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Advanced phenotyping offers opportunities for improved breeding of forage and turf species

Walter, Achim, Studer, Bruno, Kölliker, Roland
Annals of botany 2012 v.110 no.6 pp. 1271-1279
agroecosystems, application methods, biomass, breeding, climate change, color, cultivars, forage, genetic variation, genotype, grasslands, image analysis, lawns and turf, phenotype, photosynthesis, plant architecture, precision agriculture, remote sensing, stomatal movement, stress tolerance, temperature
Background and Aims Advanced phenotyping, i.e. the application of automated, high-throughput methods to characterize plant architecture and performance, has the potential to accelerate breeding progress but is far from being routinely used in current breeding approaches. In forage and turf improvement programmes, in particular, where breeding populations and cultivars are characterized by high genetic diversity and substantial genotype × environment interactions, precise and efficient phenotyping is essential to meet future challenges imposed by climate change, growing demand and declining resources. Scope This review highlights recent achievements in the establishment of phenotyping tools and platforms. Some of these tools have originally been established in remote sensing, some in precision agriculture, while others are laboratory-based imaging procedures. They quantify plant colour, spectral reflection, chlorophyll-fluorescence, temperature and other properties, from which traits such as biomass, architecture, photosynthetic efficiency, stomatal aperture or stress resistance can be derived. Applications of these methods in the context of forage and turf breeding are discussed. Conclusions Progress in cutting-edge molecular breeding tools is beginning to be matched by progress in automated non-destructive imaging methods. Joint application of precise phenotyping machinery and molecular tools in optimized breeding schemes will improve forage and turf breeding in the near future and will thereby contribute to amended performance of managed grassland agroecosystems.