Prediction of Nitrogen Dosage in 'Alicante Bouschet' Vineyards with Machine Learning Models

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dc.contributor.authorBrunetto, Gustavo
dc.contributor.authorStefanello, Lincon Oliveira
dc.contributor.authorSouza Kulmann, Matheus Severo de
dc.contributor.authorTassinari, Adriele
dc.contributor.authorSchneider de Souza, Rodrigo Otavio
dc.contributor.authorRozane, Danilo Eduardo [UNESP]
dc.contributor.authorTiecher, Tadeu Luis
dc.contributor.authorCeretta, Carlos Alberto
dc.contributor.authorAvelar Ferreira, Paulo Ademar
dc.contributor.authorSiqueira, Gustavo Nogara de
dc.contributor.authorParent, Leon Etienne
dc.contributor.institutionUniversidade Federal de Sergipe (UFS)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionRio Grande do Sul Fed Inst
dc.contributor.institutionLaval Univ
dc.date.accessioned2023-07-29T11:55:44Z
dc.date.available2023-07-29T11:55:44Z
dc.date.issued2022-09-01
dc.description.abstractVineyard soils normally do not provide the amount of nitrogen (N) necessary for red wine production. Traditionally, the N concentration in leaves guides the N fertilization of vineyards to reach high grape yields and chemical composition under the ceteris paribus assumption. Moreover, the carryover effects of nutrients and carbohydrates stored by perennials such as grapevines are neglected. Where a well-documented database is assembled, machine learning (ML) methods can account for key site-specific features and carryover effects, impacting the performance of grapevines. The aim of this study was to predict, using ML tools, N management from local features to reach high berry yield and quality in 'Alicante Bouschet' vineyards. The 5-year (2015-2019) fertilizer trial comprised six N doses (0-20-40-60-80-100 kg N ha(-1)) and three regimes of irrigation. Model features included N dosage, climatic indices, foliar N application, and stem diameter of the preceding season, all of which were indices of the carryover effects. Accuracy of ML models was the highest with a yield cutoff of 14 t ha(-1) and a total anthocyanin content (TAC) of 3900 mg L. Regression models were more accurate for total soluble solids (TSS), total titratable acidity (TTA), pH, TAC, and total phenolic content (TPC) in the marketable grape yield. The tissue N ranges differed between high marketable yield and TAC, indicating a trade-off about 24 g N kg(-1) in the diagnostic leaf. The N dosage predicted varied from 0 to 40 kg N ha(-1) depending on target variable, this was calculated from local features and carryover effects but excluded climatic indices. The dataset can increase in size and diversity with the collaboration of growers, which can help to cross over the numerous combinations of features found in vineyards. This research contributes to the rational use of N fertilizers, but with the guarantee that obtaining high productivity must be with adequate composition.en
dc.description.affiliationUniv Fed Santa Maria, Soil Sci Dept, BR-97105900 Santa Maria, RS, Brazil
dc.description.affiliationUniv Fed Santa Maria, Forest Sci Dept, BR-97105900 Santa Maria, RS, Brazil
dc.description.affiliationState Univ Paulista Julio Mesquita Filho, Fruticulture Dept, BR-11900000 Registro, Brazil
dc.description.affiliationRio Grande do Sul Fed Inst, Campus Restinga, BR-91791508 Porto Alegre, RS, Brazil
dc.description.affiliationLaval Univ, Dept Soil & Agrifood Engn, Quebec City, PQ G1V 0A6, Canada
dc.description.affiliationUnespState Univ Paulista Julio Mesquita Filho, Fruticulture Dept, BR-11900000 Registro, Brazil
dc.description.sponsorshipConselho Nacional de Desenvolvimento Cient�fico e Tecnol�gico (CNPq)
dc.description.sponsorshipFundacao de Amparo a Pesquisa do Estado do Rio Grande do Sul (FAPERGS)
dc.description.sponsorshipIdCNPq: 201975/2020-3
dc.description.sponsorshipIdCNPq: 302023/2019-4
dc.description.sponsorshipIdCNPq: 423772/2018-0
dc.description.sponsorshipIdFundacao de Amparo a Pesquisa do Estado do Rio Grande do Sul (FAPERGS): 21/2551-0000602-1
dc.format.extent14
dc.identifierhttp://dx.doi.org/10.3390/plants11182419
dc.identifier.citationPlants-basel. Basel: Mdpi, v. 11, n. 18, 14 p., 2022.
dc.identifier.doi10.3390/plants11182419
dc.identifier.urihttp://hdl.handle.net/11449/245463
dc.identifier.wosWOS:000858806200001
dc.language.isoeng
dc.publisherMdpi
dc.relation.ispartofPlants-basel
dc.sourceWeb of Science
dc.subjectN fertilization
dc.subjectmodel-building
dc.subjectanthocyanin
dc.subjecttotal titratable acidity
dc.subjectvineyard management
dc.titlePrediction of Nitrogen Dosage in 'Alicante Bouschet' Vineyards with Machine Learning Modelsen
dc.typeArtigo
dcterms.rightsHolderMdpi
unesp.author.orcid0000-0002-3174-9992[1]
unesp.author.orcid0000-0001-9892-4057[3]
unesp.author.orcid0000-0002-4883-4835[4]
unesp.author.orcid0000-0002-6903-9961[7]
unesp.author.orcid0000-0002-5544-9599[10]
unesp.departmentEngenharia Agronômica - FCAVRpt

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