Transcript Crop Yield Modeling through Spatial Simulation Model.

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
Yield Prediction Through Simulation
Weather
surface
Crop Parameters
LAI Map
Phenology map
Crop Simulation
Model
Soil Map
Soil Classes
Sandy Loam
Clay
Sandy Clay Loam
Clay Loam
Loam
Wheat
Yield
Map
Yield
map
Sand
Water
Simulated
Grid
Yield
Crop Yield
Map
Wheat area
Area Weighted Yield
Spatial Data Generation
Weather
Soil Types in India as per FAO soil map
Soil Classes
Sandy Loam
Clay
Sandy Clay Loam
Clay Loam
Loam
Sand
Water
Grain Yield (t ha -1)
Generation of Calibrated Crop Coefficient
7
Simulated
6
Observed
5
7 4
Simulated
6 3
Observed
Name of the state Calibrated Variety
5 2
Bihar
4 1
3 0
PBW343
2
HD4672
HI8498
HD2733 RAJ 3765 HD-2285
1
and PBW343
Punjab
0
PBW343
Biomass (t ha-1)
Haryana
HD2733
HD4672
HI8498
HD2733 RAJ 3765 HD-2285
16
Simulated
14
Observed
12
10
8
6
4
2
0
PBW343
HD4672
HI8498
HD2733 RAJ 3765 HD-2285
MP
Malvasakti (HI8498)
Rajasthan
Raj3765
UP
HD2285
Sowing Date Retrieval from Remote Sensing
Time series NDVI (25 Oct-15 Dec)
AWiFS Wheat mask
Wheat NDVI
2008-09
Sub-setting
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)
Sowing date: spectral emergence-7 days
8 Nov
28 Nov
8 Dec
Non-wheat
Grid LAI Generation
Real time LAI (56 m)
Average grid LAI (5 km)
LAI Forcing in WOFOST model
Computing the correction factor
12
2
WLV, WST,TAGP in t/ha; LAI in m /m
2
CF= observed LAI through remote sensing/Model derived LAI on RS
observation date
WLV
WST
TAGP
LAI
Date of forcing: 60
68days
daysafter
afteremergence
emergence
10
After forcing
8
6
Before forcing
4
2
0
0
20
40
60
80
Days after emergence
100
120
Spatial Wheat Yield for 2009-10 (5 km)
Rajasthan
Punjab
Non-wheat
< 2.5
2.5-3.5
3.5-4.5
>4.5
Input Data




Interpolated Weather Data
Calibrated Crop Coefficient
Sowing Date from Remote sensing
LAI from Remote Sensing
Non wheat
< 2 t/ha
2-3 t/ha
3-4 t/ha
>4 t/ha
Exploring WARM (Water Accounting Rice model) for rice yield
simulation
WARM version 1.9.6
WARM Downloaded from:
http://www.robertoconfalonieri.it/software_download.htm
Data used for calibration
Variety: PR 118
Daily weather data
Location: Punjab Agricultural Univ,
Ludhiana, Punjab, India
Station latitude
Rain fall, Tmax, Tmin and solar radiation
Climate: Semiarid subtropic
Crop data
Soil data
Date of sowing
Bulk density
GDDs to reach emergence
OC
GDDs from emergence to flowering
Clay
GDDs from flowering to maturity
Sand
Periodical LAI (4 times)
Field capacity
Dry biomass at harvest and grain yield at
harvest
PWP
KS
Calibration Result
18.00
Simulated
Observed
16.00
6.00
5.50
14.00
LAI (m2/m2)
5.00
12.00
10.00
8.00
6.00
4.50
4.00
3.50
3.00
2.50
2.00
4.00
1.50
2.00
1.00
observed
simulated
0.50
0.00
biomass (t/ha)
0.00
170
yield (t/ha)
180
190
200
210
220
230
240
250
260
270
DOY
Validation Result
7.00
25.00
Simulated
Observed
LAI (m2/m2)
20.00
6.00
15.00
10.00
5.00
4.00
3.00
2.00
5.00
0.00
Biomass (t/ha)
•
Simulated
Observed
1.00
Yield (t/ha)
0.00
170
180
190
200
210
220
230
240
250
DOY
N.B. Two days delay in flowering was observed, Harvesting date was same as observed
260
270
Converting Point WOFOST Model to Spatial Mode
Spatial data for weather
Spatial data for crop
WOFOST-exe
Spatial data for soil
Spatial data for sowing date
Batch mode for all grid
Output for all grid
FORTRAN
Input Data and Source
Data
1. Real time Weather Data
Maximum & Minimum Temperature
Rainfall
Daily Incoming Solar Radiation
Wind speed
Relative humidity
2. Soil Data
Soil texture
Soil moisture constants
Hydraulic properties
Source
IMD website (~80 station)
IMD website (~80 station)
Computed from temperature*
Climatic normal
Climatic normal
FAO soil map (1: 5M)
3. Management Data
Planting/sowing date
Irrigation (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
*Solar radiation Rs  Ra Ah ( Tmax  Tmin )  Bh (Hargreaves, 1985)
Where, Ah and Bh are the empirical constants and Ra is the extra terrestrial radiation (Duffie and
Beckman,1980)
Crop Growth Simulation Model
Inputs
Process
Output
Weather (Temperature,
Rainfall, solar radiation)
Phenological Development Biomass, LAI, Yield
CO2 Assimilation
Water Use
Soil Parameters (Texture,
depth, soil moisture, soil
fertility)
Transpiration
Nitrogen Uptake
Crop Parameters
(Phenology, physiology,
morphology)
Partitioning
Management (DOS,
irrigation, fertilizer)
Respiration
Dry matter Format
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
remote sensing derived parameters.
•
The source code should be open for any modification
in-season
WOFOST model has been chosen because of the availability of
source code and relatively less input requirement