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Manage Ontology
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BMS 13.0-14.0 Manual


The Breeding Management System’s crop ontology management tool uses a structured vocabulary to allow descriptors to act as variables in database queries and statistical analysis. Core crops come preloaded with a set of variables, or ontology terms, recommended by their community of practice. Preloaded ontologies are fully customizable. The BMS also provides a generic crop database to support any breeding program. Consistent variable definitions are essential for data sharing and collaboration. As you consider ontology customization, we recommend that you coordinate with collaborators and your crop community to similarly define terms to facilitate data sharing and meta analysis. See more about Crop Ontology Curation.

  • Select Manage Ontologies from the information management menu to browse, edit, or add ontology terms.
Maize Ontology Browser

Variable Identity

Each variable name is identified by 3 details. Only one ontology term can be associated with a combination of unique details.

  • Property: What is measured
  • Method: How the variable is measured
  • Scale: Categorical scale or unit of measure for the variable

Duplicate Variable Error

The BMS will not allow you to add a new variable with an identical combination of property, method, and scale to other variables.

Aliases & Favorites

Although it is important for crop communities to similarly define traits and share a common vocabulary, the BMS provides the ability to create custom aliases for ontology terms, allowing breeders to communicate variables in familiar language within their breeding programs. Once an alias has been set, the custom alias will replace the variable name in the user interface, data collection files, and the output of statistical analyses. Giving a variable an alias does not change the functional identity (property, method, and scale) of the variable. Variables which are given aliases are automatically added to your program favorites. Any variable can be made a program favorite by selecting the star symbol to the right. Deselect the blue star to remove from favorites.

Add & Edit Variable

Once a variable is used in a study property, method, and scale can not be changed from the user interface. See your system administrator for more information on editing variables in use.

  • Select Add New. Choose Variable.

Variable name is generally an abbreviation or acronym describing the variable. Variable names in the default BMS crop ontology will be recognizable between all members of a crop community. Variable name is the ontology term specified in BMS data collection files and the output of statistical analyses. Variable names cannot contain spaces or special characters with the exception of underscore, and cannot start with a number to ensure compatibility with Breeding View (BV) statistical engine and other apps. If variable names exceed 15 characters truncation of variable name will occur in in the graphical output of BV. Changing a variable name does not change the functional identity (property, method, and scale) of the variable.

  • Name & describe variable. Variable description is a plain language description of the variable. Editing or expanding a variable's definition does not change the functional identity (property, method, and scale) of the variable.
    • Name: B_Carotene
    • Describe: Beta-carotene content, mg/kg dry matter (mg/kg d.m.)
  • Property describes what is being measured. The default maize ontology has no property describing beta-carotene content, so a new one must be added. Select Add a New Property.

Find Existing or Add New Property

  • Name the property, Beta-Carotene. Tag the classes, Quality and Physiological. Tagging the property helps the ontology search engine provide selections of relevant traits. Save.

Find Existing or Add New Method

Method describes how the property is measured. Beta-carotene content is measured by high performance liquid chromatography, which is not included in the default maize ontology.

  • Select Add a New Method.

  • Name and describe the method, HPLC, high performance liquid chromatography.

Find Existing or Add New Scale

Beta-carotene content is a numeric variable measured in units of milligram per kilogram of dry matter.

  • Name: mg/kg d.m.
  • Description: milligram per kilogram of dry matter
  • Data Type: Numeric
  • Valid Range: Numeric scales offer the option of setting a range of valid values for setting limit for all variables sharing a particular scale. It is recommended to set the limits as expected range when creating each variable.

Variable Type

Variable type differentiates traits, or phenotype, from different independent variables. Variable type determines which variables are presented in different BMS tools.

  • Types
    • Analysis
    • Environment Detail
    • Germplasm Descriptor
    • Nursery Condition
    • Selection Method
    • Study Detail
    • Treatment Factor
    • Trait
    • Study Detail
  • Like most terms in the crop ontology, beta-carotene, is a trait or phenotype. Select Trait from the drop down menu of different variable types. Save.


  • Expected Range: Numeric variables offer the option of setting a range of valid values for quality control. Data outside of this range is flagged as a possible error during data import. It is recommended to set minimum and maximum expected values accordingly biological limits of the trait.



Traits whose value is derived from calculation will be associated with a formula. See more on Creating formulas for calculated variables.)

Select the variable to which a formula will be added. Select add a new formula.

The trait is now associated with the formula and can be calculated by the BMS after data for input variables is added.

Algebraic Formula

Fod_Mod_pct is the percent moisture of fodder calculated by dividing the dry weight (DFDWTKGH) by the fresh weight (FFODKGHA) and multiplying by 100
Calculation = ({{DFDWTKGH}}/{{FFODKGHA}})*100

Date to Days Formula

Calculate the number of days occurring between 2 dates = fn:daysdiff ({{DATE1}},{{DATE2}}

Average Formula

Calculate averages of subsamples of grain quality amylose percent meassures (fn:avg({{AmyCt_NIRS_Pct}})
  • Select the garbage can icon to delete a formula. Select delete to confirm that you want to delete the existing formula. *If you want to edit and existing formula, you can copy the formula before deleting and paste back into the empty formula builder to edit.

Search Ontology

Searching the maize database for "beta" now reveals the newly added B_Carotene variable.

Time Series

BMS version 11 does allow a single traits to be associated with mulitple sub-observations to record repeated measures (see more Observations & Sub-Observations). However, time series is still under development. If the v11 sub-observation solution to time series recording does not suit your needs, you are still able to create individual traits for each time points.

The nomenclature of new traits created to support time series should be standardized for each crop. The following table offers some suggestions for time series nomclature.

Time Code Type Time Code Examples
Cereals Zadoks growth/development stages GS(00-99) or DS(00-99)
  • DS65 = heading
  • DS75 = anthesis
  • DS87 = physiological maturity
Maize growth/development stages

Vegetative: VE, V1-Vn, VT

Reproductive: R1-R6

  • VE = emergence
  • V3 = third Leaf
  • VT = tasseling
  • R1 = silking
  • R6 = physiological maturity
Soybean growth/development stages VE, VC, V1-Vn, R1-R8
  • VE = emergence
  • V4 = second trifoliate
  • R2 = full flowering
  • R8 = full maturity
Generic Codes
  • hd = heading
  • flw = flowering
  • ant = anthesis
  • vg = vegetative period
  • prehd = pre-heading
  • gf = grain filling
  • mat = maturity
Days after emergence dae 45dae, 65dae....
Days after sowing das 45das, 65das...
Days after planting dap 45dap, 65dap...
Weeks after planting wap 1wap, 2wap...
Months after planting map 1map, 2map
Date yyyymmdd 20150315
Date+hr+min yyyymmdd 201503151135
Time t t1, t2...
  • First create a new method with the subsampling code.

  • Second create a new trait with the subsampling code.

  • Repeat. Create a new ontology term for each required sub-sample or time series measurement.