combining modelling and stakeholder involvement to build ...pier paolo roggero, giovanna seddaiu,...
TRANSCRIPT
Combining modelling and social learning for CC adaptation – PP Roggero
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Combining modelling and
stakeholder involvement to
build community adaptive
responses to climate change
Pier Paolo Roggero
Nucleo Ricerca Desertificazione
University of Sassari, Italy
1
Structure of the presentation• How do we understand climate change
• How do we understand adaptive responses toCC
• Lessons from the Agroscenari-Macsur case study
• Expected climate scenarios
• Expected impacts on farming systems
• Community perspectives and response-abilities
• Implications for researching and policy making
Combining modelling and social learning for CC adaptation – PP Roggero
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3
Living in a CC world
3
Climate
change
Technological change
Water, food &
energy security
Infectious
diseases
Poverty
alleviation
Globalisation (India, China)
Population
growth
Combining modelling and social learning for CC adaptation – PP Roggero
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Living in a CC world
4
Unprecedented
challenges to
ecosystems &
human systems
Innovative learning
pathways are key
More
collaborative
approaches
New ways
of working
together
Adaptation to more
frequent heat waves,
floods & droughts
Innovation to
create low carbon
economies
More
imaginative
approaches
4Combining modelling and social learning for CC adaptation – PP Roggero
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Hypotheses
• Human-induced climate change is a totally newchallenge to the human kind– It requires changes in the way of thinking and
acting
• How to develop an effective strategy to respondto CC in an adaptive way?– Key words: reflection, learning
– Stakeholders: actors, owners, customers…• what role for researchers, engineers, policy makers,
consumers…?
• Structural coupling and science/policy interfaces
5Combining modelling and social learning for CC adaptation – PP Roggero
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How we understand climate change?
• Complex
• Unintended consequences
• Multiple stakeholding
• Multiple scales
• Highly contested(Collins et al 2011 Env Pol Gov)
‘Do we have a shared
conceptual understanding of what
global warming is?’ (Brooks, 2005)
6
CC situations as wicked issues(Australian Public Service Commissioner, 2007)
Tame problem Wicked issueStable, well defined problem
clear when a solution is reached,
targeted actions can be designed
No definitive formulatio
actions relying on perceived evidence are
late and ineffective
Solutions can be objectively
evaluated as right or wrong
depending on targeted achievements
Contextualized “better” or “worse”
solutions (actions) emerging from
process quality assessment
solutions can be tried and
abandoned
Every action is “one-shot operation”:
Can be classified and approached
with tools already tested elsewhere
Every problem is unique and symptom of
other problems in specific contexts
The choice of explanation modalities
determines the nature of the problem’s
and the response pathways7Combining modelling and social learning for CC adaptation – PP Roggero
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Systems view of CC in agriculture
• Complexity emerging from multiple perspectives:
– variety of time and space scales, cropping systems,
environmental socio-economic contexts
– variety of actors, responsibilities,systems’ perspectives
• Sharing the nature of the issue is crucial for
concerted actions towards sustainability– Quantitative tools essential, but not sufficient to address the
challenge of adaptive responding to CC
• Research results to provide tools for learning
8Combining modelling and social learning for CC adaptation – PP Roggero
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A systems
view of a
situation….
9Combining modelling and social learning for CC adaptation – PP Roggero
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…recognises
multiple
stakeholders
which means….
10Combining modelling and social learning for CC adaptation – PP Roggero
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…recognising
multiple
viewpoints and
partial
understandings
of the
‘situation’
11Combining modelling and social learning for CC adaptation – PP Roggero
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A well designed
social learning
process can be
effective in
integrating
multiple
perspectives
for
understanding
issues and
designing
concerted
actions
12Combining modelling and social learning for CC adaptation – PP Roggero
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How we understand CC adaptation
• Not a route towards a fixed destination, but a
changing pathway without clear future direction
13Combining modelling and social learning for CC adaptation – PP Roggero
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Combining modelling and social learning for CC adaptation – PP Roggero
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Adapting TO vs WITH CC
• “Fitting to” (jigsaw) vs co-evolution (feet-shoes)
Ison, 2010, Systems practice: how to act in a climate change world, Springer
14
Combining modelling and social learning for CC adaptation – PP Roggero
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Adapting WITH change
• A concerted action for a desirable performance
– …changeing with players under the same sheet music
– As in a jazz orchestra, the band continuously adapts to
soloists…
Ison, 2010, Systems practice: how to act in a climate change world, Springer
15
Adapting WITH CC
• In agriculture, the performance emerges from the
interaction of three components:– The biophysical system’s dynamics (eg climate, soil, crops)
– The social system’s dynamics (eg farmers practice)
– The structural coupling of the two that co-evolve and influence
each other
Ison, 2010, Systems practice: how to act in a climate change world, Springer
interface (norms, institutions,
technologies, measures…)
16Combining modelling and social learning for CC adaptation – PP Roggero
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Framing research on CC
Business as usual Strategic changeSet targeted states to be achieved
through best practices under
“command and control” regulation
Contextualize actions and tools
towards improved performance
under changing contexts and
address self-organized systems
Delegate targeted projects to
experts to design effective
solutions through rules and
incentives
Invest on co-learning spaces,
remove barriers, promote volunteer
habits and response-ABILITIES
Farmers as users of scientific
knowledge produced by
researchers
Farmers and researchers as co-
producers of “hybrid knowledge” (Nguyen et al 2013)
17Combining modelling and social learning for CC adaptation – PP Roggero
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Adapting to CC
• When dealing with CC, business as usual at different
levels will not allow acceptable performance
18
Ison, 2010, Systems practice: how to act in a climate change world, Springer
Combining modelling and social learning for CC adaptation – PP Roggero
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19Adaptive responses to CC - Roggero
Clim
ate
change im
pacts
on a
gric
ultu
ral s
yste
ms in
Afr
ica
Case study “Oristanese”, Italy (www.macsur.eu – www.agroscenari.it)
Pier Paolo Roggero, Giovanna Seddaiu, Luigi Ledda, Luca Doro, Paolo Deligios, Thi Phuoc Lai Nguyen,
Massimiliano Pasqui, Sara Quaresima, Nicola Lacetera, Raffaele Cortignani, Gabriele Dono
Combining modeling and stakeholder involvement to build
community adaptive responses to climate change in a
Mediterranean agricultural district
Combining modelling and social learning for CC adaptation – PP Roggero
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What would be the different contributions of
different European adaptation strategies to
ensure global food security until 2050 at
different scales [farm to EU] while keeping
the GHG targets?
MACSUR meta-question
Combining modelling and social learning for CC adaptation – PP Roggero
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Infrastructured area for irrigation: 36,000 ha
14%
27%
11%5%
8%
17%
18% Silage maize
Forage crops
Other
Pasture
Rice
Vegetables
Wheat
Rainfed area: 18,000 ha
5%
30%
3%50%
2%
10%
Barley-Oats
Hay crops
Other
Pasture
Vegetables
Wheat
Oristanese
21
Case study area
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Interdisciplinary team already @work
Context data available
Very diversified Mediterranean agricultural districtIrrigated and rainfed farming systems
Variety of cropping systems, intensity levels, farm sizes
Multiple stakeholdersCooperative agro-food system
Producers’ organizations (rice, horticulture)
Variety of extensive pastoral systems
Context
Combining modelling and social learning for CC adaptation – PP Roggero
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Main farming systems
Irrigated forage systems :• silage maize, Italian
ryegrass, triticale, alfalfa
Permanent or temporary pastures in rotation with autumn-winter forage (winter pasture and hay or grain production)
Dairy Cattle
Rice
Dairy sheep
Horticulture
23Combining modelling and social learning for CC adaptation – PP Roggero
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Farm typologiesRepresented
farms (n)
Farm
land
size (ha)
Typology %
total land
area
Net Income
per farm
(NI - € 000)
Typology
% total NI
Irrigated farms 1025 30,546 58 65,496 83
Rice 24 115 5 140 4
Citrus 68 13 2 46 4
Dairy cattle A 130 31 8 199 33
Dairy cattle B 40 32 2 113 6
Greenhouse 46 13 1 30 2
Vegetables - Cereals 562 22 24 34 24
Cereals - Forages 55 146 15 126 9
Tree and arable crops 100 6 1 12 2
Rainfed farms 556 22,371 42 13,933 17
Vegetables - Fruit 100 4 1 18 2
Cereals - Forages 94 25 4 17 2
Sheep A 45 87 7 44 3
Sheep B 188 41 15 16 4
Sheep C 129 62 15 43 7
Combining modelling and social learning for CC adaptation – PP Roggero
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Combining modelling and social learning for CC adaptation – PP Roggero
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RAMS scenarios forced by sea T coupled with atm
(2000-10 vs 2020-30)
Local weather dataset (59 yrs)
Local cropping system dataset
150 years PC vs FC
Calibrated EPIC model for main cropping systems
P distributions of performance of main crops and net ET
under CP vs FC
Calibrated 3-stages DSP model for main rainfed and irrigated farm
typologies and @district scaleLocal farming systems dataset
WXGEN
@RISK
Calibration
What-if scenario analyses Farmers’ adaptive
responses under uncertainty
Farmers and local organizations, participatory field experiments
Researchers, policy makers, farmers organizations
Cnr Ibimet
NRD Uniss
Unitus
Social learning
Emerging policy options and recommendations
Social learning
Social learning
Social learning
Social learning
Social learning
Calibration
Calibration
RAMS scenarios forced by sea T coupled with atm
(2000-10 vs 2020-30)
Local weather dataset (59 yrs)
Local cropping system dataset
150 years PC vs FC
Calibrated EPIC model for main cropping systems
P distributions of performance of main crops and net ET
under CP vs FC
Calibrated 3-stages DSP model for main rainfed and irrigated farm
typologies and @district scaleLocal farming systems dataset
WXGEN
@RISK
Calibration
What-if scenario analyses Farmers’ adaptive
responses under uncertainty
Farmers and local organizations, participatory field experiments
Researchers, policy makers, farmers organizations
Cnr Ibimet
NRD Uniss
Unitus
Social learning
Emerging policy options and recommendations
Social learning
Social learning
Social learning
Social learning
Social learning
Calibration
Calibration
Combining modelling and social learning for CC adaptation – PP Roggero
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Temperature: PC vs FC
+1.5
+1.7
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12
°C
Tmin CP
Tmax CP
Tmin CF
Tmax CF
Precipitation: PC vs FC
0
10
20
30
40
50
60
70
80
90
1 2 3 4 5 6 7 8 9 10 11 12
mm Prec CP
Prec CF
PC 536 mm
FC 505 mm (-6%)
-33%
ETn=ET-prec
-50
0
50
100
150
200
250
J F M A M J J A S O N D
mm
PC irrigated
FC irrigated
PC rainfed
FC rainfed
Climate change signals
Climate change scenarios
27Combining modelling and social learning for CC adaptation – PP Roggero
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RAMS scenarios forced by sea T coupled with atm
(2000-10 vs 2020-30)
Local weather dataset (59 yrs)
Local cropping system dataset
150 years PC vs FC
Calibrated EPIC model for main cropping systems
P distributions of performance of main crops and net ET
under CP vs FC
Calibrated 3-stages DSP model for main rainfed and irrigated farm
typologies and @district scaleLocal farming systems dataset
WXGEN
@RISK
Calibration
What-if scenario analyses Farmers’ adaptive
responses under uncertainty
Farmers and local organizations, participatory field experiments
Researchers, policy makers, farmers organizations
Cnr Ibimet
NRD Uniss
Unitus
Social learning
Emerging policy options and recommendations
Social learning
Social learning
Social learning
Social learning
Social learning
Calibration
Calibration
28Combining modelling and social learning for CC adaptation – PP Roggero
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0
5
10
15
20
25
30
35
40
45
50
18 19 20 21 22 23 24 25 26
Mg ha-1 DM
%
PC
FC 380
FC 407
FC_380 adapt
FC_407 adapt
Silage maize
20.5 Mg ha-1
CV: 4%
22.2 Mg ha-1
CV: 4%
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0
5
10
15
20
25
30
35
40
45
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
Mg ha-1 DM
%
PCFC380FC407
Rainfed grassland
1.1 Mg ha-1
CV: 18%
0.9 Mg ha-1
CV: 22%
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0
5
10
15
20
25
30
2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8
Mg ha-1 DM
%
PCFC380FC407
Rainfed haycrop
3.9 Mg ha-1
CV: 9%3.5 Mg ha-1
CV: 11%
31
0
10
20
30
40
50
60
70
80
5 6 7 8 9 10 11 12Mg ha-1 DM
%
PC
FC 380
FC 407
Irrigatedhaycrop
7.7 Mg ha-1
CV: 8%
9.0 Mg ha-1
CV: 5%
32Combining modelling and social learning for CC adaptation – PP Roggero
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RAMS scenarios forced by sea T coupled with atm
(2000-10 vs 2020-30)
Local weather dataset (59 yrs)
Local cropping system dataset
150 years PC vs FC
Calibrated EPIC model for main cropping systems
P distributions of performance of main crops and net ET
under CP vs FC
Calibrated 3-stages DSP model for main rainfed and irrigated farm
typologies and @district scaleLocal farming systems dataset
WXGEN
@RISK
Calibration
What-if scenario analyses Farmers’ adaptive
responses under uncertainty
Farmers and local organizations, participatory field experiments
Researchers, policy makers, farmers
organizations
Cnr Ibimet
NRD Uniss
Unitus
Social learning
Emerging policy options and recommendations
Social learning
Social learning
Social learning
Social learning
Social learning
Calibration
Calibration
33Combining modelling and social learning for CC adaptation – PP Roggero
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Cumulative ETn in April-October
PresentFuture
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Spring Hay yield from rain-fed crops
Present
Future
PresentFuture
35Combining modelling and social learning for CC adaptation – PP Roggero
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THI max in May-September
PresentFuture
36Combining modelling and social learning for CC adaptation – PP Roggero
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d a
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RAMS scenarios forced by sea T coupled with atm
(2000-10 vs 2020-30)
Local weather dataset (59 yrs)
Local cropping system dataset
150 years PC vs FC
Calibrated EPIC model for main cropping systems
P distributions of performance of main crops and net ET
under CP vs FC
Calibrated 3-stages DSP model for main rainfed and irrigated farm
typologies and @district scaleLocal farming systems dataset
WXGEN
@RISK
Calibration
What-if scenario analyses Farmers’ adaptive
responses under uncertainty
Farmers and local organizations, participatory field experiments
Researchers, policy makers, farmers organizations
Cnr Ibimet
NRD Uniss
Unitus
Social learning
Emerging policy options and recommendations
Social learning
Social learning
Social learning
Social learning
Social learning
Calibration
Calibration
37Combining modelling and social learning for CC adaptation – PP Roggero
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38
Farm typologiesRepresented
farms (n)
Farm
land
size (ha)
Typology %
total NI
Near future vs Present
climate
% per farm Total area
Irrigated farms 1025 30,546 83 -6.1%
Rice 24 115 4 -0.7
Citrus 68 13 4 -7.1
Dairy cattle A 130 31 33 -6.4
Dairy cattle B 40 32 6 -6.1
Greenhouse 46 13 2 +0.3
Vegetables – Cereals 562 22 24 -1.2
Cereals – Forages 55 146 9 +1.0
Tree and arable crops 100 6 2 -0.9
Rainfed farms 556 22,371 17 -8.8%
Vegetables - Fruit 100 4 2 -0.1
Cereals - Forages 94 25 2 +0.0
Sheep A 45 87 3 -9.0
Sheep B 188 41 4 -5.1
Sheep C 129 62 7 -7.4
-6.5%
Combining modelling and social learning for CC adaptation – PP Roggero
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d a
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ultu
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RAMS scenarios forced by sea T coupled with atm
(2000-10 vs 2020-30)
Local weather dataset (59 yrs)
Local cropping system dataset
150 years PC vs FC
Calibrated EPIC model for main cropping systems
P distributions of performance of main crops and net ET
under CP vs FC
Calibrated 3-stages DSP model for main rainfed and irrigated farm
typologies and @district scaleLocal farming systems dataset
WXGEN
@RISK
Calibration
What-if scenario analyses Farmers’ adaptive
responses under uncertainty
Farmers and local organizations, participatory field experiments
Researchers, policy makers, farmers
organizations
Cnr Ibimet
NRD Uniss
Unitus
Social learning
Emerging policy options and recommendations
Social learning
Social learning
Social learning
Social learning
Social learning
Calibration
Calibration
39Combining modelling and social learning for CC adaptation – PP Roggero
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40Combining modelling and social learning for CC adaptation – PP Roggero
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its re
latio
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d a
gric
ultu
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RAMS scenarios forced by sea T coupled with atm
(2000-10 vs 2020-30)
Local weather dataset (59 yrs)
Local cropping system dataset
150 years PC vs FC
Calibrated EPIC model for main cropping systems
P distributions of performance of main crops and net ET
under CP vs FC
Calibrated 3-stages DSP model for main rainfed and irrigated farm
typologies and @district scaleLocal farming systems dataset
WXGEN
@RISK
Calibration
What-if scenario analyses Farmers’ adaptive
responses under uncertainty
Farmers and local organizations, participatory field experiments
Researchers, policy makers, farmers
organizations
Cnr Ibimet
NRD Uniss
Unitus
Social learning
Emerging policy options and recommendations
Social learning
Social learning
Social learning
Social learning
Social learning
Calibration
Calibration
41Combining modelling and social learning for CC adaptation – PP Roggero
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Farmers’ CC perception
Analysis of Farmers’ Perceptions and Adaptation Strategies to Climate
Uncertainties 42
Perceptions % ofinterviewees
n. agreem/total respondents
Unpredictable seasons 88% 22/25
Increased temperatures 68% 17/25
Increased rain variability 52% 13/25
Climate is not changing 8% 2/25
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Results• Likert-type questionnaires
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Results
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• Results from LikertType questionnaires
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Dairy sheep farmers’ perceptions of climate impacts
• Indicator of winter temperature: daily milk production– Winter less cool now than in the past (less frost)
• Uncertainty of autumn pasture availability– date of the first rainfall after summer– difficulties in seed bed preparation for the annual forage crop
cultivation in rainy autumn (mainly with high stocking rate)
• Uncertainty of complementary forage resources availability from annual forage crops– spring rainfall is critical to hay production to address autumn
pasture uncertainty
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Farmers’ response to
climate variability
The majority of farmers have already responded to perceived CC
46
Actions already taken Actions farmers think to take
• Change/diversify crops
• Improve irrigation systems
• Improve animal health: veterinary
services, hygiene in stables
• Change/improve the animal diet
• Follow daily weather forecast to
support actions on the spot
• Do nothing (8%)
• Improve infrastructure (farm
structure, stables, barns, sheds)
• Adopt new technologies (i.e. air
conditioning systems for animals)
• Improve water use efficiency at
farm scale
• Interact with technical advisors,
researchers, neighbors
• Participate to social networks to
enhance adaptive capacity
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Farm level possibleadaptation strategies
Adaptation agenda for rural development
Exte
nsi
vefa
rmin
gsy
ste
ms - Reduce water consumption
- Use conservation tillage- Improve new grazing areas- Improve forage self-supply- Change forage system: more
grassland less arable crops- Strengthen integrated agro-
forestry-pastoral system - Recognize the role of
graziers as landscape managers
- Support maintainamce of permanent grasslands
- Support to farms’ structureimprovement
- Improve advisory services- Link complementary districts to
exploit forage resources: encourage pro-active farmers, participatory approaches
- Investment in co-research with farmers on CC adaptation practices
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Farm level adaptation options and agenda for RDP as identified by SH
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Discussion
• Emerging frames informing scenarios:
– Type 1 - Individual adaptation
– Type 2 - Collective adaptation
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DiscussionType 1 scenario: Individual adaptation
(each farmer for himself)1.1. “Proactive”Self-establishment of adaptation practices:Increase farm size, invest on hi-tech (e.g. dairy cattle farms)
RisksEnvironmental pollution
Costs of water and energy are perceived as additional pressures
1.2. “Passive”Increased uncertainty (e.g. because of no anticipated adaptive responses)
Few investment in technologies and improved farming practices ( eg sheep)
RisksAbandonment of large grazing districts
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DiscussionType 2 scenario: collective adaptation
(all SH for all farmers)2.1: “Bottom-up”Collective adaptive responses, Hi-tech farms (e.g. dairy cattle) will continue to develop (e.g. increase farm size, diversify crops, investment in technologies…);
Low-tech farms (e.g. some dairy sheep) can be also sustained and improved.
Opportunities: more investments in research on specific farm-level adaptation practices e.g. bio-fuels, waste water treatment, irrigation mgt...
2.2. “Top-down “ a. Effective policy measures: Hi-tech farms will growLow-tech farms sustained, no changes
b. Inadequate policy measures : Hi-tech farms rely on endogenous adaptive capacity Low-tech farms risk to disappear.
Assumptions:No long-term adaptation developed, no multi-stakeholder participation to the design of RDP measures
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Conclusions and Implications
• Adaptation scenarios depend on different ways and attitudes in looking into the future – Perceptions of CC and impacts
– Investments on endogenous adaptive capacity (lay knowledge, skills, experiences, etc.) and exogenous driving forces (science, incentives, resources, etc.)
• Combining modelling with social learning was effective in generating credible scenario analysis (Nguyen et al 2013)
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For further information
http://macsur.eu/index.php/regional-case-studies/52Combining modelling and social learning for CC adaptation – PP Roggero
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Some refs
• Allan et al 2013 Ital J Agron• Collins & Ison 2012 Env Pol Governance • Colvin et al 2014 Res Pol• Dono et al 2013 Agric Sys• Dono et al 2013 Water Res Manage • Ison et al 2007 Environ Sci Policy• Ison et al 2011 Water Res Manage • Nguyen et al 2012 Ital J Agron• Nguyen et al 2013 Int J Agric Sustain • Steyaert & Jiggins 2007 Environ Sci Policy• Toderi et al 2007 Environ Sci Policy
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