Crop Yield Modeling through Spatial Simulation Model
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Transcript of 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
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