Ph. D. Credit Seminar 12/12/19

1. Welcome to
PG seminar Series
2019-20

2. Major Guide
Dr. M. J. Patel
Associate Professor
Dept. of Horticulture
B. A. College of Agriculture, AAU,
Anand- 388110
Minor Guide
Dr. N. J. Jadav
Professor & Head
Dept. of Soil Science & Agril. Chem.
B. A. College of Agriculture, AAU,
Anand- 388110
SPEAKER:
Panchaal Bhattacharjee
3rd Sem., Ph. D. (Hort.) Fruit Science
Reg. No. :1080118005
Course no. : FSC 691 (0+1)
Impact of climate change on
fruit crops

3. Source- IPCC,2014
3

4. Source- IPCC,2014
4

5. Source- IPCC,2014
5

6. Source- WHO estimations
6

7. Seminar Outline
Introduction
Climate change-Current scenario
Impacts on production of fruit crops -with relevant case studies
Impact on Phenology
Impact on Physiology-
a)Increased CO2 effect
b)Temperature induced effect
Impact on fruit quality
Impact on area suitability
Impact on disease and pest incidents
Adaptation and Mitigation strategy
Conclusion
Future thrust 7

8. Global warming
Poverty
Climate change Food insecurity Malnutrition
Yield decline
Climate change-
The definition provided by UNFCCC ‘a change that is attributed directly or
indirectly to human activity which alters the composition of the global atmosphere
and that is in addition to natural climate variability observed over comparable
time periods’.
8

9. According to IPCC report it has been projected that; there
is a probability of 10–40 % loss in crop production in
India by 2080-2100 due to global warming .
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.
The Earth’s climate, although relatively stable for the past 10,000 years or so
and it has always been changing at natural rate, mainly due to natural causes
such as volcanic activity.
Best estimates predict that to increase global annual mean
temperatures in the range of 1.8-4˚C during the year 2100, resulted
to increase variability in rainfall and enhance frequency of extreme
weather events such as heat waves, cold waves, droughts and
floods.
Brief timeline of Climate change
9

10. Causes of climate change
Natural Anthropogenic
1) Continental drift
2) Volcanoes
3) The Earth’s tectonic tilts
4) Ocean Currents
5) Intensity of Solar
Radiation
1) Green Houses Gases
• Carbon dioxide (CO2)
• Methane (CH4)
• Nitrous oxide (NO2)
• Chloro floro carbons (CFCs)
• Ozone (O3)
• Water Vapors (H2O)
2) Land Use Change
• Deforestation
• Urbanization
10
10

11. 2016-Warmest year on record (Source-NASA)
Global mean surface temperature will rise between 1.8°C to 4.0°C by
2100 (IPCC, 2014)
Changes in the 21st century: Global mean temperature
Current scenario
11

12. CO2- Pre-industrial levels of 280 ppm, will jump to 700 ppm by the
end of the century (IPCC, 2014)
Changes in the 21st century: C02 level
12

13. Changes in the 21st century: Ice melting
Changes in the 21st century: Sea Level
Decrease in ice mass
in Antarctica- 127
Giga tonnes/yr
Increase in sea level-3.3
mm/yr, worldwide.
13

14. Factors involved in climate change
Greenhouse gases
 Carbon dioxide (CO2),
 Methane (CH4)
 Nitrous oxide (N2O)
are the three main GHGs responsible for climate change.
Carbon dioxide is responsible for 77% of global warming over a
100 year period and hence the most important GHGs (Climate
analysis indicators tool, 2011).
Fig-
14

15. Melting glaciers
polar caps
Decreased
reflective surface
Rising sea level
Flooding of costal
regions
Deforestation
Fossil fuel
combustion
CO2
Aerosol
propellants CFC-11
Refrigerants CFC-12
Warm
oceans
Decreased CO2
solubility in water
Garbage
Swampy
rice fields
Cattle
CH4
N2O
Biomass
Burning-
fertilizer
O3
Photochemical
reaction
Climate
change
Elements involved in Climate change
15

16. High value
crops
Quality food
High
productivity
Reestablishment is difficult
New Variety (15-20 years)
Long Juvenile Phase
Slow to adapt
Why Fruit Crops ???
Long generation species (perennial herbs, shrubs and trees) would be a
less able to respond to new selective pressures than short generation
species (Rosenheim and Tabashnik, 1991).
16

17. Climatic variables affecting fruit production
 Temperature
 Rainfall
 Light
 Relative Humidity
Adversities like
 Drought
 Hail
 Frosts
 Most of fruit crops are very sensitive to temperature
requirement in terms of initiation of floral buds, full
blooming, fruiting, colour development and maturity.
 Temperate fruit crops need a chilling temperature for
a specific period to start flowering consequently
increased fruit set.
 Flower drop is common when temperature is high in
the tropical areas.
17

18. 7TH OCTOBER, 2019
Climate change
issues relating to
fruit production
making a
buzz!!!!!
18
Pomegranate
Apple

19. Impact of Climate Change on
Mango, in recent years
making the headlines
19

20. 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 Varu and Viradia (2015) 20

21. 24TH MAY 2019
21

22. 22
November, 2019

23. Variation in climatic situation leading to a huge economic loss
world wide
23

24. For every 1⁰C increase in temperature above 20⁰C during the
crop growing season in India, there are about 70 suicides on
average.
24

25. 25
Failure in
prediction !

26. • One of the best-documented effects of climate change is the
changing timing of plant growth activity, known as change in
phenology.
• Phenology is broadly defined as the timing of annually recurring
biological events.
• Alteration in between the duration of vegetative and reproductive
phase is taking place due to climate change.
• Flowering is one of crucial stages for fruit development affecting the
production and productivity.
Impact on Phenology
(Cleland et al., 2007).
26

27. Whiley et al. (1989) observed grafted mango
plants grown at 20°C days/15°C nights
required 20 weeks while at 30°/25°C
required only 6 weeks to complete a growth
cycle with 3 times more amount of flush.
Impact on Phenology
30/25°C 25/20°C 20/15°C 15/10°C
27Australia

28. Early spring temperature increase of 0.45
◦C/decade (1973-2009) resulted in
advanced bloom of 1.6 d/decade in apple
and pear (Grab et al., 2011).
Impact on Phenology
28South Africa

29. Crops Climate change Impact on Phenology
Grape Increase of 2-3 °C average growing temperature (1964-2009)
resulted in 13-19 days acceleration in bloom, veraison, and
harvest dates in grapes in Alsace, France. (Tomasi et al., 2011)
Veraison will be advanced upto 23 days for cv. ‘Riesling’ & cv.
‘Gewurtztraminer’ by 2100. (Deuchne et al., 2010)
Advancement of 17 days (bud burst- flowering) and 46 days
(sprouting- harvest) by 2100 for cv. ‘Gewurtztraminer’.
(Jorquera-Fontena & Orrego-Verdugo, 2010)
Pear, Peach, Apricot Advancement in the harvesting period of pear, peach and Japanese
apricot in Japan (Suijira et al., 2009)
Cherry Peak flowering date of cherry tree occurred 5.5 d earlier over the
25 years due to variation in February and March temperature.
Temperature warmed by 1.8 over the last 25 years (Miller et al.,
2007)
Plum and Apricot Flowering dates of P. davidinia and P. armeniaca were advanced
in response to increase in temperature over the years 1980-2004 in
Beijing. (Pieling et al., 2006)
29

30. Citrus phenology is particularly sensitive to changes in temperature and
precipitation.
The initiation of flowering requires a period of either cool or drought
conditions to release dormancy, followed by a period of warm conditions with
sufficient moisture availability to induce budburst.
Fitchett et al., 2014 30Iran

31. Objective-To investigate shifts in the peak
flowering (80% bloom, pooled across
orchards) dates of five citrus types (orange,
tangerine, sweet lemon, sour lemon and sour
orange), and their response to changes in
temperature and precipitation, in the Iranian
cities of Gorgan, Kerman and Shiraz, for the
period 1960–2010.
A. Gorgan: Coastal, warm semi-humid
B. Kerman: Semi arid climate
C. Shiraz: Foothills, mild temperate
The climate data for this study were obtained
from the Iranian Meteorological Organization,
and comprise daily Tmax, Tmin and rainfall
records for the period 1960–2010.
Fitchett et al., 2014
Fig 1- Experimental locations in Iran
31Iran
A.
B.C.

32. The trends in delayed flowering dates for
Gorgan (Fig -2A) are only significant for
orange and tangerine (r = 0.41, p = 0.008;
r = 0.32,p = 0.035, respectively), with
considerably weaker correlations for sweet
lemon (r = 0.24, p = 0.1285), sour lemon (r
= 0.21, p = 0.1976)and sour orange (r =
0.16, p = 0.3467).
Year
Dayoftheyear
A progressive shift towards common
flowering dates for sour orange and
sour lemon, and for orange, tangerine
and sweet lemon (Fig -2A).
Fig-2A
Fig-2B
The data reflect advanced flowering by
0.12 d/yr for orange to 0.17 d/yr for sweet
lemon and sour orange, and shifts of 0.15
d/yr for tangerine and 0.16 d/yr for sour
lemon. (Fig -2B)
Dayoftheyear
Year
Fitchett et al., 2014 32
Iran

33. Even stronger advances in flowering
dates are demonstrated for all five
citrus types in Shiraz (Fig -2C).
Reflecting advances in flowering dates
of between 0.56 d/yr for sour lemon and
0.65 d/yr for sweet lemon and shifts of
0.60–0.62 d/yr for tangerine, sour orange
and orange.
Dayoftheyear
Fig -2C
Fitchett et al., 2014 33Iran

34. Fig -3. Deviation of mean annual T max, T min
and precipitation from 1960 to 2010 mean for
(A) Gorgan, (B) Kerman and (C) Shiraz
Fitchett et al., 2014
Decrease in rainfall-4.69mm/yr in Gorgan
34Iran

35. Orange Tangerine Sweet lemon Sour lemon Sour orange
Gorgan
Tmax (r) -0.13 0.07 -0.21 -0.06 -0.26
Change (d/◦ C) 0.49 +0.62 0.98 0.35 1.48
Tmin (r) 0.22 0.12 -0.08 0.02 0.13
Change (d/◦ C) -1.06 +0.59 0.43 +0.12 -0.86
Precipitation (r) 0.28 0.30 0.05 0.02 0.13
Change (d/mm) -0.01 -0.01 -0.002 -0.001 +0.01
Kerman
Tmax (r) *0.45 *0.43 *0.59 *0.35 *0.40
Change (d/◦ C) −2.78 −2.48 −3.08 −1.85 −2.96
Tmin (r) *0.57 *0.61 *0.52 *0.56 *0.47
Change (d/◦ C) −3.66 −3.93 −3.52 −3.15 −3.65
Precipitation (r) *0.46 *0.38 *0.41 *0.32 *0.45
Change (d/mm) +0.05 +0.05 +0.05 +0.03 +0.06
Shiraz
Tmax (r) *0.60 *0.54 *0.59 *0.50 *0.59
Change (d/◦ C) −7.45 −6.99 −7.86 −6.14 −7.41
Tmin (r) *0.65 *0.67 *0.62 *0.53 *0.62
Change (d/◦ C) -5.27 -5.32 -5.10 -4.34 -5.47
Precipitation (r) 0.06 0.08 0.04 0.01 0.08
Change (d/mm) +0.01 −0.01 +0.01 −0.01 +0.01
Table 1- Correlation between flowering dates for the five citrus types and the mean annual climate
parameters for Gorgan, Kerman and Shiraz, 1960–2010.
Significant relationships are highlighted by an asterisk.
Fitchett et al., 2014 35

36. Correlation of Climatic Parameters with Flowering
Characters of Mango
Int. J. Pure App. Biosci. 6 (3): 597-601 (2018 ) DOI: http://dx.doi.org/10.18782/2320-7051.6523
Objective- To determine the mango
cultivars correlations along with their
climatic parameters of direct and indirect
effects in mango to determine the
contribution of most important characters
towards yield.
36
Rajatiya et al. (2018)JAU, Junagadh

37. Climatic parameters Mean of
climatic
parameter
for
effective
period
Days to
initiation
of flowering
Mean of
climatic
parameter
for effective
period
Days to
fruit set
Maximum temperature (˚C) 33.1 -0.991** 32.3 -0.855**
Minimum temperature (˚C) 14.1 -0.979** 13.5 -0.822**
Day temperature (˚C) 31.3 -0.989** 27.6 -0.836**
Night temperature (˚C) 18.9 -0.984** 18.2 -0.821**
Rainfall (mm) 11.6 0.299 11.6 0.039
Relative humidity (%) 50.8 0.529** 50.5 0.804**
Wind speed (km/hr) 2.3 0.949** 2.8 -0.672**
Bright sunshine (hrs/day)
8.4 -0.778** 8.3 -0.672**
Table 2: Correlation of climatic parameters with days to flower initiation and fruit
set in mango
Rajatiya et al. (2018)JAU, Junagadh 37

38. Climatic parameters Mean of
climatic
parameter
for
effective
period
Male
flowers (%)
Hermaphrodite
flowers (%)
Total no. of
flowers/panicle
Maximum temperature (˚C) 32.3 -0.656** 0.658** -0.413*
Minimum temperature (˚C) 13.5 -0.663** 0.665** -0.403*
Day temperature (˚C) 27.6 -0.669** 0.671** -0.442*
Night temperature (˚C) 18.2 -0.666** 0.668** -0.405*
Relative humidity (%) 50.5 0.733** -0.738** 0.458*
Wind speed (km/hr) 2.8 -0.619** 0.617** -0.287
Bright sunshine (hrs/day) 8.3 -0.615** 0.613** -0.408*
Table 3: Correlation of climatic parameters with percentage of flowers (male &
hermaphrodite)/panicle and total number of flowers/panicle
JAU, Junagadh 38Rajatiya et al. (2018)

39. In grapes, net photosynthate
assimilation rate was significantly
increased, whereas stomatal
conductance was reduced in elevated
CO2, leading to improvements in
intrinsic water use efficiency. (Pereira
et al., 2009)
70 % increase in biomass in citrus
exposed to 350-650 ppm of CO2
(Kimball et al., 2007)
Impact on physiology
(a) Increasing CO2
But elevated temperature and altered precipitation pattern can
offset the beneficial impact.
39

40. Effects of elevated CO2 on grapevine (Vitis vinifera L.):
Physiological and yield attributes
Vitis 48 (4), 159–165 (2009)
Objective- The main purpose of this
study was to investigate the pattern
of acclimation of grapevine
physiology and yield responses to
elevated [CO2] in Open-Top
Chamber (OTC).
To predict the grapevine
performance in a future scenario of
climate change.
Vila Real, Portugal 40Pereira et al., 2009

41. A gs A/gs Ψmd
OTC-C 15.08 77.4 28.4 -1.48
OTC-E 20.44 82.8 38.7 -1.33
P-value 0.006 0.531 0.022 0.530
A= Net photosynthesis ( μmol m-2 s-1)
gs= Stomatal conductance (mmol m-2 s-1)
A/gs=Intrinsic water use efficiency (A/gs, μmol CO2 mol-1 H2O)
Ψmd= Midday leaf water potential (MPa)
Table 4- Grapevines grown in ambient CO2 (OTC-C), elevated CO2 (OTC-E).
Differences were considered statistically significant when P<0.05 or
tendency for significant when, 0.05<P<0.10
Grape (Vitis vinifera L.) cv. 'Touriga Franca' is taken for the experiment, which is
conducted under ambient (OTC-C, 365 ± 10 ppm) and elevated carbon dioxide
[CO2] (OTC-E, 500 ± 16 ppm) under Open-top chambers.
Pereira et al., 2009
41Vila Real, Portugal

42. Table 5- Stomatal density (stomata mm-2) and leaf tissue thickness (μm) of
grapevines grown in ambient CO2 (OTC-C), elevated CO2 (OTC-E).
Differences were considered statistically significant when P<0.05 or tendency for
significant when, 0.05<P<0.10
Stomatal
density
Total
lamina
Palisade
parenchyma
Spongy
parenchyma
OTC-C 168.1 150.2 46.7 65.5
OTC-E 130.0 171.2 56.4 78.0
P-value <0.001 0.033 0.013 0.045
Yield
(kg·vine-1)
Cluster
(no’s·vine-1)
Cluster
weight
(g)
Shoot
(no’s·vine-1)
OTC-C 2.80 ± 0.26 9.7 ± 1.1 303.7 ± 22.1 18.8 ± 1.7
OTC-E 4.20 ± 0.75 12.1 ± 2.2 367.3 ± 54.2 26.4 ± 2.6
P-value 0.062 0.295 0.047 0.025
Table 6- Yield, cluster number and weight, shoot number, of grapevines grown at
elevated CO2 (OTC-E) and ambient CO2 (OTC-C)
Pereira et al., 2009 42Vila Real, Portugal

43. Seventeen years of carbon dioxide enrichment of
sour orange trees: final results
Global Change Biology (2007) 13, 2171-2183, DOI: 10.1111 / j.1365-2486.2007.01430.x
Objective:
Revealing significant inter-annual
changes in response to elevated
CO2 as the trees grew from
saplings and well into middle-age
reproductive maturity.
Kimball et al., 2007
43
Arizona, USA

44. Table 7: Means, standard errors and statistical significance of difference due to
CO2 level between the means of six organ classes of sour orange trees.
Item
Enriched Ambient
Pr.>F Significance
Mean SE Mean SE
Biomass at final harvest (spring 2005 after 17 years)
Fruit biomass (Kg tree-1) 32.9 2.1 10.9 0.6 0.0001 ***
Leaf biomass (Kg tree-1) 33.6 0.9 26.2 1.1 0.0024 **
Twig biomass (Kg tree-1) 30.1 1.2 26.8 1.6 0.2607 NS
Branch biomass (Kg tree-1) 124.6 6.4 78.8 5.1 0.0309 *
Trunk biomass (Kg tree-1) 110.0 10.9 80.4 2.0 0.1616 NS
Stump biomass (Kg tree-1) 41.0 2.6 26.3 0.3 0.0014 **
Large root biomass (Kg
tree-1)
40.6 1.7 27.7 2.2 0.0035 **
Total biomass (Kg tree-1) 413.8 16.7 274.8 9.0 0.0250 *
*,** and *** indicate significance at 0.05,0.01 and0.001 level of significance
44
Kimball et al., 2007Arizona, USA

45. High
Evaporation
Stomatal
closure
Small influx of
CO2
(b) Temperature-
1. Heat Stress
2. Inadequate chilling for temperate crops
3. Disruption in pollination activity
Reduced Winter Chilling Projected for California
Chilling hours will decline by 30% to 60% by 2050 and by up to 80% by 2100 (http://nca2014.globalchange.gov)
Heat Stress
45

46. Inadequate
chilling
Flower bud abscission
Warm winters in pome
fruits, may abscise flower
primordia (Brown, 1952)
Anthesis
In P. communis, autumnal
warming delayed anthesis,
with the response being
greater for early flowering
cultivars. (Atkinson and
Lucas, 1996).
Fruit set
Flower quality
Fruit quality
Under mild Californian
winter (1950–51) apricot
yield was limited by low
fruit set. (Brown, 1952).
Varying chilling resulted
in variation in size of
apple fruits (Grebeye and
Berg, 2000).
P. avium (Sweet cherry cv.
‘Stella’) subject to low chill
shows reduced flower size
and pedicel lengths.
(Mahmood et al., 1999)
46

47.  Insect pollination- 35 percent of global food
production (Klein et al., 2007) .
 Economic value - $153 billion annually (Gallai
et al., 2009).
 Plant-pollinator interactions: Temporal
(phenological) and spatial (distributional)
mismatches (Hegland et al., 2009)
 Temporal- Apis mellifera advanced their
activity period earlier than their preferred
forage species flowering peaks (Gordo and
Sanz, 2005)
 Spatial- Shifting of areas in impoverished
countries .
 Crop plants more vulnerable-
Self-incompatible
Pollinator limited
Pollinator specific
Disruption in pollination activity
47

48. Influence of Regional Weather Changes on Major
Fruit Production and Productivity of Navsari District
of Gujarat State, India
Current Journal of Applied Science and Technology;35(3): 1-8, 2019; DOI:10.9734/CJAST/2019/v35i330181
Objective:
An attempt to analyse the influence of weather on crop(Banana,
Mango, Sapota, Papaya productivity with special reference to Navsari
District of South Gujarat.
Swati et al., 2019
48
NAU, Navasari

49. Fig-Trend in area production and productivity of fruit crops in Navasari dist. 49

50. Table 8. Compound annual growth rate (CAGR) for banana, mango, sapota and papaya
Crop Compound annual growth rate (CAGR)
Area Production Productivity
Banana 11.47** 11.11** 0.33 NS
Mango 3.25** 4.96* 1.65 NS
Sapota 1.83** 3.91** 2.04**
Papaya 12.68** 15.06** 2.10**
** Significance at 1 percent level and * significance at 5 percent
level (student t test at p < 0.01 and 0.05
Swati et al., 2019 50

51. Table 9. Correlation of production, productivity with mean annual temperature and total annual
rainfall for major fruits of the Navsari
Crop Mean annual temperature Total annual rainfall
Production Productivity Production Productivity
Banana -0.50(-50%) 0.33(33%) -0.37(-37%) -0.039(-3.9%)
Mango -0.53(-53%) -0.61(-61%) * -0.34(-34%) -0.007(-0.7%)
Sapota -0.50(-50%) -0.62(-62%) * -0.48(-48%) -0.37(-37%)
Papaya -0.52(-52%) -0.56.(-56%) -0.49(-49%) -0.53(-53%)
** Significance at 1 percent level and * significance at 5 percent
level (student t test at p < 0.01 and 0.05
Swati et al., 2019
51

52. Strawberry yield efficiency and its correlation with
temperature and solar radiation
Horticultura Brasileira (2013) 31: 93-99
Objective: To assess the variation of
temperature and solar radiation
on strawberry cv. Camarosa
production.
52Palencia et al., 2013

53. Fig 4: Mean temperature
and solar radiation
for the years 2003-
2006.
Palencia et al., 2013
Cv. Camarosa, Strawberry
plants were transplanted in
mid October every year
(2003-06).
Observation was recorded
every week-
Early yield- 1st Jan-31st Mar
Total yield- Early yield + Apr-
May(late production)
53

54. Year Oct. Nov. Dec. Jan. Feb. Mar. Apr. May. Mean±SD
Temperat-
ure (⁰C)
2003-04 18.4 14.1 11.1 11.1 11.6 13.2 15.0 16.8 13.9±2.5
2004-05 18.3 12.7 10.8 7.5 8.3 13.3 15.9 19.0 13.2± 4
2005-06 17.5 12.0 10.7 8.3 9.7 13.3 13.3 19.7 13.1±3.6
Mean±SD
2003-06
18.1
±0.4
12.9
±0.9
10.9
±0.2
9.0±
1.5
9.9±
1.4
13.2
±0
14.7
±1.1
18.5±
1.3
13.4±0.6
Radiation
mJ/m2
2003-04 12.1 9.3 7.7 8.3 11.6 16.6 21.8 22.2 13.7±5.4
2004-05 13.3 10.6 9.6 11.1 13.9 15.2 23.3 25.8 15.4±5.6
2005-06 12.7 10.1 8.2 9.1 11.8 16.0 19.6 24.3 14.0±5.2
Mean±SD
2003-06
12.7
±0.5
10.0
±0.5
8.5±
0.8
9.5±
1.2
12.4
±1
15.9
±0.6
21.6
±1.5
24.1±
1.5
14.3±0.2
Table 10- Air temperature and solar radiation of each month during three crop
cycle (2003-2006).
Palencia et al., 2013
54

55. Early production (2003-06)-(Jan-Mar)
Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Yield(g/plant) 0 0 4 8.5 9.5 21.4 33.9 31.1 16.8 38.2 50.9 72.9 58.3 112.5
Late production (2003-06)-(Apr-May)
Week 15 16 17 18 19 20 21 22 23
Yield(g/plant) 102 83.1 108.2 51.7 58.7 49.3 46.7 42.3 0
Table 11. Mean early and late production recorded each week during three crop
cycles (2003-2006)
Palencia et al., 2013
There was a significant linear relationship of early production with temperature (R2=
0.86*) and mean radiation (R2= 0.73*).
A quadratic relationship of total production with temperature (R2= 0.69) and mean
radiation (R2=0.64) which indicates gradual decrease in yield with increasing
temperature specially in the late production of strawberry.
55

56. Fig-5 : Statistical early yield model used as related mean radiation and
temperature for the years 2003-2006.
NS= non-significant; *; **significant at p≤0.05 and p≤0.01, respectively
Palencia et al., 2013
(R2= 0.86*)(R2= 0.73*)
Mean temperature (⁰C): 2003-06Mean solar radiation (mJ/m2): 2003-06
56

57. Fig- 6 : Statistical total yield model used as related mean radiation and temperature
for the years (2003-2006).
NS = non-significant, *, ** significant at p≤0.05 and p≤0.01, respectively
Palencia et al., 2013
(R2= 0.64*) (R2= 0.69*)
Mean solar radiation (mJ/m2):2003-06 Mean temperature (⁰C):2003-06
57

58. Apple fruits had a higher
sugar content in extended
harvesting (Brooks & Fisher,
1996).
Impact on quality of fruits
Direct sunlight & high
temperature impact
Lower antioxidant activity in
‘Kent’ strawberries under
warmer days (25 °C) &
warmer nights (18–22 °C)
(Wang and Zheng, 2001)
 Impacts fruit size,
firmness, color development
etc.
- Earlier ripening by the
end of this century in
California may lead to
reduced grape quality in
the region (Hayhoe et al.,
2004)
58

59. Fruit firmness
Mandarin exposed to direct sunlight (35 °C) were 2.5 times firmer than those on the
shaded side (20 °C) (Woolf et al., 2000).
Decreased cell wall enzyme activity (cellulase and polygalacturonase) under higher
temperatures during growth and development delays ripening.
Affects chemical composition of fruits with variable precipitation and moisture stress
trends.
Abnormal coloration in grapes cv. Aki Queen due to high temperature
(Webb et al., 2007)
Fruit color-
59

60. Rind puffing of Satsuma mandarin due to high
temperatures and heavy rain (left) .
The rind separates from the flesh, undermining
the quality of fruit and storage.
Sun burned sweet orange fruits due to
high temperatures.
60

61. Climate influences on grape production, wine
composition and quality in Bordeaux, France
Am. J. Enol. Vitic. (2011), 51 (3): 249-255
Objective-The study assessed
the impact of increasing
temperature on maturity , acidity
and color of wine grapes.
61Bordeaux, France Jones et al., 2011

62. Fig -7: Effect of growing season temperature on maturity of grape.
Jones et al., 2011 62Bordeaux, France

63. Fig- 8 : Effect of growing season temperature on acidity of grape.
Jones et al., 2011 63Bordeaux, France

64. Fig- 9 : Effect of warmer ripening temperature on berry colour of grape
Jones et al., 2011 64Bordeaux, France

65. Impact on area suitability
 The increase in temperature from 0.7-1.0°C may shift the area
suitable presently for the quality production of Dashehari and
Alphonso varieties of mango (Rajan, 2008)
 Rise in temperature by 0.2°C may result into dramatic reduction
areas suitable for development of red colour on guava (Rajan,
2008)
65

66. An assessment of global banana production and
suitability under climate change scenarios
GERMAN CALBERTO1, CHARLES STAVER2 AND PABLO SILES3.
1 Bioversity International, Cali, Colombia - CGIAR Research Program on Climate Change, Agriculture and Food
Security (CCAFS)
2 Bioversity International, Montpellier, France - CGIAR Research Program on Climate Change, Agriculture and
Food Security (CCAFS)
3 CIAT, Managua, Nicaragua (previously Bioversity International, Turrialba, Costa Rica)
In; Climate change and food systems: global assessments and implications for food security and trade, Aziz
Elbehri (editor). Food Agriculture Organization of the United Nations (FAO), Rome, 2015.
Objective- To asses the changes in area
suitability worldwide in coming
future due to change in climate.
66

67. Category Current 2030 2050 2070
Not Suitable 141 224 300 134 962 475 132 472 650 130 299 200
Subtropical 41 201 350 40 346 450 40 194 675 39 829 175
Tropical 43 189 025 46 952 150 49 593 750 52 132 700
Fig- 10:
Table 12- Total area (in km2) for three categories of suitability for
banana currently and with climate change.
Calberto et al., 2015 67

68. In summary, based on this analysis of 24 sites:
 All sites demonstrate the linear increase in
temperatures – average as well as minimum
and maximum temperatures – which has made
climate change a concern for humankind.
Also indicating increase in suitable areas due
to temperature rise , mainly areas with ≥24⁰C
average temperature.
 Only three sites show trends towards
extremely high temperatures – two in India (i.e,
Bagalkot and Uttar Pradesh) and one in
Argentina – which may limit banana
growth.
Table 14- To further explore the
implications of climate change for banana-
growing, 24 sites were identified
worldwide, where banana is an important
crop, located in contrasting climatic zones
in Latin America, Africa and Asia.
Calberto et al., 2015 68

69. Fig- 12a
Fig- 12b
Fig- 12:Predicted increase in
banana area suitability under
climate change scenario.
12a: Current area suitability.
12b: 2050’s predicted area
suitability.
Calberto et al., 2015 70

70. Impact on pest and disease incidence
 Change in the flowering times in temperate regions leads to
ecological consequences such as
 Introduction of new insect-pests.
 Attaining of a pest status by non-pest insects.
 Breakdown of resistance to certain insect pests and
diseases.
Willis et al., 2008
71

71. Impacts on Insects :
Climate change could lead to :
Changes in geographical distribution
Changes in population growth rates
Increased overwintering
Increase in the number of generation
Extension of developmental seasons
Changes in crop-pest synchrony of phenology
Changes in interspecific interactions
Increased risk of invasion by migrant pests
(Parmesan, 2007)
72

72. Crop Insect pests References
Apple A warming of 2 °C will cause extra 5
generations / year in woolly aphids.
Harrington et al.,
2007
Apple Every 1 °C rise will reduce winter mortality of
stink bug (Halyomorpha halys) by 15 %.
Kiritani, 2006
Banana Severity of burrowing nematode (Radopholus
similis) will increase with drier climates.
I. van. den Bergh,
2008
Citrus Increased production of shoots in spring and
summer would increase the population of
leafhoppers (Dilobopterus costalimai,
Oncometopia facialis and Acrogonia sp.) the
main vectors of citrus variegated chlorosis.
Milanez et al., 2002
Favourable chances of pest incidents under climate change scenario
73

73. Invasive diseases-
• Climate change could alter stages and rates of development of pathogen,
modify host resistance and physiology of host-pathogen interactions (Cockley
et al., 1999)
• Warming is most deleterious for tropical insects (physiological optima) than
species at higher latitudes (Ghini et al., 2011)
• A reduction in vector population may decrease the importance of some
viruses in tropical regions. e. g.
Pineapple wilt (mealy bug) (Sether et al., 2001)
Papaya ring spot (aphid) (Rezende and Martins, 2005)
Citrus leprosis (mealy bug) (Feichtenberger et al., 2005)
Citrus leprosis
74

74.  Sigatoka disease has occurred in devastating proportion in
Maharashtra, where it was never considered a problem, which is
due to change in climate causing rise in temperature.
 The Fusarium wilt race 1 & 2 resistant, Cavendish group are
increasingly infected by tropical race-4 in Asian countries is of
great concern and the entry of this race-4 would ruin the banana
industry in India.
NRCB annual report, 2012 75

75. Earth Syst. Dynam., 3, 33–47, 2012 DOI:10.5194/esd-3-33-2012
Downscaling climate change scenarios for apple pest
and disease modelling in Switzerland
Objective: To examined the influence of
climate change in Switzerland on
the future threat of codling moth
and fire blight.
76
Switzerland Hirschi et al., 2012

76. Fig 13 : Seasonal (top row) and daily (bottom row) cycles of mean temperature (TAVG), precipitation
(PREC) and global solar radiation (SRAD) for the station Wadenswil. Synthetic data are displayed in
red, observed data in black. Database is 29 yr of insitu observations (1981–2009) and 100 yr of
synthetic weather.
Hirschi et al., 2012 77
Switzerland

77. ∆Tmean ∆Pmean ∆Pint ∆Pfreq ∆P01
Northeastern Switzerland (CHNE)
Winter 2.3 0 3 −2.9 −2
Spring 2 3.4 5.1 −1.6 −3
Summer 2.6 −10.4 2 −12.2 −17
Autumn 2.2 0.9 7 −5.7 −3
Western Switzerland (CHW)
Winter 2.3 1.4 4.9 −3.3 −2
Spring 2 −1.1 4 −4.9 −4
Summer 2.7 −17.2 −3 −14.6 −19
Autumn 2.2 −2.4 5 −7 −8
Southern Switzerland (CHS)
Winter 2.4 9.7 5 4.5 5
Spring 2.3 −6.6 −0.6 −6 −3
Summer 2.9 −13.2 −2.5 −11 −13
Autumn 2.3 −3.8 2.4 −6.1 −5
Table 13- Median climate change signals (2045–2074 vs. 1980– 2009) of mean temperature (∆Tmean) and mean
precipitation(∆Pmean ) as well as of precipitation intensity (∆Pint) frequency (∆Pfreq), and dry-to-wet transition
probability (∆P01) for the three Swiss domains and four seasons.
Figures in bold denote significant signals with respect to the distribution of the multi-model climate change scenarios (5%significance
level).
Hirschi et al., 2012 78Switzerland

78. Fig 14 : The flight start in spring from synthetic weather for present (“ctrl”, top panel) and
future (“scen”, bottom panel) climate at the stations Wadenswil and Magadino.
 The p-values for the Wilcoxon- Mann Whitney (WMW) and the Kolmogorov-Smirnov (KS) tests are displayed for the difference
between flight start from present-day and future synthetic weather.
Hirschi et al., 2012 79Switzerland

79. Fig. 15: Occurrences of important codling moth life phases for present (“ctrl”, blue) and future (“scen”, red, median
signal of the multi model projections) climate at the station at W¨adenswil .
The y-axes display the life phases for three generations (denoted “gen1–3”) over one year ,starting with the flight start in
spring). The box plots were derived from synthetic weather data representing 100 yr in the present and future climate.
The underlined phase “Start larvae hatch gen3” was used for the assessment of the potential risk of a 3rd codling moth
generation.
Occurrence [DOY]
Hirschi et al., 2012 80Switzerland

80. Fig 16 : The number of fire blight infection days per year from synthetic weather for
present (“ctrl”) and future (“scen”) at the stations Wadenswil (left panels)
and Magadino (right panels).
Hirschi et al., 2012
81
Switzerland

81. Adaptation
Necessary to limit estimated potential risks of the unavoidable residual climate
change now and in coming decades, considering the future vulnerability.
(FAO, 2012)
Sources- FAO, NICRA,
Maplecroft 82

82. Despite of rising atmospheric CO2, food production in future is
uncertain with global warming and altered precipitation.
There is limited information regarding realistic impacts of pests and
diseases in a changing climate, which otherwise may influence future
food security.
Not only productivity but also fruit quality will be impaired.
Loss in plant diversity and area suitability will further increases the
problem.
Under such threats in global fruit production a plan based strategic
scientific assessment of such impacts should be quantified with
adaptation and mitigation approaches.
83
Holistic approach of Adaptation

83. Development of crop simulation models for
perennial tropical and subtropical horticultural
crops
Assessment of combined
impact of climatic variables
Improvements in existing
models
Strategies to mitigate changes in food quality
Wild germplasm conservation and utilization
Mitigation
Path Ahead
84

84. IPCC contribution to climate science and policy making

85. Impact on
phenology
 Citrus: Significant co-relation of flowering advancement with the changes in
maximum and minimum temperature over a period of the time.
 Mango: Flowering was delayed due to increased of day/night temperature and cloudy
weather.
 In apple and pear advanced bloom in temperate regions of South Africa due to early
spring temperature increases.
Impact on
physiology
CO2 effect:
 Elevated concentration of CO2 resulted into increase net photosynthates along with
reduction in stomatal conductance which ultimately leads in higher accumulation
plant biomass and intrinsic water use efficiency in citrus and grape, respectively.
High temperature effect:
 Negative correlation of production and productivity in banana, mango, sapota and
papaya with the temperature and rainfall in Navasari district of Gujarat, India
 Reduction in total yield of strawberry as affected by increase in temperature and
solar radiation.
Impact on fruit
quality
 Grape: Level of acidity and pigment anthocyanin levels declines towards warmer
temperature in wine varieties of grape.
 Citrus: Mandarin exposed to direct sunlight (35 °C) gets delay in ripening due to
decreased cell wall enzyme activity; it also affects chemical composition of fruits
with variable precipitation and moisture stress trends.
Impact on area
suitability
 Mango: Shift in area suitable for quality production of Dashehari and Alphonso due
to increase in temperature from 0.7-1.0°C
 Guava: 0.2°C may result into dramatic reduction areas suitable for development of
red colour guava.
 Banana: Only category of area that will increase through 2070 in the temperature
range of ≥24°C in growing season.
Impact on pest
and disease
incidence
 Apple: Projected shift in the codling moth occurrence in apple and duration of life
phases relevant for pest control under future climate conditions.
Conclusion
86

86. FUTURE STRATEGIES
Need to study on physiology, phenology, growth, yield and quality of fruit crops
to elevated temperature, CO2 and excess and deficit water stress.
Identification and development of crops and varieties tolerant to heat, water and
climate aberrations for different agro-ecological regions and growing seasons.
Development of crop simulation model for horticultural crops for enabling
regional impact, adaptation and vulnerability analysis.
87