FAO Crop Monitoring and Yield Forecasting Tool (AgroMetShell version 2.0)

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1 FAO Crop Monitoring and Yield Forecasting Tool (AgroMetShell version 2.0)

2 Objectives To provide a unique shell in which to integrate all software tools to be used for crop monitoring and crop yield forecasting New_AMS will be a new concept for the FAO/NRC and one of the most important features will be to integrate in the shell a large number of tools and utilities developed over the years. Database Tools Statistical Analysis Meteorological data Remote sensing data Crop statistics Water balance model Remote sensing indicators Stepwise Actual evapotranpiration Maximum NDVI Crop yield trend analysis water deficit (pheno phase) Cumulated NDVI crop season Crop yield regression model WRSI

3 Based principles Open Source software be distributed free charge and copyright ties be multi-platform

4 Software in AMS version 2.0 Database management WABAL SEDI (method for 'assisted' interpolation) WINDISP ADDAPIX (tool for images classification) Link to AcquaCrop, a crop water productivity model developed by the Land and Water Division of FAO AURELHY (statistical computing and graphics) VAST (planting date calculation based on NDVI) Other potential software : ET, Bags, EXTRARAD, WBPP

5 MOSAICC as a climate change vulnerability and risk assessment tool MOSAICC = MOdelling System for Agricultural Impacts of Climate Change

6 4 main components Modellers Interface IPCC GCM Low Resolution Scenarios Historical weather records Historical yield records Climate Scenario Downscaling Downscaled Climate scenarios Server & Database Historical water use statistics Climate Hydrology Crops Economy Crop parameters Soil data Crop growth Simulation Hydrological Modelling Historical discharge records Soil and Land use data Technology trend scenarios Yield projections Water availability for irrigation Dam data Current state of economy Macroeconomic scenarios Economic Modelling External users interface Economic impacts AfricaAdapt 10/03/11 Addis Ababa

7 Low Resolution Climate Change Scenarios GCM Low Resolution Scenarios Low resolution climate scenarios are useful at the global and regional levels. To some extent, can also help in planning at the national level. Global Regional National Provincial Municipal

8 High Resolution Climate Change Scenarios GCM Low Resolution Scenarios Global Historical Weather Records Regional National Provincial Municipal Downscaled high resolution climate scenarios High resolution climate change scenarios are useful at the national, provincial, and even municipal levels.

9 Implementation requirements Host institution (e.g. national met office) Experts from relevant institutions: climatologists, agrometeorologists, hydrologists, economists Constitution of a multidisciplinary team, including end-users Minimum data (weather station observations, agricultural statistics, etc) Training sessions for each model MOSAICC is free (but funds needed to provide training and set up servers)

10 Step 2 Mapping of Vulnerability to Food Insecurity under Climate Change Current: FIVIMS(Food Insecurity and Vulnerability Information and Mapping System: FIVIMS) Vulnerability by 2030? New Information and Mapping System FIVIMS is a snapshot of current vulnerability as a composite of poverty rate, average family income, unemployment rate, nutrition, low body weight rate, etc. Climate, biophysical, environmental, social and economic variables, which change with time, need to be integrated to analyze changes in vulnerability under future climate change

11 Agricultural Stress Index System (ASIS) ASIS assess the severity (intensity, duration and spatial extent) of the agricultural drought and express the final results at administrative level given the possibility to compare it with the agricultural statistics of the country. ASIS works at global scale and in the near-real time on the dekadal 1 km resolution data of METOP- AVHRR. A standalone version of ASIS will be developed for monitoring agricultural drought and risk management at country and regional level. Main agricultural drought hot spots in 2011 United States, Mexico and the Horn of Africa.

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14 Future development on Agricultural Stress Index System (ASIS) A standalone version of ASIS could be use after local calibration use as a regional/country agricultural drought monitoring ASIS could be use as a remote sensing based index insurance Probability in % of having 30% of total agricultural area affected by drought Hypothetical case of payoff at province level, using the line of 70% of agricultural area affected by drought in Kenya ( ).

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