Suchergebnisse

Abstract
The Amazon basin has been subjected to unprecedented rates of land-use change over the past several decades, primarily as a result of the expansion of agriculture. Enhanced rain forest conservation efforts toward the end of the twentieth century slowed deforestation of the Amazon but, in turn, increased demand for land repurposing in the adjacent Cerrado (savanna) region, where conservation regulations are less strict. To maintain or increase yields while minimizing the need for additional land, agricultural producers adopted a form of intensification in which two rain-fed crops are planted within a single growing season (double cropping). Using 10 years (August 2002 to July 2012) of MODIS and TRMM data, it is demonstrated that there exists a threshold growing season rainfall amount (1759 mm) for double cropping. But more nuanced is the relationship between observable precipitation information available to farmers at the time of planting decision and the choice to "double crop" in a given year. An evaluation of decision-available precipitation characteristics provides strong evidence for the importance of high rainfall frequency during a critical period prior to, and including, the rainy season onset.

AbstractContinued climate change is increasing the frequency, severity, and duration of populations' high temperature exposures. Indoor cooling is a key adaptation, especially in urban areas, where heat extremes are intensified—the urban heat island effect (UHI)—making residential air conditioning (AC) availability critical to protecting human health. In the United States, the differences in residential AC prevalence from one metropolitan area to another is well understood, but its intra-urban variation is poorly characterized, obscuring neighborhood-scale variability in populations' heat vulnerability and adaptive capacity. We address this gap by constructing empirically derived probabilities of residential AC for 45,995 census tracts across 115 metropolitan areas. Within cities, AC is unequally distributed, with census tracts in the urban "core" exhibiting systematically lower prevalence than their suburban counterparts. Moreover, this disparity correlates strongly with multiple indicators of social vulnerability and summer daytime surface UHI intensity, highlighting the challenges that vulnerable urban populations face in adapting to climate-change driven heat stress amplification.

Abstract
Infectious disease surveillance frequently lacks complete information on race and ethnicity, making it difficult to identify health inequities. Greater awareness of this issue has occurred due to the COVID-19 pandemic, during which inequities in cases, hospitalizations, and deaths were reported but with evidence of substantial missing demographic details. Although the problem of missing race and ethnicity data in COVID-19 cases has been well documented, neither its spatiotemporal variation nor its particular drivers have been characterized. Using individual-level data on confirmed COVID-19 cases in Massachusetts from March 2020 to February 2021, we show how missing race and ethnicity data: (1) varied over time, appearing to increase sharply during two different periods of rapid case growth; (2) differed substantially between towns, indicating a nonrandom distribution; and (3) was associated significantly with several individual- and town-level characteristics in a mixed-effects regression model, suggesting a combination of personal and infrastructural drivers of missing data that persisted despite state and federal data-collection mandates. We discuss how a variety of factors may contribute to persistent missing data but could potentially be mitigated in future contexts.