---
title: "SoftwareX publication"
author: Adrian Correndo
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{SoftwareX publication}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width=6,
fig.height=4
)
```
## Citation
Correndo, A. A., Pearce, A., Bolster, C. H., Spargo, J. T., Osmond, D., & Ciampitti, I. A. (2023). The soiltestcorr R package: An accessible framework for reproducible correlation analysis of crop yield and soil test data. SoftwareX, 21, 101275.
## Highlights
- soiltestcorr stemmed from the Fertilizer Recommendation Support Tool (FRST) project.
- soiltestcorr is an accessible R package assembling key soil test correlation models.
- soiltestcorr facilitates the fitting of multiple models without the need of advanced programming skills.
- soiltestcorr aligns with the grammar, pipeline, and data visualization practices of the popular tidyverse.
- A web application based on shiny is also offered for users with no programming skills.
## Abstract
The soiltestcorr R package is an open-source software designed to enable accessible and reproducible computation of correlation analyses between crop yield response to fertilization and soil test values. The package compiles a series of functions for analyzing soil test correlation data: (i) Cate & Nelson data partitioning procedure (graphical and statistical versions), (ii) nonlinear regression analysis (linear-plateau, quadratic-plateau, and Mitscherlich-type exponential models), and (iii) the modified arcsine-log calibration curve. The soiltestcorr enables users to correlate crop response to soil nutrient availability and estimate a critical soil test value and visualize results with ggplot without requiring advanced R programming skills. Finally, a web application that facilitates the use of the package is also offered for users with no background in R programming.
Full publication is open-access here [https://doi.org/10.1016/j.softx.2022.101275](https://doi.org/10.1016/j.softx.2022.101275)