Next Article in Journal
Evaluation of the Improvement Effect of Chemical Fertilizer Combined with Different Additives on Newly Built Paddy Soil
Previous Article in Journal
Research and Experiment on the Ditching Performance of a Ditching and Film-Covering Machine in the Yellow Sand Cultivation Mode of Solar Greenhouses
Previous Article in Special Issue
Lentil Cultivar Evaluation in Diverse Organic Mediterranean Environments
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 14
Issue 8
10.3390/agronomy14081705
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

Optimization of Harvesting and Drying Techniques for Quality Seed Production in Specialty Crops: A Systematic Review and Meta-Analysis

by
Laura Monteiro Pedrosa
*,
Bruno Rafael de Almeida Moreira
and
Cibele Chalita Martins
College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal 14884-900, SP, Brazil
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(8), 1705; https://doi.org/10.3390/agronomy14081705
Submission received: 28 May 2024 / Revised: 10 July 2024 / Accepted: 15 July 2024 / Published: 2 August 2024
(This article belongs to the Special Issue Modern Seed Technologies for Developing Dynamic Agriculture)
Browse Figures
Versions Notes

Abstract

:
Specialty crops enhance food security, biodiversity, and economic resilience, relying on high-quality seeds. However, there is a gap in understanding how to enhance seed quality under specific conditions. This study addresses this gap by proposing that optimizing harvesting and drying can improve specialty crop seed quality. A literature review spanning 2000–2023 was conducted, followed by a meta-analysis to measure effect sizes. From an initial pool of 1589 documents, 45 met the criteria for further analysis. The results indicated that harvesting seeds at intermediate times significantly increased the logarithmic response ratio (LRR), with LRRs of 1.7 for germination and 2 for seedling count compared with early harvesting. Regarding drying methods, no significant differences were found between artificial and natural methods. However, optimal drying temperatures (30–60 °C) were identified, ensuring adequate moisture levels (10–20%) for a germination percentage of 50–100%. The variability in these findings was attributed to the twelve diverse species analyzed. Although the results supported the study’s hypothesis, limited and inconsistent data prevented the determination of optimal conditions for each species, indicating a need for further research. Despite these limitations, the study provides insights into optimizing harvesting and drying to enhance seed quality in specialty crops, contributing to emerging science in this domain.

1. Introduction

Special crops, also known as specialized or niche crops, encompass a wide array of plant species that stand out due to their unique characteristics, specialized applications, or specific cultivation prerequisites. These crops are not only diverse in their nature but also in the roles they play in various aspects of human life and the environment [1].
One of the most significant roles of special crops is their contribution to global food security. These crops provide a diverse range of food sources, which is crucial in reducing dependence on a limited range of staple crops. This diversity in food sources ensures that even if one crop fails due to disease, pests, or adverse weather conditions, others are available to fill the gap, thereby ensuring a steady supply of food [2].
In addition to food security, special crop cultivation plays a vital role in biodiversity conservation. By promoting the growth of a variety of plant species, these crops contribute to the overall health of ecosystems. They support a wide range of fauna, including pollinators and beneficial insects, and help maintain soil health through crop rotation and intercropping practices [3].
Moreover, special crops have a significant impact on the economic resilience of farming communities. These crops often have unique attributes that are highly valued in various markets such as medicinal plants, aromatic herbs, or exotic fruits. By cultivating these crops, farmers can diversify their income streams, reducing their vulnerability to price fluctuations in mainstream crop markets. Furthermore, the cultivation of special crops often requires less intensive farming practices, which can reduce input costs and contribute to sustainable farming [4].
The production of high-quality seeds is a cornerstone in the cultivation of special crops. This process is not a simple task; it requires precision, expertise, and strict adherence to good management practices [5]. Although genetic purity is important, it is the physiological traits of the seeds that often play a more significant role in determining the overall quality of the crops [6,7].
Physiological traits such as the germination percentage, vigor, and yield are directly influenced by the seed production process [8,9], particularly the stages of harvesting and drying. Harvesting is a delicate process that requires careful timing [10]. The point at which the seeds are harvested can significantly impact their quality and flavor. Harvesting too early can result in immature seeds that lack vigor and may not germinate properly [11]. On the other hand, delaying the harvest can expose the seeds to a variety of adverse effects [12].
Following the harvest, the seeds undergo a drying process. This step is essential to reduce their moisture content, which in turn facilitates storage and prevents seed deterioration [13]. Seeds with a high moisture content are prone to fungal and bacterial infections and they may also germinate prematurely. Therefore, proper drying is crucial to maintain the quality of the seeds during storage [14].
The choice between natural and artificial drying methods can also significantly impact the quality of the seeds. Natural drying methods such as sun-drying are cost-effective and easy to implement. However, they may not be suitable for all types of seeds, especially those that are sensitive to elevated temperatures [15]. Artificial drying methods such as oven-drying or freeze-drying offer more control over the drying conditions. However, these methods can be more costly and may require specialized equipment [16].
Researchers have collected and summarized evidence from numerous studies, each aimed at answering specific questions related to improving the quality of seeds from major crops. One area of focus has been the exploration of new market technologies. For instance, the use of nanoscale coatings such as nanoparticles and nano priming has been studied for its potential to enhance seed quality. These technologies can provide protective layers to the seeds, improving their resistance to environmental stresses and enhancing their germination potential [17,18,19].
Another promising technology is the use of Raman spectroscopy. This non-destructive analytical technique allows for the detailed examination of seeds, providing valuable insights into their chemical composition and structure. Such information can be instrumental in identifying factors that contribute to seed quality [20].
Proteomic analyses and the creation of molecular markers have also been highlighted in the literature. These advanced techniques allow for the detailed study of the proteins present in the seeds and the identification of genetic markers associated with desirable traits [21,22]. Such information can guide breeding programs aimed at improving seed quality.
In addition to these advanced techniques, classic methodologies such as the tetrazolium test in a laboratory [23] and various methods of seed-drying (including air-drying as well as fixed and mobile bed dryers) [24] continue to play a crucial role in seed-quality assessment and improvement. However, despite the wealth of information available, there is a notable lack of emphasis on the application of statistics to meta-data to synthesize trends and provide analytical insights. Specifically, how harvest times and drying methods affect the quality of seeds from special crops has not been thoroughly explored.
Therefore, this study’s primary objective was to address the identified gap in the existing literature. To achieve this, a systematic review was conducted to assess the impact of harvest times and drying methods on the quality of seeds in specialty crops. This was followed by a meta-analysis to measure effect sizes, providing both a descriptive and quantitative evaluation of the data.
This paper is structured into five main sections. Section 1 provides an overview of the production of seed quality in special crops. Section 2 delves into the methodology used for the systematic review and meta-analysis. Section 3 and Section 4 present and discuss the main findings from the systematic review and meta-analysis, respectively. Section 5 concludes the study, summarizing the key points and considerations for further research.

2. Methodology

2.1. Systematic Search

The PICO protocol (Table 1) was used to structure and address the research question: Can harvesting and drying techniques improve the physiological quality of seeds in specialty crops?
Adhering to the PICO and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Scopus and Web of Science were selected as the primary databases for the literature search. The focus was on peer-reviewed articles published from 2000 to 2023 in Portuguese, English, and Spanish. A “snowball” technique, alongside citation tracking, was incorporated to encompass all pertinent studies (Figure 1).
The search strings were crafted to align with the title, abstract, and keywords, combining Boolean operators, parentheses, truncation (*), and specific database filters to refine the search (Table 2).

2.2. Publication Selection

The retrieved studies from the databases underwent a thorough analysis and preliminary selection based on their title and abstract. The inclusion criteria required that the studies assess the effects of harvesting and drying techniques on seed quality in specialty crops, as indicated by physiological traits. These criteria were refined to include studies that investigated at least one alternative drying method and delineated at least three distinct harvest timings, as defined by the harvest days specified by the authors in relation to the crop cycle. Studies that met the initial selection criteria were advanced to the full-text screening stage.
Throughout the title/abstract and full-text screening processes, two independent reviewers (LMP and BRAM) scrutinized each study. Discrepancies between reviewers were resolved by a consultation with a third reviewer (CCM).
During the title/abstract screening phase, articles were excluded based on the following criteria:
  • Unrelated studies: articles not pertinent to seed quality such as those focusing on plant genetic improvements, nutrition and fertilization, diseases and pathogens, and the effects of herbicides and desiccants.
  • Studies on field crops: research that did not pertain to specialty crops, including soy, cotton, the common bean, peanut, quinoa, rice, and corn.
  • Studies on tree species: articles addressing tree species not classified as specialty crops; for example, star fruit and palm trees.
  • Studies excluding harvest or drying methods: research evaluating seed quality without directly incorporating harvest or drying methods such as those examining the maturation and physiological maturity of seeds as well as storage and seed conditioning (priming).
In the subsequent full-text screening phase, the following additional exclusion criteria were applied:
  • Non-extractable data: Studies that precluded data extraction due to illegible graphical representations such as overlapping points in regression graphs.
  • Yield-focused studies: research presenting yield indicators, including the number of pods per plant, number of seeds per plant, or quantity of seeds per fruit.

2.3. Data Collection

Data extraction from the selected studies was performed using tables, figures, and graphs from within the publications. In instances where direct extraction was not feasible, WebPlotDigitizer software (version 5) facilitated the manual retrieval of values. Organizational structuring of data was accomplished using Excel (Microsoft 365) and categorization with pertinent topics such as “harvest” and “drying.” The extracted data encompassed (1) study identification, including the author, publication year, and digital object identifier (DOI); (2) specifications detailing the crop type, harvest/drying methods, temperature, and duration; and (3) the physiological quality indicators of seeds.

2.4. Statistical Data Analysis

The statistical analysis focused on measuring the impact of harvesting and drying techniques on seed quality in specialty crops while also identifying publication trends. The natural logarithmic response ratio (LRR) was used as a metric to compare alternative treatments with a control. Harvesting was categorized into early, mid, and late. These timings corresponded with the crop’s phenological cycle, as defined by the authors. The initial harvest served as the baseline for the LRR calculations. Drying methods were compared using sun-drying as the control. Mechanical (oven)-drying and shade-drying were the alternative treatments, selected based on the authors’ distinctions. A regression analysis was performed using data from mechanical-drying experiments. This established the relationships between operational temperature, seed moisture, and germination percentage, helping to identify optimal conditions. A Welch test was used to determine significant differences between harvesting and drying groups [25]. A Bayes factor analysis [26] was used to quantify evidence supporting the hypothesis that at least one condition in harvesting and drying could enhance seed quality in specialty crops. Only indicators of physiological quality with sufficient sample sizes were included in the analytical procedures. Tests were not conducted under suboptimal conditions, adhering to strict assumption protocols. Bibliometric data entropy was calculated to reveal trends in the publication, citation, and impact in the field over time. This was based on the probability distribution of studies, as defined by Shannon’s Information Theory [27]. The findings were presented in tables and graphical diagrams such as box and whisker plots. All analyses was performed using R software (version 4.3.2), with packages such as ggplot2 and ggExtra.

3. Results

3.1. Literature Characterization

3.1.1. Dynamic Timeline and Engagement

Out of 1589 publications from across both databases, only 45 studies matched the criteria for inclusion (Table S1) [6,10,11,12,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]. The literature on harvesting, spanning from 2000 to 2021, consisted of 29 studies, reaching its zenith in 2016. Regarding drying, 13 studies were identified from 2003 to 2021, with the highest concentration in 2008. The number of publications surged from 2006 onwards, peaking in 2021 for both harvesting and drying (Figure 2A). Correspondingly, cumulative citations for harvesting (363) and drying (149) also peaked in 2021 (Figure 2B). Publication entropy for harvesting peaked in 2008 and 2009, then declined in 2021; for drying, it peaked in 2007 before dropping further in 2021 (Figure 2C). Similarly, citation entropy reached its peak in 2007 for both methods, gradually decreasing over the years (Figure 2D). Influence entropy peaked for harvesting in 2012 and for drying from 2009 to 2011, gradually waning until the study’s conclusion in 2021 (Figure 2E).

3.1.2. Most Influential Genres, Species, and Physiological Indicators

The publications included in this study covered 12 genres, with Capsicum spp. (9) and Solanum spp. (5) being the most represented for both harvesting and drying. Abelmoschus spp. (3) and Cucurbita spp. (3) were only represented for harvesting, while Physalis spp. (2) and Carica spp. (2) were dominant for drying (Figure 3A). In terms of the species contribution to the dataset of harvest times, A. esculentus (3) was the most represented, accounting for 9.68% of the total of 12 species found in the studies. This was followed by two studies each for C. annuum, Coriandrum sativum, Cucumis sativus, C. moschata, Daucus carota, Lagenaria siceraria, and Lycopersicon esculentum, each representing 6.45% of the studies found. For drying, C. annuum (2), C. papaya (2), and Physalis ixocarpa (2) were the most represented, accounting for 14.29% of the dataset (Figure 3B). The most common quality indicators found in the studies for harvesting were the germination percentage (28), moisture content (15), and mass of a thousand seeds (14). For drying, the most studied indicators were the germination percentage (14), water content (7), and first count of normal seedlings (5) (Figure 4).

3.2. Impact of Harvesting on Seed Quality

Significant effects of harvesting on key physiological indicators were found. These effects helped identify conditions conducive to high-quality seed production in specialty crops. Notably, moisture content, germination, and the emergence velocity index responded positively to these practices (Figure 5, Figure 6A and Figure 7A, respectively). However, the emergence rate and the first seedling count remained unaffected (Figure 6B and Figure 7B). The LRR indicated a decrease in water content with a progression in harvest time, with values of −0.2, −0.8, and −0.6 for early, mid, and late conditions, respectively. The germination percentage increased until mid-harvest before declining, as reflected by LRR estimates of 0.9, 1.2, and 0.2. The emergence velocity index mirrored this trend, with LRR estimates of 0.65, 0.8, and 0.2. Both these trends were statistically significant. The statistical analysis confirmed the significance of these differences.

3.3. Impact of Drying on Seed Quality

The impact of both shade- and oven-drying on moisture content was positive, resulting in LRR estimates of 7.5e1 and 2.5e−1, respectively (Figure 8A). For germination, shade- and oven-drying had positive and negative impacts; the LRR estimates were 7.5e−1 and −2.5e−1, respectively (Figure 8B). These differences were statistically significant, although the Welch test’s p-value indicated potential inconsistencies for the germination percentage. The natural logarithms of the Bayes factor were 3.6 and 4.9 for the germination percentage and moisture content, respectively, suggesting that the observed data were more likely under the alternative hypothesis than under the null hypothesis. This provided convincing evidence in favor of harvesting and drying techniques to improve the physiological quality of seeds in specialty crops. However, the Bayes factor should be interpreted in the context of the study and was not definitive proof of the hypothesis. Further research and data are needed to strengthen this evidence.
The regression analysis established functional relationships for the impact of drying temperatures on the moisture content and the germination percentage (Figure 9A). The model accounted for 85% of the observed variability in the seed germination percentage, based on the moisture content at drying temperatures ranging from 25 to 100 °C, with 25 °C serving as a reference point for “ambient conditions”. The germination percentage increased up to a certain moisture content threshold, after which it declined as the moisture content exceeded 20%. At a moisture content of 10% and drying temperatures between 30 and 60 °C, the germination percentage reached its maximum value. The specific correlation between moisture content and drying temperature was captured by a linear model, accounting for 70% of the variability (Figure 9B). This suggested a decrease in moisture content with an increase in drying temperature. However, elevated oven temperatures (> 80 °C) were found to be detrimental as they led to a reduction in moisture content to 0%, rendering the seed permanently inactive. The quadratic model derived for the germination percentage, with 75% variability, confirmed that germination exhibited its optimal values (60–100%, depending on the species) at drying temperatures of 25–35 °C (Figure 9C). Temperatures exceeding this range led to a significant reduction in the germination percentage, indicative of seed damage, thereby compromising the physiological quality.

4. Discussion

4.1. Key Learning

4.1.1. Harvesting and Drying Techniques Validated to Optimize Seed Quality

A systematic review and meta-analysis were conducted to evaluate the impact of harvesting and drying techniques on seed quality in specialty crops. It was confirmed that specific methodologies optimized the seed physiological quality, leading to increased germination percentages and better plant development. This validation substantiated the initial hypothesis and provides a foundation for subsequent research in this domain. Despite the advantages of these techniques in optimizing seed quality, certain limitations were identified that necessitate further investigation to advance this emerging field of research.

4.1.2. Declining Trends in Publication Entropy

The observed trend of decreasing influence entropy in harvesting and drying studies suggested a potential stagnation or saturation in the research output and impact in these areas. Influence entropy, which measures the diversity and distribution of influence across various studies, indicates the breadth of innovation and exploration within a field. A decline in this metric could suggest a lack of new research directions in harvesting and drying studies. This trend could limit the generation of fresh insights in these key areas of seed-quality research. However, recognizing this pattern also presents an opportunity to stimulate renewed interest, collaboration, and innovation in these studies.

4.1.3. Uneven Representation of Genres and Species

Although certain genres and species such as Capsicum spp. and Solanum spp. are extensively documented in the literature, others are under-represented. This uneven representation highlights the necessity for research that encompasses the diversity of specialty crops and their unique seed-quality traits [69]. Investigating less-studied genres and species can yield valuable insights and inform species-specific harvesting and drying best practices. For example, Abelmoschus spp. and Cucurbita spp. are predominantly featured in harvesting studies, indicating the potential for further investigations into drying techniques. Conversely, Physalis spp. and Carica spp. are frequently mentioned in drying studies, suggesting a need for research on harvesting techniques and optimization strategies.

4.1.4. Inadequacy of Traditional Indicators to Describe Physiological Quality

Commonly used metrics such as the mass of a thousand seeds, moisture content, and germination percentage provide insights into seed viability and storage potential, but there is a demand for more in-depth studies exploring additional physiological indicators of seed quality. These traditional metrics, while informative, may not provide a holistic view of seed health and performance. For instance, if seed vigor or storage resistance is identified as a critical quality indicator, research and development efforts could concentrate on harvesting and drying methodologies that preserve or enhance these characteristics [70,71].

4.1.5. Effectiveness of Shade- and Oven-Drying

Both shade- and oven-drying methods effectively reduce moisture content in seeds, which is crucial for the prevention of fungal or bacterial growth during storage [62,65]. Shade-drying, often preferred due to its less aggressive and more gradual process, preserves seed integrity and minimizes damage risk, making it ideal for delicate specialty crops. This method facilitates better moisture control and helps maintain seed viability [16]. Conversely, oven-drying, although effective at reducing moisture content, can pose risks to seed viability due to its harsh and rapid conditions. High temperatures and a fast-drying process may lead to seed damage and a reduced germination potential if not carefully managed [67,68].

4.1.6. Impact of Drying Temperatures on Seed Viability

Maintaining optimal drying temperatures of 30–60 °C and moisture content of 10–20% are essential for seed quality and viability as germination percentages reach their peak within these ranges. However, exceeding these ranges can lead to reduced germination percentages and seed viability, with temperatures above 80 °C being particularly detrimental [56]. Understanding the effects of specific drying methods and conditions on seed-quality parameters such as moisture content and germination percentage is vital for seed longevity and storage quality. Precise temperature control during mechanical-drying is necessary to prevent seed damage [68].
Our results demonstrated improvements in seed quality. However, for storage purposes, more precise conditions are necessary. Seed-bank drying conditions typically require temperatures of 10–15 °C and a water content of 5–10% [72]. Drying protocols and the optimal water content for storage vary depending on the storage temperature [71]. Monitoring and adjusting drying temperatures according to the crop species and environmental conditions can help optimize seed-drying outcomes.

4.2. Main Gaps Identified

4.2.1. Insufficient Species-Specific Data

A significant technical gap in the existing literature is the scarcity of data available for analysis at the species or genus level, necessitating the aggregation of results. Although this pooled approach provides a comprehensive overview that is beneficial to users by considering the variability and potential biases in individual studies (as reviewed by Tierney et al. [73] and Zhao et al. [74], it can obscure the unique characteristics of individual crops. This makes it challenging for growers, seed producers, and other stakeholders to replicate results in further studies or apply findings to real-world specialty cropping systems. Additionally, variations in methodologies across studies further complicate establishing clear links between harvesting and drying treatments and their impacts on specific species.

4.2.2. Lack of Detailed Experimental Conditions

Another noticeable gap in the current research is the absence of detailed descriptions of treatments, which complicates the direct extraction of control data. The LRR is a widely used, effective metric in meta-analyses to assess the impact of an alternative treatment compared with a control. However, the precision and reliability of the LRR are heavily dependent on the clarity and specificity of the control conditions described in the studies. Any ambiguity in control conditions such as those related to harvesting or drying can hinder the achievement of accurate and reliable results. This highlights the need for rigorous reporting standards in seed-quality research.

5. Conclusions

A comprehensive review and meta-analysis were conducted to evaluate harvesting and drying techniques, identifying key conditions that enhance seed physiological quality. These findings can guide evidence-based seed production, benefiting growers, seed producers, and other stakeholders. However, methodological inconsistencies and technical gaps were also noted, which warrant further research. The exploration of new physiological indicators for a holistic seed-health and performance assessment is necessary. Factors like seed vigor and storage resistance can enrich the understanding of seed physiology and contribute to reliable quality assessment protocols. Furthermore, species-specific studies are essential when tailoring practices to the unique traits of distinct crops. By considering each crop species’ specific needs, production practices can be optimized, thereby enhancing seed quality and performance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14081705/s1, Table S1: Identification of the studies used for harvesting and drying analyses on the quality of specialty crop seeds.

Author Contributions

Conceptualization, L.M.P. and B.R.d.A.M.; methodology, B.R.d.A.M.; validation, L.M.P., B.R.d.A.M. and C.C.M.; formal analysis, L.M.P. and B.R.d.A.M.; data curation, B.R.d.A.M.; writing—original draft preparation, L.M.P. and B.R.d.A.M.; writing—review and editing, L.M.P., B.R.d.A.M. and C.C.M.; visualization, L.M.P., B.R.d.A.M. and C.C.M.; supervision, C.C.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Coordination of Improvement of Higher Education Personnel—Brazil (CAPES, Financial Code No. 001).

Data Availability Statement

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

Acknowledgments

We would like to acknowledge the funding sponsors of this work.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Dar, Z.A.; Khan, A.; Lone, A.A.; Bahar, F.A.; Baghel, S.; Lone, B.A.; Mahdi, S.S.; Jan, R.; Wani, O.A.; Nissar, R.; et al. Specialty Food Crops: An Alternate Way for Increasing Farmers’ Income. In Secondary Agriculture: Sustainability and Livelihood in India; Bahar, F.A., Anwar Bhat, M., Mahdi, S.S., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 59–68. ISBN 978-3-031-09218-3. [Google Scholar]
  2. Ojuederie, O.B.; Igwe, D.O.; Ludidi, N.N.; Ikhajiagbe, B. Editorial: Neglected and Underutilized Crop Species for Sustainable Food and Nutritional Security: Prospects and Hidden Potential. Front. Plant Sci. 2024, 14, 1358220. [Google Scholar] [CrossRef] [PubMed]
  3. Yadav, S.P.S.; Adhikari, R.; Bhatta, D.; Poudel, A.; Subedi, S.; Shrestha, S.; Shrestha, J. Initiatives for Biodiversity Conservation and Utilization in Crop Protection: A Strategy for Sustainable Crop Production. Biodivers. Conserv. 2023, 32, 4573–4595. [Google Scholar] [CrossRef]
  4. Kim, H.-J. Opportunities and Challenges of Alternative Specialty Crops: The Global Picture. HortScience 2016, 51, 1316–1319. [Google Scholar] [CrossRef]
  5. Alan, O.; Eser, B. Pepper Seed Yield and Quality in Relation to Fruit Position on the Mother Plant. Pak. J. Biol. Sci. 2007, 10, 4251–4255. [Google Scholar] [CrossRef] [PubMed]
  6. Hedau, N.K.; Singh, G.; Mahajan, V.; Singh, S.R.K.; Gahlain, A. Seed Quality and Vigour in Relation to Nodal Position and Harvesting Stage of Okra under Mid Hills of North-Western Himalayas. Indian J. Hortic. 2010, 67, 251–253. [Google Scholar]
  7. Dwivedi, S.L.; Spillane, C.; Lopez, F.; Ayele, B.T.; Ortiz, R. First the Seed: Genomic Advances in Seed Science for Improved Crop Productivity and Food Security. Crop Sci. 2021, 61, 1501–1526. [Google Scholar] [CrossRef]
  8. Ligterink, W.; Joosen, R.V.L.; Hilhorst, H.W.M. Unravelling the Complex Trait of Seed Quality: Using Natural Variation through a Combination of Physiology, Genetics and -Omics Technologies. Seed Sci. Res. 2012, 22, S45–S52. [Google Scholar] [CrossRef]
  9. Wang, Y.; Chen, Y. Research on Multilateral Collaboration Strategies in Agricultural Seed Quality Assurance. Sci. Rep. 2024, 14, 11310. [Google Scholar] [CrossRef] [PubMed]
  10. Alkimim, E.R.; David, A.M.S.d.S.; Sousa, T.V.; Rodrigues, C.G.; Amaro, H.T.R. Different Harvest Times and Physiological Quality of Coriander Seeds. Rev. Bras. Eng. Agríc. Ambient. 2016, 20, 133–137. [Google Scholar] [CrossRef]
  11. Vinod, K.; Tomar, B.S.; Kaddi, G.; Kumar, S. Effect of Stage of Harvest and Post-Harvest Ripening of Fruits on Hybrid Seed Yield and Quality in Pumpkin (Cucurbita moschata). Indian J. Agric. Sci. 2014, 84, 737–741. [Google Scholar] [CrossRef]
  12. Kumar, V.; Tomar, B.S.; Singh, B.; Kumar, S. Effect of Post-Harvest Ripening and Drying Methods on Seed Quality and Storability in Pumpkin Cv Pusa Hybrid 1. Indian J. Agric. Sci. 2014, 84, 1144–1148. [Google Scholar] [CrossRef]
  13. Copeland, L.O.; McDonald, M.B. Seed Drying. In Principles of Seed Science and Technology; Springer: Boston, MA, USA, 2001; pp. 268–276. ISBN 978-1-4615-1619-4. [Google Scholar]
  14. De Vitis, M.; Hay, F.R.; Dickie, J.B.; Trivedi, C.; Choi, J.; Fiegener, R. Seed Storage: Maintaining Seed Viability and Vigor for Restoration Use. Restor. Ecol. 2020, 28, S249–S255. [Google Scholar] [CrossRef]
  15. Jangde, P.K.; Singh, A.; Arjunan, T.V. Efficient Solar Drying Techniques: A Review. Environ. Sci. Pollut. Res. 2022, 29, 50970–50983. [Google Scholar] [CrossRef] [PubMed]
  16. Yahaya, A.M.; Sinniah, U.R.; Misran, A. Seed Quality of Lablab Beans (Lablab purpureus L.) as Influenced by Drying Methods and Storage Temperature. Agronomy 2022, 12, 699. [Google Scholar] [CrossRef]
  17. Singh, N.; Bhuker, A.; Jeevanadam, J. Effects of Metal Nanoparticle-Mediated Treatment on Seed Quality Parameters of Different Crops. Naunyn. Schmiedebergs Arch. Pharmacol. 2021, 394, 1067–1089. [Google Scholar] [CrossRef] [PubMed]
  18. Eevera, T.; Kumaran, S.; Djanaguiraman, M.; Thirumaran, T.; Le, Q.H.; Pugazhendhi, A. Unleashing the Potential of Nanoparticles on Seed Treatment and Enhancement for Sustainable Farming. Environ. Res. 2023, 236, 116849. [Google Scholar] [CrossRef] [PubMed]
  19. Shah, M.A.; Shahnaz, T.; Zehab-ud-Din; Masoodi, J.H.; Nazir, S.; Qurashi, A.; Ahmed, G.H. Application of Nanotechnology in the Agricultural and Food Processing Industries: A Review. Sustain. Mater. Technol. 2024, 39, e00809. [Google Scholar] [CrossRef]
  20. Saletnik, A.; Saletnik, B.; Puchalski, C. Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality—A Review. Molecules 2022, 27, 4454. [Google Scholar] [CrossRef] [PubMed]
  21. Sliwinska, E. Nuclear DNA Replication and Seed Quality. Seed Sci. Res. 2009, 19, 15–25. [Google Scholar] [CrossRef]
  22. Pedreschi, R.; Lurie, S.; Hertog, M.; Nicolaï, B.; Mes, J.; Woltering, E. Post-Harvest Proteomics and Food Security. Proteomics 2013, 13, 1772–1783. [Google Scholar] [CrossRef]
  23. Franca-Neto, J.D.B.; Krzyzanowski, F.C. Use of the Tetrazolium Test for Estimating the Physiological Quality of Seeds. Seed Sci. Technol. 2022, 50, 31–44. [Google Scholar] [CrossRef]
  24. Barrozo, M.A.S.; Mujumdar, A.; Freire, J.T. Air-Drying of Seeds: A Review. Dry. Technol. 2014, 32, 1127–1141. [Google Scholar] [CrossRef]
  25. West, R.M. Best Practice in Statistics: Use the Welch t-Test When Testing the Difference between Two Groups. Ann. Clin. Biochem. 2021, 58, 267–269. [Google Scholar] [CrossRef] [PubMed]
  26. Hansen, B.; Avalos-Pacheco, A.; Russo, M.; De Vito, R. A Variational Bayes Approach to Factor Analysis. In Proceedings of the Bayesian Statistics, New Generations New Approaches; Avalos-Pacheco, A., De Vito, R., Maire, F., Eds.; Springer International Publishing: Cham, Switzerland, 2023; pp. 15–21. [Google Scholar]
  27. Chen, R.X. A Brief Introduction to Shannon’s Information Theory. arXiv 2021, arXiv:161209316. [Google Scholar]
  28. Schwitter, P.; Detterbeck, A.; Herforth-Rahme, J. Effect of Harvest Frequency, Seed Extraction Time Point and Post-Harvest Cooling on Organic Tomato Seed Production. Sustainability 2022, 14, 11575. [Google Scholar] [CrossRef]
  29. Pathania, K.; Kaur, N.; Kaur, D.; Singh, R. Mobilization of Fruit Pulp Reserves towards Seeds in Cucumber (Cucumis sativus L.) Fruits Harvested at Variable Developmental Stages. Plant Physiol. Rep. 2022, 27, 268–274. [Google Scholar] [CrossRef]
  30. Tetteh, R.; Aboagye, L.M.; Boateng, S.K.; Darko, R. Seed Quality of Six Eggplant Cultivars as Influenced by Harvesting Time. J. Appl. Hortic. 2021, 23, 24–27. [Google Scholar] [CrossRef]
  31. García-Ruiz, R.F.; Castañeda-Garzón, S.L.; Valdéz-Hernández, E.F. Quality of Rocoto Pepper (Capsicum Pubescens Ruiz & Pav.) Seeds in Relation to Extraction Timing. Acta Agron. 2017, 67, 246–251. [Google Scholar] [CrossRef]
  32. Ruiz, M.B.; Parera, C.A. Effect of Harvesting Time on Seed Quality of Two Bell Pepper Cultivars (Capsicum annuum). Rev. Fac. Cienc. Agrar. 2017, 49, 67–77. [Google Scholar]
  33. Sharma, R.K.; Tomar, B.S.; Jain, S.K.; Nehra, M. Seed Yield and Quality Attributes in Bottle Gourd (Lagenaria Siceraria) as Influenced by Position of Seed in Fruit. Indian J. Agric. Sci. 2017, 87, 1320–1323. [Google Scholar] [CrossRef]
  34. Khan, H.; Bedi, S.; Singh, R. Seed Quality in Pumpkin (Cucurbita Pepo L) Cv. Punjab Samrat as Affected by Fruit Harvesting and Storage Duration. Agric. Res. J. 2016, 53, 220–223. [Google Scholar] [CrossRef]
  35. Bortey, H.M.; Dzomeku, B.M. Fruit and Seed Quality of Okra [Abelmoschus esculentus (L.) Moench] as Influenced by Harvesting Stage and Drying Method. Indian J. Agric. Res. 2016, 50, 330–334. [Google Scholar] [CrossRef]
  36. Mengarda, L.H.G.; Lopes, J.C. Seed Quality and Initial Seedling Growth of Malagueta Pepper and Their Relationship to the Position of Fruit Collection. Rev. Bras. Sementes 2012, 34, 644–650. [Google Scholar] [CrossRef]
  37. Kalyanrao; Tomar, B.S.; Singh, B. Effect of Stage of Harvest and Post Harvest Ripening on Hybrid Seed Yield and Quality in Bottle Gourd. Indian J. Hortic. 2014, 71, 428–432. [Google Scholar]
  38. Pereira, F.E.C.B.; Torres, S.B.; Silva, M.I.d.L.; Grangeiro, L.C.; Benedito, C.P. Physiological Quality of Pepper Seeds in Relation to Age and Period of Post-Harvest Resting. Rev. Cienc. Agron. 2014, 45, 737–744. [Google Scholar] [CrossRef]
  39. Yogeesha, H.S.; Vasugi, C.; Somashekhar; Bhanuprakash, K.; Naik, L.B. Papaya (Carica papaya) Seed Quality as Influenced by Stage of Fruit Harvest, Postharvest Ripening and Seed Extraction. Indian J. Agric. Sci. 2013, 83, 928–932. [Google Scholar]
  40. Queiroz, L.A.F.; Pinho, É.V.d.R.V.; Oliveira, J.A.; Ferreira, V.d.F.; Carvalho, B.O.; Bueno, A.C.R. Influence of Maturation Stage and Drying on the Quality of’habanero Yellow’pepper Seeds. Rev. Bras. Sementes 2011, 33, 472–481. [Google Scholar] [CrossRef]
  41. K’Opondo, F.B. Influence of Drying Method and Fruit Position on the Mother Plant on Seed Quality of Spiderplant (Cleome gynandra L.) Morphotypes from Western Kenya. Adv. Appl. Sci. Res. 2011, 2, 74–83. [Google Scholar]
  42. Passam, H.C.; Theodoropoulou, S.; Karanissa, T.; Karapanos, I.C. Influence of Harvest Time and After-Ripening on the Seed Quality of Eggplant. Sci. Hortic. 2010, 125, 518–520. [Google Scholar] [CrossRef]
  43. Wang, Y.; Mu, C.; Hou, Y.; Li, X. Optimum Harvest Time of Vicia Cracca in Relation to High Seed Quality during Pod Development. Crop Sci. 2008, 48, 709–715. [Google Scholar] [CrossRef]
  44. Castro, M.M.; Godoy, A.R.; Inacio Cardoso, A.I. Okra Seed Quality as a Function of Age and Fruit Post Harvest Rest. Ciênc. Agrotecnologia 2008, 32, 1491–1495. [Google Scholar] [CrossRef]
  45. Alan, O.; Eser, B. The Effect of Fruit Maturity and Post-Harvest Ripening on Seed Quality in Hot and Conic Pepper Cultivars. Seed Sci. Technol. 2008, 36, 467–474. [Google Scholar] [CrossRef]
  46. Kumar, V.; Tomar, B.S.; Dadlani, M.; Singh, B.; Kumar, S. Effect of Fruit Retention and Seed Position on the Seed Yield and Quality in Pumpkin (Cucurbita moschata) cv Pusa Hybrid 1. Indian J. Agric. Sci. 2015, 85, 1210–1213. [Google Scholar] [CrossRef]
  47. Sousa, T.V.; Alkimim, E.R.; David, A.M.S.S.; Sá, J.R.; Pereira, G.A.; Amaro, H.T.R.; Mota, W.F. Harvest Season and Physiological Quality of Coriander Seeds Produced in the North of Minas Gerais. Rev. Bras. Plantas Med. 2011, 591–597. [Google Scholar] [CrossRef]
  48. Samarah, N.H. Effect of Air-drying Immature Seeds in Harvested Pods on Seed Quality of Common Vetch (Vicia sativa L.). N. Z. J. Agric. Res. 2006, 49, 331–339. [Google Scholar] [CrossRef]
  49. Vidigal, D.D.S.; Dias, D.C.F.D.S.; Naveira, D.D.S.P.C.; Rocha, F.B.; Bhering, M.C. Physiological Quality of Tomato Seeds in Relation to Fruit Age and Post-Harvest Storage. Rev. Bras. Sementes 2006, 28, 87–93. [Google Scholar] [CrossRef]
  50. Dias, D.C.F.S.; Ribeiro, F.P.; Dias, L.A.S.; Silva, D.J.H.; Vidigal, D.S. Tomato Seed Quality Harvested from Different Trusses. Seed Sci. Technol. 2006, 34, 681–689. [Google Scholar] [CrossRef]
  51. Reghin, M.Y.; Dalla Pria, M.; Otto, R.F.; Vinne, J. van der Épocas de Colheita de Umbelas e Comprimento Da Haste Floral No Rendimento e No Potencial Fisiológico de Sementes de Cebola. Hortic. Bras. 2004, 22, 286–289. [Google Scholar] [CrossRef]
  52. Barbedo, A.; Camara, F.; Nakagawa, J.; Barbedo, C. Plant Population, Harvest Method and Seed Quality of Carrot, Brasilia Cultivar. Pesqui. Agropecuária Bras. 2000, 35, 1645–1652. [Google Scholar] [CrossRef]
  53. Jing, H.; Bergervoet, J.; Jalink, H.; Klooster, M.; Du, S.; Bino, R.; Hilhorst, H.; Groot, S. Cucumber (Cucumis sativus L.) Seed Performance as Influenced by Ovary and Ovule Position. Seed Sci. Res. 2000, 10, 435–445. [Google Scholar] [CrossRef]
  54. Murugesan, P.; Vanangamudi, K. Effect of Seed and Fruit Position on Seed Quality in Ash Gourd (Benincasa hispida (Thunb.) Cogn). Seed Res. 2005, 33, 156–159. [Google Scholar]
  55. Amjad, M.; Anjum, M.A. Effect of Root Size, Plant Spacing and Umbel Order on the Quality of Carrot Seed. Int. J. Agric. Biol. 2001, 3, 239–242. [Google Scholar]
  56. Siddique, A.; Wright, D. Effects of Different Drying Time and Temperature on Moisture Percentage and Seed Quality (Viability and Vigour) of Pea Seeds (Pisum sativum L.). Asian J. Plant Sci. 2003, 2, 978–982. [Google Scholar] [CrossRef]
  57. Aroucha, E.M.M.; da Silva, R.F.; de Oliveira, J.G.; Viana, A.P.; Gonzaga, M.P. Época de Colheita e Período de Repouso Dos Frutos de Mamão (Carica papaya L.) Cv Golden Na Qualidade Fisiológica Das Sementes. Ciênc. Rural 2005, 35, 537–543. [Google Scholar] [CrossRef]
  58. Martins, G.N.; da Silva, R.F.; Pereira, M.G.; Araújo, E.F.; Posse, S.C.P. Influência Do Repouso Pós-Colheita de Frutos Na Qualidade Fisiológica de Sementes de Mamão. Rev. Bras. Sementes 2006, 28, 142–146. [Google Scholar] [CrossRef]
  59. Hunje, R.; Vyakarnahal, B.; Jagadeesh, R. Influence of Drying Methods of Fruits on Seed Quality in Chilli (Capsicum annuum L.). Karnataka J. Agric. Sci. 2007, 20, 269–271. [Google Scholar]
  60. Camacho, I.P.; Hernández, V.A.G.; Moreno, J.C.M.; Garay, Ó.J.A.; Lomelí, A.P. Efecto de Desarrollo y Secado de Semillas de Physalis ixocarpa Brot En Germinación, Vigor y Contenido de Azúcares. Interciencia 2008, 33, 762–766. [Google Scholar]
  61. Demir, I.; Tekin, A.; Ökmen, Z.A.; Okcu, G.; Kenanoğlu, B.B. Seed Quality, and Fatty Acid and Sugar Contents of Pepper Seeds (Capsicum annuum L.) in Relation to Seed Development and Drying Temperatures. Turk. J. Agric. For. 2008, 32, 529–536. [Google Scholar]
  62. Hanapiah, N.F.H.; Sinniah, U.R.; Yusoff, M.M. Seed Quality of Lablab Bean (Lablab purpureus) as Influenced by Seed Maturity and Drying Methods. Agronomy 2022, 12, 363. [Google Scholar] [CrossRef]
  63. Asbrouck, J.V.; Taridno, P. Fast Field Drying as a Method to Maintain Quality, Increase Shelf Life and Prevent Post Harvest Infections on Cucumis sativum L. As. J. Food Ag-Ind. 2009, 133–137. [Google Scholar]
  64. Resende, O.; Almeida, D.P.; Costa, L.M.; Mendes, U.C.; Sales, J.d.F. Adzuki Beans (Vigna angularis) Seed Quality under Several Drying Conditions. Food Sci. Technol. 2012, 32, 151–155. [Google Scholar] [CrossRef]
  65. de França, L.V.; Croda, M.D.; Nascimento, W.M.; Freitas, R.A.d. Physiological Quality of Eggplant Seeds with Different Extraction and Drying Methods. J. Seed Sci. 2013, 35, 51–55. [Google Scholar] [CrossRef]
  66. Santos, H.O.; Dutra, S.M.F.; Pereira, R.W.; Pires, R.M.d.O.; Von Pinho, E.V.d.R.; Rosa, S.D.V.F.d.; Carvalho, L.M.d. Physiological Quality of Habanero Pepper (Capisicum chinense) Seeds Based on Development and Drying Process. Afr. J. Agric. Res. 2016, 11, 1102–1109. [Google Scholar] [CrossRef]
  67. Botey, H.M.; Ochuodho, J.O.; Ngode, L.; Danquah, E.O.; Agyare, R. Physiological Quality of African Eggplant Seeds as Influenced by Natural Fermentation and Drying Methods. J. Hortic. For. 2021, 13, 51–57. [Google Scholar]
  68. Balbuena-Mascada, S.; Peña-Lomelí, A.; Magaña-Lira, N.; Sahagún-Castellanos, J.; Martínez-Solís, J. Drying Temperatures of Tomatillo (Physalis ixocarpa Brot. Ex Horm.) Seeds. Rev. Chapingo Ser. Hortic. 2022, 28, 79–92. [Google Scholar] [CrossRef]
  69. Sundareswaran, S.; Ray Choudhury, P.; Vanitha, C.; Yadava, D.K. Seed Quality: Variety Development to Planting—An Overview. In Seed Science and Technology: Biology, Production, Quality; Dadlani, M., Yadava, D.K., Eds.; Springer Nature Singapore: Singapore, 2023; pp. 1–16. ISBN 978-981-19588-8-5. [Google Scholar]
  70. Marcos Filho, J. Seed Vigor Testing: An Overview of the Past, Present and Future Perspective. Sci. Agric. 2015, 72, 363–374. [Google Scholar] [CrossRef]
  71. Kameswara Rao, N.; Dulloo, M.E.; Engels, J.M.M. A Review of Factors That Influence the Production of Quality Seed for Long-Term Conservation in Genebanks. Genet. Resour. Crop Evol. 2017, 64, 1061–1074. [Google Scholar] [CrossRef]
  72. Cromarty, A.; Ellis, R.H.; Roberts, E.H. The Design of Seed Storage Facilities for Genetic Conservation; Bioversity International: Rome, Italy, 1982. [Google Scholar]
  73. Tierney, J.F.; Fisher, D.J.; Burdett, S.; Stewart, L.A.; Parmar, M.K.B. Comparison of Aggregate and Individual Participant Data Approaches to Meta-Analysis of Randomised Trials: An Observational Study. PloS Med. 2020, 17, e1003019. [Google Scholar] [CrossRef] [PubMed]
  74. Zhao, Y.; Khan, B.; Negeri, Z.F. An Evaluation of Computational Methods for Aggregate Data Meta-Analyses of Diagnostic Test Accuracy Studies. BMC Med. Res. Methodol. 2024, 24, 111. [Google Scholar] [CrossRef] [PubMed]
Agronomy 14 01705 g001
Figure 1. Systematic review flowchart. This chart visually outlines our PRISMA-guided review and meta-analysis. Yellow boxes mark the main steps, while green and purple boxes show data sources and process evolution. It traces the process from database definitions and paper filtering to the final selection of analysis-ready samples.
Figure 1. Systematic review flowchart. This chart visually outlines our PRISMA-guided review and meta-analysis. Yellow boxes mark the main steps, while green and purple boxes show data sources and process evolution. It traces the process from database definitions and paper filtering to the final selection of analysis-ready samples.
Agronomy 14 01705 g001
Agronomy 14 01705 g002
Figure 2. Trends in specialty crop seed quality research. This chart incorporates various subplots to illustrate the trends in cumulative publications (A) and citations (B) from 2000 to 2023. It also includes additional subplots that present information entropy metrics related to publication (C), citation (D), and influence (E). The influence metric reflects the impact of papers in terms of their citation frequency, indicating their influence in the field. Each subplot features grey and pale-green stacked areas that represent specific trends for drying and harvesting. Vertically crossed, dotted lines indicate significant transition points in these trends.
Figure 2. Trends in specialty crop seed quality research. This chart incorporates various subplots to illustrate the trends in cumulative publications (A) and citations (B) from 2000 to 2023. It also includes additional subplots that present information entropy metrics related to publication (C), citation (D), and influence (E). The influence metric reflects the impact of papers in terms of their citation frequency, indicating their influence in the field. Each subplot features grey and pale-green stacked areas that represent specific trends for drying and harvesting. Vertically crossed, dotted lines indicate significant transition points in these trends.
Agronomy 14 01705 g002
Agronomy 14 01705 g003
Figure 3. Distribution of studies by genus and species. This chart uses subplots to display the number of studies according to genus (A) and species (B). The size of each bar corresponds with the number of studies, indicating the influence of an individual genus or species in the field. Within each subplot, the grey and pale-green bars represent the number of studies focusing on drying and harvesting, respectively.
Figure 3. Distribution of studies by genus and species. This chart uses subplots to display the number of studies according to genus (A) and species (B). The size of each bar corresponds with the number of studies, indicating the influence of an individual genus or species in the field. Within each subplot, the grey and pale-green bars represent the number of studies focusing on drying and harvesting, respectively.
Agronomy 14 01705 g003
Agronomy 14 01705 g004
Figure 4. Distribution of studies by indicator of physiological quality. This chart integrates subplots to visualize the distribution of studies by the indicator of physiological quality. The size of each bar corresponds with the number of studies, indicating the influence of each individual indicator in the field. Within each subplot, the grey and pale-green bars represent the number of studies focusing on drying and harvesting, respectively.
Figure 4. Distribution of studies by indicator of physiological quality. This chart integrates subplots to visualize the distribution of studies by the indicator of physiological quality. The size of each bar corresponds with the number of studies, indicating the influence of each individual indicator in the field. Within each subplot, the grey and pale-green bars represent the number of studies focusing on drying and harvesting, respectively.
Agronomy 14 01705 g004
Agronomy 14 01705 g005
Figure 5. Effect of harvest time on seed moisture content. This figure uses box plots to illustrate how seed moisture content changes with early (purple), mid (green), and late (yellow) harvest times. The y-axis represents harvest times, plotted against the LRR on the x-axis. The origin point differentiates the responses of later harvest times from the first harvest time. A statistical analysis, including Welch’s F-test for a mean comparison and W-square (W2) for effect sizes (0.2 = small, 0.5 = medium, and 0.8 = large), was specifically applied to larger datasets with more than thirty observations (N > 30). A vertical line separates the groups, with the p-value from the Games–Howell pairwise test indicating the probability of significant differences among them (* p-value < 0.5). Confidence intervals (CI95%) are provided for all measurements.
Figure 5. Effect of harvest time on seed moisture content. This figure uses box plots to illustrate how seed moisture content changes with early (purple), mid (green), and late (yellow) harvest times. The y-axis represents harvest times, plotted against the LRR on the x-axis. The origin point differentiates the responses of later harvest times from the first harvest time. A statistical analysis, including Welch’s F-test for a mean comparison and W-square (W2) for effect sizes (0.2 = small, 0.5 = medium, and 0.8 = large), was specifically applied to larger datasets with more than thirty observations (N > 30). A vertical line separates the groups, with the p-value from the Games–Howell pairwise test indicating the probability of significant differences among them (* p-value < 0.5). Confidence intervals (CI95%) are provided for all measurements.
Agronomy 14 01705 g005
Agronomy 14 01705 g006
Figure 6. Effect of harvest time on seed germination (A) and seedling emergence rate (B). * p-value < 0.5.
Figure 6. Effect of harvest time on seed germination (A) and seedling emergence rate (B). * p-value < 0.5.
Agronomy 14 01705 g006
Agronomy 14 01705 g007
Figure 7. Effect of harvest time on seedling emergence velocity index (A) and first count (B). * p-value < 0.5.
Figure 7. Effect of harvest time on seedling emergence velocity index (A) and first count (B). * p-value < 0.5.
Agronomy 14 01705 g007
Agronomy 14 01705 g008
Figure 8. Effect of drying method on seed moisture content and germination percentage. This figure uses box plots to compare the effects of shade-drying and oven-drying on seed germination (A) and seedling emergence rate (B), with sun-drying as the control. A statistical analysis, including Welch’s t-test (* p-value < 0.5) for a mean comparison and Hedges’ g for effect sizes (0.2 = small, 0.5 = medium, and 0.8 = large), was specifically applied to smaller datasets with fewer than thirty observations (N < 30). The Bayesian factor analysis is indicated by LN (BF), supporting alternative hypotheses over null ones, with larger LN (BF) values providing stronger evidence. The posterior distribution mean (dpost) indicates the true group difference, balancing prior knowledge with new meta-analysis information. A vertical solid line separates groups. Confidence intervals (CI95%) are provided for all measurements, suggesting a likely range of values that includes the true population parameter.
Figure 8. Effect of drying method on seed moisture content and germination percentage. This figure uses box plots to compare the effects of shade-drying and oven-drying on seed germination (A) and seedling emergence rate (B), with sun-drying as the control. A statistical analysis, including Welch’s t-test (* p-value < 0.5) for a mean comparison and Hedges’ g for effect sizes (0.2 = small, 0.5 = medium, and 0.8 = large), was specifically applied to smaller datasets with fewer than thirty observations (N < 30). The Bayesian factor analysis is indicated by LN (BF), supporting alternative hypotheses over null ones, with larger LN (BF) values providing stronger evidence. The posterior distribution mean (dpost) indicates the true group difference, balancing prior knowledge with new meta-analysis information. A vertical solid line separates groups. Confidence intervals (CI95%) are provided for all measurements, suggesting a likely range of values that includes the true population parameter.
Agronomy 14 01705 g008
Agronomy 14 01705 g009
Figure 9. Effect of drying temperature on seed moisture content and germination percentage. This figure uses scatter plots and marginal histograms to dynamically depict the variation in seed moisture content and germination percentage with an increase in oven-drying temperature. Natural drying temperatures (sun or shade) serve as references to compare and visualize the effects under artificial conditions. The subplot on the left (A) illustrates the interaction among these factors. The y-axis represents the germination percentage, plotted against the moisture content on the x-axis. This relationship was modulated by a fluctuating oven temperature, as represented by the regression curve. The subplots on the right offer visuals for individual conditions, demonstrating the response of moisture content (B) and germination percentage (C) to temperatures ranging from 25 to 100 degrees Celsius. This range is indicated on the gradient scale. The “zero” point in the visual representation indicates seeds that had not been dried, serving as a control. This control helped assess the effectiveness of drying in reducing seed susceptibility to abiotic and biotic factors such as microbial organisms, storage insects, and (physiological and biochemical) aging.
Figure 9. Effect of drying temperature on seed moisture content and germination percentage. This figure uses scatter plots and marginal histograms to dynamically depict the variation in seed moisture content and germination percentage with an increase in oven-drying temperature. Natural drying temperatures (sun or shade) serve as references to compare and visualize the effects under artificial conditions. The subplot on the left (A) illustrates the interaction among these factors. The y-axis represents the germination percentage, plotted against the moisture content on the x-axis. This relationship was modulated by a fluctuating oven temperature, as represented by the regression curve. The subplots on the right offer visuals for individual conditions, demonstrating the response of moisture content (B) and germination percentage (C) to temperatures ranging from 25 to 100 degrees Celsius. This range is indicated on the gradient scale. The “zero” point in the visual representation indicates seeds that had not been dried, serving as a control. This control helped assess the effectiveness of drying in reducing seed susceptibility to abiotic and biotic factors such as microbial organisms, storage insects, and (physiological and biochemical) aging.
Agronomy 14 01705 g009
Table 1. PICO elements tailored to this study.
Table 1. PICO elements tailored to this study.
ElementDescription
ProblemSeed quality in specialty crops
InterventionHarvesting and drying
ComparisonHarvest timings and drying methods
OutcomeImprovement in physiological traits
Table 2. Search strings tailored to bibliographic databases.
Table 2. Search strings tailored to bibliographic databases.
DatabaseSearch String
Scopus(“fruit*” OR “nut*” OR “vegetable*” OR “herb*” OR “spice*” OR “medicinal plant*” OR “nurser*” OR “flo*” OR “horticult*”) AND ((“harvest*” OR “dry*”) AND (“seed quality”))
Web Of Science(“fruit*” OR “nut*” OR “vegetable*” OR “herb*” OR “spice*” OR “medicinal plant*” OR “nurser*” OR “flo*” OR “horticult*”) AND ((“harvest*” OR “dry*”) AND (“seed quality”))
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pedrosa, L.M.; de Almeida Moreira, B.R.; Martins, C.C. Optimization of Harvesting and Drying Techniques for Quality Seed Production in Specialty Crops: A Systematic Review and Meta-Analysis. Agronomy 2024, 14, 1705. https://doi.org/10.3390/agronomy14081705

AMA Style

Pedrosa LM, de Almeida Moreira BR, Martins CC. Optimization of Harvesting and Drying Techniques for Quality Seed Production in Specialty Crops: A Systematic Review and Meta-Analysis. Agronomy. 2024; 14(8):1705. https://doi.org/10.3390/agronomy14081705

Chicago/Turabian Style

Pedrosa, Laura Monteiro, Bruno Rafael de Almeida Moreira, and Cibele Chalita Martins. 2024. "Optimization of Harvesting and Drying Techniques for Quality Seed Production in Specialty Crops: A Systematic Review and Meta-Analysis" Agronomy 14, no. 8: 1705. https://doi.org/10.3390/agronomy14081705

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