The goal of infiltrodiscR is to provide functions for the modeling of data derived from the Minidisk Infiltrometer device. If you use this package please cite using this DOI code

You can install the infiltrodiscR package from GitHub with:

```
# install.packages("devtools")
::install_github("biofisicasuelos/infiltrodiscR") devtools
```

or install it from CRAN

`install.packages("infiltrodiscR")`

A dataframe or tibble with the following columns: - texture: soil texture according to USDA: as.character() and lowercase, for example: “clay loam”. - suction: as.character() and lowercase, in this format: “2cm”. - volume: volume recorded in the infiltration measurements in mL, as.numeric(). - time: time recorded in the infiltration measurements in seconds, as.numeric().

- In the lab’s YT channel, we uploaded short sessions [ES] about data wrangling and the package.
- Slides [EN] explaining the package are available here

`infiltration()`

This function calculates cumulative infiltration and the square root of time, using time and volume recorded.

`vg_par`

This function returns the parameter *A*, *no_h* and
*alpha* related to the van Genuchten parameters (van Genuchten,
1980), from tabulated data calculated for a radius of 2.25 cm, including
12 soil texture classes and suctions from -0.5 cm to -7 cm. Data from
(Decagon Devices, Inc., 2005)

`parameter_A`

This function returns the parameter *A* calculated from the
equation proposed by Zhang
(1997).

Minidisk infiltrometers have been used to understand infiltration in soils, hydrophobicity, irrigation system design, erosion and burn severity.

While it is simple and easy to use, the data from Minidisk Infiltrometer must be processed in a reproducible manner. A common way is to use an Excel macro provided by the supplier on their website, but for handling a large number of samples this can be complex.

To determine the unsaturated hydraulic conductivity at specific suctions, the Minidisk Infiltrometer data can be fitted to the method proposed by Zhang (1997). The first step is to calculate the cumulative infiltration vs. the square root of time.

```
library(tidyverse)
library(infiltrodiscR)
<- tibble(
infiltration_data soil = c(rep("soil_a",11), rep("soil_b",11)),
time = c(0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300,
0, 35, 65, 95, 125, 155, 185, 215, 245, 275, 305),
volume = c(95, 89, 86, 83, 80, 77, 74, 73, 71, 69, 67,
83, 77, 64, 61, 58, 45, 42, 35, 29, 17, 15)
)
<-
infilt_cum_sqrt %>%
infiltration_data group_by(soil) %>%
nest() %>%
mutate(data = map(data, ~ infiltration(.), data = .x)) %>%
unnest(cols = data)
infilt_cum_sqrt
```

Now three Van Genuchten parameters: n, alpha and A are extracted from the Minidisk Infiltrometer Table (Decagon Devices, Inc., 2005). The radius (2.25 cm) corresponds to the Minidisk Infiltrometer specs.

```
<- tibble(soil = c("soil_a", "soil_b"),
soil_data texture = c("sandy loam", "clay loam"), #USDA
suction = c("4cm","2cm"),
om_content = c(1,10))
<-
infilt_cum_sqrt_par %>%
infiltration_data group_by(soil) %>%
nest() %>%
left_join(soil_data) %>%
::vg_par() %>%
infiltrodiscRmutate(data = map(data, ~ infiltration(.), data = .x))
infilt_cum_sqrt_par
```

The hydraulic conductivity of the soil K at a specific suctions is calculated as: K(h) = C1 / A. Parameter C1 is calculated fitting a polynomial function of the second degree (y = ax2+b), where a is parameter C1, x is the square root of time and y is the cumulative infiltration. For this step, we use the package broom and base R.

```
library(broom)
<-
infilt_cum_sqrt_par_fit %>%
infilt_cum_sqrt_par mutate(
fit = map(data, ~ lm(infiltration ~ poly(sqrt_time, 2, raw = TRUE), data = .x)),
tidied = map(fit, tidy)
%>%
) unnest(tidied) %>%
filter(term == "poly(sqrt_time, 2, raw = TRUE)2")
infilt_cum_sqrt_par_fit
```

The column `estimate`

corresponds to the parameter C1

```
<-
infilt_cum_sqrt_par_fit %>%
infilt_cum_sqrt_par_fit rename(C1 = estimate)
```

The hydraulic conductivity of the soil K at a specific suctions is calculated as: K(h) = C1 / A. Parameter C1 is already calculated, and parameter A is calculated using the function.

```
parameter_A(infilt_cum_sqrt_par_fit) %>%
mutate(K_h = C1 / parameter_A) %>%
select(K_h)
```

You can check that the values of A from the equation and the table are equivalent.