Impact of Climate Dynamics and Agricultural Inputs on Agricultural Greenhouse Gas Emissions in India: An ARDL Cointegration Approach

Authors

  • AADITYA JADHAV Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
  • ABHISHEK SINGH Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
  • SHRINIVASA DJ Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
  • ABHA GOYAL Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
  • SANKET CHAVAN Research and Information System for Developing Countries (RIS), New Delhi, India

DOI:

https://doi.org/10.54386/jam.v28i3.3461

Keywords:

Greenhouse Gas Emissions, ARDL Cointegration, Climate Variables, Paddy Area, India

Abstract

In India, where agriculture is both a foundation of the economy and a major source of greenhouse gas (GHG) emissions, rising temperatures due to climate change are accelerating environmental degradation while simultaneously undermining the resilience of the agrarian sector. In this context, the present study examines the long-run relationship between agricultural greenhouse gas emissions, climate variables and agricultural inputs in India. The Autoregressive Distributed Lag (ARDL) model technique is applied to time series data on six key variables from 1990 to 2021. The findings of the ARDL bound F-test confirmed the significant long-run cointegration among variables under consideration. Furthermore, area under paddy crop (0.257) and manure nitrogen content (0.842) exert significant and positive long-run effects on greenhouse gas emissions. Also, maximum temperature (0.341) and rainfall (0.086) positively contribute to the rising of greenhouse gases in the long run. With an adjusted R2 value of 0.947, the ARDL model exhibits strong explanatory power and confirms the impact of climate and agricultural drivers on agricultural emissions in India. These findings highlight the need to consider the climate variables in agricultural GHG mitigation efforts and policy strategies.

References

Ahmed, M., Shuai, J., & Ali, H. (2023). The effects of climate change on food production in India: evidence from the ARDL model. Environment Development and Sustainability, 26(6), 14601-14619. https://doi.org/10.1007/s10668-023-03209-w

Bhattacharyya, P., Bhatia, A., Jain, N., Chatterjee, D., Mohanty, S., Prabhakar, M., M, P., Jambhulkar, N., Ananthakrishnan, S., Nayak, S., Nayak, A., & Pathak, H. (2025). Modified emission and scaling factors for methane and nitrous oxide in rice system of India: A country specific disaggregate approach. Journal of Environmental Management, 377, 124595. https://doi.org/10.1016/j.jenvman.2025.124595

Climate Watch (2025). Greenhouse gas (GHG) emissions. Retrieved September 25, 2025 from https://www.climatewatchdata.org/ghg-emissions.

Chachei, K. (2024). Greenhouse gas emissions in the Indian agriculture sector and mitigation by best management practices and smart farming technologies - A review. Environmental Science and Pollution Research, 31(32), 44489–44510. https://doi.org/10.1007/s11356-024-33975-7

Data.gov.in. (2025). Open Government Data platform of India. Retrieved September 25, 2025 from https://www.data.gov.in/catalog/all-india-seasonal-and-annual-temperature-series.

FAOSTAT Database (2025). Food and Agriculture Organization of the United Nations. Available via: https://www.fao.org/faostat/en/#data. Accessed on 21 September 2025

Gao, H., Tian, H., Zhang, Z., and Xia, X. (2022). Warming-induced greenhouse gas fluxes from global croplands modified by agricultural practices: A meta-analysis. The Science of the Total Environment, 820, 153288. https://doi.org/10.1016/j.scitotenv.2022.153288

Gupta, K., Kumar, R., Baruah, K. K., Hazarika, S., Karmakar, S., & Bordoloi, N. (2021). Greenhouse gas emission from rice fields: a review from Indian context. Environmental Science and Pollution Research, 28(24), 30551–30572. https://doi.org/10.1007/s11356-021-13935-1

Hasnul, A. G., & Masih, M. (2016). “Role of instability in affecting capital flight magnitude: An ARDL bounds testing approach.” MPRA Paper No. 72086, University of Munich.

Li, C., Wang, W., Wang, K., Wang, Y., and Zhang, M. (2024). Responses of greenhouse gas emissions to increased precipitation events in different ecosystems: A meta-analysis. CATENA, 246, 108400. https://doi.org/10.1016/j.catena.2024.108400

Mohammed, A., & Tarpley, L. (2009). High nighttime temperatures affect rice productivity through altered pollen germination and spikelet fertility. Agricultural and Forest Meteorology, 149(6–7), 999–1008. https://doi.org/10.1016/j.agrformet.2008.12.003

Nkoro, E., & Uko, A. K. (2016). Autoregressive Distributed Lag (ARDL) cointegration technique: application and interpretation. Journal of Statistical and Econometric Methods, 5(4), 63-91

Patange, O., Purohit, P., Avashia, V., Klimont, Z., & Garg, A. (2024). Mitigation of non-CO2 greenhouse gases from Indian agriculture sector. Environmental Research Letters, 19(7), 074020. https://doi.org/10.1088/1748-9326/ad4e4e

Pant, K. P. (2009). Effects of agriculture on climate change: A cross country study of factors affecting carbon emissions. Journal of Agriculture and Environment, 10, 84–102. https://doi.org/10.3126/aej.v10i0.2134

Peng, S., Huang, J., Sheehy, J. E., Laza, R. C., Visperas, R. M., Zhong, X., Centeno, G. S., Khush, G. S., & Cassman, K. G. (2004). Rice yields decline with higher night temperature from global warming. Proceedings of the National Academy of Sciences, 101(27), 9971–9975. https://doi.org/10.1073/pnas.0403720101

Pesaran, M.H. and Shin, Y. (1999). An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis. In: Strom, S., Ed., Chapter 11 in Econometrics and Economic Theory in the 20th Century the Ragnar Frisch Centennial Symposium, Cambridge University Press, Cambridge, (371-413).

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3): 289–326. https://doi.org/10.1002/jae.616

Sam, C. Y., McNown, R., & Goh, S. K. (2018). An augmented autoregressive distributed lag bounds test for cointegration. Economic Modelling, 80, 130–141. https://doi.org/10.1016/j.econmod.2018.11.001

Sathya, N. J., Kanakarani, N. P., Gayathri, N. J., Babu, N. M., & Thiruppathi, N. R. (2025). Trends in temperature in India: Impact on precipitation and freshwater resources. Ecology Environment and Conservation, 31(Suppl), S214–S219. https://doi.org/10.53550/eec.2025.v31i01s.037

Saud, S., Wang, D., Fahad, S., Alharby, H. F., Bamagoos, A. A., Mjrashi, A., Alabdallah, N. M., AlZahrani, S. S., AbdElgawad, H., Adnan, M., Sayyed, R. Z., Ali, S., & Hassan, S. (2022). Comprehensive impacts of climate change on rice production and adaptive strategies in China. Frontiers in Microbiology, 13, 926059. https://doi.org/10.3389/fmicb.2022.926059

Shakoor, A., Shakoor, S., Rehman, A., Ashraf, F., Abdullah, M., Shahzad, S. M., Farooq, T. H., Ashraf, M., Manzoor, M. A., Altaf, M. M., & Altaf, M. A. (2020). Effect of animal manure, crop type, climate zone, and soil attributes on greenhouse gas emissions from agricultural soils-A global meta-analysis. Journal of Cleaner Production, 278, 124019. https://doi.org/10.1016/j.jclepro.2020.124019

Sindhuja, S., & Malik, D. (2025). Growth and Instability Analysis of rice production in India: A Zone Wise study. Archives of Current Research International, 25(9), 94–109. https://doi.org/10.9734/acri/2025/v25i91479

Singh, A., Arora, K., & Babu, S. C. (2024). Examining the impact of climate change on cereal production in India: Empirical evidence from ARDL modelling approach. Heliyon, 10(18), e36403. https://doi.org/10.1016/j.heliyon.2024.e36403

Vinnarasi, R., Dhanya, C. T., Chakravorty, A., & AghaKouchak, A. (2017). Unravelling diurnal asymmetry of surface temperature in different climate zones. Scientific Reports, 7(1), 7350. https://doi.org/10.1038/s41598-017-07627-5

Downloads

Published

07-09-2026

How to Cite

JADHAV, A., SINGH, A., DJ, S., GOYAL, A., & CHAVAN, S. (2026). Impact of Climate Dynamics and Agricultural Inputs on Agricultural Greenhouse Gas Emissions in India: An ARDL Cointegration Approach. Journal of Agrometeorology, 28(3), 314–323. https://doi.org/10.54386/jam.v28i3.3461

Issue

Vol. 28 No. 3 (2026): September

Section

Research Paper

Categories

License

Copyright (c) 2026 AADITYA JADHAV, ABHISHEK SINGH, SHRINIVASA DJ, ABHA GOYAL, SANKET CHAVAN

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This is a human-readable summary of (and not a substitute for) the license. Disclaimer.

You are free to:

Share — copy and redistribute the material in any medium or format

Adapt — remix, transform, and build upon the material

The licensor cannot revoke these freedoms as long as you follow the license terms.

    Under the following terms:

    Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

    NonCommercial — You may not use the material for commercial purposes.

    ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

    No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

    Notices:

    You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.

    No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.