Suffering caused by malnutrition has remained a challenge in many communities. To tackle this, The Nigerian Meteorological Agency (NiMet), and the Global Alliance for Improved Nutrition (GAIN) are strengthening their partnership.
This was the outcome of a strategic meeting by both organizations at a meeting held at NiMet headquarters in Abuja on Friday, 21st March 2025.
Speaking at the meeting, the Director General and Chief Executive Officer of the NiMet, Prof. Charles Anosike remarked that nutrition begins with soil health and weather has a significant impact on soil health and plant nutrition.
“It’s very important that we formalize the relationship between NiMet and GAIN so that we can increase our understanding of the relationship between weather, soil health, and nutrition. With that we have a chance of fostering sustainable agricultural practices in Nigeria and helping our people”, Prof. Anosike said.
He said that NiMet was already downscaling the 2025 seasonal climate predictions in local languages with some of its partners. “Going forward, once NiMet unveils the seasonal climate prediction, both organizations can partner to disseminate to farmers”, Prof.Anosike concluded.
Responding, the Country Director of GAIN, Dr. Michael Ojo, informed that the mandate of the organization is to tackle human suffering caused by malnutrition.
“We have been working together with NiMet for at least 3 years. NiMet has been a friend and family giving GAIN support not only through weather forecasts but also by providing training to the farmers and extension workers we work with.
We appreciate the support and commitment of the able team at NiMet. We are hoping to progress the relationship with NiMet further and include joint research and training to the list of activities”, Ojo said.
As part of the value chain activities, both organizations would partner on climate change and the environment and how that impacts on agriculture and food systems – related activities.
They would also leverage on each other’s competencies in related areas including the production of crop calendars, optimization of predictions for certain crops, and expanding climate prediction in languages of communities not presently covered.
