**About the Editor**

#### **Piotr Salachna**

Professor Piotr Salachna is a lecturer of horticulture and floristry at the West Pomeranian University of Technology in Szczecin, Poland. He was born in Debica and earned his MS (2001) and PhD (2006) from the University of Agriculture in Szczecin. His main areas of expertise include the use of various natural biostimulants, biomaterials and nanoparticles in ornamental and medicinal plant production.

### *Editorial* **Trends in Ornamental Plant Production**

**Piotr Salachna**

Department of Horticulture, West Pomeranian University of Technology, 3 Papieza Pawła VI Str., ˙ 71-459 Szczecin, Poland; piotr.salachna@zut.edu.pl

Growing ornamental plants is a dynamically developing and profitable sector of plant production. In 2019, the value of the flower market on the largest global flower exchange, Royal FloraHolland, reached 4.8 billion euros. In 2021, despite the problems related to SARS-CoV-2 and the global pandemic, the value of the annual flower trade increased to 5.6 billion euros [1]. The power of the market and the floriculture sector lies in the variety of the assortment offered for sale. This is why it is so important to constantly introduce new species and cultivars, especially those with the most environmentally friendly production process [2,3]. Other factors important for the constant expansion of the floriculture industry include implementing new strategies for plant reproduction, regulating their growth and development, adapting production technologies to fit the idea of sustainable development, and optimizing supply chain management [4,5]. All these aspects are discussed in seven papers published in this Special Issue on the 'Trends in Ornamental Plant Production'.

The modern production of ornamental plants requires solutions that combine improved production efficiency with a more rational and environmentally friendly use of resources. The principle of sustainable development is a perfect answer to these challenges, as it allows for more effective use of the means of production and better protection of the environment in which a production facility operates. One of the elements of sustainable development in floriculture is biological progress achieved by implementing the species with low thermal requirements and relatively good resistance to diseases and pests. This topic is discussed in depth in an interesting review article [6] that characterizes specialty cut flowers (SCF) and their increasingly important role in the global and local floricultural market. The SCF group is not homogeneous, and it includes annual species, biennials, perennials, bulbs, and woody plants. The main source of their genotypes is the endemic flora of South Africa, Australia, and America. In comparison with traditional cut flowers (TCF), such as roses, gerberas, or anthuriums, the production of SCF flowers is considerably less energy-consuming and safer for people and the environment. This aforementioned paper presents a SWOT analysis that comprehensively assesses the external and internal factors determining the development potential of SCF and TCF flower production. It also discusses the issues related to the harvest, storage, and extension of the vase life of little-known cut flowers.

The environmental management technique called 'life cycle assessment' (LCA) is a tool defined in ISO standards and recommended in many EU documents. It enables a comprehensive assessment of environmental hazards. LCA identifies and prioritizes individual risks and is therefore helpful in the search for technological solutions aimed at maintaining optimal environmental quality. The authors in [7] describe the use of LCA in assessing the environmental impact of *Cyclamen persicum* and *Pelargonium* ×*hortorum* production in 20 horticultural farms from the floriculture district of Treviso, Veneto region. LCA analysis showed that the production of *P.* ×*hortorum* more strongly affected the environment than that of *C. persicum*, mainly due to fossil fuel consumption for heating greenhouses. However, the production of C. persicum is more variable and affects the environment in a more diverse way, especially in the field of eutrophication, acidification, and human toxicity potential. The authors point out that growing *C. persicum* in accordance

**Citation:** Salachna, P. Trends in Ornamental Plant Production. *Horticulturae* **2022**, *8*, 413. https://doi.org/10.3390/ horticulturae8050413

Received: 1 May 2022 Accepted: 2 May 2022 Published: 6 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

with the principles of integrated pest management and using compost to significantly limit the consumption of mineral fertilizers brings about measurable benefits for the environment and human health.

Plant growth regulators (PGRs) act at very low concentrations to stimulate, inhibit or otherwise modify plant growth. Although there are many research studies on PGRs, specific responses of individual plant species and cultivars make PGR use a still up-to-date and interesting topic. In [8], an international team presents the effects of abscisic acid, Nacetyl thiazolidine, gibberellic acid, salicylic acid, indole-3-butyric acid, and oxalic acid on the flowering and antioxidant potential of *Tagetes erecta*, a popular ornamental, edible, and medicinal plant. The authors demonstrated that PGRs effectively improved the flowering and antioxidant potential of *T. erecta* inflorescences depending on the genotype and type and concentration of PGR used. In *T. erecta* cv. 'Narangi', foliar treatment with different concentrations of oxalic acid considerably enhanced inflorescence biomass, the total content of polyphenols and flavonoids, as well as antioxidant capacity. As for *T. erecta* cv. 'Basanthi', the antiradical activity of the extracts was the most strongly influenced by spraying the plants with indole-3-butyric acid at 100 mg/l.

PGRs are commonly used for root induction and development in cuttings propagated in ornamental shrub nurseries. Auxins are particularly capable of stimulating simultaneous and steady root formation. This is especially important in the intensive reproduction of species with a poor ability to form adventitious roots on cuttings. Researchers in [9] present the impact of 1-naphthylacetic acid (NAA) on rooting effectiveness in *Syringa vulgaris* cv. 'Mme Lemoine', *S. vulgaris* cv. 'President Grevy', *Ilex aquifolium*, *Cotinus coggygria*, *Cotinus coggygria* cv. 'Kanari', and *C. coggygria* cv. 'Royal Purple'. All these shrub taxa, except for *Cotinus coggygria* cv. 'Royal Purple', positively responded to NAA application. A particularly beneficial effect of NAA on the rooting percentage of the cuttings, root volume, number of roots, and root length and diameter of the cuttings was observed in *Ilex aquifolium*.

PGRs are being increasingly replaced with biostimulants to improve plant growth and quality. A valuable source of biostimulants is natural polysaccharides and their derivatives. They are safe for the environment and therefore provide a perfect alternative to synthetic PGRs. The experiments reported in [10] investigated two types of biostimulant complexes, composed of depolymerized chitosan and carrageenan and depolymerized chitosan and xanthan, and assessed their stimulating effects on the growth and quality of *Eucomis autumnalis*. *E. autumnalis* is an endemic species grown as an ornamental and medicinal plant. The biostimulants were applied using a patented method of bulb coating prior to their planting. Both biostimulant complexes effectively improved growth and flowering, increased bulb yield, shortened the period of plant production, and enhanced the content of macroelements and total sugars in *E. autumnalis*. The coating of ornamental plant bulbs in biostimulants is an environmentally friendly biostimulation method with a promising future in sustainable cultivation systems.

The production of potted plants is developing very dynamically, and the practical aspects of their cultivation are always of great importance for producers. In [11], a group of researchers presents their findings related to the effect of temperature on the growth of hydroponically cultivated *Streptocarpus formosus*. This is still a little-known but very attractive plant, native to South Africa, and recommended for cultivation in pots, flower beds, and as a cut flower. A lower root-zone temperature (18 ◦C) increased the leaf number, leaf and root length, and fresh weight, while a higher root-zone temperature limited vegetative growth of *S. formosus*. Increasing the root-zone temperature during the plant dormancy did not stimulate flowering.

To streamline the supply chain in the floriculture industry, researchers from Ecuador and Spain developed the SCOR (Supply Chain Operations Reference) model and a multicriteria decision-making method [12] based on questionnaires filled by companies representing this sector. The model can be used to assess the performance of individual companies as well as the performance of the entire floriculture sector. The authors concluded that Ecuadorian floriculture farms need to improve their planning, procurement, and manufacturing.

In summary, the production of ornamental plants, just as in other horticulture sectors, is subject to constant changes. The long-term development of this industry, faced with the current energy crisis, post-pandemic challenges, and threats to global geopolitical stability, is highly uncertain. Therefore, to continue its constant development, it is necessary to adapt cultivation methods to actual conditions and take into account the energy transformation and biological, technical, and organizational advances. It is also increasingly important that all flower companies systematically implement sustainable development strategies, which requires a favorable political and social atmosphere.

**Acknowledgments:** I am deeply grateful to the authors of the papers for sharing their research results in this Special Issue, to the reviewers for their unbiased and insightful reviews, and to the entire team of the *Horticulturae* editors I had the pleasure to work with.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**


## *Article* **Measuring the Supply Chain Performance of the Floricultural Sector Using the SCOR Model and a Multicriteria Decision-Making Method**

**Luís Oswaldo Rodríguez Mañay 1, Inmaculada Guaita-Pradas <sup>2</sup> and Inmaculada Marques-Perez 2,\***


**Abstract:** This study aims to highlight the usefulness of studying the performance of supply chains (SC) at the sectoral level in greater detail through the combination of a disaggregated supply chain operations reference (SCOR) model, with a multicriteria decision-making approach, specifically using an AHP, to adjust the analysis to the particularities of the sector under study by stakeholders' judgements. The methodology was applied to the Ecuadorian flower industry, and the data for the analysis was from a survey of a group of companies that represent this sector. In addition, a focus group of SC experts weighted the model constructs as part of the analytic hierarchy process (AHP), and then the performance level for each construct was determined. According to the results methodologies, this model allows the classification of companies by their performance, as well as the performance of the aggregate sector. The processes that Ecuadorian flower companies need to improve on are planning, procurement, and manufacturing. The study's main contribution is developing a general framework for measuring the overall performance of SCs and how the results are obtained. This tool could help managers, consultants, industries, and governments to assess the performance of SCs, as well as improving SC management in order to increase the sector's competitiveness in the international market.

**Keywords:** supply chain performance; floricultural sector; SCOR; AHP

#### **1. Introduction**

Supply chains (SC), which are understood as a system of people, organizations, activities, resources, and data that are involved in the flow of products or services from the supplier to the customer [1] have developed continuously over the past forty years [2], especially during the months following the outbreak of the pandemic [3]. SCs evolve for two reasons: (i) to improve their performance and the system's functioning, as well as the elements that make it up, and (ii) to ensure consumer satisfaction [4]. Recently, the COVID-19 pandemic has exposed the vulnerability of supply chain risk management [5]. The concept of *supply chain management* (SCM) was first introduced in the 1980s to express the need to integrate all the processes of a supply chain, from the end-user to the original suppliers [6,7]. Since then, plenty of research has been undertaken both to study supply chain management in various fields of activity (industry, transportation, distribution, and agriculture, among others), as well as to measure and determine the ability of different SC processes to achieve their set goals, and also, to identify processes which could be improved in order to make SCs more effective and efficient. Among the most recent works, the following are worth highlighting: supply chain risk management (SCRM) [8], environmental supply chain management (GSCM) [2], and supply chain performance management (SCMP) [9,10], as well as those works exploring the use of technologies, such as artificial

**Citation:** Rodríguez Mañay, L.O.; Guaita-Pradas, I.; Marques-Perez, I. Measuring the Supply Chain Performance of the Floricultural Sector Using the SCOR Model and a Multicriteria Decision-Making Method. *Horticulturae* **2022**, *8*, 168. https://doi.org/10.3390/ horticulturae8020168

Academic Editor: Piotr Salachna

Received: 14 December 2021 Accepted: 9 February 2022 Published: 16 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

intelligence (AI) [11,12], the Internet of Things (IoT) [12], 3D printing [12], big data [12], and blockchain [12,13].

It should also be stressed that, nowadays, supply chain management (SCM) needs to adapt to more dynamic environments characterized by competition, rapidly evolving technologies, and higher consumer expectations for responsiveness [14]. All of these circumstances put pressure on supply chains to be more integrative and collaborative [4]. SC integration enables SC systems to shorten their response time because it allows the frequent and rapid changes in markets and demand to be managed [2]. Silvestro and Lustrato [15] emphasized the importance of integrating physical supply chain activities for several reasons: (1) it provides quick responses to fast-moving markets under conditions of demand uncertainty [16]; (2) it enables a closer collaboration between buyers and sellers along the supply chain, resulting in significant reductions in delivery times and costs [17,18]; (3) an integrated supply chain works better than each process on its own [19,20]; and (4) it maximizes information visibility through the use of the Internet and the involvement of all the parties in the supply chain [21,22]. Given that information transparency along the supply chain has become a priority for buyers and suppliers, highly complex supply chain networks tend to improve their performance when integrated [13,23].

There are several models and techniques for assessing SC performance, of which the following stand out: (a) the supply chain operation reference (SCOR) model, which is a model that describes, communicates, assesses, and identifies opportunities for improving workflow efficiency [4]; (b) the Global Supply Chain Forum (GSCF) model, which provides a systematic overview of the balance, alignment, and management of SC technological capabilities to achieve successful management [4]; (c) the Triple-E model, developed by Simao et al. (2021) [10], which focuses on three performance dimensions: efficiency, efficacy, and environmental impact [10]; and (d) the BSC model, developed by Kaplan and Norton, which allows managers to obtain an overall view of a supply chain's performance [24,25].

Developed and endorsed by the Supply Chain Council (SCC, focuses primarily on defining the core processes that make up a supply chain system) as an industry-standard diagnostic tool, the SCOR model emerged in 1996 and, since then, it has evolved from its initial design to its current 12th version (The SCC with American Production and Inventory Control Society (APICS) produced the latest SCOR version, 12.0, in 2017) [10]. It is a powerful tool for structuring, assessing, and comparing supply chain practices and performance [26,27]. Furthermore, it is known to be an integrated approach based on the idea that the SC is an interconnected structure that combines SC processes, performance metrics, best practices, and technology into a single framework for the effective communication and the continuous improvement of the SC [5]. Moreover, it has been increasingly used by practitioners and academics involved in value chain management [28] and, in general, it is a global benchmark that enables the comparisons of SCs [29].

In recent years, several studies of supply chain management have combined the SCOR model with multi-criteria techniques to improve the analysis of SCs. Table 1 provides a list of these combinations, along with the works.

**Table 1.** Combinations of the SCOR model with multi-criteria techniques for studying supply chain management.



#### **Table 1.** *Cont.*

Source: Authors' review.

In this regard, various studies have combined the analytic hierarchy process (AHP) [41] with the SCOR model in supply chain analyses [42]. The most relevant works are listed in Table 2.

**Table 2.** Research using both the SCOR model and AHP approaches to improve supply chain performance.


Source: Authors' review.

The SCOR model is based on a hierarchical structure with four different levels. Level 1 presents the different types of processes and identifies the scope and content of the supply chain. Level 2 presents the process categories that include the operations (sub-processes), while Level 3 corresponds to the process elements that form the individual process configurations (tasks that are grouped by activities in each sub-process) [48]. The first point to consider, when analyzing the SCOR model processes, is to check which ones need to be analyzed, as well as the level of disaggregation, i.e., whether they are primary processes, sub-processes, specific activities, or tasks. In addition, it is necessary to establish a measuring system with which the values that reflect the level of performance of these processes can be calculated [51].

In general, it can be observed that previous SC assessments using the SCOR model and the associated performance metrics predominantly analyzed supply chains' main processes, but very few of them considered a division into sub-processes and activities, and almost none on them considered a disaggregation into tasks [52,53]. However, an analysis of the individual processes, sub-process, activities, and tasks could help to better identify where the problems originate in each process; in other words, it would enable us to identify which process, or sub-processes, activities, or tasks are more critical, why they are critical, what the causes are, and how they can be corrected.

This approach has been applied to the Ecuadorian flower industry.

In distributing and selling perishable products, such as flowers, supply chain management is a crucial and decisive element in improving their efficiency, productivity, and the overall distribution and sale processes. Ecuador is the third-largest producer of cut flowers in the world, where flower companies are a significant source of income and employment for this country [1]. The Ecuadorian floriculture industry is characterized by short product life cycles, a wide product variety, volatile and changing demand, and long and inflexible delivery processes [2,3]. Since 2021, due to the COVID-19 pandemic, it has also been beset by international trade and transport problems [4], which have affected the production and marketing of thousands of products traded around the world. With regard to the Ecuadorian flower sector, in particular, the greatest impact of the COVID-19 crisis has been due to a rise in the price of inputs and fertilizers [5], as well as the lack of air freight companies that could deliver floral products on time, with the required quality [5,6]. These constraints and difficulties are currently exposing the supply chain (SC) management to a variety of risks and uncertainties [7,8]. Any attempt to improve the distribution channels in the floriculture sector requires a detailed analysis of its supply chain performance. The proposed performance analysis model was applied to a set of flower companies to assess how well the supply chain was performing at the individual level, and to identify the problems. The individual values were then aggregated to establish whether the supply chain was working well in sectoral terms, and similarly, where the problems lay. Currently, the Ecuadorian flower sector does not have a methodology or model to measure the performance of the supply chain. We apply this proposal to the Ecuadorian flower industry.

The content of the manuscript is structured as follows. First, the SCOR model approach, followed by the analysis of the floriculture supply chain, is explained. Then, consultations that are carried out with the sector's companies to obtain each company's performance data is described, as well as the order of processing and aggregating the survey results to work out the individual performance values. Next, using an AHP, the performance results are interpreted and discussed by analyzing the sector's performance through the individual and aggregated results. Finally, the practical and theoretical implications of the proposed methodology are discussed, as well as the most relevant issues and suggestions for future research.

With this purpose, here, we present a methodology for examining supply chains' levels of performance at the sectoral level, combining the SCOR model, that is disaggregated to Level 4, with a multi-criteria methodology (AHP) to adjust the analysis to the specificities of the sector under study, based on stakeholders' assessments. In particular, by applying the proposed methodology, we can determine which processes are the most critical, and why, as well as the causes of performance problems and how those can be corrected.

#### **2. Materials and Methods**

Figure 1 summarizes the methodology used to analyze the Ecuadorian flower sector based on the structuring of a supply chain, as defined by the SCOR model, in combination with an AHP approach.

**Figure 1.** Methodology. Source: Authors' diagram.

As mentioned earlier, the 12th version of the SCOR model establishes a performanceanalysis system with up to four levels. Thus, in addition to the first level of the supply chain, which is composed of six main processes (planning, procurement, manufacturing, distribution, return, and management), three more levels can be differentiated, namely, sub-processes, activities, and tasks, where each one might influence the main processes' performance and should, therefore, be analyzed [54].

Previous studies dealing with SC measurement, using the SCOR model, examined four, five, or six of its processes. In this study, we examined the planning, procurement, manufacturing, distribution, and return processes, which are those that are directly linked to the supply chain [55].

For example, process 1, planning, is broken down into three sub-processes [56] (see Figure 2). Each of these sub-processes is, in turn, disaggregated into different activities. For example, sub-process 1.1, supply chain planning, is decomposed into four activities, each of which is then divided into tasks (see Figure 2).

As previously pointed out, the greater the disaggregation, the better the analysis can identify the failures and where action is needed [57,58]. Our methodological proposal is to disaggregate each of the five main supply chain processes, up to level 4, which corresponds to the individual tasks.

**Figure 2.** *Cont*.

**Figure 2.** Disaggregation of the SCOR model's supply chain main processes into sub-processes, activities, and tasks. Source: Authors' diagram.

The proposed evaluation method is to assess all the processes and activities of the SC, in regard to their compliance with the standards. Thus, our SC performance assessment is based on checking whether the tasks, activities, sub-processes, and processes were completed or not [51]. Consequently, each company was sent a survey with a proposed breakdown of the SC and was asked to indicate whether or not it carried out the different individual tasks. A good performance involved completing each one of the defined tasks (i.e., the companies replied to dichotomous questions with yes or no answers), which meant that all activities, sub-processes, and processes were performed. After collecting the answers, we assigned a one to those tasks that were performed (for answering YES), and for those that were not carried out, a zero was assigned (for answering NO). Additionally, it is necessary to first carry out a systematic evaluation of each particular process and establish how the results are aggregated afterward to obtain a metric for measuring the SC's level of performance within the sector. To calculate the SC's overall performance, the aggregate value of the performance index must be calculated by weighting each SC process according to its relevance in the sector of activity. By using the AHP technique, it

is possible to distinguish the importance of each process of the SC when aggregating the data. This distinction is made by the sector's stakeholders, based on the importance they attach to every process or activity, since the aim is to provide a metric that considers the particularities of each sector. To aggregate the single values obtained from the rating of the tasks, activities, sub-processes, and processes, Aliaga Rota et al. [51] proposed using the average of the separate scores given for each sub-process, which, in turn, are gathered from the average of the scores obtained by the activities involved in that sub-process, and so on [51,59]. The aggregation of the results is carried out, considering the importance given to each process by the stakeholders participating in the AHP [60]. As a result of this aggregation, the SC performance of each company in the sector can be analyzed.

The AHP is a technique by which experts in a given field make pairwise comparisons in order to derive priority scales. Furthermore, it provides an algorithm to solve complex decision-making problems that are broken down into a hierarchy [61,62]. This method involves two main steps [63]. First, each stakeholder completes a pairwise comparison survey, which is designed based on the hierarchy previously established, indicating which of the two elements that are compared they consider to be more important and, using Saaty's scale (Table 3), how much more important they are.

**Table 3.** Saaty's scale.


Source: Leal [63].

The second stage of the AHP is to calculate the vector of priorities, according to the following formula:

$$p\_{rj} = \frac{1}{a\_{ij} \* \sum\_{k=1}^{n} \frac{1}{a\_{ik}}} \tag{1}$$

where *j* is the element for which the priority is calculated, *i* is the base element for the comparison, *aij* is the value of the alternative *i* that is compared with the alternative *j*, by the criteri *k*, *aik* is the value of alternative *i* for the criteria *k*, *prj* is the priority of the alternative *j* against the considered criterion, and *n* is the number of criteria.

The coherence of the preferences of stakeholders was studied based on the "consistency", which should be taken into account in order to consider whether opinions are valid for determining the priorities. The consistency analysis requires calculating the "consistency index" (*CI*) of Saaty's Scale for each preferences matrix.

$$CI = \frac{\lambda\_{\max} - n}{n - 1} \tag{2}$$

The "consistency ratio" (*CR*) is calculated from the *CI*. The *CR* is a ratio of the *CI* and *RI*:

$$CR = \frac{CI}{RI} \tag{3}$$

where the *RI* is the average value of the *CI* of pair-wise comparisons matrices of the same order, randomly obtained. When the *CR* is less than 10% (0.1), the matrix is considered to offer acceptable consistency. Saaty's scale calculated the random indices of the RI for different matrix sizes to obtain *CR*.

There are two possibilities, when aggregating results, to analyze the company performance at the sectoral level. The first one is to aggregate the individual results obtained from individual analyses. It is then necessary to determine how the individual values will

be aggregated, so that they can be interpreted in sectoral terms. The results may be aggregated by the company type, but another way is to aggregate them according to the tasks performed by all of the companies. This way, the sector performance indicator for each task is calculated; the aggregation of these indicators will result in a sector performance indicator for each activity. By aggregating these, we can then calculate the performance indicators of the sub-processes. Finally, by aggregating the latter, we can establish the level of performance of each primary process. Regardless of the method, the representativeness of each company in the sector should be considered when aggregating the individual data. This representativeness can be determined by the company's turnover. Nevertheless, in both cases, the aggregation of the processes must be carried out in consideration of the importance of each process that is given by the stakeholders, who are participating in the AHP.

Once the ratings of the five main processes have been obtained and aggregated according to the weights defined by the AHP, the overall performance score of the SC can be achieved. Table 4 contains Kusrini et al.'s [64] proposal for rating supply chains' performance, according to a scale. The scale can be used to rate each company's performance and that of the sector, as well as the disaggregated results of the tasks, activities, processes, and sub-processes.



Source: Kusrini et al. [64].

#### **3. Case Study**

We tested the proposed methodology in a case study of the Ecuadorian flower industry. Ecuador is currently the third-largest exporter of cut flowers worldwide. Although Ecuador had increased its exports up until 2019, it did so at a far lower rate in both value and volume than other flower-exporting countries. The subsequent fall became more marked in 2020, due to the restrictions brought on by the pandemic [65].

Flower production has, historically, been concentrated in the provinces of Pichincha, with 62% of the production, and Cotopaxi, with 21% of production. The rest of the country's provinces, including Guayas, Imbabura, and Azuay account for the remaining 17% [66]. Furthermore, it should be noted that the industry is presently in the midst of a wave of acquisitions. In the first quarter of 2021, the largest flower company in Ecuador (Hilsea Investments, with annual sales of around USD 50,000,000) was transferred to the investment company Sunshine Bouquet, which belongs to a group of the 500 largest companies in Colombia. Additionally, a number of other small firms, namely, Alma Roses, Sisapamba, Natuflor, Romaverde, Bellarosa, Rose Connection, Qualisa, and Florasani were taken over by the investment company Elite, one of the 500 largest companies in Ecuador [67].

For the case study, we selected a representative sample of floricultural companies from the Expoflores directory, where data was accessible. Specifically, the first 96 Ecuadorian flower companies (Order established according to the income data published by the Superintendencia de Compañías del Ecuador, https://www.supercias.gob.ec/portalscvs/, accessed on 30 April 2021) were chosen. According to the value of sales, these represented approximately 70% of the more than USD 800,000,000 turnover of the sector in 2019 [68]. As seen in Table 5, the turnover in these 96 companies varies from the largest to the smallest, i.e., from USD 12,000 to USD 47,000,000. The highest concentration of companies corresponds to those with a turnover of between USD 12,000 and USD 13,500,000. This group accounts for 93% of the total turnover in the industry.


**Table 5.** Frequency distribution by turnover (USD).

Source: Authors' calculations.

Of the 96 companies to which we sent the survey, 29 answered. Table 5 shows the results of the frequency distribution analysis of the companies that answered the questionnaire. This analysis was performed to verify how representative they are. The frequencies were calculated according to the firms' turnovers. Most of the companies in the sample that answered were from the groups with the largest number of flower companies. Table 5 shows that the weight of the companies in the sample is similar to the weight of all companies in the Ecuadorian floriculture industrial sector in each turnover group.

We used a digital questionnaire (https://docs.google.com/forms/d/1GZDfiJLW5D7 IdsgrpjbXI696UlHAmH5tEOGQmmk-RKc/edit, accessed on 3 June 2020) to collect the preferences. Although various alternatives were available, we chose Google forms for this study. The form was sent to the companies' representatives by email, along with a letter explaining the study's purpose: to analyze the supply chain of Ecuador's flower industry, identify the key problems, and improve certain aspects.

The questionnaire was divided into four sections. The first section described the objective of the study and the survey and asked for the company's details. It also provided information on the Ecuadorian flower industry and the SC processes, as defined by the SCOR model. The following sections contained the questions about the supply chain processes. These were broken down to task levels. Respondents had to indicate which sub-processes, activities, and tasks they performed for each process.

Twenty-nine companies answered the survey, which accounted for approximately 20% of the total turnover of the selected sample, i.e., USD 180,000,000. Falcon Farms is the second-largest flower company, in terms of turnover, within this group of companies. The tasks were graded according to their fulfillment: a positive answer scored a one, and a negative answer scored a zero. Next, the average of the scores obtained for each sub-process was calculated, and then the average of the processes' scores was calculated for each of the 29 companies that answered the survey [51,69].

The importance of the SCOR model processes was determined by a group of stakeholders in the Ecuadorian floriculture sector by means of an AHP model. For this purpose, an online survey was undertaken. It was assumed that all members of the group had the same level of importance in the decision-making processes [70]. The stakeholders were: representatives of floriculture companies (6), supply chain teachers (2), experts in floriculture issues (1), and experts in quality control (1). The AHP methodology was applied to calculate the weights of the Level 1 metrics and the attributes of the SCOR model. A questionnaire was carried out that was divided into four sections. The first section described the study's objective and that of the questionnaire and requested information on the company or institution's identity. Furthermore, it included information on the Ecuadorian flower sector and descriptions of the performance attributes of the Ecuadorian supply chain, as well as the AHP hierarchy, with the objective of redesigning its elements, metrics, and processes, and an explanation of Saaty's scale for making the comparisons. The second section listed the questions related to the pairwise comparisons of the supply chain processes' attributes, in order to determine their importance (10 questions). The third section presented questions regarding the importance of the metrics for each attribute (7 questions). Finally, in the

fourth section, the questions about the relevance of the performance metrics to the supply chain processes were included (10 questions).

After collecting the preferences of the stakeholders by the processes considered, we aggregated the preferences of individuals and obtained the preferences matrix from stakeholders. This was used to calculate stakeholders' priorities. Table 6 shows the weights given by the stakeholders.

**Table 6.** Weighted results by process.


Source: Authors' calculations.

We have a consistency ratio of *CR* = 0.0209 ≤ 0.10, so the data comparing the main criteria pairs is appropriate and does not need to be re-evaluated.

Once the weights of the processes were calculated, the scores for each company were computed according to the results of the survey.

#### **4. Results**

We determined the supply chain performance level for each of the 29 companies that answered the questionnaire using the survey data. Then, the individual results were aggregated to determine the level of performance of the supply chain at the sector level. According to the analysis and the classification proposed by Kusrini [64], the 29 firms showed a good overall performance (see Tables 4 and 7). This rating was obtained because the score achieved by each of the five processes of the SCs of the companies studied was rated as "good" (G). However, it should be noted that, as the scores obtained for the processes were less than one, all procedures need to be reconfigured and improved.

**Table 7.** Calculation of the sector-level performance metrics.


Source: Authors' calculations.

The turnover of the 29 companies showed a relatively low correlation (0.08) with the SC performance index, which means that the supply chain performance does not explain the sales behavior.

When considering each of the SCOR processes at the sector level, it should be highlighted that the processes with the highest GAPs (GAP: gap or difference between the intended result and the actual result obtained by the research), weighted according to their weight, were planning (0.06) and manufacturing (0.04) (see Table 8).


**Table 8.** Supply chain performance GAPs at the sector level by process.

Source: Authors' calculations.

To improve our analysis results and to better identify where the most critical points of the SC are, we also examined the sub-processes.

Regarding the analysis of the sub-processes, of the 16 sub-processes examined (shown in Figure 2), four were rated as "excellent" (E), eleven as "good" (G) and one as "average" (A) (see Table 9). Hence, the floriculture sector should pay attention to the sub-processes with "good" and "average" ratings.

**Table 9.** Supply chain performance GAPs at the sector level by sub-process.


Source: Authors' calculations.

By evaluating the different activities of each sub-process, we assigned each activity the corresponding Kusrini rating. As a result, several activities with "good," "average," and "marginal" ratings need to be improved. The most critical activities, which received the lowest ratings, are:

	- One-to-one (task) training, i.e., there is a training program for new employees (45%).
	- Methods for estimating needs related to the task, i.e., statistical techniques are used to estimate the needs and validate the data sources employed to make these estimates (59%);
	- The authorization of casual purchases related to each task, i.e., casual purchases that do not exceed a certain amount, as defined by the company, are authorized (66%);
	- Feedback from customers concerning each task, i.e., the company undertakes customer satisfaction surveys at least once a year (52%);
	- Workforce and skill versatility, i.e., workers regularly switch jobs since they know how to do them (66%);
	- Sales management related to each task, i.e., the company undertakes customer satisfaction surveys (62%);
	- Returned goods management, i.e., there is a system for classifying returned goods (69%);
	- Accounting transactions, i.e., inventory adjustments are regularly carried out as part of the returned goods process (69%).

By examining the results at the company level, we can determine which companies in the sector are having the greatest problems and, therefore, need to optimize their processes. It also enables us to see which processes in each company are performing poorly. The analysis at the company level can be done individually, or by groups of companies. Table 10 shows that no company received a "poor" or "marginal" rating; four companies were rated with "average" performances, thirteen companies gave a "good" performance, and twelve gave an "excellent" performance.


**Table 10.** Summary of the SC performance metrics for the 29 companies that answered the survey.

Source: Authors' calculations.

Together, the four flower companies with an "average" performance rating (see Table 10) achieved a performance level of 60%. Their turnover ranged from USD 12,374 to USD 2,400,000 during 2012–2019. The process with the highest GAP was planning in the four companies, at 0.19; the remaining processes showed similar GAPs, close to 0.05 (see Table 11).


**Table 11.** Supply chain performance GAPs of groups of companies that answered the survey by index performance.

Source: Authors' calculations.

The thirteen floriculture companies that achieved a "good" performance rating have a turnover ranging from USD 118,000 to USD 26,400,000 during 2012-2019. All together, they achieved a performance of 84%. Planning and manufacturing stand out in these companies as the processes with the highest GAPs (see Table 11).

The minimum turnover of the twelve flower companies that achieved an "excellent" rating was USD 636,000, and the maximum turnover was USD 9,500,000 in the 2012–2019 period. Together, the companies achieved a performance level of 94% (see Table 10), which can be considered as "excellent."

The process that had the most GAPs was the manufacturing process, whereas the distribution and return processes did not show any GAPs (see Table 11).

Regarding the sub-processes' performance, in the group of companies (4) with average performances, two sub-processes obtained a "poor" rating, eight sub-processes obtained an "average" rating, and six sub-processes obtained a "good" rating. Therefore, no subprocesses achieved an "excellent" rating in this group.

The sub-processes carried out by the group of companies with the lowest scores (i.e., "poor") were supply chain planning (38%) and customer expectation management (25%). The following sub-processes received an "average" rating: the linearity of the supply chain (the alignment of supply and demand) (50%), inventory management (69%), strategic sourcing (65%), buying products and services (63%), the development of the supply chain infrastructure (63%), sales logistics (58%), receiving returned goods and storage (50%), and repair and refurbishment (50%). Those considered to have a "good" performance were supplier management (75%), the management of inbound logistics (70%), supplier relationships and collaboration (75%), the product (75%), storage and compliance (70%), and customer and business partner management (75%).

In the group of the companies (13) that achieved a good performance level, the analysis by sub-processes resulted in five sub-processes with an "excellent" rating, eight with a "good" one, and three with an "average" rating.

In this group, the sub-processes with an "excellent" rating were inventory management (96%), strategic sourcing (95%), supplier management (90%), the product (92%), and repair and refurbishment (100%). Those with a "good" rating were supply chain planning (81%), the linearity of the supply chain (the alignment of supply and demand) (83%), buying products and services (85%), the management of inbound logistics (88%), the development of the supply chain infrastructure (73%), sales logistics (74%), storage and compliance (89%), and customer and business partner management (77%), while those with average scores were supplier relationships and collaboration (69%), receiving returned goods and storage (65%), and customer expectation management (62%).

In relation to the group of companies rated as "excellent", the analysis of sub-processes resulted in twelve sub-processes with an "excellent" rating, and four with a "good" rating.

Regarding the third SCOR level, of the 48 activities studied, 24 achieved an "excellent" rating, 16 activities showed a good performance level, seven activities exhibited an average level, and one activity was considered to have a "marginal" performance level. Thus, the

activities that the floriculture sector should pay more attention to are those with "good, average, and marginal" ratings, which accounted for 51% of the studied activities.

#### **5. Discussion**

Our proposed methodology shows that it is possible to analyze the performance of the supply chain at the sectoral level by applying the SCOR model and the AHP in a representative sample of companies in the sector. In previous research, these analyses were more limited. The majority did not disaggregate the SCOR model, and only studied the first level, regarding the processes [44,46–49]. Other studies were on unique companies and the results cannot be viewed as sectoral results [42,46,47]. There are some studies where the proposed methodology only studied a stage in the supply chain, and only one element in this stage. For example, Wang et al. [43] applied the model to a raw material supplier. Other works analyzed the sector and does not use company data. These used focus groups or stakeholders' opinions instead [46]. Sutoni et al. [47] used observations, interviews, literature reviews, and information or dates, but these were from a single company.

In general, an analysis of the individual processes, sub-process, activities, and tasks would enable us to identify which process, sub-processes, activities, or tasks, are more critical why they are more critical, what the causes are, and hence, how they can be corrected. The methodology proposed makes possible this analysis at the individual level, for each company, and at the sectoral level.

The proposed SC performance analysis method can be used with any company and with any industry, since it allows the evaluation of groups of companies that make up an industry or represent it, by aggregating the individual values. Additionally, it is a tool that determines where problems lie and their causes. It also helps to increase the competitiveness of firms and industries, and achieves long-term goals by supporting company managers, governments, policymakers, and every industry in the design of policies and measures to fix issues. Managers can use the results to benchmark their company's competitiveness and performance against other companies in their sector, or in sectors with similar characteristics. In the policy field, sector-level analyses can be used for planning purposes.

#### **6. Conclusions**

This study contributes to the current literature with a methodological proposal that uses the SCOR model, combined with an analytical hierarchy process (AHP) to measure the performance of supply chains within a given sector. We applied this methodology to individual flower companies to assess the degree of compliance of their supply chain (SC) processes and activities, with the standards set by the SCOR model regarding SC performance. In addition, we determined which tasks or activities in each company were not carried out and traced the origin of potential problems back to specific SC sub-processes, which should be checked. Moreover, the aggregation of performance data at the individual level enabled us to assess the performance at the sector level.

Here, we employed the proposed methodology to identify, calculate, and handle potential SC performance issues in the Ecuadorian floriculture industry. By conducting an in-depth study of Ecuadorian flower companies, we have been able to draw a comprehensive picture of this industry.

Based on the results for the 29 companies that answered the survey, the SC performance of the Ecuadorian flower sector is 85%. The results showed that all processes need to be improved, especially the planning and manufacturing processes. When analyzing the flower companies by groups according to their rating, the planning, procurement, and manufacturing processes with an "average" rating (50–70) showed large performance GAPs. Meanwhile, the planning and manufacturing processes of companies with a score of 70–90, which is considered "good", had the largest performance GAPs. Moreover, within the group of companies with a performance score that was higher than 90, the manufacturing process is the most critical.

Therefore, Ecuadorian flower companies should work on the first five SCOR processes, applying the standards suggested in the model. To excel, they should work on all processes, which also depend on external factors, in order to improve the flower industry's supply chain.

When conducting studies such as this one, the sample must be as representative of the industry as possible. Therefore, in general, obtaining a high response rate allows for a better analysis and results that reflect the realities of the sector. Hence, the scope of future studies about the Ecuadorian flower industry must be expanded to include a larger number of companies and a broader field of analysis, considering performance attributes such as reliability in compliance, the speed of responses, agility, costs, and the efficient management of assets and their components.

**Author Contributions:** Conceptualization, I.M.-P. and I.G.-P.; methodology, I.M.-P.; software, L.O.R.M.; validation, I.M.-P. and I.G.-P.; formal analysis, L.O.R.M.; investigation, L.O.R.M.; resources, L.O.R.M.; data curation, L.O.R.M.; writing—original draft preparation, L.O.R.M.; writing—review and editing, I.M.-P. and I.G.-P.; visualization, I.G.-P.; supervision, I.M.-P.; project administration, I.M.-P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** I Superintendencia de Compañías del Ecuador (https://www.supercias. gob.ec/portalscvs/ accessed on 30 April 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Efficacy of Different Concentrations of NAA on Selected Ornamental Woody Shrubs Cuttings**

**Endre Kentelky 1,\*, Denisa Jucan 2,\*, Maria Cantor <sup>2</sup> and Zsolt Szekely-Varga <sup>1</sup>**


**Abstract:** Ornamental woody shrubs are used in landscape design worldwide. Their propagation can be made generatively and vegetatively. Vegetative propagation methods are mostly used by nurseries, as such methods are quick and the newly propagated plants inherit the genetics of the mother plant. However, rooting in some woody plants is slow and, unfortunately, sometimes produces only a small number of rooted cuttings. In this study, shoot cuttings from six selected ornamental woody shrubs were subjected to different concentrations of rooting stimulators (0.5 (NAA5) and 0.8 (NAA8) % concentrations of 1-Naphthylacetic acid; cuttings without treatment were considered as control) and propagated in two different periods (spring and summer). Our results show that significant changes were obtained in the plants under the different treatments. Most of the plants showed a positive response to both treatments, expect for *Cotinus coggygria* 'Royal Purple', which, compared to control, registered decreases in all the tested parameters under NAA5 treatment. *Ilex aquifolium* was the species that showed increments in all the parameters when NAA treatments were applied. In conclusion, our research suggests that NAA increases rooting in ornamental woody shrubs, although in some cases rooting could be a species-dependent process.

**Keywords:** 1-Naphthylacetic acid; stimulants; propagation; rooting; shrubs

#### **1. Introduction**

Interest in ornamental woody plants has been increasing in recent years and they are an important part of the horticulture industry. Ornamental shrubs are valued for their countless landscape uses and need to be part of our modern managed landscapes, for instance, as roadside trees in public parks which provide shade, shelter, clean pollutants in the air, and are a source of beauty [1,2].

With growing demand for ornamental shrubs, nurseries and horticulturists need new propagation methods in order to meet it. These types of plants can be propagated generatively and also vegetatively [3,4]. All woody plants are capable of producing flowers and seeds; however, they require favorable environmental conditions and take many years to develop [2]. Most of them are propagated by vegetative methods, by cuttings, because such methods are quicker and also because the plants will retain the characteristics and genetics of the mother plants [5,6].

The rooting of ornamental woody plants can sometimes be a hard and slow method, and does not have a high success rate. Propagation by cuttings is a vegetative method widely used for different plant species. Ornamental woody plant nurseries have developed different techniques to successfully improve the rooting of cuttings. However, in spite of controlled environmental conditions, high economic losses are still being sustained as a result of insufficient root formation [7,8]. In addition to environmental factors, the successful rooting of woody plant cuttings could be affected by different elements, such

**Citation:** Kentelky, E.; Jucan, D.; Cantor, M.; Szekely-Varga, Z. Efficacy of Different Concentrations of NAA on Selected Ornamental Woody Shrubs Cuttings. *Horticulturae* **2021**, *7*, 464. https://doi.org/10.3390/ horticulturae7110464

Academic Editor: Piotr Salachna

Received: 14 October 2021 Accepted: 1 November 2021 Published: 4 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

as nutritional levels of the mother plant, cutting type, rooting medium, and even by the manipulation and treatments applied [9].

Hormones could improve the percentage of radicals and also reduce propagation time [10]. Plant hormones are substances naturally produced by plants which control plant functions and development, such as root growth, fruit maturation, and plant growth [11,12]. Hormones are important and crucial elements which are required to control plant development through the life cycle, from embryogenesis to reproductive development [13–15].

Adventitious root formation is a physiological process enabling the propagation of cuttings of many plant species [7]. Previous reports suggested that adventitious root formation in woody plants could be associated with the action of endogenous auxin and can be triggered by the application of exogenous auxin, such as 1–Naphthylacetic acid (NAA) [16–18]. NAA is used to influence/induce and to ensure a greater rooting capacity of cuttings and the better establishment of many shrubs and trees [8,10,19]. NAA could even effectively improve the survival rate of cuttings or shorten the rooting period [20].

*Syringa vulgaris* L., commonly known as Lilac, is a deciduous shrub including more than 40 species distributed around Europe and Asia [21,22]. *Ilex aquifolium*, native to southern Europe, northwest Africa, and southwest Asia, commonly known as English holly, is a dioecious plant species with persistent leaves and female and male flowers on different plants [23,24]. *Cotinus coggygria* (Smoketree) is a woody shrub growing wildly in Europe and Asia [25,26].

The aim of the present study was to test the effect of NAA in two different concentrations on six ornamental woody shrubs often used in Romanian landscape design. *Syringa vulgaris* 'Mme Lemoine', *Syringa vulgaris* 'President Grevy', *Ilex aquifolium*, *Cotinus coggygria*, *Cotinus coggygria* 'Kanari', and *Cotinus coggygria* 'Royal Purple' were analyzed in the experiment. The influence of NAA on rooting percentage, root volume, number of roots, root length, and rooted cutting diameter were investigated. We aimed to determine the concentration most suitable for the vegetative propagation of woody ornamental plants.

#### **2. Materials and Methods**

#### *2.1. Experimental Site and Plant Material*

The study was conducted between May and October 2019 in the experimental greenhouse belonging to Sapientia Hungarian University of Transylvania, Târgu Mures, (46◦31 17 N 24◦35 54 E). The cuttings were obtained from a local nursery (Biota, Găies,ti village, Romania). The cuttings were immediately transported to the experimental sites to prevent desiccation. As plant material, the following ornamental woody shrubs were selected:


#### *2.2. Experimental Design and Rooting Conditions*

The first experiment started on 21 May (spring propagation), and in the summer on 7 July the experiment was repeated, with the same ornamental shrub species and rooting stimulants.

For each species and cultivar 10 sub-apical shoots (herbaceous spring and semihardwood summer cuttings) per replication, with three replications, were used—a total of

540 cuttings. Disease and pest-free propagation material was between 8–10 cm in length and was collected with a secateur from the nursery. The leaves on the lower one-third to one-half of the stems were removed. After treatments were applied, the cuttings were planted in 60 × 40 cm plastic trays, filled with perlite rooting medium. Planting distance was 2 cm between the cuttings. We had filled the plastic tray with perlite to a depth of 20 cm (granulation: 1–3 mm, density: 0.05 kg/L, and pH: 7–7.5) and this was well irrigated before planting the cuttings. No artificial lights were installed. Propagation trays were placed in the greenhouse with an automatic humidifier controller in order to provide the 80–90% humidity required for rooting. Humidity and temperature were measured using a Testo 175H1 (Testo Romania, Cluj-Napoca, Romania); the average temperature was between 22–28 ◦C.

From each species, 30 cuttings per treatment were immersed in Incit–5 (AMVAC Chemical UK Ltd., Surrey, UK) and Incit–8 (AMVAC Chemical UK Ltd., Surrey, UK) rooting hormones, approximately up to 1–1.5 cm. Incit-5 composition was 0.5% 1-Naphthaleneacetic acid (NAA5) and Incit-8 0.8% of 1–Naphthaleneacetic acid (NAA8), both recommended as rooting stimulants for ornamental woody plants. Cuttings without treatment were considered as control.

#### *2.3. Data Evaluation*

Data for the rooted cuttings propagated in spring were reported on 13th September (116 days after preparing the cuttings) and for the summer rooted cuttings on 28th October (114 days after preparing the cuttings).

Rooting percentage (the percentage of cuttings that developed at least one root), root volume (cm3—a measuring cylinder was filled with water, the plant was submerged in it and under the pressure of the cutting water, filled out), number of roots, root length (cm) and rooted cutting diameter (cm) were determined. Root length was measured with a tape measure and cutting diameter with a digital caliper (GartenVIP DiyLine, Alba Iulia, Romania).

#### *2.4. Statistical Analysis*

The data were tested for normality of errors and homogeneity of variance. As all data were normally distributed, ANOVA followed by Tukey's test was used to compare variances. The significance of the differences between the treatments was tested by applying two-way ANOVA, at a confidence level of 95%. When the ANOVA null hypothesis was rejected, Tukey's post hoc test was carried out to establish the statistically significant differences at *p* < 0.05.

#### **3. Results**

#### *3.1. Rooting Percentage of Cuttings*

Concerning rooting percentage, hormone type influenced the process in different ways (Figure 1). However, it no significant differences were recorded between the spring and summer cuttings propagation. SVM (Figure 1a) reported small increases compared to control. In the case of SVP (Figure 1b), differences were determined when comparing the two treatments to control, although 0.5% 1-Naphthylacetic acid, compared with the other treatment (0.8% 1-Naphthylacetic acid), highly increased the rooting percentage, with 90% of summer cuttings rooting. Similar data were reported for the IA cuttings (Figure 1c), where at both propagation times the greatest percentage of rooting was observed in plants subjected to NAA5 treatment. Regarding *Cotinus coggygria* (CC), significant increases were reported for NAA8, almost double the rooting percentage compared to control (Figure 1d). In contrast, the data reported for NAA5 were similar to those for the untreated CC (Figure 1d). In the case of CCK (Figure 1e), significant increases were determined just with NAA8. Nevertheless, for *Cotinus coggygria* 'Royal Purple' (CCR), no significance was observed between the control and the NAA8 treated plants, yet rooting percentage decreased at the CCR subjected to 0.5% 1-Naphthylacetic acid treatment (Figure 1f).

**Figure 1.** Effect of rooting stimulants (NAA8—0.8% concentration of 1-Naphthylacetic acid and NAA5—0.5% concentration of 1-Naphthylacetic acid) on rooting percentage for the six selected ornamental shrubs: (**a**) *Syringa vulgaris* 'Mme Lemoine' (SVM); (**b**) *Syringa vulgaris* 'President Grevy' (SVP); (**c**) *Ilex aquifolium* (IA); (**d**) *Cotinus coggygria* (CC); (**e**) *Cotinus coggygria* 'Kanari' (CCK); (**f**) *Cotinus coggygria* 'Royal Purple' (CCR). Bars represent the means ± SE (*n* = 30). Different lowercase letters above the bars indicate significant differences between the treatments, and different uppercase letters indicate the significant differences between the spring and summer propagated cuttings, according to Tukey's test (*α* = 0.05).

#### *3.2. Root Volume*

Under our experimental conditions, no significant differences were observed in root volume when comparing the two propagation periods (Figure 2). The root volume of *Syringa vulgaris* 'Mme Lemoine' (Figure 2a) increased under the treatments. In the case of cuttings propagated in the spring, significant differences were observed with NAA5, and summer cuttings showed increased root volume with both treatments; however, the NAA5 recorded higher increases. Additionally, increased root volume was observed with SVP (Figure 2b) during the treatments, yet the largest increase was observed in the summer cuttings subjected to NAA5, where the volume of the roots was approximately 16 times higher than in the untreated plants' root systems. Considering *Ilex aquifolium* (Figure 2c), it can be determined that treatments greatly increased root volume. IA spring cuttings under the 0.5% NAA treatment reported root volumes of 1.99 cm3 compared to control, in which case the root volume was just 0.1%. Significant differences were also observed in the treated plants (CC) compared to the controls (Figure 2d). Rooting hormone NAA8 greatly increased the root volume of CCK (Figure 2e). In the case of *Cotinus coggygria* 'Royal Purple' (CCR), the development of volume of the roots was inhibited by NAA5 treatment (Figure 2f), compared to control.

**Figure 2.** Effect of rooting stimulants (NAA8 and NAA5) on root volume in the six selected ornamental shrubs: (**a**) *Syringa vulgaris* 'Mme Lemoine' (SVM); (**b**) *Syringa vulgaris* 'President Grevy' (SVP); (**c**) *Ilex aquifolium* (IA); (**d**) *Cotinus coggygria* (CC); (**e**) *Cotinus coggygria* 'Kanari' (CCK); (**f**) *Cotinus coggygria* 'Royal Purple' (CCR). Bars represent the means ± SE (*n* = 30). Different lowercase letters above the bars indicate significant differences between the treatments, and different uppercase letters indicate the significant differences between the spring and summer propagated cuttings, according to Tukey's test (*α* = 0.05).

#### *3.3. Number of Roots*

As expected, the number of roots was significantly affected by hormone products. In the case of SVM (Figure 3a) treated with NAA5, root numbers were about four times higher than in the controls. Increments in number roots were also observed in SVP (Figure 3b). However, in the spring cuttings, only in plants treated with 0.5% 1-Naphthylacetic acid were increases reported. On the other hand, by the summer, both treatments influenced the number of roots in SVP plants in a positive way. Significant differences between treated and untreated IA plants were observed (Figure 3c) with NAA5 treatment, which increased root number at both spring and summer. Under our experimental conditions, increases in the number of roots of CC were observed with NAA8 treatment in both propagation periods, and also in the summer cycle with 0.5% 1-Naphthylacetic acid treatment (Figure 3d). Spring cuttings of CCK (Figure 3e) showed root number increases when subjected to NAA8 treatment, though no significant results were recorded for summer propagation. In the case of CCR (Figure 3f), it was concluded that NAA8 has no influence on the number of roots for spring or summer cuttings. Moreover, NAA5 had inhibited the development of roots compared to control. It is important to mention that no significant differences were determined between spring and summer propagations (Figure 3).

#### *3.4. Root Length*

From the results for the cuttings, no significant changes were observed in root length between the spring and summer periods of treatment (Figure 4). However, for SVM, increases were reported compared to control. The influence of NAA5 was greater than the other hormone type (Figure 4a). For SVP (Figure 4b), no effect was measured at the spring cutting under NNA8 treatment; by contrast, increases were reported for the spring cutting treated with NAA5. With the summer cuttings subjected to both rooting stimulants, significant results were observed. IA cuttings registered increases of root length under both treatments (Figure 4c). In the case of CC, high increments in relation to controls were reported in both propagation periods, with 0.8% 1-Naphthylacetic acid and with 0.5% 1-Naphthylacetic acid in the summer cuttings (Figure 4d). *Cotinus coggygria* 'Kanari' (Figure 4e) showed increases in spring and summer cuttings under the NAA8 treatment. NAA5 treatment had a significant negative influence on the root length of *Cotinus coggygria* 'Royal Purple'. In contrast, no effect was observed in cuttings treated with NAA8 compared to control (Figure 4f).

#### *3.5. Diameter of Cuttings*

As expected, no differences were shown in the diameter of rooted cuttings when comparing spring and summer propagation (Figure 5), and no effect of rooting stimulants was reported for SVM (Figure 5a). On the contrary, SVP cutting diameters were highly influenced by NAA5 treatment in both propagation experiments (spring, 3.14 cm; summer, 3.82 cm), and increases were also observed in summer cuttings subjected to NAA8 (Figure 5b). Comparing the control *Ilex aquifolium* to the treated cuttings, it could be concluded that the diameter of spring and summer cuttings reported high increases with both treatments (Figure 5c). In the case of CC (Figure 5d), cutting diameters showed significant increases under the NAA8 treatment. No differences were observed in the CCK rooted cuttings diameter (Figure 5e). NAA8 increased the diameter of cuttings; on the other hand, NAA5 inhibited the thickness of the cuttings' diameters for CCR (Figure 5f).

**Figure 3.** Effect of rooting stimulants (NAA8 and NAA5) on the number of roots in the six selected ornamental shrubs: (**a**) *Syringa vulgaris* 'Mme Lemoine' (SVM); (**b**) *Syringa vulgaris* 'President Grevy' (SVP); (**c**) *Ilex aquifolium* (IA); (**d**) *Cotinus coggygria* (CC); (**e**) *Cotinus coggygria* 'Kanari' (CCK); (**f**) *Cotinus coggygria* 'Royal Purple' (CCR). Bars represent the means ± SE (*n* = 30). Different lowercase letters above the bars indicate significant differences between the treatments, and different uppercase letters indicate the significant differences between the spring and summer propagated cuttings, according to Tukey's test (*α* = 0.05).

**Figure 4.** Effect of rooting stimulants (NAA8 and NAA5) on root length in the six selected ornamental shrubs: (**a**) *Syringa vulgaris* 'Mme Lemoine' (SVM); (**b**) *Syringa vulgaris* 'President Grevy' (SVP); (**c**) *Ilex aquifolium* (IA); (**d**) *Cotinus coggygria* (CC); (**e**) *Cotinus coggygria* 'Kanari' (CCK); (**f**) *Cotinus coggygria* 'Royal Purple' (CCR). Bars represent the means ± SE (*n* = 30). Different lowercase letters above the bars indicate significant differences between the treatments, and different uppercase letters indicate the significant differences between the spring and summer propagated cuttings, according to Tukey's test (*α* = 0.05).

**Figure 5.** Effect of rooting stimulants (NAA8 and NAA5) on the diameter of rooted cuttings in the six selected ornamental shrubs: (**a**) *Syringa vulgaris* 'Mme Lemoine' (SVM); (**b**) *Syringa vulgaris* 'President Grevy' (SVP); (**c**) *Ilex aquifolium* (IA); (**d**) *Cotinus coggygria* (CC); (**e**) *Cotinus coggygria* 'Kanari' (CCK); (**f**) *Cotinus coggygria* 'Royal Purple' (CCR). Bars represent the means ± SE (*n* = 30). Different lowercase letters above the bars indicate significant differences between the treatments, and different uppercase letters indicate the significant differences between the spring and summer propagated cuttings, according to Tukey's test (*α* = 0.05).

#### **4. Discussion**

Rooting stimulants can be used to increase the rooting capacity of different plants [27–31] and for obtaining the maximum number of rooted cuttings in a short period of time [32]. However, some studies concluded that rooting media is also an important factor which could affect rooting percentage in different ornamental cuttings [1,33,34]. Rooting hormones could positively influence the rooting process of woody plants, but in some cases, this depends on species or their natural rooting ability [35,36]. The rooting percentage

of *Parthenocissus quinquefolia* was increased with the use of two different stimulants, although it was mentioned in the study that growing media combined with the different stimulants could also have a positive effect on the rooting percentage of cuttings [37]. Nevertheless, blue light combined with NAA treatments could significantly improve the rooting and leaf-bud of *Chrysanthemum* cuttings [38]. Thus, propagation period is critical for the rooting process. Our observations indicated that it has no effect on the rooting capacity of the selected ornamental woody shrubs. Though it has been concluded in some studies that the season could affect the rooting of the cuttings [39–41], this could also be a species-dependent factor.

From our results, it can be concluded that rooting percentage increased in the woody shrubs subjected to treatments; however, an inhibition was observed in the case of CCR treated with NAA, compared to control. Of course, it is important to mention that even if NAA5 and NAA8 boosted rooting percentage, not all plants behaved in the same way. Adventitious root formation is a critical phase for the survival and growth of the propagated cuttings [42], involving morphological, physiological, and biological changes [43,44]. The application of NAA improved the rapid recovery of the wounded surface and also affected the rapid appearance of adventitious roots, which guaranteed the cuttings' survival rates. It was reported in a study that 0.3% of NAA concentration resulted in the highest rooting percentage of *Jasminum parkeri* [31]. It was also reported that just 0.01% NAA in combination with 0.01% GA3 can improve rooting percentage of *Hydrangea* [45]. NAA used in micropropagation improved in vitro root induction in *Magnolia sirindhorniae* [46]. Nevertheless, for *Ficus benjamina* L., it was reported that the highest rooting percentage was obtained with just 0.001% of NAA [47], which, compared to our concertation, is very low, yet still increased the rooting of the cuttings.

The data obtained show that rooting stimulants can clearly have a positive effect on the root volume of ornamental woody shrub cuttings. Under our experimental conditions, NAA5 greatly increased root volume in both *Syringa vulgaris* and *Ilex aquifolium*. On the other hand, NAA8 reported higher increases for CC, CCK, and CCR, but with *Cotinus coggygria* 'Kanari' and 'Royal Purple', root volume was inhibited. Previous studies have also reported increases in root volume under different stimulants [48,49]. In one study it was determined that a combination of IBA 1500 ppm + NAA 1000 ppm resulted in the highest root volume in *Piper nigrum* L. [50].

Application of rooting stimulants (1-Naphthyl and 2-Naphthhyl) slightly increase the number of roots of apple and mung bean [51]. In a study by Trofimuk et al. [52] it was reported that rooting stimulants influence in a positive way the number and length of roots of *Abies gracilis*, which is useful in accelerating the production of plant material, and reduces rooting time in woody ornamental plants. Previous research found that NAA could also boost the number of adventitious roots, even the growth and development of micropropagated plants [53–55]. Under our experimental conditions, root length increased under the NAA5 treatment in SVM, SVP, IA, and CC. However, with CCK and CCR, root length was similar or was even inhibited under the same treatment. Another study conducted reveled that indolebutyric acid increased the number of roots and root length of blueberry cuttings, but on the contrary, no effect was observed in rooting percentage and survival percentage of the cuttings [56]. Planting dates and NAA treatments could significantly improve root number and length [57], yet under our experimental conditions, it was determined that planting time did not influence root number or root length. Our data are similar to previous studies which have concluded that number and length of roots are positively affected by the application of NAA, at least at 0.5% concentrations [38,58–61].

From the data obtained in our experiment, it could be concluded that NAA influenced every woody shrub cutting diameter in a different way. Furthermore, no significant differences were observed in SVM. NAA5 clearly affected cutting diameters in SVP in both propagation times. NAA8 summer cuttings also showed differences compared to control, but not as high as those treated with NAA5. Both concentrations of 1-Naphthylacetic acid greatly increased the diameters of IA rooted cuttings. In the case of *Cotinus coggygria*,

significant increases were determined with NAA8. On the other hand, no significant difference was observed for CCK. Significant differences were observed in CRR cutting treated with NAA8; however, NAA5 inhibited the treated shrubs. In some studies, it was reported that diameter could have an effect on root number and on the length of cuttings [62,63]. This was clearly observed in our experiment—that where cutting diameter increased, root length and number increased with it. Similar data was recorded for *Punica granatum* L., where with increases in diameter, the length and number of roots increased under IBA + NAA treatment [64]. The rooting process depends on the cutting's diameter and on the nutrients which sustain the biological processes involved in adventitious root formation [65–67]. Altogether, it can be concluded that rooting stimulants could have beneficial effects on the development, growth and survival percentage of the plants studied [68–70].

#### **5. Conclusions**

Ornamental woody plant nurseries strive to produce rooted cuttings in a short time and to ensure that they are of good quality. The present study provides new experimental data on the comparison of two rotting stimulants on six woody shrubs often used in landscape design. According to the results, it can be concluded that 1-Naphthylacetic acid used in different concentrations could have a positive effect on the rooting of the plants selected in this experiment. Results show that NAA8 treatment positively affected the root percentage of CC and CCK, and NAA5 influenced negatively root percentage in CCR in both propagation periods. However, root percentage in CC and CCK was not significantly influenced in either treatment period. On the other hand, significant (negative) changes were reported in CCR rooting percentage for cuttings under NAA8 treatment. These data show that rhizogenesis could be a species- or cultivar-dependent process. Regarding the number of roots, NAA5 showed better results for *Syringa* tested cultivars and for *Ilex aquifolium*, while NAA8 had a greater influence on the analyzed species and cultivars of *Cotinus*. Root length increased when SVM, SVP and IA cuttings were treated with NAA5, while NAA8 increased the length of the root systems in CC and CCK. On the basis of the results presented here, it could be stated that rooting hormones/stimulants strengthen the possibility of achieving a quicker vegetative propagation method, but future experiments need to be conducted.

**Author Contributions:** Conceptualization, E.K. and Z.S.-V.; methodology, E.K. and M.C.; formal analysis, M.C. and Z.S.-V.; resources, E.K. and D.J.; writing—original draft preparation, Z.S.-V.; writing—review and editing, Z.S.-V. and D.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was partially funded by the University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** This work was supported by the Institute of Advance Horticulture Research of Transylvania, University of Agricultural Science and Veterinary Medicine of Cluj–Napoca and the Sapientia Hungarian University of Transylvania.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

