Phenotyping Whole Forests Will Help to Track Genetic Performance. (October 2018)

• Record Type: Journal Article
• Authors: Dungey, Heidi S.; Dash, Jonathan P.; Pont, David; Clinton, Peter W.; Watt, Michael S.; Telfer, Emily J.
• Is Part Of: Trends in plant science. Volume 23:Number 10(2018)
• Page Start: 854
• Page End: 864
• Publication Date: 2018-10
• Subjects: phenotyping -- remote-sensing -- forest modelling -- genetic gains; Botany -- Periodicals; Botanique -- Périodiques; Botany; Periodicals; 580.5
• DOI: 10.1016/j.tplants.2018.08.005
• Languages: English
• ISSNs: 1360-1385

Abstract

Abstract : Phenotyping is the accurate and precise physical description of organisms. Accurate and quantitative phenotyping underpins the delivery of benefits from genetic improvement programs in agriculture. In forest trees, phenotyping at an equivalent precision has been impossible because trees and forests are large, long-lived, and highly variable. These facts have restricted the delivery of genetic gains in forestry compared to other agricultural sectors. We describe a landscape-scale phenotyping platform that integrates remote sensing, spatial information systems, and genomics to facilitate the delivery of greater gains enabling forestry to catch up with other sectors. Combining remote sensing at a range of spatial and temporal scales with genomics will ultimately impact on tree breeding globally. Highlights: Planted forests with good management records are a useful resource to understand the influence of genetics and the environment on phenotype. Remote sensing is a rapidly evolving group of technologies that will allow us to phenotype individual trees in situ in planted forests. Geographical information systems, data surfaces that describe climate and other abiotic factors, are information-rich. In combination with genetics and genomics, we have presented the concept of the 'phenotyping platform' which involves the phenotyping of whole forests, eventually down to the individual-tree level. The phenotyping platform will allow us to better quantify the key drivers of forest productivity to inform and optimise future breeding and deployment programmes.