Multi-Environment and Multi-Trait Genomic Prediction for Crop Yield Improvement
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Crop Genetics, Genomics and Breeding".
Deadline for manuscript submissions: 25 December 2026 | Viewed by 138
Editors
Interests: genomic prediction; genomic selection; multi-trait genomic prediction; multi-environment genomic prediction; yield-related traits; wheat breeding; high-throughput phenotyping
Interests: genomic prediction; genomic selection; multi-trait genomic prediction; multi-environment genomic prediction; genotype-by-environment interaction; quantitative genetics; statistical genomics; prediction accuracy; bayesian models; machine learning
Special Issue Information
Dear Colleagues,
Crop yield improvement remains one of the major challenges in modern agriculture, particularly under increasing climate variability, resource limitations, and the need to ensure food security for a growing global population. In recent decades, genomic selection has become a powerful approach to accelerate breeding cycles and improve the prediction of complex quantitative traits. However, crop performance is strongly influenced by genotype-by-environment interactions, and grain yield is often associated with multiple correlated traits that jointly determine adaptation, stability, and productivity.
This Special Issue aims to present and disseminate the most recent advances related to multi-environment and multi-trait genomic prediction for crop yield improvement. We welcome contributions addressing statistical, computational, and breeding-oriented approaches that enhance the prediction of yield and related agronomic traits across environments, management conditions, and genetic backgrounds.
Particular attention will be given to innovative genomic prediction models, integration of high-throughput phenotyping, environmental covariates, crop growth models, machine learning, and artificial intelligence approaches. Studies focusing on genotype-by-environment interaction, prediction of untested genotypes and environments, multi-omics integration, and decision-support tools for breeding programs are especially encouraged.
Topics of interest for publication include, but are not limited to, the following:
- Multi-environment genomic prediction for crop yield and adaptation;
- Multi-trait genomic prediction models for complex agronomic traits;
- Genotype-by-environment interaction modeling;
- Prediction of yield stability and resilience under climate variability;
- Integration of environmental covariates and enviromics in genomic prediction;
- Use of high-throughput phenotyping and remote sensing in genomic selection;
- Machine learning and artificial intelligence for genomic prediction;
- Multi-omics approaches for crop improvement;
- Genomic prediction for stress tolerance and resource-use efficiency;
- Applications of genomic prediction in practical plant breeding programs.
Original research articles, reviews, perspectives, and methodological papers are welcome.
Dr. Damiano Puglisi
Dr. Jose Crossa
Guest Editors
Manuscript Submission Information
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Keywords
- genomic prediction
- genomic selection
- multi-environment trials
- multi-trait prediction
- crop yield improvement
- genotype-by-environment interaction
- crop breeding
- quantitative genetics
- machine learning
- enviromics
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