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Furrow irrigation, which can be combined with fertilisation by dissolving solid fertilisers in irrigation water, is the most common practice in vineyards in the Ningxia Hui Autonomous Region, and drip irrigation and fertigation have been employed in some areas. These irrigation methods and their corresponding fertilisation schemes may affect nitrous oxide (N 2 O) emissions from the soil. Therefore, it is important to use a model to simulate differences in N 2 O emissions and identify farmland management methods that limit N 2 O emissions. During the July–August 2012 and July–September 2013 growing seasons, the denitrification and decomposition (DNDC) model was tested against experimental N 2 O emissions data from vineyards in Yuquanying, Yongning, Yinchuan, Ningxia. After model validation, the simulated differences in emissions between furrow irrigation and drip irrigation were 9.86 ± 0.845 and 0.966 ± 0.464 kg ha −1 a −1 in 2012 and 2013, respectively. Thus, the emissions were reduced by approximately 72.5% and 52.4% in 2012 and 2013. In the regional simulation, the Global Warming Potential (GWP) of the annual N 2 O emission reductions reached 27,749,760 ± 5,489,160 kg CO 2 -equivalents after the vineyards in the Ningxia Autonomous Region were converted from furrow irrigation to drip irrigation. In conclusion, the DNDC model, which proved to be a powerful tool for addressing the efficacy of alternative management practices in vineyards, revealed that N 2 O emissions can be reduced by adopting drip irrigation systems rather than traditional furrow irrigation systems. [ABSTRACT FROM AUTHOR]

• · The production areas of crops under RCPs slightly shift northward in future China. • · The crop yield changes under RCPs remain stable or increase in future China. • · This is caused by the combined effect of changed climatic variables on crop yields. • · The crop yield changes are mainly driven by temperature and CO 2 factors. Maize, rice, and wheat are the major staple food crops in China and are crucial components of national food security and economic development. The cultivation and production of these crops are expected to be affected by climate change and elevated atmospheric carbon dioxide (CO 2) concentration, and have drawn considerable public attention. The objective of this experiment was to understand the impact of future climate change (including increased temperature and changed precipitation patterns) and elevated CO 2 concentration on variations of crop yields in their suitable planting areas. We conducted a spatial grid-based analysis of maize, rice, and wheat yields using projections of future climate generated by a multi-model ensemble of global climate models for three representative concentration pathway scenarios (RCP2.6, RCP4.5, and RCP8.5) in suitable planting areas in China for the 2030s (2021–2040) and the 2050s (2041–2060). Suitable areas for the planting of maize, rice, and wheat under the high-emission scenarios migrated slightly northward over time. Yield of all three crops would be expected to remain stable or to slightly increase across China in the future. A possible reason for this result may be because the positive effects of increased precipitation and CO 2 offset the negative effect of increased temperature on crop yields, resulting in a much more appropriate growth environment and increased biomass accumulation and crop yield. In addition, this study also indicated that changes in crop yields were mainly driven by temperature and CO 2 factors. The potential effects of climate change and elevated CO 2 concentration on migration of planting areas and yield fluctuations for crops should be given greater attention in the future. [ABSTRACT FROM AUTHOR]

Drought monitoring is a key topic in environmental monitoring and assessment although there is still a need to determine the correlation between drought monitoring indices and remote sensing products. We analyzed the correlation between the self-calibrating Palmer Drought Severity Index (sc_PDSI), the Standardized Precipitation Index and the Standardized Precipitation Evapotranspiration Index (SPEI) and terrestrial water storage monitored through the Gravity Recovery and Climate Experiment (GRACE) on a monthly timescale from 2002 to 2015 in China. As a consequence of anomalies in the soil water budget, the highly significant correlation between the sc_PDSI and the GRACE satellite-observed terrestrial water storage suggested that these two datasets are the most suitable for use in monitoring droughts. In comparing the three drought indices, the sc_PDSI was introduced as a means of drought monitoring in the Yangtze, Pearl, Huaihe, Southeast and Songhua River Basins, whereas the SPEI was found to be more applicable to other major river basins, such as the Inland River Basin. These diverse spatial behaviors are caused by the differences between the hydrological droughts characterized by these three drought indices. [ABSTRACT FROM AUTHOR]

Highlights • PDSI is highly correlated with the variations of P and ET 0 on 12-month timescale. • PDSI is more sensitive to P on national scales, except the northeastern China. • PDSI is preferred to recommend to estimate long-term drought in China. Abstract The self-calibrating Palmer Drought Severity Index (sc_PDSI) is developed within the frame of the PDSI model, but is considered to be more appropriate for global drought monitoring. The sc_PDSI can automatically calibrate itself at any location using dynamically computed values and can calculate evapotranspiration using the FAO-56 Penman–Monteith (P–M) equation. However, the correlation of the sc_PDSI(P–M) with some factors that drive drought, such as precipitation (P) and the reference evapotranspiration (ET 0), is still unclear in China. With the aim of solving this issue, we analyzed the correlation of the detrended sc_PDSI(P–M) with the detrended P and ET 0 on different timescales (one, three, six and 12 months) in China for the period 1951–2015. The results show that both the P and ET 0 are highly correlated with the sc_PDSI(P–M) on a 12-month timescale. On this timescale, the sc_PDSI(P–M) is more sensitive to P than to ET 0 on the national scale, except for northeastern China. Thus the sc_PDSI(P–M) may effectively fit the long-term variations in these drivers of drought, especially P. These results provide guidance on the use of the sc_PDSI(P–M) to detect the impacts of climate change on drought severity under the climatic conditions found in China. [ABSTRACT FROM AUTHOR]

Abstract: Grasslands cover about 40% of China''s land area. This paper synthesizes 133 papers from China on the impacts of land use conversion and improved management practices on soil organic carbon (SOC) in China''s grasslands. The synthesis finds that overgrazing and conversion of freely grazed grassland to cropland lead to an annual average decline of 2.3–2.8% in SOC, and have caused a loss of 30–35% of total grassland SOC in China. Improved management practices may reverse the loss of SOC. Exclosure of degraded grassland from grazing and conversion of cropland to abandoned fields (i.e. natural restoration) increased carbon content by 34% and 62% on average. Carbon sequestration rates were greatest during the first 30yr after treatments began and tended to be greatest in the top 10cm of soil. Carbon sequestration potential was negatively related to initial carbon and nitrogen concentrations in soils. Exclosure from grazing and the conversion of cropland to abandoned fields resulted in average carbon sequestration rates of 130.4gCm−2 yr−1 for 0–40cm soil and 128.0gCm−2 yr−1 for 0–30cm soil, representing annual average increases of 5.4–6.3%. Based on our results, achievement of the national objective to exclude grazing livestock from 150 million ha of China''s grasslands and to establish 30 million ha of cultivated pasture by 2020 would sequester over 0.24PgCyr−1, which is equivalent to about 16% of fossil fuel CO2 emissions in China in 2006. [Copyright &y& Elsevier]

Maize, rice, wheat and soybean-the major staple food crops in China-have a crucial role in national food security and economic development. Predictions of changes in the requirements for irrigation water in food crop production under climate change may provide scientific support for the optimum allocation of water resources and measures to mitigate climate change. We conducted a spatial grid-based analysis using projections of future climate generated by a bias-correction and spatial disaggregation multi-model ensemble for three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) adopted by the fifth phase of the Coupled Model Intercomparison Project. We investigated the effects of climate change associated with increasing temperature, changed precipitation and increased concentrations of atmospheric carbon dioxide (CO 2 ) on the irrigation water requirements of maize, rice, wheat and soybean in China at the end of the 21st century (2081-2100). Our results indicate that the irrigation water requirements of maize and wheat are driven by temperature and especially by CO 2 concentrations in the northwest interior area as a result of the low rainfall and high rates of evaporation; the irrigation water requirement of soybean is influenced by a combined effect of temperature, precipitation and CO 2 concentration, whereas the irrigation water requirement for rice is dominated by precipitation alone in the southern coastal region, which has high rainfall. The irrigation water requirements of crops decrease mainly as a result of the beneficial effects of CO 2 on plant growth in China. The regions requiring vast amounts of irrigation water as a result of climate change are mainly concentrated in northwestern China. The effects of climate change affect the requirement for irrigation water, especially under high-emission scenarios, and should be studied further to design appropriate adaptation strategies for the management of agricultural water to maintain the sustainable development of agriculture., (Copyright © 2018. Published by Elsevier B.V.)

Bai et al. suggest that in China's Inner Mongolia steppe, community-level stability arises from compensatory effects among the principal components at both the species and plant functional group (PFG) levels. By analysing a consistent 19-year data set (1980-98), we show here that their analysis of a 24-year field data set (1980-2003) is called into question by inconsistencies in sampling location and numbers after 1998; the authors' findings are further undermined because they do not distinguish temporal variation from spatial heterogeneity in analysing compensatory effects among species or PFGs. We believe that rigorous reanalysis is needed for a better understanding of grassland stability.