Delineation of management zones in integrated crop–livestock systems

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dc.contributor.authorRodriguez Miranda, Diana Alexandra
dc.contributor.authorde Oliveira Alari, Fernando [UNESP]
dc.contributor.authorOldoni, Henrique
dc.contributor.authorBazzi, Claudio Leones
dc.contributor.authordo Amaral, Lucas Rios
dc.contributor.authorGraziano Magalhães, Paulo Sérgio
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionFederal Univ. of Technology of Paraná (UTFPR)
dc.date.accessioned2022-04-28T19:47:06Z
dc.date.available2022-04-28T19:47:06Z
dc.date.issued2021-01-01
dc.description.abstractThe lack of studies on spatial variability in integrated crop–livestock systems (ICLS) hinders understanding how to increase their efficiency by implementing precision agriculture (PA) practices. As such, little is known about how grain and forage crops interact and how to improve the decision-making process on fertilization and forage management. One technique that can help manage such systems is the delineation of management zones (MZs), regions with similar yield potential and soil and topography characteristics. Thus, this paper assesses the spatial correlation between yield and potential factors affecting it, and identifies whether it is possible to establish MZs for field management of grain and forage crops in succession in ICLS. Bivariate Moran's index was used to identify the attributes most spatially correlated with the yields. Elevation, soil apparent electrical conductivity, and clay content were the most spatially correlated variables with soybean [Glycine max (L.) Merr.] yield, while soil organic matter content and elevation were the most spatially correlated with the forage yield. Spatial principal components analysis and fuzzy c-means clustering algorithm were combined to delineate MZs for each crop. The MZs created for soybean were statistically different in grain yield, available phosphorus (P) in the 0-to-0.40-m layer and pH in the 0-to-0.20-m layer. The forage MZs showed significant differences in terms of available P in the 0-to-0.40-m layer. We conclude that MZs for ICLS tends to be crop specific, demanding different MZs to characterize soybean and forage spatial variability.en
dc.description.affiliationSchool of Agricultural Engineering Univ. of Campinas (UNICAMP), Av. Cândido Rondon, 501 – Barão Geraldo CEP
dc.description.affiliationFaculty of Agricultural and Veterinary Sciences São Paulo State Univ. (UNESP), Rua Quirino de Andrade, 215 – Centro CEP
dc.description.affiliationInterdisciplinary Center of Energy Planning Univ. of Campinas (UNICAMP), Rua Cora Coralina, 330 – Barão Geraldo CEP
dc.description.affiliationDep. of Computer Science Federal Univ. of Technology of Paraná (UTFPR), Av. Brasil, 4232 CEP
dc.description.affiliationSchool of Agricultural Engineering Univ. of Campinas (UNICAMP), Av. Cândido Rondon, 501, Barão Geraldo CEP
dc.description.affiliationUnespFaculty of Agricultural and Veterinary Sciences São Paulo State Univ. (UNESP), Rua Quirino de Andrade, 215 – Centro CEP
dc.identifierhttp://dx.doi.org/10.1002/agj2.20912
dc.identifier.citationAgronomy Journal.
dc.identifier.doi10.1002/agj2.20912
dc.identifier.issn1435-0645
dc.identifier.issn0002-1962
dc.identifier.scopus2-s2.0-85118865312
dc.identifier.urihttp://hdl.handle.net/11449/222837
dc.language.isoeng
dc.relation.ispartofAgronomy Journal
dc.sourceScopus
dc.titleDelineation of management zones in integrated crop–livestock systemsen
dc.typeArtigo
unesp.author.orcid0000-0003-3218-0993[1]
unesp.author.orcid0000-0003-1346-8748[2]
unesp.author.orcid0000-0003-3862-003X[3]
unesp.author.orcid0000-0002-9009-5562[4]
unesp.author.orcid0000-0001-8071-4449[5]
unesp.author.orcid0000-0002-5374-3591[6]

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