Main Article Content
In the recent year, pre harvest crop yield forecasting has been a topic of interest for producers, policy makers, government and agricultural related organizations. Pre harvest crop forecasting is important for national food security. Construction of appropriate yield forecast promotes the output of scenario analyses of crop production at a farm level, which enables suitable tactical and strategic decision making by the farmer. Indeed, considerable benefits apply when seasonal forecasting of crop performance is applied across the whole value chain in crop production. Timely and accurate yield forecast is essential for crop production, marketing, storage and transportation decisions as well as for managing the risk associated with these activities. In present manuscript efforts were made for development of pre harvest forecast models by using different statistical approaches viz. multiple linear regression (MLR), discriminant function analysis and ordinal logistic regression. The study utilized the crop yield data and corresponding weekly weather data of last 30 years (1985-2014). The model development was carried out at 35th and 36th SMW (Standard Meteorological Week) for getting forecast well in advance of actual harvesting of the field crop. The study revealed that method of discriminant function analysis gave best pre harvest forecast as compare to remaining developed models. It was observed high value of Adj. R2= 0.94, low value of RMSE= 164.24 and MAPE= 5.30. The model can be used in different crop for reliable and dependable forecast and these forecasts have significant value in agricultural planning and policy making.
Fisher RA. The influence of rainfall on yield of wheat at Rothamsted. Philosophical Transaction of Royal Society of London, Series B, 1924;213:89-142.
Hendricks WA, Scholl JC. Technique in measuring joint relationship. The joint effect of temperature and precipitation on corn yield. N. C. Staff Agricultural Experimental Techniques Bulletin. 1943;74-78.
Agrawal R, Jain RC, Jha MP. Modes for studying rice-weather relationship. Mausam. 1980;37(1):67-70.
Agrawal R, Jain RC, Mehta SC. Yield forecast based on weather variables and agricultural inputs on agro-climatic zone basis. Ind. J. Agri. Sci., 2001;71(7):487-490.
Agrawal R, Chandrahas, Kaustav A. Use of discriminant function analysis for forecasting crop yield. Mausam. 2011;63(3):455-458.
Patel GB, Vaishnav PR, Patel JS, Dixit SK. Pre-harvest forecasting of rice (Oryza Sativa L.) yield based on weather variables and technological trend. Journal of Agrometeorology. 2007;9(2):167-173.
Chauhan VS, Shekh AM, Dixit SK, Mishra AP, Kumar S. Yield prediction model of rice in Bulsar district of Gujarat. Journal of Agrometeorology, 2009;11(2):162- 168.
Garde YA, Shukla AK, Singh S. Pre-harvest forecasting of rice yield using weather indices in Pantnagar. International Journal of Agricultural Statistical Science. 2012;8(1):233-241.
Mahdi SS, Lotus S, Singh G, Ahmad L, Singh KN, Dar LA. et al. “Forecast of rice (Oryza sativa L.) yield based on climatic parameters in Srinagar district of Kashmir Valley. Journal of Agrometeorology. 2013;15(1):89-90.
Singh RS, Patel C, Yadav MK, Singh KK. Yield forecasting of rice and wheat crops for eastern Uttar Pradesh. Journal of Agrometeorology. 2014;16(2):199-202.
Pandey KK, Rai VN, Sisodia BVS, Singh SK. Effect of Weather Variables on Rice Crop in Eastern Uttar Pradesh, India. Plant Archives. 2015;15(1):575-579.
Yadav RR, Sisodia BVS, Kumar S. Pre-harvest forecast of pigeon-pea yield using discriminant function analysis of weather variables. Mausam, 2016:67(3): 577-582.
Banakara KB, Pandya HR, Garde Y. A. Pre-harvest forecast of kharif rice yield using PCA and MLR technique in Navsari district of Gujarat. Journal of Agrometeorology. 2019;21(3):336-343.
Varmola SL, Dixit SK, Patel JS, Bhatt HM. Forecasting of wheat yield on the basis of weather variables. Journal of Agrometeorology. 2004;6(2):223-228
Agrawal R, Chandrahas, Aditya K. Use of discriminant function analysis for forecasting crop yield. Mausam, 2012;63(3):455-458.
Sisodia BVS, Yadav RR, Kumar S, Sharma MK. Forecasting of pre-harvest crop yield using discriminant function analysis of meteorological parameters. Journal of Agrometeorology. 2014;16(1):121-125.
Garde YA, Dhekale BS, Singh S. Different approaches on pre harvest forecasting of wheat yield. Journal of Applied and Natural Science. 2015;7 (2):839-843.
Diwan UK, Puranik HV, Das GK, Chaudhary JL. Yield prediction of wheat at pre-harvest stage using regression based statistical model for 8 district of Chhattisgarh, India. International Journal of current Microbiology and Applied Sciences, 2018:7(1):2180-2183.
Kumar R, Gupta BRD, Athiyaman B, Singh KK, Shukla RK. Stepwise regression technique to predict Pigeon pea yield in Varanasi district, Journal of Agrometeorology. 1999;1(2):183-186.
Sarika Iquebal M, Chattopadhyay A. Modelling and forecasting of pigeonpea (Cajanus cajan) production using autoregressive integrated moving average methodology. Indian Journal of Agricultural Sciences. 2011;81(6):520-523.
Priya SRK, Suresh KK. A study on pre-harvest forecast of sugarcane yield using climatic variables. Statistics and Applications. 2009;7&8(1&2):1-8.
Dhekale BS, Mahdi S, Sawant PK. Forecast models for groundnut using meteorological variables in Kolhapur, Maharashtra, Journal of Agrometeorology, 2014;16(2):238-239.
Goyal M. Use of different multivariate techniques for pre-harvest wheat yield estimation in Hisar (Haryana). International Journal of Computer & Mathematical Sciences, 2016;5(12):6-11.
Verma, Sudesh, P. and Verma, Urmil. Using logistic regression to predict wheat yield in western zone of Haryana. International Journal of Computer & Mathematical Sciences, 2016;5(12):26-32.