project personnel: alan cooper lead associate ane-asia david b. hannaway professor of crop science...

Project Personnel:

Alan Cooper Lead Associate ANE-Asia

David B. HannawayProfessor of Crop ScienceForage Information System

Christopher RunkleLead AssociateRunckel & Associates

Christopher DalyProfessor of Geosciences Spatial Climate Analysis Service

Agriculture, Natural Resources & Environmental consultants

Improved Agriculture and Natural Resource Management

through GIS Mapping of Climates, Soils, and Vegetation

Mekong River Commission Briefing

Project Objective: Improved Agriculture and

Natural Resource Management

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


Current Challenge: Sub-optimal decision making due to complexity and lack of integration

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 Opportunity: Development and application of current

and emerging technology tools

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


The Concept: Develop “state-of-the-science” spatial data layers, constrain by tolerances, and map

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


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


Current Techniques: for Climate Spatial Data Layers

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

Parameter-elevation Regressions on Independent Slopes Model


Current Products: Internationally recognized “best maps” for climate

Climates– USA, PRC, EU, Taiwan

40 year mean annual and mean monthly precipitation

Mean monthly minimum temperature

Mean monthly maximum temperature


Current PRC Map Products: Climate Stations


Current PRC Map Products: Digital Elevation Model


Current PRC Map Products: Annual Precipitation


Current PRC Map Products: January Min. Temperature


Current PRC Map Products: July Max. Temperature


Current Techniques: for Soil Spatial Data Layers

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

CAAS Soil & Fertilizer Institute

http://www.ftw.nrcs.usda.gov/order.html


Current Products: Variable scale soil maps

Soils– USA, PRC

Type Texture Drainage pH Salinity Alkalinity

STATSGO and SSURGO Soil Information


Current PRC Map Products: Soil texture


Current PRC Map Products: Soil drainage


Current PRC Map Products: Soil pH


Current Techniques: for Vegetation Tolerances

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”


Public access View PRISM and

suitability maps

Private access Password required Allows dynamic

modeling of species suitability

Internet Map Server: http://blitzen.oce.orst.edu/arcims/adapt/


Map services PRC and USA

Toolbar Zoom, pan,

identify, select, etc.



Zoom Functions

PanningIdentify

FindMeasure Distance Select

FeaturesClear Selection

Print

Open Map Service

Run Adaptation

Model

Toggle Legend Overview Map

Zoom Functions

Internet Map Server: Toolbar


Layers: Cities, geopolitical, rivers, roads, etc.PRISM: Mean Precipitation, Min. & Max. Temp. by MonthAdaptation: Tall Fescue, Orchardgrass, Perennial ryegrass


Map Features

PRISM layers

Adaptation Maps


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.


More layers become visible as you zoom into the

map

Legend displays for the grid data

layers.


Run the Adaptation Model:

),,( PPTMinTMaxTRfAdaptation

MaxTR = Range of July Maximum TemperatureMinT = Minimum January Minimum TemperaturePPT = Minimum Mean Annual Precipitation


The map updates to show regions of favorable and unfavorable growth based on inputs to the model.

Color legend and table of values used in the model appear

with map.

Recall previously run model results by

selecting grass type and “Refresh Map”


Viewing Plant Species as Tools

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.


Species Suitability

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


Suitability Factors

Climate– Precipitation

Annual & Seasonal– Temperature

Minimum & Maximum Soil

– pH, Drainage, Salinity Pest tolerance

– Diseases– Insects – Nematodes– Vertebrates


Forage Species Tolerances

Drought, heat, cold pH, drainage, salinity Insects, diseases, & nematodes Fertilization, defoliation severity

and intensity


Current PRC Map Products: Climatic tolerances

Well Adapted

Species July Mean Maximum

Temp (C) January Mean Minimum

Temp (C) Mean Annual

Precipitation (mm) Tall Fescue 22-32 >= -10 >= 625

Orchardgrass 22-31 >= -7.5 >= 625 Perennial Ryegrass 22-30 >= -5 >= 625

Moderately Adapted






Marginally Adapted







Current PRC Map Products: Lolium perenne


Current PRC Map Products: Dactylis glomerata


Current PRC Map Products: Festuca arundinacea


Soil Tolerances: pH

Perennial Ryegrass Orchardgrass Tall Fescue Not Adapted < 5.00 < 4.25 < 4.0

Minimally Adapted 5.00 – 5.25 4.25 – 4.75 4.0 – 4.5

Marginally Adapted 5.25 – 5.50 4.75 – 5.25 4.5 – 5.0

Moderately Adapted 5.50 – 5.75 5.25 – 5.75 5.0 – 5.5

Well Adapted 5.75 – 7.50 5.75 – 7.50 5.5 – 8.0

Moderately Adapted 7.50 – 7.75 7.50 – 7.75 8.0 – 8.5

Marginally Adapted 7.75 – 8.00 7.75 – 8.25 8.5 – 9.0

Minimally Adapted 8.00 – 8.25 8.25 – 8.50 9.0 – 9.5

Not Adapted > 8.25 > 8.50 > 9.5


Current PRC Map Products: Provincial level

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: Yunnan DEM


Current PRC Map Products: Yunnan Annual Precip.


Current PRC Map Products: Yunnan Min. Temperature


Current PRC Map Products: Yunnan Max. Temperature


Current PRC Map Products: Yunnan Festuca arundinacea


Current PRC Map Products: Sichuan Festuca arundinacea


Soil pH


Conduct Validation to Ensure Accuracy

Validate with field-based evaluation trials

Link advanced technologies with traditional approaches

Enlist local experts to assist with concept development, implementation and validation


Integrate into a Web-based DSS

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


Summary

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

Additional details can be found on web segment http://forages.orst.edu/projects/gmp/

Improved Agriculture and Natural Resource Management can be achieved by:

project personnel: alan cooper lead associate ane-asia david b. hannaway professor of crop science...

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