1. APPLICATION OF CLIMATE PREDICTION IN RICE PRODUCTION IN THE MEKONG RIVER DELTA (VIETNAM) Nguyen Thi Hien Thuan Sub-Institute of Hydrometeorology and Environment of South Vietnam

2. Introduction Mekong River Delta (MRD) in Vietnam: Largest rice producing area in VN, 12% of total country’s cultivated area but 52% of total rice production Mekong River Delta (MRD) in Vietnam: Largest rice producing area in VN, 12% of total country’s cultivated area but 52% of total rice production Rice production varies depending mostly on climate- related constraints: water availability (depending on rainfall), drought/dry spells, flood/inundation, salinity intrusion Rice production varies depending mostly on climate- related constraints: water availability (depending on rainfall), drought/dry spells, flood/inundation, salinity intrusion Preliminary studies show the connection between El Nino/La Nina phases with climate variables Preliminary studies show the connection between El Nino/La Nina phases with climate variables

3. Objectives Objectives To identify the impacts of ENSO on rainfall and temperature over the MRD. To identify the impacts of ENSO on rainfall and temperature over the MRD. Case study: To produce climate forecast for Long An province and provide these forecasts to provincial agricultural sector. Case study: To produce climate forecast for Long An province and provide these forecasts to provincial agricultural sector. To apply seasonal forecasts into a crop yield simulation model to generate crop yield forecasts in Long An province. To apply seasonal forecasts into a crop yield simulation model to generate crop yield forecasts in Long An province. To evaluate the seasonal forecasts and crop simulation model outputs. To evaluate the seasonal forecasts and crop simulation model outputs.

4. Study area

5. Data For ENSO relationship study: For ENSO relationship study: - Rainfall and temperature of 18 met. station in MRD - SSTs, SOI For crop simulation: Long An province For crop simulation: Long An province - Rainfall, temperature, sunshine duration of 2 met. station in Long An province - Rice crop data: Rice variety, planting date, crop management data - Soil properties

6. -Area: 482,000ha, of which about 433,000ha are for annual rice crops - Production: 1.7 – 2ml ton of rice/year - Population: >80% of population is engaged in rice producing activities Why is Long An province?

7. 2 communes were selected: 2 communes were selected: - Thanh Phu commune of Ben Luc district represents salinity affected areas - Tan Lap commune of Tan Thanh district (Dong Thap Muoi lowland - the Plant of Reeds): is affected by annual flood during high water season. 26 farmers from each communes participated in the survey (total of 52 farmers) Met stn Hydrol.stn Rain gauge

8. Study the relationship between ENSO and rainfall and temperature of MRD. Correlation coefficient between Nino3.4 with mean T and R in MRD Correlation coefficient between Nino3.4 with mean T and R in MRD

9. Significant correlation between monthly ENSO indices and rainfall and temperature (R: 0.4 – 0.7), better correlation during March – July (temperature) and during February – June (rainfall). Significant correlation between monthly ENSO indices and rainfall and temperature (R: 0.4 – 0.7), better correlation during March – July (temperature) and during February – June (rainfall). Highest correlation coefficients are with the lag time of 2 – 3 months for temperature and 4-5 months for rainfall. Highest correlation coefficients are with the lag time of 2 – 3 months for temperature and 4-5 months for rainfall. Regression equations were established for each location. Regression equations were established for each location. ENSO effect

10. Calculated vs. Observed Temperature Anomaly (left) and Rainfall Anomaly (right) for May (St. Tan An, Long An)

11. Surveys Two surveys have been carried out. 1st survey: 1st survey: - Defined needs for climate forecast information from farmers. - Collected crop management data - Questionnaire sheets were distributed to 52 farmers in two selected communes of two districts of Long An province.

12. Impacts of different factors on Yield and Necessity of Climate Forecast Information (by farmers) Site >> Tan Lap (Tan Thanh) Thanh Phu (Ben Luc) Criteria Priority level 12341234 Factors influencing the yield: BiologyTechnique Economy, Policy Weather12%12%0%80%68%20%8%8%20%60%24%8%0%8%60%4%18%7%4%89%29%71%11%7%31%14%15%4%22%8%7%0% Meteorological Factors: Hot weather Strong wind Drought Severe rain Flood18%16%45%14%7%33%3%20%26%18%29%18%12%36%6%024%8%18%4%4%89%0059%29%11%7%7%33%41%04%18%4%4%000 When bulletins needed? Winter-SpringSummer-AutumnSowing68%3%Tilering2%12%Flowering30%55%Ripe0%30%Sowing74%4%Tilering.0%11%Flowering22%85%Ripe4%0%

13. When and what kind of forecasts do farmers need? onset of the rainy season. onset of the rainy season. Rainfall amount/heavy rains Rainfall amount/heavy rains dry spells during the rainy season dry spells during the rainy season salinity forecast (for Thanh Phu commune) salinity forecast (for Thanh Phu commune) water level, water receding rate (for Tan Lap commune) water level, water receding rate (for Tan Lap commune)

14. Surveys Surveys The second survey focused on the use and effectiveness of climate forecast information. Questionnaires and interviews were made to obtain the information.

15. Forecasting and disseminating Forecast procedure has been set up and agreed between the forecasting bodies to take the most advantage of all available forecasting sources. The forecast period lasted 6 months since the 1 st April till the 1 st October 2003. Each forecasting bulletin contains climate and hydrological parts (water level and salinity). Forecasts with lead times of 10 days, 1 month and 3 month were prepared in each bulletin. Forecasts were disseminated directly to the 52 farmers and the 20 officials/extension workers

16. Forecasting and disseminating Forecast at SRHMC Forecast at LA Prov. Center Prov. Extensio n Center Input data Study result s Foreca st referen ce Local data District Extensio n Units Farmers

17. Rice crop simulation DSSAT 35 software has been used to simulate yields of different planting dates with: Historical weather data 1990-2002 Historical weather data 1990-2002 Actual 2003 weather Actual 2003 weather Using climate forecasts Using climate forecasts

18. SiteGroupPlating time Fertilizer application (kg)Yield (kg/ha) TanThanh15-10 April100N - 45 P 2 0 5 - 50 K 2 03570 211-15 April120N - 40 P 2 0 5 - 50 K 2 03300 316-20 April120N - 30 P 2 0 5 - 40 K 2 03150 421-25 April115N - 30 P 2 0 5 - 30 K 2 02900 526-30 April110N - 30 P 2 0 5 - 40 K 2 02830 Ben Luc14-10 May80N - 40 P 2 0 5 - 40 K 2 04050 216-20 May70N - 45 P 2 0 5 - 30 K 2 03213 321-25 May90N - 55 P 2 0 5 - 30 K 2 03625 426-31 May76N - 45 P 2 0 5 - 42 K 2 03000 Planting dates, fertilizers, yield from survey 1

19. Average yields of different planting dates with actual weather 1990-2002

20. Tan Thanh Ben Luc Yields of different planting dates with 2003 weather

21. U sing climate forecast

22. Evaluation Climate forecasts (10 days, 1 month, 3 month – temperature, rainfall, water levels, salinity) Climate forecasts (10 days, 1 month, 3 month – temperature, rainfall, water levels, salinity) Occurrence of dry spell in July Occurrence of dry spell in July Climate forecasts provided to end users were highly appreciated. Climate forecasts provided to end users were highly appreciated. Most farmers need 10-day forecasts. Managerial officials/extension workers prefer 1- 3month forecasts Most farmers need 10-day forecasts. Managerial officials/extension workers prefer 1- 3month forecasts The contents of the forecasts need to be shaped into more concise. The contents of the forecasts need to be shaped into more concise.

23. Evaluation Probabilistic forecasts were first introduced – new, not easy to interpret – farmers require categorical forecasts while officials accept but need more training for interpretation. Probabilistic forecasts were first introduced – new, not easy to interpret – farmers require categorical forecasts while officials accept but need more training for interpretation. Crop simulation can be a useful tool to help decision making, using different kinds of climate inputs, but needs more calibration and training Crop simulation can be a useful tool to help decision making, using different kinds of climate inputs, but needs more calibration and training

24. Recommendations * To continue the detailed study on ENSO impacts for the Mekong River Delta so that the findings can be used to establish forecasting tools in operational work. * To continue the detailed study on ENSO impacts for the Mekong River Delta so that the findings can be used to establish forecasting tools in operational work. * To improve forecasting capability (climate and crop simulation) * To set up forecast-dissemination line to provide the best possible benefits of climate information for users. * To conduct a study on the effects of Climate Change on Water Resources and Coastal Zone of the Mekong River Delta. * To conduct a study on the effects of Climate Change on Water Resources and Coastal Zone of the Mekong River Delta.

25. Acknowledgment START, IRI and the Packard Foundation are acknowledged for supporting the research and funding the project.

26. Thank you