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Achieving Genetic Gains in Practice

تحقيق المكاسب الوراثية في الممارسة العملية
Authors: Ravi P. Singh; Philomin Juliana; Julio Huerta‐Espino; Velu Govindan; Leonardo Crespo‐Herrera; Suchismita Mondal; Sridhar Bhavani; +8 Authors

Achieving Genetic Gains in Practice

Abstract

AbstractAccelerating the rate of genetic gain for grain yield together with key traits is pivotal for delivering improved wheat varieties. The key strategies of CIMMYT’s spring bread wheat improvement program to continuously increase genetic gains and deliver elite wheat lines to national partners in the target countries include: breeding for product profiles that prioritize selection traits; robust choice of diverse parents by leveraging all phenotypic and genotypic data; effective crossing schemes with an optimal proportion of different types of crosses; early-generation advancement using the selected-bulk breeding scheme that reduces operational costs; the two generations/year field based “shuttle-breeding” that reduces the breeding cycle time while selecting breeding populations in contrasting environments with diverse biotic and abiotic stresses; making advancement decisions for elite lines using data from intensive multi-trait, multi-year and multi-environment phenotyping; integrating new methods like genomic selection; utilizing yield and phenotypic data from international yield trials and screening nurseries generated by worldwide partners for identifying and utilizing superior lines; and maintaining effective partnerships with the National Agricultural Research Systems who serve as key leaders in developing, releasing, and disseminating varieties to farmers. In addition to these strategies, new breeding schemes to reduce the cycle time and recycle parents in 2–3 years are being piloted and optimized to further accelerate genetic gain.

Keywords

Crop Improvement, Artificial intelligence, Trait, Plant Science, Yield (engineering), Breeding, Gene, Agricultural and Biological Sciences, Selection (genetic algorithm), Cultivar Evaluation and Mega-Environment Investigation, Genetic Diversity and Breeding of Wheat, Genetics, Genetic variation, Biology, Factors Affecting Maize Yield and Lodging Resistance, Adaptation (eye), Life Sciences, Breeding program, Computer science, Agronomy, Materials science, Programming language, Plant Breeding, FOS: Biological sciences, Genetic gain, Metallurgy, Cultivar, Agronomy and Crop Science, Grain Quality, Biotechnology, Neuroscience

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    5
    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Top 10%
hybrid