of India were analyzed by time-series methods. Autocorrelation and partial autocorrelation functions were calculated for the data. The Box Jenkins ARIMA methodology has been used for forecasting. The diagnostic checking has shown that ARIMA (1, 1, 1) is appropriate. The forecasts from 2015-2016 to 2024-2025 were calculated based on the selected model. The forecasting power of autoregressive integrated moving average model was used to forecast tur production for ten leading years. These forecasts would be helpful for the policy makers to foresee ahead of time the future requirements of tur seed, import and/or export and adopt appropriate measures in this regard.

of India were analyzed by time-series methods. Autocorrelation and partial autocorrelation functions were calculated for the data. The Box Jenkins ARIMA methodology has been used for forecasting. The diagnostic checking has shown that ARIMA (1, 1, 1) is appropriate. The forecasts from 2015-2016 to 2019-2020 are calculated based on the selected model. The forecasting power of autoregressive integrated moving average model was used to forecast tobacco production for five leading years. These forecasts would be helpful for the policy makers to foresee ahead of time the future requirements of tobacco production, import and/or export and adopt appropriate measures in this regard.

Fertilizers have contributed significantly to increased agricultural yields, particularly for cereal crops and they will still be an important part of the science-based farming that is needed to feed the world's growing population. Fertilizers replenish the soil nutrients lost by the harvested crops, promote the use of high-yielding cultivars and boost biomass in tropical soils that are deficient in nutrients. In this study, data on fertilizer consumption in India was gathered from Agricultural Statistics at a Glance from 1950-51 to 2020-21 and utilized to fit the ARIMA model and forecast future usage. Forecasting has been done using the Box-Jenkins ARIMA approach. The ARIMA model is the most popular and widely applied forecasting model for time series data. The data was calculated using autocorrelation and partial autocorrelation functions. R programming software was used to estimate model parameters. The performance of the fitted model was evaluated using various goodness of fit criteria, such as AIC, BIC and MAPE. Empirical results revealed that the ARIMA (1,2,1) model was best suited to forecasting India's future total fertilizer use. Similarly, the ARIMA model was fitted for nitrogen, phosphorus, and potassium consumption in India independently. Forecasts from 2021-22 to 2030-31 are calculated using the chosen model. By 2030-31, total fertilizer use is predicted to reach 32,058.55 thousand tonnes. Policymakers should preferably base their judgments on reliable forecasts in order to tighten policies and achieve outcomes. Predicting future events using an appropriate time series model will assist policymakers, marketing strategies in making decisions related to export/ import and developing appropriate fertilizer consumption strategies.

: This paper has examined the market integration of wheat in Madhya Pradesh. Both market arrivals and prices of wheat have depicted increasing trends in almost all the selected markets of Madhya Pradesh. The present study aimed to study price movement of Wheat i.e. seasonal variation, price volatility and co-integration among the major wheat markets in Madhya Pradesh. For study purpose the data related to monthly average prices and arrivals of Wheat were collected from major markets from different markets in States viz., Bhopal, Gwalior, Indore, and Ujjain for the period 2005-2016. Moving average method used to study seasonal variation. The econometric tools like ADF test, Johansen's multiple co-integration test, Granger Causality Test and ARCH-GARCH model were used to arrive at conclusion. The results of study showed that the prices of wheat were higher in the months from March to August in all selected markets. The cyclical variation observed in the prices of Wheat in the selected markets. For all selected markets the prices series are free from the consequences of unit root and were stationary at first difference. The selected markets show long run equilibrium relationship and co-integration between them. Most of the markets showed bidirectional influence on Wheat prices of each other. Bhopal, Gwalior, Indore and Ujjain recorded low price volatility in wheat prices.

:In the present study, autoregressive integrated moving average (ARIMA) methodology has been applied for modeling and forecasting of yearly area and production of rice in India. Rice production data for the period of 1950-1951 to 2014-2015 of India were analyzed by time-series methods. Autocorrelation and partial autocorrelation functions have been estimated, which have led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future area and production.The diagnostic checking has shown that ARIMA (1, 0, 1) and ARIMA (0, 1, 1) is appropriate for rice area and production. The forecasts from 2015-2016 to 2024-2025 were calculated based on the selected model. The forecasting power of autoregressive integrated moving average model was used to forecast rice area and production for ten leading years. This projection is important as it helps to inform good policies with respect to relative production, price structure as well as consumption of rice in the country.