Efficient Breeding of Crop Plants

Kulwal, P.L., Mir, R.R. and Varshney, R.K.ORCID: 0000-0002-4562-9131 (2022) Efficient Breeding of Crop Plants. In: Yadava, D.K., Dikshit, H.K., Mishra, G.P. and Tripathi, S., (eds.) Fundamentals of Field Crop Breeding. Springer Singapore, pp. 745-777.

Abstract

In crop breeding programs, the rate of genetic gain which is achieved using the traditional breeding is insufficient to meet the increased demand of food for the rapidly expanding global population. The main constraint with the conventional breeding is the time which is required in developing crosses, followed by selection and testing of the experimental cultivars. Although, using this technique, lot of progress has been made in increasing the productivity, the time has come to think beyond this and integrate the recent advances in the area of genomics, phenomics and computational biology into the conventional breeding program for increasing its efficiency. While doing this emphasis on proper characterization and use of plant genetic resources, defining the breeding objectives and use of recent advances in holistic way are also essential. Therefore, in this chapter, we first highlight the importance of plant breeding followed by significance of the plant genetic resources in the breeding program, need of ideotype breeding and the breeding objectives for important traits including resistance against various biotic and abiotic stresses. We then discuss the limitations of conventional breeding and advantages of genomics-assisted breeding. While doing this, we also discuss various molecular breeding tools and genomic resources as well as different approaches for efficient breeding including marker-assisted selection, marker-assisted recurrent selection and genomic selection. This is followed by importance of other non-conventional approaches including the recent one on gene editing, speed breeding and role of integrated data management and bioinformatics in the breeding programs. We also discuss the significance of phenomics and phenotyping platforms in the crop breeding as well as role of computational techniques like artificial intelligence and machine learning in analysing the huge data which is being generated in the breeding programs. Finally, we conclude with a brief note on the emerging challenges in breeding which need to be addressed and the thrust areas of research for the future.

Item Type:	Book Chapter
Murdoch Affiliation(s):	Centre for Crop and Food Innovation Western Australian State Agricultural Biotechnology Centre
Publisher:	Springer Singapore
Copyright:	© 2022 The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
URI:	http://researchrepository.murdoch.edu.au/id/eprint/65162

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