ECONOMIC ASSESSMENT OF THE IMPACT OF UNCERTAINTY   ASSOCIATED WITH SHORT-RUN CHANGE IN CLIMATE  VARIABILITY IN MEDITERRANE...
AREA: Oristano, Water User Association             other           WUA facilities  pasture             11%              36...
Farm typos, land, labour and income                 Represented   Farm land Typology %    Family     Gross sales Net Incom...
Two faces of Oristanoagriculture Rainfed area (Off-consortium)  Mainly covered by Cereals and Sheep milk   sectors, it i...
Economic results for Present and Near future                                    scenarios                           Presen...
Gross Margin (GM) per typology and farm                                                         Near Future              H...
Climate Model and Scenarios   The numerical model for future climate scenarios downscaling    is the Regional Atmospheric...
Future Climate Scenarios Downscaling                                          Numerical Modelling      Numerical Modelling...
The regional downscaling strategy       Atm. Forcing:                                                                     ...
Future climate RAMSsimulationSpring A slightly increase in minimum and maximum daily   temperature. No significant variat...
Present and Future scenariosThe differences between present andfuture climatic conditions reflect trendsof climate change ...
EvapoTranspirational demand of April-October          under observed climatic conditions                                  ...
Spring Hay production from rainfed crops under                  observed climatic conditions                              ...
ETn of April-October under simulated climatic                                   scenarios
Spring Hay yield from rainfed crops under simulated                                  climatic scenarios                   ...
Discrete Stochastic Prog.
Decision Tree in DSP                         K1, R1                                             K1, R2                    ...
The DSP Farmer      Farmer’s decision making under uncertain conditions is       represented as a conditional strategy ba...
The Oristano DSP Farmer   The farmer makes choices allocating scarce    resources (land, water and labor) without    know...
Major issues for Oristano          agricultureDAIRY CATTLE   Issue: increased uncertainty on yields of reused crop, leadi...
Economic results for baseline and near future                                       scenarios.                           P...
Gross Margin (GM) per typology and farm                                                         Near Future              H...
Present          Futurepresentfuture
Conclusions   Water availability, even when reduced, is    a key strategy for adaptation of    agriculture to future clim...
Upcoming SlideShare
Loading in …5
×

Gabriele DONO "Economic assessment of the impact of uncertainty associated with short-run change in climate variability in Mediterranean farming systems"

566 views

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
566
On SlideShare
0
From Embeds
0
Number of Embeds
31
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Gabriele DONO "Economic assessment of the impact of uncertainty associated with short-run change in climate variability in Mediterranean farming systems"

  1. 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. 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 habarley-oat 5% wheat 10% pasture 50% clover 30%
  3. 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 NIWUA 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 1.5Rainfed 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. 4. Two faces of Oristanoagriculture 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. 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 facilitiesTotal revenue 203,892 178,203 25,689 -3,9 -4,2 -1,7Variables costs 124,279 110,814 13,465 -5,6 -6,8 4,5 Feeds 16,557 14,427 2,130 15,5 13,4 30,1Gross margin 109,259 89,876 19,383 -1,3 -0,4 -5,4Net income 69,387 58,004 11,383 -2,0 -0,6 -9,2
  6. 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. 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. 8. Future Climate Scenarios Downscaling Numerical Modelling Numerical Modelling Grid Point: the simulation unit.Scaling up outputs by increasing spatial resolution, increasesthe information and maintains consistency of the atmosphere physical description.
  9. 9. The regional downscaling strategy Atm. Forcing: Coherent Atmospheric CaseGlobal Model Atmos. Forcing Studies Representation RAMS model simulation domainSea Surface Temperature The physiographic description (GLC2000 land use + Digital Elevation Model + FAO soil categories dataset)
  10. 10. Future climate RAMSsimulationSpring 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. 11. Present and Future scenariosThe differences between present andfuture climatic conditions reflect trendsof climate change that have alreadyemerged in the last 30 years.These trends are reflected in the yieldand water requirement of crops, asestimated by mean of DSSAT and EPICmodels.
  12. 12. EvapoTranspirational demand of April-October under observed climatic conditions Year 1992 1995 1998 2001 2004 2007 2010 2013
  13. 13. Spring Hay production from rainfed crops under observed climatic conditions Year 1992 1995 1998 2001 2004 2007 2010 2013
  14. 14. ETn of April-October under simulated climatic scenarios
  15. 15. Spring Hay yield from rainfed crops under simulated climatic scenarios Present Future
  16. 16. Discrete Stochastic Prog.
  17. 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. 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. 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. 20. Major issues for Oristano agricultureDAIRY 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. 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 facilitiesTotal revenue 203,892 178,203 25,689 -3,9 -4,2 -1,7Variables costs 124,279 110,814 13,465 -5,6 -6,8 4,5 Feeds 16,557 14,427 2,130 15,5 13,4 30,1Gross margin 109,259 89,876 19,383 -1,3 -0,4 -5,4Net income 69,387 58,004 11,383 -2,0 -0,6 -9,2
  22. 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. 23. Present Futurepresentfuture
  24. 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

×