Forecasting pre-harvest rice (Oryza sativa) yield: A regression analysis of meteorological factors and climate change impacts on food security

ASHUTOSH KUMAR  VISHWAKARMA; NAGALAXMI M  RAMAN; AJAY  KUMAR; CHETNA; VINAY  KUMAR; ARADHNA  SAGWAL; SUMAN  GHALAWAT; KAPIL  ROHILLA; SUSHMA; RAVI PRAKASH  XALXO; SHRISHTI  SAXENA

doi:10.56093/ijas.v95i4.158592

Authors

ASHUTOSH KUMAR VISHWAKARMA ICAR-National Bureau of Plant Genetics and Resources, New Delhi 110 012, India
NAGALAXMI M RAMAN Amity University, Noida, Uttar Pradesh
AJAY KUMAR Krishi Vigyan Kendra (Chaudhary Charan Singh Haryana Agricultural University), Jhajjar, Haryana
CHETNA Chaudhary Charan Singh Haryana Agricultural University, Hisar, Haryana
VINAY KUMAR Chaudhary Charan Singh Haryana Agricultural University, Hisar, Haryana
ARADHNA SAGWAL Chaudhary Charan Singh Haryana Agricultural University, Hisar, Haryana
SUMAN GHALAWAT Chaudhary Charan Singh Haryana Agricultural University, Hisar, Haryana
KAPIL ROHILLA Haryana Space Applications Centre, Hisar, Haryana
SUSHMA Chaudhary Charan Singh Haryana Agricultural University, Hisar, Haryana
RAVI PRAKASH XALXO Haryana Space Applications Centre, Hisar, Haryana
SHRISHTI SAXENA Forest Survey of India, Dehradun, Uttrakhand

https://doi.org/10.56093/ijas.v95i4.158592

Keywords:

Crop production, Pre-harvest forecast, Statistical model, Weather indices

Abstract

In order to arrive at the findings, different statistical models have been developed as a result to examine how climate change may affect rice yield at various phases of the crop as well as it has been attempted to forecast its output for Karnal district. Time series data on rice yield for the past 37 years on crop and weather variables have been used in the Karnal district of Haryana from 1985–1986 through 2021–22. The relationship between rice (Oryza sativa L.) crop and various models was investigated. A boost in yield can be obtained by creating fresh weather indices from weekly data. The model takes various weather variables into account. It was discovered that the best models (models 1, 2, and 7, 8) for assessing the impact of specific weather variables were linear functions across weekly data, meteorological factors, and adjusted crop production for the trend impact are the independent variables. A forecast model was also built and the findings revealed that forecasting at the 15^th week of the crop period or one and a half months before harvest was found reliable.

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