Agrometeorological analysis of the soybean potentiality in an Amazonian environment

Authors

  • Marcus José Alves de Lima Universidade Federal Rural da Amazônia
  • Evandro Chaves de Oliveira Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo
  • Leila Sobral Sampaio Universidade Federal Rural da Amazônia
  • Clyde William Fraisse University of Florida
  • Paulo Jorge de Oliveira Ponte de Souza Universidade Federal Rural da Amazônia

Abstract

The use of crop models that integrate soil, climate, cultivar and management information may broaden the understanding of the interactions between soybean cropping system and local climate variability. This study aimed to analyze the potentiality of soybean in an Amazonian production environment, as well as to determine an optimal sowing window via agrometeorological modeling. A crop model was programmed to simulate the soybean yield for 18 sowing dates, obtained along 33 years of climatic data, under attainable and potential conditions. The simulated potential of soybean yield ranged from 3,785 kg ha-1 to 5,114 kg ha-1, owing to the local energy availability, whereas the average attainable yield ranged from 557 kg ha-1 to 4,700 kg ha-1, mainly because of the soil moisture conditions. The smallest difference between the potential and attainable yields was observed in the sowing dates from 01-Jan to 15-Feb. For this window, the probability of obtaining yields above 3,500 kg ha-1 was higher than 90 %.

KEYWORDS: Glycine max, CROPGRO-Soybean, optimum sowing window.

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Author Biography

Marcus José Alves de Lima, Universidade Federal Rural da Amazônia

Agrometeorológia

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Published

2019-06-03

How to Cite

LIMA, M. J. A. de; DE OLIVEIRA, E. C.; SAMPAIO, L. S.; FRAISSE, C. W.; DE SOUZA, P. J. de O. P. Agrometeorological analysis of the soybean potentiality in an Amazonian environment. Pesquisa Agropecuária Tropical [Agricultural Research in the Tropics], Goiânia, v. 49, p. e54595, 2019. Disponível em: https://revistas.ufg.br/pat/article/view/54595. Acesso em: 18 dec. 2024.

Issue

Vol. 49 (2019)

Section

Research Article

License

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