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The effects of changes in meteorological parameters on rice yield variations were considered. Weather parameters, temperature (T), rainfall (R), relative humidity (RH) and solar radiation (SR), and rice yield variation for Ibadan were analyzed. Meteorological parameters were obtained from the International Institute for Tropical Agriculture while rice yield data were obtained from the Africa Rice Centre both in Nigeria for three decades (1980–2010). Trends analysis of past and recent variations using the weather parameters obtained showed trends of variability of each parameter with respect to rice yield. Mann–Kendall trend and Sen's slope tests were performed on the respective meteorological variables while correlation, multiple regression and variability index (VI) were also computed for these parameters. Results showed that T, RH and rice yield were negative and decreased significantly (P < 0.001) while R and SR showed statistically non-significant increasing trends in the last three decades. R and T decreased at the rate of 3% per year and 0.03% per decade, respectively. Results of annual VI showed that decreases observed in RH, SR and rice yield were rather recent. T, SR and R were found to have the most significant effect on rice yield of all the meteorological parameters considered.

The development of wind energy in West Africa is essential to meet the rising energy needs due to population growth and societal development. However, only few studies have investigated the changes in turbine hub-height wind characteristics over the region under changing climate. This study aims at assessing the impact of climate change on wind power density (WPD) over West Africa using the simulations from the newly developed Coupled Model Intercomparison Project version 6 (CMIP6) models. The CMIP6 near-surface wind speed and directions simulations for the historical climate (1985–2014) were compared with ERA5 reanalysis data using multiple descriptive statistics. Relative to ERA5 reanalysis, the CMIP6 models alongside their multimodel ensemble mean (EnsMean) realistically reproduce the near-surface wind characteristics (i.e. wind speed and directions) across most subregions of West Africa, although noticeable biases still exist. Overall, the CMIP6 EnsMean performs better than most individual models at capturing the near-surface wind speed over the region. Under global warming, we find a robust projected increase (about 70%) in WPD over the Guinea coast subregion of West Africa, especially in June–July–August season. The December–January–February and March–April–May seasons show alternating projected WPD increase and decrease, with predominantly robust projected decrease over the Sahel subregion. The projected increase over the Guinea coast has strong temporal qualities, with the end of the century (2070–2099) changes showing stronger magnitude compared to the mid-century (2040–2069) changes, and thus may provide a commercially viable renewable energy source.

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The energy industry is faced with important investment and optimization choices especially for wind power as a fuel of the future, especially for China which boasts the largest installed wind power capacity. This study therefore assessed the potential status of future wind power over China using Coupled Model Intercomparison Project phase 5 (CMIP5) models. Changes in wind power density relative to the current time period 1981–2005 were then analyzed using near-surface wind speeds extrapolated to hub-height of 90 m above ground level. The results showed relatively modest differences between the models and reanalysis. The majority of the models showed any two of location, shape, and size agreement for peak areas albeit models BCC-CSM-1-1-M, BNU-ESM, and CanESM2 tended to overestimate wind speed by up to 2.5 m/s. The multi-model ensemble mean performed better than most individual models in representing the wind characteristics over the study area. Future changes in wind power density showed an increase (decrease) over the coastal areas of the South China Sea and Bay of Bengal (areas along the 30°–40° N belt). In all, the changes were not significant enough to neither warrant a move away from wind energy nor threaten considerably the marketability and profitability under the present warming scenario rate.

Trends and spatial analysis of temperature and rainfall on rice yield in Nigeria was carried out. Forty year of past trends (1970–2010) was conducted with climate data obtained from the International Institute of Tropical Agriculture, Ibadan, Nigeria while upland rice yield data were obtained from the Food and Agriculture Organization. Six cities, one in each of the six agro‐ecological zones which were Calabar, Enugu, Ikeja, Ilorin, Kaduna, and Maiduguri were selected. Geographic information systems mapping for spatial analysis of temperature and rainfall over Nigeria was carried out. Mann–Kendall, Sens' tests, Pettitt's, and Buishand's tests and multiple linear regressions were used as statistical tools for analysis. Increasing rainfall trends in Enugu, Calabar, and Ikeja but decreasing trends were observed in Ilorin Kaduna and Maiduguri while temperature showed increasing trends in all the cities in the last four decades. Statistically significant positive trends of rice yield, rainfall, and temperature were observed in Ikeja and Maiduguri in the last four decades. Mann–Kendall tests showed that rice yield and temperature had generally statistically significant positive trends in Calabar Ilorin Kaduna, and Enugu while rainfall and yield were significant in Calabar Enugu but not significant in Ilorin and Maiduguri adaptation strategies to genetically modify rice varieties and effective water use strategies in areas of rainfall deficit are recommended to ensure food security.

Estimation errors have constantly been a technology bother for wind power management, often time with deviations of actual power curve (APC) from the turbine power curve (TPC). Power output dispersion for an operational 800 kW turbine was analyzed using three averaging tine steps of 1-min, 5-min, and 15-min. The error between the APC and TPC in kWh was about 25% on average, irrespective of the time of the day, although higher magnitudes of error were observed during low wind speeds and poor wind conditions. The 15-min averaged time series proved more suitable for grid management and energy load scheduling, but the error margin was still a major concern. An effective power curve (EPC) based on the polynomial parametric wind turbine power curve modeling technique was calibrated using turbine and site-specific performance data. The EPC reduced estimation error to about 3% in the aforementioned time series during very good wind conditions. By integrating statistical wind speed forecasting methods and site-specific EPCs, wind power forecasting and management can be significantly improved without compromising grid stability.

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