land use change patterns

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Short communication

Land use change patterns in the Ro de la Plata grasslands: The influence ofphytogeographic and political boundaries

Ernesto Vega a,*, German Baldi b, Esteban G. Jobbagy b, Jose Paruelo a

a Laboratorio de Analisis Regional y Teledeteccion, IFEVA-FAUBA/CONICET; Av. San Mart n 4453 (C1417DSE) Buenos Aires, Argentinab Grupo de Estudios Ambientales-IMASL, Universidad Nacional de San Luis/CONICET, Avenida Ejercito de Los Andes 950 1er piso, D5700HHW San Luis, Argentina

1. Introduction

Land Use and Land Cover Change (LULCC) is a key component of

global change (Dale et al., 2000; Vitousek et al., 1997). Its global

importance derived mainly from theimpact of the accumulation of

small local changes. LULCC may affect climate by modifying water

dynamics (Gordon et al., 2008; Nosetto et al., 2005), increasing of

greenhouse gases (CO2 and nitrous oxide) (Searchinger et al.,

2008), or changing the surface energy balance (Pielke and Avissar,

1990). Biodiversity is directly affected by LULCC, through the effect

of agricultural practices on specific populations (Mattison and

Norris, 2005) and habitat losses (Foley et al., 2008).

The plains locatedin the southernportionof the Ro dela Plata

Basin in eastern South America(Fig.1) hold one of the largest area

of temperate grasslands of theworld,the Ro dela Platagrasslands

(RPG onwards) (Soriano, 1991, pp. 367407;Paruelo et al., 2007,

pp. 232248). Although the RPG is a physiognomically homo-

geneous region, eight phytogeographic districts can be identified

(Leon, 1991, pp. 369387) (Fig. 1). Ro de la Plata grasslands have

an area of 3.4 106 km2 at the center-east of Argentina, south of

Brazil and Uruguay. The presence of three countries adds an

important socioeconomic, political and cultural heterogeneity to

the region. A huge transformation process started in century XVI

with the introduction of domestic cattle brought by European

conquerors and it was accelerated with century XIX farming

expansion (Hall et al., 1991, pp. 413450). Nowadays RPG is

perhaps one the regions with highest rates of land use change in

theworld(Paruelo et al.,2005, 2006;Baldiand Paruelo, 2008).The

Agriculture, Ecosystems and Environment 134 (2009) 287292

A R T I C L E I N F O

Article history:

Received 12 May 2009Received in revised form 30 July 2009

Accepted 31 July 2009

Available online 4 September 2009

Keywords:

Afforestation

Crop expansion

Markovian matrix models

Matrix sensitivity analysis

A B S T R A C T

The Ro de la Plata grasslands (RPG) in South America are one of the largesttemperate grasslands regions

of the world. This region plays a key role in international crop production and land use change rates in

some areas are among the highest detected nowadays. Our objective was to characterize the spatial

heterogeneity of land use change dynamics in the RPG and to relate it with biophysical and political

boundaries. Based on Landsat imagery we characterized land use changes at two time periods (1986

1990, 20022005) and we performed a comparison of markovian models and their properties (stable

proportion vector and sensitivity analysis) for each phytogeographic district and country of the region.

Temporal transitions between natural and implanted forests (Fo), crops (Cr), and grasslands (Gr) were

calculated in order to build matrix probabilistic models. We found that 1.2 106 ha of grassland surface

has been transformed into implanted forests and croplands (6% reduction of grasslands, 60% increase of

afforestations, 3% increase of croplands). Transition probability Cr ! Cr displayed the largest spatial

variation, followed by the other three transitions linking croplands and grasslands (Cr ! Gr, Gr ! Gr,

Gr ! Cr). The less variable transition rate among districts was Cr ! Fo.

Projections for Argentina and parts of Uruguay suggest that grassland loss would continue in most of

analyzed territory, whereas in Brazil and parts of Uruguay the projected trend was the opposite,

grassland cover would increase. Forested area increment would continue in Uruguay and partially in

Argentina,while in Brazil it would decrease. Considering the whole region, crop areawould maintain theincreasing trends. Transition probabilities for the same phytogeographical district (Northern Campos)

differed significantly among the three countries that occupy it, showing the relative importance of

human context driving land use change.

2009 Elsevier B.V. All rights reserved.

* Corresponding author. Present address: Laboratorio de Ecologa Genetica,

CIECO-UNAM, antigua carretera a Patzcuaro 8701, col. Ex-Hacienda San Josede la

Huerta, CP 58190, Morelia, Michoacan, Mexico. Tel.: +52 443 322 2704x42608;

fax: +52 443 322 2719.

E-mail addresses: [email protected],

[email protected](E. Vega).

Contents lists available atScienceDirect

Agriculture, Ecosystems and Environment

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a g e e

0167-8809/$ see front matter 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.agee.2009.07.011
mailto:[email protected]:[email protected]://www.sciencedirect.com/science/journal/01678809http://dx.doi.org/10.1016/j.agee.2009.07.011http://dx.doi.org/10.1016/j.agee.2009.07.011http://www.sciencedirect.com/science/journal/01678809mailto:[email protected]:[email protected]


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country. Second, we analyzed the future dynamic of land use

change using projections of the observed trends.

2. Materials and methods

2.1. Study zone

Study zone is in Ro de la Plata grasslands and comprises three

countries: Argentina, Brazil and Uruguay (Fig. 1). This region is

occupied by steppes and grasslands co-dominated by C3 and C4(Soriano, 1991, pp. 367407;Burkart et al., 1998; Paruelo et al.,

2007, pp. 232248). Mean annual temperature ranges from 20 8C

in the north to 13 8C in the south. Annual precipitation varied from

600 mm in the southwest to 1500 mm in the northeast ( Paruelo

et al., 2007, pp. 232248). According to geomorphology, soil,

drainage, physiography and vegetation (Leon, 1991, pp. 369387)

eight phytogeograophic districts are identified: Flooding (F), West

Inland (W), Flat Inland (I), Mesopotamic (M), Austral (A) and

Rolling Pampas (R), and Northern (N) and Southern (S) Campos

(Leon, 1991, pp. 369387). The main crops in the region are

soybean, maize, sunflower,wheat, rice and oat (DIEA-MGAP, 2003;

IBGE, 2004; SAGPyA, 2002). There are important stands of

Eucalyptusspp. and Pinusspp. in the more humid zones (Jobbagy

et al., 2006).

2.2. Transition probabilities estimation

This study is based on a land use change characterization

obtained from Landsat TM and ETM imagery classification at two

time periods, 19851989 and 20022005. Total surface analyzed

comprises eight Landsat scenes (185,664 km2, which represent

27% oftotalarea ofRo de la Plata grasslands) distributed along the

main environmental gradients, phytogeographic districts and

countries in the region (Baldi and Paruelo, 2008).

This work stems out from that ofBaldi and Paruelo (2008), who

analyzed the change in abundance and distribution of three cover

types (or land uses): grasslands (natural and seminatural) (Gr),

annual crops (Cr) and forests stands (natural and commercial) (Fo)of eight Landsat scenes (Fig. 1) in two periods (19851989 and

20022005). On the each of the Landsat classifications (see details

inBaldi and Paruelo, 2008) we overlay an 8 km 8 km cell grid.

Transition probabilities were calculated with data gathered from

cells with at least 90% of their surface covered by the three land

uses. A total of 2899 cells were used, representing 85.17% of total

surface analyzed byBaldi and Paruelo (2008).

Cells were grouped according to phytogeographic districts

(eight) and countries (three). Seven districts were analyzed in

Argentina: Northern Campos (N), Austral (A), Floodable (F), Inland

(I), West (W), Rolling (R), Mesopotamic (M) and Flat (F) pampas. In

Brazil and Uruguay the Northern (N) and Southern (S) Campos

were analyzed. Changes of cover were thus evaluated in 11

subscenes (districts countries).

2.3. Markovian models of land use change

Land Use and Land Cover Change (LULCC) characterization was

assessed through the comparison of cover type proportionsin each

8 km 8 km cell. All theproportional changes from one cover type

to another (aij) were arrangedin the transition matrixA to generate

a cover change modelA n(t)=n(t+1), wheren is the vector of the

relative proportions of cover classes. In the context of this work w

is the dominant eigenvector of A and represents the stable

proportions vector of cover classes.

To find out whether matrix models of phytogeographic districts

(PD) of Argentina are different, a lineal generalized model was

applied, using aij as responsevariable, PD as a factorand a binomial

error (Crawley, 2007). In cases where overdispersion was detected,

a quasibinomial distribution was used instead (Crawley, 2007).

Tukey tests were used to compare 21 pairs of PD. Similar analysis

were applied to Northern Campos (comparing between Argentina,

Brazil and Uruguay) and to Southern Campos (comparing between

Brazil and Uruguay). All analyses were performed with R

statistical package (R Development Core Team, 2007).

2.4. Sensitivity analysis of stable vector proportions

The variation ofaij has an effect on w which can be measured

through vector sensitivity analysis:

@w1@ai j

w1 j

Xsm 6 1

vmi

l1 lmwm (1)

@w1/@aijrepresents the change onw1(the dominant eigenvector)

due toa change ofaij; w1j

is thejth element ofw; s is the size of the

matrix;vmi

is theith element ofmth left conjugated eigenvector;

lmis themth eigenvalue ofA. The result of this expression is a set

of matrices of the same size ofA, one for each element ofw. It is

possible, then, to evaluate how the variation of aij affects each

element of the final proportion vector. Sensitivity value (@w1/@aij)

is a measure of the effect that a change ofaij has on the elements ofthe stable proportions vectorw. If it is positive, then an increment

ofaijcauses an increment of an element ofw. If it is negative then

an increment ofaij causes a decrement of an element ofw. Methods

employed to calculatew and(1)were those proposed byCaswell

(2001).

Matrix models analyzed here are markovian, so a change in aijimplies the variation of the values of the rest of the elements of

columnj (or at least in one of them), so the summation along the

column is unity. Therefore, the increment of a particular cover will

always be related with the reduction of other. Sensitivity analysis

took in to account these aspects (Caswell, 2001).

2.5. Rates of land use change

Rate of change (%) (rt) for each cover was calculated as

rt= ((S1 S0)/S0) 100.S0and S1are the areas of a cover class at

(t= 0) and (t= 1) respectively. Two rates of change were obtained,

the observed and the expected. The first one is calculated from the

observed areas in the two observations periods. The expected rate

considered the area in the second period of observation (2002

2005) (S0), whileS1 was the area predicted by model projections.

Values or rtwere multiplied by (1), so a negative value would

mean a loss of cover, while a positive one is a gain.

Annual rate of change was calculated asra

100 1 1 S1 S0

S0

1=t !;

wheret= 10 years. Observed and expected rates were obtained as

above.

Four recent studies of land use change were used to comparethe

rates obtained in this work with them (White et al., 2000; Achard

et al., 2002; Morton et al., 2006; Hansen et al., 2008). These papers

were selected because they present data to calculatertandra.

2.6. Differences between observed and predicted covers

Distance between observed and predicted cover proportions at

time (t) can be measured as

cAt X

s

i1

nt i w

Ai

2(2)

E. Vega et al. / Agriculture, Ecosystems and Environment 134 (2009) 287292 289


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where nti

is theith element of proportions vector at time (t) and

wAi is the ith element of thefinal proportionsvectorw of matrixA.

Summation ofcAt along x iterations ofA n(t)=n(t+1), gives the

total distance (Z(A)), a measure of how far is the observed

proportion vector at time (t) fromw, givenA:

ZA Xxt0

cAt (3)

In this context, a particular distanceZ(A) can also be referred asZobs.

A way to evaluate the oddity ofZ(A)derives from the fact that it is

possible to find matrices different of A but having the same

eigenvectorw. To calculate Pr(Z(A)), the probability of occurrence of

Z(A), 1000 markovian matrices X(different ofA) having the same

eigenvectorw ofA were obtained randomly (Hastings, 1970) for

each combination of phytogeographic district and country. Each

matrixXwas iterated 30 times as according to X n(t)=n(t+1), to

obtain itsZ(X). Pr(Z(A)) was calculated as the fraction ofZ(X)lesser

than Z(A). Thus it is possible to estimate how feasible is to find a

shorter route from n(t)to w than the one defined by A.

3. Results

Though in the three countries the highest transition probabil-ities were those to grassland persistence (Fig. 2), the land use

change dynamic showed important differences. Transitions

probabilities towards grasslands were highest in Uruguay,

followed by those of Brazil andArgentina. Transition rates towards

afforestations were similar in Argentina and Brazil, and lower in

Uruguay. Noticeably, in this country afforestation persistence is

clearly higher than in the others. The area occupied by grassland in

19851989 (64%) decreased almost 6% during the period studied.

Projections for Argentina and Uruguay suggest that this trend

would continue until reaching 58%. In Brazil, the projected trend

was the opposite: grassland cover would increase up to 70% (see

Suplementary material, Table ST1).

The comparison of the average matrix models for each country

showed that the largest transitions are those that point towards

grasslands, while the smallest point towards afforestations (Fig. 2).

However projections showed that grasslands surface would

diminish and afforestation would increase. This apparent contra-

diction may derive from the fact that expected trends of each cover

are a linear combination of all transitions and all covers. If a

transition rate is high it does not follow strictly that the associated

cover will increase.

Northern Campos and West Inland Pampa were the phytogeo-

grahic districts that showed the greatest differences with the rest

(seven out nine (77%) and 6.5 transitions (72%) different from the

rest of districts, respectively). The districts with fewer transitions

different from the rest were Flooding (57%) and Rolling pampas

(50%). The most contrasting pairs of districts were the Northern

Campos-Austral Pampa, Northern Campos-Inland Pampa, and

Mesopotamic Pampa-Austral Pampa, with eight out of ninetransition rates different in each case (see Suplementary material,

Fig. S1a).

Transition rate Cr ! Cr was the most variable: it differed in 19

of 21 district-pair comparisons (p


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Acknowledgements

This work has been possible through funding of the Inter-

American Institute for Global Change Research (IAI, CRN II 2031),

which is supported by the US National Science Foundation (Grant

GEO-0452325) forEV. This work has also been funded underthe EU

6th Framework Programme for Research, Technological Develop-

ment and Demonstration, Priority 1.1.6.3. Global Change and

Ecosystems (European Commission, DG Research, contract 003874

(GOCE)). Its content does not represent the official position of the

European Commission and is entirely under the responsibility of

the authors.

Appendix A. Supplementary data

Supplementary data associated with this article canbe found, in

the online version, atdoi:10.1016/j.agee.2009.07.011.

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