Crop forecasting is a formidable challenge for every nation. The Government of India has developed a number of forecasting systems. The national and state governments need such pre-harvest forecasts for various policy decisions on storage, distribution, pricing, marketing, import-export and many more. In this paper, univariate forecasting models such as random walk, random walk with drift, moving average, simple exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) models are considered and analyzed for their efficiency for forecasting vegetable production in the Haryana state. The State annual data on vegetable production were divided into the training data set from 1966-67 to 2013-14 and the test data set from 2014-15 to 2018-19. Suitable models were selected on the basis of error analysis on the training data and a percent error deviation test on the test data. Model diagnostic checking was carried out on ACF and PACF in residual terms through runs above and below the median, runs up and down and Ljung-Box tests. It is inferred that ARIMA (2,1,1) was found to be optimal and that the forecast values for the years 2019-20 to 2023-24 were estimated on the basis of this model, which were 7.82,8.23,8.72,9.2 and 9.72 million tonnes for the year 2019-20 to 2023-24, respectively. The significance of the mode is that we can forecast the values using this best fit model and forecast values are very important for the policymakers and other government agencies for proper policy decision regarding food security.
ARIMA, Autocorrelations function, Forecasting models, Time series, Vegetable production, Ljung-Box tests
Habbati B., Ramdani Y.& Moulay F. (2014) A detailed modeling of photovoltaic module using MATLAB , NRIAG Journal of Astronomy and Geophysics(3)53-61 http://dx.doi.org/10.1016/j.nrjag.2014.04.001
Kyocera (2009). KC200GT High Efficiency Multi crystal Photovoltaic Module Datasheet. https://www.energy matters.com.au /images/kyocera/KC200GT.pdf
Marcelo G.M. (2016). Modelling and Control of Grid-connected Solar Photovoltaic Systems, Renewable Energy - Utilisation and System Integration, Wenping Cao and Yihua Hu, Intech Open, http://dx.doi.org/10.5772/62578
Motan N., Abu-Khaizaran M. & Quraan M.(2018). Photovoltaic array modelling and boost-converter controller-design for a 6kW grid-connected photovoltaic system - DC stage. IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe. 1-6, doi: 10.1109/EEEIC.20 18.8494003.
Nguyen, X.H. & Nguyen, M.P. (2015). Mathematical modeling of photovoltaic cell/module/arrays with tags in Matlab/Simulink. Environ Syst Res 4(24) https://doi.org/10.1186/s40068-015-0047-9
Natarajan P. & Ranganath M. (2011). Development of power electronic circuit oriented model of photovoltaic module International Journal of Advanced Engineering Technology 2(4) 118-127.
Tsai, Huan-Liang, Ci-Siang, Tu & Yi-Jie, Su. (2008). Development of generalized photovoltaic model using MATLAB/SIMULINK. Lecture Notes in Engineering and Computer Science. 2173.
Tummuru N. R., Mishra M. K. & Srinivas S.(2015).
Dynamic energy management of renewable grid integrated hybrid energy storage system. IEEE Transactions on industrial Electronics, (62)12, 7728-7737. doi: 10.1109/TIE.2015.2455063
Umanand L. (2007). Photovoltaic System Design. CEDT-Indian Institute of Science, Banglore, NPTEL course material
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) © Author (s)