Multiple Years Research Articles

School health promotion and fruit and vegetable consumption in secondary schools: a repeated cross-sectional multilevel study

BackgroundWorldwide, recommendations for fruit and vegetable consumption are not met, which can cause chronic diseases. Especially adolescence is an important phase for the development of health behaviours. Therefore, in the Netherlands, the Healthy School program was established to aid schools in promoting healthy lifestyles among their students. We examined to what extent the variation between secondary schools regarding students’ fruit and vegetable consumption could be explained by differences between schools regarding Healthy School certification, general school characteristics, and the school population. Additionally, we examined whether Healthy School certification was related to the outcomes, and whether the association differed for subgroups.MethodsWe performed a repeated cross-sectional multilevel study. We used data from multiple school years from the national Youth Health Monitor on secondary schools (grades 2 and 4, age ranged from approximately 12 to 18 years) of seven Public Health Services, and added data with regard to Healthy School certification, general school characteristics and school population characteristics. We included two outcomes: the number of days a student consumed fruit and vegetables per week. In total, we analysed data on 168,127 students from 256 secondary schools in the Netherlands.ResultsResults indicated that 2.87% of the variation in fruit consumption and 5.57% of the variation in vegetable consumption could be attributed to differences at the school-level. Characteristics related to high parental educational attainment, household income, and educational track of the students explained most of the variance between schools. Additionally, we found a small favourable association between Healthy School certification and the number of days secondary school students consumed fruit and vegetables.ConclusionsSchool population characteristics explained more variation between schools than Healthy School certification and general school characteristics, especially indicators of parental socioeconomic status. Nevertheless, Healthy School certification seemed to be slightly related to fruit and vegetable consumption, and might contribute to healthier dietary intake. We found small differences for some subgroups, but future research should focus on the impact in different school contexts, since we were restricted in the characteristics that could be included in this study.

Changes in the Average Body Mass Index of Fifth- and Sixth-Grade Korean Elementary School Students: The Role of Physical Education in Student Health.

This study aimed to emphasize the importance of physical education in maintaining sufficient physical activity by examining the average BMI of fifth- and sixth-grade elementary school students in Korea over multiple years. This study investigated changes in the average BMI of fifth- and sixth-grade elementary school students in Korea. It provided foundational data to suggest the role of physical education in student health and set future directions. The 2019-2021 Physical Activity Promotion System (PAPS) of the Korean Ministry of Education was used. Data corresponding to fifth- and sixth-grade students (124,693 from 2019, 126,226 from 2020, and 117,809 from 2021) in 1208 elementary schools in Korea were collected. Repeated measures ANOVA was conducted for the data analysis. The study findings are as follows: First, the average BMI of fifth- and sixth-grade elementary school students in Korea increased significantly in 2021 compared with 2019 and 2020. Second, changes in the yearly average BMI showed a significant difference depending on gender and grade. Obesity among fifth- and sixth-grade elementary school students in Korea increased steadily during 2019-2021, which may be due to a decrease in students' physical activity. Male students showed a steeper increase regardless of grade. During the study period, limited physical activity at school increased obesity rates. Physical education must play a role in students' health in preparation for future changes.

Loss of load probability for power systems based on renewable sources

An increasing number of small and variable power sources, such as wind and solar power plants, are being integrated into power systems, potentially affecting their reliability. This study was aimed at developing a method for evaluating reliability in terms of the loss of load probability. To this end, an algorithm capable of assessing the expanded role of renewable sources in the power system was established, which could take into account the specific roles and features of wind, solar, and hydro power plants, including pumped storage hydro power plants. Various models were developed and tested to closely emulate real power system behavior. A regional power system was evaluated over multiple years. The results indicated that the reliability of power systems fluctuates with the available capacity of renewable resources. These findings provide a foundation for a more precise specification of power reserves in such systems to prevent excessive reduction in reliability.

An optimization model for reducing thrombectomy center rotations while maintaining medical accessibility

Background/purposeThis study addresses the delicate balance between healthcare personnel burnout and medical accessibility in the context of endovascular thrombectomy (EVT) services in urban areas. We aimed to determine the minimum number of hospitals providing EVT on rotation each day without compromising patient access. MethodsEmploying an optimization model, we developed shift schedules based on patient coverage rates and volumes during the pre-pandemic (2016–2018) and pandemic (2019–2021) periods. Starting with a minimum of two hospitals on duty per day, we gradually increased to a maximum of eight. Patient coverage rates, defined as the proportion of patients meeting bypass criteria and transported to rotating hospitals capable of EVT, were the primary outcomes. Sensitivity analyses explored the impact of varying patient transport intervals and accumulating patients over multiple years. ResultsResults from 7024 patient records revealed patient coverage rates of 92.5% (standard deviation [SD] 2.8%) during the pre-pandemic and 91.4% (SD 2.8%) during the pandemic, with at least two rotating hospitals daily. No significant differences were observed between schedules based on the highest patient volume and coverage rate months. A patient coverage rate of 98.99% was achieved with four rotating hospitals per day during the pre-pandemic period, with limited improvement beyond this threshold. Changing patient transport intervals and accumulating patients over six years (p = 0.83) had no significant impact on coverage rates. ConclusionOur optimization model supports reducing the number of daily rotating hospitals by half while preserving a balance between patient accessibility and alleviating strain on medical teams.

Long-term developments in seasonal hypoxia and response to climate change: A three-decade modeling study in the Ariake Sea, Japan

Hypoxia in the Ariake Sea, Japan, is steadily increasing in both duration and spatial coverage. Hypoxia, defined as dissolved oxygen (DO) below 3 mg/L, is strongly associated with the amplified frequency of extreme rainfall events driven by climate change, which poses a mounting threat to marine ecosystems on a global scale. In this study, we employed a general three-dimensional (3-D) hydrodynamic coastal model and a phytoplankton-based ecosystem model to identify the potential cause of seasonal hypoxic events over three decades. The results indicated a substantial decrease in bottom DO levels from 1992 to 2021, with the rate of increase in hypoxic area being 8 km2/yr (95 % CI: −0.38, 16.2) and the anoxic area increasing from almost non-existent to 100 km2. Notably, among various environmental drivers, increased river discharge was identified as a pivotal factor in the occurrence of hypoxia. Large-scale river discharge events can potentially increase water stratification, leading to the formation of hypoxia. River discharge volume and the duration of bottom hypoxia in the Ariake Sea were correlated. The duration of hypoxia was strongly associated with river discharge magnitude, with correlation coefficients ranging from 0.56 to 0.82 across six observational stations. Furthermore, analysis of varied simulated environmental factors over multiple years revealed diverse responses to climate change, indicating that the Ariake Sea is prone to experiencing a decline in its physical and water quality conditions.

From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops.

Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.

Remotely mapping gullying and incision in Maryland Piedmont headwater streams using repeat airborne lidar

Headwater streams can contribute significant amounts of fine sediment to downstream waterways, especially when severely eroded and incised. Potential upstream sediment source identification is crucial for effective management of water quality, aquatic habitat, and sediment loads in a watershed. This study explored topographic openness (TO) derived from 1-m lidar for its ability to predict incision in headwater streams and to remotely detect changes in incision over time. Field surveys were conducted in one forested and two recently urbanized headwater watersheds in the Maryland Piedmont physiographic province, USA to characterize the level of stream channel incision (none, moderate, or severe) in the main stem of each watershed. Predictions of the severity of stream channel incision derived from TO were compared against the field surveys. Channel incision was detected with an overall accuracy of 67 %, with best performance in reaches with either severe or no incision (79–86 % accuracy). The method was also applied to repeat lidar collected over the same area to model the extent of channel incision in 2002 before urban development began and in 2008 and 2013 during active construction in the urban watersheds. Results showed increasing incision over time in all three watersheds, with similar patterns in the forested and urban watersheds. This new method of remotely measuring channel incision can be used to identify potential sediment sources across a watershed, enhance water and habitat quality predictions, and detect changes over time where multiple years of overlapping lidar are available.

Decomposition of Fomes fomentatius fruiting bodies - transition of healthy living fungus into a decayed bacteria-rich habitat is primarily driven by Arthropoda.

Fomes fomentarius is a widespread, wood-rotting fungus of temperate, broadleaved forests. Although the fruiting bodies of F. fomentarius persist for multiple years, little is known about its associated microbiome or how these recalcitrant structures are ultimately decomposed. Here we used metagenomics and metatranscriptomics to analyse the microbial community associated with healthy living and decomposing F. fomentarius fruiting bodies to assess the functional potential of the fruiting body-associated microbiome and to determine the main players involved in fruiting body decomposition. F. fomentarius sequences in the metagenomes were replaced by bacterial sequences as the fruiting body decomposed. Most CAZymes expressed in decomposing fruiting bodies targeted components of the fungal cell wall with almost all chitin-targeting sequences, plus a high proportion of beta-glucan-targeting sequences, belonging to Arthropoda. We suggest that decomposing fruiting bodies of F. fomentarius represent a habitat rich in bacteria, while its decomposition is primarily driven by Arthropoda. Decomposing fruiting bodies thus represent a specific habitat supporting both microorganisms and microfauna.

Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data

Abstract. Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind speed profiles and (2) low-level jets (LLJs) at three locations of high relevance to offshore wind energy deployment: the US Northeastern Atlantic Coastal Zone, the North Sea, and the Baltic Sea. The ML models are trained on multiple years of lidar profiles and utilize single-level ERA5 variables as input. The models output spatial predictions of coastal wind speed profiles and LLJ occurrence. A suite of nine ERA5 variables are considered for use in the study due to their physics-based relevance in coastal wind speed profile genesis and the possibility to observe these variables in real-time via measurements. The wind speed at 10 ma.s.l. and the surface sensible heat flux are shown to have the highest importance for both wind speed profile and LLJ predictions. Wind speed profile predictions output by the ML models exhibit similar root mean squared error (RMSE) with respect to observations as is found for ERA5 output. At typical hub heights, the ML models show lower RMSE than ERA5 indicating approximately 5 % RMSE reduction. LLJ identification scores are evaluated using the symmetric extremal dependence index (SEDI). LLJ predictions from the ML models outperform predictions from ERA5, demonstrating markedly higher SEDIs. However, optimization utilizing the SEDI results in a higher number of false alarms when compared to ERA5.

A long-term study of sport event volunteers: Cincinnati Flying Pig Marathon Weekend in 2012 and 2022

PurposeThis study examined the characteristics, motivations and satisfaction of volunteers for the Cincinnati Flying Pig Marathon Weekend across a 10-year period. The purpose was to enhance our understanding of sport event volunteers, especially in the long term. This information and insight may be useful in light of ongoing challenges with volunteer recruitment and retention, which have been exacerbated by the COVID-19 pandemic.Design/methodology/approachThe authors developed and implemented an online survey, based primarily on the Special Event Volunteer Motivation Scale (SEVMS) and the Volunteer Motivations Scale for International Sporting Events (VMS-ISE). The sample included a total of 2,038 respondents – 1,086 in 2012 and 952 in 2022. Quantitative data were analyzed using descriptive statistics and Chi-square tests; qualitative data provided additional insight.FindingsAbout two-thirds of 2022 survey respondents were “repeat volunteers.” Volunteer characteristics, motivations and satisfaction remained relatively consistent across the 10-year period. These volunteers were motivated by a set of multiple, interrelated factors which the authors call “community-based altruism” – that is, the desire to help others driven by a sense of community involvement and pride. In both years, more than 90% were satisfied with their volunteer experience. Satisfaction was higher for individuals with certain characteristics such as being a repeat volunteer and volunteering with a group.Originality/valueThis may be the first scholarly article to assess volunteer characteristics, motivations and satisfaction for a major sport event in the same location across multiple years. While it focused on a three-day running event in a midwestern city in the United States, the approach and findings may be applicable to sport event volunteers in other contexts.

Evaluation of Wintertime Precipitation Estimates and Forecasts in the Mountains of Colorado

Abstract Wintertime precipitation poses many observational and forecasting challenges, especially in the complex topography of the western United States where radar beam blockage and difficulty siting in situ observations yields more sparse observations than in the eastern United States. Uncertainty in western U.S. winter precipitation is known to be high, so much so that some studies have found model simulated precipitation to produce similar or better large-scale estimates of annual precipitation than gridded observational products during climatologically anomalous years. This study evaluates high-resolution gridded precipitation estimates from Multi-Radar Multi-Sensor (MRMS) and Stage IV as well as forecasts from NOAA’s High-Resolution Rapid Refresh (HRRR) model in the Colorado Rocky Mountains. Gridded precipitation estimates and forecasts are compared with in situ SNOTEL measurements for two seasons of wintertime precipitation. The influence of forecast length, lead time, and model elevation on seasonal precipitation predictions from the HRRR are investigated. Additional comparisons are made with the relatively dense network of observations deployed in Colorado’s East River Watershed during the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Gridded products and forecasts are found to underestimate cold-season precipitation by 25%–65% relative to in situ and aircraft measurements, with longer forecast periods and lead times (6–24 h) having smaller biases (25%–30%) than shorter forecast periods and lead times (55%–65%). The assessment of multiple years of observations indicates that these biases are related more to the data and methods used to create the gridded products and forecasts than to precipitation characteristics. Significance Statement In the mountainous western United States, it is very challenging to both observe and forecast wintertime precipitation, yet snowfall plays an important role in providing the region’s annual water supply. This study aims to increase our understanding of the biases in observations and forecasts of snowfall in the Colorado Rocky Mountains, which can in turn impact forecasts of water availability for the ensuing warm season. In this study we find high-resolution gridded precipitation estimates and forecasts to underestimate cold-season precipitation when compared with in situ observing stations, with longer-range forecasts (e.g., daily) being the least biased. These findings were consistent over two years of study and have broad implications for the hydrologic modeling and water management communities.

Simulation of Full HIV Cluster Networks in a Nationally Representative Model Indicates Intervention Opportunities.

Clusters of rapid HIV transmission in the United States are increasingly recognized through analysis of HIV molecular sequence data reported to the National HIV Surveillance System. Understanding the full extent of cluster networks is important to assess intervention opportunities. However, full cluster networks include undiagnosed and other infections that cannot be systematically observed in real life. We replicated HIV molecular cluster networks during 2015-2017 in the United States using a stochastic dynamic network simulation model of sexual transmission of HIV. Clusters were defined at the 0.5% genetic distance threshold. Ongoing priority clusters had growth of ≥3 diagnoses/year in multiple years; new priority clusters first had ≥3 diagnoses/year in 2017. We assessed the full extent, composition, and transmission rates of new and ongoing priority clusters. Full clusters were 3-9 times larger than detected clusters, with median detected cluster sizes in new and ongoing priority clusters of 4 (range 3-9) and 11 (range 3-33), respectively, corresponding to full cluster sizes with a median of 14 (3-74) and 94 (7-318), respectively. A median of 36.3% (range 11.1%-72.6%) of infections in the full new priority clusters were undiagnosed. HIV transmission rates in these clusters were >4 times the overall rate observed in the entire simulation. Priority clusters reflect networks with rapid HIV transmission. The substantially larger full extent of these clusters, high proportion of undiagnosed infections, and high transmission rates indicate opportunities for public health intervention and impact.

Long-Term Spatial Pattern Predictors (Historically Low Rainfall, Benthic Topography, and Hurricanes) of Seagrass Cover Change (1984 to 2021) in a Jamaican Marine Protected Area

Climate change and other anthropogenic factors have caused a significant decline in seagrass cover globally. Identifying the specific causes of this decline is paramount if they are to be addressed. Consequently, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in a marine protected area on Jamaica’s southern coast. Two random forest regression (RFr) models were built using 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary and 2013 Landsat 7 and 8 reflectance data as the predictors. These were used to generate 24 SAV percentage cover and benthic feature maps (SAV present, absent, and coral reef) for the period 1984–2021 (37 years) from Landsat satellite series reflectance data. These maps and rainfall data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Additionally, rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed. Finally, the most important spatial pattern predictors of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified. Predictors included rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance). SAV area/extent was largely stable, with >70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or 13%) during 2002–2006, and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by >1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change and had a positive overall effect on the spatial pattern of SAV cover percentage change (across the entire bay) and gain (close to the mouth of a river). The most important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Specifically, a hurricane that made landfall in 1988 (Gilbert), resulted in higher SAV percentage cover loss in 1987–1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002–2006. Additionally, exposure to Ivan explained percentage cover loss during 2006–2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013–2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years, SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

Landscape-wide floral resource deficit enhances the importance of diverse wildflower plantings for pollinators in farmlands

Pollinators are declining rapidly, largely due to land conversion and intensification of agriculture. To mitigate their crisis, low-disturbance habitats, such as sown wildflower plantings, could promote pollinators by restoration of their resources. However, comprehensive knowledge is lacking on how landscape context, spatial configuration and age of wildflower plantings, seasonality and flower composition affect pollinator communities, especially from East-Central Europe. To understand these effects, we established diverse native wildflower plantings within heterogeneous and homogeneous agricultural landscapes, by two spatial configurations: one large field or three smaller strips, in Hungary. We sampled floral resources and wild pollinator insects (wild bees, hoverflies, butterflies), in early and mid-summer, for two years after establishment. Flower resources of the sown plant species increased continuously, and were complemented at high rate by flowering plant species from the soil seed bank, especially in the first year. Both flower abundance and diversity increased the abundance of pollinators, highlighting the important role of using diverse seed mixtures. Wild bee abundance and species richness increased year by year and season by season, while butterfly abundance also demonstrated a yearly increase after establishment. Hoverfly abundance and species richness, however, showed an opposite trend, possibly due to the inter-annual variation. Wild bee and butterfly abundance was higher in the heterogeneous than in the homogeneous landscapes. We did not observe any significant local effects of spatial configuration itself on pollinator populations. Wildflower plantings were particularly attractive for wild bees in flower-poor (homogeneous) landscapes during the flower-poor (mid-summer) period of the year. This can be explained by the increased contrast in flower resources between the wildflower plantings and the surrounding landscape, and highlights the importance of wildflower plantings in such conditions. Our results emphasise that to support pollinators effectively, future wildflower plantings should be maintained for multiple years, aim to maximise floral diversity and ensure continuously available flower resources throughout the entire season. They can be facilitated by i) using diverse seed mixtures of native flowering plants, ii) particularly focusing on plants that bloom during the crucial mid-summer period, iii) allowing the plant species to grow and bloom from the soil seed bank, and iv) using management techniques, which help to extend the flowering season. Further results from the upcoming years and similar long-term and landscape-scale experimental studies are needed to understand all the benefits and ecological processes of diverse native wildflower plantings especially in understudied European regions.

Strategic behaviours in a labour market with mobility-restricting contractual provisions: evidence from the National Hockey League

Abstract We follow workers’ performance along an unbalanced panel dataset over multiple years and study how performance varies at the end of fixed-term contracts, in a labour market where some people face a mobility-restricting clause (i.e. a noncompete clause). Focusing on the labour market of the National Hockey League, we analyse players’ performance data and contracts with a fixed-effects estimator to address empirical limitations in previous studies. We find that, on average, National Hockey League players’ performance does not vary. However, our estimations detect substantially heterogeneous behaviours, depending on tenure, perceived expected performance, and mobility. Only younger players (i.e. restricted free agents) with high expected mobility but low expected performance tend to behave strategically and perform better. Differently, older players (i.e. unrestricted free agents) with high expected mobility tend to underperform, as the option of moving back to European tournaments is more appealing.

UV-SAM: Adapting Segment Anything Model for Urban Village Identification

Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the Sustainable Development Goals (SDGs) on poverty, adequate housing, and sustainable cities. Traditionally, governments heavily depend on field survey methods to monitor the urban villages, which however are time-consuming, labor-intensive, and possibly delayed. Thanks to widely available and timely updated satellite images, recent studies develop computer vision techniques to detect urban villages efficiently. However, existing studies either focus on simple urban village image classification or fail to provide accurate boundary information. To accurately identify urban village boundaries from satellite images, we harness the power of the vision foundation model and adapt the Segment Anything Model (SAM) to urban village segmentation, named UV-SAM. Specifically, UV-SAM first leverages a small-sized semantic segmentation model to produce mixed prompts for urban villages, including mask, bounding box, and image representations, which are then fed into SAM for fine-grained boundary identification. Extensive experimental results on two datasets in China demonstrate that UV-SAM outperforms existing baselines, and identification results over multiple years show that both the number and area of urban villages are decreasing over time, providing deeper insights into the development trends of urban villages and sheds light on the vision foundation models for sustainable cities. The dataset and codes of this study are available at https://github.com/tsinghua-fib-lab/UV-SAM.

Depth Range Extension for the Misty Grouper Hyporthodus mystacinus Documented via Deep-Sea Landers throughout the Greater Caribbean

Misty Groupers (Hyporthodus mystacinus) are one of the largest and most geographically widespread grouper species and one of the few grouper species known to occur at depths greater than 200 m. However, aspects of their basic biology, behavior, and ecology remain poorly understood, leaving significant gaps in our ability to evaluate their functional role throughout the vertical water column, as well as our understanding of their conservation needs in a changing ocean. Through in-situ video observation obtained using deep-sea landers in both The Bahamas and Cayman Islands over multiple years, we documented Misty Grouper occurrence up to 470 m depth in the mesopelagic zone. These observations provide a new depth range extension for the species and illuminate the potential importance of deep-water habitats for large grouper species in the wider Caribbean.

Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach

Droughts cause significant economic losses in many regions around the world, highlighting a need to more accurately quantify implications of drought on production and water management. Remote sensing technologies and machine learning-based models offer promising solutions for timely and accurate regional drought monitoring, but the accuracy of such approaches is constrained by both the conceptual design and algorithms underpinning such approaches. We developed a machine learning stacking ensemble approach to overcome such limitations, drawing upon Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR), Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing products, and climate zoning data to estimate 3-month scale SPEI (Standardized Precipitation Evapotranspiration Index, SPEI-3) in 9 sub-regions of China. We compared 19 individual machine learning models and used the stacking approach to select the most robust meta-model for SPEI-3 prediction. We found that CatBoost Regressor (CBR), Extra Trees Regressor (ETR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF) were the top individual models for predicting drought, while using CBR as the stacked meta-model achieved the best performance. The R2 values for the stacking model with CBR as the meta-model were 0.9065 and 0.8218 in the eastern and western regions, respectively. We then employed the stacking model with CBR as the meta-model to generate seasonal drought maps based on SPEI-3 across different years and seasons. Our predicted SPEI-3 was compared with drought maps generated by the GPCC and ERA5 reanalysis datasets. We found a strong correlation between them with R2 exceeding 0.8 across multiple years and seasons, indicating our machine learning stacking ensemble approach had a good performance in monitoring seasonal drought conditions. The findings of our study establish a standardized protocol for predicting droughts in the agricultural sector, enabling its widespread application irrespective of variations in terrain and climate.

Recombination, admixture and genome instability shape the genomic landscape of Saccharomyces cerevisiae derived from spontaneous grape ferments.

Cultural exchange of fermentation techniques has driven the spread of Saccharomyces cerevisiae across the globe, establishing natural populations in many countries. Despite this, Oceania is thought to lack native populations of S. cerevisiae, only being introduced after colonisation. Here we investigate the genomic landscape of 411 S. cerevisiae isolated from spontaneous grape fermentations in Australia across multiple locations, years, and grape cultivars. Spontaneous fermentations contained highly recombined mosaic strains that exhibited high levels of genome instability. Assigning genomic windows to putative ancestral origin revealed that few closely related starter lineages have come to dominate the genetic landscape, contributing most of the genetic variation. Fine-scale phylogenetic analysis of loci not observed in strains of commercial wine origin identified widespread admixture with European derived beer yeast along with three independent admixture events from potentially endemic Oceanic lineages that was associated with genome instability. Finally, we investigated Australian ecological niches for basal isolates, identifying phylogenetically distinct S. cerevisiae of non-European, non-domesticated origin associated with admixture loci. Our results illustrate the effect commercial use of microbes may have on local microorganism genetic diversity and demonstrates the presence of non-domesticated, potentially endemic lineages of S. cerevisiae in Australian niches that are actively admixing.

Nitrogen and carbon stocks in sandy soil cultivated with corn subjected to 17 years of animal manure and mineral fertilizer application

Animal manure can be used as a source of nutrients in cropping systems throughout the world. However, in subtropical climates, the impacts of animal manure application over multiple years on crop productivity and on the total organic carbon (TOC) and total nitrogen (TN) stocks in the soil profile are not well understood. In this study, we aimed to evaluate how animal manure application carried out over more than 17 years can impact corn productivity and TOC and TN stocks in different soil layers. This long-term study was conducted from 2004 to 2021 under no-till conditions in southern Brazil. The treatments included pig slurry (PS), cattle slurry (CS), pig deep litter (PDL), mineral fertilizer (NPK) and a control (no fertilizer application). To determine soil TOC and TN stocks, soil samples were collected from the 0.00–0.10, 0.10–0.20, 0.20–0.30, 0.30–0.40 and 0.40–0.50 m layers in December 2008, 2012, 2016 and 2020. Corn productivity was evaluated for the 2008/09, 2012/13, 2016/17, and 2020/21 crops. The highest yields and TOC and TN stocks were observed in soils with pig deep litter and cattle slurry application. In the sandy soil under a no-till system after 26 animal manure applications over 17 years, the highest TOC and TN stocks were observed in the 0.00–0.10 m layer. On average, the TOC stocks obtained for the 0.00–0.10 m layer were 11.21, 14.88, 15.82, 22.59 and 15.16 Mg ha−1 for the control, PS, CS, PDL and NPK treatments, respectively. The soil TN stocks in the 0.00–0.10 m layer were 1.06, 1.40, 1.65, 2.03 and 1.43 Mg ha−1 for the control, PS, CS, PDL and NPK treatments, respectively. The application of pig deep litter to the soil over 17 years resulted in a notable increase in TOC and TN levels, playing a crucial role in increasing the productivity of maize crops.