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Not AvailableOilseeds are an indispensable crop in India since it contributes substantially to agriculture in terms of farm income, employment and export earnings. It has been observed that there are fluctuations in the area under various oilseed crops in India. It was seen that two oilseed crops namely castor and sunflower had a high retention capability and rapeseed and mustard had a relatively stable retention capability in all the analyzed states. The low retention probability for few oilseeds may be due to tough competition presented and greater market diffusion efforts made by other oilseeds. Further, it was concluded that India should not have high reliance on one oilseed so as to evade trade risks in the long run. Therefore, suitable policies need to be envisioned to diversify the geographical concentration of oilseed production and minimize market risks.Not Availabl

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Not AvailableForecasting of volatile data is generally carried out using Generalized autoregressive conditional heteroscedastic (GARCH) model.However, there are some limitations of this methodology, such as its inability to capture empirical properties observed in time-series data. Further, the GARCH assumption that volatility is driven by past observable variables only sometimes becomes a constraint. Accordingly, in this paper, a promising methodology of Stochasticvolatility (SV) model, in which the time-varying variance is not restricted to follow a deterministic process, is considered.The estimation of parameters of this model is carried out using a powerful technique of Kalman filter (KF) in conjunction with Quasi-maximum likelihood (QML) method.As an illustration, volatile dataset of Month-wise total exports of fruits and vegetables seeds from India during the period April 2004 to January 2012 are considered. It is concluded that SV model performs quite well for modelling as well as forecasting of the volatile data under consideration.Not Availabl

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The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304- 4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304- 4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.Not Availabl

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Not AvailableWe propose a parametric nonlinear time-series model, namely the Autoregressive-Stochastic volatility with threshold (AR-SVT) model with mean equation for forecasting level and volatility. Methodology for estimation of parameters of this model is developed by first obtaining recursive Kalman filter time-update equation and then employing the unrestricted quasi-maximum likelihood method. Furthermore, optimal one-step and two-step-ahead out-of-sample forecasts formulae along with forecast error variances are derived analytically by recursive use of conditional expectation and variance. As an illustration, volatile all-India monthly spices export during the period January 2006 to January 2012 is considered. Entire data analysis is carried out using EViews and matrix laboratory (MATLAB) software packages. The ARSVT model is fitted and interval forecasts for 10 hold-out data points are obtained. Superiority of this model for describing and forecasting over other competing models for volatility, namely AR-Generalized autoregressive conditional heteroscedastic, AR-Exponential GARCH, AR-Threshold GARCH, and ARStochastic volatility models is shown for the data under consideration. Finally, for the AR-SVT model, optimal out-of-sample forecasts along with forecasts of one-step-ahead variances are obtained.Not Availabl

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Not AvailableTomato (Solanum lycopersicum L.) is commercially important crop grown worldwide in wide range of climatic conditions in field or under protected condition and it is highly accepted as fresh salad, cooked and processed food (Peralta et al., 2006). Among the different biotic stresses reported on this crop, Tomato leaf curl virus (ToLCV), a gemini virus is the most important and destructive viral pathogen in many parts of India (Saikia and Muniyappa, 1989; Harrison et al., 1991) including West Bengal. The management of leaf curl disease in tomatoes relies mainly on the application of insecticides at frequent intervals. Excessive use of insecticides not only cause environmental hazards but also triggers the development of resistance in the insect pest and also enhances the production cost of the crop. A reduction in the number of pesticide applications could be achieved by applying pesticides only at times when weather conditions are favourable for disease development. The cause and effect relationship are generally studied using simple regression model or nonlinear regression models. But in situations where data are not in ratio scale, then regression may not be appropriate as it violates the assumption of normality and constant variance. A very well used recommendation is to apply data transformation and proceed with ordinary linear regression but there may be problem in interpretability. In such situations, beta regression model may be employed as it takes into consideration all the properties of the data (Maier, 2014; Cribari‐Neto and Zeileis, 2010; Hijazi and Jernigan, 2009; Ferrari and Cribari‐Neto, 2004; Kieschnick and McCullough, 2003). In Beta regression, it is assumed that the dependent variable is distributed with beta function and the mean of the beta distribution is dependent on a set of independent variables by means of linear predictor with a link function and unknown parameters. A dispersion parameter is also included in the functionNot Availabl

People’s perception of climate change impacts and their adaptation practices in Khotokha Valley, Wangdue, Bhutan

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Not AvailableAs agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd forecast values and these forecast values are not reliable. In such cases, models need to be improved. To improve the models, we have incorporated prior knowledge through the Bayesian technique and investigate the superiority of these models under the Bayesian framework. The Bayesian technique is one of the most powerful methodologies in the modern era of statistics. We have discussed different types of prior (informative, non-informative and conjugate priors). The MCMC methodology has been briefly discussed for the estimation of parameters under Bayesian framework. To illustrate these models, production data of banana, mango and wheat yield data are taken under consideration. We compared the traditional regression model with the Bayesian regression model and conclusively infer that the models estimated under the Bayesian framework provide superior results as compared to the models estimated under the classical approach.Not Availabl

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Not AvailableIn time series literature, use of exogenous variable(s) in done to enhance the modelling as well as forecasting efficiency of the model. Various models have been proposed in literature and ARIMAX model is one of the preferred choice among the researchers. It is due to its ease of application and wide range of applicability. To deal with inherently noisy data sets such as financial series, its variant ARIMAX-GARCH is widely used. Like all other time series models, ARIMAX and ARIMAX-GARCH models are governed with some assumptions. In some practical applications, these assumptions are hard to satisfy. Under such scenarios one has to seek help of alternate methodologies such as Bayesian estimation technique. Past decade has witnessed unprecedented growth in the evolution of statistical computing. This has paved way for researchers to explore the Bayesian paradigm in time series domain. Further, on concentrated literature search we could find very few applications of these two models in agriculture domain. Also, we were unable to obtain any literature that has applied Bayesian framework for parameter estimation of ARIMAX and ARIMAX-GARCH model. Hence, these two research gaps intrigued us to take up the present investigation and document our findings. We have applied these two models on agricultural data sets and attempted to enrich the Bayesian time series literature by documenting estimation technique of ARIMAX and ARIMAXGARCH model using Bayesian framework. We strongly believe that the proposed models will find wide application in agricultural domain ranging from price forecasting to forecasting rainfall, etc. The product developed in form of R package which is freely accessible will help the researchers working in this field.ICAR-IASR

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Not AvailableSelection of informative genes, modeling and construction of Transcriptional Regulatory Networks is an important problem in gene expression genomics. The small sample size and the large number of genes in gene expression data make the selection and modeling process complex. Further, the selected informative genes from high dimensional gene expression data may act as a vital input for genetic network analysis. The identification of hub genes and module interactions in genetic networks is yet to be fully explored. Usually, the raw gene expression data is taken as input for genetic network analysis, which is inherently noisy due to different sources of variation present in gene expression experiments. Further, these noises may mislead the results obtained from the network modeling and inference algorithms and techniques. Therefore, attempts are made to develop approaches for modeling and construction of Transcriptional Regulatory Networks after denoising the raw noisy gene expression data. In this study, a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data was proposed. The comparative performance of the proposed gene selection technique (Boot-SVM-RFE) was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Further, the bootstrap procedure incorporated in this technique was able to remove the spurious association among genes and their corresponding classes. Here, attempts were also made to develop an algorithm to estimate the true gene expression value from raw expression matrix based on Wavelet methodology. Further, statistical approaches for modeling and construction of gene regulatory networks using vector autoregressive models and sparse autoregressive vector models were also developed using wavelet transformed gene expression data for time-series gene expression experiments. Further, the effect of levels (scales), filter types and filter lengths of various wavelet filters on gene regulatory network modeling and inferences was studied. For this purpose, extensive simulations (artificial gene expression data) and synthetic gene expression data (DREAM4 data for E. coli and S. cerviceae) were used. Though this, better combinations of wavelet decomposition levels, filter types and lengths for better modeling and inference of gene regulatory networks was obtained. Further, the comparative performance analysis of the proposed approach was carried out on DREAM4 data with respect to WGCNA, CLR, ARACNE, NetworkBMA and MVAR. The results indicated that our method performs better than these popular contemporary genetic network modeling and inference approaches. For identification hub genes in genetic networks is a crucial task in system biology. Therefore, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, developed approaches were applied to salinity and aluminum stresses in rice and soybean respectively. Through this, various key genes revealed the underlying molecular mechanisms of salinity and Aluminum toxic stress response in rice and soybean were reported. Here, we developed two R packages, namely, dhga (https://cran.r-project.org/web/packages/dhga) and waveletGRN for the users.Not Availabl