Henry&Hobbs, 'Developing long-term agro-ecological trial datasets for C and N modelling.'
•
1 j'aime•1,064 vues
Recommandé
Contenu connexe
En vedette
En vedette (8)
Similaire à Henry&Hobbs, 'Developing long-term agro-ecological trial datasets for C and N modelling.'
Similaire à Henry&Hobbs, 'Developing long-term agro-ecological trial datasets for C and N modelling.' (20)
Plus de TERN Australia
Plus de TERN Australia (20)
Henry&Hobbs, 'Developing long-term agro-ecological trial datasets for C and N modelling.'
- 1. New value from old trials: Developing long-term agro-ecological trial datasets for C and N modelling Australian Centre for Ecological Analysis and Synthesis (ACEAS) C&N Dynamics Working Group Beverley Henry (QUT), John Carter, Ram Dalal, Steven Reeves (Qld Department of Science, Information Technology, Innovation and the Arts); Craig Thornton (Qld Department of Natural Resources & Mines), Robyn Cowley (NT Department of Resources); Leigh Hunt, Andrew Moore (CSIRO); Bill Parton (Colorado State University); Bill Slattery (Department of Climate Change and Energy Efficiency); Peter Grace, Richard Conant (Institute for Future Environments, QUT);Alison Specht (ACEAS); Murray Unkovich (University of Adelaide). Semaphore project team (Australian National Data Service - ANDS) Vaughan Hobbs , Marco Fahmi, Beverley Henry, Alvin Sebastian, Siobhann McCafferty (Institute for Future Environments, QUT), Mingfang Wu (ANDS), Richard Conant CSU) CCRSPI Conference 27th – 29th November 2012 Melbourne
- 2. Purpose of C&N modelling • To evaluate effects of environmental change • To evaluate changes in management CCRSPI Conference 27th – 29th November 2012 Melbourne
- 3. Purpose of C&N modelling • To evaluate effects of environmental change • To evaluate changes in management Current applications include: • Understanding impacts of climate variability and change • Testing climate adaptation strategies • Evaluating practices and activities for soil carbon sequestration • Understanding Nutrient Use Efficiency and N2O emissions • Managing productivity and sustainability goals in agro- ecosystems CCRSPI Conference 27th – 29th November 2012 Melbourne
- 4. Project Objectives ACEAS C&N Dynamics Working Group To develop a database with high-quality climate, soil, management, NPP and nutrient datasets (with metadata) suitable for validation of C&N models ANDS Semaphore To develop software to [semi-]automatically extract and transform data to calibrate and validate C&N models. CCRSPI Conference 27th – 29th November 2012 Melbourne
- 5. Project Objectives ACEAS C&N Dynamics Working Group To develop a database with high-quality climate, soil, management, NPP and nutrient datasets (with metadata) suitable for validation of C&N models ANDS Semaphore To develop software to [semi-]automatically extract and transform data to calibrate and validate C&N models. Outcome: Improved predictions of carbon and nutrient dynamics in agro-ecosystems CCRSPI Conference 27th – 29th November 2012 Melbourne
- 6. Models & sites Sites Targeted – Brigalow catchment study – Hermitage cropping trial – Kidman Springs fire trial – Wambiana grazing trial – Hamilton long-term P trial Data & Information – Site information and climate files – Experimental data (treatments, measurements) – Management schedules CCRSPI Conference 27th – 29th November 2012 Melbourne
- 7. Brigalow Catchment Study Brigalow (Acacia harpophylla) bioregion (40 Mha) of central QLD Study commenced 1965 3 land uses, brigalow forest, cropping, and grazed pasture Monitored for water balance, resource condition, productivity Qld Government – Craig Thornton, John Carter CCRSPI Conference 27th – 29th November 2012 Melbourne
- 8. Hermitage Cropping Trial Hermitage Long-Term Tillage Trial near Warwick, QLD Study commenced 1968 Tillage, stubble management, N fertiliser treatments Monitored for soil C, N, yield, N2O, (& disease tolerance, LCA) etc Qld Government – Ram Dalal, Steven Reeves; CSU – Bill Parton CCRSPI Conference 27th – 29th November 2012 Melbourne
- 9. Data collation issues Examples of data issues: data in different sources (e.g. paper, electronic) with different custodians data stored in different layouts and file formats different units used different sampling strategies – may effect accuracy, comparability may be only processed data (e.g. means) – limits interpretations & uses frequency of data collection changes over time (depending on resources) metadata may be missing or incomplete type of physical or chemical analysis may change over time (e.g. for carbon - Walkley & Black vs Leco) CCRSPI Conference 27th – 29th November 2012 Melbourne
- 10. Data collation issues (2) Access to specific site history knowledge is critical for both data collation and developing model inputs, e.g. pre-trial site history, data outlier reasoning, data gap filling and actual location of data sources to ensure any model parameterisation was realistic ACEAS investment provided an opportunity to bring together data custodians, modellers & end-users Data inputs for different models require further conversion – hence the ANDS project CCRSPI Conference 27th – 29th November 2012 Melbourne
- 11. Preliminary findings - Hermitage Example: Simulated SOC (0-20 cm) zero till; (A)stubble retained, (B)stubble burnt; 3 N fertiliser rates A B CCRSPI Conference 27th – 29th November 2012 Melbourne
- 12. Preliminary findings - Hermitage Example: Simulated SOC (0-20 cm) zero till; (A)stubble retained, (B)stubble burnt; 3 N fertiliser rates A B Preliminary results using DayCent: • Trends in SOC across treatments OK • Magnitude of SOC changes less accurate • Further parameterisation required for outputs such as N2O fluxes CCRSPI Conference 27th – 29th November 2012 Melbourne
- 13. Preliminary findings - General Preliminary comparison of model outputs for FullCAM, DayCENT, Century and a Microsoft Excel version of RothC Initial estimates suggest that if the input data are the same/very similar, the 4 models will all deliver soil C stock results within 10 t C/ha of the final result. CCRSPI Conference 27th – 29th November 2012 Melbourne
- 14. Preliminary findings - General Preliminary comparison of model outputs for FullCAM, DayCENT, Century and a Microsoft Excel version of RothC Initial estimates suggest that if the input data are the same/very similar, the 4 models will all deliver soil C stock results within 10 t C/ha of the final result. Semaphore Improving the quality of modelling using scientific workflows CCRSPI Conference 27th – 29th November 2012 Melbourne
- 15. Opportunities/challenges • The rise of “data- and CPU-intensive” analysis – Multiplication of data sources and tools to analyse the data • Need for a scientific “cyber infrastructure” – Increased expectation to share data and tools in a structured way • A new practice – “eScience” – Ability for scientists to make sense of the software written by others and modify it to fit their needs
- 16. Pitfalls/solutions • Problems – Low visibility due to unavailability of data and code publicly – Poor quality of software due to bugs/heavy customisation – Lack of provenance information and documentation of procedures • The project – Ability to prepare data and run simulations remotely – Rapid sharing of well described data and tools – Allows users to examine the data analysis and re- purpose the tools for other analyses
- 17. Data processing and analysis • A manual process prone to error and inconsistency • Capture (and expose) implicit knowledge and local conditions • Integrate tools for data cleaning and preparation
- 18. How the software works Scientific workflow software to capture steps of data process and processing • Visual interface that allows user manipulation of data • Integration with data management software
- 20. Advantages • Ability to run simulations with zero software installation • Public availability of data and software for peer- review and public use • Expose data processing and analysis for easy bug fixes and re-use • Flexible design to extend to other standard modelling tools
- 21. For more information • Project blog: semaphoreblog.wordpress.com • Software prototype available at Github • Final product will available by June 2013 • Software development team – Alvin Sebastian, Marco Fahmi, Siobhann McCafferty, Jodie Vaughan, Vaughan Hobbs • Project funders – Australian National Data Service (ANDS) – Australian Centre for Ecological Analysis and Synthesis (ACEAS)
- 22. Thank You Acknowledgements Funding: Australian Centre for Ecological Analysis and Synthesis (ACEAS) Australian National Data Service (ANDS) Expert contributions: QUT Queensland Government (DSITIA, DNR&M) CSIRO Colorado State University DCCEE University of Adelaide NT Department of Resources CCRSPI Conference 27th – 29th November 2012 Melbourne
- 23. This project is supported by the Australian National Data Service (ANDS) ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
Notes de l'éditeur
- Initially for 3 to 5 long-term research trials, and having a framework and capacity for expansion to include additional datasets and trials.
- Initially for 3 to 5 long-term research trials, and having a framework and capacity for expansion to include additional datasets and trials.
- Data and metadate collation is very time-consuming and requires multiple sourcesThroughout the data collation and modelling process, the importance of individual site history knowledge was evident. Many queries which arose throughout the process could only be answered by someone with a long-term association with the trial. These include pre-trial site history, data outlier reasoning, data gap filling and actual location of data sources. Once the data was collated site historical knowledge was still important to ensure any model parameterisation was reasonable. The importance of long term trial data in refining current models, to enable them to predict carbon and nutrient dynamics in Australian agroecosystems, cannot be understated. Many models currently in use have been developed for Northern Hemisphere systems, or have only been tested on a limited number of sites in Australia. To ensure that the models are accurately able to predict long-term temporal variability in agroecosystem dynamics under Australian conditions, data collated from longer time scales in Australia are vital. This will enable the effects of different climate scenarios and management regimes on carbon and nitrogen dynamics to be confidently modelled. This will further enable primary industries to investigate a range of issues, such as carbon sequestration or nitrogen leaching, in a timely an economical manner.With the number of long term trials constantly dwindling due to financial constraints, data from existing sites needs to be actively collated with appropriate metadata and stored for future use in modelling. Without this emphasis, the scientific community will lose vital data, and our ability to utilise the knowledge associated with it will also be lost.
- Preliminary results show the Daycent model is able to model the overall trends in soil carbon across treatments (i.e. increased soil carbon with increased applied nitrogen, stubble burning reduced soil carbon compared to stubble retention and conventional tillage reduced soil carbon compared to zero till). However the magnitude of temporal soil carbon changes is not accurate. Further parameterisation is required to refine the carbon outputs, before other outputs, such as nitrous oxide fluxes, are investigated.
- Preliminary results show the Daycent model is able to model the overall trends in soil carbon across treatments (i.e. increased soil carbon with increased applied nitrogen, stubble burning reduced soil carbon compared to stubble retention and conventional tillage reduced soil carbon compared to zero till). However the magnitude of temporal soil carbon changes is not accurate. Further parameterisation is required to refine the carbon outputs, before other outputs, such as nitrous oxide fluxes, are investigated.
- Throughout the data collation and modelling process, the importance of individual site history knowledge was evident. Many queries which arose throughout the process could only be answered by someone with a long-term association with the trial. These include pre-trial site history, data outlier reasoning, data gap filling and actual location of data sources. Once the data was collated site historical knowledge was still important to ensure any model parameterisation was reasonable.
- Throughout the data collation and modelling process, the importance of individual site history knowledge was evident. Many queries which arose throughout the process could only be answered by someone with a long-term association with the trial. These include pre-trial site history, data outlier reasoning, data gap filling and actual location of data sources. Once the data was collated site historical knowledge was still important to ensure any model parameterisation was reasonable.
- Issues sharing and re-using data and code from others due to lack of documentation, need for re-purposing the software and generally crappiness of the code
- Various ways issues can be addressed using technologies that capture scientific workflows, automatic or semi-automatic data transformation and running software in a cloud environment
- Issues that scientists encounter when developing custom code for data preparation and calibration of models due to the high specificity of each
- Annex benefits of sound data management practices and ability to value the contribution of data officers, software developers and other technicians but publicising the existing of code and data, listing them in repositories and making the citable
- The scientific benefits (better due diligence, increase efficiency, enabling synthesis) that could be gleaned from software that addresses immediate needs but that is designed with sufficient flexibility so it can be examined, used and modified by others for different data sets or different modelling tools.