A data-driven framework for identifying productivity zones and the impact of agricultural droughts in sugarcane using SPI and unsupervised learning (2021)
- Authors:
- USP affiliated authors: MENDIONDO, EDUARDO MARIO - EESC ; SARAIVA, ANTONIO MAURO - EP ; DELBEM, ALEXANDRE CLÁUDIO BOTAZZO - ICMC ; SILVA, ROBERTO FRAY DA - IEA ; GESUALDO, GABRIELA CHIQUITO - EESC ; BENSO, MARCOS ROBERTO - EESC
- Unidades: EESC; EP; ICMC; IEA
- DOI: 10.1109/MetroAgriFor52389.2021.9628570
- Subjects: CANA-DE-AÇÚCAR; ZONA AGRÍCOLA; SECA
- Keywords: Agricultural drough; Framework; SPI; Unsupervised learning
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2021
- Source:
- Título: Proceedings
- Conference titles: IEEE International Workshop on Metrology for Agriculture and Forestry - MetroAgriFor
- Status:
- Artigo possui versão em acesso aberto em repositório (Green Open Access)
- Versão do Documento:
- Versão submetida (Pré-print)
- Acessar versão aberta:
-
ABNT
SILVA, Roberto Fray da et al. A data-driven framework for identifying productivity zones and the impact of agricultural droughts in sugarcane using SPI and unsupervised learning. Proceedings, 2021Tradução . . Disponível em: https://doi.org/10.1109/MetroAgriFor52389.2021.9628570. Acesso em: 01 abr. 2026. -
APA
Silva, R. F. da, Gesualdo, G. C., Benso, M. R., Fava, M. C., Mendiondo, E. M., Saraiva, A. M., & Delbem, A. C. B. (2021). A data-driven framework for identifying productivity zones and the impact of agricultural droughts in sugarcane using SPI and unsupervised learning. Proceedings. doi:10.1109/MetroAgriFor52389.2021.9628570 -
NLM
Silva RF da, Gesualdo GC, Benso MR, Fava MC, Mendiondo EM, Saraiva AM, Delbem ACB. A data-driven framework for identifying productivity zones and the impact of agricultural droughts in sugarcane using SPI and unsupervised learning [Internet]. Proceedings. 2021 ;[citado 2026 abr. 01 ] Available from: https://doi.org/10.1109/MetroAgriFor52389.2021.9628570 -
Vancouver
Silva RF da, Gesualdo GC, Benso MR, Fava MC, Mendiondo EM, Saraiva AM, Delbem ACB. A data-driven framework for identifying productivity zones and the impact of agricultural droughts in sugarcane using SPI and unsupervised learning [Internet]. Proceedings. 2021 ;[citado 2026 abr. 01 ] Available from: https://doi.org/10.1109/MetroAgriFor52389.2021.9628570 - Long term consequences of climate shocks on crop insurance in BraziL
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