Work

A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study

Year: 2024

Type: article

Abstract: Agrometeorological data are essential for understanding production using digital agriculture techniques. However, integrating agrometerological observations from multiple sources remains a challenge. Often, digital agriculture scientists download and clean the same datasets many times. We present a prototype system that simplifies the process of collecting, cleaning, integrating, and aggregating data from meteorological data sources by providing a simplified user interface, database, and application programming interface. The prototype provides a standard interface for querying multiple geospatial formats (raster and vector) and integrates observation networks including the National Oceanic and Atmospheric Administration Global Historical Climatology Network (NOAA GHCN), NOAA NClim‐Grid (NOAA's Gridded Climate Normals), and Ameriflux BASE. The system automatically checks and updates data, saving storage space and processing time, and allows users to summarize data spatially and temporally. Provided as open source code and browser‐based user interface, the application and integration system can be run across Windows, Linux, and Mac environments to support broader use of multi‐source agrometeorology data.

Source: Agricultural & Environmental Letters

DOI: 10.1002/ael2.20147

Authors: Logan Gall, Tom Glancy, Michael Kantar, Bryan C. Runck

Institutions: University of Minnesota, University of Hawaiʻi at Mānoa

Language: English

Topic: Environmental Monitoring and Data Management

Subfield: Geology

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDGs: Zero hunger, Climate action

Open Access status: gold

Estimated APC paid: $1,670