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0567485 - ÚVGZ 2023 RIV IR eng J - Journal Article
Monteiro, L. A. - Ramos, R. M. - Battisti, R. - Soares, J. R. - Oliveira, J. C. - Figueiredo, G. K. D. A. - Lamparelli, R. A. C. - Nendel, Claas - Lana, M. A.
Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil.
International Journal of Plant Production. Roč. 16, č. 4 (2022), s. 691-703. ISSN 1735-6814. E-ISSN 1735-8043
Research Infrastructure: CzeCOS III - 90123
Institutional support: RVO:86652079
Keywords : Large-scale analysis * Machine learning approaches * Public databases * Geospatial and temporal variability * Climatic and soil variables
OECD category: Agriculture
Impact factor: 2.5, year: 2022
Method of publishing: Limited access
https://link.springer.com/content/pdf/10.1007/s42106-022-00209-0.pdf?pdf=button
Permanent Link: https://hdl.handle.net/11104/0338734