For the future installation of a wind farm in Cameroon, the wind energy potentials of three of Cameroon's coastal cities (Kribi, Douala and Limbe) are assessed using NASA average monthly wind data for 31 years (1983-2013) and compared through Weibull statistics. The Weibull parameters are estimated by the method of maximum likelihood, the mean power densities, the maximum energy carrying wind speeds and the most probable wind speeds are also calculated and compared over these three cities. Finally, the cumulative wind speed distributions over the wet and dry seasons are also analyzed. The results show that the shape and scale parameters for Kribi, Douala and Limbe are 2.9 and 2.8, 3.9 and 1.8 and 3.08 and 2.58, respectively. The mean power densities through Weibull analysis for Kribi, Douala and Limbe are 33.7 W/m2, 8.0 W/m2 and 25.42 W/m2, respectively. Kribi's most probable wind speed and maximum energy carrying wind speed was found to be 2.42 m/s and 3.35 m/s, 2.27 m/s and 3.03 m/s for Limbe and 1.67 m/s and 2.0 m/s for Douala, respectively. Analysis of the wind speed and hence power distribution over the wet and dry seasons shows that in the wet season, August is the windiest month for Douala and Limbe while September is the windiest month for Kribi while in the dry season, March is the windiest month for Douala and Limbe while February is the windiest month for Kribi. In terms of mean power density, most probable wind speed and wind speed carrying maximum energy, Kribi shows to be the best site for the installation of a wind farm. Generally, the wind speeds at all three locations seem quite low, average wind speeds of all the three studied locations fall below 4.0m/s which is far below the cut-in wind speed of many modern wind turbines. However we recommend the use of low cut-in speed wind turbines like the Savonius for stand alone low energy needs.

According to data from the Food and Agricultural Organization of the United Nations (FAO), Ukraine was the fifth and ninth global producer and exporter of corn and wheat, respectively, before Russia’s invasion. The disruption of the supply chain in Ukraine in these food sectors due to the Russian–Ukrainian conflict can hamper global food security. Very little is known about how the war has impacted these important food sectors globally. This paper examines the global impact of the war on the corn and wheat sectors in the first year of the war and investigates the relationship between market vulnerability and trade ties with the conflicting regions. Analysis of FAO data shows that Ukraine suffered a 12.87% and 17.45% production decline in the corn and wheat sectors, respectively, compared to the 2012–2021 base years. Using the Acclimate economic network model, these shocks are applied to Ukraine’s corn and wheat network nodes to analyze their global impact. The production value and total losses are calculated and compared to the base year, both regionally and in some major global economic blocs. The results show that the corn sectors in Germany, Ukraine, Poland, and Belgium suffered the largest production value losses, while Ukraine, China, Venezuela, and Korea suffered the largest overall losses. In the wheat sector, Russia, Germany, Ukraine, and Canada suffered the largest production value losses, while Ukraine, Kazakhstan, Uzbekistan, and China suffered the largest overall losses. Overall, the corn sector was the hardest hit globally compared to the wheat sector, with the EU, the US, China, South America, and Africa being the hardest hit in the corn sector, while BRICS and Oceania were the hardest hit in the wheat sector. The study equally finds a strong correlation between increased regional food market vulnerability and Ukraine’s trade relations. These findings can contribute to better investment decisions, regional and sectoral emergency management planning, and the development of regulatory frameworks.

Identifying agricultural disaster risk regions before the occurrence of climate-related disasters is critical for early mitigation planning. This paper aims to identify these regions based on data from the Food and Agriculture Organization of the United Nations (FAO), the bilateral and multilateral trade network data of the World Integrated Trade Solution(WITS) and the agent-based economic model Acclimate. By applying a uniform forcing across agricultural sectors of some breadbasket regions (US, EU and China), when single and simultaneous extreme weather events occur, such as the 2018 European heatwave, production and consumption value losses and gains are calculated at regional and global levels. Comparing the FAO data sets, WITS, and Acclimate’s production value losses, the results show a strong dependence of agricultural production losses on a region’s output and connectivity level in the global supply and trade network. While India, Brazil, Russia, Canada, Australia, and Iran are highly vulnerable, the imposition of export restrictions to compensate for demand shortfalls makes Sub-Saharan Africa the most vulnerable region, as it is heavily dependent on agricultural imports. In addition, simultaneous extreme weather events can exacerbate the loss of value of agricultural production relative to single extreme weather events. Agricultural practices to increase production such as smart farming, increased investment in plantation agriculture, and diversification of trading partners can help mitigate future food security risks in Sub-Saharan Africa and other agricultural import-dependent regions.

The method of generalized extreme value family of distributions (Weibull, Gumbel, and Frechet) is employed for the first time to assess the wind energy potential of Debuncha, South-West Cameroon, and to study the variation of energy over the seasons on this site. The 29-year (1983–2013) average daily wind speed data over Debuncha due to missing values in the years 1992 and 1994 is gotten from NASA satellite data through the RETScreen software tool provided by CANMET Canada. The data is partitioned into min-monthly, mean-monthly, and max-monthly data and fitted using maximum likelihood method to the two-parameter Weibull, Gumbel, and Frechet distributions for the purpose of determining the best fit to be used for assessing the wind energy potential on this site. The respective shape and scale parameters are estimated. By making use of the P values of the Kolmogorov-Smirnov statistic (K-S) and the standard error (s.e) analysis, the results show that the Frechet distribution best fits the min-monthly, mean-monthly, and max-monthly data compared to the Weibull and Gumbel distributions. Wind speed distributions and wind power densities of both the wet and dry seasons are compared. The results show that the wind power density of the wet season was higher than in the dry season. The wind speeds at this site seem quite low; maximum wind speeds are listed as between 3.1 and 4.2 m/s, which is below the cut-in wind speed of many modern turbines (6–10 m/s). However, we recommend the installation of low cut-in wind turbines like the Savonius or Aircon (10 KW) for stand-alone low energy need.

The return of El Niño in 2023 brought record global temperatures, an increase in forest fires, floods, heatwaves, and intense and severe droughts. How the climate of 2023 affected agricultural activities in West Africa is not yet known. This paper analyses the changes in the patterns of local climate variables during the planting season and the potential consequences for agricultural productivity in West Africa. The study uses the ERA5, Normalized Difference Vegetation Index (NDVI), and Standardized Precipitation Evaporation Index (SPEI) datasets and performs a thorough spatio-temporal analysis and anomaly calculation of precipitation, air temperature, relative humidity, NDVI, drought, and soil moisture. Compared to the climatology, we observed early precipitation in March, high temperatures and humidity, low precipitation, and a loss of vegetation in the middle of the growing season (April to June). The direct influence of El Niño proved to be weak, suggesting that El Niño could be the cause, together with other factors such as global mean temperature and Atlantic circulation. Farmers in West Africa usually expect rainfall in early April to start the agricultural season. This shift in rainfall patterns can have a serious impact on agricultural activities and increase vulnerability to food insecurity in the region. Rather than relying solely on rainfall, we recommend the use of advanced irrigation techniques and the development of drought-resistant crops to combat the effects of El Niño in the future.