PDFSR (ICAR), India ICAR-NEH RC (ICAR). India BARC, BARI Bangladesh NARC, Nepal CIMMYT-Nepal

Transcription

1 Strengthening simulation approaches for understanding, projecting, and managing climate risks in stress-prone environments across the central and eastern Indo-Gangetic Basin Climate Smart System Simulation (CSSS) PDFSR (ICAR), India ICAR-NEH RC (ICAR). India BARC, BARI Bangladesh NARC, Nepal CIMMYT-Nepal

2 The IGB catchment area consists of these plains along with hilly regions of Nepal, India and Pakistan and also parts of Central India Source: Gupta et al, 2001 & CPWF, 2004 Indo-Gangetic Basin - Contrasting Landscape 1. Trans-Gangetic Plain (in Pakistan) 2. Trans-Gangetic Plain (in India) 3. Upper-Gangetic Plain (in India and Nepal) 4. Middle-Gangetic Plain (in India and Nepal) 5. Lower Gangetic Plain (in India and Bangladesh

3 Rice-wheat Sugarcane-wheat

4 Long term data on nutrient management experiment Years : & Soil data : Profile-wise (0-150 cm) bulk density, OC, NO 3, NH 4, EC & ph, LL15, DUL, SAT and Soil texture Crop data : Phenology, LAI, and Biomass partitioning at different phenology, Grain and straw yield Variety : PBW343 Fertilizer : ; N-P-K Irrigation: 5 irrigations : CRI, PI, Anthesis, Milking & Dough

5 Genetic coefficient used for APSIM wheat calibration

6 Calibration of model Crop seasons : & Comparison between simulated and actual yield Rice Wheat Rice Wheat Determined the various genetic coefficients based on phenology and yield attributes

7 Time series of Observed and simulated Biomass and LAI Biomass (kg/ha) Simulated Observed LAI Simulated Observed DAS DAS

8 Calibration of DSSAT- Genetic Coefficients (Cultivar : PBW343) Using Genotype coefficient estimator, estimated the following genetic coefficients P1V P1D P5 G1 G2 G3 PHINT However, the phenology is not matching with actual value, we have manually modified the P1D & P5 genetic coefficient as given below Run the GLUE estimator for 3000 runs, I got the following coefficient

9 Calibration (year ) (only for DSSAT) Parameter Calibration ( ) Actual DSSAT APSIM Anthesis Yield Biomass Max LAI

10 Time series of actual and simulated LAI (APSIM)

11 Time series of actual and simulated Biomass (APSIM)

12 Time series of actual and simulated Biomass and LAI (DSSAT) Biomass (kg/ha) Observed Simulated LAI Actual DAS DAS

13 Observed variability in the farm data Farm survey data of 69 farms Wide variability in dates of sowing - 17 th October to 3 rd January Date of Harvest 10 th April - 17 th May Five cultivars PBW223, PBW243,WL502, PBW343, UP232 No. of irrigations 3,4 & 5 Variability in N, P and K applications

14 Assumptions made in the dome Single cultivar PBW343 DOME Potential yields of 5 varieties are almost same Irrigation depth 5 cm Available moisture content at sowing 50 % Some of the farmers are using FYM once in 3 years, we have not mentioned in the DOME Plant density, Plant spacing as per recommendations Single soil already we have completed soil collection in 6 farms at mm depth

15 Comparison between observed and simulated farm yields DSSAT APSIM Observed wheat yield (kg/ha) Observed wheat yield (kg/ha) Simulated wheat yield (kg/ha) Simulated wheat yield (kg/ha)

16 Mean and Bias-correction with APSIM and DSSAT Observed Simulated APSIM DSSAT Mean (kg/ha) SD (kg/ha) CV (%) Bias correction (Mean obs./ Mean sim.)

17 CDF- Comparison of APSIM and DSSAT simulated wheat yield over observed farm yield Cumulative probability distribution Observed farm survey APSIM simulated DSSAT Simulated Simulated (APSIM & DSSAT) and Observed farm survey wheat yield

18 DSSAT CALIBRATION VARIABLE SIMULATED MEASURED.. Anthesis day (dap) Physiological maturity day (dap) Yield at harvest maturity (kg [dm]/ha) Unit wt at maturity (g [dm]/unit) Rainfed lowland rice at farmers field, NE India: DSSAT simulation Using DOME concept Farmers field- Farmers name Observed Simulated Ms. R. Nongsieh Ms. Shantilang Masharing Ms. Lica Masharing Mr. A Mawlong Mr. Pherlin Ripnar Ms. Lilia N. Sangma Mr. Grim Nongrum Ms. Civility Passi Ms. Lismeri Kharbani Ms. Daiophika Dohling Mr. Sawan Nongrum Mr. Brasson Mukhim Mr. Linious Kharbukhi Ms. Animery Kharbukhi Mr. Phubor Lwai Mr. Batshai Rymbai Jirang Mean 4658 ± ± 318

19 Observed Yield, kg ha No. of farmers = 17 y = 0.093x R² = y = 1.023x R² = Simualted Yield, kg ha -1 Farmers field Measured Simulated Mean(kg/ha) SD (kg/ha) CV (%) 11 7 Bias Correction = 1.028

20 What is to be done next? (fine tuning) Calibrate the DSSAT /APSIM model for other 4 varieties with sentinel site data Incorporation of 6 more soil data series More number of farms to capture yield variability Planning to collect wheat yield data from farms through crop cutting Initial AgMIP Project activities in National/ International seminar/workshops Stakeholder workshops/consultations in each countries and its documentation

21 Feedback Multi-model intercomparison learning experience as a learner and as a resource person Dome not created APSIM simulation outputs for farmscreated 69 simulations incorporating crop management practices manually created 69 farms in APSIM More useful/effective compared to separate sessions during different workshops (SA, kickoff etc) Same type of training to be implemented in Climate group also Opportunity to know where we are, how to move forward etc IGB - boot camp meeting for fine tuning the multi model analysis May end or June first week

22 Thanks to all Drs. Ken, Gerrit, John, Cheryl AgMIP leadership and Resource person ICRISAT and CIMMYT-Nepal team Participants