We examine the impact of current and future climate on crop mixes over space in the US. We find using historical data that temperature and precipitation are among the causal factors for shits in crop production location and mixes, with some crops being more sensitive than others. In particular, we find that when temperature rises, cotton, rice, sorghum and winter wheat are more likely to be chosen. We also find that barley, sorghum, winter wheat, spring wheat and hay are more likely to be chosen as regions become drier, and corn, cotton, rice and soybeans are more likely to be selected in wetter regions. Additionally, we assess how much of the observed crop mix shifts between 1970 and 2010 were contributed to by climate change. There we find climate explains about 7–50% of the shift in latitude, 20–36% in longitude and 4–28% of that in elevation. Finally, we estimate climate change impacts on future crop mix under CMIP5 scenarios. There we find shifts in US production regions for almost all major crops with the movement north and east. The estimates describe how the farmers respond to altering climate and can be used for planning future crop allocations.

This study assessed the damage and the potential economic threat of pine wilt disease, which is the most common disease caused by forest-integrated pests in Korea. To estimate the rate of damage by pine wilt disease, a structural damage function was implemented. The nonlinear panel probit model and the generalized estimated equation (GEE) were used for the estimation. The estimated damage function and representative concentration pathways (RCP)8.5 data were used to predict the future damage rate by pests caused by climate change. In the assessment of the economic impact on forests, the dynamic optimization model was introduced. The concept of environmental payment was introduced to consider the economic value of non-timber benefits. For the economic analysis, three scenarios were established, i.e., no pest outbreak (baseline), pest infestation (no control), and pest infestation (prevention and control), and the forest management revenues that included the wood and non-wood materials for each scenario were compared. On the basis of the results of the analysis, a simulation was conducted to investigate the changes in forest management revenues according to changes in timber market prices, environmental payments, and climate change. The prediction results confirmed that the future damage by pine wilt disease and the extent of the damaged areas will increase as a consequence of climate change. In addition, the analysis of the economic impact showed that the increase of pest damage caused by climate change will worsen the forest management revenues. As pest damage brought on by climate change is expected to increase uncertainties and economic losses, there is a marked need to review the policies that so far have been focusing only on post-response tasks. In addition to a proper post-incident management, it is necessary to secure the sense of control and stability over the matter through the reinforcement of pre-incident management.

This study examines the statistical association of wildfire risk with climatic conditions and non-climate variables in 48 continental US states. Because the response variable “wildfire risk” is a fractional variable bounded between zero and one, we use a non-linear panel data model to recognize the bounded nature of the response variable. We estimate the non-linear panel data model (fractional probit) using the Generalized Estimating Equation (GEE) approach to ensure that the parameter estimation is efficient. The statistical model, coupled with the future climates projected by Global Climate Models (GCMs), is then employed to assess the impact of global climate change on wildfire risk. Our regression results show that wildfire risk is positively related to spring, summer, and winter temperatures and human population density whereas it is negatively associated with precipitation. The simulation results based on GCMs and the regression model indicate that climate change will intensify wildfire risk throughout the entire US, especially in the South Central region, posing an increasing wildfire threat and thus calling for more effective wildfire management strategies.

Small gold nanoparticles (AuNPs) that possess interfacial methyl-2-(diphenylphosphino)benzoate moieties have been successfully synthesized (Staudinger-AuNPs) and characterized by multi-nuclear MR spectroscopy, transmission electron microscopy (TEM), UV-Vis spectroscopy, thermogravimetric analysis, and X-ray photoelectron spectroscopy (XPS). In particular, XPS was remarkably sensitive for characterization of the novel nanomaterial, and in furnishing proof of its interfacial reactivity. These Staudinger-AuNPs were found to be stable to the oxidation of the phosphine center. The reaction with benzyl azide in a Staudinger–Bertozzi ligation, as a model system, was investigated using ³¹P NMR spectroscopy. This demonstrated that the interfacial reaction was clean and quantitative. To showcase the potential utility of these Staudinger-AuNPs in bioorganic chemistry, a AuNP bioconjugate was prepared by reacting the Staudinger-AuNPs with a novel azide-labeled CRGDK peptide. The CRGDK peptide could be covalently attached to the AuNP efficiently, chemoselectively, and with a high loading.

Climate and socioeconomic and policy factors are found to stimulate land use changes along with changes in greenhouse gas emissions and adaption behaviors. Most of the studies investigating land use changes in the U.S. have not considered potential spatial effects explicitly. We used a two-step linearized multinomial logit to examine the impacts of various factors on conterminous U.S. land use changes including spatial lag coefficients. The estimation results show that the spatial dependences have existed for cropland, pastureland, and grasslands with a negative dependence on forests but weakened in most of the land uses except for croplands. Temperature and precipitation were found to have nonlinear impacts on the land use shares in the succeeding years by exerting opposite effects on crop versus pasture/grass shares. We also predicted land use changes under different climate change scenarios. The simulation results imply that the southern regions of the U.S. would lose cropland shares with further severity under the business-as-usual climate scenarios, while the land use shares for pasture/grass and forest would increase in those regions. As land use plays an important role in the climate system and vice versa, the results from this study may help policymakers tackle climate-driven land use changes and farmers adapt to climate change.

This study quantifies the structure similarity of nontariff measures between countries and estimates its impact on bilateral agricultural trade using a structural gravity model. The findings show that a similar structure of technical barriers to trade (TBT) between countries is likely to expand their bilateral trade. However, a similar structure of sanitary and phytosanitary measures (SPS) is shown to have negative impacts on agricultural trade. We also discuss the effects of regulatory harmonization on sustainable development.