Chinese yam (Dioscorea opposita Thunb.) is consumed and regarded as medicinal food in traditional Chinese herbal medicine, Chinese medicinal yam especially is one of the most important Chinese herbal medicines and its medicinal needs have been increasing in recent decades1. Furthermore, Chinese medicinal yam is susceptible to climate conditions during the growth period. Therefore, a better understanding of the suitability regionalization of Chinese medicinal yam under the impact of climate change is of both scientific and practical importance to spacial development and reasonable layout of Chinese yam in China. In this study, based on the Coupled Model Inter-comparison Project, Phase 5 (CMIP5) climate model projections with 5 Global Circulation Models (GCMs) developed by the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP) driven by 4 Representative Concentration Pathways (RCPs), we assessed the changes of potential planting area of Chinese medicinal yam between the baseline climatology of 1981-2010 and the future climatology of the 2050s (2041-2070) under the RCP 4.5 scenario by the Geographic Information System (GIS) technology. Results indicate that regions with high ecological similarity to the Geo-authentic producing areas of Chinese medicinal yam include northeastern Henan, southeastern Hebei and western Shandong, mainly distribute in the lower reaches of the Yellow River basin and other major floodplains. In the future, the climate suitability of Chinese medicinal yam in these areas will be weakened, but that will still be the main suitable planting regions.
KEYWORDS: Methane, Data modeling, Atmospheric modeling, Climate change, Climatology, Temperature metrology, Geographic information systems, Systems modeling, Data centers, Agriculture
Paddy field is a major source of methane (CH4) emission. Methane emission in paddy fields accounts for 31.5% of agricultural methane emissions in China. Double-rice cropping system is a part of the major paddy systems in China for rice production, accounting for only 27% of the national rice planting area while CH4 emission accounting for 60% of the national CH4 emission. Given the importance of reducing CH4 emission from double rice to mitigate climate warming, it is necessary to investigate the impact of climate change on CH4 emission of double cropping paddy field in the future. In this study, the denitrification–decomposition (DNDC-a process-based biogeochemistry model) model is employed to simulate the CH4 emission from double-rice cropping system in southern China based on the historical meteorological data of the past 50 years (1966-2015) and the observational data of rice agricultural stations in the study area. Then we combined the outputs with Geographic Information System (GIS) technology to analyze the impact of climate change on CH4 emissions from the double rice paddy. The results indicate that change of the average temperature is associated with the change of CH4 emission across the growing period of double rice paddy. Methane has increased by 8.4% in the main producing provinces of double cropping rice in southern China. Zhejiang has increased by up to 20.8%. Anhui, Hubei, Hunan has increased by 10.6%, 10.2% and 11.4%. The relatively small increase in Fujian and Yunnan is only 5%. However, in the low latitudes of Guangxi, and Guangdong province, there was a slight reduction in CH4 emission.
Peanut is one of the major edible vegetable oil crops in China, whose growth and yield are very sensitive to climate change. In addition, agriculture climate resources are expected to be redistributed under climate change, which will further influence the growth, development, cropping patterns, distribution and production of peanut. In this study, we used the DSSAT-Peanut model to examine the climate change impacts on peanut production, oil industry and oil food security in China. This model is first calibrated using site observations including 31 years’ (1981-2011) climate, soil and agronomy data. This calibrated model is then employed to simulate the future peanut yield based on 20 climate scenarios from 5 Global Circulation Models (GCMs) developed by the InterSectoral Impact Model Intercomparison Project (ISIMIP) driven by 4 Representative Concentration Pathways (RCPs). Results indicate that the irrigated peanut yield will decrease 2.6% under the RCP 2.6 scenario, 9.9% under the RCP 4.5 scenario and 29% under the RCP 8.5 scenario, respectively. Similarly, the rain-fed peanut yield will also decrease, with a 2.5% reduction under the RCP 2.6 scenario, 11.5% reduction under the RCP 4.5 scenario and 30% reduction under the RCP 8.5 scenario, respectively.
KEYWORDS: Climate change, Atmospheric modeling, Climatology, Calibration, Data modeling, Carbon dioxide, Solar radiation models, Agriculture, Process modeling, Temperature metrology
The climate is changing due to higher concentrations of greenhouse gases. If concentrations continue to increase, climate
models project climate change in this century, with significant impacts on many human sectors, and particularly agriculture.
Agriculture is a fundamental production sector for society, especially for large population countries such as China. Wheat is
the second most important crop in China. Therefore, using climate change projections and crop models in order to understand
the impacts of climate change on Chinese agriculture, especially on winter wheat, is extremely helpful to policy makers and
international agencies. CERES-Wheat, a dynamic process crop growth model, will be calibrated and validated for current
production at ten sites in the major winter wheat-growing region of China-Yellow Huai-Hai plain. Using two Global Climate
Models, it will then be used to simulate production changes under IPCC SRES A2 and B2 climate change scenarios.
Simulations will consider impacts for rainfed and irrigated winter wheat, with and without CO2 fertilization. Simulation
results indicated the possibility of significant impacts of climate change on winter wheat production in this region, with
marked differences between rainfed and irrigated production. In conclusion, this exercise successfully tested the applicability
of standard climate change impact assessment methodology to an important production region of China.
KEYWORDS: Climate change, Atmospheric modeling, Data modeling, Climatology, Gases, Solar radiation models, Geographic information systems, Agriculture, Process modeling, Carbon monoxide
The climate is changing due to higher concentrations of greenhouse gases. If concentrations continue to
increase, climate models project climate change will be more severe in this century, and with significant
impacts on many human sectors, particularly agriculture. Agriculture is a fundamental production sector for
society, especially for highly populated countries such as China. Huang Huai-Hai Plain is regarded as the
bread basket of China. With only 7.7% water resources of the whole country, it produces 39.2% of national
grain production and 32.4% of gross domestic product. According to government predictions, by 2030 this
area will have a net population increase of 104 million, while its urbanization rate will be greater than 50%.
The total irrigated area will reach about 20 million ha, with a net increase of 2 million ha/year. In this study,
DSSAT a dynamic process crop growth model, has been calibrated and validated for current production at ten
sites in the major winter wheat and summer maize-growing region of Huang-Huai-Hai Plain in China The
IPCC SRES greenhouse gase emission scenarios A2 and B2 were used in the simulation, combining with the
Regional Climate Model (PRICES) which provides long term present and future daily weather data. Using the
regional crop model and GIS technologies, the crop productivity changes of two main crops winter wheat and
summer maize were for simulated 2020s, 2050s and 2080s under both IPCC SRES A2 and B2 greenhouse
gases emission scenarios. Simulation results indicated the possibility of significant impacts of climate
change on crop production in this region, with marked differences between rainfed and irrigated production.
In conclusion, this exercise successfully tested the applicability of standard climate change impact assessment
methodology to an important production region of China.
Monitoring crop growth status and yields using remote sensing data have been a challenges both in estimating the growing parameters and quantifying the seasonal changes. Traditionally, NOAA AVHRR data was applied to estimate and predict crop yields with statistical correlation methods. However, its spatial resolution of 8-km is not satisfying in monitoring crop growth on the site level. The launch of TERRA with moderate resolution imaging spectroradiometer (MODIS) instruments onboard began a new era in remote sensing of the Earth system which is providing a series of products of unparalleled quality and sophistication for the observation and biophysical monitoring of the terrestrial environment. Crop growth models simulate biophysical processes in the soil-crop-atmospheric system provide a continuous description of crop growth and development. Combining a growth model with the input parameters derived from remote sensing data provides spatial integrity as well as a real-time "calibration" of model parameters. A field study was conducted to evaluate the applicability of the 8-day MODIS leaf area index (LAI) data product in operational assessment of wheat growth condition and yields in the region of Yucheng, ShanDong Province, in China. The MODIS LAI product were used to compared with the DSSAT LAI--the output of crop simulation model (DSSAT) and the observed LAI. The MODIS LAI corresponded comparatively well with the DSSAT LAI in the early stage which have been tested well with the observed LAI, however in the later wheat growing stage, there are still some difference between the MODIS LAI and observed LAI. Limitations of this study and its conclusions are also discussed.
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