A regional stocktake of maize yield vulnerability to droughts in the Horn of Africa.

Climate projections in sub-Saharan Africa predict increased frequency of droughts with parallel impacts on crop yield. The Horn of Africa is among the most vulnerable regions in Africa to these changes because agriculture in general and maize production in particularly is highly climate driven, and rain-fed. Current research approaches have mostly focused on the climatic and biophysical drivers of crop yield without including the socio-economic drivers of crop yield. This study fills this gap by investigating the vulnerability of maize yield in the Horn of Africa to climate and socio-economic indicators. The hypothesis is that there is an inverse relationship between vulnerability and adaptive capacity. The vulnerability index is a composite index that integrates sensitivity, exposure, and adaptive capacity sub-indices. Maize yield data to compute the sensitivity index were collected from FAOSTAT, precipitation data to compute the exposure index were collected from the Climate Research Unit (CRU), and the data for the proxies of adaptive capacity were collected from the readiness index database on figshare. From the results, Somalia records the highest vulnerability index of 1.15, followed by Ethiopia with a vulnerability index of 0.61. Kenya records the lowest vulnerability index of 0.33. Also, there is a positive relationship between the vulnerability, sensitivity, and the exposure indices and an inverse relationship between the vulnerability index and the adaptive capacity index. The high vulnerability index recorded in Somalia is accentuated by a low adaptive capacity index of 0.44 that is anchored on low literacy and high poverty rates. As Somalia records the lowest adaptive capacity index of 0.44, Ethiopia and Kenya record 0.91 and 0.99 respectively. This study has shown that to better understand vulnerability, a shift from the old paradigm that focuses on the climatic variables to integrating socio-economic variables or proxies of adaptive capacity which enhances our understanding of vulnerability. Though leveraging the benefits of climatic and non-climatic variables is important, the challenge so far has been on how to integrate these in the same model; a challenge this work has succinctly overcome by integrating adaptive capacity in the vulnerability equation.

Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria.

Rapid and accurate soybean yield prediction at an on-farm scale is important for ensuring sustainable yield increases and contributing to food security maintenance in Nigeria. We used multiple approaches to assess the benefits of rhizobium (Rh) inoculation and phosphorus (P) fertilization on soybean yield increase and profitability from large-scale conducted trials in the savanna areas of Nigeria [i.e., the Sudan Savanna (SS), Northern Guinea Savanna (NGS), and Southern Guinea Savanna (SGS)]. Soybean yield results from the established trials managed by farmers with four treatments (i.e., the control without inoculation and P fertilizer, Rh inoculation, P fertilizer, and Rh + P combination treatments) were predicted using mapped soil properties and weather variables in ensemble machine-learning techniques, specifically the conditional inference regression random forest (RF) model. Using the IMPACT model, scenario analyses were employed to simulate long-term adoption impacts on national soybean trade and currency. Our study found that yields of the Rh + P combination were consistently higher than the control in the three agroecological zones. Average yield increases were 128%, 111%, and 162% higher in the Rh + P combination compared to the control treatment in the SS, NGS, and SGS agroecological zones, respectively. The NGS agroecological zone showed a higher yield than SS and SGS. The highest training coefficient of determination (R2 = 0.75) for yield prediction was from the NGS dataset, and the lowest coefficient (R2 = 0.46) was from the SS samples. The results from the IMPACT model showed a reduction of 10% and 22% for the low (35% adoption scenario) and high (75% adoption scenario) soybean imports from 2029 in Nigeria, respectively. A significant reduction in soybean imports is feasible if the Rh + P inputs are large-scaled implemented at the on-farm field and massively adopted by farmers in Nigeria.

COVID-19 response in Africa: impacts and lessons for environmental management and climate change adaptation.

The COVID-19 pandemic adds pressure on Africa; the most vulnerable continent to climate change impacts, threatening the realization of most Sustainable Development Goals (SDGs). The continent is witnessing an increase in intensity and frequency of extreme weather events, and environmental change. The COVID-19 was managed relatively well across in the continent, providing lessons and impetus for environmental management and addressing climate change. This work examines the possible impact of the COVID-19 pandemic on the environment and climate change, analyses its management and draws lessons from it for climate change response in Africa. The data, findings and lessons are drawn from peer reviewed articles and credible grey literature on COVID-19 in Africa. The COVID-19 pandemic spread quickly, causing loss of lives and stagnation of the global economy, overshadowing the current climate crisis. The pandemic was managed through swift response by the top political leadership, research and innovations across Africa providing possible solutions to COVID-19 challenges, and redirection of funds to manage the pandemic. The well-coordinated COVID-19 containment strategy under the African Centers for Disease Control and Prevention increased sharing of resources including data was a success in limiting the spread of the virus. These strategies, among others, proved effective in limiting the spread and impact of COVID-19. The findings provide lessons that stakeholders and policy-makers can leverage in the management of the environment and address climate change. These approaches require solid commitment and practical-oriented leadership. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-023-02956-0.

Identifying gaps in actual and simulated/potential yield and growing season precipitation in Morocco.

The influence of growing season rainfall on agricultural production is indisputable. In Morocco, the production of crops such as barley, maize, and wheat is impacted by growing season rainfall. Due to persistent gaps in growing season rainfall and other drivers of crop yield, crops have experienced observed yields that are often below projected or potential yields. However, there are currently no studies that have quantified these gaps in yield and growing season rainfall in Morocco. To achieve this objective, time-series crop yield for all three crops and growing season rainfall data for the period 1991-2020 were collected from FAOSTAT and the World Bank climate portal, respectively. Growing season rainfall and crop yield data for the spatial variations were culled from System National de Suivi Agrometeorologique (GCMS) and the yield gaps atlas, respectively, for the same historical period. The data were subjected to bias correction to handle uncertainty. The projected/simulated crop yields and growing season rainfall were computed by regression analysis. Crop yield and growing season rainfall gaps were determined by establishing the difference between the projected and observed crop yields and rainfall data. The results show that observed and simulated wheat have a stronger relationship when compared to the other crops. Also, most years with crop yield gaps are associated with growing season rainfall gaps. Wheat records the lowest number of years with yield gaps and the highest number of years with growing season rainfall gaps during the entire data series. Therefore, even though yield gaps are strongly tied to growing season rainfall gaps, it is not the case for wheat, and therefore other drivers might be important because wheat has the lowest number of years with crop yield gaps and the highest number of years with growing season rainfall gaps. Spatially, yield and growing season rainfall gaps decline with increased latitude. The broader perspective and policy implication here is that a better understanding of yield and growing season rainfall gaps mandates an understanding of growing season rainfall and other drivers of yield. As a way forward, potential research should focus on identifying the drivers of yield gaps, sub-national experimentation at the plot level as well as on closing yield gaps through water and nutrient management.

A new index on agricultural land greenhouse gas emissions in Africa.

Africa emits the lowest amounts of greenhouse gases (GHGs) into the global GHG budget. However, the continent remains the most vulnerable continent to the effects of climate change. The agricultural sector in Africa is among the most vulnerable sectors to climate change. Also, as a dominant agricultural sector, African agriculture is increasingly contributing to climate change through GHG emissions. Research has so far focused on the effects of GHG emissions on the agricultural and other sectors with very little emphasis on monitoring and quantifying the spatial distribution of GHG emissions from agricultural land in Africa. This study develops a new index: African Agricultural Land Greenhouse Gas Index (AALGGI) that uses scores and specific scale ranges for carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) to map the spatial variations in regional GHG emissions across Africa. The data for the three main GHGs (CO2, CH4, and N20) were downloaded from FAOSTAT. The data were analyzed through the newly developed African Agricultural Land Greenhouse Gas Index (AALGGI). This is an empirical index with scores ranging from 0 to 10, with higher scores indicating higher levels of emissions. The results show that Southern and North African regions have the lowest amounts of agricultural land GHG emissions, with AALGGIs of 3.5 and 4.5, respectively. East Africa records the highest levels of GHG emissions, with an AALGGI of 8 followed by West Africa with an AALGGI of 7.5. With the continental mean or baseline AALGGI being 5.8, East and Middle Africa are above the mean AALGGI. These results underscore the fact that though Africa, in general, is not a heavy emitter of GHGs, African agricultural lands are increasingly emitting more GHGs into the global GHG budget. The low AALGGIs in the more developed parts of Africa such as Southern and North Africa are explained by their domination in other GHG emitting sectors such as industrialization and energy. The high rates of emissions in East Africa and Middle Africa are mainly linked to intensive traditional farming practices/processes and deforestation. These findings underscore the need to further leverage climate change mitigation actions and policy in Africa and most importantly the co-benefits of mitigation and adaptations in the most vulnerable regions.

Vulnerability of maize, millet, and rice yields to growing season precipitation and socio-economic proxies in Cameroon.

In sub-Saharan Africa growing season precipitation is affected by climate change. Due to this, in Cameroon, it is uncertain how some crops are vulnerable to growing season precipitation. Here, an assessment of the vulnerability of maize, millet, and rice to growing season precipitation is carried out at a national scale and validated at four sub-national scales/sites. The data collected were historical yield, precipitation, and adaptive capacity data for the period 1961-2019 for the national scale analysis and 1991-2016 for the sub-national scale analysis. The crop yield data were collected for maize, millet, and rice from FAOSTAT and the global yield gap atlas to assess the sensitivity both nationally and sub-nationally. Historical data on mean crop growing season and mean annul precipitation were collected from a collaborative database of UNDP/Oxford University and the climate portal of the World Bank to assess the exposure both nationally and sub-nationally. To assess adaptive capacity, literacy, and poverty rate proxies for both the national and regional scales were collected from KNOEMA and the African Development Bank. These data were analyzed using a vulnerability index that is based on sensitivity, exposure, and adaptive capacity. The national scale results show that millet has the lowest vulnerability index while rice has the highest. An inverse relationship between vulnerability and adaptive capacity is observed. Rice has the lowest adaptive capacity and the highest vulnerability index. Sub-nationally, this work has shown that northern maize is the most vulnerable crop followed by western highland rice. This work underscores the fact that at different scales, crops are differentially vulnerable due to variations in precipitation, temperature, soils, access to farm inputs, exposure to crop pest and variations in literacy and poverty rates. Therefore, caution should be taken when transitioning from one scale to another to avoid generalization. Despite these differences, in the sub-national scale, western highland rice is observed as the second most vulnerable crop, an observation similar to the national scale observation.