Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Yield Criteria for Ductile Materials under Plane Stress01:25

Yield Criteria for Ductile Materials under Plane Stress

839
In designing structural elements and machine parts using ductile materials, it is crucial to ensure that these components withstand applied stresses without yielding. Yielding is initially determined through a tensile test, which evaluates the material's response to uniaxial stress. However, tensile stress is insufficient when components face biaxial or plane stress conditions This condition requires advanced criteria to predict failure.
The Maximum Shearing Stress Criterion, also known as...
839
Methods of Medium Optimization01:28

Methods of Medium Optimization

74
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
74
In Vitro Drug Dissolution: Compendial Testing Models II01:09

In Vitro Drug Dissolution: Compendial Testing Models II

694
Various dissolution methods are utilized to assess a drug’s dissolution rate, including the flow-through cell, paddle-over-disk, cylinder, and reciprocating disk methods.The flow-through cell apparatus (USP (United States Pharmacopeia) method 4) comprises a reservoir for the dissolution medium and a pump that propels the medium through the cell containing the test sample. This method is crucial for assessing modified-release dosage forms with minimally soluble active ingredients,...
694
In Vitro Drug Dissolution: Compendial Testing Models I01:13

In Vitro Drug Dissolution: Compendial Testing Models I

584
Compendial dissolution methods are standardized procedures defined by pharmacopeias to evaluate the rate at which a drug dissolves in a specific medium. These methods ensure batch-to-batch consistency, enable quality control, and support the prediction of drug bioavailability. They are critical for both immediate and modified-release drug products.The apparatuses used for dissolution testing differ in their design and mechanical function, but all aim to simulate the physiological environment of...
584
Multiple Regression01:25

Multiple Regression

3.4K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.4K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.1K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Predictive and postdictive success of statistical analyses of yield trials.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2013
Same author

Accuracy and selection success in yield trial analyses.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2013
Same author

Imputing missing yield trial data.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2013
Same author

AMMI adjustment for statistical analysis of an international wheat yield trial.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2013
Same author

Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2013
Same author

Analysis of T-RFLP data using analysis of variance and ordination methods: a comparative study.

Journal of microbiological methods·2008
Same journal

Genome-wide association analysis and candidate gene identification for plant height in Shanxi local foxtail millet varieties.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
Same journal

Combined genome and transcriptome analysis of boll weight and lint percentage traits in Gossypium barbadense.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
Same journal

The allelic variation of anthocyanidin reductase underlies anthocyanin biosynthesis and purple leaf trait in Brassica napus.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
Same journal

Unveiling core genomic regions shaping plant architecture, productivity, and seed quality traits in sesame (Sesamum indicum L.): insights from Meta-QTL study into breeding targets.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
Same journal

Watkins wheat landraces: a treasure of stripe rust resistance alleles identified using multi-model association analyses.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
Same journal

Selection of four mutant alleles of fatty acid desaturase genes for a stable high oleic and low linolenic acid soybean seed oil trait.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
See all related articles

Related Experiment Video

Updated: May 6, 2026

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.6K

Full and reduced models for yield trials.

H G Gauch1

  • 1Department of Agronomy, Cornell University, 14853, Ithaca, NY, USA.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|November 14, 2013
PubMed
Summary
This summary is machine-generated.

The Additive Main effects and Multiplicative Interaction (AMMI) model provides more accurate yield predictions than simple treatment means. This improved accuracy stems from its statistical approach, which leverages more data for better genotype-environment interaction insights.

More Related Videos

Absolute Quantum Yield Measurement of Powder Samples
14:20

Absolute Quantum Yield Measurement of Powder Samples

Published on: May 12, 2012

29.5K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.3K

Related Experiment Videos

Last Updated: May 6, 2026

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.6K
Absolute Quantum Yield Measurement of Powder Samples
14:20

Absolute Quantum Yield Measurement of Powder Samples

Published on: May 12, 2012

29.5K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.3K

Area of Science:

  • Agricultural Science
  • Biometrics
  • Statistical Modeling

Background:

  • Traditional yield trial analysis often uses treatment means, which can be less accurate for predictions.
  • The Additive Main effects and Multiplicative Interaction (AMMI) model is an alternative statistical approach.
  • Understanding the statistical underpinnings of AMMI is crucial for its effective application.

Purpose of the Study:

  • To explain why the reduced Additive Main effects and Multiplicative Interaction (AMMI) model often outperforms the full treatment means model in predictive accuracy for yield trials.
  • To elucidate the statistical and agricultural reasons behind the superior performance of the AMMI model.

Main Methods:

  • The study discusses the statistical "Stein effect," where a small increase in bias yields a large gain in predictive accuracy.
  • It highlights how the AMMI model selectively identifies patterns related to treatment design while minimizing noise from experimental design.
  • The AMMI model utilizes the entire yield trial data, not just specific replications, for estimation.

Main Results:

  • Empirical results consistently show the reduced AMMI model's superior predictive accuracy over the treatment means model.
  • The AMMI model's accuracy surpasses that of the raw data it is derived from, a phenomenon explained by the Stein effect.
  • AMMI effectively separates treatment-related patterns from experimental noise.

Conclusions:

  • The AMMI model offers enhanced predictive accuracy for genotype yields in specific environments by utilizing comprehensive trial data.
  • The statistical principle of bias-variance tradeoff (Stein effect) explains the AMMI model's improved performance.
  • AMMI's ability to model genotype-environment interactions makes it a powerful tool in agricultural research and breeding programs.