High-throughput phenotyping technologies allow accurate selection of stay-green
| Autor: | William D. Bovill, Richard A. James, Greg J. Rebetzke, David M. Deery, José A. Jiménez-Berni |
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| Rok vydání: | 2016 |
| Předmět: |
Crops
Agricultural 0106 biological sciences 0301 basic medicine phenotyping leaf senescence Physiology drought Plant Science Biology Machine learning computer.software_genre 01 natural sciences Normalized Difference Vegetation Index 03 medical and health sciences stay-green wheat Range (statistics) Climate change Plant breeding Throughput (business) Selection (genetic algorithm) business.industry Simulation modelling High-Throughput Nucleotide Sequencing Biotechnology Plant Breeding Phenotype 030104 developmental biology genotype × environment interaction Grain yield crop breeding Artificial intelligence Insight business crop adaptation computer 010606 plant biology & botany |
| Zdroj: | Journal of Experimental Botany |
| ISSN: | 1460-2431 0022-0957 |
| DOI: | 10.1093/jxb/erw301 |
| Popis: | Improved genotypic performance in water-limited environments relies on traits, like ‘stay-green’, that are robust and repeatable, correlate well across a broader range of target environments and are genetically more tractable than assessment of yield per se. Christopher et al. (see pages 5159–5172) used multi-temporal, Normalised Difference Vegetative Index (NDVI) measurements with crop simulation modelling to demonstrate the value of various stay-green phenotype parameters for improving grain yield across different environment types. |
| Databáze: | OpenAIRE |
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