We have worked with NiMet for at least three years. NiMet has been a strong supporter, providing weather forecasts and training for farmers and extension workers. We appreciate NiMet’s commitment and hope to deepen our collaboration through joint research and training initiatives
The Global Alliance for Improved Nutrition (GAIN) and the Nigerian Meteorological Agency (NiMet) are strengthening an existing partnership to tackle malnutrition. This collaboration focuses on soil health and nutrition to support sustainable agriculture. The decision was reached during a strategic meeting at NiMet headquarters in Abuja on Friday, 21st March 2025.
Speaking at the meeting, Director General of NiMet, Professor Charles Anosike, decried malnutrition-induced human suffering. He emphasized that nutrition begins with soil health and weather significantly impacts soil and plant nutrition.
“It’s important to formalize the relationship between NiMet and GAIN to enhance understanding of weather, soil health, and nutrition. This collaboration will help foster sustainable agricultural practices in Nigeria,” Professor Anosike said.
He added that NiMet is downscaling the 2025 seasonal climate predictions in local languages through some partners. “Moving forward, once NiMet unveils the seasonal climate prediction, both organizations can work together to disseminate this information to farmers,” he noted.
Country Director of GAIN, Dr. Michael Ojo, stated that the organization’s mandate is to tackle malnutrition and alleviate human suffering.
“We have worked with NiMet for at least three years. NiMet has been a strong supporter, providing weather forecasts and training for farmers and extension workers. We appreciate NiMet’s commitment and hope to deepen our collaboration through joint research and training initiatives,” Dr. Ojo said.
Both organizations will collaborate on climate change, environmental impact on agriculture, and food systems. Additionally, they will leverage their expertise in producing crop calendars, optimizing predictions for specific crops, and expanding climate predictions in more local languages.
