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Retrieving and processing agro-meteorological data from API-client sources using R software.

Adrian A Correndo1, Luiz H Moro Rosso2, Ignacio A Ciampitti3

  • 1Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA. correndo@ksu.edu.

BMC Research Notes
|May 27, 2021
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Summary

This R tutorial simplifies accessing and processing gridded weather data from DAYMET, NASA-POWER, and CHIRPS for agronomic applications like crop yield forecasting. It enables users to derive key agricultural variables from diverse weather data sources.

Keywords:
AgricultureChirpsDaymetNasapowerProgramming

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Area of Science:

  • Agronomy
  • Data Science
  • Climate Science

Background:

  • Agronomic research and crop yield forecasting heavily rely on accurate weather data.
  • Accessing and processing gridded weather data from various sources can be complex and time-consuming.
  • Existing R packages offer access to different weather databases, but a unified approach is needed.

Purpose of the Study:

  • To provide a user-friendly R code-tutorial for retrieving and processing gridded weather data.
  • To facilitate the use of weather data for agronomic purposes, including crop yield prediction.
  • To demonstrate data retrieval from multiple Application Programming Interface (API) clients using R software.

Main Methods:

  • Developed an R code-tutorial leveraging specific R libraries for DAYMET, NASA-POWER, and CHIRPS weather databases.
  • Demonstrated data retrieval using latitude and longitude coordinates for multiple locations simultaneously.
  • Included methods for calculating agronomic variables such as growing degree days (GDD), crop heat units (CHU), and evapotranspiration (ET0).

Main Results:

  • Successfully integrated three major gridded weather data sources (DAYMET, NASA-POWER, CHIRPS) within a single R workflow.
  • Enabled efficient extraction of diverse weather variables and calculation of key agronomic indices.
  • Provided a reproducible method for accessing and analyzing weather data for research and practical applications.

Conclusions:

  • The R tutorial effectively simplifies the process of accessing and analyzing diverse gridded weather data for agronomic applications.
  • This resource empowers students, researchers, and farmers to utilize weather data for improved crop management and yield forecasting.
  • The developed workflow enhances the accessibility and utility of weather data in agricultural research.