Global Dataset Research Articles

Island colonization in flowering plants is determined by the interplay of breeding system, lifespan, floral symmetry, and arrival opportunity

Summary Among flowering plants, self‐compatibility, longer lifespan, and generalized pollination syndrome are each thought to increase the lifetime odds of finding a mate, particularly in isolated locales. An accumulated body of evidence supports the role of breeding system in island colonization, but less is known about the impact of other traits and their interactions during establishment. We employ a global dataset of 3222 flowering plant species from 169 families to estimate the effects of traits on the probability of island occurrence. Our analyses additionally account for taxonomic group membership and assess the role of island arrival opportunity. Self‐compatibility is strongly associated with island colonization. A longer lifespan and generalized pollination syndrome are also associated with increased island colonization, although this is influenced by their interaction with breeding system. The probability of island colonization is highly dependent on taxonomically conserved unmeasured traits and arrival opportunity. As expected, mate limitation appears to increase with dispersal distance, although many other factors are at play. We find that arrival opportunity and breeding system are the primary drivers of island colonization relative to other life‐history traits we account for here, lending additional support for the positive role of uniparental reproduction in establishment following long‐distance dispersal.

Planned relocation may reduce communities’ future exposure to coastal inundation but effect varies with emission scenario and geography

The planned, permanent relocation of entire communities away from sea level rise (SLR) and coastal floods is an already occurring climate change adaptation strategy. Yet, planned relocations are fraught undertakings with multiple goals, and may or may not achieve their most basic objective: to reduce risk. Here we assess risk of future coastal flooding before and after moving, for three dates and three emissions scenarios, for 17 communities from a global dataset. Most communities achieved exposure reduction with less future inundation in destinations than origin sites, but the extent varies across time and emissions scenario. In all cases, origin sites have projected exposure to SLR plus a once-per-year flood, with increasing exposure under high emissions scenarios and towards 2100. In nine cases, even destination sites have projected inundation exposure under some scenarios. Small-island-to-small-island relocations had more projected inundation in destinations than moves from a small-island-to-mainland, or from mainland-to-mainland.

Monitoring mangroves with multi-sensor Earth Observation data sets: from one-shot historical cartography to online and near-real-time monitoring tools

Abstract. This study outlines advancements in mangrove monitoring using Earth Observation (EO) technologies, highlighting a transition from historical cartography to modern, near-real-time monitoring systems. Mangroves, critical for biodiversity, climate regulation, and coastal protection, face threats from climate change and human activities. The need for accurate and recurrent mapping tools is emphasized, addressing the limitations of current global datasets like the Global Mangrove Watch (GMW) by incorporating high-resolution data and field measurements. The French National Research Institute for Sustainable Development (IRD) has pioneered mangrove monitoring since the 1990s, with key methodologies including the use of Sentinel-2 and Pleiades satellite imagery for high-resolution mapping, texture-based analysis, and the incorporation of LiDAR data for accurate biomass estimation. Historical cartography is contrasted with contemporary monitoring efforts, focusing on the integration of multi-source data and the importance of localized ground-truthing. This approach enhances the capabilities of earth observation for carbon stock assessments and supports informed decision-making in conservation and climate policy. The work also highlights the need for further integration of local knowledge and advanced EO sensor data to refine carbon sequestration models and improve mangrove management strategies.

Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data.

Despite the end of the global Coronavirus Disease 2019 (COVID-19) pandemic, the risk factors for COVID-19 severity continue to be a pivotal area of research. Specifically, studying the impact of the genomic diversity of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) on COVID-19 severity is crucial for predicting severe outcomes. Therefore, this study aimed to investigate the impact of the SARS-CoV-2 genome sequence, genotype, patient age, gender, and vaccination status on the severity of COVID-19, and to develop accurate and robust prediction models. The training set (n = 12,038), primary testing set (n = 4,006), and secondary testing set (n = 2,845) consist of SARS-CoV-2 genome sequences with patient information, which were obtained from Global Initiative on Sharing all Individual Data (GISAID) spanning over four years. Four machine learning methods were employed to construct prediction models. By extracting SARS-CoV-2 genomic features, optimizing model parameters, and integrating models, this study improved the prediction accuracy. Furthermore, Shapley Additive exPlanes (SHAP) was applied to analyze the interpretability of the model and to identify risk factors, providing insights for the management of severe cases. The proposed ensemble model achieved an F-score of 88.842% and an Area Under the Curve (AUC) of 0.956 on the global testing dataset. In addition to factors such as patient age, gender, and vaccination status, over 40 amino acid site mutation characteristics were identified to have a significant impact on the severity of COVID-19. This work has the potential to facilitate the early identification of COVID-19 patients with high risks of severe illness, thus effectively reducing the rates of severe cases and mortality.

Global mapping of oil palm planting year from 1990 to 2021

Abstract. Oil palm is a controversial crop, primarily because it is associated with negative environmental impacts such as tropical deforestation. Mapping the crop and its characteristics, such as age, is crucial for informing public and policy discussions regarding these impacts. Oil palm has received substantial mapping efforts, but accurate and up-to-date oil palm maps of both extent and age are essential for monitoring impacts and informing concomitant debate. Here, we present a 10 m resolution global map of industrial and smallholder oil palm, developed using Sentinel-1 data for the years 2016–2021 and a deep learning model based on convolutional neural networks. In addition, we used Landsat-5, Landsat-7, and Landsat-8 to estimate the planting year from 1990 to 2021 at a 30 m spatial resolution. The planting year indicates the year of establishment of the current (as of 2021) oil palm plantation by means of either newly planted or replanted oil palm in an existing oil palm plantation. We validated the oil palm extent layer using 18 812 randomly distributed reference points. The accuracy of the planting-year layer was assessed using field data collected from 5831 industrial parcels and 1012 smallholder plantations distributed throughout the global oil palm growing area. We found oil palm plantations covering a total mapped area of 23.98 Mha, and our area estimates are 16.82 ± 0.19 Mha of industrial oil palm and 7.37 ± 0.25 Mha of smallholder oil palm worldwide. The producer's and user's accuracy are 91.0 ± 2.5 % and 91.8 ± 1.2 % for industrial plantations and 71.4 ± 0.7 % and 72.4 ± 1.8 % for smallholders; these values represent an improvement compared to a previous global oil palm dataset, particularly in terms of omission of oil palm. The overall mean error between the estimated planting year and the field data was −0.24 years, and the root-mean-square error was 2.65 years, but the agreement was lower for smallholders. Mapping the extent and planting year of smallholder plantations remains challenging, particularly for wild and sparsely planted oil palm, and future mapping efforts should focus on these specific types of plantations. The average oil palm plantation age was 14.1 years, and the area of oil palm over 20 years old was 6.28 Mha. Given that oil palm plantations are typically replanted after 25 years, our findings indicate that this area will require replanting within the coming decade, starting from 2021. Our dataset provides valuable input for optimal land use planning to meet the growing global demand for vegetable oils. The global oil palm extent layer for the year 2021 and the planting-year layer from 1990 to 2021 can be found at https://doi.org/10.5281/zenodo.13379129 (Descals, 2024a).

Functional diversity and resilience of bivalves after the Permian-Triassic mass extinction

The Permian-Triassic mass extinction was the largest extinction event in the Phanerozoic eon, with profound taxonomic and ecological effects on the ecosystem function. Functional diversity, a facet of biodiversity, could reflect the ecosystem function and stability. Although previous studies have shown that the functional richness of global marine organisms was decoupled from their taxonomic diversity during the mass extinctions, the evolution of functional diversity during the Permian-Triassic mass extinction and its aftermath is still under debate. The ecologically diverse clade bivalves may be more representative for understanding the evolution of functional diversity. To investigate the evolutionary dynamics of the functional diversity of bivalves, a global bivalve dataset of 8929 occurrences from the latest Permian to the Late Triassic was constructed. Functional richness, functional evenness, and functional redundancy were calculated to reflect the functional diversity in this study. Our results showed that the functional richness of bivalves was slightly affected by the Permian-Triassic mass extinction, decoupled from the significant decrease in taxonomic diversity. Meanwhile, a decrease in functional redundancy and an increase in functional evenness were observed after the mass extinction. In addition, bivalves showed high resilience to the mass extinction by maintaining the ecospace and reducing the functional redundancy. The high taxonomic diversity, high functional richness, high functional redundancy and relatively higher proportion of infaunal bivalves during the Late Triassic indicate that the Mesozoic marine revolution was already underway.

Long-term (2000–2020) global 0.05° continuous atmospheric carbon dioxide mapping combining OCO-2 observations and model simulations

We reconstructed a global continuous 8-day XCO2 (column-averaged CO2 dry air mole fraction) product (GCXCO2) at a spatial resolution of 0.05° from 2000 to 2020, combining terrestrial/marine remote sensing data and model simulations based on developed and tested stacking machine learning method. The GCXCO2 product has the similar spatial pattern with OCO-2 satellite observations but with global seamless coverage, showing a higher spatial resolution and accuracy than CarbonTracker and CAMS model simulation data. A novel dynamic normalization strategy was developed to handle the great temporal variation issue and ensure the temporal expansion of the prediction model. The sampled based 10-fold cross-validation shows an overall satisfactory result at a global scale, with R2 = 0.974 and root-mean-square error (RMSE) = 0.551 ppm (parts per million). Further spatial extension and temporal prediction experiments also proved that dependable results could be obtained in the regions and time periods without valid OCO-2 satellite observations (R2 = 0.958 and R2 = 0.886, respectively). Compared with Total Carbon Column Observing Network (TCCON) ground station observations, the GCXCO2 demonstrates a better accuracy and a higher spatial resolution than the model simulation data. Based on the GCXCO2 product, an upward annual trend of approximately 2.105 ppm/year can be found for global XCO2 between 2000 and 2020, with greater seasonal fluctuations in the Northern Hemisphere than in the Southern Hemisphere. This product is one of some remote sensing-based global high-precision long-term XCO2 datasets and an important tool to help advance the understanding of climate change and carbon balance, but also to detect CO2 concentration anomalies. The dataset can be obtained publicly at doi:https://doi.org/10.5281/zenodo.10083102 (Guan and Sun, 2023).

Intercropping increases plant water availability and water use efficiency: A synthesis

Intercropping, involving the incorporation of annual crops with alternative crops or non-crop cash crops, has the potential to enhance water conservation and stabilize agroecosystems. However, few studies have comprehensively explored the effects of intercropping on water cycling. Here, we investigated the impacts of intercropping on five crucial water cycle processes (states): soil water content (SWC), runoff (RO), soil evaporation (E), leaf transpiration (LT), and water use efficiency (WUE). To this end, a meta-analysis was carried out utilizing a global dataset comprising 1285 paired observations from 64 publications. We found that intercropping reduced SWC (1.31 %), RO (29.17 %), and E (10.30 %), but increased LT (9.85 %) and WUE (29.46 %). The effects of intercropping on SWC, E, and RO did not exhibit significant fluctuations over the course of a year, but SWC initially decreased then increased in multi-year planting durations. Moreover, the intercropping effect was contingent upon climatic conditions (mean annual precipitation and temperature), soil characteristics (organic matter content, bulk density, and total nitrogen content), and agricultural practices (crop type, fertilization, and irrigation). We determined that resource complementarity, abiotic facilitation, and biotic feedback mechanisms may underlie the effect of intercropping on the water cycle. This research underscores the potential of using intercropping to improve plant water usage and the sustainability and productivity of cropping systems.

Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers

Abstract Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity ( S r ) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified S r and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale S r with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of S r globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with S r globally, the relation between precipitation and S r varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and S r for precipitation of up to 3 mm d − 1 , above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and S r . Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of S r , which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate S r on a global scale has the potential to be readily and easily integrated into the parameterization of S r in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.

Democracy, visa-waivers, and international mobility

In examining visa-waiver agreements, previous studies have primarily focused on economic motivations and effects. In this article, we explore the potential political motivations and consequences. Particularly, we propose that there is a positive relationship between visa-waivers and democracy. We test this using a global dataset that records bilateral travel visa requirements for all countries between 1973 and 2013. We find support for our main hypothesis. We also examine the relationship between democracy, visa waivers and economic development. Contrary to our expectations, we find that democracy continues to exert influence on visa waivers even at high levels of income. Non-democracies fail to attain visa waivers even when they are wealthy. Finally, we explore whether visa waivers could have an impact on individuals’ attitudes towards democracy. Using survey data from 18 Latin American countries, we find suggestive evidence that that visa-waivers can have a positive impact on individuals’ democratic attitudes. Taken together, the findings suggest that democracy and visa-waivers might have a mutually reinforcing relationship.

The environment is somewhat alike for different adaptive motivations: Machine learning reveals optimal motivational contexts involve collective support of parents, teachers, and peers

Achievement motivation is fundamental for human flourishing. While numerous adaptive motivational constructs have been proposed, they are often examined in isolation without considering their shared contextual roots. To identify the contextual factors underlying different forms of adaptive achievement motivation, we conducted comprehensive analyses by integrating a global student assessment dataset (n = 77,068 middle-school students across 19 countries, Mage = 15.79). We conducted a literature review and identified 27 potential predictors theoretically and empirically related to achievement motivation, including immediate contextual factors available in the dataset and distal contextual factors available from varying sources. Results from machine learning analyses showed convergent patterns of the contextual predictors for adaptive motivation (self-efficacy, learning goals, and task mastery orientation). Specifically, the optimal environment for adaptive motivation is characterized by the collective positive influence from parents, teachers, and peers, rather than depending on one exclusively. In comparison, the pattern of other less adaptive motivation constructs (fixed mindsets, performance goals, and fear of failure) is idiosyncratic. These findings provide synthesized evidence consolidating achievement motivation research, highlighting the shared contextual foundations for various adaptive motivations. This integrative approach clarifies that the optimal motivational contexts involve the collective social support of parents, teachers, and peers.

Reconstructed centennial precipitation-driven water storage anomalies in the Nile River Basin using RecNet and their suitability for studying ENSO and IOD impacts

While the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GFO) missions have offered valuable observations for monitoring total water storage anomalies (TWSA), their short record constrains our ability to study the complete range and long-term variability of TWSA in the Nile River Basin (NRB). Previous studies reconstructing TWSA in this region either relied on specific hydrological models or did not consider spatial correlations among the TWSA grids. Here, we employ RecNet, a deep learning model capable of providing independent TWSA observations without relying on hydrological models while considering spatial correlations, to reconstruct precipitation-driven TWSA in the NRB from 1923 to 2022. The reconstructed data are validated by comparisons with the Global Land Data Assimilation System (GLDAS), the WaterGAP Global Hydrology Model (WGHM), the water balance budget, long-term runoff data, and GRACE-REC (i.e., a global reconstruction dataset freely available online). Subsequently, the suitability of the reconstructed data for studying El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) impacts within the NRB is assessed. Dividing the NRB into four sub-regions, i.e., the Lake Victoria Basin (LVB), the Bahr el Jebel and Bahr el Ghazal basins (BJBG), Ethiopian Highlands region (EH), and the Lower Nile River Basin (LNRB), it is shown that RecNet successfully reconstructs precipitation-driven TWSA over BJBG and EH, achieving correlation coefficient (CC), normalized root mean square error (NRMSE), and Nash–Sutcliffeefficiency (NSE) of 0.94/0.11/0.88 and 0.96/0.09/0.91 during the testing period, respectively. Additionally, RecNet’s reconstruction shows better agreement with GLDAS and WGHM than GRACE-REC, correlating well with runoff and the water balance budget in these regions. The relatively poor performance in the LVB and LNRB regions could be attributed to the substantial influence of Lake Victoria and the arid climate, respectively. Correlation analysis and wavelet coherence analysis identify significant coherence between ENSO/IOD and the reconstructed TWSA in BJBG and EH, with CC values of −0.68/0.34 and −0.82/0.56, respectively. This study provides centennial reconstructed TWSA data that could be useful in climate change/variability studies and water resource management within the NRB.

A dynamic platform for global pullorum disease and fowl typhoid

Pullorum disease and fowl typhoid are caused by the Salmonella serovars Gallinarum biovars Pullorum and Gallinarum, respectively. The prevalence of these diseases varies across regions and is affected by different risk factors that remain insufficiently documented. To fill this knowledge gap, we have compiled a global dataset for its prevalence, drawing upon a collection of literature from the last seven decades obtained from bilingual databases. However, a more interactive and dynamic platform is still needed for both academics and policymakers to improve biosecurity measures, limit disease transmission, and prevent future outbreaks at the global and local levels. Here, we developed an advanced visualization platform to depict the prevalence of Salmonella Pullorum and Gallinarum, especially in China, which is categorized by geographical region and temporal span. The platform offers a user-friendly, efficient, and visually engaging tool to explore the prevalence of pullorum disease and fowl typhoid between 1945 and 2021 in different regions. Additionally, this platform allows users to understand the influence of various factors, such as breed, farm mode, economic usage and even the sex of the primary host, chickens, on the prevalence of this disease. We further provided a detailed overview of individual province within China. In particular, by selecting two different provinces on the interface, users can quickly visualize and grasp the disparities in disease prevalence between the chosen regions. This interactive toolkit enables a dynamic exploration of the patterns and factors contributing to the prevalence of Salmonella Pullorum and Gallinarum. This interactive platform is freely available open source at http://139.9.85.208/.

A global dataset of salmonid biomass in streams

Salmonid fishes are arguably one of the most studied fish taxa on Earth, but little is known about their biomass range in many parts of the world. We created a dataset of estimated salmonid biomass using published material of over 1000 rivers, covering 27 countries and 11 species. The dataset, spanning 84 years of data, is the largest known compilation of published studies on salmonid biomass in streams, allowing detailed analyses of differences in biomass by species, region, period, and sampling techniques. Production is also recorded for 194 rivers, allowing further analyses and relationships between biomass and production to be explored. There is scope to expand the list of variables in the dataset, which would be useful to the scientific community as it would enable models to be developed to predict salmonid biomass and production, among many other analyses.

International migration and the advent of a new demographic era

AbstractThe paper explores whether international migration is linked to currently decreasing levels of fertility in high birth rates countries, thereby to the advent of a stage of population degrowth at the world's level. Methodology is in two steps. First, a global dataset is assembled comprising 13 variables for each country. For the country itself: emigrant stock, total fertility rate, girls' enrolment rate, women's labour force participation, global gender gap, and income per capita; for a fictitious average region of destination of migrants originating from this specific country, the last six variables. In the second step, links between origin and destination countries in terms of fertility levels and determinants are analysed using bivariate correlations. A remarkable fact emerges, namely national levels of fertility and their determinants vary quasi perfectly parallel at both ends of international migration corridors. Fertility at origin is not correlated to any phenomenon as much as to fertility, girls' school enrolment and gender equity at destination. Three complementary hypotheses explaining this apparently puzzling fact are discussed: transmission of norms by migrants; cultural similarities at both ends of migrant corridors; and congruency between the global diffusion of norms and the global migration of people. The conclusion highlights the original contribution of the paper, both to the demographic discipline (migration should also be dealt with as a remote determinant of fertility) and political debates on migration (erecting barriers to migration works against the preservation of earth).

Automating global landslide detection with heterogeneous ensemble deep-learning classification

With changing climatic conditions, we are already seeing an increase in extreme weather events and their secondary consequences, including landslides. Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. However, these both rely on data from previous landslide events, which is often scarce. Many deep learning (DL) models have recently been applied for landslide mapping using medium-to high-resolution satellite images as input. However, they often suffer from sensitivity problems, overfitting, and low mapping accuracy. This study addresses some of these limitations by using a diverse global landslide dataset, using different segmentation models, such as Unet, Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an ensemble model. The ensemble model achieved the highest F1-score (0.69) when combining both Sentinel-1 and Sentinel-2 bands, with the highest average improvement of 6.87 % when the ensemble size was 20. On the other hand, Sentinel-2 bands performed well, with an F1 score of 0.61 only when the ensemble size is 20 with an improvement of 14.59 %. This result shows considerable potential in building a robust and reliable monitoring system to minimise landslide hazards by building the ensemble globally trained system based on changes in vegetation index dNDVI only.

Analysis of patterns and trends in competition manipulation in sport global data: 2018–2023

ABSTRACT Competition manipulation in sports has evolved from a localized issue to a global concern. Advances in technology and the growth of sports betting markets have attracted criminal networks that manipulate sporting events for financial gain. The aim of this study was to examine patterns and trends in competition manipulation in sport from a global dataset over a 6-year period. Manipulation of sports competition investigations and sanctions were assessed across world regions and sports. The Macolin Convention typology was also employed to categorize manipulation types and instigators. Results showed an increase in investigations and sanctions year-on-year, with Europe consistently having the highest numbers annually. The majority of investigations and sanctions were consistent across the sports of football, cricket, and tennis, and also an emerging trend in sanctions in esports. Investigation types have mostly been due to direct interference of a sports competition or use of external means. The instigator of manipulation trends shifted from opportunistic types to the exploitation of power or influence. The patterns and trends reported can help inform the work of sport management professionals.

Large disagreements in estimates of urban land across scales and their implications

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts.

Support Carbon Neutral Goal with a High-resolution Carbon Monitoring System in Beijing

Abstract China has committed to peak its carbon emissions before 2030 and achieve carbon neutrality before 2060, known as the Dual Carbon Goals (DCG). To support the DCG, monitoring and assessment of progresses in emissions reduction have become a central research focus. Here we describe the Beijing-Tianjin-Hebei (JJJ) Carbon Monitoring System, established over the last six years through a collaborative effort of 16 research institutions and universities. The monitoring capability includes ground-based high-precision reference stations, a dense network of medium-accuracy low-cost sensors, carbon satellites, vertical profile samplers, and mobile platforms. Observations using the network have shown CO2 variations from diurnal, synoptic to seasonal-interannual timescales, as well as dramatic on-road changes during the COVID-19 lockdown. High-resolution modeling and data assimilation at km-scale reveal major deficiencies in global carbon emissions datasets at regional/city scale. The inversion found the total fossil fuel CO2 emissions of the City of Beijing to be 117 megatonne (Mt), but much lower than the national-data-based inventories (152 MtCO2). The results have been used in emissions assessment by national and local environmental protection agencies. Similar systems are now being developed in other Chinese cities.

Wheat genetic progress in biomass allocation and yield components: A global perspective

BackgroundWheat is an essential food source and is subjected to intense breeding efforts for increased grain yield, but stagnation in grain yield improvements has been reported in many regions. The identification of genetically linked factors impeding further progress in wheat grain yield improvement is therefore urgently required. MethodA comparative meta-analysis of data from 66 publicly available field experiments involving multiple wheat genotypes was performed to identify traits altered in breeding programs, their relationship with grain yield, and their past and current impact on grain yield increases. ResultsWheat grain yield can be increased by increasing either the aboveground biomass (ABM) or the harvest index (HI). However, there was no correlation between these traits since a reduction in plant height can occur with increases in the HI and overall grain yield, but with no reduction in the ABM. The combined data from 32 global datasets revealed a substantial increase in wheat grain yield from 1860 to 2017, accompanied by improvement in HI and yield components. When considering only the genotypes introduced from the 1960s to 2017, there was a linear increase observed in both grain yield and HI until the mid-1980s. However, genetic progress in HI and GY has slowed down since then. Before the mid-1980s, there was a decreasing trend observed in plant height which remained relatively static thereafter. While ABM did not exhibit significant increases during this period. After the mid-1980s, significant improvements have been observed in ABM; however, no obvious increase were observed in other yield components. ConclusionsSince the 1980s, there has been an increase in the aboveground biomass of wheat, while grain weigth and grains per m2 increases trending slowly, and both harvest index and grain yield have almost stagnated. Therefore, increasing grains per m2 and/or grain weight should be the major research direction to further improve the wheat harvest index and grain yield in the future. ImplicationsThe systematic study of changes in wheat traits in past breeding efforts for improved grain yields has provided useful indicators for the direction of wheat breeding in the future.