Based on the 1965–2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998–2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized precipitation evapotranspiration index (SPEI) and the Mann–Kendall mutation test; furthermore, the relationship between meteorological factors, drought index, and maize climate yield was determined. Finally, the maize climate yields under 1.5 °C and 2.0 °C global warming scenarios were predicted. The results revealed that: (1) from 1965 to 2017, the study area experienced increasing temperature, decreasing precipitation, and intensifying drought trends; (2) the yield of the study area showed a downward trend from 1998 to 2017. Furthermore, the climate yield was negatively correlated with temperature, positively correlated with precipitation, and positively correlated with SPEI-1 and SPEI-3; and (3) under the 1.5 °C and the 2.0 °C global warming scenarios, the temperature and the precipitation increased in the maize growing season. Furthermore, under the studied global warming scenarios, the yield changes predicted by multiple regression were −7.7% and −15.9%, respectively, and the yield changes predicted by one-variable regression were −12.2% and −21.8%, respectively.
Land use/cover change (LUCC) is one of the major environmental changes and has become a hot topic in the study of global change. Based on four land use classification maps, this study used the intensity analysis method to quantitatively monitor the land use changes which occurred in Inner Mongolia during 1980–2015. The results showed that changes occurred although the trends of corresponding land use types were different (increase or decrease), and the land use changes had an obvious increasing or decreasing trend before and after 2000, respectively. Generally, woodland, high-coverage grassland, and moderate-coverage grassland decreased and the other land use types increased during 1980–2015. In addition, the changes had great differences in spatial distribution. The area of grassland had the largest decrease, indicating that the quality of grassland has declined in Inner Mongolia. The variation rate of land use in 1980–1990 was faster than the rates in 1990–2000 and 2000–2015.
Actinidia arguta (Siebold and Zucc.) Planch.ex Miq, called “hardy kiwifruit”, “baby kiwi” or “kiwi berry”, has a unique taste, is rich in nutrients and has high economic value and broad market prospects. Active research on the potential geographic distribution of A. arguta in China aims to provide a reference basis for its resource investigation, conservation, development and utilization and introduction of cultivation. In this study, the Maxent model was used to combine climatic factors, soil factors and geographical factors (elevation, slope and aspect) to predict the current and future (2041–2060 and 2081–2100) potential distribution of A. arguta and to analyze the impact of climate change on it. The results showed that the suitable distribution range of A. arguta in China was 23–43 N and 100–125 E, with a total area of about 3.4451 × 106 km2. The highly suitable area of A. arguta was mainly concentrated in the middle and low mountain areas of the south of Shaanxi, the east of Sichuan, the middle and west of Guizhou and the west of Yunnan, presenting a circular distribution. The Jackknife test was used to calculate the main environmental factors affecting the distribution of A. arguta. The first four main factors were annual mean temperature (bio_1), precipitation of the warmest quarter (bio_18), elevation (ELE) and mean temperature of the warmest quarter (bio_10), which provided a contribution up to 81.7%. Under the scenarios of three representative concentrations (SSP1_2.6, SSP2_4.5 and SSP5_8.5) in the future, the area of low and moderate suitable habitat decreased, while the area of highly suitable habitat increased. The migration direction of the centroid in the highly suitable habitat moved to the southwest in the future scenario period.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.