suitable cultivars availability [34]. Indeed, if the LD orchards worked well with traditional high-vigour cultivars [39] and the MD orchards fit best with medium-vigour cultivars [21], the SHD cropping system needs low-vigour genotypes, characterised by early bearing, slow canopy growth, and high yield efficiency [22,31,34]. Up to now, only a few cultivars have been identified as suitable for the olive SHD system [34]. The cv. Arbequina was by far the dominant cultivar used in SHD orchards, given its low-vigour, high and stable productivity and fruity oil, followed by cv. Arbosana showing, in addition to its characteristics of early bearing, low-vigour, and slow canopy growth, a higher yield efficiency under SHD conditions compared to Arbequina [22,30,40,41]. Unfortunately, these two cultivars and the most important traditional ones, such as Coratina and Frantoio, have shown susceptibility to Xfp infection [14]. Moreover, in the last decades, the Italian and the Spanish breeding programs have licensed some newly patented cultivars, such as Fs-17 (=Favolosa®), Chiquitita®, Lecciana®, Oliana® and Urano (=Tosca®) showing, in the short-term, some interesting features such as medium–low vigour and early bearing, which make them suitable to be planted under olive SHD systems [32,42,43,44]. However, there is still a need for medium to long-term studies to better understand the suitability of these cultivars for the SHD system.
Until recently, very few newly-established olive orchards were planted with Xfp-resistant cultivars in infected areas, most of them with Fs-17 following the SHD planting system. However, the crucial question would be to which available resistant cultivar(s) (Leccino and/or Fs-17) SHD planting system can be applied. In order to offer a scientific contribution to this issue, we studied eight olive cultivars within ongoing research for the selection of resistant olive cultivars to Xfp [14]. The aim was to evaluate the suitability of the Xfp-resistant cultivars to the SHD planting system. The varietal growth and yield were assessed by means of the von Bertalanffy function. This field investigation aimed to address the gap in knowledge by evaluating the main vegetative and productive traits of cv. Leccino and cv. Fs-17, together with other most diffused Italian cultivars, both the traditional (Coratina, Frantoio) and the new patented ones, compared to the well-adapted Spanish cultivars (Arbequina and Arbosana), dominating the new olive orchards planted during the last decades in Southern Italy, with the hypothesis that the cultivars Xfp-resistant can, at the same time, give good results in terms of yield and vegetative growth in new super-intensive plantings.

2. Materials and Methods

2.1. Olive Orchard

The experimental olive orchard was set up at Cassano delle Murge (Bari, Southern Italy; 40°54′ N, 16°47′ E, 307 m a.s.l.) on a not calcareous clay-loam soil, classified as Aploxeralf-Xerothent (USDA) or Luvisols-Phaeozems (FAO). The site, located about 40 km far from the buffer zone [7], is characterised by a typical Mediterranean climate with a long-term annual average rainfall of 560 mm, with 67% of precipitation occurring from autumn to winter, and a long-term annual average temperature of 15.6 °C. Agricultural practices were applied uniformly over the experimental area. Detailed information about agriculture practices and crop management can be found in a previous study [45].

2.2. Experimental Design

Within the experimental plots, eight olive cultivars (Arbequina, Arbosana, Cipressino, Coratina, Frantoio, Leccino, Fs-17 and Urano) were planted as rooted cuttings (18 months old) coming from mist propagation. The cvs. Arbequina and Arbosana, known to be well adapted to the SHD system [24,25], were considered as control. The examined Italian olive cultivars are already included in ongoing studies to investigate the Xfp resistance of olive cultivars [14].
Olive trees were trained according to the central leader system (SHD 1.0), spaced 4.0 m × 1.5 m (1660 trees ha−1) and with a North–South row orientation. The experimental design was a randomised complete block with three replicates. Every block was composed of three rows with 30 trees of each cultivar. Within each row, 10 contiguous trees were chosen and were labelled just after plantation for a total of 30 trees per cultivar. As indicated in the previous studies [46,47], for the mechanical operations in the SHD cropping system, the canopy of the cultivated olive genotype was kept under approximately 1.50 m in width and 2.50 m in height. All cultivars met this threshold at the 4th year after planting (YAP) when manual hedging of later branches with a diameter over 2 cm was operated.

2.3. Experimental Variables

The field trial was monitored yearly for five consecutive years. The measurements related to canopy growth and yield were taken starting from the second year after planting and ending in the sixth year. The following parameters were measured for each tree once a year during harvesting: tree height (H; cm), transversal crown width (W; cm) and tree stem diameter (D; cm) at 50 cm from the ground. In addition, tree stem sectional area (A; cm2), as a feature of tree vigour, was calculated as in the following equation:
A = π D 2 / 4
Every year, on the same trees, fruit yield (Y; kg tree−1) was measured by manual harvesting. Additionally, yield efficiency (YE; g cm−2) was calculated as follows:
YE   =   Y / A
Finally, the cumulated yield (CY, t ha−1) was also determined.

2.4. Statistical Analysis

Descriptive statistics were computed on canopy growth and yield parameters collected over the experimental period to synthesise the main features of data distribution. Canopy growth parameters, in particular tree transversal canopy width (W) and tree height (H), showed skewness and kurtosis coefficients close to zero (−0.283 and −0.049 respectively for W; 0.280 and 0.369 respectively for H), indicating no substantial departure from a normal distribution [48]. On the contrary, yield response variables (Y and YE) showed instead highly skewed distributions (2.517 and 7.714, respectively, for Y and YE). For this reason, a non-parametric analysis of variance was performed to investigate these two parameters, and the Nemenyi–Damico–Wolfe–Dunn test was used to assess differences among groups. Variables that showed no substantial departure from the normal distribution, as in the case of canopy growth parameters, were processed according to a repeated-measures analysis [49,50]. Sub-replicates (trees within each plot) were considered nested observations. SAS software for MS Windows was used SAS/STAT. Multiple comparisons of the main effects of time and cultivar were performed by post hoc analysis using the SNK test (p < 0.05). In addition, where significant interactions with time were identified, further analysis of simple effects was conducted with the slice test, which uses the full model degrees of freedom and MSE to assess differences among the means [51]. Considering that the fruiting potential of olive trees affects canopy size evolving over time [52,53,54,55], an appropriate mathematical function fitting growth and yield data could be very useful to describe and compare patterns among different genotypes [56]. Within the mathematical functions available, the von Bertalanffy function (VBF) has been widely applied for forest tree growth modelling as well as for the genetic study of plant development [57,58,59]. Tree growth parameters (H and A) and the experimental data of cumulated yield (CY), collected over the study years, were fitted by the VBF [60]. The fitting was not performed for W nor YE, considering the hedging made in the fourth year as a derived, non-cumulative parameter. The VBF includes three mathematical parameters, and it is expressed as in the following equation:
L t = L ∞ [ 1 − e − k ( t − t 0 ) ]
where Lt represents the growth/yield at year t; L∞ is the maximum potential growth/yield; k is the growth/yield coefficient accounting for the rate of achieving the maximum growth/yield; t0 is an estimate of the year at which the growth/yield is zero; t represents the year [61].
Olive growth/yield functions were fitted by the Iterative Marquardt Method (IMM) within the NLIN procedure of SAS/STAT [62]. The goodness of fit was evaluated through the significance of each parameter by computing the respective standard error and by the pseudo-R2 statistics [50]. In the case of non-convergence, the associated functions were excluded. In addition, a comparison among functions of each cultivar for the parameters under study (H, A, CY) was performed through the Analysis of the Residual Sum of Squares (ARSS) according to the procedure proposed by Ratkoswsky [63] and modified by Chen et al. [56] and Zar [64]. Finally, to test the differences in VBF between cultivars for each parameter (H, A, CY), the calculated F value was compared with the critical F. We repeated F comparisons for all possible cultivars twins for the three parameters. Finally, polynomial regressions were calculated using SigmaPlot software (version 11.0). The significance of equations was indicated in Statistical Normality Test (Shapiro–Wilk) with a 0.05 significant level.

3. Results

3.1. Growth Parameters

Tree height (H), crown width (W) and stem sectional area (A) increased over time and were significantly affected by the cultivars and the year (significant year × cultivar interaction (Table 1).
Detailed single effects of cultivar on H, W and A for each year are reported in Figure 1, Figure 2 and Figure 3, respectively.
As expected (Table 1), mean tree height values (H) increased, from second to sixth YAP of about 73%, with cv. Cipressino reached the highest mean value (285.5 cm). Cultivars Leccino (268.1 cm) and Fs-17 (261.8 cm), followed by Frantoio (260.8 cm) and Coratina (243.5 cm), showed greater tree height values than the cv. Arbequina (226.1 cm), Arbosana (201.4 cm) and Urano reported the lowest H value (173.3 cm). Similar behaviour was observed for W values (Table 1). Leccino (200.4 cm) and Fs-17 (192.0 cm), together with Coratina (201.4 cm), showed the highest W values throughout the experimental period, while cvs. Arbequina (164.8 cm), Arbosana (140.6 cm) and Urano (151.5 cm) presented W values lower than the mean, as in the case of the H parameter. In general, moving from the second to the fourth YAP, W values increased significantly in all cultivars and reached their maximum average (193.7 cm). At the fifth YAP, after manual hedging, the W value was reduced significantly by about 13.5% as average in all cultivars except for the cv. Cipressino, varying from a minimum value of 7% for cv. Urano to a maximum value of 20.0% for cv. Fs-17, followed by cvs. Arbosana and Arbequina with 18 and 15%, respectively. Finally, A values tend to increase gradually and significantly (about 12 times on average), reaching their maximum value in the last year as compared to the first one. Cv. Cipressino reported the highest A mean value (36.5 cm2), followed by Frantoio (29.7 cm2); Leccino (26.9 cm2) and Fs-17 (23.6 cm2) behaved no longer from Arbequina (20.3 cm2) and Arbosana (17.1 cm2) that showed the lowest A values. In order to evaluate the relation among the growth parameters, a correlation matrix was performed (Table 2). The results indicate, as expected, a high correlation among all parameters. In particular, the highest Person’s coefficient (0.88 ***) was recorded between tree height (H, cm) and stem sectional area (A, cm2). Similar results were obtained in other experiments [65].

3.2. Yield Parameters

Significant effects were detected between cultivar and year by non-parametric analysis carried out on yield parameters (Table 3); the effect of temporal variation on the yield and yield efficiency of the cultivars was reported in Figure 4 and Figure 5, respectively.
In the second YAP, only cv. Arbequina and Arbosana began their first fruiting, although with very low values (Table 3). At the third YAP cvs. Fs-17 and Leccino started to bear appreciable values, mainly for Fs-17 with over 2 kg tree−1. Between the third and the fourth YAP, all other cultivars started bearing except for cv. Frantoio. Between the fifth and the sixth YAP, the yield was reduced significantly in all the cultivars because of the hedging effect (Table 3). The highest Y mean values were obtained by cvs. Fs-17 and Arbosana (1.1 kg tree−1), followed by cvs. Urano (about 0.7 kg tree−1), Arbequina (0.5 kg tree−1) and Leccino (0.4 kg tree−1). The lowest yields were reported for cv. Cipressino and Frantoio, with negligible mean values (Table 2) (Figure 4). Finally, the highest YE mean values were recorded during the third and fourth YAP (52.7 g cm−2 and 49.4 g cm−2, respectively); then, in the following years (fifth to sixth YAP), YE significantly decreased (Table 3). Cv. Arbosana (73.3 g cm−2) and cv. Fs-17 (52.9 g cm−2) showed similar highest YE mean values, followed by cvs. Urano (33.5 g cm−2), Arbequina (28.8 g cm−2) and Leccino (13.9 g cm−2), without significant differences among them (Table 3). The lowest mean YE value was found for Frantoio (1.1 g cm−2), with values higher than 0 only during the fifth and the sixth YAP (Figure 5).

3.3. Fitting by the von Bertalanffy Function

Fitting the experimental data over the study years employing the von Bertalanffy function (VBF), the curves reported in Figure 6, Figure 7 and Figure 8 were obtained for tree height (H), stem sectional area (A) and cumulated yield (CY), respectively. The curves showed that the H and A slopes of Cipressino were higher compared to other cultivars, as expected (Figure 6 and Figure 7). For the CY, instead, cvs. Fs-17 and Arbosana slopes were much higher as compared to other cultivars; opposite trends were shown by Cipressino and Frantoio (Figure 8). This variation in the slopes for the different cultivars was clearly related to the CY data. Indeed, during the sixth YAP cvs. Fs-17 and Arbosana showed the highest CY values (9.4 t ha−1 and 8.7 t ha−1, respectively), followed by cvs. Urano (5.5 t ha−1), Arbequina (4.0 t ha−1), Leccino (3.1 t ha−1) and Coratina (2.4 t ha−1), whereas the lowest CY values were reported for cvs. Cipressino and Frantoio (0.7 t ha−1 and 0.5 t ha−1, respectively).
In Table 4, the von Bertalanffy equation parameters and the corresponding goodness of fit passed by the Iterative Marquardt Method (IMM-passed) are reported for H, A and CY parameters. The VBF results showed well adaptability for Arbequina, Arbosana and Leccino for all parameters, as confirmed by the pseudo-R2 statistics values. The best fitting was recorded for stem sectional area A, where good results were achieved, and also for Cipressino. IMM test failed to find convergence in Fs-17 for H and A parameters and in Coratina, Urano and Frantoio for A and CY parameters.
In Table 5, the twin-comparison significance for Leccino and Fs-17 vs. Arbequina and Arbosana is reported. ARSS demonstrated that the functions of all the cultivars were statistically different for the three examined parameters. A very interesting exception was observed for the cv. Fs-17 as compared to cv. Arbosana, where no statistical differences were observed between their CY functions. In the remaining cases, the cultivars behaved differently for the same growth/yield parameters. Regarding the usefulness of the Von Bertalanffy function to predict the behaviour, in terms of vegetative growth and yield of the investigated olive cultivars over the study years, our results demonstrated that Arbequina and Arbosana showed a good fitting; on the contrary, Leccino provided a good fitting only for vegetative growth parameters. Among the evaluated parameters, we found that stem sectional area (A) was a reliable descriptor of vegetative growth, showing the highest values of Pseudo-R2 (Table 4).

4. Discussion

This is one of few agronomic studies aimed at evaluating different olive cultivated genotypes for their suitability to be grown under the SHD system over a medium-term period [25,45,46]. The key factors for assessing olive cultivars’ suitability for SHD orchards are canopy size (height and width) and tree vigor (stem sectional area) [66,67,68,69]. The canopy size of the studied cultivars was smaller than those observed in the previous studies [25,46] for the same cultivars in warmer climatic conditions; however, higher values than those observed in colder areas [40,70] indicate the importance of climate conditions on the canopy size. Similarly, data on tree fruit production and cumulated yield were much lower than those reported in different cultivation areas [40,46,70]. The yield reduction of the studied cultivars was due to climate effects on the phenology of some olive cultivars, as confirmed by the work of Caruso et al. [67], who analysed Arbequina growth and yields in high-density orchards in three different Italian olive growing areas.
Canopy height and width are two important parameters that need to be measured in order to evaluate the adaptability of the studied cultivars to the SHD system. In particular, tree height has a great influence on establishing the appropriate design for hedgerow plantings [71]. In this study, the vertical growth of Arbequina, Arbosana and Urano did not overtake the threshold until the sixth YAP, whereas Cipressino and Frantoio reached the threshold at the fourth YAP. The threshold limit was exceeded at the fifth YAP for the remaining cultivars. This is a clear varietal response; in fact, vertical growth can be considered a selective varietal parameter for the best adaptability to SHD planting [23,30]. As for tree width, all cultivars overcame the width threshold at the fourth YAP; therefore, winter hedging was operated. We correlated the reduction percentages of W and Y from the fourth to fifth YAP for the selected cultivars (Arbosana, Arbequina, Fs-17 and Urano) in order to understand better how the hedging affected crop yield, and we found a positive and significant regression: r = 0.99, p < 0.001, W Statistic= 0.8645 and Passed p = 0.2765. In fact, in the studied cultivars, we found that the more pruning, the greater the Y losses, always depending on the cultivar. Indeed, the removal of peripheral branches increases the competition between vegetative and reproductive shoots, reducing total fruit yield, especially for medium-high vigor cultivars [31]. Concerning tree vigor, expressed by stem sectional area [45,72], an increase in all cultivars over the studied years was observed; however, the rate of increase was reduced in the Spanish control cultivars (Arbosana and Arbequina), and the new Italian cultivar (Urano) resulting in lower vigor. A similar result was previously reported [40,67] for the control cultivars. For the Fs-17 cultivar, our findings show that this cultivar resulted in a medium vigor, while traditional Italian such as Cipressino, Frantoio and Leccino cultivars reported a high to very high stem sectional area, in agreement with the results of Rosati et al. [73] who found that Frantoio produced higher trunk diameter compared to Leccino after seven years of study.
Other important keyfactors for classifying an olive genotype as suitable for the SHD cropping system are early bearing and high yield, which have the same importance as the canopy growth parameters [74,75]. In this regard, we found, in this medium-term study, that Fs-17 and Arbosana are the highest productive cultivars with the best yield efficiency, in contrast with previous work by De la Rosa et al. [64]. Moreover, Fs-17, the new patented cultivar, showed an early bearing similar to the control cultivars Arbequina and Arbosana, as these cultivars started to provide yields from the third YAP. The very similar productive behaviour of Arbosana and Fs-17 was confirmed by the von Bertalanffy growing function with plenty of close equation parameters for CY. Nevertheless, Fs-17 showed, at the 6th YAP, a bigger canopy growth with respect to Arbosana in terms of tree height (+40%), width (+22%) and vigor (+45%). These vegetative parameters would limit the Fs-17 cultivar for its suitability in SHD orchards since excessive canopy growth after the first years could reduce light interception and therefore yield efficiency per unit of canopy volume [22,76,77,78]. Nevertheless, it has long been assumed that it could reduce tree growth through agricultural field practices, such as regulated deficit irrigation and pruning [52,79].
The other patented Italian cultivar (Urano = Tosca®) resulted in good fruit tree production and yield efficiency. Compared to other cultivars, Urano reported the second-highest production at the sixth YAP (significantly higher after Arbosana and Fs-17), although it started bearing one year later than Arbequina and Arbosana. Moreover, Urano showed a slow canopy growth similar to that of the control Spanish cultivars. On the contrary, the cultivars; Leccino, Coratina, Cipressino and Frantoio gave lower and late production and low yield efficient results. On the other hand, cv. Leccino could be successfully considered in new intensive olive orchards [80] and for table olive production as well [81]. Once the cultivar starts to bear, the yield will compete with and reduce vegetative growth and, therefore, vigour [55,82]. Moreover, reduced growth might be the consequence of early and abundant fruiting: in mature olive trees, it is well established that vegetative growth is more abundant in off (i.e., with low yield) years [83]. The general assumption indicates that trees that spend more energy on production are expected to grow less in terms of vegetative growth because both reproductive and vegetative growth competes for the same sources within a tree [82]. Early bearing and more abundant fruiting are the major causes, rather than merely a consequence, of lower vigor in young Arbequina trees [54]. In our study, this premise is confirmed since the cultivars with earlier bearing (Spanish control cultivars in the second YAP) have a lower vigor than the other cultivars. Although the reference cultivars (Arbequina and Arbosana) proved their best fit for the SHD system, they may not be resistant to Xfp; therefore, they are not suitable to be planted in infected areas for the new olive orchard. Under the SHD planting system, cv. Fs-17 resulted in the best-cumulated yields; however, it presented some limitations due to its higher canopy growth. Nevertheless, the vegetative limitations of Fs-17 can be overcome by a proper pruning technique, as recently demonstrated by Vivaldi et al. [31], in order to better adapt this model to medium-low vigor cultivars. These findings should overcome the contradictory information up to now reported in the literature about the suitability of the cv. Fs-17 under the SHD planting system in different environments [22,44].

5. Conclusions

This medium-term field study demonstrated that the von Bertalanffy function can be successfully used to investigate and compare olive cultivars’ behavior, thanks to properly fitting in most of the experimental data. The overall results indicated that cv. Fs-17 (=Favolosa®) showed acceptable suitability to SHD plantings, thanks to its best-accumulated yield. It should increase the prospect of planting this cultivar in the infected areas sensibly.
On the contrary, cv. Leccino, like the other traditional Italian cultivars, did not perform under the SHD system in terms of both growth and yield parameters. The results of this study could be useful for optimizing integrated resistance management in Xfp-infected areas by planting resistant host plants. The identification of low-vigor, early bearing and high-yielding cultivars would meet this challenge. However, further research is needed for the investigation of the long-term varietal behaviours of other cultivars belonging to the very large Italian olive platform. Moreover, this study highlighted the crucial role of worldwide olive breeding programs for both low vigor and Xfp resistance, looking at sustainable new climate-smart planting systems.

Author Contributions

Conceptualization, S.C. and G.A.V.; methodology, S.C., A.M.S. and G.A.V.; software, A.M.S. and G.A.V.; validation, S.A.A. and C.R.T.; investigation, S.C. and G.A.V.; data curation, A.M.S. and G.A.V.; writing—original draft preparation, S.C., A.M.S. and G.A.V.; writing—review and editing, S.C., A.M.S., G.A.V., S.A.A. and C.R.T.; visualization, S.A.A. and C.R.T.; supervision, S.C.; project administration, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.

Funding

The authors wish to thank Provincia di Bari for the financial support of this research. Romero-Trigueros, C. acknowledges the financial support of Fundación Séneca (Agency of Science and Technology of the Region of Murcia) within the framework of the Regional Program of “Research Talent and its Employability” for a postdoctoral fellowship (20363/PD/17), as well as the Spanish Research Agency of the Ministry of Science and Innovation for a contract from Juan de la Cierva-Incorporación (JC2019-040871-I).

Data Availability Statement

Not applicable.

Acknowledgments

This work is dedicated to the memory of Angelo Godini.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Scortichini, M. The Multi-Millennial Olive Agroecosystem of Salento (Apulia, Italy) Threatened by Xylella Fastidiosa Subsp Pauca: A Working Possibility of Restoration. Sustainability 2020, 12, 6700. [Google Scholar]
  2. Ipsen, C. Xylella fastidiosa and the olive oil crisis in Puglia. Gastronomica 2020, 20, 55–66. [Google Scholar] [CrossRef]
  3. Saponari, M.; Boscia, D.; Nigro, F.; Martelli, G.P. Identification of DNA sequences related to Xylella fastidiosa in Oleander, Almond and Olive trees exhibiting leaf scorch symptoms in Apulia (Southern Italy). J. Plant Pathol. 2013, 95, 668. [Google Scholar]
  4. White, S.M.; Navas-Cortés, J.A.; Bullock, J.M.; Boscia, D.; Chapman, D.S. Estimating the epidemiology of emerging Xylella fastidiosa outbreaks in olives. Plant Pathol. 2020, 69, 1403–1413. [Google Scholar] [CrossRef]
  5. Frem, M.; Santeramo, F.G.; Lamonaca, E.; El Moujabber, M.; Choueiri, E.; La Notte, P.; Nigro, F.; Bozzo, F.; Fucilli, V. Landscaperestoration due to Xylella fastidiosa invasion in Italy: Assessing the hypotheticalpublic’spreferences. NeoBiota 2021, 66, 31–54. [Google Scholar] [CrossRef]
  6. Saponari, M.; Guiampetruzzi, G.; Loconsole, G.; Boscia, D.; Saldarelli, P. Xylella fastidiosa in Olive in Apulia: Wherewe Stand. Phytopathology 2019, 109, 175–186. [Google Scholar] [CrossRef] [Green Version]
  7. Ali, B.M.; van der Werf, W.; Lansink, A.O. Assessment of the environmental impacts of Xylella fastidiosa subsp. pauca in Puglia. Crop Prot. 2021, 142, 105519. [Google Scholar] [CrossRef]
  8. Giampetruzzi, A.; Morelli, M.; Saponari, M.; Loconsole, G.; Chiumenti, M.; Boscia, D.; Savino, V.; Martelli, G.P.; Saldarelli, P. Transcriptome profiling of two olive cultivars in response to infection by the CoDiRO strain of Xylella fastidiosa subsp pauca. BMC Genet. 2016, 17, 475. [Google Scholar]
  9. Fierro, A.; Liccardo, A.; Porcelli, F. A lattice model to manage the vector and the infection of the Xylella fastidiosa on olive trees. Sci. Rep. 2019, 9, 1–14. [Google Scholar] [CrossRef] [Green Version]
  10. Martelli, G.P.; Boscia, D.; Porcelli, F.; Saponari, M. The olive quick decline syndrome in south-east Italy: A threatening phytosanitary emergence. Eur. J. Plant Pathol. 2016, 144, 235–243. [Google Scholar] [CrossRef]
  11. Scortichini, M.; Loreti, S.; Pucci, N.; Scala, V.; Tatulli, G.; Verweire, D.; Oehl, M.; Widmer, U.; Codina, J.M.; Hertl, P.; et al. Progress towardsSustainable Control of Xylella fastidiosa subsp. pauca in Olive Groves of Salento (Apulia, Italy). Pathogens 2021, 10, 668. [Google Scholar] [CrossRef]
  12. Bruno, L.G.; Cariddi, C.; Botrugno, L. Exploring a suitable solution to control Xylella fastidiosa subsp pauca on olive in the Salento Peninsula, Southern Italy. Crop Prot. 2021, 1, 105288. [Google Scholar]
  13. Catalano, L.; Shoki, A.D.; Boscia, D.; Martelli, G.P. Guidelines for the Prevention, Eradication and Containment of Xylella fastidiosa in Olive-Growing Areas; Food and Agriculture Organization of the United Nations (FAO): Cairo, Egypt, 2019; p. 64. [Google Scholar]
  14. EFSA (European Food Safety Authority); Bau, A.; Delbianco, A.; Stancanelli, G.; Tramontini, S. Statement on: Susceptibility of Olea europaea L. varieties to Xylella fastidiosa subsp. pauca ST53: Systematic literature search up to 24 March 2017. EFSA J. 2017, 15, e04772. [Google Scholar] [PubMed]
  15. Richard, B.; Qi, A.; Fitt, B.D.K. Control of crop disease through Integrated Crop Management to deliver climate-smart farming system for low- and high- input crop production. Plant Pathol. 2022, 71, 187–206. [Google Scholar] [CrossRef]
  16. Vergine, M.; Meyer, J.B.; Cardinale, M.; Sabella, E.; Hartmann, M.; Cherubini, P.; De Bellis, L.; Luvisi, A. The Xylella fastidiosa-resistant olive cultivar “Leccino” has stable endophytic microbiota during the olive quick decline syndrome (OQDS). Pathogens 2020, 9, 35. [Google Scholar] [CrossRef] [Green Version]
  17. Sion, S.; Taranto, F.; Montemurro, C.; Mangini, G.; Camposeo, S.; Falco, V.; Gallo, A.; Mita, G.; SaddoudDebbabi, O.; Ben Amar, F.; et al. Genetic characterization of Apulian olive germplasm as potential source in new breeding programs. Plants 2019, 8, 268. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Cascone, P.; Quarto, R.; Iodice, L.; Cencetti, G.; Formisano, G.; Spiezia, G.; Giorgini, M.; Michelozzi, M.; Guerrieri, E. Behaviouralresponse of the mainvector of Xylella fastidiosa towards olive VOCs. Entomol. Gen. 2022, 42, 35–44. [Google Scholar] [CrossRef]
  19. EU Decision. Commission Implementing Decision (2017/2352) of 14 December 2017 Amending Implementing Decision (EU) 2015/789 as Regards Measures to Prevent the Introduction into and the Spread within the Union of Xylella fastidiosa (Wells et al.) (Notified under Document C. 8356) 2017. Available online: http://data.europa.eu/eli/dec_impl/2017/2352/oj (accessed on 10 November 2022).
  20. Saponari, M.; Boscia, D.; Martelli, G.P. Xylella fastidiosa,a new phytosanitary threat for olive crops. Acta Hortic. 2018, 1199, 251–254. [Google Scholar] [CrossRef]
  21. Vossen, P. Olive oil: History, production, and characteristics of the world’s classic oils. Conference: Colloquium on Update on Statistical Methods for Interpreting Horticultural Data held at the ASHS Annual Conference. HortScience 2007, 42, 1093–1100. [Google Scholar] [CrossRef] [Green Version]
  22. Díez, C.M.; Moral, J.; Cabello, D.; Morello, P.; Rallo, L.; Barranco, D. Cultivar and Tree Density as Key Factors in the Long-Term Performance of Super High-Density Olive Orchards. Front. Plant Sci. 2016, 7, 1226. [Google Scholar] [CrossRef] [Green Version]
  23. Connor, D.J.; Gómez-del-Campo, M.; Rousseaux, M.C.; Searles, P.S. Structure, management and productivity of hedgerow olive orchards: A review. Sci. Hortic. 2014, 169, 71–93. [Google Scholar] [CrossRef]
  24. Gómez-del-Campo, M.; Connor, D.J.; Trentacoste, E.R. Long-term Effect of Intra-Row Spacing on Growth and Productivity of Super-High Density Hedgerow Olive Orchards (cv. Arbequina). Front. Plant Sci. 2017, 8, 1790. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Godini, A.; Vivaldi, G.A.; Camposeo, S. Olive cultivars field-tested in super high-density system in southern Italy. Calif. Agric. 2011, 65, 39–40. [Google Scholar] [CrossRef] [Green Version]
  26. Pellegrini, G.; La Sala, P.; Camposeo, S.; Contò, F. Economic sustainability of the olive oil high and super-high density cropping systems in Italy. GBER 2017, 19, 553–569. [Google Scholar] [CrossRef]
  27. Mairech, H.; López-Bernal, Á.; Moriondo, M.; Dibari, C.; Regni, L.; Proietti, P.; Villalobos, F.J.; Testi, L. Is new olive farming sustainable? A spatial comparison of productive and environmental performances between traditional and new olive orchards with the model OliveCan. Agric. Syst. 2020, 181, 102816. [Google Scholar] [CrossRef]
  28. Freire, D. Changing the Olive Oil Value Chain: Food Regime and Development in Portugal. In Transforming the Rural; Emerald Publishing Limited: Bingley, UK, 2017; Volume 24, pp. 197–220. [Google Scholar]
  29. Camposeo, S.; Vivaldi, G.A.; Gattullo, C.E. Ripening indices and harvesting times of different olive cultivars for continuous harvest. Sci. Hortic. 2013, 1, 10–151. [Google Scholar] [CrossRef]
  30. Rosati, A.; Paoletti, A.; Caporali, S.; Perri, E. The role of tree architecture in super high density olive orchards. Sci. Hortic. 2013, 161, 24–29. [Google Scholar] [CrossRef]
  31. Vivaldi, G.A.; Strippoli, G.; Pascuzzi, S.; Stellacci, A.M.; Camposeo, S. Olive genotypes cultivated in an adult high-density orchard respond differently to canopy restraining by mechanical and manual pruning. Sci. Hortic. 2015, 192, 391–399. [Google Scholar] [CrossRef]
  32. Camposeo, S.; Vivaldi, G.A.; Montemurro, C.; Fanelli, V.; Cunill Canal, M. Lecciana, a new low-vigour olive cultivar suitable for super high density orchards and for nutraceutical EVOO production. Agronomy 2021, 11, 2154. [Google Scholar] [CrossRef]
  33. Camposeo, S.; Vivaldi, G.A. Yield, harvesting efficiency and oil chemical quality of cultivars Arbequina and Arbosana harvested by straddle machine in two Apulian growing areas. Acta Hortic. 2018, 394, 402–1199. [Google Scholar] [CrossRef]
  34. Romero-Trigueros, C.; Vivaldi, G.A.; Nicolás, E.N.; Paduano, A.; Salcedo, F.P.; Camposeo, S. Ripening indices, olive yield and oil quality in response to irrigation with saline reclaimed water and deficit strategies. Front. Plant Sci. 2019, 10, 1243. [Google Scholar] [CrossRef]
  35. Pellegrini, G.; Ingrao, C.; Camposeo, S.; Tricase, C.; Contò, F.; Huising, D. Application of water footprint to olive growing systems in the Apulia region: A comparative assessment. J. Clean. Prod. 2016, 112, 2407–2418. [Google Scholar] [CrossRef]
  36. Camposeo, S.; Vivaldi, G.A.; Russo, G.; Melucci, F.M. Intensification in Olive Growing Reduces Global Warming Potential under Both Integrated and Organic Farming. Sustainability 2022, 14, 6389. [Google Scholar] [CrossRef]
  37. Russo, G.; Vivaldi, G.A.; De Gennaro, B.; Camposeo, S. Environmental sustainability of different soil management techniques in a high-density olive orchard. J. Clean. Prod. 2015, 16, 498–508. [Google Scholar] [CrossRef]
  38. Morris, C.E.; Geniaux, G.; Nédellec, C.; Sauvion, N.; Soubeyrnd, S. One Health concepts and challenges for surveillance, forecasting and mitigation og plant disease beyond the traditional scope of crop production. Plant Pathol. 2022, 71, 86–97. [Google Scholar] [CrossRef]
  39. Famiani, F.; Farinelli, D.; Rollo, S.; Camposeo, S.; Di Vaio, C.; Inglese, P. Mechanical Fruit Harvest and Oil Quality in Very Large Size Olive Trees. Span. J. Agric. Res. 2014, 12, 960–972. [Google Scholar] [CrossRef] [Green Version]
  40. Tous, J.; Romero, A.; Hermoso, J.F.; Ninot, A. Mediterranean clonal selections evaluated for modern hedgerow olive oil production in Spain. Calif. Agric. 2011, 65, 34–40. [Google Scholar] [CrossRef] [Green Version]
  41. Rallo, L.; Barranco, D.; Castro-Gacía, S.; Connor, D.J.; Gómez del Campo, M.; Rallo, P. High-density olive plantations. Hortic. Rev. 2013, 41, 303–384. [Google Scholar]
  42. Fontanazza, G.; Bartolozzi, F.; Vergati, G. Fs-17. Riv. Frutticol. 1998, 5, 61. [Google Scholar]
  43. Rallo, L.; Barranco, D.; De la Rosa, R.; León, L. ‘Chiquitita’ olive. HortScience 2008, 43, 529–531. [Google Scholar] [CrossRef]
  44. Avidan, B.; Birger, R.; El Hadi, F.A.; Salmon, O.; Hekster, O.; Friedman, Y.; Lavee, S. Adopting vigorous olive cultivars to high density hedgerow cultivation by soil applications of uniconazol, a gibberellin synthesis inhibitor. Span. J. Agric. Res. 2011, 9, 821–830. [Google Scholar] [CrossRef] [Green Version]
  45. Camposeo, S.; Ferrara, G.; Palasciano, M.; Godini, A. Varietal behaviour according to the superintensive olive culture training system. Acta Hortic. 2004, 791, 271–274. [Google Scholar]
  46. Camposeo, S.; Godini, A. Preliminary observations about the performance of 13 varieties according to the super high density oliveculture training system in Apulia (southern Italy). Adv. Hortic. Sci. 2010, 24, 16–20. [Google Scholar]
  47. Tous, J.; Romero, A.; Hermoso, J.F. New trends in olive orchards design for continuous mechanical harvesting. Adv.Hortic. Sci. 2010, 24, 43–52. [Google Scholar]
  48. Webster, R. Statistics to support soil research and their presentation. Eur. J. Soil Sci. 2001, 52, 331–340. [Google Scholar] [CrossRef]
  49. Gómez, K.A.; Gómez, A.A. Statistical Procedures in Agricultural Research, 2nd ed.; Wiley: New York, NY, USA, 1984; p. 680. [Google Scholar]
  50. Schabenberger, O.; Pierce, F.J. Contemporary Statistical Models for the Plant and Soil Science; CRC Press: Boca Raton, FL, USA, 2001; p. 760. [Google Scholar]
  51. Wludyka, P.S.; Nelson, P.R. Two non-parametric, analysis-of-means-type tests for homogeneity of variances. J. Appl. Stat. 1999, 26, 243–256. [Google Scholar] [CrossRef]
  52. Villalobos, F.J.; Testi, L.; Hidalgo, J.; Pastor, M.; Orgaz, F. Modelling potential growth and yield of olive (Olea europaea L.) canopies. Eur. J. Agron. 2006, 24, 296–303. [Google Scholar] [CrossRef]
  53. Rosati, A.; Paoletti, A.; Al Hariri, R.; Famiani, F. Fruit production and branching density affect shoot and whole-tree wood to leaf biomass ratio in olive. Tree Physiol. 2018, 38, 1278–1285. [Google Scholar] [CrossRef] [Green Version]
  54. Rosati, A.; Paoletti, A.; Al Hariri, R.; Morelli, A.; Famiani, F. Partitioning of Dry Matter intoFruitExplains Cultivar Differences in Vigor in Young Olive (Olea europaea L.) Trees. HortScience 2018, 53, 491–495. [Google Scholar] [CrossRef]
  55. Rosati, A.; Paoletti, A.; Al Hariri, R.; Morelli, A.; Famiani, F. Resource investments in reproductive growth proportionately limit investments in whole-tree vegetative growth in young olive trees with varying crop loads. Tree Physiol. 2018, 38, 1267–1277. [Google Scholar] [CrossRef] [Green Version]
  56. Chen, Y.; Jackson, D.A.; Harvey, H.H. A comparison of von Bertalanffy and polynomial function in modelling fish growth data. Can. J. Fish. Aquat. Sci. 1992, 49, 1228–1235. [Google Scholar] [CrossRef]
  57. Rugmini, P.; Jayaraman, K. Intrinsic units of growth for teak trees. Trees 2009, 23, 51–58. [Google Scholar] [CrossRef]
  58. Rijal, B.; Weiskittel, A.R.; Kershaw, J.A. Development of regional height to diameter equations for 15 tree species in the North America Acadian Region. Forestry 2012, 85, 379–389. [Google Scholar] [CrossRef] [Green Version]
  59. Bartholomé, J.; Salmon, F.; Vigneron, P.; Bouvet, J.M.; Plomion, C.; Gion, J.M. Plasticity of primary and secondary growth dynamics in Eucalyptus hybrids: A quantitative genetics and QTL mapping perspective. BMC Plant Biol. 2013, 13, 120. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Von Bertalanffy, L. Quantitative Laws in Metabolism and Growth. Q. Rev. Biol. 1957, 32, 217–231. [Google Scholar] [CrossRef]
  61. Ricker, W.E. Computation and interpretation of biological statistics of fish populations. Bull. Fish. Res. Bd. Can. 1975, 191, 1–382. [Google Scholar]
  62. SAS Institute. SAS/STAT 12.1 User’s Guide: Version 9.2 Survey Data Analysis (Book Excerpt); SAS Institute Incorporated: Cary, NC, USA, 2012. [Google Scholar]
  63. Ratkoswsky, D.A. Statistical properties of alternative parameterizations of the von Bertalanffy growth curve. Can. J. Fish. Aquat. Sci. 1983, 43, 742–747. [Google Scholar] [CrossRef]
  64. Zar, J.H. Biostatistics; Prentic-Hall, Inc.: Englewood Cliffs, NJ, USA, 1984; p. 718. [Google Scholar]
  65. Chiraz, M.C. Growth of youg olive trees: Water requirements in relation to canopy and root development. Am. J. Plant Sci. 2013, 4, 1316–1344. [Google Scholar] [CrossRef] [Green Version]
  66. De la Rosa, R.; León, L.; Guerrero, N.; Rallo, L.; Barranco, D. Preliminary results of an olive cultivar trial at high density. Aust. J. Agric. Res. 2007, 58, 392–395. [Google Scholar] [CrossRef]
  67. Caruso, T.; Campisi, G.; Marra, F.P.; Camposeo, S.; Vivaldi, G.A.; Proietti, P.; Nasini, L. Growth and yields of the cultivar Arbequina in high density planting systems in three different olive growing areas in Italy. Acta Hortic. 2014, 341, 348–1057. [Google Scholar]
  68. Navas-Lopez, J.F.; Cano, J.; de la Rosa, R.; Velasco, L.; Leon, L. Genotype by environment interaction for oil quality components in olive tree. Eur. J. Agron. 2020, 119, 126115. [Google Scholar] [CrossRef]
  69. Navas-Lopez, J.F.; León, L.; Rapoport, H.F.; Moreno-Alías, I.; Lorite, I.J.; de la Rosa, R. Genotype, environment and their interaction effects on olive tree flowering phenology and flower quality. Euphytica 2019, 215, 1–13. [Google Scholar] [CrossRef]
  70. Tombesi, A.; Proietti, P.; Iacovelli, G.; Tombesi, S.; Farinelli, D. Vegetative and productive behaviour of four olive Italian cultivars and Arbequina according to super intensive olive training system in central Italy. Acta Hortic. 2011, 924, 211–218. [Google Scholar] [CrossRef]
  71. Connor, D.J. Towards optimal designs for hedgerow olive orchards. Aust. J. Agric. Res. 2006, 57, 1067–1072. [Google Scholar] [CrossRef]
  72. Gómez-del-Campo, M.; Connor, D.J.; Centeno, A. Yield determination in olive hedgerow orchards. II. Analysis of radiation and fruiting profiles. Crop Pasture Sci. 2009, 60, 443–452. [Google Scholar]
  73. Rosati, A.; Paoletti, A.; Pannelli, G.; Famiani, F. Growth is inversely correlated with yield efficiency across cultivars in Young Olive (Olea europaea L.) Trees. HortScience 2017, 52, 1525–1529. [Google Scholar] [CrossRef] [Green Version]
  74. Tous, J.; Romero, A.; Plana, J.; Hermoso, J.F. Olive oil cultivars suitable for very-high density planting conditions. Proceedings of the fifth international symposium on olive growing. Acta Hortic. 2008, 791, 403–408. [Google Scholar] [CrossRef]
  75. Proietti, P.; Nasini, L.; Ilarioni, L. Photosyntheticbehavior of Spanish Arbequina and Italian Maurino olive (Olea europaea L.) cultivars under super-intensive grove conditions. Photosynthetica 2012, 50, 239–246. [Google Scholar] [CrossRef]
  76. Connor, D.J.; Gómez-del-Campo, M.; Comas, J. Yield characteristics of N-S oriented olive hedgerow orchards, cv. Arbequina. Sci. Hortic. 2012, 133, 31–36. [Google Scholar] [CrossRef]
  77. Larbi, A.; Ayadi, M.; Ben Dhiab, A.; Msallem, M.; Caballero, J.M. Planting density affects vigour and production of ‘Arbequina’ olive. Span. J. Agric. Res. 2012, 10, 1081–1089. [Google Scholar] [CrossRef] [Green Version]
  78. Léon, L.; De la Rosa, R.; Rallo, L.; Guerrero, N.; Barranco, D. Influence of spacing on the initial production of hedgerow ‘Arbequina’ olive orchards. Span. J. Agric. Res. 2007, 5, 554–558. [Google Scholar] [CrossRef] [Green Version]
  79. Tombesi, S.; Farinelli, D. Evaluation of canopy elasticity, light penetration and reciprocal shading for optimal canopy management in high density hedgerow olive orchards. Acta Hortic. 2014, 1057, 315–320. [Google Scholar] [CrossRef]
  80. Tombesi, S.; Poni, S.; Palliotti, A.; Farinelli, D. Mechanical vibration transmission and harvesting effectiveness is affected by the presence of branch suckers in olive trees. Biosyst. Eng. 2017, 158, 1–9. [Google Scholar] [CrossRef]
  81. Boskou, D.; Camposeo, S.; Clodoveo, M.L. Table Olives as Sources of Bioactive Compounds. In Olives and Olive Oil Bioactive Constituents; Boskou, D., Ed.; AOCS Press Ltd.: Urbana, IL, USA, 2015; pp. 217–260. [Google Scholar]
  82. Lliso, I.; Forner, J.B.; Talón, M. The dwarfing mechanism of citrus rootstocks F&A 418 and #23 is related to competition between vegetative and reproductive growth. Tree Physiol. 2004, 24, 225–232. [Google Scholar] [PubMed] [Green Version]
  83. Fernández, F.J.; Ladux, J.L.; Searles, P.S. Dynamics of shoot and fruit growth following fruit thinning in olive trees: Same season and subsequent season responses. Sci. Hortic. 2015, 192, 320–330. [Google Scholar] [CrossRef]
Agronomy 12 03157 g001 550
Figure 1. Effect of cultivar on tree height (H, cm) for each year. Standard error (SE) of the mean difference and least significant difference (p < 0.001) are reported. Bars in each column indicate the standard deviation. *** means p < 0.001.
Figure 1. Effect of cultivar on tree height (H, cm) for each year. Standard error (SE) of the mean difference and least significant difference (p < 0.001) are reported. Bars in each column indicate the standard deviation. *** means p < 0.001.
Agronomy 12 03157 g001
Agronomy 12 03157 g002 550
Figure 2. Effect of cultivar on tree transversal canopy width (W, cm) for each year. Standard error (SE) of the mean difference and least significant difference (p < 0.001) are reported. Bars in each column indicate the standard deviation. *** means p < 0.001.
Figure 2. Effect of cultivar on tree transversal canopy width (W, cm) for each year. Standard error (SE) of the mean difference and least significant difference (p < 0.001) are reported. Bars in each column indicate the standard deviation. *** means p < 0.001.
Agronomy 12 03157 g002
Agronomy 12 03157 g003 550
Figure 3. Effect of cultivar on stem sectional area (A, cm2) for each year. Standard error (SE) of the mean difference and least significant difference (LSD) (p < 0.001) are reported. Bars in each column indicate the standard deviation. *** means p < 0.001.
Figure 3. Effect of cultivar on stem sectional area (A, cm2) for each year. Standard error (SE) of the mean difference and least significant difference (LSD) (p < 0.001) are reported. Bars in each column indicate the standard deviation. *** means p < 0.001.
Agronomy 12 03157 g003
Agronomy 12 03157 g004 550
Figure 4. Effect of cultivar on yield (Y, kg tree−1) for each experimental year. Standard error (SE) of the mean difference and least significant difference (LSD) (p < 0.001) are reported. *** means p < 0.001. n.s. means no significance.
Figure 4. Effect of cultivar on yield (Y, kg tree−1) for each experimental year. Standard error (SE) of the mean difference and least significant difference (LSD) (p < 0.001) are reported. *** means p < 0.001. n.s. means no significance.
Agronomy 12 03157 g004
Agronomy 12 03157 g005 550
Figure 5. Effect of cultivar on yield efficiency (YE, g cm−2) for each year. Standard error (SE) of the mean difference and least significant difference (LSD) (p < 0.001) are reported. *** means p < 0.001. n.s. means no significance.
Figure 5. Effect of cultivar on yield efficiency (YE, g cm−2) for each year. Standard error (SE) of the mean difference and least significant difference (LSD) (p < 0.001) are reported. *** means p < 0.001. n.s. means no significance.
Agronomy 12 03157 g005
Agronomy 12 03157 g006 550
Figure 6. The von Bertalanffy functions of tree height (H, cm) for eightcultivars from second to sixth year after planting.
Figure 6. The von Bertalanffy functions of tree height (H, cm) for eightcultivars from second to sixth year after planting.
Agronomy 12 03157 g006
Agronomy 12 03157 g007 550
Figure 7. The von Bertalanffy functions of stem sectional area (A, cm2) for eightcultivars from secondto sixth year after planting.
Figure 7. The von Bertalanffy functions of stem sectional area (A, cm2) for eightcultivars from secondto sixth year after planting.
Agronomy 12 03157 g007
Agronomy 12 03157 g008 550
Figure 8. The von Bertalanffy functions of cumulated yield (CY, t ha−1) for eightcultivars from second to sixth year after planting.
Figure 8. The von Bertalanffy functions of cumulated yield (CY, t ha−1) for eightcultivars from second to sixth year after planting.
Agronomy 12 03157 g008
Table
Table 1. Results of the repeated measures analysis carried out on canopy growth variables (Tree height, H; Crown width; Stem sectional area, A).
Table 1. Results of the repeated measures analysis carried out on canopy growth variables (Tree height, H; Crown width; Stem sectional area, A).
Source of VariationTree Height
(H, cm)		Crown Width
(W, cm)		Stem Sectional Area (A, cm2)
Year (Y) *********
2nd year167.48 e134.31 d3.880 e
3rd year228.04 d179.11 b16.804 d
4th year245.06 c193.68 a25.374 c
5th year271.29 b171.29 c32.965 b
6th year289.68 a192.27 a46.062 a
Cultivar (cv)********
Arbequina226.10 c164.77 c20.347 bc
Arbosana201.35 d140.60 d17.134 c
Cipressino285.53 a160.61 c36.531 a
Coratina243.50 bc201.43 a25.869 bc
Frantoio260.77 ab181.80 b29.654 ab
Fs-17261.77 ab192.00 ab23.617 bc
Leccino268.13 ab200.40 a26.915 bc
Urano175.33 e151.47 cd20.069 bc
Y × cv *********
**, *** indicate differences at p ≤ 0.01 and p ≤ 0.001.Means followed by the same letter in each column are not significantly different according to the SNK test at p < 0.05.
Table
Table 2. Matrix correlation (Person correlation) among growth parameters of all cultivars. p-values: *** p < 0.001.
Table 2. Matrix correlation (Person correlation) among growth parameters of all cultivars. p-values: *** p < 0.001.
Tree Height
(H, cm)		Crown Width
(W, cm)		Stem Sectional Area (A, cm2)
H1.0000.71 ***0.88 ***
W0.71 ***1.0000.58 ***
A0.58 ***0.58 ***1.000
Table
Table 3. Results of the repeated analysis carried out on yield response variables.
Table 3. Results of the repeated analysis carried out on yield response variables.
Source of VariationYield
(kg tree−1)		Yield Efficiency
(g cm−2)
Year (Y) ******
2nd year0.009 a6.896 a
3rd year0.736 bc52.737 b
4th year0.859 d49.438 d
5th year0.427 b15.151 b
6th year0.548 c12.811 c
Cultivar (cv)******
Arbequina0.488 a28.83 a
Arbosana1.051 b73.34 b
Cipressino0.082 c3.50 c
Coratina0.287 d12.13 d
Frantoio0.065 c1.13 c
Fs-171.126 a52.93 a
Leccino0.370 d13.91 d
Urano0.657 d33.49 d
*** indicate differences at p ≤ 0.001; ns indicates not significant difference. Means followed by the same letter in each column are not significantly different according to the Nemenyi–Damico–Wolfe Comparison test at p < 0.05.
Table
Table 4. The von Bertalanffy fitting functions passed by the Iterative Marquardt Method (IMM) for the three parameters.
Table 4. The von Bertalanffy fitting functions passed by the Iterative Marquardt Method (IMM) for the three parameters.
CultivarTree Height (H)Pseudo-R2
Arbequina L t = 2.59 ( 1 − e x p ( − 0.99 ( t − 0.08 ) ) ) 0.575
Arbosana L t = 2.19 ( 1 − e x p ( − 2.25 ( t − 0.55 ) ) ) 0.632
Coratina L t = 3.00 ( 1 − e x p ( − 0.51 ( t — ( 0.72 ) ) ) ) 0.595
Cipressino L t = 4.43 ( 1 − e x p ( − 0.30 ( t − ( − 0.72 ) ) ) ) 0.399
Frantoio L t = 3.40 ( 1 − e x p ( − 0.53 ( t − ( − 0.25 ) ) ) ) 0.681
Leccino L t = 10.00 ( 1 − e x p ( − 0.04 ( t − ( − 4.60 ) ) ) 0.671
Urano L t = 2.65 ( 1 − e x p ( − 0.27 ( t − ( − 1.21 ) ) ) 0.419
Stem sectional Area (A)
Arbequina L t = 77.12 ( 1 − e x p ( − 0.13 ( t − 0.13 ) ) ) 0.781
Arbosana L t = 62.79 ( 1 − e x p ( − 0.14 ( t − 0.64 ) ) ) 0.747
Cipressino L t = 86.78 ( 1 − e x p ( − 0.29 ( t − 0.86 ) ) ) 0.756
Leccino L t = 69.98 ( 1 − e x p ( − 0.21 ( t − 0.53 ) ) 0.758
Cumulated Yield (CY)
Arbequina L t = 4.67 ( 1 − e x p ( − 0.49 ( t − 0.99 ) ) ) 0.505
Arbosana L t = 11.31 ( 1 − e x p ( − 0.37 ( t − 1.02 ) ) ) 0.585
Cipressino L t = 0.76 ( 1 − e x p ( − 0.79 ( t − 1.02 ) ) ) 0.061
Fs-17 L t = 11.46 ( 1 − e x p ( − 0.42 ( t − 1.02 ) ) ) 0.567
Leccino L t = 49.47 ( 1 − e x p ( − 0.02 ( t − 1.04 ) ) ) 0.323
Table
Table 5. Twin-comparison significance of von Bertalanffy functions for three agronomic parameters for cultivars Fs-17 and Leccino vs. cultivars Arbequina and Arbosana (*** p= significant at p ≤ 0.001, n.s. = not significant; ARSS).
Table 5. Twin-comparison significance of von Bertalanffy functions for three agronomic parameters for cultivars Fs-17 and Leccino vs. cultivars Arbequina and Arbosana (*** p= significant at p ≤ 0.001, n.s. = not significant; ARSS).
CultivarTree HeightStem Sectional AreaCumulated Yield
Fs-17 vs. Arbequina-- -- 3.8× 10−14***
Fs-17 vs. Arbosana-- -- 3.6× 10−1n.s.
Leccino vs. Arbequina4.3 × 10−25***5.3 × 10−96***2.0× 10−52***
Leccino vs. Arbosana2.2 × 10−61***-- 2.9× 10−30***
Fs-17 vs. Leccino-- -- 2.0× 10−18***
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Camposeo, S.; Stellacci, A.M.; Romero Trigueros, C.; Alhajj Ali, S.; Vivaldi, G.A. Different Suitability of Olive Cultivars Resistant to Xylella fastidiosa to the Super-Intensive Planting System. Agronomy 2022, 12, 3157. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12123157

AMA Style

Camposeo S, Stellacci AM, Romero Trigueros C, Alhajj Ali S, Vivaldi GA. Different Suitability of Olive Cultivars Resistant to Xylella fastidiosa to the Super-Intensive Planting System. Agronomy. 2022; 12(12):3157. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12123157

Chicago/Turabian Style

Camposeo, Salvatore, Anna Maria Stellacci, Cristina Romero Trigueros, Salem Alhajj Ali, and Gaetano Alessandro Vivaldi. 2022. "Different Suitability of Olive Cultivars Resistant to Xylella fastidiosa to the Super-Intensive Planting System" Agronomy 12, no. 12: 3157. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12123157

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