Literature DB >> 35294575

Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale.

Pierre Casadebaig1, Arnaud Gauffreteau2, Amélia Landré3, Nicolas B Langlade4, Emmanuelle Mestries5, Julien Sarron3,6, Ronan Trépos7, Patrick Vincourt4, Philippe Debaeke3.   

Abstract

KEY MESSAGE: Crop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time. Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Year:  2022        PMID: 35294575     DOI: 10.1007/s00122-022-04072-5

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  14 in total

Review 1.  On systems thinking, systems biology, and the in silico plant.

Authors:  Graeme L Hammer; Thomas R Sinclair; Scott C Chapman; Erik van Oosterom
Journal:  Plant Physiol       Date:  2004-03       Impact factor: 8.340

2.  General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding.

Authors:  A Hector; Y Hautier; P Saner; L Wacker; R Bagchi; J Joshi; M Scherer-Lorenzen; E M Spehn; E Bazeley-White; M Weilenmann; M C Caldeira; P G Dimitrakopoulos; J A Finn; K Huss-Danell; A Jumpponen; C P H Mulder; C Palmborg; J S Pereira; A S D Siamantziouras; A C Terry; A Y Troumbis; B Schmid; M Loreau
Journal:  Ecology       Date:  2010-08       Impact factor: 5.499

Review 3.  Plant phenotypic plasticity in a changing climate.

Authors:  A B Nicotra; O K Atkin; S P Bonser; A M Davidson; E J Finnegan; U Mathesius; P Poot; M D Purugganan; C L Richards; F Valladares; M van Kleunen
Journal:  Trends Plant Sci       Date:  2010-10-21       Impact factor: 18.313

4.  Redefining plant systems biology: from cell to ecosystem.

Authors:  Joost J B Keurentjes; Gerco C Angenent; Marcel Dicke; Vítor A P Martins Dos Santos; Jaap Molenaar; Wim H van der Putten; Peter C de Ruiter; Paul C Struik; Bart P H J Thomma
Journal:  Trends Plant Sci       Date:  2011-01-05       Impact factor: 18.313

Review 5.  Models for navigating biological complexity in breeding improved crop plants.

Authors:  Graeme Hammer; Mark Cooper; François Tardieu; Stephen Welch; Bruce Walsh; Fred van Eeuwijk; Scott Chapman; Dean Podlich
Journal:  Trends Plant Sci       Date:  2006-11-07       Impact factor: 18.313

Review 6.  Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance.

Authors:  Carlos D Messina; Dean Podlich; Zhanshan Dong; Mitch Samples; Mark Cooper
Journal:  J Exp Bot       Date:  2010-11-01       Impact factor: 6.992

7.  Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt--spatial and temporal trends.

Authors:  Karine Chenu; Reza Deihimfard; Scott C Chapman
Journal:  New Phytol       Date:  2013-02-21       Impact factor: 10.151

Review 8.  Global consequences of land use.

Authors:  Jonathan A Foley; Ruth Defries; Gregory P Asner; Carol Barford; Gordon Bonan; Stephen R Carpenter; F Stuart Chapin; Michael T Coe; Gretchen C Daily; Holly K Gibbs; Joseph H Helkowski; Tracey Holloway; Erica A Howard; Christopher J Kucharik; Chad Monfreda; Jonathan A Patz; I Colin Prentice; Navin Ramankutty; Peter K Snyder
Journal:  Science       Date:  2005-07-22       Impact factor: 47.728

9.  Measuring the Berry phase of graphene from wavefront dislocations in Friedel oscillations.

Authors:  C Dutreix; H González-Herrero; I Brihuega; M I Katsnelson; C Chapelier; V T Renard
Journal:  Nature       Date:  2019-09-30       Impact factor: 49.962

Review 10.  Contribution of Crop Models to Adaptation in Wheat.

Authors:  Karine Chenu; John Roy Porter; Pierre Martre; Bruno Basso; Scott Cameron Chapman; Frank Ewert; Marco Bindi; Senthold Asseng
Journal:  Trends Plant Sci       Date:  2017-04-04       Impact factor: 18.313

View more
  1 in total

1.  Identification of environment types and adaptation zones with self-organizing maps; applications to sunflower multi-environment data in Europe.

Authors:  Daniela Bustos-Korts; Martin P Boer; Jamie Layton; Anke Gehringer; Tom Tang; Ron Wehrens; Charlie Messina; Abelardo J de la Vega; Fred A van Eeuwijk
Journal:  Theor Appl Genet       Date:  2022-05-07       Impact factor: 5.574

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.