The Key Laboratory for Agro environment and Climate Change


The Key Laboratory for Agro environment and Climate Change

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Xiong W.,Chinese Academy of Agricultural Sciences | Xiong W.,The Key Laboratory for Agro environment and Climate Change | Holman I.,Cranfield University | Lin E.,Chinese Academy of Agricultural Sciences | And 6 more authors.
Agriculture, Ecosystems and Environment | Year: 2010

Climate scenarios from a regional climate model are used to drive crop and water simulation models underpinned by the IPCC A2 and B2 socio-economic development pathways to explore water availability for agriculture in China in the 2020s and 2040s. Various measures of water availability are examined at river basin and provincial scale in relation to agricultural and non-agricultural water demand and current and planned expansions to the area under irrigation. The objectives are to understand the influences of different drivers on future water availability to support China's food production. Hydrological simulations produce moderate to large increases in total water availability in response to increases in future precipitation. Total water demand increases nationally and in most basins, but with a decreasing share for agriculture due primarily to competition from industrial, domestic and municipal sectors. Crop simulations exhibit moderate to large increases in irrigation water demand which is found to be highly sensitive to the characteristics of daily precipitation in the climate scenarios. The impacts of climate change on water availability for agriculture are small compared to the role of socio-economic development. The study identifies significant spatial differences in impacts at the river basin and provincial level. In broad terms water availability for agriculture declines in southern China and remains stable in northern China. The combined impacts of climate change and socio-economic development produce decreases in future irrigation areas, especially the area of irrigated paddy rice. Overall, the results suggest that there will be insufficient water for agriculture in China in the coming decades, due primarily to increases in water demand for non-agricultural uses, which will have significant implications for adaptation strategies and policies for agricultural production and water management. © 2009 Elsevier B.V.

Wu Y.,Chinese Academy of Agricultural Sciences | Wu Y.,The Key Laboratory for Agro Environment and Climate Change | Wu Y.,Engineering Consulting Center | Ma X.,Chinese Academy of Agricultural Sciences | And 5 more authors.
International Journal of Greenhouse Gas Control | Year: 2014

Carbon capture and storage (CCS) is a technology of strategic importance to global carbon reduction. However, studies indicate that CCS may likely lead to CO2 leakage in the long term. In the present study, the potential impacts of introduced CO2 fluxes on the growth and development of selected crops and soil are described. Plants were grown in restructured pots with platform bottoms to simulate stored and introduced CO2. In the initial growth stages, pure CO2 gas was continuously injected into maize and alfalfa root zones at five different fluxes, ranging between 0g/(m2d) and 2000g/(m2d), for a minimum of 30 days. The results showed inhibition of plant growth and development, and soil modification, based on introduced CO2 and control (absence of CO2) scenarios. Maize and alfalfa showed decreased height, leaf number, leaf area, and root length trends as the introduced CO2 flux increased. Photosynthesis and transpiration rates decreased, accumulated dry matter was significantly reduced, and soil pH and O2 concentrations were reduced. The results indicated alfalfa was less tolerant than maize. The relationship between soil O2 concentration and injected CO2 flux was expressed as a linear equation. Most plant indicators did not change significantly when introduced CO2 was within a flux of 500g/(m2d), but when introduced CO2 was between 500g/(m2d) and 2000g/(m2d) all indicators exhibited notably decreased values. Maize and alfalfa exposed to a 2000g/(m2d) flux rapidly approached zero (0) in terms of all physiological indicators, and plant growth and development ceased, i.e. Maize and alfalfa showed a tolerance threshold of 500-2000g/(m2d) flux for introduced CO2. This provided the tolerance thresholds for maize and alfalfa under different scenarios of introduced CO2, and clarified how the simulated introduction of CO2 interfered with plant growth and development. The results of this study can inform future preventative and remedial actions in response to potential CCS leakage. © 2014 Elsevier Ltd.

Song G.,Chinese Academy of Agricultural Sciences | Song G.,The Key Laboratory for Agro environment and Climate Change | Sun Z.,Chinese Academy of Agricultural Sciences | Sun Z.,The Key Laboratory for Agro environment and Climate Change | And 5 more authors.
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2011

In northeastern China, chilling damage is the main threat to rice production. The selection and breeding of cold tolerant rice varieties is an important goal. Research into the differing physiologies of rice strains grown under low temperature conditions can give guidance for the breeding of cold-tolerant varieties. A better understanding is needed of the physiological variation exhibited by rice during different growth stages, under exposure to low temperature stress. We selected fifteen major rice varieties that are grown in the central northeast of China for analysis under controlled environmental conditions. Following the climatic characteristics of the region and the temperature demands of rice at different developmental stages, we simulated natural temperature variation with an artificial weather box; set to provide four daily temperature gradients. A control variety was planted in the natural environment. We examined the relationship between the cold tolerance coefficient (the ratio of yields under low temperature compared with the control), and levels within the leaves of peroxidase (POD), malondialdehyde (MDA), proline (Pro), and chlorophyll, during the seedling, booting, heading and grain filling stages of development. This relationship has been shown to be related to the cold tolerance of the plants. Using the weighted average of subordinate function values (D value), of these four physiological indices, the relative cold tolerance shown by each variety was determined. We found that during each growth stage the POD, proline and chlorophyll levels decreased. This was in contrast to MDA levels; these were observed to increase. The subordinate function changes in the four indices are significantly correlated with the cold tolerance coefficient (P<0. 01). At seedling, booting, heading and grain filling stages, we were able to use the above four physiological indicators to create an index of subordinate functions. From large to small these were Pro; MDA; POD; chlorophyll; MDA; chlorophyll; Pro; POD; chlorophyll; MDA; POD; Pro and MDA; chlorophyll; Pro; POD. Quantitative analysis of the cold tolerance characters of each variety was conducted using these subordinate functions; and the contribution of each index to the cold tolerance of each variety was analyzed. The D value of the four indices not only has a significant correlation with the cold tolerance coefficient but is also higher than the result of each index. In conclusion, the D value can be used to effectively evaluate and classify the low temperature tolerance of rice. It can provide an new method of screening for low temperature tolerance in rice breeding. The changes in the physiological indices observed, when plants were subjected to different periods of low temperature stress, accurately reflect the cold tolerance of rice, and can be used to effectively classify rice varieties according to their cold tolerance.

Liu Y.T.,Chinese Academy of Agricultural Sciences | Liu Y.T.,The Key Laboratory for Agro Environment and Climate Change | Li Y.E.,Chinese Academy of Agricultural Sciences | Li Y.E.,The Key Laboratory for Agro Environment and Climate Change | And 8 more authors.
Agriculture, Ecosystems and Environment | Year: 2011

As maize requires a high input of fertilizer nitrogen, it is likely to be an important source of nitrous oxide (N2O). Detailed information on N2O emissions over long time periods, and management practices that aim to reduce N2O emissions from spring maize fields in China is lacking. Consequently we measured the emissions of N2O from a spring maize field continuously from 2007 to 2009 at Yuci, Shanxi Province, China using newly developed automated chambers and explored strategies to reduce N2O emissions. The results showed that the Optimal fertilizer treatment (120kgNha-1y-1) produced the same yield of grain as the Traditional fertilizer treatment (330kgNha-1y-1), and significantly reduced N2O emissions by 48%. Topdressing with urea was the main source of N2O, which on average accounted for 58% of the total N2O emissions each year. Uptake of N2O occurred during the late stage of maize growth when soil mineral N content was less than 46.4mgNkg-1 soil. The N2O emission factors were lower than the IPCC default value. Nitrous oxide emissions could also be reduced if farmers did not apply fertilizer N during periods of heavy rainfall and did not irrigate immediately after fertilization. © 2011 Elsevier B.V.

Xiong W.,Chinese Academy of Agricultural Sciences | Xiong W.,The Key Laboratory for Agro Environment and Climate Change | Yang H.,Chinese Academy of Agricultural Sciences | Yang H.,The Key Laboratory for Agro Environment and Climate Change | Feng Y.,Zhongkai University of Agriculture and Engineering
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2010

Crop regional simulation has emerged as a new scope for crop model application. It has been used in studies of climate change impact assessment, precision farming, food security and agricultural policy assessment etc. Key obstacles keeping the regional simulation from widely application are availability and quality issues of high resolution daily weather data, and, the diverse methods for generating high-resolution daily weather data from coarse weather observations. Various methods exist in interpolating from randomly distributed weather observation sites to high resolution grid weather data, but how the methods affect the crop regional simulation is rarely known. Evaluating the sensitivity and uncertainty of simulation to method of weather interpolation can help us to identify an appropriate interpolation method for the regional simulation. Based on observed daily weather data from 650 weather observation sites distributed across China, we use three types of interpolation approach, geometrical (choose method of Nearest Neighbor, NN), geographical statistic (choose method of Bivariate Interpolation, BI), and regional climate model interpolation (We use PRECIS Baseline run, BS) to generate gridded (50 km × 50 km) daily weather data for whole China, and input them into CERES-Maize crop model. Maize yield is simulated from 1961 -1990 and its spatial variability is generated with each interpolation method. Differences in results due to various interpolation methods are measured through (1) comparison of simulated yields to census yields, (2) identifying sensitivity of simulated yields to various interpolation approaches. The census yields from 1980 to 1990 are compared to corresponding simulation yields with each daily weather dataset as input. The comparisons demonstrate interpolated daily weather data with different methods are all able to produce reasonable projections in terms of spatial patterns of yield variability. The spatial patterns simulated by inputting the three interpolation methods are roughly identical to census one, indicating the reliability of the interpolation methods for crop simulation use. Simulated yields are correlate to census yield significantly (P < 0. 05) in all cases, suggesting the feasibility of using interpolated weather to replace observed weather if observations were not available. Difference exits between census yields and simulated yields, the differences due to different selection on interpolated weather data are within 8%, implying the limited impacts caused by different weather interpolation methods. Sensitivity analysis is operated through correlation analysis for any two of the three simulation results, it proves that there are significant correlations between any two of the three simulation results, but statistically speaking, yields/phonologies are different when comparing any two pairs within the three simulation results. These differences are also significant for most of the maize planting regions. This highlights that caution must be taken before choosing interpolation method for regional crop simulation, particularly in the case of forecasting exact local yield. We make recommendations for selection of interpolation method for crop regional simulation. According to their different characteristics of the methods and the observation data availability, geometrical interpolation is a best solution given the availability and accessibility of nicely distributed and large number of observed weather site, geographical statistic interpolation can be used if regional simulation happens in large flat regions, interpolation by regional climate model is an alternative when attentions were put on spatial variability or without observations.

Qin X.,Chinese Academy of Agricultural Sciences | Qin X.,The Key Laboratory for Agro Environment and Climate Change | Qin X.,Agriculture and Agri Food Canada | Li Y.,Chinese Academy of Agricultural Sciences | And 11 more authors.
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2012

To investigate the regression relationships between greenhouse gas (GHG) emissions and soil microbes in a double-rice paddy soil under various management practices, a two-year study was conducted to observe the seasonal variation of GHG emissions and activities of soil microbes (SMA) as well as their populations (SMP) using the closed static chamber-GC (gas chromatography) and the most probable number methods. There were seven management practices (or treatments), including CWS (Conventional Tillage + Without Straw Residues + Urea), NWS (No Tillage + Without Straw Residues + Urea), SCU (Conventional Tillage + Without Straw Residues + Controlled-Release Urea), HN (High Stubbles + No Tillage + Urea), HC (High Stubbles + Conventional Tillage + Urea), SN (Straw Cover + No Tillage + Urea) and SNF (Straw Cover + No Tillage + Urea + Continuous Flooding). The average values of seven treatments′ daily fluxes of GHGs and SMA as well SMP were used for the analysis in this study. Regression analysis was conducted using the R statistical software. Similar seasonal variations of methane flux and SMA as well as the amount of soil methanogens (MET) were found in the rice growing season of 2008-2009; and same regularity occurred in the temporal distribution of nitrous oxide flux and the amount of soil nitrifiers and denitrifiers. Furthermore, there was a strong correlation between methane flux and SMA as well as the population of MET. The relationships of methane flux vs. SMA and methane flux vs. MET can be represented by using the exponential and quadratic polynomial models, respectively. Simple regression indexed that the quantity of MET could explain individually at least 96. 96% of variance of methane flux (R 2 =0. 969, P<0. 001), but the fitting precision of multiple nonlinear regression of methane flux with two factors of SMA and MET (R 2 =0. 975, P< 0. 001) was higher than the univariate regression analysis. Besides, the pronounced positive dependency of nitrous oxide flux with soil nitrifiers and denitrifiers has also been found (P<0. 05). The mixed binary nonlinear regression of nitrous oxide flux with the SMP of the two types of microbes can explain at least 70. 4% of variance of nitrous oxide flux (R 2 ≥ 0. 704, P<0. 001), and of course the fitting precision of multiple nonlinear regression was higher than the simple regression using the SMP of either nitrifiers or denitrifiers. However, as we know, GHG emissions from paddy soils are affected by many factors, of which SMA and SMP are the most direct influential variants. In order to reasonably reveal the interactions between GHG emissions and environmental variables, the multivariate nonlinear regression analysis should be carried out based on data derived from the extensive field experiments rather than few laboratory trials.

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