Leaf Colour Chart (LCC) a Reliable Tool for Nitrogen Management: A Comprehensive Review

Kh. Chandrakumar Singh

Department of Natural Resource Management, College of Horticulture, VCSG Uttarakhand University of Horticulture & Forestry, Bharsar, Pauri Garhwal- 246123, Uttarakhand, India.

Mukesh Vishnoi

Division of Agronomy, Dr. K. N. Modi University Newai Tonk, Rajasthan - 304021, India.

Shafiya Fayaz

Division of Agronomy, Faculty of Agriculture, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Jammu & Kashmir, India.

Piyush Parihar *

Division of Plant Pathology, Faculty of Agriculture, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Jammu & Kashmir, India.

Dhamni Patyal

Division of Agronomy, Sher-e-Kashmir University of Agriculture Sciences and Technology, Jammu, 180009, Jammu & Kashmir, India.

Sheikh Amjid

Division of Soil Science, Faculty of Horticulture, Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, Jammu & Kashmir, India.

Yasir Hanif Mir

Division of Soil Science and Agricultural Chemistry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Wadura- 193201, Jammu and Kashmir, India.

Aman Tutlani *

Division of Genetics and Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Wadura- 193201, Jammu and Kashmir, India.

*Author to whom correspondence should be addressed.


Abstract

Efficient nitrogen (N) management is central to improving crop productivity while reducing adverse environmental impacts such as nutrient leaching, nitrous oxide emissions, and groundwater contamination. Among available diagnostic tools, the Leaf Colour Chart (LCC) has proven to be a simple, cost-effective, and non-destructive method for assessing crop nitrogen status and guiding site-specific nitrogen management (SSNM). Developed initially for rice cultivation, LCC has since been validated in other cereals and small millets, including sorghum, pearl millet, and finger millet, reflecting its adaptability across diverse production systems. The principle of LCC relies on visual comparison of leaf greenness with standardized colour panels, which correlates strongly with leaf chlorophyll content and nitrogen concentration. This enables farmers to make timely fertilizer applications, thereby enhancing nitrogen use efficiency (NUE), sustaining yields, and minimizing input costs. Field experiments across agro-ecological regions consistently demonstrate that LCC-based N management can match or surpass blanket fertilizer recommendations while lowering losses and improving resource use efficiency. The reliability of the tool, however, may be influenced by varietal differences in leaf colour, environmental conditions, and user subjectivity. Optimal readings are generally obtained from the youngest fully expanded leaves during mid-morning prior to topdressing. Recent advances, including digital LCCs, smartphone-based colour calibration, and integration with precision agriculture platforms, show promise in overcoming these limitations and improving accuracy. This review synthesizes current knowledge on LCC calibration, crop-specific threshold values, and correlations with chlorophyll and leaf N content. It also evaluates practical applications, benefits, and constraints of LCC across different crops and environments. Overall, evidence suggests that LCC is a reliable, adaptable, and sustainable tool for precision nitrogen management, offering significant potential for climate-resilient agriculture and providing valuable guidance to researchers, extension agents, and farmers worldwide.

Keywords: Leaf COLOR chart, nitrogen management, nitrogen use efficiency, greenhouse gas


How to Cite

Singh, Kh. Chandrakumar, Mukesh Vishnoi, Shafiya Fayaz, Piyush Parihar, Dhamni Patyal, Sheikh Amjid, Yasir Hanif Mir, and Aman Tutlani. 2025. “Leaf Colour Chart (LCC) a Reliable Tool for Nitrogen Management: A Comprehensive Review”. Journal of Scientific Research and Reports 31 (9):743-54. https://doi.org/10.9734/jsrr/2025/v31i93540.

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