=Paper=
{{Paper
|id=Vol-1498/HAICTA_2015_paper100
|storemode=property
|title=Agriculture and Sustainability: a GIS Based Model to Appraise Incentive Policy
|pdfUrl=https://ceur-ws.org/Vol-1498/HAICTA_2015_paper100.pdf
|volume=Vol-1498
|dblpUrl=https://dblp.org/rec/conf/haicta/GiuffridaGN15
}}
==Agriculture and Sustainability: a GIS Based Model to Appraise Incentive Policy==
https://ceur-ws.org/Vol-1498/HAICTA_2015_paper100.pdf
Agriculture and Sustainability: a GIS Based Model to
Appraise Incentive Policy
Salvatore Giuffrida1, Filippo Gagliano2, Grazia Napoli3
1
Department of Civil Engineer and Architecture, University of Catania, Italy,
e-mail: sgiuffri@dica.unict.it
2
Department of Civil Engineer and Architecture, University of Catania, Italy,
e-mail: fmgagliano@gmail.com
3
Department of Architecture, University of Palermo, Italy, e-mail: grazia.napoli@unipa.it
Abstract. Agriculture is the major form of protection of local identities and
sustainability and one of the most fragile Italian economic sectors, exposed to
fluctuations of the financial/economic crisis. As a consequence, boosting
agricultural policies should integrate conflicting objectives connected to
preservation and innovation, effectiveness/efficiency, and landscape features
and job opportunities. Referring to a large land area located in the central part
of Sicily (Italy) the paper proposes an assessment/planning pattern aimed at
providing some axiological items and a specific algorithm able to appraise
each specific land parcel, generating different strategies and selecting the best
format of funding allocation. The pattern combines some WebGIS tools
helpful for spatial analysis and management of the big data amount coming
from the Landscape Regional Plan and the cadastral vector database. The
general approach integrates monetary and qualitative features, as well as land
estate and landscape values within a multidimensional pattern providing the
quantitative conditions for supporting qualitative and sustainable development.
Keywords: agricultural policy, GIS, appraisal model, sustainability.
1 Introduction
Agriculture is an economic sector that has suffered the effects of the economic crisis
and has been growing slower due to the change of the relationship between the State
and the market, the increasing international competition, the uncertainty of the large-
scale investments (INEA, 2011) and the modification of the Common Agricultural
Policy (CAP) that, in 2007-2013, produced “a notable impact on the Italian and
Sicilian agriculture caused by the decreasing attention to the Mediterranean
agricultural products” (Regione Siciliana, 2008, p. 16). These difficulties are
confirmed by the low availability of financial facilities in agriculture compared to
other economic sectors (4.2% in the south, 2.5% in the centre-north Italy) (Caprara et
al., 2010), and by the inability to accumulate economic wealth (over the decade
2000-2010 the added value increased by 2%).
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The preservation of the territory, in both its environmental and local dimensions,
assumes the agricultural sector as a protection of economic wealth and a socio-
economic whose activities have assumed a strategic importance because of the
widespread a different demand in food and landscape. The new agricultural policy
improves the nutritional behaviours by boosting the biological farms and promoting
food education programmes (Pollan, 2007; Foer, 2010).
Despite the crisis, some important modifications have been improving the rural
economic structure like the diffusion of environmental sustainability, the trust
towards the responsibility of the local communities (European Commission 2011), a
greater integration of land planning, economic investments and farm tourism
reporting from 1998 to 2005 an increase of facilities of 58% in Italy and of 134% in
Sicily (OSEAAS, 2007).
In this context, the change of the relationship among job, sustainability and
territory is directed to the “degrowth, envelopes and maintenance" (Morin, 2012) and
to some indications of the reformed CAP that invokes a strong public policy
supporting those agricultural products whose production could not be guaranteed by
the market, in order to prevent marginalisation, abandonment and degradation of the
areas excluded from intensive farming and characterized by the permanence of
fragile ecological and anthropic systems.
The conjugation of quantitative purposes (increase of 70% in agricultural products
by 2050) to landscape and environmental sustainability (European Commission,
2010, pp. 2-3) asks for a global agricultural policy at a local scale which can be
realized with a consistent amplification of a planning-oriented database and a
territorial information system containing the data which allow to clearly and fairly
appraise the economic, functional and landscape values of each territorial unit.
The Italian cadastre currently reports only dimensional and administrative data
and the income from the land parcels, but its database is going to be improved with
the development of orthophoto systems, cartographic modelling, GIS and WebGIS
technologies to satisfy the ever-increasing demand for territorial data suitable for
cognitive, appraising and planning purposes.
This study proposes a pattern for representing and appraising cadastral parcels; by
applying GIS spatial calculation and by means of a great quantity of quality
information, this model aims to improve tax equalisation and subsidy choice as well
as to assess the redistributive effects of agricultural policy (Bernetti and Marinelli,
2010). These types of algorithms are common used to support the decision-making
and improve the efficiency of multi-objective spatial planning (Cowen, 1988; Fotakis
et al., 2012; Malczewski, 1999; Malczewski and Rinner, 2015).
2 Materials and methods
2.1 The cadastre: Information, Appraisal and Programming
The Cadastre has always represented the principal source of data used for an
orderly representation of the territory and for planning economic activities. The
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quantitative and qualitative increase of its technical information base allows,
therefore, to consolidate its primary vocation, i.e. tax equalisation, through the
integration of territorial policies into economic planning (Coletta et al., 2009; Rizzo,
1986) within the sustainable development. Sustainability is a concept that
encompasses a plethora of definitions (Trovato, 2012), but it tightly focuses on local
development, which involves anthropological and landscape dimensions, and this
leads to reconsider the relationship between global capital and income
accumulated/provided from the social and territorial macro-system.
Assuming a parcel1 as the starting point, this research proposes to widen the
cadastral database by integrating the economic and physical quantitative data with
the aesthetic and qualitative data (geographical and landscape-related), so as to
develop a territorial profile of each parcel through spatial calculation which is also
suitable for the evaluation purpose. The new database can support the appraisal of the
market values from the perspective of a cadastre founded on both incomes and
values, for adjusting “the fiscal imposition to the asset recovery so that it can
contribute to attenuate the effects of the crises and the speculative activities” (Rizzo,
2010), especially when the real estate becomes object of over-accumulation of capital
with the aim of profiting from the differential market value.
The basic idea of this study is that public institutions can conduct virtuous
hoarding activity by supporting the sustainable agricultural policies that raise the
social value of the lands, thus promoting the traditional cultivation and making it
competitive thanks to the introduction of innovations which not only increase the
added value, but also tend to accumulate as farm improvement. The added value,
defined as the difference between sales and purchases or as the remuneration of the
productive factors (including amortization), “could be replaced in the fiscal survey
by the social net product plus amortizations” (Rizzo, 2010, p. 146).
The recourse to GIS as a tool is consistent with the necessity to provide a value
function containing the fundamental merit elements to have access to the incentive
system as well as the territorial characteristics according to the policy for sustainable
agriculture.
2.2 The GIS Model and the Methodology of the Georeferenced Cadastral
Cartographic Database
The cartographic support has been achieved by integrating the necessary
algorithms for a georeferenced cadastral cartographic database into a GIS; the object
of the cartographic database and its spatial analyses apply the projection onto the
geographic Cassini-Solder system, while territorial information is projected onto the
geographic Gauss-Boaga system. In order to create a vector database, useful for
accurate institutional calculation, the GIS database has been integrated with an
algorithm using a rigorous methodology for the roto-translation (Di Filippo, 2004)
(Figure 1). The methodology is divided in the following phases: 1. Acquisition of the
cadastral cartography in numeric format; 2. Acquisition of the control points; 3.
1
In the Italian land register, the parcel is a continuous part of land having the following
characterizes: same owner, same type of cultivation and same ranking of productivity.
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Translation from the plane coordinate system into the ellipsoidal coordinate system;
4. Transformation of the ellipsoidal coordinates; 5. Translation from the ellipsoidal
coordinates into a new plane coordinate system (cartographic coordinates).
After correlating the two systems, the seven-parameter transformation is
completed by calculating the translation parameters, two scale factors, the angular
slide (S-band) and two rotation parameters according to the minimum quadratic
difference. The algorithm runs using .XML file parameters and cadastral vector .CXF
files, and creating a correlation between the identification number (id.) (cadastral
code) of the parcel and the corresponding polygon, so that a geographic database in
Shape format is developed (http://shapelib.maptools.org).
The database is built on the base of the id., by importing the cadastral data of the
parcels following relational functions; besides, the fields sheet and parcel have been
unified to build a specific field (KEY) and obtain a spatial join to the georeferenced
cadastral map.
2.3 The Evaluation Model and the Spreadsheet
The proposed model is applied to a portion of land (Municipality of Piazza
Armerina, Sicily, Italy) constituted by 31,254 parcels whose value has been
represented from four points of view, 1. Landscape, 2. Environment, 3. Economy, 4.
Functions. Each criterion is specified by indicators provided by a database that
applies the spatial computation functions of the GIS and works out the criteria values
by implementing specific utility functions.
The scores are assigned in an adimensional scale, ranging from 1 to 5, to each
parcel, identified and described in quantitative and qualitative terms. The scores are
positive when the characteristic constitutes an economic-monetary, functional,
landscape or environmental value, and negative when it constitutes a disvalue (i.e.
hydrogeological risk, high fire risk, etc.) (Table 1). The spatial analysis functions
turn land characteristics into value function parameters. The relationship between
characteristic and score is expressed by a utility function that associates the general
value k – obtained by aggregating the scores by means of a Work Breakdown
Structure – to each cadastral parcel.
k = ∑ kiʹ′λiʹ′ . (1)
i
where: (i = 1, 2, 3, 4), k is the overall value of each parcel, kiʹ′ is the value of
criterion i ( k1ʹ′ = landscape, k 2ʹ′ = environment, k3ʹ′ = economy, k 4ʹ′ = functions), k ʹ′jʹ′ is
i
the weight expressing the importance of criterion j compared to first-level criteria;
kiʹ′ = ∑ k ʹ′jʹ′i λ ʹ′jʹ′i (2)
ji
where (j1 = 1, 2,…, 6), (j2 = 1, 2,…, 5), (j3 = 1, 2,…, 4), (j4 = 1, 2, 3), k ʹ′jʹ′ is the
i
value of subcriterion j of criterion i, λ !!ji is the weight defining the importance of
subcriterion j – belonging to criterion i – compared to second-level criteria.
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code code code code
sheet sheet sheet sheet
parcel parcel parcel parcel
Torre, 2007).
municipality municipality municipality municipality
parcel code parcel code parcel code parcel code
2.4 Incentive Policy
cultivation type cultivation type
geology geology
plants plants ranking ranking
hectare hectare
forestry forestry
landscape
landscape
river river are are
building of archi- building of archi- centiare centiare
tectural interest tectural interest
archeologic basin archeologic basin
rental income rental income
geological risk geological risk
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land use risk land use risk agricultural income agricultural income
forest fire (summer) forest fire (summer) owner code owner code
environment
environment
forest fire (winter) forest fire (winter)
variation code variation code
road system road system
constraint constraint
rental income rental income area (m2) area (m2)
agricultural income agricultural income code part 1 code part 1
average average
economy
economy
cultivation type of cultivation type of
agricultural value agricultural value part 1 part 1
market value/m2 market value/m2 ranking part 1 ranking part 1
forest fire forest fire
hectare parte 1 hectare parte 1
land use land use are part 1 are part 1
functions
functions
slope slope centiare part 1 centiare part 1
Guidelines of the Regional Territorial Landscape Plan (Superintendence and DAU, 2008).
area (m2) part 1 area (m2) part 1
Table 1. Part of the evaluation spreadsheet model. Our processing on data provided by the
recording both the cartographic changes in the charts, and the numerical changes in
algorithm allowing both the numerical and the graphical implementation, and
The pattern integrates spatial and appraisal calculation functions by a specific
integrating firm economic performances in local identity and culture. In the Enna
boosted according to a fixed budget and a specific strategy (Sani and Rinner, 2011;
the mapping. A WebGIS interface allows displaying the distribution of the parcels
the agricultural standard value and market value, productivity, acclivity, location.
Land Registry Office and Forestry Corps of the Sicilian Region – Forestry Informatin System,
The incentive policy takes into account sustainability principles as declined by
The economic criterion is based on the cadastral incomes (landowners and firms),
district low urbanization, demographic drop, intense migration flowing towards
external labour markets, a weak economic system mostly depending on agriculture
and high unemployment rate on the one hand, and the vitality of traditional
handicrafts and the construction sector on the other, should be of great interest to an
incentive policy for the creation of added value, diversification in household
consumption and improvement of life quality.
In Sicily, the CAP is inspired by the European support strategy that intends to
integrate market, local rural development, sustainability and innovation. With
reference to some key thematic areas – economy of the agri-food sector,
environment, rural economy and population – the development programs individuate
four axis: 1. Improving the competitiveness of the agricultural and forestry sector; 2.
Improving the environment and the countryside; 3. Quality of life in rural areas and
diversification of the rural economy; 4. Leader, (ENRD, 2007).
The proposed pattern is a decision-making support helpful for determining the
financial requirement of the firms involved in the incentive program. Measure 112 of
CAP 2007-2013 is supposed to be applied: it concerns the renewal process of the
entrepreneurial culture in terms of economic actors and farm competitiveness. The
overall financial envelope of the measure is 90 mln € and is conditional upon the
execution of at least another investment measure among the following: a)
Modernisation of agricultural holdings (M.121); b) Improvement of the economic
value of forests (M.122); c) First afforestation of agricultural land (M.221); d)
Diversification into non-agricultural activities (M.311).
The financeable amount, assumed as a base for determining the requirement, is
established by adopting the current practices among local operators, that is, the
standard amount of 40,000 euros (M.112) plus further 60,000 euros for any
additional measure. The access to the funding depends on the dimension of the farm
that has to correspond to 10 ESUs (Economic Size Unit), reduced to 8 ESUs in case
of disadvantaged areas (e.g. where the municipality of Piazza Armerina is located).
3 Results of Appraisal and Programmatic Indications
The economic appraisal and spatial calculation of the area have shown the spatial
distribution of the parcels which most deserve an incentive based on a hypothetical
budget. The selection is made on the following algorithm:
n (3)
∀p∃f1 ( p) = k : k ≥ gW ; 1 ≤ k ≤ 5 ; ∑ f 2 ( pi ) = W ; (n = 31.254)
i
where p is the generic parcel selected if the value k overcomes the threshold gw;
the threshold is set so that the potential funding sum of all the selected parcels will be
equal to the total budget W (Fig. 1)
Eight different strategies have been hypothesized; each of them is defined by a
specific weight system λ j (respecting the constraint ∑ j λ j = 1 ) placing primary
importance on two of the four different qualitative dimensions (Landscape,
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Environment, Economy and Functions) (strategies 1-4) or on each dimension
(strategies 5-8) (Table 2).
Fig. 1. Funding distribution of the budget increasing from EUR 5 to 35 million.
Table 2. Weight system of the eight strategies.
Strategies
Criteria 1 2 3 4 5 6 7 8
Landscape 0,1 0,5 0,1 0,4 0,7 0,1 0,1 0,1
Environment 0,1 0,1 0,4 0,1 0,1 0,7 0,1 0,1
Economy 0,5 0,3 0,4 0,1 0,1 0,1 0,7 0,1
Functions 0,3 0,1 0,1 0,4 0,1 0,1 0,1 0,7
Budget!
5 millions!
10 millions!
15 millions!
20 millions!
25 millions!
30 millions!
35 millions!
cadastral parcel!
Fig. 2. Distribution of the parcels selected according to the budget and strategies 5 and 6.
The varied results produced by the different strategies raise issue of fairness in
funding (Fig. 2). To this end, every strategy should analyse the relationships among
the increasing budget and: the number of the selected parcels; the area receiving
funding (index of efficient and fair funding allocation); the threshold level which is
reduced for selecting the parcels (indicating the relationship between funding amount
and quality of the selected parcels); the overall evaluation (index summing all the
scores of the selected parcels).
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These characteristics are normalized on a scale from 0 to 2 (Tab. 3, Fig. 3). On the
same budget one strategy is preferred if: a greater number of parcels and a greater
area are financed; a higher total value is obtained (when the best parcels or a much
greater number of less valuable parcels are financed); the same number of parcels is
selected on the same budget but a higher threshold is imposed (index of higher-
quality parcels along the main criteria of a specific strategy).
Table 3. Weight system of the eight strategies.
Fig. 3. Evaluation of the strategies according to the efficiency/effectiveness of the budget
allocation
The model allows a further evaluation concerning the distribution of the financed
land units with reference to land qualification. The whole financed parcels are
overlapped with eight qualified sectors (for a budget of EUR 20 million)
implementing one by one the eight strategies (64 combinations) and selecting the
parcels inside (dark grey) and outside (light grey) the sector: the indexes of
effectiveness of each strategy can be achieved by connecting the area of each sector
with the whole financed area and the financed area inside the sector (Fig. 4).
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Financing
admitted parcels
no admitted parcels
V ≤ 3° quartile
V > 3° quartile
cadastral parcel
Fig. 4. Effectiveness evaluation of strategies 5 and 6 with reference to the qualified area
4 Conclusion
The proposed model allows testing how a representation and evaluation model
(through functions of spatial calculation and economic appraisal) can address the
incentive policy of the agricultural sector.
The results underlined the conditions, which justify and prefer a policy aimed at
encouraging lands with greater landscape value and lower productivity. A coherent
economic and land planning in agriculture imposes itself with some urgency and asks
for a methodology of analytical representation and evaluation of the territory which
assumes minimum units of study described by detailed spatial information, and
accurately stored and managed by spatial informative systems.
The paper has also underlined some criticalities of these procedures, due to very
poor standardised geo-spatial data; thus, a specialist competence is required for
building an evaluation database. Potentially, this model can be developed by
overcoming the additive approach and pushing the artificial intelligence to represent
complex relationships.
Acknowledgments. Salvatore Giuffrida edited paragraphs 1, 2.3, 3 and 4; Filippo
Gagliano edited the paragraph 2.2; Grazia Napoli edited paragraphs 1, 2.1, 2.4 and 4.
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