نوع مقاله : مقاله پژوهشی
نویسندگان
1 پژوهشگر پسادکتری گروه علوم خاک، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران
2 دانشیار بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان زنجان، سازمان تحقیقات، آموزش و ترویج کشاورزی، زنجان،
3 گروه علوم خاک، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Considering the climatic and soil characteristics of the Tarem region, sustainable olive production in its super-high-density systems requires precise nutrient management and the identification of nutritional disorders in trees. The utilization of multivariate statistical methods to determine the nutritional status of olive trees and identify factors causing disorders enables the analysis of nutritional patterns, optimization of fertilizer use, and enhancement of production sustainability. In this study, 50 leaf samples were collected from super-high-density olive orchards in the Tarem region of Qazvin province in July 2025. The concentrations of nutrients (N, P, K, Ca, Mg, Na, Fe, Zn, Mn, Cu, and B) were measured, and the data were processed using statistical algorithms. Principal Component Analysis (PCA) was first used to reduce data dimensionality and identify variables contributing most to total variance. Subsequently, nutrients were clustered based on nutritional similarities using cluster analysis. The results indicated that four principal components of PCA explained 66.26% of the total data variance, and cluster analysis (K-mean) divided the samples into five distinct clusters. The overlap between cluster analysis and PCA results revealed that nutritional disorders and yield reduction in the Tarem region are primarily caused by deficiencies of nutrients, including phosphorus and micronutrients, resulting from salinity, soil calcareous conditions, alkaline pH, and low organic matter. Employing a combined analytical approach significantly increases the accuracy of identifying nutritional disorders in super-high-density olive systems. Identifying the nutritional clusters enables a transition from uniform management to variable and optimized fertilization strategies in super-high-density systems.
کلیدواژهها [English]
)