The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap
Open accessAbstract
Plants are affected by complex genome×environment×management interactions which determine phenotypic plasticity as a result of the variability of genetic components. Whereas great advances have been made in the cost-efficient and high-throughput analyses of genetic information and non-invasive phenotyping, the large-scale analyses of the underlying physiological mechanisms lag behind. The external phenotype is determined by the sum of the complex interactions of metabolic pathways and intracellular regulatory networks that is reflected in an internal, physiological, and biochemical phenotype. These various scales of dynamic physiological responses need to be considered, and genotyping and external phenotyping should be linked to the physiology at the cellular and tissue level. A high-dimensional physiological phenotyping across scales is needed that integrates the precise characterization of the internal phenotype into high-throughput phenotyping of whole plants and canopies. By this means, complex traits can be broken down into individual components of physiological traits. Since the higher resolution of physiological phenotyping by 'wet chemistry' is inherently limited in throughput, high-throughput non-invasive phenotyping needs to be validated and verified across scales to be used as proxy for the underlying processes. Armed with this interdisciplinary and multidimensional phenomics approach, plant physiology, non-invasive phenotyping, and functional genomics will complement each other, ultimately enabling the in silico assessment of responses under defined environments with advanced crop models. This will allow generation of robust physiological predictors also for complex traits to bridge the knowledge gap between genotype and phenotype for applications in breeding, precision farming, and basic research.
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Citations
Cites
Field high-throughput phenotyping: the new crop breeding frontier
Araus, J. L., & Cairns, J. E. (2014) · Trends in Plant Science · doi:10.1016/j.tplants.2013.09.008 · Cited by 1,817 · cited 1×
“Thus agriculture requires a second green revolution to accelerate breeding of novel climate-resilient varieties that satisfy the rising demand for food and the increased competition for crops as sources of bioenergy and fibre, and for other industrial purposes.”
BackgroundGibon, Y., Blaesing, O. E., Hannemann, J., Carillo, P., Höhne, M., Hendriks, J. H. M., Palacios, N., Cross, J., Selbig, J., & Stitt, M. (2004) · The Plant Cell · doi:10.1105/tpc.104.025973 · Cited by 552
Cited by
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks
Ubbens, J. R., & Stavness, I. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01190 · Cited by 371
Yendrek, C. R., Tomaz, T., Montes, C. M., Cao, Y., Morse, A. M., Brown, P. J., McIntyre, L. M., Leakey, A. D. B., & Ainsworth, E. A. (2017) · Plant Physiology · doi:10.1104/pp.16.01447 · Cited by 308
Machine learning: a powerful tool for gene function prediction in plants
Mahood, E. H., Kruse, L. H., & Moghe, G. D. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11376 · Cited by 153
Metadata
- Authors
- Großkinsky, D. K., Svensgaard, J., Christensen, S., & Roitsch, T.
- Venue
- Journal of Experimental Botany
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-07-10
- Volume
- 66
- Issue
- 18
- Pages
- 5429-5440
- ISSN
- 0022-0957 (ISSN-L)
- 1460-2431
- OpenAlex
- W2127293692
- Cited by
- 266
- References
- 2
- FWCI
- 16.61
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Großkinsky, D. K., Svensgaard, J., Christensen, S., & Roitsch, T. (2015). Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. Journal of Experimental Botany. https://doi.org/10.1093/jxb/erv345
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