Genomic prediction has been used for breeding of animals and plants with complex quantitative traits by predicting Genomic Estimated Breeding Values (GEBVs) of target population. The accuracy of genomic prediction depends on various factors including sampling population, genetic architecture of target species, statistical models, etc. There are large numbers of statistical models for genomic prediction available in the literature. These models perform differently due to different genetic architecture of the datasets. In this article, performances of linear least squared regression, BLUP, LASSO, ridge regression, SpAM, HSIC LASSO, SVM, ANN along with our newly developed integrated model framework have been evaluated in wheat dataset containing 599 wheat lines and 1279 SNP markers. In general, the performances of SVM, ridge regression and integrated model framework were found to be superior for genomic prediction. This study will help researcher in selection of appropriate statistical method to predict phenotypic values. Keywords: ANN, Genomic prediction, Integrated model framework, LASSO, Ridge regression, SVM

  In the present study exogenous variable is incorporated in the long memory model to give better forecast of time series. Autoregressive Fractionally Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedastic (ARFIMA-GARCH) and Autoregressive Fractionally Integrated Moving Average with exogenous variable- Generalized Autoregressive Conditional Heteroscedastic (ARFIMAX-GARCH) models are studied for describing the volatile data. Brief description of the models are given along with parameter estimation procedure. As an illustration daily minimum market price of onion along with daily market arrival of Lasalgaon market of Maharashtra, India is taken. Comparative study of the fitted minimum market price of onion along with daily market arrival of Lasalgaon market of Maharashtra, India is taken. Comparative study of the fitted set. The superior performance of the ARFIMAX-GARCH model than ARFIMA-GARCH model is demonstrated for data under study. Keywords: Long memory, ARFIMA, ARFIMAX, ARFIMAX-GARCH, Forecasting.

  In agriculture, data on various parameters such as area, production and yield are collected over time. These data collected over time are modelled using various time-series modelling techniques. In this paper, an attempt is made to model time-series data of two important food commodities viz. Rice and Wheat using Autoregressive Integrated Moving Average (ARIMA) model and its multivariate variant Vector Autoregressive Integrated Moving Average (VARMA) model. The VARMA models are advantageous over the ARIMA models since two or more series can be modelled simultaneously besides capturing the relations between different series. The performance of ARIMA and VARMA models are compared using the measures of accuracy. Time-series data on production of rice and wheat for the period 1965-2017 is utilized for modelling and forecasting using ARIMA and VARMA statistical time-series modelling techniques. It was observed that the multivariate VARMA modelling technique is not an alternative to the univariate ARIMA modelling technique in terms of efficiency since the production of these two commodities are independent of each other. Finally, forecasting of rice and wheat production for the year 2020 was carried out and is found out to be 114 million tonnes of rice and 106 million tonnes of wheat. An increase of 4.5 % in rice production and 8.8 % in wheat production over the current production values are forecasted for the year 2020. Forecasting for future years is essential as this would help the planners in planning for eventualities arising due to vagaries of monsoon such as floods or droughts. Keywords: Time-series, ARIMA, VARMA, Forecasting, India.

  The study is based on the daily village level data of tobacco caterpillar incidence and district level data on weather variables for Maharashtra collected from Crop Pest Surveillance and Advisory Project (CROPSAP). The Project conducted at Department of Agriculture, Govt. of Maharashtra for the period 2010-2015. The study was conducted to assess the influence of weather variables on tobacco Caterpillar (Spodoptera litura) infestation in phase larvae was significantly positively correlated with relative humidity of first lag week (RH-1) and significantly negatively correlated with rainfall of current and first lag weeks (RF0, RF-1 respectively); and Solitary phase was significantly and negatively correlated with relative humidity of current and first lag week (RH0 and RH-1), and rainfall of first and second lag week (RF-1, and RF-2). Forewarning models were developed using training dataset and validated using validation dataset. Mean regression models explained 61.58%, 72.08% and 46.48% variability in the population of S. litura egg mass, gregarious and solitary larva, respectively. The pre-disposing conditions favouring the tobacco caterpillar infestation for maximum temperature, minimum temperature, relative humidity and rainfall were in the range of 27.10-32.83 ºC, 19.35-24.15 ºC, 84.21-93.38% and 14.77- 92.95 mm with high or medium rainfall in previous weeks followed by low rainfall in current week (for S. litura egg mass); 27.29-31.94ºC, 20.28- 25.63 ºC, 86.00-93.75% and 11.07-112.65 mm with low rainfall in previous weeks followed by higher rainfall in current week (for S. litura gregarious larva); and 27.06-32.45 ºC, 20.23-25.63 ºC, 82.96-94.28% and 15.03-119.18 mm with high rainfall in RF-2 and slowed in RF-1 followed by rise in larva); and 27.06-32.45 ºC, 20.23-25.63 ºC, 82.96-94.28% and 15.03-119.18 mm with high rainfall in RF-2 and slowed in RF-1 followed by rise in larva); and 27.06-32.45 ºC, 20.23-25.63 ºC, 82.96-94.28% and 15.03-119.18 mm with high rainfall in RF-2 and slowed in RF-1 followed by rise in RF0 or continuously increasing pattern from RF-2 to RF0 (for S. litura solitary larva) respectively. The models were validated by cross-validation and independent dataset methodologies. Two sample t-test, RMSE, and other validation statistics revealed no significance difference between observed and predicted values of insect population. Hence, the models could be utilized to disseminate the insect advisories to the farmers. Keywords: S. litura, Soybean, Weather variables, Forewarning, Validation

  Ranked Set Sampling (RSS) is preferred over Simple Random Sampling (SRS) when measuring an observation is expensive or time consuming, but can be easily ranked at a negligible cost. Biswas et al. (2015) proposed a Spatial Estimator (SE) of population mean under RSS through prediction approach incorporating spatial dependency among sampling units of a spatial finite population. In this present article, an attempt has been made approach incorporating spatial dependency among sampling units of a spatial finite population. In this present article, an attempt has been made to propose bootstrap techniques viz. Rescaled Spatial Stratified Bootstrap (RSSB) and Rescaled Spatial Clustered Bootstrap (RSCB) methods for unbiased variance estimation of the SE under RSS from finite populations. Simulation study reveals that both the proposed methods give approximately unbiased estimation of variance of the SE under RSS for different combination of sample and bootstrap sample sizes, but while considering relative stability, RSSB method was found to be more stable. Keywords: Ranked set sampling, Prediction approach, Inverse distance weighting, Ranks, Cycles.

  In the present study, standardized precipitation index (SPI) series was analysed for different timescales of 1, 3, 6, 9, 12 and 24 months has been used to assess the vulnerability of meteorological drought in the Ballari district of Karnataka. SPI values showed that the occurrences of droughts in the study period varied from moderately to extremely condition. Suitable Autoregressive Integrated moving average (ARIMA) model and neural network Artificial neural network (ANN) models were developed to predict drought at different 1, 3, 6, 9 and 12 month timescale and lead time of up to 6 months ahead. The best model was selected based on minimum Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). An lead time. The evaluation of model performance was carried out using root mean square error (RMSE) and mean absolute error (MAE) Furthermore, the ANN model performed well for all stations compared to ARIMA models. ARIMA was observed to forecast well at higher timescale. Keywords: ARIMA, ANN SPI, AIC, BIC, RMSE and MAE.

  Experiments for comparing the test treatments with one control treatment have special importance because in many situations it is necessary to compare test treatments against control with extra importance. In such situations, experimental designs for test treatments against control are used. Balanced Treatment Incomplete Block (BTIB) designs are usually used for the purpose. Kageyama and Mukerjee (1986) constructed a type of BTIB designs as Generalized Efficiency Balanced (GEB) block designs. The robustness properties of such designs are studied by several authors like, Srivastava et al. (1996), Singh et al. (2005), Shunmugathai and Srinivasan (2011) and others. All the authors mostly discussed the robustness of BTIB designs after missing of a single observation in a block. The presence of correlation in the form of neighbour effects among the adjacent plots in agricultural experiments is a well-established fact, Wilkinson et al.(1983), Kiefer and Wynn (1981), Gill and Shukla (1985) etc. The present paper develops the robustness criteria of BTIB designs for missing of a single test or control treatment from any block with correlated adjacent plots. A series of robust BTIB designs have been developed. The C matrices of the BTIB design and the residual BTIB design after removal of a single plot has also been presented having correlated observations. The efficiencies of the above designs for different values of plot to plot correlation coefficient (?) have been listed. Keywords: Robustness of block designs; BTIB designs; GEB designs; Correlated observations.

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  The Sustainable Development Goal of Zero Hunger is a bold commitment towards 795 million undernourished people to end all forms of hunger and malnutrition by 2030 (http://www.undp.org/sustainable-development-goals/goal-2-zero-hunger/). India, sharing a quarter of the global hunger by decentralized level planning and effective monitoring. Availability of reliable disaggregate level statistics using Small Area Estimation (SAE) approach for measuring the prevalence of food insecurity can be a potential key to the Governmental organization to take consistent steps towards framing strategic plans eyeing zero hunger. A pragmatic approach in SAE is to consider Hierarchical Bayes (HB) framework, which provide an added flexibility of using complex models without concerning much about known design variance or traditional normality assumption. However, this approach does not incorporate the survey weights that are essential for valid inference given the informative samples that are produced by complex survey designs. In this paper, involving survey design information a number of model specifications are discussed in area level HB version to generate reliable and representative district and district by social groupwise estimates of food insecurity incidence for rural areas of the State of Odisha in India by combining the Household Consumer Expenditure Survey 2011-2012 data of National Sample Survey Office and the Population census 2011. Spatial maps have been produced to observe the inequality in food insecurity distribution among the districts as well as districts cross classified by socio-economic categories. Such maps are definitely useful for policy formulation, fund disbursement purpose and for the Government in taking effective administrative decisions targeting zero hunger. Keywords: Food insecurity, Hierarchical Bayes, Small area estimation, Spatial map.

  Direct Benefit Transfer (DBT) is an initiative of Government of India (GoI) for transfer of benefits in all government schemes directly to beneficiaries. In the Department of Agricultural Research and Education (DARE)-Indian Council of Agricultural Research (ICAR), a total of twenty (20) schemes are covered under DBT wherein the beneficiaries are the farmers, students and faculty members and the benefits type are both as cash and in kind. A web based Management Information System (MIS) (https://dbtdare.icar.gov.in) to manage the DBT records of beneficiary in different schemes of DARE-ICAR has been developed and hosted in the ICAR Data Centre at Indian Agricultural Statistics Research Institute (IASRI), New Delhi. This application is developed using .NET framework and uses web service for data exchange with national level DBT Bharat portal (https:// dbtbharat.gov.in). Keywords: DARE, DBT, ICAR, MIS, WebAPI.