acdcR: Agro-Climatic Data by County

This repository is to develop and distribute a testing version of R-package acdc. The stable latest version is directly available from R-CRAN. Data and code are subject to change

GitHub Repository maintained by: Seong Yun
Department of Agricultural Economics
Mississippi State University
seong.yun@msstate.edu
https://sites.google.com/site/yunsd2004/
Last updated: Jun. 21, 2022

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1 acdcR R-package

An R-package Agro-Climatic Data by County (acdcR) is designed to provide the functions to calculate the most widely-used county-level variables in agricultural production or agro-climatic and weather analyses. acdcR applies the most recent NLCD maps (2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019) to take account of agricultural areas only weighted averages over the PRISM rasters. In the current version of acdcR, there are functions to calculate growing season degree days (GDDs) with single/double sine/triangulation methods, to produce GDDs and precipitations by the PRISM grids or County FIPS codes from the direct input of PRISM rasters, and to convert the PRISM grids data to county-level values.

2 Getting started:

2.1. Install the latest version (complete) of acdcR from the R-CRAN repository:

The latest version of acdcR is available from the R-CRAN repository. Users can install and use all functions and features directly installing it from the R-CRAN repository.

    ## In R
    install.packages("acdcR")
    library(acdcR)

2.2. Install acdcR testing version from the github repository:

For your testing purpose, a version currently developing is available from this GitHub repository.

    ## Need to install devtools packages
    install.packages("devtools")
    ## Then use
    devtools::install_github("ysd2004/acdcR")
    library(acdcR)

2.3. To get the county values from a PRISM raster:

The functions of gddprism and pptprism are directly convert a PRISM raster to county or PRISM grid values. For this purpose, users need to download a raw weater data from the PRISM Climate Group of the Oregon State University. To download the bulk raster files through the FTP, follow the instructions: link.

3 Authors

Seong D. Yun, Assistant Professor, Mississippi State University (seong.yun@msstate.edu)

Note: (Dr.) Benjamin M. Gramig, Research Agricultural Economist at USDA-ERS provides great insights, helpful data resources, and kind advice to build this package.

Maintainer/Bug report or quetion to Seong Yun (seong.yun@msstate.edu)

4 Citation

Please cite the software in publications;

4.1. acdcR (stable and latest) from the R-CRAN repository

To cite the R-package acdcR, use citation() for information on how to cite the software;

citation(package = "acdcR")
 
To cite package 'acdcR' in publications use:

  Seong D. Yun, (2022). acdcR: Agro-Climatic Data by County for R. 
  R package version 1.0.0. https://CRAN.R-project.org/package=acdcR

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {acdcR: Agro-Climatic Data by County for R.},
    author = {Seong D. Yun},
    year = {2022},
    note = {R package version 1.0.0},
    url = {https://CRAN.R-project.org/package=acdcR},
  }

4.2. acdcR (testing) from the R-CRAN repository

To cite this GitHub repository:

To cite 'acdcR' package in publications use:

  Yun, Seong D., 2022, "acdcR: Agro-Climatic Data by County for R" Data retrieved from the GitHub,
  https://github.com/ysd2004/acdcR.

A BibTeX entry for LaTeX users is

  @misc{,
    title = {acdcR: Agro-Climatic Data by County},
    author = {Seong D. Yun},
    year = {2022},
    note = {Data retrieved from the GitHub,
    \url{https://github.com/ysd2004/acdcR}}
  }

5 Acknowledgement

The author is grateful for financial support through the “Sustainable Bioenergy Production and Integrated Valuation of Ecosystem Services” project provided by United States Department of Agriculture (USDA) National Institute for Food and Agriculture (NIFA) Agriculture and Food Research Initiative (AFRI) competitive award [no. 2019-67024-29677].

The former efforts to generate the data for this package was gratefully supported by the Agriculture and Food Research Initiative (AFRI) competitive grant no. 2011-68002-30110 from the USDA National Institute of Food and Agriculture (NIFA).

The views expressed herein do not necessarily reflect the views of the USDA-NIFA.

6 References

The methods and examples in acdcR are available from:

• Yun, S. D. and B. M. Gramig, 2019, "Agro-Climatic Data by County: A Spatially and Temporally Consistent U.S. Dataset for Agricultural Yields, Wather and Soils," Data, 4(2):66. (https://doi.org/10.1086/676034)
• Yun, S. D. and B. M. Gramig, 2017, "ACDC: Agro-Climatic Data by Count, 1981-2015," Purdue University Research Repository. (https://doi.org/10.4231/R72F7KK2)

7 Development History

Below are the development history of R-package acdcR.

• 06-27-2022: The first relese of acdcR v.1.0.0 on R-Repository.

• 06-18-2022: The package name change from acdc to acdcR.

• 06-18-2022: V. 1.0.0 is installable from GitHub.

• 06-01-2022: V. 1.0.0 was embeded in GitHub.

• 04-30-2021: The first beta version was tested.

• 12-01-2020: The first R-package version acdc was initiated to code.

• 05-08-2019: Published the journal article of ACDC v1.0.0 on Data (https://doi.org/10.3390/data4020066)

• 07-08-2017: Published the first version of data in Purdue University Research Repository (https://doi.org/10.4231/R72F7KK2)