Dickson Quality Index: relation to technological impact on forest seeds

Abstract

A comprehensive indicator of predicting the quality of planting material at the time of planting – the Dickson quality Index (DQI) – is currently widely represented in studies of the growth and development of forest crops, but is not limited to them. Based on the systematization of data to a depth of 10 years returned by the term [Scholar Query = "Dickson quality index"], on the dynamics of the Dickson quality index depending on the criteria of technological impact on seeds and seedlings (seedlings), a hierarchical classification was carried out using the method of the most distant neighbors to describe the similarity between the criteria. Data grouping was performed using the square of the Euclidean distance. The change in the DQI index in most cases can be approximated by a second-order polynomial. The technological impact on seeds before sowing on the basis of the convergence and difference method is significantly (p < 0.05) allocated to a separate cluster (the square of the Euclidean distance is 4), which suggests a possible contribution to the assessment of the quality of planting material using DQI. In future studies, it is planned to get an answer to the question: what effect does the separation of seeds by spectrometric properties have on the dynamics of the DQI index in the process of ontogenesis? How does the dimensionality of the ratio of plant height to the diameter of the root neck affect the accuracy of the DQI estimate?

The Root Collar Diameter Growth Reveals a Strong Relationship with the Height Growth of Juvenile Scots Pine Trees from Seeds Differentiated by Spectrometric Feature

“…The interaction between RCD and SH will make it possible to predict the quality indicators of the seedlings: for example, the seedling sturdiness quotient (SH RCD-1) included in the Dickson Quality Index [55] as well as the possibility of excluding the operation of measuring the height of the seedling using only the RCD measurement. Moreover, a close correlation between these indicators, as shown in a recent study by Jiang et al [56], may contribute to the selection of the best trees.…”

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

“…The dataset allows for correlation and regression analyses of the effect of individual seed parameters on the indicator of seed sowing qualities, and can also be linked to other datasets in the FLR Library [2] to generate summary queries to predict the effect, for example, geometric parameters on 50-day container germination The tabular dataset includes the results of direct measurements of biometric parameters (height and diameter of the root neck) and bio-mass parameters of the underground and aboveground parts of the plant in the wet and dried state of container-grown seedlings obtained from these seeds. Also, based on the calculation of known ratios (for example, HDR -Height Diameter Ratio) between these parameters, the dataset presents integral indicators of the Dickson quality index DQI [6], the compactness index CP, the seedling health index SHI, the root quality index RQI. The dataset allows for correlation and regression analyses between these parameters, and can also be linked to other datasets in the FLR Library [2] to generate summary queries to predict the effect, for example, of VIS-spectrometric properties of seeds on DQI.…”

“…The quality of the seeds is closely correlated with their spectrometric properties. Moreover, the effectiveness of performing a group of seeding operations (including aerial seeding [231] on hard-to-cultivate areas) depends on the quality of seeds [3,6,12,13,34,72,79,85,89,107,111,115,139,143,175,203,217,[223][224][225][226][227][228][229][230].…”