Performance evaluation of yield crop forecasting models using weather index regression analysis

SANJEEV PANWAR; K N SINGH; ANIL KUMAR; RANJIT KUMAR PAUL; SUSHEEL KUMAR SARKAR; BISHAL GURUNG; ABHISHEK RATHORE

doi:10.56093/ijas.v87i2.67673

Authors

SANJEEV PANWAR Indian Council of Agricultural Research, Krishi Bhawan, New Delhi 110 012
K N SINGH Indian Council of Agricultural Research, Krishi Bhawan, New Delhi 110 012
ANIL KUMAR Indian Council of Agricultural Research, Krishi Bhawan, New Delhi 110 012
RANJIT KUMAR PAUL Indian Council of Agricultural Research, Krishi Bhawan, New Delhi 110 012
SUSHEEL KUMAR SARKAR Indian Council of Agricultural Research, Krishi Bhawan, New Delhi 110 012
BISHAL GURUNG Indian Council of Agricultural Research, Krishi Bhawan, New Delhi 110 012
ABHISHEK RATHORE Indian Council of Agricultural Research, Krishi Bhawan, New Delhi 110 012

https://doi.org/10.56093/ijas.v87i2.67673

Keywords:

Forecasting, Nonlinear regression model, Weather indices approach, Weather variables

Abstract

A crop forecast is a statement of the most likely magnitude of yield or production of a crop. It is made on the basis of known facts on a given date and it assumes that the weather conditions and damages during the remainder of the growing season will be about the same as the average of previous year. The present paper deals with use of non-linear regression analysis for developing wheat yield forecast model for Allahabad district (India). A novel statistical approach attempted in this study to use nonlinear models with different weather variables and their indices and compare them to identify a suitable forecasting model. Time series yield data of 40 years (1970-2010) and weather data for the year 1970-71 to 2009-10 have been utilized. The models have been used to forecast yield in the subsequent three years 2008-09 to 2009-10 (which were not included in model development). The approach provided reliable yield forecast about two months before harvest.

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References

Agrawal Ranjana, Jain R C, Jha M P and Singh D. 1980. Forecasting of wheat yield using climatic variables. Ind. J. Agric. Sci., 50(9) pp 680–4.

Bates D M and Watts D G. 1988. Nonlinear regression analysis and its App. Met., 12(6), pp 937–47. DOI: https://doi.org/10.1002/9780470316757

Baier W. 1977. Crop weather models and their use in yield assessments. Tech. note no. 151, WMO, Geneva, 48 pp.

Carter M M and Eisner J B. 1997. A Statistical Method for Forecasting Rainfall over Puerto Rico. Department of Meteorology, The Florida State University, U.S.A. DOI: https://doi.org/10.1175/1520-0434(1997)012<0515:ASMFFR>2.0.CO;2

Frere M and Popov G F. 1979. Agrometeorological crop monitoring and forecasting. FAO plant production and protection paper, Plant Production and Protection Division, FAO.

Panwar Sanjeev, Kumar Anil, Singh K N, Farooqi Md Samir, Rathore Abhishek. 2012. Using Nonlinear Regression Techniques for analysis of Onion Production in India. Indian Journal of Agriculture Science Vol 82 (12) pp 43–6, 2012.

Seber G A F and Wild C J. 1989. Non-linear regression. John Wiley and Sons, New York. DOI: https://doi.org/10.1002/0471725315

Panwar Sanjeev, Sharma S C and Kumar Anil. 2009. A critical study of Onion (Allium cepa) export of India: Non linear Approach” Indian Journal of Agriculture Science. Vol 78, no.2, pp 178–80.