Measurement, Modelling, and Mitigation of Methane Emissions in Agricultural Systems for Climate-Smart Agriculture

M. N. Karthik *

Department of Agronomy, S. V. Agricultural College, ANGRAU, Tirupati, Andhra Pradesh 517502, India.

V. Chandrika

Department of Agronomy, S. V. Agricultural College, ANGRAU, Tirupati, Andhra Pradesh 517502, India.

D. Subramanyam

Department of Agronomy, S. V. Agricultural College, ANGRAU, Tirupati, Andhra Pradesh 517502, India.

P. Venkata Subbaiah

Department of Soil Science, S. V. Agricultural College, ANGRAU, Tirupati, Andhra Pradesh -517502, India.

P. Latha

Department of Crop Physiology, S. V. Agricultural College, ANGRAU, Tirupati, Andhra Pradesh -517502, India.

S. S. T. Aarthi

Department of Agronomy, S. V. Agricultural College, ANGRAU, Tirupati, Andhra Pradesh 517502, India.

K. B. Hazeera

Department of Agronomy, S. V. Agricultural College, ANGRAU, Tirupati, Andhra Pradesh 517502, India.

G. P. Sathwik

Department of Agronomy, S. V. Agricultural College, ANGRAU, Tirupati, Andhra Pradesh 517502, India.

K. Deepasri

Department of Agronomy, Tamil Nadu Agricultural College, Tamil Nadu 641003, India.

*Author to whom correspondence should be addressed.


Abstract

Methane is a high-priority agricultural greenhouse gas because substantial emissions arise from enteric fermentation, anaerobic manure management, and flooded rice cultivation, while its relatively short atmospheric lifetime creates an opportunity for comparatively rapid climate benefits from sustained abatement. Yet mitigation claims are only as credible as the measurements and models used to establish baselines, attribute sources, quantify intervention effects, and detect leakage or trade-offs. This critical narrative review integrates evidence on agricultural methane measurement, emission modelling, and mitigation in a climate-smart agriculture framework. Literature was selected from agricultural, biomedical, environmental and multidisciplinary scholarly indexes, with emphasis on peer-reviewed evidence published from 2000 to 24 June 2026 and retention of earlier foundational work when necessary. The synthesis shows that no measurement method is universally superior: respiration chambers and tracer approaches offer controlled animal-level estimation, automated head-chamber systems increase throughput, micrometeorological and inverse methods enlarge the measurement footprint, and chamber, eddy-covariance and emerging remote-sensing approaches address contrasting scales in rice and manure systems. Model uncertainty often originates as much from activity data, dry matter intake, temperature, storage history, water regime and management assumptions as from model structure itself. Among mitigation options, 3-nitrooxypropanol has the most consistent evidence for substantial enteric methane suppression under controlled feeding, whereas red seaweed, nitrate, lipids and genetic selection show context-dependent promise with distinct safety, delivery or scalability constraints. Manure acidification, shortened anaerobic storage and well-managed anaerobic digestion can reduce emissions, but methane leakage can erode expected benefits. In rice, alternate wetting and drying is strongly supported for methane and water reduction, although yield and nitrous oxide responses depend on threshold, soil and management. Climate-smart deployment therefore requires scale-matched measurement, transparent uncertainty, multi-gas and productivity accounting, and verification systems capable of reconciling farm-level interventions with facility and landscape emissions. The strongest pathway is an integrated measurement-model-management architecture rather than reliance on any single technology.

Keywords: Agricultural methane, enteric fermentation, greenhouse-gas measurement, emission models, manure management, rice paddies, mitigation, climate-smart agriculture


How to Cite

Karthik, M. N., V. Chandrika, D. Subramanyam, P. Venkata Subbaiah, P. Latha, S. S. T. Aarthi, K. B. Hazeera, G. P. Sathwik, and K. Deepasri. 2026. “Measurement, Modelling, and Mitigation of Methane Emissions in Agricultural Systems for Climate-Smart Agriculture”. Archives of Current Research International 26 (9):296-316. https://doi.org/10.9734/acri/2026/v26i92133.

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