Abstract
Coffee is one of the major crops produced in Colombia which is the third largest producer of coffee after Brazil and Vietnam. Together, these three countries produce more than 50% of the world’s total coffee. One of the main challenges facing coffee producers in Colombia is to determine the effects of climate variability and climate change on their production. This paper presents CoffeeWKG, an RDF knowledge graph focused on weather conditions in the coffee-growing regions of Colombia over 15 years (2006–2020), to facilitate the understanding of climate impacts on coffee crops. CoffeeWKG enables the integration of heterogeneous sensor data collected from different weather stations and the definition of semantic metadata on agro-climatic parameters. This knowledge graph enables coffee growers and experts to explore and query historical weather conditions to establish a correlation between weather data and information on coffee crops, thus revealing the complex interaction between climate and production dynamics. This research is essential to improving the resilience of agriculture and optimizing resources in the face of changing climatic challenges.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
masl is the abbreviation of meters above sea level.
- 2.
- 3.
- 4.
- 5.
- 6.
Cenicafé yearbooks download page: https://www.cenicafe.org/es/index.php/nuestras_publicaciones/anuarios_meteorologicos.
- 7.
- 8.
WeKG-MF GitHub repository: https://github.com/Wimmics/weather-kg.
- 9.
- 10.
CoffeeWKG download page: https://zenodo.org/record/8237867.
- 11.
Apache Jena Fuseki Server: https://jena.apache.org/documentation/fuseki2.
- 12.
The concept GDD is available at the URI of the GCMD vocabulary https://gcmd.earthdata.nasa.gov/kms/concept/6d808909-ce04-4401-a883-aff4d723d025.
References
Ahmed, S., et al.: Climate change and coffee quality: systematic review on the effects of environmental and management variation on secondary metabolites and sensory attributes of Coffea arabica and Coffea canephora. Front. Plant Sci. 12, 708013 (2021). https://doi.org/10.3389/fpls.2021.708013
Atemezing, G., et al.: Transforming meteorological data into linked data. Semant. Web 4, 285–290 (2013). https://doi.org/10.3233/SW-120089
Battle, R., Kolas, D.: Enabling the geospatial semantic web with parliament and GeoSPARQL. Semant. Web 3, 355–370 (2012). https://doi.org/10.3233/SW-2012-0065
Cox, S., Little, C.: Time ontology in OWL. W3C candidate recommendation draft. Technical report, W3C (2022). https://www.w3.org/TR/owl-time/
Cyganiak, R., Reynolds, D.: The RDF data cube vocabulary. Technical report, W3C (2014). https://www.w3.org/TR/2014/REC-vocab-data-cube-20140116/
DaMatta, F.M., Ronchi, C.P., Maestri, M., Barros, R.S.: Ecophysiology of coffee growth and production. Braz. J. Plant. Physiol. 19, 485–510 (2007). https://doi.org/10.1590/S1677-04202007000400014
Davis, A.P., Gole, T.W., Baena, S., Moat, J.: The impact of climate change on indigenous arabica coffee (Coffea arabica): predicting future trends and identifying priorities. PLoS ONE 7(11), 1–13 (2012). https://doi.org/10.1371/journal.pone.0047981
Haller, A., et al.: The modular SSN ontology: a joint W3C and OGC standard specifying the semantics of sensors, observations, sampling, and actuation. Semant. Web 10, 9–32 (2018). https://doi.org/10.3233/SW-180320
Janowicz, K., Haller, A., Cox, S.J., Phuoc, D.L., Lefrançois, M.: SOSA: a lightweight ontology for sensors, observations, samples, and actuators. J. Web Semant. 56, 1–10 (5 2019). https://doi.org/10.1016/j.websem.2018.06.003
León-Burgos, A.F., Unigarro, C., Balaguera-López, H.E.: Can prolonged conditions of water deficit alter photosynthetic performance and water relations of coffee plants in central-west Colombia? South Afr. J. Botany 149, 366–375 (2022). https://doi.org/10.1016/j.sajb.2022.06.034
Matteis, L., et al.: Crop ontology: Vocabulary for crop-related concepts. In: CEUR Workshop Proceedings, vol. 979 (2013)
Michel, F., Djimenou, L., Faron-Zucker, C., Montagnat, J.: Translation of relational and non-relational databases into RDF with xR2RML. In: Proceedings of the 11th International Conference on Web Information Systems and Technologies, pp. 443–454. SCITEPRESS - Science and and Technology Publications (2015). https://doi.org/10.5220/0005448304430454
Peroni, S.: A simplified agile methodology for ontology development. In: Dragoni, M., Poveda-Villalón, M., Jimenez-Ruiz, E. (eds.) OWLED/ORE -2016. LNCS, vol. 10161, pp. 55–69. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54627-8_5
Prensa Federación Nacional de Caficultores de Colombia: en junio importaciones de café disminuyeron un 30 porciento (2023). https://federaciondecafeteros.org/wp/listado-noticias/en-junio-importaciones-de-cafe-disminuyeron-un-30/
Ronchi, C.P., Miranda, F.R.: Flowering percentage in arabica coffee crops depends on the water deficit level applied during the pre-flowering stage. Rev. Caatinga 33, 195–204 (2020). https://doi.org/10.1590/1983-21252020v33n121rc
Roussey, C., Delpuech, X., Amardeilh, F., Bernard, S., Jonquet, C.: Semantic description of plant phenological development stages, starting with grapevine. In: Garoufallou, E., Ovalle-Perandones, M.-A. (eds.) MTSR 2020. CCIS, vol. 1355, pp. 257–268. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-71903-6_25
Suarez, C., Griol, D., Figueroa, C., Corrales, J.C., Corrales, D.C.: RustOnt: an ontology to explain weather favorable conditions of the coffee rust. Sensors 22, 9598 (2022). https://doi.org/10.3390/s22249598
Subirats-Coll, I., et al.: AGROVOC: the linked data concept hub for food and agriculture. Comput. Electron. Agric. 196, 105965 (2022). https://doi.org/10.1016/j.compag.2020.105965
Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11, 93–136 (1996). https://doi.org/10.1017/S0269888900007797
Vélez-Vallejo, R.: Informe del gerente - 90 congreso nacional de cafeteros. Technical report, Federación Nacional de Cafeteros de Colombia (2022). https://federaciondecafeteros.org/app/uploads/2022/12/Informe-del-Gerente-D.pdf
Wu, J., Orlandi, F., O’Sullivan, D., Dev, S.: LinkClimate: an interoperable knowledge graph platform for climate data. Comput. Geosci. 169, 105215 (2022). https://doi.org/10.1016/j.cageo.2022.105215
Yacoubi Ayadi, N., Faron, C., Michel, F., Gandon, F., Corby, O.: A model for meteorological knowledge graphs: application to Météo-France data. In: ICWE 2022–22nd International Conference on Web Engineering. 22nd International Conference on Web Engineering, ICWE 2022, Bari, Italy (2022). https://doi.org/10.1007/978-3-031-09917-5_19
Yacoubi Ayadi, N., Faron, C., Michel, F., Gandon, F., Corby, O.: Computing and visualizing agro-meteorological parameters based on an observational weather knowledge graph. In: Ding, Y., Tang, J., Sequeda, J.F., Aroyo, L., Castillo, C., Houben, G. (eds.) Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April–4 May 2023, pp. 242–245. ACM (2023). https://doi.org/10.1145/3543873.3587357
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Figueroa, C., Ayadi, N.Y., Audoux, N., Faron, C. (2023). CoffeeWKG: A Weather Knowledge Graph for Coffee Regions in Colombia. In: Sales, T.P., Araújo, J., Borbinha, J., Guizzardi, G. (eds) Advances in Conceptual Modeling. ER 2023. Lecture Notes in Computer Science, vol 14319. Springer, Cham. https://doi.org/10.1007/978-3-031-47112-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-47112-4_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47111-7
Online ISBN: 978-3-031-47112-4
eBook Packages: Computer ScienceComputer Science (R0)