Development and evaluation of a field-based high-throughput phenotyping platform
Pedro Andrade-Sanchez A E , Michael A. Gore B C , John T. Heun A , Kelly R. Thorp B , A. Elizabete Carmo-Silva B D , Andrew N. French B , Michael E. Salvucci B and Jeffrey W. White BA Department of Agricultural and Biosystems Engineering, University of Arizona, Maricopa Agricultural Center, 37860 W. Smith-Enke Road, Maricopa, AZ 85138, USA.
B US Department of Agriculture, Agricultural Research Service, Arid-Land Agricultural Research Center, 21881 North Cardon Lane, Maricopa, AZ 85138, USA.
C Present address: Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853, USA.
D Present address: Rothamsted Research, Plant Biology and Crop Science Department, Harpenden, Hertsfordshire, AL5 2JQ, UK.
E Corresponding author. Email: pandrade@ag.arizona.edu
Functional Plant Biology 41(1) 68-79 https://doi.org/10.1071/FP13126
Submitted: 4 May 2013 Accepted: 18 July 2013 Published: 5 September 2013
Abstract
Physiological and developmental traits that vary over time are difficult to phenotype under relevant growing conditions. In this light, we developed a novel system for phenotyping dynamic traits in the field. System performance was evaluated on 25 Pima cotton (Gossypium barbadense L.) cultivars grown in 2011 at Maricopa, Arizona. Field-grown plants were irrigated under well watered and water-limited conditions, with measurements taken at different times on 3 days in July and August. The system carried four sets of sensors to measure canopy height, reflectance and temperature simultaneously on four adjacent rows, enabling the collection of phenotypic data at a rate of 0.84 ha h–1. Measurements of canopy height, normalised difference vegetation index and temperature all showed large differences among cultivars and expected interactions of cultivars with water regime and time of day. Broad-sense heritabilities (H2)were highest for canopy height (H2 = 0.86–0.96), followed by the more environmentally sensitive normalised difference vegetation index (H2 = 0.28–0.90) and temperature (H2 = 0.01–0.90) traits. We also found a strong agreement (r2 = 0.35–0.82) between values obtained by the system, and values from aerial imagery and manual phenotyping approaches. Taken together, these results confirmed the ability of the phenotyping system to measure multiple traits rapidly and accurately.
Additional keywords: cotton, genetics, Gossypium barbadense, phenomics, proximal sensing.
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