Survey of Major Crop Models in Thailand and Indonesia to Develop Evaluation System for Effects of Climate Change on Agriculture

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1 Survey of Major Crop Models in Thailand and Indonesia to Develop Evaluation System for Effects of Climate Change on Agriculture Kei TANAKA, Takuji KIURA, Hiroe YOSHIDA (NARO, Japan), Kiyoshi HONDA (Chubu Univ., Japan), and Budi I. Setiawan (IPB, Indonesia)

2 Contents 1. Research plan 2. Evaluation system 3. Survey of major crop models 4. Visiting fields 5. Collaboration network 2012/3/4 1st GRENE Workshop 2

3 AER2: Elucidation of the effects of climatic changes on major crops in Asian monsoon region Construction of an Evaluation System Using Meteorological Data and Crop Models (NARO) Collecting crop models & params. (for Asian Monsoon Region) Rice Cultivar A Rice Cultivar B Spinach Cassava Developing cultivation simulators with flexibility which can exchange a crop model Rain Fed Rice Field Improvement of the framework Previous Results Clop models Web applications Framework for developing applications Rice cultivation possibility prediction tool Simulator execution Climate change evaluation Verification Correction Results of Climate Change Research Meteorological Data + Climate Change Coef Implementing Web applications Deploying to DIAS Improvement of simulators 2012/3/4 1st GRENE Workshop 3

4 Structure of Evaluation System for Effects of Climate Change on Agriculture Meteorological Data MetBroker Agro Climate Database Air Temp. Solar Radiation Soil Moisture Crop Models Rice Cassava Maize Sugarcane Oil Palm Rubber Field Data Result Data Crop Yield Growth Period with Effects of Climate Change Data Viewer App AER1 compare to validate CCR1, AER3 Climate Change Parameters Adaptation and Mitigation Strategies 2012/3/4 1st GRENE Workshop 4

5 Agricultural Model Framework The functions provided by the framework Model Execution Engine Data Structure Weather Generator GUI Components Execution Data Parameter (Location Cultivar) Kind of met. data Model Execution Engine (Repetition calculation until conditions are satisfied) Weather Generator MetBroker Estimation data Normal year data User data Model Calculation Weather database Meteorological model Normal year XML file CSV file Result Data DVI, Dry weight (sequential data) Harvest date, Yield (date, value) Result display, save 2012/3/4 1st GRENE Workshop 5 Chart Map Table Download (XML, CSV)

6 MetBroker Meteorological data mediation software Rice Model Cassava Model Corn Model Sugarcane Model Application Layer MetBroker Meta data Mediation Layer Unified access method to a weather databases about 20 weather databases, 100,000 weather stations Java class library to treat meteorological data Driver Program Agro Climate DB Data from Field GD DR&TR Database Layer CCR1 AER3 AER1 2012/3/4 1st GRENE Workshop 6

7 Cultivation Possibility Predicting System (Data Viewer) 2012/3/4 1st GRENE Workshop 7

8 Cultivation Possibility Predicting System Start (Data Generator) Execution condition setup Acquisition of the meteorological station list from MetBroker Thread N Execution of station id: Meteorological data acquisition from MetBroker Thread partitioning Thread 1 Thread 2 Thread N All threads finished Output summary results of all stations Setup parameters Model Execution Output result Repetition with parameter change times Repetition with station change 15000/N times Finish Thread finish 2012/3/4 1st GRENE Workshop 8

9 Survey of Major Crop Models Countries Thailand, Indonesia Crops Rice, Cassava, Corn, Sugarcane, Maize, Oil Palm, Rubber Plantation Survey Methods Collecting literature, Interview to experts, Analysis on the model characteristics 2012/3/4 1st GRENE Workshop 9

10 Thailand by Dr. Honda (former Associate Professor of Asian Institute of Technology: AIT) RICE DSSAT CERES Rice, SWAP WOFOST, ORYZA 2000, H08, CATCHCROP Sugarcane DSSAT Canegro Cassava DSSAT GUMCAS 2012/3/4 1st GRENE Workshop 10

11 DSSAT Decision Support System for Agrotechnology Transfer 2012/3/4 1st GRENE Workshop 11

12 Crop Environmental Resource Synthesis (CERES) Rice A plant module in DSSAT Estimate rice yield under different crop conditions: rainfed, irrigation and rice varieties. Simulate crop water use under difference environments Ability to simulate nitrogen transformation under different crop conditions /3/4 1st GRENE Workshop 12

13 DSSAT GUMCAS Cassava model DSSAT Canegro Sugarcane Model 2012/3/4 1st GRENE Workshop 13

14 Indonesia by Dr. Budi I. Setiawan (Bogor Agricultural University) Dynamic Rice Model (Makarim 2012) 2012/3/4 1st GRENE Workshop 14

15 Expert Meetings Bangkok Bogor 2012/3/4 1st GRENE Workshop 15

16 Visiting Fields in Chiang Mai Rainfed Rice Field Irrigated Rice Field 2012/3/4 1st GRENE Workshop 16

17 Visiting Fields in Chiang Mai Longan Potato Maize 2012/3/4 1st GRENE Workshop 17

18 Development Collaboration Network Agriculture WG, Sensor Network WG 33rd Meeting, Chiang Mai, Feb th Meeting, Colombo, Aug th Meeting, Hawaii, Jan th Meeting, Korea, Aug th Meeting, Indonesia, Jan/Feb /3/4 1st GRENE Workshop 18