Abstract
The first ever increased contribution to the GHG from human activities was due to emissions from agriculture. Because of the large area covered under agriculture and comprehensive practices, the agriculture sector has a significant impact on the earth’s GHG. Therefore, agriculture emissions being one of the main emissions and GHG being the cause of climate change, it is necessary to determine the pattern of emission and forecast of the future. The aim of this paper is to determine the emission pattern from manure management, agriculture soils, enteric fermentation and rice cultivation, also forecasting the emissions. The study object was India. Different univariate time series models were built and best model was selected on the basis of evaluation criteria for forecasting. The performed modelling was based on the FAOSTAT data of India.
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Deshpande, A., Belsare, T., Sharma, N., De, P. (2022). Univariate Time Series Forecasting of Indian Agriculture Emissions. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_28
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