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Climate variability and its impacts on agriculture production and future prediction using autoregressive integrated moving average method (ARIMA)
Author(s) -
Praveen Bushra,
Sharma Pritee
Publication year - 2020
Publication title -
journal of public affairs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.221
H-Index - 20
eISSN - 1479-1854
pISSN - 1472-3891
DOI - 10.1002/pa.2016
Subject(s) - autoregressive integrated moving average , agriculture , food security , environmental science , climate change , productivity , agricultural productivity , production (economics) , food prices , agricultural economics , geography , economics , mathematics , time series , statistics , ecology , biology , macroeconomics , archaeology
This study assesses the impacts of climate variation on land productivity for major Indian food and non‐food grain crops. We collected data for 50 years from (1967–2016) with 15 crops across India. To estimate the variation of agriculture production for each crop by different variables, for instance, rainfall and temperature estimation and future prediction for 20 years, that is, until 2036. Our results specify that land productivity drops with a rise in annual mean temperature in most of the crops. The adverse impact of climate variation on agricultural production recommends food security risk to minor and marginal agricultural families, badly affected by climatic variations. Results show that a rise in temperature would reduce agricultural productivity and assessed sensitivity of Indian agriculture to climate change. We did forecasting using the autoregressive integrated moving average model for 20 years. It shows that as temperature and rainfall upturns in the future, production of some crops, such as gram, sesamum (til), jowar, groundnut, sugarcane, and bajra, will also increase. Some crops are climate sensitive, such as arhar, wheat, rice, cotton, and tea. As temperature increases, the production of these crops slightly increase or decrease.