Climate Change Dynamics and Imperatives for Food Security in Nigeria

Decadal variability in African rainfall is projected from General Circulation Models (GCMs) to continue under elevated greenhouse gas scenarios. Effects on rain intensity, spatio-temporal variability of growing seasons, flooding, drought, and land-use change impose feedbacks at regional-local scales. Yet, empirical knowledge of associated impacts on crop yield is limited; thus, we examined the imperatives for food security in Nigeria. Bivariate correlation and multiple regression suggests impending drought in the northern region where livestock farming is predominant. Relative contributions of climate independent variables in determining crop yield by backward selection procedures with stepwise approach indexed the impacts of annual climate variability by a parameter computed as annual yield minus mean annual yield divided by the standard deviation. Results show Z-distribution approximately 5 to + 5, when 3 indicate impacts significant at 95% confidence levels. In conclusion, we established the interwoven relationship between climatic change and food security

pSESYNTH project: Community mobilization for a multi-disciplinary paleo database of the Global South

How to enhance paleoscientific research, collaboration and application in the Global South? The INQUA-funded multi-year pSESYNTH project envisions the first multi-disciplinary Holocene paleo database through a collaborative vision for past human-environmental systems in the Global South, and their future sustainability

Analysing the rate of land use and land-cover changes in Gambari Forest Reserve, Nigeria

This research work discusses the phenomenon of land use and land cover, which has undergone constant changes over the past few decades due to major variations in the environment caused by anthropogenic and natural factors. This study is supported by a long time series of land use and land-cover satellite data of the Gambari Forest Reserve boundary map for 1984, 2004, and 2020. A maximum likelihood classification scheme was employed to classify the satellite imageries using ArcGIS 10.1 software to derive the spatial patterns and temporal variation of the land-use and land-cover change (LULC) classes: dense forest (DF), light forest (LF) and non-forest (NF). Data on deforestation of the study area showed that the area of DF increased from 31.7 km2 (23.4%) to 72.8 km2 (54.4%) within a 36-year time series, with a percentage change of 31.0%. The area of LF decreased from 79.2 km2 (65.4%) to 51.2 km2 (41.5%), with a percentage change of −23.9%, and that of NF decreased from 14.7 km2 (11.2%) to 2.6 km2 (4.1%), with a percentage change of −7.1%. This indicates that it would be reasonable to anticipate an increase in deforestation in the future