Projects – The Gore Lab

Drones are an effective tool to collect large-scale scientific measurements

Remote sensing: measuring the invisible

We capitalize on the advancements in robotics and proximal sensing to access information about root system, soil, and the root-soil interface (rhizosphere) in fields.

Why it matters?

These new technologies allow us to see part of the plants and their environment in a non-destructive manner, allowing us to gather data that was previously unavailable.

We use these data to improve our understanding of factors that determine plant phenotypes and predict these phenotypes early in growing plants.

Some examples of our current projects

We are exploring how genotype and environment affect the spatial and temporal distribution of metabolites exuded by maize roots, which in turn affects organic carbon in soil.
We are predicting agronomic traits in maize field trials by integrating phenotype + environment –both above and below ground—from data collected by worm-like robots that swim in soil to sense and monitor roots and the soil environment.
We are advancing maize phenotyping by using data from root and root-soil-carbon interactions. These data are collected by technologies that allow dynamic access to the rhizosphere and sense chemical and biological factors such as root interactions with soil water and carbon.
We are modeling dry matter content in growing cassava roots to allow early detection of phenotype characteristics using data collected by NMR (nuclear magnetic resonance), a non-destructive method for quality detection of underground roots in fields.

Combining RNA-seq and metabolic profiles allows prediction of nutritional quality in oat

Nutritional quality: harnessing genetic information to assist breeding

We integrate genomics and environment data to predict plant phenotypes, identify superior germplasm, and accelerate the selection of new crop varieties.

Why it matters?

Phenotypes are the result of many parameters. Disentangling the effect of environment and genetics facilitate the breeding of plants with desired characteristics. These can be nutritional content (e.g. vitamin, minerals, etc.) or adaptation to stress (e.g. drought, heat, etc.).

Some examples of our current projects

We generate and analyze RNA-seq data from an oat diversity panel together with their metabolomic profile to predict nutritional quality for new oat lines.
We are developing an open-source digital tool that collects high-dimensional data from genotypes and environment, model GxE in the field, and predict phenotypes.
We are creating user-friendly, open-source software that models plant growth and development for specific genotypes in order to facilitate breeding and selection of crop varieties adapted to our changing environment.
We are developing phenomics technologies (GxE interactions and their effect on phenotypes) to support breeding goals and objectives of NARIs (National Agricultural Research Institutes) in several countries.
We are developing a new breeding method combining speed breeding and genomic selection to improve carotenoid, tocochromanols, zinc and iron levels in sweet corn.