Data/hora: 21/06/2024 13:16:22

Biblioteca(s): 	Embrapa Amazônia Oriental.	
Data corrente: 	06/09/2019	
Data da última atualização: 	23/01/2020	
Autoria: 	APARECIDO, L. E. de O.; MORAES, J. R. da S. C. de; ROLIM, G. de S.; MARTORANO, L. G.; MENESES, K. C. de; VALERIANO, T. T. B.	
Afiliação: 	Lucas Eduardo de Oliveira Aparecido, IFMS; José Reinaldo da Silva Cabral de Moraes, UNESP; Glauco de Souza Rolim, UNESP; LUCIETA GUERREIRO MARTORANO, CPATU; Kamila Cunha de Meneses, UNESP; Taynara Tuany Borges Valeriano, UNESP.	
Título: 	Neural networks in climate spatialization and their application in the agricultural zoning of climate risk for sunflower in different sowing dates.	
Ano de publicação: 	2019	
Fonte/Imprenta: 	Archives of Agronomy and Soil Science, v. 65, n. 11, p. 1477-1492, 2019.	
DOI: 	10.1080/03650340.2019.1566715	
Idioma: 	Inglês	
Conteúdo: 	Sunflower is a species that is sensitive to local climate conditions. However, studies that use artificial neural networks (ANNs) to evaluate this influence and create tools such as agricultural zoning of climate risk (ZARC) have not been conducted for this species. Due to the importance of sunflower as a human food source and for biodiesel production, and also the necessity of conducting research to evaluate the suitability of this oleaginous species under different climatic conditions. Thus, we seek to construct a ZARC for sunflower in Brazil simulating sowing on different dates and using meteorological elements spatialized by ANNs. Climate data were used: air temperature (T), rainfall (P), relative air humidity (UR), solar radiation (MJ_m&#8722;2_d&#8722;1) and wind velocity (U2). Climatic regions considered suitable for the cultivation of sunflower had average annual values for T between 20 and 28°C, P between 500 and 1.500 mm per cycle, and soil water deficit (DEF) below 140 mm per cycle. A neural network is an efficient tool that can be used in spatialization of climate variables quickly and accurately. Sunflower sowing in the spring and summer are the ones that provide the largest suitable areas in southeastern Brazil, with 58.13 and 64.36% of suitable areas, respectively	
Thesagro: 	Clima; Girassol; Zoneamento Agrícola.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
Registro original: 	Embrapa Amazônia Oriental (CPATU)	
Exemplares: 	CPATU - (55901-1) - UPC - AP - DD -  -  -  - DIS