Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals.

Area of Science:

• Agricultural Science
• Genetics
• Plant Breeding

Background:

• Rising global population and climate change necessitate increased agricultural production and climate-resilient crops.
• Anthropogenic activities cause environmental degradation, impacting crop yield and biodiversity.
• Traditional plant breeding methods are being advanced by molecular techniques for trait selection.

Purpose of the Study:

• To review integrated genomic selection (IGS) strategies for developing climate-smart crops.
• To highlight the potential of IGS in enhancing crop yield and stress tolerance.
• To discuss the application of IGS in cereal crops for sustainable agriculture.

Main Methods:

• Genomic selection (GS) utilizing molecular genetic markers for trait evaluation.
• Integration of advanced technologies like speed breeding and machine learning with GS.
• Analysis of crop morpho-physiology, statistical tools, and bioinformatics.

Main Results:

• Genomic selection (GS) offers advantages over traditional methods by shortening breeding periods and increasing genetic gain for complex traits.
• Integrated genomic selection (IGS) further enhances breeding efficiency by incorporating speed breeding, machine learning, and environmental data.
• IGS shows significant potential for developing crops with improved abiotic stress tolerance.

Conclusions:

• IGS is a powerful tool for developing climate-resilient crops to ensure food security.
• The integration of advanced technologies in IGS accelerates the breeding cycle and improves selection accuracy.
• IGS strategies are crucial for adapting agriculture to climate change and meeting global food demands.