Pulse crops are widely cultivated and consumed across the globe, playing a vital role as key sources of protein and other essential nutrients. Many traditional and modern culinary dishes rely heavily on staple pulse crops like chickpea, lentil, mung bean, pigeon pea and black gram, while some pulses have industrial uses. This compilation explores the latest advancements in breeding pulse crops to make them resilient to climate change, with an emphasis on improving resistance to biotic and abiotic stresses, along with yield traits. The varietal development process is becoming more efficient and precise thanks to new methods such as genome editing, genomic selection, haplotype-based breeding and speed breeding, which also help in reducing resource utilisation. Also, to further enhance production, there is a need to focus on adopting artificial intelligence (AI) and machine learning (ML)-driven breeding tools, thereby lowering developmental costs and saving time. The current challenge is to effectively integrate and apply these advanced techniques and methods into ongoing crop improvement initiatives.

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Introduction: Breeding Climate Resilient and Future-Ready Pulse Crops

  • Muraleedhar S. Aski,
  • Mallana Gowdra Mallikarjuna,
  • Hirenallur Chandappa Lohithaswa,
  • Manish K. Pandey,
  • Sanjeev Gupta

摘要

Pulse crops are widely cultivated and consumed across the globe, playing a vital role as key sources of protein and other essential nutrients. Many traditional and modern culinary dishes rely heavily on staple pulse crops like chickpea, lentil, mung bean, pigeon pea and black gram, while some pulses have industrial uses. This compilation explores the latest advancements in breeding pulse crops to make them resilient to climate change, with an emphasis on improving resistance to biotic and abiotic stresses, along with yield traits. The varietal development process is becoming more efficient and precise thanks to new methods such as genome editing, genomic selection, haplotype-based breeding and speed breeding, which also help in reducing resource utilisation. Also, to further enhance production, there is a need to focus on adopting artificial intelligence (AI) and machine learning (ML)-driven breeding tools, thereby lowering developmental costs and saving time. The current challenge is to effectively integrate and apply these advanced techniques and methods into ongoing crop improvement initiatives.