Forecasting the rice yield in Rio Grande do Sul using the SimulArroz model

Authors

  • Michel Rocha da Silva Crops Team, Avenida Roraima, no 1.000, Prédio 61H, Sala 7B, Camobi, CEP 97105-900 Santa Maria, RS.
  • Nereu Augusto Streck Universidade Federal de Santa Maria, Avenida Roraima, no 1.000, Cidade Universitária, Camobi, CEP 97105-900 Santa Maria, RS.
  • Jossana Ceolin Cera Instituto Rio Grandense do Arroz, Avenida Missões, no 342, São Geraldo, CEP 90230-100 Porto Alegre, RS.
  • Ary José Duarte Junior Universidade Federal de Santa Maria, Avenida Roraima, no 1.000, Cidade Universitária, Camobi, CEP 97105-900 Santa Maria, RS.
  • Giovana Ghisleni Ribas Grupo Dom Mario, Rua Antonio Rasteiro Filho, no 2.700, CEP 86183-751 Cambé, PR.
  • Ioran Guedes Rossato Universidade Federal de Santa Maria, Avenida Roraima, no 1.000, Cidade Universitária, Camobi, CEP 97105-900 Santa Maria, RS.
  • Lorenzo Dalcin Meus Universidade Federal de Santa Maria, Avenida Roraima, no 1.000, Cidade Universitária, Camobi, CEP 97105-900 Santa Maria, RS.
  • Vladison Fogliato Pereira Universidade Federal de Santa Maria, Avenida Roraima, no 1.000, Cidade Universitária, Camobi, CEP 97105-900 Santa Maria, RS.
  • Isabela Bulegon Benedetti Universidade Federal de Santa Maria, Avenida Roraima, no 1.000, Cidade Universitária, Camobi, CEP 97105-900 Santa Maria, RS.
  • Romulo Pulcinelli Benedetti Crops Team, Avenida Roraima, no 1.000, Prédio 61H, Sala 7B, Camobi, CEP 97105-900 Santa Maria, RS.
  • Francisco Tonetto Universidade Federal de Santa Maria, Avenida Roraima, no 1.000, Cidade Universitária, Camobi, CEP 97105-900 Santa Maria, RS.
  • Alencar Junior Zanon Universidade Federal de Santa Maria, Avenida Roraima, no 1.000, Cidade Universitária, Camobi, CEP 97105-900 Santa Maria, RS.

DOI:

https://doi.org/10.1590/S1678-3921.pab2022.v57.27081

Keywords:

Oryza sativa, crop modeling, decision-support systems, supply balance

Abstract

The objective of this work was to evaluate a flooded-rice yield forecasting method for the state of Rio Grande do Sul, Brazil, using the SimulArroz model. Version 1.1 of this model and historical meteorological data were used, with six different scenarios composed of the following levels of field information: number of sowing dates (1 to 4) and number of cultivars and/or development cycles (1 to 3) during four growing seasons (2014/2015 to 2017/2018). The root mean square error (RMSE) for comparing the actual yield with the simulated yield for Rio Grande do Sul was of 618.3 and 1,024.8 kg ha-1, i.e., of 8 and 13%, respectively. The forecast of rice yield by applying the SimulArroz model and historic meteorological data for Rio Grande do Sul shows a good predictability, and the recommended scenario is complex 1, using three sowing dates per site and the three most representative rice cultivars per region.

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Published

2022-07-22

How to Cite

Silva, M. R. da, Streck, N. A., Cera, J. C., Duarte Junior, A. J., Ribas, G. G., Rossato, I. G., … Zanon, A. J. (2022). Forecasting the rice yield in Rio Grande do Sul using the SimulArroz model. Pesquisa Agropecuaria Brasileira, 57(Z), e02069. https://doi.org/10.1590/S1678-3921.pab2022.v57.27081