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| Registros recuperados : 34 | |
| 11. |  | GRUTZMACHER, A.D.; NAKANO, O.; MARTINS, J.F. da S.; GRUTZMACHER, D.D.; LOECK, A.E. Danos da lagarta-da-folha, Spodoptera frugiperda, no arroz irrigado em diferentes condicoes hidricas. Agropecuaria Clima Temperado, Pelotas,RS, v.3, n.2, p.152-163, dez., 2000.| Biblioteca(s): Epagri-Sede. |
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| 12. | | GRUTZMACHER, A.D.; NAKANO, O. MARTINS, J.F.da.S.; GRUTZMACHER, D.D.; LOECK, A.E. Danos de Spodoptera frugiperda (J.E.Smith, 1797) (Lepidoptera: noctuidae) e seus efeitos sobre a producao de graos na cultivar de arroz irrigado Embrapa 6-Chui. Revista Brasileira de Agrociencia, Pelotas, v.5, n.2, p.135-141, maio/ago. 1999.| Biblioteca(s): Epagri-Sede. |
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| 15. | | GRUTZMACHER, A.D.; NAKANO, O.; MARTINS, J.F.da S.; GRUTZMACHER, D.D.; LOECK, A.E. Efeito do desfolhamento artificial sobre a producao e ciclo do arroz irrigado Embrapa 6-Chui. Revista Brasileira de Agrociencia, Pelotas, v.5, n.1, p.68-74, jan./abr. 1999.| Biblioteca(s): Epagri-Sede. |
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| 16. | | GRUTZMACHER, A. D.; NAKANO, O.; MARTINS, J. F. da S.; GRUTZMACHER, D.; LOECK, A. E. Simulacao do dano causado por lagartas-da-folha em arroz irrigado. Revista Cientifica Rural, Bage, v. 6, n. 2, p. 40-50, 2001.| Biblioteca(s): Epagri-Sede. |
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| 19. | | CUNHA, U. S. da; MARTINS, J. F. da S; GRUTZMACHER, A. D.; PAN, E. A. Recupercao de plantas de arroz irrigado danificadas por larvas de Oryzophagus oryzae (Costa Lima, 1936)(Coleoptra: Curculionidae) pela adubacao nitrogenada em cobertura. Revista Brasileira de Agrociencia, Pelotas, v. 7, n. 1, p.58-63, jan./abr. 2001.| Biblioteca(s): Epagri-Sede. |
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| Registros recuperados : 34 | |
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Registro Completo
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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
23/05/2022 |
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Data da última atualização: |
23/05/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
HAHN, L.; PARENT, L.; PAVIANI, A. C.; FELTRIM, A. L.; WAMSER, A. F.; ROZANE, D. E.; ENDER, M. M.; GRANDO, D. L.; MOURA-BUENO, J. M.; BRUNETTO, G. |
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Título: |
Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Plos One, California, USA, v. 17, n. 5, p. 1-13, 2022. |
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Idioma: |
Inglês |
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Conteúdo: |
Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers? observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production. MenosBrazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers? observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential t... Mostrar Tudo |
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Thesagro: |
Fertilidade do solo; modelos de precição; nutrição de plantas. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 02627naa a2200265 a 4500
001 1131993
005 2022-05-23
008 2022 bl uuuu u00u1 u #d
100 1 $aHAHN, L.
245 $aGarlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models.$h[electronic resource]
260 $c2022
520 $aBrazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers? observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production.
650 $aFertilidade do solo
650 $amodelos de precição
650 $anutrição de plantas
700 1 $aPARENT, L.
700 1 $aPAVIANI, A. C.
700 1 $aFELTRIM, A. L.
700 1 $aWAMSER, A. F.
700 1 $aROZANE, D. E.
700 1 $aENDER, M. M.
700 1 $aGRANDO, D. L.
700 1 $aMOURA-BUENO, J. M.
700 1 $aBRUNETTO, G.
773 $tPlos One, California, USA$gv. 17, n. 5, p. 1-13, 2022.
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