Figure 1: Abbreviated schema for BETYdb, focusing on tables used to store plant trait and yield data. This figure excludes other tables used for PEcAn workflow system provenance and data management and for synchronizing independent instances of BETYdb across many servers. A complete and up-to-date interactive schema is published at https://www.betydb.org/schemas.
BETYdb is designed as a relational database, somewhat normalized as shown in the structure diagram Figure 1. Each table has a primary key field,
id, which serves as surrogate key, a unique identifier for each row in the table. Most tables have a natural key defined as well, by which rows can be uniquely identified by real-world attributes. In addition, most tables have a
created_at and an
updated_at column to record row-insertion and update timestamps, and the traits and yields tables each have a
user_id field to record the user who originally entered the data.
A complete list of tables along with short descriptions is provided in Table 2, and a comprehensive description of the contents of each table is provided below. Note: An up-to-date list of the tables in BETYdb along with their descriptions and diagrams of their interrelationships may be found at https://www.betydb.org/schemas.
mean, n, variance estimate,date, time,site, citation,species, treatment
Trait, yield,,and ecosystem service data, including values and summary statistics.
Name, Definition, Units
Definitions,,description, units, and allowable ranges of specific traits and ecosystem,services contained in the database
Defines primary,data, covariates, and priors
Context required,to interpret a particular data point, for example the time, temperature, or,location of a measurement
Contextual,information necessary to interpret data
Plant Functional Type
Name, Definition, reference
Context required,to interpret a particular data point, for example the time, temperature, or,location of a measurement
USDA Plants database, amended with additional species and links to other tables within BETYdb
Specific,genotype bred for cultivation
Probability distributions,that quantify knowledge of a variable in the absence of information at the,level of functional type, species or cultivar
Stores expert,knowledge, used in QA/QC and data analysis
Qualitative,descriptions of treatments described in the primary publication
Categorize,experimental treatments, permits reference to original publication
Quantitative record of management activities performed on all plots or specific experimental interventions
Name, Lat, Lon
Location and,basic climate and soil information
Author, Year, Title, doi
Unique reference,for source of information, not necessarily published
Used in many,tables to independently record source of information that may come from,multiple publications
Links related,trait records
Used to identify measurments made on the same unit of replication (e.g. leaf, plant, or plot),when information is available; used to ’pivot’ data from long to wide.
Each table is given a name that describes the information that it contains. For example, the table containing trait data is called
traits, the table containing yield data is
yields, and so on. Each table also has a primary key; the primary key is always
id, and the primary key of a specific table might be identified as
yields.id . One table can reference another table using a foreign key; the foreign key is given a name using the singular form of the foreign table, an underscore, and
In some cases, two tables can have multiple references to one another, known as a ’many to many’ or ’m:n’ relationship. For example, one citation may contain data from many sites; at the same time, data from a single site may be included in multiple citations. Such relationships use join tables (also known as "association tables" or "junction tables"). Join tables (e.g. Table 4, Table 5, Table 10, Table 12, Table 13) combine the names of the two tables being related. For example, the table used to link
sites is named
citations_sites. These join tables have two foreign keys (
site_id in this example) which together uniquely identify a row of the table (and thus constitute a candidate key). (For various implementational reasons, these tables also have a surrogate key named
id, but in general such a key is extraneous.)
While foreign key columns are identified implicitly by the naming convention whereby such columns end with the suffix
_id, foreign keys can be made explicit by imposing a foreign-key constraint at the database level. Such a constraint identifies the table and column which the foreign key refers to and in addition guaranties that a row with the required value exists. Thus, if there is a foreign-key constraint saying that the column
yields.citation_id refers to
citations.id, then if there is a row in the yields table where
cititation_id = 9, there must also be a row in the citations table where
id = 9. Explicit foreign keys show up in the schema documentation as an entry in the References column of the table listing and as a line between tables in the schema diagrams.
The two data tables, traits and yields, contain the primary data of interest; all of the other tables provide information associated with these data points. These two tables are structurally very similar as can be seen in Table 17 and Table 20.
The traits table contains trait data (Table 17). Traits are measurable phenotypes that are influenced by a plants genotype and environment. Most trait records presently in BETYdb describe tissue chemistry, photosynthetic parameters, and carbon allocation by plants.
The yields table includes aboveground biomass in units of Mg per ha (Table 20). Biomass harvested in the fall and winter generally represents what a farmer would harvest, whereas spring and summer harvests are generally from small samples used to monitor the progress of a crop over the course of the growing season. Managements associated with Yields can be used to determine the age of a crop, the fertilization history, harvest history, and other useful information.
Each site is described in the sites table (Table 15). A site can have multiple studies and multiple treatments. Sites are identified and should be used as the unit of spatial replication; treatments are used to identify independent units within a site, and these can be compared to other studies at the same site with shared management. "Studies" are not identified explicitly, but independent studies can be identified via shared management entries at the same site.
The treatments table provides a categorical identifier of a study’s experimental treatments, if any (Table 18).
Any specific information such as rate of fertilizer application should be recorded in the managements table. A treatment name is used as a categorical (rather than continuous) variable, and the name relates directly to the nomenclature used in the original citation. The treatment name does not have to indicate the level of treatment used in a particular treatment—if required for analysis, this information is recorded as a management.
Each study includes a control treatment; when there is no experimental manipulation, the treatment is considered "observational" and listed as "control". In studies that compare plant traits or yields across different genotypes, site locations, or other factors that are built in to the database, each record is associated with a separate cultivar or site so these are not considered treatments.
For ambiguous cases, the control treatment is assigned to the treatment that best approximates the background condition of the system in its non-experimental state; for this reason, a treatment that approximates conventional agronomic practice may be labeled "control".
The managements table provides information on management types, including planting time and methods, stand age, fertilization, irrigation, herbicides, pesticides, as well as harvest method, time and frequency.
The managements and treatments tables are linked through the
managements_treatments table (Table 10).
Managements are distinct from treatments in that a management is used to describe the agronomic or experimental intervention that occurs at a specific time and may have a quantity whereas treatment is a categorical identifier of an experimental group. Managements include actions that are done to a plant or ecosystem—for example the planting density or rate of fertilizer application.
In other words, managements are the way a treatment becomes quantified. Each treatment can be associated with multiple managements. The combination of managements associated with a particular treatment will distinguish it from other treatments. Each management may be associated with one or more treatments. For example, in a fertilization experiment, planting, irrigation, and herbicide managements would be applied to all plots but the fertilization will be specific to a treatment. For a multi-year experiment, there may be multiple entries for the same type of management, reflecting, for example, repeated applications of herbicide or fertilizer.
The covariates table is used to record one or more covariates associated with each trait record (Table 6). Covariates generally indicate the environmental or experimental conditions under which a measurement was made. The definition of specific covariates can be found in the variables table (Table 19). Covariates are required for many of the traits because without covariate information, the trait data will have limited value.
The most frequently used covariate is the temperature at which some respiration rate or photosynthetic parameter was measured. For example, photosynthesis measurements are often recorded along with irradiance, temperature, and relative humidity.
Other covariates include the size or age of the plant or plant part being measured. For example, root respiration is usually measured on fine roots, and if the authors define fine root as < 2mm, the covariate
root_diameter_max has a value of 2.
The plant functional type (PFT) table pfts is used to group plants for statistical modeling and analysis. Each row in pfts contains a PFT that is linked to a set of species in the species table. This relationship requires the lookup table pfts_species (Table 13). Alternatively, a PFT may be linked to a set of cultivars in the cultivars table via the cultivars_pfts lookup table. (A PFT can not comprise both cultivars and species.) Furthermore, each PFT can be associated with a set of trait prior probability distributions in the priors table (Table 14). This relationship requires the lookup table pfts_priors (Table 12).
In many cases, it is appropriate to use a pre-defined default PFT (for example
tempdecid is temperate deciduous trees). In other cases, a user can define a new PFT to query a specific set of priors or subset of species. For example, there is a PFT for each of the functional types found at the EBI Farm prairie. Such project-specific PFTs can be named using the binomial scheme projectname.pftname—for example,
ebifarm.c4grass instead of simply
The variables table includes definitions of different variables used in the traits, covariates, and priors tables (Table 19). Each variable has a
name field and is associated with a standardized value for
description field provides additional information or context about the variable.
Join tables are required when each row in one table may be related to many rows in another table, and vice-versa; this is called a ’many-to-many’ relationship.
Because a single study may use multiple sites and multiple studies may use the same site, these relationships are tracked in the citation_sites table (Table 4).
Because a single study may include multiple treatments and each treatment may be associated with multiple citations, these relationships are recorded in the citations_treatments table (Table 5).
The cultivars_pfts table allows a many-to-many relationship between the pfts and cultivars tables. A PFT that is related to a set of cultivars may not also be related to one or more species (except indirectly, by virtue of its associated cultivars belonging to particular species). A database-level constraint ensures this.
It is clear that one treatment may have many managements, e.g. tillage, planting, fertilization. It is also important to note that any managements applied to a control plot should, by definition, be associated with all of the treatments in an experiment; this is why the many-to-many association table managements_treatments is required.
The pfts_priors table allows a many-to-many relationship between the pfts and priors tables (Table 12). This allows each pft to be associated with multiple priors and each prior to be associated with multiple pfts.
The pfts_species table allows a many-to-many relationship between the pfts and species tables (Table 13). A PFT that is related to a set of species may not also be related to one or more cultivars (except perhaps indirectly, by virtue of the associated species having certain cultivars). A database-level constraint ensures this.