Calculation Functions

This vignette describes the included calculation functions.

Invertebrate mass

The ff_calc_inv_mass and ff_calc_inv_mass_ts functions use the rice field geometries in the ff_fields dataset to predict invertebrate mass production over time based on field acreage. Moss et al. (2009)1 estimated that invertebrate biomass in flooded post-harvest agricultural fields increased at an average rate of 0.186 g/m² per day. This relationship between area and invertebrate mass production forms the basis for these functions.

We begin with the fields dataset, which contains the rice field geometries and their areas. Techically, any data.frame, tibble, or sf object with an area_ac column containing area in acres can be used for this function.

ff_fields
unique_id county elev_grp geometry group_id area_ac volume_af
1103539 Glenn 90 to 100 POLYGON ((6526707 2289953, … 1802010405 23.428379 9.7618244
1102849 Glenn 110 to 120 POLYGON ((6517766 2307611, … 1802010405 23.476331 9.7818048
1103353 Glenn 120 to 130 POLYGON ((6501679 2309168, … 1802010405 1.055052 0.4396049
1103425 Glenn 120 to 130 POLYGON ((6525986 2324730, … 1802010403 1.339829 0.5582621
1103546 Glenn 70 to 80 POLYGON ((6535820 2277869, … 1802010403 48.900074 20.3750307

Pass the fields to the calc_inv_mass function, along with the desired number of days (for example, 14 days) to add two new columns.

  • daily_prod_kg calculates the production per day based on area
  • total_prod_kg multiplies this value by the number of days provided
ff_calc_inv_mass(ff_fields, 14)
unique_id county elev_grp geometry group_id area_ac volume_af area_m2 daily_prod_kg total_prod_kg
1103539 Glenn 90 to 100 POLYGON ((6526707 2289953, … 1802010405 23.428379 9.7618244 94814.648 17.6355246 246.89734
1102849 Glenn 110 to 120 POLYGON ((6517766 2307611, … 1802010405 23.476331 9.7818048 95008.713 17.6716207 247.40269
1103353 Glenn 120 to 130 POLYGON ((6501679 2309168, … 1802010405 1.055052 0.4396049 4269.794 0.7941817 11.11854
1103425 Glenn 120 to 130 POLYGON ((6525986 2324730, … 1802010403 1.339829 0.5582621 5422.288 1.0085455 14.11964
1103546 Glenn 70 to 80 POLYGON ((6535820 2277869, … 1802010403 48.900074 20.3750307 197898.598 36.8091393 515.32795

If no number of days is provided, then only the daily_prod_kg column is created.

A plotting function shorthand is also provided to map the result:

These functions can be applied in combination with the other datasets to synthesize and ask additional questions. For example, which watersheds produce the most invertebrate biomass?

inv_mass_14_days <- ff_calc_inv_mass(ff_fields, 14)

total_prod_by_group <- inv_mass_14_days |>
  group_by(group_id) |> 
  summarize(sum_total_prod_kg = sum(total_prod_kg)) |>
  st_drop_geometry()

ff_watersheds |> 
  left_join(total_prod_by_group) |>
  ggplot() + 
    geom_sf(aes(fill = sum_total_prod_kg, color = sum_total_prod_kg)) + 
    geom_sf(data=ff_streams) + geom_sf(data=ff_canals) + geom_sf(data=ff_returns) +
    scale_fill_viridis_c(aesthetics = c("colour", "fill"),
                                  option="cividis",
                                  direction=-1,
                                  name="Total 14-day production (kg) \nby watershed") +
    ggplot2::scale_y_continuous(breaks = seq(38, 40, by=0.5)) +
    ggplot2::scale_x_continuous(breaks = seq(-122.5, -120.5, by=0.5))

These results will always be directly proportional to total rice field acreage, as this is the only input into the relationship.

The ff_calc_inv_mass_ts function returns the growth in biomass over time for a selected number of days. This returns a simple tibble in the following format…

ff_calc_inv_mass_ts(ff_fields, 14)
day unique_id total_prod_kg
1 1103539 17.6355246
1 1102849 17.6716207
1 1103353 0.7941817
1 1103425 1.0085455
1 1103546 36.8091393
1 1103560 11.3354323
1 1103561 26.1411859
1 1103563 18.7069138
1 1103564 56.2166164
1 1103566 42.1784267

…which can be pivoted as so if needed…

ff_calc_inv_mass_ts(ff_fields, 7) |> 
  pivot_wider(names_from = day, values_from = total_prod_kg, names_prefix = "day_")
unique_id day_1 day_2 day_3 day_4 day_5 day_6 day_7
1103539 17.6355246 35.271049 52.906574 70.542098 88.177623 105.813148 123.448672
1102849 17.6716207 35.343241 53.014862 70.686483 88.358103 106.029724 123.701345
1103353 0.7941817 1.588363 2.382545 3.176727 3.970909 4.765090 5.559272
1103425 1.0085455 2.017091 3.025637 4.034182 5.042727 6.051273 7.059819
1103546 36.8091393 73.618279 110.427418 147.236557 184.045696 220.854836 257.663975
1103560 11.3354323 22.670865 34.006297 45.341729 56.677162 68.012594 79.348026
1103561 26.1411859 52.282372 78.423558 104.564743 130.705929 156.847115 182.988301
1103563 18.7069138 37.413828 56.120742 74.827655 93.534569 112.241483 130.948397
1103564 56.2166164 112.433233 168.649849 224.866466 281.083082 337.299698 393.516315
1103566 42.1784267 84.356853 126.535280 168.713707 210.892133 253.070560 295.248987

…or plotted to show growth in biomass over time:

ff_fields |> 
  head(n = 20) |>
  ff_calc_inv_mass_ts(10) |>
  ggplot() + geom_line(aes(x=day, y=total_prod_kg, color=unique_id))

  1. Moss, R.C., Blumenshine, S.C., Yee, J. & Fleskes, J.P. (2009). Emergent insect production in post-harvest flooded agricultural fields used by waterbirds. Wetlands 29(3) pp. 875-883. doi:10.1672/07-169.1↩︎