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Registro Completo |
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
06/04/2011 |
Data da última atualização: |
06/04/2011 |
Autoria: |
BRAND, M. A.; BOLZON DE MUÑIZ, G. I. |
Título: |
Influência da época de colheita da biomassa florestal sobre sua qualidade para a geração de energia. |
Ano de publicação: |
2010 |
Fonte/Imprenta: |
Scientia Forestalis, Piracicaba, v. 38, n. 88, p. 619-628, dez. 2010. |
Idioma: |
Português |
Palavras-Chave: |
Eucalyptus dunnii. |
Thesagro: |
Energia; Pinus Taeda. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00505naa a2200157 a 4500 001 1885177 005 2011-04-06 008 2010 bl uuuu u00u1 u #d 100 1 $aBRAND, M. A. 245 $aInfluência da época de colheita da biomassa florestal sobre sua qualidade para a geração de energia. 260 $c2010 650 $aEnergia 650 $aPinus Taeda 653 $aEucalyptus dunnii 700 1 $aBOLZON DE MUÑIZ, G. I. 773 $tScientia Forestalis, Piracicaba$gv. 38, n. 88, p. 619-628, dez. 2010.
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Registro original: |
Embrapa Florestas (CNPF) |
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Registro Completo
Biblioteca(s): |
Embrapa Clima Temperado; Embrapa Uva e Vinho. |
Data corrente: |
01/07/2020 |
Data da última atualização: |
01/07/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
BETEMPS, D. L.; PAULA, B. V. de; PARENT, S.-E.; GALARÇA, S. P.; MAYER, N. A.; MARODIN, G. A. B.; ROZANE, D. E.; NATALE, W.; MELO, G. W. B. de; PARENT, L. E.; BRUNETTO, G. |
Afiliação: |
DÉBORA LEITZKE BETEMPS, UFSM; UFFS; BETANIA VAHL DE PAULA, UFSM; SERGE-ÉTIENNE PARENT, LAVAL UNIVERSITY; SIMONE P. GALARÇA, ASCAR EMATER; NEWTON ALEX MAYER, CPACT; GILMAR A. B. MARODIN, UFRGS; DANILO E. ROZANE, UNESP; WILLIAM NATALE, UFC; GEORGE WELLINGTON BASTOS DE MELO, CNPUV; LÉON E. PARENT, UFSM; LAVAL UNIVERSITY; GUSTAVO BRUNETTO, UFSM. |
Título: |
Humboldtian Diagnosis of Peach Tree (Prunus persica) Nutrition Using Machine-Learning and Compositional Methods. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Agronomy, v. 10, n. 6, June 2020. |
Páginas: |
21 p. |
ISSN: |
2073-4395 |
DOI: |
10.3390/agronomy10060900 |
Idioma: |
Inglês |
Conteúdo: |
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 diered 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. |
Thesagro: |
Pêssego; Porta Enxerto; Prunus. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/214306/1/agronomy-10-00900-v2.pdf
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Marc: |
LEADER 02364naa a2200313 a 4500 001 2123550 005 2020-07-01 008 2020 bl uuuu u00u1 u #d 022 $a2073-4395 024 7 $a10.3390/agronomy10060900$2DOI 100 1 $aBETEMPS, D. L. 245 $aHumboldtian Diagnosis of Peach Tree (Prunus persica) Nutrition Using Machine-Learning and Compositional Methods.$h[electronic resource] 260 $c2020 300 $a21 p. 520 $aRegional 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 diered 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. 650 $aPêssego 650 $aPorta Enxerto 650 $aPrunus 700 1 $aPAULA, B. V. de 700 1 $aPARENT, S.-E. 700 1 $aGALARÇA, S. P. 700 1 $aMAYER, N. A. 700 1 $aMARODIN, G. A. B. 700 1 $aROZANE, D. E. 700 1 $aNATALE, W. 700 1 $aMELO, G. W. B. de 700 1 $aPARENT, L. E. 700 1 $aBRUNETTO, G. 773 $tAgronomy$gv. 10, n. 6, June 2020.
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Embrapa Clima Temperado (CPACT) |
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