Crop Yield Modeling through Spatial Simulation Model

Simulation Model-WOFOST

WOFOST (WOrld FOod STudies, Supit et al.,1994) is particularly suited to quantify the combined effect of changes in CO2, temperature, rainfall and solar radiation, on crop development, crop growth and crop water use, as all the relevant processes are simulated separately while taking due account of their interactions

Crop Yield Map

Soil Classes

ClaySandy Loam

LoamClay Loam

Water

Sandy Clay Loam

Sand

Soil Classes

ClaySandy Loam

LoamClay Loam

Water

Sandy Clay Loam

Sand

Soil MapCrop Simulation

Model

Crop Parameters

Wheat Yield MapWheat Yield Map

LAI MapWeather surface

Wheat area

Phenology map

Area Weighted Yield

Yield map

Simulated Grid Yield

Yield Prediction Through Simulation

Spatial Data Generation

Weather

Soil Types in India as per FAO soil map

Soil Classes

ClaySandy Loam

LoamClay Loam

Water

Sandy Clay Loam

Sand

Soil Classes

ClaySandy Loam

LoamClay Loam

Water

Sandy Clay Loam

Sand

Generation of Calibrated Crop Coefficient

Name of the state

Bihar

Haryana and

Punjab

MP

Rajasthan

UP

Calibrated Variety

HD2733

PBW343

Malvasakti (HI8498)

Raj3765

HD2285

0

1

2

3

4

5

6

7

PBW343 HD4672 HI8498 HD2733 RAJ 3765 HD-2285

Simulated

Observed

Gra

in Y

ield

(t

ha

-1)

0

1

2

3

4

5

6

7

PBW343 HD4672 HI8498 HD2733 RAJ 3765 HD-2285

Simulated

Observed

0

2

4

6

8

10

12

14

16

PBW343 HD4672 HI8498 HD2733 RAJ 3765 HD-2285

Simulated

Observed

Bio

ma

ss

(t

ha

-1)

Sowing Date Retrieval from Remote Sensing

Sowing date: spectral emergence-7 days

Time series NDVI (25 Oct-15 Dec)

Wheat NDVI

AWiFS Wheat mask

State-wise wheat NDVI

ISODATA Classification

Plotting temporal NDVI of each class

3rd order polynomial curve fit

Spectral emergence (The Day with first positive change in NDVI which

is greater than the soil NDVI)

Sub-setting

8 Nov28 Nov8 DecNon-wheat

Grid LAI Generation

Real time LAI (56 m) Average grid LAI (5 km)

LAI Forcing in WOFOST model

Computing the correction factor

CF= observed LAI through remote sensing/Model derived LAI on RS observation date

0

2

4

6

8

10

12

0 20 40 60 80 100 120

WLV WST TAGP LAI

Days after emergence

WL

V, W

ST

,TA

GP

in k

g/h

a;

LA

I in

m2 /m

2W

LV

, WS

T,T

AG

P in

t/h

a; L

AI i

n m

2/m

2

Days after emergence

0

2

4

6

8

10

12

0 20 40 60 80 100 120

WLV WST TAGP LAI

Days after emergence

WL

V, W

ST

,TA

GP

in k

g/h

a;

LA

I in

m2 /m

2W

LV

, WS

T,T

AG

P in

t/h

a; L

AI i

n m

2/m

2

Days after emergence

Before forcing

After forcing

Date of forcing: 60 days after emergence

68 days after emergence

Spatial Wheat Yield for 2009-10 (5 km)

Input Data

Interpolated Weather Data Calibrated Crop Coefficient Sowing Date from Remote sensing LAI from Remote Sensing

RajasthanPunjab

< 2.52.5-3.53.5-4.5>4.5

Non-wheat Non wheat< 2 t/ha2-3 t/ha3-4 t/ha>4 t/ha

Exploring WARM (Water Accounting Rice model) for rice yield simulation

WARM Downloaded from: http://www.robertoconfalonieri.it/software_download.htm

WARM version 1.9.6

Data used for calibration

Daily weather data

Station latitude

Rain fall, Tmax, Tmin and solar radiation

Crop data

Date of sowing

GDDs to reach emergence

GDDs from emergence to flowering

GDDs from flowering to maturity

Periodical LAI (4 times)

Dry biomass at harvest and grain yield at harvest

Soil data

Bulk density

OC

Clay

Sand

Field capacity

PWP

KS

Variety: PR 118

Location: Punjab Agricultural Univ, Ludhiana, Punjab, India

Climate: Semiarid subtropic

Calibration Result

LA

I (m

2/m

2)

Validation Result

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

170 180 190 200 210 220 230 240 250 260 270

Simulated

Observed

LA

I (m

2/m

2)

DOY

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

biomass (t/ha) yield (t/ha)

Simulated

Observed

• N.B. Two days delay in flowering was observed, Harvesting date was same as observed

Converting Point WOFOST Model to Spatial Mode

WOFOST-exe

Spatial data for weather

Spatial data for crop

Spatial data for soil

Spatial data for sowing date Batch mode for all grid

Output for all grid

FORTRAN

Data Source

1. Real time Weather DataMaximum & Minimum TemperatureRainfallDaily Incoming Solar RadiationWind speedRelative humidity

IMD website (~80 station)IMD website (~80 station)Computed from temperature*Climatic normalClimatic normal

2. Soil DataSoil textureSoil moisture constantsHydraulic properties

FAO soil map (1: 5M)

3. Management DataPlanting/sowing dateIrrigation (Date & Amount)Fertilizer (Date & Amount)

Remote sensing (SPOT-VGT/INSAT-CCD)Not required for potential simulation

4. Crop data•Phenology•Physiology•Morphology

Derived for a major variety in each state through calibration

Input Data and Source

*Solar radiation

Where, Ah and Bh are the empirical constants and Ra is the extra terrestrial radiation (Duffie and Beckman,1980)

hhas BTTARR )( minmax (Hargreaves, 1985)

Crop Growth Simulation Model

Inputs Process Output

Weather (Temperature, Rainfall, solar radiation)

Soil Parameters (Texture, depth, soil moisture, soil fertility)

Crop Parameters (Phenology, physiology, morphology)

Management (DOS, irrigation, fertilizer)

Phenological Development

CO2 Assimilation

Transpiration

Respiration

Partitioning

Dry matter Format

Biomass, LAI, Yield

Water Use

Nitrogen Uptake

Choice of Simulation Models in FASAL

• The model needs to be sufficiently process based to simulate crop productivity over a range of environments, while being simple enough to avoid the need for large amounts location specific input data

• It should be possible to run the model spatially, in large number of grids.

• The user interface of the model should be simple enough for multi-disciplinary users.

• There needs to be a scope for assimilation of in-season remote sensing derived parameters.

• The source code should be open for any modification

WOFOST model has been chosen because of the availability of source code and relatively less input requirement