ECOLOGICAL TESTING OF PROSO MILLET COLLECTION UNDER CONDITIONS OF WESTERN AND NORTHERN KAZAKHSTAN
DOI:
https://doi.org/10.26577/EJE2025821014Abstract
Proso millet (Panicum miliaceum L.) is a highly valuable cereal crop and one of the key food crops grown by humans. Proso millet is widespread in America, Europe and Asia. Therefore, the search for ways to increase the production of millet crops due to the growth of the world's population is very relevant today. Of all grain crops, proso millet stands out for its biological characteristics, such as drought and heat resistance. For this reason, proso millet can be grown in different soil and climatic conditions of Kazakhstan. However, this requires a comprehensive assessment of the adaptability of genotypes from various origins for the subsequent expansion of the cultivation range of this crop. For this purpose, domestic and foreign collection varieties of proso millet were selected for ecological testing in two regions: Northern and Western Kazakhstan, which are characterized by different climatic conditions. In our studies, the agricultural technology developed for these regions was used, as well as the guidelines for variety study of the world collection of proso millet. According to the methodology, phenological observations of plant growth and development and analysis of crop structure elements were carried out during the study. The statistical analysis revealed a significant relationship between the traits of productive tillering, weight of 1000 seeds, and number of grains in the main panicle, which can enhance the efficiency of plant breeding efforts aimed at increasing variety yield. Based on the assessment results, highly productive genotypes were identified for the two regions: K-10215 (671.9 g/m²), K-2468 (885.3 g/m²), Yarkoe 5 (572 g/m²), Shortandinskoe 7 (548 g/m²), and Saratovskoe 3 (492.6 g/m²). These genotypes can be utilized in breeding programs to develop new highly adaptive proso millet varieties.
Keywords: proso millet, germplasm, ecological testing, correlation analysis
