Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

Hedging our bet on forest permanence for the economic viability of climate targets

Achieving the Paris Agreement's CO2 emission reduction goals heavily relies on enhancing carbon storage and sequestration in forests globally. Yet, the increasing vulnerability of carbon stored in forests to both climate change and human intervention is often neglected in current mitigation strategies. Our study explores modelled interactions between key emission sectors, indicating that accelerated decarbonization could meet climate objectives despite forest carbon losses due to disturbances. However, delaying action on forest carbon loss by just five years consistently doubles the additional mitigation costs and efforts across key sectors, regardless of the assessed forest disturbance rates. Moreover, these myopic responses to forest carbon loss are as stringent, or even more demanding, than immediate responses to twice the forest disturbance rate. Our results underline the urgent need to monitor and safeguard forests for the economic feasibility of the Paris Agreement's climate goals.

Chemical application improves stress resilience in plants

In recent years, abiotic stresses, including droughts, floods, high temperatures, and salinity, have become increasingly frequent and severe. These stresses significantly hinder crop yields and product quality, posing substantial challenges to sustainable agriculture and global food security. Simultaneously, the rapidly growing global population exacerbates the need to enhance crop production under worsening environmental conditions. Consequently, the development of effective strategies to strengthen the resilience of crop plants against high temperatures, water scarcity, and extreme environmental conditions is critical for mitigating the impacts of abiotic stress. Plants respond to these environmental challenges by reprogramming their transcriptome and metabolome. Common strategies for developing stress-tolerant plants include screening germplasm, generating transgenic crop plants, and employing genome editing techniques. Recently, chemical treatment has emerged as a promising approach to enhance abiotic stress tolerance in crops. This technique involves the application of exogenous chemical compounds that induce molecular and physiological changes, thereby providing a protective shield against abiotic stress. Forward and reverse genetic approaches have facilitated the identification of chemicals capable of modulating plant responses to abiotic stresses. These priming agents function as epigenetic regulators, agonists, or antagonists, playing essential roles in regulating stomatal closure to conserve water, managing cellular signaling through reactive oxygen species and metabolites to sustain plant growth, and activating gluconeogenesis to enhance cellular metabolism. This review summarizes recent advancements in the field of chemical priming and explores strategies to improve stress tolerance and crop productivity, thereby contributing to the enhancement of global food security.

Climate change threatens crop diversity at low latitudes

Climate change alters the climatic suitability of croplands, likely shifting the spatial distribution and diversity of global food crop production. Analyses of future potential food crop diversity have been limited to a small number of crops. Here we project geographical shifts in the climatic niches of 30 major food crops under 1.5-4 °C global warming and assess their impact on current crop production and potential food crop diversity across global croplands. We found that in low-latitude regions, 10-31% of current production would shift outside the climatic niche even under 2 °C global warming, increasing to 20-48% under 3 °C warming. Concurrently, potential food crop diversity would decline on 52% (+2 °C) and 56% (+3 °C) of global cropland. However, potential diversity would increase in mid to high latitudes, offering opportunities for climate change adaptation. These results highlight substantial latitudinal differences in the adaptation potential and vulnerability of the global food system under global warming.

Cost-effective adaptations increase rice production while reducing pollution under climate change

Rice is a major source of greenhouse gas (GHG) and nitrogen pollution. While best management practices have been developed to enhance the sustainability of rice production under current climates, their adaptability and efficacy under future climate scenarios remain uncertain. Here we evaluated 49 best management practices across global grid cells of rice-producing areas in terms of increasing rice production, reducing GHG emissions and minimizing nitrogen pollution under future climate conditions. Optimal climate adaptation measures were assigned to each grid cell. We show that implementing the proposed adaptation strategy could increase global rice production by 36% while reducing GHG emissions and nitrogen losses by 23% and 32%, respectively. This approach could lead to a global benefit of US$117 billion for food supply, resource saving, climate mitigation and environmental protection, with total implementation costs of US$13 billion. Establishing practical and cost-effective adaptation strategies is critical for the sustainable development of the global agricultural system in the face of climate challenges.

The importance of genotyping within the climate-smart plant breeding value chain - integrative tools for genetic enhancement programs

The challenges faced by today's agronomists, plant breeders, and their managers encompass adapting sustainably to climate variability while working with limited budgets. Besides, managers are dealing with a multitude of issues with different organizations working on similar initiatives and projects, leading to a lack of a sustainable impact on smallholder farmers. To transform the current food systems as a more sustainable and resilient model efficient solutions are needed to deliver and convey results. Challenges such as logistics, labour, infrastructure, and equity, must be addressed alongside adapting to increasingly unstable climate conditions which affect the life cycle of transboundary pathogens and pests. In this context, transforming food systems go far beyond just farmers and plant breeders and it requires substantial contributions from industry, global finances, transportation, energy, education, and country developmental sectors including legislators. As a result, a holistic approach is essential for achieving sustainable and resilient food systems to sustain a global population anticipated to reach 9.7 billion by 2050 and 11.2 billion by 2100. As of 2021, nearly 193 million individuals were affected by food insecurity, 40 million more than in 2020. Meanwhile, the digital world is rapidly advancing with the digital economy estimated at about 20% of the global gross domestic product, suggesting that digital technologies are increasingly accessible even in areas affected by food insecurity. Leveraging these technologies can facilitate the development of climate-smart cultivars that adapt effectively to climate variation, meet consumer preferences, and address human and livestock nutritional needs. Most economically important traits in crops are controlled by multiple loci often with recessive alleles. Considering particularly Africa, this continent has several agro-climatic zones, hence crops need to be adapted to these. Therefore, targeting specific loci using modern tools offers a precise and efficient approach. This review article aims to address how these new technologies can provide a better support to smallholder farmers.

A global open-source dataset of monthly irrigated and rainfed cropped areas (MIRCA-OS) for the 21st century

Crop production is among the most extensive human activities on the planet - with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development.

Landscape-scale predictions of future grassland conversion to cropland or development

Grassland conservation planning often focuses on high-risk landscapes, but many grassland conversion models are not designed to optimize conservation planning because they lack multidimensional risk assessments and are misaligned with ecological and conservation delivery scales. To aid grassland conservation planning, we developed landscape-scale models at relevant scales that predict future (2021-2031) total and proportional loss of unprotected grassland to cropland or development. We developed models for 20 ecoregions across the contiguous United States by relating past conversion (2011-2021) to a suite of covariates in random forest regression models and applying the models to contemporary covariates to predict future loss. Overall, grassland loss models performed well, and explanatory power varied spatially across ecoregions (total loss model: weighted group mean R2 = 0.89 [range: 0.83-0.96], root mean squared error [RMSE] = 9.29 ha [range: 2.83-22.77 ha]; proportional loss model: weighted group mean R2 = 0.74 [range: 0.64-0.87], RMSE = 0.03 [range: 0.02-0.06]). Amount of crop in the landscape and distance to cities, ethanol plants, and concentrated animal feeding operations had high variable importance in both models. Total grass loss was greater when there were moderate amounts of grass, crop, or development (∼50%) in the landscape. Proportional grass loss was greater when there was less grass (∼<30%) and more crop or development (∼>50%). Some variables had a large effect on only a subset of ecoregions, for example, grass loss was greater when ∼>70% of the landscape was enrolled in the Conservation Reserve Program. Our methods provide a simple and flexible approach for developing risk layers well suited for conservation that can be extended globally. Our conversion models can support conservation planning by enabling prioritization as a function of risk that can be further optimized by incorporating biological value and cost.

Effects of Production System With or Without Growth-Promoting Technologies on Growth and Blood Expression of (Cyto)Chemokines and Heat Shock and Tight Junction Proteins in Bos taurus and indicus Breeds During Summer Season

Heat stress (HS) induced by global warming is a real welfare, productivity, and economic burden of cattle production. However, some cattle breeds have superior physiological adaptive traits to others, yet the underlying molecular mechanisms are not fully defined. The present study aimed, therefore, to determine the expression profile of stress-related molecular signatures in the blood of thermosensitive Angus (Bos taurus) and thermotolerant Brahman (Bos indicus) cattle breeds managed without (CON) or with growth-promoting technology (TRT) during the summer (April-October, 2023) season in Colorado, US. Body weight (BW) was significantly increased from April to October, and the amplitude was greater for the Angus compared to the Brahman breed. The TRT system slightly increased BW, mainly in the Angus breed. Molecular analyses showed that all tested genes were expressed in beef cattle blood. When comparing production systems, the expression of HSP1A1 was significantly upregulated, and HSP90 was downregulated in CON compared to TRT cattle. The expression of IL6, CCL20, and OCLN was induced by the CON system only in the Angus and not in the Brahman breed. At the breed level, Angus cattle exhibited greater expression of IL10, CCL20, and CLDN1 compared to their Brahman counterparts. There was a significant period by production system as well as period by breed interactions. The expression of HSP1A1 increased in both breeds during October. The expression of IL10, CXCL14, CXCR2, and CLDN1 was affected by the production systems in a period-dependent manner. However, the expression of IL6, CXCL14, CCL5, and CXCR2 was upregulated in Angus cattle in a period-sensitive manner. In summary, HSPs, (chemo)cytokines, and tight junction proteins are expressed in the whole blood of beef cattle, and their expression is regulated in a breed-, period-, and/or production system-dependent manner. This could open new vistas for future research to identify molecular signatures for non-invasive stress monitoring and/or marker-assisted genetic selection for robustness and resilience to HS.

Current status and obstacles of narrowing yield gaps of four major crops

BACKGROUND

Maize, wheat, rice and soybean production are intimately linked to food security. Identifying the key factors affecting crop yields and determining the countries where increased irrigation and nitrogen application most effectively enhance yields are essential steps towards achieving sustainable development goals and ensuring food security. Identifying these areas is crucially dependent on yield gaps. However, the lack of comparability between different regions in current regional-scale yield gap studies stems from varied methodologies. Moreover, global yield gap research, relying on statistical models and regression methods, tends to neglect the crop growth process. In this study, we used a random forest model, based on statistical and meteorological data, to pinpoint the key factors influencing crop yields. Subsequently, using unified yield data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), derived from crop models simulations, we applied the yield gap method to calculate the potential yield increase for four crops across countries, under conditions of full irrigation and nitrogen application.

RESULTS

Our research finds that nitrogen application is the main factor affecting yields globally, while irrigation plays a crucial role in the major producing countries. The countries with high potential for yield increases are located at the border between Africa and Eurasia. The global average yield of the four major crops increased 13.7-29.8% under full irrigation, 2.9-39.1% under full nitrogen application and 29.4-97.8% under both conditions.

CONCLUSION

This study provides crucial insights into global crop yield changes and their determinants, which are highly important for global sustainable agriculture and food security efforts. © 2024 Society of Chemical Industry.

Cropland expansion links climate extremes and diets in Nigeria

Climate change threatens smallholder agriculture and food security in the Global South. While cropland expansion is often used to counter adverse climate effects despite ecological trade-offs, the benefits for diets and nutrition remain unclear. This study quantitatively examines relationships between climate anomalies, forest loss from cropland expansion, and dietary outcomes in Nigeria, Africa's most populous country. Combining high-resolution data on forest cover and climate variables within random forest and panel regression models, we find that 25 to 31% of annual forest loss is linked to climate variability. Using georeferenced household survey data, we then find that changes in forest cover have a significant positive association with changes in child diet diversity-a key proxy of nutritional adequacy-while cropland expansion does not, suggesting that such forest conversions may be an ineffective climate adaptation strategy for improving nutrition. Our findings highlight the potential of nutrition-sensitive climate adaptation to enhance yields, promote nutritious cropping choices, and protect remaining forests.

Comprehensive prediction of potential spatiotemporal distribution patterns, priority planting regions, and introduction adaptability of Elymus sibiricus in the Chinese region

The natural grassland in China is facing increasingly serious degradation. Elymus sibiricus L., as an important native alpine grass, is widely used in the restoration and improvement of natural grassland. In this study, the geographical distribution and environmental data of E. sibiricus in China were collected, and the potential spatiotemporal distribution pattern, planting pattern, and introduction adaptability of E. sibiricus were comprehensively predicted by using ensembled ecological niche model and Marxan model. The results show that (1) the potential spatial distribution of E. sibiricus mainly spans 33°-42°N and 95°-118°E. It was widely distributed in Qilian Mountains (northeast of Qinghai-Tibet Plateau), Taihang Mountains (junction of Loess Plateau and Inner Mongolia Plateau), and Tianshan Mountains; (2) with the passage of time, the suitable distribution regions of E. sibiricus generally showed a collapse trend, but its main distribution regions did not show obvious change, and the suitable regions (centroid) generally migrated to the southwest of China by 2.93 km; (3) the spatial distribution of E. sibiricus in the current period was significantly affected by the annual range of monthly near-surface relative humidity, mean annual air temperature, annual range of monthly potential evapotranspiration, mean monthly potential evapotranspiration, annual range of monthly climate moisture index, elevation, exchangeable Ca2+, available P, mean monthly near-surface relative humidity, exchangeable H+, and annual precipitation amount, respectively; (4) the potential planting area of E. sibiricus cover 2.059 × 105 km2, which was mainly distributed in Qilian Mountains (northeast of Qinghai-Tibet Plateau), Taihang Mountains (southeast of Inner Mongolia Plateau), the middle part of Tianshan Mountains, and the southeast of Altai Mountains; (5) the introduction adaptability regions of six E. sibiricus germplasm (LM01-LM06) were all distributed in the high-elevation regions of western China. The study aims to provide an effective theoretical basis for the collection, preservation, and utilization of E. sibiricus germplasm resources in China.

Current food trade helps mitigate future climate change impacts in lower-income nations

The risk of national food supply disruptions is linked to both domestic production and food imports. But assessments of climate change risks for food systems typically focus on the impacts on domestic production, ignoring climate impacts in supplying regions. Here, we use global crop modeling data in combination with current trade flows to evaluate potential climate change impacts on national food supply, comparing impacts on domestic production alone (domestic production impacts) to impacts considering how climate change impacts production in all source regions (consumption impact). Under 2°C additional global mean warming over present day, our analysis highlights that climate impacts on national supply are aggravated for 53% high income and 56% upper medium income countries and mitigated for 60% low- and 71% low-medium income countries under consumption-based impacts compared to domestic impacts alone. We find that many countries are reliant on a few mega-exporters who mediate these climate impacts. Managing the risk of climate change for national food security requires a global perspective, considering not only how national production is affected, but also how climate change affects trading partners.

Five levels of famine prevention: towards a framework for the twenty-first century and beyond

In recent years, the world has faced a rapid rise in humanitarian needs and an increasing risk of famine. Given the potential threats posed by conflict, climate change, economic shocks, and other issues, it is important to be prepared for the possibility of new crises in the future. Drawing on key informant interviews and a literature review, this paper assesses the state of the art in famine prevention, examining a range of technical and political approaches and analysing emerging lessons. Based on the findings, it identifies five levels of famine prevention: (i) averting famine; (ii) anticipating famine; (iii) reducing famine risks; (iv) altering famine risks; and (v) preventing famine risks. The paper argues that the current focus only partially addresses a relatively narrow set of levels. It concludes that a more comprehensive approach that engages all five levels simultaneously could contribute to a global famine prevention framework for the twenty-first century and beyond.

Soil phosphorus stocks could prolong global reserves and improve water quality

Combining existing databases, we estimated global phosphorus stocks in croplands and grasslands that are not readily available to plants as 32-41% of the 2020 estimated geologic phosphorus reserves, representing 146-186 years of the 2020 mass of phosphorus fertilizer applied annually. Especially if accessed by more efficient crops, this stock could reduce the need for additional fertilizer, improve water quality and contribute to all-round phosphorus sustainability.

Optimizing cover crop practices as a sustainable solution for global agroecosystem services

The practice of cover crops has gained popularity as a strategy to improve agricultural sustainability, but its full potential is often limited by environmental trade-offs. Using meta-analytic and data-driven quantifications of 2302 observations, we optimized cover crop practices and evaluated their benefits for global agroecosystems. Cover crops have historically boosted crop yields, soil carbon storage, and stability, but also stimulated greenhouse gas emissions. However, combining them with long-term implementation (five years or more) and climate-smart practices (such as no-tillage) can enhance these services synergistically. A biculture of legume and non-legume cover crops, terminated 25 days before planting the next crop and followed by residue mulching, is the optimal portfolio. Such optimized practices are projected to increase agroecosystem multiservices by 1.25%, equivalent to annual gains of 97.7 million metric tons in crop production, 21.7 billion metric tons in carbon dioxide sequestration, and 2.41 billion metric tons in soil erosion reduction. By 2100, the continued implementation of optimized practices could mitigate climate-related yield losses and contribute to climate neutrality and soil stabilization, especially in harsh and underdeveloped areas. These findings underscore the promising potential of optimized cover crop practices to achieve the synergy in food security and environmental protection.

An assessment of the relationship between spring frost indicators and global crop yield losses

Reports on the influences of spring frost on crop losses are not consistent, which may be because insufficient indicators of spring frost were included in the analysis. To bridge this gap, we analyzed global temperature datasets and production data for the three major crops of maize, winter wheat, and rice from 1981 to 2016. Five indicators of spring frost events: temperature fluctuation (Tv), temperature difference (Td), duration (Thour), occurrence date (Tdate), and frequency (Tnum) were considered to assess their relationship with yield losses. Linear regression was employed to analyze the change trends in five indicators and random forest was utilized to investigate the relationship between yield loss and indicators of spring frost. Our findings reveal that, despite a decline in the number of spring frost events during global warming, not all the five indicators declined over time. Tv is the most important indicator for yield losses in maize and winter wheat, which shows an increasing trend in their growing regions and provides an explanation for the increasing yield losses of maize and winter wheat over time. Td is the most important indicator of rice yield losses but it shows a decreasing trend in rice-growing areas, which explains why rice yield losses from spring frosts in recent years are not significant.

Improved alternate wetting and drying irrigation increases global water productivity

Rice is the staple food for half of the world's population but also has the largest water footprint among cereal crops. Alternate wetting and drying (AWD) is a promising irrigation strategy to improve paddy rice's water productivity-defined as the ratio of rice yield to irrigation water use. However, its global adoption has been limited due to concerns about potential yield losses and uncertainties regarding water productivity improvements. Here, using 1,187 paired field observations of rice yield under AWD and continuous flooding to quantify AWD effects (ΔY), we found that variation in ΔY is predominantly explained by the lowest soil water potential during the drying period. We estimate that implementing a soil water potential-based AWD scheme could increase water productivity across 37% of the global irrigated rice area, particularly in India, Bangladesh and central China. These findings highlight the potential of AWD to promote more sustainable rice production systems and provide a pathway toward the sustainable intensification of rice cultivation worldwide.

Optimized irrigation and fertilization can mitigate negative CO2 impacts on seed yield and vigor of hybrid maize

Seed yield and vigor of hybrid maize determine the planting, yield, and quality of maize, and consequently affect food, nutrition, and livelihood security; however, the response of seed yield and vigor to climate change is still unclear. We established an optimization-simulation framework consisting of a water‑nitrogen crop production function, a seed vigor and a gridded process-based model to optimize irrigation and nitrogen fertilization management, and used it to evaluate seed yield and vigor in major seed production locations of China, the USA, and Mexico. This framework could reflect the influence of water and nitrogen inputs at different stages on seed yield and vigor considering the spatio-temporal variability of climate and soil properties. Projected seed yield and vigor decreased by 5.8-9.0 % without adaptation by the 2050s, due to the 1.3-5.8 % decrease in seed number and seed protein concentration. Seed yield was positively correlated with CO2 and negatively correlated with temperature, while seed vigor depended on the response of components of seed vigor to climatic factors. Under optimized management, the direct positive effects of temperature on seed protein concentration and CO2 on seed number were strengthened, and the direct negative effects of temperature on seed number and CO2 on seed protein concentration were weakened, which mitigated the reductions in both seed yield and vigor. Elevated CO2 was projected to exacerbate the 2.6 % seed vigor reduction and mitigate the 2.9 % seed yield loss without adaptation, while optimized management could increase seed yield by 4.1 % and mitigate the 2.2 % seed vigor reduction in the Hexi Corridor of China, and decrease the seed yield and vigor reduction by 2.4-5.8 % in the USA and Mexico. Optimized management can strengthen the positive and mitigate the negative effects of climate change on irrigated hybrid maize and inform high-yield and high-quality seed production globally.

Ecological intensification index: reducing global footprint of agriculture

Because of the growing human population, increasing agricultural yields is becoming increasingly more important. However, various environmental crises have led society to demand a reduction in the environmental damage caused by agriculture. Until now, the economic and ecological aspects of plant cultivation have developed largely independently. Here, we propose a novel ecological intensification index (EII) that integrates both economic and ecological goals, measured in relative units as the realized proportion of a possible maximum value. The EII can incorporate multiple ecological and/or economic measures with different weights to balance societal needs, environmental concerns, and scientific knowledge. Using the EII will provide a quantitative target for breeders, agronomists, and farmers to catalyze innovation toward a minimal ecological impact of agriculture.

Does vegetation greening have a positive effect on global vegetation carbon and water use efficiency?

Terrestrial ecosystems have undergone significant changes as a result of climate change, profoundly affecting global carbon and water cycling processes. Notably, the synergistic changes in vegetation carbon use efficiency (CUE) and water use efficiency (WUE) and their response to patterns of climate change over the last 40 years are unknown. Therefore, in this study, global vegetation WUE and CUE were inverted using Gross primary productivity (GPP), Net primary productivity (NPP) and total evaporation (ET) data from 1981 to 2019 to reveal their temporal and spatial patterns of change through trend analysis and stability analysis. A stepwise regression algorithm was used to reveal the potential driving law of environmental factors on vegetation WUE and CUE. The results shows that (1) From 1981 to 2019, the global vegetation WUE and CUE showed in a relatively stable state, and the trends of WUE and CUE were -0.00004/year and 0.006 g C m-2 mm-1/year, respectively; (2) the greening of vegetation was the most important cause of the changes in WUE and CUE, and the driving force of rain and heat conditions on the CUE of vegetation was smaller than that of solar radiation and soil water, the regions where CO2 is the dominant factor affecting CUE and WUE are mainly in the north temperate zone; (3) the region of synergistic growth of WUE and CUE accounts for about 31.38 % of the global terrestrial area, and this pattern of change suggests that the global vegetation carbon sink potential is huge, and the popularization of vegetation planting patterns under the synergistic growth of CUE and WUE should be strengthened. The research has shown that vegetation greening is a key factor influencing changes in the WUE and CUE of vegetation, therefore, the implementation of ecological engineering will be an important step in combating climate change.

Contribution of irrigation to the production of maize, wheat, and rice in the major global producing countries

This study offers new insights into the heterogeneity behind the widely accepted notion that irrigated crops contribute 40% to global food production. It also highlights the potential of irrigation to mitigate the negative effects of climate change on crop yields.

Embracing complexities in agricultural water management through nexus planning

A major challenge for agricultural water management (AWM) in the 21st century is to feed a growing population in the face of increasing intersectoral resource competition, evolving diets, degradation, pandemics, geopolitical conflicts and climate change. This has to be achieved within the planetary boundaries and without compromising the livelihood and environmental (ecosystem) objectives linked to water, including provisioning, supporting and regulating services. This paper uses a systems and nexus lens to unravel the centrality and complexities in AWM, with particular emphasis on the interconnected dimensions and objectives of AWM, as well as its practices and technologies. AWM exists beyond water and food with linkages to human and environmental well-being. AWM needs to catalyse transformation and integrate approaches across systems, users and scales to meet its objectives in a changing climate. It must provide perspectives beyond productivity, managing water risks and safeguarding food security - as important as these are - and integrate our understanding of the interconnected climate, land, water, food and ecosystems to address planetary health outcomes. By doing so, AWM could catalyse contextualised, equitable, innovative solutions that acknowledge local socio-economic and institutional structures and limitations while catalysing sustainable development and climate resilience.

Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield

High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001-2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha- 1 with a maximum RUE trait value, compared to a control cultivar. No significant yield improvement (< 0.4 t ha- 1) was observed with increases in K and FE alone. Utilizing field-scale input data showed the greatest yield improvement per unit increment in RUE. Resolution of water related inputs (soil characteristics and precipitation) had a notably higher impact on simulated yield than of temperature. However, it did not alter the effects of high-yielding traits on yield. Simulated yields were only slightly affected by data aggregation for the different trait combinations. Warm-dry conditions diminished the benefits of high-yielding traits, suggesting that benefits from high-yielding traits depend on environments. The current findings emphasize the critical role of input data resolution and source in quantifying a large-scale impact of high-yielding traits.

Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates

Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well-studied major crops. Using 727 maize (Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre- and postanthesis maximum temperature, mid-season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well-studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates.

Reducing phosphorus losses from agricultural land to surface water

Phosphorus (P) enrichment of water impairs its quality by stimulating algal growth and eutrophication, affecting an estimated 1.7 billion people. Remediation costs are substantial, estimated at $1 billion annually in Europe and $2.4 billion in the USA. Agricultural intensification over the past 50 years has increased P use brought into the system from mined fertiliser sources. This has enriched soil P concentrations and loss to surface waters via pathways such as surface runoff and subsurface flow, which are influenced by precipitation, slope, and farming practices. Effective mitigation of losses involves managing P sources, mobilisation, and transport/delivery mechanisms. The cost-effectiveness of mitigation actions can be improved if they are targeted to critical source areas (CSAs), which are small zones that disproportionately contribute to P loss. While targeting CSAs works well in areas with variable topography, flatter landscapes require managing legacy sources, such as enriched soil P to prevent P losses.

Maize mutant screens: from classical methods to new CRISPR-based approaches

Mutations play a pivotal role in shaping the trajectory and outcomes of a species evolution and domestication. Maize (Zea mays) has been a major staple crop and model for genetic research for more than 100 yr. With the arrival of site-directed mutagenesis and genome editing (GE) driven by the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), maize mutational research is once again in the spotlight. If we combine the powerful physiological and genetic characteristics of maize with the already available and ever increasing toolbox of CRISPR-Cas, prospects for its future trait engineering are very promising. This review aimed to give an overview of the progression and learnings of maize screening studies analyzing forward genetics, natural variation and reverse genetics to focus on recent GE approaches. We will highlight how each strategy and resource has contributed to our understanding of maize natural and induced trait variability and how this information could be used to design the next generation of mutational screenings.

Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models' representation of PCI. Moreover, the models' poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB.

Identification of the Novel Small Compound Stress Response Regulators 1 and 2 That Affect Plant Abiotic Stress Signaling

Abiotic stresses, such as drought, salinity, and extreme temperatures, limit plant growth and development, reducing crop yields. Therefore, a more comprehensive understanding of the signaling mechanisms and responses of plants to changing environmental conditions is crucial for improving sustainable agricultural productivity. Chemical screening was conducted to find novel small compounds that act as regulators of the abiotic stress signaling pathway using the ABA-inducible transgenic reporter line. Small molecules called stress response regulators (SRRs) were isolated by screening a synthetic library composed of 14,400 small compounds, affecting phenotypes such as seed germination, root growth, and gene expression in response to multiple abiotic stresses. Seeds pretreated with SRR compounds positively affected the germination rate and radicle emergence of Arabidopsis and tomato plants under abiotic stress conditions. The SRR-priming treatment enhanced the transcriptional responses of abiotic stress-responsive genes in response to subsequent salt stress. The isolation of the novel molecules SRR1 and SRR2 will provide a tool to elucidate the complex molecular networks underlying the plant stress-tolerant responses.

Climate change exacerbates the environmental impacts of agriculture

Agriculture's global environmental impacts are widely expected to continue expanding, driven by population and economic growth and dietary changes. This Review highlights climate change as an additional amplifier of agriculture's environmental impacts, by reducing agricultural productivity, reducing the efficacy of agrochemicals, increasing soil erosion, accelerating the growth and expanding the range of crop diseases and pests, and increasing land clearing. We identify multiple pathways through which climate change intensifies agricultural greenhouse gas emissions, creating a potentially powerful climate change-reinforcing feedback loop. The challenges raised by climate change underscore the urgent need to transition to sustainable, climate-resilient agricultural systems. This requires investments that both accelerate adoption of proven solutions that provide multiple benefits, and that discover and scale new beneficial processes and food products.

Climate risk and green total factor productivity in agriculture: The moderating role of climate policy uncertainty

In light of the escalating global warming and the escalating frequency of extreme weather events, the agricultural sector, being a fundamental and pivotal industry worldwide, is encountering substantial challenges due to climate change. Using Chinese provincial panel data for 2000-2021, this paper utilizes a two-way fixed-effect model to investigate the impact of Climate Risk (CR) on green total factor productivity in agriculture (AGTFP), with China's climate policy uncertainty (CPU) being introduced as a moderating variable within the research framework to scrutinize its influence in this context. The findings reveal a noteworthy adverse effect of CR on AGTFP, further exacerbated by CPU. Heterogeneity analysis results show that there is a clear regional variation in the effect of CR on AGTFP across different Chinese regions, with CR significantly inhibiting AGTFP development in the northern regions and provinces in major grain producing regions. Consequently, there is a pressing necessity to bolster the establishment of climate change monitoring infrastructures, devise tailored climate adaptation strategies at a regional level, and enhance the clarity and predictability of climate policies to fortify the resilience and sustainability of agricultural production systems.

A flexible model for thermal performance curves

Temperature responses of many biological traits-including population growth, survival, and development-are described by thermal performance curves (TPCs) with phenomenological models like the Briere function or mechanistic models related to chemical kinetics. Existing TPC models are either simple but inflexible in shape, or flexible yet difficult to interpret in biological terms. Here we present flexTPC: a model that is parameterized exclusively in terms of biologically interpretable quantities, including the thermal minimum, optimum, and maximum, and the maximum trait value. FlexTPC can describe unimodal temperature responses of any skewness and thermal breadth, enabling direct comparisons across populations, traits, or taxa with a single model. We apply flexTPC to various microbial and entomological datasets, compare results with the Briere model, and find that flexTPC often has better predictive performance. The interpretability of flexTPC makes it ideal for modeling how thermal responses change with ecological stressors or evolve over time.

Site-directed genotype screening for elimination of antinutritional saponins in quinoa seeds identifies TSARL1 as a master controller of saponin biosynthesis selectively in seeds

Climate change may result in a drier climate and increased salinization, threatening agricultural productivity worldwide. Quinoa (Chenopodium quinoa) produces highly nutritious seeds and tolerates abiotic stresses such as drought and high salinity, making it a promising future food source. However, the presence of antinutritional saponins in their seeds is an undesirable trait. We mapped genes controlling seed saponin content to a genomic region that includes TSARL1. We isolated desired genetic variation in this gene by producing a large mutant library of a commercial quinoa cultivar and screening the library for specific nucleotide substitutions using droplet digital PCR. We were able to rapidly isolate two independent tsarl1 mutants, which retained saponins in the leaves and roots for defence, but saponins were undetectable in the seed coat. We further could show that TSARL1 specifically controls seed saponin biosynthesis in the committed step after 2,3-oxidosqualene. Our work provides new important knowledge on the function of TSARL1 and represents a breakthrough for quinoa breeding.

Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector

Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N2O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N2O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process-based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low-ambition nitrogen regulation policies, EF would increase from 1.18%-1.22% in 2010 to 1.27%-1.34% by 2050, representing a relative increase of 4.4%-11.4% and exceeding the IPCC tier-1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%-0.35% (relative increase of 11.9%-17%). In contrast, high-ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying-wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N2O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N2O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio-economic assessments and policy-making efforts.

One Health as a potential platform to rescue the neglected fruit trees in Yucatan, Mexico

Neglected and underutilized species of plants (NUS) have been identified by the Food and Agriculture Organization as valuable resources for fighting poverty, hunger and malnutrition as they can help make agricultural production systems more sustainable and resilient. Adaptation of NUS to changing environments over several millennia has rendered most of these plants resistant to pests and climate change. In this paper, we explore the potential values of some of the Mayan fruit trees justifying conservation efforts in their native habitats. Our research was primarily based on a scoping review using Google Scholar. We considered articles published in English, Spanish and Portuguese. Our review rendered two sets of articles including those focusing on the nutritional and medicinal properties of NUS and their products, and those focusing on their uses in traditional medicine. Both sets of papers strongly support arguments for conservation of NUS. Additionally, our scoping review expands and includes a case study on the conservation of NUS, highlighting the critical role of civil society on how it can spearhead rescue efforts of botanical resources through the creation of what is possibly the first arboretum of its kind in the Americas. Among the project's key selling points was not only the rescue of an important component of Yucatan's cultural heritage but its nutritional value as well as its potential medicinal properties. Our paper is not prescriptive on how to preserve or even commercially exploit NUS. It is intended as a thought-provoking piece on the potential of a One Health approach as a multisectoral platform to support conservation efforts, while stimulating greater interest in the subject and encouraging more action from the academic and pharmaceutical sectors as well as civil society.

Prediction of suitable regions of wild tomato provides insights on domesticated tomato cultivation in China

Climate change is one of the biggest challenges to the world at present. Tomato is also suffered from devastating yield loss due to climate change. The domesticated tomato (Solanum lycopersicum) is presumed to be originated from the wild tomato (S. pimpinellifolium). In this study, we compared the climate data of S. pimpinellifollium with the domesticated tomato, predicted the suitable regions of S. pimpinellifollium in China using MaxEnt model and assessed their tolerance to drought stress. We found that the predicted suitable regions of wild tomato are highly consistent with the current cultivated regions of domesticated tomato, suggesting that the habitat demand of domesticated tomato descended largely from its ancestor, hence the habitat information of wild tomato could provide a reference for tomato cultivation. We further predicted suitable regions of wild tomato in the future in China. Finally, we found that while average drought tolerance between wild and domesticated tomato accessions shows no difference, tolerance levels among wild tomato accessions exhibit higher variation, which could be used for future breeding to improve drought resistance. To summarize, our study shows that suitable regions of wild tomato provide insights into domesticated tomato cultivation in China.

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.

Unraveling root and rhizosphere traits in temperate maize landraces and modern cultivars: Implications for soil resource acquisition and drought adaptation

A holistic understanding of plant strategies to acquire soil resources is pivotal in achieving sustainable food security. However, we lack knowledge about variety-specific root and rhizosphere traits for resource acquisition, their plasticity and adaptation to drought. We conducted a greenhouse experiment to phenotype root and rhizosphere traits (mean root diameter [Root D], specific root length [SRL], root tissue density, root nitrogen content, specific rhizosheath mass [SRM], arbuscular mycorrhizal fungi [AMF] colonization) of 16 landraces and 22 modern cultivars of temperate maize (Zea mays L.). Our results demonstrate that landraces and modern cultivars diverge in their root and rhizosphere traits. Although landraces follow a 'do-it-yourself' strategy with high SRLs, modern cultivars exhibit an 'outsourcing' strategy with increased mean Root Ds and a tendency towards increased root colonization by AMF. We further identified that SRM indicates an 'outsourcing' strategy. Additionally, landraces were more drought-responsive compared to modern cultivars based on multitrait response indices. We suggest that breeding leads to distinct resource acquisition strategies between temperate maize varieties. Future breeding efforts should increasingly target root and rhizosphere economics, with SRM serving as a valuable proxy for identifying varieties employing an outsourcing resource acquisition strategy.

Calcium Hexacyanoferrate Nanozyme Enhances Plant Stress Resistance by Oxidative Stress Alleviation and Heavy Metal Removal

Oxidative damage, exacerbated by the excessive accumulation of reactive oxygen species (ROS), profoundly inhibits both crop growth and yield. Herein, a biocompatible nanozyme, calcium hexacyanoferrate nanoparticles (CaHCF NPs), targeting ROS is developed, to mitigate oxidative damage and sequestrate heavy metal ions during plant growth. Uniquely, CaHCF NPs feature multifaced enzyme-like activities, involving superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), glutathione peroxidase, thiol peroxidase, and ascorbate peroxidase, which enable them to neutralize excessive ROS. Furthermore, CaHCF NPs promote calcium-cadmium exchange process, diminishing the uptake of heavy metals. Importantly, 120 µg mL-1 of CaHCF NPs alleviate the inhibitory effects of hydrogen peroxide and cadmium chloride on Arabidopsis and tomato. The activities of SOD, POD, and CAT increase by 46.2%, 74.4%, and 48.3%, respectively, meanwhile the glutathione level rises by 72.4% in Arabidopsis under cadmium stress. Moreover, CaHCF NPs boost the expression of genes associated with antioxidation, heavy metal detoxification, nutrient transport, and stress resistance. These findings unveil the significant potential of nanoplatforms equipped with nanozymes in alleviating oxidative stress in plants, which not only regulate crop growth but also substantially ameliorate yield and quality, heralding a new era in agricultural nanotechnology.

Pathways to identify and reduce uncertainties in agricultural climate impact assessments

Both climate and impact models are essential for understanding and quantifying the impact of climate change on agricultural productivity. Multi-model ensembles have highlighted considerable uncertainties in these assessments, yet a systematic approach to quantify these uncertainties is lacking. We propose a standardized approach to attribute uncertainties in multi-model ensemble studies, based on insights from the Agricultural Model Intercomparison and Improvement Project. We find that crop model processes are the primary source of uncertainty in agricultural projections (over 50%), excluding unquantified hidden uncertainty that is not explicitly measured within the analyses. We propose multidimensional pathways to reduce uncertainty in climate change impact assessments.

Climate adaptation through crop migration requires a nexus perspective for environmental sustainability in the North China Plain

Crop migration can moderate the impacts of global warming on crop production, but its feedback on the climate and environment remains unknown. Here we develop an integrated framework to capture the climate impacts and the feedback of adaptation behaviours with the land-water-energy-carbon nexus perspective and identify opportunities to achieve the synergies between climate adaptation and environmental sustainability. We apply the framework to assess wheat and maize migration in the North China Plain and show that adaptation through wheat migration could increase crop production by ~18.5% in the 2050s, but at the cost of disproportional increment in land use (~19.2%), water use (~20.2%), energy use (~19.5%) and carbon emissions (~19.9%). Irrigation and fertilization management are critical mitigation opportunities in the framework, through which wheat migration can be optimized to reduce the climatic and environmental impacts and avoid potential carbon leakage. Our work highlights the sustainable climate adaptation to mitigate negative environmental externalities.

Changes in climate attributes and harvest area structures jointly determined spatial-temporal variations in water footprint of maize in the Beijing-Tianjin-Hebei region

Irrigation contributes significantly to boosting crop yield and ensuring food security. However, in the Beijing-Tianjin-Hebei (BTH) region, unsustainable irrigation practices have led to serious outcomes on freshwater resources. Balancing irrigation with crop productivity in this region, currently facing complex challenge, requires a comprehensive understanding of its spatial pattern and thus to seeking for potential optimization of current crop structures. In this study, we employed the concept of water footprint (WFP) to assess the spatial-temporal patterns of water footprint for maize in BTH region at the county level for the years 2005, 2010, 2015, and 2020, untangled the relative impacts on WFP from climate attributes and harvest area structures. Our results showed significant regional heterogeneities in both blue water requirement and green water requirement, ranging from 64.6 mm to 290.7 mm. Yearly anomalies of climate attributes and maize harvest jointly influenced water footprints, with the highest value of 1.06 × 1011 m3 occurring in the year 2015. The green water footprints, linked to precipitation, dominated the total water footprint compared to the blue water footprint associated with irrigation. Additionally, we observed an increasing influence of maize harvest area on the temporal changes in water footprints, with these changes becoming more concentrated in the east-central region over time. Our findings underscore the respective contributions of annual climate attribute changes and harvest area variations at the county level, highlighting regions where urgent interventions are required to enhance the sustainability of water usage for agriculture.

The interplay of future solar energy, land cover change, and their projected impacts on natural lands and croplands in the US

Projections for deep decarbonization require large amounts of solar energy, which may compete with other land uses such as agriculture, urbanization, and conservation of natural lands. Existing capacity expansion models do not integrate land use land cover change (LULC) dynamics into projections. We explored the interaction between projected LULC, solar photovoltaic (PV) deployment, and solar impacts on natural lands and croplands by integrating projections of LULC with a model that can project future deployment of solar PV with high spatial resolution for the conterminous United States. We used scenarios of LULC projections from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios from 2010 to 2050 and two electricity grid scenarios to model future PV deployment and compared those results against a baseline that held 2010 land cover constant through 2050. Though solar PV's overall technical potential was minimally impacted by LULC scenarios, deployed PV varied by -16.5 to 11.6 % in 2050 from the baseline scenario. Total land requirements for projected PV were similar to other studies, but measures of PV impacts on natural systems depended on the underlying land change dynamics occurring in a scenario. The solar PV deployed through 2050 resulted in 1.1 %-2.4 % of croplands and 0.3 %-0.7 % of natural lands being converted to PV. However, the deepest understanding of PV impacts and interactions with land cover emerged when the complete net gains and losses from all land cover change dynamics, including PV, were integrated. For example, one of the four LULC projections allows for high solar development and a net gain in natural lands, even though PV drives a larger percentage of natural land conversion. This paper shows that integrating land cover change dynamics with energy expansion models generates new insights into trade offs between decarbonization, impacts of renewables, and ongoing land cover change.

Daily bias-corrected weather data and daily simulated growth data of maize, millet, sorghum, and wheat in the changing climate of sub-Saharan Africa

Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies -WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias-corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995-2014), 2030 (2020-2039), and 2050 (2040-2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water-limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights about agro-meteorological conditions and water-limited crop output to inform adaptation priorities and benchmark (gridded) crop simulations. Finally, the weather and simulated growth data can also be used for training machine learning techniques for extrapolation purposes.

Seedling root system adaptation to water availability during maize domestication and global expansion

The maize root system has been reshaped by indirect selection during global adaptation to new agricultural environments. In this study, we characterized the root systems of more than 9,000 global maize accessions and its wild relatives, defining the geographical signature and genomic basis of variation in seminal root number. We demonstrate that seminal root number has increased during maize domestication followed by a decrease in response to limited water availability in locally adapted varieties. By combining environmental and phenotypic association analyses with linkage mapping, we identified genes linking environmental variation and seminal root number. Functional characterization of the transcription factor ZmHb77 and in silico root modeling provides evidence that reshaping root system architecture by reducing the number of seminal roots and promoting lateral root density is beneficial for the resilience of maize seedlings to drought.

Strategies for Enhancing Plant Immunity and Resilience Using Nanomaterials for Sustainable Agriculture

Research on plant-nanomaterial interactions has greatly advanced over the past decade. One particularly fascinating discovery encompasses the immunomodulatory effects in plants. Due to the low doses needed and the comparatively low toxicity of many nanomaterials, nanoenabled immunomodulation is environmentally and economically promising for agriculture. It may reduce environmental costs associated with excessive use of chemical pesticides and fertilizers, which can lead to soil and water pollution. Furthermore, nanoenabled strategies can enhance plant resilience against various biotic and abiotic stresses, contributing to the sustainability of agricultural ecosystems and the reduction of crop losses due to environmental factors. While nanoparticle immunomodulatory effects are relatively well-known in animals, they are still to be understood in plants. Here, we provide our perspective on the general components of the plant's immune system, including the signaling pathways, networks, and molecules of relevance for plant nanomodulation. We discuss the recent scientific progress in nanoenabled immunomodulation and nanopriming and lay out key avenues to use plant immunomodulation for agriculture. Reactive oxygen species (ROS), the mitogen-activated protein kinase (MAPK) cascade, and the calcium-dependent protein kinase (CDPK or CPK) pathway are of particular interest due to their interconnected function and significance in the response to biotic and abiotic stress. Additionally, we underscore that understanding the plant hormone salicylic acid is vital for nanoenabled applications to induce systemic acquired resistance. It is suggested that a multidisciplinary approach, incorporating environmental impact assessments and focusing on scalability, can expedite the realization of enhanced crop yields through nanotechnology while fostering a healthier environment.

Real-time emulation of future global warming reveals realistic impacts on the phenological response and quality deterioration in rice

Decreased production of crops due to climate change has been predicted scientifically. While climate-resilient crops are necessary to ensure food security and support sustainable agriculture, predicting crop growth under future global warming is challenging. Therefore, we aimed to assess the impact of realistic global warming conditions on rice cultivation. We developed a crop evaluation platform, the agro-environment (AE) emulator, which generates diverse environments by implementing the complexity of natural environmental fluctuations in customized, fully artificial lighting growth chambers. We confirmed that the environmental responsiveness of rice obtained in the fluctuation of artificial environments is similar to those exhibited in natural environments by validating our AE emulator using publicly available meteorological data from multiple years at the same location and multiple locations in the same year. Based on the representative concentration pathway, real-time emulation of severe global warming unveiled dramatic advances in the rice life cycle, accompanied by a 35% decrease in grain yield and an 85% increase in quality deterioration, which is higher than the recently reported projections. The transcriptome dynamism showed that increasing temperature and CO2 concentrations synergistically changed the expression of various genes and strengthened the induction of flowering, heat stress adaptation, and CO2 response genes. The predicted severe global warming greatly alters rice environmental adaptability and negatively impacts rice production. Our findings offer innovative applications of artificial environments and insights for enhancing varietal potential and cultivation methods in the future.

Finger millet: a hero in the making to combat food insecurity

Climate change and population growth pose challenges to food security. Major crops such as maize, wheat, and rice are expected to face yield reductions due to warming in the coming years, highlighting the need for incorporating climate-resilient crops in agricultural production systems. Finger millet (Eleusine coracana (L.) Gaertn) is a nutritious cereal crop adapted to arid regions that could serve as an alternative crop for sustaining the food supply in low rainfall environments where other crops routinely fail. Despite finger millet's nutritional qualities and climate resilience, it is deemed an "orphan crop," neglected by researchers compared to major crops, which has hampered breeding efforts. However, in recent years, finger millet has entered the genomics era. Next-generation sequencing resources, including a chromosome-scale genome assembly, have been developed to support trait characterization. This review discusses the current genetic and genomic resources available for finger millet while addressing the gaps in knowledge and tools that are still needed to aid breeders in bringing finger millet to its full production potential.

Maize miRNAs and their putative target genes involved in chilling stress response in 5-day old seedlings

BACKGROUND

In the context of early sowing of maize as a promising adaptation strategy that could significantly reduce the negative effects of climate change, an in-depth understanding of mechanisms underlying plant response to low-temperature stress is demanded. Although microRNAs (miRNAs) have been recognized as key regulators of plant stress response, research on their role in chilling tolerance of maize during early seedling stages is scarce. Therefore, it is of great significance to explore chilling-responsive miRNAs, reveal their expression patterns and associated target genes, as well as to examine the possible functions of the conserved and novel miRNAs. In this study, the role of miRNAs was examined in 5d-old maize seedlings of one tolerant and one sensitive inbred line exposed to chilling (10/8 °C) stress for 6 h and 24 h, by applying high throughput sequencing.

RESULTS

A total of 145 annotated known miRNAs belonging to 30 families and 876 potentially novel miRNAs were identified. Differential expression (DE) analysis between control and stress conditions identified 98 common miRNAs for both genotypes at one time point and eight miRNAs at both time points. Target prediction and enrichment analysis showed that the DE zma-miR396, zma-miR156, zma-miR319, and zma-miR159 miRNAs modulate growth and development. Furthermore, it was found that several other DE miRNAs were involved in abiotic stress response: antioxidative mechanisms (zma-miR398), signal transduction (zma-miR156, zma-miR167, zma-miR169) and regulation of water content (zma-miR164, zma-miR394, zma-miR396). The results underline the zma-miRNAs involvement in the modulation of their target genes expression as an important aspect of the plant's survival strategy and acclimation to chilling stress conditions.

CONCLUSIONS

To our understanding, this is the first study on miRNAs in 5-d old seedlings' response to chilling stress, providing data on the role of known and novel miRNAs post-transcriptional regulation of expressed genes and contributing a possible platform for further network and functional analysis.