Po Valley
General information
| Name of region | Po Valley, Italy (Grana and Parmigiano districts) |
| Global Environmental Zone(s) (Metzger) | K. Warm temperate and mesic |
| Population density (persons per km2) | |
| Contact (general) | Pier Paolo Roggero |
| Contact (ag. scenarios) | Pier Paolo Roggero |
| Location (NUTS code) | ITD51, ITD52 |
| Dominant regional farming system(s) (SEAMLESS nomenclature) |
Permanent crops and arable/specialised crops |
| The three most important farming systems in region (SEAMLESS nomenclature) |
|
| Main crop species | |
| Main livestock species |
Regional development goal in rural spatial planning
Specific issues the region deals with/will deal with
Dairy farming in an intensive cropping systems:- Nitrate Vulnerable Zone, 30,000 cattle in 5,500 ha- High dependence on extra-farm and costly inputs (feeds, fertilizers...)- nitrate pollution of groundwater- phosphorus pollution of surface water
Regional challenges with regard to climate change
Near future downscaled RAMS climatic scenarios revealed:- Increasing maximum temperature paprticularly in summer, resulting in increased irrigation water requirements- increasing precipitation variability in spring, reduction in spring rainfall, resulting in increased irrigation water requirements for hay-crops (or reduced hay production under rainfed conditions)- drop in milk production, cow's fertility and increased animal mortality related to forecasted increased temperature-humidity index- farm management in conditions of increased climatic and market uncertainty- serious drops in farmers' income
Proposed solutions to overcome the challenges
Contribution to answering the focus question
The dairy system is a strategic agro-business all over Europe which is facing a variety of challenges, among which climate change uncertainties.We propose that the Italian pilots on dairy farming is networked through MACSUR with other similar regional pilots to develop a Pan European pilot to address climate change adaptation strategies.
Important adaptation measures that are or will be considered in the study
| Water management | is important to this region. |
| Irrigation | is important to this region AND is/will be included in the modelling exercise. |
| Drainage | is important to this region. |
| Species/varietal choice | is important to this region. |
| Plant breeding | is important to this region. |
| Changed planting/sowing days | is important to this region AND is/will be included in the modelling exercise. |
| Crop rotations | is important to this region AND is/will be included in the modelling exercise. |
| Alternative tillage methods | is important to this region AND is/will be included in the modelling exercise. |
| Pest/weed management | |
| Housing of livestock | is important to this region AND is/will be included in the modelling exercise. |
| Land consolidation | |
| Management of feeding and reproduction of livestock | is important to this region AND is/will be included in the modelling exercise. |
| Structure and scale of production adjustment | is important to this region AND is/will be included in the modelling exercise. |
| Crop insurance | |
| Exit from agriculture | is important to this region. |
| Climate alertness | |
| Political regulations at various administrative levels | is important to this region. |
| Others | is important to this region. |
| Increased local production of forage and feeds; Reduction of the cattle stocking rate in the district, also by relocating part of the livestock in a neighboring areas to reduce costs (to be assessed through the DSP model)GHG emissions both from animals and fertilizersWater pollutionPlease note that not all the entered |
Models, stakeholders, advancement of knowledge
| Socio-economy | Crops | Grassland | Livestock |
|---|---|---|---|
| A4SMOD: Discrete Stochastic Programming Model (territorial and representative farms)Farm Budget analysisInput (available from P62 UNISS, UNIMI, CRA, FADN, ISTAT)Farm structures: FADN, 2010 Agricultural Census Land Use: FADN, 2010 Agricultural Census, Corinne Land CoverUse of inputs and yields: UNISS, UNIMI, CRA | EPIC v. 0810DSSAT v. 4.5Armosa v. 4.13 Cropsyst 4.09.01 | EPIC v. 0810Armosa v. 4.13 | The basic input will be the THI: Daily Tmax Tmin (measured) RHmax RHmin (estimated) are available for both sitesObserved data (1958-2012), downscaled RAMS scenarios for current climate (2000-10) and near future (2020-30) are already available from CNR IBIMET (M Pasqui) The model is based on the calculation of a 2-phase linear regression procedure (Nickerson et al., 1989), which detects an inflection point, if one exists, in the relationship between the independent (temperature humidity index THI) and dependent variable (milk yield, milk quality, mortality, etc.).. |
| How are results of of crop and livestock models assimilated in socio-economic models? | How is technological progress in arable agriculture taken into account? | How is technological progress in livestock farming taken into account? | |
Participating stakeholders
| Agro-business or agro-food chain | Administrative bodies or regional or national governments |
|---|---|
| Consorzio del Parmigiano Reggiano - http://www.parmigianoreggiano.com/default.aspxConsorzio Grana Padanohttp://www.granapadano.it/aspx/Home.aspx?idAmb=103&idMenu=-1&liv=0Consorzi di bonifica e Irrigazione (Water Users Associations)Associazione nazionale bonifiche, irrigazione e miglioramenti fondiari (ANBI), http://www.anbi.it | Ministero delle Politiche Agricole, Agroalimentari e Forestali http://www.politicheagricole.itAssociazione Regionale Allevatori Lombardia http://www.aral.lom.it/ |
| Approaches for involving stakeholders | |
Improvement of the modelling capability by involving stakeholders
| How did the modelling capability improve by involving stakeholders? | Effect of the involvement of stakeholders on the questions asked, on the assessment, or on the solutions suggested |
|---|---|
| Points that researchers learned from stakeholders | Points that stakeholders learned from researchers |
|---|---|
Further information
Collaborators of the Italian team:
NRD UNISS - Roggero P.P., Deligios P., Doro L., Ledda L., Mula L., Seddaiu G.http://nrd.uniss.it
UNITUS - Dono G, Cortignani R, Giraldo L., Mazzapicchio G,
UNIMI - Acutis M, Fumagalli M, Perego A, Sanna
MCNR IBIMET - Pasqui M, Tomozeiu R
References
Nickerson, D. M., D. E. Facey, and G. D. Grossman. 1989. Estimating physiological thresholds with continuous two-phase regression. Physiol. Zool. 62:866–877
Dono G, Cortignani R, Doro L, Giraldo L, Ledda L, Pasqui M, Roggero PP, An Integrated Assessment of the Impacts of Changing Climate Variability on Agricultural Productivity and Profitability in an Irrigated Mediterranean Catchment, WATER RESOURCES MANAGEMENT, DOI: 10.1007/s11269-013-0367-3
Dono G., Cortignani R., Deligios P., Doro L., Giraldo L., Ledda L., Mazzapicchio G., Pasqui M., Quaresima S., Roggero P.P., Economic Assessment of impact of uncertainty due to short-term changes in climate variability for in Mediterranean farming systems, Communication at the 2nd UNCCD Scientific Conference on Economic assessment of desertification, sustainable land management and resilience of arid, semi-arid and dry sub-humid areas 9 - 12 April 2013 - Bonn, Germany