Forecasting of prices of commodities, especially those of agricultural commodities, is very difficult because they are not only governed by demand and supply but also by so many other factors which are beyond control, such as weather vagaries, storage capacity, transportation, etc. In this paper time series models namely ARIMA (Autoregressive Integrated Moving Average) methodology given by Box and Jenkins has been used for forecasting prices of Groundnut oil in Mumbai. This approach has been compared with ANN (Artificial Neural Network) methodology. The results showed that ANN performed better than the ARIMA models in forecasting the prices.
Investigations on atmospheric deposition (AD) and water chemistry along a 35 km stretch of Ganga River indicated that although N:P stoichiometry of AD did not change, there were over 1.4-2.0 fold increase in AD-NO₃⁻, AD-NH₄⁺ and AD-PO₄³⁻ overtime. Concentration of dissolved inorganic-N (DIN) in river showed significant positive correlations with AD-NO₃⁻ and runoff DIN. Similarly, dissolved reactive-P (DRP) in river showed significant positive correlation with AD-PO₄³⁻ and runoff DRP. The study shows that AD has become an important source of N and P input to Ganga River.
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