सांख्यिकीय आनुवंशिकी – ICAR-Indian Agricultural Statistics Research Institute, Government of India

About

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 7-9^th 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.

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

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

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

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

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

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

AI based tools for statistical genetics and genomics

corrDNA: An R-package for finding associations in position-wise aligned sequence dataset (https://cran.r-project.org/web/packages/corrDNA/index.html) (downloaded: 14,836)
EncDNA: An R-package for encoding of nucleotide sequences into numeric feature vectors (https://cran.r-project.org/web/packages/EncDNA/index.html) (downloaded: 18,478)
PredCRG: An R-package for computational prediction of proteins encoded by circadian genes (https://cran.r-project.org/web/packages/PredCRG/index.html) (downloaded: 4,407)
funbarRF: An R-package for fungal species identification using DNA barcode with Random Forest (https://cran.microsoft.com/snapshot/2018-03-27/web/packages/funbarRF/index.html) (downloaded: 11,386)
dhga: An R package for identification of hubs and performing differential hub gene analysis in gene co-expression networks, freely available at https://CRAN.R-project.org/package=dhga (Number of downloads: 18,657)
BootMRMR: An R package for informative genes selection from high-dimensional gene expression data including Microarrays data, available at https://CRAN.R-project.org/package=BootMRMR (downloads: 24,028)
GSAQ: An R package for Gene Set Analysis with Quantitative Trait Loci for Microarrays Gene Expression Studies, freely available at https://CRAN.R-project.org/package=GSAQ (downloads: 22,471)
BSM: An R package for Selection of Biologically Relevant Genes from High-dimensional Gene Expression Data, freely available at https://github.com/sam-uofl/BSM
SwarnSeq: An R package for Analysis of Single cell RNA-sequencing data, freely available at https://github.com/sam-uofl/SwarnSeq
R package “WaveletANN” for modelling by combining wavelet and ANN method (https://cran.r-project.org/package=WaveletANN); Total download: 10,110 (till August 20, 2021)
R package “VIRF” for Computation of volatility impulse response function for multivariate time series model (https://cran.r-project.org/package=VIRF) (Total download: 9,307)
R package “WaveletGARCH” to Fit the Wavelet-GARCH Model to Volatile Time Series Data (https://CRAN.R-project.org/package=WaveletGARCH) (Total download: 8,915)
WaveletLongMemory: An R package developed for estimation of long memory parameter using wavelet technique (https://CRAN.R-project.org/package=WaveLetLongMemory) (Total download: 12,866)
TSF: An R package developed for forecasting time series having long memory and structural break. (https://CRAN.R-project.org/package=TSF) (Total download: 14,658)
WaveletArima: An R package developed for forecasting time series using hybrid models based on wavelet and ARIMA (https://CRAN.R-project.org/package=WaveletArima) (Total download: 16,497)

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.

Projects

Ongoing Projects:

Institute Funded:

S. No.	PROJECT NAME	PROJECT TEAM
1	Modelling and forecasting for time-to-event analysis in Agriculture	PI: Dr. H. Ghosh Co-PIs: Dr. A.K. Paul Dr. S.R. Jacob
2	Statistical Approaches for Analysis of Zero-inflated and Over-dispersed Counts Data and their Applications in Single-cell Studies.	PI: Dr. Samarendra Das Co-PIs: Mr. U. K. Pradhan Dr. S. Srivastava Mr. Prakash Kumar
3	Development of Machine learning models and Bayesian network for discovery of Nucleic acid-binding protein and their application in disease/pest surveillance.	PI: Mr. U. K. Pradhan Co-PIs: Dr. Samarendra Das Dr. P. K. Meher
4	An effective approach for combining time series and deep learning models	PI: Dr. Md. Yeasin Co-PI: Dr. R. K. Paul
5	#Potential irrigated area An effective approach for combining time series and deep learning models mapping through remotely sensed high resolution data.	Dr. R. K. Jena* Dr. R. R. Sethi* Dr. Nirmal Kumar* Dr. S. Khedikar* Mr. U. K. Pradhan

External Funded

S. No.	PROJECT NAME	PROJECT TEAM
1	Leveraging Institutional Innovations for Inclusive and Market led Agricultural Growth in Eastern India	PI: Dr. P Kumar (ICAR-IARI) Dr. P. S. Badal (BHU) Dr. B. Mondal (ICAR-NRRI) Mr S. Kumar (CCS-NIAM) Dr. R. K Paul Dr. Amit Kar (ICAR-IARI) Dr. G.K. Jha (ICAR-IARI) Dr. P.Venkatesh (ICAR-IARI) Dr. B. Subramanian (ICAR-IARI) Dr. H. S. Roy Dr. Kamalavanshi (BHU) Dr S. C. Pant (CCS-NIAM)
2	Production system agribusiness and institutions” Component 3: “Market Information System.	Dr. P. Sharma (ICAR-NIAP) Dr. R. K. Paul Dr.Md. Yeasin Dr. A. K. Paul Dr. Ajit
3	Genomic prediction for micro-nutritional traits in bread wheat: A study on machine learning algorithms	Dr. P. K. Meher
4	Modeling Insect Pests and Diseases Under Climate Change and Development of Digital Tools for Pest Management National Innovations in Climate Resilient Agriculture (NICRA).	Dr. S Vennila (ICAR-NCIPM) Dr. M. Prabhakar (ICAR-CRIDA) Dr. MS Rao (ICAR-CRIDA) Dr. M.N. Bhat (ICAR-NCIPM) Mr. N. Singh (ICAR-NCIPM) Dr. R. K. Paul

Completed Projects:

Institute Funded:

S. No.	PROJECT NAME	PROJECT TEAM
1	Modeling and construction of transcriptional regulatory networks using time-series gene expression data (2017)	*Samarendra Das, Bishal Gurung, Sanjeev Kumar, S.D. Wahi
2	Estimation of Heritability under correlated errors (2016)	A.K.Paul* and S.D.Wahi
3	Estimation of Breeding Value Using Longitudinal Data (2016)	*U.K. Pradhan, P.K. Meher, A.R. Rao and A.K. Paul
4	A 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
5	A study on modeling and forecasting of time-series with long memory process. (2015)	*R. K. Paul, Himadri Ghosh, and Bishal Gurung
6	Development 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
7	Development 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
8	A study of Stochastic Volatility Models Through Particle Filtering. I.A.S.R.I., New Delhi. (2013)	*Bishal Gurung and Himadri Ghosh
9	Estimation of breeding value using generalized estimation equation and Bayesian Approach	PI: Dr. H.S. Roy Co-PIs: Dr. L M Bhar Dr. A K Paul
10	A study on detection and interpretation of expression quantitative trait loci (eQTL) mapping	PI: Dr. H.S. Roy Co-PIs: Dr. L. M. Bhar Dr. R. K. Paul Dr. A. K. Paul
11	Study on Robust Estimation of Heritability	PI: Dr. A K Paul Co-PIs: Dr. H.S. Roy Dr. L. M. Bhar Dr. R. K. Paul

External Funded:

S. No.	PROJECT NAME	PROJECT TEAM
1	Doubling Farmers’ Income in India by 2021-22: Estimating Farm Income and Facilitating the Implementation of Strategic Framework.	Dr. R. Saxena (ICAR-NIAP) Dr. N.P. Singh (ICAR-NIAP) Dr U.R. Ahuja (ICAR-NIAP) Dr. R. K. Paul
2	Stochastic differential equation models and their applications to agriculture. Funded by SERB, DST, Government of India: CCPI	*Prajneshu, Himadri Ghosh and L.M. Bhar
3	Elucidating the mechanism of pashmina fibre development: An OMICS approach.	SKUST: Nazir A. Ganai; NDRI : Jai K. Kaushik; IASRI: A.R.Rao, P.K. Meher
4	Studying Dynamics of market integration and price transmission of agricultural commodities	PI: Dr. Ranjit Kumar Paul
5	Statistical approach for genome-wide association studies and genomic selection for multiple traits in Structured plant and Animal population	PI: Dr. L. M. Bhar Co-PI: Dr. Himadri Shekhar Roy and Dr. P. K. Meher
6	Creation 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 Framework	CC-PI: Dr. Ranjit Kumar Paul
7	Modelling insect pests and diseases under climate change and development of digital tools for pest management	CC-PI: Dr. Ranjit Kumar Paul
8	Study 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 and Dr. A. K. Paul
9	Leveraging Institutional Innovations for Inclusive and Market led Agricultural Growth in Eastern India	CC-PI: Dr. Ranjit Kumar Paul
10	Creating a fully characterized genetic resource pipeline for mustard improvement programme in India	CC-PI: Dr. D. K. Yadav CO-PI: Dr. P.K. Meher, Dr. Cini Varghese
11	Metagenomic profiling for assessing microbial biodiversity in River Ganga for ecosystem health monitoring	PI: Dr. Anil Rai Co-PI: Dr. P.K. Meher et al.
12	Exploring the Epigenetic Control of Heat Stress Responses in Wheat for Characterizing the regulatory Networks Associated with Thermotolerance	PI: Dr. D. C. Mishra Co-PI: Dr. P.K. Meher et al.
13	Modeling and construction of transcriptional regulatory networks using time-series gene expression data (2017)	*Samarendra Das, Bishal Gurung, Sanjeev Kumar, S.D. Wahi
14	Estimation of Heritability under correlated errors (2016)	A.K.Paul* and S.D.Wahi
15	Estimation of Breeding Value Using Longitudinal Data (2016)	*U.K. Pradhan, P.K. Meher, A.R. Rao and A.K. Paul
16	A 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
17	A study on modeling and forecasting of time-series with long memory process. (2015)	*R. K. Paul, Himadri Ghosh, and Bishal Gurung
18	Development 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
19	Development 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
20	A study of Stochastic Volatility Models Through Particle Filtering. I.A.S.R.I., New Delhi. (2013)	*Bishal Gurung and Himadri Ghosh
21	Studying Dynamics of market integration and price transmission of agricultural commodities.	PI: Dr. R. K. Paul
22	Statistical approaches for genome-wide association studies and genomic selection for multiple traits in structured plant and animal population.	PI: Dr. L. M. Bhar Co-PI: Dr. P.K Meher Dr. H.S. Roy

Publications

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
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.
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
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
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
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.

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.

Training

S. NO.	NAME OF THE TRAINING	TYPE OF TRAINING	VENUE	DURATION	Manual
	Statistical Techniques for Data Analysis in Agriculture	CAFT Training	ICAR-IASRI, New Delhi	4-13 October 2021	Reference Manual of “Statistical Techniques for Data Analysis in Agriculture”
1	Statistics genetics and its applications in agriculture	Workshop	ICAR-IASRI, New Delhi	18.03.21 - 20.03.21
2	Data Analysis in Agriculture using Statistical Software Packages	Winter School, CAFT,	ICAR-IASRI, New Delhi	16.01.20 - 05.02.20
3	Advances Statistical Analysis of Breeding Data	CAFT Training	ICAR-IASRI, New Delhi	27.08.19 - 16.09.19
4	Modern Statistical Techniques in Genetics Under the aegis of Agricultural Education Division, ICAR	CAFT Training	ICAR-IASRI, New Delhi	01.02.19 - 21.02.19
5	Recent Advances in Statistical Techniques for Data Analysis and Agriculture (Winter School)	Winter School, CAFT, Education Division ICAR	ICAR-IASRI, New Delhi	10.01.19 - 30.01.19
6	Statistical and Computational Analaysis of Phenotypic and Genotypic Data of Mustard Germplasm	Workshop	ICAR-IASRI, New Delhi	March 07-09, 2018
7	Advanced Statistical Techniques in Biometrics	CAFT Training	ICAR-IASRI, New Delhi	10-30 August, 2017
8	Advanced Statistical Techniques in Genetics and Genomics. Co-ordinator: A.K. Paul. Co-Course Coordinator: Samarendra Das	Winter School, CAFT, Education Division ICAR	ICAR-IASRI, New Delhi	02 – 22 March, 2017
9	Recent Analytical Techniques in Statistical Genetics and Genomics. Co-ordinator: L.M. Bhar. Co-Course Coordinator: Samrender Dass	CAFT, Education Division ICAR	ICAR-IASRI, New Delhi	17 January – 06 February, 2017
10	Statistical Techniques in Biometrics. Program Co-ordinator: Prakash Kumar. Co-Course Coordinator: R. K. Paul	Workshop in Hindi	ICAR-IASRI, New Delhi	31 May – 02 June, 2016
11	Recent Advances in Statistical Genetics. Co-ordinator: A.K. Paul. Co-Course Coordinator: R.K. Paul	CAFT, Education Division ICAR	ICAR-IASRI, New Delhi	03-23 February 2015
12	Recent Advances in Statistical Modelling Techniques. Co-ordinator: R.K. Paul. Co-Course Coordinator: Bishal Gurung and A.K. Paul	CAFT, Education Division ICAR	ICAR-IASRI, New Delhi	31 May- 20 June 2013
13	Advances in Statistical Genetics. Co-ordinator: Wasi Alam. Co-Course Coordinator: R. K. Paul and A.K. Paul	CAFT, Education Division ICAR	ICAR-IASRI, New Delhi	02-22 July 2013