CIMMYT’s approach to breeding for wide adaptation

Braun, Hans‐Joachim; Rajaram, S.; Ginkel, M. van

doi:10.1007/978-94-015-8806-5_25

Cited by 56 publications

(39 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genotypes tested in IBYT trials spanned the period 1979 through 2005 and were bred in Mexico (Table 1) by Enrique Rodriguez (1976)(1977)(1978)(1979)(1980)(1981)(1982)(1983)(1984), Hugo Vivar (1984), and Flavio Capettini (2000 using a shuttle breeding approach as described by Braun et al (1996). Segregating materials were shuttled between the Centro de Investigaciones Agricolas del Noroeste (CIANO) (27°20′ N lat and elevation 38 m above sea level), an arid, irrigated site in northwestern Mexico near Ciudad Obregon Sonora, and CIMMYT's high rainfall research station at Toluca in the central Mexican Highlands (19°16′ N lat and elevation 2640 m above sea level).…”

Section: Locations and Genotypesmentioning

confidence: 99%

“…shuttling segregating generations between two contrasting environments in Mexico, one located near Toluca in Central Mexico and the other close to Ciudad Obregon in the northwestern Mexico (Braun et al, 1996). The germplasm produced is distributed to collaborators worldwide through CIMMYT's International Wheat Improvement Network.…”

mentioning

confidence: 99%

“…When there is COI, it is observed as a change of genotype rank across environments (Russell et al, 2003). The CIMMYT wheat breeding program has identifi ed 12 diff erent MEs that represent wheat production globally (Braun et al, 1996), this classifi cation helps target germplasm distribution and improves selection effi ciency (Calhoun et al, 1994;Rajaram et al, 1994), however, similar MEs still need to be defi ned for barley. To reduce the COI Braun et al (1996) proposed the classifi cation of mega-environments (MEs) and defi ned them as the growing region of a crop species (not necessarily continuous) where the environmental conditions are relatively homogenous.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Mega‐Environment Identification for Barley Based on Twenty‐Seven Years of Global Grain Yield Data

et al. 2009

View full text Add to dashboard Cite

Knowledge of target environments in breeding programs is important to better direct the development of germplasm. The objectives of this study were to identify associations among barley (Hordeum vulgare L.) growing environments to identify mega‐environments to select the best locations to breed barley. Twenty‐seven years of grain yield data from the International Barley Yield Trial (IBYT) conducted by the ICARDA‐CIMMYT Barley Breeding Program, consisting of 750 grain yield trials of two replications representing 235 locations in 75 countries, were analyzed using pattern analysis to group sites across years that represent similar selection environments. The shifted multiplicative model (SHMM) was employed to group sites within each year. Environments clustered into three main groups and squared Euclidean distances were used to identify a representative location within each cluster. Group 1 locations were characterized as being cool with intermediate precipitation; Group 2 locations were warmer and drier; and Group 3 sites were generally cool and had the highest average precipitation. The respective representative key locations for each of the three groups were Leida, Spain; Boulifa, Tunisia; and Setif, Algeria. All three key locations are located in the Northern Hemisphere between 36° and 41° latitude. The results of this study show that the global adaptation of barley is possible and can be improved by breeding and selection for adaptation within the three main mega‐environments identified.

show abstract

Section: Locations and Genotypesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Mega‐Environment Identification for Barley Based on Twenty‐Seven Years of Global Grain Yield Data

et al. 2009

View full text Add to dashboard Cite

show abstract

“…Obregon, Mexico, alternatively in a shuttle breeding approach as described by Braun et al (1996). Obregon, Mexico, alternatively in a shuttle breeding approach as described by Braun et al (1996).…”

Section: Locations and Genotypesmentioning

confidence: 99%

Adaptation and Performance of CIMMYT Spring Wheat Genotypes Targeted to High Rainfall Areas of the World

et al. 2010

View full text Add to dashboard Cite

The high rainfall mega‐environment [mega environment 2 (ME2)], characterized by more than 500 mm rainfall during wheat season, is one of the 12 mega‐environments on which the global wheat breeding program of the International Maize and Wheat Improvement Center (CIMMYT) has been assembling and distributing high rainfall wheat yield trials (HRWYT) since 1992. In this study, our objectives were to evaluate the adaptation and performance of entries included in the 1st through the 12th HRWYT (1992–2004), identify best lines as parents for crossing and recommend for direct release by the National Agricultural Research Systems (NARS), and test the association among sites and determine yield progress in HRWYTs. Combined ANOVA across locations for grain yield showed highly significant difference among genotypes and genotype × environment interactions (p < 0.001) for each of the HRWYTs. At regional level, wide range of yield variability was observed in Africa (0.69–9.614 t ha−1) followed by Asia (0.389–6.93 t ha−1) with the highest mean yields in Europe (4.392 t ha−1). Two widely grown mega‐cultivars have been developed from Attila cross: entry number 4 (CM85836‐4Y‐0M‐0Y‐8M‐0Y) from 1stHRWYT, which is released as PBW343 in India, and entry number 48 from 4thHRWYT (CM85836‐50Y‐0M‐0Y‐3M‐0Y), which is released in Ethiopia, Tunisia, Iran, Pakistan, and Sudan with local names of Kubsa, Utique96, Chamran, MH97, and Imam, respectively. There is a significant positive association between plant height and grain yield in rain‐fed environments whereas the association between heading dates and yield is location specific indicating the importance of selecting broad range of materials for heading date at the main station in Mexico. Correlation analysis shows that the sites giving the best prediction of global performance are Marcos Juarez (Argentina), Pergamino (Argentina), Izmir (Turkey), Bethlehem (South Africa), Sakha (Egypt), and Tassaout (Morocco). In each year, it was possible to find a line out yielding the local check in at least 52% of the sites. However, at the trial level, lines were beating the local checks more frequently in the first trials than in the last trials. Regular assessment of the current and future HRWYTs in terms of adaptation, resistance, diversity, and quality will be of paramount importance in improving the breeding process.

show abstract

“…Most early breeding efforts sought to develop cultivars with wide adaptation (e.g., Braun et al, 1996). Later efforts aimed to target niche environments with unique abiotic or biotic stresses (e.g., Wilhelmi et al, 2002; Annicchiarico et al, 2005).…”

Section: Siting and Regional Targeting Of Genotypesmentioning

confidence: 99%

Spatial analysis to support geographic targeting of genotypes to environments

Hyman¹,

Hodson²,

Jones³

2013

Front. Physiol.

View full text Add to dashboard Cite

Crop improvement efforts have benefited greatly from advances in available data, computing technology, and methods for targeting genotypes to environments. These advances support the analysis of genotype by environment interactions (GEI) to understand how well a genotype adapts to environmental conditions. This paper reviews the use of spatial analysis to support crop improvement research aimed at matching genotypes to their most appropriate environmental niches. Better data sets are now available on soils, weather and climate, elevation, vegetation, crop distribution, and local conditions where genotypes are tested in experimental trial sites. The improved data are now combined with spatial analysis methods to compare environmental conditions across sites, create agro-ecological region maps, and assess environment change. Climate, elevation, and vegetation data sets are now widely available, supporting analyses that were much more difficult even 5 or 10 years ago. While detailed soil data for many parts of the world remains difficult to acquire for crop improvement studies, new advances in digital soil mapping are likely to improve our capacity. Site analysis and matching and regional targeting methods have advanced in parallel to data and technology improvements. All these developments have increased our capacity to link genotype to phenotype and point to a vast potential to improve crop adaptation efforts.

show abstract

CIMMYT’s approach to breeding for wide adaptation

Cited by 56 publications

References 11 publications

Mega‐Environment Identification for Barley Based on Twenty‐Seven Years of Global Grain Yield Data

Mega‐Environment Identification for Barley Based on Twenty‐Seven Years of Global Grain Yield Data

Adaptation and Performance of CIMMYT Spring Wheat Genotypes Targeted to High Rainfall Areas of the World

Spatial analysis to support geographic targeting of genotypes to environments

Contact Info

Product

Resources

About