To investigate the interdependence between Indian onion markets in terms of price volatility, the present study was conducted in four different vital onion markets in India, viz. Mumbai, Nashik, Delhi and Bengaluru. The long term monthly data, from March, 2003 to September, 2015 was collected from the website of agmarknet.nic.in. We have employed the VEC-MGARCH model to estimate mean and volatility spillover simultaneously among the different markets and also examined the nature of dynamic correlation using the DCC model. The presence of mean and volatility spillover was found between the markets. This type of significant interaction between the volatility of different markets is highly useful for cross market hedging and for sharing of common information by market participants. The empirical results also suggest for a very close observation on different market behavioral pattern since, “news” in one market may impact other market through the number of interdependencies. Key words: Dynamic conditional correlations, Market behavior, Price volatility spillover

Study of scenario and weather based prediction of severity of early blight (Alternaria solani Ell. & Mart) on tomato (Solanum lycopersicum L.) for five Indian states, viz. Rajendranagar (Telangana), Bengaluru (Karnataka), Rahuri (Maharashtra), Raipur (Chhattisgarh) and Ludhiana (Punjab) was made using advanced statistical method of support vector regression (SVR) with its accuracy compared with conventional multiple linear regression (MLR) model. Comparisons of early blight severity for mean and maximum severity levels across seasons for each location was carried out using Duncan’s Multiple Range Test (DMRT). Early blight mean and maximum severity levels were in order: Bengaluru (KA) > Rajendranagar (TS) > Rahuri (MH) > Raipur (CG) > Ludhiana (PB). Ludhiana (PB) had nil incidence during 2015 and not greater than 5% of either mean or maximum severity in any season. Both minimum temperature and morning relative humidity of one and two lagged weeks had negative and positive influence respectively, on mean and maximum severity of early blight at Rajendranagar (TS), Bengaluru (KA) and Rahuri (MH), which had higher blight severity over Raipur (CG) and Ludhiana (PB). MLR indicated 22–56% and 21–61% of variability with respect to mean and maximum severity of early blight due to weather factors that varied with locations. SVR predicted early blight severity nearer to actual values over MLR in terms of goodness of fit as well as Root Mean Square Error (RMSE).

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A crop forecast is a statement of the most likely magnitude of yield or production of a crop. It is made on the basis of known facts on a given date and it assumes that the weather conditions and damages during the remainder of the growing season will be about the same as the average of previous year. The present paper deals with use of non-linear regression analysis for developing wheat yield forecast model for Allahabad district (India). A novel statistical approach attempted in this study to use nonlinear models with different weather variables and their indices and compare them to identify a suitable forecasting model. Time series yield data of 40 years (1970-2010) and weather data for the year 1970-71 to 2009-10 have been utilized. The models have been used to forecast yield in the subsequent three years 2008-09 to 2009-10 (which were not included in model development). The approach provided reliable yield forecast about two months before harvest.

A study was carried out to forecast the yield of the wheat crop for five districts of Uttar Pradesh namely Lucknow, Kanpur, Banda, Jhansi and Faizabad. The daily weather data on variables such as maximum temperature, rainfall, minimum temperature, and relative humidity were arranged week wise from sowing to harvesting and the relations between the weather variables and yield was worked out using statistical tools like correlation and regression. The yield has been detrended by obtaining the parameter estimates of the model and subsequently the detrended yield was used to forecast the yield of the crop using ARIMA model. The proposed method of obtaining pre-harvest forecasting of yield of crops was compared with the traditional approaches of forecasting and the proposed method was evaluated in terms of criteria's such as goodness of fit of the model. It was observed that in all the districts the proposed model performed better as compared to the traditional method both in terms of goodness of fit as well as forecasting performance. Thus it can be concluded that the proposed approach is better and more suitable as compared to the traditional approach for forecasting the wheat yield in the five districts of Uttar Pradesh.