Random walk and ARIMAX modeling for cotton yield in western zone of Haryana
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Date
2018
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CCSHAU
Abstract
Crop yield models are abstract presentation of interaction of the crop with its environment and can range from simple
correlation of yield with a finite number of variables to the complex statistical models with predictive end. The pre-harvest
forecasts are useful to farmers to decide in advance their future prospects and course of action. The study has been categorized
into three parts i.e. the fitting of Random Walk, ARIMA and ARIMAX models for cotton yield forecasting in Hisar, Fatehabad,
Sirsa and Bhiwani districts of Haryana. The Random Walk and ARIMA models have been fitted using the time-series cotton
yield data for the period 1980-81 to 2010-11 of Hisar and Sirsa districts and 1997-98 to 2010-11 of Fatehabad district. The
fortnightly weather data have been utilized as input series from 1980-81 to 2016-17 for fitting/testing the Random walk/ARIMA
with weather input i.e. ARIMAX models. Models have been validated using the data on subsequent years i.e. 2011-12 to 2016-
17, not included in the development of the models.The multiple linear regression models with crop condition term as dummy
regressor were fitted for Bhiwani district as the cotton yield data being stationary in nature and showing non-significant
autocorrelations was not suitable for ARIMA modeling. Though, the MA models were tried but the yield forecasts were beyond
acceptable limits.
Random Walk i.e. I(1) and ARIMA(0,1,1) for Hisar, Fatehabad and Sirsa districts have been fitted for pre-harvest
cotton yield forecasting. Alternatively, the Random Walk models with exogenous input were tried by utilizing the fortnightly
weather variables (viz., TMIN1, RF11, SSH3 and SSH4 over the crop growth period). Lastly, the ARIMA models with
alternative combinations of weather variables were tried for fitting the ARIMAX models. Following the steps required in SPSS;
ARIMA(2,1,0) for Hisar and Fatehabad and ARIMA(0,1,1) for Sirsa districts along with fortnightly weather variables (viz.,
TMAX5, RF7, SSH4 and RH4 over the crop growth period) as input were finalized as ARIMAX models for district-level cotton
yield forecasting. The predictive performance(s) of the contending models i.e. Random Walk, ARIMA and
ARIMAX models were observed in terms of the percent deviations of cotton yield forecasts in relation to the
observed yield(s) and root mean square error(s) as well. The level of accuracy achieved by ARIMA model(s) with
weather input was considered adequate for estimating the cotton yield(s) i.e. the ARIMAX models consistently
showed the superiority over Random Walk and ARIMA models in capturing the percent relative deviations
pertaining to cotton yield forecasts. The ARIMAX models performed well with lower error metrics as compared
to the Random Walk and ARIMA models in all time regimes.
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