A Study on Impact of Climate Change on Wheat Production   in Kurukshetra District of Haryana

Chetna; Pushpa; Aditi; Urmil Verma

doi:10.56093/jisas.v77i02.171444

Authors

Chetna Chaudhary Charan Singh Haryana Agricultural University, Hisar
Pushpa Chaudhary Charan Singh Haryana Agricultural University, Hisar
Aditi Chaudhary Charan Singh Haryana Agricultural University, Hisar
Urmil Verma Chaudhary Charan Singh Haryana Agricultural University, Hisar

https://doi.org/10.56093/jisas.v77i02.171444

Keywords:

Weather variables; Weather indices; Climate change and Regression model.

Abstract

The present paper attempts to study the effect of Wheat production in the Kurukshetra area of Haryana, India, is affected by changes in meteorological conditions. The study examined 35 years of time series data on wheat yield as well as weekly data on five weather variables for the crop season from 1985-86 to 2019-20. Using weather indices and time trend as regressor variables and wheat yield as regress and the effect of various factors was investigated using step-wise regression analysis. It has been found that weighted weather indices of each weather variable including time trend have exhibited significant effect on the wheat yield. It has also been found that rise in all five weather variables except relative humidity has been detrimental to wheat yield during harvesting phase of the crop. The overall results indicate the fact that changes in climatic variables show detrimental as well as beneficial the role depending upon the phases of crop production in getting out its final output. On the basis of root mean square error the Model-P5 has been proven to be best among all the models the average percent standard error (PSE) value of the Model-P5 is 0.94 which shows that these models are better for forecast. Principal component techniques are best created model.

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References

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

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

Azfar, M., Sisodia, B.V.S., Rai, V.N. and Kumar, S. (2014). A Study on Impact of Changes in Climatic variables on Rice Production in Faizabad district of Uttar Pradesh. Mausam, 66(2), 281-286.

Jain, R.C., Agrawal, Ranjana and Jha, M.P. (1980). Effect of climatic variables on rice yield and its forecast. Mausam, 31(4), 591-596.

Mall, R.K., Singh, R., Gupta, A., Srinivasan, G. and Rathore, L.S. (2006). Impact of climate change on Indian agriculture: A Review. Climatic Change, 78, 445-478.

Muema, F., Home, P. and Raude, J. (2018). Application of benchmarking and principal component analysis in measuring performance of public irrigation schemes in Kenya. Journal of Agriculture, 8(10), 162.

Rathore, L.S., Singh, K.K. and Saseendran, S.A. (2001). Modelling the impact of climate change on rice production in India. Mausam, 52(1), 263-274.

Saseendran, S.A., Singh, K.K., Rathore, L.S., Singh, S.V. and Sinha, S.K. (1999). Effect of climate change on rice production in the tropical humid climate of Kerala, India. Climatic Change, 1-20.

Sisodia, B.V.S. and Kumar, S. (2015). Pre-harvest forecast models for wheat yield based on biometrical characters. Economic Affairs, 9, 89-93.

Verma, U., Aneja, D.R. and Hooda, B.K. (2015). Principal component technique for pre-harvest estimation of cotton yield based on plant biometrical characters. Journal of Cotton Research and Development, 29(2), 339-343.

Verma, U., Piepho, H.P., Ogutu, J.O., Kalubarme, M.H. and Goyal, M. (2014). Development of agromet models for district-level cotton yield forecasts in Haryana State. International Journal of Agricultural and Statistical Sciences, 10(1), 59-65.