Document Type : Research Paper
Authors
1 Postdoctoral research Department of Soil Science, Faculty of Agriculture, Urmia University, Urmia, Iran
2 2. Assistant Professor, Research Department, Zanjan Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zanjan, Iran.
3 3. Department of Soil Science, Faculty of Agriculture, Urmia University, Urmia, Iran
Abstract
Keywords
Main Subjects
The Tarom region, endowed with unique climatic and soil characteristics, holds significant potential for olive cultivation. However, the advent of modern agricultural systems, particularly Super-High-Density (SHD) planting, has introduced novel challenges in achieving sustainable crop management, especially for olives. In SHD systems, the high density of trees and intense competition for resources—namely water, light, and nutrients—necessitate a highly precise approach to nutrient management. Insufficient attention to the nutritional requirements of trees and the delayed identification of nutritional disorders can lead to reduced yields, impaired product quality, and ultimately, compromised production sustainability in these potentially high-yielding systems. This research endeavors to employ advanced statistical methodologies to identify the primary factors responsible for nutritional disturbances and to ascertain the precise nutritional status of olive trees, thereby paving the way for optimized fertilizer use and enhanced production sustainability. The overarching goal of this study was to identify the key factors contributing to nutritional disorders in olive trees cultivated under SHD systems in the Tarom region of Qazvin Province, and to determine their nutritional status using a multivariate statistical approach.
This field-based research was conducted in the Tarom region of Qazvin Province, a prominent olive-producing area in Iran. Sampling was performed in olive orchards managed under SHD systems. Leaf samples were collected from olive trees across 50 selected orchards. Collected samples were transported to the laboratory, where the concentrations of nutrients (N, P, K, Ca, Mg, Na, Fe, Zn, Mn, Cu, and B) were measured using standard chemical analysis methods. Additionally, data pertaining to crop yield were gathered for each orchard. The data obtained from leaf analysis and yield information were subjected to multivariate statistical analyses. Initially, Principal Component Analysis (PCA) was employed. PCA facilitates the identification of variables contributing most to overall data variability and reveals hidden interrelationships among them. Following dimensionality reduction and the identification of principal components, Cluster Analysis was utilized. The objective of this analysis was to identify distinct groups of orchards exhibiting similar nutritional profiles, thereby aiding in a better understanding of the variability in nutritional status across the region and pinpointing orchards facing specific nutritional challenges.
The results from the Principal Component Analysis (PCA) revealed that four principal components were sufficient to explain 66.26% of the total variance observed in the nutrient concentration data. The first principal component, which exhibited the highest factor loading, demonstrated a strong association between the concentrations of Nitrogen (N) and Manganese (Mn). This correlation may reflect simultaneous uptake or a degree of metabolic interdependence between these two nutrients. The substantial loading of this component in certain observations highlights the prominent role of N and Mn in determining the overall nutritional status. The second principal component revealed a significant correlation between crop yield and the concentration of Phosphorus (P). Iron (Fe) and zinc (Zn) primarily influence the third and fourth components, respectively, showing positive factor loadings that align directly with yield. These findings suggest a direct relationship between leaf P and micronutrients levels and olive yield in these SHD systems, indicating that P and Zn deficiencies could be key factors limiting productivity. Cluster analysis divided the studied orchards into five distinct clusters, each characterized by a unique nutritional profile and yield status:
Cluster 1: This cluster comprised orchards exhibiting low yields and poor nutritional status. The primary contributing factor identified was high concentrations of Sodium (Na) and high soil salinity, which likely impaired the uptake of other essential nutrients and potentially induced phytotoxicity.
Clusters 2 and 3: These clusters presented moderate yields but displayed differing nutrient-element patterns, suggesting that nutrient dynamics are complex and influenced by various factors even at intermediate yield levels.
Cluster 4: Orchards in this cluster showed signs of deficiency in critical elements such as Phosphorus (P), Potassium (K), Zinc (Zn), and Copper (Cu). These deficiencies could potentially lead to reduced yield and quality in the future if unaddressed.
Cluster 5: This cluster consisted of orchards demonstrating optimal yields and more balanced nutrient concentrations in their leaf tissues, representing well-managed and nutritionally sound plots.
A comparative analysis of the PCA and Cluster Analysis results revealed a notable overlap. A key finding of this study is the critical role of phosphorus and micronutrients, alongside effective management of soil salinity and pH, in achieving optimal olive yields in the Tarom region. The deficiencies identified, rooted in the region’s specific edaphic conditions, have a direct and significant impact on yield and production sustainability. In conclusion, this study highlights the imperative of integrating scientific knowledge (such as multivariate statistics) with practical regional understanding (soil and plant needs) to achieve precise and sustainable agriculture. Accurate identification and remediation of nutritional disorders through targeted management strategies represent a fundamental step towards elevating the Tarom region’s standing as a key center for high-quality, sustainable olive production.
The study was funded by Iran National Science Foundation (INSF) under project No 4044109.
“Conceptualization, M.S. and M.T.; methodology, E.S. and M.T.; software, E.S.; validation, M.T., and E.S.; investigation, M.S.; resources, E.S.; data curation, M.T.; writing—original draft preparation, M.S.; writing—review and editing, M.S.; supervision, E.S.; project administration, M.T. All authors have read and agreed to the published version of the manuscript.”
All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.
There is no use of any type of artificial intelligence-based technology in this paper.
Data available on request from the authors.
The authors thank the Agricultural Research, Education, and Extension Organization, and Tat Sabz Qazvin Company for their participation in the present research.
The authors would like to thank anonymous reviewers for their constructive comments and valuable suggestions in manuscript revision and improvement.
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The authors declare no conflict of interest.
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