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CROPPING SYSTEM ANALYSIS
&
CLIMATE CHANGE IMPACT
DATA USE
•
IRS Advanced Wide Field Sensor
(AWiFS) data acquired from
October 2004 to May 2005
•
ScanSAR Narrow Beam-2 (SN2) of
RADARSAT
•
10-day composite (S10) NDVI
(Normalized Difference Vegetation
Index)
product
of
SPOT
Vegetation (VGT)
•
Ground truth collected for each
crop in each state
•
Survey of 1000 farmers in 100
villages
•
Soil and water sampling in these
villages
Multi-date Resourcesat AWiFS
06 SEP2004
13 NOV 2004
05 DEC 2004
14 JAN 2005
21 FEB 2005
01 May 2005
Multi-date Radarsat ScanSAR Narrow-2
(22Jun,16 Jul, 09 Aug)
10-DAY COMPOSITE NDVI PRODUCT OF
SPOT VGT
CROPPING SYSTEM INDICES
1. Multiple Cropping Index (MCI)
n
MCI 
 ai
i 1
A
 100
ai= area occupied by the ith crop planted and
harvested within a year
n = total number of crops
A= total cultivated land
area
2. Area Diversity Index (ADI)
ADI 
1
 ai


i 1 
n

ai 

i 1

n
2
3. Cultivated Land Utilisation Index (CLUI)
n
CLUI 
 ai.di
i 1
A  365
ai= area under each crop
n = number of crops in a
season
ai= area occupied by the i-th
crop
Di = days that i-th crop
occupied
n = total number of crops
A= total cultivated land area
CROPPING SYSTEM OF PUNJAB STATE
Crop Rotation Statistics
Rotation
% of Agricultural Area
Rice-Wheat
Cotton-Wheat
Rice-Others
Others-Wheat
Maize based
Sugarcane based
Triple Cropping
Other Rotations*
50.87
10.62
8.07
19.60
2.31
2.14
2.43
3.06
•
Cropping Intensity: 204%
•
Diversity
•
•
Kharif : 2.23
•
Rabi: 1.64
Land Utilsation Index: 0.80
Suggestions
•
Diversification both in Kharif
and Rabi
•
Increase cropping intensity
by adopting short-duration
summer legume crop
Bathinda
CROPPING SYSTEM BATHINDA DISTRICT
Crop Rotation Statistics
Rotation
% of Agricultural Area
Cotton-wheat
Rice-wheat
Rice-others
Cotton-others
Other-wheat
Other rotations
43.75
32.48
3.64
4.72
7.83
7.58
•
Cropping Intensity: 202%
•
Diversity
•
•
Kharif : 2.43
•
Rabi: 1.39
Land Utilsation Index: 0.78
Suggestions
•
Diversification both in Kharif
and Rabi
•
Alternative cropping pattern
to substitute rice
CROPPING SYSTEM OF HARYANA STATE
CROPPING SYSTEM OF UTTAR PARDESH
ROTATIONS
Rice-Wheat
Rice-Other crops
Rice-Fallow
Sugarcane-Sugarcane
Sugarcane-Other crops
Maize/Pmillet-Wheat
Maize/Pmillet -Other crops
Pulse-Pulse
Fodder/fallow-Wheat/others
Maize/Pmillet/Pulse-Fallow
Fallow-Pulse
Non-Agriculture
District Boundary
CROPPING SYSTEM OF WEST BENGAL
CROP ROTATION IN INDO-GANGETIC PLAINS
Rice-Wheat
Sugarcane Based
Cotton-Wheat
Rice-Potato
Maize-Wheat
Pearlmillet-Wheat
Rice-Fallow-Rice
Rice-Fallow-Fallow
Rice-Fallow-Jute
Rice-Wheat-Other
Fallow-Pulse
Fallow-Wheat
Minor Crop Rotations
Fallow
Non-Arable
Punjab
Haryana
Major Cropping Systems Area (% of NSA)
Rice-Wheat
42.76
Rice-Fallow-Fallow
9.59
Maize-Wheat
8.13
Sugarcane Based
7.00
Pearlmillet-Wheat
3.80
Fallow-Wheat
3.36
Rice-Fallow-Rice
1.91
Cotton-Wheat
1.89
Rice-Wheat- Other
1.87
Fallow-Pulse
1.83
Rice-Fallow-Jute
0.46
Rice-Potato
0.51
Minor Cropping Systems
15.03
Uttar Pradesh
Bihar
West Bengal
MAXIMUM NDVI (CROP VIGOUR) PATTERN
Kharif Season
Rabi Season
RICE PLANTING PATTERN MAP
Very Early (01 July)
Early (28 July)
Medium (17 Aug)
Late (10 Sept)
Non-Rice Area
WHEAT SOWING PATTERN MAP
Very Early
Early
Medium
Late
Non-Wheat Area
Map 19
Major crop Rotations and number of rotations
observed in agroclimatic subregions of IGP
through ground survey
Alternate Cropping Pattern Planning
Punjab
Rice-Wheat
Cot/Maz/Puls-Wheat
Maize-Sugarcane
Rice-Mustard
Cotton-Mustard
Groundnut/Maize
Bajra-Gram
Baj/Fod- Mustard
Vegetables
Agroforestry
Non-Agriculture
District Boundary
Major Road
Climate Change Impact Analysis
State of the Art : Indian Studies
Scenario
Model
Inputs
Crops
Findings
Assumed Temp. Rise, Double CO2, GCM Projection, RCM Projection
Statistical Models, Simulation Models, Spatial Mode
Temperature, Temp + CO2, Temp. +CO2+ Rainfall
Rice, Wheat, Soybean, Mustard
Yield Change, Phenolgy Change, Shift of Iso-yield-Lines, Adaptation
Approach
Objectives:
1.
Sensitivity Analysis : Temperature
and Crop Yield
2.
Cropping System Productivity Under
Future Climate Scenario
3.
Uncertainty in Impact Assessment
4.
Adaptation Study through Adjustment
in Sowing Date
Rice-Wheat
Sugarcane Based
Cotton-Wheat
Rice-Potato
Maize-Wheat
Pearlmillet-Wheat
Rice-Fallow-Rice
Rice-Fallow-Fallow
Rice-Fallow-Jute
Rice-Wheat-Other
Fallow-Pulse
Fallow-Wheat
Minor Crop Rotations
Fallow
Non-Arable
RS
Cropping
System Map
NBSS
&LUP
Soil Map
Field
Expt.
Crop
Parameters
Mitigation Measures
Current
Weather
RCM
Projections
CROPSYST
MODEL
Current
Productivity
Productivity
2020/2050/2080
Comparison
Vulnerability Analysis
Reduction in grain yield (%)
Sensitivity Analysis : Temperature
70
60
Wheat
Rice
Maize

Crop simulation Model used:
CropSyst (Stockle et.al., 1994)

Most sensitive crop: wheat
(around 66 % reduction with 50C
rise in daily temperature)

Least sensitive crop: Maize
(around 15 % with 50C rise in
daily temperature)
Pearl Millet
50
40
30
20
10
0
1
2
3
4
5
Rise in Temperature (0C)
No adaptation and no CO2 impact
Yield Decrease Shown by other Authors:
•
8-31% decrease in wheat yield with 1-30 Temp. Rise: Pandey et al., 2009
•
Increase in temperature by 0.5-2°C decreases grain yield by 8- 40% : Patil et al., 2009
•
Decrease in grain yield per degree rise in temp. ranges from 0.56 q/ha (UP) to 4.29 q/ha (Haryana): Kalra et al. 2008
Cropping System Productivity
under Future Climate Scenario
Location
Ludhiana :
Bhatinda:
Ballowal:
Rice-Wheat
Cotton-Wheat
Maize-Wheat
Yield (t ha-1)
Location
Patna :
Rice-Wheat
Santiniketan: Rice-Rice

Climate model: HadCM3 (A2)

Impact crop simulation model:
CropSyst

Weather parameters: Tmax,
Tmin and Rain fall

CO2 : 380 ppm at current
situation, 420 ppm at 2020, 480
ppm 2050 and 540 at 2080
Impact of Climate Change on Crop Yield
(Current vs. 2080)
• Study using yield response model and RCM projection for the
period 2071-2080 (A2 scenario)
• Yield reduction in wheat is maximum in Eastern Rajasthan
Cropping System Response (Yield reduction ,%) to
Climate change
2020






Climate model: HadCM3 (A2)
Impact Model: CropSyst
Weather parameters: Tmax, Tmin and
Rainfall
CO2 : 380 ppm at current situation, 420
ppm at 2020, 480 ppm 2050 and 540 at
2080
Major rotation under study: Rice-Wheat,
Maize-Wheat and Cotton- Wheat
Crop rotation map: RS Data
2050
>Current (-1.12%-0)
Other rotation or
non-agriculture
0-5 %
5-10%
10-15 %
15-20 %
20-25 %
25-30 %
30-40 %
40-50 %
50-62 %
2080
C-R Map
Punjab
R-W
M-W
C-W
Other
or NA
Uncertainty in the Impact Assessment
Change in System Productivity (%)
Due to climate model
Due to impact model
0
2020
2050
2080
-5
-10
-15
CGCM2
-20
HADCM3
-25
-30
Change in System productivity of RiceWheat cropping system under A2 scenario
projected by two climatic GCMs
Temperature sensitivity to rice yield predicted
by two crop simulation model
Findings:
• CGCM2 model predict more rise in maximum temperature and hence
the reduction in yield simulated for the CGCM2 was more than that
for the HADCM3 predicted climate scenario
• Crop yield predicted by InfoCrop model is less sensitive to
Adaptation Study through
Adjustment in Sowing Date (R-W System)
System Yield (Mg ha-1)
Scenario: HadCM3_A2
Scenario: HadCM3_B2
14
14
12
12
10
10
8
8
6
6
4
4
2
2
0
2020
0
2020
Actual_wth_NSD
2050
NSD
NSD-15
2050
2080
2080
NSD+7
NSD+15
NSD: Normal Sowing Date:
Actual_wth_NSD
NSD
NSD-15
NSD+7
NSD+15
Wheat (R-W and M-W): 15 November
Rice: 20 June, Maize: 20 July
Findings:
• 7 days delay in sowing in both rice and wheat may help to reduce the impact by 1.67% and 1.55
% in A2 and B2 scenarios, respectively during 2020.
• For 2050, 15 days delay in sowing under A2 scenario resulted in 6 % increase and 7 days delay in
sowing under B2 scenario resulted in 11 % increase.
• For 2080, 15 days delay in sowing resulted in maximum improvement in both A2 and B2 (9.27
and 6.48%, respectively) scenarios as compared to normal sowing date.
Future Studies
•
Impact of Extreme Climates
•
Understanding the vulnerability of Rainfed Agroecosystems
•
Mitigation: Soil Carbon Sequestration