Relationship between Laboratory Germination and Biometric Parameters of Betula jarmolenkoana Golosk. Aglets

Abstract

This study evaluates the relationship between laboratory germination and biometric parameters of aglets of the critically endangered birch tree species Betula jarmolenkoana Golosk (Yarmolenko birch). The Yarmolenko birch is a very rare, narrowly endemic species, growing at an altitude of 1,900-2,100 m above sea level and only in the basins of three rivers (Kakpak, Bayynkol, and Tekes) in the Almaty region of Kazakhstan. Therefore, research is currently needed to increase the stability of birch forests and determine the optimal ways to preserve them. The study aimed to establish the nature of the relationship between laboratory germination and biometric parameters of birch aglets. To conduct surveys on the Yarmolenko birch forests, six sampling areas were selected, and five trees of close age (VI-VII age classes) were selected and marked in each sampling area. At the end of summer (August), after the seeds had reached physiological maturity (yellowing of the aglet core), 100 pieces of aglets were collected from trees at a height of 2.0 to 2.5 m and weighed on electronic scales. The data obtained were processed using methods of variational statistics, and the average values, coefficient of variability, and standard error of the average value were calculated. Student's t-test was used to compare features between the areas. The relationship between biometric parameters and seed germination was identified and statistically substantiated. According to three-year observations, in some areas of the forest, especially in areas with young and medium-aged plants, the morphometric parameters of aglets were not the same and did not repeat in different years. This was due to the weather conditions of the year of aglet formation and the growth and development of seeds.

Influence of Varietal Characteristics and Planting Schemes of Remontant Raspberries on their Productivity

“…Statistical methods can be used to quantify factors in plant disease incidence indices, disease development (Dutbayev et al, 2022), physiological indices (Dutbayev et al, 2023;Rakymbekov et al, 2023), mycotoxin composition and yield parameters (Kuldybayev et al, 2021). These methods can be widely used to quantify the impact of climatic index changes on observed yield by means of principal component analysis (Kuldybayev et al, 2021;Dutbayev et al, 2022;.…”