Statistical Genetics Division

Dr. Ajit
Head (A)
Phone (O).: 25848720
ajit@icar.gov.in

About Us

Statistical Genetics Division deals with theoretical and applied research in Statistical Genetics with special emphasis on computational aspects among the statisticians, practicing breeders, researchers, and scientists in the National Agricultural Research System. The major objective of the division is to conduct research, impart training in the field of Statistical Genetics and Bio-statistics and to provide advisory services on theory and application of these subjects with special emphasis on agriculture. To meet this objective, new theoretical developments were made from time to time and numerous methodologies for application in plant and animal breeding and related areas were developed. The Scientists of this division provide proper statistical techniques applicable in plant and animal breeding data with the support of online programs for analysis, e-learning, etc. Genotype-environment (GE) interaction and yield stability is an important area in the crop improvement programme. The division is engaged in developing various selection indices for selecting genotypes simultaneously for both yield and stability.  Heritability is another important area in plant and animal breeding programme. The division has studied extensively on the precise estimation of heritability and provided some improved estimation procedures both parametric and nonparametric. Scientists are also engaged in studying various aspects of genetic parameters like repeatability, breeding value, etc.  Scientists also study the role of spatial patterns in the analysis of agricultural field experiments and developed methodology for estimating genetic trends realized over the years. Studies are also carried on yield survival relationship in dairy cattle, progeny testing for auxiliary traits, analysis of animal epidemiology, etc. Division also works on estimation of breeding value using marker data and developed appropriate software for analysis of data using such methodologies.  Recently scientists of the division developed some statistical techniques for Genome-Wide Association Studies (GWAS). They are also involved in developing statistical techniques and platforms for understanding complex traits and diseases in plants and animals and expressed QTL modelling. Scientists also provide advisory and consultancy to the statistician, breeders and biometricians. They also provide a platform to establish a network among agricultural statisticians, animal and plant breeders. The Scientists of this division also involved in research related to the development of the methodology for nonparametric modelling of time-series data and its application in Agriculture. Stochastic differential equation models and their applications to agriculture is also one area of research.

History: The major contribution of Division of Statistical Genetics was started in 1940, when the Institute in its formative stages and was started working as a statistical section of ICAR. At that time, the research methodologies and the findings of this division under the visionary leadership of Professor P. V. Sukhatme, received wide accolade from various sections of ICAR including Animal and Crop Breeding researchers. This established the power of Statistics in general and Statistical Genetics in particular to draw valid inferences and conclusions on issues in animal sciences and other fields. During the 7-9th decades of twentieth century, the role of Statistical Genetics in Agricultural and Animal science research was its peak. During this period, this division contributed many statistical and applied methodologies for analyzing plant and animal breeding data, obtained from Crop and Animal Science studies across the country.

Mandate

  • To undertake research, teaching and training in the field of Statistical Genetics.

Thrust Areas

  • Statistical approaches/techniques for Genome-Wide Association Studies (GWAS).
  • Statistical approaches/techniques and platforms for understanding complex traits and diseases in plants and animals.
  • Expressed QTL modelling.
  • Population Genetics, Computational Biology, Statistical Genomics.
  • Studies on gene action, estimation of genetic parameters and genetic merit, genetic  progress and other related statistical methods in genetics.
  • Computer simulation studies and applications of re-sampling techniques, like bootstrap, Jackknife, balanced repeated replications in Agricultural Statistics.
  • Non-linear statistical modeling of  biological and ecological phenomena.
  • Development of methodology for nonparametric modelling of time-series data.
  • Stochastic differential equation models and their applications to agriculture.
  1. Estimation of genetic parameters:

Most of the crop and animal traits are quantitative nature and controlled by polygenes, hence, a wider range of variability is observed for such traits characterized by the individual’s genetic architecture and interaction with the environment. Breeders use this variability for getting improvement in economic characters through efficient selection strategies. The information on genetic parameters, such as heritability, repeatability and genetic correlation is a prerequisite for making efficient selection strategies by the geneticists and breeders. Keeping in view the importance of these parameters, Division of Statistical Genetics has developed a number of statistical methods for efficient estimation of genetic parameters, which briefly described as:

  • Methods of estimation of heritability
  • Intra-sire regression of offspring on dam
  • Variance components from full-sib and half-sib data (under uncorrelated and correlated error structure)
  • Variance components using half-sib data (under uncorrelated and correlated error structure)
    • Tackling the problem of inadmissible estimates
  • Heritability estimation through unbalanced hierarchical model
  • Methods for optimum sample sizes and genetic structures
  • Probabilistic techniques for of inadmissible estimates of heritability
  • heritability estimation for finite population
    • Improved estimators of heritability
  • Estimators of mortality, infertility, abortion, sex-ratio, involuntary culling, etc. in Indian cattle and buffalo population
  • Restricted estimators of heritability under various constraints
    • Improved variance component estimation
  • Estimation of variance components for random effects of offspring and dam
  • Probabilistic estimation of variance components
  • Bootstrap based estimators of variance components
  • Optimum sample size determination for half-sib and full-sib mating designs
  • Variance components estimation under this optimum structures, depending upon the level of population heritability
  • Estimation under non-genetic fixed effects in half/full-sib mating design
  • Estimation of variance of sample heritability from full-sib breeding design
    • Estimation using Resampling techniques (Application of bootstrap technique for the variance estimation of genetic parameters was initiated in 1992)
  • Confidence intervals estimation of genetic parameters
  • Bootstrap based robust estimates of heritability
  • Nonparametric Bootstrap based estimation of genetic parameters
  • Nonparametric and parametric bootstrapping procedures for estimation of confidence intervals for half-sib and offspring-parent regression heritabilities
  • Estimation of genetic parameters for unbalanced data
  • Non-parametric bootstrap technique to obtain the standard errors, percentile and bias-corrected percentile intervals for genetic parameters
    • Bayesian estimator of genetic parameters
  • Bayesian estimator using GIBSS sampling
  • Bayesian estimator using BUGS approach
    • Robust estimation of genetic parameters
  • Robust estimator for low-inherited traits
  • Multivariate approach to estimate genetic parameters
  • Estimator for genetic parameters under the aberrant values, outliers, and non-Gaussian data
    • Heritability of herd-life in dairy animals
  • Retention time and its relationship with production and reproduction traits of dairy cattle and buffalo populations
  • Non-parametric approach for studying culling patterns in dairy animals
  • Estimation and comparison of retention times in dairy cattle as well as relationships between the retention time and other explanatory variables.
  • Heritability of herd-life estimation from the correlation between half-sib for traits using Path analysis
  • Measures of heritability of stayabilty from herd data
  • Estimates of heritability of stayability for various quantitative traits
  • Heritability of threshold character (mastitis disease) in sahiwal breed of cows
    • Yield survival relationships and culling patterns
  • Methods to determine culling patterns of different categories of crossbred animals
  • Studies on genetic aspects of stayability of animals with different proportions of exotic inheritance
  • Bayes discriminatory analysis in identifying important traits influencing culling in dairy cattle
  • Studies on association between various traits with their continuous discrete distribution mixture
    • Estimation of generalized heritability
  • Methods on estimation of generalized heritability for important traits for studying growth performance of crossbred goats
    • Repeatability estimation in biennial bearing crops
  • Moving averages based technique for repeatability estimation
  • ANOVA and principal components in terms of robustness to biennial disturbances
  • Theoretical expression for variance of the repeatability estimators
    • Estimation of genetic correlation
  • Estimator of genetic correlation using variance partitioning technique
  • Estimator of genetic correlation for non-normal breeding data
  • Estimator for bias in genetic correlation in presence of outliers
  • Bootstrap based estimation of genetic correlation
  • Optimum number of bootstrap replications required for estimating the variance of the genetic correlation
  1. Optimal selection strategies for genetic improvement studies
  • Gene-flow Technique for Optimum Selection Strategies
  • Selection indices for non-random mating and stage-structured populations
  • Estimator for transmittable genetic value for an individual
  1. Crossbreeding Studies
  • Methods for optimum level of exotic inheritance for better performance, inferring about the nature of gene action as also in determining the heterosis
  • Methods for gene action in crossbred cattle found that the polygenes controlling the milk yield traits and age
  • Methods for studying heterozygosity of individuals heterozygous at one more loci
  • Methods for gene interaction in dairy cattle
  • Methods for genetic parameters in various generation mean models in terms of the proportions of parental exotic inheritance
  • Genetic models for gene action and heterosis in various lifetime traits of Holstein-Friesian x Sahiwal crosses
  • Methods for curvilinear relation between production and level of exotic inheritance
  1. Selection of stable genotypes of crops
  • Theoretical basis for selection of stable crop genotypes
  • Development of non-parametric stability measures
  • Robust measure for stability in presence of outliers
  • Methods for estimation of linear genotype-environment interaction
  • Methods for estimation of nonlinear genotype-environment interaction
  • Software for stability analysis of genoypes
  1. Simultaneous selection measures
  • Innovative indices for selection of genotypes simultaneously for yield and stability
  • Statistical procedures based on AMMI model for selecting genotypes simultaneously for high yield and stability
  • Computer programmes for genotype selection simultaneously for yield and stability
  • Methods for stability analysis using MCDM techniques
  1. Statistical Genomics

6.1 Sequence data analytics

  • Methods for splice-site prediction
  • Non-parametric methods for splice-site prediction
  • Machine learning techniques for splice-site prediction for eukaryotic and pro-karyotic species
  • Deep-learning techniques for biological sequence data analysis

6.2 Methods for gene expression data analysis

  • Innovative statistical methods for biologically relevant gene selection
  • Improved machine learning techniques for gene selection
  • Hybrid methods for informative gene selection from crop gene expression data

6.3 Methods for Network biology

  • Statistical techniques for gene co-expression network analysis
  • State space Models for gene regulation network modeling
  • Wavelet based technique for gene network modeling
  • Statistical approach for hub-gene detection in gene networks
  • Application of network biological techniques for key genes identification in various crops

6.4 Methods for recent single-cell studies

  • Methods for differential expression downstream analysis for single-cell studies
  • Methods for differential zero inflation downstream analysis for single-cell studies
  • Techniques for gene set analysis
  1. AI based tools for statistical genetics and genomics

 

  1. Non-linear modeling of biological phenomenon:

Generally, biological phenomena including fishery management is based on the concept of Maximum sustainable yield (MSY), i.e., at any given population level less than the carrying capacity, a surplus production exists. To assess MSY, the statistical models like Schaefer and Fox models were to determine the optimum fishing effort. These models are widely used for efficient fishery management as these require only the information regarding time-series data on catch and effort, which is readily available in our country for a large number of fish species. As the surplus production models, like Schaefer and Fox models are ‘intrinsically linear’, the usual practice followed for fitting of these models had been to get rid of the nonlinearity by converting these to a linear model by applying a suitable transformation, like logarithmic, and reciprocal. Hence, this division contributed several nonlinear statistical models and nonlinear estimation procedures to analyze the biological data coming genetics and growth studies.

 

 

Ongoing Projects:

  • Institute Funded:
S. No.PROJECT NAMEPROJECT TEAM
1Estimation of breeding value using generalized estimation equation and Bayesian ApproachPI: Dr. Himadri Sekhar Roy
Co-PI: Dr. L.M. Bhar and Dr. A. K. Paul
2A study on detection and interpretation of expression quantitative trait loci (eQTL) mappingPI: Dr. Himadri Sekhar Roy
Co-PI: Dr. L.M. Bhar, Dr. R. K. Paul and Dr. A. K. Paul
3Stochastic differential equation models and their applications to agriculturePI: Dr. Prajneshu, Ex. Emeritus Scientist, ICAR
Co-PI: Dr. Himadri Ghosh, Dr. L.M. Bhar
4A Study on Heritability EstimationPI: Dr. Amrit Kumar Paul
CC-PI: Dr. Himadri Shekhar Roy, Dr. L.M. Bhar, Dr. Ranjit Kumar Paul
5Development of methodology for nonparametric modelling of time-series data and its application in agriculturePI: Dr. Himadri Ghosh
Co-PI: Dr. Soumen Pal
6Study of long memory and periodicities in climate
variables in different Meteorological Subdivisions of
India
PI: Dr. Ranjit Kumar Paul
Co-PI: Dr. L.M. Bhar, Dr. Amrit Kumar Paul
7Development of methodology for nonparametric modelling of time-series data and its application in Agriculture.Himadri Ghosh* and Soumen Pal
8Development of Rank Based Stability Measures for Selecting Genotypes.Prakash kumar*, A.K. Paul,  Samarendra Das and L.M. Bhar
9Gene selection for classification of crop gene expression dataSamarendra Das, **P.K. Meher, R.K. Paul and Prakash Kumar
10A study on sequence encoding based approaches for splice site prediction in agricultural  species*P.K. Meher, Prakash   Kumar and A.R. Rao
11Study of Long Memory and Periodicities in Climate Variables in Different Agro-Climatic Zones of IndiaRanjit Paul, L M Bhar, A K Paul
  • External Funded
S. No.PROJECT NAMEPROJECT TEAM
1Stochastic differential equation models and their applications to agriculture. Funded by SERB, DST, Government of India: CCPI*Prajneshu, Himadri Ghosh and L.M. Bhar
2Creation of Policy and Strategy Cell (PSC) at ICAR-NIAP for Doubling Farmers’ Income in India by 2021-22: Estimating Farm Income and Facilitating the Implementation of Strategic FrameworkRaka Saxena (NIAP), Naveen P. Singh (NIAP), Usha Ahuja (NIAP) and Ranjit Kumar  Paul (IASRI)
3Modelling insect pests and diseases under climate change and development  of digital tools for pest management (National Initiative on climate resilient agriculture (NICRA), Government of India)S Vennila (NCIPM), M N Bhat (NCIPM), Niranjan Singh (NCIPM), Ranjit Kumar Paul (IASRI), M Prabhakar (CRIDA) and M S Rao (CRIDA)
4Elucidating the mechanism of pashmina fibre development: An OMICS approach.SKUST: Nazir A. Ganai; NDRI : Jai K. Kaushik; IASRI: A.R.Rao, P.K. Meher

Completed Projects:

  • Institute Funded:
S. No.PROJECT NAMEPROJECT TEAM
1Modeling and construction of transcriptional regulatory networks using time-series gene expression data (2017)*Samarendra Das, Bishal  Gurung, Sanjeev Kumar, S.D. Wahi
2Estimation of Heritability under correlated errors (2016)A.K.Paul* and S.D.Wahi
3Estimation of Breeding Value Using Longitudinal Data (2016)*U.K. Pradhan, P.K.  Meher, A.R. Rao and A.K. Paul
4A study on STAR and SV families of nonlinear time-series models for describing cyclicity and volatility in Agriculture. (2015)*Bishal Gurung and Himadri Ghosh,  and R. K. Paul
5A study on modeling and forecasting of time-series with long memory process. (2015)*R. K.  Paul, Himadri  Ghosh, and Bishal Gurung
6Development of Methodology of Estimation of compound Growth Rate and Its Web Based Solutions. I.A.S.R.I., New Delhi. (2013)*S. Pal,  Himadri  Ghosh, and Prajneshu
7Development of Weather-based Crop Yield Forecasting Models using GARCH and Wavelet Techniques. I.A.S.R.I., New Delhi. (2013)*R. K. Paul , Himadri Ghosh, and Prajneshu
8A study of Stochastic Volatility Models Through Particle Filtering. I.A.S.R.I., New Delhi. (2013)*Bishal Gurung and Himadri  Ghosh
  • External Funded:
S. No.PROJECT NAMEPROJECT TEAM
1Studying Dynamics of market integration and price transmission of agricultural commodities PI: Dr. Ranjit Kumar Paul
2Statistical approach for genome-wide association studies and genomic selection for multiple traits in Structured plant and Animal populationPI: Dr. L. M. Bhar
Co-PI: Dr. Himadri Shekhar Roy and Dr. P. K. Meher
3Creation of Policy and Strategy Cell (PSC) at ICAR-NIAP for Doubling Farmers’ Income in India by 2021-22: Estimating Farm Income and Facilitating the Implementation of Strategic FrameworkCC-PI: Dr. Ranjit Kumar Paul
4Modelling insect pests and diseases under climate change and development of digital tools for pest management CC-PI: Dr. Ranjit Kumar Paul
5Study of Long Memory and Periodicities in Climate Variables in Different Meteorological Subdivisions of IndiaPI: Dr. Ranjit Kumar Paul
Co-PI: Dr.L. M. Bhar and Dr. A. K. Paul
6Leveraging Institutional Innovations for Inclusive and Market led Agricultural Growth in Eastern India CC-PI: Dr. Ranjit Kumar Paul
7Creating a fully characterized genetic resource pipeline for mustard improvement programme in IndiaCC-PI: Dr. D. K. Yadav
CO-PI: Dr. P.K. Meher, Dr. Cini Varghese
8Metagenomic profiling for assessing microbial biodiversity in River Ganga for ecosystem health monitoringPI: Dr. Anil Rai
Co-PI: Dr. P.K. Meher et al.
9Exploring the Epigenetic Control of Heat Stress Responses in Wheat for Characterizing the regulatory Networks Associated with ThermotolerancePI: Dr. D. C. Mishra
Co-PI: Dr. P.K. Meher et al.
10Modeling and construction of transcriptional regulatory networks using time-series gene expression data (2017)*Samarendra Das, Bishal  Gurung, Sanjeev Kumar, S.D. Wahi
11Estimation of Heritability under correlated errors (2016)A.K.Paul* and S.D.Wahi
12Estimation of Breeding Value Using Longitudinal Data (2016)*U.K. Pradhan, P.K.  Meher, A.R. Rao and A.K. Paul
13A study on STAR and SV families of nonlinear time-series models for describing cyclicity and volatility in Agriculture. (2015)*Bishal Gurung and Himadri Ghosh,  and R. K. Paul
14A study on modeling and forecasting of time-series with long memory process. (2015)*R. K.  Paul, Himadri  Ghosh, and Bishal Gurung
15Development of Methodology of Estimation of compound Growth Rate and Its Web Based Solutions. I.A.S.R.I., New Delhi. (2013)*S. Pal,  Himadri  Ghosh, and Prajneshu
16Development of Weather-based Crop Yield Forecasting Models using GARCH and Wavelet Techniques. I.A.S.R.I., New Delhi. (2013)*R. K. Paul , Himadri Ghosh, and Prajneshu
17A study of Stochastic Volatility Models Through Particle Filtering. I.A.S.R.I., New Delhi. (2013)*Bishal Gurung and Himadri  Ghosh

2021

  • Paul, RK, Sarkar, S and Yadav, SK. (2021). Wavelet based long memory model for modelling wheat price in India. Indian Journal of Agricultural Sciences, 91(2): 227–31. http://krishi.icar.gov.in/jspui/handle/123456789/44375.
  • Singla, S., Paul, R.K., and Shekhar, S. (2021). Modelling price volatility in onion using wavelet based hybrid models. Indian Journal of Economics and Development, 17(02): 256:265.
  • Kumar, P., Badal, PS, Paul, RK, Jha, GK, Venkatesh, P, Kamalvanshi, Anbukani P, Balasubramanian M and Patel, P. (2021). Forecasting onion price for Varanasi market of Uttar Pradesh, India. Indian Journal of Agricultural Sciences, 91(2): 249–53
  • Kumar, P., Badal, P.S., Jha, G.K., Paul, R.K., Venkatesh, P., Kamalvanshi, V., Balasubramanian M., Anbukkani P. and Patel, P. (2021). Enabling Informed Resource Allocation Decision by Vegetable Growers of Varanasi, Uttar Pradesh: Price Forecasting using ARIMA. Agricultural Situation in India, LXXVII (10): 16: 24.
  • Deka, H., Barman, T., Dutta, J. Devi, A., Tamuly, P., Paul, R.K., and Karak, T. (2021). Catechin and caffeine content of tea (Camellia sinensis L.) leaf significantly differ with seasonal variation: A study on popular cultivars in North East India. Journal of Food Composition and Analysis, 96, 103684. https://doi.org/10.1016/j.jfca.2020.103684.
  • Rakshit, D., Paul, R.K. and Panwar, S. (2021). Asymmetric Price Volatility of Onion in India. Indian Journal of Agricultural Economics, 76 (2): 245:260.
  • Meher, P.K., Rai, A. and Rao, A.R. (2021). mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net. BMC Bioinformatics, 22(1), pp.1-24. DOI: 10.1186/s12859-021-04264-8.
  • Meher, P.K., Mohapatra, A., Satpathy, S., Sharma, A., Saini, I., Pradhan, S.K. and Rai, A. (2021). PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel. Plant Methods, 17(1), pp.1-15. DOI:10.1186/s13007-021-00744-3.
  • Malik, P., Kumar, J., Singh, S., Sharma, S., Meher, P.K., Sharma, M.K., Roy, J.K., Sharma, P.K., Balyan, H.S., Gupta, P.K. and Sharma, S., (2021). Single-trait, multi-locus and multi-trait GWAS using four different models for yield traits in bread wheat. Molecular Breeding, 41(8),1-21. DOI: 10.1007/s11032-021-01240-1.
  • Saurabh, K., Rao, K.K., Mishra, J.S., Kumar, R., Poonia, S.P., Samal, S.K., Roy, H.S., Dubey, A.K., Choubey, A.K., Mondal, S. and Bhatt, B.P., (2021). Influence of tillage based crop establishment and residue management practices on soil quality indices and yield sustainability in rice-wheat cropping system of Eastern Indo-Gangetic Plains. Soil and Tillage Research, 206, 1-9.
  • Ekka, U., Kumar, A. And Roy, H.S. (2021). Particulate matter exposure of combine harvester operator during wheat harvesting in northern India. Indian Journal of Agricultural Sciences, 91 (5): 678–682.
  • Tanvi Sharma, Nitesh Kumar Sharma, Prakash Kumar, Ganesh Panzade, Tanuja Rana, Mohit Kumar Swarnkar, Anil Kumar Singh, Dharam Singh, Ravi Shankar, Sanjay Kumar.(2021) The first draft genome of Picrorhiza kurrooa, an endangered medicinal herb from Himalayas, Scientific Reports, volume 11, Article number: 14944. https://www.nature.com/articles/s41598-021-93495-z.
  • Das, S. and Rai, S.N. (2021). Statistical Approach of Gene Set Analysis with QTL for Crop Gene Expression Studies. Entropy, 23(8), 945 https://doi.org/10.3390/e23080945.
  • Das, S. and Rai, S.N. (2021). SwarnSeq: An Improved Statistical Approach for Differential Expression Analysis of Single-Cell RNA-Seq Data. Genomics, 113(3), 1308- 1324. org/ 10.1016/ j.ygeno. 2021.02.014
  • Verma, A., Kumar, P., Soni, M.L., Pawar, N., Pradhan, U.K. and Kumar, S. (2021). Nutrient dynamics of three species under agroforestry system in arid western region of Rajasthan, India. Communicated to Biological Agriculture and Horticulture (In press).
  • Sharma NK, Gupta S, Kumar P, Kumar A, Pradhan UK, Shankar R. (2021). RBPSpot: Learning on Appropriate Contextual Information for RBP Binding Sites Discovery. bioRxiv. https://doi.org/10.1101/2021.06.07.447370.
  • Yeasin M., Dhandapani A., and Ravichandran S. (2021). Artificial Intelligence in Agriculture. In: Ch. Srinivasarao et al., (Eds). Agricultural Research, Technology and Policy: Innovations and Advances, ICAR-National Academy of Agricultural Research Management (NAARM), Hyderabad, Telangana, India, 291-306.

 

2020

  • Paul, R.K., Paul, A.K. and Bhar, L. M. (2020). Wavelet-based combination approach for modeling sub-divisional rainfall in India. Theoretical and Applied Climatology, 139, (3–4), 949–963. DOI:10.1007/s00704-019-03026-0.
  • Paul, R.K., Sarkar, S., Mitra, D., Panwar, S., Paul, A.K. and Bhar, L.M. (2020) Wavelets based estimation of trend in sub-divisional rainfall in India. Mausam, 71 (1), 551-560.
  • N M Ahmadi, T K Das, N Nasrat, S.S. Rathore and A. K. Paul (2020). Effect of phosphorus on yield and economics of maize (Zea mays) under semi-arid conditions of Afghanistan. Indian Journal of Agricultural Sciences, 90 (2): 439–41.
  • T. Das, R.K Paul, L.M. Bhar and A.K. Paul (2020). Application of Machine Learning Techniques with GARCH Model for Forecasting Volatility in Agricultural Commodity Prices. Journal of The Indian Society of Agricultural Statistics, 74(3): 187–194.
  • Narendra Khode, B.P.Singh, Mahesh Chander, D. Bardhan, Med Ram Verma and A.K. Paul (2020). Article Impact of dairy trainings on productivity of herd, generation of income and employment. Indian Journal of Animal Sciences, 90 (8): 1191–1194.
  • Paul, R.K., Vennila, S., Yadav, S.K., Bhat, M.N., Kumar, M., Chandra, P., Paul, A.K. and Prabhakar, M. (2020). Weather based Forecasting of Sterility Mosaic Disease in Pigeonpea using Machine Learning Techniques and Hybrid Models. Indian Journal of Agricultural Sciences, 90 (10), 1952-1958.
  • Sarkar, K.P., Singh, K.N., Paul, A.K., Ramasubramanian, V., Kumar, M., Lama, A. and Gurung, B., (2020). Forecasting long range dependent time series with exogenous variable using ARFIMAX model. Indian Journal of Agricultural Sciences, 90 (7): 1302-5.
  • Khode, N., Singh, B.P., Chander, M., Bardhan, D., Verma, M.R. and Paul, A.K., (2020). Impact of dairy trainings on productivity of herd, generation of income and employment. The Indian Journal of Animal Sciences, 90(8): 1191–1194.
  • Paul, R.K., Paul, A.K. and Bhar, L.M (2020). Wavelet-based combination approach for modeling sub-divisional rainfall in India. Theoretical and Applied Climatology, 139, 3–4, 949–963.
  • Kumar,S. ,Dwivedi S.K., Basu S., Kumar G., Mishra J.S., Koley T.K., K.K. Rao, A.K. Choudhary, Mondal S.,Kumar S.,Bhakta N., Bhatt B.P., Paul, R.K., Kumar A., (2020). Anatomical, agro-morphological and physiological changes in rice under cumulative and stage specific drought conditions prevailed in eastern region of India. Field Crops Research, 245 (107658).
  • Ray, M., Singh, K.N., Ramasubramanian, V., Paul, R.K., Mukherjee, A. and Rathod, S. (2020). Integration of Wavelet Transform with ANN and WNN for Time Series Forecasting: An Application to Indian Monsoon Rainfall. National Academy of Science Letter. Volume 43, pages 509–513. https://doi.org/10.1007/s40009-020-00887-2.
  • Paul, R.K., Sarkar, S., Mitra, D., Panwar, S., Paul, A.K. and Bhar, L.M. (2020) Wavelets based estimation of trend in sub-divisional rainfall in India. Mausam, 71 (1), 551-560.
  • Mitra, D. and Paul, R.K. (2020). Forecasting of price of rice in India using long memory time series model. National Academy of Science Letter, 44, pages 289–293. DOI 10.1007/s40009-020-01002-1.
  • Paul, R.K., Vennila, S., Yadav, S.K., Bhat, M.N., Kumar, M., Chandra, P., Paul, A.K. and Prabhakar, M. (2020). Weather based Forecasting of Sterility Mosaic Disease in Pigeonpea using Machine Learning Techniques and Hybrid Models. Indian Journal of Agricultural Sciences, 90 (10), 1952-1958. http://krishi.icar.gov.in/jspui/handle/123456789/47600.
  • Borah, P., Gujre, N., Rene, E.R., Rangan, L., Paul, R.K., Karak, T. and Mitra, S. (2020). Assessment of mobility and environmental risks associated with copper, manganese and zinc in soils of a dumping site around a Ramsar site. Chemosphere, Volume 254,126852.
  • Das, T., Paul, R.K., Bhar, L.M. and Paul, A.K. (2020). Application of Machine Learning Techniques with GARCH Model for Forecasting Volatility in Agricultural Commodity Prices. Journal of The Indian Society of Agricultural Statistics, 74(3): 187–194.
  • Deka, H., Barman, T., Sarmah, PP, Devi, A., Tamuly, P., Paul, R. K., and Karak, T. (2020). Quality characteristics of infusion and health consequences: a comparative study between orthodox and CTC green teas. RSC Advance, 10, 32833–32842. DOI: 10.1039/D0RA06254E.
  • Borgohain, A., Konwar, K., Buragohain, D., Varghese, S., Dutta, A. K., Paul, R. K., Khare, P. and Karak, T. (2020). Temperature effect on biochar produced from tea (Camellia sinensis L.) pruning litters: A comprehensive treatise on physico-chemical and statistical approaches. Bioresource Technology, 318, 124023. https://doi.org/10.1016/j.biortech.2020.124023.
  • Vennila S, Shabistana Nisar , Murari Kumar , Yadav Sk, Paul RK, Srinivasa Rao M and Prabhakar M. (2020). Impact of Climate Variability on Species Abundance of Rice Insect Pests across Agro Climatic Zones of India. Journal of Agrometeorology 22, 60-67.
  • Saxena, R., Paul, RK and Kumar, R. (2020). Transmission of price shocks and volatility spillovers across major onion markets in India. Agricultural Economics Research Review, 33 (347-2020-1414). DOI: 10.22004/ag.econ.304155.
  • Yeasin, Md., Singh, K.N., Lama, A. and Paul, R.K. (2020). Modelling Volatility Influenced by Exogenous Factors using an Improved GARCH-X Model. Journal of The Indian Society of Agricultural Statistics. 74(3): 209–216. http://krishi.icar.gov.in/jspui/handle/123456789/44375.
  • Kumar, P, Badal, PS, Paul, RK, Jha, GK, Venkatesh, P., Kingsly, IT, Kamalvanshi, V., Balasubramanian, M and Anbukkani, P. (2020). Empowering farmers through future price information: A case study of price forecasting of Brinjal in eastern Uttar Pradesh. Indian Journal of Economics and Development, 16(4):  479-488. DOI:10.35716/IJED/2.
  • Sahu, T.K., Gurjar, A.K.S., Meher, P.K., Varghese, C., Marwaha, S., Rao, G.P., Rai, A., Guleria, N., Basagoudanavar, S.H., Sanyal, A. and Rao, A.R. (2020). Computational insights into RNAi-based therapeutics for foot and mouth disease of Bos taurus. Scientific Reports10(1),1-13.
  • Meher, P.K., Satpathy, S. and Rao, A.R. (2020). miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides. Scientific Reports, 10(1),1-12. doi: 10.1038/s41598-020-71381-4.
  • Navathe, S., Yadav, P.S., Chand, R., Mishra, V.K., Vasistha, N.K., Meher, P.K., Joshi, A.K. and Gupta, P.K., 2020. ToxA–Tsn1 interaction for spot blotch susceptibility in Indian wheat: An example of inverse gene-for-gene relationship. Plant Disease, 104(1), 71-81.
  • Nath, K., Jain, R., Marwaha, S., Roy, H.S. (2020). Identification of optimal crop plan using nature inspired metaheuristic algorithms. Indian Journal of Agricultural Sciences 90 (8): 1587–1592.
  • Das, S. and Rai, S.N. (2020). Statistical Approach for Biologically Relevant Gene Selection from High-Throughput Gene Expression Data. Entropy, 22(11), 1205. doi.org/10.3390/e22111205.
  • Das, S., McClain, C, J., and Rai, S.N. (2020). Fifteen Years of Gene Set Analysis for Genomic Studies: A Review of Statistical Approaches and Future Challenges. Entropy, 22(4), 427, org/10.3390/e22040427.
  • Das S, Chhuria S, Rouchka EC, Rai SN. (2020). A Computational Network Biology Approach to Understand Salinity Stress Response in Rice (Oryza Sativa L.). Bioinform. Int., 1(1), 1003.
  • Pradhan, U.K., Anand, P., Sharma, N.K., Kumar, P., Kumar, A., Pandey, R., Padwad, Y., Shankar, R., (2020). Various RNA-binding proteins and their conditional networks explain miRNA biogenesis and help to reveal the potential SARS-CoV-2 host miRNAome system. bioRxiv. https://doi.org/10.1101/2020.06.18.156851.
  • Moharana,P.C., Jena,R.K., Pradhan,U.K., Nogiya,M., Tailor,B.L., Singh,R.S. and Singh,S.K(2020). Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India. Precision Agriculture, 21:426-448. https://doi.org/10.1007/s11119-019-09671-9.
  • Yeasin, M., Singh, K.N., Lama, A. and Paul, R.K. (2020). Modelling Volatility Influenced by Exogenous Factors using an Improved GARCH-X Model. Journal of the Indian Society of Agricultural Statistics, 74(3): 209–216. http://krishi.icar.gov.in/jspui/handle/123456789/44375.

 

2019

  • Sarkar, S., Paul, R.K., Paul, A.K. and Bhar, L.M. (2019). Wavelet based Multi-scale Auto-Regressive (MAR) Model: An Application for Prediction of Coconut Price in Kerala. Journal of The Indian Society of Agricultural Statistics, 73 (1), 1-10.  http://krishi.icar.gov.in/jspui/handle/123456789/42975.
  • Paul, R.K., Das, T., Panwar, S., Paul, A.K. and Bhar, L.M. (2019). Volatility and spillover in onion prices in major markets of Karnataka, India. Indian Journal of Agricultural Marketing, 33 (2), 65-76.
  • N M Ahmadi, T K Das, N Nasrat, S S Rathore and A K Paul (2019). Effect of phosphorus on growth and yield of maize (Zea mays) under arid conditions of Kandahar, Afghanistan. Indian Journal of Agronomy, 64 (4): 538-540.
  • Prajneshu and Ghosh, Himadri (2019).  Stochastic Differential Equation Models and Their Applications to Agriculture: An Overview. Statistics and Applications, 17, 73-83. http://krishi.icar.gov.in/jspui/handle/123456789/42313.
  • Ghosh, H. and Prajneshu (2019). Optimum fitting of Richards growth model in random environment. Journal of Statistical Theory and Practice,13, online. DOI:10.1007/s42519-018-0004-9.
  • Paul, R.K., Vennila, S., Bhat, M.N., Yadav, S.K., Sharma, V.K., Nisar, S. and Panwar,S. (2019). Prediction of early blight severity in tomato (Solanum lycopersicum) by machine learning technique. Indian Journal of Agricultural Sciences, 89 (1): 169-175.
  • Paul, R.K., Das, T., Panwar, S., Paul, A.K. and Bhar, L.M. (2019). Volatility and spillover in onion prices in major markets of Karnataka, India Indian Journal of Agricultural Marketing, 33 (2), 65-76.
  • Panwar, S., Kumar, N., Kumar, A., Paul, R.K., and Sarkar, S.K. (2019). Analysis of trend in area, production and productivity of okra (Abelmoschus esculentus) in India. Current Horticulture, 7 (2) :56. DOI:5958/2455-7560.2019.00021.9.
  • Panwar, S., Kumar, A., Paul, R.K., Alam, N.M., Tomar, S., Kumar, N. and Rathore, A. (2019). An alternative method for yield forecasting using weather indices approach and non-linear statistical modelling. Indian Journal of Extension Education, 55 (2), 111-115.
  • Saxena, R., Singh, N.P, Paul, R.K. and Kumar, R. (2019). Market linkages for the major onion markets in India. Indian Journal of Horticulture76 (1), 133-140. DOI:10.5958/0974-0112.2019.00019.7.
  • Sarkar, S., Paul, R.K., Paul, A.K. and Bhar, L.M. (2019). Wavelet based Multi-scale Auto-Regressive (MAR) Model: An Application for Prediction of Coconut Price in Kerala. Journal of The Indian Society of Agricultural Statistics 73 (1), 1-10. http://krishi.icar.gov.in/jspui/handle/123456789/42975.
  • Vennila S, Paul R K, Bhat M N, Yadav S K, Vemana K, Chandrayudu E, Nisar S, Kumar M, Tomar A, Rao M S and Prabhakar M. (2019). Impact of climate variability on recent and future status of jassid infestation in groundnut at Kadiri, a hot arid region of A.P. State. Indian Journal of Plant Protection, 47 (1&2):66-68.
  • Meher PK, Sahu TK, Gahoi S, Tomar R and Rao AR. (2019). funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model. BMC Genetics , 20:2. DOI:10.1186/s12863-018-0710-z.
  • Meher, P.K., Sahu, T.K., Gahoi, S., Satpathy, S. and Rao, A.R., (2019). Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition. Gene705, 113-126. DOI: 10.1016/j.gene.2019.04.047.
  • Gupta, M.C., Sharma, A.K., Singh, A.K., Roy, H. S., Bhadauria, S.S. (2019). Assessment of Genetic Diversity in Thirty-Five Genotypes of Oilseed Brassica Species using Principal Component Analysis. International Journal of Current Microbiology and Applied Sciences. 8(1). https://doi.org/10.20546/ijcmas.2019.801.039.
  • Manglesh Kumari, Shweta Thakur, Ajay Kumar, Robin Joshi, Prakash Kumar, Ravi Shankar, Rajiv Kumar (2019). Regulation of color transition in purple tea (Camellia sinensis), Planta 251(35):35,  https://doi.org/ 10.1007/s00425-019-03328-7.

2018

  • Kshandakar, Shashank, Verma, Med Ram, Singh, Yashpal, Kumar, Sanjay, Paul, Amrit Kumar (2018). Effect of clinical mastitis on lactation curves of Murrah buffaloes. Indian Journal of Animal Sciences, 88(5), 585-592.
  • Kumar. P., Bhar, L.M., Paul, A.K., Das, S. and Roy, H. S. (2018). Development of Composite Stability Measure using Multi Criteria Decisions Making (MCDM) Techniques. Journal of the Indian Society of Agricultural Statistics, 72(2), 121–127.
  • Mitra, D., Paul, R.K., Paul, A.K. and Bhar, L.M. (2018). Forecasting Time-Series Allowing for Long Memory and Structural Break. Journal of Indian Society of Agricultural Statistics, 72(1), 49-60.
  • Mondal Surajit, Das Anupam, Pradhan Sanatan, Tomar R.K., Behera U.K. , Sharma A.R., Paul, A.K. and Chakraborty Debashis (2018). Impact of Tillage and Residue Management on Water and Thermal Regimes of a Sandy Loam Soil under Pigeonpea-Wheat Cropping System. Journal of the Indian Society of Soil Science, Vol. 66, No. 1, pp 40-52.
  • Yashavanth, B.S., Singh, K.N., Singh, Paul, A.K. and Kumar, Amrender (2018). An Empirical Evaluation of Parameter Shrinkage Techniques for Vector Autoregressive Models. Journal of the Indian Society of Agricultural Statistics,72(2), 113-120.
  • Ghosh, H. and Prajneshu (2018). Gompertz Stochastic Differential Equation Grwoth Model with Exogenous Variables and Time-Dependent Diffusion. Journal of the Indian Society of Agricultural Statistics, 72(2), 97-104. http://krishi.icar.gov.in/jspui/handle/123456789/42849.
  • Ghosh, Himadri and Prajneshu (2018).  Von bertalanffy Stochastic Differential Equation Growth Model with Multiplicative Noise. Journal of Statistics and Management Systems, 21, 1407-1417. https://doi.org/10.1080/09720510.2018.1467644.
  • Mitra, D., Paul, R.K., Paul, A.K. and Bhar, L.M. (2018). Forecasting Time-Series Allowing for Long Memory and Structural Break. Journal of Indian Society of Agricultural Statistics, 72(1), 49-60.
  • Hanumanthaiah, R., Singh, A., Rathod, S. and Paul, R. K. (2018). Wavelet analysis for Forecasting Prices and Arrivals of Black Pepper in Karnataka, India. International Journal of Current Microbiology and Applied Sciences 7(5): 677-687.
  • Mitra, D., Paul, R K., Kumar, A., and Panwar, S., (2018). Multivariate time series model for forecasting urad price in different zones of India. Indian Journal of Agricultural Marketing 31 (3): 42-47.
  • Paul, R. K., Vennila, S., Singh, N., Chandra, P., Yadav, S.K., Sharma, O.P., Sharma V.K., Nisar S., Bhat, M.N., Rao, M. S. and Prabhakar, M. (2018). Seasonal Dynamics of Sterility Mosaic of Pigeonpea and its Prediction using Statistical Models for Banaskantha Region of Gujarat, India. Journal of The Indian Society of Agricultural Statistics, 72 (3), 213-223. http://krishi.icar.gov.in/jspui/handle/123456789/43015.
  • Pardhi, R., Singh, R. and Paul, R.K. (2018). Price Forecasting of Mango in Lucknow Market of Uttar Pradesh. International Journal of Agriculture, Environment and Biotechnology, 11 (2) 357-363. DOI: 10.30954/0974-1712.04.2018.17.
  • Bora, K., Sarkar, D., Konwar, K., Payeng, B., Sood, K., Paul, R.K., Datta, R., Das, S., Khare, P., Karak, T. (2018). Disentanglement of the secrets of aluminium in acidophilic tea plant (Camellia sinensis L.) influenced by organic and inorganic amendments. Food Research International, 120:851-864. https://doi.org/10.1016/j.foodres.2018.11.049.
  • Pardhi, R., Singh, R. and Paul, R.K. (2018). Price Forecasting of Mango in Varanasi Market of Uttar Pradesh. Current Agriculture Research Journal, 6 (2), 218-224. DOI:10.12944/CARJ.6.2.12.
  • Panwar, S., Kumar, A., Singh, K.N., Paul, R.K., Gurung, B., Ranjan, R., Alam, N.M., Rathore, A. (2018). Forecasting of crop yield using weather parameters–two step nonlinear regression model approach. Indian Journal of Agricultural Sciences, 88 (10), 117-119.
  • Karak, T., Abollino, O., Paul, R. K., Dutta, A. K., Giacomino, A. and Boruah, R. K. (2018). Achievability of municipal solid waste compost for tea (Camellia sinensis L.) cultivation: Does cadmium pose threat in tea infusion vis-à-vis human health? CLEAN – Soil, Air, Water, 46, 1:13. DOI: 10.1002/clen.201800093.
  • Vennila, S., Paul, R.K., Bhat, M.N., Yadav, S.K., Vemana, K., Chandrayudu, E., Nisar, S., Kumar, M., Tomar, A., Rao, M.S. and Prabhakar, M. (2018). Abundance, infestation and disease transmission by thrips on groundnut as influenced by climatic variability at Kadiri, Andhra Pradesh. Journal of Agrometeorology, 20 (3), 227-233.
  • Sinha, K., Panwar,S., Alam, W., Singh,K.N., Gurung, B., Paul, R.K., and Mukherjee, A. (2018). Price volatility spillover of Indian onion markets: A comparative study. Indian Journal of Agricultural Sciences,88 (1), 114-120. http://krishi.icar.gov.in/jspui/handle/123456789/47467.
  • Meher P.K., Sahu TK., Mohanty J., Gahoi S., Purru S., Grover M and Rao AR (2018). nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine. Frontiers in Microbiology 9:1100. doi: 10.3389/fmicb.2018.01100.
  • Sharma D., Tiwari A., Sood S., Jamra G., Singh N.K., Meher P.K. and Kumar A. (2018). Genome wide association mapping of agro-morphological traits among a diverse collection  of finger millet (Eleusine coracana L.) genotypes using SNP markers. PLoS ONE, 13(8):  e0199444. https://doi.org/10.1371/journal.pone.0199444.
  • Meher P.K., Sahu T.K., Gahoi S., and Rao A.R. (2018). ir-HSP: Improved recognition of heat shock proteins, their families and sub-types based on g-spaced di-peptide features and support vector machine. Frontiers in Genetics, 8, 235. doi: 10.3389/fgene.2017.00235.
  • Kumar, J., Saripalli, G., Gahlaut, V., Goel, N., Meher, P.K., Mishra, K.K., Mishra, P.C., Sehgal, D., Vikram, P., Sansaloni, C. and Singh, S., (2018). Genetics of Fe, Zn, β-carotene, GPC and yield traits in bread wheat (Triticum aestivum L.) using multi-locus and multi-traits GWAS. Euphytica, 214(11), 1-17. DOI:10.1007/s10681-018-2284-2.
  • Gupta,M. C., Sharma,A. K., Singh, A. K., Roy, H. S. and Bhadauria, S. S.(2018). Assessment of genetic divergence in thirty-five genotypes of oilseed Brassica species.Journal of Pharmacognosy and Phytochemistry,7(6), 2076-2080. http://krishi.icar.gov.in/jspui/handle/123456789/42316.Misra, T., Priyadarshini, S., ARORA, A., Marwaha, S., Roy, H. S. and Ray, M.(2018). A comparative study of chlorophyll content estimation techniques through image analysis, Journal of Crop and Weed.14(3),165-168.Lama, A., Singh, K.N., Gurung, V., Singh. R. S., and Roy, H.S., (2018). Observation of Time Series Model. Bhartiya Krishi Anusandhan Patrika. 33. 58-61.
  • Kumar,P., Bhar, L. M., Paul, A. K., Das, S. and Roy, H. S.(2018). Development of Composite Stability Measure by using Multi Criteria Decisions Making (MCDM) Techniques. Journal of the Indian Society of Agricultural Statistics, 72(2),121–127.
  • Gurumurthy, Ajay Arora1, Basudeb Sarkar, Harikrishna, V.P. Singh, S.K. Meena, Prakash Kumar and P.K. Singh (2018).Individual Heat and Combined Heat Drought Stresses in Wheat: Variation in NDVI and Canopy Temperature, International Journal of Current Microbiology and Applied Sciences, 7(10): 2676-2684 https://doi.org/10.20546/ijcmas.2018.710.311.
  • Prakash Kumar, Krishan Lal, Anirban Mukherjee, Upendra Kumar Pradhan, Mrinmoy Ray and Om Prakash (2018). Development of Composite Stability Measure using Multi Criteria Decisions Making (MCDM) Techniques. Journal of the Indian Society of Agricultural Statistics, 72(2) 121-127.
  • Prakash Kumar, Anil Kumar, Sanjeev Panwar, Sukanta Dash, Kanchan Sinha and Mrinmoy Ray (2018). Role of big data in Agriculture-A Statistical Prospective, Annals of Agriculture Research, 39:210-215.
  • Gurumurthy, Ajay Arora1, Basudeb Sarkar, Harikrishna, V.P. Singh, S.K. Meena, Prakash Kumar and P.K. Singh (2018).Individual Heat and Combined Heat Drought Stresses in Wheat: Variation in NDVI and Canopy Temperature, International Journal of Current Microbiology and Applied Sciences, 7(10): 2676-2684 https://doi.org/10.20546/ijcmas.2018.710.311.
  • Das, S., Rai, A., Mishra, D.C. Rai, S.N. (2018). Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci. Scientific Reports, 8, 2391. org/10.1038/s41598-018-19736-w.
  • Das, S., Rai, A., Mishra, D.C. Rai, S.N.(2018). Statistical approach for selection of biologically informative genes. Gene, 655, 71-83. (doi: 10.1016/j.gene.2018.02.044).
  • Kour, S, Shitap,M.S, Pradhan,U.K. and Vaishnav P.R.(2018). Forecasting of rice yield based on weather parameters in Kheda District of Gujarat, India. International Journal of Agricultural and Statistical Sciences. 14(2):611-615, http://krishi.icar.gov.in/jspui/handle/123456789/42332.
  • Kour,S., Pradhan, U.K., Patel,J.S. and Vaishnav,P.R.(2018). Comparative Study of Selection Indices Based on Different Weights in Forage Sorghum [Sorghum bicolor (L.) Moench]. Journal of Crop and Weed.14(1): 17-23.
    http://krishi.icar.gov.in/jspui/handle/123456789/42338
    .
  • Kumar, P., Lal, K., Mukherjee, A., Pradhan, U.K., Ray, M and Prakash, O.(2018). Advanced row column designs for animal feed experiments. Indian Journal of Animal Sciences, 88(4):499-503. http://krishi.icar.gov.in/jspui/handle/123456789/42989.
  • Gurung, B., Singh, K.N., Shekhawat, R.S. and Yeasin, M., An insight into technology diffusion of tractor through Weibull growth model. Journal of Applied Statistics, 45(4), pp.682-696. https://doi.org/10.1080/02664763.2017.1289504.

 

2017

  • Anjoy, P. and Paul, R.K. (2017). Wavelet based hybrid approach for forecasting volatile potato price. Journal of the Indian Society of Agricultural Statistics, 71(1), 7–14
  • Anjoy, P., Paul, R. K., Sinha, K., Paul, A. K. and Ray, M. (2017) A hybrid wavelet based neural networks model for predicting monthly WPI of pulses in India. Indian Journal of Agricultural Sciences, 87 (6): 834–839
  • Das, Samarendra, Meher P.K, Rai A, Bhar L.M and Mandal B.N. (2017). Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.). PLoS ONE 12(1): e0169605.
  • Das, Samarendra, Meher , P.K., Pradhan U.K., Paul A.K. (2017). Inferring gene regulatory networks using Kendall’s tau correlation coefficient and identification of salinity stress responsive genes in rice. Current Science 112 (6): 1257-1262.
  • Das, Samarendra, Paul, A.K., Wahi, S.D., Pradhan, U.K. (2017). Comparative performance of imputation methods for different proportions of missing data in classification of crop genotypes. Journal of the Indian Society of Agricultural Statistics, 71(2): 147–153
  • Ghosh, Himadri (2017). Nonorthogonal optimal partial diallel cross designs for consistent estimation of heritability. Journal of Japan Statistical Society 47(1): 37-48
  • Ghosh, Himadri and Prajneshu (2017). Gompertz growth model in random environment with time-dependent diffusion. Journal of Statistical Theory and Practice.
  • Ghosh, Himadri and Prajneshu (2017). Richards Stochastic Differential Equation Growth model and its Application. Journal of the Indian Society of Agricultural Statistics 71(2):127-137
  • Gurung, B., Singh, K.N., Paul, R. K., Panwar, S., Gurung, B. & Lepcha, L. (2017). An Alternative Method for Forecasting Price Volatility by Combining Models. Communications in Statistics – Simulation and Computation, 46 (6), 4627-4636
  • Karak, T., Bora, K., Paul, R.K., Das, S., Khare, P., Dutta, A.K. and Boruah, R.K. (2017). Paradigm shift of contamination risk of six heavy metals in tea (Camellia sinensis L.) growing soil: A new approach influenced by inorganic and organic amendments. Journal of Hazardous Materials, 338, 250–264.
  • Karak, T., Kutu, F. R., Nath, J. R., Sonar, R., Paul, R. K., Boruah, R. K., Sanyal, S., Sabhapondit, S. and Dutta, A. K. (2017). Micronutrients (B, Co, Cu, Fe, Mn, Mo and Zn) content in made tea (Camellia sinensis L.) and tea infusion with health prospect: A critical review. Critical reviews in food science and nutrition, 57(14), 2996-3034
  • Karak, T., Kutu, F.R., Paul, R.K., Bora, K., Das, D.K., Khare, P., Das, K., Dutta, A.K., Boruah, R.K. (2017). Co-composting of cow dung, municipal solid waste, roadside pond sediment and tannery sludge: Role of human hair. International journal of Environmental Science and Technology, 14(3), 577-594.
  • Kour, S., Pradhan, U.K., Paul, R.K. and Vaishnav, P.R. (2017). Forecasting of Pearl millet productivity in Gujarat under time series framework. Economic Affairs, 62 (1), 121-127.
  • Kumar, N., Mukherjeea, I., Sarkar, B. and Paul, R. K. (2017). Degradation of tricyclazole: Effect of moisture, soil type, elevatedcarbon dioxide and Blue Green Algae (BGA). Journal of Hazardous Materials, 321, 517–527
  • Kumar Paritosh, Kaur Ravinder, Suman Archna, Singh Alpana and Kumar Prakash. (2017). Nickel Bioremediation by Different Wetland Macrophytes Root Associated Bacteria. Chemical Science Review and Letters. 6(22), 987-994.
  • Kumar Prakash, Lal Krishanl, Mukherjee Anirban Kumar Upendra Pradhan Ray Mrinmoy  and Prakash.Om (2018). Advanced Row-Column Designs for Animal feed Experiments. The Indian journal of animal sciences.vol. 88 (4).
  • Meher P.K., Sahu TK., Banchariya A. and Rao AR. (2017). DIRProt: A computational approach for discriminating insecticide resistant proteins from non-resistant proteins. BMC Bioinformatics, 18: 190.
  • Meher, P.K., and Rao, A.R. (2017) A Non-parametric Regression based Computational Approach for Prediction of Donor Splice Sites. Journal of the Indian Society of Agricultural Statistics, 71(2): 159–166.
  • Meher, P.K., Sahu,T.K., Saini, V. and Rao, A.R. (2017). Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Scientific Reports 7, 42362.
  • Mitra, D., Paul, R.K. and Pal, S. (2017). Hierarchical time-series models for forecasting oilseeds and pulses production in India. Economic Affairs, 62 (1), 103-111.
  • Mitra, D. and Paul, R.K. (2017). Hybrid time-series models for forecasting agricultural commodity prices. Model Assisted Statistics and Applications, 12, 255–264.
  • Mitra, D., Paul, R K. and Paul, A. K. (2017). Statistical modelling for forecasting volatility in potato prices using ARFIMA-FIGARCH model. Indian Journal of Agricultural Sciences 88 (2): 268–72
  • Mitra, D., Paul, R.K., Kumar, A. and Panwar, S. (2017). Multivariate time series model for forecasting Urad price in different zones of India. Indian Journal of Agricultural Marketing, 31 (3), 36-41.
  • Okendro, N. Singh, Gopimohan, N. Singh, Paul, A.K., Kumar, Surinder and Gangmei, Sobha (2017). Modelling of Population Growth for s Seasonal Incidence of Mustart Aphid. Lipaphis erysimi. Journal of the Indian Society of Agricultural Statistics, 71(2): 155–157
  • Paritosh Kumar, Ravinder Kaur, Archna Suman, Alpana Singh and Prakash Kumar. (2017). Nickel Bioremediation by Different Wetland Macrophytes Root Associated Bacteria. Chemical Science Review and Letters. 6(22), 987-994.
  • Paul, R.K. (2017). Modelling long memory in maximum and minimum temperature series in India. Mausam, 68 (2), 317-326
  • Paul, A.K., Paul, R.K., Prabhakaran, V.T., Singh, I. and Singh, O. (2017). Study of different parametric stability measures when the basic data/variables are non-normal. Indian Journal of Agricultural Sciences, 87 (9): 1252–1256.
  • Paul, R.K. (2017). Modelling long memory in maximum and minimum temperature series in India. Mausam, 68 (2), 317-326.
  • Prajneshu, Ghosh, Himadri and Pandey N.N. (2017). Fitting of von Bertalanffy growth model: Stochastic differential equation approach. Indian J. Fish., 64(3): 24-28
  • Rathod, S., Singh, K.N., Paul, R.K., Meher, S.K., Mishra, G.C., Gurung, B., Ray, M. and Sinha, K. (2017). An Improved ARFIMA Model using Maximum Overlap Discrete Wavelet Transform (MODWT) and ANN for Forecasting Agricultural Commodity Price. Journal of the Indian Society of Agricultural Statistics, 71(2), 103–111
  • Rathod, S., Singh, K.N., Arya, P. Ray, M., Mukherjee, A., Sinha, K. and Kumar, P. (2017). Towards the effective forecasting of maize yield through ARIMA-Genetic Algorithm approach. Outlook on agriculture, 46(4), 265-271.
  • Roy, H.S., Paul, R.K., Bhar, L.M., Kumar, A. (2017). Modelling binary data by Bayesian logistic regression with random intercepts. Journal of the Indian Society of Agricultural Statistics, 71(3), 207-215.     
  • Samanta, S., Paul, R.K. and Roy, A. (2017). Modelling repeated measures with multiple endpoints. Journal of the Indian Society of Agricultural Statistics, 71(2), 113–120
  • Saxena, R., Singh, N. P., Choudhary, B. B., Balaji S.J., Paul, R. K., Ahuja, U., Joshi, D., Kumar, R. and Khan, M.A. (2017). Can Livestock Sector be the Game Changer in Enhancing the Farmers’ Income? Reinvesting Thrust with Special Focus on Dairy Sector. Agricultural Economics Research Review, 30, 59:76.
  • Sinha, K., Gurung, B., Paul, R.K., Kumar, A., Panwar, S., Alam, W., Ray, M. and Rathod, S. (2017). Volatility Spillover using Multivariate GARCH Model: An Application in Futures and Spot Market Price of Black Pepper. Journal of the Indian Society of Agricultural Statistics, 71(1), 21–28
  • Srivastava, R., Bajaj, D., Sayal, Y.K., Meher, P.K., Upadhyaya, H.D., Kumar, R., Tripathi, S., Bharadwaj, C., Rao, A.R., Parida S.K. (2016). Genome-wide development and deployment of informativeintron-spanning and intron-length polymorphism markers forgenomics-assisted breeding applications in chickpea. Plant Science, 252: 374–387.
  • Yashavanth, B. S., Singh, K. N., Paul, A. K. and Paul, R. K. (2017). Forecasting prices of coffee seeds using Vector Autoregressive Time Series Model. Indian Journal of Agricultural Sciences, 87 (6): 754–758.
  • Ziar Y.K., Das T.K. , Hakimi A.R., Shekhawat Kapila and Paul A.K. (2017) Chemical weed management in wheat (Triticum aestivum) under semi-aridconditions of Kandahar, Afghanistan. Indian Journal of Agronomy, 62 (3) ; 359-362.

 2016

  • Das Samarendra, Paul A.K., Wahi S.D., Raman R.K. (2016). A comparative study of various classification techniques in multivariate skew-normal data. of the Ind. Soc. of Ag. Stat. 69 (3): 271-280.
  • Das, K., Dang, R., Sutar V, G., Einstein, J. W., Paul, R. K., and Karak, T. (2016). Influence of Metals In Soil on The Comparative Phytochemical Characterization and Antioxidant Study of Indian Golden Shower (Cassia Fistula). Indian Journal of Pharmaceutical Education and Research, 50(3):9-24
  • Das, P., Paul, A.K. and Paul, R.K. (2016). Non-linear mixed effect models for estimation of growth parameters in Goats. Journal of the Indian Society of Agricultural Statistics, 70(3), 205-210
  • Das, Samarendra, Paul A.K., Wahi S.D., Raman R.K. (2016). A comparative study of various classification techniques in multivariate skew-normal data. of the Ind. Soc. of Ag. Stat. 69 (3): 271-280.
  • Das,S., Meher,P.K., Pradhan, U.K., Paul, A.K. (2016) Inferring gene regulatory networks using Kendall’s tau correlation coefficient and identification of salinity stress responsive genes in rice. Current Science, 112(6): 1257-1262.
  • Ghosh, Himadri, Chowdhury, Sumit, and Prajneshu (2016). An improved – fuzzy time series method of forecasting based on L-R fuzzy sets and its application. Appl. Stat., 43,1128-1139
  • Gurung, B., Paul, R. K., Singh, K.N., Panwar, S., Lama, A. and Lepcha, L. (2016). An alternative approach to capture cyclical and volatile phenomena in time-series data. Model Assisted Statistics and Application,11(3): 221-230
  • Jaiswal, V., Gahlau,t V.,Meher, P.K., Mir, R.R., Jaiswal, J.P., Rao, A.R., Balyan, H.S. and Gupta, P.K. (2016). Genome wide single locus single trait, multi-locus and multi-trait association mapping for some important agronomic traits in common wheat ( aestivum l.). PLoS ONE, 11(7): e0159343.
  • Joshi, D., Anwer, E., Kumar, R., Rana, S., Paul, R. K., Kumar, A. and Saxena, R. (2016). Agricultural marketing system in Uttarakhand: Structure and functioning. Economic Affairs, 61(3): 549-559
  • Kumar, M., Paul, R. K. and Singh, B.K. (2016). Estimating area, production and productivity trends of cotton crop in Haryana state. Cotton Res. Dev. 30 (2), 317-323.
  • Kumar, P., Lal, K., Parsad, R. and Gupta, V.K. (2016). Block Designs with Nested Row-Column for Factorial Experiments, Communications in Statistics – Theory and Methods, DOI: 10.1080/03610926.2016.1161800.
  • Lama, A., Jha, G. K., Gurung, B., Paul, R. K., Bharadwaj, A. and Parsad, R. (2016). A Comparative Study on Time-delay Neural Network and GARCH Model for Forecasting Agricultural Commodity Price Volatility. Journal of the Indian Society of Agricultural Statistics 70(1): 7-18
  • Lama, A., Jha, G. K., Gurung, B., Paul, R. K. and Sinha, K. (2016). VAR-MGARCH models for volatility modelling pulses prices. Journal of the Indian Society of Agricultural Statistics, 70(2): 145-151
  • Meher, P.K., Sahu, T.K. and Rao, A.R. (2016). Prediction of donor splice sites using random forest with a new sequence encoding approach. BioData Mining, 9: 4.
  • Meher, P.K., Sahu, T.K., Rao, A.R. and Wahi, S.D. (2016). Discriminating coding from non-coding regions based on codon structure and methylation-mediated substitution: An application in rice and cattle. Computers and Electronics in Agriculture, 129: 66–73.
  • Meher, P.K., Sahu, T.K., Rao, A.R.,Wahi, S.D. (2016). A computational approach for prediction of donor splice sites with improved accuracy. Journal of Theoretical Biology, 404: 285–294.
  • Meher, P.K., Sahu, T.K., Rao, A.R.,Wahi, S.D. (2016). Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features. Algorithms for Molecular Biology, 11:16.
  • Meher, P.K., Sahu,T.K. and Rao, A.R. (2016). Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier. Gene, 592: 316–324.
  • Meher, PK., Sahu, TK., Rao, AR. (2016). Performance evaluation of neural network, support vector machine and random forest for prediction of donor splice sites in rice. Indian J. Genet. 76(2): 173-180.
  • Pal, S. and Ghosh, Himadri (2016) Web ECGR: Web Solution for Estimation of Compound Growth Rates Using Parametric and Nonparametric Methodologies.RASHI, 1(2), 7-15
  • Pal, S. and Paul, R. K. (2016). Modelling and Forecasting Sorghum Production in India using Hierarchical Time-Series Models. Indian Journal of Agricultural Sciences, 86 (6): 803–808.
  • Panwar, S., Kumar, A., Sarkar, S.K., Paul, R.K., Gurung, B. and Rathore, A. (2016). Forecasting of Common Carp fish production from ponds using nonlinear growth models – A modelling Approach. Journal of the Indian Society of Agricultural Statistics, 70(2): 139-144
  • Paul, A. K., Kundu, M. G., Paul, R. K. and Gurung, B. (2016). Usefulness of Growth Curve Parameters in early selection of pigs, Rashi, 1(2), 27–34
  • Paul, A. K., Paul, R.K., Singh, N.M.D., Wahi, S.D. and Singh, N.O. (2016). Genetic variability of growth curve parameters in goats: application of bootstrap techniques. Journal of the Indian Society of Agricultural Statistics, 70(3), 211-218
  • Paul, A.K and  Wahi S.D. (2016).  Estimation  of  heritability  under  correlated  Error, Project Report ICAR- IASRI New Delhi.
  • Paul, A.K., Kundu, M. G., Paul, R.K. and Gurung, B. (2016). Usefulness of Growth Curve Parameters in early selection of pigs.  Journal of the Society for Application of Statistics in Agriculture and Allied Sciences (SASAA), 1(2),  27-34.
  • Paul, R. K. and Sinha, K. (2016). Forecasting crop yield: a comparative assessment of ARIMAX and NARX model. RASHI, 1(1): 77-85.
  • Paul, R. K., Rana, S. and Saxena, R. (2016). Effectiveness of price forecasting techniques for capturing asymmetric volatility for onion in selected markets of Delhi. Indian Journal of Agricultural Sciences, 86 (3): 303–309
  • Paul, R.K., and Bhardwaj, S.P. (2016). Econometric modeling for optimal hedging in commodity futures: An empirical study of soybean trading. Economic Affairs, 61(3): 447-453.
  • Paul, R.K., Gurung, B., Paul, A.K. and Samanta, S. (2016). Long memory in conditional Journal of the Indian Society of Agricultural Statistics, 70(3), 243-254
  • Paul, R.K., Saxena, R. and Bhat, S.A. (2016). How Price Signals in Pulses are Transmitted across Regions and Value Chain? Examining Horizontal and Vertical Market Price Integration for Major Pulses in India. Agricultural Economics Research Review, 29: 75-86.
  • Ranganath, H.K., Ghosh, Himadri and Prajneshu (2016)Nonlinear Exponential Autoregressive Time-Series Model with Moving Average Errors: An Application”. Int. J. Agricult. Stat. Sci., Vol. 12, 409-414
  • Roy, H. S., Paul, R. K., Bhar, L. M. and Arya, P. (2016). Application of INAR model on the pest population dynamics in Agriculture. Journal of Crop and Weed, 12(2): 96-101.
  • Saxena, R., Joshi, D., Paul, R. K., Kumar, A., Anwer, E., Pal, K., Rana, S. and Chaudhary, K. R. (2016). How equipped are the regulated agricultural markets? Evidences based on selected markets in Uttarakhand. Economic Affairs-Quarterly journal of Economics, 61(2), 203-213
  • Sinha, K., Paul, R. K. and Bhar, M. (2016). Price Transmission and Causality in major onion markets of India, Rashi, 1(2), 35–40
  • Srivastava, R., Bajaj, D., Sayal, Y.K., Meher, P.K., Upadhyaya, H.D., Kumar, R., Tripathi, S., Bharadwaj, C., Rao, A.R., Parida S.K. (2016). Genome-wide development and deployment of informativeintron-spanning and intron-length polymorphism markers forgenomics-assisted breeding applications in chickpea. Plant Science, 252: 374–387.

2015

  • Bhar, L.M. (2015). Regression analysis diagnostics and remedial measures. Decision support system in agriculture using quantitative analysis. Agrotech Publishing Academy, ISBN: 978-81-8321-395-0. 245-270.
  • Ghosh, Himadri, Gurung, B. and Prajneshu (2015) Fitting EXPAR models through the extended Kalman filter.  Sankhya, Series B, 77-B, Part 1, 27-44
  • Ghosh, Himadri, Prajneshu and SandipanSamanta (2015). Corrigendum to ‘Fitting forecasts’. Statistics 49, 1422-22
  • Gurung, B., Singh, K. N., Paul, R. K., Arya, P., Panwar, S., Paul, A. K. and Lama, A. (2015). Fitting stochastic volatility model through genetic algorithm. International Journal of Agricultural and Statistical Sciences, 11, 257-264.
  • Himadri Ghosh, B. Gurung, and Prajneshu (2015). Kalman filter-based modelling and forecasting of stochastic volatility with threshold. Appl. Stat.,42, 492–507
  • Ojha, S and Bhar, LM (2015). Detection of outliers in designed experiments with correlated error. J. Ind. Soc. Agril. Statist., 69(1), 57-63.
  • Pal, S., Prajneshu and Ghosh, Himadri (2015). Development of a Novel Web-based WebECGR Package for Estimation of Compound Growth Rates for Monotonically Non-decreasing Situations. Econ. Res. Rev.28, 163-170
  • Pal, S., Prajneshu and Ghosh Himadri(2015). Estimation of Compound Growth Rates for Non-Monotonic Situations through Nonlineargrowth models using WebECGR package. Ind. Soc. Ag. Stat.,69, 95-100
  • Paul, A. K., Paul, R. K., Das, S., Behera, S. K. and Dhandapani, A. (2015), On some development of new nonparametric stability measures. Indian Journal of Agricultural Sciences, 85(8), 113-117.
  • Paul, A. K., Paul, R. K., Prabhakaran, V. T., Singh, I. and Dhandapani, A. (2015). Performance of parametric and non-parametric stability measures. Journal of the Indian Society of Agricultural Statistics, 69(3), 289-299
  • Paul, R. K., Birthal, P.S., Paul, A. K. and Gurung, B. (2015). Temperature trend in different agro-climatic zones in India. Mausam, 66(4), 841-846
  • Paul, R. K., Gurung, B., Samanta, S. and Paul, A. K. (2015). Modeling long memory in volatility for spot price of lentil with multi-step ahead Out-of-sample forecast using AR-FIGARCH Model. Economic Affairs-Quarterly journal of Economics, 60(3), 457-466.
  • Paul, R. K., Sinha, K. (2015). Spatial Market Integration among Major Coffee Markets in India. Journal of the Indian Society of Agricultural Statistics, 69(3), 281-287
  • Paul, R.K., Birthal, P.S., Paul, A.K. and Gurung, B. (2015). Temperature trend in different agro-climatic zones in India. Mausam, 66(4), 841-846
  • Paul, R.K., Gurang, B. and Paul, A.K. (2015). Modelling and forecasting of retail price of arhar dal in Karnal, Haryana. Indian Journal of Agricultural Science, 85 (1), 69-72.
  • Paul, RK, Bhar, LM, Panwar, S and Kumar, A (2015). Robust analysis of agricultural field experiments. Ind. J. Agric. Sci., 85(1), 55-58.
  • Paul, RK, Gurung, B, Samanta, S and Paul, AK (2015). Modeling long memory in volatility for spot price of lentil with multi-step ahead out-of-sample forecast using AR-FIGARCH Model. Eco. Affairs, 60(3), 457-466.
  • Raman, R.K., Paul, A. K., Das, Samarendra. and Wahi, S. D.(2015) Empirical Comparison of the performance of linear discriminant function under multivariate non-normal and normal data. J.Agricult.Stat.Sci..11(2), 403-409.
  • Sahu, T.K., Rao, A.R., Meher, P.K., Sahoo, B.C., Gupta, S. and Rai, A. (2015). Computational prediction of MHC class I epitopes for most common viral diseases in cattle (Bos taurus). Indian J Biochem Biophys, 52(1): 34-44.
  • Sarkar, R.K., Rao, A.R., Meher, P.K., Nepolean, T. and Mohapatra, T. (2015). Evaluation of random forest regression for prediction of breeding value from genome-wide SNPs. Journal of Genetics, 94(2): 187-192.
  • Singh, N. M. D., Paul, A. K. and Paul, R. K. (2015). Selecting Appropriate Nonlinear Growth Models Using Bootstrap Technique. Indian Journal of Animal Sciences, 85(8), 104-107.
  • Singh, S, Paul, AK, Paul, RK, Bhar, LM, Kumar, A and Alam, W (2015). Study of growth pattern of cattle under different error structures. Model Assisted Statist. Appl., 10, 109-115.
  • Karak, T., Sonar, I., Paul, R. K., Frankowski, M., Boruah, R. K., Dutta, A. K. and Das, D. K. (2015). Aluminium dynamics from soil to tea plant (Camellia sinensis L.): Is it enhanced by municipal solid waste compost application? Chemosphere, 119, 917-926.

2014

 Publisher: Biomass Energy Research Association; Biomass and Biofuels Association (Great Britain), Elsevier (ISSN: 0960-8524)

Articles

  • Alam, W., Chaturvedi, A., Singh, K. N., Kumar, A., Paul, A. K., Paul, R. K. and Sinha, K. (2014). Maximum likelihood and uniformly minimum variance unbiased estimation of p(y<x) for Gompertz distribution. International Journal of Agricultural and Statistical Sciences, 10(2), 267-274
  • Behera, S. K., Paul, A. K., Wahi, S. D., Iquebal, M. A., Das, S., Paul, R. K., Alam, W. and Kumar, A. (2014). Estimation of heritability of mastitis disease using moment estimators. International Journal of Agricultural and Statistical Sciences, 10(1), 243-247.
  • Bhaduri, Debarati, Purakayastha TJ, Bhar, LM, Patra, AK and Sarkar, Binoy (2014). Impact of integrated management on yield sustainability in relation to soil quality under a rice–wheat cropping system, Nat. Acad. Sci. Lett., 37(1), 25-31.
  • Bhar , LM (2014). Robustness of variance balanced block designs. Sankhya B, 76(2), 305-316.
  • Bhar, LM and Ojha, Sankalpa (2014). Outliers in multi-response experiments. Comm. Statist.- Theory Methods, 43(13), 2782-2798.
  • Bhardwaj, S. P., Paul, R. K., Singh, D. R. and Singh, K. N. (2014). An Empirical Investigation of ARIMA and GARCH models in Agricultural Price Forecasting. Economic Affairs-Quarterly journal of Economics, 59(3), 415-428
  • Bhattacharyya, P., Karak, T., Chakrabarti, K., Chakraborty, A., Paul, R. K. and Tripathi, S. (2014). Is Tsunami tremor jolted on microbial biomass and their activities in soils?-A case study in Andaman Island, India. Environmental Earth Sciences, 72, 1443-1452
  • Bioresource Technology, 169, 731–741.
  • Chattaraj, S., Chakraborty, D., Sehgal, V.K., Paul, R. K., Singh, S.D., Daripa, A. and Pathak, H. (2014). Predicting the impact of climate change on water requirement of wheat in the semi-arid Indo-Gangetic Plains of India. Agriculture, Ecosystems and Environment, 197, 174–183
  • Ghosh, Himadri, Sarkar, K.A. and Prajneshu (2014).  Fourier-autoregressive (F-AR) coefficient nonlinear time-series model for forecasting asymmetric cyclical data. J. An. Sci. ,84, 802-806
  • Ghosh, Himadri, Prajneshu and Samanta, S. (2014). Fitting of SETARMA nonlinear time-series model through Genetic algorithm and development of out-of-sample forecasts. Statistics, 48, 1166-1184
  • Gurung, B., Paul, R. K. and Ghosh, H. (2014). Fitting Smooth Transition Autoregressive nonlinear time-series model using Particle Swarm Optimization technique. Journal of the Indian Society of Agricultural Statistics, 68(3), 327-332.
  • Gurung, B., Paul, R. K. and Lepcha, L. (2014). Volatility and cointegration in export of livestock and marine products of India. Indian Journal of Animal Sciences, 84(11), 104-107.
  • Gurung, Bishal, Prajneshu and Ghosh, Himadri (2014). Forecasting volatile time-series data through Stochastic volatility model. J. Ag. Sci., 83, 1368-1371
  • Gurung, Bishal, Prajneshu and Ghosh, Himadri(2014). Stochastic volatility model fitting using Particle filter: An application. J. Ind. Soc. Ag. , 68, 343-350
  • Karak, T., Paul, R. K and Das, D. K. (2014). Thermodynamics of cadmium sorption on different soils of west Bengal, India. The Scientific World Journal, Article ID 216451, DOI: 10.1155/2014/216451
  • Karak, T., Paul, R. K., Boruah, R. K., Sonar, I., Bordoloi, B., Dutta, A. K. and Borkotoky, B. (2014). A Report on Major Soil Chemical Properties of Upper Assam: the Worldwide Fascinating Tea (Camellia sinensis) Producing Region in India. Pedosphere, 25(2), 316-328
  • Karak, T., Paul, R. K., Sonar, I., Sanyal, S., Ahmed, K. Z., Boruah, R. K., Das, D. K. and Dutta, A. K. (2014). Chromium in soil and tea (Camellia sinensis L.) infusion: Does soil amendment with municipal solid waste compost make sense? Food Research International, 64, 114–124
  • Karak, T., Sonar, I., Paul, R. K., Das, S. , Boruah, R. K., Dutta A. K , Das, D. K. (2014)  Composting of cow dung and crop residues using termite mounds as bulking agent, Bioresource Technology (BIORESOURCE TECHNOL)
  • Lepcha, L., Gurung, B., Paul, R. K. and Sinha (2014). Stochastic model for sticklac forecasting in India. Economic Affairs-Quarterly journal of Economics, 59(3), 479-483
  • Meher, P.K., Sahu, T.K., Rao, A.R. and Wahi, S.D. (2014). A statistical approach for 5’ splice site prediction using short sequence motifs and without encoding sequence data. BMC Bioinformatics, 15: 362.
  • Ojha, Sankalpa and Bhar, LM (2014). Cook statistic for detecting outliers in block designs with correlated errors. Int. J. Agric. Statist. Sci., 10(2), 503-512.
  • Panwar, S., Singh, K N, Kumar, A., Sarkar, S. , Paul, R., Rathore, A. and Sivaramane, N. (2014). Forecasting of growth rates of wheat yield of Uttar Pradesh through non-linear growth models, Indian Journal of Agricultural Science, 84(7), 68-71.
  • Paul, R. K. (2014). Forecasting Wholesale Price of Pigeon Pea Using Long Memory Time-Series Models. Agricultural Economics Research Review, 27(2), 167-176.
  • Paul, R. K. and Bhar, L. (2014). Robust Analysis of Experimental Data: An Application of LMS Technique. International Journal of Agricultural and Statistical Sciences, 10(2), 387-392
  • Paul, R. K., Alam, W. and Paul, A. K. (2014). Prospects of livestock and dairy production in India under time series framework. Indian Journal of Animal Sciences, 84(4), 130-134.
  • Paul, R. K., Birthal, P. S. and Khokhar, A. (2014). Structural Breaks in Mean Temperature over Agro-climatic Zones in India. The Scientific World Journal. doi.org/10.1155/2014/434325
  • Paul, R. K., Ghosh, H. and Prajneshu (2014). Development of out-of-sample forecast formulae for ARIMAX-GARCH model and their application. Journal of the Indian Society of Agricultural Statistics, 68(1), 85-92.
  • Paul, R.K, Alam, W. and Paul, A.K. (2014). Prospects of livestock and dairy production in India under time series framework. Indian Journal of Animal Sciences, 84(4), 130-134
  • Paul, RK and Bhar, LM (2014). Robust analysis List of Publications 84 ICAR-IASRI Annual Report 2014-15 of experimental data: An application of LMS technique. Int. J. Agric. Statist. Sci., 10(2), 387- 392.
  • Ramasubramanian, V and Bhar, LM (2014). Crop yield forecasting by markov chain models and simulation. Statist. Appl., 12(1&2), 1-14.
  • Ranganath, H K, Prajneshu and Ghosh, Himadri (2014).Descriptive statistics for Symbolic interval-valued data. J. Ag. Sci., 84, 424-427
  • Sarkar, R.K., Meher, P.K., Wahi, S.D., Mohapatra, T. and Rao, A.R. (2014). An approach to the development of a core set of germplasm using a mixture of qualitative and quantitative data. Plant Genetic Resource, 13(2): 96-103.
  • Shekhar, S, Bhar, LM and Gupta, VK (2014). Incomplete block designs for multiple asymmetric parallel line assays. Int. J. Comput. Theo. Statist., 1(1), 29-35.

2013

  • Bhar, Lalmohan (2013). A diagnostic tool for detecting outliers in experimental data. Model Assist. Statist. Appln., 8(1), 61-68.
  • Bhar, Lalmohan, Gupta, VK and Parsad, Rajender (2013). Detection of outliers in designed experiments in presence of masking. Statist. Appl., 11(1&2), 147-160.
  • Das, T.K.and Paul, A K and Yaduraju, N T (2013). Density-effect and economic threshold of purple nutsedge (Cyperus rotundus) in soybean. Journal of Pest Science. 1-10. ISSN 1612-4766
  • Iquebal, M.A., Ghosh, Himadri and Prajneshu (2013). Application of genetic algorithm for fitting SETAR three-regime nonlinear time-series model. J. Ag. Sci., 83, 1406-1408
  • Karak, T., Bhattacharyya, P., Das, T., Paul, R. K. and Bezbaruah, R. (2013). Non-segregated municipal solid waste in an open dumping ground: A potential contaminant vis-à-vis environmental health. International Journal of Environmental Science and Technology, 10 (3), 503-518.
  • Karak, T., Bhattacharyya, P., Paul, R. K. (2013) Assessment of Co-compost Quality by Physico-chemical and Exploratory Data Analysis. CLEAN – Soil, Air, Water, 42(6), 836-848
  • Karak, T., Bhattacharyya, P., Paul, R. K. and Das, D. K. (2013). Metal accumulation, biochemical response and yield of Indian mustard grown in soil amended with rural road side pond sediment. Ecotoxicology and Environmental Safety. 92,161-173.
  • Karak, T., Bhattacharyya, P., Paul, R. K. and Das, T. (2013). Evaluation of composts from agricultural wastes with fish pond sediment as bulking agent. CLEAN – Soil, Air, Water, 41(7), 711-723
  • Paul, A. K., Paul, R. K. and Alam, W. (2013). Effect of Non-normality and inadmissible estimates on estimation of heritability. Indian Journal of Animal Sciences, 83 (12), 114–116
  • Paul, AK, Paul, RK and Alam, Wasi (2013). Effect of non-normality and inadmissible estimates on estimation of heritability. Indian Journal of Animal Science, 83 (12), 1355–1357.
  • Paul, R. K. and Das, M. K. (2013). Forecasting of average annual fish landing in Ganga Basin. Fishing chimes, 33 (3), 51-54
  • Paul, R. K., Panwar, S., Sarkar, S. K., Kumar, A. Singh, K. N., Farooqi, S. and Chaudhary, V. K. (2013). Modelling and Forecasting of Meat Exports from India. Agricultural Economics Research Review, 26 (2), 249-256.
  • Paul, R. K., Prajneshu, and Ghosh, H. (2013). Statistical modelling for forecasting of wheat yield based on weather variables. Indian Journal of Agricultural Science, 83 (2), 180-183
  • Paul, R. K., Prajneshu, and Ghosh, H. (2013). Wavelet Frequency Domain Approach for Modelling and Forecasting of Indian Monsoon Rainfall Time-Series Data. Journal of the Indian Society of Agricultural Statistics, 67 (3), 319-327
  • Paul, R. K.,Prajneshu, and Ghosh, Himadri(2013). Statistical modelling for forecasting of wheat yield based on weather variables. Ind. J. Ag. Sci. 83, 18-183
  • Prasad, Sanjay Kumar and Bhar, LM (2013). Bayesian analysis of experimental data. Pak. J. Statist. Operation Res., 9(2), 225-239.
  • Shekhar, S and Bhar, LM (2013). Incomplete block designs for parallel line assays. Int. J. Agric. Statist. Sci., 9, 1-10.
  • Singh, N Okendro, Paul, AK, Kumar, Surinder, Alam, Wasi, Singh, N Gopimohon, Singh, KN and Singh, Pal (2013). Fitting of partial reparameterized logistic growth model to oil palm yield data. Int. J. Agril. Statist. Sci., 9, Supplement 1, 55-62.
  • Singh, N. Okendro, Kumar, Surinder, Singh, N. Gopimohon, Paul, A.K., Singh, K.N. and Singh, Pal (2013). Fitting of Fox Model with Autoregressive of Order One Using Expected Value Parameters, Indian Journal of Animal Sciences, 83(2), 201-203. (NARS rating 6.6)

2012

  • Iquebal, M.A., Prajneshu and Ghosh, Himadri (2012). Genetic algorithm optimization technique for linear regression models with heteroscedastic errors. Ind. J. Ag. Sci., 82, 422-425
  • Paul, AK, Singh, S, Singh, KN, Kumar, A and Singh, NO (2012). Modelling for body growth of crossbred piglets. Indian Journal of Animal Sciences 82 (9): 1098–1099(NARS rating 6.6)
  • Paul, RK and Bhar, L.M. (2012). Robust analysis of block designs: A new objective function. Int. J. Agril. Statist. Sci., 8(1), 243-250.
  • Raman, RK, Wahi, SD and Paul, AK (2012). Linear discriminant function under multivariate non-normal rice (Oryza sativa) and maize (Zea mays) data. J. Agril. Sci., 82(5), 436–439. (NARS rating 6.6)
  • Singh, N Okendro, Prem Kumar, Bhar, L.M, Singh, KN and Singh, Pal (2012). Forecasting of fish production from ponds – A non-linear model approach. Ind. J. Fish., 60(2), 67-71.
  • Surendra Singh, K. Paul, K.N. Singh and Ashok Kumar (2012). Study of Growth Patten of Cattle Under Different Climatic Conditions.  IUP Journal of Genetics & Evolution, Vol. V  No.1, 41-46.
S. NO.NAME OF THE TRAININGTYPE OF TRAININGVENUEDURATIONManual
Statistical Techniques for Data Analysis in AgricultureCAFT TrainingICAR-IASRI, New Delhi4-13 October 2021Reference Manual of “Statistical Techniques for Data Analysis in Agriculture”
1Statistics genetics and its applications in agricultureWorkshopICAR-IASRI, New Delhi18.03.21 - 20.03.21
2Data Analysis in Agriculture using Statistical Software PackagesWinter School, CAFT, ICAR-IASRI, New Delhi16.01.20 - 05.02.20
3Advances Statistical Analysis of Breeding Data CAFT TrainingICAR-IASRI, New Delhi27.08.19 - 16.09.19
4Modern Statistical Techniques in Genetics Under the aegis of Agricultural Education Division, ICAR CAFT TrainingICAR-IASRI, New Delhi01.02.19 - 21.02.19
5Recent Advances in Statistical Techniques for Data Analysis and Agriculture (Winter School)Winter School, CAFT, Education Division ICARICAR-IASRI, New Delhi10.01.19 - 30.01.19
6Statistical and Computational Analaysis of Phenotypic and Genotypic Data of Mustard GermplasmWorkshopICAR-IASRI, New DelhiMarch 07-09, 2018
7Advanced Statistical Techniques in BiometricsCAFT TrainingICAR-IASRI, New Delhi10-30 August, 2017
8Advanced Statistical Techniques in Genetics and Genomics. Co-ordinator:   A.K. Paul. Co-Course Coordinator:   Samarendra DasWinter School, CAFT, Education Division ICARICAR-IASRI, New Delhi02 – 22 March, 2017
9Recent Analytical Techniques in Statistical Genetics and Genomics. Co-ordinator: L.M. Bhar. Co-Course Coordinator:  Samrender DassCAFT, Education Division ICARICAR-IASRI, New Delhi17 January – 06 February, 2017
10Statistical Techniques in Biometrics. Program Co-ordinator: Prakash Kumar. Co-Course Coordinator: R. K. PaulWorkshop in HindiICAR-IASRI, New Delhi31 May – 02 June,  2016
11Recent Advances in Statistical Genetics. Co-ordinator:  A.K. Paul. Co-Course Coordinator:  R.K. PaulCAFT, Education Division ICARICAR-IASRI, New Delhi03-23 February 2015
12Recent Advances in Statistical Modelling Techniques. Co-ordinator: R.K. Paul. Co-Course Coordinator: Bishal Gurung and A.K. PaulCAFT, Education Division ICARICAR-IASRI, New Delhi31 May- 20 June 2013
13Advances in Statistical Genetics. Co-ordinator: Wasi Alam. Co-Course Coordinator: R. K. Paul and A.K. PaulCAFT, Education Division ICARICAR-IASRI, New Delhi02-22 July 2013

Scientific Staff

  • Profile Photo

    Dr. Ajit

    Ajit[at]icar[dot]gov[dot]in

    Head (A) Statistical Genetics

Technical Staff

Sh S P Singh

Assistant Chief Technical Officer, Division of Statistical Genetics
spsingh@icar.gov.in

Sh. Nitin Joshi

Technical Assistant
nitin.joshi@icar.gov.in

 

 

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