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Not AvailableThis Newsletter is a compilation of the key research achievements, awards and recognitions received, training programmes conducted, workshops and conferences organized/ attended, advisory services provided and significant publications of our Institute during the period under report. It is worth mentioning that most of the period being reported pertains to lockdown due to COVID-19 and even then our Scientists have contributed immensely by adapting to the changing times. An R-package BayesARIMA to estimate the ARIMAX model using Bayesian framework has been developed. A machine learning-based method for prediction of Gigantea proteins has been developed. A supervised learning-based methodology ASRpro for multi-label prediction of abiotic stress responsive proteins has also been developed. An online software Web generation of Generalized Row-Column Designs (WebGRC) has been developed. Algorithm of Multiple Kernel Extreme Learning Machine (MK-ELM) for drought index forecasting and procedure for estimation of the parameter of the Multiple Kernel Extreme Learning Machine (MK-ELM) has also been developed. Our Scientists have brought recognitions to our institute by way of serving as Expert Members in various high-level committees, delivering invited talks in prestigious forums. Two training programmes were conducted on topics viz., Statistics: Experimental Designs and Analysis and Data Science in Agriculture. Orientation training for newly joined 11 scientists in the institute was also conducted during this period.Not Availabl

ICAR-IASRI NEWS October-December, 2020

Not AvailableThis Newsletter brings to you the key research achievements, awards and recognitions received, training programmes conducted, workshops and conferences organized/ attended, advisory services provided and significant publications of our institute during the period under report. ICAR-IASRI is adapting itself to the needs and trends of what the present era demands and is indeed working on a couple of projects relating to Artificial Intelligence (AI) having useful applications in the field of agriculture such as detection of crop pests and incidence of diseases. The scientists at the Institute are broadening their horizon of research capabilities in AI tools like Deep Learning. Such a skill strengthening for development of data analytics aided solutions is the best step that can happen for the agricultural research and education. Of late, one can see many universities in India and elsewhere offering Masters in Data Science courses which include statistical computing combined in a packaged format along with R, Python and other computing solutions. The institute is also planning several human resource development programmes in Data Science. In the field of design of experiments, developed methods of construction for obtaining pairwise and/or variance balanced Structurally Incomplete Row-Column (SIRC) and Sliced Latin Hypercube Designs (SLHDs) of equal and unequal run sizes in all batches (slices). For agroforestry experiments, a class of variance balanced network designs for the estimation of direct as well as network effects of trees from adjacent plots has been obtained.Not Availabl

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Not AvailableThis Newsletter throws light on the key research achievements, awards and recognitions received, training programmes conducted, workshops and conferences organized/ attended and significant publications of our Institute during the period under report. A portal developed under the project “Integrated Sample Survey Solution for Major Livestock Products” became live during the reporting period and a total of 35 States/UTs have successfully logged in using the credentials provided. It is heartening to note that our Scientists continue to bag young scientist awards from prestigious scientific societies. Our senior level Scientists who also perform other key roles like ADG (ICT), ICAR National Fellow and Coordinator of ICAR Network project KRISHI have brought recognitions to our institute by way of serving as Expert Members in various high level committees, delivering invited talks in prestigious conferences and Co-chairing a session in international conference. Six training programmes were conducted on a wide range of topics viz., Sampling Techniques for Crop Cutting Experiment (CCE), Tools and Techniques for Data Analysis and Management, Advanced Bioinformatics Techniques for Mapping and GWAS using NGS Data, Experimental Data Analysis, Recent Advances in Econometric Modeling and Forecasting in Agriculture, Statistical Designs and Experimental Data Analysis. Our Scientists have also acted as Faculty Coordinators for the “Field Exposure Program” in the area of “Time Series Analysis, Forecasting Techniques and R Software” as part of M.F.Sc. (Fisheries Economics) curriculum of students of ICAR-Central Institute of Fisheries Education, Mumbai.Not Availabl

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Not AvailableGeneralized incomplete Trojan-type designs have been obtained for experiments where it is required to control two sources of variability in the experimental units and the number of treatments may be substantially larger than the number of replicates. Several families of distance balance sampling plans have been obtained using linear integer programming. These plans are useful for sampling from populations in which nearer units provide similar observations due to natural ordering of units in time or space.Not Availabl

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Not AvailableApproaches for modeling and construction of Transcription Regulatory Networks, after denoising the raw noisy gene expression data and approaches using vector autoregressive models and sparse autoregressive vector models using wavelet transformed gene expression data for time-series gene expression experiments, have been developed. The developed approaches were applied to salinity and aluminum stresses in rice and soybean respectively. Two R packages namely dhga (https://cran.r-project.org/web/packages/dhga) and waveletGRN have also been developed. ICAR-Data Centre was inaugurated and KVK Mobile App was launched at ICAR-IASRI by Shri Radha Mohan Singh, Union Minister for Agriculture and Farmers Welfare. This digitization process in agriculture will help the farming community by encouraging technology, training and analysis of data by KVK portal and through its mobile app hosted at ICAR Data Centre of the Institute.Not Availabl

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Not AvailableIn the study on “Some investigations on design and analysis of agro-forestry experiments”, the concept of strongly neighbour balanced designs has been defined and some methods of constructing complete block designs for two factors have been obtained. Some series of incomplete block designs balanced for adjacent tree effects have also been obtained. These designs are shown to be partially variance balanced for direct effects. Another study is on “Statistical and algorithmic approach for improved estimation of treatment effects in repeated measurements designs(RMDs)”. Designs in which each experimental unit receives some or all of the treatments, one at a time, over a period of time are called repeated measurements designs (RMDs). A class of reference balanced RMDs for estimating direct effects of formulations useful for bioequivalence trials has been obtained using Williams Square RMDs.Not Availabl

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Not AvailableDesign Resources Server has been strengthened by adding new link namely “SAS Online Doc:9.1.3” to the already created link “Important Links” on Design Resources Server (www.iasri.res.in/ design). Under the study “Weather based models for forecasting potato yield” Complex polynomial (C.P.) models, using GMDH technique, are developed by taking both weighted and unweighted indices.Not Availabl

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Not AvailableOnion Genomic Research (OGR), with three-tier architecture is an open web resource accessible freely at http://webtom.cabgrid.res. in/ogr/. It is built in My SQL database and PHP that cataloges the genomic developments specific to onion. The resource contains information on gene annotations and is linked with KEGG pathways. Gene prediction was carried out for the annotated sequences and over 200 different ready-to-use experimentally validated molecular markers were also mined to enrich the lab-based studies targeting variety improvement. Krishi Vigyan Kendra Knowledge Network (http://kvk.icar.gov.in), an online portal has been developed to disseminate knowledge from KVKs to farmers. As of now, 635 KVKs have already been enrolled and more than 15000 KVKs events (past and future) detail have been uploaded on the portal. The portal is linked with http://www.enam.gov.in/ for providing information related to agricultural marketing. A beta version of mobile app consisting of all the functionalities of the portal has been developed. A Dash board monitoring system for various components of portal has also been developed. Further, a live cluster map has been incorporated into the portal to keep track of the users. The Hindi version of Home Page of the portal has also been developed.Not Availabl

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Not AvailableA general method of construction of multilevel factorial experiments that are linear trend-free for main effects and few lower order interaction effects has been developed. Breed descriptor has been developed to identify breed that cover only “pure breed” or true to the breed type animals excluding undefined or admixture population. Moreover, in case of semen, ova, embryo and breed product, the breed cannot be identified due to lack of visible phenotypic descriptors. Advent of molecular markers like microsatellite and SNP have revolutionized breed identification from even small biological tissue or germplasm. Microsatellite DNA marker based breed assignments has been reported in various domestic animals. Such methods have limitations viz. non availability of allele data in public domain, thus each time all reference breed has to be genotyped which is neither logical nor economical. Even if such data is available, computational methods need expertise of data analysis and interpretation. The first goat breed identification server has been developed using microsatellite DNA markers and is available at http://cabin.iasri.res.in/gomi/. A user friendly software has been developed for estimating the compound growth rate (WebECGR) and the same has been uploaded at http://iasri.res.in/cgrNot Availabl

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Not AvailableAn attempt has been made to develop hybrid models by combining time series models viz., Auto-Regressive Integrated Moving Average i.e. ARIMA / ARIMA with eXplanatory variables i.e. ARIMAX and machine learning techniques viz., Artificial Neural Networks (ANN) / Support Vector Machines (SVM) for forecasting rice yield using weather based covariates (explanatory variables) namely, minimum temperature, maximum temperature and rainfall for Aligarh and Meerut districts of Uttar Pradesh. For this, time series has been considered as a function of linear and nonlinear components and ARIMA/ ARIMAX models were employed to fit and predict the linear component while the residuals have been predicted employing ANN/ SVM models. Eventually, the predicted linear and nonlinear components are combined to obtain aggregate prediction. The proposed hybrid approach was found to be better than the traditional time series models in terms of their forecasting performance.Not Availabl