Legume Genomics and Genetics 2024, Vol.15, No.5, 221-231 http://cropscipublisher.com/index.php/lgg 226 different water regimes (Lauterberg et al., 2023). These advancements in phenomics are pivotal for precision agriculture, allowing for the identification of superior genotypes with enhanced stress tolerance and productivity. The integration of high-throughput genotyping and phenotyping technologies into breeding programs has significantly accelerated genetic gains in chickpea. Modern breeding approaches, such as marker-assisted selection and genomic selection, rely heavily on these technologies to identify and select desirable traits (Varshney et al., 2012; Hou et al., 2024). For example, the development of high-density SNP arrays and genotyping platforms has facilitated the creation of detailed genetic maps and the identification of quantitative trait loci (QTLs) associated with important agronomic traits (Roorkiwal et al., 2017). By combining high-resolution phenotypic and genotypic data, breeders can enhance selection efficiency and develop superior chickpea varieties more rapidly (Varshney et al., 2018; Roorkiwal et al., 2020). 5.2 Systems biology and multi-omics approaches Systems biology approaches that integrate transcriptomics, proteomics, and metabolomics are transforming chickpea breeding by providing a comprehensive understanding of the molecular mechanisms underlying important traits. These multi-omics strategies enable the identification of key genes, proteins, and metabolites involved in stress responses and other agronomic traits (Roorkiwal et al., 2020; Singh et al., 2022). For instance, advances in next-generation sequencing and high-throughput genotyping have led to the development of extensive genomic resources, including transcriptome assemblies and SNP markers, which are essential for multi-omics studies (Varshney et al., 2012). The integration of these omics data sets facilitates a holistic view of the plant's biology, aiding in the discovery of novel targets for genetic improvement. The application of multi-omics approaches in chickpea breeding has led to significant advancements in trait discovery and improvement. By leveraging transcriptomics, proteomics, and metabolomics data, researchers can identify candidate genes and pathways associated with traits such as drought tolerance, disease resistance, and nutritional quality (Roorkiwal et al., 2020; Singh et al., 2022). For example, genomics-assisted breeding has successfully introgressed QTLs for drought tolerance into elite chickpea varieties, resulting in lines with higher yield under stress conditions (Thudi et al., 2014). These multi-omics approaches are crucial for developing climate-resilient and high-yielding chickpea cultivars. 5.3 Machine learning and artificial intelligence in breeding Machine Learning (ML) and Artificial Intelligence (AI) are increasingly being used in chickpea breeding to develop predictive models and decision support tools. These technologies can analyze large and complex data sets, including genomic, phenotypic, and environmental data, to predict the performance of breeding lines and guide selection decisions (Roorkiwal et al., 2020; Varshney et al., 2018). For instance, ML algorithms can be used to predict the genetic potential of lines for specific traits, thereby enhancing the efficiency of breeding programs. The integration of AI in breeding pipelines helps in optimizing resource allocation and accelerating the development of improved chickpea varieties. AI plays a crucial role in accelerating genetic gains in chickpea breeding by enabling more precise and efficient selection processes. By utilizing AI-driven tools, breeders can identify the best-performing genotypes with greater accuracy and speed, thus reducing the breeding cycle time (Varshney et al., 2018). AI technologies, such as deep learning and neural networks, can also uncover complex patterns and interactions within the data that are not easily detectable through traditional methods (Roorkiwal et al., 2020). This capability is particularly valuable for addressing the challenges posed by climate change and ensuring the development of robust and high-yielding chickpea varieties. 6 Challenges and Opportunities in Chickpea Improvement 6.1 Genetic diversity and germplasm utilization Genetic diversity is crucial for the success of breeding programs as it provides the raw material for selection and adaptation to changing environmental conditions. Diverse germplasm lines offer a wide range of alleles that can
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