Global climate change and increasing climatic variability are recently considered a huge concern worldwide due to enormous emissions of greenhouse gases to the atmosphere and its more apparent effect on fruit crops because of its perennial nature. The changed climatic parameters affect the crop physiology, biochemistry, floral biology, biotic stresses like disease-pest incidence, etc., and ultimately resulted to the reduction of yield and quality of fruit crops. So, it is big challenge to the scientists of the world.

2. Presented by
Rajatiya Jignasa H.
M.Sc. (Horti.) Fruit Science
Climate Change: Impact, Adaptation
and Mitigation in Fruit Crops

3. ContentsContent

4. Introduction
The Earth’s climate, although relatively stable for the past 10,000 years or so,
has always changing, mainly due to natural causes such as volcanic
activity.
But since the 1900s more rapid changes have taken place and these are thought
to be mainly man-made.
Global mean temperatures increased by 0.74˚C during last 100 years and the
year 2100 best estimates predict that to increase global annual mean
temperature in the range of 1.8 - 4˚C; increase the variability in rainfall and
enhance frequency of extreme weather events such as heat waves, cold waves,
droughts and floods (IPCC, 2007).
4

5. Climate change is a big threat to human food supply. Around
12% of world's population is already at risk of hunger, but if
temperature rises by only 2 to 3 ˚C it will increase the people
at risk of hunger, potentially by 30 - 200 million (Stern, 2006).
Food production will be particularly sensitive to climate
change, because crop yields depend directly on climatic
conditions and could lead to food yields being reduced by as
much as a third in the tropics and subtropics (Pender, 2008).
5

6. Weather
Sunshine
Rain
Storms
Daily
All over the world
Climate
Determined over time
Determined by years Depends on where you live
Weather makes up climate
6

7. The layer of gases which protects the earth surface from ultra
violet rays and other radiation are known as green house
gases.
7
CFC

8. 8

9. 0
9

10. Contribution of major economic sectors to
emission of GHGs in the World and India.
IPCC (2014)
Figure 1 Figure 2
10

11. 28.21%
15.99%
6.24%
4.53% 3.67%
2.23% 1.75% 1.72% 1.71% 1.56%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
ShareofglobalCO2emissions
Statista (2017)
Fig. 3: Top ten CO2 producer countries in worldwide.
11

12. “A change in the state of the climate that can
be identified by changes in the mean or
variability of its properties and that persists
for an extended period, typically decades or
longer.”
IPCC (2007)12

13. Natural causes
13

14. Anthropogenic causes
14

15. Linkages between climate change and other environmental issues
15

16. Fig. 4: Change in Concentrations of carbon dioxide in the Earth’s atmosphere.
Source: scripps.ucsd.edu16

17. Temperature
Latest annual average: 2016
Fig. 5: Change in global surface temperature relative to
1951-1980 average temperature.
0.99 °C
TemperatureAnomaly(˚C)
Source: climate.nasa.gov.
1998
Sixteen of the 17
warmest years in
the 136-year
17

18. Fig. 6: Change in global mean sea level.
SealevelAnomaly(mm)
AVISCO (2017)18

19. Green/Blue show increase of precipitation.
Pink/Red show decrease of precipitation.
Fig. 7: Change in average annual precipitation (mm) during
2000–2050 in the World.
Nelson (2011)
19

20. Climate change in India
Indian agriculture is facing challenges
Probability of 10–40% loss in crop production in India by
2080–2100 due to global warming (IPCC, 2014).
Indians will be vulnerable to Climate change in three ways
 Due to higher oceans level lead to huge problems in
coastal cities
 Dependence on Agriculture
 Increase in food demand
20

21. 550C temperature
in June
Tawang 190C
in June
Drass -450C in
December night
Tiruvanantapuram
& Chennai 200C in
December night
Kerala Diurnal range
of temperature 80C
Thar desert
Diurnal range of
temperature 300C
Cherrapunji &
Mawsynram have
1080cm rain
Monsoon regime is the unity of India
Jaisalmer
receives 9cm
rainfall
Climate diversity of India
21

22.  Gujarat is state with semi-arid zones and includes the longest
coastline. These characteristic features make the state
relatively more vulnerable to climate change.
 Temperature varies from 6 to 45 ˚C.
 Annual rainfall varies from 250 mm in the North West and to
more than 1500 mm in South Gujarat.
 According to the survey of Drought Prone Area Programme
(DPAP) out of 33 districts, 14 districts are drought prone.
Climate change in Gujarat
22

23. Table-1: Classification of selected districts under different degrees
of vulnerability to climate change for the year 2008.
Less
vulnerable
Moderately
vulnerable
vulnerable Highly
vulnerable
Very highly
vulnerable
Vadodara Surat ------- Suredranagar Amreli
Junagadh Jamnagar ------- Rajkot Ahmedabad
Panchamahal Kheda ------- Bharuch
------- Banaskantha
------- Mehsana
------- Sabarkantha
JAU, Junagadh Deepa and Shiyani (2012)23

24. Impact of climate change in fruit crops

25. Climate change, particularly increasing temperatures, altered
rainfall patterns and climate variability will affect dramatically
the productivity of crops and their regional distribution in the
next decades with severe impacts on food security.
Over the past decade or so the fruit growers experienced by the
most commonly encountered climatic conditions.
Quality and yield of the any crop is only possible through its
optimum climatic requirements.
The changed climatic parameters affects the
- Crop physiology
- Crop biochemistry
- Floral biology
- Biotic stresses like disease-pest incidence
25

26.  Disturbance in flowering pattern and fruit setting
 Increase the pollination failures
 Reduce Colour development in many fruits
 Photosensitive crops are mature faster
 Increased incidence of physiological disorders
 Shifting of major area of potential suitable zones e.g. apple
 Heavy rains at fruit maturity results in reduction of fruit quality
 Inadequate chilling hours affect dormancy breaking & yield in
temperate fruits
Impact of climate change in fruit crops
26

27.  Low perfect flowers, floral abortions,
flower and fruit drop will be occurred
frequently in mango
 Changes in the distribution of existing
pests, diseases & weeds and increased
threat of new incursions
 Delayed panicle emergence in mango
due to low temperature
 Strawberries will reduce runner
production at low temperature, etc.
27

28. 28

29. Damage of mango flowering and fruits in Gujarat during the year 2015.
80-90 % loss in mango production during the
year 2015 due to
1) Unseasonal rain during fourth week of
February
2) Heavy dew attack during flowering season
3) Unseasonal rains and dew attack reduce
fruit setting and increase fruit drop at pea
& marble stage
4) Increase incidence of sooty mould &
powdery mildew diseases
JAU, Junagadh Viradia and Varu (2015)29

30. Table-2: Correlation of area and production of major fruit
crops with weather parameters in India.
Crop
Rainfall Min. Temp. Max. Temp.
Area Production Area Production Area Production
Mango -0.4350 -0.3990 -0.0272 0.0462 0.2751 0.3937
Banana -0.4180 -0.3096 0.1807 0.1828 0.4545 0.3955
Citrus -0.6258 -0.6582 -0.0636 -0.0144 0.3305 0.3774
Apple -0.4214 0.2094 0.0268 0.2584 0.3409 0.2091
Guava -0.5779 -0.5382 0.3523 0.3227 0.3523 0.3227
Sapota -0.3870 -0.3786 0.3811 0.3744 0.3811 0.3744
Papaya -0.4360 -0.4970 -0.0897 0.0005 0.2263 0.3267
Grape -0.4705 0.2583 0.0841 -0.6582 0.4171 -0.6699
Pineapple -0.4980 -0.4408 -0.4980 -0.4408 0.2485 0.2992
University of Agril. Sciences, Dharwad Patil et al. (2015)30

31. Table-3: Correlation co-efficient of various weather variable
and flowering parameters of mango.
Weather
variable
Days to
flower-
ing
No. of
inflo. per
plant
Flowering
intensity
(%)
No. of male
flowers per
inflorescence
No. of herma.
flowers per
inflorescence
Total no. of
flowers per
inflorescence
Day Temp. (˚C) -0.3684 0.2655 -0.0220 -0.1547 -0.0384 -0.1587
Night Temp. (˚C) -0.4846 0.2916 0.0519 -0.1100 -0.0629 -0.1175
RH (%) -0.8091 0.6364 0.4145 -0.3860 0.0696 -0.3747
BSS (hrs) 0.8344 -0.6583 -0.3854 0.4476 -0.1540 0.4250
Rain (cm) -0.6371 0.5071 0.3625 -0.5385 -0.0264 -0.5388
JAU, Junagadh Varu et al. (2015)31

32. Table-4: Correlation co-efficient of various weather variable
and fruiting parameters of mango.
JAU, Junagadh Varu et al. (2015)
Weather
variable
No. of fruits
No. of ferti.
fruits at pea
stage
No. of
parthe. fruits
at pea stage
No. of
fruits per
plant
Fruit
yield
(kg/plant)
at pea
stage
At
marble
stage
Day Temp. (˚C) 0.0537 -0.3317 -0.1391 0.1744 -0.0721 -0.1326
Night Temp. (˚C) 0.0472 -0.2900 -0.1574 0.1617 -0.1199 -0.1706
RH (%) 0.5960 0.3198 0.5078 0.5778 0.4088 0.4385
BSS (hrs) -0.4390 0.0214 -0.3736 -0.4488 -0.3228 -0.4018
Rain (cm) 0.3335 -0.1777 0.3341 0.3384 0.0225 0.0741
32

33. Table-5: Correlation co-efficient between weather variables and
flowering and fruiting characters of mango (main season).
Weather
variables
Number of
inflorescence m-2
Hermaphrodite
flower (%)
Fruit
set (%)
Number of
fruits/tree
Yield
(kg/plant)
Max. Temp. (˚C) 0.568** 0.814** 0.368* 0.853** 0.754**
Min. Temp. (˚C) 0.493* 0.327 NS 0.270NS -0.215NS 0.296NS
RH (%) -0.152 0.444* 0.281NS -0.247NS -0.191
Rainfall (mm) 0.843** 0.148NS 0.542* 0.059 0.206
Soil moisture 0.206 NS 0.095NS 0.147NS 0.105 0.171
Kumar et al. (2014)TNAU, Tamil Nadu
** and * indicate significant differences at 5 % and 1 %, respectively.
33

34. Weather parameters
No. of hopper per
Inflorescence
No. of Flower bug
per Inflorescence
No. of Thrips per
leaf
No. of Thrips per
Inflorescence
Max. Temp. (˚C) 0.0064 0.0180 0.1184 0.0576
Min. Temp. (˚C) -0.4030 -0.5959* -0.7788** -0.5642*
Mean Temp. (˚C) -0.2912 -0.4280 -0.5096 -0.3824
Morning RH1 (%) -0.5998* -0.7717** -0.8141** -0.7628**
Evening RH1 (%) -0.4270 -0.5875* -0.7579** -0.5756*
Wind speed (km/hr) -0.0973 -0.0986 -0.2793 -0.0999
BSS Hours (hr/day) 0.2674 0.3730 0.6137* 0.3794
Evaporation (mm) 0.1824 0.2453 0.3663 0.2664
Rainfall (mm) -0.2350 -0.2899 -0.4378 -0.2825
Rainy days -0.2596 -0.3491 -0.5455* -0.3401
JAU, Junagadh Bhut and Jethva (2013)
Table-6: Correlation co-efficient between weather parameters
and inflorescence pests of mango.
** and * indicate significant differences at 5 % and 1 %, respectively.
34

35. T1V1 T1V2 T1V3 T2V1 T2V2 T2V3 T3V1 T3V2 T3V3
Patel et al. (2012)NAU, Navsari
Date Max. Temp. (˚C) Min. Temp.(˚C)
T1: 9th Feb-2012 23.0 6.2
T2: 17th Feb-2012 30.3 12.0
T3: 25th Feb-2012 32.0 14.3
Table-7: Influence of temperature on pollen viability in mango.
V1 : Kesar
V2 : Alphonso
V3 : Rajapuri
35

36. Parmar et al. (2012)NAU, Navsari
Table-8: Effect of weather parameters on yield of mango in
south Gujarat.
Year Yield (t/ha) BSS (hr)
Rainfall
(mm)
T max (˚C) T min (˚C)
2005-06 6.9 9.0 2435 32.0 15.5
2006-07 8.1 8.8 1855 30.9 15.9
2007-08 7.1 8.5 1696 30.1 13.9
2008-09 2.8 8.0 2030 32.3 16.3
Correlation 0.85 -0.16 -0.67 -0.46
36

37. Table-9: Total fruit dry weight and total fruit number per plant of
strawberry cultivated under different conditions.
0
5
10
15
20
25
30
35
CT CTN C CN T TN ck N
Treatments
Fruit dry
weight/plant (g)
Total fruit
number/plant
*
*
** CT= Increase in CO2 and temperature
CTN= Increase in CO2, Temperature and N2 input
C= Increase in CO2 (720 ppm)
CN= Increase in CO2 and N2 input
T= Increase in Temp. (25˚C/20˚C; Day Temp./Night Temp.)
TN= Increase in Temperature and N2 input
ck= Control (360 ppm x 20˚C/15˚C x without N2 input)
N= Increase in N2 fertilizer input (50 ml of 0.1% NH4NO3
twice a week/plant
Treatment detail
Peng Sun et al. (2012)Jinhua, China
** and * indicate significant differences at 5 % and 1 %, respectively.
37

38. Year
Days to fruit maturity (days) Fruit retention (%)
2007 2008 2009 2010 Pooled 2008 2009 2010 Pooled
Sel. Mean 70.86 70.86 81.95 79.33 75.75 2.68 6.24 8.29 5.74
S. Em + 2.388 2.388 2.104 2.186 2.172 0.2730 0.3407 0.9318 1.185
CD at 5% NS NS 6.48 6.74 NS 0.84 1.05 2.87 3.65
CV% 5.84 5.84 4.45 4.77 5.19 17.64 9.46 19.46 17.94
Y X T
SEm + - - - - 2.270 - - - 0.776
CD (P=0.05) - - - - 6.46 - - - 2.39
Effective
months
Temperature (˚C) Humidity (%) Rainfall (mm)
2007 2008 2009 2010
‘07 ‘08 ‘09 ‘10 ‘07 ‘08 ‘09 ‘10
Max Min Max Min Max Min Max Min
June 36.5 26.9 34.2 26.5 35.7 26.4 37.1 26.6 79 82 79 84 164.1 148.7 101.5 59.0
July 31.5 26.2 31.2 25.2 31.1 25.3 31.1 25.5 88 88 93 93 336.9 436.1 660.6 181.2
August 30.3 24.8 29.7 24.2 32.2 25.2 30.4 24.8 92 92 89 94 649.4 110.8 52.7 244.6
Sept. 31.4 24.5 31.7 23.8 34.2 24.5 31.3 23.7 88 88 84 91 231.0 508.8 10.4 46.2
Year mean 33.7 21.1 33.2 20.3 34.6 21.2 34.7 21.3 74 75 73 85 1417 1236 825 668
JAU, Junagadh Varu et al. (2010)
Table-10: Effect of climatic parameters on days to maturity
and fruit retention percentage in custard apple.
38

39. Year
Mealy bug (%) Black spot (%)
2007 2008 2009 2010 Pooled 2007 2008 2009 2010 Pooled
Sel. Mean 10.84 29.88 46.46 29.48 29.16 16.80 27.95 35.63 26.74 26.78
S. Em + 0.692 1.952 2.391 1.353 3.751 0.772 6.554 3.027 1.325 2.406
CD at 5% 2.13 6.01 7.37 4.17 NS 2.38 NS NS 4.08 NS
CV% 11.07 11.31 8.92 7.95 10.22 7.96 40.62 14.71 8.58 23.87
Y X T
SEm + - - - - 1.720 - - - - 3.690
CD (P=0.05) - - - - 4.90 - - - - 10.50
Effective
months
Temperature (˚C) Humidity (%) Rainfall (mm) Wind speed (km/hr)
2007 2008 2009 2010
‘07 ‘08 ‘09 ‘10 ‘07 ‘08 ‘09 ‘10 ‘07 ‘08 ‘09 ’10
Max Min Max Min Max Min Max Min
August 30.3 24.8 29.7 24.2 32.2 25.2 30.4 24.8 92 92 89 94 649 110 52 244 - - - -
Sept. 31.4 24.5 31.7 23.8 34.2 24.5 31.3 23.7 88 88 84 91 231 508 10 46 4.3 5.1 4.6 4.5
October 34.8 20.4 35.7 20.5 36.5 21.5 35.2 22.7 70 74 74 81 0.0 2.8 0.0 0.0 3.4 3.3 4.7 4.7
Nov. 34.5 17.3 34.1 17.5 34.3 17.6 31.9 20.3 73 63 62 75 0.0 0.0 0.0 0.0 2.8 3.8 3.4 4.0
Year
mean
33.7 21.1 33.2 20.3 34.6 21.2 34.7 21.3 74 75 73 85 1417 1236 825 668 3.5 3.9 4.2 4.4
JAU, Junagadh Varu et al. (2010)
Table-11: Effect of climatic parameters on mealy bug (%) and
black spot (%) infestation in custard apple.
39

40. • Apple belt has moved 30 kms upwards [northwards] over the last 50 years
• The new areas of apple cultivation have appeared in Lahaul & Spitti and
upper reaches of Kinnaur district of H.P.
• The total area under apple state have fallen from 92.82 thousands ha in 2001 to
86.2 thousands ha in 2005. Avg. state productivity decreased from 7.06 t/ha in
1980-81 to 4.65 t/ha in 2004-05.
NPCC (2004-07)
Cumulative chill units treads (Utah model) at Bajaura in
kullu valley
40

41. Projected beneficial impacts of climate change
on agriculture
More frost sensitive crops like potato, pea, mustard, etc. and
several seed spices during winter may be benefited due to
slightly increase temperature or no frost condition in rabi
season. So, assumed that the yield and quality of above
mention crops are increased in future.
Long et al. (2005) shown that in field environment, 550 ppm
CO 2 leads to a benefit of 8-10 % in yield of wheat and rice, up
to 15 % in soybean and almost negligible in maize and
sorghum.
41

42. Fig. 9: Rising temperature response of photosynthesis in C3,
C4 and CAM plants.
Japan Yamori et al. (2013)42

43. Fig. 10: C3 and C4 plants photosynthesis response to
atmospheric CO2 enrichment.
USA Rogers et al. (1994)43

44. NPCC (2004-07)
Component
Altitude range
Low (1100 m) Mid (1800-2000 m) High (2600-2700 m)
Crop change People have shifted to
vegetable crops-
cabbage, pea and
cauliflower
People are shifting from
apple toward kiwi,
pomegranate and
vegetable cultivation
From traditional crop
to apple
Land use change Fruit crops to vegetable
cultivation
Fruit crops to vegetable
cultivation
Agriculture to fruit
crops
Varietal change Some people are shifting
to low chilling cultivars
of apple like Coop-12
People are adopting low
chilling cultivars of apple
like red spur
No
Whether the
change- beneficial
or harmful
Majority response is
beneficial
Beneficial
Beneficial for apple
cultivation
44

45. adaptation to climate
change in fruit crops

46. What is Adaptation ?
“Adjustment in natural or human systems in
response to actual or expected climatic stimuli and
their effects, which moderates harm or exploits
beneficial opportunity”.
Climate change brings into focus the mutual
relationship between society and nature...
46

47. Climate smart agriculture
Incorporation of
Adaptation
Mitigation
Other practices
increases the capacity of
the system to respond to
various climate related
disturbances
47

48. Strategies for adaptation
Altered agronomy of crops
 Altering dates of planting & spacing
 Use stress tolerant crops or cultivars
 Change in cropping system
HDP mango orchard
Mixed cropping Inter cropping
48

49. Conservation agriculture
Sod culture
49

50. Soil and water conservation
 Use nutrient efficient fertigation system &
manuring
 Terraces and bunds
 Rain water harvesting structures and
systems
 Use of water efficient irrigation systems
and crops, etc.
50

51. Windbreak and Mulching
Use of anti-traspirants
Improved land use & NRM policies
Recycling of waste water and solid wastes in agriculture
Early warning systems and crop insurances
51

52. Table-13: List of some fruit cultivar tolerant to abiotic stress.
Sr. no. Crop cultivars Tolerant
1 Pomegranate Ruby Drought
2 Custard apple Arka Sahan Drought
3 Fig Deanna and Excel Drought
4 Grape TRY (G)-1 Drought
5 Mango Bappakai Salinity
6 Lime Rangpur lime and Cleopatra mandarin Salinity
7 Papaya Pusa Giant strong wind
Bose and Mitra (1996)52

53. Table-14: Fruit crop cultivars tolerant to different abiotic stresses.
Crop Cultivars Tolerant
Apricot Badami, Rannil Drought & Heat stress
Aonla Francis Chakkia Heat Stress
Peach Flordasun, Sunlet Heat Stress
Banana Shrimanti, Grand Nain Heat Stress
Poovan, Karpuravali Heat & Cold stress
Guava L-49, Allahabad Safeda Drought & Cold stress
Mango Sindhuri, Arka Neelechal Drought
Ber Sev, Seb, Gola, Mundia, Umran Drought
Apple York Imperial Drought
Sweet orange Mosambi Drought & Heat stress
Indira Nain Cold stress
Maheswari et al. (2015)53CRIDA, Hyderabad

54. Table-15: Rootstocks tolerant to different abiotic stresses.
Crop Rootstocks Specific features
Mango Kurrukan, Nekkare Salt tolerant
Custard apple Pond apple Tolerant to flood condition
Ber Z. nummularia Salt tolerant
Z. rotundifolia Drought tolerant
Citrus Trifoliate orange Cold hardy
Rangpur lime Drought tolerant
Grape Dogridge Drought & Salt tolerant
Apple MM111 & MM104 Drought tolerant
MM104 Cold hardy
Plum Myrobalan B Cold hardy
Cherry Gisela Cold hardy
Mahaleb Drought tolerant
Kumar & Kumar (2013)54

55. Table-16: Low chill cultivars of some temperate fruits.
Fruit
crops
Cultivars CR
Apple
Anna, Mayan, Tamma, Vered, Tropical Beauty,
Parlin’s Beauty, Schlomit, Michel, Neomi, Coop-2,
Red Spur
<800
hours
Peach and
Nectarine
Flordasun, Flordared, Sunred Nectarine, Sun Gold,
Saharanpur Prabhat, Shan-e-Punjab, Sharbati
<500
hours
Pear
Gola, Pathernakh (Sand Pear), Leconte, Kieffer,
Punjab Nectar
150
hours
CR: Chilling Requirement
Rai et al. (2015)Pantnagar, Uttarakhand 55

56. Table-17: Effect of plastic mulching on yield of different
fruit crops.
Crop Yield (t/ha) Increase in Yield
(%)Unmulched Mulched
Guava 18.36 23.12 25.93
Mango 4.93 7.16 45.23
Papaya 73.24 120.29 64.24
Ber 7.02 8.92 27.06
Pineapple 10.25 11.75 14.63
Banana 53.99 73.32 33.95
Litchi 111.0 125.0 12.61
Patil et al. (2013)Environmental Sci. Res. Lab., Mumbai 56

57. Table-18: Effect of mulch treatments on strawberry
runner plant yields at three locations.
Mulch treatment
Location
Kinston Laurel Springs Reidsville
Black plastic 111.14 165.91 88.23
Bare ground 137.19 109.67 87.98
White-on-black
plastic 179.34 153.13 114.77
LSD=31.71
Sandra (2002)North Carolina, US 57

58. Table-19: Effect of different barrier condition on mortality percentage
of fruit plants affected by frost in Doon Valley.
Barriers Mango Litchi Guava Aonla Papaya
Wind breaks 19.44 2.97 11.98 28.30 30.81
Physical barriers 32.61 8.92 16.41 41.92 42.74
Open 90.50 30.43 43.34 91.43 90.81
CD at 5 % 8.80 5.92 4.82 8.67 6.55
Dehradun, India Rathore et al. (2012)58

59. Table-20: Effect of different frost reduction approaches
on grapes grown in moderate cold climate.
Yadollahi (2011)Iran
Treatments
Decrease of
photosynthesis
(%)
Increase of
respiration (%)
Decrease of
grape yield (%)
Tissue damages
(%)
Heater 47.3b 51.8b 53.0b 34.2a
sprinkler
irrigation
59.3c 70.0d 75.4d 48.0b
FC 56.0c 60.3c 63.9c 39.7b
Bordeaux
mixture
22.3a 25.3a 27.7a 33.3a
Control 97.0d 99.0e 99.9e 98.0e
FC: Increasing soil moisture up to field capacity
Values have the same letter are not significantly different at 5% level using Duncan's Test
59

60. Table-21: Effect of spraying some anti-transpirants on yield
parameters of banana.
Treatment Av. finger weight (g) Av. hand weight (kg) Bunch weight (kg)
Control 82.5 1.75 17.9
Green miracle at 2% 84.6 1.91 19.0
Salicylic acid at 0.05% 87.6 2.09 19.9
Vapor guard at 2% 91.5 2.25 20.9
Pureshade at 2% 94.0 2.50 22.0
Sunscreen at 2% 96.5 2.66 22.8
Calcium carbonate at 2% 99.0 2.82 23.6
Kaolin at 2% 102.0 2.98 24.5
Chitosane at 2% 108.0 3.15 25.3
LSD at 5% 2.0 0.15 0.8
Giza, Egypt Ahmed (2014)60

61. Table-22: Effect of bagging type on fruit cracking (%) and sunburn
fruits (%) in pomegranate during 2011 & 2012 seasons.
Treatments
Fruit cracking (%) Sunburn fruits (%)
2011 2012 2011 2012
Control 5.00c 6.00c 23.0b 25.0a
Brown paper bag 2.00e 2.00e 2.00e 3.00g
White paper bag 5.00c 7.00b 2.00e 5.00f
Prgmen bag 1.00f 1.00f 2.00e 2.00h
Agrail white bag 3.00d 4.00d 5.00d 10.0e
Agrail red bag 6.00b 7.00b 10.0c 15.0d
Agrail blue bag 6.00b 6.00c 10.0c 16.0c
Plastic bag 8.00a 10.0a 25.0a 24.0b
Values have the same letter are not significantly different at
5% level using Duncan's Test
Control
Prgmen
bag
Plastic
bag
Egypt Mohamed (2014)61

62. Table-23: Fruit quality distribution in the Terra alba and
control treatments in pomegranate.
Treatment
Fruit grade (%)
Premium (no
sunburn damage)
First (<10% sunburn
damage)
Unmarketable (>10%
sunburn damage)
Control 78.1a 10.3a 11.6a
Terra alba 90.6b 5.6b 3.9b
Values within columns marked with different letters showed statistically significant differences (P < 0.05)
Jhansi, UP Parashar and Ansari (2012)
Effect of terra alba application on air, fruit and leaf average temperature.
Temperature Control Terra alba
Air (˚C) 40.5 40.5
Fruit (˚C) 47.1a 42.2b
Leaf (˚C) 38.1a 35.6b
Values within rows marked with different letters showed statistically significant differences (P < 0.05)
62

63. Table-24: Effect of kaolin and gibberellic acid on sunburn intensity
and percent in pomegranate cv. Rabbab Neiriz.
Treatments Sunburn intensity Sunburn (%)
control 2.22 a 54.07 a
Kaolin (2.5%) 1.22 c 28.89 c
Kaolin (5%) 1.08 c 29.63 c
Gibberellic acid 1.67 b 37.54 b
Iran Ehteshami et al. (2011)
Similar letters in each column show non-significant differences at 5% level of probability using DMRT.
63

64. Table-25: Overall recovery of heat shock affected plants after spray
of 3 % sucrose solution in different varieties of mango.
Variety
No. of plant
observed
No. of plant
with new
growth
Per cent
(%)
No. of plant
without
growth
Per cent
(%)
Dashehari 44 16 36.36 28 63.34
Baneshan 50 31 62.00 19 38.00
Rajapuri 56 36 64.28 20 35.71
Langra 42 38 90.48 4 9.52
Andhra Pradesh Reddy and Singh (2011)64

65. Table-26: Influence of bagging of fruits at marble stage on fruit
retention, shelf life and spongy tissue in mango.
Dapoli, Maharashtra Haldankar et al. (2015)
Treatment
Fruit
retention (%)
Weight of
fruit (g)
Spongy tissue
(%)
Newspaper bag 71.25 264.07 0
Brown paper bag 71.67 254.47 0
Scurting bag 71.67 243.53 1.72
Plastic bag with perforations 65 225.78 6.17
Butter paper bag 68.75 245.65 0.67
Muslin cloth bag 68.58 239.24 0.84
Brown paper bag with polythene
coating
67.92 251.37 2.39
No Bagging 66.25 232.46 9
SEm ± 68.88 4.98 0.27
CD at 5 % 0.86 15.09 0.82
65

66. Table-27: Effect of cover crops and mulches on occurrence of
spongy tissue in Alphonso fruits under different locations.
Treatments
Mean occurrence of spongy tissue (%) Mean occurrence
over two locations
(%)Paria (Valsad) Ghadoi (Valsad)
Cover crop-green gram 5.55 (2.48) 3.38 (2.06) 4.72 (2.27)
Cover crop-cowpea 3.33 (1.75) 3.33 (1.48) 1.66 (1.48)
Mulch-paddy straw 0.55 (1.66) 1.11 (1.32) 0.83 (1.24)
Mulch-mango leaves 1.11 (1.32) 1.66 (1.48) 1.40 (1.40)
Control-clean 15.55 (4.05) 18.33 (4.35) 16.94 (4.20)
Treatment Locations (T x L)
S.Em± 0.21 0.06 0.1282 0.81 0.181
CD at 5 % 0.68 0.17 0.36* NS NS
Figures in parentheses indicate square root transformed value
* = significant at 1 % level
GAU, Navsari Katrodia and Sheth (1988)66

67. Mitigation to climate
change in fruit crops

68. What is mitigation ?
“Climate mitigation means a human
intervention to reduce the sources or enhance
the sinks of greenhouse gases, which
permanently eliminate or reduce the long term
risk and hazard of climate change to human
life and their properties.”
68

69. Crops and farming system management
Improve residue management e.g. avoid biomass burning
Include nitrogen fixing plants into crop rotations
Fertilizer, manure and biomass management
Reduce use and production of synthetic fertilizers
Avoid leaching and volatilization of N from organic fertilizers
during storage and application
Use slow-releasing fertilizers, Nitrification inhibitors, etc.
Improve storage management of manures
Biogas production (methane capture)
strategies FOR Mitigation
69

70. Soil management
Use organic fertilizers
Reduced tillage
Avoid soil compaction
Use cover crops, intercropping
and biochar
Afforestation
Use wind and Solar power
Increase fuel efficiency in
agricultural machinery
Carbon sequestration
Carbon credit
Conti...
70

71. Ways that can be sequestered carbon
71

72. Rajasthan Nimbalkar et al. (2017)
Table-28: Carbon stock and sequestration by aonla and mango.
Particulars Aonla Mango Total
Number of trees sampled 191 113 304
Trees in study orchards 723 544 1267
Area (ha) 7.4 5.7 13.1
Volume (m3) 50 46 95
Above-ground biomass(tons) 56 38 94
Below-ground biomass(tons) 15 10 24
Total biomass (tons) 70 47 117
Carbon stock in biomass(tons) 38 26 64
Equivalent CO2 in biomass 139 96 235
Carbon stock t ha-1 5.1 4.5 9.6
Equivalent CO2 t ha-1 18.7 16.8 35.5
Carbon stock (CS) = 0.55 x total biomass
eCO2 or sequestration of CO2 = CS x 3.6663
72

73. Table-29: Influence of different types of orchard soil on carbon
stock and equivalent CO2.
Crop
Bulk density
(g cm-3)
OC (%)
Carbon stock
(t ha-1)
Equivalent
CO2 (t ha-1)
Mango 1.11 0.85 28.33 103.98
Sapota 1.03 1.07 33.12 121.55
Teak 1.08 1.11 35.84 131.54
Coconut 1.11 0.81 27.00 99.09
Carbon stock (t ha-1) = Organic Carbon (%) x Bulk density (g cm-3) x depth (30 cm)
eCO2 or sequestration of CO2 = carbon stock x 3.6663
Chennai Ananthi et al. (2016)73

74. Table-30: Carbon sequestration potential of different pool area of
fruit tree plantation.
Fruit
plantation
Fruit Trees
(t/ha)
Understory
(t/ha)
Litter
(t/ha)
Roots
(t/ha)
Soil
(t/ha)
Total
Rambutan 11.12b 0.50a 1.79ab 2.01b 96.76a 112.18
Mango 45.29a 0.70a 1.62b 7.17a 67.56a 122.34
Santol 75.02a 0.36a 3.43a 11.60a 113.21a 203.62
CV (%) 16.30 44.56 19.13 16.71 12.70
Philippines Mark and Marin (2016)
Mean of the same letters are not significantly different at 5% level of significance using DMRT.
Santol74

75. Table-31: Soil organic carbon (%) under different
cropping systems at 0-15 cm soil depth.
Treatments Soil OC (%)
Coconut + banana 1.25
Coconut + maize 1.21
Coconut + pineapple 1.30
CD (P=0.05) 0.04
Thiruvananthapuram, Kerala Sudha and George (2011)75

76. Table-32: Above and below ground carbon density (t/ha) under
different tree plantation.
Treatments
Above ground
(t/ha)
Below ground
(t/ha)
Total (t/ha)
Sapota 30.39 7.60 38.00
Mango 50.12 12.53 62.65
Teak 344.22 86.06 430.28
White siris 334.39 83.60 417.99
Eucalyptus 80.14 20.04 100.17
Casuarina 73.29 18.32 91.61
Indian rosewood 177.43 44.36 221.79
Jatropha 16.40 4.10 20.51
Arjun tree 153.68 38.42 192.09
CD (P=0.05) 16.20 4.34 18.73
NAU, Navsari Bhalawe (2014)76

77. Table-33: Above and below ground carbon density (t/ha)
under agroforestry.
Treatments
Above ground
(t/ha)
Below ground
(t/ha)
Total C
(t/ha)
Rice + Teak 52.47 13.12 65.59
Sugarcane + Casuarina 52.17 13.04 65.21
Banana + Teak 41.17 10.29 51.45
CD (P=0.05) 6.01 0.95 5.21
NAU, Navsari Bhalawe (2014)77

78. Table-34: Carbon sequestration potential of different fruit tree
species.
Name of crop
Average organic carbon (t/tree)
Above ground Below ground Total
Bael 0.03 0.007 0.03
Papaya 0.03 0.008 0.038
Lime 0.011 0.003 0.014
Sweet orange 0.031 0.008 0.039
Loquat 0.08 0.02 0.1
Walnut 0.26 0.067 0.327
Apple 0.06 0.015 0.075
Mulberry 0.2 0.052 0.252
Apricot 0.186 0.05 0.234
Guava 0.073 0.018 0.092
Pomegranate 0.078 0.02 0.098
Ber 0.048 0.012 0.06
Rajouri, J & K Gupta and Sharma (2014)78

79. Technique Effects
Nitrification inhibitors (e.g.,
nitrapyrin, DCD, DMPP).
Slows the conversion of NH4 to NO3, reducing
the potential for N2O and N2 losses and NO3
leaching.
Urease inhibitors (e.g. NBPT)
Slow conversion of urea to NH4, allowing
improved NUE by reduced NH3 losses and
delayed N supply.
Slow and controlled release
fertilizers (mainly urea based)
Slows nutrient release, leading to reduced NH3
and N2O emission and to less NO3 leaching.
Urea deep placement
Improves NUE by reducing NH3 losses and by
targeting roots.
Table-35: Green economy perspective on N2O mitigation through
improved fertilizer techniques.
NERC, UK Mark et al. (2014)79

80. Table-36: Efficiency of nitrification inhibitors in mitigating
nitrous oxide emission.
Nitrification inhibitors Mitigation (%)
Dicyandiamide (DCD) 13-42
Neem cake 10-21
Neem oil 15-21
Nimin 25-35
Coated Ca-carbide 12-29
Thiosulphate 15-20
New Delhi Pathak et al. (2010)80

81. 26.63
24.18
32.55
29.85
0
5
10
15
20
25
30
35
N1 + DCD N2+DCD N3 + DCD N4 + DCD
N2Oemissiondecreased(%)
Treatments
N1 + DCD N2 + DCD N3 + DCD N4 + DCD
Treatment Detail :-
N 1 = 100% of N (urea)
N 2 = 75% of N (urea) + 25% of N (rice
straw) + DCD (15% of applied N)
N 3 = 75% of N (urea) + 25% of N (cow
dung) + DCD (15% of applied N)
N4 = 75% of N (urea) + 25% of N
(poultry dung) + DCD (15% of
applied N)
*DCD = Dicyandiamide
Table-37: Influence of nitrification inhibitor dicyandiamide (DCD)
on reduction of cumulative N2O emission.
Malaysia Mohamed et al. (2016)81

82. Carbon credit
 A permit that allows a country or organization to produce a
certain amount of carbon emissions and which can be traded if
the full allowance is not used.
 Carbon credits are a key component of national and
international attempts to mitigate the growth in conc. of GHGs.
 A credit can be sold in the international market at a prevailing
market rate.
At present, price of 1 carbon
credit is 10.5 $
82

83. Fig. 11: Leading countries in earning carbon credit
UNFCCC (2011)83

84. Types of carbon credit
1) Carbon offset credits: consists of
clean forms of energy production
through wind, solar, hydro and
biofules.
2) Carbon reduction credits: consists of
the collection and storage of carbon
from our atmosphere through
reforestations, ocean and soil.
84

85. Leading companies of India among top 200 carbon clean
firms in the World
Rank- 68 Rank- 106
Rank- 114
Rank- 139
Rank- 153
Rank- 155
Rank- 166
85

86. Conclusion
 Global climate changes and increasing climatic variability are likely to
exert pressure on fruit quality and production system.
 Available adaptation strategies can help to reduce negative impacts of
climate change on fruit crops like bagging of mango and pomegranate fruit
reduce incidence of physiological disorders, use of certain chemicals like
anti-transpirant chitosane at 2% increase bunch weight in banana, kaolin
at 5% reduce sunburn intensity and terra alba gave maximum premium
grade fruit 90.6% in pomegranate and 3% sucrose solution found effective
for recovering of heat shock affected mango plants.
 In case of mitigation, fruit crops were indeed potential to reduce GHGs
through carbon sequestration. CO2 sequestration potential of santol
(203.62 t/ha), mango (122.34 t/ha), sapota (38 t/ha), etc. can be comparable
to forest trees and the fact that these crops are also providing food and
income to the farmers. 86

87. Future thrust
1. Strengthen research on development of ‘adverse climate tolerant’ genotypes
and land-use systems to ensure adequate food production.
2. Provide value-added climatic risk management services to farmers in the
form of reliable weather forecasts and associated agro-advisories for
farmers in different agro-climatic region.
3. Provide financial incentives to farmers for resource (carbon, water, energy)
conservation and efficient use.
4. Enhance investment in water storage and efficient water-use technologies.
5. Mobilize national and international opinion to make food security and
poverty alleviation central in climate negotiations.
87