Neighborhood greenspace exposure as a protective factor in dementia risk among U.S. adults 75 years or older: a cohort study

Background Research suggests that greenspace may confer neurocognitive benefits. This study examines whether residential greenspace is associated with risk of dementia among older adults. Methods Greenspace exposure was computed for 3047 participants aged 75 years and older enrolled in the Gingko Evaluation of Memory Study (GEMS) across four U.S. sites that prospectively evaluated dementia and its subtypes, Alzheimer’s disease (AD), vascular dementia (VaD), and mixed pathologies, using neuropsychiatric evaluations between 2000 and 2008. After geocoding participant residences at baseline, three greenspace metrics—Normalized Difference Vegetative Index, percent park overlap within a 2-km radius, and linear distance to nearest park—were combined to create a composite residential greenspace measure categorized into tertiles. Cox proportional hazards models estimated the associations between baseline greenspace and risk of incident all-cause dementia, AD, and Mixed/VaD. Results Compared to low residential greenspace, high residential greenspace was associated with a reduced risk of dementia (HR = 0.76 95% CI: 0.59,0.98) in models adjusted for multiple covariates. After additional adjustment for behavioral characteristics, Apolipoprotein E ɛ4 status, and other covariates, the association was slightly attenuated (HR = 0.82; 95% CI:0.63,1.06). Those exposed to medium levels of greenspace also had 28% lower risk (HR = 0.72; CI: 0.55, 0.95) of dementia compared to those with low greenspace in adjusted models. Subtype associations between high residential greenspace and AD were not statistically significant. Greenspace was not found to be significantly associated with mixed/vascular pathologies. Conclusions This study showed evidence for an association between residential greenspace and all-cause dementia among older adults. Future research with larger sample size, precise characterization of different dementia subtypes, and assessment of residential greenspace earlier in life may help clarify the role between exposure to greenspace and dementia risk.

Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)

Optical sensors have grown in popularity for estimating plant health, and they form the basis of midseason yield estimations and nitrogen (N) fertilizer recommendations, such as the Oklahoma State University (OSU) nitrogen fertilization optimization algorithm (NFOA). That algorithm uses measurements of normalized difference vegetative index (NDVI), yet not all producers have access to the sensors required to make these measurements. In contrast, most producers have access to smartphones, which can measure fractional green canopy cover (FGCC) using the Canopeo app, but the usefulness of these measurements for midseason yield estimations remains untested. Our objectives were to (1) quantify the relationship between NDVI and FGCC, (2) assess the potential for using FGCC values in place of NDVI values in the current OSU Yield Prediction Model, and (3) compare the performance of NDVI and FGCC-based yield prediction models from the collected dataset. This project, implemented on 13 winter wheat sites over the 2019-2020 growing season, used a range of nitrogen (N) rates (0, 34, 67, 101, and 134 kg N ha−1) to provide different levels of yield. Our results indicated that while NDVI and FGCC are highly correlated (r2 = 0.76), FGCC is not suitable for direct insertion into the current yield prediction model. However, a yield prediction model derived from FGCC provided similar estimates of yield compared to NDVI (Nash Sutcliffe Efficiency = −3.3). This new FGCC-based model will give more producers access to sensor-based yield prediction and N rate recommendations.

АЛМАТЫ ОБЛЫСЫНЫҢ (TILLETIA CARIES (D.C.) TUL. & C. TUL) ПОПУЛЯЦИЯСЫНА ВЕНГРИЯЛЫҚ БИДАЙ ЛИНИЯЛАРЫНЫҢ ТӨЗІМДІЛІГІ

Қатты қаракүйе ауруы (Tilletia caries (DC.) Tul.) күздік бидайдың кең таралған ауруы. Ол әлемнің бидай өсіретін барлық аймақтарында кездеседі. Эпифитотия жылдары бидай өнімінің азаюы мен сапасының нашарлауына алып келеді. Жасанды індет аясында Алматы облысының Tilletia caries (D.C.) Tul. & C. Tul патогеніне венгриялық 21 бидай сорттарының төзімділігі сыналды. Зерттеу жұмысының барысында мақсатқа жету үшін бірнеше әдістер қолданылды. Олар: Tilletia caries (D.C.) Tul. & C. Tul патогенімен бидайды инокуляциялауда А.И. Борггардта-Анпилогованың әдісі қолданылды, Green Seeker (Trimble Navigation Limited, USA) – аппараты арқылы өсімдіктің биомассасының индексі өлшенді (NDVI – Normalized Difference Vegetative Index). Үлгілерді Tilletia caries (D.C.) Tul. & C. Tul қоздырғышымен залалдануын бағалауда М. Қойшыбаев шкаласы қолданылды. Зерттеу нәтижесінде ауруға жоғары төзімді деп 7 бидай сорты ерекшеленді. Олар: Békés, Szemes, Rege, Rába, Ati, Pilis және Vitorlás. Индекс биомасса көрсеткішін (NDVI) есептеу нəтижесінде 6 генотиптің NDVI көрсеткіші жоғары деп табылды. Құрылымдық белгілеріне талдау нəтижесінде Pilis, Rege және Rába сорттары төрт бірдей белгілері бойынша жоғары көрсеткіш көрсетті. Ерте масақтануымен 4 бидай сорты ерекшеленді. Бұл сорттарды селекция бағдарламасына қатты қаракүйе ауруына төзімді үлгі ретінде ұсынуға болады.

Walkability and Greenness Do Not Walk Together: Investigating Associations between Greenness and Walkability in a Large Metropolitan City Context

Background: The existing environment literature separately emphasizes the importance of neighborhood walkability and greenness in enhancing health and wellbeing. Thus, a desirable neighborhood should ideally be green and walkable at the same time. Yet, limited research exists on the prevalence of such “sweet spot” neighborhoods. We sought to investigate this question in the context of a large metropolitan city (i.e., Sydney) in Australia. Methods: Using suburb level normalized difference vegetative index (NDVI), percentage urban greenspace, Walk Score® (Walk Score, Seattle, WA, USA), and other data, we explored the global and local relationships of neighborhood-level greenness, urban green space (percent park area) with walkability applying both non-spatial and spatial modeling. Results: We found an overall negative relationship between walkability and greenness (measured as NDVI). Most neighborhoods (represented by suburbs) in Sydney are either walkable or green, but not both. Sweet spot neighborhoods that did exist were green but only somewhat walkable. In addition, many neighborhoods were both less green and somewhat walkable. Moreover, we observed a significant positive relationship between percentage park area and walkability. These results indicate walkability and greenness have inverse and, at best, mixed associations in the Sydney metropolitan area. Conclusions: Our analysis indicates an overall negative relationship between greenness and walkability, with significant local variability. With ongoing efforts towards greening Sydney and improving walkability, more neighborhoods may eventually be transformed into becoming greener and more walkable.

Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI < 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes.

Normalized Difference Vegetative Index Response of Nonirrigated Kentucky Bluegrass and Tall Fescue Lawn Turf Receiving Seaweed Extracts

Turf managers are continually seeking improved grasses, management practices, and products that enhance heat and drought tolerance and reduce supplemental irrigation needs. To this end, products like seaweed extract (SWE) have been extensively studied on short-cut (≤12 mm) golf turf and seedlings of various turfgrass species exposed to stress conditions. Few studies, however, have reported SWE effects on mature, higher cut (≥38 mm) cool-season turfgrass swards. A 3-year field study (2013–15) was conducted in Connecticut to determine the effect of various SWE treatments on the normalized difference vegetative index (NDVI) response of nonirrigated kentucky bluegrass (Poa pratensis L.) and tall fescue (Festuca arundinacea Schreb.) turf managed as a lawn and cut at 76.2 mm. Separate experiments for each species were set out as randomized complete block designs with three replicates. Throughout the growing season in each year, various liquid SWEs were applied at a concentration of 9.55 L·ha−1 weekly or 19.1 L·ha−1 biweekly. A nontreated control was included. The study lacked extreme heat stress conditions during the yearly growing seasons, but periodic moisture deficits below normal were present. Within each year, there were no significant SWE effects on the NDVI of either species. The results suggest that there is no improvement in the NDVI by applying SWEs to mature, higher cut cool-season turfgrass lawns under less than extreme heat-stress conditions, water-stress conditions, or both. Because this study was conducted only at one site without extreme stress, further research of SWE applications to established, higher cut cool-season turfgrass lawns should be conducted across different locations and soils to determine the effects of applying SWE to these stands under extreme heat-stress conditions, water-stress conditions, or both.