7. Segnalini et al., in press
Distribution of Mediterranean summer THI anomalies versus CliNo
(climate normal, 1971–2000 period) for the four decades 2011–2020,
2021–2030, 2031–2040, and 2041–2050
18. Effects of elements of climate change on grassland
productivity and herbage quality in Central Europe
Grassland
productivity
Herbage
quality
Increased
temperature
+/- +/- Depends on water availability
and soil processes
Longer
growing
season
+ 0
Water
scarcity
- -
Extreme
rainfall events
- 0/- Water logging, trafficability
Increased CO2
concentration
+ -
CO2=carbon dioxide
Gauly et al.,2012
20. Trends of the presence of mycotoxins in cereal grains in North Western
Europe over the period 1989-2009 for 3-acetyl-deoxynivalenol (3-Ac-DON) in oats (a),
deoxynivalenol in oats (b) and wheat (c) and of zearalenone in maize (d). The presence of mycotoxins is
indicated by the percentage of samples that contained the toxin above the level of detection.
Except for the presence of DON in wheat (c), the effect of year on the
presence of the toxin was significant.
Van der Fels-Klerx Hietaniemi et al., 2012
22. Region No. of outbreaks
Number of
animals in the
outbreaks
Number of
clinical
cases
Number
of deaths
Number of
animals
slaugthered
Basilicata 63 4,766 152 16
Calabria 427 52,722 10,176 236 9,877
Campania 1 64 3 2
Lazio 62 23,040 431 69 356
Sardegna 6,090 1,294,365 239,178 72,502 159,636
Sicilia 6 853 29 29
Toscana 158 33,988 693 119 560
Total 6,807 1,409,798 250,662 72,973 170,429
Culicoides spp BTV
23. Wittmann et al.,
(2001)
Suitability of sites
in Europe for the
occurrence of
Culicoides imicola
Suitability of sites
in Europe for the
occurrence of
Culicoides imicola
with a 2° C
increase in
temperature
24. Climate change was
associated with a
northwards extension, in
Europe, of Culicoides
vectors
1998: Northern limit C. imicola midge
2004: Northern limit C. imicola midge
1998: Northern range of virus (in sheep)
2004: Northern range* of virus (in sheep)
Purse et al, 2005
C. pulcaris C. obsoletus C. imicola
Current northern limit
Northern limit < 1998
Northern limit
Southern limit
Midge species: potential vectors of BT Virus
25. Positive latitudinal shifts indicate movement toward the north (pole); negative
values indicate shifts toward the south (Equator). Horizontal lines mark the
Median. Open Circles are outliers.
Observed latitudinal
shifts (kilometres)
of the northern
range boundaries of
species belonging to
4 taxonomic groups
(spiders, ground
beetles, butterflies
and grasshoppers),
studied over 25
years in Britain.
26. Overview of
some of the
mosquitoes
and vector-
borne viruses
detected in
Europe in the
last five
years
41. 6.7
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
J F M A M J J A S O N D
Months
Cheeseyield(%)
100
110
120
130
140
150
160
170
Suppliedmilk(q/d)
3,20
3,25
3,30
3,35
3,40
3,45
3,50
J M M J S N
Months
Soxhlet-Henkeldegrees/50ml
14
15
16
17
18
19
20
21
22
23
24
25
10-giu
20-giu
30-giu
10-lug
20-lug
30-lug
09-ago
19-ago
29-ago
08-set
Days
Clottingtime(min)
60
65
70
75
80
MeandailyTHI
35
40
45
50
55
60
10-giu
20-giu
30-giu
10-lug
20-lug
30-lug
09-ago
19-ago
29-ago
08-set
18-set
Days
Milkcreaming(%)
60
65
70
75
80
MeandailyTHI
Adapted from Bernabucci & Calamari, 1998
42. Milk protein fractions (%) in summer (su) and
spring (sp) cows (Bernabucci et al., 2002)
αs-CN β-CN k-CN α-La β-LG sp
SU 1.12A
0.79A
0.27 0.16 0.38 0.29B
SP 1.36B
0.97B
0.25 0.17 0.38 0.18A
A,B = P < 0.01
43. “High ambient temperatures are associated
with higher incidence of health problems”
(Martin et al., 1975; Webster et al., 1983; Silanikove,
2000; Kadzere et al., 2002; Lopes-Gatius et al., 2002)
44. BCS changes in spring (sp) and summer (su)
transition dairy cows
1.0
1.5
2.0
2.5
3.0
3.5
4.0
-30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35
Days from calving
BCS
BCS-sp BCS-su
**
**
**b
a
a
b
c
c
a
a a
b
a
b
Bernabucci et al., 2005
45. Plasma NEFA in spring and summer cows
0.2
0.3
0.4
0.5
0.6
0.7
0.8
LBCS MBCS HBCS LBCS MBCS HBCS
A
B
C
B
B
AB
Summer Spring
BCS groups
mmol/l
46. 0.3
0.5
0.7
0.9
1.1
0 10 20 30 40 50
Days of lactation
BHBA(mmol/l)
TC
WE
Lacetera et al., 1996
* * *
Post-calving plasma BHBA in cows kept
under Thermal comfort or Warm
environment
47. Cows pant to cool off
Respiratory Alkalosis (CO2)
Bicarbonate excretion in the urine
Loss of saliva from drooling
Metabolic Acidosis
Abnormal blood flow in the feet
Increased standing
time
Weakened hooves: connective tissue breaks down;
developing hoof horn is of poor quality; bloody
areas or abscesses may develop on sole
LAMENESS
(Shearer, 1999)
48. • Changes in geographical ranges and incidence of
vector-borne disease
• Changed incidence of diarrheal and other
infectious diseases
• Malnutrition and hunger, and consequent
impairment of child growth and development
• Increased risk of infectious disease,
psychological disorders
• Asthma and allergies; other acute and chronic
respiratory disorders and deaths
• Wide range of public health consequences: mental
health and nutritional impairment, infectious
diseases, civil strife
• Disturbances of ecological systems: effect on
range and activity of vectors and infective parasites
• Changed local ecology of water-borne and food-
borne infective agents
• Changed food productivity (especially crops)
through changes in climate and associated pests and
diseases
• Sea level rise with population displacement and
damage to infrastructure
• Biological impact of air pollution changes (including
pollens and spores); improper actions to reduce GHG
emissions
• Social, economic, and demographic dislocation
through effects on economy, infrastructure, and
resource supply
Indirect effects
• Changes rates of illness and death related to heat
and cold
• Deaths, injuries, psychological disorders; damage to
public health infrastructure
• Thermal stress (altered immunity)
• Changed frequency or intensity of extreme and less
severe weather events
Direct effects
Health outcomeMediating process
MEDIATING PROCESSES AND DIRECT/INDIRECT HEALTH EFFECTS
Adapted from McMichael, A.J., A. Haines, R. Sloof, and S. Kovats (eds),
(1996). Climate Change and Human Health (Geneva: WHO, WMO, UNEP)
49. Post-colostral increase of plasma Ig in
Holstein calves (Lacetera, 1998)
0
5
10
15
20
0 4 8 12 16 20 24 36
Hours of life
PlasmaIg,g/l
__
Thermal comfort
__
Heat stress
*
*
51. SP: either day-time (9-20 h) or night-time (21-8
h) THI were below the UCTHI (72)
SU: daytime and night-time THI were 79.5 ± 2.9
and 70.1 ± 4.7, respectively
THI data
(summer 2003)
Heat wave - Duration (d) - Max THI - Mean THI > 72
1 5 83.4 78.9
2 6 85.8 79.1
3 15 90.5 79
52. Isolines of summer temperature humidity index (THI) anomalies for the
year 2003 versus CliNo (Climate Normal, 1961-1990 period)
Segnalini et al., 2011
53. ____________________________________________________
Years Winter Spring Summer Autumn Total
______________________________________________________________________
2002 12,562 11,773(0.9) 15,652(1.2) 14,180 54,169
2003 13,850 13,077(0.8) 21,830(1.4) 14,106 62,836
2004 13,095 11,947(0.9) 15,020(1.1) 13,264 53,326
2005 11,930 11,063(0.9) 12,773(1.1) 11,982 47,748
2006 11,529 10,579(0.9) 14,200(1.2) 12,778 49,086
2007 11,259(0.9)11,275 15,165 15,229(1.2) 52,928
02-07 74,225 69,716(0.9) 94,640(1.2) 81,539 320,120
_______________________________________________________________
Number and risk of deaths (in parenthesis)/season
Vitali et al., 2009
54. y = 232.75x - 18,118.73
y = -0.5421x + 443.04
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
Maximum THI
Adjustednumberofdeathsx1,000§§
Break Point
79,6
Adjusted number of deaths (AND) in relation to values of maximum
temperature humidity index (THI).
55. 0
500
1,000
1,500
2,000
2,500
3,000
3,500
30-74 75-77 78-80 81-83 84-86 87-89 90-92
THI categories of maximum THI
Adjustednumberofdeathsx1,000
a
dd
c
ab
a
b
Least squares means (± SE) of the adjusted number of deaths by
categories grouping values of maximum temperature humidity index
(THI).
56. Descriptive statistics of two pig studies
Study period (years) 2003-2007
Geographic area North Italy
Seasonal pattern study
Number of journeys 24,099
Number of in-transit pigs 3,676,153
Number of farms 1,618
Number of in-transit losses 1,161
Number of lairage losses 675
THI-mortality relationships study
Number of journeys 14,316
Number of in-transit pigs 2,156,322
Number of farms 1,209
Number of in-transit losses 664
Number of lairage losses 399
Vitali et al., 2010
57. 95% confidence interval (CI) estimates of risk ratios (RR) for season in
heavy pigs died during transport to the slaughter house and in lairage
RR 95% CI P value
Transport
Winter 0.716 0.566 - 0.905 0.005375
Spring 0.565 0.435 - 0.734 0.000019
Summer 1.107 1.008 - 1.217 0.033166
Fall 1.023 0.906 - 1.154 0.708065
Lairage
Winter 0.645 0.465 - 0.894 0.008624
Spring 0.575 0.409 - 0.808 0.001437
Summer 1.143 1.011 - 1.292 0.031910
Fall 0.973 0.828 - 1.144 0.745810
58. In-transit mortality rate of pigs in relation to temperature-
humidity index (THI).
Two phases regression_in transit pigs losses
0
2
4
6
8
10
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
THI
Mortalityrate*10000
y=1,47827+0,02095 * THI y'=-109,20+1,43013 * THI Mortality rate_moving average
Break point a THI = 78,54
59. Mortality rate at lairage of pigs in relation to temperature-
humidity index (THI).
Two phases linear regression_lairage pigs losses
0
2
4
6
8
10
29
32
35
38
41
44
47
50
53
56
59
62
65
68
71
74
77
80
83
THI
Mortalityrate*10000
y'=-42,76398+0,6048 * THI y=0,69974+0,01389 * THI mortality rate_moving average
Break point a THI = 73,55
60. No riskNo risk
Mild discomfortMild discomfort
DiscomfortDiscomfort
AlertAlert
DangerDanger EmergencyEmergency
Temperature Humidity Index (THI):
Livestock Welfare Categories
61. Maps of summer THI expressed as
livestock welfare categories for CliNo
period (1971-2000) and the decades
2011-2020, 2021-2030, 2031-2040, and
2041-2050.
Different colors correspond to
different livestock welfare categories:
THI < 68, 68 ≤ THI < 72, 72 ≤ THI <
75,75 ≤ THI < 79, 79 ≤ THI < 84, and
THI ≥ 84.
Segnalini et al., in press
62. Maps of vulnerability
index to climate
change based on the
Vulnerability-
Resilience Indicator
Model (VRIM), which
not only presents
sensitivity to climate
change stresses but
reflects also the
adaptive capacity of
different Countries
(IPCC).
64. Global warming affects GHG emissions from
livestock systems
1. Lower quality of feedstuffs: ↑ CH4 emissions from EF.
2. Reduced efficiency in feed utilization due to heat stress:
↑ CH4 emissions from EF.
3. Reduced life expectancy: a rapid turnover of milkers
means that energy inputs and GHG outputs are ‘wasted’ in
the process of rearing heifers before they reach first
pregnancy and lactation.
4. Higher mortality rate: ↑ emissions of GHG for disposal of
carcasses.
5. Higher environmental temperatures: ↑ N2O emission from
manure.
6. Less need for warming in cold climates: ↓ on farm fossil
fuel need.
66. Adaptation (definitions)
• IPCC: Adjustment in natural or human systems in response
to actual or expected climatic stimuli or their effects,
which moderates harm or exploits beneficial opportunities.
• European Commission: Cost-effective actions taken to cope
with a changing climate and aimed at reducing the risk and
damage from current and future impacts.
67. • Structural interventions: Buildings orientation,
insulation and reflectance; shading; ventilation with
or without use of water
• Management: feeding, water sources (crops and
livestock), reproduction, disease control and
surveillance (crops and livestock), genetic selection
(crops and livestock)
• Proactive behavior: heat alert, insurance systems,
regulation
Adaptations
68. Administration of high doses of Vitamin E and
Selenium in late pregnant-summer dairy cows
DIM DIM
DIM
Milk/litres
(Bernabucci et al., unpublished)
69. Simulated selection of local and cosmopolitan high yielding dairy
breeds to produce milk under hot environment
(modified from Nardone et Valentini, 2000)
Genetic progress
I h2
a
GP =
GI
I = selection intensity
h2
= heritability
a = accuracy
GI = generation interval
*50% bull selected (on progeny test)
**20% bull selected (on progeny test)
Parameters Local HY
Milk production (Kg) 2000 5000
RT (°C) 40.1
σ milk 500
σ RT 0.5
h2
milk 0.25
h2
RT 0.25
Sel. Diff. (st. dev.) 0.80* 1.40** 2.00
Accuracy milk 0.79
Accuracy RT 0.86
GI (years) 6 5
Annual progress
on RT (°C) 0.043
on milk (Kg) 13.2 23.0
After 20 years
progress (Kg) 263 461
yield (Kg) 2263 2461 5000
RT(°C) 39.2
75. http://ec.europa.eu/clima/sites/change/what_is_eu_doing/index_en.ht
m
• The EU’s role:
• Spreading knowledge on climate change
impacts and risks;
• Providing guidance on best approaches;
• Coordinating national activities,
particularly when impacts cross borders
such as in river basins;
• Adopting legislation and targets;
• Providing funding.
76. European Commission→Agriculture and
Rural Development→Climate change
http://ec.europa.eu/agriculture/climate-ch
Projected impacts from climate change in different EU regions
77. http://ec.europa.eu/clima/sites/change/what_is_eu_doing
/agriculture_en.htm
Finding ways of adapting to climate change will be crucial for the
agricultural sector in the years to come. Coupling adaptation efforts
to climate-change mitigation efforts will enable farmers to work
with, and not against, the environment.
Measures include:
• Introducing crop rotation to make the most of the available water;
• Rewarding farmers who carry out sustainable practices;
• Using different crop varieties which are more resistant to heat or
drought;
• Altering sowing dates and other practices to adapt to changing
weather patterns;
• Modernizing farm buildings and machinery to make them more
energy efficient;
• Providing more information and training to keep farmers informed
on climate change impacts.
78. Seventh Framework Programme (FP7)
Community Research and Development Information
Service ( CORDIS):
http://cordis.europa.eu/fp7/projects_en.html
- 116 Projects with Climate Change + Adaptation
- 37 Projects with Climate Change + Scenario
- 9 Projects with Climate Change + Scenario + Adaptation
- 3 Projects with Climate Change + Scenario + Agriculture
- 25 Projects with Climate Change + Adaptation +
Agriculture
- 2 Projects with Climate Change + Scenario + Adaptation +
Agriculture
79. 1. CLIMSAVE
Title: Climate change integrated assessment methodology for cross-sectorial
adaptation and vulnerability in Europe
Research area: ENV.2009.1.1.6.1 Integrated methodology providing a common
platform for an improved assessment of climate change impacts vulnerability
and related cost effective adaptation measures
Project start date: [2010-01-01]
2. IMPACT2C
Title: Quantifying projected impacts under 2°C warming
Research area: ENV.2011.1.1.6-1 Impacts of a global temperature increase up
to 2°C from pre-industrial level, in Europe and most vulnerable regions of the
world
Project start date: [2011-10-01]
Seventh Framework Programme (FP7)
80. • Take Home Messages
• Analysis of regional bioclimate scenarios may help
farmers, nutritionists, veterinarians, and policy-
makers to develop appropriate anticipatory adaptation
strategies to limit the consequences of climate change
for the livestock sector
• Bioclimate scenarios indicate that climate changes will
impact dramatically on animal health, welfare and
performances
• Need for regional economic analysis aimed at
establishing costs and potential benefits of
anticipatory adaptation actions in a context of
economic and bioclimate uncertainty
81. • Acknowledgements
Academic
- Loredana Basiricò
- Umberto Bernabucci
- Patrizia Morera
- Bruno Ronchi
Clinical biochemistry
and immunology labs
- Piero Bondi
- Giorgina Kuzminsky
Feedstuffs analysis lab
- Corrado BrutiPost-doc students
- Maria Segnalini
- Andrea Vitali
Major fundings
- CNR
- EU
- MiPAF (Agriculture)
- MinSal (Health)
- MIUR (Research)
- Università della Tuscia
Literature reviews and
bibliographic searches
- Maria Stella Ranieri
