Food Insecure Countries Research Articles

A community-engaged tool for evaluating food sovereignty in Haiti and beyond

ABSTRACT Researchers and community organizations are increasingly operationalizing food sovereignty, and argue that participatory, community-engaged food sovereignty metrics can enhance insights about local contexts, and opportunities for food sovereignty. In Haiti, one of the most food-insecure countries in the world, peasant and civil society organizations have been calling for food sovereignty for over a decade, and the state recently published a food sovereignty policy document, marking an important paradigm shift. In this paper, we share a case study of a food sovereignty tool that was designed through a participatory, community-based process in Northern Haiti, offering a blueprint and lessons learned that we hope will be useful to others working to operationalize food sovereignty.

ENSO-based outlook of droughts and agricultural outcomes in Afghanistan

Drought is one of the key drivers of food insecurity in Afghanistan, which is among the most food insecure countries in the world. In this study, we build on previous research and seek to answer the central question: “What is the influence of El Niño-Southern Oscillation (ENSO) on drought outlooks and agricultural yield outcome in Afghanistan, and how do these influences vary spatially?” We do so by utilizing multiple indicators of droughts and available wheat yield reports. We find a clear distinction in the probability of drought (defined as being in the lower tercile) in Afghanistan during La Niña compared to El Niño events since 1981. The probability of drought in Afghanistan increased during La Niña, particularly in the North, Northeast, and West regions. La Niña events are related to an increase in the probability of snow drought, particularly in parts of the Amu Darya basin. It is found that relative to El Niño events, snow water equivalent [total runoff] during La Niña events January–March (March–July total runoff) decreases between 9% and 30% (28%–42%) for the five major basins in the country. The probability of agricultural drought during La Niña events is found to be higher than 70% in the rainfed and irrigated areas of the Northeast, North, and West regions. This result is at least partly supported by reported wheat yield composites related to La Niña events that tend to be lower than for El Niño events across all regions in the case of rainfed wheat (statistically significant in Northeast, West, and South regions) and in some cases for irrigated wheat. The results of this study have direct implications for improving early warning of worsening food insecurity in Afghanistan during La Niña events, given that we now have long-lead and skillful forecasts of ENSO up to 18–24 months in advance, which could potentially be used to provide earlier warning of worsening food insecurity in Afghanistan

Redistribution of nitrogen to feed the people on a safer planet.

Lack of nitrogen limits food production in poor countries while excessive nitrogen use in industrial countries has led to transgression of the planetary boundary. However, the potential of spatial redistribution of nitrogen input for food security when returning to the safe boundary has not been quantified in a robust manner. Using an emulator of a global gridded crop model ensemble, we found that redistribution of current nitrogen input to major cereals among countries can double production in the most food-insecure countries, while increasing global production of these crops by 12% with no notable regional loss or reducing the nitrogen input to the current production by one-third. Redistribution of the input within the boundary increased production by 6-8% compared to the current relative distribution, increasing production in the food-insecure countries by two-thirds. Our findings provide georeferenced guidelines for redistributing nitrogen use to enhance food security while safeguarding the planet.

Co-existence of maternal overweight/obesity, child undernutrition, and anaemia among mother-child pairs in Ethiopia.

Ethiopia is currently known to be the most food-insecure country in sub-Saharan Africa, where childhood undernutrition remains endemic. While attention is increasingly being paid to childhood undernutrition in Ethiopia, a current surge of "triple burden of malnutrition" (TBM) has received less attention. The purpose of this study was to determine the prevalence of TBM and identify the associated factors in Ethiopia. Data were from the Ethiopian Demographic and Health Surveys (2005-2016) and a total of 20,994 mother-child pairs were examined in this study. The TBM was our primary outcome variable, which encompasses three types of nutritional problems-when a mother may be overweight/obese, while her child is stunted, wasted, or underweight plus has anaemia under the same roof. A multilevel logistic regression explored the individual- and community-level factors associated with TBM. Our study indicated that children under-five years of age were anaemic, stunted, wasted, and underweight [49.3% (95% CI: 48.7-49.9), 43.1% (95% CI: 42.4-43.7), 10.3% (95% CI: 9.9-10.7), and 27.6% (95% CI: 27.0-28.1)] respectively. The overall prevalence of TBM was 2.6% (95% CI: 2.39-2.83). Multilevel analyses revealed that TBM was more likely to occur among children aged 12-23 months (AOR: 2.54, 95% CI: 1.68-3.83), 24-35 months (AOR: 1.54, 95% CI: 1.03-2.29), children perceived by their mothers to be smaller than normal at birth (AOR: 1.94, 95% CI: 1.48-2.56), who experienced fever in the past 2 weeks (AOR: 1.58, 95% CI: 1.24-2.01), and lived in urban settings (AOR: 1.79, 95% CI: 1.13-2.86). Lower odds of TBM were reported among female children (AOR: 0.59, 95% CI: 0.47-0.72), and those who lived in rich households (AOR: 0.69: 95% CI: 0.49-0.98). TBM was found to be present in almost three percent of households in Ethiopia. Addressing the TBM through double-duty actions will be of critical importance in achieving malnutrition in all its forms in Ethiopia.

Household food insecurity and associated factors among postpartum women in southern Ethiopia: a community based cross sectional study

Approximately two billion individuals, or 26.4% of the global population, live in moderate- to severely food-insecure circumstances. It was discovered that not receiving all recommended antenatal care throughout one's pregnancy is one of the effects of household insecurity. The majority of women in Ethiopia, one of the most food-insecure countries in the world, with 10% of the population facing food poverty, devote more of their time to household duties, making food insecurity in the home the primary effect of poor prenatal care utilization. The main objective this study was to assess the status of household food insecurity among postpartum women at Arba Minch town, southern Ethiopia. A community-based cross-sectional study was conducted among 381 mothers who were enrolled from December 1, 2022, to January 30, 2023. The total sample size was allocated proportionately to the number of postpartum women living in each kebeles taking from the town registry of health extension workers for immunization. Thus, systematic sampling was applied. Kobo Toolbox was used for data collection and cleaning, which was then analyzed using the statistical package of Social Science Version 26 (SPSS). In this study, the prevalence of food insecurity was 30.2% (95% CI 25.5–34.5). The associated factors with household food-insecurity were maternal occupation (AOR = 0.5, 95% CI 0.27, 0.90), late antenatal care initiation (AOR = 3.5, 95% CI 2.13, 5.91), and low monthly income (AOR = 3.1, 95% CI 1.38, 6.93). Food insecurity among postpartum mothers in the study area is high. Families who are severely food insecure require quick assistance to lower poor maternal and neonate’s outcomes. Furthermore, enhancing the occupation of mothers is crucial in reducing the morbidities and mortality of food insecure mothers, such as delayed prenatal care services, anemia, low birth weight, and stillbirth.

Is deeper always better? Evaluating deep learning models for yield forecasting with small data

Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based on deep 1D and 2D convolutional neural networks using limited data. This approach meets the operational requirements—public and global records of satellite data in an application ready format with near real time updates—and can be transferred to any country with reliable yield statistics. Three-dimensional histograms of normalized difference vegetation index (NDVI) and climate data are used as input to the 2D model, while simple administrative-level time series averages of NDVI and climate data to the 1D model. The best model architecture is automatically identified during efficient and extensive hyperparameter optimization. To demonstrate the relevance of this approach, we hindcast (2002–2018) the yields of Algeria’s three main crops (barley, durum and soft wheat) and contrast the model’s performance with machine learning algorithms and conventional benchmark models used in a previous study. Simple benchmarks such as peak NDVI remained challenging to outperform while machine learning models were superior to deep learning models for all forecasting months and all tested crops. We attribute the poor performance of deep learning to the small size of the dataset available.

Cryopreservation of Seeds of Neonotonia Wightii Wight & Arn: A Strategy for Conservation, Dormancy Breaking and Preservation of Nutritional Status

BACKGROUND: N. wightii (Leguminosae) is valued as a cover crop and as a potential source of protein in food insecure countries. However, plantlet establishment is limited by physical dormancy. Our previous work has shown that exposure of N. wightii seeds to cryogenic temperatures is able to overcome physical dormancy. OBJECTIVE: The current study is an extension of that work where the field performance and nutritional composition of plants regenerated from N. wightii seeds was investigated. RESULTS: It was evident that plants regenerated from cryopreserved seeds displayed faster growth rates than those from control seeds. In addition, cryopreservation did not alter the nutritional profile of plants produced from cryo-stored seeds. CONCLUSION: Collectively, the results indicate that cryopreservation serves as a suitable strategy for the preservation of seeds of N. wightii with the added benefit of also serving as a dormancy breaking mechanism upon retrieval from cryogenic temperatures.

Global Patterns of Agricultural Investment and Food Security: Evidence from the fDi Markets Database.

The number of hungry people is on the rise and more efforts are needed to improve the global food security status. The Food and Agriculture Organization (FAO) proposes more investment in the agricultural sector to boost production and alleviate hunger. However, there are fewer papers that distinguish enterprises investment from public investment. In this case, we take advantage of detailed investment data in the fDi Markets database to explore the global patterns of agricultural investment. In particular, we identify the top destination countries based on aggregate and sub-sectoral agricultural investment data. Then we investigate the relationship between agricultural investment and food security, which is measured by per capita protein intake. Finally, we propose some suggestions from the investment motivation perspective to help food-insecure countries to attract overseas investment. We find that developed countries are the primary sources of global agricultural investment and these sources have been becoming more diverse in the past decade. It implies the trend towards a more inclusive investment environment worldwide. However, the global distribution of agricultural investment is uneven as food-insecure countries only receive 20% of the global agricultural investment. The top three destination countries, USA, China, and Russia, have a relatively high food security level. In contrast, countries suffering from food insecurity receive fewer investment projects, and most of which are on a small scale. Given the limited socio-economic development status in food-insecure countries, it is essential for all levels of society to help them and contribute to ending hunger.

Predicting food crises using news streams.

Anticipating food crisis outbreaks is crucial to efficiently allocate emergency relief and reduce human suffering. However, existing predictive models rely on risk measures that are often delayed, outdated, or incomplete. Using the text of 11.2 million news articles focused on food-insecure countries and published between 1980 and 2020, we leverage recent advances in deep learning to extract high-frequency precursors to food crises that are both interpretable and validated by traditional risk indicators. We demonstrate that over the period from July 2009 to July 2020 and across 21 food-insecure countries, news indicators substantially improve the district-level predictions of food insecurity up to 12 months ahead relative to baseline models that do not include text information. These results could have profound implications on how humanitarian aid gets allocated and open previously unexplored avenues for machine learning to improve decision-making in data-scarce environments.

Evaluating farmland ecosystem resilience and its obstacle factors in Ethiopia

In the context of global warming, the sustainability of farmland ecosystems is increasingly impacted by multiple disturbances from both natural and human-induced sources. This study constructed a conceptual model and indicator system of farmland ecosystem resilience (FER) based on the disturbance-response processes of farmland ecosystems. FER assessment, supported by 30 specific indicators, was tested in Ethiopia, one of the most food-insecure countries in the world and the factors impending farmland ecosystem sustainability were discussed based on the obstacle degree values (ODVs). The results showed that the FER change rate in Ethiopia was 0.3 %/year during 2003–2018, indicating positive sustainability on the whole. However, in the five years of 2004, 2007, 2008, 2011, and 2017, the FER decreased, among which the largest decline was −0.017 in 2017. Natural comprehensive disturbance (ND), human activity disturbance (HD), resilience of farmland supply (RFS), and resilience of social support (RS) were the key constraints of FER, while the ODVs of ND and RFS showed increasing trends. We also found that there were general antagonistic and synergistic effects among the response factors and the synergistic enhancement between disturbance factors was more pronounced than the antagonistic buffering effect. This study provides a fresh perspective for farmland ecosystem managers and stakeholders to examine disturbance risks and assess the sustainability of farmland ecosystems.

Climate Change and Food Security: Are Asian Economies Tracking Sustainable Development Goals?

Sustainable Development Goals 2030 is a sustainable development track approved by the United Nations in 2015. All member countries are trying to attain these SDGs. Unfortunately, Asian countries are still far from this track. Achieving SDG 2 (zero hunger) under the shadow of climate change (SDG 13) requires an indispensable line of action for the developing world and, specifically, Asian countries. Food security is the first step to achieving SDG 2 with changing climate. This study investigates the probable footprints of climate change and other socioeconomic determinants on food security in 8 Asian food-insecure countries from 1990 to 2019. The study secrutinizes climate change and food security literature as they relate to an SDG-oriented policy agenda. The study results reveal that climate change and socioeconomic determinants substantially affect food security. The Panel Quantile Autoregressive Distributed Lag Model (PQARDL) outcomes illustrate that undernourishment prevalence (SDG 2.1.1) will likely be reduced due to increased agricultural productivity, food availability, and economic growth. However, this is compromised by the negative effects of greater variations in temperature and food supply. Increasing rainfall is likely to reduce the prevalence of undernourishment (SDG 2.1.1) in the long run. Contrarily, a temperature rise will exacerbate the prevalence of undernourishment (SDG 2.1.1). Based on the outcomes, comprehensive SDG-oriented policy recommendations have been put forward to realize the goals of SDG 2, SDG 8 and SDG 13 for Asian food-insecure countries.

Degree-Granting Nutrition Programs in the Republic of Yemen: A Status Report on Postsecondary Education.

Yemen is noted as one of the most food-insecure countries in the Arab world coupled with high rates of malnutrition. To address the public health outcomes related to nutrition, trained nutrition professionals are needed. This report provides a snapshot of current nutrition-affiliated programs offered in postsecondary institutions in Yemen. Partnering with or creating independent organizations responsible for defining the scope of practice is warranted. Additionally, ensuring educational quality, program accreditation, and competence of graduating students and readiness to practice will be central to progressively moving Yemen’s dietetics profession forward.

Insights into countries\u2019 exposure and vulnerability to food trade shocks from network-based simulations

In the context of a global food system, the dynamics associated to international food trade have become key determinants of food security. In this paper, we resort to a diffusion model to simulate how shocks to domestic food production propagate through the international food trade network and study the relationship between trade openness and vulnerability. The results of our simulations suggest that low-income and food insecure countries tend to be the more exposed to external shocks and, at the same time, they are usually not in a position to take full advantage of international food trade when it comes to shield themselves from shocks to domestic production. We also study and discuss how nodes characteristics are associated with the propagation dynamics and with countries’ vulnerability, finding that simple centrality measures can significantly predict the magnitude of the shock experienced by individual countries.

Food security and emerging infectious disease: risk assessment and risk management.

Climate change, emerging infectious diseases (EIDs) and food security create a dangerous nexus. Habitat interfaces, assumed to be efficient buffers, are being disrupted by human activities which in turn accelerate the movement of pathogens. EIDs threaten directly and indirectly availability and access to nutritious food, affecting global security and human health. In the next 70 years, food-secure and food-insecure countries will face EIDs driving increasingly unsustainable costs of production, predicted to exceed national and global gross domestic products. Our modern challenge is to transform this business as usual and embrace an alternative vision of the biosphere formalized in the Stockholm paradigm (SP). First, a pathogen-centric focus shifts our vision of risk space, determining how pathogens circulate in realized and potential fitness space. Risk space and pathogen exchange are always heightened at habitat interfaces. Second, apply the document-assess-monitor-act (DAMA) protocol developing strategic data for EID risk, to be translated, synthesized and broadcast as actionable information. Risk management is realized through targeted interventions focused around information exchanged among a community of scientists, policy practitioners of food and public health security and local populations. Ultimately, SP and DAMA protect human rights, supporting food security, access to nutritious food, health interventions and environmental integrity.

Food security prediction from heterogeneous data combining machine and deep learning methods

After many years of decline, hunger in Africa is growing again. This represents a global societal issue that all disciplines concerned with data analysis are facing. The rapid and accurate identification of food insecurity situations is a complex challenge. Although a number of food security alert and monitoring systems exist in food insecure countries, the data and methodologies they are based on do not allow for comprehending food security in all its complexity. In this study, we focus on two key food security indicators: the food consumption score (FCS) and the household dietary diversity score (HDDS). Based on the observation that producing such indicators is expensive in terms of time and resources, we propose the FSPHD (Food Security Prediction based on Heterogeneous Data) framework, based on state-of-the-art machine and deep learning models, to enable the estimation of FCS and HDDS starting from publicly available heterogeneous data. We take into account the indicators estimated using data from the Permanent Agricultural Survey conducted by the Burkina Faso government from 2009 to 2018 as reference data. We produce our estimations starting from heterogeneous data that include rasters (e.g., population density, land use, soil quality), GPS points (hospitals, schools, violent events), line vectors (waterways), quantitative variables (maize prices, World Bank variables, meteorological data) and time series (Smoothed Brightness Temperature — SMT, rainfall estimates, maize prices). The experimental results show a promising performance of our framework, which outperforms competing methods, thus paving the way for the development of advanced food security prediction systems based on state-of-the-art data science technologies.

Yield forecasting with machine learning and small data: What gains for grains?

Forecasting crop yields is important for food security, in particular to predict where crop production is likely to drop. Climate records and remotely-sensed data have become instrumental sources of data for crop yield forecasting systems. Similarly, machine learning methods are increasingly used to process big Earth observation data. However, access to data necessary to train such algorithms is often limited in food-insecure countries. Here, we evaluate the performance of machine learning algorithms and small data to forecast yield on a monthly basis between the start and the end of the growing season. To do so, we developed a robust and automated machine-learning pipeline which selects the best features and model for prediction. Taking Algeria as case study, we predicted national yields for barley, soft wheat and durum wheat with an accuracy of 0.16–0.2 t/ha (13-14% of mean yield) within the season. The best machine-learning models always outperformed simple benchmark models. This was confirmed in low-yielding years, which is particularly relevant for early warning. Nonetheless, the differences in accuracy between machine learning and benchmark models were not always of practical significance. Besides, the benchmark models outperformed up to 60% of the machine learning models that were tested, which stresses the importance of proper model calibration and selection. For crop yield forecasting, like for many application domains, machine learning has delivered significant improvement in predictive power. Nonetheless, superiority over simple benchmarks is fully achieved after extensive calibration, especially when dealing with small data.

Nutritional Qualities and Enhancement of Edible Insects.

Over the last decade, the urgency to find alternative and sustainable protein sources has prompted an exponential increase in the interest in insects as a human food source. Edible insects contribute suitable amounts of energy and protein, fatty acids, and micronutrients to the human diet. Nutritional values of insects can be manipulated to meet specific needs. Edible insects in food-insecure countries can contribute to improving diets and preventing undernutrition. Bioactive compounds in insects may reduce health risks. Food safety risks are low and mainly relate to those of allergenicity. Strategies to overcome barriers to the consumption of insect products include emphasizing their sustainability, increasing their tastiness, and developing the ability to disguise insects in familiar products. A new sector of insects as food and feed is emerging. Major challenges include legislation, lowering prices by automation and cheap substrates, developing insect products that appeal to consumers, and exploring the health benefits.

Socio-Economic Considerations and Potential Implications for Gene-Edited Crops

Regulatory clarity and efficiency are increasingly important for the successful commercialization of technologies resulting from public and private R&D investments. This article examines recent developments in the movement away from mostly science-based risk assessment regulatory and variety approval systems focusing on human and animal health and environmental safety to hybrid systems that include assessment of socio-economic considerations allowed for under the auspices of the Cartagena Protocol on Biosafety. We propose that socio-economic consideration assessments can be grouped into three methodological categories: empirically based, legally grounded and consensus approaches. Our exploration of developments in the three categories reveals gaps in data, consistency and methodology rigor, that must be addressed for efficient and reliable socio-economic consideration assessment. We assess the potential impacts of these gaps on the regulation of gene-edited crop varieties, concluding that if gene-edited crops are regulated as genetically modified crops, they will endure the same fate, that is, lengthy regulatory approval processes and failure to be commercialized. The end result being that the regulatory burdens in potential adopting and food insecure countries will prevent important new crop varieties from reaching farmers and producers for their use. https://doi.org/10.21423/jrs-v09i2ludlow

The impact of climate and societal change on food and nutrition security: A case study of Malawi

AbstractSociety is currently facing an unprecedented challenge in terms of achieving food and nutrition security for a rapidly expanding global population while also minimising and reversing damage to the natural environment. Compounding this issue is climate change, which adversely affects the four pillars of food security: availability, access, utilisation and stability. This study aims to quantify the potential impact of future climate and societal change on food and nutrition security under a range of plausible scenarios. Malawi is used as a case study given it is one of the most food insecure countries in the world. Using the Food Estimation and Export for Diet and Malnutrition Evaluation modelling framework, the quantity and quality of the national food supply are assessed under a suite of future (2050) climate and socioeconomic scenarios. The results indicate that undernourishment prevalence could be reduced in Malawi under a best‐case scenario; however, undernourishment is likely to increase assuming either a business‐asusual or a pessimistic scenario. On the other hand, the quality of the food supply in Malawi (in terms of micronutrient provision) is likely to decrease even under a best‐case scenario. Moreover, projected dietary change in the form of nutrition transition in Malawi is unlikely to improve micronutrient provision sufficiently to meet requirements. This is a consequence of the already low supply of micronutrient dense foods in Malawi, the negative impact of climate change on micronutrient dense crops and an insufficient increase in micronutrient dense foods associated with nutrition transition. This study highlights the importance of moving beyond the focus on dietary energy supply as a measure of food security since nutrient adequacy of diets may be a more pressing issue in the future than simply the quantity of food and supply of energy.

The potential of agricultural programs for improving food security: A multi-country perspective

Although an emerging literature evaluates the impact of agricultural interventions on food security, studies taking a multi-country perspective are scarce. We construct a cross-project dataset - encompassing 14 household surveys from IFAD-funded project-level impact evaluations in 13 different countries - to analyze the overall effect of different agricultural interventions on food security. We use the Household Dietary Diversity Score (HDDS) as a measure for household-level access to dietary energy and quality, and the prevalence of food insecurity based on the Food Insecurity Experience Scale (FIES) as a complementarity, subjective indicator for food security. Results of random coefficient models show that agricultural interventions can improve food security, especially for beneficiaries of agricultural projects living in food insecure countries. We further show a slight dietary shift towards animal-sourced foods attributed to agricultural interventions implemented in low-income countries. Thus, especially in these settings, adding a specific nutrition component to agricultural projects could be effective in preventing a further shift towards unhealthy diets and halting the double (or even triple) burden of malnutrition before it can develop further.