Quality of meteorological data used in the context of agriculture: an issue
Abstract
Meteorological data (DM)is very important for crop and livestock production, since the climate is an agent of great influence for agricultural crops development, as well for farm animals' performance. The objective of this work was to highlight critical points of DM quality and its implications for studies in agriculture and livestock. The literature reports problems of accuracy, completeness and consistency in the quality of some datasets, being also apparent issues related to the dimensions of temporality and trustiness of these data. Considering the origin of these problems, issues were observed both from the DM collection systems and from the sensors used. In conclusion, DM quality studies applied in the agricultural and zootechnical context, although current, are still few, being considered an issue for data management in these fields.
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References 37 · Cited by 1
- Authors
- de Castro Junior, S. L., da Silva, I. J. O., Alves-Souza, S. N., & de Souza, L. S.
- Affiliations
- Universidade de São Paulo (BR)
- Venue
- 2020 15th Iberian Conference on Information Systems and Technologies (CISTI)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1-6
- OpenAlex
- W3043481460
- Cited by
- 1
- References
- 37
- Citations by year
- 2023: 1
- FWCI
- 0.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Iberian Conference on Information Systems and Technologies (CISTI) (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Earth and Planetary Sciences › Geology › Environmental Monitoring and Data Management
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Keywords
- meteorological data quality, agrometeorology, animal husbandry, crop production, data accuracy, data completeness, data consistency, data trustworthiness, weather sensors, data collection system, data management
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