Precision production environments for sugarcane fields

Autor(es)

Guilherme Martineli Sanches ^[1] ; Maria Thereza Nonato de Paula ^[2] ; Paulo Sérgio Graziano Magalhães ^[3] ; Daniel Garbellini Duft ^[4] ; André César Vitti ^[5] ; Oriel Tiago Kolln ^[6] ; Bernardo Melo Montes Nogueira Borges ^[7] ; Henrique Coutinho Junqueira Franco ^[8]

Afiliação do(s) autor(es)

^[1] Universidade Estadual de Campinas. FEA. Depto. de Bioenergia - Brasil

^[2] Universidade Estadual de Campinas. FEAGRI - Brasil

^[3] Universidade Estadual de Campinas. FEAGRI - Brasil

^[4] Laboratório Nacional de Ciência e Tecnologia do Bioetanol/CTBE - Brasil

^[5] Agência Paulista de Tecnologia dos Agronegócios. Polo Regional Centro Sul - Brasil

^[6] Laboratório Nacional de Ciência e Tecnologia do Bioetanol/CTBE - Brasil

^[7] Laboratório Nacional de Ciência e Tecnologia do Bioetanol/CTBE - Brasil

^[8] Laboratório Nacional de Ciência e Tecnologia do Bioetanol/CTBE - Brasil

• Tipo de documento: Artigo Científico
• Fonte: Scientia Agricola; v. 76, n. 1, p. 10-17, 2019-02-00.

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. (AU)

• Processo FAPESP: 13/50942-2 - Reflectância multiespectral por espectroscopia aplicada na predição de propriedades químicas de solo para o uso em agricultura de precisão em cana de açúcar
• Beneficiário: Paulo Sergio Graziano Magalhães
• Modalidade de apoio: Auxílio à Pesquisa - Programa BIOEN - Regular
• Processo FAPESP: 14/14965-0 - Contribuição da Tecnologia de Agricultura de Precisão com a sustentabilidade da produção da cana-de-açúcar para produção de energia
• Beneficiário: Paulo Sergio Graziano Magalhães
• Modalidade de apoio: Auxílio à Pesquisa - Regular