Agriculture Development and its Impact: A Comprehensive Time Series Analysis of Climate Variables

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Research ID 142U2

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Abstract

Climate change poses significant challenges that necessitate the development of policies aimed at managing aggregate inputs and social costs. For formulating such policies, an analysis of its factors and their current trends needs to be studied. This paper explores the factors influencing climate change and provides insights into their impact through changes in arable land and greenhouse gas (GHG) emissions in India from 1990 to 2020. Utilizing time series analysis, the study examines trends in GHG emissions from agriculture and develops a simulation model to estimate overall GHG emissions through methane and nitrous oxide emissions. Results indicate that enteric fermentation and agricultural soil are major contributors to methane and nitrous oxide emissions, respectively, with enteric fermentation contributing approximately 69.33% to methane emissions and agricultural soil contributing approximately 97.66% to nitrous oxide emissions. Additionally, a higher growth rate is observed for nitrous oxide emissions than methane emissions, with nitrous oxide emissions showing a 161% increase from 1960 to 2010. Furthermore, a positive correlation (i.e. r=0.587) between GHG emissions and changes in annual mean temperature underscores the direct impact of agricultural emissions on climate dynamics in India, with a regression coefficient factor of 0.176. It is estimated that the overall GHG emission from agriculture through methane and nitrous oxide emission will be approximately 695.87 to 818.73 MMTCDE in the year 2030; while the change in annual mean temperature is estimated to be about 1.65 ± 0.58o C from 1990 to 2030 in India. The findings highlight the urgent need for effective mitigation strategies within the agricultural sector to address the growing threat of climate change.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

Not applicable

Data Availability

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

Funding

This work did not receive any external funding.

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  • Classification

    QC903

  • Version of record

    v1.0

  • Issue date

    21 June 2024

  • Language

    English

Article Placeholder
Open Access
Research Article
CC-BY-NC 4.0
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