| Records |
| Author |
Pasqui, M.; Quaresima, S.; Tomozeiu, R.; Dono, G.; Doro, L.; Cortignani, R.; Ledda, L.; Roggero, P.P. |
| Title |
A comprehensive climate characterization of the Oristano (Sardinia) regional pilot case study |
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Conference Article |
| Year |
2014 |
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In order to assess probability distributions of critical response variables in a full crop modelling system, a complete climate characterization has been implemented to identify principal variability components in the Oristano (Sardinia) regional pilot study area with a particular emphasis on current vs near future climate. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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no |
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MA @ admin @ |
Serial |
5046 |
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| Author |
Roggero, P.P.; Seddaiu, G.; Ledda, L.; Doro, L.; Deligios, P.; Nguyen, T.P.L.; Pasqui, M.; Quaresima, S.; Lacetera, N.; Cortignani, R.; Dono, G. |
| Title |
Combining modeling and stakeholder involvement to build community adaptive responses to climate change in a Mediterranean agricultural district |
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Conference Article |
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2014 |
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The case study area (54,000 ha) is located at Oristano, Italy. The main cropping systems are based on forages (silage maize, Italian ryegrass and alfalfa under irrigation, winter cereals and grasslands under rainfed conditions), rainfed cereals (durum wheat, barley), vegetables (e.g. artichokes), rice, citrus, olives and vineyards. Some 36,000 ha are served by irrigation. The area includes the dairy cows cooperative system of Arborea (30,000 cows, 5500 ha, nitrate vulnerable zone). The rainfed dairy sheep includes 372,000 sheep and a number of small milk processing plants. The research aims to support adaptive responses to climate change through the combination of modeling approaches and stakeholder engagement. Present (2000-2010) and future (2020-2030) climatic scenarios were developed by combining global climate models with Regional Atmospheric Modelling Systems to produce calibrated time series of daily temperature and precipitation for the case study. The EPIC model was calibrated to simulate the impact of climate scenarios on the main cropping systems. The impact of THIndex on milk yield, milk quality and mortality was also simulated for dairy cows. A territorial farm-type Discrete Stochastic Programming model was implemented to simulate choices for thirteen farming typologies as influenced by crop yields and water consumptions. Participatory activities, including field experiments, interviews, focus groups and interactive workshops, involved farmers and other stakeholders in the most critical phases of the research. The assessment of uncertainties and opportunities were proposed as a basis for discussion with policy makers to identify priorities for agro-climatic measures in 2014-2020. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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no |
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MA @ admin @ |
Serial |
5065 |
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Dono, G.; Cortignani, R.; Doro, L.; Roggero, P.P. |
| Title |
The adaptation of farm and awareness of ongoing climate change (CC) |
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Conference Article |
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2014 |
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Farm planning is based on awareness of climate variability, here assumed to depend on experience gained over the years, and to generate expectations on climatic variables. Expectations are based on probability distributions (pdfs) estimated on climate data and used to generate managing choices by means of Discrete Stochastic Programming. The model simulates the income losses in case farmers do not recognize the ongoing CC, and continue to plan assuming climate stability. In particular, the use of resources in 2010 is simulated based on the pdfs of the early 2000s, despite CC has changed the probabilities of the various states of nature. The model, calibrated with Positive Mathematical Programming, generates a 0.9% income increase when is allowed to adapt to 2010 climate pdfs. The model is also calibrated according to pdfs of 2010, i.e. recognizing CC: in this case income falls of 0.7% when farmers are simulated to use their soil mistakenly based of the 2000 pdfs. Given the short period of CC, the differences represent an appreciable error that farmers may be already committing. Properly specifying with the CC at local level can help building farmers’ awareness on it, and to properly manage their resources, recovering profitability. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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Approved |
no |
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MA @ admin @ |
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5131 |
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| Author |
Dono, G. |
| Title |
Awareness of climate change for adaptation of the farm sector |
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2015 |
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FACCE MACSUR Reports |
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4 |
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SP4-5 |
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TradeM International Workshop 2014 »Economics of integrated assessment approaches for agriculture and the food sector«, 25–27 November 2014, Hurdalsjø, Norway |
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no |
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MA @ admin @ |
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2195 |
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Dono, G. |
| Title |
The economic impact of changes in climate variability on milk production in the area of Grana Padano |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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5 |
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Sp5-18 |
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Climate variability (CV) normally influences production and farm management, and climate change (CC) has precisely the effect of changing this variability. Thus, models that estimate the economic impact of CC, integrating with climatic models, agronomic, and livestock, must represent the implications of this variability on farm management. This study describes an economic model based on Discrete Stochastic Programming (DSP) which assesses the impact of CC on milk production in the Grana Padano area. The model is based on 23 farm typologies from FADN that represent 856 farms in Piacenza and Cremona, two of the most important provinces for Grana Padano production. The results of the model were projected at the regional scale. The climate scenarios, current and future, are generated with a Regional Atmospheric Modeling System. The forage production under these scenarios is estimated with the EPIC agronomic model. Estimates on milk production and livestock mortality are based on studies conducted in the Po valley. The nutritional needs of the cattle are estimated with the CNCPS model. Probability distribution functions (PDF) express the relations between the CV and the productive variables under both climate scenarios. These PDFs represent the expectations of farmers on the productive-climate variability in the DSP model, which is PMP calibrated based on land distribution observed in a reference year. Comparing the model results in the two scenarios indicates the effects of CC, given the opportunity to adapt the use of resources and techniques of cultivation. The structure of the model, and its economic results are presented and discussed, along with the strengths and weaknesses of this approach. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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no |
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MA @ admin @ |
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2133 |
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