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ICAR AICE-JRF/SRF Groups of Experiments, Multi-Location Trials & Genotype x Environment Analysis: practice questions

ICAR AICE-JRF/SRF - Agricultural Statistics, Computer Application, Bioinformatics (PhD admission and fellowship) · India · National Testing Agency for ICAR

Original practice questions written to the published syllabus · not past papers
10worked here, free
86written to this exam in this subject
9syllabus units

What this subject covers

Combining trials across sites and seasons: testing homogeneity of error variances, pooled analysis, genotype by environment interaction, the classical stability models and AMMI and GGE biplots that decide variety release in national coordinated trials.

In the exams: AICE-JRF Ag Stat Units 3-4 'groups of experiments', 'stability parameters' (heavy); AICE-JRF Agronomy Unit 6 'pooled analysis' (heavy); ASRB NET Ag Stat (present)

What ICAR AICE-JRF/SRF asks from it

ICAR AICE-JRF/SRF Agricultural Statistics (Units 3 and 4)

Groups of experiments, homogeneity of error variances, pooled analysis; G x E interaction and stability parameters (Eberhart-Russell and related).

ICAR AICE-JRF/SRF Agronomy (Unit 6) and Genetics & Plant Breeding

Pooled analysis of experiments over locations and seasons; stability analysis of varieties.

Where the 86 questions fall

How our questions for ICAR AICE-JRF/SRF spread across the units of Groups of Experiments, Multi-Location Trials & Genotype x Environment Analysis. Difficulty: 17 easy, 45 medium, 24 hard.

Pooled analysis over locations and years11
Finlay-Wilkinson and Eberhart-Russell stability models11
Mixed-model analysis of multi-environment trials11
AMMI models10
GGE biplots and mega-environment analysis10
Other stability parameters: Shukla, Wricke's ecovalence and Perkins-Jinks9
Groups of experiments and testing homogeneity of error variances8
Genotype x environment interaction and its partitioning8
Analysis of perennial and repeated-season experiments8

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Finlay-Wilkinson and Eberhart-Russell stability models · medium

Why did Finlay and Wilkinson analyse log yields?

  1. To avoid computing environment means
  2. To make proportional differences additive
  3. To make every slope equal to one
  4. To remove all genotype main effects
Answer and working

B. To make proportional differences additive

On the log scale, a constant percentage advantage gives parallel lines; on the original scale it inflates slopes for high yielders.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

2 · AMMI models · medium

For 10 genotypes and 6 environments, after IPCA1 (13 df), IPCA2 (11 df) and IPCA3 (9 df), how many df remain for the interaction residual?

  1. 12
  2. 45
  3. 33
  4. 9
Answer and working

A. 12

Interaction df = 9 x 5 = 45; 45 - 33 = 12.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

3 · AMMI models · medium

What does the horizontal axis of an AMMI1 biplot usually show?

  1. Ecovalences
  2. IPCA2 scores
  3. Genotype and environment means
  4. Regression slopes
Answer and working

C. Genotype and environment means

AMMI1 plots main effects (means) against IPCA1 scores.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

4 · GGE biplots and mega-environment analysis · medium

In a which-won-where view, what do the vertex genotypes of the polygon represent?

  1. The genotypes with the lowest means
  2. The most stable genotypes across all the environments tested
  3. The best genotypes in the environments within their sectors
  4. Genotypes excluded as outliers
Answer and working

C. The best genotypes in the environments within their sectors

Perpendiculars to polygon sides define sectors; each vertex genotype wins in the environments of its sector.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

5 · GGE biplots and mega-environment analysis · hard

Which scaling suits comparing genotypes in a GGE biplot?

  1. Genotype-focused singular value partitioning
  2. No centring of environments
  3. Standardising genotypes by their means
  4. Environment-focused singular value partitioning
Answer and working

A. Genotype-focused singular value partitioning

Allocating singular values to genotype scores makes genotype distances interpretable.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

6 · GGE biplots and mega-environment analysis · medium

What is a limitation of biplots?

  1. They cannot display environments at all
  2. They require balanced data with equal error variances in every site
  3. They show only two components and no standard errors
  4. They always explain all variation
Answer and working

C. They show only two components and no standard errors

If PC1 and PC2 explain little, patterns may mislead; nearby genotypes may not differ significantly.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

7 · Mixed-model analysis of multi-environment trials · hard

What allows prediction of a genotype's performance in an environment where it was not tested?

  1. Treating environments as fixed effects with no genetic covariance at all
  2. Using only the pooled error variance
  3. Genetic correlations between environments in the mixed model
  4. Setting all G x E effects to zero
Answer and working

C. Genetic correlations between environments in the mixed model

BLUPs borrow from correlated environments; adequate connectivity between sites is needed.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

8 · Other stability parameters: Shukla, Wricke's ecovalence and Perkins-Jinks · easy

Who proposed the stability variance statistic?

  1. Shukla
  2. Wricke
  3. Gauch
  4. Yan
Answer and working

A. Shukla

Shukla (1972) proposed the stability variance; Wricke ecovalence; Gauch AMMI; Yan GGE.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

9 · AMMI models · hard

How do Cornelius's F tests for AMMI axes compare with Gollob's?

  1. They always retain every possible axis
  2. They are identical in every case
  3. They ignore the pooled error entirely
  4. They are more conservative and often retain fewer axes
Answer and working

D. They are more conservative and often retain fewer axes

Cornelius tests are more conservative; cross-validation may retain fewer still.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

10 · Analysis of perennial and repeated-season experiments · hard

In a mixed model for repeated harvests, which structure would reveal alternate bearing?

  1. Independent harvests with one variance
  2. Compound symmetry with a positive covariance
  3. A temporal structure estimating a negative between-year correlation
  4. A fixed effect for replicates only
Answer and working

C. A temporal structure estimating a negative between-year correlation

An AR(1) or similar structure allows a negative correlation, revealing alternate bearing.

Further reading: Gomez and Gomez, Statistical Procedures for Agricultural Research; Kang and Gauch, Genotype-by-Environment Interaction

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Groups of Experiments, Multi-Location Trials & Genotype x Environment AnalysisThe full study manual, free ICAR AICE-JRF/SRFFormat, sections and how it has changed

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