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

Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

21.4K
Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
21.4K
Trihybrid Crosses02:27

Trihybrid Crosses

25.3K
Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal...
25.3K
Multiple Regression01:25

Multiple Regression

3.7K
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...
3.7K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.4K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.4K
Monohybrid Crosses01:20

Monohybrid Crosses

238.7K
Overview
238.7K
Dihybrid Crosses01:18

Dihybrid Crosses

80.8K
Overview
80.8K

You might also read

Related Articles

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

Sort by
Same author

Beyond electronic stabilization: towards a multicomponent conceptual density-functional theory for positron-driven bonding.

Physical chemistry chemical physics : PCCP·2026
Same author

Biological Approaches to Carbon Sequestration and Bioenergy Production.

Indian journal of microbiology·2026
Same author

NMDA and glycine receptors provide dual presynaptic regulation on climbing fibre inputs to cerebellar Purkinje cells.

The Journal of physiology·2026
Same author

Wild barley cytoplasms reduce grain weight plasticity, with environment-dependent cytonuclear epistasis at the <i>ari-e</i> locus.

Molecular breeding : new strategies in plant improvement·2026
Same author

Role of allied health professions in falls prevention for community-dwelling older adults: a scoping review.

BMJ open·2026
Same author

Ensemble-based genomic prediction for maize flowering time improves prediction accuracy and reveals novel insights into trait genetic variation.

G3 (Bethesda, Md.)·2026
Same journal

Validation of the International Weed Genomics Consortium genome annotation pipeline through reannotation of the model species Arabidopsis thaliana.

The plant genome·2026
Same journal

High-density mutation tracks are associated with proton-beam irradiation patterns in Sorghum bicolor.

The plant genome·2026
Same journal

Application of deep learning in crop research: From genomics to phenomics.

The plant genome·2026
Same journal

Haplotype‑resolved comparison of transcription factor superfamilies between wild and cultivated autotetraploid green jujube and prioritization of candidate transcription factors via machine learning.

The plant genome·2026
Same journal

A public mid-density genotyping platform for pecan [Carya illinoinensis (Wangenh.) K. Koch].

The plant genome·2026
Same journal

Missing comparability: When genomic selection faces field variability. A case study in soybeans.

The plant genome·2026
See all related articles

Related Experiment Video

Updated: Jan 12, 2026

Scalable Transfection of Maize Mesophyll Protoplasts
08:38

Scalable Transfection of Maize Mesophyll Protoplasts

Published on: June 23, 2023

3.6K

The Big BIT maize experiment: A large multi-location, multi-year, multi-tester, multi-population predictive breeding

Michael Jines1, Michael Chandler2, Dean Podlich3

  • 1Seed Product Development, Corteva Agriscience, Johnston, Iowa, USA.

The Plant Genome
|October 31, 2025
PubMed
Summary
This summary is machine-generated.

The Big Breeding Innovation Team (Big BIT) maize experiment validated genomic prediction for breeding. Early genetic evaluation using wide-area testing data optimizes maize inbred and variety development.

More Related Videos

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
05:55

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

7.4K
A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines
07:09

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines

Published on: January 3, 2014

8.9K

Related Experiment Videos

Last Updated: Jan 12, 2026

Scalable Transfection of Maize Mesophyll Protoplasts
08:38

Scalable Transfection of Maize Mesophyll Protoplasts

Published on: June 23, 2023

3.6K
High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
05:55

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

7.4K
A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines
07:09

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines

Published on: January 3, 2014

8.9K

Area of Science:

  • Plant genetics and breeding
  • Genomic prediction
  • Maize (Zea mays L.)

Background:

  • The Big Breeding Innovation Team (Big BIT) conducted a large-scale maize experiment to validate genomic data-informed predictive breeding.
  • The study involved multi-location, multi-year, multi-tester, and multi-population trials of F1 maize hybrids.

Purpose of the Study:

  • To describe the design and unique features of the Big BIT experiment.
  • To discuss practical learnings for optimizing predictive breeding strategies in maize.

Main Methods:

  • Utilized descriptive summary statistics on multi-year field data.
  • Performed large-scale cross-validations to assess predictive abilities.
  • Analyzed genomic estimated breeding values (GEBVs) for individuals and families.

Main Results:

  • Provided multi-year field data-based evidence for predictive breeding strategies.
  • Demonstrated the effectiveness of wide-area testing training datasets for early genetic evaluation.
  • Confirmed findings for selection candidates with and without phenotypic records.

Conclusions:

  • Early-stage genetic evaluation in maize improvement should leverage GEBVs from wide-area testing datasets.
  • This approach is effective for inbred and variety development.
  • The Big BIT experiment offers valuable insights for plant breeders aiming to optimize predictive breeding.