Data/hora: 11/06/2024 04:22:30

Provedor de dados: | 63
País: | Brazil
Título: | Precision production environments for sugarcane fields
Autores: | Sanches,Guilherme Martineli; Paula,Maria Thereza Nonato de; Magalhães,Paulo Sérgio Graziano; Duft,Daniel Garbellini; Vitti,André César; Kolln,Oriel Tiago; Borges,Bernardo Melo Montes Nogueira; Franco,Henrique Coutinho Junqueira.
Data: | 2019-02-01
Ano: | 2019
Palavras-chave: | Proximal soil sensors; Site-specific soil management; Soil apparent electrical conductivity; Precision agriculture technologies.
Resumo: | ABSTRACT: Sugarcane (saccharum spp.) in Brazil is managed on the basis of “production environments”. These “production environments” are used for many purposes, such as variety allocation, application of fertilizers and definition of the planting and harvesting periods. A quality classification is essential to ensure high economic returns. However, the classification is carried out by few and, most of the time, non-representative soil samples, showing unreal local conditions of soil spatial variability and resulting in classifications that are imprecise. One of the important tools in the precision agriculture technological package is the apparent electrical conductivity (ECa) sensors that can quickly map soil spatial variability with high-resolution and at low-cost. The aim of the present work was to show that soil ECa maps are able to assist classification of the “production environments” in sugarcane fields and rapidly and accurately reflect the yield potential. Two sugarcane fields (35 and 100 ha) were mapped with an electromagnetic induction sensor to measure soil ECa and were sampled by a dense sampling grid. The results showed that the ECa technique was able to reflect mainly the spatial variability of the clay content, evidencing regions with different yield potentials, guiding soil sampling to soil classification that is both more secure and more accurate. Furthermore, ECa allowed for more precise classification, where new “production environments”, different from those previously defined by the traditional sampling methods, were revealed. Thus, sugarcane growers will be able to allocate suitable varieties and fertilize their agricultural fields in a coherent way with higher quality, guaranteeing greater sustainability and economic return on their production.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019000100010
Editor: | São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"
Relação: | 10.1590/1678-992x-2017-0128
Formato: | text/html
Fonte: | Scientia Agricola  v.76 n.1 2019
Fonte: | info:eu-repo/semantics/openAccess