Santos, P.A.B.d.; Schwerz, F.; Carvalho, L.G.d.; Baptista, V.B.d.S.; Marin, D.B.; Ferraz, G.A.e.S.; Rossi, G.; Conti, L.; Bambi, G.
Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios. Agronomy 2023, 13, 2366.
https://doi.org/10.3390/agronomy13092366
AMA Style
Santos PABd, Schwerz F, Carvalho LGd, Baptista VBdS, Marin DB, Ferraz GAeS, Rossi G, Conti L, Bambi G.
Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios. Agronomy. 2023; 13(9):2366.
https://doi.org/10.3390/agronomy13092366
Chicago/Turabian Style
Santos, Pietros André Balbino dos, Felipe Schwerz, Luiz Gonsaga de Carvalho, Victor Buono da Silva Baptista, Diego Bedin Marin, Gabriel Araújo e Silva Ferraz, Giuseppe Rossi, Leonardo Conti, and Gianluca Bambi.
2023. "Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios" Agronomy 13, no. 9: 2366.
https://doi.org/10.3390/agronomy13092366
APA Style
Santos, P. A. B. d., Schwerz, F., Carvalho, L. G. d., Baptista, V. B. d. S., Marin, D. B., Ferraz, G. A. e. S., Rossi, G., Conti, L., & Bambi, G.
(2023). Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios. Agronomy, 13(9), 2366.
https://doi.org/10.3390/agronomy13092366
