BMS Example

Khaled Al-Shamaa



This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like EBS, BMS, BreedBase, and GIGWA (using BrAPI calls). Extra helper functions support environmental data sources, including TerraClimate and FAO HWSDv2 soil database.

Breeding Management System

Breeding Management System (BMS) is an information management system developed by the Integrated Breeding Platform to help breeders manage the breeding process, from programme planning to decision-making. The BMS is customizable for most crop breeding programs, and comes pre-loaded with curated ontology terms for many crops (bean, cassava, chickpea, cowpea, groundnut, maize, rice, sorghum, soybean, wheat, and others). The BMS is available as a cloud application, which can be installed on local or remote servers and accessed by multiple users.


The Breeding API (BrAPI) project is an effort to enable interoperability among plant breeding databases. BrAPI is a standardized RESTful web service API specification for communicating plant breeding data. This community driven standard is free to be used by anyone interested in plant breeding data management.


if (!require("remotes")) install.packages("remotes")

If you are not already an active BMS user, you can contact IBP support to get access to a trial BMS server.

From BMS version 25 onward, users must possess the necessary permissions to utilize the BrAPI services. This table outlines the permissions needed for accessing specific services.


# load the QBMS library

# config your BMS connection (by providing your BMS login page URL)

# login using your BMS account (interactive mode)
# or pass your BMS username and password as parameters (batch mode)

# list supported crops in the current bms server

# select a crop by name

# list all breeding programs in the selected crop

# select a breeding program by name
set_program("Wheat International Nurseries")

# list all studies/trials in the selected program

# filtered by year of starting date

# select a specific study/trial by name

# list all environments/locations information in the selected study/trial

# select a specific environment/location by name
set_study("IDYT39 Environment Number 9")

# select a specific study by location name (first match)
studies <- list_studies()
set_study(studies[studies$locationName == "Amlaha", "studyName"][1])

# retrieve data, general information, and germplasm list 
# of the selected environment/location
data <- get_study_data()
info <- get_study_info()
germplasm <- get_germplasm_list()

# get observation variable ontology in the selected study/trial
ontology <- get_trial_obs_ontology()

# get the pedigree table
pedigree_table <- get_pedigree_table(germplasm, "germplasmName", "pedigree")

# retrieve multi-environment trial data of the selected study/trial
MET <- get_trial_data()

# retrieve all environments/locations information in the selected program
program_studies <- get_program_studies()

# retrieve observations data of given germplasm aggregated from all trials 
# in the selected program
# e.g.,
germplasm_observations <- get_germplasm_data("Jabal")

# retrieve germplasm attributes for a given germplasm in a crop
germplasm_attributes <- get_germplasm_attributes("Jabal")