Statistical Genetics Division

Dr. L. M. Bhar
Director (A) & Head
Phone (O).: 25848720
lm.bhar@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 provides proper statistical techniques applicable in plant and animal breeding data with support of online programs for analysis, e-learning, etc. Genotype-environment (GE) interaction and yield stability is an important area in crop improvement programme. 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 non-parametric. 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 analysis of agricultural field experiments and developed methodology for estimating genetic trends realized over years. Studies are also carried on yield survival relationship in dairy cattle, progeny testing for auxiliary traits, analysis of animal epidemiology etc. Division also work 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 development of 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.

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.

Ongoing Projects:

  • Institute Funded:
S. No.PROJECT NAMEPROJECT TEAM
1Development of methodology for nonparametric modelling of time-series data and its application in Agriculture.Himadri Ghosh* and Soumen Pal
2Development of Rank Based Stability Measures for Selecting Genotypes.Prakash kumar*, A.K. Paul,  Samarendra Das and L.M. Bhar
3Gene selection for classification of crop gene expression dataSamarendra Das, **P.K. Meher, R.K. Paul and Prakash Kumar
4A study on sequence encoding based approaches for splice site prediction in agricultural  species*P.K. Meher, Prakash   Kumar and A.R. Rao
5Study 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
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

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.
S. NO.NAME OF THE TRAININGTYPE OF TRAININGVENUEDURATION
1Advanced 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
2Recent 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
3Statistical Techniques in Biometrics. Program Co-ordinator: Prakash Kumar. Co-Course Coordinator: R. K. PaulWorkshop in HindiICAR-IASRI, New Delhi31 May – 02 June,  2016
4Recent Advances in Statistical Genetics. Co-ordinator:  A.K. Paul. Co-Course Coordinator:  R.K. PaulCAFT, Education Division ICARICAR-IASRI, New Delhi03-23 February 2015
5Recent 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
6Advances 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

Technical Staff

1

Smt. Savita Wadhwa

Chief Technical Officer, Division of Statistical Genetics
savita.wadhwa@icar.gov.in

2

Sh. O.P. Singh

Assistant Chief Technical officer
op.singh@icar.gov.in

3

Sh. Anil Garg

Assistant Chief Technical officer
anil.garg@icar.gov.in

Sh. S.P.Singh

Assistant Chief Technical officer
sp.singh@icar.gov.in

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