59 research outputs found
Not Available
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
Not Available
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
Not Available
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
Not Available
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
Not Available
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
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