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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
1001
A
aiMCI
n
i
n
i
n
i
ii aaADI
1
2
1
1
365
.1
A
diaiCLUI
n
i
1. Multiple Cropping Index (MCI)
2. Area Diversity Index (ADI)
3. Cultivated Land Utilisation Index (CLUI)
ai= area under each cropn = number of crops in a season
ai= area occupied by the i-th crop planted and harvested within a yearn = total number of cropsA= total cultivated land area
ai= area occupied by the i-th crop Di = days that i-th crop occupiedn = total number of cropsA= total cultivated land area
CROPPING SYSTEM INDICES
Bathinda
Rotation % of Agricultural Area
Rice-Wheat 50.87 Cotton-Wheat 10.62 Rice-Others 8.07 Others-Wheat 19.60 Maize based 2.31 Sugarcane based 2.14 Triple Cropping 2.43 Other Rotations* 3.06
Crop Rotation Statistics
• 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
CROPPING SYSTEM OF PUNJAB STATE
CROPPING SYSTEM BATHINDA DISTRICTCrop Rotation Statistics
• 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
Rotation % of Agricultural Area
Cotton-wheat 43.75 Rice-wheat 32.48 Rice-others 3.64 Cotton-others 4.72 Other-wheat 7.83 Other rotations 7.58
CROPPING SYSTEM OF HARYANA STATE
Rice-Wheat
Rice-Other crops
Sugarcane-Sugarcane
Maize/Pmillet-Wheat
Maize/Pmillet -Other crops
ROTATIONS
Maize/Pmillet/Pulse-Fallow
Pulse-PulseFodder/fallow-Wheat/others
Fallow-Pulse
Rice-Fallow
Sugarcane-Other crops
Non-AgricultureDistrict Boundary
CROPPING SYSTEM OF UTTAR PARDESH
CROPPING SYSTEM OF WEST BENGAL
Rice-WheatSugarcane BasedCotton-WheatRice-PotatoMaize-WheatPearlmillet-WheatRice-Fallow-RiceRice-Fallow-FallowRice-Fallow-JuteRice-Wheat-OtherFallow-PulseFallow-WheatMinor Crop RotationsFallowNon-Arable
Punjab
Haryana
Uttar Pradesh
Bihar
West Bengal
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
CROP ROTATION IN INDO-GANGETIC PLAINS
Kharif Season Rabi Season
MAXIMUM NDVI (CROP VIGOUR) PATTERN
RICE PLANTING PATTERN MAP
Very Early (01 July)Early (28 July)Medium (17 Aug)Late (10 Sept)Non-Rice Area
Very EarlyEarlyMediumLateNon-Wheat Area
Map 19
WHEAT SOWING PATTERN MAP
Major crop Rotations and number of rotations observed in agroclimatic
subregions of IGP through ground survey
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
Punjab
Alternate Cropping Pattern Planning
Climate Change Impact AnalysisClimate Change Impact Analysis
Scenario
Model
InputsTemperature, Temp + CO2, Temp. +CO2+ Rainfall
Assumed Temp. Rise, Double CO2, GCM Projection, RCM Projection
Statistical Models, Simulation Models, Spatial Mode
Crops Rice, Wheat, Soybean, Mustard
FindingsYield Change, Phenolgy Change, Shift of Iso-yield-Lines, Adaptation
State of the Art : Indian Studies
Rice-WheatSugarcane BasedCotton-WheatRice-PotatoMaize-WheatPearlmillet-WheatRice-Fallow-RiceRice-Fallow-FallowRice-Fallow-JuteRice-Wheat-OtherFallow-PulseFallow-WheatMinor Crop RotationsFallowNon-Arable
Cropping System Map
Soil Map
Crop Parameters
Current Weather
RCM Projections
CROPSYSTMODEL
Productivity2020/2050/2080
CurrentProductivity
Comparison
Mitigation Measures
RS
FieldExpt.
NBSS&LUP
Vulnerability Analysis
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
Objectives:
ApproachApproach
Sensitivity Analysis : TemperatureSensitivity Analysis : Temperature
0
10
20
30
40
50
60
70
1 2 3 4 5
Wheat Rice Maize Pearl Millet
Red
uct
ion
in g
rain
yie
ld (%
)
Rise in Temperature (0C)
0
10
20
30
40
50
60
70
1 2 3 4 5
Wheat Rice Maize Pearl Millet
Red
uct
ion
in g
rain
yie
ld (%
)
Rise in Temperature (0C)
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)
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
No adaptation and no CO2 impact
Cropping System Productivity Cropping System Productivity under Future Climate Scenariounder Future Climate Scenario
LocationLudhiana : Rice-WheatBhatinda: Cotton-Wheat Ballowal: Maize-Wheat
LocationPatna : Rice-WheatSantiniketan: Rice-Rice
Yie
ld (
t h
a-1)
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
(Current vs. 2080)(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• Reduction in rice yield is maximum in Haryana followed by
Punjab.
Impact of Climate Change on Crop YieldImpact of Climate Change on Crop Yield
Cropping System Response (Yield reduction ,%) to Cropping System Response (Yield reduction ,%) to Climate changeClimate change
2020 2050 2080
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
>Current (-1.12%-0)Other rotation or non-agriculture0-5 %5-10%10-15 %15-20 %20-25 %25-30 %30-40 %40-50 %50-62 %
R-WM-WC-WOther or NA
C-R Map Punjab
Uncertainty in the Impact AssessmentUncertainty in the Impact Assessment
Due to climate model Due to impact model
-30
-25
-20
-15
-10
-5
0
2020 2050 2080
CGCM2
HADCM3
Ch
an
ge
in S
ys
tem
Pro
du
cti
vit
y (
%)
Change in System productivity of Rice-Wheat cropping system under A2 scenario projected by two climatic GCMs
• 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 temperature as compared to CropSyst
Temperature sensitivity to rice yield predicted by two crop simulation model
Findings:
Adaptation Study through Adaptation Study through Adjustment in Sowing Date (R-W System)Adjustment in Sowing Date (R-W System)
NSD: Normal Sowing Date: Wheat (R-W and M-W): 15 NovemberRice: 20 June, Maize: 20 July
Sys
tem
Yie
ld (
Mg
ha-1
)
Scenario: HadCM3_A2 Scenario: HadCM3_B2
0
2
4
6
8
10
12
14
2020 2050 2080
Actual_wth_NSD NSD NSD-15 NSD+7 NSD+15
0
2
4
6
8
10
12
14
2020 2050 2080
Actual_wth_NSD NSD NSD-15 NSD+7 NSD+15
• 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.
Findings:
Future StudiesFuture Studies
• Impact of Extreme Climates
• Understanding the vulnerability of Rainfed Agro-ecosystems
• Mitigation: Soil Carbon Sequestration