Presentation on theme: "Implementing CGMS in Morocco and the Huaibei/Juanghuai plains Allard de Wit & Raymond van der Wijngaart."— Presentation transcript:
Implementing CGMS in Morocco and the Huaibei/Juanghuai plains Allard de Wit & Raymond van der Wijngaart
Workshop contents Introduction MARS Crop Yield Forecasting System (MCYFS) and Crop Growth Monitoring System (CGMS): What is it and what is needed to set it up Strengths and limitations How to sustain it in the future Discuss with partners INRA and APEI Collection of necessary inputs (weather, experimental data, soil data, irrigation, regional statistics, etc.) – Deliverable 2.1 Usability of CGMS for pilot areas: main drivers of yield level and variability, identify missing elements and improvements. Take into account synergies with WP3
Information chain in the MCYFS Meteorological information Agrometeoinformation Analysts On-demand elaboration (extreme events & critical condition) (extreme events & critical condition) Yield estimate Statisticalinformation Satelliteinformation Domain of CGMS
Level 1Level 2Level 3 CGMS overview and levels of operation CGMS.exe program
Daily estimates at grid level: Precipitation (daily total) Temperature (daily maximum, daily minimum) Global radiation (daily total) or a proxy (sunshine duration, cloud cover) Vapour pressure Wind speed (daily average) Reference evapotranspiration (derived from the above) Potential evaporation of water surface Potential evaporation of wet bare soil Potential evapotranspiration of a crop canopy Level1: Weather data requirements in CGMS
Level1: How to get weather data Use station observations: CGMS can process, store, make quality checks and substitute missing data. CGMS can interpolate to a regular grid Use output from numerical weather prediction models: Often easier to obtain Beware for strong biases for some variables and/or regions!
Level 2: WOFOST Crop Model in CGMS
Level2: WOFOST profile WOFOST is a semi-deterministic crop simulation model of physiological processes (daily time steps), phenology (sowing- flowering- maturity) Light interception Photosynthesis Respiration Assimilate partitioning Leaf area dynamics Senescence of canopy Evapotranspiration Soil water balance
Level2: daily flow of dry matter in WOFOST
Production ecological principles of yield levels Production level (t/ha) Van Ittersum and Rabbinge, 1997 Potential Water- and nutrient limited CO 2 Radiation Temperature Crop features Rainfall Irrigation Nutrients Weeds/Pests Critical periods Diseases Pollutants/salt Defining factors Reducing factors Production situation Limiting factors Attainable yield Actual yield WOFOST 7.1
Level2: Output variables of WOFOST in CGMS Crop development stage Crop total biomass and yield under potential & water-limited conditions Crop leaf area index under potential & water- limited conditions Soil moisture, transpiration
Level2: Limitations of WOFOST Multi-parameter model, difficult to calibrate and validate Sensitive for initial state of soil and crop Processes not simulated: Irrigation, nutrients, winter-kill, cold stress, heat stress, damage from excess water, flooding No recovery mechanisms
Level2: Implementing WOFOST Needed for setting up CGMS Level2 (WOFOST) Spatial information about soil type and parameters Regional crop calendars and crop masks for winter- wheat Winter-wheat experimental data for calibration: 1. phenology (sowing, emergence, flowering, maturity). 2. Crop total biomass, maximum LAI. 3. Time-series of crop biomass (roots, stems, leaves, organs), LAI, yield under potential conditions. 4. As point 3, under water-limited conditions.
Level3: Actual yield forecasting Statistical infrastructure to forecast crop yield/production in the current year using: Time-series of historic reported crop yield and area Time-series of crop yield indicators (e.g. CGMS output, meteorology or remote sensing indicators) Needed for setting up: Time-series of historic crop yield & area at national, provincial and (if possible) district level
How to sustain CGMS A clear political mandate for agricultural monitoring with a stable source of funding and a clear entry into the political decision making process. Institutional arrangements to operate the system and a stable project organization with clear functional delegation of responsibilities to the various partners; Long term dedication of key personnel to the project not only at the institution with the political mandate, but also with supporting institutions (research institutes, universities). In this way, knowledge can be build up and shared across a pool of personnel; Good technical know-how: particularly with regard to the management of database system, the handling of spatial information layers, statistical analysis of system results and visualization; A stable stream of input data consisting of weather data, but also historical regional crop yield statistics and crop experimental data;
WP2: Adapting CGMS for winter-wheat monitoring in Huaibei/Juanghuai and Morocco WP2.1: Data collection WP2.2/2.3: Evaluation of usability, strategy development, system adaptation for target regions WP2.4/2.5: System testing and piloting in target regions
WP2 data collection activities - China (D2.1) Huaibei plains WhatDescriptionWhoWhenBackup solution Station weather data ECMWF data from MO3 Soil map and parameters FAO 1: Crop masks SAGE crop masks at 0.05 degrees Regional crop calendars FAO or MO3 calendars Crop experimental data None winter-wheat regional statistics None
WP2.1 data collection activities - Morocco (D2.1) Morocco WhatDescriptionWhoWhen Backup solution Station weather data ECMWF data from MO3 Soil map and parameters 50,000 soil map FAO 1: Crop masks SAGE crop masks at 0.05 degrees Regional crop calendars FAO or MO3 calendars Crop experimental data None winter-wheat regional statistics Province level statisticsRiad 15 aprilNone
WP2.2/2.3: Evaluation of usability What are main driving factors of yield level and inter-annual variability at regional scale? What are missing components in the current CGMS for the target regions? Are there special requirements for system output? How to answer these questions: Analyze time-series of crop yield at regional level in combination with weather, model output. Design questionnaire to be circulated with local experts in the target regions.