Extreme climate events increase risk of global food insecurity and adaptation needs

Metal-Organic Framework-Enabled Sustainable Agrotechnologies: An Overview of Fundamentals and Agricultural Applications

With aggravated abiotic and biotic stresses from increasing climate change, metal-organic frameworks (MOFs) have emerged as versatile toolboxes for developing environmentally friendly agrotechnologies aligned with agricultural practices and safety. Herein, we have explored MOF-based agrotechnologies, focusing on their intrinsic properties, such as structural and catalytic characteristics. Briefly, MOFs possess a sponge-like porous structure that can be easily stimulated by the external environment, facilitating the controlled release of agrochemicals, thus enabling precise delivery of agrochemicals. Additionally, MOFs offer the ability to remove or degrade certain pollutants by capturing them within their pores, facilitating the development of MOF-based remediation technologies for agricultural environments. Furthermore, the metal-organic hybrid nature of MOFs grants them abundant catalytic activities, encompassing photocatalysis, enzyme-mimicking catalysis, and electrocatalysis, allowing for the integration of MOFs into degradation and sensing agrotechnologies. Finally, the future challenges that MOFs face in agrotechnologies were proposed to promote the development of sustainable agriculture practices.

Key predictors of food security and nutrition in Africa: a spatio-temporal model-based study

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other three models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.

Perovskite Nanocrystals: Superior Luminogens for Food Quality Detection Analysis

With the global limited food resources receiving grievous damage from frequent climate changes and ascending global food demand resulting from increasing population growth, perovskite nanocrystals with distinctive photoelectric properties have emerged as attractive and prospective luminogens for the exploitation of rapid, easy operation, low cost, highly accurate, excellently sensitive, and good selective biosensors to detect foodborne hazards in food practices. Perovskite nanocrystals have demonstrated supreme advantages in luminescent biosensing for food products due to their high photoluminescence (PL) quantum yield, narrow full width at half-maximum PL, tunable PL in the entire visible spectrum, easy preparation, and various modification strategies compared with conventional semiconductors. Herein, we have carried out a comprehensive discussion concerning perovskite nanocrystals as luminogens in the application of high-performance biosensing of foodborne hazards for food products, including a brief introduction of perovskite nanocrystals, perovskite nanocrystal-based biosensors, and their application in different categories of food products. Finally, the challenges and opportunities faced by perovskite nanocrystals as superior luminogens were proposed to promote their practicality in the future food supply.

Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years

Climate change-induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (Nw). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in Nw, ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme Nw. This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme Nw with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

Nano-enabled seed treatment: A new and sustainable approach to engineering climate-resilient crops

Under a changing climate, keeping the food supply steady for an ever-increasing population will require crop plants adapted to environmental fluctuations. Genetic engineering and genome-editing approaches have been used for developing climate-resilient crops. However, genetically modified crops have yet to be widely accepted, especially for small-scale farmers in low-income countries and some societies. Nano-priming (seed exposure to nanoparticles, NPs) has appeared as an alternative to the abovementioned techniques. This technique improves seed germination speed, promotes seedlings' vigor, and enhances plant tolerance to adverse conditions such as drought, salinity, temperature, and flooding, which may occur under extreme weather conditions. Moreover, nano-enabled seed treatment can increase the disease resistance of crops by boosting immunity, which will reduce the use of pesticides. This unsophisticated, farmer-available, cost-effective, and environment-friendly seed treatment approach may help crop plants fight climate change challenges. This review discusses the previous information about nano-enabled seed treatment for enhancing plant tolerance to abiotic stresses and increasing disease resistance. Current knowledge about the mechanisms underlying nanomaterial-seed interactions is discussed. To conclude, the review includes research questions to address before this technique reaches its full potential.

Intensifying rice production to reduce imports and land conversion in Africa

Africa produces around 60% of the rice the continent consumes, relying heavily on rice imports to fulfill the rest of the domestic demand. Over the past 10 years, the rice-agricultural area increased nearly 40%, while average yield remained stagnant. Here we used a process-based crop simulation modelling approach combined with local weather, soil, and management datasets to evaluate the potential to increase rice production on existing cropland area in Africa and assess cropland expansion and rice imports by year 2050 for different scenarios of yield intensification. We find that Africa can avoid further increases in rice imports, and even reduce them, through a combination of cropland expansion following the historical trend together with closure of the current exploitable yield gap by half or more. Without substantial increase in rice yields, meeting future rice demand will require larger rice imports and/or land conversion than now.

Dietary quality and cardiometabolic indicators in the USA: A comparison of the Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension

BACKGROUND

The Planetary Health Diet Index (PHDI) measures adherence to the sustainable dietary guidance proposed by the EAT-Lancet Commission on Food, Planet, Health. To justify incorporating sustainable dietary guidance such as the PHDI in the US, the index needs to be compared to health-focused dietary recommendations already in use. The objectives of this study were to compare the how the Planetary Health Diet Index (PHDI), the Healthy Eating Index-2015 (HEI-2015) and Dietary Approaches to Stop Hypertension (DASH) relate to cardiometabolic risk factors.

METHODS AND FINDINGS

Participants from the National Health and Nutrition Examination Survey (2015-2018) were assigned a score for each dietary index. We examined disparities in dietary quality for each index. We used linear and logistic regression to assess the association of standardized dietary index values with waist circumference, blood pressure, HDL-C, fasting plasma glucose (FPG) and triglycerides (TG). We also dichotomized the cardiometabolic indicators using the cutoffs for the Metabolic Syndrome and used logistic regression to assess the relationship of the standardized dietary index values with binary cardiometabolic risk factors. We observed diet quality disparities for populations that were Black, Hispanic, low-income, and low-education. Higher diet quality was associated with improved continuous and binary cardiometabolic risk factors, although higher PHDI was not associated with high FPG and was the only index associated with lower TG. These patterns remained consistent in sensitivity analyses.

CONCLUSIONS

Sustainability-focused dietary recommendations such as the PHDI have similar cross-sectional associations with cardiometabolic risk as HEI-2015 or DASH. Health-focused dietary guidelines such as the forthcoming 2025-2030 Dietary Guidelines for Americans can consider the environmental impact of diet and still promote cardiometabolic health.

Building soil to reduce climate change impacts on global crop yield

Improving soil health and resilience is fundamental for sustainable food production, however the role of soil in maintaining or improving global crop productivity under climate warming is not well identified and quantified. Here, we examined the impact of soil on yield response to climate warming for four major crops (i.e., maize, wheat, rice and soybean), using global-scale datasets and random forest method. We found that each °C of warming reduced global yields of maize by 3.4%, wheat by 2.4%, rice by 0.3% and soybean by 5.0%, which were spatially heterogeneous with possible positive impacts. The random forest modeling analyses further showed that soil organic carbon (SOC), as an indicator of soil quality, dominantly explained the spatial heterogeneity of yield responses to warming and would regulate the negative warming responses. Improving SOC under the medium SOC sequestration scenario would reduce the warming-induced yield loss of maize, wheat, rice and soybean to 0.1% °C-1, 2.7% °C-1, 3.4% °C-1 and - 0.6% °C-1, respectively, avoiding an average of 3%-5% °C-1 of global yield loss. These yield benefits would occur on 53.2%, 67.8%, 51.8% and 71.6% of maize, wheat, rice and soybean planting areas, respectively, with particularly pronounced benefits in the regions with negative warming responses. With improved soil carbon, food systems are predicted to provide additional 20 to over 130 million tonnes of food that would otherwise lose due to future warming. Our findings highlight the critical role of soil in alleviating negative warming impacts on food security, especially for developing regions, given that sustainable actions on soil improvement could be taken broadly.

Generation of three-dimensional meat-like tissue from stable pig epiblast stem cells

Cultured meat production has emerged as a breakthrough technology for the global food industry with the potential to reduce challenges associated with environmental sustainability, global public health, animal welfare, and competition for food between humans and animals. The muscle stem cell lines currently used for cultured meat cannot be passaged in vitro for extended periods of time. Here, we develop a directional differentiation system of porcine pre-gastrulation epiblast stem cells (pgEpiSCs) with stable cellular features and achieve serum-free myogenic differentiation of the pgEpiSCs. We show that the pgEpiSCs-derived skeletal muscle progenitor cells and skeletal muscle fibers have typical muscle cell characteristics and display skeletal muscle transcriptional features during myogenic differentiation. Importantly, we establish a three-dimensional differentiation system for shaping cultured tissue by screening plant-based edible scaffolds of non-animal origin, followed by the generation of pgEpiSCs-derived cultured meat. These advances provide a technical approach for the development of cultured meat.

Greening of human-dominated ecosystems in India

Satellite data show the Earth has been greening and identify croplands in India as one of the most prominent greening hotspots. Though India's agriculture has been dependent on irrigation enhancement to reduce crop water stress and increase production, the spatiotemporal dynamics of how irrigation influenced the satellite observed greenness remains unclear. Here, we use satellite-derived leaf area data and survey-based agricultural statistics together with results from state-of-the-art Land Surface Models (LSM) to investigate the role of irrigation in the greening of India's croplands. We find that satellite observations provide multiple lines of evidence showing strong contributions of irrigation to significant greening during dry season and in drier environments. The national statistics support irrigation-driven yield enhancement and increased dry season cropping intensity. These suggest a continuous shift in India's agriculture toward an irrigation-driven dry season cropping system and confirm the importance of land management in the greening phenomenon. However, the LSMs identify CO2 fertilization as a primary driver of greening whereas land use and management have marginal impacts on the simulated leaf area changes. This finding urges a closer collaboration of the modeling, Earth observation, and land system science communities to improve representation of land management in the Earth system modeling.

Regional inequalities of future climate change impact on rice (Oryza sativa L.) yield in China

The implications of climate change for rice yield have significant repercussions for food security, particularly in China, where rice cultivation is diverse, involving various cropping intensities, management practices, and climate conditions across numerous regions. The regional discrepancies in the impact of climate change on rice yield in China, however, are yet to be fully understood. Using the ORYZA(v3) model and future climate data from 2025 to 2084, gathered from ten climate models and three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5), we conducted an investigation into these regional discrepancies. Our findings suggest a projected average decline in rice yield ranging from 3.7 % to 16.4 % under both rainfed and fully irrigated conditions across different scenarios. Central, eastern, and northwestern China could face the most significant climate change impacts on both rainfed and irrigated rice, with yield reductions reaching 41.5 %. In contrast, low levels of climate change under the RCP2.6 scenario may benefit northeastern (2.4 %) and southern (1.0 %) regions for rainfed and irrigated rice, respectively. Fertilization effects from elevated CO2 could counterbalance climate change's negative impact, resulting in yield increases in all Chinese rice-growing regions, excluding the northwest. The primary factor influencing rice yield changes in all regions under the RCP4.5 and RCP8.5 scenarios was temperature. However, precipitation, solar radiation, and relative humidity had notable and sometimes dominant effects, especially under the RCP2.6 scenario. These results highlight the divergent, even contradictory, rice yield responses to climate change across China, underlining the need to account for regional differences in large-scale impact studies. The study's findings can inform future policy decisions regarding ensuring regional and national food security in China.

Food retail environments, extreme weather, and their overlap: Exploratory analysis and recommendations for U.S. food policy

Extreme weather events are increasing in frequency and severity due to climate change, yet many of their impacts on human populations are not well understood. We examine the relationship between prior extreme weather events and food environment characteristics. To do so, we conduct a U.S. county-level analysis that assesses the association between extreme weather events and two common food retail environment dimensions. Overall, we find a relationship between higher levels of historic extreme weather exposure and lower food availability and accessibility. In addition, we find heterogeneity in association across the distribution of the number of extreme weather events and event type. Specifically, we find that more localized extreme weather events are more associated with a reduction of access and availability than broad geographic events. Our findings suggest that as extreme weather events amplify in intensity and increase in frequency, new approaches for mitigating less acute and longer-term impacts are needed to address how extreme weather may interact with and reinforce existing disparities in food environment factors. Furthermore, our research argues that integrated approaches to improving vulnerable food retail environments will become an important component of extreme weather planning and should be a consideration in both disaster- and food-related policy.

Geospatial Overlap of Undernutrition and Tuberculosis in Ethiopia

Undernutrition is a key driver of the global tuberculosis (TB) epidemic, yet there is limited understanding regarding the spatial overlap of both diseases. This study aimed to determine the geographical co-distribution and socio-climatic factors of undernutrition and TB in Ethiopia. Data on undernutrition were found from the Ethiopian Demographic and Health Survey (EDHS). Data on TB were obtained from the Ethiopia national TB prevalence survey. We applied a geostatistical model using a Bayesian framework to predict the prevalence of undernutrition and TB. Spatial overlap of undernutrition and TB prevalence was detected in the Afar and Somali regions. Population density was associated with the spatial distribution of TB [β: 0.008; 95% CrI: 0.001, 0.014], wasting [β: -0.017; 95% CrI: -0.032, -0.004], underweight [β: -0.02; 95% CrI: -0.031, -0.011], stunting [β: -0.012; 95% CrI: -0.017, -0.006], and adult undernutrition [β: -0.007; 95% CrI: -0.01, -0.005]. Distance to a health facility was associated with the spatial distribution of stunting [β: 0.269; 95% CrI: 0.08, 0.46] and adult undernutrition [β: 0.176; 95% CrI: 0.044, 0.308]. Healthcare access and demographic factors were associated with the spatial distribution of TB and undernutrition. Therefore, geographically targeted service integration may be more effective than nationwide service integration.

Extreme low-temperature events can alleviate micronutrient deficiencies while increasing potential health risks from heavy metals in rice

Despite global warming, extreme low-temperature stress (LTS) events pose a significant threat to rice production (especially in East Asia) that can significantly impact micronutrient and heavy metal elements in rice. With two billion people worldwide facing micronutrient deficiencies (MNDs) and widespread heavy metal pollution in rice, understanding these impacts is crucial. We conducted detailed extreme LTS experiments with two rice (Oryza sativa L.) cultivars (Huaidao 5 and Nanjing 46) grown under four temperature levels (from 21/27 °C to 6/12 °C) and three LTS durations (three, six, and nine days). We observed significant interaction effects for LTS at different growth stages, durations and temperature levels on the contents and accumulation of mineral elements. The contents of most mineral elements (such Fe, Zn, As, Cu, and Cd) increased significantly under severe LTS at flowering, but decreased under LTS at the grain-filling stage. The accumulations of all mineral elements decreased at the three growth stages under LTS due to decreased grain weight. The contents and accumulation of mineral elements were more sensitive to LTS at the peak flowering stage than at the other two stages. Furthermore, the contents of most mineral elements in Nanjing 46 show larger variation under LTS compared to Huaidao 5. Accumulated cold degree days (ACDD, °C·d) were found to be suitable for quantifying the effects of LTS on the relative contents and accumulations of mineral elements. LTS at the flowering stage will help alleviate MNDs, but may also increase potential health risks from heavy metals. These results provide valuable insights for evaluating future climate change impacts on rice grain quality and potential health risks from heavy metals.

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.

Artificial intelligence achieves easy-to-adapt nonlinear global temperature reconstructions using minimal local data

Understanding monthly-to-annual climate variability is essential for adapting to future climate extremes. Key ways to do this are through analysing climate field reconstructions and reanalyses. However, producing such reconstructions can be limited by high production costs, unrealistic linearity assumptions, or uneven distribution of local climate records. Here, we present a machine learning-based non-linear climate variability reconstruction method using a Recurrent Neural Network that is able to learn from existing model outputs and reanalysis data. As a proof-of-concept, we reconstructed more than 400 years of global, monthly temperature anomalies based on sparse, realistically distributed pseudo-station data and show the impact of different training data sets. Our reconstructions show realistic temperature patterns and magnitude reproduction costing about 1 hour on a middle-class laptop. We highlight the method's capability in terms of mean statistics compared to more established methods and find that it is also suited to reconstruct specific climate events. This approach can easily be adapted for a wide range of regions, periods and variables.

Widening mismatch between UK seafood production and consumer demand: a 120-year perspective

Developed countries are increasingly dependent on international trade to meet seafood requirements, which has important social, environmental, and economic implications. After becoming an independent coastal state following Brexit, the UK faces increased trade barriers and changes in seafood availability and cost. We compiled a long-term (120-year) dataset of UK seafood production (landings and aquaculture), imports, and exports, and assessed the influence of policy change and consumer preference on domestic production and consumption. In the early twentieth century, distant-water fisheries met an increasing demand for large, flaky fish such as cod and haddock that are more abundant in northerly waters. Accordingly, from 1900 to 1975, the UK fleet supplied almost 90% of these fish. However, policy changes in the mid-1970s such as the widespread establishment of Exclusive Economic Zones and the UK joining the European Union resulted in large declines in distant-water fisheries and a growing mismatch between seafood production versus consumption in the UK. While in 1975, UK landings and aquaculture accounted for 89% of seafood consumed by the British public, by 2019 this was only 40%. The combination of policy changes and staunch consumer preferences for non-local species has resulted in today's situation, where the vast majority of seafood consumed in the UK is imported, and most seafood produced domestically is exported. There are also health considerations. The UK public currently consumes 31% less seafood than is recommended by government guidelines, and even if local species were more popular, total domestic production would still be 73% below recommended levels. In the face of climate change, global overfishing and potentially restrictive trade barriers, promoting locally sourced seafood and non-seafood alternatives would be prudent to help meet national food security demands, and health and environmental targets..

Supplementary Information

The online version contains supplementary material available at 10.1007/s11160-023-09776-5.

Trophic transfer of silver nanoparticles shifts metabolism in snails and reduces food safety

Food security and sustainable development of agriculture has been a key challenge for decades. To support this, nanotechnology in the agricultural sectors increases productivity and food security, while leaving complex environmental negative impacts including pollution of the human food chains by nanoparticles. Here we model the effects of silver nanoparticles (Ag-NPs) in a food chain consisting of soil-grown lettuce Lactuca sativa and snail Achatina fulica. Soil-grown lettuce were exposed to sulfurized Ag-NPs via root or metallic Ag-NPs via leaves before fed to snails. We discover an important biomagnification of silver in snails sourced from plant root uptake, with trophic transfer factors of 2.0-5.9 in soft tissues. NPs shifts from original size (55-68 nm) toward much smaller size (17-26 nm) in snails. Trophic transfer of Ag-NPs reprograms the global metabolic profile by down-regulating or up-regulating metabolites for up to 0.25- or 4.20- fold, respectively, relative to the control. These metabolites control osmoregulation, phospholipid, energy, and amino acid metabolism in snails, reflecting molecular pathways of biomagnification and pontential adverse biological effects on lower trophic levels. Consumption of these Ag-NP contaminated snails causes non-carcinogenic effects on human health. Global public health risks decrease by 72% under foliar Ag-NP application in agriculture or through a reduction in the consumption of snails sourced from root application. The latter strategy is at the expense of domestic economic losses in food security of $177.3 and $58.3 million annually for countries such as Nigeria and Cameroon. Foliar Ag-NP application in nano-agriculture has lower hazard quotient risks on public health than root application to ensure global food safety, as brought forward by the United Nations Sustainable Development Goals.

Engineering Climate-Resilient Rice Using a Nanobiostimulant-Based "Stress Training" Strategy

Under a changing climate, cultivating climate-resilient crops will be critical to maintaining food security. Here, we propose the application of reactive oxygen species (ROS)-generating nanoparticles as nanobiostimulants to trigger stress/immune responses and subsequently increase the stress resilience of plants. We established three regimens of silver nanoparticles (AgNPs)-based "stress training": seed training (ST), leaf training (LT), and combined seed and leaf training (SLT). Trained rice seedlings were then exposed to either rice blast fungus (Magnaporthe oryzae) or chilling stress (10 °C). The results show that all "stress training" regimes, particularly SLT, significantly enhanced the resistance of rice against the fungal pathogen (lesion size reduced by 82% relative to untrained control). SLT also significantly enhanced rice tolerance to cold stress. The mechanisms for the enhanced resilience were investigated with metabolomics and transcriptomics, which show that "stress training" induced considerable metabolic and transcriptional reprogramming in rice leaves. AgNPs boosted ROS-activated stress signaling pathways by oxidative post-translational modifications of stress-related kinases, hormones, and transcriptional factors (TFs). These signaling pathways subsequently modulated the expression of defense genes, including specialized metabolites (SMs) biosynthesis genes, cell membrane lipid metabolism genes, and pathogen-plant interaction genes. Importantly, results showed that the "stress memory" can be transferred transgenerationally, conferring offspring seeds with improved seed germination and seedling vigor. This may provide an epigenetic breeding strategy to fortify stress resilience of crops. This nanobiostimulant-based stress training strategy will increase yield vigor against a changing climate and will contribute to sustainable agriculture by reducing agrochemical use.

Food insecurity and sleep health by race/ethnicity in the United States

Food insecurity, poised to increase with burgeoning concerns related to climate change, may influence sleep, yet few studies examined the food security-sleep association among racially/ethnically diverse populations with multiple sleep dimensions. We determined overall and racial/ethnic-specific associations between food security and sleep health. Using National Health Interview Survey data, we categorised food security as very low, low, marginal and high. Sleep duration was categorised as very short, short, recommended and long. Sleep disturbances included trouble falling/staying asleep, insomnia symptoms, waking up feeling unrested and using sleep medication (all ≥3 d/times in the previous week). Adjusting for socio-demographic characteristics and other confounders, we used Poisson regression with robust variance to estimate prevalence ratios (PRs) and 95 % confidence intervals (95 % CIs) for sleep dimensions by food security. Among 177 435 participants, the mean age of 47⋅2 ± 0⋅1 years, 52⋅0 % were women, and 68⋅4 % were non-Hispanic (NH)-White. A higher percent of NH-Black (7⋅9 %) and Hispanic/Latinx (5⋅1 %) lived in very low food security households than NH-White (3⋅1 %) participants. Very low v. high food security was associated with a higher prevalence of very short (PR = 2⋅61 [95 % CI 2⋅44-2⋅80]) sleep duration as well as trouble falling asleep (PR = 2⋅21 [95 % CI 2⋅12-2⋅30]). Very low v. high food security was associated with a higher prevalence of very short sleep duration among Asian (PR = 3⋅64 [95 % CI 2⋅67-4⋅97]) and NH-White (PR = 2⋅73 [95 % CI 2⋅50-2⋅99]) participants compared with NH-Black (PR = 2⋅03 [95 % CI 1⋅80-2⋅31]) and Hispanic/Latinx (PR = 2⋅65 [95 % CI 2⋅30-3⋅07]) participants. Food insecurity was associated with poorer sleep in a racially/ethnically diverse US sample.

Extreme rainfall reduces one-twelfth of China's rice yield over the last two decades

Extreme climate events constitute a major risk to global food production. Among these, extreme rainfall is often dismissed from historical analyses and future projections, the impacts and mechanisms of which remain poorly understood. Here we used long-term nationwide observations and multi-level rainfall manipulative experiments to explore the magnitude and mechanisms of extreme rainfall impacts on rice yield in China. We find that rice yield reductions due to extreme rainfall were comparable to those induced by extreme heat over the last two decades, reaching 7.6 ± 0.9% (one standard error) according to nationwide observations and 8.1 ± 1.1% according to the crop model incorporating the mechanisms revealed from manipulative experiments. Extreme rainfall reduces rice yield mainly by limiting nitrogen availability for tillering that lowers per-area effective panicles and by exerting physical disturbance on pollination that declines per-panicle filled grains. Considering these mechanisms, we projected ~8% additional yield reduction due to extreme rainfall under warmer climate by the end of the century. These findings demonstrate that it is critical to account for extreme rainfall in food security assessments.

On the forecastability of food insecurity

Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges included in the 2030 Agenda for Sustainable Development. Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk. Here, using food consumption observations in combination with secondary data on conflict, extreme weather events and economic shocks, we build a forecasting model based on gradient boosted regression trees to create predictions on the evolution of insufficient food consumption trends up to 30 days in to the future in 6 countries (Burkina Faso, Cameroon, Mali, Nigeria, Syria and Yemen). Results show that the number of available historical observations is a key element for the forecasting model performance. Among the 6 countries studied in this work, for those with the longest food insecurity time series, that is Syria and Yemen, the proposed forecasting model allows to forecast the prevalence of people with insufficient food consumption up to 30 days into the future with higher accuracy than a naive approach based on the last measured prevalence only. The framework developed in this work could provide decision makers with a tool to assess how the food insecurity situation will evolve in the near future in countries at risk. Results clearly point to the added value of continuous near real-time data collection at sub-national level.

Mapping Local Variations and the Determinants of Childhood Stunting in Nigeria

INTRODUCTION

Understanding the specific geospatial variations in childhood stunting is essential for aligning appropriate health services to where new and/or additional nutritional interventions are required to achieve the Sustainable Development Goals (SDGs) and national targets.

OBJECTIVES

We described local variations in the prevalence of childhood stunting at the second administrative level and its determinants in Nigeria after accounting for the influence of geospatial dependencies.

METHODS

This study used the 2018 national Nigeria Demographic and Health Survey datasets (NDHS; N = 12,627). We used a Bayesian geostatistical modelling approach to investigate the prevalence of stunting at the second administrative level and its proximal and contextual determinants among children under five years of age in Nigeria.

RESULTS

In 2018, the overall prevalence of childhood stunting in Nigeria was 41.5% (95% credible interval (CrI) from 26.4% to 55.7%). There were striking variations in the prevalence of stunting that ranged from 2.0% in Shomolu in Lagos State, Southern Nigeria to 66.4% in Biriniwa in Jigawa State, Northern Nigeria. Factors positively associated with stunting included being perceived as small at the time of birth and experience of three or more episodes of diarrhoea in the two weeks before the survey. Children whose mothers received a formal education and/or were overweight or obese were less likely to be stunted compared to their counterparts. Children who were from rich households, resided in households with improved cooking fuel, resided in urban centres, and lived in medium-rainfall geographic locations were also less likely to be stunted.

CONCLUSION

The study findings showed wide variations in childhood stunting in Nigeria, suggesting the need for a realignment of health services to the poorest regions of Northern Nigeria.

Possible factors determining global-scale patterns of crop yield sensitivity to drought

In recent decades, droughts have critically limited crop production, inducing food system shocks regionally and globally. It was estimated that crop yield variability in around one-third to three-fourths of global harvested areas is explained significantly by drought, revealing the notable vulnerability of crop systems to such climate-related stressors. However, understanding the key factors determining the global pattern of crop yield sensitivity to drought is limited. Here, we investigate a wide range of physical and socioeconomic factors that may determine crop-drought vulnerability in terms of yield sensitivity to drought based on the Standardized Precipitation Index at 0.5° resolution from 1981 to 2016 using machine learning approaches. The results indicate that the spatial variations of the crop-drought sensitivity were mainly explained by environmental factors (i.e., annual precipitation, soil water-holding capacity, soil acidity, annual potential evapotranspiration) and crop management factors (i.e., fertilizer rate, growing season). Several factors might have a positive effect in mitigating crop-drought vulnerability, such as annual precipitation, soil water holding capacity, and fertilizer rate. This study quantitatively assesses the possible effect of various determinants which might control crop vulnerability to drought. This understanding may provide insights for further studies addressing better crop vulnerability measures under future drought stress.

Changes in soil microbial biomass and organic C pools improve the sustainability of perennial grass and legume system under organic nutrient management

Introduction

The perennial grass-legume cropping system benefits soil because of its high biomass turnover, cover cropping nature, and different foraging behaviors. We investigated the response of soil organic carbon (SOC) pools and their stock to organic and inorganic nutrient management in the Guinea grass and legume (cowpea-Egyptian clover) cropping system.

Methods

Depth-wise soil samples were collected after harvesting the Egyptian clover. Based on the ease of oxidation with chromic acid, different pools of SOC oxidizable using the Walkley-Black C method, very labile, labile, less labile, non-labile; and dissolved organic C (DOC), microbial biomass C (MBC), and total organic C (TOC) in soils were analyzed for computing several indices of SOC.

Result and discussion

After 10 years of crop cycles, FYM and NPKF nutrient management recorded greater DOC, MBC, SOC stocks, and C sequestration than the NPK. Stocks of all SOC pools and carbon management index (CMI) decreased with soil depth. A significant improvement in CMI, stratification ratio, sensitivity indices, and sustainable yield index was observed under FYM and NPKF. This grass-legume intercropping system maintained a positive carbon balance sequestered at about 0.8Mg C ha^-1 after 10 years without any external input. Approximately 44-51% of the applied carbon through manure was stabilized with SOC under this cropping system. The DOC, MBC, and SOC in passive pools were identified for predicting dry fodder yield. This study concludes that the application of organics in the perennial grass-legume inter cropping system can maintain long-term sustainability, enhance the C sequestration, and offset the carbon footprint of the farm enterprises.

The pervasive impact of global climate change on plant-nematode interaction continuum

Pest profiles in today's global food production system are continually affected by climate change and extreme weather. Under varying climatic conditions, plant-parasitic nematodes (PPNs) cause substantial economic damage to a wide variety of agricultural and horticultural commodities. In parallel, their herbivory also accredit to diverse ecosystem services such as nutrient cycling, allocation and turnover of plant biomass, shaping of vegetation community, and alteration of rhizospheric microorganism consortium by modifying the root exudation pattern. Thus PPNs, together with the vast majority of free-living nematodes, act as ecological drivers. Because of direct exposure to the open environment, PPN biology and physiology are largely governed by environmental factors including temperature, precipitation, humidity, atmospheric and soil carbon dioxide level, and weather extremes. The negative effects of climate change such as global warming, elevated CO₂, altered precipitation and the weather extremes including heat waves, droughts, floods, wildfires and storms greatly influence the biogeographic range, distribution, abundance, survival, fitness, reproduction, and parasitic potential of the PPNs. Changes in these biological and ecological parameters associated to the PPNs exert huge impact on agriculture. Yet, depending on how adaptable the species are according to their geo-spatial distribution, the consequences of climate change include both positive and negative effects on the PPN communities. While assorting the effects of climate change as a whole, it can be estimated that the changing environmental factors, on one hand, will aggravate the PPN damage by aiding to abundance, distribution, reproduction, generation, plant growth and reduced plant defense, but the phenomena like sex reversal, entering cryptobiosis, and reduced survival should act in counter direction. This seemingly creates a contraposition effect, where assessing any confluent trend is difficult. However, as the climate change effects will differ according to space and time it is apprehensible that the PPNs will react and adapt according to their location and species specificity. Nevertheless, the bio-ecological shifts in the PPNs will necessitate tweaking their management practices from the agri-horticultural perspective. In this regard, we must aim for a 'climate-smart' package that will take care of the food production, pest prevention and environment protection. Integrated nematode management involving precise monitoring and modeling-based studies of population dynamics in relation to climatic fluctuations with escalated reliance on biocontrol, host resistance, and other safer approaches like crop rotation, crop scheduling, cover cropping, biofumigation, use of farmyard manure (FYM) would surely prove to be viable options. Although the novel nematicidal molecules are target-specific and relatively less harmful to the environment, their application should not be promoted following the global aim to reduce pesticide usage in future agriculture. Thus, having a reliable risk assessment with scenario planning, the adaptive management strategies must be designed to cope with the impending situation and satisfy the farmers' need.

Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway

Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.

Mutation of glucose-methanol-choline oxidoreductase leads to thermosensitive genic male sterility in rice and Arabidopsis

Thermosensitive genic male sterility (TGMS) lines serve as the major genetic resource for two-line hybrid breeding in rice. However, their unstable sterility under occasional low temperatures in summer highly limits their application. In this study, we identified a novel rice TGMS line, ostms18, of cultivar ZH11 (Oryza sativa ssp. japonica). ostms18 sterility is more stable in summer than the TGMS line carrying the widely used locus tms5 in the ZH11 genetic background, suggesting its potential application for rice breeding. The ostms18 TGMS trait is caused by the point mutation from Gly to Ser in a glucose-methanol-choline (GMC) oxidoreductase; knockout of the oxidoreductase was previously reported to cause complete male sterility. Cellular analysis revealed the pollen wall of ostms18 to be defective, leading to aborted pollen under high temperature. Further analysis showed that the tapetal transcription factor OsMS188 directly regulates OsTMS18 for pollen wall formation. Under low temperature, the flawed pollen wall in ostms18 is sufficient to protect its microspore, allowing for development of functional pollen and restoring fertility. We identified the orthologous gene in Arabidopsis. Although mutants for the gene were fertile under normal conditions (24°C), fertility was significantly reduced under high temperature (28°C), exhibiting a TGMS trait. A cellular mechanism integrated with genetic mutations and different plant species for fertility restoration of TGMS lines is proposed.

Showcasing MESMER-X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models

Emulators of Earth System Models (ESMs) are complementary to ESMs by providing climate information at lower computational costs. Thus far, the emulation of spatially resolved climate extremes has only received limited attention, even though extreme events are one of the most impactful aspects of climate change. Here, we propose a method for the emulation of local annual maximum temperatures, with a focus on reproducing essential statistical properties such as correlations in space and time. We test different emulator configurations and find that driving the emulations with global mean surface temperature offers an optimal compromise between model complexity and performance. We show that the emulations can mimic the temporal evolution and spatial patterns of the underlying climate model simulations and are able to reproduce their natural variability. The general design and the good performance for annual maximum temperatures suggest that the proposed methodology can be applied to other climate extremes.

The future of CRISPR gene editing according to plant scientists

This study surveyed 669 plant scientists globally to elicit how (which outcomes of gene editing), where (which continent) and what (which crops) are most likely to benefit from CRISPR research and if there is a consensus about specific barriers to commercial adoption in agriculture. Further, we disaggregated public and private plant scientists to see if there was heterogeneity in their views of the future of CRISPR research. Our findings suggest that maize and soybeans are anticipated to benefit the most from CRISPR technology with fungus and virus resistance the most common vehicle for its implementation. Across the board, plant scientists viewed consumer perception/knowledge gap to be the most impeding barrier of CRISPR adoption. Although CRISPR has been hailed as a technology that can help alleviate food insecurity and improve agricultural sustainability, our study has shown that plant scientists believe there are some large concerns about the consumer perceptions of CRISPR.

Probabilistic forecasting of remotely sensed cropland vegetation health and its relevance for food security

In a world where climate change, population growth, and global diseases threaten economic access to food, policies and contingency plans can strongly benefit from reliable forecasts of agricultural vegetation health. To inform decisions, it is also crucial to quantify the forecasting uncertainty and prove its relevance for food security. Yet, in previous studies both these aspects have been largely overlooked. This paper develops a methodology to anticipate the agricultural Vegetation Health Index (VHI) while making the underlying prediction uncertainty explicit. To achieve this aim, a probabilistic machine learning framework modelling weather and climate determinants is introduced and implemented through Quantile Random Forests. In a second step, a statistical link between VHI forecasts and monthly food price variations is established. As a pilot implementation, the framework is applied to nine countries of South-East Asia (SEA) with consideration of national monthly rice prices. Model benchmarks show satisfactory accuracy metrics, suggesting that the probabilistic VHI predictions can provide decision-makers with reliable information about future cropland health and its impact on food price variation weeks or even months ahead, albeit with increasing uncertainty as the forecasting horizon grows. These results - ultimately allowing to anticipate the impact of weather shocks on household food expenditure - contribute to advancing the multidisciplinary literature linking vegetation health, probabilistic forecasting models, and food security policy.

Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia

One of the potential impacts of climate change is enhanced groundwater contamination by geogenic and anthropogenic contaminants. Such impacts should be most evident in areas with high land-use change footprint. Here, we provide a novel documentation of the impact on groundwater nitrate (GW_NO3 ) pollution with and without climate change in one of the most intensely groundwater-irrigated areas of South Asia (northwest India) as a consequence of changes in land use and agricultural practices at present and predicted future times. We assessed the probabilistic risk of GW_NO3 pollution considering climate changes under two representative concentration pathways (RCPs), i.e., RCP 4.5 and 8.5 for 2030 and 2040, using a machine learning (Random Forest) framework. We also evaluated variations in GW_NO3 distribution against a no climate change (NCC) scenario considering 2020 status quo climate conditions. The climate change projections showed that the annual temperatures would rise under both RCPs. The precipitation is predicted to rise by 5% under RCP 8.5 by 2040, while it would decline under RCP 4.5. The predicted scenarios indicate that the areas at high risk of GW_NO3 pollution will increase to 49 and 50% in 2030 and 66 and 65% in 2040 under RCP 4.5 and 8.5, respectively. These predictions are higher compared to the NCC condition (43% in 2030 and 60% in 2040). However, the areas at high risk can decrease significantly by 2040 with restricted fertilizer usage, especially under the RCP 8.5 scenario. The risk maps identified the central, south, and southeastern parts of the study area to be at persistent high risk of GW_NO3 pollution. The outcomes show that the climate factors may impose a significant influence on the GW_NO3 pollution, and if fertilizer inputs and land uses are not managed properly, future climate change scenarios can critically impact the groundwater quality in highly agrarian areas.

Elevating Air Temperature may Enhance Future Epidemic Risk of the Plant Pathogen Phytophthora infestans

Knowledge of pathogen adaptation to global warming is important for predicting future disease epidemics and food production in agricultural ecosystems; however, the patterns and mechanisms of such adaptation in many plant pathogens are poorly understood. Here, population genetics combined with physiological assays and common garden experiments were used to analyze the genetics, physiology, and thermal preference of pathogen aggressiveness in an evolutionary context using 140 Phytophthora infestans genotypes under five temperature regimes. Pathogens originating from warmer regions were more thermophilic and had a broader thermal niche than those from cooler regions. Phenotypic plasticity contributed ~10-fold more than heritability measured by genetic variance. Further, experimental temperatures altered the expression of genetic variation and the association of pathogen aggressiveness with the local temperature. Increasing experimental temperature enhanced the variation in aggressiveness. At low experimental temperatures, pathogens from warmer places produced less disease than those from cooler places; however, this pattern was reversed at higher experimental temperatures. These results suggest that geographic variation in the thermal preferences of pathogens should be included in modeling future disease epidemics in agricultural ecosystems in response to global warming, and greater attention should be paid to preventing the movement of pathogens from warmer to cooler places.

Assessment and Prediction of Grain Production Considering Climate Change and Air Pollution in China

This study examines the spatial and temporal impacts of climate change on grain production in China. This is achieved by establishing a spatial error model consisting of four indicators: the climate, air pollution, economic behavior, and agricultural technology, covering 31 provinces in China from 2004 to 2020. These indicators are used to validate the spatial impacts of climate change on grain production. Air pollution data are used as instrumental variables to address the causality between climate and grain production. The regression results show that: First, climatic variables all have a non-linear “increasing then decreasing” effect on food production. Second, SO2, PM10, and PM2.5 have a negative impact on grain production. Based on the model, changes in the climatic production potential of grain crops can be calculated, and the future spatial layout of climate production can also be predicted by using random forests. Studies have shown that the median value of China’s grain production potential is decreasing, and the low value is increasing.

Climate Change and Food Security Prioritizing Indices: Applying Analytical Hierarchy Process (AHP) and Social Network Analysis (SNA)

Food security and climate change are multidimensional issues. Therefore, a lack of knowledge about the most essential variables made these concepts more complex for decision-making and highlighted the need for credible decision support methods. Here, we aim to develop an accurate tool by using the analytic hierarchy process (AHP) method to explore the priority indicator of food security under climate change in Iran and social network analysis (SNA) to support decisions. The following steps were conducted for the AHP approach: a literature review, a Likert questionnaire and experts’ interviews for variable selection and the variables’ weight determination and prioritization by pairwise comparison questionnaire, designed based on the hierarchy matrix of the criteria and sub-criteria of food security and climate change. The SNA was employed to understand the robustness of the informants’ points of view for indicator selection. After the analysis, 61 criteria were extracted. Sustainability was the important criterion, weighted 0.248. The most important sub-criteria (indicators): groundwater sources, household income, underweight adolescent ratio, food wastage and an annual average of precipitation, weighted 0.095, 0.091, 0.125, 0.227 and 0.236, respectively. The SNA showed that professionals with academic origins focused on the sustainability component. The AHP tool is a credible technique to distinguish the most important criteria. The results might be employed to estimate or predict food security under climate change and simplify decision making in Iran.

Extreme Low-Temperature Stress Affects Nutritional Quality of Amino Acids in Rice

Global climate change has increased the frequency of extreme climate events, and their effects on the nutritional quality, especially on amino acids in rice, have not been quantified. The data from a 3-year low temperature stress (LTS) experiment including two rice varieties (Huaidao 5 and Nanjing 46), seven minimum/maximum temperature levels (one optimal 21/27°C and six LTS levels from 17/23 to 6/12°C), and three LTS durations (3, 6, and 9 days) after flowering, revealed significant interactive effects of LTS at different stages, durations, and temperature levels on the content and accumulation of amino acids. LTS increased rice total amino acid content, while decreasing its accumulation, with higher sensitivities to LTS at the flowering stage than at the grain filling stage. In most treatments, the lysine (the first limiting amino acid) and phenylalanine content were increased under LTS at early and peak flowering stages but decreased at the grain filling stage in both varieties, and only leucine content was increased at all three stages after flowering, while the content of other essential amino acids differed among the two varieties. With an increase of 1°C·d per day in the accumulated cold degree days, the relative content of the essential amino acids was increased by 0.01-0.41%, depending on the rice variety and growth stage. Our results suggest that LTS can improve nutritional quality of amino acids of rice grains in terms of amino acids content, especially at flowering stage. These results provide critical insights for assessing the potential impact of extreme climates on the nutrient quality of rice under future climate change.

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.

Attributing changes in food insecurity to a changing climate

It is generally accepted that climate change is having a negative impact on food security. However, most of the literature variously focuses on the complex and many mechanisms linking climate stressors; the links with food production or productivity rather than food security; and future rather than current effects. In contrast, we investigate the extent to which current changes in food insecurity can be plausibly attributed to climate change. We combine food insecurity data for 83 countries from the FAO food insecurity experience scale (FIES) with reanalysed climate data from ERA5-Land, and use a panel data regression with time-varying coefficients. This framework allows us to estimate whether the relationship between food insecurity and temperature anomaly is changing over time. We also control for Human Development Index, and drought measured by six-month Standardized Precipitation Index. Our empirical findings suggest that for every 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }\hbox {C}$$\end{document}∘C of temperature anomaly, severe global food insecurity has increased by 1.4% (95% CI 1.3–1.47) in 2014 but by 1.64% (95% CI 1.6–1.65) in 2019. This impact is higher in the case of moderate to severe food insecurity, with a 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }\hbox {C}$$\end{document}∘C increase in temperature anomaly resulting in a 1.58% (95% CI 1.48–1.68) increase in 2014 but a 2.14% (95% CI 2.08–2.20) increase in 2019. Thus, the results show that the temperature anomaly has not only increased the probability of food insecurity, but the magnitude of this impact has increased over time. Our counterfactual analysis suggests that climate change has been responsible for reversing some of the improvements in food security that would otherwise have been realised, with the highest impact in Africa. Our analysis both provides more evidence of the costs of climate change, and as such the benefits of mitigation, and also highlights the importance of targeted and efficient policies to reduce food insecurity. These policies are likely to need to take into account local contexts, and might include efforts to increase crop yields, targeted safety nets, and behavioural programs to promote household resilience.

Land-based climate change mitigation measures can affect agricultural markets and food security

Earlier studies have noted potential adverse impacts of land-related emissions mitigation strategies on food security, particularly due to food price increases-but without distinguishing these strategies' individual effects under different conditions. Using six global agroeconomic models, we show the extent to which three factors-non-CO2 emissions reduction, bioenergy production and afforestation-may change food security and agricultural market conditions under 2 °C climate-stabilization scenarios. Results show that afforestation (often simulated in the models by imposing carbon prices on land carbon stocks) could have a large impact on food security relative to non-CO2 emissions policies (generally implemented as emissions taxes). Respectively, these measures put an additional 41.9 million and 26.7 million people at risk of hunger in 2050 compared with the current trend scenario baseline. This highlights the need for better coordination in emissions reduction and agricultural market management policies as well as better representation of land use and associated greenhouse gas emissions in modelling.