Agriculture and food security under a changing climate: An underestimated challenge

AI for food: accelerating and democratizing discovery and innovation

By 2050, feeding nearly 10 billion people will require transformative changes to ensure nutritious, sustainable food for all. Our current food system is inefficient and unsustainable. Traditional attempts to transform the global food system are too slow to drive innovation at scale. Here we explore the potential of artificial intelligence to reshape the future of food. We review the state of the art in food development, discuss the data needed to define a new food product, and highlight seven challenges where AI can help us design nutritious, delicious, and sustainable foods for all. By leveraging AI to democratize food innovation, we can accelerate the transition to resilient global food systems that meet the urgent challenges of food security, climate change, and planetary health.

Integrating Genetic Diversity and Agronomic Innovations for Climate-Resilient Maize Systems

Maize is a vital staple crop significantly affected by climate change, necessitating urgent efforts to enhance its resilience. This review analyzes advanced methodologies for maize improvement, focusing on the identification of genetic determinants through QTL mapping, candidate gene mining, and GWAS. We highlight the transformative potential of CRISPR gene editing for identifying key regulators in maize development and the utility of virus-induced gene silencing (VIGS) for functional genomics. Additionally, we discuss breeding strategies leveraging the genetic diversity of maize wild relatives and innovations such as speed breeding and genomic selection (GS), which accelerate breeding cycles. Marker-assisted selection (MAS) plays a critical role in developing superior maize varieties. The review also encompasses agronomic practices and technological innovations, including GS, aimed at climate mitigation. High-throughput phenotyping and omics-based approaches, including transcriptomics and metabolomics, are essential tools for developing climate-resilient maize. Climate changes have a significant impact on maize production and pose unprecedented challenges to its cultivation.

Adaptation and Acclimation of Gametophytic Traits to Heat Stress in a Widely Distributed Wild Plant Along a Steep Climatic Gradient

Climate change-induced heat waves often reduce seed yields and quality via high-temperature effects in the gametophytic phase. Yet, in contrast to model and crop species, the ability of pollen and ovules to adapt or acclimate to heat stress in wild plants remains poorly understood. To address this gap, we examined the adaptation and acclimation potential of six gametophytic traits in 11 wild Silene vulgaris populations across a temperature gradient in Europe. First, we cultivated plants in a common garden to reveal differences in gametophytic traits indicative of adaptation. Next, we assessed their acclimation potential by subjecting flowering plants to two chronic heat stress (CHS) treatments: moderate (35°C/30°C) and severe (40°C/35°C) for 18 days. Also, we estimated the CHS effects on seed quantity and quality. The common garden experiment showed no intraspecific variation in gametophytic traits across the temperature gradient, suggesting these traits may not influence reproductive adaptation to local habitats. During CHS, the female gametophyte was less temperature-sensitive than the male. Moderate CHS led to larger ovaries with more large-sized ovules, while severe CHS reduced ovule numbers but increased their size. Both CHS treatments decreased pollen grain numbers, size, and anther length, with severe CHS causing greater reductions. These reductions in gametophytic traits led to lower seed yield and quality. Under both CHS treatments, acclimation potential did not vary along the temperature gradient, except for pollen size under severe CHS, which was larger in warmer climates. Our findings revealed the lack of adaptation and acclimation mechanisms in the gametophytic traits (except for pollen size) of wild Silene vulgaris populations along the temperature gradient. These findings suggest that Silene plants may rely on alternative strategies, such as shifts in gametophyte physiology and biochemistry or flowering phenology, to respond to thermal stress associated with heat waves.

Patterns and Drivers of Pollen Temperature Tolerance

Pollen, a pivotal stage in the plant reproductive cycle, is highly sensitive to temperature fluctuations, impacting seed quality and quantity. While the importance of understanding pollen temperature limits (Tmin, Topt, Tmax - collectively PTLs) is recognized, a comprehensive synthesis of underlying drivers is lacking. Here, we examined PTLs, correlating them with vegetative tissue thermotolerance and assessing variability at the intra- and interspecific levels across 191 species with contrasting phylogeny, cultivation history, growth form and ecology. At the species level, the PTLs range from 9.0 to 42.4°C, with considerable differences among individual species. Vegetative tissue showed greater tolerance to both low and high temperatures than pollen. A significant, though weak, correlation was observed between PTLs and leaf temperature tolerance. Pollen heat tolerance was independent of that in leaves and stems. The greatest intraspecific variability was observed in pollen cold tolerance (Tmin), followed by Topt and Tmax. Phylogenetic analysis revealed family-level conservation in all three pollen temperature tolerance measures. Climate emerged as a significant PTL driver of pollen cold tolerance, with species from colder and stable climates exhibiting enhanced cold tolerance. Cultivated and wild species did not differ in their pollen temperature tolerances. Herbaceous plants showed higher tolerance to high temperatures compared to shrubs and trees, potentially reflecting divergent thermal conditions during anthesis. This study provides the first formal analysis of complex relationships between pollen temperature limits, plant characteristics and environmental factors, providing crucial insights into climate change impacts on plant reproduction.

Comparative transcriptome reveals lignin biosynthesis being the key molecular pathway regulating oilseed rape growth treated by SiO2 NPs and biochar

Biochar and SiO2 NPs are effective soil conditioners, but the impacts and mechanisms of combined application in oilseed rape are not yet clear. Therefore, an experiment was designed to investigate oilseed rape growth, physiological indexes, and transcriptome sequencing under four treatments: control (CK), Platanus orientalis L. leaf biochar (B), SiO2 NPs (S), and BS. Our results showed that B, S and BS treatments all promoted the root growth, root activity and biomass of oilseed rape, especially the root length and fresh weight in BS, which were increased by 77.48% and 279.07%, respectively. Moreover, the three-dimensional fluorescence spectra of B and BS were similar, and the tyrosine-like substance proportion in B, S and BS increased from 7.8 to 9.4%, 10.2% and 19.5%, respectively. In transcriptome analysis, there were 10,280 differentially expressed genes (DEGs) shared in B and BS, 3431 DEGs shared in S and BS, and 2815 DEGs shared in B, S and BS. We also found that B, S and BS all regulated oilseed rape growth by inducing the lignin biosynthesis and the relevant genes encoding BBE-like, BGL, UDP in the phenylpropanoid biosynthesis pathway. The results provide gene regulation associated with the phenylpropanoid biosynthesis applying the biochar and SiO2 NPs, which can be used to increase biomass.

Coupling relationship and development patterns of agricultural emission reduction, carbon sequestration, and food security

In-depth exploration of the coupling relationship between agricultural emission reduction and carbon sequestration (ERCS) and food security provides an important basis for promoting sustainable low-carbon development in agriculture. This research investigates the coupling mechanisms and the current state of coordinated development of agricultural ERCS and food security using provincial panel data from 2001 to 2022 in China. The agricultural ERCS level shows an upward trend, with higher levels in the north and lower in the south; externalities are positive in the north but negative in the south. Significant dynamic interactions and spatial correlations between the agricultural ERCS and food security exist, with a local spatial agglomeration pattern of "north-south opposition". Areas of high-high agglomeration are mainly concentrated in the north, while low-low agglomeration areas are primarily in the south. High-high agglomeration areas drive growth in transitional and low-growth areas through diffusion effects. The average coupling coordination degree of provinces increased from 0.432 to 0.473, indicating more coordinated development, and with a decreasing polarization trend. The spatial distributions of the coupling coordination degree and the relative development index are higher in the north and lower in the south, with many areas showing high adjustment, low adjustment, and high antagonism, particularly in the south where the number of high antagonistic areas has decreased. Implementing differentiated development strategies between the north and the south, using spatial agglomeration characteristics to optimize regional policies, focusing on the diffusion effects of high-coupling coordination areas to drive the development of low-growth and transitional areas, and enhancing the lagged terms can promote sustainable coordinated agricultural development.

Mitigating excessive heat in Arabica coffee using nanosilicon and seaweed extract to enhance element homeostasis and photosynthetic recovery

BACKGROUND

Global warming-related temperature increases have a substantial effect on plant and human health. The Arabica coffee plant is susceptible to growing in many places across the world where temperatures are rising. This study examines how nanosilicon and seaweed extracts can improve Arabica coffee plant resilience during heat stress treatment (49.0 ± 0.3 °C) by maintaining mineral homeostasis and photosynthetic ability upon recovery.

RESULTS

The principal component analysis arrangement of four treatments, nanosilicon (Si), seaweed extract (SWE), Si + SWE, and control (CT), showed each element ratio of magnesium, phosphorus, chloride, potassium, manganese, iron, copper, and zinc per silicon in ambient temperature and heat stress that found influenced upper shoot rather than basal shoot and root within 74.4% of largest feasible variance as first principal component. Magnesium and iron were clustered within the silicon group, with magnesium dominating and leading to a significant increase (p ≤ 0.05) in magnesium-to-silicon ratio in the upper shoot under heat conditions, especially in Si and Si + SWE treated plants (1.11 and 1.29 fold over SWE treated plant, respectively). The SWE and Si + SWE treated plants preserved chlorophyll content (15.01% and 28.67% over Si-treated plant, respectively) under heat stress, while the Si and Si + SWE treated plants restored photosynthetic efficiency (Fv/Fm) better than the SWE treated plant.

CONCLUSIONS

The concomitant of the Si + SWE treatment synergistically protected photosynthetic pigments and Fv/Fm by adjusting the magnesium-silicon homeostasis perspective in Arabica coffee to protect real-world agricultural practices and coffee cultivation under climate change scenarios.

GSP-AI: An AI-Powered Platform for Identifying Key Growth Stages and the Vegetative-to-Reproductive Transition in Wheat Using Trilateral Drone Imagery and Meteorological Data

Wheat (Triticum aestivum) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

Steps towards operationalizing One Health approaches

One Health recognizes the health of humans, agriculture, wildlife, and the environment are interrelated. The concept has been embraced by international health and environmental authorities such as WHO, WOAH, FAO, and UNEP, but One Health approaches have been more practiced by researchers than national or international authorities. To identify priorities for operationalizing One Health beyond research contexts, we conducted 41 semi-structured interviews with professionals across One Health sectors (public health, environment, agriculture, wildlife) and institutional contexts, who focus on national-scale and international applications. We identify important challenges, solutions, and priorities for delivering the One Health agenda through government action. Participants said One Health has made progress with motivating stakeholders to attempt One Health approaches, but achieving implementation needs more guidance (action plans for how to leverage or change current government infrastructure to accommodate cross-sector policy and strategic mission planning) and facilitation (behavioral change, dedicated personnel, new training model).

Effects of extreme temperatures on public sentiment in 49 Chinese cities

The rising sentiment challenges of the metropolitan residents may be attributed to the extreme temperatures. However, nationwide real-time empirical studies that examine this claim are rare. In this research, we construct a daily extreme temperature index and sentiment metric using geotagged posts on one of China's largest social media sites, Weibo, to verify this hypothesis. We find that extreme temperatures causally decrease individuals' sentiment, and extremely low temperature may decrease more than extremely high temperature. Heterogeneity analyses reveal that individuals living in high levels of PM2.5, existing new COVID-19 diagnoses and low-disposable income cities on workdays are more vulnerable to the impact of extreme temperatures on sentiment. More importantly, the results also demonstrate that the adverse effects of extremely low temperatures on sentiment are more minor for people living in northern cities with breezes. Finally, we estimate that with a one-standard increase of extremely high (low) temperature, the sentiment decreases by approximately 0.161 (0.272) units. Employing social media to monitor public sentiment can assist policymakers in developing data-driven and evidence-based policies to alleviate the adverse impacts of extreme temperatures.

Deciphering the distinct biocontrol activities of lipopeptides fengycin and surfactin through their differential impact on lipid membranes

Lipopeptides produced by beneficial bacilli present promising alternatives to chemical pesticides for plant biocontrol purposes. Our research explores the distinct plant biocontrol activities of lipopeptides surfactin (SRF) and fengycin (FGC) by examining their interactions with lipid membranes. Our study shows that FGC exhibits a direct antagonistic activity against Botrytis cinerea and no marked immune-eliciting activity in Arabidopsis thaliana while SRF only demonstrates an ability to stimulate plant immunity. It also reveals that SRF and FGC exhibit diverse effects on membrane integrity and lipid packing. SRF primarily influences membrane physical state without significant membrane permeabilization, while FGC permeabilizes membranes without significantly affecting lipid packing. From our results, we can suggest that the direct antagonistic activity of lipopeptides is linked to their capacity to permeabilize lipid membrane while the stimulation of plant immunity is more likely the result of their ability to alter the mechanical properties of the membrane. Our work also explores how membrane lipid composition modulates the activities of SRF and FGC. Sterols negatively impact both lipopeptides' activities while sphingolipids mitigate the effects on membrane lipid packing but enhance membrane leakage. In conclusion, our findings emphasize the importance of considering both membrane lipid packing and leakage mechanisms in predicting the biological effects of lipopeptides. It also sheds light on the intricate interplay between the membrane composition and the effectiveness of the lipopeptides, providing insights for targeted biocontrol agent design.

Biofilms formation in plant growth-promoting bacteria for alleviating agro-environmental stress

Biofilm formation represents a pivotal and adaptable trait among microorganisms within natural environments. This attribute plays a multifaceted role across diverse contexts, including environmental, aquatic, industrial, and medical systems. While previous research has primarily focused on the adverse impacts of biofilms, harnessing their potential effectively could confer substantial advantages to humanity. In the face of escalating environmental pressures (e.g., drought, salinity, extreme temperatures, and heavy metal pollution), which jeopardize global crop yields, enhancing crop stress tolerance becomes a paramount endeavor for restoring sufficient food production. Recently, biofilm-forming plant growth-promoting bacteria (PGPB) have emerged as promising candidates for agricultural application. These biofilms are evidence of microorganism colonization on plant roots. Their remarkable stress resilience empowers crops to thrive and yield even in harsh conditions. This is accomplished through increased root colonization, improved soil properties, and the synthesis of valuable secondary metabolites (e.g., ACC deaminase, acetin, 2,3-butanediol, proline, etc.). This article elucidates the mechanisms underpinning the role of biofilm-forming PGPB in bolstering plant growth amidst environmental challenges. Furthermore, it explores the tangible applications of these biofilms in agriculture and delves into strategies for manipulating biofilm formation to extract maximal benefits in practical crop production scenarios.

The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat

Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.

Past and future impacts of land-use changes on ecosystem services in Austria

Environmental and socio-economic developments induce land-use changes with potentially negative impacts on human well-being. To counteract undesired developments, a profound understanding of the complex relationships between drivers, land use, and ecosystem services is needed. Yet, national studies examining extended time periods are still rare. Based on the Special Report on land use, land management and climate change by the Austrian Panel on Climate Change (APCC), we use the Driver-Pressure-State-Impact-Response (DPSIR) framework to (1) identify the main drivers of land-use change, (2) describe past and future land-use changes in Austria between 1950 and 2100, (3) report related impacts on ecosystem services, and (4) discuss management responses. Our findings indicate that socio-economic drivers (e.g., economic growth, political systems, and technological developments) have influenced past land-use changes the most. The intensification of agricultural land use and urban sprawl have primarily led to declining ecosystem services in the lowlands. In mountain regions, the abandonment of mountain grassland has prompted a shift from provisioning to regulating services. However, simulations indicate that accelerating climate change will surpass socio-economic drivers in significance towards the end of this century, particularly in intensively used agricultural areas. Although climate change-induced impacts on ecosystem services remain uncertain, it can be expected that the range of land-use management options will be restricted in the future. Consequently, policymaking should prioritize the development of integrated land-use planning to safeguard ecosystem services, accounting for future environmental and socio-economic uncertainties.

WHIRLY1 Acts Upstream of ABA-Related Reprogramming of Drought-Induced Gene Expression in Barley and Affects Stress-Related Histone Modifications

WHIRLY1, a small plant-specific ssDNA-binding protein, dually located in chloroplasts and the nucleus, is discussed to act as a retrograde signal transmitting a stress signal from the chloroplast to the nucleus and triggering there a stress-related gene expression. In this work, we investigated the function of WHIRLY1 in the drought stress response of barley, employing two overexpression lines (oeW1-2 and oeW1-15). The overexpression of WHIRLY1 delayed the drought-stress-related onset of senescence in primary leaves. Two abscisic acid (ABA)-dependent marker genes of drought stress, HvNCED1 and HvS40, whose expression in the wild type was induced during drought treatment, were not induced in overexpression lines. In addition, a drought-related increase in ABA concentration in the leaves was suppressed in WHIRLY1 overexpression lines. To analyze the impact of the gain-of-function of WHIRLY1 on the drought-related reprogramming of nuclear gene expression, RNAseq was performed comparing the wild type and an overexpression line. Cluster analyses revealed a set of genes highly up-regulated in response to drought in the wild type but not in the WHIRLY1 overexpression lines. Among these genes were many stress- and abscisic acid (ABA)-related ones. Another cluster comprised genes up-regulated in the oeW1 lines compared to the wild type. These were related to primary metabolism, chloroplast function and growth. Our results indicate that WHIRLY1 acts as a hub, balancing trade-off between stress-related and developmental pathways. To test whether the gain-of-function of WHIRLY1 affects the epigenetic control of stress-related gene expression, we analyzed drought-related histone modifications in different regions of the promoter and at the transcriptional start sites of HvNCED1 and HvS40. Interestingly, the level of euchromatic marks (H3K4me3 and H3K9ac) was clearly decreased in both genes in a WHIRLY1 overexpression line. Our results indicate that WHIRLY1, which is discussed to act as a retrograde signal, affects the ABA-related reprogramming of nuclear gene expression during drought via differential histone modifications.