M. Radha¹, M. Nirmala Devi², S. Vishnu Shankar³, P. Jeyalakshmi⁴*
¹ Assistant Professor (Statistics), Department of Agricultural Economics, Anbil Dharmalingam Agricultural College and Research Institute, Trichy, Tamil Nadu, India
*Corresponding author: radha@tnau.ac.in
² Assistant Professor (Statistics), Department of Physical Science and Information Technology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
³ Teaching Assistant (Agricultural Statistics), Department of Physical Science and Information Technology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
⁴ Assistant Professor (English), Department of Agricultural Extension and Communication, VOCAC&RI, Killikulam, Tamil Nadu, India
Email: jeyalakshmi.p@tnau.ac.in
Introduction
Canonical Correlation Analysis (CCA) is a multivariate statistical method that identifies and quantifies relationships between two sets of variables. In agriculture, where biological, environmental, and agronomic factors often interact in complex ways, CCA has proven highly valuable. It enables researchers to understand associations across datasets, thereby facilitating crop improvement, environmental management, and precision farming.
Applications in Crop Improvement and Breeding
In plant breeding, yield is influenced by multiple morphological and physiological traits. CCA helps breeders assess the association between traits such as plant height, leaf area, or root development and yield components like grain weight or fruit size (Hossain et al., 2011). Identifying traits with strong canonical correlations to yield guides targeted selection strategies, ultimately improving breeding efficiency.
Soil–Plant–Environment Interactions
Sustainable agriculture requires a clear understanding of how soil, plant, and environmental factors interact. CCA is particularly effective for examining variables such as soil nutrient levels, water availability, climatic conditions, and plant nutrient uptake (Khan et al., 2014). For example, CCA-based studies have clarified how soil fertility influences plant growth under stress, thereby informing better nutrient and water management practices.
Disease Diagnosis and Management
In plant pathology, CCA has been used to explore relationships between disease incidence, pathogen dynamics, and environmental factors. By linking disease prevalence to variables such as temperature, humidity, and plant physiology, CCA helps predict outbreaks and design preventive measures (Sarker et al., 2017). This improves disease forecasting and supports timely interventions to reduce crop losses.
Precision Agriculture and Resource Optimization
Modern precision agriculture relies on integrating large datasets from sensors, drones, and satellites. CCA enables correlation of multispectral or soil sensor data with crop growth and yield parameters (Yadav et al., 2019). Such applications support optimized use of fertilizers, irrigation, and pesticides, reducing input costs while improving productivity and sustainability.
Conclusion
Canonical Correlation Analysis offers a robust framework for understanding complex multivariate interactions in agriculture. Its applications span breeding, soil–plant–environment analysis, disease management, and precision farming. By integrating diverse data types, CCA enhances the accuracy of decision-making, supporting sustainable crop production, resilience, and efficient resource use. With the growing need for climate-resilient and resource-efficient farming systems, CCA stands out as a critical tool for agricultural research and practice.
References
Hossain, M. A., et al. (2011). Multivariate analysis of yield-related traits in rice genotypes. Australian Journal of Crop Science, 5(2), 164–168.
Khan, M. S., et al. (2014). Soil–plant–environment interactions in arid regions. Journal of Plant Nutrition, 37(15), 2410–2424.
Sarker, N., et al. (2017). Application of multivariate analysis to plant disease management. Plant Disease, 101(4), 495–501.
Yadav, P., et al. (2019). Remote sensing-based precision agriculture: A review. Agricultural Systems, 172, 15–30.