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Simulation models and statistical methods to assist in targeting and breeding for drought tolerance

By: Chapman, S.C | Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT), Mexico DF (Mexico).
Contributor(s): Barreto, H.J [coaut.] | Cooper, M [coaut.] | Edmeades, G.O.|Banziger, M.|Mickelson, H.R.|Pena-Valdivia, C.B [eds.] | Hammer, G.L [coaut.].
Material type: materialTypeLabelBookAnalytics: Show analyticsPublisher: Mexico, DF (Mexico) CIMMYT : 1997ISBN: 968-6923-93-4.Subject(s): Breeding methods | Drought resistance | Genotype environment interaction AGROVOC | Selection | Simulation models | CIMMYT | Zea mays AGROVOC | Plant breeding AGROVOCDDC classification: 633.153 Summary: Simulation models can be used in combination with spatial (geographic information systems) and historical data to determine how different sites and seasons (the target population of environments - TPE) provide different challenges to plant cultivars. Examples for maize in Central America and southern Africa and for sorghum in northern Australia are used to demonstrate aspects of the nature of these three TPEs. Models can be used to simulate the value of different traits over a range of environments from the TPE. For such 'specific-adaptation' cultivars, models might also be used to determine where the cultivars would be best deployed. Further, if models are used to determine the 'types' of abiotic challenges that exist in the TPE, we can ensure that the combination of test environments sampled in the multi environment trials matches the frequency of challenges in the TPE. It is argued that, in smaller, more-targeted multi environment trials, it should be feasible to identify the same superior genotypes that would have been selected in more extensive and costly random testing in the production environment. The paper also illustrates how pattern analysis of multi-environment trials can elucidate different aspects of the interaction of genotypes with dryland environments. For maize, this analysis demonstrates the necessity of testing in both dry and irrigated environments to facilitate drought tolerance gains in a diverse TPE. In the case of sorghum, the same statistical techniques demonstrate differences among locations within a geographically large TPE. These results correlate with independent measures of the environment (simulation model output). The linkage between real data and simulation output allows breeders to weight selection decisions by location and season, depending on how representative the sampled environments are of the real TPE.Collection: CIMMYT Publications Collection
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Conference proceedings CIMMYT Knowledge Center: John Woolston Library

Lic. Jose Juan Caballero Flores

 

CIMMYT Publications Collection 633.153 EDM (Browse shelf) 1 Available 3P624179
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Simulation models can be used in combination with spatial (geographic information systems) and historical data to determine how different sites and seasons (the target population of environments - TPE) provide different challenges to plant cultivars. Examples for maize in Central America and southern Africa and for sorghum in northern Australia are used to demonstrate aspects of the nature of these three TPEs. Models can be used to simulate the value of different traits over a range of environments from the TPE. For such 'specific-adaptation' cultivars, models might also be used to determine where the cultivars would be best deployed. Further, if models are used to determine the 'types' of abiotic challenges that exist in the TPE, we can ensure that the combination of test environments sampled in the multi environment trials matches the frequency of challenges in the TPE. It is argued that, in smaller, more-targeted multi environment trials, it should be feasible to identify the same superior genotypes that would have been selected in more extensive and costly random testing in the production environment. The paper also illustrates how pattern analysis of multi-environment trials can elucidate different aspects of the interaction of genotypes with dryland environments. For maize, this analysis demonstrates the necessity of testing in both dry and irrigated environments to facilitate drought tolerance gains in a diverse TPE. In the case of sorghum, the same statistical techniques demonstrate differences among locations within a geographically large TPE. These results correlate with independent measures of the environment (simulation model output). The linkage between real data and simulation output allows breeders to weight selection decisions by location and season, depending on how representative the sampled environments are of the real TPE.

English

9802|AGRIS 9702

Jose Juan Caballero

CIMMYT Publications Collection

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Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) © Copyright 2015. Carretera México-Veracruz. Km. 45, El Batán, Texcoco, México, C.P. 56237.
Si tiene cualquier pregunta, contáctenos a CIMMYT-Knowledge-Center@cgiar.org