Chandan Kumar Jha 1 and Vijaya Gupta 2 1 Research Scholar, Economics, National Institute of Industrial Engineering (NITIE), Mumbai-400087, India email id: [email protected]2 Professor, Economics, National Institute of Industrial Engineering (NITIE), Mumbai-400087, India email id: [email protected]
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Chandan Kumar Jha and Vijaya Gupta - The ISEE · 2016-11-02 · Chandan Kumar Jha1 and Vijaya Gupta2 1 Research Scholar, Economics, National Institute of Industrial Engineering (NITIE),
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Chandan Kumar Jha1 and Vijaya Gupta2
1 Research Scholar, Economics, National Institute of Industrial Engineering (NITIE), Mumbai-400087, India
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