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

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

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

Rice-wheat

Sugarcane-wheat

Long term data on nutrient management experiment

Years : 2007-08 & 2008-09

Soil data : Profile-wise (0-150 cm) bulk density, OC, NO3, NH4, 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 : 120-60-40 ; N-P-K

Irrigation: 5 irrigations : CRI, PI, Anthesis, Milking & Dough

Genetic coefficient used for APSIM wheat calibration

Calibration of model Crop seasons : 2007-08 & 2008-09

Determined the various genetic coefficients based on phenology and yield attributes

Comparison between simulated and actual yield

Rice Rice

Wheat

Wheat

Time series of Observed and simulated Biomass and LAI

DAS0 20 40 60 80 100

Bio

mass (

kg/h

a)

0

1000

2000

3000

4000

5000

6000

Simulated

Observed

DAS

0 20 40 60 80 100LA

I0

1

2

3

4

5

Simulated

Observed

Calibration of DSSAT- Genetic Coefficients (Cultivar : PBW343)

• Using Genotype coefficient estimator, estimated the following genetic coefficients

• P1V P1D P5 G1 G2 G3 PHINT

64 74 748 21 32 1.1 100

• However, the phenology is not matching with actual value, we have manually modified the P1D & P5 genetic coefficient as given below

64 89 430 21 32 1.1 100

• Run the GLUE estimator for 3000 runs, I got the following coefficient

32 88 584 18 40 1.1 100

• Calibration (year 2007-08) (only for DSSAT)

Parameter Calibration (2007-08)

Actual DSSAT APSIM

Anthesis 131 131 129

Yield 3473 3520 4109

Biomass 11072 9104 10958

Max LAI 4.6 4.0 4.6

Time series of actual and simulated LAI (APSIM)

Time series of actual and simulated Biomass (APSIM)

Time series of actual and simulated Biomass and LAI (DSSAT)

DAS

20 40 60 80 100 120 140

Bio

mass (

kg/h

a)

0

2000

4000

6000

8000

10000

12000

Observed

Simulated

DAS

0 20 40 60 80 100 120 140

LA

I0

1

2

3

4

5

Actual

Observed variability in the farm data

Farm survey data of 69 farms

Wide variability in dates of sowing - 17th October to 3rd January

Date of Harvest – 10th April - 17th May

Five cultivars – PBW223, PBW243,WL502, PBW343, UP232

No. of irrigations – 3,4 & 5

Variability in N, P and K applications

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 0-180 mm depth

Comparison between observed and simulated farm yields

Simulated wheat yield (kg/ha)

2000 3000 4000 5000 6000 7000 8000

Ob

se

rve

d w

hea

t yie

ld (

kg

/ha)

2000

3000

4000

5000

6000

7000

8000

Simulated wheat yield (kg/ha)

2000 3000 4000 5000 6000 7000 8000

Observ

ed w

heat yie

ld (

kg/h

a)

2000

3000

4000

5000

6000

7000

8000

DSSAT APSIM

Mean and Bias-correction with APSIM and DSSAT

Observed Simulated

APSIM DSSAT

Mean (kg/ha) 5209 5451 5005

SD (kg/ha) 872 551 636

CV (%) 17 10 13

Bias correction (Mean obs./ Mean sim.) 0.96 1.04

CDF- Comparison of APSIM and DSSAT simulated wheat yield over observed farm yield

Simulated (APSIM & DSSAT) and Observed farm survey wheat yield

3000 4000 5000 6000 7000 8000

Cum

ula

tive p

robabili

ty d

istr

ibution

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Observed farm survey

APSIM simulated

DSSAT Simulated

Farmers name Observed Simulated

Ms. R. Nongsieh 4400 5205

Ms. Shantilang Masharing 4500 4914

Ms. Lica Masharing 5500 4848

Mr. A Mawlong 4400 4243

Mr. Pherlin Ripnar 4800 4507

Ms. Lilia N. Sangma 5000 4603

Mr. Grim Nongrum 4400 4867

Ms. Civility Passi 4000 4222

Ms. Lismeri Kharbani 4100 4178

Ms. Daiophika Dohling 6000 4288

Mr. Sawan Nongrum 5000 4769

Mr. Brasson Mukhim 4100 4264

Mr. Linious Kharbukhi 5100 4342

Ms. Animery Kharbukhi 5000 4188

Mr. Phubor Lwai 4400 4264

Mr. Batshai Rymbai 4400 4814

Jirang 4100 4512

Mean 4658 ± 548 4531 ± 318

VARIABLE SIMULATED MEASURED

…………………………………………………………………………………………………………..

Anthesis day (dap) 94 95

Physiological maturity day (dap) 136 129

Yield at harvest maturity (kg [dm]/ha) 5476 5550

Unit wt at maturity (g [dm]/unit) 0.020 0.019

DSSAT CALIBRATION

Farmers field-

Rainfed lowland rice at farmers field, NE India: DSSAT simulation Using DOME concept

y = 1.023xR² = -0.28

y = 0.093x + 4233.R² = 0.003

3000

3500

4000

4500

5000

5500

6000

6500

3500 4000 4500 5000 5500

Ob

serv

ed

Yie

ld, kg

ha

-1

Simualted Yield, kg ha-1

No. of farmers = 17

Farmers field Measured Simulated

Mean(kg/ha) 4658 4531

SD (kg/ha) 548 318

CV (%) 11 7

Bias Correction = 1.028

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

Feedback Multi-model intercomparison –learning experience

as a learner and as a resource person

Dome not created APSIM simulation outputs for farms- created 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

• Thanks to all

Drs. Ken, Gerrit, John, Cheryl

AgMIP leadership and Resource person

ICRISAT and CIMMYT-Nepal team

Participants