19 th icabr conference “impacts of the bioeconomy on agricultural sustainability, the environment...
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19th 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)
Outline Background Objectives Methodology Results Discussion
University of Pisa – Department of Agriculture, Food and Environment
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
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
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
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
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
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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
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
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
Cluster obtianed
Code Farming system
Cluster weight (%) UAA
(ha)Rented
land (ha)
Labour Livestock inventory (LU **#)
Household (FTE * #) Hired (FTE* #)
C1 arable 2.54 116 71 1.66 - -C2 arable 1.46 193.54 143.54 1.66 - -C3 arable 5.23 72 27 1.45 - -C4 arable 0.59 6.15 - 0.82 0.82 -C5 arable 16.68 17 - 0.91 - -C6 arable 42.47 2.6 - 0.48 - -C7 arable 8.26 36.5 - 1.36 - -C8 vegetable 0.65 18.33 5.68 1.59 0.44 -C9 vegetable 5.45 1.11 - 1.64 - -
C10 vegetable 1.24 7 3 1.82 0.53 -C11 livestock 0.43 153.96 - 3.02 - 128C12 livestock 8.09 1.3 - 1.66 - 2C13 livestock 1.30 52.33 15.43 1.94 - 32C14 livestock 0.22 259.12 - - 2.82 168C15 livestock 0.65 78.24 8.05 2.75 - 56C16 livestock 1.89 35.43 6.73 3.66 - 62C17 livestock 0.05 7.02 1.75 2.25 - 13C18 livestock 2.75 20 - 1.66 - 24
University of Pisa – Department of Agriculture, Food and Environment
Results (No adoption)
CodeNPV SFP/VAN UAA Labour (hours) Nitrogen
input (kg)Water input (m3)
Nitrogen kg/ha
Water m3 /ha
Shannon index
C1 1,897,400 0.11 139.2 3,044 680 1,192 122.05 914.55 0.51
C2 2,747,313 0.19 165.81 3,579 523 313 62.25 128.58 0.27
C3 1,361,658 0.21 93 1,791 430 565 81.37 540.6 0.39
C4 114,170 - 8.15 373 25 95 130.42 1,660.52 0.09
C5 337,691 0.18 19 633 64 216 103.2 1,285.88 0.39C6 51,513 0.18 3 117 2 54 29.3 - 0.38
C7 865,136 0.15 41 1,503 121 489 98.92 1,290.82 0.37
C8 506,241 0.13 21.33 1,143 38 322 87.67 1,449.18 0.28
C9 44,732 - 2 200 3 24 136.74 1,818.39 0.2
C10 188,491 0.15 9 634 18 112 130.29 1,768.32 0.18
C11 20,225,947 0.02 147.96 12,238 147 2,768 74.97 1,533.74 0.24
C12 72,729 - 2.3 307 2 31 113.66 1,700.66 0.18
C13 3,185,463 0.04 43.33 4,511 45 660 98.87 1,646.18 0.22
C14 21,101,226 - 189.12 13,783 5 70,116 1.83 742.83 0.28
C15 5,026,697 0.05 74.35 5,338 116 1,084 102.66 1,636.79 0.2
C16 3,047,662 0.04 43.34 3,446 61 632 102.36 1,633.59 0.26
C17 425,075 0.07 9.2 646 16 119 112.12 1,580.14 0.13
C18 1,781,085 - 27.5 3,243 26 385 102.34 1,567.92 0.17
University of Pisa – Department of Agriculture, Food and Environment
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%
C1 arable 2.54 116 - 108kwC2 arable 1.46 193.54 -C3 arable 5.23 72 - 108kwC4 arable 0.59 6.15 -C5 arable 16.68 17 -C6 arable 42.47 2.6 -C7 arable 8.26 36.5 -C8 vegetable 0.65 18.33 -C9 vegetable 5.45 1.11 - 254kw
C10 vegetable 1.24 7 - 254kwC11 livestock 0.43 153.96 128 108kW 254kw 254kwC12 livestock 8.09 1.3 2C13 livestock 1.30 52.33 32 108kW 108kW 108kwC14 livestock 0.22 259.12 168 254kwC15 livestock 0.65 78.24 56 108kW 108kwC16 livestock 1.89 35.43 62 108kwC17 livestock 0.05 7.02 13 108kwC18 livestock 2.75 20 24 108kW 108kw
Results (impacts)
Sce. NPV (1000 €)
SFP/NPV UAA
lab (100 0 hours)
Energy crops (share)
Energy (kw/h)
Nitrogen (1000 kg)
Water (1000 m3)
Nitr. (kg /ha)
Water (m3 /ha )
Shannon index
no 690,200 0.12 31,290 1,331 - - 3,835 37,954 87.42 1,213 0.61 current 705,790 0.12 31,824 1,331 0.01 3,456 3,916 38,397 88.03 1,207 0.59 +20% 799,619 0.11 32,527 1,340 0.04 6,028 3,815 37,289 87.89 1,183 0.54 +50% 830,568 0.12 37,149 1,438 0.30 52,868 4,460 40,176 87.45 1,081 0.54
University of Pisa – Department of Agriculture, Food and Environment
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
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
