为数字农业整合农业气象观测数据的工具:明尼苏达州案例研究

IF 2.3 4区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Logan Gall, Tom Glancy, Michael Kantar, Bryan C. Runck
{"title":"为数字农业整合农业气象观测数据的工具:明尼苏达州案例研究","authors":"Logan Gall,&nbsp;Tom Glancy,&nbsp;Michael Kantar,&nbsp;Bryan C. Runck","doi":"10.1002/ael2.20147","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":48502,"journal":{"name":"Agricultural & Environmental Letters","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.20147","citationCount":"0","resultStr":"{\"title\":\"A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study\",\"authors\":\"Logan Gall,&nbsp;Tom Glancy,&nbsp;Michael Kantar,&nbsp;Bryan C. Runck\",\"doi\":\"10.1002/ael2.20147\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":48502,\"journal\":{\"name\":\"Agricultural & Environmental Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.20147\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Agricultural & Environmental Letters\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ael2.20147\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural & Environmental Letters","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ael2.20147","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

农业气象数据对于利用数字农业技术了解生产情况至关重要。然而,整合多个来源的农业气象观测数据仍是一项挑战。数字农业科学家通常要多次下载和清理相同的数据集。我们提出了一个原型系统,通过提供简化的用户界面、数据库和应用程序接口,简化了从气象数据源收集、清理、整合和汇总数据的过程。原型系统提供了查询多种地理空间格式(栅格和矢量)的标准接口,并整合了观测网络,包括美国国家海洋和大气管理局全球历史气候学网络(NOAA GHCN)、NOAA NClim-Grid(NOAA 的网格气候标准)和 Ameriflux BASE。该系统可自动检查和更新数据,节省存储空间和处理时间,并允许用户对数据进行空间和时间汇总。该应用和集成系统以开放源代码和基于浏览器的用户界面提供,可在 Windows、Linux 和 Mac 环境中运行,支持更广泛地使用多源农业气象数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.70
自引率
3.80%
发文量
28
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信