Evaluating breeding for broad versus narrow adaptation for cassava in Nigeria using stochastic simulation

Bakare, Moshood A.; Kayondo, Siraj Ismail; Kulakow, Peter; Rabbi, Ismail Yusuf; Jannink, Jean‐Luc

doi:10.1002/csc2.21170

Cited by 3 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cost-effective simulations can inform decisions in designing breeding strategies by assessing the impact of changes in crossing, evaluation, or selection procedures on genetic gain. Several studies have been conducted using that approach (Bakare et al, 2023;Lin et al, 2023;Wu et al, 2023). Since 2021, CGIAR has been engaged in annually estimating realized genetic gain rates for its breeding programs.…”

Section: Core Ideasmentioning

confidence: 99%

Q&A: Methods for estimating genetic gain in sub‐Saharan Africa and achieving improved gains

Dieng,

Gardunia,

Covarrubias‐Pazaran

et al. 2024

The Plant Genome

View full text Add to dashboard Cite

Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi‐environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics‐IITA/Estimating‐Realized‐Genetic‐Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.

show abstract

Section: Core Ideasmentioning

confidence: 99%

Q&A: Methods for estimating genetic gain in sub‐Saharan Africa and achieving improved gains

Dieng,

Gardunia,

Covarrubias‐Pazaran

et al. 2024

The Plant Genome

View full text Add to dashboard Cite

show abstract

Plant breeding simulations with AlphaSimR

Bančič,

Greenspoon,

Gaynor

et al. 2024

Crop Science

View full text Add to dashboard Cite

Plant breeding plays a crucial role in the development of high‐performing crop varieties that meet the demands of society. Emerging breeding techniques offer the potential to improve the precision and efficiency of plant breeding programs; however, their optimal implementation requires refinement of existing breeding programs or the design of new ones. Stochastic simulations are a cost‐effective solution for testing and optimizing new breeding strategies. The aim of this paper is to provide an introduction to stochastic simulation with software AlphaSimR for plant breeding students, researchers, and experienced breeders. We present an overview of how to use the software and provide an introductory AlphaSimR vignette as well as complete AlphaSimR scripts of breeding programs for self‐pollinated, clonal, and hybrid crops, including relevant breeding techniques, such as backcrossing, speed breeding, genomic selection, index selection, and others. Our objective is to provide a foundation for understanding and utilizing simulation software, enabling readers to adapt the provided scripts for their own use or even develop completely new plant breeding programs. By incorporating simulation software into plant breeding education and practice, the next generation of plant breeders will have a valuable tool in their quest to provide sustainable and nutritious food sources for a growing population.

show abstract

A framework for simulating genotype-by-environment interaction using multiplicative models

Bančič,

Gorjanc,

Tolhurst

2024

Theor Appl Genet

View full text Add to dashboard Cite

Key message The simulation of genotype-by-environment interaction using multiplicative models provides a general and scalable framework to generate realistic multi-environment datasets and model plant breeding programmes. Abstract Plant breeding has been historically shaped by genotype-by-environment interaction (GEI). Despite its importance, however, many current simulations do not adequately capture the complexity of GEI inherent to plant breeding. The framework developed in this paper simulates GEI with desirable structure using multiplicative models. The framework can be used to simulate a hypothetical target population of environments (TPE), from which many different multi-environment trial (MET) datasets can be sampled. Measures of variance explained and expected accuracy are developed to tune the simulation of non-crossover and crossover GEI and quantify the MET-TPE alignment. The framework has been implemented within the R package FieldSimR, and is demonstrated here using two working examples supported by R code. The first example embeds the framework into a linear mixed model to generate MET datasets with low, moderate and high GEI, which are used to compare several popular statistical models applied to plant breeding. The prediction accuracy generally increases as the level of GEI decreases or the number of environments sampled in the MET increases. The second example integrates the framework into a breeding programme simulation to compare genomic and phenotypic selection strategies over time. Genomic selection outperforms phenotypic selection by $$\sim $$ ∼ 50–70% in the TPE, depending on the level of GEI. These examples demonstrate how the new framework can be used to generate realistic MET datasets and model plant breeding programmes that better reflect the complexity of real-world settings, making it a valuable tool for optimising a wide range of breeding methodologies.

show abstract

Evaluating breeding for broad versus narrow adaptation for cassava in Nigeria using stochastic simulation

Cited by 3 publications

References 46 publications

Q&A: Methods for estimating genetic gain in sub‐Saharan Africa and achieving improved gains

Q&A: Methods for estimating genetic gain in sub‐Saharan Africa and achieving improved gains

Plant breeding simulations with AlphaSimR

A framework for simulating genotype-by-environment interaction using multiplicative models

Contact Info

Product

Resources

About