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