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This study used daily observation data obtained from the Nigerian Meteorological Agency (NiMet) to investigate the temperature trend of Nigeria from 1981-2015. The data were homogenised using the Quantile Matching (QM) method and Quality Controlled. The data have been transformed into three sets of data with different periods: daily, monthly and yearly. The datasets (daily, monthly and yearly) were checked for autocorrelation and if they were found auto correlated, the Modified Mann Kendall (MMK) and the PreWhitening (PW) methods were used and compared, if not the normal Mann Kendall (MK) test was applied. The results showed for the different methods, variations in the trend from one station to another and for the minimum and maximum temperature. These variations were observed in the different methods and data screening the performance of each of the methods in the datasets. The general trend was found to be increasing. The variations in the temperature increase the Diurnal Temperature Range (DTR) that impact human health and increase the probability of occurrence of extreme events.

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Climate change impacts have been the major subject of discussion for scientists from different fields of study, including the agriculture sector. This study investigates the effects and implications of future climate change on rice production in the Lower River Region of The Gambia. The study seeks the following specific objectives: i) Analyse temperature and rainfall trends over Lower River Region; ii) Determine the relationship between temperature, rainfalls and rice production in the study area; and iii) Simulate temperature, rainfalls and rice production as well as the existing relationship among those parameters in the future using seasonality. The trend was examined after an exploratory data analysis, a unit root test and a correlation analysis. The study revealed an increase in maximum temperature (Tmax) and a variation in minimum temperature (Tmin) where the increase is not constant over 1981-2015. Also, the harvested area, production and rainfall increased while yield decreased. The data was extrapolated to 2035 using a VARMA statistical forecast method. Ordinary Least Squares and robust linear regression models were applied to find out the future implications (2035 and subsequent near years) of the climate parameters on rice production using 1981 to 2015-year series. The model shows that by 2035, yields will negatively be affected by the increase in Tmax and positively by the very little variation in Tmin. But the risk is that the ratio is not balanced, the damages of Tmax will be greater than the good productions of Tmin. The Tmin will also decrease as a general trend occasioning severe conditions for rice production in the region. This reveals the effects of climate change on rice production even though the relationship between climatic and rice variables remains low, because of the numerous parameters in rice production. This calls for an urgent need to improve rice varieties that will thrive well in the anticipated new climatic conditions (high yielding, heat tolerant, saline tolerant and early maturing) and promotion of good cultural practices that save water to cope with future climate. This study suggests that more studies should include other parameters of rice production for improved predictions.