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This is an ORACLE package containing stored procedures developed to aggregate crop simulations to EMU, grid and regional levels and gridded weather to regional levels while differentiating for different land covers or crops. The package have to be compiled for every regional window: EUR, RUK, IND, CHN, SAM and GLO.  
 
This is an ORACLE package containing stored procedures developed to aggregate crop simulations to EMU, grid and regional levels and gridded weather to regional levels while differentiating for different land covers or crops. The package have to be compiled for every regional window: EUR, RUK, IND, CHN, SAM and GLO.  
  
The core of the aggregation is the Aggregator.aggregation procedure used to aggregate weather data from GRID to all regional levels for every land cover using the information from table {{Object|LINK_GRID_REGION_COVER}}.
+
The core of the package is the Aggregator.aggregation procedure that select a specific aggregation procedure given the provided arguments. The arguments can be
  
The Aggregator.aggregation procedure is also used to aggregate WOFOST, BLAST and WARM simulations from:
+
*Theme Crop indictors or Weather indicators (values: ‘SIM’ or ’WEATHER’)
* STU to EMU
+
*Model Crop model (values: ‘WOFOST’,’WARM’,’BLAST’)
* EMU to GRID
+
*Meteo Meteo model (values: ‘OBS’,’HRES’,’OPE’,’HIS’,’ERA’,’ENS’,’ENSEXT’,’SEA’)
* GRID to Lowest regional level
+
*Crop Selected crop (values: crop number or null for all crops)
* Lowest regional level to upper regional levels
+
*Start Selected start-date
 +
*End Selected end-date
 +
*From Start resolution (values: ‘STU’,’EMU’,’GRID’,’REGION_LOW’,’REGION_UPPER’)
 +
*To Target resolution (values: ’EMU’,’GRID’,’REGION_LOW’,’REGION_UPPER’)
 +
*Region Selected region (values: region code or null for all regions)
 +
*Continent Selected continent (values: continent code or null for all continents)
 +
*Regional level Select the regional level from which data must be aggregated
  
Aggregation to the levels GRID and lowest regional level is not crop specific but based on the land cover associated with the crop parametrization (table {{Object|CROP_PARAMETRIZATIONS}}). Area weights of these land covers are given in tables {{Object|LINK_SMU_GRID_COVER}} and {{Object|LINK_SMU_GRID_REGION_COVER}}. Aggregation from the lowest regional level to upper regional levels is based on crop specific area weights coming from table {{Object|STAT_REGION_AREAS}}. Further simulation results of crop parametrization can be linked to different crops available in the statistics.
+
 
For example in the regional RUK window we simulate winter wheat with parametrization 1 but we aggregate the outputs at regional level for aggregation 1 and 3, thus for winter wheat and winter barley.
+
Two general types (themes) of aggregation procedures are distinguished:   
 +
*Aggregate weather data from GRID resolution to all regional levels for every land cover using the information from table {{Object|LINK_GRID_REGION_COVER}}.
 +
*Aggregated crop simulation results from
 +
** STU to EMU resolution
 +
** EMU to GRID resolution
 +
** EMU to Lowest regional level resolution
 +
** GRID to Lowest regional level resolution
 +
** Lowest regional level to Upper regional resolutions
 +
 
 +
Aggregation to the levels GRID and lowest regional level is not crop specific but based on the land cover associated with the crop parametrization (table {{Object|CROP_PARAMETRIZATIONS}}). Area weights of these land covers are given in tables {{Object|LINK_SMU_GRID_COVER}} and {{Object|LINK_SMU_GRID_REGION_COVER}}. Aggregation from the lowest regional level to upper regional levels is based on crop specific area weights coming from table {{Object|STAT_REGION_AREAS}}. Further simulation results of crop parametrization can be linked to different crops available in the statistics. For example in the regional RUK window we simulate winter wheat with parametrization 1 but we aggregate the outputs at regional level for aggregation 1 and 3, thus for winter wheat and winter barley.
  
  
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==AMDAC==
 
==AMDAC==
Models of Yield Production is one of the fields covered by the Agriculture Project of the Institute for Remote Sensing Applications at the Joint Research Centre of the Commission of the European Communities in Ispra (Italy). The goal of one of the studies in this field (see operation 3.2 in the MARS Project Call for Proposals: General Conditions and Detailed Specifications of August 1990) was to provide the Agriculture Project with a software package able to perform decoding, filing and quality evaluation of actual meteorological data which are used as input for agro-meteorological models. For this purpose the Actual Meteorological Database Construction (AMDaC) package is developed by MeteoConsult (Wageningen, The Netherlands), which is described in this manual.
+
Models of Yield Production is one of the fields covered by the Agriculture Project of the Institute for Remote Sensing Applications at the Joint Research Centre of the Commission of the European Communities in Ispra (Italy). The goal of one of the studies in this field (see operation 3.2 in the MARS Project Call for Proposals: General Conditions and Detailed Specifications of August 1990) was to provide the Agriculture Project with a software package able to perform decoding, filing and quality evaluation of actual meteorological data which are used as input for agro-meteorological models. For this purpose the Actual Meteorological Database Construction (AMDaC) package has been developed by MeteoConsult (Wageningen, The Netherlands), which is described in this manual. AMDAC has been retired in January 2020 and is succeeded by the [[Software Tools#QUACKME|QUACKME]] software which was developed by the Joint Research Centre.
 
{{Expert_box|{{PDFlink|[[media:AMDAC_manual.pdf|AMDAC manual]]}}
 
{{Expert_box|{{PDFlink|[[media:AMDAC_manual.pdf|AMDAC manual]]}}
 
{{PDFlink|[[media:AMDAC_manual_AppendixA.pdf|AMDAC manual Appendix A station list]]}}}}
 
{{PDFlink|[[media:AMDAC_manual_AppendixA.pdf|AMDAC manual Appendix A station list]]}}}}
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Within the framework of the ASEMARS project, the calibration platform Calplat was developed, at Alterra, in close consultation with the Joint Research Centre (JRC) at Ispra. It was developed during 2005 and 2006, and was, in first instance, meant to calibrate the basic crop phenology parameters. Later Calplat has been extended to be able to calibrate a larger set of crop parameters for the WOFOST crop growth model and the LINGRA grass growth model, which are both part of the CGMS monitoring system.
 
Within the framework of the ASEMARS project, the calibration platform Calplat was developed, at Alterra, in close consultation with the Joint Research Centre (JRC) at Ispra. It was developed during 2005 and 2006, and was, in first instance, meant to calibrate the basic crop phenology parameters. Later Calplat has been extended to be able to calibrate a larger set of crop parameters for the WOFOST crop growth model and the LINGRA grass growth model, which are both part of the CGMS monitoring system.
 
{{Expert_box|{{PDFlink|[[Media:CALPLAT_manual.pdf|Calplat manual]]}}}}
 
{{Expert_box|{{PDFlink|[[Media:CALPLAT_manual.pdf|Calplat manual]]}}}}
 +
 +
==Calibration Manager==
 +
The calibration Manager is a Python packages that combines Python wofost (PCSE) with an open optimization tool NLopt. This makes it possible to calibrate a combination of crop parameters at the same time (e.g. TSUM1 and TSUM2, SPAN and SLATB and TDWI, etc.) using one or more target variables (e.g. day of anthesis, day of maturity, lai-max, harvest index etc.). The selected target variables are combined in a combined objective function that is optimized. Additional functionalities are added to the tool, such as normalizing target variables, assigning weights to experiments and calendars, criteria when expert knowledge is taken into account, applying additional crop masks to exclude regional observations of non agricultural areas. 
 +
{{scientific_box_2|[[Calibration_Manager]]}}
  
 
==CGMS==
 
==CGMS==
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In order to facilitate the work of the MARS analysts the COBO (Control board) was developed. The COBO is a mixture of data warehousing and data search engines organised through a common interface based on web-portal concept. COBO represents a base tool for the analyst.
 
In order to facilitate the work of the MARS analysts the COBO (Control board) was developed. The COBO is a mixture of data warehousing and data search engines organised through a common interface based on web-portal concept. COBO represents a base tool for the analyst.
  
{{scientific_box_1|[[Control board]]}}
+
{{scientific_box_1|[[CoBo_(Control_Board)_-_the_tool_used_for_statistical_forecasting]]}}
 +
 
 +
==COPdate==
 +
To estimate grid specific sowing dates following sowing dates rules, the oracle package COPdate was developed
 +
{{scientific_box_2|[[COPdate]]}}
  
 
==GIS interface==
 
==GIS interface==
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*[http://cidportal.jrc.ec.europa.eu/home/idp/thematic-portals/agri4cast-imageserver/ Image download portal Remote Sensing data]
 
*[http://cidportal.jrc.ec.europa.eu/home/idp/thematic-portals/agri4cast-imageserver/ Image download portal Remote Sensing data]
 
}}
 
}}
 +
 +
==ISW==
 +
To estimate initial soil water content for crop simulations, the implemented approach is to start a water balance long before the actual crop simulation. Using this approach, recharge of the soil moisture by rainfall and water use of a pseudo crop simulation or bare soil will help to find a more representative estimate of the soil moisture level when the actual crop simulation for the year of interest starts.
 +
 +
{{scientific_box_2|[[ISW]]}}
  
 
==LTA_YIELD==
 
==LTA_YIELD==
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*{{PDFlink|[[Media:analyst_manual_2_0.pdf|Analyst manual]]}}
 
*{{PDFlink|[[Media:analyst_manual_2_0.pdf|Analyst manual]]}}
 
*{{PDFlink|[[Media:web_manual_2_0.pdf|Web manual]]}}}}
 
*{{PDFlink|[[Media:web_manual_2_0.pdf|Web manual]]}}}}
 +
 +
==PCSE==
 +
PCSE is the abbreviation for Python Crop Simulation Environment. PCSE is a Python package for building crop simulation models, in particular the crop models developed in Wageningen (Netherlands). PCSE provides the environment to implement crop simulation models, the tools for reading ancillary data (weather, soil, agromanagement) and the components for simulating biophysical processes such as phenology, respiration and evapotranspiration. PCSE also includes implementations of the WOFOST and LINTUL3.
  
 
==Project Management Board==
 
==Project Management Board==
Line 360: Line 391:
  
 
{{expert_box|{{PDFlink|[[Media:MANUAL_PMB.pdf‎|PMB manual]]}}}}
 
{{expert_box|{{PDFlink|[[Media:MANUAL_PMB.pdf‎|PMB manual]]}}}}
 +
 +
==QUACKME==
 +
In 2018, the JRC developed the Quality Checks Meteorological Station Data Software (QUACKME) as a successor of [[Software Tools#AMDAC|AMDAC]] . The Technical Guide describes the architecture, the implemented data quality checks and the applied data aggregation methods. The Manual describes how the system can be operated.
 +
{{Expert_box|{{PDFlink|[[media:Quackme_TechGuide_JRC128152_01.pdf|QUACKME Technical Guide]]}}
 +
{{PDFlink|[[media:QUACKME_UserManual.pdf|QUACKME User Manual]]}}}}
  
 
==ReferenceWeather==
 
==ReferenceWeather==
Windows software tool, released in 2000 to calculate long term average station weather. Currently installed as Oracle Package WEATHER_OBS_LTA.
+
Oracle package (WEATHER_OBS_LTA) to calculate average station weather.
  
 
{{hidden
 
{{hidden
|Calculation steps
+
|'''Configuration'''
 
|
 
|
#Accumulate data per day and station retrieved from the tables {{Object|WEATHER_OBS_STATION}} and {{Object|WEATHER_OBS_STATION_CALCULATED}}. The data is only accumulated when it is not NULL (excluding NoData). Each datafield has its own counter to perform the average later.
+
First, the time window for looking back and forth can be set. Without setting the time window a default of 7 days (back and forth) is used; a total of 15 days. The window can be set by calling the function 'set_window_limit' with one argument.
#Average the data by dividing the accumulated total by the value of the counter (only when there is data present).
+
 
#For days that have missing values try to correct this by stepping back through the days, and substituting todays NULL with the first value found in the past. Do not go back more than 30 days.
+
To calculated the long term averages, function 'stations' is called and configured using four arguments:
#For all weather stations in the {{Object|STATION}} table, delete the existing stations in {{Object|WEATHER_OBS_STATION_LTA}} table.
+
 
#For every day of the reference year (leap year), add the calculated average values per station and day to table {{Object|WEATHER_OBS_STATION_LTA}}.
+
*Select stations and years that should be included in the calculation of average station weather by setting arguments for :
 +
** argument 1: lowest station number to include
 +
** argument 2: highest station number to include
 +
** argument 3: lowest year to include
 +
** argument 4: highest year to include
 +
*If no arguments are provided (i.e. null, null, null, null), all stations and all years are included in the calculation of the averages.
 +
*If only one argument is provided (e.g. null, null, 1990, null), it limits only that part of the data. In this example, only including years greater or equal to 1990. Likewise, also 2, 3 or all 4 arguments can be set in any combination. The arguments are interpreted with AND operators.
 +
*When no values are set for arguments 3 and 4, results will be written to the {{Object|WEATHER_OBS_STATION_LTA}} table (long term average).
 +
*When a non-null value is provided for arguments 3 or 4, it is assumed the average is a medium term average and results are written to the {{Object|WEATHER_OBS_STATION_MTA}} table.
 +
 
 +
'''Calculation steps:'''
 +
*Starting at 1st of March (index 1), for each combination of station and element, accumulate all observations in a 15 day window (+/- 7 days) around each 1st of March of all selected years (by arguments 3 and/or 4) in {{Object|WEATHER_OBS_STATION}} and {{Object|WEATHER_OBS_STATION_CALCULATED}}.
 +
*Count for each combination of station and element the number of included values. NoData/null records are excluded from the counter.
 +
*Average the data by dividing the accumulated total by the value of the counter (only when there is data present).
 +
*Repeat for the next day (i.e. 2nd of March, index 2).
 +
*Repeat until index 366, where index 366 represents 29th of February.
 +
*For all selected stations, delete the corresponding records in {{Object|WEATHER_OBS_STATION_MTA}} or {{Object|WEATHER_OBS_STATION_LTA}} table.
 +
*Add the calculated averages to table {{Object|WEATHER_OBS_STATION_MTA}} or {{Object|WEATHER_OBS_STATION_LTA}}.
 +
 
 +
'''Important remarks:'''
 +
* By using a window around the target day, the calculated average is less sensitive to missing values; compared to a simple average.
 +
* By using a window the results are smoothed.
 +
* The windows always consist of adjacent days. In leapyears they can also include leapdays.
 +
* When processing index day 366, windows in leapyears will be located around 29th of February and windows in non-leapyears will be located around 1st of March. The average for index day 366 will be based on the same number of years as other index days. The results will be similar to the average of index day 1 (1st of March), but not identical!
 +
* Averages for days 1 to 7 January (index days 307-313) can also include some values from days before lowest year (if set by argument 3).
 +
* Averages for days 25 to 31 December (index days 300-306) can also include some values from days after the highest year (if set by argument 4).
 
}}
 
}}
  
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[[Category:MCYFS introduction]]
 
[[Category:MCYFS introduction]]
 
[[Category:Software Tools]]
 
[[Category:Software Tools]]
 +
 +
==WOFOST Control Centre==
 +
WOFOST Control Centre (WCC) is a graphical user interface that runs the WOFOST (version 7.1.7) crop growth model. WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. By interactively changing model parameters and analysing the results, the tool can be used to manually calibrate a crop under local conditions. More detailed documentation and software download is available on [https://www.wur.nl/en/Expertise-Services/Research-Institutes/Environmental-Research/Facilities-Products/Software-and-models/WOFOST.htm WOFOST - WOrld FOod STudies].

Latest revision as of 09:25, 15 February 2022



Aggregator

This is an ORACLE package containing stored procedures developed to aggregate crop simulations to EMU, grid and regional levels and gridded weather to regional levels while differentiating for different land covers or crops. The package have to be compiled for every regional window: EUR, RUK, IND, CHN, SAM and GLO.

The core of the package is the Aggregator.aggregation procedure that select a specific aggregation procedure given the provided arguments. The arguments can be

  • Theme Crop indictors or Weather indicators (values: ‘SIM’ or ’WEATHER’)
  • Model Crop model (values: ‘WOFOST’,’WARM’,’BLAST’)
  • Meteo Meteo model (values: ‘OBS’,’HRES’,’OPE’,’HIS’,’ERA’,’ENS’,’ENSEXT’,’SEA’)
  • Crop Selected crop (values: crop number or null for all crops)
  • Start Selected start-date
  • End Selected end-date
  • From Start resolution (values: ‘STU’,’EMU’,’GRID’,’REGION_LOW’,’REGION_UPPER’)
  • To Target resolution (values: ’EMU’,’GRID’,’REGION_LOW’,’REGION_UPPER’)
  • Region Selected region (values: region code or null for all regions)
  • Continent Selected continent (values: continent code or null for all continents)
  • Regional level Select the regional level from which data must be aggregated


Two general types (themes) of aggregation procedures are distinguished:

  • Aggregate weather data from GRID resolution to all regional levels for every land cover using the information from table LINK_GRID_REGION_COVER.
  • Aggregated crop simulation results from
    • STU to EMU resolution
    • EMU to GRID resolution
    • EMU to Lowest regional level resolution
    • GRID to Lowest regional level resolution
    • Lowest regional level to Upper regional resolutions

Aggregation to the levels GRID and lowest regional level is not crop specific but based on the land cover associated with the crop parametrization (table CROP_PARAMETRIZATIONS). Area weights of these land covers are given in tables LINK_SMU_GRID_COVER and LINK_SMU_GRID_REGION_COVER. Aggregation from the lowest regional level to upper regional levels is based on crop specific area weights coming from table STAT_REGION_AREAS. Further simulation results of crop parametrization can be linked to different crops available in the statistics. For example in the regional RUK window we simulate winter wheat with parametrization 1 but we aggregate the outputs at regional level for aggregation 1 and 3, thus for winter wheat and winter barley.



AMDAC

Models of Yield Production is one of the fields covered by the Agriculture Project of the Institute for Remote Sensing Applications at the Joint Research Centre of the Commission of the European Communities in Ispra (Italy). The goal of one of the studies in this field (see operation 3.2 in the MARS Project Call for Proposals: General Conditions and Detailed Specifications of August 1990) was to provide the Agriculture Project with a software package able to perform decoding, filing and quality evaluation of actual meteorological data which are used as input for agro-meteorological models. For this purpose the Actual Meteorological Database Construction (AMDaC) package has been developed by MeteoConsult (Wageningen, The Netherlands), which is described in this manual. AMDAC has been retired in January 2020 and is succeeded by the QUACKME software which was developed by the Joint Research Centre.

Red.gif

More information
AMDAC manual (pdf)

AMDAC manual Appendix A station list (pdf)


Calplat

Within the framework of the ASEMARS project, the calibration platform Calplat was developed, at Alterra, in close consultation with the Joint Research Centre (JRC) at Ispra. It was developed during 2005 and 2006, and was, in first instance, meant to calibrate the basic crop phenology parameters. Later Calplat has been extended to be able to calibrate a larger set of crop parameters for the WOFOST crop growth model and the LINGRA grass growth model, which are both part of the CGMS monitoring system.

Red.gif

More information
Calplat manual (pdf)


Calibration Manager

The calibration Manager is a Python packages that combines Python wofost (PCSE) with an open optimization tool NLopt. This makes it possible to calibrate a combination of crop parameters at the same time (e.g. TSUM1 and TSUM2, SPAN and SLATB and TDWI, etc.) using one or more target variables (e.g. day of anthesis, day of maturity, lai-max, harvest index etc.). The selected target variables are combined in a combined objective function that is optimized. Additional functionalities are added to the tool, such as normalizing target variables, assigning weights to experiments and calendars, criteria when expert knowledge is taken into account, applying additional crop masks to exclude regional observations of non agricultural areas.

Orange red.gif

More information
Calibration_Manager


CGMS

The CGMS is the combination of the WOFOST crop growth model, a relational database and a statistical yield prediction module. From 2004 onwards the development of CGMS continued in the framework of the MARSOP2, ASEMARS and MARSOP3 projects, leading to the current version CGMS 10.0.3.2. The linked document below describes version 9.2

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More information
CGMS manual (pdf)


CGMS statistical tool

The CGMS statistical tool has been developed for JRC’s MARS project in the framework of the contract study “Actions in Support of the Enlargement of the MARS Crop Yield Forecasting System (MCYFS) Lot I (ASEMARS Lot I)”. The tool is designed for use by the crop analysts and is an improved version of the CGMS statistical module which was in use since 1994 to facilitate national and sub national crop yield forecasting. Time trend analyis of yield statistics is followed by regression or scenario analysis using biophysical indicators to explain yield statistics and search for similar years. Constructed models are used to predict yield of the current growing season.

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More information
CGMS Statistical Tool manual (pdf)


CMETEO

The software package has been developed to aggregate grid weather to some levels of administrative and agri-environmental regions. It is a generic package that can be used with input datasets such as observed weather and forecasted weather of several models and regions of interest (ROIs) as supplied in the MARS database. For each of the input dataset the procedure is repeated using weighing methods based on occupied areas. The output of the process is merged into resulting datasets for the choosen theme and resolution.

Orange red.gif

More information
CMETEO


Control board

In order to facilitate the work of the MARS analysts the COBO (Control board) was developed. The COBO is a mixture of data warehousing and data search engines organised through a common interface based on web-portal concept. COBO represents a base tool for the analyst.


COPdate

To estimate grid specific sowing dates following sowing dates rules, the oracle package COPdate was developed

Orange red.gif

More information
COPdate


GIS interface

CGMS does not use or need a GIS to produce its results. However, a GIS is necessary for a meaningful presentation of the results, and is also indispensable for the initial creation of the database. The link to the GIS is formed by the concept of the EMU’s, grid cells, administrative regions(NUTS) and Agro-environmental zones. Meteorological data are stored in the resolutions 'grid', 'NUTS' and 'agro-environmental zones'. Simulated yields are stored in the resolutions 'EMU', 'grid' and 'NUTS'.

In the cgms database all grid cells, administrative regions and agri-environmental zones have a unique code or number that can be used to provide a link to a GIS. The EMU's are stored as a unique combination of grid number and SMU number. This unique combination can be used to provide the link to the GIS.

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More information
GIS interface manual (pdf)


Remote Sensing Software

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More information
REMOTE SENSING SOFTWARE


Image server

ECMWF weather data, aggregated for 10-daily periods and 10-daily and monthly remote sensing based indicators can be downloaded from image servers hosted by the JRC. Documentation, terms of use and download links can be found on the homepages of these image servers.


ISW

To estimate initial soil water content for crop simulations, the implemented approach is to start a water balance long before the actual crop simulation. Using this approach, recharge of the soil moisture by rainfall and water use of a pseudo crop simulation or bare soil will help to find a more representative estimate of the soil moisture level when the actual crop simulation for the year of interest starts.

Orange red.gif

More information
ISW


LTA_YIELD

LTA_YIELD is a dedicated software package that calculates long term averages of simulated crop yield for each individual geographical feature on a 10 daily base. The geographical features can be be EMU's, grid cells or adminstrative regions (NUTS).

Orange red.gif

More information
LTA_YIELD


Marsop viewer

The operational MARS services deliver and store large amounts of data. These data vary from static reference layers and input weather data originating from different supplying meteorological sources (weather stations, ECMWF) and data quick looks to the data that is generated in the various operational levels of the MARS services through downscaling, simulation of crop indicators, estimation of crop yields etcetera. The MARS viewers enable users to perform spatial and temporal analysis of these data sets in a customized way. The documentation of the Marsop3 viewer is split into two manuals: an analyst viewer version with full functionality and a web version with limited functionality.

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More information


PCSE

PCSE is the abbreviation for Python Crop Simulation Environment. PCSE is a Python package for building crop simulation models, in particular the crop models developed in Wageningen (Netherlands). PCSE provides the environment to implement crop simulation models, the tools for reading ancillary data (weather, soil, agromanagement) and the components for simulating biophysical processes such as phenology, respiration and evapotranspiration. PCSE also includes implementations of the WOFOST and LINTUL3.

Project Management Board

The Project Management Board (PMB) was developed to keep track of the operational processing activities. Within the MCYFS many production lines are operational. The source date come from different locations, generally in large quantities and high frequencies (daily-, decadal, monthly and seasonal updates). Different parties are involved in processing the data before they are injected in a database. And eventually most data is mirrored to a duplicate database elsewhere.

A single error could interrupt one of the production lines and may cause a complete dataset to be inaccessible (including derived results). A real-time overview of the states of the processing steps in the different production lines could help to quickly trace potential problems. This is what the PMB does. It consists of a database that stores all scheduled processing steps and their real-time status. The status of processing steps is updated by the various production lines automatically.

A web-based user interface can generate various overviews of processing steps. By default it displays the events that are delayed and not yet delivered. The user interface can also be used to manually update status information in case issues are resolved (and automatic status request can't be generated).

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More information
PMB manual (pdf)


QUACKME

In 2018, the JRC developed the Quality Checks Meteorological Station Data Software (QUACKME) as a successor of AMDAC . The Technical Guide describes the architecture, the implemented data quality checks and the applied data aggregation methods. The Manual describes how the system can be operated.

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More information
QUACKME Technical Guide (pdf)

QUACKME User Manual (pdf)


ReferenceWeather

Oracle package (WEATHER_OBS_LTA) to calculate average station weather.


SupitConstants

Supit constants are need to be available for all weather stations in the table WEATHER_STATION to be able to calculate solar radiation from other weather indicators as measured solar radiation is only seldom available. The SupitContstants application is developed to interpolated supit constants from the table SUPIT_REFERENCE_STATIONS to all weather stations and restuls are stored in the SUPIT_CONSTANTS table.

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More information
SupitConstants


Supporting software

Within the MCYFS a number of software packages are developed that support processing steps in automated production lines. Some of these software packages can also be used as a separate tool by analysts.

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More information

  • REGLISTS (supplies specific lists of regions directed by input parameters)
  • DATE_GENERATOR (generic tool to generate specific lists of dates according to the input parameters)
  • PROCESS_LOGGING (procedures to send some info to a user interface)
  • PROCMAN (generic tool to assist other procedures to run in heterogeneous environments)

WOFOST Control Centre

WOFOST Control Centre (WCC) is a graphical user interface that runs the WOFOST (version 7.1.7) crop growth model. WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. By interactively changing model parameters and analysing the results, the tool can be used to manually calibrate a crop under local conditions. More detailed documentation and software download is available on WOFOST - WOrld FOod STudies.