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ICAR Agriculture Statistics Syllabus 2026, Exam Pattern
ICAR Agriculture Statistics Syllabus 2026, ICAR JRF Exam Pattern, Exam Date 2026
Detail Information about ICAR AIEEA has published notification 2026 for the notification of JRF Agriculture Statistics entrance exam. Those Candidates who are Interested to the following entrance exam and completed all Eligibility Criteria can read the Notification & Apply Online. In this page we provide the Complete Syllabus of this Notification with Latest Update Exam Pattern and the Exam Date also.
ICAR AIEEA PG Notification 2026 - Overview
ICAR AIEEA PG Notification 2026 - Overview
| Name of the exam | ICAR AIEEA |
| Full-Form | Indian Council of Agricultural Research All India Entrance Examination for Admission |
| Exam conducting body | National Testing Agency |
| Counseling conducting authority | ICAR (Indian Council of Agricultural Research) |
| Program Offered | Postgraduate (PG) |
| Application mode | Online |
| Exam Type | National level |
| Marking Scheme | +4 for each correct answer -1 for each incorrect answer 0 for no attempt |
| Category | Syllabus and Exam Pattern |
| Medium of Exam | English only |
| Exam Centre Cities | 90 cities in India |
| Official Website | www.icar.nta.nic.in |
NTA ICAR JRF Agriculture Statistics Syllabus 2026
Unit 1: Statistical Methods I
Descriptive statistics. Elements of probability theory, conditional probability, Bayes’ theorem. Random variablediscrete and continuous. Mathematical expectation. Moment generating and characteristic functions. Laws of large numbers. Central limit theorem. Discrete probability distributions-binomial, Poisson, negative binomial and hypergeometric. Continuous probability distributions-normal, rectangular, Cauchy exponential, gamma and beta. Sampling distributions-chi-square, t, and F. Bivariate normal distribution-conditional and marginal. Point estimation-unbiasedness, consistency, efficiency, sufficiency. Completeness Minimum variance unbiased estimator. Cramer-Rao Inequality. Rao-Blackwell theorem and Lehman-Scheffe theorem. Methods of point estimation like Maximum likelihood, Moments, Minimum chi-square. Confidence interval estimation. Testing of hypotheses -two types of errors, level of significance and power of a test. Neyman-Pearson Lemma. Uniformly most powerful tests and their construction. Unbiased test, Likelihood ratio test. Tests of significance based on Z, t, chi-square and F distributions.
Unit 2: Statistical Methods II
Correlation, rank correlation, correlation ratio, intra-class correlation. Simple and multiple regression analysis, partial and multiple correlation. Examination of residuals. Model-adequacy, Selecting best regression. Order statistics. Non-parametric tests-run, sign, rank, Wilcoxon, Kruskal-Wallis, Mann-Whitney, Cochran and Friedman’s tests. Contingency tables. Log linear models. Sequential analysis sequential probability ratio test. Elements of stochastic processes. Multivariate normal distribution-estimation of mean vector and dispersion matrix. Wishart distribution, Hotelling T2, multivariate analysis of variance, principal component analysis, factor analysis, discriminant analysis, cluster analysis.
Unit 3: Statistical Genetics I
Statistical analysis of segregation, detection and estimation of linkage. Gene and genotypic frequencies. Random mating and equilibrium in large populations. Disequilibrium due to linkages for two pairs of genes and for sex linked genes. Selection, mutation and migration. Equilibrium between forces in large population. Polymorphism. Fisher’s fundamental theorem of natural selection. Polygenic systems for quantitative characters, Concepts of breeding value, dominance, average effect of gene and epistatic interactions
Unit 4: Statistical Genetics II
enetic variance and its partitioning. Correlation between relatives. Regular system of inbreeding, effects of inbreeding. Genotype and environment interaction, stability parameters. Estimation of heritability, repeatability and genetic correlation. Path coefficient analysis. Heterosis, concepts of general and specific combining abilities. Diallel crosses and line × tester analysis. Response due to selection. Prediction of response to individual, family and combined selections. Construction of selection index.
Unit 5: Design of Experiments I
Linear models-Random, fixed and mixed effects. Nested and crossed classifications. Gauss-Markoff theorem. Analysis of variance. Principles of design of experiments. Uniformity trials. Completely randomized design. Randomized complete block design. Latin square design. Factorial experiments-2n and 3n series and asymmetrical factorial experiments, confounding in 2n and 3n experiments, split and strip-plot designs, change over designs. Missing plot techniques. Analysis of covariance. Variance stabilizing transformations.
Unit 6: Design of Experiments II
Balanced incomplete block designs and their analysis with and without recovery of inter block information. Partially balanced incomplete block designs with two associate classes, lattice designs. Youden square design. Multiple comparison procedures. Fractional replication of symmetrical factorials, confounding in asymmetrical factorial experiments. Response surface designs, second order rotatable designs. Combined analysis of groups of experiments. Sampling in field experiments. Experiments on cultivators’ fields.
Unit 7: Sample Surveys I
Sampling versus complete enumeration. Concept of probability sampling. Simple random sampling. Stratified sampling, allocation in stratified sampling, choice of strata, construction of strata boundaries and collapsing of strata. Use of auxiliary information in sample surveys, ratio and regression methods of estimation. Systematic sampling. Cluster and multi-stage sampling with equal probability.
Unit 8: Sample Surveys II
Sampling with unequal probabilities with and without replacement, sampling schemes with inclusion probabilities proportional to size. Unbiased ratio type of estimators, Double sampling, sampling on successive occasions, inverse sampling. Non-sampling errors-sources and classification. Non-response in surveys interpenetrating sub-samples, randomized response techniques, imputation methods. Design and organization of pilot and large scale surveys. National sample surveys. Agricultural statistics system in the country-land use statistics, crop estimation surveys, livestock and fishery statistics.
Unit 9: Computer Applications
Computer Organization and Architecture- number system, input/output unit, memory, arithmetic logic unit and control unit. Computer algorithms. Programming in C-Building blocks, control structures, arrays, pointers, dynamic memory allocation, file management. Data Structures linked list, stack, queue, tree, graph, sorting and searching algorithms. Data Base Management System-definition and features, data models, relational database. Object oriented programming-encapsulation, inheritance, polymorphism with C++/JAVA. Networking-need, basic concepts, types of networks. Connecting computers-local area networks, wide area networks. Value added network services-E-mail, on-line services, Internet, etc. Hyper Text Markup Language (HTML), Building static and dynamic web pages. Numerical analysis interpolation, numerical integration, solution of ordinary differential equations, solution of linear and non-linear system of equations. Modeling and simulation-random number generation and testing, discrete simulation models, simulation of stochastic events and processes, design of simulation xperiments, analysis of data generated by simulation experiments, validation of simulation models. Linear Programming formulation and graphical solution, simplex method, duality, transportation and assignment problems.
Unit 2: Statistical Methods II
Correlation, rank correlation, correlation ratio, intra-class correlation. Simple and multiple regression analysis, partial and multiple correlation. Examination of residuals. Model-adequacy, Selecting best regression. Order statistics. Non-parametric tests-run, sign, rank, Wilcoxon, Kruskal-Wallis, Mann-Whitney, Cochran and Friedman’s tests. Contingency tables. Log linear models. Sequential analysis sequential probability ratio test. Elements of stochastic processes. Multivariate normal distribution-estimation of mean vector and dispersion matrix. Wishart distribution, Hotelling T2, multivariate analysis of variance, principal component analysis, factor analysis, discriminant analysis, cluster analysis.
Unit 3: Statistical Genetics I
Statistical analysis of segregation, detection and estimation of linkage. Gene and genotypic frequencies. Random mating and equilibrium in large populations. Disequilibrium due to linkages for two pairs of genes and for sex linked genes. Selection, mutation and migration. Equilibrium between forces in large population. Polymorphism. Fisher’s fundamental theorem of natural selection. Polygenic systems for quantitative characters, Concepts of breeding value, dominance, average effect of gene and epistatic interactions
Unit 4: Statistical Genetics II
enetic variance and its partitioning. Correlation between relatives. Regular system of inbreeding, effects of inbreeding. Genotype and environment interaction, stability parameters. Estimation of heritability, repeatability and genetic correlation. Path coefficient analysis. Heterosis, concepts of general and specific combining abilities. Diallel crosses and line × tester analysis. Response due to selection. Prediction of response to individual, family and combined selections. Construction of selection index.
Unit 5: Design of Experiments I
Linear models-Random, fixed and mixed effects. Nested and crossed classifications. Gauss-Markoff theorem. Analysis of variance. Principles of design of experiments. Uniformity trials. Completely randomized design. Randomized complete block design. Latin square design. Factorial experiments-2n and 3n series and asymmetrical factorial experiments, confounding in 2n and 3n experiments, split and strip-plot designs, change over designs. Missing plot techniques. Analysis of covariance. Variance stabilizing transformations.
Unit 6: Design of Experiments II
Balanced incomplete block designs and their analysis with and without recovery of inter block information. Partially balanced incomplete block designs with two associate classes, lattice designs. Youden square design. Multiple comparison procedures. Fractional replication of symmetrical factorials, confounding in asymmetrical factorial experiments. Response surface designs, second order rotatable designs. Combined analysis of groups of experiments. Sampling in field experiments. Experiments on cultivators’ fields.
Unit 7: Sample Surveys I
Sampling versus complete enumeration. Concept of probability sampling. Simple random sampling. Stratified sampling, allocation in stratified sampling, choice of strata, construction of strata boundaries and collapsing of strata. Use of auxiliary information in sample surveys, ratio and regression methods of estimation. Systematic sampling. Cluster and multi-stage sampling with equal probability.
Unit 8: Sample Surveys II
Sampling with unequal probabilities with and without replacement, sampling schemes with inclusion probabilities proportional to size. Unbiased ratio type of estimators, Double sampling, sampling on successive occasions, inverse sampling. Non-sampling errors-sources and classification. Non-response in surveys interpenetrating sub-samples, randomized response techniques, imputation methods. Design and organization of pilot and large scale surveys. National sample surveys. Agricultural statistics system in the country-land use statistics, crop estimation surveys, livestock and fishery statistics.
Unit 9: Computer Applications
Computer Organization and Architecture- number system, input/output unit, memory, arithmetic logic unit and control unit. Computer algorithms. Programming in C-Building blocks, control structures, arrays, pointers, dynamic memory allocation, file management. Data Structures linked list, stack, queue, tree, graph, sorting and searching algorithms. Data Base Management System-definition and features, data models, relational database. Object oriented programming-encapsulation, inheritance, polymorphism with C++/JAVA. Networking-need, basic concepts, types of networks. Connecting computers-local area networks, wide area networks. Value added network services-E-mail, on-line services, Internet, etc. Hyper Text Markup Language (HTML), Building static and dynamic web pages. Numerical analysis interpolation, numerical integration, solution of ordinary differential equations, solution of linear and non-linear system of equations. Modeling and simulation-random number generation and testing, discrete simulation models, simulation of stochastic events and processes, design of simulation xperiments, analysis of data generated by simulation experiments, validation of simulation models. Linear Programming formulation and graphical solution, simplex method, duality, transportation and assignment problems.
ICAR AIEEA JRF Agriculture Statistics Exam Pattern 2026
Duration : 120 Minutes
Negative Mark : -1
| S.No. | Subject | No. of Question | Marks |
| 1. | Agriculture Statistics | 120 | 480 |
| Total | 120 | 480 |
ICAR JRF Exam Date 2026 : 04th July 2026
Starting Date of Application Form : 08th May 2026
Last Date of Application Form : 07th June 2026
FAQs
Icar Agriculture Statistics 2026 Exam Syllabus Frequently Asked Questions (FAQ's)
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In this article Page, we have provided the required syllabus of the ICAR Agriculture Statistics exam.
Being familiar with ICAR Agriculture Statistics Exam Pattern will help you understand the types of questions asked, difficulty level of the exam and important topics that require your keen attention. It will also acquaint you with the marking scheme and time allotted to each Topics.
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The candidates must be well versed and acquainted with the syllabus and the exam pattern. Select the areas where improvement is required and schedule your preparation accordingly. To ace this exam, the applicants ought to maintain speed with high accuracy. Practicing previous years question papers will help you understand the exam pattern and the level of difficulty of the exam. You need to hone your strengths and improve upon your weaknesses. Toppersexam.com will aid you through the preparation and drive you to success. At Toppersexam.com, you can practice a complete set of test series along with a free ICAR Agriculture Statistics mock test designed by our expert faculty.
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The ICAR Agriculture Statistics Exam pattern includes details like the number of sections, total questions, marks distribution, and exam duration. Toppersexam provides a comprehensive guide on exam format, marking scheme, and question types.
Yes, the majority of questions in ICAR Agriculture Statistics Exam are MCQs. Toppersexam provides practice MCQs with answers to help candidates understand the question pattern and improve accuracy.
Yes, questions are divided section-wise. Toppersexam provides a clear guide on how many questions are asked per section and their difficulty level for effective preparation.
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