What are the best practices for data management in agronomy?

Data management is a crucial aspect of agronomy, the science of crop production and soil management. Agronomists collect, process, and analyze various types of data, such as soil properties, weather conditions, crop yields, pest and disease incidence, and genetic markers. These data can help agronomists improve crop performance, optimize resource use, and enhance sustainability. However, data management also poses many challenges, such as data quality, security, integration, and sharing. In this article, you will learn about some of the best practices for data management in agronomy, based on the principles of FAIR (Findable, Accessible, Interoperable, and Reusable) data.

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1 Data quality

Data quality is essential for reliable and valid data analysis and interpretation. To ensure data quality, it is important to plan your data collection methods and protocols carefully, and document them clearly. Additionally, standardized formats, units, and terminologies should be used for data and metadata. Quality checks and validations should also be performed on the data to check for outliers, errors, missing values, and duplicates. Appropriate tools and software, such as spreadsheets, databases, or data management platforms can also be used to clean, organize, and store the data.

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Ahmed Ibrahem

Agronomist / Horticulturist
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Devil in the detailes: 1-Data Storage - Any data related to production (dates/varities/area/soil specifications/seeds or plantings/applications/wether data). 2-Documentation - It becomes like an ID for each crop and production time, {in sensitive plants it's very essintial to mange your data in the best way in order to keep your investment growing}. 3-Commitment to data culture - Keeping recording data and applying analatical on regular basis, finding new inputs of your data shall always help any production process. 4- Data quality - When your are depending on those data, ouditing on data type and collection methods is an advantage to keep feeding your analytical system with the right aspects to help achiving the major target.

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2 Data security

Data security is essential for preserving the confidentiality, integrity, and availability of the data. To ensure data security, you should use strong passwords and encryption for your data and devices, and change them regularly. Additionally, it’s important to backup your data regularly, and store them in multiple locations. Furthermore, you should adhere to ethical and legal guidelines for data collection, processing, and sharing. This includes obtaining consent, anonymizing sensitive data, and respecting intellectual property rights. Finally, you should report any data breaches or incidents to the relevant authorities and stakeholders.

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3 Data integration

Data integration refers to the combination of data from different sources, formats, and scales, which is useful to enrich the data, increase the sample size, and enable cross-disciplinary analysis. To achieve this, you should first identify common variables, attributes, and identifiers that link the data from different sources. Additionally, it is necessary to use standardized formats, units, and terminologies for your data and metadata and convert them if necessary. Appropriate tools and software are needed to merge, join, or align the data such as scripts, APIs, or data integration platforms. Finally, quality checks and validations should be performed on the integrated data to check for inconsistencies, conflicts, or redundancies.

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4 Data sharing

Data sharing, which is the dissemination of data to other researchers, stakeholders, or the public, is beneficial to increase the visibility, impact, and reproducibility of the data. To facilitate data sharing, you should use open and interoperable formats, standards, and protocols for your data and metadata such as CSV, JSON, XML, or RDF. Additionally, it is important to use persistent and unique identifiers for your data and metadata such as DOIs, URIs, or ORCIDs. Furthermore, you should use appropriate tools and software to publish, archive, or deposit your data such as repositories, journals, or data portals. Lastly, clear and comprehensive metadata and documentation should be used to describe your data including title, author, date, description, keywords, license, and citation.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for data management in agronomy?

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1 Data quality

2 Data security

3 Data integration

4 Data sharing

5 Here’s what else to consider

Bioengineering

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