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Mango on an average account approximately 75 per cent of total production quantity. India is the largest mango producer, accounting for about half of the world-wide mango production. Forecasting of area, production and price fluctuations are the key to provide support in decision making and proper planning for sustainable growth of farmers and other people who are dependent on horticulture. The prices of mango are affected by cultivated area and yield of mango but in other ways pre or post-harvest management also affects it. The problems regarding the price fluctuations arise due to seasonality in arrival and its perishable nature. Therefore, the present study was carried out with time series intervention modelling in forecasting area, productivity and prices of mangoes. In the current investigation, simple exponential smoothing (SES) implemented to develop the forecasting models for area and productivity of mango. Under the SES, the error measurements at different values of alpha (α) for forecasting of area and productivity were observed that the value 0.8 and 0.9 of alpha (α) showed minimum Mean Absolute Percentage Error (MAPE) error i.e. 3.11 per cent, and 12.73 per cent, respectively. The study also developed time series ARIMA models for forecasting the prices of the mango (Keshar and Alphonso) for Valsad markets of Gujarat. It was showed that ARIMA (6, 1, 2) and ARIMA (1, 1, 2) were found good models for forecasting the prices of the Keshar and Alphonso, respectively in Valsad district of Gujarat. [ABSTRACT FROM AUTHOR]

The early shoot borer, top borer, root borer, internode borer are major insect occurs in most of the sugarcane growing areas of the Gujarat and cause extensive damage to the sugarcane crop, which leads to losses in the crop yield. The weather discrepancies acting an important role in development of sugarcane insect and pest. The proper management of cropping practices may leads to overcome on it. Therefore it need to develop weather based approaches for forewarning the insect incidence which helps to farmers takes timely control measures to reduce the damage and yield losses due to this borer complex. Current study, relationship between insects incidence with weekly average weather parameters has been workout by using Karl-Pearson's correlation approach on the 18 years of the data (2000-01 to 2017-18) in the Navsari district. The some of the weather variables were found significantly correlated with insect incidence. The multiple linear regression (MLR) and discriminant function analysis approach were adopted for statistical forewarning of the insect incidence. It was observed that MLR technique found better than discriminant function analysis for forecasting of insect incidence for forewarning of early shoot borer at 90 DAP and top shoot borer incidence at 5th month of crop season respectively. [ABSTRACT FROM AUTHOR]