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Spatial Configuration of Land-use/Land-cover in the Pujal-Coy Project Area,Huasteca Potosina Region, MexicoAuthor(s): Humberto Reyes Hernández, Miguel Aguilar Robledo, Juan Rogelio Aguirre Rivera, andJavier Fortanelli MartínezSource: AMBIO: A Journal of the Human Environment, 37(5):381-389. 2008.Published By: Royal Swedish Academy of SciencesDOI: http://dx.doi.org/10.1579/07-A-294.1URL: http://www.bioone.org/doi/full/10.1579/07-A-294.1

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Report Humberto Reyes Hernandez, Miguel Aguilar Robledo, Juan Rogelio Aguirre Rivera andJavier Fortanelli Martınez

Spatial Configuration of Land-use/Land-coverin the Pujal-Coy Project Area, HuastecaPotosina Region, Mexico

This article analyzes the relationship between the config-uration and spatial reorganization of land-use and land-cover in the Pujal-Coy project area, Huasteca Potosinaregion, eastern San Luis Potosı, Mexico, as well as therelationship between these changes and the environmen-tal conditions prevailing in the area. Land-use and land-cover changes were determined through the analysis andinterpretation of satellite images from different dates. Thechanges identified in the different study periods werecorrelated with the prevailing physical factors. The resultsshow that the spatial configuration of farming activities,initially induced by the implementation of a regionaldevelopment project, is highly correlated to the presenceof limiting factors such as soil type, slope, and climate.Particularly, the former represents the element that hasled to the establishment of the current distribution patternof farming activities.

INTRODUCTION

Land-use and land-cover represent two of the elements that bestevidence the earth’s surface transformation derived from humanactivities over time. On the one hand, land-cover is defined asthe spontaneous vegetation cover of the earth’s surface; on theother hand, land-use is characterized by the land arrangement,activities, and production of goods by people in a given covertype with the consequent change or persistence of this land-cover. Unfortunately, the impact derived from this process isfrequently related to deforestation, degradation and fragmen-tation of ecosystems, desertification and disruption of hydro-logical cycles, loss of biological diversity, and an increase in thevulnerability of human groups (1–4). Depending on the natural,socioeconomic, and technological conditions that prevail in anygiven region, land-use changes may be either transient orpermanent, trigger other changes, or lead to soil degradationultimately resulting in land abandonment. In general, thesetransformations, as well as the likelihood to revert them, may beas profound or superficial as the associated causes: the moreprofound and permanent a land-use mode, the harder will be itspotential reversion or reorientation (5, 6).

During the second half of the 20th century, a series of regionaldevelopment projects were implemented in tropical and sub-tropical Mexico’s regions to boost stagnant economic regionsbased on the development of collective mechanized agricultureand intensive cattle-raising systems. The Pujal-Coy project wasone portion of what officially would be ‘‘the largest irrigationdistrict in Latin America,’’ including a total of 7200 km2 ofirrigated land in the lower basin of the Panuco River.Specifically, the Pujal-Coy project is located in the Huastecaregion in the lowlands of San Luis Potosı State, along thefloodplains of the Tampaon-Moctezuma and Tamesı rivers; itwas planned as a complex land-use change plan, which wouldinvolve the replacement of the prevailing extensive cattle-raising

by intensive agriculture based on irrigation. This project,planned in two stages, started in 1973 in the Ebano and Tamuınmunicipalities, comprising 720 km2. The second one, started in1978, comprised 2300 km2 and also included land in the SanVicente Tancuayalab municipality (6). Overall, the irrigationarea covered about 3000 km2.

Although during the early onset of the Pujal-Coy project theconversion of the rangeland with spontaneous vegetation highlyaltered by cattle and the ‘‘idle lands’’ (the official name for areascovered with primary vegetation) to irrigated lands cultivatedwith oil-producing crops, produce, and staple grain gave rise toan accelerated expansion of the agricultural frontier, theplanned transition from extensive stockraising to intensiveagriculture was only partially achieved. More recently the land-use pattern promoted by the project has reverted to the extentthat, at present, the land-use/cover changes evidence a clearpredominance of cattle raising in pastures on lands initiallydevoted to crop production.

Although the reorientation toward cattle raising is attributedto socioeconomic, cultural, and political factors widely docu-mented by several authors (7–9), the influence of environmentalconditions in the constitution and spatial reorganization ofcultivated and cattle raising land-use in the region has not beenanalyzed in depth to date. Within this context, this paperanalyzes the influence of environmental conditions in thecurrent configuration and reorganization of land-use/cover inthe Pujal-Coy project area, in the Huasteca potosina region,during the period 1973–2000. The article shows how theenvironmental conditions play an important role in the spatialland-use/land-cover configuration in some tropical and sub-tropical regions of the world.

Study Area and Description

The Pujal-Coy project area is located between 218370–228280Nand 988070–988500W in the physiographic province denominatedGulf of Mexico Coastal Plain, comprising an area of 3056 km2

that includes the Ebano, Tamuın, and San Vicente Tancuayalabmunicipalities, eastern San Luis Potosı state, Mexico.

The Tampaon River, the main water stream running acrossthe plain, is located in the center of the study area, and its wateris used in the 092 Irrigation District. The Moctezuma River,located east of San Vicente Tancuayalab, serves as a naturalboundary between the San Luis Potosı and Veracruz states, andits water is used for irrigation. Where the Moctezuma and theTampaon rivers merge, they give rise to the Panuco River (Fig.1).

The region is basically a broad plain with a slight slopetoward the southwest, including small areas of low hills andscattered low mountains that stand out across the highland,located north of Tamuın, with altitudes between 100 m and 150m and slopes from 4% to 15%; the minimum altitude is 15 m,located in the zone where the Tampaon and Moctezuma riversmerge. A geologic substrate consisting of sedimentary rocks,

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mainly Upper-Cretaceous lutites, emerges in 80% of the area.

Quaternary alluviums occupy the rest of the area, mainly at the

floodplains of the rivers. A narrow fringe of limestone exists

west of Tamuın, along with small areas of conglomerate and

swampy soil in areas occupied by lagoons and other permanent

water bodies (10).

Theprevailing climate in the north, center, andwest of the study

area, according to Koppen’s classification modified, corresponds

to warm subhumid with summer rainfall (Aw0(e)gw0), being the

driest of its kind, and comprises more than 85% of the total area.

This climate presents a mean annual temperature of 24.58C and a

mean annual rainfall of 870 mm. Toward the south, the climate

becomes more humid and is classified as subhumid with summer

rainfall (Aw1(e)gw0); it is intermediate between the former (dryer)

and the one with the greatest precipitation, Aw2, located in a

narrow strip to the west of the study area. This climate presents a

mean annual rainfall of 1350 mm and a mean annual temperature

of 258C. The region’s rainfall pattern is characterized by a marked

seasonality, with the presence of a relative drought spell during

July and August.

METHODS

In order to define the previous land-use configuration and itssubsequent reorganization, four georeferenced Landsat satelliteimages were used: three multispectral scanner system (MSS)images (May 1973, October 1985, and October 1990) with a 60-m spatial resolution and an enhanced thematic mapper plus(ETMþ) image dated April 2000 with a 30-m resolution. Theimages were projected to Universal Transverse Mercator system(UTM) Zone 14, using the 1866 Clark ellipsoid. For the presentanalysis, a 1099 3 1459-pixel subscene was considered in thecase of MSS images, and a 252133161 subscene was consideredfor the ETMþ image (totaling 5770 km2), jointly comprising thetotal study area. The digital imagery previously georeferencedwas provided by the Geography Institute at UniversidadNacional Autonomo de Mexico (UNAM).

These images were digitally processed using the IDRISI Ksoftware and bands 2, 3, and 4 from the MSS images, as well as5 and 7 bands from the ETMþ image. Training samples for eachland-cover attribute under study were selected based on aprevious knowledge throughout the study area in order to

Figure 1. The Pujal-Coy project area location in the Huasteca Potosina region, Mexico.

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obtain good representative samples and field data gathering.The training samples were on-screen digitizing and editing fromthe display of various enhanced image composites. At first, thepolygons used as training fields were defined for the followingland-use types: three different types of tropical forest (lowthorny, low deciduous, and mid-height subdeciduous), irriga-tion agriculture, nonirrigated agriculture, pasture lands, sec-ondary shrubland, secondary forests, water bodies, and humansettlements.

The signature files generated by this process containstatistical information that aids in determining whether theclasses are good in terms of their separability in themultidimensional attribute space. The corresponding land-coversignatures for the training sites were evaluated using measuresof separability such as divergence, transformed divergence, andEuclidean distance to determine the spectral pattern response ofeach cover type in each band. The corresponding histogramswere analyzed along with the separation of each band of land-cover types. When these did not show a normal distribution,training field boundaries were redefined, and the procedure wascarried out again. Further, because of the difficulty to spectrallyseparate the three tropical forest types, these were pooled into asingle class, as in the case of the two types of agriculture.

The final signatures were then used in a maximum likelihoodsupervise classification method (11–13), to produce categoricalland-use/cover maps for change detection. In the supervisedclassification approach, the spectral response vectors of eachclass are modeled to have a multivariate normal distribution,and parameters of the models are estimated from trainingsamples. The maximum likelihood classifier assigns each pixelto one among the set of candidate land-cover classes, dependingonly on the spectral response of the unique pixel.

Several field trips were conducted to verify the current land-use/ cover, physical conditions of the terrain, and the land-useassociated to them. Any errors found in the classification werecorrected, and misclassified pixels were assigned to the correctclass. The accuracy of results of land-cover maps was evaluatedthrough comparison with ground reference data. At first, inorder to contrast the accuracy of the classification used in theresulting maps of 1973, 1985, and 1990, these were comparedwith maps published by the National Institute of Geography,Statistics and Informatics (14), the Technical Commission toAssess the Stocking Rates (15), and the vegetation map of thestate of San Luis Potosı drafted by the botanist J. Rzedowski(16).

The accuracy of the 2000 image classification was evaluatedthrough the comparison with reference data previously collectedin the field. To do so, a digital archive was built representing‘‘true’’ land coverage corresponding with each one of the classespreviously defined; these were finally compared with theclassification obtained. This procedure gave rise to a table withcrossed-data or a matrix of error that shows the relationshipbetween the ‘‘true’’ land coverage and the classes represented inthe map, as well as the proportional error. The overall accuracywas measured, resulting in 87% of pixels correctly classified.

The output map scale was 1 : 125 000. In this way, land-useand land-cover maps were elaborated for the years 1973, 1985,1990, and 2000 including the following classes: i) tropicalforests, ii) pasture lands, iii) crop lands, iv) secondaryshrublands, v) secondary forests, and iv) water bodies.

Given that the spatial resolution of the MSS images—originally with a pixel size of 79 3 79 m and later resampled at60360 m—differs from the 2000 ETMþ image resolution (pixelsize of 30 3 30 m), before comparing the drafted maps, theETMþ image pixel size was reduced through the imagecontraction procedure to homogenize the spatial resolution ofall the images at 60 3 60 m per pixel.

By means of a cartographic superposition and a cross-tabulation (2, 17), areas that changed land-use and those thatremained unchanged were located and quantified for theperiods 1973–1985, 1985–1990, and 1990–2000. Thus, mapsand tables illustrating changes in land-use and cover wereconstructed for each period of study.

The vector layers previously elaborated corresponding to theedaphic regions, isohyets, climate type, geological substrate,vegetation type, potential land-use, irrigated area, elevation,and slope were exported to the IDRISI K software, with which anew cartographic superposition was made using the changemaps previously developed. This procedure enabled theestablishment of the correspondence between land-use changesand each of the described variables.

A number of statistical analyses were done using the datafrom the cross-tabulation between change maps and each coverfor the abiotic factors. First, a correlation analysis was done toinvestigate the statistical behavior of variables. This revealedthat all variables were significantly correlated (r2 . 0.83) andthat soil type represented most of the correlated variables. Forthis reason, soil type was selected as the indicator variable. Incontrast, precipitation was the variable that showed the greatestindependence, so it was also selected for the next stage of theanalysis, along with soil type.

The information regarding the perceived changes and thecorresponding coverage were captured in a database andexported to the PC-ORD v. 4 software. The goal was to applya multivariable ordination analysis using detrended correspon-dence analysis (DCA) through the DECORANA program forthe next study period: 1985–1990 and 1990–2000. Only theseperiods were included because the objective is to identify thetrends of the changes that occurred since 1986, when the statefunding to develop the Pujal-Coy project ended.

The main goal of DCA is to represent the relations betweensampling units and their attributes, in this case the geographicunits (edaphic, climatic, and agricultural) and their correspond-ing processes of change in vegetative cover as accurately aspossible in a space of small dimensions. The final product is agraph, usually bidimensional (although not necessarily), inwhich the units or the similar attributes are represented close toeach other and the dissimilar entities are located separated fromone another. The DCA is based on reciprocal averaging butwithout the problems of a spurious axis and compression of theopposite points of the first axis, which characterize factorialcorrespondences analysis (FCA) (18).

Additionally, a cover including 500 control points of thestudy area was derived from the IDRISI K software using arandom stratified sampling. The detected change and theassociated environmental conditions (soil type, precipitation,slope, and presence/absence of irrigation) were verified for eachpoint. Information on these points was exported to theSTATISTICA 6.0 software to conduct a multiple discriminationanalysis (MDA) through the stepwise statistical method.

Multiple discriminant analysis is an extension of discrimi-nant analysis and a relative of multiple analysis of variance(MANOVA), sharing many of the same assumptions and tests.Usually, it includes several variables in a study in order todetermine which one(s) contribute to the discriminationbetween groups. In that case, we have a matrix of totalvariances and covariances; likewise, we have a matrix of pooledwithin-group variances and covariances.

We compare those two matrices via multivariate F tests inorder to determine whether there are any significant differences(with regard to all variables) between groups. This procedure isidentical to multivariate analysis of variance or MANOVA.This analysis has two steps: i) an F test (Wilks’ lambda) is usedto test if the discriminant model as a whole is significant and

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ii) if the F test shows significance, then each individualindependent variable is assessed to see which differs significant-ly, in its mean value by group, and these are used to classify thedependent variable. In this case, MDA is used to determinewhich variable(s) is the best predictor(s) of land-use change.

RESULTS AND DISCUSSION

At the beginning of the project in 1973, the prevailing activityacross the study lowlands was the extensive cattle raising basedon the free grazing in pasture lands and secondary shrublands.Agriculture, deemed a secondary activity of less importancethan cattle raising, was confined to river banks (fertilefloodplains dominated by alluvial soils). So, grazing landsoccupied 667.1 km2 of the total area (21.8%), cultivated areasaccounted for 449.6 km2 (14.7%), and areas covered by tropicalforests covered 982.7 km2 (32.1%) (Table 1, Fig. 2). Grazinglands were distributed mainly across the Tamuın municipality,surrounded to a great extent by disturbed tropical forests thatpersisted to the east and west at that time. Areas dedicated toagriculture were concentrated mainly at the Tampaon andMoctezuma river banks, northeast of Ebano (Fig. 3a).

From 1973 to 1985, more than 731 km2 of tropical forestswere cut; this was due to the project implementation, whichincluded actions such as providing peasants with completelydeforested land. During the same period, grazing land areasunderwent a negligible 3.82 km2 reduction, whereas cultivatedland increased by 259.4 km2 (Table 1, Fig. 2). The acceleratedtransformation of cattle raising lands and forested areasresulted in the expansion of cultivated areas, most noticeablyto the north of Ebano and Tamuın. However, the projectedtransition from extensive cattle raising to intensive agriculturebased on irrigation was never completed. By 1990, this becameevident at the beginning of the project’s second phase because ofthe visible rise in areas covered with secondary vegetation,distributed mostly to the north and in the middle of San VicenteTancuayalab and Tamuın (Fig. 3b).

In 1986 when the project financial support was officiallysuspended, changes in the spatial reconfiguration of land-useand land-cover in the study area were already in motion, butwith a higher environmental cost. In fact, since 1978 there wassome evidence of the return to cattle raising, an activity thathistorically prevailed in the region. Further, during 1985–1990there was a sudden shift regarding farming activities in theregion. In this period, grazing land areas dramatically increasedmore than 510 km2, whereas cultivated land decreased by185.69 km2. This already evidenced a reversal toward theprevious land-use, which became stronger during the nextdecade when it consolidated. By 1990, grazing lands accountedfor 1175.4 km2 (38.5%) of the total area, more than twice thearea of agriculture, which covered only 523.3 km2 (17.3%)(Table 1, Figs. 2 and 3b,c).

In 2000, grazing lands spread throughout the region tocomprise almost the total area in the Tamuın and San VicenteTancuayalab municipalities. The surface devoted to this activity

underwent a 792.9 km2 increase, from 1175.4 km2 in 1990 to1968.2 km2 in 2000, accounting for more than 64% of the totalarea (Table 1, Fig. 2). This growth is particularly evident northof Tamuın and southwest of San Vicente Tancuayalab. Thecrop lands cover only 16% of the whole project surface, namely,just 73 km2 more than in 1973. The area with irrigationinfrastructure covers only 670 km2; moreover, most of theinfrastructure built in the stage II area of the project is useless oris ruined (Fig. 3d).

Although the land-use reorientation in the Pujal-Coy projectarea was attributed to socioeconomic, cultural, and politicalfactors, the findings reported here indicate that the nature ofsoils and relief are additional elements to explain theconfiguration of land-use and land-cover in this region afterthe government stopped funding the project. The correlationbetween the spatial distribution of the edaphic units and land-use forms demonstrates so.

Five main soil types prevail in the Pujal-Coy project area:Vertisol, Gleysol, Leptosol, Regosol, and Feozem. Generally,these soil units are not isolated but form defined microregionsbased on the predominance of a given soil unit and the presenceof one or several associated soil units, each having differenttexture classes, along with chemical and physical phases. Thevarious soil combinations make it possible to identify 13edaphic microregions (Table 2, Fig. 4).

Leptosols and Regosols are located at the highest lands,located north and west of Tamuın. Regosols are shallow soils,located on hilly areas and low ranges; these soils are frequentlymixed with Leptosols and present rock outcrops. Fertility ofthis soil is variable, and its agricultural use is restrained by itsdepth and other physical limitations. Leptosols are usually evenmore shallow and frequently rockier, and are located in abruptareas with steep to moderate slope. Most of these soils showrestrictions on agricultural practices and production (19).

Gleysols are distributed in flood-prone areas south of Ebanoand Tamuın. These soils show fewer restrictions for agricultureand stockraising because they contain less expansive clays and

Table 1. Land-use/land-cover for 1973, 1985, 1990, and 2000 in the Pujal-Coy project area, Huasteca Potosina region, Mexico.

Land-use/land-cover

Area in km2 y�1

1973 1985 1990 2000

Forests 982.72 251.64 178.42 102.16Pasture lands 667.10 663.28 1175.45 1968.20Crop lands 449.62 709.06 523.37 510.33Secondary shrublands 615.13 1032.83 523.37 189.44Secondary forests 287.16 368.22 576.68 249.11Water 54.32 31.02 65.57 36.81

Figure 2. Land-use and land-cover change in the Pujal-Coy projectarea, Huasteca Potosina region, Mexico, 1973–2000.

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more organic material than Vertisols. Gleysols include a layerthat periodically gets soaked, but are fertile enough foragriculture if kept properly drained. Phaeozems occur in thescarce hills in the northern part of the study area. These soilshave a layer with plenty of organic matter and nutrients;although depth varies depending on the local relief, it is usuallyhigher than a meter. Phaeozems are used for livestock raisingwith satisfactory results.

Vertisols, being the most abundant soil type, are locatedalong the lowlands in the three municipalities and account forabout 66% of the total area. These soils are located in flat sites,gentle slopes, terraces, or lowlands; they have a fine-grainedtexture and low organic matter content. One of the commontraits of Vertisols is the predominance of expansive clay, usuallymontmorillonite, which hinders agricultural use because of theirhigh sensitivity to moisture. On the one hand, Vertisols areprone to flooding when superficial water is in excess; on theother, they easily crack when they are dry: the top soil movesdownward so that the soil horizons become ‘‘inverted,’’ thesetraits make its cultivation difficult (19).

The results of the DCA for 1985–1990 show a gradient ofchange in land-use and cover in the first ordination axis, with atropical forest–secondary vegetation–grazing lands–cultivatedlands sequence (Fig. 5a,b). However, changes toward secondaryvegetation comprise a great extent of the gradient and suggest aperiod of a lack of definition in the trend. Likewise, theordination of environmental conditions (Fig. 5c) reflects unclearpatterns. In the right of axis 1 only shallow soils with lowagricultural quality are evident (I, II, and IV), which matchtropical forests that are either nondeforested or undergoing atransformation process toward grazing lands or crop lands.Microregions V, VIII, and X appear at the center of theordination space, dominated by intermediate-quality soils (i.e.,Regosols and saline Vertisols) associated with an importantchange component toward grazing lands. Microregions XI, XII,and XIII appear in this same space, which despite being theareas where top-quality soils occur, seem to be related to areasthat are changing toward secondary vegetation (Fig. 5b).

Vertisols in smooth hills in microregions IX and VII arelocated to the left of the first axis, in relation to traditionalnonirrigated crop lands. Irrigated (R) and nonirrigated (S) landappear close to each other in the first axis, suggesting anindependence from plant-cover spatial patterns in relation tosoil moisture management. Areas with different precipitationlevels are ranked from left to right in a low-to-high precipitationgradient, suggesting the agriculture-low precipitation, tropicalforest-high precipitation, and grazing land-moderate precipita-tion associations. However, the 1250 mm and 1350 mmprecipitation zones show a lack of coordination in relation tothis pattern (Fig. 5a,c).

The overall tropical forest–secondary vegetation–grazinglands–cultivated lands gradient remains in 1990–2000, butchanges toward the grazing lands and secondary vegetationconcentrates toward the center of axis 1 (Fig. 5d,e). It is alsoevident that changes toward secondary vegetation undergo areduction in dispersion as compared with the gradientcorresponding to the previous period, whereas changes towardcultivated land are clearly located to the left end of axis 1 (Fig.5e).

Consistent with the overall gradient in land-use and land-cover, the ordination of edaphic microregions suggests agradient of agricultural quality soil ranging from Leptosolsand Regosols in mountain ranges and steep slopes tointermediate-quality soils like Feozems or Vertisols associatedwith Regosols in the piedmont and nonriverside lowlands, up toVertisols in low steep hills and river bank areas. A gradientbecomes evident in this way, in which low-quality soils in terms

of slope and depth are associated with areas covered by tropicalforests that are either primary or undergoing a transformationprocess toward farming activities; soils of an intermediatequality, in terms of depth or presence of gravel or salts, areassociated with grazing lands, whereas river bank soils areassociated with areas prone to become cultivated lands (Fig.5d).

Irrigated and nonirrigated lands are related to high-qualityand low-to-intermediate quality soils, respectively. Likewise, aprecipitation gradient occurs ranging from low (850 mm) tohigh (1350 mm) from left to right of axis 1 (except for theslightly uncoordinated 1250 mm region). This seems to suggestan inverted pattern in relation to moisture derived from rainfall,that is, the rainiest areas overlap with shallow soils and steepslopes, whereas areas with low precipitation match intermedi-ate- and high-agricultural quality soils (Fig. 5d,f ).

In both periods, the second axis is not interpreted because ofits very small contribution to the explained variation (r2¼ 0.10and 0.04, respectively) as compared with that of the first axis (r2

¼ 0.83 and 0.85, respectively). The importance of the axis inrelation to the relative quantity of the explained variation isdefined in FCA through the proper or characteristic value(eigenvalue); however, in DCA the correction of the FCA flawsaffects the correspondence between the characteristic value andthe structure of the axis. This suggests evaluating theeffectiveness of ordering through the determination coefficient(r2) between the distances in the ordering space and thedistances in the original multidimensional space. As a functionof the observed values, it can be decided whether to analyzeonly the data structure of the first axis, as in this case, or toinclude one or more of the subsequent axis.

Figure 3. Land-use/cover transformation in the Pujal-Coy project area,Huasteca Potosina region, Mexico, in 1973, 1985, 1990, and 2000.

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Coinciding with the previous analysis, the results from thediscriminating analysis indicate that slope and edaphic micro-region are the variables that best discriminate among the groupsanalyzed for 1985–1990 and 1990–2000 (Table 3). It means thatboth variables are statistically correlated with coverage andland-use changes in the Pujal-Coy area project.

For the 1985–1990 period, model 1 indicates that slope is thevariable that best discriminates the changes that took place(canonical correlation ¼ 0.71). In contrast, the Lambda value(0.50) shows an acceptable discriminating power of the model—

values close to 0 indicate a perfect discriminating power. Thesecond model, with the inclusion of the edaphic microregionvariable, diminishes the F value and the canonic correlation in50% (0.38%). Further, it raises the Lambda value, which meansthat when both variables mingle the discriminatory value of thistest drops; nonetheless, both models were statistically signifi-cant.

For the 1990–2000 period, the first model indicates that sloperemains as the variable that best discriminates the changesdetected, with a 0.74 canonical correlation. In contrast, Lambda(0.45) indicates a slightly higher discriminating power of themodel compared to 1985–1990. In the second model, theinclusion of the edaphic microregion variable, like the formermodel, provokes a drop in the F value (the first model 35.2; thesecond model 17.0), and a raise in the Lambda value (0.77),similar to the 1985–1990 period when the inclusion of thevariable soil diminishes the test discriminatory value. However,both models are also statistically significant.

Considering the characteristics of each of the soil unitspresent in the study area, a tight relation between them andrelief can be found. Thus, Regosols are distributed mainly onthe low hilly areas, whereas the Leptosols dominate mainlyabrupt sites and areas with steep and moderate slopes. Incontrast, Vertisols are located mainly on flat sites or gentleslopes, generally on terraces or lowlands.

Here it is worth noting i) that Vertisols, which are hard-to-manage soil types, comprise more than 60% of the study area;ii) that most of the colonists (ejidatarios) were unfamiliar withirrigated, modern agricultural systems; iii) that agriculturaltradition was virtually nonexistent in the Pujal-Coy lowlands;and iv) particularly, that only 420 km2 was irrigated across thestudy area, with the remaining land being nonirrigated (20).Although detailed agrological studies were carried out (21), soilmanagement (e.g., Vertisols) and its particular characteristicswere apparently either ignored or underestimated both beforeand during the project implementation. The top-down planningmodel instrumented by the state at that time, along with theurgency of the federal agencies involved to meet the pro-grammed actions and goals, are the two main reasons whytraining in tropical soil management and its strategic imple-mentation were never considered.

Furthermore, Vertisols need optimum moisture for plowingand weeding, as a function of cohesion, plasticity, andadhesiveness (22, 23), which can only be reached through the

Table 2. Edaphic microregions identified in the Pujal-Coy project area, Huasteca Potosina region, Mexico.

Edaphicmicroregion Soil units Description Area in km2

I LPqþLPkþR Lithic Leptosol, Rendzic Leptosol and Calcaric Regosol 63.95II LPkþPHhþRGc (L) Rendzic Leptosol, Haplic Phaeozem and Calcaric Regosol, with a

superficial hard shallow rock cap.72.30

III RGc+LPq (L) Calcaric Regosol and Lithic Leptosol, with a superficial hard shallowrock cap.

6.00

IV LPk+VRh, (D L) Rendzic Leptosol and Haplic Vertisol, with constant hard rock cap. 300.92V PHh+RGc+VRc (L) Calcaric Phaeozems, Calcaric Regosols and Calcic Vertisol, with a

superficial hard shallow rock cap.34.45

VI VRh+RGc+PHh (G) Haplic Vertisols, Calcaric Regosols and Haplic Phaeozems, withgravels less than 7.5 cm length.

553.30

VII VRh+PHh (G) Haplic Vertisols and Haplic Phaeozems, with gravels less than 7.5cm length.

20.60

VIII VRc+VRs (S) Chromic Vertisols and Saline Vertisols, with soluble salts of the earthand sodium.

133.60

IX VRs (S) Saline Vertisols, with soluble salt of the earth and sodium. 839.45X VRh Haplic Vertisols, without physical or chemical restrictions. 636.20XI VRh+VRc Haplic Vertisols and Chromic Vertisols, without physical or chemical

restrictions93.13

XII VRh+GLh Haplic Vertisols and Haplic Gleysol, without physical or chemicalrestrictions.

214.20

XIII VRh (G) Haplic Vertisols with gravels less than 7.5 cm length. 62.50

Figure 4. Edaphic microregions distribution in the Pujal-Coy projectarea, Huasteca Potosina region, Mexico.

386 Ambio Vol. 37, No. 5, July 2008� Royal Swedish Academy of Sciences 2008http://www.ambio.kva.se

Figure 5. Attributes ordination during 1985–1990 period in the Pujal-Coy project area, Huasteca Potosina region, Mexico. Symbols are C, Vs-P: cultivated land and secondary shrublands change to grassland; P, Vs-C: grassland and secondary shrublands change to cultivated land;P-P: grassland persistence; C-C: cultivated land persistence; S-P: forest change to grassland; S-C: forest change to cultivated land; S-Vs:forest change to secondary shrublands; S-S: forest persistence; P-Vs: grassland change to secondary shrublands; C-Vs: cultivated landchange to secondary shrublands; Vs-Vs: secondary shrublands persistence; R: irrigated land; S: nonirrigated land; a, b, c, d, e, f:precipitation levels (isohyet); I, II, III, IV, V, VI, VII, VIII, IX, X, XI, XII, XIII: Edaphic microregion.

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regulated application of water by irrigation, a goal that wasonly partially met, and the availability of proper machinery totill this soil type under dry-farming conditions.

This may be one of the reasons explaining that, between 1985and 2000, more than 66% of the area occupied by this soil typewas reconverted to grazing lands—an activity that requiresalmost no plough. In addition to the difficult management ofthese soils, the lack of irrigation would be another of the mainfactors that forced farmers to widen the fallow periods in theirland (lowering the cost of farming labors) and, in most cases,implement a rotation of annual crops and perennial-grasspastures, a common practice in other tropical regions (24).

In order to achieve adequate management of Vertisols, analternated system of crop cultivation is required during a limitedperiod of time followed by the pastures during severalagricultural cycles for grasses to increase organic matter contentand hence improve soil properties (25). This has been one of thestrategies to face the inadequacy of the farming model(successful in other regions of the country with temperate andsemiarid climates) introduced in tropical and subtropicalregions, including the Huasteca Potosina region (6, 26).Therefore, it is reasonable to think of an alternating system ofannual crops production use of these soils after havingremained as grazing lands for a given time.

In fact, this alternated land-use already occurred during1985–1990 (Fig. 3) when the reconfiguration of land-use started,that is, the expansion of grazing lands to the north andsouthwest of the study area along with the concentration ofcultivated land in the Tampaon and Moctezuma river banks. Inthis sense, the edaphic microregions XII and XIII dominated byVertisols associated to Gleysols (Table 2), besides showing lessinconveniences for cultivation, are irrigated in more than 70%of the total area, and for this reason have maintained asignificant agricultural activity since the beginning of the project(27), which has become clearly defined in the most recent period(1990–2000).

It is worth mentioning here that, as an effect of the generaltrend toward cattle raising, some terrains located in the irrigatedarea have also been reconverted into grazing lands; in othercases, the chosen crops have included sorghum (Sorghumvulgare Pers.), soybean (Glycine max L.), and safflower(Carthamus tinctorius L.), which require lower capital invest-ment and technological levels and still yield an acceptable profitunder more adverse environmental conditions, as in the case ofmicroregion IX.

Most of the areas occupied by Leptosols do not seem to haveundergone deforestation and, along with the Regosols micro-region, concentrate the last remnants of tropical forest. Someother areas within these microregions, which were deforested at

the beginning of the project and abandoned soon after,concentrate 50% of the remaining secondary forests along withthose that survived the deforestation carried out by the state atthe onset of the Pujal-Coy project (Fig. 3d). The characteristicsof these soils impose serious restrictions for agriculturalpractices, which in addition to the lack of irrigation wouldexplain why these were the first areas to be abandoned orimmediately reconverted to grazing lands after deforestation. Inaddition, several authors have pointed out that slope andedaphic conditions enable the conservation of fragments oftropical forest (28, 29).

Apart from the previous factors, the erratic rainfall patternrecorded for the area would explain the high frequency of firesduring the first years of the project and in the 1990s,accelerating the conversion of land initially allocated to cropproduction into grazing lands. Furthermore, consider that morethan 80% of this area is managed under the so-called ‘‘technifieddry-farming’’ scheme, meaning it lacks effective irrigationalthough it has the due infrastructure in place. This may haveaccelerated the transition from crop lands into grazing landsand the subsequent agricultural specialization of the mostfavored floodplain areas.

Although the analyses conducted here reveal that rainfallwas not a significant variable, it still contributes to theexplanation of the reversal of land initially devoted toagriculture into grazing lands, especially during 1990–2000.The analysis of precipitation for 23 years from six climaticstations located in the study area shows several years that failedto reach the minimum precipitation level for the region—870mm per year—particularly in 1977–1980, 1985–1990, and 1993–1996. According to the Environmental Ministry of Mexico (30),during the last century four major drought periods wereregistered in Mexico (1948–1952, 1960–1964, 1970–1978, and1993–1996), which mainly affected the northern states. It isworth mentioning that San Luis Potosı is considered as one ofthe Mexican states with a higher decline in rainfall during the1990s (�20% variation) compared with its historical annualaverage; for this reason, we propose that such behavior mighthave also accelerated the land-use changes and influencedtoday’s configuration of land-use/cover.

CONCLUSIONS

Although at the beginning of the Pujal-Coy project theexpansion of areas devoted to agriculture was induced throughthe state support, soil type, slope, irrigation, and climateconditions are driving the current distribution pattern offarming activities. The conversion of agriculture lands tograzing lands and forage crops for intensive cattle raising isnow resumed; but with a higher environmental impact. Earlier

Table 3. Discriminant analysis models and variables relates with changes in the Pujal-Coy project area, Huasteca Potosina region, Mexico,1985–1990 and 1990–2000 periods.

Models and variables F Canonical correlation Wilkins Lambda Chi-squared

1985–1990 Period

1 25.98 0.714 0.503 220.709 **P , 0.001Slope 0.9422 13.44 0.329 0.762 30.44 **P , 0.001Slope 0.308Edaphic Microregion 0.798

1990–2000 Period

1 35.24 0.743 0.454 245.732 **P , 0.001Slope 0.9792 17.07 0.331 0.772 31.039 **P , 0.001Slope 0.273Edaphic Microregion 0.811

**Highly statistically significant.

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studies claimed that the reasons for resuming the previous land-use were cultural, socio-economic, and political. In this respect,the multivariable analysis revealed transition gradients fromtropical forests to secondary vegetation, grazing lands, and croplands, as well as the ordination of environmental conditionsaccording to their conditions. This analysis affirms that theaccumulated knowledge by residents about the nature of soilsand the corresponding proper soil management, along with thelack of irrigation and the hard climatic conditions that prevailedin the last decade, explain today’s configuration of land-use/cover in the sub-tropical region of Mexico.

References and Notes

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31. The authors thanks the Institute of Geography, UNAM, Mexico, for providing satelliteimages; SAGARPA and CNA for the valuable information provided; FOMIX-SLP forproviding the financing required to carry out the project (FMSLP-2002–5715);anonymous referees for their comments to improve this paper; and Fortunata Velazquezfor the English revision. The first author wishes to thank CONACYT and DGEP-UNAM for the grants provided to successfully carry out his doctoral studies.

32. First submitted 5 January 2007. Accepted for publication 20 December 2007.

Humberto Reyes Hernandez is a full-time professor in theDepartment of Geography at Coordinacion de CienciasSociales y Humanidades, Universidad Autonoma de San LuisPotosı. His address: Department of Geography at Coordina-cion de Ciencias Sociales y Humanidades, UniversidadAutonoma de San Luis Potosı, Avenida Industrias #101,Fracc. Talleres, San Luis Potosı. S. L. P., Mexico C. P. 78494.E-mail: hreyes@uaslp.mx

Miguel Aguilar Robledo is a full-time professor in theDepartment of Geography at Coordinacion de CienciasSociales y Humanidades, Universidad Autonoma de San LuisPotosı. His address: Department of Geography at Coordina-cion de Ciencias Sociales y Humanidades, UniversidadAutonoma de San Luis Potosı, Avenida Industrias #101,Fracc. Talleres, San Luis Potosı. S. L. P., Mexico C. P. 78494.E-mail: aguilarm@uaslp.mx

Juan Rogelio Aguirre Rivera is a full-time professor in theInstituto de Investigacion de Zonas Deserticas, UniversidadAutonoma de San Luis Potosı. His address: Instituto deInvestigacion de Zonas Deserticas, Universidad Autonoma deSan Luis Potosı, San Luis Potosı, S. L. P., Mexico C. P. 78494.E-mail: iizd@uaslp.mx

Javier Fortanelli Martınez is a full-time professor in the Institutode Investigacion de Zonas Deserticas, Universidad Autonomade San Luis Potosı. His address: Instituto de Investigacion deZonas Deserticas, Universidad Autonoma de San Luis Potosı,San Luis Potosı, S. L. P., Mexico C. P. 78494.E-mail: fortanel@uaslp.mx

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