Jove
Visualize
Contact Us

Related Concept Videos

Trihybrid Crosses02:27

Trihybrid Crosses

23.0K
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...
23.0K
Chi-square Analysis02:46

Chi-square Analysis

37.2K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
37.2K
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
  1. Home
  3. Sparse Testing Designs For Optimizing Resource Allocation In Multi-environment Cassava Breeding Trials.
  1. Home
  3. Sparse Testing Designs For Optimizing Resource Allocation In Multi-environment Cassava Breeding Trials.

Related Experiment Video

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections
06:35

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections

Published on: April 17, 2015

9.0K

Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials.

Nelson Lubanga1, Beatrice E Ifie1, Reyna Persa2

  • 1Insitute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK.

The Plant Genome
|February 6, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Sparse testing in cassava breeding can reduce phenotyping costs for multi-environment trials (METs). Implementing models with genotype-by-environment interaction (G × E) improves predictive ability, suggesting fewer overlapping genotypes are needed for training.

More Related Videos

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

18.7K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K

Related Experiment Videos

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections
06:35

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections

Published on: April 17, 2015

9.0K
Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

18.7K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K

Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Genetics

Background:

  • Developing improved crop cultivars necessitates multi-environment trials (METs) to assess genotype performance across diverse conditions.
  • High phenotyping costs in METs restrict the evaluation of numerous genotypes in all target environments.

Purpose of the Study:

  • To investigate the effectiveness of sparse testing strategies in cassava breeding programs for reducing phenotyping expenses in METs.
  • To evaluate prediction models incorporating genomic data and genotype-by-environment interaction (G × E) for optimizing sparse testing designs.

Main Methods:

  • Utilized a population of 435 cassava genotypes evaluated across five Nigerian environments for dry matter and fresh root yield.
  • Developed sparse testing designs based on non-overlapping (NOL) and completely overlapping (OL) genotype allocations.
  • Assessed three prediction models: one phenotype-only and two incorporating genomic data, with and without G × E modeling.

    • Main Results:

    • All models demonstrated higher predictive ability and lower mean square error (MSE) with larger training datasets.
    • Modeling G × E significantly improved predictive ability and reduced MSE for given training set sizes.
    • Increased OL genotypes led to decreased predictive ability and increased MSE, indicating a need for minimal OL genotypes in training sets.

    Conclusions:

    • Sparse testing, particularly when incorporating G × E, offers a viable approach to reduce phenotyping costs in cassava METs.
    • Optimizing the size and distribution of training populations in sparse testing can enhance predictive ability and cost-efficiency.
    • Integrating crop growth models (CGMs) with genomic prediction presents future potential for further improving predictive accuracy.