Genomic prediction of maize yield across European environmental conditions
The development of germplasm adapted to changing climate is required to ensure food security1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios3–7 (genotype × environment interaction), in spite of promising results for flowering time8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype - specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Texto biblioteca |
| Language: | eng |
| Subjects: | GENETICS, PHYSIOLOGY, PLANT SCIENCES, |
| Online Access: | http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=47674 http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The development of germplasm adapted to changing climate is required to ensure food security1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios3–7 (genotype × environment interaction), in spite of promising results for flowering time8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype - specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach. |
|---|