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CAgM ICT on Support Systems for Agrometeorological Services

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OPAG 2 – Support Services

• 2.1 ICT on Support Services for Agrometeorological Services –India Feb 2009

• 2.2 ET on Collection and Evaluation of Operational Agrometeorological Tools and Methodologies – Kenya Oct 2008

• 2.3 ET on Communication of Agrometeorological Products and Services – Australia May 2009

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WMO OMMWorld Meteorological Organization

Summary of CAgM Expert Team Meeting on the Collection and Evaluation of

Operational Agrometeorological Tools and Methodologies

Nairobi, Kenya21-24 October 2008

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Summary of ET 2.2 Meeting

• Hosted by the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT)-Nairobi and the assistance of the Kenyan Meteorological Department.

• Sixteen participants from 7 countries attended the meeting

• Half-day meeting with Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA) project Group

• International Livestock Research Institute (ILRI), the University of Nairobi

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WMO OMMWMO Sec. PPT

• Overview of WMO• Overview of the Commission for Agricultural

Meteorology• Review of Previous CAgM ET on same topic• Review of Data Policies• Overview of World Agrometeorological

Information Service (WAMIS)

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Previous ET Meeting

• CAgM Expert Team Meeting on Database Management, Validation and Application of Models, and Research Methods at the Eco-Regional Level

• Gaborone, Botswana from 21 to 23 November 2005.

• Twelve participants attended the workshop

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FAO WinddspSome of the following: Arcview,Arc/INFO, ERDAS, PCI, ENVI

FAO WindsipRemotely Sensed Data Management Software

FAO AgroMetShellSome of the following: ArcView, Manifold, Surfer, GRADS, GRASS, Open Source GIS

FAO AgroMetShellMapping/GIS

FAO AgroMetShellCrop Models (e.g. WOFOST, CropSyst, APSIM, DSSAT, EPIC, CropWatand ClimWat)

FAO AgroMetShellFAO CropWat and ClimWat

Agromet software

InstatSome of the following: GenStat,SysStat, R, ClimLabFAO New LoClim

InstatFAO New LoClim

Statistical SoftwareGeostatistical

ClimSoft or CliDataLogbook

ClimSoftDBMS

FAO New LoClimFAO AgroMetShell

FAO New LoClimFAO AgroMetShell

Data SourcesRegional/NationalLocalSoftware

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ET 2.2 TORs

• TOR (a): To prepare a comprehensive review of the different tools and methodologies available for operational applications in agrometeorology in different regions

• TOR (b): To evaluate the actual performance and operational use of such tools and methodologies and assess the feasibility of their application in different regions

• TOR (c): To promote the application of different tools and methodologies in different regions through the use of case studies and to evaluate the impacts of such applications on services provided to the farming sector

• TOR (d): To recommend ways and means to enhance the use of the promising tools and methodologies by various agencies in different regions

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ET Leader – Roger Stern

• Overview of 4 types of tools and methods– data generation tools (e.g. Marksim); – crop simulation models (e.g. ApSim/DSSAT), – data analysis tools (e.g. INSTAT, Genstat);

• Computer Assisted Statistics Teaching (CAST) – learning management systems (e.g. Moodle)

• Statistics in Applied Climatology (SIAC)

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RA I - Africa

• Observation (PUMA, AMESD)

• Analytical (GRADS, INSTAT)

• Dissemination tools (RANET).

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RA III – South America

• Agritempo• The Agrometeorological Monitoring System

(Agritempo) monitors and disseminates, since 2003, the climatic risk mapping out aiming at reducing possible crop losses.

• www.agritempo.gov.br

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• stores and manages daily data of 1280 weather stations;

• stores historical data from 4870 meteorological stations;

• manages more than 50,000,000 records;

• stores and uses data from estimates of 1 to 15 days;

• allows the registration of users with different profiles;

• incorporates migration process and validation of data received from institutions;

• provides resources for generation of graphics, research and statistical summary;

• agricultural zoning;

• daily generation (automatic), 27 agroclimatological bulletins

• daily generation (automatic) of 810 thematic maps for all of Brazil

Characteristics

Agritempo

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RA IV – North & Cen. America

• Statistical Analysis• Evapotranspiration, Soil Water Balance and

Irrigation Estimation• Growing Seasons• Rainfall Monitoring and Forecasting• Crop Simulation• Land Evaluation

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WINISAREG (Pereira et al., 2003), includes programsfor ETo computation and crop parameterization following the FAO 56 methodology

Algorithm to consider soil salinity impacts on ETc and yield (Pereira et al., 2007) and parametric functions for groundwater contribution and percolation (Liu et al., 2006).

Water stress impacts on crop yields are evaluated by estimating the relative yield losses as a function of the relative evapotranspiration deficit through the water–yield response factor Ky (Stewart et al., 1977).

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SIMDualKc, A Software Tool for Water Balance Simulation Based on Dual Crop CoefficientPublished by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Computers in Agriculture and Natural Resources, 4th World Congress Conference, Proceedings of the 24-26 July 2006 (Orlando, Florida USA) Publication Date 24 July 2006 701P0606.Authors: João Rolim, Pedro Godinho, Bruno Sequeira, Ricardo Rosa, Paula Paredes and Luis Santos Pereira

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• Short term forecasts out to 7 days ahead– Forecast rainfall using a probability matched ensemble mean.– Frost potential using the Operational Consensus Forecast.

• Longer term forecasts out to 3 to 12 months ahead.– Seasonal forecasts– POAMA and ACCESS

• Decision support tools– APSIM– Yield Prophet– Whopper Cropper– Pycal– Aussiegrass

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WMO OMMDecision support tools

• Agricultural Production Systems sIMulator APSIM from APRSU– Plant, soil and management modules– Simulates growth of crops, pastures and trees– Inputs: Climate, location and management – License fees. May be reduced for National Agricultural

Research Systems in developing countries.

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WMO OMMYield Prophet®

Wheat, canola, sorghum & barley - dryland & irrigatedReal-time information during crop growthInputs: paddock-specific soil, crop & climate informationOutputs: impact of management decisions [crop type & variety, sowing time, fertilisers, irrigation] on yield & qualityOnline, subscriber-only service

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Whopper Cropper

Grain & cotton - district-level outputTargeted at advisors to explore crop management strategies at start of seasonDatabase of pre-run APSIM simulations (10 districts in Vic)Inputs: soil conditions, sowing date, plant density, N application ratesOutputs: yield, protein levels, WUE, days to flowering, leaf area indexCD-ROM based

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WMO OMMAussieGRASS

Grazing and pasture application, providing regional-level outputSimulation model based - accounts for interactions between climate, soils, fires, grazing managementOutputs:

forecasts potential grass growth & cover; projected grazing pressure; risk of degradation; impact of management decisions

Internet-based, password protected; broadly “field calibrated”across all mainland states

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RA VI – Europe

• “Operational agrometeorological support of Russian agriculture”

– operational agricultural support, – based on-line agrometeorological information obtained

from Russian hydrometeorological network; – agrometeorological, agroclimatic and statistical

databases; – methods of agrometeorological assessments and

forecasts

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More

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Outline of Final Report

• Introduction• Data Acquistion• Data analysis and management• Agromet Models (i.e. Crop models)• Capacity development (Learning Management)• Dissemination tools• Summary and Recommendations

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Categories of Operational Agromet Tools and Methodologies

• Data Acquistion– Weather observations– Generation (wx simulation) MARKSIM– Remotely sensed (PUMA, radar)

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Categories of Operational Agromet Tools and Methodologies

• Data analysis and management– DBMS, Remotely Sensed Data Management Software– Statistical Software

• Stats packages (INSTAT, GENSTAT, CAST)• User-friendly Rainfall forecasts (value added)• Frost forecasts

– Mapping/GIS– Geostatistical analysis

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Categories of Operational Agromet Tools and Methodologies

• Agromet Models (i.e. Crop models)– Yield Prophet– Whopper Cropper– APSIM– CROPWAT– INSTAT– AUSSIEGRASS– DSSAT

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Categories of Operational Agromet Tools and Methodologies

• Capacity development (Learning Management)– CAST– Moodle

• Dissemination tools– RANET– Extension Services

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Key Attributes of Operational Tools

• Operational• Support• Ease of Use• Scientific Validity• Cost• Plans for Enhancement

• Draft definition of operational tool: tool being used and has demonstrated an impact to the farming sector.

• Accessibility• Data requirements• Training Requirements• Free dissemination• Case Studies

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Draft List of Collaborations

Roger Stern• within SSC• UK Met Office• Algerian Met Service• Kenyan Meteorological

Department• CGIAR centers

Emmanuel Bongkiyungand Roger Stern

• ACMAD• Agromet service – IRAD• Ministry of Agriculture• Global Forest Watch

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Draft List of Collaborations

Vernon Carr• IRRI• USQ – Roger Stone• Regional DPI (Australia) • Regional reps in each state• Jim Salinger (Stefanski)• Fiji• Philippines (Stefanski)• Regional CC centres• Private sector

Saeed Bazgeer• Dr Stigter (INSAM)• Dr Sivakumar• Ministry of Agriculture• Other countries• ICARDA (Roger Stern)• India (Dr Das)• Sri Lanka (IWMI)

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Timeline

• Categorize tools presented in Kenya into the 5 classes (Nov 2008)• Develop an evaluation plan for ET members (Nov 2008)

– What questions to ask partners– Need to write CV of each tool, what are the categories

• Meeting Report (by mid-Nov)• Decide what the final report will look like (Jan 2009)• Requesting assistance from partners and the collection of other

tools – Status report - Feb 2009– Report - May 2009

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Timeline

• Categorize the tools from partners (by Jun 2009)• Begin Writing Report (Jun 2009)• First draft (Aug 2009)• First draft reviewed by (Sep 2009)• Final Report Delivered by (Nov 2009)

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Conclusions • If the role of the historical data were fully recognised then National

Met Service (NMS) staff might be recognised as key players and that making use of the existing climate records would become more relevant to climate change.

• Coping and adapting to current climate variability is a first step in tackling long-term climate change and that much can be done with simple rainfall analysis. Crop simulation models can then investigate the impact of climate change scenarios and are useful tools

• The replacement of obsolete tools with modern and more efficientones is slow and implemented at different levels in different countries putting into question the overall objective for global food security.

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Conclusions 2

• Agrometeorologists tend to think they can only interpret weather information for farmers but that farmers obtain a lot of weatherinformation themselves and can also make their own interpretations

• Climate- driven tools are useful to quantify climate-induced risk in supporting farmers’ decisions and historical daily data is essential

• For Ag Research Centers, partnerships with NMS are vital for capacity building and climate data access

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Recommendations

• Use tools that have been scientifical validated (do not want the ease of use of tools to comprise the science)

• Promote the use of historical data and existing climate records by National Met Service (NMS) in climate change studies

• Promote a variety of tools such as simple rainfall analysis forcoping and adapting to current climate variability and the use of and crop simulation models to investigate the impact of climate change scenarios on agriculture

• Promote the full implementation of new agrometeorological tools in developing countries, especially in Africa

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Recommendations 2

• Capacity building is needed on the use of modern agrometeorological tools to address the persistent food insecurity in Africa and other developing countries.

• Fit the appropriate tool to the product (user), the most advanced tool output is not always the best

• Users need to have improved understanding of climate change versus climate variability.

• Build better partnerships between Ag Research Centers and NMS