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Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.

Background Older adults are particularly vulnerable to the adverse health effects of extreme temperature-related events. A growing body of literature highlights the importance of the natural environment, including air pollution and sunlight, on cognitive health. However, the relationship between exposure to outdoor temperatures and cognitive functioning, and whether there exists any differences across climate region, remains largely unexplored. We address this gap by examining the temperature-cognition association, and whether there exists any variation across climate regions in a national cohort of aging adults. Methods In this cross-sectional study, we obtained data on temperature exposure based on geocoded residential location of participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. For each participant, this information was linked to their cognitive scores from Word List Learning and Recall tests to assess cognitive functioning. We used distributed lag non-linear models (dlnm) to model temperature effects over 2 days. Multivariable linear regression was used to compute temperature-cognitive functioning associations, adjusted for important covariates. Region-specific (“Dry”, “Mediterranean/oceanic”, “Tropical” and “Continental”) associations were examined by including an interaction term between climate region and temperature. Results Amongst 20,687 individuals (mean age = 67.8; standard deviation = 9.2), exposure to region-specific extreme cold temperatures in the “dry” region (e.g., Arizona) over 2 days was associated with lower cognitive scores (Mean Difference [MD]: -0.76, 95% Confidence Interval [CI]: − 1.45, − 0.07). Associations remained significant for cumulative effects of temperature over 2 days. Extremely cold exposure in the “Mediterranean/oceanic” region (e.g., California) over 2 days was also associated with significantly lower cognitive performance (MD: -0.25, 95% CI: − 0.47, − 0.04). No significant associations were observed for exposure to hot temperatures. Cognitive performance was slightly higher in late summer and fall compared to early summer. Conclusion We noted adverse cognitive associations with cold temperatures in traditionally warmer regions of the country and improved cognition in summer and early fall seasons. While we did not observe very large significant associations, this study deepens understanding of the impact of climate change on the cognitive health of aging adults and can inform clinical care and public health preparedness plans.

Environmental science is increasingly reliant on remotely sensed observations of the Earth's surface and atmosphere. Observations from polar-orbiting satellites have long supported investigations on land cover change, ecosystem productivity, hydrology, climate, the impacts of disturbance, and more and are critical for extrapolating (upscaling) ground-based measurements to larger areas. However, the limited temporal frequency at which polar-orbiting satellites observe the Earth limits our understanding of rapidly evolving ecosystem processes, especially in areas with frequent cloud cover. Geostationary satellites have observed the Earth's surface and atmosphere at high temporal frequency for decades, and their imagers now have spectral resolutions in the visible and near-infrared regions that are comparable to commonly used polar-orbiting sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or Landsat. These advances extend applications of geostationary Earth observations from weather monitoring to multiple disciplines in ecology and environmental science. We review a number of existing applications that use data from geostationary platforms and present upcoming opportunities for observing key ecosystem properties using high-frequency observations from the Advanced Baseline Imagers (ABI) on the Geostationary Operational Environmental Satellites (GOES), which routinely observe the Western Hemisphere every 5–15 min. Many of the existing applications in environmental science from ABI are focused on estimating land surface temperature, solar radiation, evapotranspiration, and biomass burning emissions along with detecting rapid drought development and wildfire. Ongoing work in estimating vegetation properties and phenology from other geostationary platforms demonstrates the potential to expand ABI observations to estimate vegetation greenness, moisture, and productivity at a high temporal frequency across the Western Hemisphere. Finally, we present emerging opportunities to address the relatively coarse resolution of ABI observations through multisensor fusion to resolve landscape heterogeneity and to leverage observations from ABI to study the carbon cycle and ecosystem function at unprecedented temporal frequency.

Surface‐atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub‐grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub‐kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not continuously available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES‐16 and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station [ECOSTRESS]) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to independent observations from a network of 20 micrometeorological towers and piloted aircrafts in addition to Landsat‐based LST retrieval and drone‐based LST observed at one tower site. The downscaled 50‐m hourly LST showed good relationships with tower (r2 = 0.79, RMSE = 3.5 K) and airborne (r2 = 0.75, RMSE = 2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio‐temporal variation compared to a geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hot and cold spots across the landscape as evidenced by independent drone LST, with significant reduction in RMSE by 1.3 K. These results demonstrate a simple pathway for multi‐sensor retrieval of high space and time resolution LST. Plain Language Summary The temperature of the Earth’s surface over land—land surface temperature (LST)—is an important variable to observe and forecast. Variation in LST over space and time at scales of meters and hours influence processes in the atmosphere, soils, vegetation, and water. For the worldwide coverage of LST, we rely on Earth‐observing satellites. However, there are trade offs in how finely LST can be observed over space versus how often LST can be observed over time, given the characteristics of any one satellite's orbit, not to mention the obscuring effect of clouds. Therefore, methods are needed that enable data from multiple satellites as well as aircraft and towers if we want to observe LST at high space and time resolution. Here, we develop such an approach and test its accuracy over a test bed of extensive LST observations made by towers, drones, and aircraft during a field experiment in Northern Wisconsin USA. Key Points Fusion of satellites with models for high space and time resolution land surface temperature needed for many surface‐atmosphere studies Developed an approach that evaluates well across array of towers and aircraft observations from an intensive field experiment Additional downscaling with airborne hyperspectral imagery further refines the identification of hot spots as evaluated with drone observations

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks. Plain Language Summary Rangelands play a crucial role in providing various ecosystem services, including potential climate change mitigation through increased soil organic carbon (SOC) storage. Accurate estimates of changes in carbon (C) storage are challenging due to the heterogeneous nature of rangelands and the limited availability of field observations. In this work, we leveraged remote sensing observations, tower‐based C flux measurements from over 60 rangeland sites in the Western and Midwestern US, and other environmental data sets to build the process‐based Rangeland Carbon Tracking and Management (RCTM) modeling system. The RCTM system is designed to simulate the past 20 years of rangeland C dynamics and is regionally calibrated. The RCTM system performs well in estimating spatial and temporal rangeland C fluxes as well as spatial SOC storage. Model simulation results revealed increased SOC storage and rangeland productivity driven by annual precipitation patterns. The RCTM system developed by this work can be used to generate accurate spatial and temporal estimates of SOC storage and C fluxes at fine spatial (30 m) and temporal (every 5 days) resolutions, and is well‐suited for informing rangeland C management strategies and improving broad‐scale policy making. Key Points The Rangeland Carbon Tracking and Monitoring System was calibrated to simulate vegetation type‐specific rangeland C dynamics Regional variability in carbon fluxes and soil organic carbon is well represented by a remote sensing‐driven process modeling approach Soil organic carbon stocks in Western and Midwestern US rangelands increased over the past 20 years due to increased precipitation

Background Compared to ST-segment elevation myocardial infarction (STEMI) patients who present at centres with catheterization facilities, those transferred for primary percutaneous coronary intervention (PCI) have substantially longer door-in to door-out (DIDO) times, where DIDO is defined as the time interval from arrival at a non-PCI hospital, to transfer to a PCI hospital. We aimed to identify potentially modifiable factors to improve DIDO times in Ontario, Canada and to assess the impact of DIDO times on 30-day mortality. Methods A population-based, retrospective cohort study of 966 STEMI patients transferred for primary PCI in Ontario in 2012 was conducted. Baseline factors were examined across timely DIDO status. Multivariate logistic regression was used to examine independent predictors of timely DIDO as well as the association between DIDO times and 30-day mortality. Results The median DIDO time was 55 min, with 20.1% of patients achieving the recommended DIDO benchmark of ≤30 min. Age (OR > 75 vs 18–55 0.30, 95% CI: 0.16–0.56), symptom-to-first medical contact (FMC) time (OR 61-120mins vs < 60mins 0.60, 95% CI: 0.39–0.90; OR >120mins vs < 60mins 0.53, 95% CI:0.35–0.81) and emergency medical services transport with a pre-hospital electrocardiogram (ECG) (OR EMS transport + ECG vs self-transport 2.63, 95% CI:1.59–4.35) were the strongest predictors of timely DIDO. Patients with timely ECG were more likely to have recommended DIDO times (33.0% vs 12.3%; P  < 0.001). A significantly higher proportion of those who met the DIDO benchmark had timely FMC-to-balloon times (78.7% vs 27.4%; P  < 0.001). Compared to patients with DIDO time ≤ 30 min, those with DIDO times > 90 min had significantly higher adjusted 30-day mortality rates (OR 2.82, 95% CI:1.10–7.19). Conclusions While benchmark DIDO times were still rarely achieved in the province, we identified several potentially modifiable factors in the STEMI system that might be targeted to improve DIDO times. Our findings that patients who received a pre-hospital ECG were still being transferred to non-PCI capable centres suggest strategies addressing this gap may improve patient outcomes.

BackgroundImmigrants have been shown to possess a health advantage, yet are also more likely to reside in arduous economic conditions. Little is known about if and how the socioeconomic gradient for all-cause, premature and avoidable mortality differs according to immigration status.MethodsUsing several linked population-based vital and demographic databases from Ontario, we examined a cohort of all deaths in the province between 2002 and 2012. We constructed count models, adjusted for relevant covariates, to attain age-adjusted mortality rates and rate ratios for all-cause, premature and avoidable mortality across income quintile in immigrants and long-term residents, stratified by sex.ResultsA downward gradient in age-adjusted all-cause mortality was observed with increasing income quintile, in immigrants (males: Q5: 13.32, Q1: 20.18; females: Q5: 9.88, Q1: 12.51) and long-term residents (males: Q5: 33.25, Q1: 57.67; females: Q5: 22.31, Q1: 36.76). Comparing the lowest and highest income quintiles, male and female immigrants had a 56% and 28% lower all-cause mortality rate, respectively. Similar trends were observed for premature and avoidable mortality. Although immigrants had consistently lower mortality rates compared with long-term residents, trends only differed statistically across immigration status for females (p<0.05).ConclusionsThis study illustrated the presence of income disparities as it pertains to all-cause, premature, and avoidable mortality, irrespective of immigration status. Additionally, the immigrant health advantage was observed and income disparities were less pronounced in immigrants compared with long-term residents. These findings support the need to examine the factors that drive inequalities in mortality within and across immigration status.

A substantial portion of tropospheric O3 dry deposition occurs after diffusion of O3 through plant stomata. Simulating stomatal uptake of O3 in 3D atmospheric chemistry models is important in the face of increasing drought-induced declines in stomatal conductance and enhanced ambient O3. Here, we present a comparison of the stomatal component of O3 dry deposition (egs) from chemical transport models and estimates of egs from observed CO2, latent heat, and O3 flux. The dry deposition schemes were configured as single-point models forced with data collected at flux towers. We conducted sensitivity analyses to study the impact of model parameters that control stomatal moisture stress on modeled egs. Examining six sites around the Northern Hemisphere, we find that the seasonality of observed flux-based egs agrees with the seasonality of simulated egs at times during the growing season, with disagreements occurring during the later part of the growing season at some sites. We find that modeled water stress effects are too strong in a temperate–boreal transition forest. Some single-point models overestimate summertime egs in a seasonally water-limited Mediterranean shrubland. At all sites examined, modeled egs was sensitive to parameters that control the vapor pressure deficit stress. At specific sites that experienced substantial declines in soil moisture, the simulation of egs was highly sensitive to parameters that control the soil moisture stress. The findings demonstrate the challenges in accurately representing the effects of moisture stress on the stomatal sink of O3 during observed increases in dryness due to ecosystem-specific plant–resource interactions.

Bangladesh Bank, the central Bank of Bangladesh is committed to maintain price stability and facilitate economic growth through conventional monetary policy. Although Bangladesh entered the regime of floating exchange rate in 2003, central bank intervenes to minimize excessive fluctuation in exchange rate through foreign exchange intervention policy. While these two policies work simultaneously, there was no study to examine the performance and effectiveness of these policies and understand their interaction in the framework of a joint analysis. The paper intends to fill up this gap. The paper develops a structural vector autoregression (SVAR) model to understand how foreign exchange intervention and conventional monetary policy affects the economy along with the endogenous reaction of policy variables. The intervention policy and conventional monetary policy are jointly analyzed to understand the interaction between them and also their impact on exchange rate and other economic variables i.e., price levels, real economic activity and real financial asset prices. The SVAR model is identified through relevant exclusion restriction based on the literature and the reality of the economy of Bangladesh. Monthly data of the relevant variables from July 2003 to December 2014 are used to estimate the model. Impulse response function is used to reveal the impact of policy and other shocks.Results reveal that foreign exchange intervention shocks have significant effects on the Taka / US $ exchange rate and the contribution of foreign exchange intervention shocks to exchange rate fluctuation is larger than that of monetary policy shocks. However, intervention shock has virtually no impact on price level, economic activity and real asset price supporting sterilization. On the other hand, while conventional monetary policy has insignificant effect on relevant variables when its effect is examined excluding foreign exchange intervention policy, it has mostly significant impact on economic variables when joint analysis is conducted. Both monetary policy variable and foreign exchange intervention variable react to each other significantly, suggesting the usefulness of this joint analysis. Results also reveal appropriate policy reaction to economic situation. Central Bank’s policy interventions are working pretty well in achieving internal and external stability of the value of local currency. Hence, present framework of exchange rate intervention and monetary policy may be continued without any major overhaul. However, monetary policy has limited impact on economic activity measured by industrial production. Hence, to the extent growth is adversely affected by inflation and exchange rate fluctuation, the central bank may work towards achieving higher growth indirectly by maintaining low inflation and exchange rate stability.

Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was \"averaged out\" at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an 'optimum' spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.