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The effect of monetary and non-monetary drivers on agricultural
and forest land-use change in Andalusia using logit modelsAuthors and e-mail of them: Jose L. Oviedo1 (jose.oviedo@csic.es)Alejandro Caparrós1 (alejandro.caparros@csic.es)Pedro J. Moreno Rodríguez2 (pedroj.moreno@uca.es)
Department: 1 Institute of Public Goods and Policies2 Departamento de Economía General
University:
1 Consejo Superior de Investigaciones Científicas2 Universidad de Cádiz
Subject area:
Environmental economics, natural resources and public policies
Abstract:
We investigate transitions between the main agricultural and forest land-uses in Andalucía (Spain) using conditional and mixed logit models. We observe individual land use decisions using the Corine Land Cover database in a sample of 10.000 random points, and we use monetary and non-monetary drivers such as, net returns, subsidies, natural protection figures, slope, distance to urban centers, rainfall and temperature associated to each observation. We analyze (i) a 3-uses model that aggregate crop, pasture and forest uses, and (ii) a 7-uses model that differentiate four type of crops (herbaceous, fruit trees, olive groves and vineyards) and two type of forests (hardwood and conifer). The results of the two specifications are consistent and show that net returns, subsidies, protection figures, slope and rainfall increase the probability of land use change. Net returns have a positive effect on crop and forest and a negative effect on pasture. Pasture is the land use that changes the most to to other land-use alternatives. In addition, we obtain that the largest land use changes come from hypothetical scenarios (up to 2022) with large variations in subsidies, while the effect of climate variables is moderate. These results show the feasibility of a European-scale application.
Keywords: land use transitions, land use drivers, discrete choice models
JEL codes: C25, Q15, Q18, Q24
1. Introduction
European agricultural and environmental policies have affected land uses in Europe in
the past, and future climate and bio-energy policies will further influence land use choices. In
the last 20 years, the European Union has provided subsides to specific land uses and the
associated production through the Common Agricultural Policy (CAP) and related
reforestation and afforestation programs. Current concern about climate change, and the
growing demand of land for bio-energy production, will likely increase further the incentives
for reforestations and bio-energy uses, therefore increasing land use competition and the
effects on land use allocation.
Existing models predict that carbon sequestration in forests and bioenergy produced in
Europe can contribute significantly to European climate policy (Ovando and Caparrós 2009).
However, these models are either focused on the physical possibilities or on Computable
General Equilibrium (CGE) models. They typically do not consider factors which are
relevant in explaining land-use transitions, such as landowners inertia to maintain current
uses or liquidity constraints, the impact of non-commercial and non-monetary factors (bio-
physical and environmental variables), the wish to retain options for future land-use decisions
or any other benefits and costs associated to land uses of which the analyst is unaware. By
omitting these factors, these models overestimate the effects of land use changes as, in
general, they will tend to attribute the land use change decision to a higher monetary return
from an alternative land use. For the reasons explained above, however, this may not always
be the case, especially for rural land with low development pressure.
A direct way to incorporate these variables in land use decision modelling is to observe
past land use decisions and regress them on a set of explanatory variables that intend to
capture all potential factors affecting land use choice. These econometric land use models,
originally developed by Stavins (1999), have been further developed by Lubowski et al.
(2006), Rashford et al. (2010) and Claasen et al. (2013) mainly focusing on monetary
variables (net returns from and subsidies to specific land uses) corrected by land class
productivity variable.
In this paper we present an econometric model of land use that incorporates both
monetary (net returns and subsidies) and environmental variables (slope, protection figure,
rainfall, temperature and distance to the nearest urban center) in an application to the
agroforestry lands of the Andalucía region (south of Spain). We use the model to assess the
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impact on land use allocation in the region of different scenarios that considered changes in
these explanatory variables. These scenarios consider both drivers that can be controlled by
the policy maker and the landowner and external drivers of land use choice.
Using the Corine Land Cover (CLC) database (Commission of the European
Communities 1994), we develop and compare two different land use models. We first
perform a model that analyze 3 possible initial and final uses (3-uses model), where we group
land uses in general categories (crop, grass and forest) similarly to Lubowski et al. (2006),
Rashford et al. (2010) and Claasen et al. (2013). We then explore a model which considers 7
possible initial and final land uses (7-uses model), breaking down crop and forest uses in
different subgroups with the goal of capturing land use change drivers beyond the general
groups so far analyzed in the literature. Crop uses are broken down into herbaceous crop,
fruit trees, olive trees and vineyards, while forests uses are breaking down into hardwood and
conifer tree uses. We expect that, despite being more complex data-intensive, the 7-uses
model provides richer results and more insights into the drivers of land use in the analyzed
region. We do not include in these models burnt areas, developed land and water areas
(including beaches), despite the CLC identifies these uses. The first two cases fall outside our
object of study (agroforestry land) and the drivers of these land uses are too different to the
drivers of the analyzed land uses. In the case of water areas, except for rare cases, this land
use hardly changes and therefore has no interest for our models and objectives.
We analyze the Andalucía region due to its large diversity and data available on the
different land uses analyzed, making a first assessment of the potential of this type of models
in a European context (previous studies have been performed for the US). Andalucía is a
large territory, almost the size of Austria and Portugal and doubling the size of the
Netherlands or Belgium. It covers a wide variety of ecosystems, making this region a good
pilot study for these land use models as it likely resembles the problems and the
heterogeneity that can be found at larger scales. In addition, a recent research project carried
out in this region (Caparrós et al., 2016) makes available data at micro-scale on the net
returns and capital values associated to forest uses, an information that is hardly available in
official statistics as opposed to crop and pasture uses.
We contribute to the existing literature on econometric models of land use choice in
three ways. First, compared to previous models applied in the United States (Stavins 1999;
Lubowski et al. 2006 and 2008; Rashford et al. 2010) our main contribution is the explicit
analysis of non-monetary factors in explaining land use transitions. When they are relevant,
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the omission of these variables will artificially assign explanatory power to monetary
variables included in the model by averaging the effect of the omitted variable in the
regressed coefficients, biasing the estimates of how land use decisions are affected by a
change in the model variables. Our results confirm that models including these non-monetary
variables outperform models including only net returns and subsidies as explanatory
variables. Second, we use mixed logit models in order to capture heterogeneity across
observations. This type of models has been used only in Claasen et al. (2013), which used a
discrete specification (latent class). Finally, we explore the potential of a model that breaks
down crop and forest uses in subgroups of uses with differing characteristics that are relevant
in terms of land use and the potential provision of ecosystem services. For example, although
they all are considered as agricultural land by the CLC, there are differences between
herbaceous crops, olive tree and vineyards in terms of erosion, carbon and biodiversity.
Similarly, we also find difference in terms of biodiversity, carbon and scenic values between
hardwood and conifer forests, being the latter in general exotic species in Andalucía
introduced through plantations.
2. Econometric specification
For modeling land use decisions, we assume a risk-neutral and price-taking
agroforestry landowner that can allocate a parcel i of his/her property among a set of k
alternative uses. For K potential uses (j, k = 1,…,K), the decision rule for the landowner is to
choose, for parcel i in use j, the use k at time t that maximizes his/her utility after conversion
costs (Uik) minus the current one-period expected opportunity cost of undertaking conversion
(Stavins 1999).
, [1]
where Ukt and Ujt represent the expected utility at time t for a parcel in use k and j,
respectively; Cjkt is the expected marginal cost of converting that land parcel from j to k use at
time t; and r is the discount rate (Lubowski et al. 2006). In our modelling approach, we
assume that this decision is made given the landowner’s expectations about the benefits
associated to each land use and that these benefits may include both monetary and non-
monetary returns (e.g., scenic values of a specific area, which may be reflected in land prices
but not in annual commercial net returns). We assume that landowners form these
expectations in the years previous to the moment in which the land use decision is observed.
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We specify a linear-in-parameters landowner utility function for parcel i and land use k
starting in use j at time t. This function includes both an observed (Vijkt) and an unobserved
(εijkt) component characterized as a random error:
[2]
As Uijkt can be specified for each initial land use j and for each time period t, for simplicity we
drop j and t in the equation, leaving us with the function: Uik = Vik + εik. The probability that
the landowner chooses land use k for parcel i (Pik) over any land use h ( h K) is:
, [3]
This probabilistic problem can be solved using multinomial logit models. Depending on the
error specification, we obtain different econometric specifications that estimate parameters
for the explanatory variables in Vik:
, [4]
where αk is the intercept for land use k; βk is a vector of parameters for land use k; and Xik is a
vector of explanatory variables for observation i and land use k.
If the errors are assumed to be independently and identically distributed with an
extreme value distribution across the h alternatives and i individuals, the probability model
gives the conditional logit:
, [5]
where µ is the scale parameter (normalized to 1 in this model). The error distribution in the
conditional logit implies that the ratio of the probabilities of choosing any two land uses is
independent of the remaining land use alternatives. In other words, the unobserved
component of the land use k function is independent of the unobserved error of land use h
function when choosing land use j. The violation of this assumption may arise in situations
where some alternatives are qualitatively similar to others, as we expect to happen for grass
and forest uses. In the discrete choice modeling literature, the most recent and widely used
approach to address the violation of this assumption is the mixed logit.
The continuous-mixed logit assumes that all attribute parameters consist of a
component that is common to all individuals and that some attribute parameters have an
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individual specific component. The common component is independently and identically
distributed with an extreme value distribution while the individual specific component
follows a distribution specified by the researcher (θ). Thus, the landowner utility for parcel i
and land use k is:
, [6]
Where and represent vectors of mean parameters common to all individuals and
and represents a vector of deviation parameters from these means. This results in
individual-specific parameters and in the utility function. This model has the
advantage of allowing correlated error terms between alternatives, not assuming the
independence of irrelevant alternatives hypothesis and modeling unobservable heterogeneous
landowners’ preferences (Train 2009).
The probability of choosing land use k in parcel i is the integral of CL probabilities in
[5] over a density of parameters according to the distribution θ. This integral has no closed-
form solution but it can be evaluated through simulation for any value of θ. Being R the
number of draws from θ (in our models R=500), the unbiased estimator of Pr ik is defined as
(Train 2009):
[7]
The consensus in the literature for this model is to assume a normal distribution for the
random parameter.
3. A land use model for Andalucía
The region of Andalusia is located in the south of Spain and covers 17.2% of the
country area. Andalusia has a strong tradition in agriculture, livestock and forestry activities,
and is part of the Mediterranean basin, a world region considered as a biodiversity hotspot
(Myers et al. 2000). The agroforestry areas of Andalusia are habitat for endangered species
and endemism that cannot be found in other parts of the world. The savannah type forest with
mixed trees, shrubs and grassland provides habitat to thousands of species. Its strategic
position between the Mediterranean and the Atlantic sea makes this region a key area for bird
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migration from Europe to Africa. Natural and semi-natural land in Andalusia has been
targeted by several protection figures in the last 30 years. This has resulted in that 19% of the
total Andalusia area and 35% of the total forest land area (including treeless grassland and
shrublands) is protected (IECA 2011).
A unique feature of the Andalucía region is its variety of ecosystems. From the lowland
crop fields of the Guadalquivir Valley, 300 km to the east you find the alpine mountain
ecosystem of the Sierra Nevada with a large ski resort and the highest peak mountain in
continental Spain. Then, 50 km south you find the subtropical coast of Granada and 200 km
east the Tabernas desert in Almería. Andalusia is also home of the Sierra de Grazalema, the
area with the highest average rainfall in the Iberian Peninsula. This area is located in the
southwest, along with the cork oak woodland of Cádiz a dense forest which houses the
canutos, a subtropical forest habitat unique in continental Europe.
This diversity of ecosystems, the scale of the region, which allows to produce and
obtain some additional data when needed, the fact that most of the problems potentially
arising in national-scale analysis are already prevalent, and the availability of detailed data on
forest uses (Caparrós et al., 2016) made us selecting this region for our application.
3.1 Data and materials
We construct the land use model based on the information on real land use decisions
and on a set of explanatory variable which are associated to each land use observation over
different years. For the land use decision variable (the dependent variable in the model), we
made a random draw of 10,000 geographic points in the Andalucía map using ArcGIS-
ArcInfo 9.3.0 software over a geographical layer that has all CLC classes, but that it is
referred geographical only to the studied land uses: CLC class 2 (crop uses) and CLC class 3
(forest and grass uses). Each point-level observation corresponds to a land parcel (polygon in
CLC terminology) classified by the CLC with a specific land use and characterized by an
area (hectares) and a shape (meters). We created a spatial link between the layer of points and
the CLC databases so that each point is associated to a specific land use from the polygon.
The CLC database provides this information for years 1990, 2000 and 2006, but our
analysis focuses on the final use in 2006 considering the initial use in 1990 as this period
captures more land use variability. Thus, each sampling point is assigned a land use choice k
in 2006 and a starting land use j in 1990. The final outcome of the sampling process for our
dependent variable is a vector layer of land use observations located in Andalusia with the
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information of classes 2 and 3 of CLC for years 1990 and 2006. We use the European CLC
that works at three levels of disaggregation of land uses, because of its potential extension to
a model at European level and for its availability for the two years in a homogeneous manner.
From the CLC database, we identify crop, grass and forestland uses for the model that
considers 3 possible initial and final land uses. Then, for the model considering 7 possible
initial and final land uses, we identify herbaceous, fruit, vineyards, olive (these four
integrated in crop uses in the 3-uses model), grass, hardwood and conifer forests (these two
integrated in forest uses in the 3-uses model). The 7-uses model offers, a priori, more
information on how changes in the explanatory variable affect to land uses in the region.
While the 3-uses model it less intensive in data, it only considers changes among the three
main groups of agroforestry land uses. We estimate a different model for each one of the j
starting land uses, as this reduces computational efforts and makes the models easier to
estimate without losing any information. This implies that j is fixed in equations [2], [3] and
[4] in each land use choice models. The land use information from 1990 is used for
constructing the subsamples of observations starting in the same land use.
The explanatory variables of the model are constructed using data from different
statistical and geographical databases. We obtained the information for these variables at
parcel, municipality, province or region level, depending on their availability, and we
assigned them to the different land uses for each point-level observation. We use five type of
variables that could drive land use choices differently: (i) two monetary variables that are
directly or indirectly related to the Common Agricultural Policy (CAP): net returns and
subsidies to specific land uses and the associated production; (ii) two variables related to
climate change: average temperature and average rainfall; (iii) a variable controlling for
physical constraints to land uses: the slope in the point-level observation; (iv) a variable
related to environmental protection policies: whether the land use observation is in a
protected natural area (protection); and (v) a variable capturing the effect of development
pressures: the distance to the nearest urban center (distance).
The three latter variables also try to capture the non-pecuniary benefits that landowners
obtain from a land use, explaining why landowners choose land uses regardless of their
potential monetary benefits (e.g., land parcels located in protected areas or in high slopes are
likely to have higher scenic values for landowners). These values are recognized to have an
important role on land use decisions on agroforestry lands (Lubowsky et al. 2006; Newburn
et al. 2006; Campos et al. 2009). Subsidies are a component of the commercial benefits from
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the land use, but landowners perceive them differently than benefits from market transactions
and could have a different impact in the model. That is why we separate them from net
returns as a land use choice driver.
For the explanatory variables, we gathered information about net returns and subsidies
per hectare for crop and grass uses from the Farm Accountancy Data Network (FADN)
(European Union 1999). These data are available for 14 categories of crop and livestock for
the Andalusia region1. Using this information, we estimated values for each municipality
according to the predominant production associated to these uses in the period 2001-2006.
We estimated a weighted mean for net returns and subsidies for the three main crop and
livestock products observed in the municipality in the period 2001-2006 (IECA, 2011). This
mean is weighted by the occupied area in the case of the crop uses and by the number of
animal units in the case of grass uses. The mean for the period 2001-2006 was converted to
2006 €.
For the net returns and subsidies for forest uses, we first identified the main forest
species of Andalusia with commercial interest, resulting in nine. Then, we took the
estimations of the net present value (NPV) associated to an entire rotation cycle for each of
this forest species from Caparrós et al. (2016), and we annualized this NPV using a 5%
discount rate. The obtained annualized figure is assigned to each corresponding species as the
average annual net returns (in € per hectare) from a forest stand with this species. The
estimated values are transformed to 2006 €. This NPV is estimated in Caparros et al., (2016)
with and without net subsidies so that we also have estimates of the annualized net subsidies
received by the landowner associated to each forest species. The NPV figure includes both
the benefits from tree products and from livestock and game grazing rent from understory
grass and shrubland.
For the environmental and biophysical characteristics we obtained data at point level
using spatially-explicit data from geographical information systems (AEMET 2011; Junta de
Andalucía 2011; RAIF 2011; RIA 2011). The point-level variables are the average slope of
the observation measured in a 500x500 meter grid (slope), whether the land use observation
poimt is located in a protected area listed in the Natura 2000 network (protection), the
average annual mean temperature (Cº) (temperature) and the average annual rainfall (mm)
(rainfall) in the period 2001-2006 in the area where the observation point is located, and the 1 Although the FADN was our primary source of data, in some cases the FADN provides no information and we
used data from the Red Contable Agraria (RECAN) (MARM 2011), which is the Spanish branch of the FADN.
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distance (km) “as the crow flies” from the observed point to the nearest urban center defined
as the a city with more 100.000 inhabitants (distance).
4. Results
From our sample we identify 62 observations located in a parcel with a land use
category that do not correspond to either crop, grass or forest. This could be caused by an
error in the sampling process or by a change from either crop, grass or forest category in 1990
to a different category (developed, burnt or water) in 2006. Additionally, one observation fell
outside the Andalusia territory borders. These points are removed from the sample that is
finally made of 9,937 observations.
We present two type of models for each initial land use. Model I includes as
explanatory variables alternative specific constants for grass and forest uses (crop is the
reference level and normalized to zero), and the net returns and subsidies associated to each
land use. Model II adds slope, protection, temperature, rainfall and distance as explanatory
variables. The idea is to test whether these variables are relevant in establishing the
probability of land use choice. For all models, our baseline is the conditional logit and then
we progress towards the mixed logit models.
4.1 The 3-uses models
An analysis of our dependent variable shows that land use transitions between 1990 and
2006 (Table 1) are more present for observations starting in grassland, which change mainly
to crop uses. Changes for uses starting in forest are evenly distributed between cropland and
grassland, while observations starting in crop uses hardly change. If we look at the land use
observations absorbed by alternative uses in 2006, crop is the one getting more new
observations, followed by forestland. Grassland hardly gets new observations from other uses
(Table 1).
The first main result from our models (Tables 2 and 3) is that model II (the one
including non-monetary variables) for both the conditional and the mixed logit implies a
substantial increase in the pseudo r2 and decrease in the Akaike Information Criterion (AIC).
This shows the importance of these variables to explain land use decisions. We also find that
the mixed logit models outperform the conditional logit models according to these statistics.
Thus, heterogeneity is significant in land use decisions. We note that the mixed logit models
presented are the best models found. In all cases the initial goal was to set all parameters as
random. However, in some cases, we needed to drop some random parameters to allow the
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model to converge and to find a maximum for the log-likelihood function (either local or
global is uncertain). Although this makes the models not perfectly comparable (they do not
have the same number of parameters), it is a better option that not reporting model results.
The analysis of drivers of land use only focuses on those variables that have turned out
significant in some of the models. Taking together all model results, we find that net returns
and subsidies affects positively to the probability of moving to crop uses for all initial uses,
except for subsidies when starting in forest. However, this variable affects negatively to the
probability of moving to grassland when starting in both grass and forest uses and to the
probability of moving to forestland when starting in grass uses, although the latter finding is
only obtained for one model. The latter result shows that it is more likely to change to grass
and forest uses in areas where these uses offer lower net returns. Subsidies affects positively
to the probability of moving to grass or forestland when starting in any of these uses. This
result can also explain the previous one if higher subsidies are given to low return uses,
which would increase the probability of land use change despite these lower net returns.
Protection and slope increases the probability of moving to grass and forestland in
almost all cases (recall that these parameters are only estimated for grass and forest use
alternatives as the dummy coding set crop alternatives as the reference level). Distance
increases the probability of moving to forest uses when starting in both crop and forest uses
and of moving to grassland uses when starting in forest uses. This means that the further is
the observation from an urban center, the higher is the probability of a transition between
forest and grassland uses. Rainfall always shows a positive effect of changing either to grass
or to forest, while temperature decreases the probability of moving to forest from crop or
grass uses and of staying in grassland uses. In summary, other things being equal, an increase
in temperature and a decrease in precipitation will reduce the probability of changing to either
grass or forestland in Andalucía, and therefore will favor cropland uses.
The model results also show that the model considering grassland starting use
observations offer more significant explanatory variables, probability due to the higher
variation of the dependent variable (Table 1). Models using cropland starting use
observations are by far the worst for finding significant drivers of land use decisions.
4.2 The 7-uses models
The dependent variable of the 7-uses model identifies transitions among the three
general land use groups, but also transitions within crop and within forest uses (Table 4).
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Observations starting in herbaceous crop, vineyard and grassland are the ones changing more
in the period. Herbaceous crop and vineyards change mostly to other crop uses (the former to
olive and fruit trees; the latter to herbaceous crops). Grasslands change to all uses but
vineyard, with most changes to herbaceous crops and to hardwood forests. Observations
starting in fruit and olive tree uses only change to other crop uses. Hardwood forests change
to almost all other uses, while conifer forests mostly change to hardwood forest and
grassland.
For land use observations added to alternative land uses in 2006, we observe that
herbaceous crop uses absorb all changes from land starting in vineyards and fruit trees. Olive
tree uses gets mainly observations starting in herbaceous uses, and hardwood forests gets
observations from both initial grassland and conifer forest uses. Vineyards practically receive
no new points and fruit trees mainly receive points from herbaceous crop and grassland.
Grassland and conifer forests increase mostly by getting points starting in hardwood forests
(Table 4).
The results of the 7-uses models (Tables 5 and 6) also show that the models including
non-monetary variables (models II) increases the pseudo r2 and decrease in the Akaike
Information Criterion (AIC) and that mixed logit models outperform conditional logit models
in most cases. Net returns affect positively to the probability of changing to herbaceous crop
and olive tree uses and negatively the probability of choosing grassland uses. The only
exception is that when starting in hardwood forest, net return affects negative the probability
of changing to herbaceous crops. These results are consistent with the 3-uses model results,
and provide additional information on the type of crops that are affected by this variable. Net
subsidies have a positive effect in all cases, increasing the probability of choosing herbaceous
crop, fruit tree, olive tree, grassland and hardwood forest uses, depending in each case on a
specific initial use. While the 3-uses model only show this effect for grass and forest uses, the
7-uses model identifies this effect for several crops and only for hardwood forest types.
Protection increase the probability of choosing both grassland and forestland when
starting in these uses. This makes sense as there is hardly initial cropland located in a
protected area (7.6% of the sample), and it seems that for this small share there is no change
to grass or forest. We also find that being in a protected area decreases the probability of
moving from herbaceous to olive and of staying in grasslands. The variable slope show the
same positive effect on grassland and hardwood forest. It also shows that staying in olive tree
use is less likely for points located in areas with higher slopes. Distance increases the
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probability of moving to olive tree, grass and hardwood forest uses. For olive tree, this
happens when the point starts both in herbaceous and in olive tree uses. For grass and
hardwood, this change happens when starting in hardwood forest uses. These results closely
follow the ones from the 3-uses model for grass and forestland uses, but they additionally
identify a significant and positive effect in the probability of moving to olive tree uses caused
by a higher distance from an urban center. Temperature and rainfall show similar effects to
the ones found in the 3-uses models (negative for temperature and positive for rainfall),
affecting to both grass and hardwood forests. The only exception is that moving from
herbaceous crop to olive tree is less likely when rainfall is higher.
While the 3-uses model offer poor results in terms of identifying significant drivers for
crop uses, the 7-uses model perform relatively well for observations starting in herbaceous
crop but nor for observations starting in olive tree uses. Regarding forest uses, while the 3-
uses model offer significant results for aggregated forest uses, the 7-uses model shows that
this results are only relevant for hardwood forests. For conifer forest uses there are not
enough observations both to obtain a model for points in this initial land use and to include
this as an alternative in models starting in alternative uses. These two differences show a
comparative advantage of the 7-uses model versus the 3-uses model in terms of information
on land use dynamics in Andalucía.
4.2 Simulation scenarios
Using the estimated models, we now simulate the variation of five variables (net
returns, subsidies, protection, temperature and rainfall) in four scenarios to predict the impact
of these changes on the land use allocation in the Andalucía region. We set 2006 as our base
year and we simulate the evolution of these explanatory variables for the period 2006-2022,
as 16 years is the interval analyzed in our models.
The four hypothetical scenarios are inspired on the Intergovernmental Panel for
Climate Change (IPCC) scenarios (Nakicenovic et al. 2000), which are discussed and
extended in Rounsevell et al. (2006) and in Kankaapää and Carter (2004), and on the three
scenarios presented and discussed in Nowicki et al. (2009). Each scenario involves different
changes in the explanatory variables considered.
Scenario 1 represents the business-as-usual (BAU) situation where net returns evolves
similarly than in the period 1990-2006, with an 8% increase in real terms (Eurostat 2013).
Subsidies evolve according to the baseline scenario from Nowicki et al. (2009), which is
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interpreted as a reduction of 20% in crops, but maintaining the same subsidies for grass and
forest due to climate change and biofuel policies. Protected areas remains stable as there is no
increasing environmental regulation. Climatic variables evolve according to IPCC (2007)–
mean annual temperature increase by 0.5 ºC and mean annual rainfall falls by 10 mm.
Scenario 2 follows the liberalization and rapid economic growth pattern described in
scenario A1F1 in Nakicenovic et al. (2000) and in Nowicki et al. (2009). The scenario is
mostly driven by market forces, which imply an increase in net returns for all uses, although
higher for crop, and a reduction in subsidies. As the scenario A1F1 in Nakicenovic et al.
(2000) and in Nowicki et al. (2009) does not provide specific estimates, we assume that the
increase in net returns doubles the one in scenario 1 for crops and goes up to 10% for grass
and forests. For net subsidies, we assume that they do not disappear but falls by half. Due to
increasing population and economic growth protected areas are reduced 2.5%. Climatic
variables evolve as reflected in scenario A1F1 in IPCC (2007)–mean annual temperature
increase by 0.6 ºC and mean annual rainfall falls by 10 mm.
Scenario 3 presents a protectionist economy, as in scenario A2 in Nakicenovic et al.
(2000), and in the conservative scenario from Nowicki et al. (2009). Net returns grow
moderately (around 2%), dropping as compared to the BAU scenario, and all subsidies are
reduced 20% according to Nowicki et al. (2009). Protected areas are reduced according to
scenario A2 from Nakicenovic et al. (2000). Climatic variables evolve below the figures from
the BAU scenario, as expected due to a slower economic growth, and follow the values from
the A2 scenario in IPCC (2007)–mean annual temperature increase by 0.4 ºC and mean
annual rainfall remains constant.
Scenario 4 resembles a balanced economy based on the B1 scenario from Nakicenovic
et al. (2000). Subsidies increase for all uses but more for grass and forest (10%) than for
crops (5%) due to incentive to biofuel production and climate change mitigation. Net returns
growth follow the same pattern but more moderately, an increase of 4% for crop and of 6%
for grass and forest uses. Environmental protection is encouraged resulting in an increase of
10% in protected areas. Climate variables follow the evolution described for scenario B1 in
IPCC (2007), which implies a drop in values as compared to the BAU situation due to a
successful climate change mitigation policy–mean annual temperature increase by 0.3 ºC and
mean annual rainfall remains constant.
14
According to model II from the CL and ML specification of the 3-uses models, the
simulation scenarios for the period 2006-2022 (Table 7) show that there are large changes in
land use in scenarios 2 and 4. Crops are favored in scenarios with less environmental
regulation (reducing protection) and subsidies, but that considers either a sharp or a moderate
increase in returns. Scenario 2, the one with the sharpest reduction in subsidies, implies the
highest increase in crop uses mainly taken from forest uses. Interestingly, grassland, the land
use which changed more between 1990-2006, remain relatively stable in three scenarios,
probably because of the combination of positive and negative drivers for this land use. Only
in scenario 4, this land use gets a notable reduction mostly in favor of forestland uses. This
scenario 4 implies active subsidy and environmental regulation policies, which seems to
mainly drive changes to forest uses. Forestland hardly changes in scenarios 1 and 3, which
jointly implies a moderate growth in return and a reduction in subsidies. It does not seem that
climate variables have an important impact on land use transitions as the value ranges are
narrow in the analyzed time horizon and the effect of these variables may be dominated by
environmental regulation and subsidies.
5. Discussion and concluding remarks
The econometric model presented analyzes land use choices in Andalusia based on real
land use decisions made by landowners between 1990 and 2006. The model observes when
land use transitions have occurred in the past and quantifies the probability that selected
drivers (using proxy variables) have had a significant impact in that transition. The presented
approach uses real landowner decision, which may be implictly incorporating inertia,
liquidity constraints, non-commencial values (partly captured in some of the non-monetary
variables used; e.g., slope or protection figure as proxy of landscape view or proximity to
significant natural assets), and benefits and costs of which the analyst is unaware. Results
from these models are expected to resemble the changes in land uses in hypothetical
scenarios better that models that assume optimal allocation based solely on profit
maximization.
Our model results show that both monetary and non-monetary factors are significant in
explaining land use transitions, and the inclusion of non-monetary factors increase
substantially the performance of the different models tested. Among monetary factors, we
find that subsidies always have a positive effect in the probability of changing to a land use,
while net returns have a positive effect for crop and forest uses and a negative effect for
grassland uses. A possible explanation to the latter result is that net returns for observations
15
that change to grassland is also likely to be low for cropland in the areas where these
observations are found and the opportunity cost of change drops, increasing the likelihood of
moving to alternative uses with lower returns but other environmental benefits. Also, the
effect of subsidies, which usually target low-return production, could be implicitly causing
the higher probability of change to low-return grassland uses (and forestland uses in one
case). Among the non-monetary land use drivers, our main findings are that protection, slope
and rain favours grass and forest uses and temperature favour crop uses. The variable
distance presents mixed results, although it mainly increase the probability of change to
forest. The results obtained for the climate variables are consistent with the findings from
other analyses in other countries about the effect of climate change on agriculture (Schenkler
et al., 2005).
Among the studied land uses, grassland is the one that present the highest probability of
change in both the 3-uses and the 7-uses models. However, the more complex and data
intensive 7-uses models allows the identification of two additional insights in the dynamics of
land use change in Andalucía: (i) among crop uses, herbaceous show nearly as much changes
as grassland and we identify drivers both for olive tree and herbaceous uses, although more
significant for the latter use–in contrast the 3-uses models for crop initial uses do not perform
well and identify few drivers; and (ii) for forest uses, we cannot identify drivers for conifer
forest use both as initial use and as final use. Thus, the drivers of land use change from and to
forest use gets limited to hardwood species in the case of Andalucía.
Our simulation of land use changes for the period 2006-2022 shows the largest land use
changes for scenarios with the largest variations in subsidies and environmental regulation.
Overall the results show that crop and forest uses are the ones changing more in the scenarios
presented. Grasslands gets reduced by 5% in favor of forest uses in the only scenario that
considers simultaneously higher environmental protection and an increase in subsidies. Crops
are favored in scenarios that considers both reductions in subsides and in environmental
protection regulations. In our scenario analysis, it is not clear what the effect of climate
change variables is, as the value changes chosen for subsidies and environmental protection
variables seem to dominate the overall effect on land use allocation in Andalucía.
In summary, the presented Andalucía models show that policy makers should take into
account non-monetary factors (e.g., environmental and bio-physical) when designing policies
devoted to influence land uses (e.g., carbon sequestration policies and bio-fuel production),
not focusing solely on monetary variables that are believed to rule landowner decisions. The
16
significant results of our 3-uses and 7-uses models in most of the cases for both the
conditional and mixed logit models also show the feasibility of extending the methodology to
the rest of Europe with existing data. The only exception is forest net returns, which were
available for the Andalucía region in Caparrós et al., (2016) but for a European application
they will need to be obtained from models at larger scales and for much more aggregated data
(e.g., Sohngen and Tennity, 2004). In any case, forest net returns have turned out to be not
significant in some of the models and this limitation does not overcome the potential of this
approach at the European level as an important tool for land use policy design.
Acknowledgements
We are thankful for the comments and suggestions from participants in the PASHMINA
projects. We gratefully acknowledge Financial support from the European Commission
(Project PASHMINA, C-SSH/0652) and the I + D National Plan of the Ministry of Science
and Innovation (Project UTPCC, ECO2009-10267). The usual disclaimer applies.
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Tables
Table 1. Land use transitions for the period 1990-2006 for crop, grass and forest land
uses in Andalusia. 9,937 sampling points
Initial land use (1990) Final land use (2006)Crop Grass Forest
Crop 99.79% 0.06% 0.15%
Grass 5.70% 91.17% 3.14%
Forest 0.95% 0.90% 98.15%
Table 2. Conditional logit models for the 3-uses model
Final land useInitial land use
Crop Grassland ForestModel I Model II Model I Model II Model I Model II
Paramete Parameter Parameter Paramete Parameter ParameterCrop
Log(net
returns)
0.7320 0.8197* 0.1978** 0.2406** 0.4898** 0.6765**
*Subsidies 0.0036 0.0050 0.0017 0.0029** -
0.0085**
-0.0048**Grassland
Constant -6.1358 -39.0094 5.8445*** 10.3021*
**
2.2787 -0.3792Log(net
returns)
-0.0792 -0.2729 -
0.5231***
-0.3105* -
0.8125**
-
0.9075**Subsidies 0.0218 0.0389 0.0130 0.0092 0.0166* 0.0166*Protection 1.6850 1.3585**
*
1.0404*Slope -0.2786 0.2858**
*
0.2197**Distance 1.2533 -0.3579* 1.2575**
20
Temperature 0.8134 -
0.2961**
-0.0774Rainfall 0.0234** 0.0008 0.0034
ForestConstant -18.7622 -27.2198 -
41.6719**
-
21.5784*
1.0838 0.6975Log(net
returns)
0.1022 0.1321 -0.1177 -0.2621 0.1997 -0.0290Subsidies 0.0485 0.0810 0.1207*** 0.0787** 0.0075 0.0140**
*Protection 2.5614**
*
1.1264**
*
1.3149**
*Slope 0.0582 0.2079**
*
0.3274**
*Distance 0.3136 0.0563 0.8049**Temperature -0.3690 -.43652*
**
-0.2504Rainfall 0.0033 0.0053**
*
0.0029*n 4,673 4,672 1,913 1,910 3,351 3,350Pseudo r2 0.04 0.19 0.04 0.14 0.07 0.14AIC 164 161 1,321 1,197 666 635The crop alternative is set as the reference level and the constant for this alternative, as well the variables that interact with this constant
(protection, slope, distance, temperature and rainfall) are normalize to zero.
Asterisks (e.g.,***, **, *) denote significance at the 1%, 5% and 10% level, respectively; AIC: Akaike Information Criteria; n: number
of observations.
Table 3. Mixed logit models for the 3-uses model
Final land use
Initial land useCrop Grassland Forest
Model I Model II Model I Model II Model I Model IIParameters Parameters Parameters Parameters Parameters Parameters
CropLog(net returns) 1.4454* 7.4094 0.3559 2.4132* 0.5444* 4.2007Subsidies 0.0238* 0.1688*** 0.0016 0.0033 -0.0137*** -0.0744
GrasslandConstant -2.5417 -41.974 8.5565** 86.3127** 1.1657 16.4296Log(net returns) -0.3521 16.7398 -1.4288* -3.1314 -1.2907*** -14.6822***Subsidies 0.0188 -0.6521 0.0603* 0.1258 0.0257* 0.2635Protection -5.9448 32.2576*** 10.1880Slope -1.5247 4.8431*** 0.8272Distance 11.7643 -4.8691* 10.5419Temperature -2.4863 -3.0575** -3.6749Rainfall 0.1665*** 0.0216* 0.0310
ForestConstant -14.770 -28.4926 -54.7917** -41.8748 0.1697 -74.0657Log(net returns) 0.0115 4.4754 -0.0520 -6.9133* 0.4749 -0.1866Subsidies 0.0496 0.2739 0.1597** 0.4111 0.0110 0.2266*Protection 18.0124*** 16.2803* 23.9458*Slope -1.5430 1.0385 5.5852**Distance 13.1129* 2.6309 11.4127Temperature -8.5207** -9.7724*** -1.8403Rainfall 0.0175 0.1068*** 0.0319
St. deviation parametersCrop
Log(net returns) 0.3891* 0.2129 0.7192 0.2030** 2.0709Subsidies 0.0091** 0.0668*** 0.0058* 0.0357*** 0.0018 0.0318
GrasslandConstant 3.5185* 1.0342 6.4278** 1.5531** 15.8051***Log(net returns) 0.0810 0.1286 0.0333 0.1852* 0.2549
21
Subsidies 0.0036 0.0193* 0.0105 0.0037 0.0175Protection 20.1191*** 2.2768Slope 2.2028*** 1.7552**Distance 0.5342 3.6916**Temperature 0.5766*** 1.7320***Rainfall 0.0195*** 0.0133
ForestConstant 0.7588 0.7529 12.2255*** 1.7157*** 0.2506Log(net returns) 0.1347 0.0997 1.8387** 0.1128 2.03030***Subsidies 0.0046 0.0033 0.0758*** 0.0045** 0.0130Protection 16.2413** 8.7938 15.7376**Slope 1.3311** 1.93102***Distance 1.6332** 3.6275*** 0.9462Temperature 0.9181* 0.5166** 0.4323**Rainfall 0.0244* 0.0009 0.0051
n 4,673 4,672 1,913 1,910 3,351 3,350Pseudo r2 0.11 0.27 0.05 0.17 0.09 0.22AIC 169 158 1,320 1,183 666 317The crop alternative is set as the reference level and the constant for this alternative, as well the variables that interact with this constant
(protection, slope, distance, temperature and rainfall) are normalize to zero.
Asterisks (e.g.,***, **, *) denote significance at the 1%, 5% and 10% level, respectively; AIC: Akaike Information Criteria; n: number
of observations.
Table 4. Land use transitions for the period 1990-2006 for herbaceous, fruit tree, olive grove,
vineyard, grassland, hardwood and conifer land uses in Andalusia. 9,937 sampling points
Initial land
use (1990)
Final land use (2006)
HerbaceousFruit
tree
Olive
groveVineyard Grassland Hardwood Conifer
Herbaceous 94.50% 0.68% 4.47% 0.03% 0.10% 0.19% 0.03%
Fruit tree 1.33% 98.67% 0.00% 0.00% 0.00% 0.00% 0.00%
Olive grove 0.51% 0.14% 99.35% 0.00% 0.00% 0.00% 0.00%
Vineyard 6.67% 0.00% 0.00% 93.33% 0.00% 0.00% 0.00%
Grassland 4.44% 0.68% 0.58% 0.00% 91.17% 2.93% 0.21%
Hardwood 0.76% 0.29% 0.07% 0.00% 0.94% 97.04% 0.90%
Conifer 0.17% 0.00% 0.00% 0.00% 0.68% 1.37% 97.77%
22
Table Conditional logit models for the 7-uses model
Final useInitial use
Herbaceous crop Olive tree Grassland Hardwood forestModel I Model II Model I Model II Model I Model II Model I Model II
Paramete Paramete Paramete Paramete Paramete Parameter Paramete ParameteHerbaceous
Log(net 0.0147 0.0125 0.8763 1.1876 0.1790** 0.1267* 0.0002** 0.0440Subsidies -0.0006 -0.0005 0.0084* 0.0102 0.0004 0.0006 -0.0024* -0.0010
Fruit treeConstant - - 6.8056 13.0953 - -11.2607 - -9.1276Log(net -0.2707 -0.3392 0.2170 0.4636 -0.1869 -0.0014 0.0036 0.1438Subsidies 0.0126* 0.0146* 0.0089 -0.0003 0.0100* 0.0050 -0.0233 0.0018Protection -0.7953 -0.8670Slope 0.0102 -0.8507 0.0928 0.2018Distance 0.6242 0.5507 -0.3755 0.5365Temperatu 0.1646 -0.6926 0.5398 0.2645Rainfall 0.5011D-04 0.0166 0.0032 0.0015
Olive treeConstant - - 13.6487* 20.9559 -40.6394 -10.9440Log(net 0.3132** 0.2730** 1.0371 1.0901 6.2194 2.1447Subsidies 0.0009 0.0015** -0.0100 -0.0113 -0.0033 0.0051Protection -Slope 0.0181 -0.1754 0.0668Distance 0.5262** 0.9052** 0.2760Temperatu 0.0528 -0.5206 -0.3931Rainfall - 0.0046 -0.0041
GrasslandConstant -12.2573 - 4.4985** 7.40810* 2.2934* -2.4429Log(net -0.1605 -0.1625 - - - -Subsidies 0.0271 0.0346 0.0198** 0.0176** -0.0074 0.0179*Protection 1.4002 1.3384** 1.3635*Slope -0.1269 0.2849** 0.2845*Distance 1.1412 -0.2661 1.9082**Temperatu 1.4103 -0.3085** -0.2917Rainfall 0.0215** 0.0017* 0.0051
Hardwood Constant -0.4873 1.2919 3.5279** 1.2321Log(net 0.1086 -0.0292 0.0011 -0.0046Subsidies 0.0023 0.0032 0.0021** 0.0038Protection 1.0659** 1.3205**Slope 0.1699** 0.3975**Distance 0.1732 1.3502**Temperatu -0.3825** -0.3656Rainfall 0.0066** 0.0041*
n 3,102 3,101 1,383 1,383 1,909 1,907 2,740 2,739Pseudo r2 0.08 0.11 0.14 0.22 0.07 0.17 0.07 0.16AIC 1,336 1,325 117 123 1,408 1,293 627 599
23
Table 6. Mixed logit models for the 7-uses model
Final useInitial use
Herbaceous crop Olive tree Grassland Hardwood forestModel I Model Model I Model Model I Model II Model I Model IIParamet Paramet Parameters Paramet Parameter Parameter Paramet Paramete
Herbaceous Log(net -0.0274 0.1363 0.9366 4.8411 0.3890 1.7536 -0.1326 0.0738Subsidies 0.0008 -0.0007 0.0091* 0.0490 -0.0065 -0.0181 -0.0057 -0.0010
Fruit treeConstant - - 7.3180 15.3151 -4.5626* -51.1981 - -13.2461Log(net -0.3319 -0.5163 0.2314 2.5983 0.1018 1.2516 0.2749 0.1228Subsidies 0.0112 0.0223* 0.0103 -0.0027 -0.0116 -0.0010 -0.0303 0.0029Protection -1.6768 -1.0154Slope 0.0345 -5.3235 -0.1144 -0.9623Distance 1.2260 9.5116 -10.6520 -0.8234Temperatu -0.1179 -2.2271 2.5711 0.8578Rainfall -0.0028 0.0995 0.0531 -0.0062
Olive treeConstant - - 14.4854** 23.6391 -18.9650 -5.0839Log(net 0.4874* 0.2906* 1.1754 11.5210 2.2897 18.9785Subsidies -0.0021 0.0017* -0.0090 -0.0541 0.0014 -0.0103Protection -Slope 0.0116 - -0.5875Distance 0.5885* 8.7179* -2.3523Temperatu 0.0311 0.6933 -6.4329Rainfall - 0.0331 -0.0037
GrasslandConstant -10.1998 - 4.4563** 96.1411* 1.9884 -2.7509Log(net -0.2197 -0.0592 - - - -Subsidies 0.0062 0.0281 0.0541** 0.4912** .02542* .02288**Protection 1.3076 24.6034* 1.3684*Slope -0.1252 13.6873* 0.2836*Distance 1.1567 -8.4071 1.9749**Temperatu 1.3822 -4.8779* -0.2952Rainfall 0.0220* 0.0557** 0.0053
Hardwood Constant -0.8047 105.9630 3.50965 1.0456Log(net -0.0575 -0.0496 0.1852 0.0124Subsidies 0.0026 0.0611 0.0040 0.0044Protection 6.5713 1.3583**Slope 2.2409* 0.3991**Distance 1.3243 1.3772**Temperatu - -0.3752Rainfall 0.0533** 0.0047**
St. deviation parametersHerbaceous
Log(net 0.0918 0.2102* 0.8733* 0.2508 0.0514 0.3137*Subsidies 0.0026* 0.0119* 0.0070** 0.0528** 0.0034*
Fruit treeConstant 1.4623* 0.4178 3.0312* 4.9321 1.6048*Log(net 0.0798 0.0149 0.2445 0.1171Subsidies 0.0057 0.4826D 0.0151* 0.0201ProtectionSlope 1.7976 0.5924*Distance 0.3282 3.4700 0.5866Temperatu 0.1952* 0.1996Rainfall 0.0036 0.0049*
Olive treeConstant 1.3995* 1.2595Log(net 0.0809 0.2017 2.8143* 0.0850Subsidies 0.0039* 0.0021 0.0285* 0.0044*ProtectionSlope 1.6709DistanceTemperatu
24
Continuation of Table 6. Rainfall
GrasslandConstant 0.0975 0.4738 0.4608Log(net 0.0107 0.2512* 0.2933 0.1361 0.1488Subsidies 0.0109 0.0111** 0.0815** 0.0023Protection 6.0930Slope 7.1491**DistanceTemperatu 1.6206**Rainfall 0.0460**
Hardwood Constant 0.7157 0.8090Log(net 0.4326 0.1917* 0.0750Subsidies 0.0007 0.0037*Protection 3.6566SlopeDistanceTemperatu 0.6633**Rainfall 0.0564** 0.0011
n 3,102 3,101 1,383 1,383 1,909 1,907 2,740 2,739Pseudo r2 0.09 0.11 0.14 0.27 0.08 0.20 0.08 0.16AIC 1,339 1,324 120 129 1,413 1,270 635 602
Table 7. Simulation results under different scenarios (changes from 2006 to 2022).
Results from model II in Table 2.
Variations in key variablesScenario 1 Scenario 2 Scenario 3 Scenario 4
BAU Liberalisation Protection Balanced
Crop net returns (%) 8.00 15.00 2.00 4.00 Grass net returns (%) 8.00 10.00 2.00 6.00 Forest net returns (%) 8.00 10.00 2.00 6.00
Crop subsidies (%) -20.00 -75.00 -20.00 10.00 Grass subsidies (%) 0.00 -75.00 -20.00 20.00 Forest subsidies (%) 0.00 -75.00 -20.00 20.00 Protection figure (%) 0.00 -2.50 -2.50 10.00
Temperature variation (ºC) 0.50 0.60 0.40 0.30 Rainfall variation (mm dav) -0.10 -0.10 0.00 0.00
Resulting changes in land uses1
Crop (%) -0.23 5.83 0.99 -1.74 Grass (%) -0.23 0.08 0.39 -4.68 Forest (%) 0.00 -5.91 -1.38 6.421 Percentages of changes refer to the total area.
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