Data/hora: 28/05/2024 20:10:38

Biblioteca(s): 	Embrapa Mandioca e Fruticultura; Embrapa Semiárido.	
Tipo da produção científica: 	Artigo em Periódico Indexado	
Circulação/Nível: 	A - 1	
Data corrente: 	15/10/2019	
Data da última atualização: 	22/01/2020	
Autoria: 	VITOR, A. B.; DINIZ, R. P.; MORGANTE, C. V.; ANTONIO, R. P.; OLIVEIRA, E. J. de.	
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.	
Título: 	Early prediction models for cassava root yield in different water regimes.	
Ano de publicação: 	2019	
Fonte/Imprenta: 	Field Crops Research, v. 239, p. 149-158, 2019.	
DOI: 	10.1016/j.fcr.2019.05.017	
Idioma: 	Inglês	
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.	
Palavras-Chave: 	Défic hídrico; Fenotipagem; Raízes de mandioca.	
Thesagro: 	Fisiologia; Mandioca; Manihot Esculenta.	
Thesaurus Nal: 	Cassava; Physiology; Plant breeding.	
Categoria do assunto: 	--
G Melhoramento Genético	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/205097/1/Early-prediction-models-for-cassava-2019.pdf	
Registro original: 	Embrapa Semiárido (CPATSA)	
Exemplares: 	CNPMF - (32752-1) - UPC - AP - DD - Publicação digital -  -  - DIS
CPATSA - (58805-1) - UPC - AP - DD -  -  -  - DIS