Scientists estimate that U.S. Corn Belt crop yields will increase or decrease, on average, and become more variable with climate change. Corn and soybeanfarming dominates this region, but studies typically do not assess the joint impact of new distributions of corn and soybean yields on markets. We use a structural economic model with projections of climate-driven yield changes to simulate these effects. Our findings suggest that a narrow focus on a single crop in this key growing region risks underestimating the impact on price distributions and average crop receipts, and can lead to incorrect signs on estimated impacts.

Climate change has the potential to affect crop prices and price volatility. However, the economic models used in prior assessments largely do not include known, automatic, stabilizing factors. Crop storage can stabilize prices and U.S. crop policy tends to provide support that moves opposite prices. We quantify effects of circa 2050 climate forcing on the inter-annual variability of U.S. Corn Belt corn and soybean yields using statistical crop models and climate scenarios from regional and global climate models. Climate change generally reduces mean yields and increases the inter-annual variability of yields in the Midwestern U.S. Using these yield impacts and an economic model with automatic market stabilizers, we find only modest increases in price volatility. Although individual producers and states are negatively affected by the yield reductions, the aggregate effect for all corn and soybean producer returns can be positive because of price increases. Moreover, agricultural policies based on price levels or revenue variation offset some of the impacts of market variation on farm income. Our results differ from other recent results and temper concerns that increasing climate instability necessarily translates to greater uncertainty about agricultural commodity uses, including as food and biofuels, in the near future.

The Palaeocene-Eocene Thermal Maximum (PETM) was a significant global warming event in Earth's deep past (56 Mya). The warming across the PETM boundary was driven by a rapid rise in greenhouse gases. The event also coincided with a time of maximum insolation in Northern Hemisphere summer. There is increased evidence that the mean warming was accompanied by enhanced seasonality and/or extremes in precipitation (and flooding) and drought. A high horizontal resolution (50 km) global climate model is used to explore changes in the seasonal cycle of surface temperature, precipitation, evaporation minus precipitation and river run-off for regions where proxy data are available. Comparison for the regions indicates the model accurately simulates the observed changes in these climatic characteristics with North American interior warming and drying, and warming and increased river run-off at other regions. The addition of maximum insolation in Northern Hemisphere summer leads to a drier North America, but wetter conditions at most other locations. Long-range transport of atmospheric moisture plays a critical role in explaining regional changes in the water cycle. Such high-frequency variations in precipitation might also help explain discrepancies or misinterpretation of some climate proxies from the same locations, especially where sampling is coarse, i.e. at or greater than the frequency of precession.This article is part of a discussion meeting issue 'Hyperthermals: rapid and extreme global warming in our geological past'.

Scientists estimate that U.S. Corn Belt crop yields will increase or decrease, on average, and become more variable with climate change. Corn and soybean farming dominates this region, but studies typically do not assess the joint impact of new distributions of corn and soybean yields on markets. We use a structural economic model with projections of climate-driven yield changes to simulate these effects. Our findings suggest that a narrow focus on a single crop in this key growing region risks underestimating the impact on price distributions and average crop receipts, and can lead to incorrect signs on estimated impacts.

Rationale

Obesity and underweight are contraindications to lung transplantation based on their associations with mortality in studies performed before implementation of the lung allocation score (LAS)-based organ allocation system in the United States Objectives: To determine the associations of body mass index (BMI) and plasma leptin levels with survival after lung transplantation.

Methods

We used multivariable-adjusted regression models to examine associations between BMI and 1-year mortality in 9,073 adults who underwent lung transplantation in the United States between May 2005 and June 2011, and plasma leptin and mortality in 599 Lung Transplant Outcomes Group study participants. We measured body fat and skeletal muscle mass using whole-body dual X-ray absorptiometry in 142 adult lung transplant candidates.

Measurements and main results

Adjusted mortality rates were similar among normal weight (BMI 18.5-24.9 kg/m(2)), overweight (BMI 25.0-29.9), and class I obese (BMI 30-34.9) transplant recipients. Underweight (BMI < 18.5) was associated with a 35% increased rate of death (95% confidence interval, 10-66%). Class II-III obesity (BMI ≥ 35 kg/m(2)) was associated with a nearly twofold increase in mortality (hazard ratio, 1.9; 95% confidence interval, 1.3-2.8). Higher leptin levels were associated with increased mortality after transplant surgery performed without cardiopulmonary bypass (P for interaction = 0.03). A BMI greater than or equal to 30 kg/m(2) was 26% sensitive and 97% specific for total body fat-defined obesity.

Conclusions

A BMI of 30.0-34.9 kg/m(2) is not associated with 1-year mortality after lung transplantation in the LAS era, perhaps because of its low sensitivity for obesity. The association between leptin and mortality suggests the need to validate alternative methods to measure obesity in candidates for lung transplantation. A BMI greater than or equal to 30 kg/m(2) may no longer contraindicate lung transplantation.

Chronic lung allograft dysfunction (CLAD) remains the major complication limiting long-term survival among lung transplant recipients (LTRs). Limited understanding of CLAD immunopathogenesis and a paucity of biomarkers remain substantial barriers for earlier detection and therapeutic interventions for CLAD. We hypothesized the airway transcriptome would reflect key immunologic changes in disease. We compared airway brush-derived transcriptomic signatures in CLAD (n = 24) versus non-CLAD (n = 21) LTRs. A targeted assessment of the proteome using concomitant bronchoalveolar lavage (BAL) fluid for 24 cytokines/chemokines and alloimmune T cell responses was performed to validate the airway transcriptome. We observed an airway transcriptomic signature of differential genes expressed (DGEs) in CLAD marked by Type-1 immunity and striking upregulation of two endogenous immune regulators: indoleamine 2, 3 dioxygenase 1 (IDO-1) and tumor necrosis factor receptor superfamily 6B (TNFRSF6B). Advanced CLAD staging was associated with a more intense airway transcriptome signature. In a validation cohort using the identified signature, we found an area under the curve (AUC) of 0.77 for CLAD LTRs. Targeted proteomic analyses revealed a predominant Type-1 profile with detection of IFN-γ, TNF-α, and IL-1β as dominant CLAD cytokines, correlating with the airway transcriptome. The airway transcriptome provides novel insights into CLAD immunopathogenesis and biomarkers that may impact diagnosis of CLAD.