To achieve this, the WAVE project established demonstration plots in the three senatorial zones of the state – Umuahia, Ohafia and Obingwa, using five of their different varieties – TMS 980505, NR 97184 (Resistant), UMUCASS 44, NR 8082 (Susceptible) and TME 419 as the check, as each location provided one of their best local varieties.
The Principal Investigator of the project, Dr. Chukwuemeka Nkere, disclosed that the demonstration plots were established to educate farmers on how to detect, manage and sustain the improved varieties, stressing the need for farmers to adopt the right measures in controlling the spread of these diseases.
He added that the WAVE project is taking measures to stop the spread of the diseases beyond Nigeria, in order to ensure food security.
The Principal Research Scientist under the WAVE project, Mrs Linda Nwachukwu, said the harvested demonstration plot, was established in April 2022, with the aim of evaluating both resistant and susceptible varieties with respect to yield.
The WAVE project has trained farmers on the best agronomic practices for cassava production, disease recognition and management, especially with the use of an Artificial Intelligence APP “Nuru,” which has helped farmers on disease identification and can as well help them in selecting disease free planting materials for next farming season.
“After harvesting, a total evaluation on the six different cassava varieties was carried out to ascertain the effect of Cassava mosaic diseases on the yield. At the conclusion of the exercise, NR 8082 and UMUCASS 44 were found to be susceptible varieties, which had impact on the yield, while NR 87184 and TMS 980505 performed better.”
Testifying to the impact of the WAVE project, the Abia State Agricultural Development Programme (ADP) Farm Manager, Mr. Nwadibia Chukwuemeka, said the project has helped farmers to be aware of the coming of the new Cassava brown streak disease into the country and how it can be prevented.
He also appreciated the WAVE project for releasing Android phones to some farmers, which were embedded with applications using artificial intelligence to detect plant diseases on the farm.
