Corrado Zoppi and Sabrina Lai on "Empirical evidence on agricultural land-use change in Sardinia (Italy) from GIS-based analysis and a Tobit model"

1. Seventh International Conference on
Informatics and Urban and Regional Planning
Cagliari, May 10-12, 2012
Planning support tools: policy analysis, implementation and evaluation
Empirical evidence on agricultural
land-use change in Sardinia (Italy)
from GIS-based analysis and a Tobit model
Corrado Zoppi & Sabrina Lai
Università di Cagliari - Dipartimento di Ingegneria Civile, Ambientale e Architettura
Via Marengo, 2 – 09123 Cagliari, Italy
Tel.: +39 070 6755216/6755206, telefax: +39 070 6755215
zoppi@unica.it; sabrinalai@unica.it

2. Layout
1. Introduction
2. Methodology: Tobit model
3. A GIS-based taxonomy of Sardinian municipalities
4. The impact of investment in public services and
infrastructure from the 2000-2006 ROP-EAGGF
5. Discussion and conclusions: Tobit models are a useful
tool to analyze and interpret spatial phenomena in
conjunction with GIS representations

3. 1.a – Introduction
• to assess whether, and to what extent, the 2000-2006 ROP-EAGGF was effective in maintaining agricultural
land use compared to other physical, economic and social characteristics concerning local development.
Aim
• a Tobit model combined with Geographic Information System.
Method
• the towns of the Sardinian Region.
Case-
Case
study
• some interesting insights concerning investment policies to maintain, reinforce and possibly expand
agriculture land use, whose relevance can be clarified only through a spatial representation.
• the model’s results show evidence of a strong correlation between the stability/increase of agricultural
Results land use and the ROP-EAGGF investment, even though its impact is quantitatively less important
with respect to other physical, economic and social characteristics concerning local development.

4. 1.b – Introduction: the 2000-2006 ROP-EAGGF.
The period to assess land-use change
A fundamental pillar of the 2000-2006 cohesion policy of the EU in Sardinia,
the 2000-2006 ROP was a plurifund program based on:
• EAGGF – European Agricultural Guidance and Guarantee Fund (approx. 770 million euros, 20 % of the
total investment of the program)
• ERDF – European Regional Development Fund
• ESF – European Social Fund
• FIFG – Financial Instrument for Fisheries Guidance
More than 90% of the expenditure of the 2000-2006 ROP-EAGGF occurred
in the period 2003-2008:
• measures aimed at simplifying payment procedures were adopted only in 2003: only from that year did
the program really start
• expenditure of the 2000-2006 ROP had to be entirely realized by Jun 30, 2009 due to the impossibility to
meet the original deadline of Dec 31, 2006
2003-2008 is the most suitable time period to assess if the
2000-2006 ROP-EAGGF succeeded in maintaining agricultural land use

5. 1.c – Introduction: the 2000-2006
ROP-EAGGF investment
Measures of the 2000-2006 ROP-EAGG
(Regione Autonoma della Sardegna, 2009a, pp. 300, 313, 367-391)
Expenditure
Measure Type of operations
(million €)
1.2 - Integrated cycle of water resource 43.494 Public infrastructure and services
management: irrigation systems of agricultural zones
1.9 - Prevention and control concerning forest fires, 11.000 Public infrastructure and services
and reforestation
4.9 - Investment in agricultural firms 183.092 Funds granted under the aid regime rules
4.10 - Improvement of transformation and marketing 137.848 Funds granted under the aid regime rules
of agricultural products
4.11 - Marketing of agricultural products 27.796 Funds granted under the aid regime rules
characterized by a high qualitative level
4.12 - Diversification of agricultural activities and the 10.000 Funds granted under the aid regime rules
like
4.13 - Essential services for the economy and the 15.659 Public infrastructure and services
rural population
4.14 - Promotion of adaptation and development of 31.740 Public infrastructure and services
rural areas
4.17 - Restoration of the rural environment damaged 16.000 Public infrastructure and services
by fire and natural disasters, and prevention
4.18 - Professional education referred to all of the 6.000 Education
2000-2006 ROP-EAGGF measures
4.19 - Rural reparcelling 59.957 Feasibility studies.
Funds granted under the aid regime rules
4.20 – Development and improvement of rural 143.626 Public infrastructure and services
infrastructure for agriculture
4.21 - Young farmers’ start-up support 84.325 Funds granted under the aid regime rules

6. 1.c (continued) – Introduction: the 2000-2006
ROP-EAGGF investment
The thirteen measures of the 2000-2006 ROP-EAGGF show a
comprehensive approach which entails addressing several
aspects of rural development:
conservation of natural resources and of rural cultural heritage
protection from fire and natural disasters
restoration and renovation of rural buildings and settlements
capacity building and professional education
tourism and tourism-related activities
reforestation
support to agriculture, in terms of infrastructure and services

7. 1.c (continued) – Introduction: the 2000-2006
ROP-EAGGF investment
Objective
not to evaluate the comprehensive impact and effectiveness
of the program
but only to assess its impact on agricultural land use
Therefore, only the subset of measures that finance public
infrastructure and services for agricultural land use is here
considered, that is measures 1.2, 1.9, 4.13, 4.14, 4.17, 4.20.
Assumption: the other measures (funds granted to agricultural
firms under the aid regime rules, and to promote professional
education) do not influence agricultural land use, at least in the
short run, even though they could possibly have an impact on
rural economy.
The investment expenditure referred to the six measures is
about one third of the total investment.

8. 2.a – Methodology: a Tobit model
The spatial configuration of agricultural land-use change between
2003 and 2008 in Sardinian towns is considered to be dependent
on the following variables:
Variable Definition Mean St.dev.
PLUAA3_8 Percentage change in a municipality's agricultural land use 4.6660 17.2699
between 2003 and 2008
DENS Residential density of a municipality in 2008 (residents per 0.7744 2.0981
hectare)
VARUU3_8 Percentage change in a municipality's urbanized land 22.8301 41.1650
COAST Dummy - A municipality overlaps a coastal landscape unit 0.4430 0.4974
as defined by the Regional Landscape Plan
PERVARPI Percentage change in a municipality's per-capita income in 21.8675 9.5568
the period 2003-2008
EXPEAGF 2000-2006 ROP-EAGGF per-capita expenditure for 456.1207 669.9753
measures 1.2, 1.9, 4.13, 4.14, 4.17, 4.20 concerning a
municipality (thousand Euros)
The most relevant methodological point of this study is
to demonstrate how the Tobit model can be used
to address an important problem of spatial analysis.

9. 2.b – Applications of Tobit models
concerning rural development
Gebremedhin and Swinton (2003): tenure rights and farmers' attitude
towards soil conservation, Northern Ethiopia
Alene et al. (2000): relationship between adoption and intensity of
utilization of improved maize varieties, and household characteristics, West
Shoa, Central Highlands, Ethiopia
Coomes et al. (2000): relationships between non-market mediated access
to land and labor, and forest fallow management and duration, Peruvian
Amazon
Pfaffermayr et al. (1991): assessment of the labor-supply decisions of
farm workers between part-time off-farm and full-time in-farm options,
Austria
Baidu Forson (1999): application of a Tobit model to analyze the
determinants of level and intensity of adoption of innovative technologies for
soil conservation and water resource management, Niger
Rajasekharan and Veeraputhran (2002): relationship between
intercropping decisions and availability of family labor and the type of
intercrops during the initial gestation periods of natural rubber cultivation,
Kerala Region, India
The Tobit model used in this paper is based
on Greene (1993, pp. 694-700)

10. 2.c – The model
We consider a censored or
latent dependent variable, y*, •y=L if y* ≤ L
which is related to an
observable variable, y, as • y = y* if y* > L (1)
follows:
The model operationalizes by
assuming that y* is linearly • y* = β’x + ε (2)
dependent on a vector of
explanatory variables, • where β = (β1,…, βm) is a
x = (x1,…, xm), through the vector of coefficients
following relation:

11. 2.c (continued) – The model
If we substitute (2) in (1) we obtain the following model, which, under the usual
hypotheses of ordinary least-squares models concerning the mean, variance and
covariance of the ε term, can be estimated by the maximum likelihood
estimator (MLE), which is unbiased and efficient, even though non-linear
(Greene, ibid.):
y=L if y* ≤ L
y = β’x + ε if y* > L (3)
The MLE for β is the vector b = (b1,…, bm) that maximizes the following maximum
likelihood function:
(4)
where s2 is the MLE estimator for the variance of the error term and Φ is the
cumulative normal distribution operator (Greene, ibid.).
The values of the vectors of coefficients bj’s which maximize (4) are the solution of
the system which comes from equalizing to zero the derivatives of ln L with respect
to the m components of vector b.

12. 2.c (continued) – The model
The values of the estimates bj’s of the vector of coefficients βj’s make it
possible to estimate the marginal effects of a change of the vector of
explanatory variables x on the censored variable y as follows (Greene, ibid.):
(5)
The model assumes that:
the error term ε is normally distributed
E(ε|x) = 0 (i.e., the error terms in the regression have a 0 conditional mean)
Var(ε) = σ2 (i.e., the error term has the same variance at each observation)
E[ εi εj | X ] = 0 (i.e., the error terms are uncorrelated between observations)
s2 = where y is the vector of observations of the censored
variable y and x is the matrix (n,m) of the n observations of
the m explanatory variables.

13. 3.a – A GIS-based taxonomy of
agricultural land use & related variables
Aim: to analyze each city/town changes in land use by
implementing a spatial database
Basic spatial units: municipalities
Integration of available (both spatial
and non-spatial) data, e.g.
spreadsheets (demography, income,
levels of expenditure, …)
Need
spatial datasets (distribution of land uses, for
delimitation of coastal landscape units, …)
was required to develop new knowledge GIS
and obtain new layers of either
spatial or non-spatial information

14. 3.b – Land cover and land-cover changes
1990-2006: data produced by the
European Environmental Agency
Freely downloadable from
www.eea.europa.eu
Distinction between real evolution
processes and different
interpretations of the same
subject
Not detailed enough (scale,
minimum mapping units)
2003 & 2008 regional Land cover changes involving areas classed as agricultural
between 1990 and 2000 and between 2000 and 2006
“land-use maps” according to EEA’s land-cover maps

15. 3.c – 2003 and 2008 regional land-use
maps
Freely downloadable from
http://www.sardegnageoportale.it
4 levels
hierarchical nomenclature, compliant with
the CORINE Land Cover project
strictly speaking, the so-called “regional
land-use maps” are actually land-cover
maps …
… however, thanks to their level of detail,
they do also provide reliable information on
how land is used by humans, especially as
far as agricultural areas and forests are
concerned Land-use changes in the 2003-2008 time
the expression “land-use map” is frame, taking into account only areas that in
2003 were classed as agricultural according
therefore used here (but we are
to the regional land-use maps
aware of the difference!)

16. 3.d – Agricultural land uses in Sardinia
according to the 2003 and 2008 land-use maps
Total area Total area
2nd level 3rd level 4th level in 2003 in 2008
[hectares] [hectares]
2111 Non-irrigated arable land 144,537.8 251,181.45
211 Non-irrigated arable land
2112 Artificial meadows 142,587.2 164,575.47
2121 Arable land and
21 346,524.70 205,735.63
horticultural crops in open fields
Arable land
212 Permanently irrigated land 2122 Rice fields 4,584.17 4,660.69
2123 Nurseries 240.08 341.00
2124 Crops in greenhouses 1,184.98 1,769.04
22 221 Vineyards 15,957.87 24,686.43
Permanent 222 Fruit trees and berry plantations 10,268.18 11,907.84
crops 223 Olive groves 43,790.91 48,777.62
23 Pastures 231 Pastures 9,517.21 10,316.08
2411 Annual crops associated
9,608.73 11,714.50
with vineyards
241 Annual crops associated with 2412 Annual crops associated
163.70 296.13
24 permanent crops with vineyards
Heterogeneous 2413 Annual crops associated
53,164.42 58,067.58
agricultural with other permanent crops
areas 242 Complex cultivation patterns 43,107.79 42,206.07
243 Land principally occupied by agriculture, with significant
27,271.17 29,282.01
areas of natural vegetation
244 Agro-forestry areas 50,493.25 57,429.67
Total 903,002.15 922,947.21

17. 3.e – Descriptive attributes
ISTAT census code of each municipality in the Italian Census system
Original
AREA_CITY land area
PERIMETER length of the boundary
NAME_CITY name of the municipality
POP_2003 Resident population as of December 31, 2003
POP_2008 Resident population as of December 31, 2008
Used in MNL
DENS Residential density in each municipality in 2008 [residents/hectare]
Derived
COAST Dummy - A municipality overlaps a coastal landscape unit as defined by the RLP
PLUAA3_8 Percentage change in a municipality's agricultural land use bw 2003 & 2008
VARUU3_8 Percentage change in a municipality's urbanized land bw 2003 & 2008
PERVARPI Percentage change in a municipality's per-capita income in the period 2003-2008
EXPEAGF 2000-2006 ROP-EAGGF per-capita expenditure for measures 1.2, 1.9, 4.13, 4.14, 4.17,
4.20 concerning a municipality (Euros per resident)
VARPOP Percentage change in a municipality’s population bw 2003 & 2008
AGR03 Total amount of agricultural land in 2003 [ha]
PERCAGR03 Percentage of agricultural land in a given municipality in 2003
AGR08 Total amount of agricultural land in 2008 [ha]
PERCAGR08 Percentage of agricultural land in a given municipality in 2008
LUURB_03 Total amount of urbanized land in 2003 [ha]
Derived – not used in MNL
PERCURB03 Percentage of urbanized land in a given municipality in 2003
LUURB_08 Total amount of urbanized land in 2008 [ha]
PERCURB08 Percentage of urbanized land in 2008
LUC_AU Land-use change from “agricultural” to “urbanized” bw 2003 & 2008 [ha]
LUC_AA Land-use change from “agricultural” to “agricultural” (≠sub-types) bw 2003 & 2008 [ha]
LUC_AF Land-use change from “agricultural” to “forestry” bw 2003 & 2008 [ha]
LUC_AS Land-use change from “agricultural” to “scrubs” bw 2003 & 2008 [ha]
LUC_AB Land-use change from “agricultural” to “bare soil” bw 2003 & 2008 [ha]
LUC_AP Land-use change from “agricultural” to “wetlands” bw 2003 & 2008 [ha]
LUC_AW Land-use change from “agricultural” to “inner & maritime waters” bw 2003 & 2008 [ha]
% LUC_AU Land-use change from “agricultural” to “urbanized” bw 2003 & 2008 [%]
% LUC_AF Land-use change from “agricultural” to “forestry” bw 2003 & 2008 [%]
% LUC_AS Land-use change from “agricultural” to “scrubs” bw 2003 & 2008 [%]
% LUC_AB Land-use change from “agricultural” to “bare soil” bw 2003 & 2008 [%]
% LUC_AP Land-use change from “agricultural” to “wetlands” bw 2003 & 2008 [%]
% LUC_AW Land-use change from “agricultural” to “inner & maritime waters” bw 2003 & 2008 [%]
TOTINC2003 Per-capita income in 2003
TOTINC2008 Per-capita income in 2008

18. 3.e (continued) – Descriptive attributes: examples
1. PLUAA3_8: % change in a municipality’s agricultural land use 2003-2008
2. DENS: residential density in 2008
3. VARUU3_8: % change in artificial surfaces 2003-2008

19. 3.e (continued) – Descriptive attributes: examples
1. COAST: municipalities that overlap/do not overlap any coastal landscape units
2. PERVARPI: % change in per-capita income 2003-2008
3. EXPEAGF: 2000-2006 ROP-EAGGF per-capita investment on public
infrastructure and services for agriculture

20. 4. – The impact of investment in public services
and infrastructure from the 2000-2006 ROP-
EAGGF
Hypothesis test:
Variable Coefficient bi Standard error z-statistic
βi=0
DENS 0.3479 0.0680 5.116 0.0000
VARUU3_8 0.0394 0.0220 1.791 0.0732
COAST 4.1603 1.8525 2.246 0.0247
PERVARPI 0.2073 0.0737 2.812 0.0049
EXPEAGF 0.0070 0.0007 10.434 0.0000
Marginal Hypothesis test:
Variable Standard error z-statistic
effect βi=0
DENS 0.3470 0.0678 5.119 0.0000
VARUU3_8 0.0393 0.0219 1.791 0.0732
COAST 4.1498 1.8466 2.247 0.0246
PERVARPI 0.2067 0.0736 2.809 0.0050
EXPEAGF 0.0070 0.0007 10.429 0.0000

21. 4. (continued) – The impact of investment in public
services and infrastructure from the 2000-2006
ROP-EAGGF
• An increase of about 3.5% in • This variable has a positive and
agricultural land would occur if significant marginal effect, which
DENS increased by 10 residents puts in evidence that investment in
per hectare, which puts in infrastructure and services to
evidence a significant improve agricultural land use is
agglomeration effect. more effective in coastal areas
than in inner ones.
The results imply, ceteris paribus, a
4% positive differential in
agricultural land use.
Residential A municipality
overlaps a coastal
density landscape unit (RLP)

22. 4. (continued) – The impact of investment in public
services and infrastructure from the 2000-2006
ROP-EAGGF
• The change in a municipality’s • This implies not only that there is an
urbanization also reveals a positive income effect that does not displace
marginal effect, even though not at all agricultural activities (0.2% per
quantitatively strong: approximately a 1% increase in RPCI), but also that,
only a 3.9% of a percentage point for in principle, whichever policy aiming
a 1% increase. However, this is at increasing local people’s income
important since it shows that can be considered an indirect
increasing urbanization does not support to maintaining and possibly
seem to prevent conservation or increasing agricultural land use.
expansion of agricultural land use.
Change in a
Real per capita
municipality's
income increase
urbanization

23. 4. (continued) – The impact of investment in public
services and infrastructure from the 2000-2006
ROP-EAGGF
Finally, the marginal impact of the investment concerning a municipality
from the 2000-2006 ROP-EAGGF is positive, as expected, and highly
significant.
However, this impact is quantitatively weak, since it indicates that a
100€ increase in per-capita investment implies approximately a 0.7%
increase in agricultural land use.
In other words, this means that an investment of nearly 165 million
Euros, that is more than one fifth of the total 2000-2006 ROP-
EAGGF expenditure, would have been required to increase agricultural
land in 2008 with respect to 2003 by approximately 63 km2.

24. 5. – Conclusions
This paper proposes a discussion on quantitative change in agricultural land use
at the municipal level as a phenomenon influenced by physical, economic, social
and investment policy variables. The methodological approach is based on the
intermixed use of GIS techniques and econometric models.
We feel that the use of a Tobit model based on a GIS-based taxonomy of
dependent and explanatory variables could be easily and effectively exported to
other European Union regional contexts, since the 2000-2006 ROP’s of the
European regions at the NUTS 2 level were fairly standard.
The reproducibility of the proposed approach makes it possible to assess the
results concerning the impact of ROP-EAGGF’s on agricultural land-use change
and to compare such impacts across regions, at the intra-national and inter-
national levels.
Moreover, the results are useful in terms of
ex-post assessment
definition and implementation of regional policies concerning investment
aimed at maintaining and increasing agricultural land use, that is in terms of
ex-ante and on-going assessment.

25. 5.(continued) – Conclusions: policy implications
from the model results
In the rest of this concluding remarks we use GIS to comment and
discuss policy implications of our results through a spatial representation.
Background policy implication: it should be more effective to invest in
agriculture in municipalities
having significant values of residential density
whose territory overlaps a coastal landscape unit
“What-if” scenario built upon marginal effects from the Tobit model.
Two steps:
1. If a single explanatory variable increased by a given quantity (ten
percentiles in its distribution) …
… what would the magnitude of the impact on the % change in a
municipality’s agricultural land use between 2003 and 2008 be?
2. What impacts would be produced by implementing policies that
increase four variables (DENS, VARUU3_8, PERVARPI, EXPEAGF)?

26. 5.(continued) – Conclusions: a spatial
representation of policy implications
Impacts on agricultural land use stemming from policies that increase…
1. … a municipality’s residential density (Imp. DENS)
2. … % change in a municipality’s urbanized land between 2003 and 2008 (Imp. VARUU3_8)
3. … per-capita income at the municipal level (Imp. PERVARPI)
4. … per-head investment on public infrastructure and services for agriculture (Imp. EXPEAGF)

27. 5.(continued) – Conclusions: a spatial
representation of policy implications
The map shows impacts on preservation of
agricultural land uses produced by implementing
policies that increase:
residential density
urbanized land
per-capita income
per-capita investment on
public infrastructure and
services for agriculture
It therefore gives clear indications on which are
the “best” possible areas that policies should be
targeted in order to preserve and reinforce
agricultural land uses.