Retrieving and processing agro-meteorological data from API-client sources using R software
Abstract Objectives The main purpose of this publication is to help users (students, researchers, farmers, advisors, etc.) of weather data with agronomic purposes (e.g. crop yield forecast) to retrieve and process gridded weather data from different Application Programming Interfaces (API client) so...
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| Format: | Article |
| Language: | English |
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BMC
2021-05-01
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| Series: | BMC Research Notes |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13104-021-05622-8 |
| _version_ | 1819047605177417728 |
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| author | Adrian A. Correndo Luiz H. Moro Rosso Ignacio A. Ciampitti |
| author_facet | Adrian A. Correndo Luiz H. Moro Rosso Ignacio A. Ciampitti |
| author_sort | Adrian A. Correndo |
| collection | DOAJ |
| description | Abstract Objectives The main purpose of this publication is to help users (students, researchers, farmers, advisors, etc.) of weather data with agronomic purposes (e.g. crop yield forecast) to retrieve and process gridded weather data from different Application Programming Interfaces (API client) sources using R software. Data description This publication consists of a code-tutorial developed in R that is part of the data-curation process from numerous research projects carried out by the Ciampitti’s Lab, Department of Agronomy, Kansas State University. We make use of three weather databases for which specific libraries were developed in R language: (i) DAYMET (Thornton et al. in https://daymet.ornl.gov/ , 2019; https://github.com/bluegreen-labs/daymetr ), (ii) NASA-POWER (Sparks in J Open Source Softw 3:1035, 2018; https://github.com/ropensci/nasapower ), and (iii) Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (Funk et al. in Sci Data 2:150066, 2015; https://github.com/ropensci/chirps ). The databases offer different weather variables, and vary in terms of spatio-temporal coverage and resolution. The tutorial shows and explain how to retrieve weather data from multiple locations at once using latitude and longitude coordinates. Additionally, it offers the possibility to create relevant variables and summaries that are of agronomic interest such as Shannon Diversity Index (SDI) of precipitation, abundant and well distributed rainfall (AWDR), growing degree days (GDD), crop heat units (CHU), extreme precipitation (EPE) and temperature events (ETE), reference evapotranspiration (ET0), among others. |
| first_indexed | 2024-12-21T11:03:01Z |
| format | Article |
| id | doaj.art-1ba6c2083b8446d9a820e935bde69625 |
| institution | Directory Open Access Journal |
| issn | 1756-0500 |
| language | English |
| last_indexed | 2024-12-21T11:03:01Z |
| publishDate | 2021-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj.art-1ba6c2083b8446d9a820e935bde696252022-12-21T19:06:18ZengBMCBMC Research Notes1756-05002021-05-011411310.1186/s13104-021-05622-8Retrieving and processing agro-meteorological data from API-client sources using R softwareAdrian A. Correndo0Luiz H. Moro Rosso1Ignacio A. Ciampitti2Department of Agronomy, Kansas State UniversityDepartment of Agronomy, Kansas State UniversityDepartment of Agronomy, Kansas State UniversityAbstract Objectives The main purpose of this publication is to help users (students, researchers, farmers, advisors, etc.) of weather data with agronomic purposes (e.g. crop yield forecast) to retrieve and process gridded weather data from different Application Programming Interfaces (API client) sources using R software. Data description This publication consists of a code-tutorial developed in R that is part of the data-curation process from numerous research projects carried out by the Ciampitti’s Lab, Department of Agronomy, Kansas State University. We make use of three weather databases for which specific libraries were developed in R language: (i) DAYMET (Thornton et al. in https://daymet.ornl.gov/ , 2019; https://github.com/bluegreen-labs/daymetr ), (ii) NASA-POWER (Sparks in J Open Source Softw 3:1035, 2018; https://github.com/ropensci/nasapower ), and (iii) Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (Funk et al. in Sci Data 2:150066, 2015; https://github.com/ropensci/chirps ). The databases offer different weather variables, and vary in terms of spatio-temporal coverage and resolution. The tutorial shows and explain how to retrieve weather data from multiple locations at once using latitude and longitude coordinates. Additionally, it offers the possibility to create relevant variables and summaries that are of agronomic interest such as Shannon Diversity Index (SDI) of precipitation, abundant and well distributed rainfall (AWDR), growing degree days (GDD), crop heat units (CHU), extreme precipitation (EPE) and temperature events (ETE), reference evapotranspiration (ET0), among others.https://doi.org/10.1186/s13104-021-05622-8ProgrammingAgricultureDaymetNasapowerChirps |
| spellingShingle | Adrian A. Correndo Luiz H. Moro Rosso Ignacio A. Ciampitti Retrieving and processing agro-meteorological data from API-client sources using R software BMC Research Notes Programming Agriculture Daymet Nasapower Chirps |
| title | Retrieving and processing agro-meteorological data from API-client sources using R software |
| title_full | Retrieving and processing agro-meteorological data from API-client sources using R software |
| title_fullStr | Retrieving and processing agro-meteorological data from API-client sources using R software |
| title_full_unstemmed | Retrieving and processing agro-meteorological data from API-client sources using R software |
| title_short | Retrieving and processing agro-meteorological data from API-client sources using R software |
| title_sort | retrieving and processing agro meteorological data from api client sources using r software |
| topic | Programming Agriculture Daymet Nasapower Chirps |
| url | https://doi.org/10.1186/s13104-021-05622-8 |
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