Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models

Abstract

Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA) models and was compared with the forecasted all Indian data. The autoregressive (p) and moving average (q) parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF) and autocorrelation function (ACF) of the different time series. ARIMA (2, 1, 0) model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1) was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC) and Schwarz-Bayesian information criteria (SBC). The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE), which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.

Forecasting Area, Production and Productivity of Vegetable Crops in Nepal using the Box-Jenkins ARIMA Model

“…Although various strategies and plans are made for the betterment of the agriculture sector, a sudden decrease in production lowers the farmers' income, decreases the marketable surplus and ultimately increases in price could be foreseen. Likewise, a boost in agricultural production can lead to a sharp fall in prices and affects the farmers' incomes (Tripathi et al, 2014). An accurate forecast could suggest appropriate surplus and deficit management, stabilize the price and ensure profits for the farmers (Kumar, 2020).…”

“…Furthermore, forecasting could be of immense importance in reinforcing the policy decisions, ensuring food security, managing import/export and implementing price policy (Badmus and Ariyo, 2011;Sharma et al, 2018). Besides, land use allocation, food safety, choosing high yielding varieties, conducting training for improved cultural practices, adequate supply of inputs, adoption of latest technologies, and security and environmental issues could also be addressed by the appropriate forecast (Mahapatra and Dash, 2020;Tripathi et al, 2014). Thus, it is imperative to understand the trend of vegetable production, productivity and area over time, and forecasting these parameters could be of great essence.…”

“…Various literature mentions different statistical and econometric forecasting models that could be suitable for making a forecast on different issues, like agricultural production (Ali et al, 2015). Several techniques like simulation modelling and remote sensing are essentially being used to forecast the productivity and production area (Tripathi et al, 2014). But sometimes, forecasting is felt necessary before crop harvest or in some cases even before the plantation of crops.…”

Method of Holt-Winters and Back Propagation FOR Prediction of Rice Production by Considering Rat Pest Attack

“…That research was using Autoregressive Integrated Moving Average (ARIMA) method. The data used in the study were historical data of planting area, productivity and rice production from 1950 to 2008 for predict the rice production for the next 3 years [8]. In that study, the prediction result was done using only 3 variables as input without considering the variable of pest attack such as rat pest attack which can affect rice production on prediction in the future.…”

Rice Productivity Growth During Nine Years in Badung Regency, Bali Province

“…The area with unstable yield needs more attention and treatment than of the stable area. In previous research, analysis of rice productivity using secondary data from year to year has been done in some areas such as Myanmar, India, Nigeria, Malaysia (Denning et al, 2013;Tripathi et al, 2014;Mundhe, 2015;Adedeji and Owolabi, 2016;Merem et al, 2017;Ismail and Ngadiman, 2017). In general, the purpose of those study is for forecasting, mapping or as an input for policy decision.…”