1. 19 th ICABR Conference “IMPACTS OF THE BIOECONOMY ON AGRICULTURAL SUSTAINABILITY, THE ENVIRONMENT AND HUMAN HEALTH” Ravello : June 16 - 19, 2015 Bartolini F., Brunori G., Gava O. Department of Agriculture, Food and Environment (DAFE) The potential impact of agroenergy on sustainability. The case study of tuscany region (italy)

2. Outline  Background  Objectives  Methodology  Results  Discussion University of Pisa – Department of Agriculture, Food and Environment

3. Background  The European energy strategy towards 2020 builds on a set of binding Community-wide targets, with the explicit purposes of reducing dependence on imported fossil fuels and boosting new energy technologies  In Italy, farm-based biogas installations have recently boomed.  The public support system, “green certificates”  diversifying the production could help stabilise incomes  three interdependent global crises at the energy, environmental and agricultural level may have contributed to biogas success (Carrosio, 2013).  Geopolitical trends, with rising political and social instability in fossil fuel-producer countries and the emergence of state-owned energy champions had a relevant role in the global increase of traditional fuels prices until 2008 (Umbach, 2010). University of Pisa – Department of Agriculture, Food and Environment

4. Objective of the paper  to deliver an analysis of the impact of the diffusion of biogas installations at the territorial level,  To take the Italian province of Pisa (NUTS 3) as a case study University of Pisa – Department of Agriculture, Food and Environment

5. Methodology  we simulate farmers’ behaviour facing a decision over the adoption of a farm-scale biogas plant, to assess decisions’ impact at the territorial level by applying a three-step methodology.  (i) representative farms;  (ii) simulation of farmers’ behaviour;  (iii) impact assessment at farm level & at territorial level University of Pisa – Department of Agriculture, Food and Environment

6. Methodology (Representative farms)  Model applied at farm level  Representative farms results for a non-hierarchical cluster analysis across the province of Pisa  clustering Variables are: farm size; labour used; amount of SFP received  Farm profiles result from value averaging at the cluster level University of Pisa – Department of Agriculture, Food and Environment

7. Methodology (simualtion of farmers’ behaviours)  simplified version of the farm-household model  Dynamic model (mixed-integer non linear model) University of Pisa – Department of Agriculture, Food and Environment  Simulation of 5 alternative methane digesters  Diversified by installed power (from 108 to 972 kW/h), for the investment costs, for the annual maintenance costs and for the labour requirements

8. Methodology (impact assessment)  A set of indicators is measured at the cluster level  Impact upscaling at the territorial level results from the comparison of the weighted sum of clusters' performances with area clusters' weights  Economical  NPV  Reliance on paymment (SFP/NPV)  Social  plant installed power kW/h  dedicated crops demand for biogas production (i.e.silage maize)  labour force employed in the agricultural sector  Environmental  Input use  Water  Nitrogen  Biodiversity (Shannon index) University of Pisa – Department of Agriculture, Food and Environment

9. Data  Micro-data from 2010 Italian Census of Agriculture  1852 farms located in the province of Pisa (UAA>1ha): arable, vegetable, livestock  no biogas plants are currently operating  18 farm profiles (from cluster analysis) University of Pisa – Department of Agriculture, Food and Environment

10. Cluster obtianed CodeFarming system Cluster weight (%) UAA (ha) Rented land (ha) Labour Livestock inventory (LU **#) Household (FTE * #) Hired (FTE* #) C1arable 2.54 116711.66-- C2arable 1.46 193.54143.541.66-- C3arable 5.23 72271.45-- C4arable 0.59 6.15-0.82 - C5arable 16.68 17-0.91-- C6arable 42.47 2.6-0.48-- C7arable 8.26 36.5-1.36-- C8vegetable 0.65 18.335.681.590.44- C9vegetable 5.45 1.11-1.64-- C10vegetable 1.24 731.820.53- C11livestock 0.43 153.96-3.02-128 C12livestock 8.09 1.3-1.66-2 C13livestock 1.30 52.3315.431.94-32 C14livestock 0.22 259.12--2.82168 C15livestock 0.65 78.248.052.75-56 C16livestock 1.89 35.436.733.66-62 C17livestock 0.05 7.021.752.25-13 C18livestock 2.75 20-1.66-24 University of Pisa – Department of Agriculture, Food and Environment

11. Results (No adoption) Code NPVSFP/VANUAALabour (hours) Nitrogen input (kg) Water input (m3) Nitrogen kg/ha Water m3 /ha Shannon index C1 1,897,4000.11139.23,0446801,192122.05914.550.51 C2 2,747,3130.19165.813,57952331362.25128.580.27 C3 1,361,6580.21931,79143056581.37540.60.39 C4 114,170-8.153732595130.421,660.520.09 C5 337,6910.181963364216103.21,285.880.39 C6 51,5130.18311725429.3-0.38 C7 865,1360.15411,50312148998.921,290.820.37 C8 506,2410.1321.331,1433832287.671,449.180.28 C9 44,732-2200324136.741,818.390.2 C10 188,4910.15963418112130.291,768.320.18 C11 20,225,9470.02147.9612,2381472,76874.971,533.740.24 C12 72,729-2.3307231113.661,700.660.18 C13 3,185,4630.0443.334,5114566098.871,646.180.22 C14 21,101,226-189.1213,783570,1161.83742.830.28 C15 5,026,6970.0574.355,3381161,084102.661,636.790.2 C16 3,047,6620.0443.343,44661632102.361,633.590.26 C17 425,0750.079.264616119112.121,580.140.13 C18 1,781,085-27.53,24326385102.341,567.920.17 University of Pisa – Department of Agriculture, Food and Environment

12. Results (adoption) University of Pisa – Department of Agriculture, Food and Environment Code Farming system Cluster weight (%) UAA (ha) Livestock inventory (LU **#) Current energy price +20%+50% C1arable 2.54 116- 108kw C2arable 1.46 193.54- C3arable 5.23 72- 108kw C4arable 0.59 6.15- C5arable 16.68 17- C6arable 42.47 2.6- C7arable 8.26 36.5- C8vegetable 0.65 18.33- C9vegetable 5.45 1.11- 254kw C10vegetable 1.24 7- 254kw C11livestock 0.43 153.96128 108kW254kw C12livestock 8.09 1.32 C13livestock 1.30 52.3332 108kW 108kw C14livestock 0.22 259.12168 254kw C15livestock 0.65 78.2456 108kW108kw C16livestock 1.89 35.4362 108kw C17livestock 0.05 7.0213 108kw C18livestock 2.75 2024108kW108kw

13. Results (impacts) Sce. NPV (1000 €) SFP/ NPV UAA lab (100 0 hours) Energy crops (share) Energy (kw/h) Nitroge n (1000 kg) Water (1000 m3) Nitr. (kg /ha) Water (m3 /ha ) Shan non index no690,2000.1231,2901,331 -- 3,835 37,95487.421,2130.61 current 705,7900.1231,8241,3310.01 3,456 3,916 38,39788.031,2070.59 +20% 799,6190.1132,5271,3400.04 6,028 3,815 37,28987.891,1830.54 +50% 830,5680.1237,1491,4380.3052,868 4,460 40,17687.451,0810.54 University of Pisa – Department of Agriculture, Food and Environment

14. discussion  Bioeconomy sustainability central in policy and scientific debates  Biogas diffusion has multiple impacts  Trade-offs among indicators  Increased farm incomes & increased land demand  Competition among different land uses (energy and food)  Environmental indicators assessement is complex  Coherence of Bioeconomy with other EU policy and goals (i.e biodiversity)  Interaction of several policy key issues (regionalised payments, greening; milk quota abolishment) University of Pisa – Department of Agriculture, Food and Environment

15. discussion  Limits of the paper  No interaction among agents  Only changes in demand, not productive factors market  Cluster are qualified only for farm strucuture; intra-cluster heterogeneity(risk attitude; networking; life-cycle etc)  Next steps  Using a utilty functions based  Attempting a spatial equilibrium model or agent based perspectives University of Pisa – Department of Agriculture, Food and Environment

16. Thank you for your attention fabio.bartolini@unipi.it