71 research outputs found
The effects of cow genetic group on the density of raw whole milk
peer reviewedThe density of milk is dependent upon various factors including temperature, processing conditions, and
animal breed. This study evaluated the effect of different cow genetic groups, Jersey, elite Holstein Friesians
(EHF), and national average Holstein Friesians (NAHF) on the compositional and physicochemical properties
of milk. Approximately 1,040 representative (morning and evening) milk samples (~115 per month during
9 mo) were collected once every 2 wk. Milk composition was determined with a Bentley Dairyspec instrument.
Data were analysed with a mixed linear model that included the fixed effects of sampling month, genetic
group, interaction between month and genetic group and the random effects of cow to account for repeated
measures on the same animal. Milk density was determined using three different analytical approaches –
a portable and a standard desktop density meter and 100 cm3 calibrated glass pycnometers. Milk density was
analysed with the same mixed model as for milk composition but including the analytical method as a fixed effect.
Jersey cows had the greatest mean for fat content (5.69 ± 0.13%), followed by EHF (4.81 ± 0.16%) and NAHF (4.30
± 0.15%). Milk density was significantly higher (1.0313 g/cm³ ± 0.00026, P < 0.05) for the milk of Jersey breed when
compared to the EHF (1.0304 ± 0.00026 g/cm³) and NAHF (1.0303 ± 0.00024 g/cm³) genetic groups. The results from
this study can be used by farmers and dairy processors alike to enhance accuracy when calculating the quantity
and value of milk solids depending upon the genetic merit of the animal/herd, and may also improve milk payment
systems through relating milk solids content and density
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
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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
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 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 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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