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Project Personnel:

Improved Agriculture and Natural Resource Management through GIS Mapping of Climates, Soils, and Vegetation. Project Personnel:. David B. Hannaway Professor of Crop Science Forage Information System. Christopher Daly Professor of Geosciences Spatial Climate Analysis Service.

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Project Personnel:

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  1. Improved Agriculture and Natural Resource Management through GIS Mapping of Climates, Soils, and Vegetation Project Personnel: David B. Hannaway Professor of Crop Science Forage Information System Christopher Daly Professor of Geosciences Spatial Climate Analysis Service Alan Cooper Lead Associate ANE-Asia Agriculture, Natural Resources & Environmental consultants Christopher Runkle Lead Associate Runckel & Associates

  2. To produce a decision support system (DSS) for improving agriculture and natural resource management through: optimal matching of location, use, and crop/natural resource characteristics Using GIS spatial data layers for climates and soils and a vegetation database Integrating into a web-based DSS Project Objective: Improved Agriculture and Natural Resource Management

  3. Optimal decision making requires integration of: Climate, geo-physical, biological, socio-economic, and geo-political information GIS spatial data layers for better understanding of their inter-relationships Current Challenge: Sub-optimal decision making due to complexity and lack of integration

  4. State-of-the-science tools can be applied to: Create GIS spatial data layers for climates and soils Develop a quantitative tolerances vegetation database Integrate all information into a web-based DSS Current Opportunity: Development and application of current and emerging technology tools

  5. Base information layers of climates and soils are constrained by vegetation tolerances to produce suitability maps, based on: Precipitation, min., max. temperature Soil drainage, pH, salinity, alkalinity Quantitative tolerances for plants Socio-economic conditions Other limiting factors The Concept: Develop “state-of-the-science” spatial data layers, constrain by tolerances, and map

  6. OSU has developed similar products for the USA, PRC, and Europe through: Cooperation with governments, universities, agencies, and organizations Assembling necessary data Creating GIS layers Applying spatial data layers within DSS The Process: Identify cooperators, obtain data,apply proven model used in USA, PRC, EU

  7. PRISM is a Knowledge- Based System developed over 11 years USA mapping for NRCS using 8600 climate stations PRISM software uses point data, DEM, and expert knowledge to create gridded estimates of climate elements Now being applied to EU, China, and Taiwan Current Techniques: for Climate Spatial Data Layers Parameter-elevation Regressions on Independent Slopes Model

  8. Climates USA, PRC, EU, Taiwan 40 year mean annual and mean monthly precipitation Mean monthly minimum temperature Mean monthly maximum temperature Current Products: Internationally recognized “best maps” for climate

  9. Current PRC Map Products: Climate Stations

  10. Current PRC Map Products:Digital Elevation Model

  11. Current PRC Map Products: Annual Precipitation

  12. Current PRC Map Products: January Min. Temperature

  13. Current PRC Map Products: July Max. Temperature

  14. Based on soil survey information USA national and county digital soil surveys (STATSGO and SSURGO) are 1:250,000 and 1:24,000 scale PRC surveys and mapping are 1:4,000,000 and 1:1,000,000 scale More detailed maps are being developed for PRC Current Techniques: for Soil Spatial Data Layers http://www.ftw.nrcs.usda.gov/order.html CAAS Soil & Fertilizer Institute

  15. Soils USA, PRC Type Texture Drainage pH Salinity Alkalinity Current Products: Variable scale soil maps STATSGO and SSURGO Soil Information

  16. Current PRC Map Products: Soil texture

  17. Current PRC Map Products: Soil drainage

  18. Current PRC Map Products: Soil pH

  19. Traditional approach has been qualitative Need quantitative approach to work with spatial data layers and integrate with crop simulation models Initial estimates have been developed Internet Map Server for web-based improvements via “dynamic mapping” Current Techniques: for Vegetation Tolerances

  20. Internet Map Server: http://blitzen.oce.orst.edu/arcims/adapt/ Public access • View PRISM and suitability maps Private access • Password required • Allows dynamic modeling of species suitability

  21. Internet Map Server: http://blitzen.oce.orst.edu/arcims/adapt/ Map services • PRC and USA Toolbar • Zoom, pan, identify, select, etc.

  22. Internet Map Server: Toolbar Toggle Legend Overview Map Zoom Functions Zoom Functions Panning Identify Find Measure Distance Select Features Clear Selection Print Run Adaptation Model Open Map Service

  23. Layers: Cities, geopolitical, rivers, roads, etc. PRISM: Mean Precipitation, Min. & Max. Temp. by Month Adaptation: Tall Fescue, Orchardgrass, Perennial ryegrass Internet Map Server: http://blitzen.oce.orst.edu/arcims/adapt/ Map Features PRISM layers Adaptation Maps

  24. 1. Use Find tool to search for a value. 2. Clicking on the table of search results… 3. Will zoom the map to your selection.

  25. More layers become visible as you zoom into the map Legend displays for the grid data layers.

  26. Run the Adaptation Model: MaxTR = Range of July Maximum Temperature MinT = Minimum January Minimum Temperature PPT = Minimum Mean Annual Precipitation

  27. The map updates to show regions of favorable and unfavorable growth based on inputs to the model. Recall previously run model results by selecting grass type and “Refresh Map” Color legend and table of values used in the model appear with map.

  28. DSS will provide manager with capability to: View the various plant species as tools Know the characteristics of the various “tools” to use them effectively for specific jobs: forage-livestock systems soil conservation preserving species diversity beautification etc. Viewing Plant Species as Tools

  29. Optimal selection is based on species suitability Manager’s job is to match climate, soil, use, and management level to vegetation characteristics New tools, coupled with field testing, make this possible Species Suitability

  30. Climate Precipitation Annual & Seasonal Temperature Minimum & Maximum Soil pH, Drainage, Salinity Pest tolerance Diseases Insects Nematodes Vertebrates Suitability Factors

  31. Drought, heat, cold pH, drainage, salinity Insects, diseases, & nematodes Fertilization, defoliation severity and intensity Forage Species Tolerances

  32. Current PRC Map Products: Climatic tolerances

  33. Current PRC Map Products: Lolium perenne

  34. Current PRC Map Products: Dactylis glomerata

  35. Current PRC Map Products: Festuca arundinacea

  36. Soil Tolerances: pH

  37. Selected USA states, and provinces in Canada and China have been mapped for climate, soils, and forage species suitability Additional data is needed for higher resolution maps Current PRC Map Products: Provincial level

  38. Current PRC Map Products: Yunnan DEM

  39. Current PRC Map Products: Yunnan Annual Precip.

  40. Current PRC Map Products: Yunnan Min. Temperature

  41. Current PRC Map Products: Yunnan Max. Temperature

  42. Current PRC Map Products: Yunnan Festuca arundinacea

  43. Current PRC Map Products: Sichuan Festuca arundinacea

  44. Soil pH

  45. Validate with field-based evaluation trials Link advanced technologies with traditional approaches Enlist local experts to assist with concept development, implementation and validation Conduct Validation to Ensure Accuracy

  46. Individual pieces are important, nevertheless: For truly helpful product, these pieces need to integrated into an easy-to-use Decision Support System Web-based DSS provides easy access in an efficient delivery tool for farmers and policy makers leading to sustainable systems Integrate into a Web-based DSS

  47. Optimal matching of species and varieties to environment and management as a key to long-term success Combining new computer-based tools with traditional field-based techniques Summary Improved Agriculture and Natural Resource Management can be achieved by: Additional details can be found on web segment  http://forages.orst.edu/projects/gmp/

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