Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil-Reference-Cited by-同舟云学术

Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil

Published:2022-09-07 Issue:4 Volume:16 Page:691-703
ISSN:1735-6814
Container-title:International Journal of Plant Production
language:en
Short-container-title:Int. J. Plant Prod.

Author:

Monteiro Leonardo A.^ORCID,Ramos Rafael M.,Battisti Rafael,Soares Johnny R.,Oliveira Julianne C.,Figueiredo Gleyce K. D. A.,Lamparelli Rubens A. C.,Nendel Claas,Lana Marcos Alberto

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

Springer Science and Business Media LLC

Subject

Plant Science,Agronomy and Crop Science

Link

https://link.springer.com/content/pdf/10.1007/s42106-022-00209-0.pdf

Reference57 articles.

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2. Battisti, R., Sentelhas, P. C., & Boote, K. J. (2017). Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil. Field Crops Research, 200, 28–37. https://doi.org/10.1016/j.fcr.2016.10.004

3. Battisti, R., Sentelhas, P. C., Pascoalino, J. A. L., Sako, H., de Sá Dantas, J. P., & Moraes, M. F. (2018). Soybean yield gap in the areas of yield contest in Brazil. International Journal of Plant Production, 12, 159–168. https://doi.org/10.1007/s42106-018-0016-0

4. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.

5. Carauta, M., Libera, A.A.D., Hampf, A., Chen, R.F.F., Silveira, J.M.F.J., Berger, T. (2017). On-farm trade-offs for optimal agricultural practices in Mato Grosso, Brazil. Revista de Economia e Agronegócio. https://doi.org/10.25070/rea.v15i3.505

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