Precision nitrogen management enhances maize productivity, efficiency and soil health in the trans-gangetic plains

This study evaluates real-time nitrogen (N) management using the leaf color chart (LCC) and chlorophyll content meter (CCM) on maize (Zea mays L.) yield, nitrogen use efficiency and soil health in Punjab’s Trans-Gangetic Plains. A split-plot field experiment with three maize varieties (PMH-14, PMH-13, ADV-9293) and six N-management strategies-including LCC and CCM threshold-based applications, nano-nitrogen blends and recommended dose fertilizer regimes-was conducted with three replications during the 2024 Kharif season. Key findings reveal that synchronizing N supply with crop demand via CCM-50 and LCC-5 significantly enhances soil nitrogen content, soil enzymatic activities (urease, dehydrogenase and alkaline phosphatase enzymes) and organic carbon. The PMH-14 variety paired with CCM-50 delivered the highest yields (up to 9.26 t/ha), cob girth, test weight, grains per cob and plant growth parameters. Precision N management enhanced resource use efficiency and reduced fertilizer inputs by 25-50 % compared with conventional practices and increased farmer profitability. Soil properties, including pH, electrical conductivity and macronutrients, remained stable, while real-time N regimes promoted greater crops and soil resilience. Economic analysis indicated superior net returns for sensor-based interventions. The results support integrating sensor-guided N management with responsive genotypes for sustainable, climate-smart maize intensification. Multi-location validation is recommended to further refine these approaches.

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Publication Details

Journal
Plant Science Today
Published
2025-11-08
DOI
https://doi.org/10.14719/pst.11541
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Precision nitrogen management enhances maize productivity, efficiency and soil health in the trans-gangetic plains

Navjot Rana, Tomar Tarun, Baliyan Ayush, Pawar Amol R et al.
Plant Science Today
Remote Sensing in Agriculture
article

Precision nitrogen management enhances maize productivity, efficiency and soil health in the trans-gangetic plains

Navjot Rana, Tomar Tarun, Baliyan Ayush, Pawar Amol R, Bhatu Patil Mayur, Kaundal Munish
article en

Abstract

This study evaluates real-time nitrogen (N) management using the leaf color chart (LCC) and chlorophyll content meter (CCM) on maize (Zea mays L.) yield, nitrogen use efficiency and soil health in Punjab’s Trans-Gangetic Plains. A split-plot field experiment with three maize varieties (PMH-14, PMH-13, ADV-9293) and six N-management strategies-including LCC and CCM threshold-based applications, nano-nitrogen blends and recommended dose fertilizer regimes-was conducted with three replications during the 2024 Kharif season. Key findings reveal that synchronizing N supply with crop demand via CCM-50 and LCC-5 significantly enhances soil nitrogen content, soil enzymatic activities (urease, dehydrogenase and alkaline phosphatase enzymes) and organic carbon. The PMH-14 variety paired with CCM-50 delivered the highest yields (up to 9.26 t/ha), cob girth, test weight, grains per cob and plant growth parameters. Precision N management enhanced resource use efficiency and reduced fertilizer inputs by 25-50 % compared with conventional practices and increased farmer profitability. Soil properties, including pH, electrical conductivity and macronutrients, remained stable, while real-time N regimes promoted greater crops and soil resilience. Economic analysis indicated superior net returns for sensor-based interventions. The results support integrating sensor-guided N management with responsive genotypes for sustainable, climate-smart maize intensification. Multi-location validation is recommended to further refine these approaches.

Plant Science TodayVol. 12(sp4)
Chandigarh University (IN), Lovely Professional University (IN), Sardar Vallabhbhai Patel University of Agriculture & Technology (IN)
Lovely Professional University
Openalex Percentile: Top 68%
Remote Sensing in Agriculture
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