Autor(es):
Betemps, Débora Leitzke ; De Paula, Betania Vahl ; Parent, Serge-Étienne ; Galarça, Simone P. ; Mayer, Newton A. ; Marodin, Gilmar A.B. ; Rozane, Danilo E. [UNESP] ; Natale, William ; Melo, George Wellington B. ; Parent, Léon E. ; Brunetto, Gustavo
Data: 2020
Identificador Persistente: http://hdl.handle.net/11449/200710
Origem: Oasisbr
Assunto(s): Centered log ratio; Compositional entity; Humboldtian data sets; Local diagnosis; Machine learning; Nutrient limitations; Peach trees; Random forest; Centered log ratio; Centered log ratio; Compositional entity; Compositional entity; Humboldtian data sets; Humboldtian data sets; Local diagnosis; Local diagnosis; Machine learning; Machine learning; Nutrient limitations; Nutrient limitations; Peach trees; Peach trees; Random forest; Random forest
Descrição
Made available in DSpace on 2020-12-12T02:13:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-01
Regional nutrient ranges are commonly used to diagnose plant nutrient status. In contrast, local diagnosis confronts unhealthy to healthy compositional entities in comparable surroundings. Robust local diagnosis requires well-documented data sets processed by machine learning and compositional methods. Our objective was to customize nutrient diagnosis of peach (Prunus persica) trees at local scale. We collected 472 observations from commercial orchards and fertilizer trials across eleven cultivars of Prunus persica and six rootstocks in the state of Rio Grande do Sul (RS), Brazil. The random forest classification model returned an area under curve exceeding 0.80 and classification accuracy of 80% about yield cutoff of 16 Mg ha-1. Centered log ratios (clr) of foliar defective compositions have appropriate geometry to compute Euclidean distances from closest successful compositions in “enchanting islands”. Successful specimens closest to defective specimens as shown by Euclidean distance allow reaching trustful fruit yields using site-specific corrective measures. Comparing tissue composition of low-yielding orchards to that of the closest successful neighbors in two major Brazilian peach-producing regions, regional diagnosis differed from local diagnosis, indicating that regional standards may fail to fit local conditions. Local diagnosis requires well-documented Humboldtian data sets that can be acquired through ethical collaboration between researchers and stakeholders.
Departamento dos Solos Universidade Federal de Santa Maria, Av. Roraima, 1000 Camobi
Campus Cerro Largo Universidade Federal da Fronteira Sul, Av. Jacob Reinaldo Haupenthal, 1580-Bairro São Pedro
Department of Soils and Agrifood Engineering Laval University
Ascar Emater—Piratini, Rua 20 de Setembro, 158-Centro
Embrapa Clima Temperado Centro de Pesquisa Agropecuária de Clima Temperado, BR 392, km 78
Departemento de Horticultura e Silvicultura Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 7712, C.P. 15.100, Agronomia
Departamento de Engenharia Agronômica Universidade Estadual de São Paulo (UNESP) Campus de Registro, Av. Nelson Brihi Badur
Departamento de Fitotecnia Universidade Federal do Ceará (UFC), Av. Mister Hull, 2977-Campus do Pici
Embrapa Uva e Vinho, Rua Livramento, 515
Departamento de Engenharia Agronômica Universidade Estadual de São Paulo (UNESP) Campus de Registro, Av. Nelson Brihi Badur
