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Trans-boundary infrastructure, accessconnectivity, and household landuse in a tri-national frontier in theSouthwestern AmazonStephen Perza, Andrea Birgit Chavezb, Rosa Cossioc, JeffreyHoelled, Flavia L Leitea, Karla Rochae, Rafael O Rojasa, AlexanderShenkina, Lucas Araujo Carvalhoe, Jorge Castillof & Daniel RojasCespedesg

a University of Florida, Gainesville, FL, USAb Universidad Nacional Amazonica de Madre de Dios, GeospatialCenter for Natural Resource Management Research, CiudadUniversitaria, Puerto Maldonado, Peruc CGIAR, Dhaka, Bangladeshd University of California at Santa Barbara, Santa Barbara, CA,USAe Universidade Federal do Acre, Rio Branco, Brazilf Universidad Nacional Amazonica de Madre de Dios, PuertoMaldonado, Perug Universidad Amazonica de Pando, Cobija, BoliviaPublished online: 20 Mar 2014.

To cite this article: Stephen Perz, Andrea Birgit Chavez, Rosa Cossio, Jeffrey Hoelle, Flavia LLeite, Karla Rocha, Rafael O Rojas, Alexander Shenkin, Lucas Araujo Carvalho, Jorge Castillo &Daniel Rojas Cespedes (2014): Trans-boundary infrastructure, access connectivity, and householdland use in a tri-national frontier in the Southwestern Amazon, Journal of Land Use Science, DOI:10.1080/1747423X.2014.898104

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Trans-boundary infrastructure, access connectivity, and householdland use in a tri-national frontier in the Southwestern Amazon

Stephen Perza*, Andrea Birgit Chavezb, Rosa Cossioc, Jeffrey Hoelled, Flavia L Leitea,Karla Rochae, Rafael O Rojasa, Alexander Shenkina, Lucas Araujo Carvalhoe,

Jorge Castillof and Daniel Rojas Cespedesg

aUniversity of Florida, Gainesville, FL, USA; bUniversidad Nacional Amazonica de Madre de Dios,Geospatial Center for Natural Resource Management Research, Ciudad Universitaria, Puerto

Maldonado, Peru; cCGIAR, Dhaka, Bangladesh; dUniversity of California at Santa Barbara, SantaBarbara, CA, USA; eUniversidade Federal do Acre, Rio Branco, Brazil; fUniversidad NacionalAmazonica de Madre de Dios, Puerto Maldonado, Peru; gUniversidad Amazonica de Pando,

Cobija, Bolivia

(Received 21 May 2013; final version received 21 February 2014)

The land science literature has consistently documented the importance of infrastruc-ture for land use. Less attention has gone to land use around national borders receivingtrans-boundary infrastructure upgrades for cross-border integration. We take up thecase of the Inter-Oceanic Highway, a trans-boundary road being paved in the tri-national ‘MAP’ frontier of the southwestern Amazon. We draw on a tri-national surveyof households in rural communities across the MAP frontier to evaluate the effects ofaccess connectivity on land use. At the time of fieldwork, paving was complete inAcre/Brazil, underway in Madre de Dios/Peru, and planned in Pando/Bolivia. Thispermits a tri-national comparative analysis. The results confirm different effects ofaccess connectivity on land use by paving status; further, they also document cross-border processes stemming from trans-boundary infrastructure that affect land use. Thefindings call for more attention to the impacts of regional integration initiatives onlandscapes.

Keywords: infrastructure; land use; rain forest; South America

1. Introduction

One of the most consistent findings among research on the drivers of land changeconcerns accessibility to land due to infrastructure (Gutman et al., 2004; Keller,Bustamante, Gash, & Silva Dias, 2009; Lambin & Geist, 2006). Lands made moreaccessible by infrastructure tend to exhibit greater land change. Forested regions, suchas the Amazon, provide a useful case in point: new roads and road improvements, such aspaving, are associated with increments in land change, and lands closer to infrastructureexhibit greater land change (Alves, 2002; Anderson, Granger, Reis, Weinhold, & Wunder,2002; Pfaff et al., 2007; Southworth et al., 2011; Wood & Porro, 2002).

By extension, related work on land use intensification highlights the importance ofaccessibility for profitability and thus the types of land use at play (Angelsen &Kaimowitz, 2001). Following work on the spatial economy, lands closer to roads (espe-cially paved roads), and those closer to markets (particularly large markets) are more

*Corresponding author. Email: sperz@ufl.edu

Journal of Land Use Science, 2014http://dx.doi.org/10.1080/1747423X.2014.898104

© 2014 Taylor & Francis

valuable (Walker, 2004). To the extent that agricultural activities are more profitable thanforest extractivism, more accessible lands exhibit greater forest clearing, use of technolo-gical inputs, and productivity per hectare (Angelsen & Kaimowitz, 2001; Walker, 2004).In the Amazon, types of land use vary considerably by accessibility, ranging from forestextractivism in more remote areas to extensive ranching, then annual crop cultivation, treeplantations, and fish farming, as one draws closer to roads and cities (Serrão & Homma,1993).

While the relationship of accessibility via infrastructure and land change is clear, therehas been less attention to land use along roads crossing national boundaries. To the extentthat different countries with shared borders have different legal and administrativeregimes, populations and economies of differing sizes, and barriers to the flow of capital,labor and goods, they are likely to exhibit different land use profiles. Prior work on landchange in the Amazon shows large differences in deforestation across the countriessharing the basin (Perz, Aramburú, & Bremner, 2005).

That said, such differences are likely to be modified by a new generation of infra-structure projects which feature trans-boundary highways (Perz, Qiu, et al., 2013). Trans-boundary infrastructure is increasingly prioritized by governments and multilateral banksseeking economic development via regional integration among neighboring countries(Bourguignon & Pleskovic, 2008). In Latin America, trade liberalization among neighbor-ing countries, referred to as ‘open regionalism’, provided a basis for economic revitaliza-tion and reduction of national debts (Bulmer-Thomas, 2001). Trans-boundaryinfrastructure complements trade agreements by providing the physical wherewithal forinternational exchange, fostering cross-border flows of goods and capital.

This raises questions about regional economic opportunities as manifest in land use atnational frontiers where trans-boundary infrastructure projects are implemented on theground. If infrastructure upgrades permit the expansion of land use in areas closer to roadsand markets, trans-boundary infrastructure is likely to have similar effects across nationalboundaries. But if paving of a trans-boundary highway is only partially complete in afrontier region, land use may yet differ. That said, those differences may themselvesdepend on local paving status. A key question then concerns the differences in land useacross national boundaries but in the same region in the presence of trans-boundaryinfrastructure. In particular, land use is likely to vary by access connectivity, the combina-tion of paving status and market distance as they jointly affect travel time.

In Latin America, no program for trans-boundary infrastructure is more significantthan the Initiative for the Integration of Regional Infrastructure in South America (IIRSA).IIRSA was constituted by a meeting of presidents from 12 South American countries in2000 (CEPEI, 2002; IIRSA, 2008). Subsequent IIRSA meetings included multilateraldevelopment banks and led to agreements to fund 350 projects at a total projected cost of$38 billion. IIRSA was explicitly conceptualized in terms of a spatial strategy organizedaround ‘axes of integration’ targeted for infrastructure investments. Such axes constitutestrategic growth corridors for commerce among neighboring countries. Because theAmazon basin lies in the heart of northern South America, shared borders there are akey focus for IIRSA infrastructure projects.

We focus on the case of the Inter-Oceanic Highway (IOH), one of the first IIRSAprojects, which advanced during 2005–2010 (Dourojeanni, Barandiaran, & Dourojeanni,2010). The IOH constitutes the ‘Peru–Brazil’ axis of integration under IIRSA, and pavingof the IOH advanced in the southwestern Amazon in Peru to the Brazilian border alongtheir shared frontier with Bolivia (CEPEI, 2002; IIRSA, 2008). As its name suggests, theIOH spans South America, and constitutes a strategic investment intended to integrate the

2 S. Perz et al.

interior of the continent into the global economy by providing export outlets via Atlanticports in southern Brazil as well as Pacific ports in Peru. Paving of the IOH in thesouthwestern Amazon is also key for regional land use and product marketing, sincepaved roads permit year-round transportation in a humid tropical region with a pro-nounced rainy season.

Paving of the IOH in the southwestern Amazon occurred in the tri-national frontierwhere Bolivia, Brazil, and Peru meet (Figure 1). We therefore take up this tri-nationalfrontier, called the ‘MAP’ region, named after the three states that constitute the area:Madre de Dios (Peru), Acre (Brazil), and Pando (Bolivia) (Iniciativa MAP, 2008;Mendoza, Perz, Schmink, & Nepstad, 2007). The MAP frontier constitutes a single regionwith a similar biophysical environment. At the same time, the MAP frontier spansadministrative boundaries of three countries with distinct administrative regimes (includ-ing very different land tenure regimes), populations and economies of very different sizes,and contrasting profiles of economic activity (Perz et al., 2012). Into this mix, there areimportant differences in infrastructure: whereas paving of the IOH in Acre, Brazil, wascompleted in 2002, paving proceeded in Madre de Dios, Peru, under IIRSA, but primaryroads in Pando, Bolivia, remain largely unpaved. The exceptionally high biodiversity ofthe southwestern Amazon (Myers, Mittermeier, Mittermeier, da Fonseca, & Kent, 2000)and the largely intact forest cover there (Southworth et al., 2011) have stimulated con-siderable concern as to the environmental consequences of the IOH (Dourojeanni et al.,2010; Killeen, 2007). These considerations make the IOH in the MAP frontier a usefulstudy case for evaluating the importance of trans-boundary infrastructure for land use.

The differences in paving across the MAP frontier bear important implications for landuse. The earlier and more complete paving in Acre, Brazil, corresponds to faster and moreextensive deforestation there from 2005 to 2010 than in Madre de Dios, Peru, wherepaving was underway, or Pando, Bolivia, where paving was largely planned but not yet

Figure 1. Rural communities visited and households interviewed in Madre de Dios/Peru, Acre/Brazil, and Pando/Bolivia, 2008–2009.

Journal of Land Use Science 3

implemented (Perz, Qiu, et al., 2013). Those findings, however, beg questions about theparticularities of land use, especially along gradients of accessibility, which are modifiedby paving itself. Highway paving not only improves access connectivity by reducingtravel times over a given distance; it also often reflects or catalyzes broader public and/orprivate investments, which, in turn, may expand regional product markets. Together, thesemechanisms may stimulate land use expansion farther from markets as well as intensifica-tion nearer to markets.

Trans-boundary infrastructure presents certain analytical opportunities. The GlobalLand Project’s (2005) science plan devotes attention to questions of globalization andlocal land use as well as comparative case studies on multiple scales. In the context oftrans-boundary infrastructure initiatives, this strategy needs to prioritize cross-border,regional-scale analyses of land use in shared national frontiers (Perz, Qiu, et al., 2013).Study of a tri-national frontier being integrated permits cross-border comparisons of landuse within the same regional ambit. For one thing, this permits comparisons of land usealong gradients of access connectivity across the frontier, particularly where there aredifferences in paving status. For another, to the extent that areas near national frontiers canbe singled out, we can look for differences in land use at frontiers as opposed to locationsfarther from borders. Both constitute evidence of impacts of the trans-boundary infra-structure on land use.

The study of trans-boundary infrastructure’s effects on land use across countries in thesame region also presents important challenges. Differing administrative regimes amongcountries often imply very different land tenure rules across national borders in frontierregions. This complicates an evaluation of the effects of infrastructure, since tenuredefines use rules and may thus yield very different land use even among lands in thesame place along the same highway. That said, such challenges also constitute valuableanalytical opportunities insofar as they permit comparisons across an array of land tenuretypes as to their efficacy in regulating land use. Hence, while we focus on the ramifica-tions of highway paving and accessibility for land use, we situate that focus in a broadertheoretical framework by considering land tenure and other explanatory factors (cf.Lambin & Geist, 2006; Wood & Porro, 2002). We can thus address questions of whetherintegration via trans-boundary highways yields similar effects of access connectivity ondifferent land uses across national borders in the same frontier region, while accountingfor the effects of other factors.

2. Methods and data

For our analysis, we draw on a tri-national survey of rural households across the MAPfrontier of the southwestern Amazon. Faculty and students at the University of Florida(UF) coordinated this effort via partnerships with counterparts at the AmazonianUniversity of Pando (UAP) for fieldwork in Bolivia, the Federal University of Acre(UFAC) for fieldwork in Brazil, and the National Amazonian University of Madre deDios (UNAMAD) for fieldwork in Peru.

The first phase of our socioeconomic data collection involved a survey of roughly 300leaders from roughly 100 communities across the MAP frontier (Perz et al., 2012). Wesampled leaders from communities based on their location along the IOH. Specificdefinitions of rural communities vary across MAP, because land tenure regimes varyamong countries. In Pando, Bolivia, communities refer to communal land tenure units(predios comunales) consisting of nucleated settlements, where member families use thesurrounding lands (Cronkleton & Pacheco, 2010; Stoian & Henkemanns, 2000). In Madre

4 S. Perz et al.

de Dios, Peru, communities are constituted by local associations consisting of memberfamilies with private individual land parcels (Chavez, 2009; IIAP, 2002). In Acre, Brazil,communities vary in definition, ranging from agricultural settlement projects to agro-extractive projects, agroforestry poles, extractive reserves, and others (Governo do Acre,2006). Together, these tenure types encompass considerable diversity due to differences inaccess to land and rules of use. Land per family ranges from roughly 5 hectares (ha) inagroforestry poles in Acre to 500+ ha in extractive reserves in Acre, forest concessions inMadre de Dios, and communal lands in Pando. Further, use rules vary among tenuretypes, with greater restrictions on deforestation for agriculture in agro-extractive settle-ments in Acre, forest concessions in Madre de Dios, and communal lands in Pando. Oursample included a wide range of communities in terms of their land tenure, populationsize, paving status, and livelihood profiles (Perz et al., 2012).

For the second phase of the socioeconomic component, we revisited a subset ofcommunities we knew to be representative of the larger community sample in order toconduct household interviews (Perz, Rosero, et al., 2013). We selected roughly one in fourcommunities for revisits, based on their location (and thus their paving status) and tenuretype. Within each community, we pursued random sampling as feasible. In Madre de Diosand Pando, random sampling was viable insofar as lists of member families were avail-able. In Acre, we worked from cadastral maps of individual properties and sampledproperties systematically based on property numbers. In all cases, we worked frompopulation estimates provided by community leaders in the first phase to establishsampling ratios, such that smaller communities had larger ratios to ensure adequaterepresentation. In 2008, teams of faculty and students from UF and UAP visited eightcommunities in Pando and conducted 164 household interviews.1 The same year, facultyand students from UF and UNAMAD visited 12 communities in Madre de Dios andconducted 312 household interviews. We visited communities along the entire stretch ofthe IOH in Madre de Dios, including those in the ‘castaña zone’, where households oftenhad both agricultural plots (chacras) as well as forest concessions for harvesting ofcastaña nuts (a key non-timber forest product, NTFP). In 2009, UF and UFAC facultyand students visited seven distinct tenure areas in Acre and conducted 266 householdinterviews.2 We visited agricultural settlements, agro-extractive settlements, and agrofor-estry poles, each at regular intervals along the IOH in Acre. Figure 1 shows the commu-nities we visited and the locations of the households we interviewed in the MAP frontier.

We developed a questionnaire based on previous instruments applied in the MAPfrontier. We strived to make the questionnaire as comparable as possible across the threesides of the frontier, while ensuring that each version of the instrument would beculturally appropriate in terms of local idiom and institutionally relevant, given distinctland tenure regimes. The questionnaire was translated and/or reviewed by native speakersfrom the respective side of the MAP frontier.

The questionnaire included sections on household location (including GPS coordi-nates and information on roads traveled to reach markets), migration history, family labor,assets owned, social capital, capital inputs for productive activities, land tenure (includingaccess to private, communal, concession and other lands, as well as land rentals), healthand well-being, and future plans. The questionnaire featured items on livelihood activities,notably resource management, including agriculture (annual and perennial crops, live-stock and related products such as milk), and forest-extractive activities (includingNTFPs), which included information on land use.

Journal of Land Use Science 5

2.1. Land use

Land use questions were related to both the lands to which a household had access and tothe products of land use. For a given parcel of land, we elicited information on landallocation in ha among mature forest, cropland (annual and perennial crops), pasture, andsecondary vegetation. We followed these questions up with items on agricultural andextractive products. For crops, we elicited information on the area planted for eachproduct, including which crops were inter-planted, as a check on estimation of cropareas. For livestock and NTFPs, we included items on the lands where those activitiesoccurred, including concessions and rented lands, in order to accurately measure land useon accessible lands.

As a result, we obtained information on land allocation in ha among mature forest,crops, pasture, and secondary vegetation. In the Amazonian context, each land use has adistinct role in household production systems along highway corridors (Serrão & Homma,1993; Walker, 2004). Crops are relatively intensive land uses for both household con-sumption and product marketing; pasture tends to occur farther from cities and is oftenextensive and of low productivity, though that is changing (Faminow, 1998; Walker et al.,2009); secondary vegetation is necessary insofar as household resources are limited forfertilizers and pesticides to maintain productivity (Perz & Walker, 2002); and matureforest usually reflects land tenure rules for conservation or a lack of household assets orincentives to expand production.

Table 1 provides the descriptive statistics for the land use measures for householdssurveyed in Madre de Dios, Acre, and Pando. We present total land area followed bymature forest, annual and perennial crops, pasture, and secondary vegetation. Total landarea was calculated as the sum of the land use categories. Table 1 shows differences acrossthe MAP frontier: households in Pando averaged nearly 340 ha, followed by those inMadre de Dios with 107 ha, and then households in Acre with almost 80 ha. These figureshide considerable variation within each side. There was variation in Madre de Dios andAcre because land tenure categories have very different land areas per household, suchthat a subset of households in Madre de Dios had forest concessions and thus, roughly,additional 500 ha of land, a figure similar to those households in agro-extractive reservesin Acre. In Pando, while communities were the same in terms of de jure tenure, such that

Table 1. Measures of land use, rural households in Madre de Dios, Acre, and Pando, 2008–2009.

Madre de Dios Acre Pando

n 312 266 164Ha total land area 107.711 79.00 339.92Ln Ha total land area 2.92 3.57 4.31Ha mature forest 98.85 42.66 302.70Ln Ha mature forest 2.11 2.56 3.95Ha annual and perennial crops 3.59 2.97 2.44Ln Ha annual and perennial crops 1.05 0.98 0.91Ha pasture 5.68 20.18 12.02Ln Ha pasture 1.02 2.24 0.92Ha secondary vegetation 6.97 4.90 11.13Ln Ha secondary vegetation 1.36 1.18 1.41

Notes: 1Reported values are weighted based on sampling weights from community sizes and sampling ratios.Weighted values are more representative of the households in the study region as samples of the populations inthe communities visited. n denotes sample size.

6 S. Perz et al.

all were officially designed to give each household 500 ha, there was some migration andpopulation growth in those communities (Perz, Cabrera, Araujo Carvalho, CastilloHurtado, & Barnes, 2010), such that newcomers had much more restricted access toland. The consequence of this variation in all three sides is a set of skewed distributionsfor the land variables. For purposes of modeling, we therefore calculated natural logs (ln)of the raw values in order to normalize distributions.

Table 1 also shows that most land in the communities visited remained in forest. InMadre de Dios and Pando, households reported roughly 90% of their lands in matureforest, whereas in Acre, this figure was closer to 50%. Care is needed to avoid over-generalizing from these findings to the entire MAP frontier; however, these figures doreflect deforestation in communities with these tenure rules (Perz, Qiu, et al., 2013).Forest loss is greater in Acre than elsewhere in the MAP frontier, likely due in part toearlier and completed highway paving there.

Areas under annual and perennial crops do not vary substantially across the three sidesof the MAP frontier. Mixes of crops do vary; while rice is important across the frontier,beans are more so in Acre than elsewhere, and there are other differences (Perz, Rosero,et al., 2013). In addition, as with other land use measures, there is substantial variation incrop areas cultivated within sides, as some households were engaged in larger-scalecommercial production of selected crops than most. This leaves open questions aboutaccess connectivity and crop cultivation among households.

By contrast, land areas under pasture differ substantially across the MAP frontier, withconsiderably more pasture in Acre than elsewhere. This reflects the cultural importance ofbeef in Brazil (Hoelle, 2011) as well as a favorable political economy for ranching in thatcountry (Walker et al., 2009). There is also considerable within-side variation in pasture,particularly in Pando, where a few households had considerable pasture areas. Loggedvalues better capture the differences in pasture areas, but leave open questions as to howpastures vary among households with respect to accessibility and other factors.

Secondary vegetation was greater in Pando than Madre de Dios and more prevalentthere than in Acre. To an extent, this likely reflects differences in population densities inthe three sides, which is greater in Acre (Perz et al., 2010), and thus might imply moreintensive land use and less fallowing. This leaves open questions of within-side variation,which may reflect an inverse relationship with access connectivity.

2.2. Factors influencing land use

We pursued a multivariate analysis of these land uses, featuring the role of accessconnectivity as determined by highway paving and distance to markets, while accountingfor the effects of other factors. To organize our analysis, we drew on the householdcapitals and capabilities framework (Bebbington, 1999). The capabilities frameworkhighlights the importance of land, labor, and various types of capital (e.g., cultural,institutional, financial, and social) as determinants of livelihood indicators such as landuse. Household capabilities serve as control variables, so we can evaluate the importanceof access connectivity for land use.

Table 2 presents descriptive statistics for measures of household capabilities andaccess connectivity for the three sides of the MAP frontier. We organized the explanatoryvariables into six groups: cultural capital (social background), land (focusing on landtenure), labor, financial (productive) capital, social capital, and access connectivity.

Cultural capital reflects experience in the MAP frontier. In Bolivia, Brazil, and Peru,the Amazon has a distinct environment and history, and as a result, Amazonian

Journal of Land Use Science 7

communities have distinct identities and knowledge concerning land use. We thereforeconsidered region of birth, place of birth, and years of residence in the community whereinterviewed. We measured region of birth slightly differently in the three sides of theMAP frontier. In Madre de Dios, we distinguished between birth in Madre de Dios and therest of Peru, since Madre de Dios is a lowland department of Peru tied to the rest of thecountry via the IOH, where it climbs up into the Andean highlands via Cusco and Puno.Similarly, in Pando, we differentiated between birth in Pando or the neighboring lowlanddepartment of Beni, together designated as ‘northern Bolivia’, as distinct from the rest ofBolivia (Stoian & Henkemanns, 2000). In Acre, we differentiated between Brazil’s

Table 2. Measures of capitals, capabilities and connectivity, rural households in Madre de Dios,Acre and Pando, 2008–2009.

Madre de Dios Acre Pando

n 312 266 164Cultural capitalRegion of birth (0 = Other region, 1 = Same region) 0.331 0.76 0.92Place of birth (0 = Rural, 1 = Urban) 0.40 0.20 0.34Years of residence 20.40 15.04 14.36Land tenureAgricultural settlement project (0 = No, 1 = Yes) N/A 0.75 N/AAgro-extractive settlement (0 = No, 1 = Yes) N/A 0.17 N/AAgroforestry pole (reference category, Acre) N/A 0.08 N/AProvisional title (0 = No, 1 = Yes) 0.21 0.46 N/ADefinitive title (0 = No, 1 = Yes) 0.46 0.44 N/ACastaña concession (0 = No, 1 = Yes) 0.12 N/A N/ACommunity land (0 = No, 1 = Yes) N/A N/A 0.72Access to other Castaña land (0 = No, 1 = Yes) 0.05 N/A 0.15Ln Ha land rented from others 0.10 0.28 0.29Ln Ha land rented to others 0.40 0.37 0.01LaborNumber of children in household (under age 6) 0.45 0.34 0.92Number of children in household (ages 6–14) 0.98 0.88 1.26Number of adults in household (ages 15–64) 2.77 2.71 2.32Adults squared 9.94 9.19 6.79Number of elderly in household (Ages 65 +) 0.14 0.23 0.19Number of absent family members 1.14 1.47 0.91Ln Days of labor hired N/A 1.78 N/AFinancial/productive capitalFormal credit previous year (0 = No, 1 = Yes) 0.16 0.30 0.05Chainsaw ownership (0 = No, 1 = Yes) 0.40 0.41 0.29Transport/communication goods owned 3.24 3.94 2.41Transport/communication goods other access 0.53 1.12 2.51Social capitalNumber of types of organizational memberships 0.58 1.22 0.79Access connectivityTravel time to nearest market (minutes) 24.39 92.16 59.04Travel time to capital (minutes) 93.12 183.15 176.10Time to nearest market × time to capital 1418.28 5301.24 12,318.39Tambopata west IOH segment 0.39 N/A N/ARoad from Cobija to Extrema N/A N/A 0.09

Notes: 1Reported values are weighted based on sampling weights from community sizes and sampling ratios.Weighted values are more representative of the households in the study region as samples of the populations inthe communities visited. n denotes sample size.

8 S. Perz et al.

‘North’ (Amazon) region and the rest of the country, following Brazil’s state statisticalagency (IBGE, 2008). In each case, origins in the Amazon region may lead to distinctland use, notably greater use of forests, though this is changing (e.g., Ehringhaus, 2005;Hoelle, 2011). We also considered cultural capital in terms of place of birth, whether in arural or an urban area. Rural birthplaces are likely to relate to knowledge of traditionalagricultural and extractive activities, while urban origins may proxy for a market orienta-tion and thus greater agricultural land use. Finally, we account for years of residence onthe argument that more time in the community of residence permits learning about localland use, as well as time to develop a production system. Along roadsides in the Amazon,longer residence durations usually imply a more extensive agricultural production system,including more secondary vegetation. Findings for cultural capital in Table 2 indicate thatmost rural households in Madre de Dios originated in other parts of Peru, but that mosthouseholds in Acre and Pando were from the Amazon. However, households in Madre deDios also had longer residence durations than their counterparts in Acre and Pando. Mosthouseholds in all three sides of the MAP frontier had rural rather than urban origins.

Table 2 also presents findings concerning land tenure among the households surveyed.We first consider access to land with private, individual tenure, as to their specific landuse rules and whether the household held a title to that land. We then account for access toother types of lands including concessions, communal lands, and rented land. With regardto private lands, in Acre, we distinguished between the three de jure land tenure cate-gories: agricultural settlements (with parcels of 50–100 ha and a 20% deforestation limit,intended for small-scale agriculture), agro-extractive settlements (with parcels of 300–500 ha and a 10% deforestation limit, intended for agriculture and forest extractivism),and agoforestry poles (with parcels of 5–10 ha and no deforestation limit, intended forintensive land use). Given these different rules, we should expect large effects on land use,such as more agriculture in the agricultural settlements and more forest in agro-extractivesettlements. In Madre de Dios and Acre, lands have de jure private individual rights andtitling, so we differentiate whether those households had definitive or provisional titles. Inboth cases, titling takes time and an application process. Property rights theory suggeststhat secure title permits long-term investments in land and thus larger production systems(Feder & Feeny, 1991). On that account, households with provisional but especiallydefinitive titles should have larger areas in crops and pasture than untitled households.But the time required to secure titles may also mean that titled households may also havemore secondary growth, a reflection of older production systems. Table 2 also listsvariables related to other types of lands which are important for household land use inone or another part of the MAP frontier. Castaña concessions in Madre de Dios present animportant livelihood component, but are restricted for NTFP harvesting (Rojas, 2012). InPando, communities are defined, to a great extent, by their communal lands, which aresimilarly intended for castaña harvesting. However, those ‘communal’ lands are infor-mally parcelized among households, which permits measurement of communal land towhich a household has de facto access (Cronkleton, Pacheco, Ibargüen, & Albornoz,2009). Finally, in both Madre de Dios and Pando, some households also had access to‘other’ lands for castaña harvesting. These are usually concessions or communal landsheld by other households. Access to a concession, communal land, or ‘other’ land forcastaña harvesting should correspond to greater forest area; whether this also affects otherland uses is an intriguing question (Rojas, 2012). Finally, land tenure also refers toparticipation in land rental markets. Land scarcity in the Amazon is unusual, given therelatively low population densities. However, the need to fallow land or lose productivity,as well as use rules that limit deforestation, can nonetheless make land scarce, and this has

Journal of Land Use Science 9

impelled rental markets. Renting from others expands a household’s land assets and maypermit a larger production system; renting to others has the opposite effect. Rentalsgenerally involve agricultural land use, so renting from should permit expanded cropsand pasture, while renting to others should restrict the same land uses. Table 2 shows thatin Acre, most households resided in agricultural settlements, a reflection of their size andpopulation density. In both Madre de Dios and Acre, roughly 45% of households haddefinitive titles, but titling and title processing were more extensive in Acre than Madre deDios. A minority of households in Madre de Dios had castaña concessions, while mosthouseholds in Pando had access to community land. ‘Other’ access to castaña lands andrentals occurred among minorities of households. The rental market seems most active inAcre, where population densities were highest and restrictions on deforestation applied tomost types of rural lands.

Labor is crucial to livelihood systems in rural areas of the Amazon, where capital isoften scarce. Prior work on land use in the Amazon highlighted the importance ofhousehold life cycles as they influence labor availability (Perz, Walker, & Caldas, 2006;Walker & Homma, 1996). We therefore measured the number of adults (men and womenages 15–64) as well as children (under age 6 and ages 6 to 15) and elderly familymembers (ages 65+) in the household, as well as absent family members and (in Acre)days of labor hired. We differentiated age groups since contributions to land use are likelyto be higher for adults than for children or the elderly, and for older than younger children.For purposes of modeling, we also considered a squared term for adults, as larger familiesmay increasingly allocate labor away from agriculture, yielding a nonlinear effect. Greaterlabor availability should result in larger agricultural systems, particularly land use forcrops, which is more labor intensive than ranching. Larger families are also likely to yieldmore secondary vegetation due to fallowing of crop fields, and less mature forest due tolarger production systems. Similarly, families that hire more labor should have largerproduction systems, evident in larger areas of crops and secondary growth and less matureforest. Average family sizes (adults, children, and elderly) were similar in the three sides,though all family indicators varied among households. Absent family members varied,with more absent members in Acre than elsewhere. This may reflect greater regionalintegration and the presence of larger urban markets.

Financial (productive) capital is also important for land use. We therefore accountedfor access to credit, chainsaw ownership, and durable goods tied to transportation andcommunication. Access to credit refers to whether a household had received formal creditfrom a bank. Credit constitutes liquid capital beyond household assets and may facilitatethe expansion of agricultural activities, whether crop cultivation or pasture for ranching.Chainsaws constitute a form of capital that can increase household labor productivity interms of clearing land for crops or pasture. Finally, we considered ownership of durablegoods tied to transportation and communication, a proxy for socioeconomic status, to alsobe relevant to land use. Transportation goods included boats, trucks, cars, motorcycles,bikes, and animals. Communication goods included radios, televisions, cell phones, landlines, and internet access. We queried whether households themselves possessed suchgoods, and whether they had ‘other’ means of access (via community associations,neighbors, extended family, friends, etc.). We constructed an index of household con-nectivity capacity by summing the transport and communication goods possessed byhouseholds, and an ‘other’ connectivity capacity measure by summing the same goodsfor which households reported other means of access. These indexes controlled forsocioeconomic status as regards regional integration via transportation and communica-tion, in order to better evaluate the effects of integration itself via highway paving and

10 S. Perz et al.

travel time. Table 2 shows that the households in the rural communities surveyed did nothave large amounts of capital assets. However, differences among the three countriesfollow highway paving, with more capital in Acre (paving complete) than Madre de Dios(paving underway) or Pando (paving planned). The one exception is ‘other’ access todurable goods, where households in Pando reported more access, likely a reflection ofresiding in communally tenured communities.

We also controlled for social capital, which refers to relations of trust that beget socialsupport which may offset scarcities of other inputs to productive activities. We focused onsocial capital in the form of membership in different kinds of organizations, ranging fromcommunity associations, religious organizations, producer cooperatives, sports clubs, andothers. This seeks to capture access to different sources of information and support, whichmay substitute for other inputs via, e.g., labor reciprocity, and thereby expand agriculturalland use. Households in Acre exhibited greater diversity in organizational memberships,reflective of the history of social mobilization there (e.g., Ehringhaus, 2005). Socialcapital was lowest in Madre de Dios, where community associations are loosely con-stituted and have incurred conflicts in recent years (Rojas, 2012).

Our focal explanatory concept concerns access connectivity, which, following locationtheory, involves accessibility among locations via infrastructure (e.g., Haggett, Cliff, &Frey, 1977; Isard, 1956; King, 1984; Walker, 2004).3 To evaluate access connectivity, wemeasured travel times, by which we seek to account for both distance to market andhighway paving status. To calculate travel times to markets, we used GPS points forhousehold and market locations along with GIS coverage of road maps, combined withestimates of travel speeds on paved highways (90 km/h), unpaved highways (60 km/h),and unpaved secondary roads (30 km/h). The GPS points and road coverage permittedmeasurement of distances along roads from houses to markets; the estimated travel speedsby types of road permitted calculation of travel times along routes from communities totowns. Travel times thus account for both market distance as well as highway pavingstatus. We anticipate that households with shorter travel times, whether due to proximityor paving or both, will exhibit more extensive cropland and pasture and less mature forest.We calculated travel times to the nearest market town as well as the regional capital. Wealso considered an interaction term for travel times to nearest market by times to theregional capital, on the expectation that households closer to both may exhibit moreextensive agricultural land use than those the same distance from the nearest market butmuch farther from the capital. Finally, we also differentiated among different highwaysegments that fall in the same ranges of travel times from selected regional capitals. TheIOH crosses much of Madre de Dios, with the capital of Puerto Maldonado situated in themiddle. We therefore distinguished the ‘Tambopata west’ segment of the IOH (from30 km west of Puerto Maldonado to Mazuko; see Figure 1) from the ‘TambopataNorth’ segment (from 30 km north of Puerto Maldonado to near San Lorenzo), as thetwo are equally distant from Puerto Maldonado (Perz et al., 2012), to see if this accountedfor differences in land use. Similarly, in Pando, there are two roads along which wesampled that include segments of equal distances from the capital city of Cobija. Wetherefore distinguished between the ‘Extrema’ road and the main road to Sena (seeFigure 1). Table 2 shows substantial differences in access connectivity across the MAPfrontier. With regard to time to the nearest market, the values were larger in Acre despitecompleted paving of the IOH, because many communities were farther from the IOHalong unpaved secondary roads. With regard to travel times to regional capitals, valueswere again larger in Acre, not only due to long travel times along secondary roads, butalso because the regional capital, Rio Branco, is at one end of the IOH in Acre and some

Journal of Land Use Science 11

communities were very far from the capital (up to 330 km; see Figure 1). By contrast,communities in Madre de Dios and Pando are more nucleated along highways, andcapitals are either located in the middle of the IOH corridor (Puerto Maldonado inMadre de Dios) or along road segments that are not as long as those in Acre (Pando).

2.3. Model estimation

Prior statistical work on explanatory models of land use has employed various specifica-tions to address particular problems in estimation of parameters. One issue concernsvarying community populations and sampling ratios, which motivates the use of weighteddata and thus weighted estimation of parameters in order to obtain results truly represen-tative of the populations from which we sampled. A second concern is that not allhouseholds reported all four types of land use (forest, crops, pasture, and regrowth),resulting in clustering of observations at zero and thus distributions that appear truncated,which can cause estimation problems. A third issue concerns the nonindependence ofdecisions to allocate land among various land uses, which makes independent estimationof parameters for different land uses a questionable proposition. Models are available toaddress each of these problems.

We therefore pursued a multipronged estimation strategy to evaluate the importance ofeach of these issues. To account for varying sampling ratios, we weighted our data and ranweighted least squares (WLS) models; to account for truncated distributions at zero, weused Tobit estimation; and to recognize the correlations among land uses, we usedseemingly unrelated regression (SURE). We conducted all estimation in Stata version12.1 (StataCorp, 2011). Because we estimated models for each of the four land uses, ineach of three countries in the MAP frontier, using each of three types of estimation, theoutput was extensive. We therefore feature the SURE findings by presenting the coeffi-cients with indications of their statistical significance, along with indications of statisticalsignificance for the WLS and Tobit estimations.

3. Findings from multivariate models

Table 3 presents the findings for models of land use in Madre de Dios; Table 4 repeats theexercise for Acre; and Table 5 does the same for Pando.4 In each table, we presentfindings by type of land use beginning with forest, then crops, pasture, and regrowth.Because most land use measures had non-normal distributions, for purposes of modeling,we use the log transformed measures of land use.

3.1. Madre de Dios, Peru

Findings for land use in Madre de Dios (Table 3) show that different explanatory factorsaccount for the different land uses.5 For mature forest, the models are quite strong and thefindings are generally similar across SURE, WLS, and Tobit specifications. Householdsreported larger forest areas if they (1) had provisional or definitive land titles (as opposedto no title); (2) had a castaña concession; (3) had fewer adults; (4) owned a chainsaw; (5)had longer travel times to the capital of Puerto Maldonado; and (6) were not in theTambopata west subregion. The findings largely confirm expectations. The main excep-tion concerns chainsaw ownership, which one might expect to have a negative effect.Here chainsaw may be serving as a proxy for socioeconomic status. The effect for

12 S. Perz et al.

Table

3.Hou

seho

ldcapacitiesandconn

ectiv

itymod

elsof

land

usein

Madre

deDios,Peru,

2008

.

LnHaForest

LnHaCrops

LnHaPasture

LnHaRegrowth

Independ

entvariables

SURE

WLS,Tob

itSURE

WLS,To

bit

SURE

WLS,Tob

itSURE

WLS,To

bit

(Pseud

o-)R

20.72

0.71

,0.26

10.38

0.42

,0.21

0.44

0.43

,0.21

0.41

0.43

,0.20

F-test

31.54*

*2**

,**

7.72

****

,**

9.83

****

,**

8.74

****

,**

Valid

n30

130

2,30

230

130

4,30

430

130

4,30

430

130

4,30

4Left-censored

observations

(Tob

it)N/A

111

N/A

66N/A

152

N/A

104

Culturalcapital

Regionof

birth(0

=Other,

1=Madre

deDios)

0.07

NS,NS

−0.10

NS,+

−0.45

****

,**

−0.13

NS,NS

Place

ofbirth(0

=Rural,1=Urban)

−0.01

NS,NS

−0.01

**,*

0.02

****

,**

−0.01

NS,NS

Years

ofresidence

−0.01

NS,NS

−0.00

2NS,NS

0.02

****

,**

0.00

NS,NS

Landtenu

reProvision

altitle

(0=No,

1=Yes)

1.37

****

,**

0.96

****

,**

0.62

****

,**

1.29

****

,**

Definitive

title

(0=No,

1=Yes)

1.69

****

,**

0.96

****

,**

1.00

****

,**

1.27

****

,**

Castaña

concession

(0=No,

1=Yes)

4.49

****

,**

0.14

NS,NS

0.32

+*,

+−0.18

NS,NS

Accessto

otherCastaña

land

(0=No,

1=Yes)

−0.26

NS,NS

−0.15

NS,NS

0.09

NS,NS

0.17

NS,NS

LnHarented

from

others

0.11

NS,NS

0.36

****

,**

−0.02

NS,NS

−0.44

*NS,*

LnHarented

toothers

−0.29

NS,NS

0.02

NS,NS

0.18

*,*

0.16

NS,NS

Labor

Num

berof

child

renin

household

(und

erage6)

−0.01

NS,NS

−0.05

NS,NS

−0.13

+,+

−0.20

*+,*

Num

berof

child

renin

household

(ages6–

14)

−0.05

NS,NS

−0.04

NS,NS

−0.04

NS,NS

0.01

NS,NS

Num

berof

adultsin

household

(Ages15

–64)

−0.34

++,+

0.09

NS,NS

0.25

+NS,+

0.11

NS,NS

Adu

ltssquared

0.05

**,

+−0.00

3NS,NS

−0.03

NS,+

−0.01

NS,NS

Num

berof

elderlyin

household

(ages65

+)

0.07

NS,NS

−0.02

NS,NS

0.16

NS,NS

0.30

+NS,*

Num

berof

absent

family

mem

bers

0.06

NS,NS

0.02

NS,NS

0.02

NS,NS

−0.04

NS,NS

Financial/produ

ctivecapital

Formal

creditprevious

year

(0=No,

1=Yes)

0.08

NS,NS

−0.02

NS,NS

0.03

NS,NS

0.05

NS,NS

(Con

tinued)

Journal of Land Use Science 13

Table

3.(Con

tinued).

LnHaForest

LnHaCrops

LnHaPasture

LnHaRegrowth

Independ

entvariables

SURE

WLS,Tob

itSURE

WLS,To

bit

SURE

WLS,Tob

itSURE

WLS,To

bit

Chainsaw

ownership(0

=No,

1=Yes)

0.66

****

,**

0.06

NS,NS

0.16

NS,NS

0.22

+NS,+

Transpo

rt/com

mun

icationgo

odsow

ned

−0.09

NS,+

−0.02

NS,NS

0.03

NS,NS

0.05

NS,NS

Transpo

rt/com

mun

icationgo

odsotheraccess

0.03

NS,NS

0.05

NS,NS

0.13

+NS,+

−0.11

+,NS

Socialcapital

Num

berof

typesof

organizatio

nalmem

berships

0.17

+NS,+

0.05

NS,+

0.16

+NS,*

0.15

+NS,+

Accessconn

ectiv

ityTraveltim

eto

nearestmarket(m

inutes)

0.00

6*,

NS

0.00

2NS,NS

0.00

6+NS,+

0.00

0NS,NS

Traveltim

eto

capital(m

inutes)

0.00

4**

NS,*

−0.00

1NS,NS

0.00

2*+,NS

0.00

0NS,NS

Tim

eto

nearestmarket×tim

eto

capital

0.00

0NS,NS

0.00

0NS,NS

0.00

0++,*

0.00

0NS,NS

Tam

bopata

westSub

region

(0=No,

1=Yes)

−0.61

**NS,*

−0.16

++,+

−0.74

****

,**

−0.25

++,+

Notes:1Pseud

oR2sarecalculated

differently

forSUREandTo

bitregressions;

2+p<0.15

,*p

<0.05,**p<0.01.NSmeans

notsign

ificant,i.e.p>0.15.ndenotessamplesize.

14 S. Perz et al.

Table

4.Hou

seho

ldcapacitiesandconn

ectiv

itymod

elsof

land

usein

Acre,

Brazil,20

09.

LnHaForest

LnHaCrops

LnHaPasture

LnHaRegrowth

Independ

entvariables

SURE

WLS,Tob

itSURE

WLS,To

bit

SURE

WLS,Tob

itSURE

WLS,To

bit

(Pseud

o-)R

20.76

0.71

,0.31

10.15

0.27

,0.09

0.62

0.64

,0.26

0.18

0.27

,0.06

F-test

31.51*

*2**

,**

1.83

***

,**

16.66*

***

,**

2.16

****

,**

Valid

n23

123

1,23

123

123

6,23

623

123

5,23

523

123

1,23

1Left-censored

observations

(Tob

it)N/A

40N/A

26N/A

42N/A

67Culturalcapital

Regionof

birth(0

=Other,1=North

Region)

0.08

NS,NS

−0.15

NS,NS

−0.03

NS,NS

0.29

+*,

+Place

ofbirth(0

=Rural,1=Urban)

0.00

2NS,NS

0.01

+**

,**

0.00

1NS,NS

0.00

1NS,NS

Years

ofresidence

0.02

****

,*

0.01

***

,+

0.01

*NS,*

0.01

+NS,+

Landtenu

reAgriculturalsettlem

ent(0

=No,

1=Yes)

1.93

****

,**

−0.23

+*,

*1.57

****

,**

0.26

NS,NS

Agro-extractiv

esettlem

ent(0

=No,

1=Yes)

3.64

****

,**

0.03

NS,NS

1.53

****

,**

−0.13

NS,NS

Agroforestrypo

le(reference

catego

ry)

Provision

altitle

(0=No,

1=Yes)

0.42

*NS,+

0.12

NS,+

−0.24

NS,NS

0.12

NS,NS

Definitive

title

(0=No,

1=Yes)

0.19

NS,NS

−0.04

NS,NS

−0.00

3NS,NS

0.42

+*,

+LnHarented

from

others

0.04

NS,NS

−0.03

NS,NS

0.19

***,

**0.01

NS,NS

LnHarented

toothers

0.01

NS,NS

0.08

+**

,+

0.23

****

,**

0.05

NS,NS

Labor

Num

berof

child

renin

household(und

erage6)

−0.16

+NS,*

−0.06

NS,NS

0.04

NS,NS

−0.04

NS,NS

Num

berof

child

renin

household(ages6–

14)

−0.06

NS,NS

0.04

NS,NS

0.08

+,+

0.02

NS,NS

Num

berof

adultsin

household(A

ges15

–64)

0.38

+NS,+

0.10

NS,NS

−0.51

**,

**−0.11

NS,NS

Adu

ltssquared

−0.05

+NS,NS

−0.00

2NS,NS

0.08

****

,**

0.03

+,NS

Num

berof

elderlyin

household(ages65

+)

0.13

NS,NS

0.01

NS,NS

0.01

NS,NS

−0.17

NS,NS

Num

berof

absent

family

mem

bers

−0.01

NS,NS

0.01

NS,NS

0.03

NS,NS

−0.02

NS,NS

LnDayshiredLabor

paid

−0.01

NS,NS

0.04

+NS,NS

0.01

NS,NS

0.02

NS,NS

Financial/produ

ctivecapital

Formal

creditprevious

Year(0

=No,

1=Yes)

−0.27

+NS,+

−0.07

NS,NS

0.18

NS,NS

−0.25

++,+

Chainsaw

ownership(0

=No,

1=Yes)

−0.00

4*,

NS

0.00

3NS,NS

−0.00

3+,NS

−0.01

**,NS

Transpo

rt/com

mun

icationgo

odsow

ned

0.04

NS,NS

0.00

1NS,NS

0.24

****

,**

−0.05

NS,NS

Socialcapital

(Con

tinued)

Journal of Land Use Science 15

Table

4.(Con

tinued).

LnHaForest

LnHaCrops

LnHaPasture

LnHaRegrowth

Independ

entvariables

SURE

WLS,Tob

itSURE

WLS,To

bit

SURE

WLS,Tob

itSURE

WLS,To

bit

Num

berof

typesof

organizatio

nalmem

berships

0.09

NS,NS

0.00

1+,NS

−0.04

NS,NS

0.12

++,+

Accessconn

ectiv

ityTraveltim

eto

nearestmarket(m

inutes)

−0.01

3**

**,**

0.00

2*,

NS

−0.01

2**

**,**

−0.00

5**

**,**

Traveltim

eto

capital(m

inutes)

0.00

7**

**,**

−0.00

2**,

+0.00

8**

**,**

0.00

1NS,NS

Tim

eto

nearestmarket×tim

eto

capital

0.00

0**

**,**

0.00

0NS,NS

0.00

0**

**,**

0.00

0++,+

Notes:1Pseud

oR2sarecalculated

differently

forSUREandTo

bitregressions.

2+p<0.15,*p

<0.05

,**p<0.01.NSmeans

notsign

ificant,i.e.p>0.15.ndeno

tessamplesize.

16 S. Perz et al.

Table

5.Hou

seho

ldcapacitiesandconn

ectiv

itymod

elsof

land

usein

Pando

,Bolivia,20

08.

LnHaForest

LnHaCrops

LnHaPasture

LnHaRegrowth

Independ

entvariables

SURE

WLS,To

bit

SURE

WLS,Tob

itSURE

WLS,Tob

itSURE

WLS,To

bit

(Pseud

o-)R

20.68

0.68

,0.28

10.35

0.33

,0.18

0.48

0.47

,0.24

0.37

0.38

,0.17

F-test

14.06*

*2**

,**

3.57

****

,**

6.15

****

,**

3.97

****

,**

Valid

n14

114

1,14

114

114

6,14

614

114

5,14

514

114

6,14

6Left-censored

observations

(Tob

it)N/A

40N/A

38N/A

86N/A

49Culturalcapital

Regionof

birth(0

=Other,1=Madre

deDios)

−0.09

NS,NS

−0.27

NS,+

−0.22

NS,NS

0.10

NS,NS

Place

ofbirth(0

=Rural,1=Urban)

0.01

*,NS

−0.00

2+,NS

−0.00

3+,*

−0.02

+**

,NS

Years

ofresidence

0.02

+,+

−0.00

4NS,NS

0.00

3NS,NS

0.02

**,

*Landtenu

reCom

mun

ityland

(0=No,

1=Yes)

4.57

****

,**

0.52

****

,**

0.58

***

,**

1.18

****

,**

Accessto

otherCastaña

Land(0

=No,

1=Yes)

0.26

NS,NS

0.05

NS,NS

−0.24

NS,NS

0.15

+*,

*LnHarented

from

others

0.31

***

,**

−0.05

NS,NS

−0.00

1NS,NS

−0.53

+NS,+

Labor

Num

berof

child

renin

household(und

erage6)

0.41

****

,**

−0.04

NS,NS

−0.02

NS,NS

0.03

NS,NS

Num

berof

child

renin

household(ages6–

14)

−0.36

***,

**0.01

NS,NS

0.12

+NS,NS

0.02

NS,NS

Num

berof

adultsin

household(ages15–6

4)−0.07

NS,NS

0.33

**,

**−0.01

NS,NS

0.18

NS,NS

Adu

ltssquared

0.04

NS,NS

−0.03

+*,

*−0.00

1NS,NS

−0.01

NS,NS

Num

berof

elderlyin

household(ages65

+)

0.42

NS,NS

0.18

NS,NS

0.38

NS,NS

0.13

NS,NS

Num

berof

absent

family

mem

bers

−0.06

NS,NS

0.06

+NS,+

0.17

**,

*0.14

*+,+

Financial/produ

ctivecapital

Formal

creditprevious

year

(0=No,

1=Yes)

−1.36

*+,NS

0.13

NS,NS

0.12

NS,NS

0.24

NS,NS

Chainsaw

ownership(0

=No,

1=Yes)

−0.00

2NS,NS

−0.01

***

,**

−0.00

4NS,NS

−0.00

1NS,NS

Transpo

rt/com

mun

icationgo

odsow

ned

0.41

****

,**

−0.01

NS,NS

0.25

***,

*0.02

NS,NS

(Con

tinued)

Journal of Land Use Science 17

Table

5.(Con

tinued).

LnHaForest

LnHaCrops

LnHaPasture

LnHaRegrowth

Independ

entvariables

SURE

WLS,To

bit

SURE

WLS,Tob

itSURE

WLS,Tob

itSURE

WLS,To

bit

Transpo

rt/com

mun

icationgo

odsotheraccess

−0.02

NS,NS

0.02

NS,NS

0.00

1NS,NS

0.01

NS,NS

Socialcapital

Num

berof

typesof

organizatio

nalmem

berships

0.24

NS,+

−0.04

NS,NS

−0.38

****

,*

0.28

*+,NS

Accessconn

ectiv

ityTraveltim

eto

nearestmarket(m

inutes)

0.01

7+*,

**0.00

1NS,NS

−0.00

5NS,NS

0.00

1NS,NS

Traveltim

eto

capital(m

inutes)

−0.00

4NS,NS

−0.00

4*+,+

−0.01

7**

**,**

−0.00

3NS,NS

Tim

eto

nearestmarket*tim

eto

capital

−0.00

0NS,NS

0.00

0NS,NS

0.00

0**

**,*

0.00

0NS,NS

Roadto

Extrema(0

=No,

1=Yes)

−1.05

NS,NS

−0.29

NS,NS

−0.80

NS,NS

0.10

NS,NS

Notes:1Pseud

oR2’ sarecalculated

differently

forSUREandTo

bitregressions.

2+p<0.15,*p<0.05

,**

p<0.01

.NSmeans

notsign

ificant,i.e.p>0.15.ndeno

tessamplesize.

18 S. Perz et al.

Tambopata west reflects the lack of castaña forests there, and thus greater reliance onagriculture for incomes, which results in less forest cover.

The cropland models in Madre de Dios are moderately strong and also broadly similar.Households reported more crops if they (1) had a household head born in a rural area; (2)they had a provisional or definitive title; and (3) they rented land from others. Thesefindings correspond to expectations. More interesting was the nonsignificance of severalother variables, notably labor availability, financial capital and access connectivity. Cropcultivation in Madre de Dios focuses on several staples for home consumption, and manyrural households along the IOH get their income from castaña and informal wage work.These findings suggest that the effects of regional integration via the IOH in Madre deDios, which was receiving paving during fieldwork, did not (yet) involve the formation ofmarkets for crops. Visits to the region since the 2008 fieldwork suggest that this has begunto change, with expanded plantings of tree crops (papaya, bananas, etc.).

Pasture models for households in Madre de Dios were also moderately strong and alsosimilar among specifications. Households reported more pasture if they (1) had a house-hold head born outside of Madre de Dios; (2) had a household head born in an urban area;(3) had resided longer in their community; (4) had a provisional or (especially) a definitivetitle; (5) rented land to others; (6) had longer travel times to Puerto Maldonado, especiallyif they also had longer travel times to local markets; and (7) they resided outsideTambopata west. Livestock management in Madre de Dios emphasizes chickens ratherthan cattle, despite past government policies to encourage ranching (Chavez, 2009). Thecultural variables reflect this to an extent, especially for long-time households who were inMadre de Dios when cattle incentives were on offer. The results for access connectivitysuggest cross-border processes at work. One is the paving of the IOH in Tahuamanu,which is farther from Puerto Maldonado (see Figure 1) but exhibited larger pasture areas.Another is greater interaction in Tahuamanu with Brazilians, since that province is at theBrazilian border, and Brazilians value ranching (Hoelle, 2011). In these respects, regionalintegration and highway paving are affecting land use practices via cross-border culturalexchanges.

The final models in Table 3 present findings for secondary vegetation among ruralhouseholds in Madre de Dios. Models of regrowth are moderately strong and lead tosimilar conclusions across specifications. Households reported more secondary vegetationif they (1) had provisional or definitive titles; (2) they rented land from others; and (3)they had older families (fewer children and more elderly members). The significantfindings correspond to expectations regarding tenure security, land scarcity (the need tofallow drives land rentals), and household domestic life cycles. The findings indicate alack of land use intensification in areas closer to markets, or farther from the regionalcapital, where the IOH had been paved. Per the discussion of the cropland models above,the findings for secondary growth reflect the still incipient formation of a spatial economyfor crops in Madre de Dios.

3.2. Acre, Brazil

Table 4 presents models of the same four land uses for households in Acre.6 Here, theIOH had been completely paved, and the findings are, in many ways, very different fromthose seen for Madre de Dios. The models of mature forest area are also very strong inAcre, and the coefficients are mostly, but not always, the same across specifications.Households reported more forest cover if they (1) had lived longer in their community; (2)lived in an agricultural settlement or (especially) an agro-extractive settlement (as opposed

Journal of Land Use Science 19

to an agroforestry pole); (3) had fewer children (primarily in the Tobit model); (4) didn’town a chainsaw (in the WLS model); and (5) lived farther from the regional capital of RioBranco, especially if they also lived farther from the nearest market. These findings followexpectations, though the access connectivity results require some further explanation. Thenegative coefficient for the direct effect of travel time to nearest market results frominclusion of the interaction term; excluding the interaction renders the coefficient positivein accordance with expectations. The positive interaction also indicates that longer traveltimes to nearest markets, especially in areas with longer travel times to the regionalcapital, correspond to larger forest areas.

The cropland models are relatively weak, especially the SURE specification; hence,there are differences in significant explanatory factors among the specifications. Wetherefore note variables for which at least two of the three models had significant findings.Households reported more cropland if they (1) had a household head born in an urbanarea; (2) had lived longer in their community; (3) lived in an agroforestry pole (as opposedto an agricultural settlement); and (4) lived closer to Rio Branco. By contrast, as in Madrede Dios, labor and financial capital are insignificant. The cropland models in Acre offersome nonintuitive findings that require further context to interpret. Agricultural settle-ments have less cropland than agroforestry poles; this is due to the shift toward cattleranching in agricultural settlements, which we discuss further below. By contrast, agro-forestry poles tend to be located close to towns and have rules designed to encourage cropcultivation for sale, but on small properties. The findings for these tenure categories andaccess connectivity to Rio Branco indicate the formation of a spatial economy of land usefor crops in Acre, a reflection of highway paving as well as regional markets.

The pasture model for Acre is strong and the findings are generally similar acrossspecifications. Households in Acre reported more pasture if they (1) had lived longer intheir community; (2) lived in an agricultural settlement or an agro-extractive settlement (asopposed to an agroforestry pole); (3) they rented land to or from others; (4) they had feweradults; (5) they owned more transportation and communication goods; and (6) they livedcloser to the nearest market and farther from Rio Branco. Overall, the findings herecorrespond to expectations. Domestic life cycle theory suggests pasture formation later inthe life cycle, and ranching is not labor-intensive (Perz et al., 2006; Walker & Homma,1996). Households in both agricultural and agro-extractive settlements (among other typesof lands) shifted to cattle in the 1990s and 2000s, when prices for many crops declined orfluctuated, while beef prices remained strong and stable (Faminow, 1998; Walker et al.,2009). Of greatest interest for present purposes are the clear indications of a spatialeconomy in Acre, such that extensive ranching occurs farther from Rio Branco, andprevails around local markets. In this case, removal of the interaction term does notmodify the direct effects of travel times. Paving of the length of the IOH in Acre thuscorresponds to a clear spatial organization of pastures among rural communities.

The secondary vegetation models in Acre parallel those for cropland in being some-what weak and rather variable among specifications. This likely reflects the importantrelationship of crop cultivation and fallowing in rural lands of many parts of the Amazon.Households in Acre reported more secondary growth if they (1) were born in Brazil’sNorth (Amazon) region (in the WLS model); (2) had a definitive title (in the WLS model);(3) did not own a chainsaw (in the WLS model); and (4) had shorter travel times to thenearest market. The last finding is the most consistent across the specifications, andindicates more regrowth closer to local towns. The interaction with travel time to RioBranco was weakly positive, suggestive of more regrowth near towns farther from thecapital. While the interaction effect is expected, the direct effect of travel time to local

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towns requires more explanation. One key finding from our community interviews inAcre was that community leaders complained about the lack of access to sufficient creditfor fertilizers or machinery to sustain production on older cropland. This likely explainsthe finding of more regrowth near towns. The negative effect for chainsaw, if viewed as aproxy for wealth, reinforces this interpretation; while a spatial economy was operating,poorer households were often unable to sustain crop production or to shift old cropland topasture, resulting in secondary vegetation.

3.3. Pando, Bolivia

Table 5 presents the findings for models of land use in Pando, which largely lackedpaving at the time of fieldwork. Pando is nonetheless interesting since the IOH runs alongPando’s borders, notably meeting the border at Pando’s capital, Cobija.7 The mature forestmodel is very strong and the findings are often, but not always, consonant acrossspecifications. We report important findings as those significant in two or more of thethree specifications. Households reported more forested land if they (1) had access tocommunity land; (2) rented land from others; (3) had more young children and fewerolder children; (4) owned more transportation and communication goods; and (5) hadlonger travel times to the nearest town. The findings largely correspond to expectations, inthat families originally in the communities obtained their 500+ ha of communal land andare often the more established (relatively wealthier) households. The findings for childrenwere a surprise, and may reflect older multigenerational households with grandchildren ofthe family members who established their claim in the community.

The cropland models are moderately strong and generally consistent across specifica-tions. Households had more cropland if they (1) had access to communal land; (2) hadmore adults; (3) did not have a chainsaw; and (4) had shorter travel times to Cobija. Thesefindings correspond to expectations, particularly those for labor availability. The inverserelationship of travel time and crop cultivation area reflects the emphasis on forestextractivism in Pando, such that most cleared land is close to the capital. While, in part,this reflects a long-standing extractive economy in Pando, it also reflects the more recentinfluence of the IOH and Cobija’s growing urban population (Rojas, Zampieri, & Aguilar,2005).

The pasture model is strong and the findings generally consonant among the specifi-cations. Households in Pando reported more pasture if they (1) had access to communalland; (2) had more absent family members; (3) owned more transportation and commu-nication goods; (4) belonged to fewer organizations; and (5) had shorter travel times toCobija, especially if they had longer travel times to the nearest market. This modelindicates that wealthier households and those with better access to the regional capitalhad more pasture. Most of Pando remains forested and most households continue to relyon castaña extractivism and informal jobs for income (Cronkleton & Pacheco, 2010).Households with land and capital assets can afford to diversify into ranching, and thisoccurs close to Cobija, where there is more deforestation (Perz, Qiu, et al., 2013).Ranching near Cobija (and other locations along the Bolivian frontier with Brazil inPando) also likely reflects the influence of Brazilians seeking to expand ranching intoother lands across borders. As was seen in Madre de Dios, the findings for ranching inPando suggest cultural exchanges with Brazilians in Acre encouraging greater pastureformation.

The final model concerns secondary growth among households in Pando. The modelsare moderately strong, but the significant variables differ among specifications.

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Households had more secondary growth (according to significant findings in at least twospecifications) if they (1) had lived longer in their community; (2) had access to commu-nal land; and (3) had access to ‘other’ lands for castaña harvesting. The SURE modelsalso suggest that households also had more regrowth if they (1) had more absent familymembers; and (2) participated in more types of organizations. The findings, overall,confirm expectations, in that longer durations of residence and greater access to landspermit more fallowing. The access connectivity measures were insignificant, though theyparallel the findings for cropland.

4. Discussion

In Madre de Dios, Acre, and Pando, there are significant findings for the effect of accessconnectivity on land use among rural households. Notably, travel time matters more formature forest, which is cleared more extensively in more accessible lands, and for pasture,which is associated with cattle ranching, an eminently market-oriented activity that issensitive to accessibility. The findings are thus consistent with prior work on the impor-tance of access connectivity for land use, in the Amazon and elsewhere.

That said, the fact that the findings come from communities arrayed across a tri-national frontier with differences in the extent of paving of a trans-boundary highway, andthe fact of results showing important national differences at the regional level, adds toprior work. Here it is key to recall that paving of the IOH was complete in Acre at the timeof fieldwork, underway in Madre de Dios, and incipient, but planned, in Pando. Thispermits useful comparisons of land use in different circumstances with regard to pavingand thus access connectivity in the same region. Controlling for cultural capital, landtenure, labor, financial capital, and social capital, access connectivity exhibits differenteffects on different land uses across the MAP frontier.

For one thing, accessibility was more important for land use in Acre, where pavingwas complete, than in Madre de Dios, where paving was underway, or Pando, wherepaving was incipient. Coefficients for travel times with forest and pasture in Acre wereroughly twice those estimated for the same land uses in Madre de Dios. We suggest thatthis reflects the more advanced formation of a spatial economy in Acre after paving,where transportation was relatively fast and reliable year-round, than in Madre de Diosduring paving, where construction itself and the ongoing reorganization of space likelyobscured somewhat the effects of paving and distance. We note again that paving inMadre de Dios was complete by the time of fieldwork only in areas far from the regionalcapital of Madre de Dios, which led to unexpected coefficients as land use expanded farfrom the capital. Interestingly, coefficients in Pando were also large, especially for traveltime to the capital and pasture. We interpret this result as reflective of the influence of theIOH where it touches Pando at the capital of Cobija.

In addition, the findings for access connectivity suggest some cross-border processesinvolving cultural influences reflected in land use practices, particularly for pastureformation. In both Madre de Dios and Pando, pasture is more prevalent near borderswith Acre, where pastures are much more prevalent overall. While there is reason tobelieve that prior policies encouraging cattle are also likely responsible (cf. Chavez (2009)on Tahuamanu), our models controlled for those effects via duration of residence as aproxy for adoption of earlier policy incentives. More recent cultural influences are evidentin roadside landscapes near the Brazilian border in Madre de Dios and Pando, where thetraditional Brazilian style of indicating a ranch entrance, via a sign hung on a log laidacross log posts, can be found. Paving of the IOH facilitates such exchanges, whether via

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adoption of ranching practices by Peruvians and Bolivians or Brazilians informally buyinglands across the border.

Overall, the findings for access connectivity and land use indicate that the MAPfrontier is experiencing reorganization of space as a result of paving the IOH. Giventhat conclusion, three caveats are in order. One is that the IOH, as invoked in this paper,represents one part of a broader process of regional development in the MAP frontier,which is being driven by larger political economic forces. We have focused on the IOHsince highway paving makes integration initiatives concretely manifest in a physicallandscape where land use occurs. But we recognize that funding of the IOH is part of alarger effort, impelled in large measure by Brazil via IIRSA, to stimulate integration withPeru and Brazil’s other neighbors (CEPEI, 2002; IIRSA, 2008). Hence the spatialeconomy of pasture in the MAP frontier in part reflects accessibility facilitated by theIOH, but it also reflects Brazil’s larger political economy impelling continued growth inits ranching sector (Walker et al., 2009).

Second, while there are clear indications of a spatial economy for forest loss andpasture expansion, the effects of the IOH via travel time on cropland and secondaryvegetation were much less evident. This suggests that the reorganization of space remainsselective of land uses, and was limited at the time of our fieldwork. Since data collection,we have noted some important shifts in this regard. One is the expansion of roadside treecrop plantations in Madre de Dios; another is the expansion of mechanized agriculture,particularly for sugar cane, in several places along the IOH in Acre. Both are suggestive ofintensified land use along the roadsides due to improved accessibility. Hence, ourrelatively weak findings for crops and regrowth, including those in Acre, may reflectthe relatively early stages of the spatial reorganization of land use in the MAP frontier. Inother words, a reanalysis with more recent or future data might reveal strongercoefficients.

Finally, our findings for land use highlighted agricultural land use (via cropland andpasture) and, to some extent, forest extractivism (generally practiced in mature forests).Shortly after our fieldwork, other nonagricultural land uses began to expand in the MAPfrontier, also a reflection of improved accessibility proffered by paving of the IOH. Innear-market landscapes in both Acre and Madre de Dios, fish farming has emerged. Thisrequires heavy up-front investments for the formation of ponds and reliable cold transport.Fish farming thus constitutes a key form of land use intensification, likely to generatesubstantial income per hectare, especially compared to extensive ranching (Chavez,2009). Fish farming largely occurs on already deforested lands, and represents a shiftfrom crops, pasture, or secondary growth. By contrast, the other key nonagricultural landuse that has recently expanded in the MAP frontier is gold mining in Madre de Dios,which occurs largely on forested lands. High gold prices have drawn many migrants downfrom the Andean highlands, and they follow the IOH to gold fields established in creeksand rivers. Estimates of deforestation due to mining suggest rapid forest clearing(Swenson, Carter, Domec, & Delgado, 2011).

The case of the IOH in the MAP frontier not only confirms older work that pavinghighways brings multifaceted change dynamics to affected regions; it also highlights howtrans-boundary infrastructure upgrades not only influence land use within each side of afrontier, but also catalyze cross-border processes that drive landscape dynamics. Animportant step beyond the analysis presented would be to more closely link the productsassociated with specific land uses to their respective commodity chains in order to relatetrans-boundary infrastructure to market teleconnections. Investment in the IOH followsthe logic of regional integration, which, in turn, should become evident in commodity

Journal of Land Use Science 23

flows – along the new infrastructure – between production and processing or consumptionsites. Traditional products like castaña have established transport routes, such as down theMadre de Dios River in Pando to the city of Riberalta and beyond (Cronkleton &Pacheco, 2010; Stoian & Henkemanns, 2000). It will be interesting to see if newinfrastructure, such as the IOH, modifies those routes. The same applies to more recentlyestablished commodities such as beef, not only in Brazil but also elsewhere across theMAP frontier. Insofar as trans-boundary infrastructure permits longer-distance teleconnec-tions to larger, more numerous, and distant markets, it is likely to expand land use infrontier regions beyond that seen historically. But the details of the products, teleconnec-tions, and land use changes require careful monitoring to be able to establish the spatialrelationships and thus more clearly illustrate the impacts of trans-boundary infrastructureupgrades on land use.

AcknowledgmentsThe coauthors are coordinators of the socioeconomic component of the NSF HSD grant and theythank the students and other collaborators who contributed to the community survey fieldwork anddata entry in Madre de Dios, Peru (Mara Cardenas, Rosmery Chacacanta, Eder Chulla, WendyCueva Cueto, Yeni Franco Solano, Mercedes Perales, Aldo Ramirez, and Benjamin Zevallos), Acre,Brazil (Cleilton Amaral, Arthur Lima Carvalho, João Paulo Ferraz, Raionery Gonçalves, GenildoMacedo, Robson Belo Nogueira, and Karla Rocha), and Pando, Bolivia (Rolando Arces, MirnaAyaviri, Kelly Biedenweg, Nilda Cortes, David Elliott, Marcos Flores, and Eddy Ovalez). Forlogistical support, we thank Veronica Passos, Gabriel Alarcón, and Daniel Rojas.

FundingFinancial support for this research came from the National Science Foundation, Human and SocialDynamics Program, [grant number 0527511]; the Coupled Natural and Human Systems Program,[grant number 1114924], and from the US Agency for International Development, Latin Americaand Caribbean program in Environment, Cooperative Agreements RLA-A-00-06-00071-00 and512-A-00-08-00003-00.

Notes1. Our original intention was to visit two additional communities in Pando, but political violence

in September 2008 rendered this unviable.2. Data collection in Acre was delayed by a year due to permitting requirements.3. We use the term ‘access connectivity’ to differentiate accessibility from ‘interaction connectiv-

ity’ (cf. Perz et al., 2012). Whereas access connectivity depends on proximity and travel speeds,and thus provides an indicator of potential connectivity, interaction connectivity refers to actualinteractions among two or more locations. Access connectivity should thus theoretically predictinteraction connectivity, but this is subject to empirical testing.

4. We have used very similar explanatory variables for all three sets of models, but due to e.g.,tenure differences, there are some variations among the three sides of the MAP frontier. Insubsequent footnotes, we outline differences for each side of the MAP frontier.

5. In Madre de Dios, we added the castaña concession variable, since some households have forestconcessions, which greatly modify access to land and thus land use. We also added the‘Tambopata west subregion’ variable to account for communities along highway segmentsrunning in different directions but at similar distances from the regional capital of PuertoMaldonado. We note that some households in Madre de Dios live in rural communities butdo not have land. We kept those cases and evaluated the implications via the Tobit specification.

6. In Acre, we excluded the index of ‘other’ access to durable goods wealth related to transporta-tion and communication, as there were many missing cases and the variable was neversignificant. We also exclude the variable for ‘other’ access to lands for castaña harvesting

24 S. Perz et al.

since no households reported such access, making this a constant rather than a variable. Weadded the day labor variable, since this is a common practice in Acre.

7. In Pando, we excluded the variables for definitive and provisional titles, for two reasons. First,the communities in Pando were titled communally rather than at the level of individual house-hold properties, and second, all communities visited in Pando had been titled, making titlingstatus a constant rather than a variable. We added the variable for access to community land,which involves large land areas and makes a big difference to land use among households inPando. We excluded the ‘rentals to’ variable since this is not a legal practice in Pando and veryfew households reported renting their community land, resulting in likely underreporting andthus bias. We included the ‘Extrema road’ variable since we visited communities at similardistances along two roads going in different directions from the regional capital of Cobija.Finally, we excluded one case of an outlier household that controlled a forest estate of 26,000 haoutside the community and which had considerable missing data.

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