Xiaodong Song

Impact of agricultural management practices on soil organic carbon: simulation of Australian wheat systems

Quantifying soil organic carbon (SOC) dynamics at a high spatial and temporal resolution in response to different agricultural management practices and environmental conditions can help identify practices that both sequester carbon in the soil and sustain agricultural productivity. Using an agricultural systems model (the Agricultural Production Systems sIMulator), we conducted a high spatial resolution and long-term (122 years) simulation study to identify the key management practices and environmental variables influencing SOC dynamics in a continuous wheat cropping system in Australias 96 million ha cereal-growing regions. Agricultural practices included five nitrogen application rates (0-200 kg N ha(-1) in 50 kg N ha(-1) increments), five residue removal rates (0-100% in 25% increments), and five residue incorporation rates (0-100% in 25% increments). We found that the change in SOC during the 122-year simulation was influenced by the management practices of residue removal (linearly negative) and fertilization (nonlinearly positive) - and the environmental variables of initial SOC content (linearly negative) and temperature (nonlinearly negative). The effects of fertilization were strongest at rates up to 50 kg N ha(-1) , and the effects of temperature were strongest where mean annual temperatures exceeded 19 °C. Reducing residue removal and increasing fertilization increased SOC in most areas except Queensland where high rates of SOC decomposition caused by high temperature and soil moisture negated these benefits. Management practices were particularly effective in increasing SOC in south-west Western Australia - an area with low initial SOC. The results can help target agricultural management practices for increasing SOC in the context of local environmental conditions, enabling farmers to contribute to climate change mitigation and sustaining agricultural production.

Effects of rapid urban sprawl on urban forest carbon stocks: Integrating remotely sensed, GIS and forest inventory data

Research on the effects of urban sprawl on carbon stocks within urban forests can help support policy for sustainable urban design. This is particularly important given climate change and environmental deterioration as a result of rapid urbanization. The purpose of this study was to quantify the effects of urban sprawl on dynamics of forest carbon stock and density in Xiamen, a typical city experiencing rapid urbanization in China. Forest resource inventory data collected from 32,898 patches in 4 years (1972, 1988, 1996 and 2006), together with remotely sensed data (from 1988, 1996 and 2006), were used to investigate vegetation carbon densities and stocks in Xiamen, China. We classified the forests into four groups: (1) forest patches connected to construction land; (2) forest patches connected to farmland; (3) forest patches connected to both construction land and farmland and (4) close forest patches. Carbon stocks and densities of four different types of forest patches during different urbanization periods in three zones (urban core, suburb and exurb) were compared to assess the impact of human disturbance on forest carbon. In the urban core, the carbon stock and carbon density in all four forest patch types declined over the study period. In the suburbs, different urbanization processes influenced forest carbon density and carbon stock in all four forest patch types. Urban sprawl negatively affected the surrounding forests. In the exurbs, the carbon stock and carbon density in all four forest patch types tended to increase over the study period. The results revealed that human disturbance played the dominant role in influencing the carbon stock and density of forest patches close to the locations of human activities. In forest patches far away from the locations of human activities, natural forest regrowth was the dominant factor affecting carbon stock and density.

Quantifying the influences of various ecological factors on land surface temperature of urban forests

Identifying factors that influence the land surface temperature (LST) of urban forests can help improve simulations and predictions of spatial patterns of urban cool islands. This requires a quantitative analytical method that combines spatial statistical analysis with multi-source observational data. The purpose of this study was to reveal how human activities and ecological factors jointly influence LST in clustering regions (hot or cool spots) of urban forests. Using Xiamen City, China from 1996 to 2006 as a case study, we explored the interactions between human activities and ecological factors, as well as their influences on urban forest LST. Population density was selected as a proxy for human activity. We integrated multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) to develop a database on a unified urban scale. The driving mechanism of urban forest LST was revealed through a combination of multi-source spatial data and spatial statistical analysis of clustering regions. The results showed that the main factors contributing to urban forest LST were dominant tree species and elevation. The interactions between human activity and specific ecological factors linearly or nonlinearly increased LST in urban forests. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. In conclusion, quantitative studies based on spatial statistics and GeogDetector models should be conducted in urban areas to reveal interactions between human activities, ecological factors, and LST.

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM2.5). This approach has been used in many studies to estimate biomass and forest disturbance patterns and to monitor carbon sinks. However, the approach has rarely been used to comprehensively analyze the individual and interactive influences of anthropogenic factors (e.g., population density, impervious surface percentage) and ecological factors (e.g., canopy density, stand age, and elevation) on PM2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM2.5), then integrated a forest management planning inventory (FMPI), population density distribution data, meteorological data, and topographic data in a Geographic Information System database, and applied a spatial statistical analysis model to identify aggregated areas (hot spots and cold spots) of particulates in the urban area of Jinjiang city, China. A geographical detector model was used to analyze the individual and interactive influences of anthropogenic and ecological factors on PM2.5 concentrations. We found that particulate concentration hot spots are mainly distributed in urban centers and suburbs, while cold spots are mainly distributed in the suburbs and exurban region. Elevation was the dominant individual factor affecting PM2.5 concentrations, followed by dominant tree species and meteorological factors. A combination of human activities (e.g., population density, impervious surface percentage) and multiple ecological factors caused the dominant interactive effects, resulting in increased PM2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas.

Sensitivity in Ecological Modeling: From Local to Regional Scales

Abstract Global climate change and underlying feedbacks of the terrestrial ecosystems constitute one of the most challenging environmental issues of the present age. The endeavor to understand the complex interactions between climate system and land surface has stimulated active researches in terrestrial ecological modeling. It has been generally acknowledged that only by adopting a systematic approach and taking the land surface components as a whole by modeling strategies can many pressing environmental and climate change problems be fully understood theoretically at relative large, even global scales. With the fast-growing model complexity, the role of global sensitivity analysis (GSA) is more prominent in model correctness validation and parameter calibration. In this chapter, we discuss some typical applications of GSA in ecological modeling, e.g., parameter sensitivity analysis including its temporal feature, spatial application of social-ecological modeling with scenario settings, and specific computing strategies to cope with the huge amount of model runs in GSA. We also discuss the sensitivity of social-ecological modeling to land map errors. We conclude that GSA is an essential step in ecological modeling development and calibration, but care should be taken when extrapolating GSA results to different regions in model simulation due to its strong reliance on model forcing data.

Porting a process-based crop model to a high-performance computing environment for plant simulation

Increasing concerns about food security have stimulated integrated assessment of the sustainability of agricultural systems at regional, national and global scales with high-resolution. Traditionally, the process-based agricultural models are designed for field scale studies that obtain inputs, run the simulations and provide outputs through the graphic interface. The graphic interface based model dose not suit for modelling practices requiring a large number of simulations. Here, we developed a high performance approach which concurrently executed the Agricultural Production Systems sIMulator (APSIM) simulations using parallel programming techniques. In this approach, an APSIM simulation template with replaceable parameters was firstly designed, and new simulations based on the template was then constructed by dynamically replacing parameters of climate, soil and management options. We parallelized the batched running method in a shared-memory multiprocessor system using Pythons Multiprocessing module. We tested the approach with a case study that simulated the productivity of continuous wheat cropping system during 20 years period along the Australian cereal-growing regions under management practices of 5 levels nitrogen application and 3 stubble management practices. More than 170 K runs were finished in 43h by using 64 workers, achieved a speedup ratio of 60. The parallelized method proposed in this study makes large-scale and high-resolution agricultural systems assessment possible.