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Sorghum is an environment-friendly crop. It is known for its high efficiency in conversion of solar energy, its performance on poor soil, and its ability to grow in dry conditions. It is also a very diverse crop with multiple uses and high economic importance.

Grain sorghum –used as flour or in various food products– is the third most produced grain in the US after corn and wheat, and before rice. Biomass sorghum, in turn, does not produce grain until very late in the growing season. Instead, the plant puts all its energy in growing tall and can reach 3-4 meters at the end of the growing season. This form has long been used as forage, but it has recently attracted attention as a potential source of domestic, environmentally sustainable, renewable and affordable biofuel.

However, to meet the challenge of producing the large amount of biomass required for biofuels while maintaining sustainable production practices, improvements in sorghum productivity and efficiency are still required.

To achieve these goals at a faster pace than generally possible through traditional breeding cycles, the TERRA-MEPP (Transportation Energy Resources from Renewable Agriculture-Mobile Energy Crop Phenotyping Platform) project relies on an entirely innovative and multi-disciplinary approach to crop improvement. It brings three disciplines together: cutting-edge robotics, data analytics, and plant breeding and genetics.

Precisely, an robot has been designed to survey autonomously an entire sorghum field and take measurements of individual plants using visual, thermal, and multi-spectral sensors along the growing season. These data are analyzed in-situ by newly developed software that reconstruct the plant image in 3D and extract real-time information about plant performance. Combined with genome-wide, high-density genomic information for each plant in the field, these data allow the selection of superior sorghum plants much earlier than currently possible, and with much more accuracy as to their genetic potential.

Our team is responsible for the characterization of the genetic diversity present in biomass sorghum. Genetic data will be used to create a functional landscape of the sorghum genome specifically designed to support GWAS (Genome-Wide Association Study) of sorghum phenotypes, variance component estimates, the statistical modeling of sorghum growth, and inform yield predictions.

Funding: DOE ARPA-E DE-AR000598

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