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Reorganize harvest point data into a table

biomass_table.Rd

This function reorganizes the information contained in one or more harvest_point objects into a data.frame.

Usage

biomass_table(..., zero_when_missing = character())

  # S3 method for class 'harvest_point'
biomass_table(..., zero_when_missing = character())

Arguments

...

One or more harvest_point objects.

zero_when_missing

A character vector indicating any biomass columns whose values should be set to zero when they were not explicitly measured. For example, early harvest data points for soybeans may not include a value for pod; by default, pod would be set to NA for these points, but it would make more sense to set it to 0.

Details

The following harvest_point elements will be included as columns in the final data.frame, where each row represents the information from one of the harvest_point objects passed to biomass_table:

  • crop

  • variety

  • location

  • plot

  • year

  • doy

  • hour

  • time

  • SLA

  • LMA

  • LAI_from_LMA

  • LAI_from_planting_density

  • LAI_from_measured_population

  • agb_per_plant_row

  • agb_per_plant_partitioning

  • measured_population

  • row_spacing

  • plant_spacing

  • planting_density

  • Any elements contained in components_biocro (see process)

  • Any additional arguments (such as comments) that were passed to harvest_point when creating the data points

See harvest_point and process for more information about these elements. In general, any missing values will be set to NA in the final data.frame.

Value

A data.frame as described above.

See also

harvest_point

Examples

# Example: Creating, processing, and reorganizing a harvest_point object that
# includes (optional) comments about the stem and the leaf litter.
hp <- harvest_point(
  crop = 'soybean',
  variety = 'ld11',
  location = 'energy farm',
  plot = 1,
  year = 2023,
  doy = 186,
  hour = 12,
  planting_density = 140000,
  row_spacing = 0.7,
  partitioning_leaf_area = 500,
  partitioning_component_weights = list(leaf = 2.5, stem = 1.5, root = 1.4, leaf_litter = 0.2),
  agb_components = c('leaf', 'stem'),
  agb_row_length = 2,
  agb_weight = 50,
  trap_area = 0.185,
  trap_component_weights = list(leaf_litter = 0.4, stem_litter = 0.5),
  partitioning_nplants = 6,
  agb_nplants = 50,
  stem_comment = 'The stem weight includes petioles',
  stem_litter_comment = 'The stem litter is entirely petioles',
  leaf_litter_comment = 'Senesced leaves were present on the plants and in the trap'
)


hpp <- process(hp)

biomass <- biomass_table(hpp)

str(biomass)
#> 'data.frame':	1 obs. of  27 variables:
#>  $ crop                        : chr "soybean"
#>  $ variety                     : chr "ld11"
#>  $ location                    : chr "energy farm"
#>  $ plot                        : num 1
#>  $ year                        : num 2023
#>  $ doy                         : num 186
#>  $ hour                        : num 12
#>  $ time                        : num 186
#>  $ leaf                        : num 0.223
#>  $ stem                        : num 0.134
#>  $ root                        : num 0.125
#>  $ leaf_litter                 : num 0.0395
#>  $ stem_litter                 : num 0.027
#>  $ SLA                         : num 2
#>  $ LMA                         : num 50
#>  $ LAI_from_LMA                : num 0.446
#>  $ LAI_from_planting_density   : num 0.288
#>  $ LAI_from_measured_population: num 0.298
#>  $ agb_per_plant_row           : num 1
#>  $ agb_per_plant_partitioning  : num 0.667
#>  $ measured_population         : num 144536
#>  $ row_spacing                 : num 0.7
#>  $ plant_spacing               : num 0.0413
#>  $ planting_density            : num 140000
#>  $ stem_comment                : chr "The stem weight includes petioles"
#>  $ stem_litter_comment         : chr "The stem litter is entirely petioles"
#>  $ leaf_litter_comment         : chr "Senesced leaves were present on the plants and in the trap"