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


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Authors

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

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 15th week of the crop period or one and a half months before harvest was found reliable.

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References

Agnolucci P and De L V. 2020. Long-run trend in agricultural yield and climatic factors in Europe. Climatic Change 159(3): 385–405.

Agarwal R, Jain R C and Jha M P. 1983. Joint effects of weather variables on rice yields. Mausam. 34(2): 177–181.

Agarwal R, Jain R C and Jha M P. 1986. Models for studying rice crop weather relationship. Mausam. 37(1): 67–70.

Agrawal R, Jain R C and Singh D. 1980. Forecasting of rice yield using climatic variables. The Indian Journal of Agricultural Sciences 50(9): 680–84.

Cao J, Wang H, Li J, Tian Q and Niyogi D. 2022. Improving the forecasting of winter wheat yields in northern China with machine learning dynamical hybrid subseasonal-to-seasonal ensemble prediction. Remote Sensing 14(7): 170–77.

George N A and McKay F H. 2019. The public distribution system and food security in India. International Journal of Environmental Research and Public Health 16: 3221.

Jain R C, Agrawal R and Jha M P. 1980. Effect of climatic variables on rice yield and its forecast. Mausam 31(4): 591–96.

Pham H T, Awange J, Kuhn M, Nguyen B V and Bui L K. 2022. Enhancing crop yield prediction utilizing machine learning on satellite-based vegetation health indices. Sensors 22(3): 719.

Shammi S A and Meng Q. 2021. Modeling the impact of climate changes on crop yield: Irrigated vs. non-irrigated zones in

Mississippi. Remote Sensing 13(12): 22–49.

Sisodia B V S, Yadav R R, Kumar S and Sharma M K. 2014. Forecasting of pre-harvest crop using discriminant function analysis of meteorological parameter. Journal of Agrometeorology 16(1): 121–25.

Yadav R R, Sisodia B V S and Kumar S. 2014. Application of principal component analysis in developing statistical models to forecast crop yield using weather variables. Mausam 65(3): 357–60.

Submitted

2024-10-21

Published

2025-05-26

Issue

Section

Articles

How to Cite

VISHWAKARMA, A. K. ., RAMAN, N. M. ., KUMAR, A. ., CHETNA, KUMAR, V. ., SAGWAL, A. ., GHALAWAT, S. ., ROHILLA, K. ., SUSHMA, XALXO, R. P. ., & SAXENA, S. . (2025). Forecasting pre-harvest rice (Oryza sativa) yield: A regression analysis of meteorological factors and climate change impacts on food security. The Indian Journal of Agricultural Sciences, 95(5), 541–546. https://doi.org/10.56093/ijas.v95i4.158592
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