in_02_horizon_properties.qmd

---
title: "Horizon properties"
editor: source
editor_options: 
  chunk_output_type: console
---

```{r}
library(dplyr)
library(tibble)
library(gt)
library(gtExtras)
```

## Size classes {#sec-in-size}

### Size classes for solid soil components {#sec-in-solsz}

The following size classification may be applied to size data gathered for peds and other solid soil components.

```{r}
#| label: tbl-in-size
#| tbl-cap: "Size classification for soil materials"

dat_in_size <-
  tribble(~Code, ~Name, ~Description,
          1, 'Microfine', '< 1',
          2, 'Extremely fine', '1 - 2',
          3, 'Very fine', '2 - 6',
          4, 'Fine', '6 - 10',
          5, 'Medium', '10 - 20',
          6, 'Coarse', '20 - 60',
          7, 'Very Coarse', '60 - 100',
          8, 'Extremely coarse', '100 - 200',
          9, 'Gross', '> 200'
  )

tbl_in_size <- gt(dat_in_size) |>
  tab_options(
    column_labels.font.weight = 'bold', 
    heading.title.font.weight = 'bold',
    table.align = 'center',
    table.width = '85%'
  ) |>
  tab_style(style = list(cell_text(color  = 'red', 
                                   weight = 'bold',
                                   align  = 'center')),
            locations = cells_body(columns = 1)) |>
  cols_label(Description = 'Size range (mm)') |>
  cols_width(Code ~pct(10), Name ~ pct(30))
  
tbl_in_size

```

### Size classes for rock fragments

The following classification may be applied to directly measured size data for rock fragments, or used when assessing rock fragment composition in detail (@sec-rockfrag). 

```{r}
#| label: tbl-in-rockfrsz
#| tbl-cap: "Rock fragment size classes"

dat_in_rockfrsz <-
  tribble(~Code, ~Name, ~Description,
         1, 'Fine gravel',    '2 - 6',
         2, 'Medium gravel',  '6 - 20',
         3, 'Coarse gravel',  '20 - 60',
         4, 'Cobbles',        '60 - 200',
         5, 'Stones',         '200 - 600',
         6, 'Boulders',       '600 - 2000', 
         7, 'Large boulders', '>2000') 

tbl_in_rockfrsz <- gt(dat_in_rockfrsz) |>
  tab_options(
    column_labels.font.weight = 'bold', 
    heading.title.font.weight = 'bold',
    table.align = 'center',
    table.width = '85%'
  ) |>
  tab_style(style = list(cell_text(color  = 'red',
                                   weight = 'bold',
                                   align  = 'center')),
            locations = cells_body(columns = 1)) |>
  cols_label(Description = 'Size range (mm)') |>
  cols_width(Code ~pct(10), Name ~ pct(20))
  
tbl_in_rockfrsz
```

### Estimating size classes {#sec-in-sizepics}

The following images can help accurately estimate the size of soil features.

[images go here]{.diags}

## Abundance classes {#sec-in-abund}

### Abundance classes for roots {#sec-in-abrts}

```{r}
#| label: tbl-hr-rtab
#| tbl-cap: "Plant root abundance classes"

dat_hr_rtab <-
  tribble(~Code,      ~Name,  ~Description,
              0,     'None',          '0%',
              1,      'Few',   '> 0 - 5%',
              2,   'Common',  '> 5 - 25%',
              3,     'Many',  '> 25 - 50%',
              4, 'Abundant',       '> 50%')

tbl_hr_rtab <- gt(dat_hr_rtab) |>
  tab_options(
    column_labels.font.weight = 'bold', 
    heading.title.font.weight = 'bold',
    table.align = 'center',
    table.width = '85%'
  ) |>
  tab_style(style = list(cell_text(color  = 'red',
                                   weight = 'bold',
                                   align  = 'center')),
            locations = cells_body(columns = 1)) |>
  cols_width(Code ~pct(10), Name ~ pct(20))
  
tbl_hr_rtab

```

### Abundance classes for other soil components {#sec-in-abgen}

```{r}
#| label: tbl-in-abgen
#| tbl-cap: "General abundance classes"

dat_in_abgen <-
  tribble(~Code,      ~Name,  ~Description,
              0,     'None',          '0%',
              1, 'Very Few',    '> 0 - 2%',
              2,      'Few',    '> 2 - 10',
              3,   'Common',  '> 10 - 35%',
              4,     'Many',  '> 35 - 50%',
              5, 'Abundant',  '> 50 - 75%',
              6,  'Dominant',      '> 75%' )

tbl_in_abgen <- gt(dat_in_abgen) |>
  tab_options(
    column_labels.font.weight = 'bold', 
    heading.title.font.weight = 'bold',
    table.align = 'center',
    table.width = '85%'
  ) |>
  tab_style(style = list(cell_text(color  = 'red', 
                                   weight = 'bold',
                                   align  = 'center')),
            locations = cells_body(columns = 1)) |>
  cols_width(Code ~pct(10), Name ~ pct(20))
  
tbl_in_abgen
```

### Estimating abundance classes {#sec-in-abundpics}

The following images can help accurately estimate percentage abundance of soil features by area.

[images go here]{.diags}

## Horizon drainage {#sec-in-hdrng}

Soil drainage is an assessment of how fast water leaves a soil profile relative to supply. This characteristic can be controlled by morphological characteristics that occur in a range of combinations.

Drainage should first be assessed on a per-horizon basis. These interpretations are later summarised at a profile level (see @sec-in-pdrng). @tbl-in-hdrng below highlights the common characteristics associated with each horizon drainage class.

```{r}
#| label: tbl-in-hdrng
#| tbl-cap: "Horizon-level drainage characteristics"

dat_in_hdrng <-
  tribble(~Code, ~Name, ~HNames, ~sws, ~mcol, ~secfts,
          'VP', 'Very poor', 'O*, *r', 'Saturated or wet', 'Low-chroma', 'Few to none',  
          'PO', 'Poor', '*r, *g', 'Wet', 'Low-chroma', 'Few to none, mottles more often on ped surfaces than internals',
          'IP', 'Imperfect', '*(g)', 'Wet to Moist', 'Low-chroma patterns on brighter matrix', 'Fe and/or Mn-rich segregations, nodules and pans',
          'MW', 'Moderately Well', '*(f)', 'Wet to Dry', 'Colour patterns but no low chroma colours', 'Mottles more often in ped interiors' ,
          'WE', 'Well', 'No redox suffixes', 'Moist to Dry', 'No low chroma colours',  'Few to none')

tbl_in_hdrng <- gt(dat_in_hdrng) |>
   tab_options(
    column_labels.font.weight = 'bold', 
    heading.title.font.weight = 'bold',
    table.align = 'center',
    table.width = '85%'
  ) |>
  tab_style(style = list(cell_text(color  = 'red', 
                                   weight = 'bold',
                                   align  = 'center')),
            locations = cells_body(columns = 1)) |>
  tab_style(style = list(cell_text(weight = 'bold')),
            locations = cells_body(columns = 2)) |>
  cols_label(HNames = 'Horizon Names',
             sws    = 'Typical Moisture',
             secfts  = 'Redox concentrations',
             mcol   = 'Main colours') |>
  cols_width(Code ~pct(10), Name ~ pct(20))

tbl_in_hdrng
```

### Classifying topsoil drainage

Morphological features associated with drainage status can be hard to spot in [A]{.ceg} horizons, mostly due to their darker colour. *When otherwise in doubt*, a well developed [A]{.ceg} horizon should be assigned a drainage class one better than the horizon below it (usually a [B]{.ceg}, [E]{.ceg}, or [C]{.ceg} horizon). A poorly developed [A]{.ceg} horizon should receive the same class assignment as the horizon below. Use the 'distinct topsoil' NZSC diagnostic criteria to identify well developed [A]{.ceg} horizons.

The reasoning behind this rule of thumb is that the lack of topsoil development may be partially explained by lack of contrast in the drainage regime between the topsoil and the layer below.

<!-- example diagram here --->

::: {#nte-anoxia .callout-note appearance="minimal" collapse="true" title="Wet vs wet-and-anoxic soils"}
Soils that are persistently wet may also become anoxic. This is more common in low-permeability soils, where slow percolation allows enough time for oxygen to be consumed entirely. <!--- add more --->
:::