Breeding maize (Zea mays) for striga resistance: past, current and prospects in sub-Saharan Africa

Yacoubou, A.M.; Wallis, Z.N.; Menkir, A.; Zinsou, V.A.; Onzo, A.; Garcia‐Oliveira, A.L.; Meseka, S.; Mengesha Abera, W.; Gedil, M.; Agre, P.

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Journal Article (1.451Mb)

Date

2021-04

Author

Yacoubou, A.M.

Wallis, Z.N.

Menkir, A.

Zinsou, V.A.

Onzo, A.

Garcia‐Oliveira, A.L.

Meseka, S.

Mengesha Abera, W.

Gedil, M.

Agre, P.

Type

Journal Article

Review Status

Peer Review

Target Audience

Scientists

Metadata

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Abstract/Description

Striga hermonthica, causes up to 100% yield loss in maize production in Sub‐Saharan Africa. Developing Striga‐resistant maize cultivars could be a major component of integrated Striga management strategies. This paper presents a comprehensive overview of maize breeding activities related to Striga resistance and its management. Scientific surveys have revealed that conventional breeding strategies have been used more than molecular breeding strategies in maize improvement for Striga resistance. Striga resistance genes are still under study in the International Institute for Tropical Agriculture (IITA) maize breeding programme. There is also a need to discover QTL and molecular markers associated with such genes to improve Striga resistance in maize. Marker Assistance Breeding is expected to increase maize breeding efficiency with complex traits such as resistance towards Striga because of the complex nature of the host‐parasite relationship and its intersection with other environmental factors. Conventional alongside molecular tools and technical controls are promising methods to effectively assess Striga in Sub‐Saharan Africa.

Acknowledgements

Laboratoire de Phytotechnie, d'Amélioration et de Protection des Plantes (LaPAPP), Département des Sciences et Techniques de Production Végétale, Faculté d'Agronomie de l'Université de Parakou and the Stress Tolerant Maize for Africa Project are highly acknowledged. The authors express their appreciation to all scientist involved in the improvement of this manuscript. We acknowledge financial support for the Bill and Melinda Gates Foundation.