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There are interesting debates on the influence of foreign aid to agriculture on economic growth in Africa. Some scholars have argued that, despite the inflows, majority of rural smallholder farmers in the continent are extremely poor. The precise channels through which foreign aid is to promote sectoral growth has been inadequately understood from the literature. This paper is a systematic literature review on the empirical evidence of the relationship between agricultural aid and growth in Sub Saharan African countries. The Generalized Methods of Moments and the Granger causality test are the main methodological approaches of papers reviewed and the relationship between agricultural aid and productivity growth is positive and quite significant. However, the results demonstrate a weak synergy between the various forms of agricultural aid and growth. The main recommendation is to have a broader conceptual, theoretical or analytical frameworks that clearly define how agricultural aid influences productivity when measured against other influencing factors. Aid is only a catalyst to growth so, governments must invest and provide the necessary infrastructure and a conducive policy environment for increased productivity and growth. [ABSTRACT FROM AUTHOR]

Climate change significantly impacts global agricultural productivity. Therefore, a more dynamic farming system is needed to enable farmers to better adapt to climate change while contributing to efforts to produce enough food to feed the growing world population. In the context of climate change, this study analyzed the empirical scientific literature on the link between climate-smart agriculture and farm productivity. To evaluate the relevant articles, the authors used the search term "climate-smart agriculture amidst climate change to enhance agricultural production (CSA-CCAP)" to find studies published between 2009 and March 2022 using innovative bibliometric techniques. One hundred and sixteen published papers in BibTeX format were downloaded for further analysis. The most successful selected CSA approaches in Africa, such as in the Congo Basin forest, including sustainable land management practices, water-efficient maize hybrids, and others, aim to counteract climate change with signs of 200 percent output gains. The findings showed an annual growth rate of about 19%, demonstrating that research on CSA-CCAP expanded over time during the study period. Nonetheless, the research output on CSA-CCAP varied, with 2021 accounting for 30%, followed by 2020 with 16% as of March 2022. The study concluded that boosting agricultural productivity in the face of climate change may be accomplished through CSA to end hunger, eradicate poverty, and improve people's well-being. [ABSTRACT FROM AUTHOR]

This paper explores the nexus between agricultural production, renewable energy, foreign direct investment (FDI), and carbon emissions in Africa, where there is limited evidence on the topic. Relying on panel data covering thirty-one African countries obtained from the World Bank World Development Indicators and FAOSTAT databases, we answered the question of whether agricultural production (proxied by livestock production, fertilizer consumption, and land under cereal cultivation), the use of renewable energy, and FDI increase or reduce carbon emissions. Using the panel autoregressive distributed lag model for analysis, our results show that net FDI, fertilizer consumption, livestock production significantly increased carbon emissions, both in the short run and long run. Meanwhile, renewable energy use consumption significantly decreased carbon emissions, both in the short run and long run. Specifically, a 1% increase in net FDI increased total carbon emissions by 0.003% in the short run and by 0.01% in the long run. Renewable energy consumption significantly decreased carbon emissions, both in the short run and long run. A 1% increase in renewable energy consumption decreased total carbon emissions by 0.16% in the short run and by 0.22% in the long run. Additionally, fertilizer consumption and livestock production significantly increased carbon emissions in the short run and long run. A 1% increase in fertilizer consumption increased total carbon emissions by 0.01% in the short run and by 0.04% in the long run, while a 1% increase in livestock production increased total carbon emissions by 0.20% in the short run and by 0.56% in the long run. The findings call for investment in renewable energy technologies and consumption while advocating for large-scale uptake of climate-smart agriculture, and environmentally friendly targeted foreign direct investments on the continent. [ABSTRACT FROM AUTHOR]

Local communities in Africa have used indigenous knowledge (IK) for centuries in agricultural productivity, preservation of food, and conservation of resources such as water. Using IK, communities have been able to ensure sustainable agriculture productivity and local food security. However, due to a range of intricate and intertwined factors, IK of agricultural practices is rapidly eroding and disappearing. One of the factors is a lack of IK documentation for preservation. This paper explores the use of digital technologies to preserve and disseminate IK of agricultural practices, as well as identify potential barriers, using a systematic review methodology. The findings reveal that the management of IK of agriculture practices using digital technologies in Africa is still in its infancy. Several contextual barriers, such as the linguistic diversity of IK custodians across the continent, impede the process. The variety of communication methods and languages makes IK documentation difficult and costly. In the face of severe food insecurity exacerbated by a decline in agricultural productivity, the role of IK in improving and sustaining agricultural productivity cannot be overlooked. The preservation, dissemination, and use of IK as an adaptation strategy for addressing declining agricultural productivity and food insecurity should be prioritised. [ABSTRACT FROM AUTHOR]

Meeting growing demand for water and food in Africa, and other parts of the Global South, presents a significant and critical challenge over the next 50 years. This paper draws on an ongoing project in Africa to outline the research-for-development work that is urgently required to facilitate a paradigm shift in agricultural water management. Such work should lead to increased productivity and profitability of agricultural water use to allow agriculture to release some water to meet the growing needs of other sectors, while still meeting food security needs and contributing to a prosperous rural population. [ABSTRACT FROM AUTHOR]

Our purpose here is to provide an introduction to the set of papers in this Special Edition on "The measurement of agricultural productivity in Africa" and to draw lessons for further research. [ABSTRACT FROM AUTHOR]

Cassava is one of the six food items identified as a critical food product for Africa, owing to its importance to African farmers' lives and ability to alter African economies. However, Cassava plant diseases have affected the yield of farmers significantly which has led to a decline in the agricultural production of cassava. Therefore, the aim of this research work is to develop a web and mobile applications-based system that would be able to detect cassava diseases based on its leaf images. To achieve this aim, pre-trained Convolutional Neural Network (CNN) models were selected using their previous performance and the application of transfer learning technique, new models were developed to classify cassava diseases based on the dataset curated and pre-processed. The best three models were selected: MobileNetV2, VGG16 and ResNet50. After training, the accuracy for each model was: 98%, 92% and 75% for MobileNetV2, VGG16 and ResNet50 respectively. Following evaluation of performance, the model with the best accuracy (MobileNetV2) was deployed using a web application interface. After deploying as a web and mobile apps interface, it was further tested to see how it would perform on the field. This research work was found capable of aiding farmers in being able to timely detect the type of disease affecting their cassava plants and the correct treatment to utilize; this also contribute towards Sustainable development goals (SDG) 2 and 12. [ABSTRACT FROM AUTHOR]

Purpose: Accelerating the adoption of intensification methods is critical for improving African agricultural productivity growth. This study synthesizes the effect of determinants underlying the speed of adoption of agricultural intensification methods in Africa. Design/Methodology/Approach: A systematic literature search was conducted using online databases, covering 20 years of publication research. Eleven studies met the inclusion criteria and were included in the meta-analysis. Findings: After controlling for innovation type, a subgroup analysis reveals that gender and formal education influence the speed of adoption of improved staple crop varieties and other agricultural intensification methods. Practical implications: Continued investment in approaches that are gender sympathetic and in agricultural education and extension is implied. Theoretical implications: Accelerating agricultural productivity growth requires a broad understanding of the potential determinants. This extends beyond farmer characteristics and includes factors that describe the context of agricultural systems, policies, and technology. Originality/Value: This is the first study quantifying the effect size of factors that influence the pace of adoption of agricultural intensification methods in Africa. [ABSTRACT FROM AUTHOR]

The impact of agricultural productivity growth on the process of structural change, an increase in the share of labor employed in manufacturing, is a much debated topic. One perspective argues that agricultural productivity promotes structural change via its contribution of various resources to the manufacturing sector. Another hypothesis proposed is that agricultural productivity growth will draw resources away from agriculture. This paper hypothesizes that agricultural productivity growth plays a key role in the structural change process, but not necessarily through the mechanisms usually proposed. It is hypothesized here that agricultural productivity growth improves the quality of human labor (measured by the human development index, human capital accumulation, and reductions in infant mortality). In turn, improvements in the quality of human labor make it more profitable to employ labor in manufacturing and thus structural change, measured by the share of manufacturing employment in total employment, increases. These hypotheses are tested using dynamic ordinary least squares (DOLS) techniques utilizing data from seventeen countries in Sub-Saharan Africa. The findings indicate that agricultural productivity growth has a significant positive impact human labor quality as measured by the human development index, human capital accumulation, and reductions in infant mortality. In addition, improvements in the quality of human labor in turn have a significant positive impact on the extent of structural change as measured by the share of manufacturing employment as a share of total employment. In addition, trade, as measured by exports plus imports divided by GDP, also has a significant positive impact on the extent of structural change. Finally, inflation appears to slow the structural change process. The policy implications are straightforward. Agricultural productivity does indeed seem to be important in the process of structural change. Thus development programs involving Sub-Saharan Africa should focus on investment in the development of agricultural technologies that will raise agricultural productivity. In a broader sense, governments should focus investment on improving the overall quality of labor. [ABSTRACT FROM AUTHOR]

We discuss recent trends in agricultural productivity in Africa and highlight how technological progress in agriculture has stagnated on the continent. We briefly review the literature that tries to explain this stagnation through the lens of particular constraints to technology adoption. Ultimately, none of these constraints alone can explain these trends. New research highlights pervasive heterogeneity in the gross and net returns to agricultural technologies across Africa. We argue that this heterogeneity makes the adoption process more challenging, limits the scope of many innovations, and contributes to the stagnation in technology use. We conclude with directions for policy and what we feel are still important, unanswered research questions. [ABSTRACT FROM AUTHOR]

Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross-correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re-analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of bias-adjustment methods. The results show that multivariate BA treatment should be preferred to univariate ones. The error obtained by comparing crop simulation obtained with AgMERRA with those obtained with multivariate bias-adjusted climate prediction is systematically lower. The error reduction varies as a function of the variable, the location, the crop model, and the climate model though the tendency is for smaller errors when multivariate methods are used to treat the latter. The results are attributed to the nature of crop models and the fact that multivariate methods consider more adequately the correlation existing between the meteorological variables. The study shows the importance of considering the nature of a model and the selection of input data that best suited to the former. In this case the improvements produced when using multivariate data appears to be significant especially in the light of the variety of crop models used and the similar response obtained and it is therefore recommended. [Display omitted] • The impact of climate data multivariate bias-adjustment methods versus univariate on crop model results was estimated. • Crop model results improved when input data was treated using multivariate methods compared to univariate methods. • Multivariate methods maintain the variables correlation as required by crop models. • This result is attributed to the parameterized nature of the participating crop-model formulations. • Climate, bias adjustment and the crop modelling communities joined efforts in this unprecedented collaboration. [ABSTRACT FROM AUTHOR]

Understanding the extent and adapting to the impacts of climate change in the agriculture sector in Africa requires robust data on which technical and policy decisions can be based. However, there are no publicly available comprehensive data of which crops are suitable where under current and projected climate conditions for impact assessments and targeted adaptation planning. We developed a dataset on crop suitability of 23 major food crops (eight cereals, six legumes & pulses, six root & tuber crops, and three in banana-related family) for rainfed agriculture in Africa in terms of area and produced quantity. This dataset is based on the EcoCrop model parameterized with temperature, precipitation and soil data and is available for the historical period and until mid-century. The scenarios used for future projections are SSP1:RCP2.6, SSP3:RCP7.0 and SSP5:RCP8.5. The dataset provides a quantitative assessment of the impacts of climate change on crop production potential and can enable applications and linkages of crop impact studies to other socioeconomic aspects, thereby facilitating more comprehensive understanding of climate change impacts and assessment of options for building resilience. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max L.) is an important oil crop with production below the real need in Africa. To increase production, substantial upgrading must be accomplished by enlarging the genetic potential of new cultivars that relies on the parents' genetic diversity. We aimed to assess the genetic diversity and the population structure of soybean accessions, therefore, evaluate soybean value in terms of use for breeding purposes. To accomplish this, a set of 282 soybean accessions were genotyped using the diversity array technology sequencing with a high throughput of the single-nucleotide polymorphisms (SNPs). A total of 6,935 high-quality SNPs were identified across the whole soybean genome. The mean value of genetic diversity, major alleles frequency, minor alleles frequency, expected heterozygosity, and the polymorphism information content were estimated at 0.35, 0.77, 0.22, 0.33, and 0.29, respectively. The analysis of molecular variance reveals that the variance among the populations is lower than the variance within the populations. The soybean accessions could be classified into two groups based on the principal coordinate analysis. The information provided in this study will be helpful for breeders. [ABSTRACT FROM AUTHOR]

A lot of attention has been paid to environmental pollution worldwide, due to the increase in anthropogenic activities. Massive investment in non-renewable energy options raises questions regarding environmental sustainability and how to maximize food and non-food output while still preserving a healthy ecosystem. To this end, the present study explores the three-way nexus between economic growth, CO2 emission, and agriculture-value added will accounting for other control variables across a balanced panel of selected African economies from 1997 to 2020. Panel econometrics method of the generalized method of moments (two-step difference GMM) is used to obtain a robust result. From the present study, the environmental pollution model shows that economic growth significantly contributes to environmental pollution in Africa. Additionally, the food price index, capital, and FDI promote pollution, while agricultural production and labor decrease pollution. In the case of the economic growth model, the findings reveal that environmental pollution supports the growth-led pollution hypothesis. Also, the food price index and capital ameliorate economic growth, while foreign direct investments decrease economic growth. Finally, the agricultural production model indicates that economic growth increases agricultural production when the interaction term between GDPC and FDI is included in the model. In summary, the combination of explanatory variables, environmental pollution, capital, and foreign direct investment decreases agricultural production. On the contrary, the food price index and labor promote agricultural production in Africa. Furthermore, the study provides a lot of policies for authorities and stakeholders in Sub-Saharan African countries and other developing economies. [ABSTRACT FROM AUTHOR]

Many studies have been conducted to examine the direct effect of agriculture on the prevalence of malnutrition; however, there is little solid evidence on spatial spillover effects and much less on the heterogeneous effects stemming from spatial differences in nutritional conditions. We make up this gap by using a dynamic spatial Durbin model to characterize the impact of agricultural productivity on malnutrition in Africa. Our results show that countries in Eastern Africa are more likely to suffer from severe malnutrition than other regions. We find evidence for convergence in agricultural productivity across countries with moderate and high prevalence of malnutrition as disparities in their agricultural productivity narrow down over the sample period. It appears that the negative effect of agriculture on malnutrition is more evident in countries where the prevalence of malnutrition is lower. This implies that agricultural development does not play a substantial role in reducing malnutrition in the worst affected areas. We also report that poor agricultural development can deteriorate the nutritional status among neighboring countries in the short term, consistent with the spatial‐locking effect of agriculture. [ABSTRACT FROM AUTHOR]

While information and communication technologies (ICT) have proven to be useful in boosting agricultural production and productivity, regardless of the geographical location, much of the discussion on ICT and their impact focus on the global north, with deficient literature on the global south. The limited account of the global south shows mixed conclusions on the impact of information and communication technologies on agricultural production, with most studies focusing on crop production, as a proxy for agricultural production, leaving out livestock production. Animated by this concern, this article explores the impact of ICTs on agricultural production (crop and livestock) in Africa using panel data from 32 African countries and the panel autoregressive distributed lag model as the estimation technique. We find that individuals using internet significantly increased crop production in the long run. Specifically, a percentage increase in internet patronage increases crop production by 0.071% but significantly decreases the livestock production index, both in the short and long run. Mobile phone subscriptions had a significant negative impact on crop production in the long run but had a significant positive impact on livestock production in the long run. Fixed phone subscriptions significantly increased crop production in the long run but significantly decreased livestock production index in the long run. The findings show bidirectional causality between crop production and internet patronage, livestock production and individuals using internet, crop production and mobile cellular subscription, crop production and net national income, and rural population and both crop and livestock production. We recommend that governments in Africa increase funding investment in digital technologies to foster increased agricultural production while addressing structural challenges that constrain increased access to digital agricultural technologies. It might be useful if governments in sub-Saharan Africa (SSA) incentivize the telecommunication companies to extend digital coverage to rural areas through tax rebates and holidays to encourage rural inclusion in the digital space to bridge the digital divide. [ABSTRACT FROM AUTHOR]

Earlier research largely ignored the effects of climate change on the growth of agricultural total factor productivity (TFP) in Africa. This study shows how climate inputs impact TFP growth in addition to other productivity growth indicators and metrics, as well as how they can impact overall input efficiency as productivity drivers. We use a panel of 42 African nations from 1999 to 2019 and a nonparametric data envelopment analysis-Malmquist technique. The non-parametric analysis revealed that the average growth rate of the non-climate-induced TFP estimates was 1.9%, while the average growth rate of the climate-induced TFP estimates was 2.4%. Accounting for temperature and precipitation separately, TFP grew by 2.3% on average. This growth rate (2.3%) is slightly less than the combined effect of temperature and precipitation (2.4%) but higher than the typical TFP growth rate (1.9%) that ignores climate variables, indicating that TFP growth in African agriculture risks being underestimated when climate inputs are ignored. We also find the distribution of the climate effects to vary across regions. In northern Africa, for example, the temperature-induced TFP growth rates were negative due to rising temperature in the region. Evidence from the decomposed TFP estimates indicates that climate variables also influence productivity determinants. However, technology improvement is fundamental to mitigating the effects of extreme weather inputs on TFP growth in Africa's agriculture. As a result, a few policy suggestions are provided to help policymakers deal with the effects of climate change on TFP growth in Africa's agriculture and ensure food security. The study advocated for a reevaluation of the climate-agriculture effect in order to fully comprehend the role of climate factors and their contributions to agricultural TFP growth in Africa. [ABSTRACT FROM AUTHOR]

Diversified cropping systems, especially those including legumes, have been proposed to enhance food production with reduced inputs and environmental impacts. However, the impact of legume pre-crops on main crop yield and its drivers has never been systematically investigated in a global context. Here, we synthesize 11,768 yield observations from 462 field experiments comparing legume-based and non-legume cropping systems and show that legumes enhanced main crop yield by 20%. These yield advantages decline with increasing N fertilizer rates and crop diversity of the main cropping system. The yield benefits are consistent among main crops (e.g., rice, wheat, maize) and evident across pedo-climatic regions. Moreover, greater yield advantages (32% vs. 7%) are observed in low- vs. high-yielding environments, suggesting legumes increase crop production with low inputs (e.g., in Africa or organic agriculture). In conclusion, our study suggests that legume-based rotations offer a critical pathway for enhancing global crop production, especially when integrated into low-input and low-diversity agricultural systems. Crop rotations including legumes have been proposed as a strategy to enhance food production. Here, the authors conduct a global meta-analysis on legume-based crop rotations, showing that legume pre-crops increase 20% of yield in average across various crops and climatic regions. [ABSTRACT FROM AUTHOR]

The effort to increase agricultural productivity continues to receive interest in Africa as low productivity levels, poverty and food insecurity remain or even increase. This study used the Färe-Primont Index to estimate agricultural total factor productivity growth for 49 African countries. Panel data consisting of 833 observations for the period 2000 to 2016 were obtained from the United State Department of Agriculture Economic Research Service database. The results show that the average growth rate for agriculture in Africa is 0.73% per annum. The sector experienced increased growth after the Maputo Declaration, which was sustained during the global financial crisis. West Africa experienced the largest growth while Southern Africa suffered a substantial decline. The study also discovered that growth differed between countries indicating that customization of the Comprehensive Africa Agriculture Development Programme into regional and country-specific policy interventions is important to boost agricultural productivity. Finally, the growth was achieved through technical change, while efficiency change constrained growth. Policy-makers should increase investment in agricultural extension services, education and training to enhance managerial capacity (efficiency change) because improved managerial capacity could increase agricultural growth and thereby increase food security and alleviate poverty in Africa. [ABSTRACT FROM AUTHOR]

Africa is one of the regions with high mycotoxin contamination of foods and continues to record high incidences of liver cancers globally. The agricultural sector of most African countries depends largely on climate variables for crop production. Production of mycotoxins is climate-sensitive. Most stakeholders in the food production chain in Africa are not aware of the health and economic effects of consuming contaminated foods. The aim of this review is to evaluate the main factors and their degree of contribution to the high levels of mycotoxins in African foods. Thus, knowledge of the contributions of different factors responsible for high levels of these toxins will be a good starting point for the effective mitigation of mycotoxins in Africa. Google Scholar was used to conduct a systemic search. Six factors were found to be linked to high levels of mycotoxins in African foods, in varying degrees. Climate change remains the main driving factor in the production of mycotoxins. The other factors are partly man-made and can be manipulated to become a more profitable or less climate-sensitive response. Awareness of the existence of these mycotoxins and their economic as well as health consequences remains paramount. The degree of management of these factors regarding mycotoxins varies from one region of the world to another. [ABSTRACT FROM AUTHOR]

Flood-based Agricultural Systems (FAS), which rely on temporary floods, provide livelihoods for nearly 50 million smallholder farmers across water-stressed African basins. This paper analyses the impact of externally driven agricultural production and productivity improvement interventions in FAS, taking the Ethiopian Fogera floodplain as a case study. Ostroms' Governing the Commons Principles were used as analytical framework. Field data was gathered through focus group discussions and interviews with 266 farmers and pastoralists, and 10 local administration staff, while the AquaCrop model was used to simulate maize yield under varied floodwater management and farming practices. The interventions in the Fogera floodplain replaced vital maize and teff food crops with high-return upstream rice cultivation. It has characteristics similar to other interventions across Africa: 1) inadequate integration of local agricultural water management practices; 2) narrow focus on short-term economic gains and insufficient attention to long-term sustainability of livelihoods and environmental issues; and 3) lack of detailed ex-ante analysis of basin-wide consequences – it failed to prioritize the needs of downstream vegetable producers and pastoralists using shallow wells. The intervention missed several low-cost opportunities, including the establishment of rules to protect downstream water rights; the construction of gabion-strengthened on-farm structures to efficiently distribute floods or raised brick-walls to reinforce shallow wells; and the implementation of measures for improved soil fertility and weed management. The study establishes that these missed opportunities could have enhanced livelihoods by doubling rice yield to 6 tons/ha; increased teff and maize harvests by one-third, to 3 and 5 tons/ha respectively; and mitigated 25% vegetable yield loss and 40% reduction in grazing land. These opportunities could have also produced environmental benefits, including reduced soil moisture and fertility depletion. Lessons from the Fogera floodplain on making interventions cost-effective and considering basin-wide livelihood impacts are relevant to FAS globally. • Analysed external improvement interventions in Flood-based Agricultural Systems. • Farmer-led systems have fair floodwater sharing, but low agricultural productivity. • Implemented interventions have not improved floodwater management and productivity. • Many low-cost improvement opportunities missed due to lack of basin-wide approach. • Identified affordable and up-scalable basin-wide livelihood improving interventions. [ABSTRACT FROM AUTHOR]

Improved agricultural management plays a vital role in protecting soils from degradation in eastern Africa. Changing practices such as reducing tillage, fertilizer use, or cover crops are expected to enhance soil organic carbon (SOC) storage, with climate change mitigation co-benefits, while increasing crop production. However, the quantification of cropland management effects on agricultural ecosystems remains inadequate in this region. Here, we explored seven management practices and their potential effects on soil carbon (C) pools, nitrogen (N) losses, and crop yields under different climate scenarios, using the dynamic vegetation model LPJ-GUESS. The model performance is evaluated against observations from two long-term maize field trials in western Kenya and reported estimates from published sources. LPJ-GUESS generally produces soil C stocks and maize productivity comparable with measurements and mostly captures the SOC decline under some management practices that is observed in the field experiments. We found that for large parts of Kenya and Ethiopia, an integrated conservation agriculture practice (no-tillage, residue and manure application, and cover crops) increases SOC levels in the long term (+11 % on average), accompanied by increased crop yields (+22 %) in comparison to the conventional management. Planting nitrogen-fixing cover crops in our simulations is also identified as a promising individual practice in eastern Africa to increase soil C storage (+4 %) and crop production (+18 %), with low environmental cost of N losses (+24 %). These management impacts are also sustained in simulations of three future climate pathways. This study highlights the possibilities of conservation agriculture when targeting long-term environmental sustainability and food security in crop ecosystems, particularly for those with poor soil conditions in tropical climates. [ABSTRACT FROM AUTHOR]

In a warming climate, drought events are projected to increase in many regions across the world, which would have detrimental impacts on water resources for agriculture activity and human life. Thus, projecting drought changes, especially the frequency of future drought events, is very important for the African continent. This study investigates the future changes in drought events based on the France Centre National de Recherches Météorologiques (CNRM-CM6) model in the Coupled Model Intercomparison Project phase six (CMIP6) datasets for four shared socio-economic pathways (SSP): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5; and three time slices: near future (2020–2039), mid-century (2050–2069), and end-of-century (2080–2099), relative to a historical baseline period (1995–2014). The interannual variability and trends of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith methods for measuring potential evapotranspiration (PET) are used to estimate future droughts. The temporal analysis shows that the drought frequency, intensity, and affected area will increase throughout the 21st century. Among the scenarios, SSP3-7.0 and SSP5-8.5 project a larger upward trend in drought characteristics than SSP1-2.6 and SSP2-4.5. The spatial pattern shows drought frequency decreases in humid regions and increases in non-humid regions across Africa. For all SSP scenarios, the projected wetting trend per decade ranges from 0.05 to 0.25, while the drying trend per decade ranges from −0.05 to 0.25. A regional trend analysis revealed key differences in spatial pattern, with varied trend projections of wetter and drier conditions in humid and non-humid regions under all SSP scenarios. Drier conditions are expected to intensify in Southern Africa under all SSP scenarios but are projected to be more intense under either SSP3-7.0 and SSP5-8.5. In general, the projected wetter trends in humid areas may favor agricultural production and ecological conservation, and drier trends in non-humid regions may call for the possible adoption of tailor-made drought adaptation strategies and development programmes to minimize impacts. [ABSTRACT FROM AUTHOR]

Climate change constitutes a rising challenge to the agricultural base of developing countries. Most of the literature has focused on the impact of changes in the means of weather variables on mean changes in production and has found very little impact of weather upon agricultural production. Instead, we focus on the relationship between extreme events in weather and extreme losses in crop production. Indeed, extreme events are of the greatest interest for scholars and policy makers only when they carry extraordinary negative effects. We build on this idea and for the first time, we adopt a conditional dependence model for multivariate extreme values to understand the impact of extreme weather on agricultural production. Specifically, we look at the probability that an extreme event drastically reduces the harvest of any of the major crops. This analysis, which is run on data for six different crops and four different weather variables in a vast array of countries in Africa, Asia and Latin America, shows that extremes in weather and yield losses of major staples are associated events. We find a high heterogeneity across both countries and crops and we are able to predict per country and per crop the risk of a yield reduction above 90% when extreme events occur. As policy implication, we can thus assess which major crop in each country is less resilient to climate shocks. [ABSTRACT FROM AUTHOR]