Using a Participatory Approach and Legume Integration to Increase the Productivity of Early Maturing Maize in the Nigerian Sudan SavannasA YKamaraauthorH AAjeigbeauthorNNdaghuauthorLucyKamsangauthorTAdemulegunauthorRSolomonauthorDrought, infestation of cereal crops by the parasitic weed Striga hermonthica, and poor soil fertility are the major constraints to maize production by smallholder farmers in the Sudan savannas of northern Nigeria. Four innovation platforms (IPs) were therefore established in 2008 in the Sudan savanna (SS) agroecological zone of northern Nigeria to create a stakeholder forum to address these identified food production challenges in the target areas. )e IPs comprised researchers from Bayero University, Kano; Institute for Agricultural Research, Zaria; International Institute of Tropical Agriculture; state and local government extension programs in Kano and Katsina states; input and output dealers; community-based organisations; and media organisations in the two states. )e current study reports on the effects of legume integration on maize performance in farmer fields and the adoption of Striga management technologies introduced in the IPs over a four-year period. )e deployment of drought- Striga-tolerant and early-maturing maize varieties along with legume rotation reduced Striga infestation by 46–100% when cowpea was rotated with maize, 80–97% when groundnut was rotated with maize, and 59–94% when soybean was rotated with maize. Grain yield of maize increased by 63–88% when cowpea was rotated with maize, 69–128% when groundnut was rotated with maize, and 9–133% when soybean was rotated with maize. Participatory and detailed questionnaire-based adoption surveys showed high adoption of improved maize varieties, five years after program interventions. )e maize variety 99EVDT-W-STR C0 was the most popular among all the IPs because it is early maturing, Striga-resistant, and drought-tolerant. )e high maize yields and high adoption rates suggest that the IP approach was effective in disseminating maize technologies.Participatory ModelingNigeriaMaize2019-12Article