Literature DB >> 25873681

Variation and impact of drought-stress patterns across upland rice target population of environments in Brazil.

Alexandre Bryan Heinemann1, Camilo Barrios-Perez2, Julian Ramirez-Villegas3, David Arango-Londoño2, Osana Bonilla-Findji4, João Carlos Medeiros5, Andy Jarvis4.   

Abstract

The upland rice (UR) cropped area in Brazil has decreased in the last decade. Importantly, a portion of this decrease can be attributed to the current UR breeding programme strategy, according to which direct grain yield selection is targeted primarily to the most favourable areas. New strategies for more-efficient crop breeding under non-optimal conditions are needed for Brazil's UR regions. Such strategies should include a classification of spatio-temporal yield variations in environmental groups, as well as a determination of prevalent drought types and their characteristics (duration, intensity, phenological timing, and physiological effects) within those environmental groups. This study used a process-based crop model to support the Brazilian UR breeding programme in their efforts to adopt a new strategy that accounts for the varying range of environments where UR is currently cultivated. Crop simulations based on a commonly grown cultivar (BRS Primavera) and statistical analyses of simulated yield suggested that the target population of environments can be divided into three groups of environments: a highly favorable environment (HFE, 19% of area), a favorable environment (FE, 44%), and least favourable environment (LFE, 37%). Stress-free conditions dominated the HFE group (69% likelihood) and reproductive stress dominated the LFE group (68% likelihood), whereas reproductive and terminal drought stress were found to be almost equally likely to occur in the FE group. For the best and worst environments, we propose specific adaptation focused on the representative stress, while for the FE, wide adaptation to drought is suggested. 'Weighted selection' is also a possible strategy for the FE and LFE environment groups.
© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  Breeding; Oryza sativa; environment classification; modelling; water deficit.

Mesh:

Substances:

Year:  2015        PMID: 25873681     DOI: 10.1093/jxb/erv126

Source DB:  PubMed          Journal:  J Exp Bot        ISSN: 0022-0957            Impact factor:   6.992


  6 in total

1.  Overexpression of a phospholipase (OsPLDα1) for drought tolerance in upland rice (Oryza sativa L.).

Authors:  Fernanda Raquel Martins Abreu; Beata Dedicova; Rosana Pereira Vianello; Anna Cristina Lanna; João Augusto Vieira de Oliveira; Ariadna Faria Vieira; Odilon Peixoto Morais; João Antônio Mendonça; Claudio Brondani
Journal:  Protoplasma       Date:  2018-05-30       Impact factor: 3.356

2.  Combining Limited Multiple Environment Trials Data with Crop Modeling to Identify Widely Adaptable Rice Varieties.

Authors:  Tao Li; Jauhar Ali; Manuel Marcaida; Olivyn Angeles; Neil Johann Franje; Jastin Edrian Revilleza; Emmali Manalo; Edilberto Redoña; Jianlong Xu; Zhikang Li
Journal:  PLoS One       Date:  2016-10-10       Impact factor: 3.240

3.  Mapping abiotic stresses for rice in Africa: Drought, cold, iron toxicity, salinity and sodicity.

Authors:  P A J van Oort
Journal:  Field Crops Res       Date:  2018-04-15       Impact factor: 5.224

4.  Projected impact of future climate on water-stress patterns across the Australian wheatbelt.

Authors:  James Watson; Bangyou Zheng; Scott Chapman; Karine Chenu
Journal:  J Exp Bot       Date:  2017-12-16       Impact factor: 6.992

5.  Environment Characterization in Sorghum (Sorghum bicolor L.) by Modeling Water-Deficit and Heat Patterns in the Great Plains Region, United States.

Authors:  Ana J P Carcedo; Laura Mayor; Paula Demarco; Geoffrey P Morris; Jane Lingenfelser; Carlos D Messina; Ignacio A Ciampitti
Journal:  Front Plant Sci       Date:  2022-03-03       Impact factor: 5.753

6.  Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches.

Authors:  Sylvain Delerce; Hugo Dorado; Alexandre Grillon; Maria Camila Rebolledo; Steven D Prager; Victor Hugo Patiño; Gabriel Garcés Varón; Daniel Jiménez
Journal:  PLoS One       Date:  2016-08-25       Impact factor: 3.240

  6 in total

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