Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/340583
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampSanches, Guilherme Martineli-
dc.contributor.authorunicampPaula, Maria Thereza Nonato de-
dc.contributor.authorunicampMagalhães, Paulo Sergio Graziano-
dc.typeArtigopt_BR
dc.titlePrecision production environments for sugarcane fieldspt_BR
dc.contributor.authorSanches, Guilherme Martineli-
dc.contributor.authorNonato de Paula, Maria Thereza-
dc.contributor.authorGraziano Magalhaes, Paulo Sergio-
dc.contributor.authorDuft, Daniel Garbellini-
dc.contributor.authorVitti, Andre Cesar-
dc.contributor.authorKolln, Oriel Tiago-
dc.contributor.authorMontes Nogueira Borges, Bernardo Melo-
dc.contributor.authorJunqueira Franco, Henrique Coutinho-
dc.subjectSolos - Manejopt_BR
dc.subject.otherlanguageSoil managementpt_BR
dc.description.abstractSugarcane (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 productionpt_BR
dc.relation.ispartofScientia agricolapt_BR
dc.relation.ispartofabbreviationSci. agric.pt_BR
dc.publisher.cityPiracicaba, SPpt_BR
dc.publisher.countryBrasilpt_BR
dc.publisherUniversidade de São Paulo/Escola Superior de Agricultura "Luiz de Queiroz"pt_BR
dc.date.issued2019-
dc.date.monthofcirculationJan./Feb.pt_BR
dc.language.isoengpt_BR
dc.description.volume76pt_BR
dc.description.issuenumber1pt_BR
dc.description.firstpage10pt_BR
dc.description.lastpage17pt_BR
dc.rightsAbertopt_BR
dc.sourceWOSpt_BR
dc.identifier.eissn1678-992Xpt_BR
dc.identifier.doi10.1590/1678-992X-2017-0128pt_BR
dc.identifier.urlhttps://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019000100010pt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsordocumentnumber2013/50942-2; 2014/14965- 0pt_BR
dc.date.available2020-05-11T17:27:45Z-
dc.date.accessioned2020-05-11T17:27:45Z-
dc.description.provenanceSubmitted by Sanches Olivia (olivias@unicamp.br) on 2020-05-11T17:27:45Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-08-27T19:16:18Z : No. of bitstreams: 1 000446980300002.pdf: 969056 bytes, checksum: 8297df3d24f8dab56431a7f4249dacc3 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-05-11T17:27:45Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/340583-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia de Alimentospt_BR
dc.contributor.unidadeFaculdade de Engenharia Agrícolapt_BR
dc.contributor.unidadeFaculdade de Engenharia Agrícolapt_BR
dc.subject.keywordProximal soil sensorspt_BR
dc.subject.keywordSoil apparent electrical conductivitypt_BR
dc.subject.keywordPrecision agriculture technologiespt_BR
dc.identifier.source000446980300002pt_BR
dc.creator.orcid0000-0001-7718-8142pt_BR
dc.creator.orcid0000-0002-5262-9658pt_BR
dc.creator.orcid0000-0002-5374-3591pt_BR
dc.type.formArtigopt_BR
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