Next Article in Journal
The Effect of Meat and Bone Meal (MBM) on Crop Yields, Nitrogen Content and Uptake, and Soil Mineral Nitrogen Balance
Previous Article in Journal
Wild Mustard (Sinapis arvensis) Competition and Control in Rain-Fed Spring Wheat (Triticum aestivum L.)
Previous Article in Special Issue
Worldwide Trends in Agronomy Research: Bibliometric Studies
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Plant Agronomic Features Can Predict Quality and Field Performance: A Bibliometric Analysis

Escuela Superior de Ingeniería, Departamento de Agronomía, Universidad de Almería, 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Agronomy 2021, 11(11), 2305; https://doi.org/10.3390/agronomy11112305
Submission received: 27 September 2021 / Revised: 2 November 2021 / Accepted: 9 November 2021 / Published: 15 November 2021
(This article belongs to the Special Issue Worldwide Trends in Agronomy Research: Bibliometric Studies)

Abstract

:
Plant quality and survival prediction tools are useful when applied in the field in different agricultural sectors. The objectives of this study were to conduct a review and bibliometric analysis of the Dickson Quality Index (DQI) as a key plant quality indicator and with respect to its scientific applications. A third objective was to identify the main morphological and physiological parameters used in plant production research. The methodology and findings of 289 scientific articles were analysed based on the morphological, physiological, and mathematical parameters used as plant quality indicators in research on forest, medicinal, horticultural, aromatic, and ornamental species. During the last 10 years, the number of publications that have used the DQI as a plant quality parameter has increased by 150%, and Brazilian researchers stand out as the most frequent users. Forestry is the discipline where quality parameters and their biometric relationships are most often used to facilitate intensive plant production. Use of the DQI increases the certainty of prediction, selection, and productivity in the plant production chain. The DQI is a robust tool with scientific application and great potential for use in the preselection of plants with high quality standards among a wide range of plant species.

1. Introduction

In all agricultural sectors, the use of certified seeds and seedlings with high quality standards has been a key to increasing seedling survival and crop productivity and to preventing crop pest problems. According to the data reported by the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) in 2018 [1], arable land totals more than 4826 million ha worldwide, of which only 32.48% correspond to farmland. In turn, forest areas amount to 4068 million ha, where natural or anthropogenic degradation and deforestation problems pose a great challenge. In 2018, the area of forests regenerated through reforestation programmes was estimated at only 296,500.52 ha worldwide, and reforestation mainly occurred in (mainland) China (21.20%), the United States (7.87%), the Russian Federation (13.19%) and other countries of the former USSR (7.43%), and Canada (3.76%) [1]. According to Luis et al. [2] and Haase and Davis [3], the success of reforestation and conservation programmes is strongly affected by the quality of seedlings grown in nurseries or seedbeds. Furthermore, some countries also have intensive horticultural systems where grafted and non-grafted quality plants are produced on a large scale in similar proportions [4]. The total production of grafted vegetable plants is estimated at more than 500 million per year. More than 300 million of these plants are produced in Asia alone, whereas 90 million are produced in Europe [5] and more than 10 million in North America. These three regions are the main producers and exporters of grafted vegetable plants. In North Africa, Morocco is the main producer of tomato plants, at a rate of more than 12 million per year [6], whereas on the coast of Almería (Spain), the annual production of vegetable plants exceeds 1500 million and uses an area of more than 200 ha of seedbeds [7].
The concept of plant quality has been widely discussed in the literature. Generally, the quality of a plant is determined based on the characteristics of its morphology (phenotypic characteristics), physiology (internal factors that regulate and determine plant appearance), and performance (measurements, such as vigour, that indicate plant behaviour when subjected to tests and specific conditions), as well as on the quality of its root system (the physiological capacity to readily generate new, healthy, and vigorous roots). These characteristics may indicate that a plant meets the necessary requirements to survive and develop properly after being transplanted into the field, while showing high vigour and increased resistance to adverse growth conditions [8,9,10,11,12,13,14,15,16].
According to the International Seed Testing Association (ISTA), normal or quality plants show high potential for continued development into satisfactory plants when grown in good quality soil under favourable conditions of moisture, temperature, and light [13]. Mañas et al. [17] and Ellison et al. [18] mentioned that the quality of a plant depends on its ability to rapidly generate new roots and to have a well-developed root system, high photosynthetic efficiency, and a large stem diameter, as well as a favourable shoot/root ratio, sufficient carbohydrate reserves, and an optimal nutritional status. Recently, Kim and Hwang [19] reported that plant quality is related to high dry matter contents, low Stem/Root (S/R) ratios, high Specific Leaf Areas (SLAs), and short hypocotyls. However, in large-scale production centres, such as commercial seedbeds or nurseries, plant quality is determined by the staff, based on their technical expertise [3,20]. The production of uniform plants, with high vigour and quality, also largely depends on the level of knowledge of the technical staff and on seedbed technification [21,22,23].
Considering the above information about plant quality, quantitative parameters and robust tools must be used to produce high-quality plants more efficiently in any of the agricultural sectors [11,24,25]. However, the quality of a plant may be determined by its own genetic information and by genotype–environment interactions, together with the agronomic management of the crop and the technological level of the production facility [26,27]. Accordingly, preselecting plants at the seedbed stage, based on their morphological and physiological attributes, makes it possible to quickly discard those that do not meet the required standards. The main advantages of this non-destructive method are that it is easily applied on a large scale and facilitates the analysis of many plants [28,29,30]. However, plants with optimal morphological parameters at the seedbed stage do not always show the best vigour or growth in the field [31].
Indices that integrate root quality parameters appear to be effective in predicting field performance, vigour, and survival under adverse growth conditions [10,32,33,34]. In addition, in some studies, plant quality is determined using biometric indices or ratios, such as the stem/root dry weight, stem Height/Diameter (H/D) ratios, total dry weight, and the Dickson Quality Index (DQI) [35,36,37]. The DQI is a quality parameter initially developed for forest species [38] that is now widely used as a quality, vigour, and yield potential indicator for a broad range of species [15,39]. However, few studies have described plant quality using this index for fruit [40,41,42,43] and ornamental [24] species and for herbaceous crops, including aromatic species and horticultural crops [44]. Therefore, the objectives of this study were to conduct a review and bibliometric analysis of the application potential of the DQI as a main parameter for assessing plant quality during plant production, as well as in relation to other scientific applications of this index. A third objective was to identify the biometric ratios and main morphological and physiological parameters used to facilitate the production of quality plants.

2. Materials and Methods

Data from the 1989–2020 period were reviewed and analysed in the Scopus database using Scopus smart tools and Boolean (AND, OR, and NOT) and proximity (PRE/and W/) operators. The descriptor used as the central axis was the DQI. Quantitative analyses of key- or co-words ‘Dickson quality index’ OR quality AND index* W/5 seedling* were performed using the search field ‘Article title, Abstract, and Keywords’, resulting in a total of 662 articles and 3216 keywords.
To visualise the research topics, a bibliometric map was developed based on the keyword co-occurrence ratio and on the similarity index, where the unit of analysis was the set of keywords that includes the author’s keywords and indexed keywords, establishing a keyword frequency equal to or greater than 8 (number of times that a keyword appears in the selected publications) according to the criteria established by Chen et al. [45].
Following a similar procedure, an overlay visualisation map was drawn to identify the evolution of keywords used in the set of articles analysed in this study. A thesaurus file was constructed with synonyms or repeated concepts to increase the consistency of the main research topics.
The data were processed and mathematically analysed using the clustering algorithm of the VOSviewer® software version 1.6.15.
From the total sample of articles (n = 662), a representative and random sample of 289 articles was extracted, and were synthesised, exposing the usefulness of the DQI as a potential tool to determine plant quality in a significant sample of forest, fruit, medicinal, horticultural, aromatic, and ornamental species. Data were retrieved and analysed by relevance from Scopus database 2020.
A quantitative, detailed, and meticulous analysis of the 289 articles was performed to collect the data of interest, such as plant species under study, the main goal of each study. In addition, the biometric parameters and plant quality indicators used in each article were identified, described, and quantified.
The different themes and research topics were identified and grouped within the main aspects of agricultural sciences that influence crops and their productivity [46].

3. Plant Morphological and Physiological Parameters

3.1. Height, Stem Diameter, and Leaf Number

Height, stem diameter, and leaf number are easy-to-measure parameters that can be determined non-destructively. Height is defined as the distance between the apical meristem and the level of the substrate (or 1 cm above the substrate level) and is one of the main morphological parameters used as a quality indicator in different areas of agriculture. Plant height plays a key role in plant survival and development, especially after transplanting and during the first years of growth. Some researchers suggest using small plants in reforestation programmes, because they are considered more robust plants with less sensitivity to wind, drought, and cold stress [2,28,35,47,48].
Stem diameter is a parameter used for predicting field survival. Tsakaldimi et al. [34] found that plants with a larger stem diameter and a higher total biomass have higher percentages of field survival than smaller plants. Similarly, plants with a larger stem diameter have a greater resistance to cold damage, because they dissipate heat more efficiently through their stems [28,35,49]. In addition, stem diameter variations have been used as indicators of the water status of plants and as a tool to schedule greenhouse crop irrigation [50]. In general, stem diameter shows a positive correlation with the post-transplanting performance of crops, similar to that of the DQI [32,39,48].
The emergence and increase in the number of leaves in crops is generally associated with an increase in photosynthetic activity, improving yield and vigour after transplanting. According to Castro Paes et al. [51], container size is a key factor in nutrient availability to the root system, affecting the distribution of assimilates in shoots and, consequently, the leaf number. Lima et al. [52] have reported that using organic substrates in aubergine production increases both leaf number and photosynthetic activity, improving post-transplanting development and vigour. The use of pig farm wastewater (with concentrations ranging from 50% to 75%) in the nutrition of Khaya senegalensis seedlings increases both the leaf number and area and improves the plant quality [53].

3.2. Leaf Area

Leaf Area (LA) is one of the most important morphological parameters and is frequently used in crop growth modelling and simulation [54,55]. Leaf area is a key factor that determines the amount of photoassimilates produced by crops and thus, it substantially affects their growth, development, and productivity. A large LA is essential for high radiation interception [56]. Generally, plants with a greater LA show a higher photosynthetic capacity; consequently, LA is important for producing plants with optimal quality and the capacity for vigorous establishment in the field [57,58]. However, when the LA is large, water loss by transpiration may increase. Moreover, a reduction in LA may occur as a survival response to different types of stresses such as salinity [59,60]. According to Bantis et al. [5], LA is considered a highly efficient quality indicator in the production of watermelon and interspecific squash seedlings. Some factors, such as light quality and radiation intensity [61], plant nutrition [62,63], substrate type [64], and container design and volume [65], may affect LA and biomass accumulation in crops. Leaf area is often determined using an LA metre or by analysing images with specialised software, such as Windias or ImageJ [66]; however, LA can also be determined using simple models such as the product of the leaf length × width.

3.3. Fresh Weight

Fresh weight is a good indicator of plant volume. Generally, productivity in horticultural crops is determined by the fresh weight of the organ of interest (the stem, leaf, root or fruit), rather than by its dry weight, most likely because the sale price of the product is based on the fresh weight of the usable product. However, the quality of the product is often related to its dry matter content. For example, simulation models enable users to estimate fruit fresh yield based on knowledge of the dry weight/fresh weight ratio of the fruits [57]. However, if a constant dry weight is considered, the error is significant and surpasses 25% [67].

3.4. Dry Weight

Plant biomass accumulation, expressed as dry weight gain, is a key growth measure, as it reflects a plant’s response to several factors, such as photosynthetic activity, CO2 concentration, and to a lesser extent, temperature [68]. Total Dry Matter (TDM) is often used as an indicator of plant field survival. It represents the net gain in dry matter from and is considered one of the best parameters for indicating plant quality, as plants with a high TDM content show high growth potential and quality [17,69].

3.5. Root System Quality Parameters

Root system parameters are highly relevant in plant quality research; however, root system analysis is a destructive process. In addition, the time required to conduct root system analysis can be a limiting factor given that relatively long periods of time are needed to prepare samples in order to avoid incorrect values. Owing to these drawbacks associated with root system analysis, field survival and plant productivity have been widely correlated with biometric parameters and ratios instead of root system parameters [33], and the indices that integrate root parameters have shown greater precision and prediction capacities than certain of the other parameters, such as height, stem diameter, or the H/D ratio [10,22]. Root system parameters, such as root length, root volume, root dry matter, Specific Surface Area of Roots (SSAR), number of First Order Lateral Roots (FOLRs), root architectural parameters, and Root Growth Potential (RGP), may provide more effective indicators of seedling performance [34] compared to other parameters.
A rapid and balanced development of the root system is a key plant survival and growth strategy, mainly in low rainfall areas with low soil fertility levels. In addition, some plants can develop different types of root systems in response to the different types of biotic and abiotic stresses faced during the early stages of field crop development [70,71].

3.5.1. Root Length

Root length quantitatively describes the quality of the root system of a plant and its analysis is non-destructive. In plants with long roots, this parameter is associated with enhanced growth and an increase in the capacity to withstand post-transplanting stress, as evidenced by the high exploration capacity of such roots in growing media [29]. Some forest species, when grown in culture media with high porosity and low fertility levels, have longer roots and a better root distribution in the containers compared to when cultivated in rich, non-porous media [32]. However, different studies suggest that root development may be limited by copper supplementation [72] and by the container type and colour [73].

3.5.2. Root Volume

Determining root volume using the water displacement method is a simple way to measure plant root abundance. According to Chirino et al. [71] and Oliveira et al. [57], the greater the root volume, the greater the exploration of the growth medium, facilitating water and nutrient absorption. An increase in root volume can improve the adaptation of a plant to limiting conditions, thus increasing field survival [35,50]; the opposite occurs when plants have limited root systems [34].

3.5.3. Specific Surface Area of Roots

The SSAR (cm2) is a root system quality indicator. Plants with a high SSAR have a greater capacity to absorb and translocate water and essential nutrients for growth, because they have more fine roots than those with a low SSAR. Together with root volume, SSAR is positively correlated with field performance and survival [10,74]. The SSAR can be calculated using the method described by Tennant [75] as well as by using specific software such as WinRhizo Pro®, ImageJ, GiA Roots, or RhizoVision Explorer [76,77,78].
Root quality may be affected by crop management or developmental conditions. It has been shown that supplementing the substrate with 40% N-urea (in the form of hydrogel) improves the plant quality, including the SSAR system, of pepper plants [66]. Conversely, Pimentel et al. [79] found no effects of container type or season on root quality, total volume, or SSAR of yerba mate plantlets propagated by mini-cuttings. Similarly, Marco et al. [80] have mentioned that incorporating peat into copper-contaminated soils improves plant qualities or quality indices, such as root dry matter, SSAR, stem diameter, the DQI, and the copper tolerance index, by improving the physical and chemical properties of the soil. These same authors found that a gradual increase in copper doses in the soil (300 mg of Cu kg−1 of soil) decreases the SSAR by 40% [12,80]. Generally, plants with a high SSAR show high tolerance to soils contaminated with heavy metals [81], perhaps due to defence mechanisms such as melanin accumulation in the roots.

3.5.4. Number of First Order Lateral Roots

The number of FOLRs (roots with a diameter ≥ 1 mm that branch from the taproot) may predict the potential post-transplanting productivity of a plant [28,82]. Roots are classified by counting the approximate number and type of high-order lateral roots in each 10 cm segment of the FOLRs [83,84]. A large number of FOLRs and a fibrous root system improve survival and stimulate the rapid establishment of plants in the field [83]. Both the number of FOLRs and the RGP are effective tools for quality plant selection [85].
Some practices, such as the use of mixtures of organic and inorganic substrates, have been shown to increase the number of FOLRs in forest plants [84]. Similarly, treating forest plants with supplementary light improves the quality of the root system by causing an increase in root density and leads to higher order lateral roots by increasing the number of FOLRs with a diameter ≥ 1 mm [83].

3.5.5. Root Dry Matter

Root dry matter is one of the most important variables for determining field survival of plants. Binotto et al. [39] have mentioned that root dry matter can be used as a quality indicator in forest species due to its high correlation with the DQI.

3.5.6. Root Growth Potential

The physiological quality of plants can be determined based on their RGP, i.e., their ability to generate and grow new roots (>1 cm) in the medium into which they are transplanted [10,86]. A high RGP is desirable as it is associated with a more vigorous root system and, therefore, with a higher water and nutrient absorption potential, which is further associated with greater adaptation and a higher post-transplanting survival rate [2,50,87].
In ornamental species, out of 13 morphological and physiological parameters used to evaluate plant quality, RGP has been found to be the best tool for predicting plant vigour in all tested treatments [24]. According to Chirino et al. [71], in forest species, the design, volume, and depth of the container substantially affect the RGP during the early stages after transplanting. Nevertheless, Sánchez-Aguilar et al. [73] have mentioned that the colour of the container does not have a marked effect on root growth. Conversely, the quality of plants has been shown to increase substantially in substrates enriched with different concentrations of manure, as evidenced by an increase in their RGP values, and in greater field survival and vigour [88]. The RGP is considered a potential tool for species selection during early growth stages. Deans et al. [85] found that Sitka spruce (Picea sitchensis) clones with a high RGP (≥30) show higher plant quality and field survival three years after transplanting than clones with a low RGP. In addition, a high correlation (p = 0.05) between RGP and the main morphological indicators of plant quality has been found.

3.5.7. Aggregation of Roots to the Substrate

Aggregation of roots to the substrate is used as a quality indicator and as a tool for choosing the substrate that enhances the root growth of a plant. Wendling et al. [89] have mentioned that aggregation of roots to the substrate can be evaluated using a numerical scale (from 0 to 10), where zero corresponds to the worst quality plants, with a completely disintegrated root ball, and 10 corresponds to the best quality plants, with a compact and intact root ball after a free fall from a height of approximately one metre.
Effective root systems are highly aggregated; therefore, the production of plants with disintegrated root balls should be avoided, as this condition exposes the roots to damage by desiccation, thus hindering plant survival [90]. According to Dalanhol et al. [91], incorporating vermicompost (30% and 50%) into the substrate when growing Campomanesia xanthocarpa (Mart.) stimulates an increase in aggregation of the roots to the substrate, increasing the plant quality and the ease of seedling extraction. Similarly, Dalanhol et al. [92] assessed that mixing vermicompost with the substrate, without further fertilisation, increases Eugenia uniflora L. plant quality, because of a high aggregation of roots to the substrate, showing a higher percentage of fine roots and a more compact root ball compared to plants grown without vermicompost.

3.5.8. Seedling Extraction Ease

Seedling Extraction Ease (SEE) has been used as a parameter for assessing plant quality at the seedbed stage. It is related to handiness at the time of extraction, where plants grown in difficult-to-remove substrates can experience root ball disintegration and root rupture on extraction [89]. Seedling extraction ease is a parameter that should be considered when choosing the best cultivation substrate, as it will affect the post-extraction plant quality [2,90].
To evaluate SSE with the least possible damage, Hassen and Davis [3] have proposed the following numerical scale: 1 corresponds to a difficult extraction (requiring an exerting force and pressure to extract the seedling, resulting in mechanical damage to the root and plant); 2 corresponds to medium extraction ease (where complications may occur during extraction, but ultimately the seedlings can be removed without apparent mechanical damage); and 3 corresponds to the greatest extraction ease (where the seedlings are extracted from the container without any mechanical damage and with a compact root ball).

3.5.9. Primary and Secondary Metabolite Content (Soluble Sugar, Starch, Total Phenols, and Flavonoids)

Primary and secondary metabolite contents and their distribution among different plant tissues play key roles in adaptation strategies in response to different types of stress.
The sum of soluble sugars and starch is referred to as total Non-Structural Carbohydrates (NSC). This is a key physiological attribute that describes the quality of a plant and predicts its response to transplanting [9]. The amount of NSC in a plant is affected by the season, water and nutrient availability, temperature, and light levels. Therefore, a high concentration of NSC is necessary for the successful establishment of plants in the field and is a key indicator of the carbon source and sink capacity of the vegetation. Conversely, low carbohydrate reserves in the different plant organs decrease plant growth and survival rates [2,93]. Recently, Liu et al. [94] reported that plants found to be the most resistant to drought show the highest concentrations of NSC and the main storage organ is the root. Similarly, a high concentration of NSC in the roots is associated with species with high shade tolerance [95]. In turn, Liu et al. [61] have mentioned that enrichment with CO2 (1050 μmol mol−1 CO2) and supplementary lighting (100 μmol m−2 s−1) can improve the quality of plants, by promoting a higher photosynthetic activity and increasing their total carbohydrate content. Similarly, Liu et al. [96] have reported that DIFs of 0 °C or >10 °C substantially increase the content of primary and secondary metabolites in A. membranaceus and C. lanceolata seedlings. In addition, Liu et al. [97] found that the physiological quality of different medicinal species improves at a night temperature of 15 °C due to an increase in CO2 assimilation and in the synthesis of carbohydrates, including soluble sugars, starch, total phenols, and flavonoids.

4. Plant Biometric Ratios or Indices

4.1. Height/Diameter Ratio

The H/D ratio (cm mm−1), also known as the slenderness coefficient, sturdiness quotient, or robustness index, is a robust plant quality indicator that has the advantage of being non-destructive. This indicator is used to predict post-transplanting plant growth and field survival [35,98]. The H/D ratio determines the growth balance between plant height and thickness [93]. As suggested by some researchers, the ideal value of this index for a wide range of forest and fruit species should be lower than or equal to 6 in order for a plant to be considered in balance or of a suitable quality [21,28,99,100]. In addition, a low H/D value is associated with more robust plants with a higher probability of field survival, especially in areas with strong winds, landslides, drought, and salinity [14,100,101]. This is also true for container plants [102]. Conversely, a high H/D value is an indicator of thin plants, which are more likely to experience post-transplanting stress [2]. Similarly, an imbalance in this indicator may be associated with a decrease in plant growth and development [103].

4.2. Shoot/Root Ratio

The S/R ratio (g g−1) is the quotient between the shoot and root dry weights of a plant; it indicates the balance between the transpiration area (the leaves) and the plant roots (water and nutrient absorption regulation) [99]. This indicator is used to determine the growth capacity of plants under adverse conditions [26,35]. Shoot/root values lower than or equal to 2 are considered adequate. Some studies on forest species have found that plants with ratios very close to 2 have higher field survival and drought resistance potential than plants with lower S/R values, most likely due to their better physiological uniformity and balance (considering photosynthesis, transpiration, and water and nutrient absorption by the roots) [2,17,36]. A low S/R value may be indicative of limited leaf development and, consequently, of reduced photosynthetic activity in a plant. Nevertheless, in the early stages of plant growth, a large root system is recommended to ensure an increased water and nutrient absorption [17,20].

4.3. Dickson Quality Index

The DQI is derived from the integration of different morphological parameters, specifically the total dry weight (g), the stem H/D ratio, and the S/R ratio, as indicated in Equation (1).
DQI = total   dry   weight   ( g ) height   ( cm ) stem   diameter   ( mm ) + shoot   dry   weight   ( g ) root   dry   weight   ( g )
The DQI was initially developed to evaluate the quality of forest species [82,104] and has been applied to fruit [105,106], aromatic [23,107], ornamental [11], and horticultural [108,109,110] species. This index was suggested and developed by Dickson et al. [38] for forest species (Picea glauca and Pinus albicaulis) as a tool to predict the behaviour of plants in the seedbed stage and their performance in the field.]. According to different authors, a high DQI value is desirable, as typically found in robust plants with an optimal balance between shoot and root biomass, predicting a high field performance due to high vigour [17,24,31,40,111]. Furthermore, the DQI describes the plant survival and growth potential in the field [22,32,71]. According to Hunt [26], for forest species such as Douglas-fir (Pseudotsuga menziesii), lodgepole pine (Pinus contorta), and white spruce (Picea glauca), a value greater than or equal to 0.20 is considered to indicate that a plant meets quality standards. However, some researchers suggest that DQI calibration tests should be established for each of the forest species of interest [39,56].
To increase the reliability of plant quality indicators, thus justifying their use, some researchers correlate and integrate the main parameters that describe plant growth with the DQI, increasing prediction and decision-making capacity for the selection of plants with high quality attributes [29]. In this regard, Puttonen [9] mentions that the confidence limit of any parameter with the potential to predict field behaviour, growth, and survival must be higher than 70%. Tsakaldimi et al. [34] found a significant correlation between field survival and the DQI, with R2 values ranging from 0.60 to 0.89, reaching survival surpassing 90% when the DQI value is 0.35 and 1.1 for Pinus halepensis and Pistacia lentiscus, respectively. Similarly, Binotto et al. [39] have found a positive correlation between TDM, stem diameter, and the DQI in Eucalyptus grandis and Pinus elliottii var. elliottii. Additionally, in the production of high quality Carica papaya plants, a high correlation has been found between the DQI and the H/D ratio, S/R ratio, and TDM [43].
In contrast, Puttonen [9] has mentioned that using morphological indices describing the yield potential, such as the DQI and the RGP, is questionable because prediction errors may be generated when applying indices describing characteristics of a single tree or of a reduced batch of plants to describe the behaviour and characteristics of a complete population. In addition, integrating certain morphological and physiological parameters may give rise to nonsensical units. Accordingly, Ivetić et al. [28] have found that the ability of the DQI to predict the survival of forest seedlings is lower than that of height and the H/D ratio, which are non-destructive and easy-to-measure parameters. In turn, Mota et al. [40] analysed three quality indices, namely the H/D ratio, the R/S ratio, and the DQI, and were unable to confirm that these indicators could represent the quality of Eugenia dysenterica plants, as they found plants with lower growth and weight that had a higher quality. However, preselecting plants based on their morphological and physiological attributes such as height or H/D ratio do not always overlap with the best vigour or growth in the field [31].
In this sense, those indices that integrate root quality parameters or indices that integrate different growth variables seem to be effective in predicting field performance, vigour, and survival under adverse environments for a wide range of growth conditions [10,32,33,34].

4.4. Root/Shoot Ratio

In addition to using the S/R ratio, some researchers also use the inverse relationship, the Root/Shoot (R/S) dry matter ratio (g g−1) as a plant quality and hardiness indicator. A higher value of this index indicates a greater development of the root system relative to the shoot system [112].
The allocation of plant resources to root and shoot biomass production is considered a key factor in water use and field survival strategies, and in expressing maximum performance [34,113]. For example, when nutrient availability is low, plants allocate more resources to root growth and decrease assimilate availability to shoot growth, thereby, increasing the R/S ratio without affecting their nutritional status [114,115,116]. Chirino et al. [71] grew Quercus suber L. plants in deep containers and found a higher accumulation of root biomass and, as a result, a higher R/S ratio compared to plants grown in shallow containers. Furthermore, the substrate mixed with organic waste used for growing Chamaecrista desvauxii (Collad.) optimises plant quality [117]. Similarly, the R/S ratio increases in E. dysenterica plants when grown using different mixtures of rice husk and vermiculite as substrates, ultimately reaching an R/S ratio of 3.91, are most likely due to the improvement in the structure of these substrates and to their high oxygenation capacity in the container [40]. In physic nut plants, with and without mycorrhizae, the R/S ratio shows a positive correlation with a gradual increase in soil phosphorus levels. These results are likely due to increased synthesis of carbohydrates and long-distance transport rates between the root and stem at higher levels of phosphorus fertility [112]. In addition, Magonia pubescens production at a 70% shade level improves the overall plant quality, increasing the R/S ratio (to 3.63). In addition, Magonia pubescens production at a 70% shade level improves the overall plant quality, increasing the R/S ratio (to 3.63), as well as the root dry matter and TDM [52]. In guava plants, a moderate increase in salinity decreases the relative growth rate, the shoot biomass, and the R/S ratio by 28% at an electrical conductivity of 3.5 dS m−1 [118].

4.5. Plant Height/Shoot Dry Matter Ratio

The plant Height/Shoot Dry Matter (H/SDM) ratio (cm g−1), where SDM is the Sum of the Dry Weight of both the stem and leaves, is a high-potential index used to predict field survival potential. This ratio is also used as a quality indicator in fruit [119] and forest species [120,121,122,123]. Some studies suggest that a low value of this index is desirable, as it is associated with more lignified and higher-quality plants [93,124,125]. According to Silva et al. [58], incorporating controlled-release fertilisers into plant nutrition programmes in forest species has a positive effect on plant quality, as shown by the H/SDM ratio and DQI index values obtained at doses of 7.5 and 7.8 kg m−3, respectively. Furthermore, in substrate base saturation treatments, Cruz et al. [69] found a positive quadratic relationship (R2 = 0.56) for the H/SDM ratio, reaching its minimum (1.32) at a 54.8% base saturation, thus, indicating that it is a reference for the field survival of Tabebuia impetiginosa (Mart.) Standley seedlings. Similarly, in Machaerium nictitans (Vell.) Benth., Souza et al. [126] found that the best quality plants have an intermediate H/SDM value (6.5) at a 45% base saturation. However, these same authors mention that this ratio may vary with the type of soil. In Mimosa caesalpiniaefolia Benth. and in Piptadenia gonoacantha J.F. Macbr., nitrogen nutrition management substantially improves plant quality, as shown by the observed H/SDM ratios of 4.1 and 3.3, respectively, regardless of the nitrogen source [124,127]. Valadão et al. [128] have found that increasing shading from 30% to 70% increases plant quality when the H/SDM ratio is low (3.3), in line with the highest values of the DQI.

4.6. Shoot Dry Matter/Plant Height Ratio

The Shoot Dry Matter/plant Height (SDM/H) ratio (mg cm−1) is known as the compactness index. A high value for this index is desirable, given that more robust plants with high dry weight show high compactness [19,99]. Accordingly, biomass accumulation is essential for producing high-quality plants in the nursery [20].
The SDM/H ratio has been used to predict plant quality in different medicinal species [96,97] and in horticultural crops [6]. Recently, Bantis et al. [5] evaluated different quantitative criteria for producing quality plants and found that the SDM/H ratio is one of the best indicators for predicting the physiological and commercial quality of watermelon and interspecific squash seedlings. Similarly, Kim and Hwang [19] have found that light quality and the proportions between far-red and the red/far-red ratio substantially affect the dry matter accumulation and the SDM/H ratio in tomato plants.

4.7. Root Dry Matter/Root Length Ratio

The Root Dry Matter/Root Length (RDW/RL) ratio (g cm−1) is considered a root quality indicator, albeit underused among the physiological parameters that reflect plant quality. Recently, Liu et al. [96,97] evaluated the effects of differences in temperature (DIF) between day and night on Astragalus membranaceus and Codonopsis lanceolata seedlings and found that a DIF of more than 10 °C was recommended for the production of high-quality plants with the highest RDW/RL ratios for both species. Prior et al. [129] have reported that a gradual increase in atmospheric CO2 concentration and moderate water stress significantly improves the quality of the root system (both root density and dry weight) and the RDW/RL ratio in cotton plants.

4.8. Root Quality Index

The Root Quality Index (RQI) describes the quality and characteristics of the adventitious root system. This index was proposed by Saha et al. [33] based on the DQI (Equation (2)).
RQI = T M S R + R S Q
where the RQI represents the quality of the adventitious root system; TM, the total biomass of the rooted cutting (g·m−1); S/R, the stem/root dry weight ratio; and RSQ, the root sturdiness quotient (average diameters of roots/total length of the root system). The information provided by the RQI index may be complemented with the average root diameter, an indicator of the ability of the roots to penetrate the soil.
The RQI has a high potential for root quality evaluation and is a robust and easy-to-analyse tool. A positive correlation has been found between grey relational analysis grades and the RQI [33]. In addition, indicators that integrate root quality parameters are known to efficiently predict post-transplanting behaviour [10]. Currently, Saha et al. [33] propose using algorithms to evaluate root quality and reduce measurement biases in different eucalyptus species propagated by cuttings, thereby, increasing confidence in the choice of genotypes with high morphological and root qualities in short periods.

4.9. Leaf Area/Root Dry Matter Ratio

The Leaf Area/Root Dry Matter (LA/RDM) ratio (cm2 g−1) is a parameter used as a plant survival and resistance prediction tool for plants grown in soils subjected to drought. According to Thomas [113], low irrigation of Eucalyptus pilularis Sm. seedlings and vegetative cuttings reduces the stem diameter, leaf number, and LA. These morphological changes due to drought-induced plant hardening modify the plant biomass distribution, increasing the balance between shoot and root development, thus, increasing the R/S ratio, and decreasing the LA per unit of root dry weight (cm2 g−1), promoting field survival in all treatments with limited irrigation.

4.10. Root Length/Leaf Area

The Root Length/Leaf Area (RL/LA) ratio (cm cm−2) reflects a plant’s structural water and nutrient absorption expenditure required to maintain its specific gas exchange capacity [130]. This ratio expresses the relative amounts of the shoot and root biomass more accurately than the R/S ratio [131].
Körner and Renhardt [130] have found that plants that grow at high altitudes develop, on average, 4.5 × longer fine roots per unit of LA than plants that grow at low altitudes, which means that the growth success of herbaceous perennials at high elevations does not necessarily depend on a large fraction of underground biomass, but rather on longer fine roots. In turn, Tani et al. [132] concluded that a sudden increase in radiation of Pteridophyllum racemosum plants improves the RL/LA ratio and the specific root length (root length/root mass ratio) due to a higher carbon allocation to the roots.

5. Crop Growth and Development Analysis

Crop growth and development analysis encompasses quantitative methods of analysis used to understand and predict the effects of different factors that, themselves, modify plant growth and development. Plant growth has been analysed since ancient times [133,134] and is widely accepted as a tool in crop growth modelling and simulation [54,58]. Plant growth analysis includes the following components: LA, Leaf Area Ratio (LAR), SLA, Leaf Weight Ratio (LWR), and the Leaf Area Index (LAI).

5.1. Leaf Area Ratio

The Leaf Area Ratio (LAR; cm2 g−1) is the ratio between LA and the total dry weight. This ratio indicates the efficiency of a plant in producing one gram of dry matter, as determined by its leaves [135]. The LAR is the product of the SLA and the quotient between the Leaf Dry Weight and the TDM (LWR). A low LAR may indicate a high plant efficiency dry matter productivity, whereas a high LAR is desirable and associated with a rapid growth rate. Certain factors, such as plant nutrition [37], salinity [59], and growth conditions [136], may affect the LAR of a plant. In addition, LAR varies with time due to changes in photoassimilate distribution during growth. In general, LAR tends to decrease as the plant grows given the increase in total biomass in relation to the development of the LA over time [112] (Equation (3)).
LAR = LA   ( cm 2 ) total   dry   weight   ( g )

5.2. Specific Leaf Area

The SLA (cm2 g−1) is the ratio between the LA and the leaf dry mass. A high SLA has been considered as a good plant quality indicator. Generally, in plants growing under favourable conditions, the SLA is a reliable predictor of relative growth rate, regardless of plant species and growth habits [19,137]. In addition, this is a crucial variable for modelling crop growth [67] (Equation (4)).
  SLA = LA   ( cm 2 ) leaf   dry   mass   ( g )
In some studies, SLA, the DQI, and the K/Na ratio have been regarded as good indicators for determining the effect of salinity stress [138]. According to Hunt and Cornelissen [135], a high growth rate is strongly related to a high SLA in herbaceous crops and forest species.
Generally, thin leaves have a high SLA, which reflects a large LA per unit of leaf weight. For thick leaves, the SLA has a low value [68]. Consequently, for horticultural crops, Diánez et al. [139] recommend a low SLA value, as it may be associated with thicker leaves, which may reduce post-transplanting stress.

5.3. Leaf Weight Ratio

The LWR (g g−1) is the quotient between the leaf dry weight and the TDM of the plant and reflects the fraction of assimilates assigned to the leaves [68].

5.4. Leaf Area Index

The LAI (m2 m−2) is the total LA of a plant or crop per square metre of surface area. The LAI is the most widely used variable in crop microclimate modelling and varies with crop growth and development, reaching the lowest values during the first stages of growth, and peaking when the crop is fully grown. According to Stanghellini et al. [68], when the LAI is high, more photosynthetically active radiation is intercepted and absorbed, leading to greater photosynthetic activity and crop growth. Furthermore, when the LAI is low, the moisture level of the microclimate inside the plant canopy decreases, which may reduce the presence of pathogenic microorganisms in the crop canopy. Considering that the LAI of many crops is high, Marcelis et al. [56] have proposed that horticultural crop yields may be increased by limiting leaf formation when a certain LA has been reached (a LAI of approximately 3 to 4 m2 m−2). This excludes herbaceous crops with leaves as the harvestable products (Equation (5)).
LAI = LA   ( m 2 ) Land   area   above   the   ground   ( m 2 )

6. Dickson Quality Index Evolution, Distribution, and Application

Figure 1 shows the positive exponential trend (R2 = 0.94) in the use of the DQI in scientific research, indicating an increase of more than 150% in the number of articles published from 2009 to 2019. Figure 2 shows the global distribution and the percentage of research studies conducted by country in which the DQI has been used as a quality indicator. Studies conducted in Brazil contribute 41.6%, followed by India (20.9%), China (16.0%), the United States (3.3%), Iran (1.6%), and, with lower percentages, Mexico, Nigeria, and Spain (1.3%). These data correspond to the countries with the strongest reforestation, conservation, and recovery programmes for degraded areas worldwide [140].

6.1. Clustering

Our network visualisation map shows the 77 main descriptors used as keywords in the set of publications analysed in this study (Figure 3). The different items were grouped into five clusters, represented by different colours on the map. Each cluster shows a set of closely related words from the same field of research. According to Chen et al. [45], who conducted a bibliometric study based on keyword analysis, cluster size and number may indicate variations in lines of research. The keywords that stand out most in the network visualisation map, due to their high occurrences and total link strength are germination, seed quality, seedling, vigour, seed, forestry, and seedling quality, which highlight the main research topics in the studies due to their close relationship with the different lines of agricultural research. Furthermore, within the study period, the map shows a line of research with 25 items (cluster 1; red) that includes studies related to biomass, composting, containers, cultivation, deciduous tree, the DQI, ecology, forestry, fungi, growth rate, growth response, morphology, nitrogen, peat, plant nutrition, plant (botany), reforestation, seed, seedling, seedling growth, seedling production, seedling quality, soil, substrate, and Zea mays. Cluster 1 stands out for encompassing the current research trends in the agricultural sciences. The overlay visualisation map (Figure 4) shows the evolution of keywords used to describe the main content of a research study, with the most recent terms, also being the most relevant terms, highlighted in yellow. These keywords are: controlled study, plant growth, root length, shoot, plant root, cluster analysis, photosynthesis, and DQI.

6.2. Main Plant Species

Figure 5 shows that, among all the articles analysed in this study (n = 289), 68.6% of the studies focused on sustainable production of forest species, followed by those centring on fruit (17.3%), horticultural (6.9%), medicinal (4.2%), and to a lesser extent aromatic (2.1%) and ornamental (1.0%) species. The percentage of studies on aromatic and ornamental species was low; this may be because different quality parameters, such as colour, stem length, or shelf life, are used for these crops compared to the others [141].

6.3. Study Variables and Conditions

Factorial experimental designs were followed in 40.8% and 4.1% of the articles analysed in this study, considering two and three independent quantitative variables that affected the production of quality plants, respectively (Table 1). Studies related to growth on substrates and plant nutrition stood out. Accordingly, more than one agronomic management strategy (factor) for increasing the production of quality plants, with high vigour and production potential, should be assessed. In addition, it is widely known that the growth and development of a plant is strongly affected by the management and growth medium [21,31,102].
Among the main research topics, growing crops on substrates and crop nutrition were the main plant growth conditions evaluated in more than 50% of the article samples analysed in this study (Figure 6). In addition, 29.0% of the articles focused on plant production using different substrates, analysing the physicochemical characteristics as well as the substrate type and proportion used in mixtures with different types of soil amendments (organic or manure), and alternative substrates (sewage sludge, urban solid waste, and harvest pruning). Together, plant nutrition and fertilisation constituted the second most abundant topic in the articles (25.6%); this subject included fertilisation with different doses of macronutrients and sources (10.4%), followed by salinity management and nutrient solutions (9.7%), and soil base saturation, controlled-release fertiliser use, and phytoremediation (5.5%). Similarly, 6.2% of the publications were related to water use and integrated management, and to different irrigation strategies because efficient plant nutrition, salinity, and fertigation management, in both intensive and soilless crops, is key to sustainable development in horticultural systems [142]. In addition, light intensity management using shading and specific spectra through LED lights in controlled environments accounted for 9.34% of the studies. Currently, the use of complementary lighting with LED lights in the cultivation of different herbaceous crops has made it possible to improve plant quality, thus, increasing productivity and improving the organoleptic properties of fruits [143].
Regarding plant protection, 6.57% of the studies evaluated the use of beneficial microorganisms, such as mycorrhizae, different species of the genus Trichoderma, and plant growth-promoting fungi and bacteria, in the cultivation of a wide variety of forest, fruit, and horticultural species. According to Diánez et al. [139], plant growth-promoting microorganisms and biological control agents are of agricultural interest as they are alternatives to synthetic products (fertilisers and pesticides).
Other fields of study, a grouping of 19.03% of the article samples, included environment and crop growth (5.88%), the effect of the cultivation unit (containers) (5.54%), different plant propagation techniques (5.19%), and plant selection and genetic improvement (2.42%). According to some authors [23,71], the characteristics of the seedbeds and the types of containers in which plants grow are the main factors that should be considered in the production of quality plants. Furthermore, Nyoka et al. [29] have mentioned that technical training and crop management play a key role in producing quality plants. Based on their results, it is evident that plants produced in governmental (>83%) and private (58.3%) seedbeds have a higher quality than those grown in communal seedbeds (33.3%).
Lastly, only 4.15% of the publications evaluated the correlation between plant quality indices and different morphological parameters. These publications mainly focussed on forest species, because quality indicators are widely used in research on such species.

6.4. Dickson Quality Index Values

Table S1 (The data presented in this study are available as Supplementary Materials) shows the 214 plant species for which the DQI has been used as a quality parameter, grouped by family, genus, and species following the recommendations of Blanca et al. [144] and Castroviejo [145]. In total, 49 plant families were identified, among which the dominant families were Fabaceae, Pinaceae, Myrtaceae, and Bignoniaceae, as they included more than 51.8% of the diversity of the species analysed in these studies. The dominant genera were Pinus spp. (7.0%), Eucalyptus spp. and Picea spp. (5.6% each), and Acacia spp., Senna spp. and Handroanthus spp. (1.8% each). Most likely, these results can be attributed to the high efficiency of reforestation programmes which in turn resulted from their extensive ornamental and industrial use and economic importance worldwide [3].
The high DQI values are related to plants with excellent quality and vary with crop management, cultivation system, treatment, relative plant age, and plant material. The DQI values range from 0.014 to 25.00 in forest species, 0.10 to 3.40 in fruit species, 0.00032 to 18.87 in medicinal species, 0.00058 to 0.21 in horticultural species, 0.10 to 4.29 in aromatic species, and 0.035 to 3.47 in ornamental species.
Of the 214 species analysed in these studies, 26.2% have a DQI value lower than 0.20, without affecting the quality standards of the plants. For 36.4% and 31.3% of the species, the DQI value ranges from 0.20 to 1 and from 1 to 6, respectively, and plant nutrition management, substrate type, and level of seedbed technification are the factors that are reported to have the strongest effect on quality plant production. Finally, 6.7% of the species have higher values (from 6 to 25), especially forest and medicinal species. For instance, in Enterolobium contortisiliquum (Vell.) Morong, the plants with the best quality have the highest DQI values (25), most likely due to the positive effect of the substrate (a mixture of organic substrate and vermiculite), which increases nutrient availability whilst retaining moisture in the rhizosphere [146] Miscanthus sinensis Andersson and Thysanolaena maxima (Roxb.) Kuntze plants have DQI values of 10.8 and 21.8, respectively, most likely associated with their high genetic potential and enhanced resistance and survival capacity under limiting growth conditions in manganese-contaminated soils [147]. Furthermore, in Elaeis guineensis Jacq., the DQI value is 20.0, possibly due to different factors, including the increase in the crop growth rate, the positive effect of using plant growth-promoting rhizobacteria that alter the plant hormonal balance, and the improvement of both the crop yield and the plant quality biometric attributes [25]. In medicinal plants such as Moringa oleifera Lam., controlled-release fertilisers enhance plant vigour and quality, as shown by a DQI of 18.8 at a fertilisation dose of 5.37 kg m−3 of substrate [148] (Refs. [149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329] in the Supplementary Materials). In general terms, these values are similar to the Dickson threshold value considered optimal for forest species [38]. However, standard values are not reported in the literature for most of the species analysed in these studies, even though the DQI has been used in plant production as a tool to predict responses to a wide variety of treatments under different cultivation systems, and to increase efficiency in the production of high-quality plants.

6.5. Plant Quality Morphometric Parameters

Figure 7 shows the main morphological and physiological parameters and the biometric ratios used as quality indicators, and their ability to predict survival, vigour, and performance potential (Table 2).
Regarding the morphological characteristics of plants, leaf number and area were used as the quality parameters in 31.8% and 25.3% of the studies (n = 289), respectively (Figure 7A), followed by root morphological and physiological parameters, which were used in 44.3% of such studies (Figure 7B), particularly root length, which accounted for more than 20.1% of these. Furthermore, within the physiological quality parameters, the content of primary and secondary root metabolites and the root growth potential were the parameters most utilised in 3.4% and 2.4% of the studies, respectively (Figure 7C). Plant nutritional status has also been established as a quality parameter, and classical nutritional diagnosis and the chlorophyll metre are the most commonly used soil-plant development analysis methods. Although absolute and relative growth analyses are crucial tools for understanding plant growth, the data from this review shows that only 3.8% of the articles simultaneously included these concepts along with plant quality parameters (Figure 7D).

6.6. Plant Quality Indicators and Their Biometric Ratios

As the DQI was the main descriptor, the parameters that make up the DQI, such as the plant total dry weight, the S/R ratio, and the H/D ratio accounted for 100% of the articles. Figure 8 shows the biometric ratios that were used in plant quality studies under a wide range of growing conditions and crops.
At 13.8%, the H/SDM ratio was the most widely used indicator, followed by the R/S ratio (10.0%). Both these parameters stood out for their potential use as plant quality predictors. The other key components of plant growth analysis were the SLA (6.2%), the LAR (2.4%), and to a lesser extent the LAI (1%). In contrast, the SDM/H ratio was primarily used as a quality indicator in horticultural and medicinal species in 1.7% of the articles. These findings indicate that the indices that integrate physiological and root quality parameters show great relevance and efficacy due to their high precision in predicting post-transplanting crop quality and survival.

7. Conclusions

The use of morphophysiological characteristics of plants and biometric ratios as quantitative and qualitative tools reinforces safety in the commercial, technical, and scientific production of plants with high quality standards. The DQI increases the efficiency in the selection and mass screening of plants with high quality attributes and improves crop survival and growth capacity in a wide range of plant species after transplanting. This bibliometric analysis has revealed that the agronomic characteristics of plants and the quality indicators are positively correlated, indicating that they are robust, reliable tools, capable of predicting plant productivity and quality. This review has gathered the reference values of the DQI for more than 200 species of agronomic interest. Calibration tests should be conducted, because the relative age of the plant, the genotypic variation, and the cultivation conditions affect plant agronomic traits and their biometric ratios.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/agronomy11112305/s1, Table S1A–F. Dickson’s values (DQI) associated with the production of hight quality plants related to the research topic and relative planta age in forest species. Table S1B. Dickson’s values (DQI) associated with the production of hight quality plants related to the research topic and relative planta age in fruit species. Table S1C. Dickson’s values (DQI) associated with the production of hight quality plants related to the research topic and relative planta age in ornamental species. Table S1D. Dickson’s values (DQI) associated with the production of hight quality plants related to the research topic and relative planta age in aromatic species. Table S1E. Dickson’s values (DQI) associated with the production of hight quality plants re-lated to the research topic and relative planta age in medicinal species. Table S1F. Dickson’s values (DQI) associated with the production of hight quality plants related to the research topic and relative planta age in horticultural species.

Author Contributions

V.M.G.-C. and F.D. conceived and designed the review; V.M.G.-C., C.N. and F.D. did the bibliographic research and analysed the data; V.M.G.-C. and M.S. wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

The present work benefitted from the input of the project RTC-2017-6486-2 and was supported by the Spanish Ministry of Science, Innovation and Universities.

Conflicts of Interest

The authors declare that there is no conflict of interests regarding the publication of this manuscript.

References

  1. FAOSTAT Database. Available online: http://www.fao.org/faostat/es/#data/RL/visualize (accessed on 1 April 2021).
  2. Luis, V.C.; Peters, J.; González–Rodríguez, A.M.; Jiménez, M.S.; Morales, D. Testing nursery plant quality of Canary Island pine seedlings grown under different cultivation methods. Phyton 2004, 44, 231–244. [Google Scholar]
  3. Haase, D.L.; Davis, A.S. Developing and supporting quality nursery facilities and staff are necessary to meet global forest and landscape restoration needs. Reforesta 2017, 4, 69–93. [Google Scholar] [CrossRef] [Green Version]
  4. Colla, G.; Pérez–Alfocea, F.; Schwarz, D. Vegetable Grafting: Principles and Practices; CABI: Wallingford, UK, 2017; pp. 1–278. [Google Scholar]
  5. Bantis, F.; Koukounaras, A.; Siomos, A.S.; Radoglou, K.; Dangitsis, C. Optimal LED wavelength composition for the production of high–quality watermelon and interspecific squash seedlings used for grafting. Agronomy 2019, 9, 870. [Google Scholar] [CrossRef] [Green Version]
  6. Lee, J.M.; Kubota, C.; Tsao, S.J.; Bie, Z.; Hoyos Echeverria, P.; Morra, L.; Oda, M. Current status of vegetable grafting: Diffusion, grafting techniques, automation. Sci. Hortic. 2010, 127, 93–105. [Google Scholar] [CrossRef]
  7. Asehor Database. Available online: https://pitalmeria.es/empresas/asehor/ (accessed on 30 October 2020).
  8. Duryea, M.L. Evaluating seedling quality: Importance to reforestation. In Evaluating Seedling Quality: Principles, Procedures, and Predictive Abilities of Major Tests; Duryea, M.L., Ed.; Forest Research Laboratory, Oregon State University: Corvallis, OR, USA, 1985; pp. 1–4. [Google Scholar]
  9. Puttonen, P. Criteria for using seedling performance potential tests. New For. 1989, 3, 67–87. [Google Scholar] [CrossRef]
  10. Davis, A.S.; Jacobs, D.F. Quantifying root system quality of nursery seedlings and relationship to outplanting performance. New For. 2005, 30, 295–311. [Google Scholar] [CrossRef]
  11. Wu, C.W.; Lin, K.H.; Lee, M.C.; Peng, Y.L.; Chou, T.Y.; Chang, Y.S. Using chlorophyll fluorescence and vegetation indices to predict the timing of nitrogen demand in Pentas lanceolata. Korean J. Hortic. Sci. Technol. 2015, 33, 845–853. [Google Scholar] [CrossRef] [Green Version]
  12. De Marco, R.; da Silva, R.F.; Andreazza, R.; Da Ros, C.O.; Scheid, D.L.; Bertollo, G.M. Copper phytoaccumulation and tolerance by seedlings of native Brazilian trees. Environ. Eng. Sci. 2016, 33, 176–184. [Google Scholar] [CrossRef]
  13. Haase, D.L. Understanding Forest Seedling Quality: Measurements and Interpretation. Tree Plant. Notes 2008, 52, 24–30. [Google Scholar]
  14. Grossnickle, S.C.; MacDonald, J.E. Why seedlings grow: Influence of plant attributes. New For. 2018, 49, 1–34. [Google Scholar] [CrossRef]
  15. Grossnickle, S.C.; MacDonald, J.E. Seedling Quality: History, Application, and Plant Attributes. Forests 2018, 9, 283. [Google Scholar] [CrossRef] [Green Version]
  16. Don, R.; ISTA Germination Committee. ISTA Handbook on Seedling Evaluation, 3rd ed.; The International Seed Testing Association (ISTA): Bassersdorf, Switzerland, 2006; pp. 1–23. [Google Scholar]
  17. Mañas, P.; Castro, E.; de las Heras, J. Quality of maritime pine Pinus pinaster Ait. seedlings using waste materials as nursery growing media. New For. 2009, 37, 295–311. [Google Scholar] [CrossRef]
  18. Ellison, D.S.; Schutzki, R.; Nzokou, P.; Cregg, B. Root growth potential, water relations and carbohydrate status of ash alternative species following pre-plant storage. Urban For. Urban Gree. 2016, 18, 59–64. [Google Scholar] [CrossRef] [Green Version]
  19. Kim, H.M.; Hwang, S.J. The growth and development of ‘Mini Chal’ tomato plug seedlings grown under various wavelengths using light emitting diodes. Agronomy 2019, 9, 157. [Google Scholar] [CrossRef] [Green Version]
  20. Bantis, F.; Koukounaras, A.; Siomos, A.; Menexes, G.; Dangitsis, C.; Kintzonidis, D. Assessing quantitative criteria for characterization of quality categories for grafted watermelon seedlings. Horticulturae 2019, 5, 16. [Google Scholar] [CrossRef] [Green Version]
  21. Takoutsing, B.; Tchoundjeu, Z.; Degrande, A.; Asaah, E.; Gyau, A.; Nkeumoe, F.; Tsobeng, A. Assessing the quality of seedlings in small–scale nurseries in the highlands of Cameroon: The use of growth characteristics and quality thresholds as indicators. Small Scale For. 2014, 13, 65–77. [Google Scholar] [CrossRef]
  22. Chauhan, S.K.; Sharma, R. Growth and quality indices of different nitrogen fixing tree nursery plants. Indian J. Ecol. 2017, 44, 344–347. [Google Scholar]
  23. Touhami, I.; Khorchani, A.; Bougarradh, M.; Elaieb, M.T.; Khaldi, A. Assesing the quality of seedlings in smallscale nurseries using morphological parameters and quality indicators to improve outplanting success. Plant Sociol. 2017, 54, 29–32. [Google Scholar]
  24. Kuan–Hung, L.I.N.; Chun–Wei, W.U.; Chang, Y.S. Applying Dickson quality index, chlorophyll fluorescence, and leaf area index for assessing plant quality of Pentas lanceolata. Not. Bot. Horti Agrobot. Cluj-Napoca 2019, 47, 169–176. [Google Scholar]
  25. Lima, J.V.; Tinôco, R.S.; Olivares, F.L.; Moraes, A.J.G.D.; Chia, G.S.; Silva, G.B.D. Hormonal imbalance triggered by rhizobacteria enhance nutrient use efficiency and biomass in oil palm. Sci. Hortic. 2020, 264, 109161. [Google Scholar] [CrossRef]
  26. Hunt, G.A. Effect of styroblock design and copper treatment on morphology of conifer seedlings. In Proceedings of the Target Seedling Symposium: Proceedings, Combined Meeting of the Western Forest Nursery Association, Roseburg, Oregon, 13–17 August 1990; General Technical Report RM–200; Rose, R., Campbell, S.J., Landis, T.D., Eds.; Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station: Fort Collins, CO, USA, 1990; pp. 218–222. [Google Scholar]
  27. Park, B.B.; Park, G.E.; Bae, K. Diagnosis of plant nutrient and growth responses on fertilization with vector analysis and morphological index. For. Sci. Technol. 2015, 11, 1–10. [Google Scholar] [CrossRef]
  28. Ivetić, V.; Grossnickle, S.; Škorić, M. Forecasting the field performance of Austrian pine seedlings using morphological attributes. iForest-Biogeosciences For. 2016, 10, 99–107. [Google Scholar] [CrossRef] [Green Version]
  29. Nyoka, B.I.; Kamanga, R.; Njoloma, J.; Jamnadass, R.; Mng’omba, S.; Muwanje, S. Quality of tree seedlings produced in nurseries in Malawi: An assessment of morphological attributes. For. Trees Livelihoods 2018, 27, 103–117. [Google Scholar] [CrossRef]
  30. Toprak, B. Early growth performance of mycorrhizae inoculated Taurus Cedar (Cedrus libani A. Rich.) seedlings in a nursery experiment conducted in inland part of Turkey. J. Plant Nutr. 2020, 43, 165–175. [Google Scholar] [CrossRef]
  31. Ritchie, G.A.; Landis, T.D.; Dumroese, R.K.; Haase, D.L. Assessing plant quality. In The Container Tree Nursery Manual, Seedling Processing, Storage, and Outplanting; Agricultural Handbook No., 674; Landis, T.D., Dumroese, R.K., Haase, D.L., Eds.; United States Department of Agriculture, Forest Service: Washington, DC, USA, 2010; Volume 7, pp. 17–82. [Google Scholar]
  32. Bayala, J.; Dianda, M.; Wilson, J.; Ouedraogo, S.J.; Sanon, K. Predicting field performance of five irrigated tree species using seedling quality assessment in Burkina Faso, West Africa. New For. 2009, 38, 309–322. [Google Scholar] [CrossRef] [Green Version]
  33. Saha, R.; Ginwal, H.S.; Chandra, G.; Barthwal, S. Integrated assessment of adventitious rhizogenesis in Eucalyptus: Root quality index and rooting dynamics. J. For. Res. 2020, 31, 2145–2161. [Google Scholar] [CrossRef]
  34. Tsakaldimi, M.; Ganatsas, P.; Jacobs, D.F. Prediction of planted seedling survival of five Mediterranean species based on initial seedling morphology. New For. 2013, 44, 327–339. [Google Scholar] [CrossRef]
  35. Thompson, B.E. Seedling morphological evaluation: What you can tell by looking. In Evaluating Seedling Quality: Principles, Procedures, and Predictive Ability of Major Tests; Duryea, M.L., Ed.; Oregon State University: Corvallis, OR, USA, 1985; pp. 59–71. [Google Scholar]
  36. Bernier, P.Y.; Lamhamedi, M.S.; Simpson, D.G. Shoot: Root ratio is of limited use in evaluating the quality of container conifer stock. Tree Plant. Notes 1995, 46, 102–106. [Google Scholar]
  37. De Sousa Leite, M.; de Freitas, R.M.; de Sousa Leite, T.; Dombroski, J.L.D. Growth and morphological responses of Handroanthus impetiginosus (Mart. ex DC.) Mattos seedlings to nitrogen fertilization. Biosci. J. 2017, 33, 88–94. [Google Scholar] [CrossRef]
  38. Dickson, A.; Leaf, A.L.; Hosner, J.F. Quality appraisal of white spruce and white pine seedling stock in nurseries. For. Chron. 1960, 36, 10–13. [Google Scholar] [CrossRef]
  39. Binotto, A.F.; Lúcio, A.D.C.; Lopes, S.J. Correlations between growth variables and the Dickson quality index in forest seedlings. Cerne 2010, 16, 457–464. [Google Scholar] [CrossRef] [Green Version]
  40. Mota, C.S.; Silva, F.G.; Dornelles, P.; Costa, A.C.; Souza Araujo, E.L.; Mendes, G.C. Use of physiological parameters to assess seedlings quality of Eugenia dysenterica DC. grown in different substrates. Aust. J. Crop Sci. 2016, 10, 842–851. [Google Scholar] [CrossRef]
  41. Oliveira, F.Í.F.D.; Souto, A.G.D.L.; Cavalcante, L.F.; Medeiros, W.J.F.D.; Bezerra, F.T.C.; Bezerra, M.A.F. Quality of jackfruit seedlings under saline water stress and nitrogen fertilisation. Semin. Ciências Agrárias 2017, 38, 2337–2350. [Google Scholar] [CrossRef] [Green Version]
  42. Souza, A.G.; Smiderle, O.J.; Muraro, R.E.; Bianchi, V.J. Patents for the morphophysiological quality of seedlings and grafted peach trees: Effects of nutrient solution and substrates. Recent Pat. Food Nutr. Agric. 2018, 9, 111–118. [Google Scholar] [CrossRef] [PubMed]
  43. Matias, S.S.R.; Dias, I.D.L.; Camelo, Y.M.; Souza, I.S.; de Castelo, F.R.; de Aguiar, W.R.; de Souza Ferreira, M.D. Quality of Carica papaya seedlings grown in an alternative substrate based on buriti wood (Mauritia flexuosa Lf). Científica 2019, 47, 337–343. [Google Scholar] [CrossRef] [Green Version]
  44. Costa, E.; Durante, L.G.Y.; Nagel, P.L.; Ferreira, C.R.; Santos, A.D. The quality of eggplant seedlings under different production methods. Rev. Ciência Agronômica 2011, 42, 1017–1025. [Google Scholar] [CrossRef] [Green Version]
  45. Chen, X.; Chen, J.; Wu, D.; Xie, Y.; Li, J. Mapping the research trends by co–word analysis based on keywords from funded project. Proced. Comput. Sci. 2016, 91, 547–555. [Google Scholar] [CrossRef] [Green Version]
  46. Dixon, G.R. Horticultural science—A century of discovery and application. J. Hortic. Sci. Biotech. 2019, 94, 550–572. [Google Scholar] [CrossRef]
  47. Thornley, J.H. Modelling stem height and diameter growth in plants. Ann. Bot. 1999, 84, 195–205. [Google Scholar] [CrossRef] [Green Version]
  48. Grossnickle, S.C. Why seedlings survive: Influence of plant attributes. New For. 2012, 43, 711–738. [Google Scholar] [CrossRef]
  49. Dardengo, M.C.J.; Sousa, E.F.D.; Reis, E.F.D.; Gravina, G.D.A. Growth and quality of conilon coffee seedlings produced at different containers and shading levels. Coffee Sci. 2013, 8, 500–509. [Google Scholar]
  50. Gallardo, M.; Thompson, R.B.; Valdez, L.C.; Fernández, M.D. Use of stem diameter variations to detect plant water stress in tomato. Irrig. Sci. 2006, 24, 241–255. [Google Scholar] [CrossRef]
  51. De Castro Paes, É.; Fernandes, I.O.; da Mata Camilo, G.B.; Pereira, E.G.; Dias, F.P.M.; Rocabado, J.M.A.; Nobrega, J.C.A. Quality of Myracrodruon urundeuva seedlings in different container sizes and organic compost proportion. Aust. J. Crop Sci. 2019, 13, 1309–1317. [Google Scholar] [CrossRef]
  52. Lima, S.L.; Marimon–Junior, B.H.; Petter, F.A.; Tamiozzo, S.; Buck, G.B.; Marimon, B.S. Biochar as substitute for organic matter in the composition of substrates for seedlings. Acta Sci. Agron. 2013, 35, 333–341. [Google Scholar] [CrossRef] [Green Version]
  53. Araújo, E.F.; Arauco, A.M.S.; Dias, B.A.S.; De Jesus, L.J.; Boechat, C.L.; Porto, D.L.; Arauco, L.R.R. Wastewater from swine farming in the growth and nutrition of Khaya senegalensis (DESR.) A Juss seedlings. Biosci. J. 2019, 35, 1378–1389. [Google Scholar] [CrossRef]
  54. Heuvelink, E. Growth, development and yield of a tomato crop: Periodic destructive measurements in a greenhouse. Sci. Hortic. 1995, 61, 77–99. [Google Scholar] [CrossRef]
  55. Rodríguez, F.; Berenguel, M.; Guzmán, J.L.; Ramírez–Arias, A. Modeling and Control of Greenhouse Crop Growth; Springer International Publishing: Cham, Switzerland, 2015; pp. 1–250. [Google Scholar]
  56. Marcelis, L.F.M.; Kaiser, E.; van Westreenen, A.; Heuvelink, E. Sustainable crop production in greenhouses based on understanding crop physiology. Acta Hortic. 2018, 1227, 1–12. [Google Scholar] [CrossRef] [Green Version]
  57. Oliveira, B.D.; Reis, S.M.; Morandi, P.S.; Valadão, M.B.X.; de Oliveira, E.A.; Marimon, B.S.; Marimon, J. Germination and seedling development of Magonia pubescens A. St. Hil (Sapindaceae) under different shade levels. Sci. For. 2016, 44, 905–916. [Google Scholar]
  58. Silva, L.D.D.; Lima, A.P.L.; Lima, S.F.D.; Silva, R.C.; Paniago, G.F. Controlled–release fertilizer in the production and quality of Acacia mangium seedlings. Floram 2019, 26, e02092017. [Google Scholar] [CrossRef]
  59. Bezerra, M.A.F.; Pereira, W.E.; Bezerra, F.T.C.; Cavalcante, L.F.; da Silva Medeiros, S.A. Nitrogen as a mitigator of salt stress in yellow passion fruit seedlingss. Semin. Ciências Agrárias 2019, 40, 611–622. [Google Scholar] [CrossRef] [Green Version]
  60. Mesquita, F.D.O.; Cavalcante, L.F.; de Fátima, R.T.; Souto, A.D.L.; Batista, R.O.; Bezerra, F.S. Bovine biofertilizer and saline water on jackfruit seedling production. Irriga 2019, 24, 392–404. [Google Scholar] [CrossRef]
  61. Liu, Y.; Ren, X.; Jeong, B.R. Carbon dioxide enrichment combined with supplemental light improve growth and quality of plug seedlings of Astragalus membranaceus Bunge and Codonopsis lanceolata Benth. et Hook. f. Agronomy 2019, 9, 715. [Google Scholar] [CrossRef] [Green Version]
  62. Paulus, D.; Zorzzi, I.C.; Rankrape, F.; Nava, G.A. Wastewater from fish farms for producing Eucalyptus grandis seedlings. Floram 2019, 26, e2017580. [Google Scholar] [CrossRef] [Green Version]
  63. Rocha, J.S.; Calzavara, A.K.; Bianchini, E.; Pimenta, J.A.; Stolf–Moreira, R.; Oliveira, H.C. Nitrogen supplementation improves the high–light acclimation of Guazuma ulmifolia Lam. seedlings. Trees 2019, 33, 421–431. [Google Scholar] [CrossRef]
  64. Aimi, S.C.; Araujo, M.M.; Fermino, M.H.; Tabaldi, L.A.; Zavistanovicz, T.C.; Mieth, P. Substrate and fertilization in the quality of Myrocarpus frondosus seedlings. Floresta 2019, 49, 831–840. [Google Scholar] [CrossRef]
  65. Gallegos, J.; Álvaro, J.E.; Urrestarazu, M. Container design affects shoot and root growth of vegetable plant. HortScience 2020, 55, 787–794. [Google Scholar] [CrossRef]
  66. De Castro e Melo, R.A.; dos Santos Butruille, N.M.; Jorge, M.H.A.; Cajamarca, S.M.N. Using nanocomposite hydrogel with N–urea in substrates for production of pepper seedlings. Bioagro 2019, 31, 167–176. [Google Scholar]
  67. Marcelis, L.F.M.; Heuvelink, E.; Goudriaan, J. Modelling biomass production and yield of horticultural crops: A review. Sci. Hortic. 1998, 74, 83–111. [Google Scholar] [CrossRef]
  68. Stanghellini, C.; Oosfer, B.; Heuvelink, E. Greenhouse Horticulture: Technology for Optimal Crop Production; Wageningen Academic Publishers: Wageningen, The Netherlands, 2019; pp. 1–311. [Google Scholar]
  69. Cruz, C.A.F.; Paiva, H.N.; Gomes, K.C.O.; Guerrero, C.R.A. Effect of different rates of base saturation on the growth and quality of ipê–roxo seedlings (Tabebuia impetiginosa (Mart.) Standley). For. Sci. 2004, 66, 100–107. [Google Scholar]
  70. Cairns, M.A.; Brown, S.; Helmer, E.H.; Baumgardner, G.A. Root biomass allocation in the world’s upland forests. Oecologia 1997, 111, 1–11. [Google Scholar] [CrossRef] [PubMed]
  71. Chirino, E.; Vilagrosa, A.; Hernández, E.I.; Matos, A.; Vallejo, V.R. Effects of a deep container on morpho–functional characteristics and root colonization in Quercus suber L. seedlings for reforestation in Mediterranean climate. For. Ecol. Manag. 2008, 256, 779–785. [Google Scholar] [CrossRef]
  72. Silva, R.F.D.; Saidelles, F.L.F.; Silva, A.S.D.; Bolzan, J.S. Influence of copper soil contamination on growth and quality of Acoita–Cavalo (Luehea divaricata Mart. & Zucc.) and Aroeira–Vermelha (Schinus therebinthifolius Raddi) seedlings. Ciência Florest. 2011, 21, 111–118. [Google Scholar]
  73. Sánchez–Aguilar, H.; Aldrete, A.; Vargas–Hernández, J.; Ordaz–Chaparro, V. Influence of container type and color on seedling growth of pine in nursery. Agrociencia 2016, 50, 481–492. [Google Scholar]
  74. Vieira, L.M.; Gomes, E.N.; Brown, T.A.; Constantino, V.; Zanette, F. Growth and quality of Brazilian pine tree seedlings as affected by container type and volume. Ornam. Hortic. 2019, 25, 276–286. [Google Scholar] [CrossRef]
  75. Tennant, D. A test of a modified line intersect method of estimating root length. J. Ecol. 1975, 63, 995–1001. [Google Scholar] [CrossRef]
  76. Galkovskyi, T.; Mileyko, Y.; Bucksch, A.; Moore, B.; Symonova, O.; Price, C.A.; Topp, C.N.; Iyer–Pascuzzi, A.S.; Zurek, P.R.; Fang, S.; et al. GiA Roots: Software for the high throughput analysis of plant root system architecture. BMC Plant Biol. 2012, 12, 116. [Google Scholar] [CrossRef] [Green Version]
  77. Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 2012, 9, 671–675. [Google Scholar] [CrossRef]
  78. Seethepalli, A.; Dhakal, K.; Griffiths, M.; Guo, H.; Freschet, G.T.; York, L.M. RhizoVision Explorer: Open–source software for root image analysis and measurement standardization. bioRxiv 2021. [Google Scholar] [CrossRef]
  79. Pimentel, N.; Lencina, K.H.; Pedroso, M.F.; Somavilla, T.M.; Bisognin, D.A. Morphophysiological quality of yerba mate plantlets produced by mini–cuttings. Semin. Ciências Agrárias 2017, 38, 3515–3528. [Google Scholar] [CrossRef] [Green Version]
  80. Marco, R.D.; Silva, R.F.D.; Ros, C.O.D.; Vanzam, M.; Boeno, D. Senna multijuga and peat in phytostabilization of copper in contaminated soil. Rev. Bras. Eng. Agríc. Ambient. 2017, 21, 421–426. [Google Scholar] [CrossRef] [Green Version]
  81. Da Silva, R.F.; Missio, E.L.; Steffen, R.B.; Weirich, S.W.; Kuss, C.C.; Scheid, D.L. Effects of copper on growth and quality of Stryphnodendron polyphyllum Mar. and Cassia multijuga Rich. Ciência Florest. 2014, 24, 717–726. [Google Scholar]
  82. Kormanik, P.P. Lateral root morphology as an expression of sweetgum seedling quality. For. Sci. 1986, 32, 595–604. [Google Scholar]
  83. Smirnakou, S.; Ouzounis, T.; Radoglou, K.M. Continuous spectrum LEDs promote seedling quality traits and performance of Quercus ithaburensis var. macrolepis. Front. Plant Sci. 2017, 8, 188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Shalizi, M.N.; Goldfarb, B.; Burney, O.T.; Shear, T.H. Effects of five growing media and two fertilizer levels on polybag—Raised Camden whitegum (Eucalyptus benthamii Maiden & Cambage) seedling morphology and drought hardiness. Forests 2019, 10, 543. [Google Scholar]
  85. Deans, J.D.; Mason, W.L.; Harvey, F.J. Clonal differences in planting stock quality of Sitka spruce. For. Ecol. Manag. 1992, 49, 101–107. [Google Scholar] [CrossRef]
  86. Simpson, D.G.; Ritchie, G.A. Does RGP predict field performance? A debate. New For. 1997, 13, 253–277. [Google Scholar] [CrossRef]
  87. Gallardo–Salazar, J.L.; Rodríguez–Trejo, D.A.; Castro–Zavala, S. Seedling quality and survival of a true fir [Abies religiosa (Kunth) Schltdl. et Cham.] forest plantation from two provenances in central Mexico. Agrociencia 2019, 53, 631–643. [Google Scholar]
  88. Oliveira Júnior, O.A.D.; Cairo, P.A.R.; Novaes, A.B.D. Morphophysiological characteristics associated to quality of Eucalyptus urophylla seedlings produced on different substrates. Rev. Árvore 2011, 35, 1173–1180. [Google Scholar] [CrossRef] [Green Version]
  89. Wendling, I.; Guastala, D.; Dedecek, R. Physical and chemical characteristics of substrates for the production of Ilex paraguariensis St. Hil. seedlings. Rev. Árvore 2007, 31, 209–220. [Google Scholar] [CrossRef] [Green Version]
  90. Boene, H.C.A.M.; Nogueira, A.C.; Sousa, N.J.; Kratz, D.; de Souza, P.V.D. Effects of different substrates in production of Sebastiania commersoniana seedling. Floresta 2013, 43, 407–419. [Google Scholar] [CrossRef] [Green Version]
  91. Dalanhol, S.J.; Nogueira, A.C.; Gaiad, S.; Kratz, D. Effect of mycorrhizae and fertilization on growth seedlings of Campomanesia xanthocarpa (Mart.) O. Berg., produced in different substrates. Ciência Florest. 2017, 27, 931–945. [Google Scholar] [CrossRef] [Green Version]
  92. Dalanhol, S.J.; Nogueira, A.C.; Gaiad, S.; Kratz, D. Effect of arbuscular mycorrhizal fungi and fertilization on seedlings growth of Eugenia uniflora L., produced in different substrates. Rev. Bras. Frutic. 2016, 38, 117–128. [Google Scholar] [CrossRef] [Green Version]
  93. Gomes, G.A., Jr.; Pereira, R.A.; Sodré, G.A.; Gross, E. Growth and quality of mangosteen seedlings (Garcinia mangostana L.) in response to the application of humic acids. Rev. Bras. Frutic. 2019, 41, e-104. [Google Scholar]
  94. Liu, W.; Su, J.; Li, S.; Lang, X.; Huang, X. Non–structural carbohydrates regulated by season and species in the subtropical monsoon broad–leaved evergreen forest of Yunnan Province, China. Sci. Rep. 2018, 8, 1083. [Google Scholar] [CrossRef]
  95. Kobe, R.K. Carbohydrate allocation to storage as a basis of interspecific variation in sapling survivorship and growth. Oikos 1997, 80, 226–233. [Google Scholar] [CrossRef]
  96. Liu, Y.; Ren, X.; Jeong, B.R. Manipulating the difference between the day and night temperatures can enhance the quality of Astragalus membranaceus and Codonopsis lanceolata Plug Seedlings. Agronomy 2019, 9, 654. [Google Scholar] [CrossRef] [Green Version]
  97. Liu, Y.; Ren, X.; Jeong, B.R. Night temperature affects the growth, metabolism, and photosynthetic gene expression in Astragalus membranaceus and Codonopsis lanceolata plug seedlings. Plants 2019, 8, 407. [Google Scholar] [CrossRef] [Green Version]
  98. Opio, C.; Jacob, N.; Coopersmith, D. Height to diameter ratio as a competition index for young conifer plantations in northern British Columbia, Canada. For. Ecol. Manag. 2000, 137, 245–252. [Google Scholar] [CrossRef]
  99. Mandre, M. Ecophysiological study of suitability of Picea mariana L. for afforestation in alkalized territories in Northeast Estonia. Oil Shale 2003, 20, 143–160. [Google Scholar]
  100. Luqui, L.L.; Salles, J.S.; Costa, E.; Alves, V.C.D.; Souza, L.G.P.; Vieira, M.T.; Salles, J.S.; Souza, V.C.M. Seedlings production and fruit yield of cucumber on different organic substrates. Rev. Agric. Neotrop. 2019, 6, 1–7. [Google Scholar] [CrossRef]
  101. Prates, F.B.D.S.; Lucas, C.D.S.G.; Sampaio, R.A.; Júnior, B.; da Silva, D.; Fernandes, L.A.; Junio, G.R.Z. Growth of Jatropha seedlings in response to single superphosphate and rock–flour fertilization. Rev. Ciência Agronômica 2012, 43, 207–213. [Google Scholar] [CrossRef] [Green Version]
  102. Landis, T.D.; Dumroese, R.K.; Haase, D.L. The Container Tree Nursery Manual: Seedling Processing, Storage, and Outplanting; Agricultural Handbook Nº 674; U.S. Department of Agriculture, Forest Service: Washington, DC, USA, 2010; Volume 7, pp. 1–199.
  103. Mandre, M.; Ots, K. Influence of industrial air pollutants on growth and physiological state of Douglas fir (Pseudotsuga menziesii) in Estonia. Indian J. Agr. Sci. 2002, 75, 263–266. [Google Scholar]
  104. Yücedağ, C.; Bilir, N.; Özel, H.B. Phytohormone effect on seedling quality in Hungarian oak. For. Syst. 2019, 28, e005. [Google Scholar] [CrossRef]
  105. Dias, M.J.T.; de Souza, H.A.; Natale, W.; Modesto, V.C.; Rozane, D.E. Fertilization with nitrogen and potassium in guava seedlings in a commercial nursery. Semin. Ciências Agrárias 2012, 33, 2837–2848. [Google Scholar]
  106. Cavalcante, A.G.; Cavalcante, A.C.P.; de Luna Souto, A.G.; Zuza, J.F.C.; Dantas, M.M.M.; Araújo, R.C. Growth and nutrition of Pitombeira (Tasilia esculenta Radlk) seedlings in different substrates and biofertilizer application. Aust. J. Crop Sci. 2019, 13, 105–114. [Google Scholar] [CrossRef]
  107. Martínez-Hernández, R.; Villa-Castorena, M.M.; Catalán-Valencia, E.A.; Inzunza-Ibarra, M.A. Production of oregano (Lippia graveolens Kunth) seedling from seeds in nursery for transplanting. Rev. Chapingo Ser. Cienc. For. Ambient. 2017, 23, 61–73. [Google Scholar]
  108. Costa, E.; Leal, P.A.; Benett, C.G.; Benett, K.S.; Salamene, L.C. Production of tomato seedlings using different substrates and trays in three protected environments. Eng. Agríc. 2012, 32, 822–830. [Google Scholar] [CrossRef] [Green Version]
  109. Guisolfi, L.P.; Monaco, P.A.V.L.; Haddade, I.R.; Krause, M.R.; Meneghelli, L.A.M.; Almeida, K. Production of cucumber seedlings in alternative substrates with different compositions of agricultural residues. Rev. Caatinga 2018, 31, 791–797. [Google Scholar] [CrossRef] [Green Version]
  110. Wei, H.; Wang, M.; Jeong, B.R. Effect of supplementary lighting duration on growth and activity of antioxidant enzymes in grafted watermelon seedlings. Agronomy 2020, 10, 337. [Google Scholar] [CrossRef] [Green Version]
  111. Braga, M.D.M.; Furtini Neto, A.E.; Oliveira, A.H. Influence of base saturation in quality and growth of australian cedar seedlings (Toona ciliata M. Roem var. australis). Ciência Florest. 2015, 25, 49–58. [Google Scholar]
  112. Balota, E.L.; Machineski, O.; Viviane Truber, P.; Scherer, A.; Souza, F.S.D. Physic nut plants present high mycorrhizal dependency under conditions of low phosphate availability. Braz. J. Plant Physiol. 2011, 23, 33–44. [Google Scholar] [CrossRef]
  113. Thomas, D.S. Survival and growth of drought hardened Eucalyptus pilularis Sm. seedlings and vegetative cuttings. New For. 2009, 38, 245–259. [Google Scholar] [CrossRef]
  114. Marschner, P. Marschner’s Mineral Nutrition of Higher Plants, 3rd ed.; Academic Press: London, UK, 2012; pp. 1–651. [Google Scholar]
  115. Faria, J.C.T.; Caldeira, M.V.W.; Delarmelina, W.M.; Lacerda, L.C.; Gonçalves, E.D.O. Substrates of sewage sludge in the production of seedlings of Senna alata. Comun. Sci. 2013, 4, 342–351. [Google Scholar]
  116. Da Silva, D.S.; Venturin, N.; Rodas, C.L.; Macedo, R.L.; Venturin, R.P.; Melo, L.A.D. Growth and mineral nutrition of baru (Dipteryx alata Vogel) in nutrient solution. Rev. Bras. Eng. Agríc. Ambient. 2016, 20, 1101–1106. [Google Scholar] [CrossRef] [Green Version]
  117. Delarmelina, W.M.; Caldeira, M.V.W.; Faria, J.C.T.; Lacerda, L.C. Use of organic waste in formulation of substrate for Chamaecrista desvauxii (Collad.) Killip var Latistipula (Benth.) production. Cerne 2016, 21, 429–437. [Google Scholar] [CrossRef] [Green Version]
  118. Souza, L.D.P.; Nobre, R.G.; Silva, E.M.; Gheyi, H.R.; Soares, L.A.D.A. Production of guava rootstock grown with water of different salinities and doses of nitrogen. Rev. Ciência Agronômica 2017, 48, 596–604. [Google Scholar] [CrossRef]
  119. Jala, I.M.; da Silva, C.C.; Sampaio Filho, J.S.; de Oliveira, E.J.; Nóbrega, R.S.A. Seedlings of cassava varieties are responsive to organic fertilization. Semin. Ciências Agrárias 2019, 40, 2051–2164. [Google Scholar] [CrossRef] [Green Version]
  120. Bernardino, D.C.D.S.; Paiva, H.N.D.; Neves, J.C.D.L.; Gomes, J.M.; Marques, V.B. Growth and seedling quality of Dalbergia nigra (Vell.) Fr. All. ex Benth.) in response to base saturation in the substrate. Rev. Árvore 2007, 31, 567–573. [Google Scholar] [CrossRef] [Green Version]
  121. Nóbrega, R.S.A.; Ferreira, P.A.A.; dos Santos, J.G.D.; Boas, R.C.V.; Nóbrega, J.C.A.; de Souza Moreira, F.M. Effect of urban waste compost and liming on initial growth of Enterolobium contortisiliquum (Vell.) Morong seedlings. Sci. For. 2008, 36, 181–189. [Google Scholar]
  122. Nóbrega, R.S.A.; Paula, A.M.D.; Vilas Boas, R.C.; Nóbrega, J.C.A.; Moreira, F.M.D.S. Morphological parameters of Sesbania virgata (CAZ.) Pers and Anadenanthera peregrina (L.) seedlings cultivated in substrate fertilized with urban waste compost. Rev. Árvore 2008, 32, 597–607. [Google Scholar] [CrossRef]
  123. Sabonaro, D.Z.; Galbiatti, J.A.; de Paula, R.C.; Gonzales, J.L.S. Seedlings production of Tabebuia heptaphylla in different substrates and levels of irrigation, in greenhouse conditions. Bosque 2009, 30, 27–35. [Google Scholar]
  124. Marqués, V.B.; de Paiva, H.N.; Gomes, J.M.; Neves, J.C.L. Effects of nitrogen sources and levels on the growth of sabia (Mimosa caesalpiniaefolia Benth.) seedlings. Sci. For. 2006, 71, 77–85. [Google Scholar]
  125. Dutra, T.R.; Massad, M.D.; Sarmento, M.F.Q.; Oliveira, J.C.D. Alternative substrates and methods of breaking dormancy for the production of canafístula seedlings. Rev. Ceres 2013, 60, 72–78. [Google Scholar] [CrossRef] [Green Version]
  126. Souza, P.H.D.; Paiva, H.N.D.; Neves, J.C.L.; Gomes, J.M.; Marques, L.S. Influence of substratum base saturation on seedling growth and quality of Machaerium nictitans (Vell.) Benth. Rev. Árvore 2008, 32, 193–201. [Google Scholar] [CrossRef]
  127. Marques, L.S.; Paiva, H.N.D.; Neves, J.C.L.; Gomes, J.M.; Souza, P.H.D. Jacaré (Piptadenia gonoacantha JF Macbr.) seedling growth in different soil types and nitrogen sources and doses. Rev. Árvore 2009, 33, 81–92. [Google Scholar] [CrossRef] [Green Version]
  128. Valadão, M.B.X.; Marimon Junior, B.H.; Morandi, P.S.; Reis, S.M.; Oliveira, B.D.; Oliveira, E.A.; Marimon, B.S. Initial development and biomass partitioning of Physocalymma scaberrimum Pohl (Lythraceae) under different shading levels. Sci. For. 2014, 42, 129–139. [Google Scholar]
  129. Prior, S.A.; Rogers, H.H.; Runion, G.B.; Mauney, J.R. Effects of free–air CO2 enrichment on cotton root growth. Agr. For. Meteorol. 1994, 70, 69–86. [Google Scholar] [CrossRef]
  130. Körner, C.H.; Renhardt, U. Dry matter partitioning and root length/leaf area ratios in herbaceous perennial plants with diverse altitudinal distribution. Oecologia 1987, 74, 411–418. [Google Scholar] [CrossRef]
  131. Mortimer, S.R. Root length/leaf area ratios of chalk grassland perennials and their importance for competitive interactions. J. Veg. Sci. 1992, 3, 665–673. [Google Scholar] [CrossRef]
  132. Tani, T.; Kudoh, H.; Kachi, N. Responses of root length/leaf area ratio and specific root length of an understory herb, Pteridophyllum racemosum, to increases in irradiance. Plant Soil 2003, 255, 227–237. [Google Scholar] [CrossRef]
  133. Briggs, G.E.; Kidd, F.; West, C. A quantitative analysis of plant growth: Part I. Ann. Appl. Biol. 1920, 7, 103–123. [Google Scholar] [CrossRef] [Green Version]
  134. Fisher, R.A. Some remarks on the methods formulated in a recent article on “The quantitative analysis of plant growth”. Ann. Appl. Biol. 1921, 7, 367–372. [Google Scholar] [CrossRef] [Green Version]
  135. Hunt, R.; Cornelissen, J.H.C. Components of relative growth rate and their interrelations in 59 temperate plant species. New Phytol. 1997, 135, 395–417. [Google Scholar] [CrossRef]
  136. Zuffo, A.M.; Andrade, F.R.; Petter, F.A.; de Souza, T.R.S.; Piauilino, A.C. Position and depth of sowing on the emergence and early development of seedlings of Anacardium microcarpum Ducke. Rev. Bras. Ciên. Agrár. 2014, 9, 556–561. [Google Scholar]
  137. Gibert, A.; Gray, E.F.; Westoby, M.; Wright, I.J.; Falster, D.S. On the link between functional traits and growth rate: Meta-analysis shows effects change with plant size, as predicted. J. Ecol. 2016, 104, 1488–1503. [Google Scholar] [CrossRef] [Green Version]
  138. Elfeel, A.A.; Abohassan, R.A. Response of Balanites aegyptiaca (L.) Del. var. aegyptiaca seedlings from three different sources to water and salinity stresses. Pak. J. Bot. 2015, 47, 1199–1206. [Google Scholar]
  139. Diánez, F.; Santos, M.; Parra, C.; Navarro, M.J.; Blanco, R.; Gea, F.J. Screening of antifungal activity of 12 essential oils against eight pathogenic fungi of vegetables and mushroom. Lett. Appl. Microbiol. 2018, 67, 400–410. [Google Scholar] [CrossRef]
  140. United Nations Development Programme. The Future We Want Biodiversity and Ecosystems—Driving Sustainable Development; United Nations Development Programme Biodiversity and Ecosystems Global Framework 2012–2020; United Nations: New York, NY, USA, 2012; pp. 1–80.
  141. Carvalho, S.M.; Heuvelink, E.; Eveleens–Clark, B. Plant height formation in different cultivars of kalanchoe. Acta Hortic. 2005, 691, 83. [Google Scholar] [CrossRef] [Green Version]
  142. Gallegos–Cedillo, V.M. Effect of Fertigation on Productivity Parameters through Mineral Nutrition in Soilless Culture. Ph.D. Thesis, Almeria University, Almeria, Spain, 2019. [Google Scholar]
  143. Nájera, C.; Guil–Guerrero, J.L.; Álvaro, J.E.; Urrestarazu, M. LED-enhanced dietary and organoleptic qualities in postharvest tomato fruit. Postharvest Biol. Technol. 2018, 145, 151–156. [Google Scholar] [CrossRef]
  144. Blanca, G.; Cabezudo, B.; Cueto, M.; Salazar, C.; Morales Torres, C. Flora Vascular de Andalucía Oriental, 2nd ed.; Universidades de Almería, Granada, Jaén y Málaga: Granada, Spain, 2011; pp. 1–1750. [Google Scholar]
  145. Castroviejo, S. Flora Ibérica 1–8, 10–15, 17–18, 21; Real Jardín Botánico, CSIC: Madrid, Spain, 1986–2012; pp. 1–784. [Google Scholar]
  146. Gonçalves, F.G.; Alexandre, R.S.; Silva, A.G.D.; Lemes, E.D.Q.; Rocha, A.P.D.; Ribeiro, M.P.D.A. Emergency and quality of Enterolobium contortisiliquum (Vell.) Morong (Fabaceae) seedlings in different substrates. Rev. Árvore 2013, 37, 1125–1133. [Google Scholar] [CrossRef] [Green Version]
  147. Villacís, J.; Armas, C.; Hang, S.; Casanoves, F. Selection of adequate species for degraded areas by oil-exploitation industry as a key factor for recovery forest in the Ecuadorian Amazon. Land Degrad. Dev. 2016, 27, 1771–1780. [Google Scholar] [CrossRef] [Green Version]
  148. Rosa, T.L.M.; Jordaim, R.B.; Alexandre, R.S.; de Araujo, C.P.; Gonçalves, F.G.; Lopes, J.C. Controlled release fertilizer in the growth of Moringa oleifera LAM. seedlings. Floresta 2018, 48, 303–310. [Google Scholar] [CrossRef]
  149. Hou, X.; Liu, S.; Zhao, S.; Beazley, R.; Cheng, F.; Wu, X.; Xu, J.; Dong, S. Selection of suitable species as a key factor for vegetation restoration of degraded areas in an open-pit manganese-ore mine in Southern China using multivariate-analysis methods. Land Degrad. Dev. 2019, 30, 942–950. [Google Scholar] [CrossRef]
  150. Dantas, R.D.P.; Oliveira, F.D.A.D.; Cavalcante, A.L.G.; Pereira, K.T.O.; Oliveira, M.K.T.D.; Medeiros, J.F.D. Quality of Tabebuia aurea (Manso) Benth. & Hook. seedlings in two environments and levels of fertigation. Ciência Florest. 2018, 28, 1253–1262. [Google Scholar]
  151. Oliveira, J.R.D.; Costa, C.A.S.D.; Bezerra, A.M.E.; Abud, H.F.; Lucena, E.M.P.D. Characterization of seeds, seedlings and initial growth of Jacaranda mimosifolia D. Don. (Bignoniaceae). Rev. Árvore 2018, 42, e420403. [Google Scholar] [CrossRef]
  152. Pinto Junior, A.S.; Guimarães, V.F.; Dranski, J.A.L.; Steiner, F.; Malavasi, M.D.M.; Malavasi, U.C. Storage of physic nut seeds in different environments and packaging. Rev. Bras. Sementes 2012, 34, 636–643. [Google Scholar] [CrossRef]
  153. Barbeiro, C.; Firmino, T.P.; de Novaes, A.H.O.; Romagnolo, M.B.; Pastorini, L.H. Germination and growth of Albizia niopoides (Bentham) Burkart (Fabaceae). Acta Sci. Biol. Sci. 2018, 40, e39073. [Google Scholar] [CrossRef]
  154. Nourmohammadi, K.; Rahimi, D.; Naghdi, R.; Kartoolinejad, D. Effects of physical and chemical treatments of seed dormancy breaking on seedling quality index (QI) of Caspian locust (Gleditsia caspica Desf.). Austrian J. For. Sci. 2016, 133, 157–171. [Google Scholar]
  155. Sanches, C.F.; Costa, E.; Costa, G.G.; Binotti, F.F.D.S.; Cardoso, E.D. Hymenaea courbaril seedlings in protected environments and substrates. Eng. Agríc. 2017, 37, 24–34. [Google Scholar] [CrossRef] [Green Version]
  156. Pereira, D.D.S.; Sousa, J.E.S.; Pereira, M.D.S.; Gonçalves, N.R.; Bezerra, A.M.E. Emergence and initial growth of Copernicia prunifera (Arecaceae) as a function of fruit maturation. J. Seed Sci. 2014, 36, 9–14. [Google Scholar] [CrossRef]
  157. Dranski, J.A.L.; Malavasi, U.C.; Malavasi, M.D.M.; Jacobs, D.F. Effect of ethephon on hardening of Pachystroma longifolium seedlings. Rev. Árvore 2013, 37, 401–407. [Google Scholar] [CrossRef] [Green Version]
  158. Dellai, A.; da Silva, R.F.; Perrando, E.R.; Jacques, R.J.; Grolli, A.L.; Marco, R.D. Eucalyptus oil and Pisolithus microcarpus in the growth of bracatinga in soil contaminated by copper. Rev. Bras. Eng. Agric. Ambient. 2014, 18, 927–934. [Google Scholar] [CrossRef] [Green Version]
  159. Pierezan, L.; Scalon, S.D.P.Q.; Pereira, Z.V. Jatobá seedlings emergence and growth with the use of biostimulant and shading. Cerne 2012, 18, 127–133. [Google Scholar] [CrossRef] [Green Version]
  160. Silva, D.D.; Stuepp, C.A.; Wendling, I.; Helm, C.; Angelo, A.C. Influence of seed storage conditions on quality of Torresea acreana seedlings. Cerne 2019, 25, 60–67. [Google Scholar] [CrossRef]
  161. Da Silva, M.P.S.; Barroso, D.G.; de Souza, J.S.; de Alvarenga Ferreira, D.; de Oliveira, T.P.D.F.; Lamônica, K.R.; Marinho, C.S. Growth and quality of australian cedar saplings originated from different multiclonal minigarden systems. Semin. Ciências Agrárias 2016, 37, 1127–1134. [Google Scholar] [CrossRef] [Green Version]
  162. Tsukamoto Filho, A.D.A.; Carvalho, J.L.O.; Costa, R.B.D.; Dalmolin, A.C.; Brondani, G.E. Irrigation methods and substrate coverage affects the initial growth of Myracrodruon urundeuva seedlings. Floram 2013, 20, 521–529. [Google Scholar]
  163. Menegatti, R.D.; Navroski, M.C.; Guollo, K.; Fior, C.S.; de Souza, A.; das Graças Souza, A.; Possenti, J.C. Formation of seedlings of guatambu on substrate with hidrogel and controlled release fertilizer. Espacios 2017, 38, 35–47. [Google Scholar]
  164. Mews, C.L.; de Sousa, J.R.L.; Azevedo, G.D.O.; Souza, A.M. Effect of hydrogel and urea on the seedling production of Handroanthus ochraceus (Cham.) Mattos. Floram 2015, 22, 107–116. [Google Scholar] [CrossRef] [Green Version]
  165. Konzen, E.R.; Navroski, M.C.; Friederichs, G.; Ferrari, L.H.; Pereira, M.D.O.; Felippe, D. The use of hydrogel combined with appropriate substrate and fertilizer improve quality and growth performance of Mimosa scabrella Benth. seedlings. Cerne 2017, 23, 473–482. [Google Scholar] [CrossRef] [Green Version]
  166. Wang, J.; Hui, D.; Lu, H.; Wang, F.; Liu, N.; Sun, Z.; Ren, H. Main and interactive effects of increased precipitation and nitrogen addition on growth, morphology, and nutrition of Cinnamomum burmanni seedlings in a tropical forest. Global Ecol. Conserv. 2019, 20, e00734. [Google Scholar] [CrossRef]
  167. Navroski, M.C.; Araújo, M.M.; Reininger, L.R.S.; Muniz, M.F.B.; Pereira, M.D.O. Doses of hydrogel influencing growth and nutritional content in seedlings Eucalyptus dunnii. Floresta 2015, 45, 315–328. [Google Scholar] [CrossRef] [Green Version]
  168. Navroski, M.C.; Araujo, M.M.; Reiniger, L.R.S.; Fior, C.S.; Schafer, G.; Pereira, M.D.O. Initial growth of seedlings of Eucalyptus dunnii Maiden as influenced by the addition of natural polymer and farming substrates. Rev. Árvore 2016, 40, 627–637. [Google Scholar] [CrossRef] [Green Version]
  169. Queiroz, T.B.; Rocha, S.M.G.; da Fonseca, F.S.A.; Alvarenga, I.C.A.; Martins, E.R. Effects of water stress on production of eucalypt seedlings. Irriga 2017, 22, 659–674. [Google Scholar] [CrossRef]
  170. Ávila–Flores, I.J.; Prieto–Ruíz, J.A.; Hernández–Díaz, J.C.; Wehenkel, C.A.; Corral–Rivas, J.J. Preconditioning Pinus engelmannii Carr. seedlings by irrigation deficit in nursery. Rev. Chapingo Ser. Cienc. For. Ambient. 2014, 20, 237–245. [Google Scholar]
  171. Rossa, Ü.B.; Angelo, A.C.; Westphalen, D.J.; Oliveira, F.E.M.D.; Silva, F.F.D.; Araujo, J.C.D. Slow release fertilizer on the growth of seedlings Anadenanthera peregrina (L.) Speg. and Schinus terebinthifolius Raddi. Ciência Florest. 2015, 25, 841–852. [Google Scholar]
  172. De Sousa Leite, T.; de Freitas, R.M.; Nogueira, N.W.; Ferreira, H.; de Sousa Leite, M. Growth and quality of Handroanthus impetiginosus (Mart. ex DC.) Mattos seedlings irrigated with saline fish effluent. Aust. J. Crop Sci. 2017, 11, 1457–1461. [Google Scholar] [CrossRef]
  173. Oliveira, A.R.D.; Boechat, C.L.; Amorim, S.P.D.N.; Souza, M.E.L.D.; Duarte, L.D.S.L.; Silva, H.F. Growth and quality of Handroanthus serratifolius seedlings in soils incorporating amendments and inorganic residues. Rev. Ceres 2019, 66, 235–242. [Google Scholar] [CrossRef] [Green Version]
  174. Pinto, J.R.D.S.; de Freitas, R.M.; Leite, T.D.S.; Oliveira, F.D.A.D.; Ferreira, H.; Leite, M.D.S. Growth of young Tabebuia aurea seedlings under irrigation with wastewater from fish farming. Rev. Bras. Eng. Agri. Ambient. 2016, 20, 519–524. [Google Scholar] [CrossRef] [Green Version]
  175. Vieira, C.; Weber, O. Base saturation on growth and on nutrition of Yellow Ipê seedlings. Floresta E Ambient. 2017, 24, e20160019. [Google Scholar]
  176. Freitas, E.C.S.D.; Paiva, H.N.D.; Leite, H.G.; Oliveira Neto, S.N.D. Growth and quality of Cassia grandis Linnaeus f. seedlings in response to phosphate fertilization and liming. Ciência Florest. 2017, 27, 509–519. [Google Scholar]
  177. Da Silva, R.F.D.; Saidelles, F.L.; Kemerich, P.D.; Steffen, R.B.; Swarowsky, A.; Silva, A.S.D. Growth and quality of Ateleia glazioviana and Lafoensia pacari seedlings cultivated in copper contaminated soil. Rev. Bras. Eng. Agríc. Ambient. 2012, 16, 881–886. [Google Scholar]
  178. Da Silva, R.F.; da Ros, C.O.; Dellai, A.; Grolli, A.L.; Scheid, D.L.; Viel, P. Interference of doses of copper on growth and quality of Bauhinia forficata Link, Pterogyne nitens Tul and Enterolobium contortisiliquum Vell. seedlings. Ciência Florest. 2016, 26, 647–655. [Google Scholar] [CrossRef] [Green Version]
  179. Cuzzuol, G.R.F.; Milanez, C.R.D.; Gomes, J.M.L.; Labate, C.A.; Canal, E.C. Relationship between N, P, and K and the quality and stem structural characteristics of Caesalpinia echinata Lam. plants. Trees 2013, 27, 1477–1484. [Google Scholar] [CrossRef]
  180. Reis, B.E.; Paiva, H.N.; Barros, T.C.; Ferreira, A.L.; Cardoso, W.D.C. Growth and seedling quality of Jacarandá-da-bahia in response to potassium and sulfur fertilization. Ciência Florest. 2012, 22, 389–396. [Google Scholar]
  181. Pinho, E.K.C.; Costa, A.C.; Vilar, C.C.; Souza, M.E.D.; Silva, A.B.V.; Oliveira, C.H.G.D. Phosphate and nitrogen fertilization in the production of Barueiro (Dipteryx alata Vog.) seedlings. Rev. Bras. Frutic. 2019, 41, e-008. [Google Scholar] [CrossRef] [Green Version]
  182. Zuffo, A.M.; Júnior, J.M.Z.; Carvalho, R.M.; dos Santos, A.S.; Oliveira, J.B.D.S.; Fonseca, W.L. Response of baru (Dipteryx alata Vog.) seedlings to liming and NPK application. J. Plant Nutr. 2017, 40, 1332–1338. [Google Scholar] [CrossRef]
  183. Cavalcante, A.L.G.; Oliveira, F.D.A.; Pereira, K.T.O.; Dantas, R.D.P.; de Oliveira, M.K.T.; da Cunha, R.C.; Souza, M.D.L. Development of seedlings Mulungu fertigated with different nutrient solutions. Floresta 2016, 46, 47–55. [Google Scholar] [CrossRef] [Green Version]
  184. Rebouças, J.R.L.; Ferreira Neto, M.; Dias, N.D.S.; Gomes, J.W.S.; Gurgel, G.D.S.; de Queiroz, I.S.R. Quality of sabiá seedlings irrigated with domestic effluent. Floresta 2018, 48, 173–182. [Google Scholar]
  185. Souza, N.H.D.; Marchetti, M.E.; Carnevali, T.D.O.; Ramos, D.D.; Scalon, S.D.P.Q.; Silva, E.F.D. Nutrition study of Canafístula (I): Initial growth and seedlings quality of Peltophorum dubium in response to fertilization with nitrogen and phosphorus. Rev. Árvore 2013, 37, 717–724. [Google Scholar] [CrossRef] [Green Version]
  186. Freitas, E.C.S.D.; Paiva, H.N.D.; Leite, H.G.; Oliveira Neto, S.N.D. Effect of phosphate fertilization and base saturation of substrate on the seedlings growth and quality of Plathymenia foliolosa Benth. Rev. Árvore 2017, 41, e410111. [Google Scholar] [CrossRef] [Green Version]
  187. Da Silva Araújo, M.; Coneglian, A.; Hodecker, B.E.R.; Pelá, A.; Gonçalves, R.N.; Rocha, E.C. Initial growth of Brazilian firetree (Schizolobium parahyba (Vell.) SF Blake) fertilized with phosphorus in Red–Yellow Latosol. Aust. J. Crop Sci. 2018, 12, 1108–1113. [Google Scholar] [CrossRef]
  188. De Souza, P.H.; de Paiva, H.N.; Neves, J.C.L.; Gomes, J.M.; Marques, L.S. Growth and seedling quality of Senna macranthera (Collad.) Irwin et Barn. in response to calagem. Rev. Árvore 2010, 34, 233–240. [Google Scholar] [CrossRef]
  189. Escamilla–Hernández, N.; Obrador–Olán, J.J.; Carrillo–Ávila, E.; Palma–López, D.J. Effect of controlled release fertilizers on growth of teak plants (Tectona grandis) on nursery. Rev. Fitotec. Mex. 2015, 38, 329–333. [Google Scholar]
  190. Smiderle, O.J.; das Graças Souza, A. Production and quality of Cinnamomum zeylanicum Blume seedlings cultivated in nutrient solution. Rev. Bras. Ciências Agrárias 2016, 11, 104–110. [Google Scholar] [CrossRef]
  191. Das Graces Souza, A.; Smiderle, O.J.; Chagas, E.A. Nutrition and accumulation of nutrients in Pochota fendleri seedlings. Rev. Bras. Ciências Agrárias 2018, 13, e5559. [Google Scholar]
  192. Mesquita, F.D.O.; Nunes, J.C.; de Lima Neto, A.J.; Souto, A.D.L.; Batista, R.O.; Cavalcante, L.F. Formation of NIM seedlings under salinity, biofertilizer and soil drainage. Irriga 2015, 20, 193–203. [Google Scholar] [CrossRef] [Green Version]
  193. Carmo, É.R.D.; Silva, C.F.D.; Freitas, M.S.M.; Lima, K.B.; Martins, M.A. Production of Australian cedar seedlings inoculated with arbuscular mycorrhizal fungi in different types of containers. Rev. Árvore 2016, 40, 269–278. [Google Scholar] [CrossRef] [Green Version]
  194. Lucchese, J.R.; Lazarotto, M.; Hilgert, M.A.; Fior, C.S.; de Sá, L.C.; Brose, C.B. Nitrogen fertilization for the nursery production of Australian red cedar. Floresta 2019, 49, 431–438. [Google Scholar] [CrossRef]
  195. Leite, T.D.S.; Dombroski, J.L.D.; Freitas, R.M.O.D.; Leite, M.D.S.; Rodrigues, M.R.D.O. Production of Enterolobium contortisiliquum seedlings and assimilate partitioning in response to phosphorus fertilization and inoculation with mycorrhizal fungi. Ciência Florest. 2017, 27, 1157–1166. [Google Scholar] [CrossRef] [Green Version]
  196. Sousa, W.C.; Nóbrega, R.S.A.; Nóbrega, J.C.A.; Brito, D.R.S.; Moreira, F.M.S. Nitrogen sources and Mauritia flexuosa decomposed stem on nodulation and growth of Enterolobium contortsiliquum. Rev. Árvore 2013, 37, 969–979. [Google Scholar] [CrossRef] [Green Version]
  197. De Almeida, J.P.; de Freitas, R.M.; Nogueira, N.W.; Oliveira, F.D.A.D.; Ferreira, H.; Leite, M.D.S. Production of Piptadenia stipulacea (Benth.) Ducke seedlings irrigated with fish farming wastewater. Rev. Bras. Eng. Agríc. Ambient. 2017, 21, 386–391. [Google Scholar] [CrossRef] [Green Version]
  198. Souza, F.M.D.; Pereira, W.E.; Dantas, J.S.; Nóbrega, J.S.; Lima, E.C.S.; Sá, F.V.D.S. Initial growth of Moringa oleifera Lam. as a function of poultry litter doses and granulometry. Pesq. Agropec. Trop. 2018, 48, 399–406. [Google Scholar] [CrossRef] [Green Version]
  199. Batista, R.O.; Martinez, M.A.; Paiva, H.N.D.; Batista, R.O.; Cecon, P.R. Effect of hog production wastewater in the development and quality of Eucalyptus urophylla substrate produced in urban solid waste. Rev. Ambient Água 2013, 8, 180–191. [Google Scholar] [CrossRef]
  200. Batista, R.O.; Martinez, A.M.; Paiva, H.N.; Batista, R.O.; Cecon, P.R. The effect of swine wastewater in the development and quality of seedling of Eucalyptus urophylla. Ciência Florest. 2014, 24, 127–135. [Google Scholar]
  201. Guimarães, M.M.C.; Cairo, P.A.R.; Neves, O.S.C. Growth of Eucalyptus urophylla in hydroponic medium with different ratios of nitrate and ammonium. Floram 2014, 21, 52–61. [Google Scholar] [CrossRef] [Green Version]
  202. Morais, T.C.B.D.; Prado, R.D.M.; Traspadini, E.I.F.; Wadt, P.G.S.; Paula, R.C.D.; Rocha, A.M.S. Efficiency of the CL, DRIS and CND methods in assessing the nutritional status of Eucalyptus spp. rooted cuttings. Forests 2019, 10, 786. [Google Scholar] [CrossRef] [Green Version]
  203. Gutiérrez-García, J.V.; Rodríguez-Trejo, D.A.; Villanueva-Morales, A.; García-Diaz, S.; Romo-Lozano, J.L. Water quality for the forest nursery production of Pinus cembroides Zucc. Agrociencia 2015, 49, 205–219. [Google Scholar]
  204. Schoen, C.; Aumond, J.J.; Stürmer, S.L. Efficiency of the on–farm mycorrhizal inoculant and phonolite rock on growth and nutrition of Schinus terebinthifolius and Eucalyptus saligna. Rev. Bras. Ciênc. Solo 2016, 40, e0150440. [Google Scholar] [CrossRef] [Green Version]
  205. Smiderle, O.J.; Souza, A.G.; Schwengber, L.A.M.; Schwengber, D.R. Shading of seedlings of pau–rainha and the use of fertilized substrate. Espacios 2017, 38, 21–30. [Google Scholar]
  206. Porto, D.S.; Farias, E.D.N.C.; Chaves, J.D.S.; Souza, B.F.; Medeiros, R.D.D.; Zilli, J.É.; Silva, K.D. Symbiotic effectiveness of Bradyrhizobium ingae in promoting growth of Inga edulis Mart. seedlings. Rev. Bras. Ciênc. Solo 2017, 41, e0160222. [Google Scholar] [CrossRef] [Green Version]
  207. Araújo, K.S.; Carvalho, F.D.; Moreira, F.M.D.S. Bukholderia strains promote Mimosa spp. growth but not Macroptilium atropurpureum. Rev. Ciência Agronômica 2017, 48, 41–48. [Google Scholar] [CrossRef] [Green Version]
  208. de Andrade, A.V.N.; Santana, J.S.; da Silva, J.P.; da Silva, T.F.R.; Lima, Y.M.W.; da Silva, W.A. Growth of Schizolobium amazonicum (Huber ex Ducke) seedlings inoculated with arbuscular mycorrhizal fungi. Floresta 2019, 49, 651–660. [Google Scholar] [CrossRef]
  209. Weirich, S.W.; Silva, R.F.D.; Perrando, E.R.; Ros, C.O.D.; Dellai, A.; Scheid, D.L.; Trombeta, H.W. Influence of ectomycorrhizae on the growth of seedlings of Eucalyptus grandis, Corymbia citriodora, Eucalyptus saligna and Eucalyptus dunnii. Ciência Florest. 2018, 28, 765–775. [Google Scholar] [CrossRef] [Green Version]
  210. De Azevedo, G.B.; de Novaes, Q.S.; Azevedo, G.D.O.; Silva, H.F.; Rocha Sobrinho, G.G.; de Novaes, A.B. Effect of Trichoderma spp. on Eucalyptus camaldulensis clonal seedlings growth. Sci. For. 2017, 45, 343–352. [Google Scholar] [CrossRef] [Green Version]
  211. Dellai, A.; Silva, R.F.D.; Andreazza, R. Ectomycorhyza on the growth of Eucalyptus saligna in soil contaminated with copper. Ciência Florest. 2018, 28, 624–631. [Google Scholar] [CrossRef] [Green Version]
  212. Gandini, A.M.M.; Grazziotti, P.H.; Rossi, M.J.; Grazziotti, D.C.F.S.; Gandini, E.M.M.; Silva, E.D.B.; Ragonezi, C. Growth and nutrition of eucalypt rooted cuttings promoted by ectomycorrhizal fungi in commercial nurseries. Rev. Bras. City Solo 2015, 39, 1554–1565. [Google Scholar] [CrossRef] [Green Version]
  213. Brunetta, J.M.F.C.; Alfenas, A.C.; Mafia, R.G.; Gomes, J.M.; Binoti, D.B.; Fonseca, É.D.P. Evalution of specificity of Rhizobacteria isolated from different species of Pinus sp. Rev. Árvore 2007, 31, 1027–1033. [Google Scholar] [CrossRef] [Green Version]
  214. Valdés, R.C.; Villarreal, R.M.; García, F.G.; Morales, S.G.; Peña, S.S. Improved parameters of Pinus greggii seedling growth and health after inoculation with ectomycorrhizal fungi. South. For. 2019, 81, 23–30. [Google Scholar] [CrossRef]
  215. Godoy, T.G.; Rosado, S.C.D.S. Estimates of genetic gains for growth traits in young plants of Eucalyptus urophylla ST Blake. Cerne 2011, 17, 189–193. [Google Scholar] [CrossRef] [Green Version]
  216. Barajas-Rodríguez, J.E.; Aldrete, A.; Vargas-Hernandez, J.J.; Lopez-Upton, J. Chemical pruning in the nursery increases root density in young trees of Pinus greggii. Agrociencia 2004, 38, 545–553. [Google Scholar]
  217. Oliet, J.; Planelles, R.; Arias, M.L.; Artero, F. Soil water content and water relations in planted and naturally regenerated Pinus halepensis Mill. seedlings during the first year in semiarid conditions. New For. 2002, 23, 31–44. [Google Scholar] [CrossRef]
  218. Ferraz, A.D.V.; Engel, V.L. Effect of the root trainers size on seedling quality of Jatobá (Hymenaea courbaril L. var. stilbocarpa (Hayne) Lee et Lang.), Ipê-amarelo (Tabebuia chrysotricha (Mart. ex DC.) Sandl.) and Guarucaia (Parapiptadenia rigida (Benth.) Brenan). Rev. Árvore 2011, 35, 413–423. [Google Scholar] [CrossRef]
  219. Andrade, F.R.; Petter, F.A.; Marimon Junior, B.H.; Zuffo, A.M.; de Souza, T.R.S.; Gonçalves, L.G.V. Formation of castor bean seedlings in different containers. Rev. Bras. Ciências Agrárias 2012, 7, 274–279. [Google Scholar]
  220. Melo, L.A.D.; Abreu, A.H.M.D.; Leles, P.S.D.S.; Oliveira, R.R.D.; Silva, D.T.D. Quality and initial growth of seedlings Mimosa caesalpiniifolia Benth. produced in different volumes of containers. Ciência Florest. 2018, 28, 47–55. [Google Scholar] [CrossRef]
  221. Parra, S.; Maciel, N. Effect of sowing and transplantation to conical container on the initial growth of Pithecellobium dulce and Platymiscium diadelphum. Bioagro 2018, 30, 125–134. [Google Scholar]
  222. Lisboa, A.C.; dos Santos, P.S.; Neto, S.N.O.; de Castro, D.N.; De Abreu, A.H.M. Effect of volume of tubes on the production of seedlings of Calophyllum brasiliense and Toona ciliata. Rev. Árvore 2012, 36, 603–609. [Google Scholar] [CrossRef] [Green Version]
  223. Aimi, S.C.; Araujo, M.M.; Leon, E.B.; de Oliveira, G.G.; Cunha, F.D.S. Container volume and controlled–release fertilizer in growing Cabralea canjerana plants produced in nursery. Bosque 2016, 37, 401–407. [Google Scholar]
  224. Eloy, E.; Caron, B.O.; Schmidt, D.; Behling, A.; Schwers, L.; Elli, E.F. Quality assessment of Eucalyptus grandis seedlings using morphological parameters. Floresta 2013, 43, 373–383. [Google Scholar] [CrossRef] [Green Version]
  225. Storck, E.B.; Schorn, L.A.; Fenilli, T.A.B. Growth and quality of Eucalyptus urophylla ST seedlings Blake x Eucalyptus grandis Hill (ex Maiden) in different containers. Floresta 2016, 46, 39–46. [Google Scholar] [CrossRef] [Green Version]
  226. Castro–Garibay, S.L.; Aldrete, A.; Lopez–Upton, J.; Ordaz–Chaparro, V.M. Effect of container, substrate and fertilization on Pinus greggii var. australis growth in the nursery. Agrociencia 2018, 52, 115–127. [Google Scholar]
  227. Turchetto, F.; Mezzomo, J.C.; Araujo, M.M.; Griebeler, A.M.; Berghetti, Á.L.P.; Barbosa, F.M. Growth and physiology of Balfourodendron riedelianum seedlings in the nursery and in the field. Floresta 2019, 49, 763–772. [Google Scholar] [CrossRef]
  228. Leal, C.C.; Torres, S.B.; de Freitas, R.M.; Nogueira, N.W.; Farias, R.M.D. Light intensity and type of container on producing Cassia grandis L. f. seedlings. Rev. Bras. Eng. Agríc. Ambient. 2015, 19, 939–945. [Google Scholar] [CrossRef] [Green Version]
  229. Reis, S.M.; Marimon–Junior, B.H.; Morandi, P.S.; Oliveira–Santos, C.; Oliveira, B.D.; Marimon, B.S. Initial development and quality of saplings of Copaifera langsdorffii Desf. under different levels of shading. Ciência Florest. 2016, 26, 11–20. [Google Scholar]
  230. Da Silva Santos–Moura, S.; Alves, E.U.; Ursulino, M.M.; Bruno, R.D.L.A.; dos Anjos Neto, A.P. Effect of shading on Dimorphandra gardneriana Tul. seedling production. Biosci. J. 2018, 34, 1147–1157. [Google Scholar] [CrossRef]
  231. Lopes, M.J.D.S.; Dias–Filho, M.B.; Menezes Neto, M.A.; Cruz, E.D. Morphophysiological behavior and cambial activity in seedlings of two amazonian tree species under shade. J. Bot. 2015, 1, 1–10. [Google Scholar] [CrossRef] [Green Version]
  232. César, F.R.C.F.; Matsumoto, S.N.; Viana, A.E.S.; Bonfim, J.A. Initial growth and quality of Pterogyne nitens Tull. seedling under artificial shading gradient. Ciência Florest. 2014, 24, 357–366. [Google Scholar]
  233. Mazzanatti, T.; Calzavara, A.K.; Pimenta, J.A.; Oliveira, H.C.; Stolf–Moreira, R.; Bianchini, E. Light acclimation in nursery: Morphoanatomy and ecophysiology of seedlings of three light–demanding neotropical tree species. Braz. J. Bot. 2016, 39, 19–28. [Google Scholar] [CrossRef]
  234. Reis, R.; Pereira, M.D.S.; Goncalves, N.R.; Pereira, D.D.S.; Bezerra, A.M.E. Emergence and quality of Copernicia prunifera seedlings in function of the imbibition of seeds and shading. Rev. Caatinga 2011, 24, 43–49. [Google Scholar]
  235. Azevedo, G.T.D.O.S.; Novaes, A.B.D.; Azevedo, G.B.D.; Silva, H.F. Development of indian neem seedlings under different levels of shading. Floram 2015, 22, 249–255. [Google Scholar] [CrossRef] [Green Version]
  236. Pereira, M.A.; Gonçalves, D.S.; Souza, P.A.D.; Lucena, F.R.; Silva, R.R.D.; Brondani, G.E. Luminosity levels affect the initial seedlings growth and nutrient accumulation in Khaya senegalensis A. Juss. Cerne 2018, 24, 344–351. [Google Scholar] [CrossRef]
  237. Marco, R.D.; Conte, B.; Perrando, E.R.; Fortes, F.D.O.; Somavilla, L.; Burgin, M.B. Effect of shading screens on growth and protection of Toona ciliata seedlings under low temperatures. Floresta 2014, 44, 607–616. [Google Scholar]
  238. Santelices, R.; Cerrillo, R.M.N.; Drake, F.; Mena, C. Effect of cover and fertilization on the early development of Nothofagus alessandrii nursery container seedlings. Bosque 2011, 32, 85–88. [Google Scholar] [CrossRef]
  239. Reis, S.M.; Morandi, P.S.; Oliveira, B.; Oliveira, E.A.; Valadão, M.B.X.; Marimon, B.S.; Marimon, B.H., Jr. Influence of shading on the initial development and nutrient use efficiency of Dilodendron bipinnatum Radkl (Sapindaceae). Sci. For. 2015, 43, 581–590. [Google Scholar]
  240. De Azevedo, I.M.G.; de Alencar, R.M.; Barbosa, A.P. Study of growth and quality of marupá (Simarouba amara Aubl.) nursery seedlings. Acta Amaz. 2010, 40, 157–164. [Google Scholar]
  241. Kratka, P.C.; Correia, C.R.M.D.A. Initial growth of Aroeira of Sertão (Myracrodruon urundeuva Allemão) in different substrates. Rev. Árvore 2015, 39, 551–559. [Google Scholar] [CrossRef] [Green Version]
  242. Abreu, A.H.M.; dos Santos, L.P.S.; Amaral, M.L.; Rodrigues, O.R.; Alves, F.D.H.A. Characterization and potential of formulated substrate with biosolids in Schinus terebinthifolius Raddi. and Handroanthus heptaphyllus (Vell.) Mattos seedling production. Ciência Florest. 2017, 27, 1179–1190. [Google Scholar] [CrossRef] [Green Version]
  243. Faria, J.C.T.; de Melo, L.A.; Brondani, G.E.; Delarmelina, W.M.; da Silva, D.S.N.; Nieri, E.M. Substrates formulated with organic residues in the production of seedlings of Moquiniastrum polymorphum. Floresta 2017, 47, 523–532. [Google Scholar] [CrossRef] [Green Version]
  244. Boechat, C.L.; Ribeiro, M.D.O.; Ribeiro, L.D.O.; Santos, J.A.G.; Accioly, A.D.A. Urban and industrial sewage sludge in the initial growth and quality of physic nut seedlings. Biosci. J. 2014, 30, 782–791. [Google Scholar]
  245. Afonso, M.V.; Martinazzo, E.G.; Aumonde, T.Z.; Villela, F.A. Physiological parameters of Albizia niopoides seedlings produced in different substrate compositions. Ciência Florest. 2017, 27, 1395–1402. [Google Scholar] [CrossRef] [Green Version]
  246. Caldeira, M.V.W.; Peroni, L.; Gomes, D.R.; Delarmelina, W.M.; Trazzi, P.A. Different proportions of sewage sludge bio solids in the composition of substrates for the production of seedlings of timbo (Ateleia glazioveana Baill). Sci. For. 2012, 40, 15–22. [Google Scholar]
  247. Caldeira, M.V.W.; Delarmelina, W.M.; Faria, J.C.T.; Juvanhol, R.S. Alternative substrates in the production of seedlings of Chamaecrista desvauxii. Rev. Árvore 2013, 37, 31–39. [Google Scholar] [CrossRef] [Green Version]
  248. Oliveira, H.F.E.; de Souza, C.L.; Félix, D.V.; Fernandes, L.D.S.; Xavier, P.S.; Alves, L.M. Initial development of Baruzeiro (Dipteryx alata vog) seedling as function of substrates and irrigations levels. Irriga 2017, 22, 288–300. [Google Scholar] [CrossRef]
  249. Amaral, F.H.; Nóbrega, J.C.; Nóbrega, R.S.; Amorim, S.P.D.N. Growth of Leucaena leucocephala (Lam.) de Wit favored by organic waste in the Brazilian semiarid region. Rev. Bras. Eng. Agríc. Ambient. 2016, 20, 612–617. [Google Scholar] [CrossRef] [Green Version]
  250. Faria, J.C.T.; Caldeira, M.V.W.; Delarmelina, W.M.; Rocha, R.L.F. Alternative substrates in the seedling production of Mimosa setosa Benth. Ciência Florest. 2016, 26, 1075–1086. [Google Scholar] [CrossRef]
  251. Dutra, A.F.; Araujo, M.M.; Turchetto, F.; Rorato, D.G.; Aimi, S.C.; Gomes, D.R.; Nishijima, T. Substrate and irrigation scheme on the growth of Parapiptadenia rigida (angico–vermelho) seedlings. Ciência Rural 2016, 46, 1007–1013. [Google Scholar] [CrossRef] [Green Version]
  252. Scheer, M.B.; Carneiro, C.; dos Santos, K.G. Parapiptadenia rigida (Benth.) Brenan seedling production using substrates based on composted sewage sludge. Sci. For. 2010, 38, 637–644. [Google Scholar]
  253. Sabonaro, D.Z.; Galbiatti, J.A. Seedling growth of Schizolobium parahyba on different substrates and irrigation levels. Rodriguésia 2011, 62, 467–475. [Google Scholar] [CrossRef]
  254. Delarmelina, W.M.; Caldeira, M.V.W.; Faria, J.C.T.; Gonçalves, E.D.O.; Rocha, R.L.F. Different substrates for the production of Sesbania virgata Seedlings. Floram 2014, 21, 224–233. [Google Scholar] [CrossRef] [Green Version]
  255. Caldeira, M.V.W.; Delarmelina, W.M.; Lübe, S.G.; Gomes, D.R.; Gonçalves, E.D.O.; Alves, A.F. Biosolids in composition of substrate for Tectona grandis Linn. F. seedlings production. Floresta 2012, 42, 77–84. [Google Scholar] [CrossRef] [Green Version]
  256. Trazzi, P.A.; Caldeira, M.V.W.; Passos, R.R.; Gonçalves, E.D.O. Substrates of organic origin for production of seedlings of Teak (Tectona grandis Linn. F.). Ciência Florest. 2013, 23, 401–409. [Google Scholar]
  257. Trazzi, P.A.; Caldeira, M.V.W.; Reis, E.F.D.; Silva, A.G.D. Seedling production of Tectona grandis on substrates formulated with biosolids. Cerne 2014, 20, 293–302. [Google Scholar] [CrossRef] [Green Version]
  258. Caldeira, M.V.; Santos, F.E.; Kunz, S.H.; Klippel, V.H.; Delarmelina, W.M.; Gonçalves, E.D.O. Solid urban waste in the production of Aegiphila sellowiana Cham. seedlings. Rev. Bras. Eng. Agríc. Ambient. 2018, 22, 831–836. [Google Scholar] [CrossRef]
  259. Maranho, Á.S.; de Paiva, A.V. Physocalymma scaberrimum seedlings production in substrate composed by different percentages of organic residue of açaí. Floresta 2012, 42, 399–408. [Google Scholar] [CrossRef] [Green Version]
  260. Gasparin, E.; de Avila, A.L.; Araujo, M.M.; Dorneles, D.U.; Foltz, D.R.B. Influence of substrate and container volume on the quality of Cabralea canjerana (Vell.) Mart. seedlings in nursery and field. Ciência Florest. 2014, 24, 553–563. [Google Scholar]
  261. Petter, F.A.; Andrade, F.R.; Marimon Junior, B.H.; Goncalves, L.G.; Schossler, T.R. Biochar conditioner as substrate for the production of Eucalipto seedlings. Rev. Caatinga 2012, 25, 44–51. [Google Scholar]
  262. Menegatti, A.; de Arruda, G.O.S.F.; Nesi, C.N. The poultry manure fertilizer in the production and in the quality of Eucalyptus dunnii Maiden seedlings. Sci. Agrar. 2017, 18, 43–49. [Google Scholar]
  263. Caldeira, M.V.; Delarmelina, W.M.; Peroni, L.; Gonçalves, E.D.; Gomes da Silva, A. Use of sewage sludge and vermiculite for producing Eucalyptus seedlings. Pesqui. Agropecu. Trop. 2013, 43, 155–163. [Google Scholar] [CrossRef] [Green Version]
  264. Da Silva, R.F.; Marco, R.D.; da Ros, C.O.; de Almeida, H.S.; Antoniolli, Z.I. Influence of different concentrations of vermicompost in the development of eucalyptus and pine seedlings. Floram 2017, 24, e20160269. [Google Scholar]
  265. Gonzaga, M.I.S.; Mackowiak, C.; Almeida, A.Q.D.; Carvalho Júnior, J.I.T.D. Sewage sludge derived biochar and its effect on the growth and morphological traits of Eucalyptus grandis W. Hill ex Maiden seedlings. Ciência Florest. 2018, 28, 687–695. [Google Scholar] [CrossRef] [Green Version]
  266. Silva, M.I.; Mackowiak, C.; Minogue, P.; Reis, A.F.; Moline, E.F.D.V. Potential impacts of using sewage sludge biochar on the growth of plant forest seedlings. Ciência Rural 2017, 47, e20160064. [Google Scholar] [CrossRef] [Green Version]
  267. Sallesses, L.F.; Rizzo, P.F.; Riera, N.; Torre, V.D.; Crespo, D.E.; Pathauer, P.S. Effect of poultry manure compost in the production of hybrid clones of Eucalyptus grandis x Eucalyptus camaldulensis. Ciência Suelo 2015, 33, 221–228. [Google Scholar]
  268. De Barros, D.L.; de Rezende, F.A.; Campos, A.T. Production of Eucalyptus urograndis plants cultivated with activated biochar. Rev. Bras. Ciêc. Agrár. 2019, 14, e5649. [Google Scholar] [CrossRef]
  269. Madrid–Aispuro, R.E.; Prieto-Ruíz, J.Á.; Aldrete, A.; Hernández–Díaz, J.C.; Wehenkel, C.; Chávez–Simental, J.A.; Mexal, J.G. Alternative substrates and fertilization doses in the production of Pinus cembroides Zucc. in nursery. Forests 2020, 11, 71. [Google Scholar] [CrossRef] [Green Version]
  270. Velázquez, B.; López, M.Á.; Cetina Alcala, V.M.; Diakite, L. Substrates and nutrient addition rates affect morphology and physiology of Pinus leiophylla seedlings in the nursery stage. iForest-Biogeosciences For. 2016, 10, 115–120. [Google Scholar] [CrossRef] [Green Version]
  271. Romero–Arenas, O.; Flores, A.D.P.; Rivera Tapia, J.A.; Hernandez Aldana, F.; Parraguirre Lezama, J.F.C.; Villa Ruano, N.; Landeta Cortes, G. Production of Pinus pseudostrobus seedlings in nurseries in compost based on shiitake waste. Madera Bosques 2019, 25, e2511675. [Google Scholar]
  272. Bonamigo, T.; Scalon, S.D.P.Q.; Pereira, Z.V. Substrates and levels of light intensity on initial growth of seedlings of Tocoyena formosa (Cham. & Schltdl.) K. Schum (Rubiaceae). Ciência Florest. 2016, 26, 501–511. [Google Scholar]
  273. Dutra, A.F.; Araujo, M.M.; Tabaldi, L.A.; Rorato, D.G.; Gomes, D.R.; Turchetto, F. Optimization of water use in seedling production of arboreal species. Cerne 2018, 24, 201–208. [Google Scholar] [CrossRef]
  274. Costa, E.; Sassaqui, A.R.; Silva, A.K.D.; Rego, N.H.; Fina, B.G. Soursop seedlings: Biomasses and biometric relations in different farming environments and substrates–part II. Eng. Agríc. 2016, 36, 229–241. [Google Scholar] [CrossRef] [Green Version]
  275. Costa, E.; Leal, P.A.M.; Rego, N.H.; Benatti, J. Initial seedling development of Jatobazeiro do cerrado in Aquidauana, MS. Rev. Bras. Frutic. 2011, 33, 215–226. [Google Scholar] [CrossRef] [Green Version]
  276. Souza, A.D.G.; Spinelli, V.M.; Souza, R.O.D.; Smiderle, O.J.; Bianchi, V.J. Optimization of germination and initial quality of seedlings of Prunus persica tree rootstocks. J. Seed Sci. 2017, 39, 166–173. [Google Scholar] [CrossRef]
  277. Paixão, M.V.S.; Lopes, J.C.; Schmildt, E.R.; Alexandre, R.S.; Meneghelli, C.M. Avocado seedlings multiple stems production. Rev. Bras. Frutic. 2016, 38, e-221. [Google Scholar] [CrossRef] [Green Version]
  278. Souza, A.D.G.; José Smiderle, O.; Fischer, C.; João Bianchi, V. Post–harvest management of Prunus persica stones and the effects on seed and seedling quality. Agric. Conspec. Sci. 2019, 84, 257–261. [Google Scholar]
  279. Ferreira, B.C.; Lima, S.F.D.; Simon, C.A.; Andrade, M.G.D.O.; Ávila, J.D.; Alvarez, R.D.C.F. Effect of biostimulant and micronutrient on emergence, growth and quality of Arabica coffee seedlings. Coffee Sci. 2018, 13, 324–332. [Google Scholar] [CrossRef]
  280. Silva, A.R.; Bezerra, F.T.; Cavalcante, L.F.; Pereira, W.E.; Araújo, L.M.; Bezerra, M.A. Frequency of irrigation with saline water in sugar–apple seedlings produced on substrate with polymer. Rev. Bras. Eng. Agríc. Ambient. 2018, 22, 825–830. [Google Scholar] [CrossRef] [Green Version]
  281. De Melo Jr, J.C.F.; Lima, A.M.N.; Teixeira, M.V.; da Conceicao, G.C.; dos Santos, L.R. Water depletion on substrate and Osmocote fertilizer dose in the papaya seedlings formation. Comun. Sci. 2014, 5, 499–508. [Google Scholar]
  282. De Melo Júnior, J.C.F.; Costa, D.D.S.; Gervásio, E.S.; Lima, A.M.N.; Sediyama, G.C. Effects of water depletion levels in the substrate and fertilizer rates of controlled release on the yield of passion seedlings. Irriga 2015, 20, 204–219. [Google Scholar]
  283. De Azevedo, J.M.G.; dos Reis, E.F.; Tomaz, M.A.; Garcia, G.D.O.; Nogueira, N.O.; Dardengo, M.C.J.D. Quality index and Conilon coffee seedling growth under irrigation and hydroretentive. Rev. Bras. Ciências Agrárias 2014, 9, 432–439. [Google Scholar]
  284. Veloso, L.L.; Nobre, R.G.; Lima, G.S.; Barbosa, J.L.; Melo, E.N.; Gheyi, H.R.; Gonçalves, E.B.; Souza, C.M. Quality of soursop (Annona muricata L.) seedlings under different water salinity levels and nitrogen fertilization. Aust. J. Crop Sci. 2018, 12, 306–310. [Google Scholar] [CrossRef]
  285. De Freitas, R.M.O.; Nogueira, N.W.; Pinto, J.D.S.; Tosta, M.D.S.; Dombroski, J.L.D. Phosphate fertilizer in the initial development of sugar apple seedlings. Biosc. J. 2013, 29, 319–327. [Google Scholar]
  286. De Almeida, J.P.; Mendonça, V.; Alves, A.A.; Cardoso Neto, R.; Costa, L.P.; Silva, F.S. Morphometric responses and tolerance of pomegranate seedlings irrigated with saline water. Rev. Bras. Eng. Agric. Ambient. 2019, 23, 341–346. [Google Scholar] [CrossRef] [Green Version]
  287. Melo, E.N.; Nobre, R.G.; Pinheiro, F.W.A.; Souza, L.P.; de Lima, G.S.; Gheyi, H.R.; Silva, W.L. Evaluation of west Indian cherry (Malpighia emarginata) rootstock under saline water irrigation and nitrogen fertilization. Aust. J. Crop Sci. 2018, 12, 1034–1040. [Google Scholar] [CrossRef]
  288. Moura, R.S.; Gheyi, H.R.; Coelho Filho, M.A.; Jesus, O.N.; Lima, L.K.S.; Cruz, C.S. Formation of seedlings of species from the genus Passiflora under saline stress. Biosci. J. 2017, 33, 1197–1207. [Google Scholar] [CrossRef]
  289. Medeiros, S.D.S.; Cavalcante, L.F.; Bezerra, M.A.F.; do Nascimento, J.A.M.; Bezerra, F.T.C.; Prazeres, S.D.S. Saline water and bovine manure biofertilizer in the formation and quality of yellow passion fruit seedlings. Irriga 2016, 21, 779–795. [Google Scholar] [CrossRef]
  290. Lemos, V.T.; França, A.C.; Silva, E.B.; Marinho, R.L.S.; Franco, M.H.R.; Avellar, M.D.; Freitas, A.F.D.; Reis, L.A.C.; Corrêa, J.M.; Carvalho, G.R. Citric acid and phosphorus on development and nutritional status of coffee seedling. Coffee Sci. 2015, 10, 298–308. [Google Scholar]
  291. Marana, J.P.; Miglioranza, É.; Fonseca, É.D.P.; Kainuma, R.H. Seedling quality in coffee grown in containers. Ciência Rural 2008, 38, 39–45. [Google Scholar] [CrossRef]
  292. Santinato, F.; Caione, G.; Tavares, T.O.; Prado, R.M. Doses of phosphorus associated with nitrogen on development of coffee seedlings. Coffee Sci. 2014, 9, 419–426. [Google Scholar]
  293. De Melo Filho, J.S.; Véras, M.L.M.; Alves, L.D.S.; Da Silva, T.I.; De Melo Gonçalves, A.C.; Dias, T.J. Hydrical salinity, bovine biofertilizer and dead cover vegetal on production of Pitombeira (Talisia esculenta). Sci. Agrar. 2017, 18, 131–145. [Google Scholar]
  294. Tosta, M.D.S.; de Almeida, J.P.; Góes, G.B.D.; Freire, P.D.A.; Mendonça, V. Nitrogen fertilization in the production of seedlings of Talisia esculenta (A. St. Hil) Radlk. Rev. Bras. Eng. Agríc. Ambient. 2017, 21, 443–447. [Google Scholar] [CrossRef] [Green Version]
  295. Véras, M.L.M.; da Silva Arruda, R.; de Sousa Alves, L.; de Melo Filho, J.S.; da Silva Irineu, T.H.; Dias, T.J. Growth and dry matter of pitombeira seedlings under salinity levels and application of biofertilizer. Comun. Sci. 2017, 8, 486–492. [Google Scholar] [CrossRef] [Green Version]
  296. Covre, A.M.; Canal, L.; Partelli, F.L.; Alexandre, R.S.; Ferreira, A.; Vieira, H.D. Development of clonal seedlings of promising Conilon coffee (Coffea canephora) genotypes. Aust. J. Crop Sci. 2016, 10, 385–392. [Google Scholar] [CrossRef]
  297. Almeida, U.O.D.; Andrade Neto, R.D.C.; Lunz, A.M.P.; Nogueira, S.R.; Costa, D.A.D.; Araújo, J.M.D. Environment and slow–release fertilizer in the production of Euterpe precatoria seedlings. Pesqui. Agropecu. Trop. 2018, 48, 382–389. [Google Scholar] [CrossRef]
  298. Salles, J.S.; de Lima, A.H.; Costa, E.; Binotti, E.D.; Binotti, F.F.D.S. Papaya seedling production under different shading levels and substrate compositions. Eng. Agríc. 2019, 39, 698–706. [Google Scholar] [CrossRef]
  299. Marana, J.P.; Miglioranza, É.; Fonseca, É.D.P. Quality of jaracatia seedling submitted to different periods of shade in nursery. Rev. Árvore 2015, 39, 275–282. [Google Scholar] [CrossRef] [Green Version]
  300. Da Silva, B.L.; Costa, E.; Salles, J.S.; Binotti, F.F.D.S.; Benett, C.G. Protected environments and substrates for Achachairu seedlings. Eng. Agríc. 2018, 38, 309–318. [Google Scholar] [CrossRef]
  301. Gomes Júnior, G.A.; Pereira, R.A.; Santos, D.J.D.; Sodré, G.A.; Gross, E. Substrate and quality mangosteen seedlings. Rev. Bras. Frutic. 2019, 41, e-135. [Google Scholar] [CrossRef] [Green Version]
  302. Mota, C.S.; Araújo, E.L.S.; Silva, F.G.; Dornelles, P.; Freiberger, M.B.; Mendes, G.C. Physiology and quality of Eugenia dysenterica DC seedlings grown in vermiculite and rice husk–based substrates. Rev. Bras. Frutic. 2018, 40, e-049. [Google Scholar] [CrossRef] [Green Version]
  303. Scalon, S.P.; Jeromini, T.S.; Mussury, R.M.; Dresch, D.M. Photosynthetic metabolism and quality of Eugenia pyriformis Cambess. seedlings on substrate function and water levels. An. Acad. Bras. Ciênc. 2014, 86, 2039–2048. [Google Scholar] [CrossRef] [Green Version]
  304. Oliveira, F.T.D.; Hafle, O.M.; Mendonça, V.; Moreira, J.N.; Pereira, E.B.; Rolim, H.O. Responses of guava rootstocks under different sources and proportions of organic materials. Comun. Sci. 2015, 6, 17–25. [Google Scholar]
  305. Da Costa, G.G.S.; Costa, E.; Cardoso, E.D.; Binotti, F.F. da S.; Zuffo, A.M.; Jorge, M.H.A.; Zoz, T. Substrates to produce Jambolan (Syzygium cumini) seedlings. Aust. J. Crop Sci. 2018, 12, 1997–2003. [Google Scholar] [CrossRef]
  306. Berilli, S.S.; Zooca, A.A.F.; Ferraz, T.M.; de Assis, F.A.M.M.; Rodrigues, W.P.; Berilli, A.P.C.G.; Campostrini, E. Influence of tannery wastewater sludge doses on biometric and chlorophyll fluorescence parameters in Conilon coffee. Biosci. J. 2018, 34, 556–564. [Google Scholar] [CrossRef] [Green Version]
  307. Jaeggi, M.E.P.C.; Saluci, J.C.G.; Rodrigues, R.R.; Gravina, G. deA.; de Lima, W.L. Alternative substrates in different containers for production of conilon coffee seedlings. Coffee Sci. 2018, 13, 80–89. [Google Scholar] [CrossRef]
  308. Mota, C.S.; Silva, F.G.; Dornelles, P.; Freiberger, M.B.; Mendes, G.C. Growth, nutrition and quality of Pouteria garderiana (A. DC.) Radlk. seedlings produced in organic substrates. Cerne 2016, 22, 373–380. [Google Scholar] [CrossRef] [Green Version]
  309. Rodrigues, A.S.L.; Mesak, C.; Silva, M.L.G.; Silva, G.S.; Leandro, W.M.; Malafaia, G. Organic waste vermicomposting through the addition of rock dust inoculated with domestic sewage wastewater. J. Environ. Manag. 2017, 196, 651–658. [Google Scholar] [CrossRef]
  310. Campos, L.F.C.; Vendruscolo, E.P.; Campos, C.D.A.; da Costa, R.B.; dos Santos, M.M. Production of Ocimum basilicum L. (basil) cuttings on organic substrates. Rev. Cuba. Plantas Med. 2018, 23. Available online: https://www.cabdirect.org/cabdirect/abstract/20203431073 (accessed on 13 November 2021).
  311. Francisco, J.P.; Jose, J.V.; De Sousa Andrade, I.P.; Folegatti, M.V.; Marques, P.A.A. Quality of Basil seedlings (Ocimum basilicum L.) in a greenhouse, with different substrates and concentrations of indolbutiric acid. Rev. Agro. Amb. 2015, 8, 401–419. [Google Scholar] [CrossRef] [Green Version]
  312. Marques, P.A.A.; Fontinelle, G.B.; de Lima, A.G.; José, J.V.; da Rocha, H.S.; Alves, D.S. Artemisia seedlings quality produced in greenhouse under different irrigation system and fertilizer doses. Irriga 2017, 22, 301–313. [Google Scholar] [CrossRef]
  313. De Aquino Arantes, C.R.; Pallaoro, D.S.; Correa, A.R.; Camili, E.C.; Barbosa Coelho, M.D.F. Shading and substrate in the production of Lactuca canadensis L. seedlings. Iheringia Ser. Bot. 2019, 74, e2019005. [Google Scholar]
  314. Santos, L.W.; Coelho, M.F.B. Shading and substrate on the production of seedlings of Erythrina velutina Willd. Ciência Florest. 2013, 23, 571–577. [Google Scholar]
  315. Gonçalves, M. daP.M.; da Silva, M.I.O.; Grugiki, M.A.; Feliciano, A.L.P.; da Silva, L.B. Alternative substrates in the production of seedlings of Harpalyce brasiliana Benth. Oecol. Aust. 2019, 23, 464–472. [Google Scholar] [CrossRef]
  316. Coelho, M.F.B.; Souza, R.L.C.; Albuquerque, M.C.F.; Weber, O.S.; Nogueira Borges, H.B. Quality of Heteropteris aphrodisiaca O. Mach. seedlings in different substrates. Rev. Bras. Plantas Med. 2008, 10, 82–90. [Google Scholar]
  317. Rodrigues, L.A.; Muniz, T.A.; Samarão, S.S.; Cyrino, A.E. Quality of Moringa oleifera Lam. seedlings cultivated in substrates with green coconut fiber and organic compounds. Rev. Ceres 2016, 63, 545–552. [Google Scholar] [CrossRef]
  318. Dornelles, P.; Silva, F.G.; Freiberger, M.B.; da Costa Severiano, E.; Tavares, G.G. Initial development and nutrition of Eugenia dysenterica DC. on substrates formulated with sugarcane bagasse and filter cake. Aust. J. Crop Sci. 2018, 12, 1459–1464. [Google Scholar] [CrossRef]
  319. Costa, E.; Santo, T.L.; Batista, T.B.; Curi, T.M. Different type of greenhouse and substrata on pepper production. Hortic. Bras. 2017, 35, 458–466. [Google Scholar] [CrossRef] [Green Version]
  320. Neto, F.J.D.; Dalanhol, S.J.; Machry, M.; Pimentel, A.; Rodrigues, J.D.; Ono, E.O. Effects of plant growth regulators on eggplant seed germination and seedling growth. Aust. J. Crop Sci. 2017, 11, 1277–1282. [Google Scholar] [CrossRef]
  321. Vendruscolo, E.P.; Campos, L.F.C.; Nascimento, L.M.; Seleguini, A. Quality of muskmelon seedlings treated with thiamine in pre–sowing and nutritional suplementation. Sci. Agrar. 2018, 19, 164–171. [Google Scholar]
  322. Diánez, F.; Santos, M.; Carretero, F.; Marín, F. Trichoderma saturnisporum, a new biological control agent. J. Sci. Food Agric. 2016, 96, 1934–1944. [Google Scholar] [CrossRef]
  323. Song, J.X.; Meng, Q.W.; Du, W.F.; He, H.X. Effects of light quality on growth and development of cucumber seedlings in controlled environment. Int. J. Agr. Biol. Eng. 2017, 10, 312–318. [Google Scholar]
  324. De Almeida, R.N.; Ferraz, D.R.; Silva, A.S.; Cunha, E.G.; Vieira, J.C.; Souza, T.D.S.; Berilli, S.D.S. Use of tannery sludge in complementation to the commercial substrate in the production of pepper seedlings. Sci. Agrar. 2017, 18, 20–33. [Google Scholar]
  325. Zhang, R.H.; Duan, Z.-Q.; Li, Z.-G. Use of spent mushroom substrate as growing media for tomato and cucumber seedlings. Pedosphere 2012, 22, 333–342. [Google Scholar] [CrossRef]
  326. Neto, M.; Lopes, J.L.; Araújo, W.F.; Oliveira Vilarinho, L.B.; de Oliveira Nunes, T.K.; da Silva, E.S.; da Silva Maia, S.; Albuquerque, J.D.A.A.D.; Alves Chagas, E.; da Silva, S.; et al. Seedlings productionof two tomato (Solanum licopersicum L.) cultivars under different environments and substrates. Acta Agron. 2018, 67, 270–276. [Google Scholar] [CrossRef]
  327. Herrera, F.; Castillo, J.E.; Lopez–Bellido, R.J.; Bellido, L.L. Replacement of a peat–lite medium with municipal solid waste compost for growing melon (Cucumis melo L.) transplant seedlings. Compost Sci. Util. 2009, 17, 31–39. [Google Scholar] [CrossRef]
  328. De Freitas, G.A.D.; Silva, R.R.D.; Barros, H.B.; Vaz-de-Melo, A.; Abrahão, W.A.P. Production of lettuce seedlings for different combinations of substrata. Rev. Cienc. Agron. 2013, 44, 159–166. [Google Scholar]
  329. Simões, A.C.; Alves, G.K.; Ferreira, R.L.; Araújo Neto, S.E. Seedling quality and yield of organic lettuce using different substrate conditioners: Development of clonal seedlings of promising Conilon coffee (Coffea canephora) genotypes. Hortic. Bras. 2015, 33, 521–526. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Trend in the number of published studies in which the Dickson Quality Index has been used as an indicator of plant quality.
Figure 1. Trend in the number of published studies in which the Dickson Quality Index has been used as an indicator of plant quality.
Agronomy 11 02305 g001
Figure 2. Global distribution of the main countries in which researchers have used the Dickson Quality Index in studies on plant quality. The darker the colour, the higher the percentage of studies in which this index has been used. Prepared in Arcgis® with data from Scopus, 2020.
Figure 2. Global distribution of the main countries in which researchers have used the Dickson Quality Index in studies on plant quality. The darker the colour, the higher the percentage of studies in which this index has been used. Prepared in Arcgis® with data from Scopus, 2020.
Agronomy 11 02305 g002
Figure 3. Bibliometric map generated from an analysis of the most repeated keywords in articles published during the period 1989–2020. Different colours represent the diversity of thematic clusters found and the associated keywords: Red (cluster 1), green (cluster 2), blue (cluster 3), yellow (cluster 4), and purple (cluster 5).
Figure 3. Bibliometric map generated from an analysis of the most repeated keywords in articles published during the period 1989–2020. Different colours represent the diversity of thematic clusters found and the associated keywords: Red (cluster 1), green (cluster 2), blue (cluster 3), yellow (cluster 4), and purple (cluster 5).
Agronomy 11 02305 g003
Figure 4. Overlay visualisation map. Different colors indicate the evolution of research keywords over time based on the average publication year. Following a similar procedure, an overlay visualisation map was drawn to identify the evolution of keywords used in the set of articles analysed in this study. Earlier research topics are coloured purple and more recent items are shown in yellow. The data were processed and mathematically analysed using the clustering algorithm of the VOSviewer® software version 1.6.15.
Figure 4. Overlay visualisation map. Different colors indicate the evolution of research keywords over time based on the average publication year. Following a similar procedure, an overlay visualisation map was drawn to identify the evolution of keywords used in the set of articles analysed in this study. Earlier research topics are coloured purple and more recent items are shown in yellow. The data were processed and mathematically analysed using the clustering algorithm of the VOSviewer® software version 1.6.15.
Agronomy 11 02305 g004
Figure 5. Main groups of plant species for which the Dickson Quality Index has been used as a quality indicator. A representative and random sample of 289 articles was extracted from a total sample of articles of n = 662. Data were retrieved from the Scopus database 2020.
Figure 5. Main groups of plant species for which the Dickson Quality Index has been used as a quality indicator. A representative and random sample of 289 articles was extracted from a total sample of articles of n = 662. Data were retrieved from the Scopus database 2020.
Agronomy 11 02305 g005
Figure 6. Main research topics in the agricultural sciences. Values are expressed as percentages (n = 289).
Figure 6. Main research topics in the agricultural sciences. Values are expressed as percentages (n = 289).
Agronomy 11 02305 g006
Figure 7. Morphological and physiological parameters and quality indicators referred to in the study database. Morphological characteristics of plants: (A) leaf number, leaf area and fresh weight. (B) Root length, fibrosity, volume and density, SSA: Specific Surface Area; FOLRs: First Order Lateral Roots. (C,D) Different physiological parameters. The values are expressed as percentages (n = 289).
Figure 7. Morphological and physiological parameters and quality indicators referred to in the study database. Morphological characteristics of plants: (A) leaf number, leaf area and fresh weight. (B) Root length, fibrosity, volume and density, SSA: Specific Surface Area; FOLRs: First Order Lateral Roots. (C,D) Different physiological parameters. The values are expressed as percentages (n = 289).
Agronomy 11 02305 g007
Figure 8. Main plant quality indicators observed in the analysed publications. Data are expressed as percentage (n = 289).
Figure 8. Main plant quality indicators observed in the analysed publications. Data are expressed as percentage (n = 289).
Agronomy 11 02305 g008
Table 1. Main research topics identified in the set of publications analysed in this study (n = 289).
Table 1. Main research topics identified in the set of publications analysed in this study (n = 289).
Research Topic1st Factor (%)2nd Factor (%)3rd Factor (%)
Substrate
Plant nutrition and fertilisation
Lighting control
29.0711.420.69
25.6113.842.42
9.341.730.00
Plant protection
Irrigation and water management
6.572.080.00
6.232.770.35
Environment and crop growth
Growth in containers
Growth regulation and plant propagation
Evaluation of plant quality indices
5.880.350.00
5.544.500.69
5.192.770.00
4.151.380.00
Plant selection and genetic improvement2.420.000.00
100.0040.834.15
Table 2. Potential tools for prediction of growth, development, survival, vigour, plant performance, and plant quality for a representative sample of forest, fruit, horticultural, aromatic, and ornamental species (n = 289).
Table 2. Potential tools for prediction of growth, development, survival, vigour, plant performance, and plant quality for a representative sample of forest, fruit, horticultural, aromatic, and ornamental species (n = 289).
ParameterGrowthDevelopmentQualitySurvivalVigourPerformanceDesired ValueDestructive Nature
Height **** No
Stem Diameter *** No
Leaf Number *** ** No
Leaf Area* ** * Yes
Fresh Weight ** Yes
Dry Weight* *** *HighYes
Root Length ** No
Root Volume * * No
Root Dry Weight *** HighYes
Root Density ** HighYes
Root Fibrosity * Yes
Specific Surface Area of the Roots (SSAR (cm2)) * * No
Number of First Order Lateral Roots (FOLRs) *** * Yes
Root Growth Potential (RGP) ******HighYes
Root Aggregation to the Substrate ** No
Seedling Extraction Ease (SEE) ** No
Height/Basal Diameter Ratio [H/D ratio cm mm−1]* *** Low (≤6)No
Shoot/Root Dry Weight Ratio [S/R ratio (g g−1)] *** Low (≤2)Yes
Dickson Quality Index (DQI)* *****High (≥0.20)Yes
Root/Shoot Ratio [R/S ratio (g g−1)] *** Low (≤10)Yes
Height/Shoot Dry Matter Ratio [H/SDM ratio (cm g−1)] ** HighYes
Shoot Dry Matter/Height Ratio [SDM/H (mg cm−1)] *** *HighYes
Root Dry Matter/Root Length [RDW/RL ratio (g cm−1)] *** Yes
Root Quality Index (RQI)* ** HighYes
Height/Root Length Ratio [H/RL ratio (cm cm−1)] *** HighYes
Leaf Area/Root Dry Matter Ratio [LA/RDM ratio (cm2 g−1)] ** HighYes
Root Length/Leaf Area [RL/LA ratio (cm cm−2)] **** High
Total Non-Structural Carbohydrates (NSC) **** HighYes
Leaf Area Ratio [LAR (cm2 g−1)]* ** HighYes
Specific Leaf Area [SLA (cm2 g−1)] ** HighYes
Leaf Weight Ratio [LWR (g g−1)]**
Leaf Area Index [LAI (m2 m−2)] ** HighYes
Absolute and Relative Growth* *** HighYes
Physiological Measurements* *** Yes
Vegetation Indices ** No
Plant Analysis and Nutritional Status**** May be
* Predictive ability associated with the quality parameter; ** Morphological quality indicator; *** Parameter used to assess physiological plant quality.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Gallegos-Cedillo, V.M.; Diánez, F.; Nájera, C.; Santos, M. Plant Agronomic Features Can Predict Quality and Field Performance: A Bibliometric Analysis. Agronomy 2021, 11, 2305. https://doi.org/10.3390/agronomy11112305

AMA Style

Gallegos-Cedillo VM, Diánez F, Nájera C, Santos M. Plant Agronomic Features Can Predict Quality and Field Performance: A Bibliometric Analysis. Agronomy. 2021; 11(11):2305. https://doi.org/10.3390/agronomy11112305

Chicago/Turabian Style

Gallegos-Cedillo, Victor M., Fernando Diánez, Cinthia Nájera, and Mila Santos. 2021. "Plant Agronomic Features Can Predict Quality and Field Performance: A Bibliometric Analysis" Agronomy 11, no. 11: 2305. https://doi.org/10.3390/agronomy11112305

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