Climate change is projected to increase uncertainty for Mediterranean farming systems in Oristano, Italy. The analysis found that under a near future climate scenario: (1) Total agricultural revenue and net income are projected to decline by 3.9% and 2% respectively; (2) Irrigated farms under water user associations face revenue declines up to 4.2% while rainfed farms see smaller 1.7% declines; (3) Adaptations like more drought tolerant crops, improved irrigation efficiency, and sustaining rainfed agriculture can help address increased climate variability and uncertainty.

1. ECONOMIC ASSESSMENT OF THE IMPACT OF UNCERTAINTY
ASSOCIATED WITH SHORT-RUN CHANGE IN CLIMATE
VARIABILITY IN MEDITERRANEAN FARMING SYSTEMS
Gabriele Dono, Raffaele Cortignani, Paola Deligios, Luca Doro,
Luca Giraldo, Luigi Ledda, Graziano Mazzapicchio,
Massimiliano Pasqui, Sara Quaresima, Pier Paolo Roggero
----------------------------
Tuscia University – Sassari University and National Research
Council (Italy)
UNCCD 2nd Scientific Conference,
Bonn – Germany, 9-12 April 2013

2. AREA: Oristano, Water User Association
other WUA facilities
pasture
11% 36,000 ha
5% wheat
18%
corn
silage rice
14% 8%
vegetable
s
forage 17%
27%
vegetable
s other
Rainfed area
2% 3% 18,000 ha
barley-oat
5%
wheat
10%
pasture
50%
clover
30%

3. Farm typos, land, labour and
income
Represented Farm land Typology % Family Gross sales Net Income Typoligy %
farms (N) (ha) total land Labour Units (€ 000) (NI - € 000) total NI
WUA facilities
Rice 24 115.3 5.2 2.0 303.0 139.5 4.2
Citrus 68 12.6 1.6 1.7 73.7 45.7 3.9
Cattle A 130 30.9 7.6 4.4 507.2 199.2 32.6
Cattle B 40 31.9 2.4 6.3 452.5 112.7 5.7
Greenhouse 46 12.9 1.1 3.5 146.9 29.7 1.7
Mixed 1 562 22.2 23.5 1.7 97.6 34.2 24.2
Mixed 2 55 146.4 15.2 1.2 236.3 126.3 8.7
Mixed 3 100 5.8 1.1 2.0 43.6 11.8 1.5
Rainfed
Mixed 4 100 4.1 0.8 1.7 64.6 18.2 2.3
Mixed 5 94 24.5 4.4 1.2 40.7 16.9 2.0
Sheep A 45 86.9 7.4 2.1 110.5 43.6 2.5
Sheep B 188 41.2 14.6 1.5 34.5 16.1 3.8
Sheep C 129 62.4 15.2 1.6 82.4 42.5 6.9

4. Two faces of Oristano
agriculture
 Rainfed area (Off-consortium)
 Mainly covered by Cereals and Sheep milk
sectors, it is relevant for avoiding the
abandonment of lands
 Irrigated area (Consortium)
 Intensive production and relevant economic
dimension (dairy, citrus, vegetables)

5. Economic results for Present and Near future
scenarios
Present Near Future
(000 €) (% changes over baseline)
Total WUA Rainfed Total WUA Rainfed
area facilities area facilities
Total revenue 203,892 178,203 25,689 -3,9 -4,2 -1,7
Variables costs 124,279 110,814 13,465 -5,6 -6,8 4,5
Feeds 16,557 14,427 2,130 15,5 13,4 30,1
Gross margin 109,259 89,876 19,383 -1,3 -0,4 -5,4
Net income 69,387 58,004 11,383 -2,0 -0,6 -9,2

6. Gross Margin (GM) per typology and farm
Near Future
Hectares per GM at baseline (000 €)
(% changes over
farm
Typology Farm baseline )
Rice 115,3 3,876 161,5 -0,9
Citrus 12,6 2,768 40,7 -8,5
Cattle A 30,9 37,277 286,7 0,5
Cattle B 31,9 10,406 260,2 0,9
Greenhouse 12,9 1,858 40,4 0,1
Mixed 1 22,2 26,011 46,3 -1,5
Mixed 2 146,4 4,894 89,0 0,8
Mixed 3 5,8 2,786 27,9 -1,2
Mixed 4 4,1 1,381 13,8 -0,1
Mixed 5 24,5 3,671 39,0 -0,1
Sheep A 86,9 2,742 60,9 -8,7
Sheep B 41,2 4,575 24,3 -5,2
Sheep C 62,4 7,013 54,4 -8,0

7. Climate Model and Scenarios
 The numerical model for future climate scenarios downscaling
is the Regional Atmospheric Modelling System - RAMS
(www.atmet.com).
 RAMS is forced from a global simulation model, from surface
temperatures of the sea coming from the ocean model coupled
with the atmosphere.
 The global climate change is simulated by ECHAM 5.4
developed and used by the Euro - Mediterranean Centre for
Climate Change (CMCC - www.cmcc.it).
 The greenhouse gas emissions scenario is A1B.
 Two periods were simulated:
◦ 2000 - 2010 present climate
◦ 2020 - 2030 near future climate.

8. Future Climate Scenarios Downscaling
Numerical Modelling
Numerical Modelling Grid
Point: the simulation unit.
Scaling up outputs by increasing spatial resolution, increases
the information and maintains consistency of the atmosphere
physical description.

9. The regional downscaling strategy
Atm. Forcing:
Coherent Atmospheric Case
Global Model Atmos. Forcing Studies Representation
RAMS model simulation domain
Sea Surface Temperature The physiographic description
(GLC2000 land use + Digital Elevation
Model + FAO soil categories dataset)

10. Future climate RAMS
simulation
Spring
 A slightly increase in minimum and maximum daily
temperature. No significant variations identified on
precipitation (observations highlight a decreasing trend in the
recent years)
Fall – Winter
 A pronounced increase in minimum and maximum
temperature along with an increased rainfall variability
coupled to a decreasing rainfall (aligned with the observed
long term trends). Potentially due to an increased occurrence
of high pressure systems over the Mediterranean.
Summer
 Increased maximum daily temperature and even more
pronounced minimum as climate change footprint (aligned to
long term observed trend by Baldi et al. regarding “hot days”
and “heat waves”)

11. Present and Future scenarios
The differences between present and
future climatic conditions reflect trends
of climate change that have already
emerged in the last 30 years.
These trends are reflected in the yield
and water requirement of crops, as
estimated by mean of DSSAT and EPIC
models.

12. EvapoTranspirational demand of April-October
under observed climatic conditions
Year
1992
1995
1998
2001
2004
2007
2010
2013

13. Spring Hay production from rainfed crops under
observed climatic conditions
Year
1992
1995
1998
2001
2004
2007
2010
2013

14. ETn of April-October under simulated climatic
scenarios

15. Spring Hay yield from rainfed crops under simulated
climatic scenarios
Present
Future

16. Discrete Stochastic Prog.

17. Decision Tree in DSP K1, R1
K1, R2
K1
K2, R1
Z K2
K2, R2
K3
K3, R1
K3, R2
1° stage 2° stage 3° stage

18. The DSP Farmer
 Farmer’s decision making under uncertain conditions is
represented as a conditional strategy based on
◦ expectations on possible states of nature (probabilities)
◦ defensive behaviours against the consequences of non-optimal outcomes.
 Farmer can adapt after knowing the state of nature actually
occurred, by undertaking corrective actions
◦ Choices made are only partially reversible.
 Farmer tries to minimize the impact of sub-optimality by
choosing the state with the highest expected income.
◦ The resulting income is lower than optimal solution that would be chosen
under certainty.
 This cost may increase if CC alters the states of nature or
their probabilities of occurrence

19. The Oristano DSP Farmer
 The farmer makes choices allocating scarce
resources (land, water and labor) without
knowing with certainty irrigation requirements
and yields of crops.
 Once the events have occurred, corrective
actions can be done by drawing groundwater
and buying feeds: sub-optimal outcomes.
 The modeled states of nature regard yields and
irrigation needs

20. Major issues for Oristano
agriculture
DAIRY CATTLE
 Issue: increased uncertainty on yields of reused crop, leading to greater forage cropping and purchasing of
feeds. Worsening of the economic results also considering water pricing.
 Adaptation: intensify fodder production with the integration (and partly replace) of the system ryegrass -
corn silage with crops less water demanding as triticale and sorghum.
SHEEP MILK
 Issue: increased uncertainty on yields of reused crop, as hay and grains.
 Adaptation: increased grazing where possible and purchasing of hay. Worsening of economic results.
MIXED FARMS
 Issue: more uncertain yields (along with prices) increasing diversification and extensification. Larger
irrigation requirements and water pricing might worsen economic results, especially for farms focused on
vegetables.
 Adaptation: opportunities for extensive farming from silage corn, hay and feeding grain, and energy crops.
CITRUS
 Issue: Increasing water pricing water and irrigation requirements will lead to higher irrigation
costs, worsening their profitability.
 Adaptation: Investment to improve the irrigation efficiency may be a winning strategy for them.
RISE
 Issue: Specialized farms consider land as suitable almost exclusively for rice cropping, short term
response will be very rigid to different scenarios, resulting in inevitable decline in profitability.

21. Economic results for baseline and near future
scenarios.
Present Near Future
(000 €) (% changes over baseline)
Total WUA Rainfed Total WUA Rainfed
area facilities area facilities
Total revenue 203,892 178,203 25,689 -3,9 -4,2 -1,7
Variables costs 124,279 110,814 13,465 -5,6 -6,8 4,5
Feeds 16,557 14,427 2,130 15,5 13,4 30,1
Gross margin 109,259 89,876 19,383 -1,3 -0,4 -5,4
Net income 69,387 58,004 11,383 -2,0 -0,6 -9,2

22. Gross Margin (GM) per typology and farm
Near Future
Hectares per GM at baseline (000 €)
(% changes over
farm
Typology Farm baseline )
Rice 115,3 3,876 161,5 -0,9
Citrus 12,6 2,768 40,7 -8,5
Cattle A 30,9 37,277 286,7 0,5
Cattle B 31,9 10,406 260,2 0,9
Greenhouse 12,9 1,858 40,4 0,1
Mixed 1 22,2 26,011 46,3 -1,5
Mixed 2 146,4 4,894 89,0 0,8
Mixed 3 5,8 2,786 27,9 -1,2
Mixed 4 4,1 1,381 13,8 -0,1
Mixed 5 24,5 3,671 39,0 -0,1
Sheep A 86,9 2,742 60,9 -8,7
Sheep B 41,2 4,575 24,3 -5,2
Sheep C 62,4 7,013 54,4 -8,0

23. Present
Future
present
future

24. Conclusions
 Water availability, even when reduced, is
a key strategy for adaptation of
agriculture to future climatic scenarios
 Water accumulation is to be considered
for dealing with the changing variability
of climatic variables – allows flexibility
 Rainfed agriculture must be sustained for
the preservation of territory