climate role in crop productivity in salt affected soil

1. Role of climate in
crop productivity
in salt affected
soils
Bhaskar Narjary
Division of Irrigation and Drainage Engineering
ICAR- Central Soil Salinity Research Institute
E mail: bhaskar@cssri.ernet.in

2. WEATHER
AND
CLIMATE
Are Weather and Climate the
Same?
Weather is defined as the state of the
atmosphere at some place and time,
usually expressed in terms of
temperature, air pressure, humidity,
wind speed and direction,
precipitation, and cloudiness.
Meteorologists study weather.
Climate is defined in terms of the
average (mean) of weather elements
(such as temperature and
precipitation) over a specified period
of time (30 years according to the
World Meteorological Organization).

3. Weather Impacts on Agriculture
 Rainfall drives water availability and determines Sowing time (rain fed
crops)
 Temperature drives crop growth, duration; influences milk production in
animals and spawning in fish
 Temperature , RH influence pest and diseases incidence on crops,
livestock and poultry
 Radiation influences the photosynthetic productivity
 Wet & dry spells cause significant impact on standing crops, physiology,
loss of economic products (eg. fruit drop)
 Extreme events (eg. high rainfall/floods/heat wave/cold wave/cyclone
/hail/frost) cause enormous losses of standing crops, live stock and
fisheries

4. All plants have maximum, optimum and minimum temperature limits. The limits
are cardinal temperature points. Optimum temperature range is very important.
Wheat
3 – 4°C minimum
25° optimum
30° - 32°C maximum
Rice
10-12°C minimum
30-32°C optimum
36-38°C maximum
Significance of temperature
Low temperature:
Low temperature affects several aspects of crop growth viz., survival, cell division,
photosynthesis, water transport, growth and finally yield.
Chilling
injury
If the plants grown in hot temperature are exposed to low
temperature, they will be killed (or) severely injured. When the night
temperature is below 15°C field crops may show yellowing symptoms
(eg) Tropical annuals.
Freezing
injury
When the plants are exposed to extreme low temperature, water
freezes into ice crystals in the intercellular spaces.
(eg) Cell dehydration Temperate crops (potato, tea etc.,)

5. Significance of temperature
 Higher T causes faster crop development and thus shorter crop
duration, which in most cases is associated with lower yields
 T impacts the rates of photosynthesis, respiration, and grain filling
Warming leads to an exponential increase in the saturation vapor
pressure of air. Increased VPD leads to reduced water-use efficiency,
because plants lose more water per unit of carbon gain. Plants respond to
very high VPD by closing their stomata’s, but at the cost of reduced
photosynthesis rates and an increase in canopy T, which in turn may
increase heat-related impacts. (Ray et al., 2002).
warming increases the likelihood of heat stress during the critical
reproductive period, which can lead to sterility, lower yields, and the risk
of complete crop failure

6. HIGH TEMPERATURE INJURIES
High temperature adversely affects mineral nutrition, shoot growth and pollen
development resulting in low yield.
Mineral Nutrition
• High temperature stress causes reduction in absorption and subsequent
assimilation of nutrients.
• Absorption of calcium is reduced at temperature of 28º C in Maize.
• Nutrient uptake is affected by both soil and air temperature in rice.
• Nitrate reductase activity decrease under high temperature.
Shoot growth
• High temperature, even for short period, affects crop growth especially
in temperate crops like wheat.
• High air temperature reduces the growth of shoots and in turn reduces
root growth.
• High soil temperature is more crucial as damage to the roots is severe
resulting in substantial reduction in shoot growth.
• High temperature at 38º C in rice reduced plant height, root elongation
and smaller roots.
Significance of temperature

7. Significance of Moisture
 Crop plants will respond to reduced soil moisture by closing their stomata and
slowing carbon uptake to avoid water stress, thereby raising canopy T and
potentially increasing heat-related impacts.
More intense rainfall events may lead to flooding and waterlogged soils, also
pathways for damaged crop production.

8. Significance of CO2
 Rising atmospheric CO2 concentrations provide some counteracting tendencies to
the otherwise negative impacts of rising T and reduced soil moisture.
Higher CO2 has a fertilization effect in C3 species such as wheat, rice, and most fruit
and vegetable crops, given that photo respiratory costs in the C3 photosynthesis
pathway are alleviated by higher CO2
Impact of atmospheric CO2 enrichment on Photosynthetic assimilation
and bio molecular composition in berseem (Trifolium alexandrium)
Saha et al., 2014

9. Elevated CO2 impact
on crop yield of two
pigeon pea cultivars
(a) Pusa-992 and (b)
PS-2009 (*significant
at 5% level;
**significant
at 1% level).
Saha et al., 2012

10. Relationship between crop
water use and biomass
under elevated and
ambient CO2 levels (Saha
et al., 2011).
Relationship between
aboveground biomass and
cumulative under elevated and
ambient CO2 (Saha et al., 2012)
Impact on resource utilization efficiency under semi arid
condition of New Delhi in pigeon pea

11. Influence of
atmospheric CO2
enrichment on the
oil content and fatty
acid composition in
castor bean
Vanaja et al., 2008

12. Climate and salt salinity and sodicity

13.  High temperature and high ET has been found to cause accumulation of salts in
the upper soil horizon with decreased rate of downward leaching resulting into soil
salinization/alkalization even in places that were not found affected earlier
 Climate plays an important role in maintaining the soil properties. It can have
adverse effects on all type of soils yet can have even more deleterious effects on
sodic lands.
Increased sodicity affects the soil physical properties like dispersion and slaking,
and cause dispersion of aggregates and loss of carbon within aggregates and
physically protected from decomposition
 Altered pattern of rainfall can affect the capacity of soil to maintain the required
level of organic carbon and also the soil structure. Sodic soils suffer from ponding
on surface due to their lower infiltration rates.
Climate and salt salinity and sodicity

14. Climate variability
Climatic variability refers to Variation in Climate
 In the mean State
 In other statistics such as standard deviation and the occurrence
of extremes
Variability around the mean as a characteristics of climate

15. Climate change refers to a statistically significant variation in
either the mean state of the climate or in its variability,
persisting for an extended period (typically decades or longer).
Climate change may be due to natural internal processes or
external forcings, or to persistent anthropogenic changes in the
composition of the atmosphere or in land use.
Climate change
Observed change in surface temperature 1901-2012 IPCC, 2013

16. Observed globally averaged combined land and ocean surface temperature
anomaly 1850-2012
IPCC, 2013
According to the IPCC,2013 an increase in the average global temperature is very likely
to lead to changes in precipitation and atmospheric moisture because of changes in
atmospheric circulation and increases in evaporation and water vapor.

17. Impact of Climate Changes On Agriculture
Shifts in Agro ecological zones
Impact on crop production and productivity
Salinization and alkalization
Effect on soil organic matter and soil fertility
Soil erosion and sediment transport
Reduced soil water availability
Pest , Diseases and Weeds
Effect on biological health of soil
Reduced ground water recharge

18.  Impacts of climate change are expected to be severe
in India due to its large population, predominance in
agriculture, and its limited water resources. Haryana, one
of the green revolutionary state, facing the tremendous
challenges to agriculture due to changing climatic
scenario.
 Moreover, both saline and fresh ground water
resources further diversified the challenges for
agriculture to cope up with changing climate.

19. Total Monsoon Summer Post Monsoon
Rainfall
Rainy
Days Rainfall
Rainy
Days Rainfall
Rainy
days Rainfall
Rainy
days
Mean 757.6 47.6 595.9 32.0 67.7 6.9 94.0 8.7
Standard Error 41.6 2.2 38.2 1.6 9.0 0.8 8.8 0.6
Median 706.3 45.0 585.1 29.0 55.0 5.0 84.5 9.0
Standard
Deviation 259.8 14.0 238.8 10.3 56.2 5.0 55.0 3.7
Minimum 340.7 24.0 215.3 11.0 0.2 0.0 14.9 1.0
Maximum 1399.9 81.0 1271.3 51.0 252.9 20.0 233.0 16.0
CV 34.3 29.5 40.1 32.1 83.0 72.3 58.5 42.4
Statistical analysis of long period rainfall (mm) distribution and number of rainy days at
Karnal (1972-2010)
Narjary et al., 2014

20. 0
200
400
600
800
1000
1200
1400
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
Monsoon
Rainfall
(mm)
Year
Exces Deficit Monsoon
0
50
100
150
200
250
300
1972 1977 1982 1987 1992 1997 2002 2007 2012
1
day
maximum
rainfall,
mm
Year
June July August September
Monsoon rainfall categorization as deficient, normal and Excess at Karnal, Haryana
 Categorization of
monsoon rainfall based on
long period average (LPA)
and coefficient of variation
(CV) indicates during last
10 years (2004-2013), 6
years during the received
deficient rainfall (18-57%
lower than LPA), 2 years
normal rainfall and 2 years
excess rainfall (9-70%
higher than LPA).
Frequency of one day maximum rainfall in monsoon months at Karnal, Haryana.
 In early two decades
(1981-1990 and 1991-
2000), one day maximum
rainfall (≥ 50 mm) mainly
confined in June and July
months but in last 13 years
decades (2001-2013) it was
shifted in August and
September months.
Narjary et al., 2014

21. Changes in monthly rainfall and reference evapotranspiration in Karnal in last
decade (2001-2010) as compare to average (1972-2010)
Season Months Rainfall ETo Rainfall ETo Rainfall ETo
Average
Value
(1972-
2010)
Average
Value
(1980-
2010)
Average
Value
(2001-
2010)
Average
Value
(2001-
2010)
Change in
% over
average
(1972-
2010)
value
Change in
% over
average
(1980-
2010)
value
Winter January 26 47.07 23 45.45 -12 -3.43
February 31 65.44 40 67.23 +29 2.74
Summer March 27 112.07 19 114.22 -30 1.93
April 14 162.64 11 167.63 -21 3.07
May 28 202.34 37 203.40 +32 0.52
Monsoon June 92 187.12 101 185.69 +9 -0.76
July 198 155.01 137 155.88 -31 0.57
August 189 137.90 131 140.51 -31 1.90
September 106 121.76 150 119.56 +41 -1.80
Post
Monsoon
October 16 94.89 10 94.73 -37 -0.16
November 6 60.89 3 61.67 -50 1.29
December 14 45.31 8 46.32 -43 2.25
Narjary et al., 2014

22. Kendall's tau Sen' slope p-value (Two-tailed) alpha
Jan -0.33 -0.04 0.00 0.10
Feb 0.08 0.01 0.48 0.10
Mar 0.22 0.06 0.05 0.10
Apr 0.25 0.06 0.03 0.10
May -0.11 -0.03 0.32 0.10
Jun -0.15 -0.03 0.18 0.10
Jul 0.16 0.02 0.17 0.10
Aug 0.12 0.01 0.28 0.10
Sep -0.21 -0.03 0.07 0.10
Oct 0.02 0.00 0.84 0.10
Nov 0.21 0.02 0.06 0.10
Dec -0.11 -0.02 0.31 0.10
Average 0.10 0.01 0.39 0.10
Mann- Kendall trend test for Maximum Temperature
Narjary et al., 2014

23. Kendall's tau Sen' slope p-value (Two-tailed) alpha
Jan -0.02 0.00 0.86 0.10
Feb 0.20 0.04 0.08 0.10
Mar 0.19 0.03 0.09 0.10
Apr 0.10 0.01 0.39 0.10
May 0.12 0.02 0.29 0.10
Jun -0.04 0.00 0.72 0.10
Jul 0.25 0.02 0.03 0.10
Aug 0.27 0.01 0.02 0.10
Sep 0.15 0.01 0.20 0.10
Oct 0.09 0.02 0.40 0.10
Nov 0.09 0.02 0.40 0.10
Dec 0.17 0.03 0.14 0.10
Average 0.33 0.02 0.00 0.10
Mann- Kendall trend test for Minimum Temperature
Narjary et al., 2014

24. Sinha et al., (1998 ) observed that there was a 10 % decline in solar radiation in
northwestern India during last two decades. It is widely perceived that in all the major
cities of India, aerosol concentration has been increasing, resulting in decreased solar
radiation and increased minimum temperature
Hundal and Kaur, 1996; Aggarwal et al., 2000
Trend analysis of BSS during 1981- 2010
y = -0.0604x + 128.1
R² = 0.7415
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
1980 1985 1990 1995 2000 2005 2010
BSS
(hr)
Year

25. Mann- Kendall trend test for Rainfall, Rainy days and Evapotranspiration
Rainfall Evapotranspiration
Kendall's
tau
Sen's
slope
p-value (Two-
tailed)
Kendall's
tau
Sen's
slope
p-value
(Two-tailed)
Jan 0.07 0.17 0.53 -0.26 -0.27 0.05
Feb 0.08 0.20 0.47 0.11 0.14 0.40
Mar -0.10 -0.11 0.40 0.04 0.03 0.78
Apr -0.03 0.00 0.81 0.05 0.12 0.72
May 0.13 0.34 0.26 0.03 0.09 0.80
Jun 0.10 0.77 0.36 -0.13 -0.43 0.32
Jul -0.19 -2.17 0.09 -0.10 -0.15 0.44
Aug -0.14 -2.41 0.23 -0.06 -0.13 0.65
Sep 0.24 2.64 0.03 -0.23 -0.36 0.08
Oct -0.21 0.00 0.08 0.03 0.02 0.83
Nov 0.01 0.00 0.98 0.09 0.09 0.52
Dec -0.10 -0.03 0.37 0.02 0.01 0.92
Total -0.03 -0.70 0.81 -0.07 -0.76 0.62
SW -0.09 -2.50 0.43 -0.14 -1.02 0.30
W 0.12 0.73 0.30 -0.03 -0.04 0.86
Summer 0.02 0.11 0.84 0.07 0.41 0.62
post -0.16 -0.45 0.15 0.08 0.23 0.55
Rainy days -0.33 -0.57 0.00
Narjary and Kamra, 2013

27. A1
World: Market oriented
Economic: Rapid Economic Growth
Population : Peaks in 2050 and then gradually
declines
Governance: A convergent world, income and
way of life converge between regions. Extensive
social and cultural interaction worldwide.
Technology: There are three subsets to the A1
family
A1FI - fossil-fuels intensive.
A1B - balanced on all energy sources.
A1T - non-fossil energy sources.
A2
World: Divided World
Economy: Regionally Oriented, lowest per capita
income
Population: Continuously increasing population.
Governance: Independently operating, self-reliant
nations
Technology: slower and more fragmented
B1
World: convergent
Economy: Service and information based, lower
growth than A1
Population: Same as A1
Governance: Global solutions to economic, Social
and environmental stability
Technology: Clean and resource efficient
technologies
B2
World: Local Solutions
Economy: intermediate levels of economic
development
Population: continuously increasing population,
but at a slower rate than A2
Governance: Local solutions to economic, Social
and environmental stability
Technology: more rapid A2, less rapid more
diverse A1/B1
IPCC, 2007
IPCC SRES Scenarios (Old Concept)

28. Climate models : Mathematic models used to simulate the
behaviour of climate system. They incorporate information
regarding climate processes, current climate variability and the
response of the climate to the human-induced drivers.
Global Circulation Models (GCM): Incorporate oceanic and
atmospheric physics and dynamics and represent the general
circulation of the planetary atmosphere or ocean.
Usually coarse resolution 30 X30
Regional Circulation Models (RCM): Incorporation of local factors
Usually higher resolution 0.50 X0.50

29. GCM: Hadley Centre’s Coupled Model (HadCM3)
RCM: Providing Regional Climate for Impact Studies (PRECIS)
Resolution : 0.440 x 0.440
State action plan on climate change Haryana, 2012

30. Projected Change in mean annual precipitation and temperature in Haryana
State action plan on climate change Haryana, 2012

31. Mean Daily Maximum Temperature (0C)
JF MAM JJAS OND Annual
Haryana Baseline 21.4 38.2 34.5 24.5 29.7
Haryana Mid Century 22.6 40.3 36.2 24.9 31.0
Haryana End Century 25.7 42.8 38.8 28.2 33.9
Mean Daily Minimum Temperature (0C)
Haryana Baseline 5.8 21.2 26.5 11.7 16.3
Haryana Mid Century 7.6 23.7 28.3 13.8 18.4
Haryana End Century 10.3 26.5 30.6 16.7 21.0
Comparison of projected changes in temperatures for IPCC SRES scenario with respect to
baseline **
Change in Mean Daily Maximum Temperature (0C)
JF MAM JJAS OND Annual
Change from Baseline to Mid Century 1.2 2.1 1.7 0.4 1.3
Change from Baseline to End Century 4.3 4.6 4.3 3.7 4.2
Change in Mean Daily Minimum Temperature (0C)
Change from Baseline to Mid Century 1.8 2.5 1.8 2.1 2.1
Change from Baseline to End Century 4.5 5.3 4.1 5 4.7
Temperature for IPCC SRES baseline and A1B scenario as simulated by PRECIS for Haryana
State action plan on climate change Haryana, 2012

32. Projected Change in seasonal precipitation in Haryana
State action plan on climate change Haryana, 2012

33. Projected Change in seasonal precipitation in Haryana
State action plan on climate change Haryana, 2012

34. Projected Change in precipitation in Haryana
State action plan on climate change Haryana, 2012

35. Description of the Representative Concentration Pathways (RCPs) and their
SRES equivalents
Name Radiative forcing Developed by CO2
Equiv
(ppm)
Temperatu
re anomaly
(0C)
Pathway SRES*
temperatur
e anomaly
equivalent
RCP8.5 8.5 W/m2 in 2100. It is
characterized by increasing
greenhouse gas emission over
time representative of scenarios
in the literature leading to high
greenhouse gas concentration
levels.
MESSAGE modeling
team and the IIASA
Integrated Assessment
Framework at the
International Institute
for Applied Systems
Analysis (IIASA),
Austria.
1370 4.9 Rising SRES A1F1
RCP6.0 6 W/m2 post 2100. It is a
stabilization scenario where total
radiative forcing is stabilized
after 2100 without overshoot by
employing a range of
technologies and strategies for
reducing greenhouse gas
emissions.
AIM modeling team
at the National
Institute for
Environment Studies,
Japan.
850 3.0 Stabilizati
on without
overshoot
SRES B2
IPCC,2013

36. Name Radiative forcing Developed by CO2
Equiv
(ppm)
Temperature
anomaly (0C)
Pathway SRES*
temperature
anomaly
equivalent
RCP4.5 4.5 W/m2 post 2100. It is a
stabilization scenario where
total radiative forcing is
stabilized before 2100 by
employing a range of
technologies and strategies for
reducing greenhouse gas
emissions
MiniCAM modeling
team at the Pacific
Northwest National
Laboratory’s Joint
Global Change
Research Institute.
650 2.4 Stabilizati
on
without
overshoot
SRES B1
RCP2.6
(RCP3P
D)
3 W/m2 before 2100, declining
to 2.6 W/m2 by 2100. Its
radiative forcing level first
reaches a value around 3.1
W/m2 mid-century, returning to
2.6 W/m2 by 2100. Under this
scenario greenhouse gas
emissions and emissions of air
pollutants are reduced
substantially over time.
IMAGE modeling
team of the
Netherlands
Environmental
Assessment Agency.
490 1.5 Peak and
decline
None
Source: Chaturvedi et al. (2012); Rogelj et al. (2012)

37. Comparison of observed and simulated climate change All Figures © IPCC 2013
IPCC,2013

38. Mean Temp. Change (◦C)- CMIP5 Model
Scenarios 2030s 2060 2080
RCP2.6 1.70 1.92 1.95
RCP4.5 1.85 2.49 2.87
RCP6.0 1.72 2.37 3.27
RCP8.5 2.02 3.31 4.78
Under the business as
usual scenarios (RCP6.0 &
RCP8.5), projected increase
in the mean temperature in
the range of 1.7-2.0 C by
2030s and 3.3-4.8 C by
2080s
Source: Chaturvedi et al. (2012)

39. Precipitation Change (%)- CMIP5 Model
Scenarios 2030s 2060 2080
RCP2.6 1.2 2.7 3.5
RCP4.5 0.8 4.3 7.0
RCP6.0 1.6 3.5 6.8
RCP8.5 2.4 6.6 11.3
Under the business as usual
scenarios (RCP6.0 & RCP8.5),
precipitation is projected to
increase in the range of 4-5% by
2030s and 6-14% by 2080s
Positive trend in frequency of
extreme precipitation days (40
mm/day) for decades 2060 and
beyond
Source: Chaturvedi et al. (2012)

40. Extreme events becoming a matter of concern
Year SWM Rainfall Departure (%)
2000 -8
2001 -15
2002 -19
2003 +2
2004 -13
2005 -1
2006 -1
2007 +5
2008 -2
2009 -23
2010 +2
2011 +1
2012 -8
2013 +6
2014 -12 (upto 30.9.2014)
 2002 drought
 20 day heat wave during May 2003 in Andhra
Pradesh
 Extreme cold winter in the year 2002-03
 Drought like situation in India in July 2004
 Abnormal temperatures during March 2004
and Jan 2005
 Floods in 2005
 Cold wave 2005 - 06
 Floods in arid Rajasthan & AP and drought in
NE regions in 2006
 Abnormal temperatures during 3rd week of Jan
to 1st week of Feb 2007
 All India Severe drought 2009
 2010 – One of warmest years
 2011 – Failure of September rains in AP
 2012 – Drought in Punjab, Haryana, Gujarat
and Karnataka. Neelam cyclone, AP floods
 2013- Uttarakhand Floods, and hailstorms
 2014- J & K Flood, Cyclones:Hudhud,Nilofur

41. Source: Chaturvedi et al. (2012)
Projected change in the frequency of extreme rainfall days for future decades relative to the
1861–1870
Based on the
MIROC-ESM-CHEM
model for RCP4.5
scenario.

42. Trends in GHGs Emissions from Indian Agriculture
In 2010, World emitted 50 billion
tons CO2 eq.
India emitted ~5% of global
GHGs. Indian agriculture ~1%
of global GHGs.
From 1970 to 2010, GHGs
emission from Indian agriculture
increased by 70%.
Emission per ha increased by
90%, but per ton food grain
production decreased by 15%.
0
5
10
15
20
25
30
35
0
100
200
300
400
500
1970 1980 1990 2000 2010
Emission
(%
total
GHGs)
Emission
(Mt
CO
2
eq.)
Emission from Indian
agriculture
Relative to India's total
GHGs
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1970 1980 1990 2000 2010
Emission
(t
CO
2
eq.)
Emission per hectare
Emission per ton foodgrain
Pathak et al. (2014)

43. HadCM3,
CSIRO-Mk2
and CCCMA-
CGCM2
Crop-Syst Rice, Wheat 7%, 15% and 25%
decrease in rice and
10%, 20% and 34% in
wheat for the years
2020, 2050 and 2080
respectively in Central
Punjab
Jalota et al.
(2013)
A1FI scenario
of IPCC
WOFOST Potato In 2055, a mean
decrease of 17.9 (Kufri
Badshah), 21.1 (Kufri
Jyoti) and 22% (Kufri
Pukhraj) is likely in the
productivity due to rise
in temperature in
Punjab
Dua et al.
(2013),
Climatic model Crop model Objective crop Predicted impacts Researchers
Summary of crop models used for the study of climate change impacts

44. Climatic model Crop model Objective crop Predicted impacts Researchers
PRECIS GLAM Rice, Wheat Extreme temperature
has a negative effect
on crop yield
Challinor et al.
(2007)
CERES-Rice
CERES-Wheat
Rice, Wheat Increase in minimum
temperature up to 1.0
to 3.0 degrees Celsius
above normal has led
to decline in
productivity of rice
and wheat by 3% and
10% respectively in
Punjab.
Hundal and
Prabhjyot-Kaur
(2007)
Global climate
model
(MIROC3.2.HI)
and a regional
climate model
(PRECIS)
InfoCrop-rice
model
Rice In the 2050 scenario,
projected yield loss is
expected to stand at
15–17 % in Punjab and
Haryana
Soora et al.
(2011)
Summary of crop models used for the study of climate change impacts

45. Estimated crop water requirement (mm) of two crops in major growing districts of the
Haryana, and Punjab under climate change scenario
District (State) 1990 2020 2050 % Change over
1990
2020 2050
Mustard
Bharatpur (Raj) 276.2 283.8 295.1 2.8 6.8
Hisar (Haryana) 357.0 369.6 380.8 3.5 6.7
Wheat
Sirsa (Haryana) 281.8 293.1 301.4 4.0 7.0
Sangrur (Punjab) 391.1 405.4 416.3 3.7 6.4

46. Crop water requirements to increase (wheat)
S.
No
State
Area
(ha)
Crop water
requirement (mm)
water requirement
(million cubic meter)
%
Deviatio
n (2020-
1990)
%
Deviation
(2050-
1990)
1990 2020 2050 1990 2020 2050
1 J&K 253023 217.4 224.3 229.7 823.9 851.7 874.5 3.1 5.3
2 HP 367770 281.8 291.8 299.1 1051.1 1089.7 1119.2 3.7 6.3
3
Punjab 3468000 359.1 371.8 380.8 12553.6 13002.5 13317.5 3.6 6.1
4 Haryana 2316674 452.1 467.1 480.9 10475.4 10825.4 11158.0 3.4 6.4
5 UP 9443104 423.7 434.6 447.9 39717.8 40750.1 41990.4 2.6 5.8
6 Bihar 2076727 438.6 449.3 465.0 9046.2 9271.1 9593.9 2.5 6.1
7 West Bengal 366729 399.8 407.2 425.2 1449.5 1479.8 1543.1 1.9 6.4
8 Rajasthan 2010241 485.3 498.6 511.6 9923.6 10208.4 10479.5 2.7 5.4
9 Gujarat 727400 604.7 614.7 630.9 4603.3 4683.2 4807.1 1.7 4.3
10
Madhya
Pradesh 4188248 502.0 513.3 526.2 21176.9 21655.3 22200.1 2.3 4.9
11 Maharashtra 932800 606.417 617.82 633.5 5613.8 5718.3 5859.4 1.9 4.5
AICRPAM - CRIDA

47. What to Do?

48. Climate change: Twin challenges
Lower capacity to adapt Impacts are worse

49. Mitigation
• Not Easy
Adaptation
Mitigation + Adoption
Resilient

50. Achieve layers of resilience through
Institutional and policy
Responses to policies and
institutions
Technological adaptation:
micro-irrigation technologies, water
harvesting, flood mitigation, land
drainage
Social adaptation:
Group action - social networks,
information dissemination
o SHGs, community projects, coping
strategies,
o local water management techniques,
in-house conflict resolution,
Farm level
o changes in inputs, timings, tillage,
o irrigation practices,
o crop rotation, crop choice, crop
diversification,
o crop harvesting and processing

51. More water
More labour
More methane
Mitigating methane emission from rice
Puddled, transplanted, continuously
submerged
Aerobic rice
SRI
Direct seeded rice
Less water, Less labour
Less methane

52. About 70% of GWP
in transplanted rice
was due to methane.
The GWP in rice was
728.1 kg CO2 eq. ha-1
in DSR vs. 1922.6 kg
CO2 eq. ha-1 in
transplanted rice.
The GWP in DSR was
only 38% of
transplanted rice.
GHG mitigation potential of DSR

53. Direct-seeded rice (DSR) and zero-till wheat (ZTW) gave similar yield with less
irrigation water and lower global warming potential.
Conservation Agriculture for Climate Change
Adaptation and GHGs Mitigation
0.0
0.5
1.0
1.5
2.0
2.5
0
5
10
15
20
25
TPR- CTW TPR -ZTW DSR - ZTW
DSR- ZTW+RR
Global
warming
potential
Rice
eq.
yield;
System
water
demand
Rice eq. yield (t)
System water demand (cm)
Global warming potential (t CO2 eq.)

54. Comparison of DSR and conventional
transplanted rice (TPR)
Parameter TPR DSR
Yield (t ha-1) 4.1-7.7 4.0-7.4
Irrigation water (cm) 16-22 12-16
Labour (man days) 55-60 35-40
Tractor (hours) 10-12 5-6
GWP (kg CO2) 1661-1922 409-728

55. GWP of transplanted (TPR) and direct-seeded rice
(DSR) in different districts of Punjab
GWP in TPR GWP in DSR
Legends GWP_TPR (t CO2/ha) GWP-DSR (t CO2/ha)
2.0-2.5 1.3-1.5
2.5-3.0 1.5-2.0
3.0-4.5 2.0-3.0

56. Sources of nitrous oxide
Soil and Fertilizer
Manure management Burning of crop residue

57. Leaf colour chart
Urea tablet/
Nitrification inhibitor
Smart Nitrogen Management
Mitigation Options for Nitrous Oxide
Emission from Agricultural Soils

58. What if CA is implemented partly, wrongly or
discntinued after some time?
Conservation agriculture (CA) promotes mitigation
and C sequestration

59. When CA are implemented partly
Happy seeder in wheat
• No residue burning
• C sequestration
• Less GHG emission
• But extra N? more N2O
Puddling/submergence of
C & N rich soil in rice
•More methane
•More nitrous oxide

60. Pathways for Development
Developing Countries
Developed Countries Climate-Smart Development
Low carbon
Low nitrogen
Low energy
Low profit
Low interest of stakeholders