Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1186871
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dc.contributor.authorNAIME, J. de M.
dc.contributor.authorLOPES, I. de O. N.
dc.contributor.authorSPERANZA, E. A.
dc.contributor.authorVAZ, C. M. P.
dc.contributor.authorFRANCHINI, J. C.
dc.contributor.authorINAMASU, R. Y.
dc.contributor.authorCHAGAS, S. das
dc.contributor.authorSCHELP, M. X.
dc.contributor.authorVECCHI, L.
dc.date.accessioned2026-05-24T00:35:19Z-
dc.date.available2026-05-24T00:35:19Z-
dc.date.created2026-05-14
dc.date.issued2025
dc.identifier.citationAgriEngineering, v. 7, n. 11, 382, Nov. 2025.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186871-
dc.descriptionThis study develops a practical, on-farm methodology for optimizing cotton cultivation through Variable Rate Seeding (VRS), utilizing existing farm data and remote sensing, while minimizing operational interference. The methodology involved an experimental design across five rainfed cotton fields on a Brazilian commercial farm, testing four seeding rates (90%, 100%, 110%, 120%) within grid cells using a 4 × 4 Latin square design. Management zones (MZs) were defined using existing soil clay content and elevation data, augmented by twelve vegetation indices from Sentinel-2 satellite imagery and K-Means clustering. Statistical analysis evaluated plant population density’s effect on cotton yield and its association with MZs. For the 2023/2024 season, results showed no positive yield response to increasing plant density above field averages, with negative responses in many plots (e.g., 84% in Field A), suggesting potential gains from reducing rates. The association between population density effect classes and MZs was highly significant with moderate to relatively strong Cramer’s V values (up to 0.47), indicating MZs effectively distinguished response areas. Lower clay content consistently correlated with yield losses at higher densities. This work empowers farm managers to conduct their own site-specific experimentation for optimal seed populations, enhancing precision agriculture and resource efficiency.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectVariabilidade espacial
dc.subjectSemeadura em locais específicos
dc.subjectPopulação de sementes
dc.subjectExperimentação on-farm
dc.subjectSpatial variability
dc.subjectSeeding density
dc.subjectSite-specific seeding
dc.subjectSeed population
dc.subjectOn-farm experimentation
dc.titleOptimizing cotton cultivation through variable rate seeding: an enabling methodology.
dc.typeArtigo de periódico
dc.subject.thesagroAgricultura de Precisão
dc.subject.thesagroDensidade de Semeadura
riaa.ainfo.id1186871
riaa.ainfo.lastupdate2026-05-22
dc.identifier.doi10.3390/agriengineering7110382
dc.contributor.institutionJOAO DE MENDONCA NAIME, CNPDIA; IVANI DE OLIVEIRA NEGRAO LOPES, CNPSO; EDUARDO ANTONIO SPERANZA, CNPTIA; CARLOS MANOEL PEDRO VAZ, CNPDIA; JULIO CEZAR FRANCHINI DOS SANTOS, CNPSO; RICARDO YASSUSHI INAMASU, CNPDIA; SÉRGIO DAS CHAGAS, AMMAGI GROUP; MATHIAS XAVIER SCHELP, BOSCH BRASIL; LEONARDO VECCHI, BOSCH BRASIL.
Aparece nas coleções:Artigo em periódico indexado (CNPDIA)

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