The effect of the individual seed mass of Negorelskaya variety Scots pine (Pinus sylvestris L.) on 30-day germination in 40-cell SideSlit growing containers

Novikov, Arthur; Rabko, Siarhei; Novikova, Tatyana; Petrishchev, E.

doi:10.34220/issn.2222-7962/2023.2/4

Cited by 3 publications

(5 citation statements)

References 67 publications

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“…Dataset 3. Total Number -ordinal global seed number (from 1 to 1200), key field, data typenumeric, variable type -categorical, nominal; Sample's number -number of a sample (batch) of seeds selected from different parts of the seed pile by quartering [232], data type -numeric; variable type -categorical, nominal (takes values from 1 to 3); Seed mass: data type -numeric; variable type -quantitative (direct measurement using analytical scales); Seed length: data type -numeric; variable type -quantitative (direct measurement using a micrometer); Seed width: data typenumeric; variable type -quantitative (direct measurement using a micrometer); Seed thickness: data type -numeric; variable type -quantitative (direct measurement using a micrometer); Seed square: data type -numeric; variable type -quantitative (calculation according to subsection 3.1); Ellipsoid volume: data type -numeric; variable type -quantitative (calculation according to subsection 3.1). Three main folders of the file dataset were formed in accordance with Figure 1, corresponding to three samples of scots pine seeds (P. sylvestris L., var.…”

Section: Table 1 the Table Data Format Of The 23-26-00228-rsf-dataset...mentioning

confidence: 99%

“…Формирование данных в блоке происходит в соответствии с таблицами 2 и 3. Total Number -ordinal global seed number (from 1 to 1200), key field, data type -numeric, variable type -categorical, nominal; Sample's number -number of a sample (batch) of seeds selected from different parts of the seed pile by quartering [232], data type -numeric; variable type -categorical, nominal (takes values from 1 to 3); Seed's number -the number of the seed inside the sample (batch) selected from the seed pile, data type -numeric; variable type -categorical, nominal (takes values from 1 to 400); Container's number -the number of the container in which the studied seeds were sown, data type -numeric; variable type -categorical, nominal (takes values from 1 to 40); G30 -germination index of each individual seed on the 30th day from the moment of sowing in a side-slit container, data type -numeric; variable type -categorical, rank (takes only two values 0there is no seed germination; and 1 -there is a viable plantlet).…”

Section: Seed Germination Blockmentioning

confidence: 99%

“…Negorelskaya) when moving seeds according to A.I. Novikov and co-authors [9] to the place of sowing according to the climatic gradient -"dependence of accumulated precipitation on accumulated degree days" [9], used in this study, is based on the hypothesis of the existence of the influence of morphometric and spectrometric parameters of seeds on sowing qualities (indicators of germinationtinplate for 30 and 50 days [10] in containers of automated forestry equipment).…”

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confidence: 99%

“…The accuracy of non-destructive [11][12][13] detection of forest seeds using electromagnetic waves from different regions increases with the assessment of each single seed [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] individually, determined by the focal distance [33] from the sensor to the place of reflection of the electromagnetic beam from the top of the seed coat [34]. At the same time, depending on the type and nature of exposure to electromagnetic radiation, the biophysical [13,35] (spectrometric) parameters of the seed correlate with different properties and indicators of a single seed [2,5,7,9,11,12,[12][13][14][15][16]21,[23][24][25][26][27][28]31,34,…”

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confidence: 99%

“…The table data format of the 23-26-00228-RCF-DataSet-SeedNG-G50.xlsx file from Dataset 3. Total Number -ordinal global seed number (from 1 to 1200), key field, data type -numeric, variable type -categorical, nominal; Sample's number -number of a sample (batch) of seeds selected from different parts of the seed pile by quartering[232], data type -numeric; variable type -categorical, nominal (takes values from 1 to 3); Seed's number -the number of the seed inside the sample, data…”

mentioning

confidence: 99%

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The VIS-Spectrometric Data of Scots Pine Individual Seed Reveal Forecasting Potential for Container-Grown Germination and Seedling’s Dickson Quality Index

Novikova,

Petrishchev,

Novikov

2023

Preprint

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To follow the growth and development of each tree (N = 1200) Pinus sylvestris L., var. Negorelskaya starting from the seed and continuing at the juvenile (or even generative) stage is the goal of an ambitious project to create a plant passport if possible. Five datasets of replenished empirical (tab-ular and file) and systematic data reflect the early results of a unique ongoing experiment conducted with families moved along a climatic gradient from the collection area (1731 degree days, 722 mm) to the experimental area (2326 degree days; 786 mm). Datasets of morphometric, VIS-spectrometric, and germination seed data, combined with the first results of biometric data (including the Dickson quality index) of seedlings produced from these seeds, will allow scientists in the future to conduct correlation and other statistical analysis, or train a neural network to determine the presence of connections between a seed and a plant. These data contribute to the early non-destructive diag-nosis and grading of seeds according to VIS-spectrometric properties. This dataset structure can complement the FLR-Library. In the future, by tracing seedlings in their early growth (for example, over three growing seasons at the site being restored), it will be possible to determine the degree of intensification of FRM production depending on the initial properties of the seed.

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Section: Table 1 the Table Data Format Of The 23-26-00228-rsf-dataset...mentioning

confidence: 99%

Section: Seed Germination Blockmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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The VIS-Spectrometric Data of Scots Pine Individual Seed Reveal Forecasting Potential for Container-Grown Germination and Seedling’s Dickson Quality Index

Novikova,

Petrishchev,

Novikov

2023

Preprint

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FLR-Library reference information system for adaptive forest restoration: cluster analysis of descriptors

Novikova,

Novikov,

Petrishchev

2023

Forestry Engineering Journal

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The lack of uniform terminological and technological approaches to the procedure of adaptive reforestation suggests the justification of the principles of creating a reference information system (FLR-Library), taking into account the specifics of implementing a joint decomposed FLR algorithm to reduce its execution time and rational forecasting of future forest landscape restoration projects. Collected and analyzed more than 120 definitions for more than 30 descriptors, such as "forest", "forest landscapes", "adaptive reforestation", "concepts of adaptive reforestation", "reforestation", "afforestation", "stand", "forest plantation", "forest complex", "forest landscape", "type of forest" and others. Both distinctive features and common points in the definitions of different countries and authors of the same descriptor are found. Technically, an approach to clustering of the most common descriptors is implemented: "forest", "forest landscapes", "reforestation", "afforestation". To do this, for each descriptor, based on the hierarchical clustering module, the combination of a cluster of recalibrated distances was determined and a cluster diagram was constructed based on «variables» (criteria for the descriptors effectiveness) and «observations» (by countries containing a source of information with a descriptor). Common to all sources will be that adaptive reforestation implies adaptation to climate change, but then there are two options for development: 1) planting (sowing) breeds resistant to climate change; 2) import and adaptation of "southern" breeds to northern latitudes. In the future, based on the analysis of descriptors and the principle of decomposition, it is planned to develop an algorithm for the functioning of the FLR system.

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Studying the spectrometric features of forest seeds to improve sowing qualities: a retrospective cluster analysis of the scientific landscape trends

Novikova,

Novikov,

Petrishchev

2024

Forestry Engineering Journal

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Forest seeds spectral data in the visible and infrared regions of electromagnetic radiation lengths quite effectively differentiate the origin, viability, types of seeds, their infestation with pests and diseases, the ability to absorb and lose water. The search for a method of seed testing that is both experimentally simple, fast and effective for predicting germination is necessary to increase the energy efficiency of forest nurseries in the production of planting material. The retrospective references systematization (N = 55, 1998-2023, terms [Scholar Query = seeds* AND (spectr* OR optic*) (properties OR features) AND analysis]) into clusters was carried out on the basis of eight performance criteria represented by rank variables. The level of similarity and difference between clusters is determined by the method of the most distant neighbors with the grouping of data by the square of the Euclidean distance. The most distant criterion from other criteria is the level of invasiveness of testing (the square of the Euclidean distance is 25, p < 0.05). Correlation analysis of nonparametric criteria indicates a direct strong interaction between the level of financial and organizational costs (Spearman coefficient ρ = 0.77; p = 0.0008), time costs and low machine learning capability (ρ = 0.725; p = 0.0008). In the future, it is planned to periodically supplement the set of systematic data to obtain an objective assessment of seed testing methods, as well as using a seed passport to evaluate the relationship of RGB spectral data of more than 1 000 individual seeds with early growth of seedlings in a post-pyrogenic experimental site of the forest landscape of the Voronezh region by example (Pinus sylvestris L. var. Negorelskaya).

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The effect of the individual seed mass of Negorelskaya variety Scots pine (Pinus sylvestris L.) on 30-day germination in 40-cell SideSlit growing containers

Cited by 3 publications

References 67 publications

The VIS-Spectrometric Data of Scots Pine Individual Seed Reveal Forecasting Potential for Container-Grown Germination and Seedling’s Dickson Quality Index

The VIS-Spectrometric Data of Scots Pine Individual Seed Reveal Forecasting Potential for Container-Grown Germination and Seedling’s Dickson Quality Index

FLR-Library reference information system for adaptive forest restoration: cluster analysis of descriptors

Studying the spectrometric features of forest seeds to improve sowing qualities: a retrospective cluster analysis of the scientific landscape trends

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