Trait Ontology is a controlled vocabulary used to describe and codify a collection of phenotypic traits in breeding programs. In DeltaBreed, ontology is defined as a controlled vocabulary to support the standardization of observation variables (or ontology terms) so that breeding data are unambiguous, interoperable and reusable. DeltaBreed follows the cropontology.org conceptual model that defines variables as a combination of a trait, a method and a scale. Note: breeders may use the term “trait” interchangeably with observation variable or phenotype, but DeltaBreed uses the term “trait” more specifically and the ontology is designed to manage any type of observation variable, not just phenotypes.

When phenotyping, quantitative variables should always be a breeder preference, because quantitative measures are the best way to characterize quantitative traits and support parametric statistical inference. DeltaBreed supports both qualitative (nominal) and quantitative (ordinal) categorical variables. Nominal variables are described by 1-n categories. Ordinal variables are described by 2-n categories with their corresponding value ranges in DeltaBreed.

Best Practice:

• When phenotyping use a quantitative variable when possible.
• If a categorical phenotype is quantitative, create an ordinal variable.
• To avoid ambiguity you must define both the category and the value range ordinal traits.
• If you having a hard time assigning values to your categories, consider using fewer categories. Using fewer categories (<6) has been shown to be a good compromise between accuracy and error.
• If you are curating an historical dataset, where values are unknown for an ordinal categories, you will need to create a nominal variable. Once you have loaded the historical data, consider archiving this variable and creating a new ordinal variable to use moving forward.