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 | Acesso ao texto completo restrito à biblioteca da Epagri-Sede. Para informações adicionais entre em contato com biblio@epagri.sc.gov.br. |
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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
15/08/2003 |
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Data da última atualização: |
15/08/2003 |
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Autoria: |
ALMEIDA, F.T.de; BERNARDO, S.; SOUSA, E.F.de; MARIN, S.L.D.; GRIPPA, S. |
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Título: |
Growth and yield of papaya under irrigation. |
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Ano de publicação: |
2003 |
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Fonte/Imprenta: |
Scientia Agricola, Piracicaba, SP, v.60, n.3, p.419-424, jul./set. 2003. |
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Idioma: |
Inglês |
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Palavras-Chave: |
Carica papaya; Desenvolvimento; Irrigacao; Mamao; Produtividade. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00581naa a2200217 a 4500
001 1026328
005 2003-08-15
008 2003 bl uuuu u00u1 u #d
100 1 $aALMEIDA, F.T.de
245 $aGrowth and yield of papaya under irrigation.
260 $c2003
653 $aCarica papaya
653 $aDesenvolvimento
653 $aIrrigacao
653 $aMamao
653 $aProdutividade
700 1 $aBERNARDO, S.
700 1 $aSOUSA, E.F.de
700 1 $aMARIN, S.L.D.
700 1 $aGRIPPA, S.
773 $tScientia Agricola, Piracicaba, SP$gv.60, n.3, p.419-424, jul./set. 2003.
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Epagri-Sede (Epagri-Sede) |
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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
05/04/2024 |
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Data da última atualização: |
05/04/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
HAHN, L.; KURTZ, C.; PAULA, B. V.; FELTRIM, A. L.; HIGASHIKAWA, F. S.; MOREIRA, C.; ROZANE, D. E.; BRUNETTO, G.; PARENT, L. |
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Título: |
Feature-specifc nutrient management of onion (Allium cepa) using machine learning and compositional methods. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Scientific Reports, Washingtons, USA, v. 14, p. 1-12, 2024. |
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Idioma: |
Inglês |
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Conteúdo: |
While onion cultivars, irrigation and soil and crop management have been given much attention in Brazil to boost onion yields, nutrient management at field scale is still challenging due to large dosage uncertainty. Our objective was to develop an accurate feature-based fertilization model for onion crops. We assembled climatic, edaphic, and managerial features as well as tissue tests into a database of 1182 observations from multi-environment fertilizer trials conducted during 13 years in southern Brazil. The complexity of onion cropping systems was captured by machine learning (ML) methods. The RReliefF ranking algorithm showed that the split-N dosage and soil tests for micronutrients and S were the most relevant features to predict bulb yield. The decision-tree random forest and extreme gradient boosting models were accurate to predict bulb yield from the relevant predictors (R2 > 90%). As shown by the gain ratio, foliar nutrient standards for nutritionally balanced and high-yielding specimens producing > 50 Mg bulb ha−1 set apart by the ML classification models differed among cultivars. Cultivar × environment interactions support documenting local nutrient diagnosis. The split-N dosage was the most relevant controllable feature to run future universality tests set to assess models? ability to generalize to growers? fields. |
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Thesagro: |
Allium cepa; fertilidade do solo; machine learning; nutrição mineral. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02171naa a2200265 a 4500
001 1134365
005 2024-04-05
008 2024 bl uuuu u00u1 u #d
100 1 $aHAHN, L.
245 $aFeature-specifc nutrient management of onion (Allium cepa) using machine learning and compositional methods.$h[electronic resource]
260 $c2024
520 $aWhile onion cultivars, irrigation and soil and crop management have been given much attention in Brazil to boost onion yields, nutrient management at field scale is still challenging due to large dosage uncertainty. Our objective was to develop an accurate feature-based fertilization model for onion crops. We assembled climatic, edaphic, and managerial features as well as tissue tests into a database of 1182 observations from multi-environment fertilizer trials conducted during 13 years in southern Brazil. The complexity of onion cropping systems was captured by machine learning (ML) methods. The RReliefF ranking algorithm showed that the split-N dosage and soil tests for micronutrients and S were the most relevant features to predict bulb yield. The decision-tree random forest and extreme gradient boosting models were accurate to predict bulb yield from the relevant predictors (R2 > 90%). As shown by the gain ratio, foliar nutrient standards for nutritionally balanced and high-yielding specimens producing > 50 Mg bulb ha−1 set apart by the ML classification models differed among cultivars. Cultivar × environment interactions support documenting local nutrient diagnosis. The split-N dosage was the most relevant controllable feature to run future universality tests set to assess models? ability to generalize to growers? fields.
650 $aAllium cepa
650 $afertilidade do solo
650 $amachine learning
650 $anutrição mineral
700 1 $aKURTZ, C.
700 1 $aPAULA, B. V.
700 1 $aFELTRIM, A. L.
700 1 $aHIGASHIKAWA, F. S.
700 1 $aMOREIRA, C.
700 1 $aROZANE, D. E.
700 1 $aBRUNETTO, G.
700 1 $aPARENT, L.
773 $tScientific Reports, Washingtons, USA$gv. 14, p. 1-12, 2024.
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