In Northeastern Nigeria seasonal rainfall is critical for the availability of water for domestic use through surface and sub-surface recharge and agricultural production, which is mostly rain fed. Variability in rainfall over the last 60 years is the main cause for crop failure and water scarcity in the region, particularly, due to late onset of rainfall, short dry spells and multi-annual droughts. In this study, we analyze 27 years (1980-2006) of gridded daily rainfall data obtained from a merged dataset by the National Centre for Environmental Prediction and Climate Research Unit reanalysis data (NCEP-CRU) for spatial-temporal variability of monthly amounts and frequency in rainfall and rainfall trends. Temporal variability was assessed using the percentage coefficient of variation and temporal trends in rainfall were assessed using maps of linear regression slopes for the months of May through October. These six months cover the period of the onset and cessation of the wet season throughout the region. Monthly rainfall amount and frequency were then predicted over a 24-month period using the Auto Regressive Integrated Moving Average (ARIMA) Model. The predictions were evaluated using NCEP-CRU data for the same period. Kolmogorov Smirnov test results suggest that despite there are some months during the wet season (May-October) when there is no significant agreement (p < 0.05) between the monthly distribution of the values of the model and the corresponding 24-month NCEP-CRU data, the model did better than simply replicating the long term mean of the data used for the prediction. Overall, the model does well in areas and months

Deforestation, soil degradation and other forms of environmental degradation that could lead to desert encroachment have become a major concern in the world more especially in the sub Saharan Africa. Consequently, environmentalist and agriculturalists have led a campaign on afforestation and agroforestry to control this problem. This paper appraises tree planting and tree planting programs in the study area. Primary and secondary data sets ware collected on the socio economic activities of people as well as their perception on tree planting. The data was analyzed using descriptive statistics. Results show that there is a high level of awareness (86.7 %) on the importance of tree planting in the study area. Government's effort has been in the area of tree planting campaign and seedlings supply. However, farmers lack modern sivicultural skills and do not have enough seedlings coupled with improper timing of planting. Finally, the paper recommends the use fruit and leguminous trees since it gives immediate benefit to the farmers and as well conserve the environment.

Over the years, West African Sahel&rsquo;s people developed some strategies for predicting the seasonal weather using meteorological indicators to plan for extreme weather events. This study used information on local indicators of seasonal weather prediction and mean monthly rainfall and temperature record (1981-2017) from Nguru weather station located at Latitude 14&deg;N in achieving the aim of the study. Both qualitative and quantitate (descriptive and inferential) statistical tools were employed in analysing the collected data. The study found that the local population of the study area used meteorological indicators in predicting the seasonal weather. The results of the analysis revealed that the variability of the annual rainfall during the study period was large. An increasing trend of 3.1mm annually was observed. While decreasing trend in the cold, dry and hot dry season temperature and an increasing trend in warm moist temperature by 0.025&deg;C, 0.05&deg;C and 0.0004&deg;C respectively, was observed. Annual rainfall amount accounts for 31% and 2% variability in cold dry and warm moist season temperature, respectively. Cold, dry season and warm moist season temperature respond to any 1mm increase in annual rainfall by decreasing by 0.012&deg;C and 0.002&deg;C, respectively. The Hot, dry season temperature also accounts for 4% of the variability in annual rainfall. The model&rsquo;s result revealed anyone 1&deg;C increase in hot dry season temperature lowers the annual rainfall by 10mm. This study confirmed that the observed relationship between seasons weather conditions by local population exist. Therefore annual rainfall is the major determinant of cold dry seasonal temperature in the study area.

For this study, geospatial technology was used to assess agricultural lands vulnerable to flooding in Makurdi, Benue State, Nigeria. Six thematic layers of factors influencing flood occurrences in the study area were generated from monthly rainfall, land use/cover, drainage density, soil, digital elevation model and slope. Pairwise comparison of the Analytical Hierarchy Process was used to derive the weights for each factor using expert&rsquo;s judgements and literature. Weighted overlay model from the spatial analysis tool in the ArcGIS 10.4 environment was used to perform the vulnerability modelling. Expert&rsquo;s judgement on the relative factors influencing flood in the study area was: rainfall (25%), elevation (22%), slope (20%), drainage density (13%), soil type (8%) and land use/cover (12%). The consistency ratio of the analysis was reasonable: (CR= 0.078). Results from the model demonstrated land vulnerability to urban agricultural flooding in the study area ranging from areas of very highly vulnerable to very low vulnerable areas, with farmlands along the floodplains of River Benue falls within the very highly vulnerable areas. The elements at Risk are; Farmland 537.6 (66.1%), Irrigation Land 40.5 (5.0%) and Built-up Land 125.8 (15.5%).