Chandan Kumar Jha1 and Vijaya Gupta2

1 Research Scholar, Economics, National Institute of Industrial Engineering (NITIE), Mumbai-400087, India

email id: chandan1929@gmail.com

2 Professor, Economics, National Institute of Industrial Engineering (NITIE), Mumbai-400087, India

email id: vijaya298@gmail.com

Climate change presently a cause of global unrest for humanity, repercussion of which is likely to follow our futuregenerations.

Climatic factors i.e. temperature, precipitation, atmospheric CO2 concentration, humidity, light, wind determines-

Crop yield (mean crop yield, year-to-year crop yield variability)

Livestock (effects on forage, feed, water availability for livestock, livestock diseases)

Soil quality (water retention capacity)

Fluctuations or departure of temperature, precipitation from normal pose serious threat to agricultural production further raisingcomplex socio-economic challenges especially for developing and less developed countries

Food insecurity

Malnutrition

Rural poverty and vulnerability

Rise in temperature of 2°C or more in tropical and temperate regions will negatively impact production especially wheat, rice,and maize without adaptation

Extent varies at temporal and spatial scales

By crops varietiese.g. rice, wheat, maize, oilseeds etc. and crop variants.

Increase/decrease in rainfall and temperature may have different impacts

Climate extremes leave the most unpredictable and devastating impact

Despite of rise in temperature by roughly 0.13°C per decade since 1950 (IPCC 2007), impact on agriculture is not wellunderstood (Lobell and Field 2007).

Global impact:

• Lobell et al. (2011)- Global yield impact

for 1980-2008 of

• temperature trends: -4.9%

• rainfall trends: -0.6%

• Impact on wheat prices:

• +11% (Kaiser,1991),

• -2.5% to -10% (Darwin et al. 1995)

• Rainfed and irrigated wheat likely to be

affected differently in different regions

Adaptation of food system and value

chains:

Wheat & rice based cropping pattern

more adaptable than other system (IPCC,

2014)

Adaptation improves yield by 15-18% of

current yields (IPCC, 2014)

Adaptation involves local coping

practices to reduce vulnerability

Objective of study

Analyze climate change impact on wheat production and how adaptation helps

Assess how socio-economic factors of farm households determine adaptation behaviour of

Indian farmers

Study region

Bihar state in eastern India with geographical area of 93.6lakh ha.

Population – 103.8 million 3rd most populated state ofIndia

Contribution of agriculture & allied activities to GSDP atconstant prices (2004-05) is 18.1% for period 2012-13 to2014-15.

90% of rural population dependent on agriculture

Average rainfall: 1013 mm

Net sown area: 57.7% of GA

Cropping pattern: Food grains- 93.25% of GA, Cereals-86.16%of GA

Zones Avg. Rainfall (mm) Temperature

(ᴼC) (min.-

max.)

Selected sample district

Agro- climatic zone

I (Northern West)

1245 7.7-36.6 East Champaran,

Madhubani, Darbhanga

Agro-climatic Zone

II (Northern East)

1450 8.8-33.8

Saharsa, Supaul

Agro-climatic zone

III (Southern East

& West)

1115 7.8-37.1

Gaya, Buxar

November December January February March April

Rainfall 6.4 6 11.5 9.9 12 23.8

Minimum Temp. (*C) 15.2 10.3 9.3 11.4 15.9 21.3

Maximum Temp. (*C) 29 24.9 23.5 26.3 32.1 36.6

05

10152025303540

Rainfall and Temperature condition in Rabi Season (1952-2010)

Ln(Yield) (Yield)

Temp .013***

(.000899)

.021***

(.00115)

.0789*

(.0412)

.0696

(.0526)

Temp2 -.000131***

(.0000206)

-.00658***

(.0000256)

-.00101

(.000942)

-.00184

(.00117)

Precip .00215***

(.0000201)

-.0000686

(.000211)

.00175*

(.000919)

-.0191**

(.00969)

Precip2

-.0000276***

(3.13e-07)

-5.38e-06***

(1.43e-06)

-.0000328**

(.0000143)

.00015**

(.0000656)

Temp*

Precip

-.000705***

(.0000153)

-.00082

(.0007)

Temp2

*Preci

p

.0000352***

(3.37e-07)

.0000789**

*

(.0000155)

Temp*

Precip2

-1.30e-07*

(7.07e-08)

-7.21e-06**

R2 0.996 0.996 0.978 0.978

Source: Moorthy et al. 2012

India

Overall temperature increase of 2-40C by 2100

(Kavikumar, 2009)

Seasonal temperature changes (Khan et al., 2009):

Rabi: 1.1–4.5ᴼC and Kharif: 0.4–2.0ᴼC

Every 1ᴼC rise in regional mean temperature-loss in

wheat yield of 6.6 million tonnes (Aggarwal et al.,

2009)

Decline in rainfed wheat yield in South Asia by 44%

by 2050 due to regional climate change (Nelson et

al., 2009)

Oritz et al. (2008) climate effects in the Indo-

Gangetic Plains (region of high yield potential) can

turn into heat-stressed region due to climate effects

by 2050

Lal et al. (1998) acute water shortage along with

thermal stress negatively affect wheat in North

West India even under +ve effects of elevated CO2

The model is based on the work of Nhemachena and Rashid Hassan (2007), Deressa et al. (2008),

Deressa, T. (2009), Kurukulasuriya (2008) and Falco, S.D. and Veronesi, M. (2013).

In the first stage, using a selection model for climate change adaptation where a representative risk

adverse farm household chooses to implement climate change adaptation strategies if it generates net

benefits.

Let A* be the latent variable that captures the expected benefits from the adaptation choice with

respect to not adapting. This model specify the latent variable as

𝐴𝑖∗= 𝑍𝑖𝛼 + 𝜂𝑖with ቊ

1 𝑖𝑓 𝐴𝑖∗

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

That is farm household i will chose to adapt ( through the implementation of some strategies in

response to long term change in mean temperature and rainfall, if 0 otherwise.

To account for selection biases this model adopted an endogenous switching regression

model of food productivity where farmers face two regimes (1) to adapt, and (2) not to

adapt defined as follows:

Regime 1 𝛾1𝑖 = 𝑋1𝑖𝛽1 + 𝜀1𝑖 𝐴𝑖 = 1 …..(1)

Regime 2 𝛾2𝑖 = 𝑋1𝑖𝛽2 + 𝜀2𝑖 𝐴𝑖= 0 ….(2)

Where is the quality of produce per hectare in regime 1 and regime 2, and represents a

vector of inputs (e.g. seeds, fertilizers, manures, labor), and of the farmers head’s and

the farm household’s characteristic, assets, and the climatic factors include in Z.

Analysis based on recorded responses of farmers/farm households during survey (questionnaire based).

Sample collected- 700, Sample used- 500

Key variables for analyzing farmers’ perception and adaptation strategies for Rabi season:

1. Perceptions on climate change- farmers were asked if they observed any change in temperature or rainfall over the past

20 years.

number of hot or rainy days had increased, decreased, or stayed the same over the past 20 years

Scale the magnitude of impact of extreme climate events (flood, drought, cyclone), hotness and coldness on

agricultural productivity in last 20 years. scale 1 to 5 (1= No impact, 2= low, 3= medium, 4= high, 5= very high).

Farmers perception on sensitivity of crops planted

2. Socio-economic factors- farmer’s age, gender of farmer, gender HOF, hh size, education of farmer, primary and

secondary occupation, farm size,

3. Farm level adaptation strategies: change land under cultivation, change crop variety, water conservation, soil

conservation, increase irrigation, plant vegetables, horticulture, lease land, switch from farm-to non-farm, urban

migration, buy insurance.

4. Barriers to adaptation- Changes are expensive, lack of markets, limited access to water, lack of quality seed, lack of

info on short and long term climate change, credit access

5. Access to extension services- credit (banks, informal money-lenders, co-operative societies) and climate information

(info from IMD, Mobile, Radio, TV, random prediction, views of HOF, farmer to farmer, weather info from govt., own

prediction)

Crop Sensitivity-% of respondents Experience on climate change (%

respondents)

38.6

30.6

21.8

9

Rice Wheat Kharif pulses Rabi pulses

500

00

100

200

300

400

500

600

Yes No

Experienced in Change in

Temperature(past 10-20

years)

Experienced in Change in

Rainfall(past 10-20 years)

500

312

496

188

40

100

200

300

400

500

600

No. of hot days No. of cold days changes in rainfall change

Perceptions on rainfall and temperature changes

Increased Decreased

257

74102

52

15

53

125 104

95123

176

39

105

87 93

191

18 17

138 136

0

50

100

150

200

250

300

1 2 3 4 5

Flood

2010-15 2005-10 2000-05 1995-2000

99.6

99.8

45.6

93.2

24.4

14.2

21.8

47.6

7

54.8

0 20 40 60 80 100 120

Own prediction current year climatecondition

Own prediction previous year climatecondition

Weather info from govt. department

Discussion with farmer group

Views of HOF

Random prediction

TV

Radio

Mobile

Information from IMD

10096.8

23.425.4

62.6

2.2

48.8

0

20

40

60

80

100

120

Ownexperience

Consultother

farmers

Consultagricultural

officers

TV Radio Mobile Newspaper

1.Enhance awareness

2.Removes asymmetric

information

)

97.8

25.4

91.4

95.4

0

20

40

60

80

100

120

Banks Money lenders Cooperativesocities

Problem inavailaing loans

Eases burden of adaptation cost

99.4

77.2 76

84.6

39.4

81.8

65.4

86.8

0

20

40

60

80

100

120Identifying barriers helps in

designing well targeted

adaptation networks

Adaptation choices identified based

on detailed questionnaire survey in

two specific farming seasons i.e.

Kharif (summer season) and Rabi

(winter season).

Farmers were enquired upon their

choice of adaptation strategies

among 11 different alternatives for-

two separate cases i.e. changes in

temperature and rainfall

two different seasons i.e. Kharif and

Rabi

How socio-economic factors affects

choice of adaptation strategies of

farmers assessed by MNL model.

Focus on total number of strategies

adopted by farmers in Rabi season

74.4

51

91

39.4

20

16

8.4

10.8

31.4

73.6

51.8

89.6

37.8

20.8

43.6

35.4

16.4

8.2

11.4

30.8

0 20 40 60 80 100

Crop variety

Change land

Increase irrigation

Soil conservation

Water conservtion

Buy insurance

Migrate to urban area

Switch from farm to non-

farm

Lease land

Horticulture

Plant vegetables

Rainfall Temperature

Socio Economics characteristic of farm

household

Farmers perception to climate change

Climate information services

Agricultural Extension Services

Credit availability

Farmer’s adaptation to climate change is the final outcome of their complete

decision making process which is based on number of factors such as

Number of adaptation strategies opted by respondents Rabi (%)

8

5.2

1413.4

14

15.4

109.6

54.4

0.4 0.6

0

2

4

6

8

10

12

14

16

18

•Farmers adopting none of the

adaptation strategy is consider

‘non-adapting farmers’

•Farmers adopting any strategy

(ranging from 1-11) considered

as adapting farmers

•Basic adaptation rule followed

by Indian farmers-

maximization of net revenue

constrained by

socio-economic

characteristics

access extension services

and credit

Iteration 14: log likelihood = -894.88677 (RNOST 0- base outcome)

Number of obs = 498

LR chi2(99) = 462.73

Prob > chi2 = 0.0000

Log likelihood = -894.88677 Pseudo R2 = 0.2054

Explanator

y variables

RNOST1 RNOST2 RNOST3 RNOST4 RNOST5 RNOST6 RNOST7 RNOST8 RNOST9 RNOST10 RNOST11

Age -0.0372

(-0.87)

-0.028

(-0.76)

-0.083

(-2.27)**

-0.035

(-1.02)

-0.045

(-1.27)

-0.071

(-1.87)*

-.0681

(-1.83)**

-0.074

(-1.78)*

-0.038

(-0.82)

-0.301

(-1.40)

0.023

(0.33)

Male -1.455

(-0.95)

16.624

(0.00)

-1.997

(-1.57)

-1.868

(-1.43)

15.526

(0.00)

15.046

(0.00)

13.434

(0.00)

12.534

(0.00)

4.684

(0.01)

-3.983

(-0.00)

6.402304

(0.00)

Head of

family

being

male

0.496

(0.42)

0.642

(0.62)

1.058

(1.12)

1.285

(1.32)

0.283

(0.29)

0.366

(0.37)

0.901

(0.90)

1.539

(1.31)

0.243

(0.20)

16.269

(0.01)

13.93923

(0.01)

Education -0.016

(-0.24)

-0.166

(-2.97)***

-0.117

(-2.14)**

.0159

(0.32)

0.053

(1.01)

0.164

(2.65)***

0.229

(3.38)***

0.295

(3.30)***

0.569

(4.14)***

0.890

(0.97)

0.451

(1.74)*

Household

size

0.0139

(0.12)

0.070

(0.79)

0.068

(0.76)

0.012

(0. 4)

-0.107

(-1.12)

.081

(0.82)

0.179

(1.88)*

0.097

(0.90)

0.140

(1.11)

-0.034

(-0.10)

0.136

(0.57)

Livestock 0.584

(2.57)***

0.374

(1.90)*

0.439

(2.17)**

0.533

(2.71)***

.506

(2.55)**

0.521

(2.45)**

0.237

(1.06)

0.402

(1.68)*

0.877

(3.58)***

1.413

(2.24)**

0.244

(0.50)

Land

holding

1.264

(1.89)*

1.291

(2.06)**

0.778

(1.20)

0.884

(1.41)

1.716

(2.85)***

1.933

(3.15)***

2.135

(3.46)***

2.338

(3.72)***

1.712

(2.59)***

2.529

(1.75)*

2.609

(2.93)***

Owner

type

-0.728

(-1.00)

0.607

(0.88)

-0.035

(-0.05)

.0184

(0.03)

0.824

(1.17)

0.377

(0.49)

1.758

(1.49)

1.055

(0.88)

12.853

(0.02)

8.497

(0.01)

12.731

(0.01)

Farm

distance

0.761

(2.32)**

0.147

(0.48)

0.212

(0.68)

.184

(0.60)

0.505

(1.68)*

-0.041

(-0.12)

-0.234

(-0.68)

0.023

(0.07)

0.298

( 0.83)

0.477

(0.46)

-0.369

(-0.54)

Constant -0.118

(-0.05)

-17.351

(-0.00)

4.271

(2.14)

1.219

(0.63)

-16.024

(-0.00)

-16.144

(-0.00)

-17.416

(-0.00)

-17.863

(-0.00)

-27.546

(-0.02)

-28.941

(-0.01)

-44.618

(-0.01)

Note: *** significant at 1% level, ** significant at 5% level, *significant at 10% level

Figures in the table gives the co-efficient and figures in brackets show the respective z-Scores of the co-efficient

RNOST0 is chosen as the base outcome the results of the model is in comparison to the base

outcome i.e. no adaptation.

Age- Age of the farmer negatively affect the likelihood of adopting adaptation strategies

(RNOST3, RNOST6, RNOST7 and RNOST8).

Age of the farmer is indicative of farming experience

Aged farmers are more risk averse and are reluctant to adopt new farming practices or farm

technology.

Gender of the farmer being male- No significant impact of gender of respondent/farmers on

adoption of adaptation strategies found in Rabi season.

Head of the family being male- no significant impact

Education level- Education level has two separate impacts on adaptation in Rabi season.

Negative effect on likelihood of adopting (RNOST2 and RNOST3)

Positively affect the likelihood of adopting higher number of adaptation strategies (RNOST6, RNOST7, RNOST8

and RNOST9).

Implying educating farmers can facilitate adaptation

Household size- household size turns out to positively affect the likelihood for RNOST7 for Rabi season.

Large household size ensures supply of family labour

Climate change

Change in

average

temperature

Change in

average

rainfall

Inter-annual

variations in

temp./rainfall

Occurrence of

climate extremes

i.e. floods/droughts

Observed over long-run and

farmers find them less convincing

and unreliable

Observed over short-term, is more erratic and

farmers are more concerned for such changes

in climate attributes

Farmer’s perception on

climate change

Necessary but not sufficient

to adapt

Personnel

experiences

Information from external

sources (climate extension

services)

Farmers start considering

adaptation

Incentive to adapt Attitude towards risk

Ability to

adapt

Most important for developing countries, varies

across regions, farms and farm households

Farm characteristics Socio economic

characteristics of farm

households

Age of farmer, gender of household head,

household size, education level of farmer, off-

farm sourced of income, access to credit and

extension services

Farm size, tenure rights, soil

characteristics, distance of farm from

market and farmer’s house, access to

irrigation facility

Choice of adaptation strategies

Social, technological

and financial

frontiers

Livestock- Livestock turnsout to be an importantvariable affecting thelikelihood of adoptingdifferent adaptationstrategies.

Land holding size-positively affects thelikelihood of adopting morethan one adaptationstrategies as it facilitatesbetter farm management.

Owner type- Ownershiptype does not appear to be asignificant variable for Rabiseason.

Distances of farm- Farmdistance positively affectsthe likelihood of adoptingRNOST1 and RNOST5strategies.

Cost item % of per ha

operational cost

How socio-economic factors can affect operating cost?

Total Human Labour 32

Farmers with large household size can get cheap family

labor and can go away with casual and attached labour

Casual 15

Attached 0

Family 17

Total animal Labour 2 Livestock ownership can reduce the hiring cost of animal

labour and also farmers can also generate additional income

through milk, or selling of livestock when required Hired 0

Owned2

Total Machine Labour 21 Education, Aged farmers is usually more reluctant in

investing on new machinery & therefore prefer to hire,

Credit access facilitates ownership of machineryHired 21

Owned0

Seed 11 Extension services, credit- help in better management of

agricultural inputs

Large farm size-better operation, generate economies of

scale

Education

Total Fertilisers and

Manure 18

Fertilisers 18

Manure 0

Total Other Inputs total16

Education , large farm size may though require more water

but irrigation system can be well managed and applied

Irrigation Charges 13

Interest on Working

Capital

3

Cost data Source: Department of Agriculture and Cooperation, Ministry of Agriculture, Govt. of India

Wheat productivity in Bihar is likely to remain sensitive to climate change.

Micro level analysis of farmer’s adaptation is necessary to understand how farmers view

climate change and how they adjust their farm practices.

Farm-level decisions in India taken over short-run and majorly affected by inter annual or

seasonal variations in climate.

Primary objective to adapt for Indian farmers-

minimize risks from crop failure

maintain sustained flow of income throughout the year

consumption smoothening

Basic adaptation rule followed by Indian farmers-

maximization of net revenue constrained by

socio-economic characteristics

access extension services and credit

Main Socio-economic factors- age, education, household size, farm size, livestock

ownership are crucial for adaptation of wheat system.

Main Adaptation strategies adopted in Rabi season- increase irrigation, change crop

variety and area under cultivation for adaptation in Rabi season

Main Barriers- perceive changes in farm management as expensive, lack on

knowledge on LR and SR climate change/variations and profitable ways to adapt, lack

of credit

Credit- Most farmers take loan from banks and also face challenge in availing loans

Climate information & extension services- Most farmers rely on their own prediction

based on current and previous year weather experience and also discuss with fellow

farmers.

Radio and news papers are also useful source of information

TV, mobiles, and information from government sources have less influence

Therefore, for enabling adaptation government must focus on-

Providing timely and easy reach of information on climate before planting period

Ease credit environment and bring crops under insurance

Design well targeted extension services across the value chain

Thank you