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.
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
largescale investments
The preservation of the territory, in both its environmental and local dimensions,
assumes the agricultural sector as a protection of economic wealth and a
socioeconomic 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
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
In this context, the change of the relationship among job, sustainability and
territory is directed to the “degrowth, envelopes and maintenance"
The conjugation of quantitative purposes (increase of 70% in agricultural products
by 2050) to landscape and environmental sustainability
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
The Cadastre has always represented the principal source of data used for an
orderly representation of the territory and for planning economic activities. The
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
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”
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”
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.
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
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 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.
where: (i = 1, 2, 3, 4), k is the overall value of each parcel, kiʹ′ is the value of criterion i ( k1ʹ′ = landscape, kʹ′ = environment, k ʹ′ = economy, kʹ′ = functions), k ʹ′ʹ′ is 2 3 4 ji the weight expressing the importance of criterion j compared to first-level criteria; k = ∑ k ʹλ′ ʹ′.
i i i kiʹ′ = ∑ kʹʹ′′ λ ʹʹ′′ ji ji ji (1) (2) where (j1 = 1, 2,…, 6), (j2 = 1, 2,…, 5), (j3 = 1, 2,…, 4), (j4 = 1, 2, 3), k ʹ′ʹ′ is the ji value of subcriterion j of criterion i, λ !! is the weight defining the importance of ji subcriterion j – belonging to criterion i – compared to second-level criteria. landscape environment economy
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The economic criterion is based on the cadastral incomes (landowners and firms),
the agricultural standard value and market value, productivity, acclivity, location.
The pattern integrates spatial and appraisal calculation functions by a specific
algorithm allowing both the numerical and the graphical implementation, and
recording both the cartographic changes in the charts, and the numerical changes in
the mapping. A WebGIS interface allows displaying the distribution of the parcels
boosted according to a fixed budget and a specific strategy
The incentive policy takes into account sustainability principles as declined by integrating firm economic performances in local identity and culture. In the Enna 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).
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: 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 ∑ jλ j = 1) placing primary importance on two of the four different qualitative dimensions (Landscape, Environment, Economy and Functions) (strategies 1-4) or on each dimension (strategies 5-8) (Table 2).
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).
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 higherquality parcels along the main criteria of a specific strategy).
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). Financing admitted parcels no admitted parcels V ≤ 3° quartile V > 3° quartile cadastral parcel
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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