GSDI-9 Conference Proceedings, 6-10 November 2006, Santiago, Chile
Spatial Information Systems in Crop Monitoring: Developing New Global Models and Sharing the Data Felix Rembold*, Jacques Delincé,* Hendrik Boogard**, Armin Burger* * European Commission, DG Joint Research Centre, via E. Fermi 1, 21020 Ispra (VA), Italy Email:
[email protected] ** Alterra, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands Abstract The AGRIFISH Unit of the Joint Research Centre, European Commission, has now nearly 20 years of experience with crop monitoring in and outside Europe. During this period, a number of research and development activities have been carried out, leading to several operational systems which are all based on spatial information. One of the best known is the MARS Crop Yield Forecasting System (MCYFS) for Europe, which is providing independent quantitative crop statistics at EU and national levels, in near real time. Similar tools have been developed in the last 5 years at a global scale and with the main aim of providing crop status information and yield forecasts in many areas of frequent food insecurity around the world. The most recent example of global crop monitoring system is the Global Water Satisfaction Index (GWSI) currently under development by a JRC project. The models are run directly by JRC or by its contractors, and the output data are disseminated directly or in form of crop monitoring bulletins to food security analysts and food security policy makers everywhere in the world. To make this possible, a huge spatial data distribution structure has been set up and both spatial data and analyzed data are made available to the broad public. Millions of maps are accessible online since the MARSOP website was first launched at the beginning of 2001 and thousands of satellite images and meteorological records both at the European and the global scale have become available to the users thanks to the recently setup AGRIFISH image portal. Finally, in addition to sharing the spatial input and output data of the AGRIFISH crop monitoring systems, the analyzed results are summarized and made available as crop monitoring reports or bulletins both through mailing lists and through the internet. These bulletins are issued ten-daily for some of the most vulnerable countries in Eastern Africa like Somalia, Sudan and Ethiopia and monthly for Europe, for the IGAD region in Eastern Africa, for the MERCOSUR countries in South America, for Central Asia and for the Mediterranean basin. For more information please visit: http://AGRIFISH.jrc.it
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1. The use of spatial data for crop monitoring The Monitoring of Agriculture with Remote Sensing (MARS) project, started in 1988, was initially designed to apply emerging space technologies for providing independent and timely information on crop areas and yields. To accomplish this task a Crop Yield Forecasting System (MCYFS) was developed, which consists of several independent modules integrated to monitor crop behaviour and produce crop yield forecasts. Running these modules means analysing and combining in a GIS environment the following main groups of spatial data: meteorological parameters, soil and land cover classifications, agrometeorological simulated crop growth indicators, low-resolution satellite data and agricultural statistics. More precisely MCYFS includes the maintenance of the meteorological database, the application of an agro-meteorological model, the processing of low resolution satellite data, statistical analyses and yield forecasts of the main crops at national level across the European Union (EU) (Micale and Genovese, 2004). Since 2000, the expertise in crop yields has been extended outside the EU. Services have been developed to support EU aid and assistance policies and provide building blocks for a European capability for global agricultural monitoring and food security assessment (Nègre et al., 2003). Classically these activities finally result in crop monitoring bulletins containing analysis of the crop situation in different regions of the world, maps of weather and crop indicators and yield expectations. However, with the system becoming more and more operational and driven by user requirements, the AGRIFISH Unit (former MARS project) started by implementing spatial data distribution structures which make available to the broad public not only the final interpretations of the crop analysts, but also maps and graphs of the input data and in many cases the original data itself. This development became even more important with the team expanding in other sectors based on spatial and remote sensing data analysis. The most important activity in this sense is the work for a more effective and efficient management of the Common Agricultural Policy through the provision of a broader range of technical support services to DG Agriculture and MemberState Administrations. The MARS PAC mission is to provide scientific support and technical guidelines to DG AGRI and Member States for the sound implementation and management of the EU Common Agriculture Policy, and especially area based subsidies. 2. Developing global tools for crop monitoring: The case of the GWSI (Global Water Satisfaction Index) Since spatial agro-climatic data and remote sensing data are more and more commonly available, a number of methods have been developed to monitor vegetation conditions and yield expectations both for highly productive agricultural areas and for regions frequently stricken by food security crises at the global level. The FAO CSWB or CSSWB (Crop Specific (Soil) Water Balance) was first published in 1986 in FAO’s Plant Production and Protection Paper No 73 “Early agrometeorological crop yield assessment” (Frere and Popov, 1986) with examples from Niger, Tanzania, Zambia and India: semi-arid tropical climates. The main output of CSWB was the Water Requirement Satisfaction Index (WRSI or WSI). Since then the index has become the most widely used crop yield indicator for the assessment of yield reduction due to drought, all over the world.
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The index is a qualitative index, expressed as a percentage of maximum yield, which can be used independently, or combined with other models, such as FAO’s crop yield functions according to FAO’s yield response to water stress (Reynolds et al., 2000). FAO has developed a special application called Agrometshell (AMS) to run the model, which was developed for supporting national meteorological services to setup their local early warning systems (Gommes, 1993). To respond to this objective, the FAO Agrometshell software is meteo-station oriented and is typically run at the national or subnational scale. The GWSI developed by JRC is based on the same algorithm proposed by FAO but follows a completely different spatial approach by using grid cells of 0.1 degrees to simulate the WSI at a worldwide scale for the most important food crops. The main objective of the system is to quickly identify hot spots of crop stress at the global scale. This implies that contrarily to the operator oriented approach of AMS, the GWSI is a centrally run system, with a client application allowing users in different parts of the World to display and analyze the output data and maps. Though the model is robust and simple compared to much more sophisticated algorithms used at the local scale in situations of large data availability, like for example the CGMS model used in Europe, retrieving and combining the input data at global scale was a great challenge. Global simulated meteo data from the European Centre for Medium Range Weather Forecast (ECMWF) had to be stored for every 10-day period over a reference climatic time series, while global crop and soil masks had to be elaborated and global agronomic data to be found (see Table 1 for a list of input data). Table 1: Main GWSI input data selected worldwide and their sources. Global input data Weather data: • Actual ten-days rainfall sums • Actual ten-days potential evapotranspiration sums Soil data: • Rooting depth • Crop specific water storage capacity Crop data • Main food crops statistics • Planting dates, crop calendar • Crop masks • Crop coefficients
Data source European Centre for Medium Range Weather Forecast (ECMWF) model The digital FAO soil map of the world (1:5.000.000) DSMW. ISRIC rules to compute water holding capacity. Food balance sheet study based on FAOSTAT data Average FAO planting dates and climatic rules GLC2000 Land Cover and national statistics FAO publications Doorenbos and Pruitt, 1977; Doorenbos and Kassam, 1979
Also, a dedicated study of national food balance sheets was necessary to find out which are the crops with the highest relative importance as food crop for each country worldwide. A basic set of 7 food crops was finally selected to be monitored worldwide and includes the following crops: wheat, barley, maize, rice, potato, sorghum and soybeans. In addition groundnut, millet, beans and cowpea (including other pulses) are monitored where they play an important role as food crops. Table 2 shows the ranking of food crops on the basis of world production and world consumption volumes. Table 2. Ranking of food crops on the basis of world production volume (left part) and of world consumption volume (right part). Source: FAO, FAOSTAT, Food Balance Sheets. 3
Production figures in 1000 Metric tons crop rank prod 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
percent Production share in Crop group or Cereal world product Equivalent prodctn cum % Maize 591987 18.4 18.4 Wheat 585298 18.2 36.7 Rice (Milled Equivalent) 401941 12.5 49.2 Soyabeans 161422 5.0 54.2 Barley 134421 4.2 58.4 Sugar Cane 125333 3.9 62.3 Potatoes 82291 2.6 64.9 Vegetables, Other 58661 1.8 66.7 Sorghum 56046 1.7 68.4 Coconuts - Incl Copra 51244 1.6 70.0 Cassava 44687 1.4 71.4 Rape and Mustardseed 40189 1.3 72.7 Sweet Potatoes 34726 1.1 73.8 Cottonseed 33205 1.0 74.8 Millet 27636 0.9 75.7 Pulses, Other 26891 0.8 76.5 Sunflowerseed 26205 0.8 77.3 Oats 25838 0.8 78.1 Sugar Beet 24514 0.8 78.9 Groundnuts (Shelled Eq) 24509 0.8 79.6 Rye 19642 0.6 80.2 Cereals, Other 19545 0.6 80.9 Beans 16739 0.5 81.4 Olives 15911 0.5 81.9 Fruits, Other 12463 0.4 82.3 Tomatoes 10703 0.3 82.6 Peas 10604 0.3 82.9 Yams 9557 0.3 83.2 Oilcrops, Other 9207 0.3 83.5 Oranges, Mandarines 8241 0.3 83.8 Coffee 7410 0.2 84.0 Bananas 6633 0.2 84.2 Grapes 6442 0.2 84.4 Palmkernels 6281 0.2 84.6 Apples 5928 0.2 84.8 Onions 4827 0.2 84.9 Roots, Other 4135 0.1 85.1 Tea 3820 0.1 85.2 Cocoa Beans 3414 0.1 85.3 Plantains 3027 0.1 85.4 Sesameseed 2883 0.1 85.5 Spices, Other 2488 0.1 85.6 Pineapples 1392 0.0 85.6 Lemons, Limes 1103 0.0 85.6 Dates 586 0.0 85.6 Grapefruit 544 0.0 85.7 Citrus, Other 527 0.0 85.7 Pimento 221 0.0 85.7 Cloves 91 0.0 85.7 Pepper 28 0.0 85.7 Sugar 167122 5.2 90.9 Vegetable oils 236308 7.4 98.3 Alcoh. Drinks 56022 1.7 100.0 3210887 100
share in crop Consumpti world rank Crop group or on cereal human cons product equivalnt consum cum % 1 Wheat 411481 25.5 25.5 2 Rice (Milled Equivalent) 345777 21.5 47.0 3 Maize 111595 6.9 53.9 4 Vegetables, Other 51518 3.2 57.1 5 Potatoes 49124 3.0 60.2 6 Cassava 24731 1.5 61.7 7 Sorghum 23868 1.5 63.2 8 Millet 21687 1.3 64.5 9 Coconuts - Incl Copra 20059 1.2 65.8 10 Pulses, Other 18299 1.1 66.9 11 Sweet Potatoes 16678 1.0 68.0 12 Soyabeans 15268 0.9 68.9 13 Beans 13338 0.8 69.7 14 Fruits, Other 11135 0.7 70.4 15 Tomatoes 9365 0.6 71.0 16 Groundnuts (Shelled Eq) 9215 0.6 71.6 72.1 17 Oranges, Mandarines 7581 0.5 18 Barley 6684 0.4 72.5 19 Coffee 6569 0.4 72.9 20 Rye 6405 0.4 73.3 21 Bananas 5421 0.3 73.6 22 Apples 5065 0.3 73.9 23 Cereals, Other 4861 0.3 74.2 24 Yams 4467 0.3 74.5 25 Onions 4397 0.3 74.8 26 Sugar Cane 3733 0.2 75.0 27 Tea 3668 0.2 75.2 28 Peas 3419 0.2 75.5 29 Cocoa Beans 3212 0.2 75.7 30 Oats 2939 0.2 75.8 31 Roots, Other 2795 0.2 76.0 32 Spices, Other 2466 0.2 76.2 33 Grapes 2144 0.1 76.3 34 Olives 1990 0.1 76.4 35 Plantains 1977 0.1 76.5 36 Pineapples 1281 0.1 76.6 37 Lemons, Limes 1112 0.1 76.7 38 Sesameseed 1068 0.1 76.8 39 Rape and Mustardseed 711 0.0 76.8 40 Oilcrops, Other 642 0.0 76.8 41 Sunflowerseed 481 0.0 76.9 42 Grapefruit 480 0.0 76.9 43 Citrus, Other 476 0.0 76.9 44 Dates 470 0.0 77.0 45 Pimento 216 0.0 77.0 46 Cloves 30 0.0 77.0 47 Pepper 28 0.0 77.0 48 Palmkernels 6 0.0 77.0 49 Sugar Beet 2 0.0 77.0 50 Cottonseed 0 0.0 77.0 Sugar 152081 9.4 86.4 Vegetable oils 163288 10.1 96.6 Alcoh. Drinks 55518 3.4 100.0 1610821 100
The main GWSI output variables are: • Water Satisfaction Index (WSI) of the whole cropping season, but updated for the current dekad; • Water Deficit (D) for the current dekad and cumulated until the current dekad; • Water Surplus (S) for the current dekad and cumulated until the current dekad (water damage index);
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• •
NB
• • •
ETa (actual evapotranspiration) per dekad and cumulated until the current dekad; Progress index per dekad (the time since planting divided by the crop growth duration); Rainfall per dekad and cumulated until the current dekad; Planting date per season but updated for the current dekad; Yield (on regional level); - Cumulated values are summed since planting dekad. - Variable WSI is calculated for different rainfall scenarios (optimum, average, minimum)
Results are shown per individual crop and per crop season (main or second) in map (spatial) or graph (temporal) format for a specific ECWMF grid cell or administrative region. Per geographic area maps of output variables are created and made available to the end users (with administrative boundaries for orientation) presenting the most detailed spatial variability. Specific software has been developed by JRC contractor Alterra to run the GWSI model and update the MARSOP website in real time with the model outputs. The system also includes a client which allows the AGRIFISH analysts to access the model outputs directly. The structure of the system is shown in figure 1 while an example of the client application can be seen in figure 2. The system is currently running in a preoperational way, while single groups of input data, like the global crop masks and planting dates have still to be improved. Weather data
SimpleClient Preprocessing of WSI indicator results (on multiple computers)
Viewing of maps and import of weather data
Administrator
MARSOP website
COMServer Viewing of maps
GWSI engine AgroMetShell
Oracle Data storage (200 Gb)
Production Experiments
Figure 1: Components of the GWSI application (Alterra 2006, modified)
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Figure 2: Example of WSI output map selected in the GWSI client application. 3. Sharing crop monitoring indicators with the broad public Monitoring agricultural vegetation in near real time and at world wide coverage implies the processing of massive volumes of spatial data, ranging from meteo data to satellite images and to global crop growth model outputs. While AGRIFISH analysts are working mainly on the evaluation and interpretation of these spatial datasets, they are also involved in their acquisition, storage and redistribution. Together with external partners, large geographical databases have been developed, and in compliance with the INSPIRE (http://inspire.jrc.it) proposal adopted by the European Commission in July 2004, massive efforts are being made to share them with the scientific community and end users through the internet. The following tools were developed by the AGRIFISH unit to share most of the spatial data with the user community: • The MARSOP website; • The AGRIFISH image servers; • The AGRIFISH bulletins. 3.1 The MARSOP website The MARSOP website (http://www.marsop.info/) was first launched at the beginning of 2001. The site, initially called MARS Extranet was originally meant for EC internal purposes but was then made accessible via registration to the whole Internet community. Most of the maps made available on the MARSOP site are static maps that show the
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results run by the AGRIFISH crop monitoring and crop forecasting models. The maps include historical data since 1975. Recently, the system was updated to make possible the visualization of on the flight maps. These systems include the Crop Growth Monitoring System (CGMS) model outputs for Europe and the Global Water Satisfaction Index (GWSI) indicators worldwide. At the subnational level most of the meteo, remote sensing and crop modeling variables are also visible as time profiles in form of graphs or tables. This web site was developed and is currently maintained by ALTERRA within the MARSOP2 project. The access is public; however a registration is required in order to obtain a password. 3.1.1 Main structure The interface of the MARSOP-2 website consists of three parts: 1. a navigation menu on the left 2. a main frame where the actual information is presented 3. and a top frame containing logo’s of the EC and JRC. The site presents the users with a hierarchical menu. In this menu the user makes a fixed set of choices, leading to a single map, graph or table. The navigation is transparent, giving the users a clear overview over the navigational structure and available data. The site is helpful to the user by retaining input throughout a session and preventing infeasible combinations of choices. 3.1.2 Different choices in navigation menu The choices are: 1. Area: This listbox presents the area for which a map, graph or table can be selected. On the top level so-called top-level Regions of Interest (ROI) are offered. These are: o World: the world, only excluding Antartica o Europe: this includes the countries of the European Union, Norway, Eastern Europe, the European part of Russia, Ukraine, Yugoslavia and its former republics, Turkey, Armenia, Azerbeidjan, Georgia, Morocco, Algeria and Tunisia; but not the countries Iceland and Switzerland and not the territories Isle of Man (UK), Gibraltar (UK), Channel Islands (UK), Aaland (FI) and Ceuta and Melilla (ES) o Mediterranean basin: this includes the area bordering the Mediterranean, but the maps and remote sensing products do not necessarily show the complete area of the countries involved. o Russia / Central Asia: this includes the whole of Russia and other former Soviet republics o Horn of Africa: this includes Eritrea, Ethiopia, Somalia, Kenya, Uganda, Burundi and Ruanda o Mercosur states: this includes Argentina, Chile, Uruguay, Paraguay, Bolivia and Brazil 2. Theme: The available options here depend on the area selected. Below an overview is given of the theme names and the corresponding products. Theme name
Related products
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Weather indicators (observed) Crop indicators (simulated) Yield forecasts Crop indicators (remote sensing) Weather indicators (simulated) Global water satisfaction index
maps of weather indicators based on observations maps and time profiles of crop indicators based on agro-meteorological models (CGMS) maps and time profiles of yield forecasts based on agro-meteorological models (CGMS) maps and time profiles of crop indicators based on remote sensing images maps of weather indicators based on numerical weather models (ECMWF) maps of water satisfaction index calculated on the basis of the FAO approach.
In general, maps are available for regions of interest (ROI) like Europe, Mediterranean basin etcetera and time profiles - or in other words graphs and tables - for countries and subdivisions of countries. 3. Variable: The items that appear here also very much depend on the selected theme. The following table gives an indication as to what kind of items to expect here: Theme name Weather indicators (observed) Crop indicators (simulated) Yield forecasts Crop indicators (remote sensing)
Weather indicators (simulated) Global water satisfaction index
Variables weather indicators for normal conditions, for alarming summer conditions as well as for alarming winter conditions crop indicators for normal conditions as well as for alarming conditions during the growing season yield forecast indicators for vegetation growth, dry matter productivity etcetera, based on remote sensing images as well as other particulars pertaining to the acquired data weather indicators for normal conditions, for alarming summer conditions as well as for alarming winter conditions global water satisfaction index indicator and related variables to this index based on the GWSI approach.
4. Values: The following table gives an explanation of the options that can appear here: Item name Explanation Current year the year as currently selected further down in this menu Long-term average the average determined over a number of years since a certain start date. The latter is usually indicated below the result. Current year minus previous year the difference between the value for the year as currently selected further down in this
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menu and the value for the year preceding that year Current year minus long-term average the difference between the value for the year as currently selected further down in this menu and the value of the long-term average Relative range this way of representing the values is particularly relevant for the variable NDVI or Normalized Difference Vegetation Index (remote sensing). It corresponds with the VCI or Vegetation Condition Index Historical probability this way of representing the values is particularly relevant for the variable NDVI. It corresponds with the VPI or Vegetation Productivity Index 5. Representation: Basically, the system enables the user to indicate what kind of result is desired: map, graph or table. Whether all 3 options are available depends on the selections made further up in the menu. In general, maps will be available for regions, but not for countries and subdivisions thereof. However, when "current year minus previous year" or "current year minus long-term average" in the values pick_list have been selected, an additional choice has to be made in this picklist: whether the results have to be represented as an absolute difference or as percentual difference. When dealing with any other theme than "Crop indicators (simulated)", comparison between values of current year and values valid for one or more previous years is done "as to decade". However, when dealing with theme "Crop indicators (simulated)", comparison between values of current year and values valid for one or more previous years is done either "as to decade" or "as to development stage". For the latter case this means that it is taken into account that in some years a crop develops quicker than in other years. Comparison of a value for a crop indicator for the current year with a value from the previous year or the long term average is done in such a way that both values apply to the time that the crop was in the same development stage (e.g. flowering stage). Figure 3 shows a sample page of the MARSOP website where the left hand navigation menu explained above is clearly visible.
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Figure 3: Example of the MARSOP web site interface with the navigation menu on the left side and the main visualization area on the center-right side. 3.2 The AGRIFISH image servers Apart from the sharing of crop indicators as maps or time profiles through the MARSOP website, in several cases also the full spatial data are made available to the public, by image servers and geographic online databases. This is the case for meteorological data for Europe and modelized meteorological data at the global scale. In addition, most of the massive amount of satellite images available at AGRIFISH have been made accessible through the image server http://imageportal.jrc.it . In the context of the EU Common Agriculture Policy appropriate administration and control systems were implemented by Member States to ensure that aid is correctly granted. The Control with Remote Sensing (CwRS) was developed with the technical support of the JRC and uses satellite images of high to very high resolution for the controls. The satellite image acquisition for the CwRS campaigns is performed by JRC on behalf of DG Agriculture. The images are distributed to contractors working on behalf of Member States. After processing and analysis the images are returned to JRC and are archived in the PAC ImageServer system that allows identification, visualisation and delivery of the
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imagery. The images are loaded onto the internal disk storage and are pre-processed (georeferenced, color-balanced, etc.) for further usage. All necessary metadata are extracted and stored in a PostgreSQL database with the PostGIS spatial extension. These metadata provide the base for identifying and retrieving the images. The current image repository consists of more than 9000 satellite images with an emphasis on High Resolution (HR) images (SPOT, Landsat, IRS, ERS, Radarsat). Current trends of CwRS campaigns are increasing very much the amount of Very High Resolution (VHR) data from the Quickbird, IKONOS and EROS satellites. At present, the image archive is primarily accessed via a web-based application called ‘ImageBrowser’ based on the UMN MapServer and the ER Mapper Image Web Server. It allows interactive query and identification of CwRS imagery based on the available metadata using attribute and spatial filters, e.g. satellite, acquisition date, image resolution, area of interest. The identified images can be easily and quickly previewed in the browser. Via a unique URL every image can be directly loaded into major GIS and image processing software via the ECWP protocol using freely available plug-ins. A currently running project will provide the authentication mechanisms that will then also allow the access to the archive via open protocols like Web Map Server and Web Coverage Server for authorized users. Additionally it will provide a Catalogue Service as internal and external metadata interface. A ortho-rectification engine will further on allow a semi-automated creation of ortho imagery. The imagery collected in the frame of the MARS Crop Yield Forecasting System (MCYFS) serves different purposes but is archived and served via similar system architecture. AGRIFISH regularly produces 10-daily and monthly vegetation state parameters derived from SPOT-VEGETATION, NOAA-AVHRR and MODIS. The archive partly exists since 1989 and currently comprises NDVI, SAVI and DMP. Moreover daily, 10-daily, and monthly MSG-SEVIRI products like land surface temperature, sunshine duration, albedo and radiation are available since January 2005. After upload via FTP the images are referenced with the required metadata in a database. This database is queried by a Web interface allowing the user to select parameters like satellite, product and period he is interested in. Then the images can be previewed using an interactive zoom/pan interface and the native access of UMN MapServer to the images (http://imageportal.jrc.it/statgaia.shtml). The current image map can be printed or downloaded as PDF document. Registered users can select single or multiple images and an area of interest and download the selected data in GeoTIFF or ERDAS Imagine format. Finally, a global Meteo application (http://imageportal.jrc.it/foodmeteo.shtml) provides access to grid data derived from ECMWF (European Centre for Medium-Range Weather Forecast) atmospherical model1. After upload the data are pre-processed and referenced in a meta-database. A time series for more than 40 years is now available thanks to the ERA40 reanalysis project. The Meteo application allows to query the database and interactively preview the grid data of the archive. The user can select areas of interest and download series of grids as floating point GeoTIFF or ASCII Grid files.
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The data made available here differ from the original ECMWF data and are only processed products like cumulated variables or complex weather indicators derived from the original data. The original data can be obtained only from ECMWF directly.
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3.3 The AGRIFISH Bulletins In addition to sharing the spatial input data of the AGRIFISH crop monitoring systems, the analyzed outputs are made available as crop monitoring reports or bulletins both through mailing lists and through the internet. The bulletins are issued ten-daily for some of the most vulnerable countries in Eastern Africa like Somalia, Sudan and Ethiopia and monthly for Europe, the IGAD region in Eastern Africa, South America, Central Asia and the Mediterranean basin. MARS Bulletin Europe The MARS bulletin is published almost every month during the main growing season and is supplied to land and agriculture policy makers of the European Union, DG Agriculture, EUROSTAT and national organisations. Its main contents, surface area changes estimations and yield forecasts, are the results of a synthesis of data as produced by the MCYFS. Usually, the bulletin provides a general agro-meteorological overview of Europe for the current situation and indicates the areas of concern, for instance areas with too wet or too dry conditions. Yield forecasts for cereals and oil seeds are given for the various countries in the EU-15 and central European countries. This is followed by a more detailed analysis of the agro-meteorological conditions per country or group of countries. Differences in vegetation indices between current and previous are shown as maps and time profiles and commented. The following releases are normally issued in one year: • Full Analysis are published from 6 to 8 times a year on European Crop Monitoring and Yield Forecasting. These publications are also available on request on paper format. • Quick Look releases (i.e. shorter digital versions or e-mail versions) of the same analyses are available to facilitate downloading and are loaded in the site before the final Full analysis. • Climatic Updates are brief intermediate analyses between two main bulletins and are available only in digital version. Crop monitoring for Food Security Bulletins For the initial phase of crop monitoring outside Europe, four pilot areas had been selected: Mediterranean Basin, Russia and Central Asia; Eastern Africa and South America. The choice was based on potential food insecurity threats and also on the interest of Commission services in yield data for certain regions, as for example Russia and the former Soviet Union countries. The bulletins were conceived for supporting Food Security analysts working in the regions and as support to the Commission DG’s involved in international cooperation and humanitarian interventions. DG AIDCO and RELEX, are the main Commission customers for this work. AGRIFISH is also delivering to the same customers data and maps based on specific requests and not contained in the bulletins. While the monitoring activities have in the meantime extended to the rest of the World, the currently available operationally released crop monitoring bulletins do still reflect the initial pilot areas and are available for the following countries and regions. • • • • •
Eastern Africa (monthly) South and East Mediterranean Countries (monthly) Russia and Central Asian Countries (monthly) South America (MERCOSUR and Bolivia) (monthly) Somalia (ten-daily)
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• • •
Sudan (ten-daily) Ethiopia (ten-daily) Eritrea (ten-daily)
All bulletins can be downloaded from our ftp site: ftp://mars.jrc.it/bulletin 4. Conclusions The systems developed are able to provide a very large amount of spatial data to a broad community of end users ranging from Commission Services like EUROSTAT or EuropeAid to International organizations like FAO and the United Nations Office for the Coordination of Humanitarian Affairs, but also national administrations and research organizations. In this sense, both the development of global crop monitoring systems and the development of spatial data sharing systems answer in a direct way the Global Monitoring for Environment and Security (GMES) objectives. The main mission of GMES is in fact expressed as: a concerted effort to bring data and information providers together with users, so they can better understand each other and make environmental and securityrelated information available to the people who need it through enhanced or new services. At the same time the described activities are an important step towards the objectives of the INSPIRE initiative aiming at the creation of a European spatial information infrastructure that delivers to the users integrated spatial information services. Substantial efforts have been dedicated to the development of geographical online database and mapservers to facilitate access by the user community by using modern data sharing tools and resources. Global crop monitoring systems like the GWSI are still under development and require considerable resources in terms of data collection, data standardization, model improvement and computing time. Many problems have still to be solved for making these models completely operational. The AGRIFISH bulletins have become an important reference for policy makers and food security analysts worldwide and are continuously expanded and improved thanks to research and users feedback.
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5. Acknowledgements The authors would like to thank the following organizations for providing information or data for this paper: FAO, Alterra (Nl), VITO (Be), ECMWF (Uk) and MCWETTER (D) and O. Rojas (JRC) for his useful comments on paragraph 2. 6. References Alterra/VITO/Meteo Consult, November 2005. “OPERATIONAL ACTIVITIES FOR MARS STAT AND MARS FOOD AID ACTIONS – Period 2” LOT A, B, C, (MARSOP-2) Contract nr 21508-2003-12 FISC ISP NL, Period 1 January- 30 June 2005 Doorenbos J., Kassam A., 1979. Yield response to water, FAO Irrigation and Drainage Paper No. 33, FAO, Rome. Doorenbos J., Pruitt W., 1977, Guidelines for predicting crop water requirements, FAO Irrigation and Drainage Paper No. 24, FAO, Rome, 1977. FAO, 1995. The Digitized Soil Map of the World Including Derived Soil Properties. (version 3.5), FAO Land and Water Digital Media Series 1, Rome, Italy. Frere, M. and G. F. Popov, 1986. Early agrometeorologicalcrop yield assessment. Plant Production and Protection PaperNo. 73, FAO, Rome GOMMES, R.: FAOINDEX, 1993, Version 2.1. Agrometeorology Group FAO. Rome. MICALE, F. and GENOVESE, G. (editors) 2004, Methodology of the MARS Crop Yield Forecasting System, Vol. 1, Meteorological Data Collection, Processing and Analysis. Contributions from: E. Van der Goot, I. Supit, H. Boogard, K. Van Diepen, F. Micale, S. Orlandi, H. Otten, M. Geuze, D. Schulze. EUR 21291 EN/1. NEGRE, T, REMBOLD, F., ROJAS, O. 2004, Integrated agricultural monitoring and yield forecasting for Eastern Africa : The JRC approach. In: Crop and Rangeland Monitoring in Eastern Africa for early warning and food security. Proceedings of an International Workshop organized by JRC-FAO. Nairobi, 28-30 January 2003. Reynolds C., Yitayew D., Slack D., Hutchinson C., Huetes A., Petersen M., 2000, Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data, Int. J. Remote Sens. 21, 3487–3508. ROJAS, O.; REMBOLD, F.; ROYER, A.; NEGRE, T. 2005. Real-time agro-meteorological crop yield monitoring in Eastern Africa. AGRONOMIE, 25 (2005) 63-77.
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