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Fed Govt distributes food items to 80,405 households in Sokoto

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Our Reporter

THE Federal Government has begun the distribution of 3,659.7 metric tonnes of grains to 80,405 households in Sokoto State, as part of measures to cushion the effects of COVID-19 lockdown.

Director General, National Emergency Management Agency (NEMA) AVM Muhammadu Muhammed stated this at the presentation of the food items to the state government yesterday in Sokoto.

Muhammed, represented by Dr. Onimode Bandele, Director Relief of the Agency, said the Federal Government had provided assorted grains for distribution to vulnerable households in the state, to mitigate the effects of COVID-19 lockdown aimed at stemming further spread of the pandemic.

He said: “We are presenting assorted grains from the National Food Reserve, approved by the President Muhammadu Buhari.

“The items are to serve as palliative for distribution to vulnerable persons affected by the recent restriction of movement to curtail the spread of COVID-19 pandemic in the country.”

Muhammed listed the food  items to include 1, 701.6 tonnes of maize; 173.10 tonnes of millet and  1,785 tonnes of sorghum.

He noted that the food items were supplied in batches of 57; 59½ and six trailer loads respectively.

Read Also: Sokoto targets 58,000 households for SIP

The NEMA D-G drew the attention of the state government to the 2020 Seasonal Rainfall Prediction and the Annual Flood Outlook.

“These documents have forecasted flood situation for different parts of the country.

“The outlook informed that 102 Local Government Areas in 28 states fall within the highly probable risk areas, while 275 LGAs in the 36 states of the federation, including the Federal Capital Territory fall within the moderately probable flood risk areas.

“However, the remaining 397 LGAs fall within the low probable flood risks areas, unfortunately, the predicted flood have started to occur in many parts of the country,” he said.

Muhammad said it was imperative for the government to activate proactive plans and measures against the prediction as the season moves towards the peak.

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