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

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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

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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

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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 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 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