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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2017)
[Effective from January 1, 2017]
For more details click here

ICV 2017: 100.00
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00
For more details click here
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2018.7(11): 2687-2696
DOI: https://doi.org/10.20546/ijcmas.2018.711.307


Time Series Models for Forecasting the Impact of Climate Change on Wheat Production in Varanasi District
Manvendra Singh1, G.C. Mishra1 and R.K. Mall2
1Department of Farm Engineering, Institute of Agricultural Sciences, BHU, Varanasi – 221005, India
2DST-Mahamana Centre of Excellence for Climate Change Research, Institute of Environment and Sustainable Development, BHU, Varanasi – 221005, India
*Corresponding author
Abstract:

Weather is the major threats for wheat production in South East Asia region and highly influenced by the environmental conditions, sowing date, nature of genotypes and growth stages of wheat. Climate change is a serious concern for the food security and lively hood of small farmers as reported from all over world. A period of 30 years is decided as a period of climate change study by World Meteorological Organisation (WMO).So the period from 1985-2016 is taken for the study. Weather forecasting is very important for decision making processes in management practices applying for controlling the damage caused by climate change. The objective of present study was to develop Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average (ARIMA) model, Autoregressive integrated moving average with exogenous variable (ARIMAX) model, Artificial Neural Network (ANN) models for forecasting climate change impact for Varanasi region of India. For development of models, weather indices were computed from weekly data related to maximum temperature, minimum temperature, Rainfall and Solar radiation.


Keywords: Climate change, Autoregressive Integrated Moving Average with exogenous variable (ARIMAX), Autoregressive Integrated Moving Average (ARIMA) model, Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Wheat, Root mean squared error (RMSE), We
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How to cite this article:

Manvendra Singh, G.C. Mishra and Mall, R.K. 2018. Time Series Models for Forecasting the Impact of Climate Change on Wheat Production in Varanasi District.Int.J.Curr.Microbiol.App.Sci. 7(11): 2687-2696. doi: https://doi.org/10.20546/ijcmas.2018.711.307