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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