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| 13. |  | BAMBINI, M. D.; BONACELLI, M. B. M.; HIGA, R. H. Pesquisa agropecuária no contexto da e-science: monitoramento de temas e plataformas de data science. In: CONGRESSO DA SOCIEDADE BRASILEIRA DE ECONOMIA, ADMINISTRAÇÃO E SOCIOLOGIA RURAL, 56., 2018, Campinas. Transformações recentes na agropecuária brasileira: desafios em gestão, inovação, sustentabilidade e inclusão social: anais. Brasília, DF: SOBER, 2018. Não paginado. Sober 2018.| Biblioteca(s): Embrapa Agricultura Digital. |
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Registro Completo
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura; Embrapa Semiárido. |
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Data corrente: |
15/10/2019 |
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Data da última atualização: |
22/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
VITOR, A. B.; DINIZ, R. P.; MORGANTE, C. V.; ANTONIO, R. P.; OLIVEIRA, E. J. de. |
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Afiliação: |
Alison Borges Vitor, Universidade Federal do Recôncavo da Bahia, Cruz das Almas, BA; Rafael Parreira Diniz, Universidade Federal do Recôncavo da Bahia, Cruz das Almas, BA; CAROLINA VIANNA MORGANTE, CPATSA; RAFAELA PRISCILA ANTONIO, CPATSA; EDER JORGE DE OLIVEIRA, CNPMF. |
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Título: |
Early prediction models for cassava root yield in different water regimes. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Field Crops Research, v. 239, p. 149-158, 2019. |
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DOI: |
10.1016/j.fcr.2019.05.017 |
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Idioma: |
Inglês |
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Conteúdo: |
The development of cassava (Manihot esculenta Crantz) varieties with greater tolerance of water deficit depends on optimized phenotyping tools. The objective of this work was to develop early prediction models of final root yield (12 months after planting - MAP) using physiological and agronomic data obtained at 4 MAP under two water regimes. Nine genotypes of cassava were evaluated under two treatments (irrigated and with water deficit), using a complete randomized block design, in a factorial scheme of 2 harvest periods (at 4 and 12 MAP) × 9 genotypes, with four replications. Both treatment groups were irrigated until 3 MAP. After this period, irrigation was interrupted for the water deficit treatment group. Fourteen physiological and agronomic traits were evaluated in all harvest periods. Four prediction models were evaluated: linear regression with stepwise selection (LRSS), linear regression with backward selection (LRBS), Bayesian ridge regression (BRR), and partial least squares (PLS). Most of the models presented a high predictive ability for final root yield (R2 ranging from 0.83 to 0.91). However, in all prediction scenarios, the PLS model presented a high R2 (0.84 to 0.91) associated with the lowest root-mean-square error (RMSE) (0.82 to 1.60). Differences in the predictive ability of the models may have occurred due to the relative importance of the early traits. In the case of PLS, the most important traits for the model were stomatal conductance, root yield at 4 MAP, leaf area index and number of roots. Regardless of the water condition, the physiological and agronomic data collected at an early stage could successfully be used to predict the final root yield with great efficiency. This strategy can reduce the cost of phenotyping, increasing the capacity for analysis and optimization of genetic gains for tolerance to drought in cassava. MenosThe development of cassava (Manihot esculenta Crantz) varieties with greater tolerance of water deficit depends on optimized phenotyping tools. The objective of this work was to develop early prediction models of final root yield (12 months after planting - MAP) using physiological and agronomic data obtained at 4 MAP under two water regimes. Nine genotypes of cassava were evaluated under two treatments (irrigated and with water deficit), using a complete randomized block design, in a factorial scheme of 2 harvest periods (at 4 and 12 MAP) × 9 genotypes, with four replications. Both treatment groups were irrigated until 3 MAP. After this period, irrigation was interrupted for the water deficit treatment group. Fourteen physiological and agronomic traits were evaluated in all harvest periods. Four prediction models were evaluated: linear regression with stepwise selection (LRSS), linear regression with backward selection (LRBS), Bayesian ridge regression (BRR), and partial least squares (PLS). Most of the models presented a high predictive ability for final root yield (R2 ranging from 0.83 to 0.91). However, in all prediction scenarios, the PLS model presented a high R2 (0.84 to 0.91) associated with the lowest root-mean-square error (RMSE) (0.82 to 1.60). Differences in the predictive ability of the models may have occurred due to the relative importance of the early traits. In the case of PLS, the most important traits for the model were stomatal conductance, root yield at ... Mostrar Tudo |
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Palavras-Chave: |
Défic hídrico; Fenotipagem; Raízes de mandioca. |
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Thesagro: |
Fisiologia; Mandioca; Manihot Esculenta. |
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Thesaurus NAL: |
Cassava; Physiology; Plant breeding. |
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Categoria do assunto: |
--
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/205097/1/Early-prediction-models-for-cassava-2019.pdf
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Marc: |
LEADER 02688naa a2200289 a 4500
001 2114761
005 2020-01-22
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1016/j.fcr.2019.05.017$2DOI
100 1 $aVITOR, A. B.
245 $aEarly prediction models for cassava root yield in different water regimes.$h[electronic resource]
260 $c2019
520 $aThe development of cassava (Manihot esculenta Crantz) varieties with greater tolerance of water deficit depends on optimized phenotyping tools. The objective of this work was to develop early prediction models of final root yield (12 months after planting - MAP) using physiological and agronomic data obtained at 4 MAP under two water regimes. Nine genotypes of cassava were evaluated under two treatments (irrigated and with water deficit), using a complete randomized block design, in a factorial scheme of 2 harvest periods (at 4 and 12 MAP) × 9 genotypes, with four replications. Both treatment groups were irrigated until 3 MAP. After this period, irrigation was interrupted for the water deficit treatment group. Fourteen physiological and agronomic traits were evaluated in all harvest periods. Four prediction models were evaluated: linear regression with stepwise selection (LRSS), linear regression with backward selection (LRBS), Bayesian ridge regression (BRR), and partial least squares (PLS). Most of the models presented a high predictive ability for final root yield (R2 ranging from 0.83 to 0.91). However, in all prediction scenarios, the PLS model presented a high R2 (0.84 to 0.91) associated with the lowest root-mean-square error (RMSE) (0.82 to 1.60). Differences in the predictive ability of the models may have occurred due to the relative importance of the early traits. In the case of PLS, the most important traits for the model were stomatal conductance, root yield at 4 MAP, leaf area index and number of roots. Regardless of the water condition, the physiological and agronomic data collected at an early stage could successfully be used to predict the final root yield with great efficiency. This strategy can reduce the cost of phenotyping, increasing the capacity for analysis and optimization of genetic gains for tolerance to drought in cassava.
650 $aCassava
650 $aPhysiology
650 $aPlant breeding
650 $aFisiologia
650 $aMandioca
650 $aManihot Esculenta
653 $aDéfic hídrico
653 $aFenotipagem
653 $aRaízes de mandioca
700 1 $aDINIZ, R. P.
700 1 $aMORGANTE, C. V.
700 1 $aANTONIO, R. P.
700 1 $aOLIVEIRA, E. J. de
773 $tField Crops Research$gv. 239, p. 149-158, 2019.
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Embrapa Semiárido (CPATSA) |
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