Software Optimisation for Mechanised Sugarcane Planting Scenarios to Aid in Decision-Making

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniv Sorocaba-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.creatorNardo, L. A. S. [UNESP]-
Autor(es): dc.creatorPaixao, C. S. S.-
Autor(es): dc.creatorGonzaga, A. R. [UNESP]-
Autor(es): dc.creatorOliveira, L. P. [UNESP]-
Autor(es): dc.creatorVoltarelli, M. A.-
Autor(es): dc.creatorSilva, R. P. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:06:12Z-
Data de disponibilização: dc.date.available2022-02-22T00:06:12Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-08-06-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s12355-020-00868-1-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/195571-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/195571-
Descrição: dc.descriptionWith advancements in the mechanisation of sugarcane farming, studies have been fundamental to improving the process-from soil preparation to harvest. Faced with increasing challenges of economic scenarios, alternatives should be sought aimed at optimising resources, reducing costs, improving operational efficiency, logistics, among others. Planting is one of the main agricultural operations, any deviation in this phase harms the crop during the crop cycle, so planning in advance the area to be planted is essential for better results. Analysis of better planting scenarios prior to harvest combined with the use of autopilot requires knowledge of the systematisation areas and skilled labour to guarantee the quality of the process and reduce losses and damages. The objective of this study is to both evaluate and optimise sugarcane planting scenarios based on travel and manoeuvre time, travel distance, number of manoeuvres, and fuel consumption. The study was conducted in the municipality of Tanabi, SP, during the 2013 planting season. The results showed fewer manoeuvres and longer planting lines in the optimised area, increased the availability of the machine and generated possible cost reduction.-
Descrição: dc.descriptionSao Paulo State Univ, Dept Engn & Exact Sci, Lab Agr Machinery & Mechanizat, Sao Paulo, Brazil-
Descrição: dc.descriptionUniv Sorocaba, Sao Paulo, Brazil-
Descrição: dc.descriptionUniv Fed Sao Carlos, Sao Paulo, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Dept Engn & Exact Sci, Lab Agr Machinery & Mechanizat, Sao Paulo, Brazil-
Formato: dc.format8-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationSugar Tech-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectPrecision agriculture-
Palavras-chave: dc.subjectAgroCAD(R)-
Palavras-chave: dc.subjectAgricultural planning-
Palavras-chave: dc.subjectRunning time-
Título: dc.titleSoftware Optimisation for Mechanised Sugarcane Planting Scenarios to Aid in Decision-Making-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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