ofpetrial: Design on-Farm Precision Experiments

The ofpetrial package allows the user to design agronomic input experiments in a reproducible manner without using ArcGIS or QGIS. The vignette for this package provides more detailed guidance on how to use the package.

Installation

CRAN version

You can install the CRAN version of the ofpetrial package.

install.packages("ofpetrial")

Development version

You can install the development version of ofpetrial from Github:

devtools::install_github("DIFM-Brain/ofpetrial")

Example

Here, we demonstrate how to use the ofpetrial package to create a single-input on-farm experiment trial design (more detailed instructions on the basic workflow is provided in this article).

library(ofpetrial)

Create experimental plots

We start with specifying plot and machine information for inputs using prep_plot.

n_plot_info <-
  prep_plot(
    input_name = "NH3",
    unit_system = "imperial",
    machine_width = 30,
    section_num = 1,
    harvester_width = 30,
    plot_width = 30
  )
#> 

Now, we can create experiment plots based on them using make_exp_plots().

exp_data <-
  make_exp_plots(
    input_plot_info = n_plot_info,
    boundary_data = system.file("extdata", "boundary-simple1.shp", package = "ofpetrial"),
    abline_data = system.file("extdata", "ab-line-simple1.shp", package = "ofpetrial"),
    abline_type = "free"
  )
#> Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0;
#> sf_use_s2() is TRUE
#> Warning: There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `experiment_plots_dissolved = list(...)`.
#> ℹ In row 1.
#> Caused by warning:
#> ! package 'sf' was built under R version 4.2.3

viz(exp_data, type = "layout", abline = TRUE)

Assign rates

We first prepare nitrogen rates.

#!===========================================================
# ! Assign rates
# !===========================================================
n_rate_info <-
  prep_rate(
    plot_info = n_plot_info,
    gc_rate = 180,
    unit = "lb",
    rates = c(100, 140, 180, 220, 260),
    design_type = "ls",
    rank_seq_ws = c(5, 4, 3, 2, 1)
  )

dplyr::glimpse(n_rate_info)
#> Rows: 1
#> Columns: 12
#> $ input_name          <chr> "NH3"
#> $ design_type         <chr> "ls"
#> $ gc_rate             <dbl> 180
#> $ unit                <chr> "lb"
#> $ tgt_rate_original   <list> <100, 140, 180, 220, 260>
#> $ tgt_rate_equiv      <list> <82.0, 114.8, 147.6, 180.4, 2…
#> $ min_rate            <lgl> NA
#> $ max_rate            <lgl> NA
#> $ num_rates           <int> 5
#> $ rank_seq_ws         <list> <5, 4, 3, 2, 1>
#> $ rank_seq_as         <list> <NULL>
#> $ rate_jump_threshold <lgl> NA

We can now use assign_rates() to assign rates to experiment plots.

trial_design <- assign_rates(exp_data, rate_info = n_rate_info)

Here is the visualization of the trial design done by viz.

viz(trial_design)

Along with the spatial pattern of the input rates, the applicator/planter ab-line and harvester ab-line are drawn by default.

Write the trial design files for implementation

You can write out the trial design as a shape file.

write_trial_files(td)

Acknowledgement

This project was funded in part by a United States Department of Agriculture—National Institute of Food and Agriculture (USDA—NIFA) Food Security Program Grant (Award Number 2016-68004-24769) and by United States Department of Agriculture (USDA) -Natural Resources Conservation Service (NRCS), Commodity Credit Corporation (CCC), Conservation Innovation Grants On-Farm Conservation Innovation Trials (Award Number USDA-NRCS-NHQ-CIGOFT-20-GEN0010750).