Enhancing Food Security in Anambra State, Nigeria Using Remote Sensing Data

Abstract

Timely availability of information on food production is vital for taking informed decisions on food security issues. Remote sensing technology and Geographic Information System (GIS) are very effective means for generating regular updates on crop production statistics, early yield prediction and providing inputs to achieve sustainable agriculture. In this study, satellite based crop prediction model was used to estimate crops statistics of major crops which include rice, cassava, yam, and maize in all local government area in Anambra State, South Eastern part of Nigeria. Landsat 7 and 8 images of 2002, 2014 and 2016 were used to generate the Normalized Difference Vegetation Index (NDVI) of the period of study. Regression model was used to predict yield of the crops under study. The NDVI, soil moisture (SM), surface temperature (ST) and rainfall estimate (RFE) were used in the prediction model. The results showed that The Normalized Difference Vegetation Index was quite efficient for monitoring crop production as it was the major factor for both yield prediction and determination of the extent of cultivated land. Also, the result showed that the total area of cultivated land in 2002 was 78.179 km2, while the total area in 2014 was 67.3578 km2. So it was ascertained that there was a decline in the agricultural area between the year 2002 and 2014 with about 10.8207 km2, this decline might be caused by increase in built-up area, migration from rural to urban etc. The results of the crop area extent reveals that Anambra East Local Government Area has the highest land for agricultural purposes of about 19.25km2 while Nnewi North has the lowest land area for agricultural purposes of about 0.673km2. Finally, from the yield prediction made it is observed that there might be a decline in the yield in the year 2016 for most of the crops apart from Cassava which is predicted to have a slight increase.

Keywords: Food security, Remote Sensing, GIS, Vegetation Index