Principle Component Analysis for Forecasting of Pre-harvest Rapeseed and Mustard Yield Based on Meteorological Parameters

Sarvesh Kumar *

Department of Agricultural Statistics, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, U.P., India.

K.K. Mourya

Crop Research Station, Masodha, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, U.P., India.

R.P. Gupta

Department of Agricultural Statistics, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, U.P., India.

B.V.S. Sisodia

Department of Agricultural Statistics, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, U.P., India.

*Author to whom correspondence should be addressed.


Abstract

The pre-harvest forecast model was developed using time series data on Rapeseed and Mustard yields as well as weekly data on six weather variables from the 40th SMW of one year to the 8th SMW of the next year, which covers the years 1990–1991 to 2014–2015. Multiple regression and principal component analysis are two statistical techniques that have been reported for creating pre-harvest forecast models. The most accurate model found by applying step-by-step regression analysis to weekly weather data was based on adj R2, RMSE, and%SE. One and a half months prior to harvest, these models can be utilised to obtain a trustworthy forecast of the yield of Rapeseed and Mustard.

Keywords: Meteorological parameters, crop yield, multiple regression principle component function analysis, forecast model


How to Cite

Kumar, S., Mourya, K., Gupta, R., & Sisodia, B. (2024). Principle Component Analysis for Forecasting of Pre-harvest Rapeseed and Mustard Yield Based on Meteorological Parameters. Journal of Experimental Agriculture International, 46(6), 478–483. https://doi.org/10.9734/jeai/2024/v46i62499

Downloads

Download data is not yet available.

References

Agrawal,Ranjana, Jain RC, Jha MP. Models for studying rice crop-weather relationship. Mausam.0 1986;37(1):67-70.

AzfarMohd, Sisodia BVS, RaiVN, DeviMonoka. Pre-harvest forecast models for rapeseed & mustard yield using principal component analysis of weather variables Journal ofAgrometeorology. 2015;66(4).

Fisher RA. The influences of rainfall on the yield of wheat at Rothamsted. Philosophical Transaction of Royal Society of London, Series B. 1924;213:89- 142.

WA, Scholl GC. Technique in measuring joint relationship: The joint effects of temperature and precipitation on crop yield. N. Carolina Agric. Exp. Stat.; 1943.

Jain RC, Agrawal Ranjana, Jha MP. Effect of climatic variables on rice yield and its forecast. Mausam. 1980;31(4):591-96.

Jain RC, Sridharan H, Agrawal Ranjana. Principal component technique for forecasting of sorghum yield. Indian Journal of Agril. Sci.. 1984;LI(1): 61-72.

Jain RC, JhaMP, Agrawal, Ranjana. Use of growth indices in yield forecast. Biometrical Journal. 1985;27(4):435-439.

Pandey KK, Rai VN, Sisodia BVS, Bharti AK, Gairola KC. Pre -Harvest Forecast Models Based On Weather Variable And Weather Indices For Eastern U.P.Adv. Biores.. 2013;4(2):118- 122.

SisodiaBVS, Yadav, RR, Kumar, S, Sharma MK. Forecasting of Pre- harvest crop yield using discriminant function analysis of meteorological parameter. Journal ofAgrometeorology. 2014;16(1):121- 125.

Kumar S, Rai VN, Mourya KK, Annu, Gupta RP. Pre-harvest forecast model using linear regression model based on weather indices International Journal of Chemical Studies. 2019; 7(6): 2960-2962.

Kumar S, Rai VN, Azfar Mo, Annu, Gupta RP.Forecasting of pre-harvest rapeseed and mustard yield using discriminant function analysis of meteorological parameters. International Journal of Chemical Studies.2019;7(3):1897-1900.

Yadav RR, Sisodia, BVS, Kumar S. Application of principal component analysis in developing statistical models to forecast crop yield using weather variables. Mausam; 2014.