From Sustainable Development to “Productive Conservation:” Forest Conservation Options and Agricultural Income and Assets in the Brazilian Amazon

From Sustainable Development to “ProductiveConservation:” Forest Conservation Options andAgricultural Income and Assets in the Brazilian Amazon*

Stephen G. PerzDepartment of Sociology and Center for Latin American StudiesUniversity of Florida

Abstract This paper assesses arguments for the “productive conserva-tion” thesis, which contends that family farming in tropical regions suchas the Amazon can generate significant income and assets while maintain-ing forest cover. Data come from a 1996 survey of households in Uruará,a community along the Transamazon highway in Brazil. A descriptiveanalysis shows that households engaged in intensification, diversification,and forestry, proposed as important methods for forest conservation, andthat they earned U.S.$1,600 from agricultural production and ownedproperties worth U.S.$16,500. A multivariate analysis shows that compo-nents of small farm systems associated with forest conservation made sig-nificant contributions to agricultural incomes and property values. I sug-gest that productive conservation requires community action which fostersties with outside institutions while educating producers.

In recent years, discussions of human-environment interactionshave centered on the vague concept of “sustainable development”(WCED 1987). Few areas of the world face more daunting chal-lenges to operationalizing “sustainability” than the Brazilian Ama-zon. This region is important for both the negative environmentaleffects of deforestation and the struggles of local communities try-ing to secure livelihoods. As deforestation has continued in theAmazon during the 1990s, scholars have sought to articulate a con-cept that more concretely captures both the social and the envi-ronmental dimensions of “sustainability.” In this context, Hall(1997) suggested the concept of “productive conservation,” whichrefers to the multifaceted task of generating incomes while sustain-ing the forest resource base.

In this paper I present an empirical analysis of “productive con-servation” as a more sharply focused, more concrete term than“sustainable development.” I examine whether components ofAmazon farming systems that are believed to encourage forest con-

Rural Sociology 66(1), 2001, pp. 93–112Copyright © 2001 by the Rural Sociological Society

*This research was supported by Grant SBR-9511965 from the National ScienceFoundation. I thank Charles Wood and Robert Walker for support in the UnitedStates, Adilson Serrão and Alfredo Homma for support in Brazil, and team membersAndré Caetano, Roberto Porro, Fabiano Toni, Célio Palheta, Rui Carvalho, and LuizGuilherme Teixeira, as well as the people of Uruará, for discussions of the issuespursued here. I also thank three anonymous RS reviewers for their comments,though I take responsibility for remaining errors. Address correspondence to the au-thor at: Department of Sociology, 3219 Turlington Hall, University of Florida, POBox 117330, Gainesville, FL 32611-7330; e-mail: [email protected].

servation also have positive effects on agricultural incomes andproperty values. The analysis draws on data from a survey of farmhouseholds in the state of Pará, Brazil. Descriptive findings showthat these households employed intensification, diversification, andforestry in their farming systems, which are often proposed as op-tions for forest conservation, and that they generated notable in-comes from agriculture and owned rural properties of substantialvalue. Results from multivariate analysis indicate that intensificationand diversification contribute significantly to higher incomes andproperty values; this point suggests that conservation is not incon-sistent with agricultural production.

Background: Arguments for Productive ConservationRevisionism Regarding Small Farm ProductivityAlthough the state has invested heavily in road construction andland settlement in the Amazon since the 1960s, a large literaturechronicles the failures of colonization efforts to generate significantagricultural production (e.g., Moran 1985).1 Three cases illustratethis point. First, the Belém-Brasília highway, a north-south roadconstructed during the 1960s to link the Amazon’s largest city withthe national capital, attracted 108,000 migrant farmers to settle theroadsides, but many abandoned their claims because few found ac-cess to markets (e.g., Martine 1978). Second, along the Transama-zon highway, the state directed colonization in an east-west corridorfrom the impoverished Brazilian northeast westward into the Ama-zon, but because support was insufficient, only 6,000 of a projected100,000 families were settled, and crop production was limited(e.g., Moran 1981). Finally, in the case of BR-364, the highway fromsouthern Brazil into the western Amazon, road improvementsspurred in-migration of more than a half-million persons. State at-tempts at semidirected settlement, however, created a situationcharacterized as much by land speculation as by agricultural pro-duction (e.g., Martine 1980).

A close reading of the literature on Amazon colonization revealsthat the reasons for small farm failures in the Amazon stem largelyfrom three external constraints rather than from farmers them-selves. First, because legal institutions in frontier areas are weak,property titles for claims are provided very slowly; thus small farm-ers are subject to competition for land by more powerful interests(Schmink and Wood 1992). Insecurity of tenure is one reason why

94 Rural Sociology, Vol. 66, No. 1, March 2001

1In this paper I focus on farming systems among smallholders in roadside frontiersettlements, and do not consider large ranches (e.g., Faminow 1998) nor indigenousgroups and caboclos (e.g., Nugent 1993). Colonization, however, carries many im-plications for indigenous rights (e.g., Ramos 1998) and highlights some importantcontrasts with resource use by indigenous peoples (e.g., Redford and Padoch 1992).

farmers do not invest in long-term production, and this situationleads to declining yields (Alston, Libecap, and Mueller 1999). Sec-ond, agricultural credit and technical assistance are limited forsmall farmers. Farm households in the Amazon desperately needstart-up capital and extension support, but Brazilian agriculturalpolicies have focused subsidies on larger operations (e.g., Martine1993). And third, family farms often find market access limited inthe Amazon. During the early years of colonization, urban marketswere few, and returns to farmers generally are minimal because ofthe low prices paid by middlemen in commodity chains.

A revisionist literature is now emerging, and it includes four ar-guments that small farmers in the Amazon can generate significantincomes and assets from agricultural production. First, the “life cy-cle” argument, drawing on farm labor economics, contends thatfamilies arrive on the frontier as young couples with small children,and face labor scarcity (Walker and Homma 1996). As a result,farm households initially focus on minimizing risk rather than max-imizing returns; this strategy is not commercially “rational” but is“reasonable” nonetheless (Pichón 1996a). When parents are olderand children are grown, households have more labor to invest andcan generate larger returns.

Second, according to the “learning” argument, derived from the-ories of adaptation in ecological anthropology (e.g., Moran 1990),income generation and asset accumulation occur, but not until sev-eral years after initial settlement. Successful agriculture amongcolonists requires an experimentation period that may last five to10 years, during which farmers develop more effective strategies(Moran 1989).

Third, the “differential wealth” argument, with roots in neoclas-sical microeconomics, contends that families who bring assets tothe frontier can invest in agricultural capital or hire labor, and canrealize returns sooner and to a greater degree. Differences in ini-tial wealth help to explain why some farm families succeed in gen-erating incomes while their neighbors fail (Murphy, Bilsborrow,and Pichón 1997; Ozório de Almeida 1992).

Fourth, the “organization” argument, derived from theory on so-cial movements, contends that social mobilization of neighboringproducers is needed to secure returns from frontier agriculture(Hall 1997:ch. 5). Whether by grassroots organization or by collab-oration with outside institutions such as extension agencies, com-munities may secure stronger market footholds for produce(Walker and Wood 1998).

Revisionism Regarding Frontier Agriculture and Forest ConservationFarming has caused rapid deforestation, however, where ruralhouseholds in Amazon frontiers had adequate labor, time to gain

From Sustainable Development to “Productive Conservation” — Perz 95

expertise in local agriculture, initial wealth, or mobilized neighbor-hoods (Ozório de Almeida and Campari 1995). The Belém-Brasília,the Transamazon, and especially the BR-364 corridor in Rondôniashow substantial deforestation in satellite images (e.g., Skole andTucker 1993). As a result, many observers believe that agricultureis environmentally unsustainable in the Amazon (e.g., Fearnside1990). The reason is that biophysical constraints combined with so-cial and economic constraints leave many rural households withfew options in their farming systems. Given poor soils and limitedcapital, the need to procure a subsistence, and the desire to marketthe surplus, farm households often are prompted to practice ex-tensive, low-input agriculture. Rather than using fertilizers to sus-tain production on one plot, farmers take advantage of the abun-dance of land that characterizes frontiers, and clear more forest fornew plots. This is particularly true when farmers shift land into pas-ture for production of high-value commodities such as beef cattle(e.g., Ozório de Almeida and Campari 1995).

Yet like the conventional belief that colonization failed to gener-ate agricultural production, the menace of pasture expansion in theAmazon has been questioned in recent revisionist literature(Faminow 1998). Three arguments suggest opportunities to reducefurther deforestation in small farming systems of the Amazon.

First, the “intensification” argument from agronomic research as-serts that increased used of inputs can sustain production on agiven plot of land while avoiding degradation (Almeida et al. 1996).Recent work on small-scale cattle ranches suggests that intensifica-tion affords high productivity per hectare, which allows for furtherinvestment in land productivity (Almeida et al. 1996:ch. 3; Tour-rand et al. 1996).

Second, the “diversification” argument from agricultural systemsresearch contends that farm systems with multiple plots permit ro-tation of crops that return different nutrients to soils, and/or fal-lowing to allow soils time to recover (Pichón 1996b; Smith et al.1995).

And third, “forestry” arguments hold that proceeds from lucra-tive but environmentally damaging activities can be invested inplantings of native trees to restore canopy cover and provide in-come from timber when mature (Smith et al. 1995). Conversely,farm households can hold valuable timber in forest, which providesenvironmental services as well as a capital reserve.

Productive Conservation and the Components of Farm SystemsThe revisionist arguments about returns to farm production andoptions for forest conservation are based on a contention that bothproduction and conservation depend on the components of farm-ing systems. With regard to returns from agriculture, the “life cycle”


argument implies that households with larger labor pools can makegreater investments in agriculture and realize greater returns; ac-cording to the “learning” argument, accumulation results from ju-dicious crop selection and cultivation regimes; the “differentiation”argument implies that farmers with wealth are better able to investin lucrative farming options; and the “organization” argument sug-gests that farmers who select crops with prices guaranteed by coop-eratives or favored by extension agents will realize greater incomes.

With regard to forest conservation, the “intensification” argumentfavors shifting land into production of commodities that commandhigher prices and require less land per unit produced; the “diversifi-cation” argument calls for mixed land-use regimes consisting of farmsystem components carefully chosen for their interdependence; and“forestry” arguments imply an important role for reforestation or for-est conservation in sustaining frontier agriculture. These observa-tions call for explication of the components of Amazonian farm sys-tems, in order to clarify the options available to small farmers.

Small-scale agriculture in the Amazon involves a farm system withsome combination of four basic components (Serrão and Homma1993). From an economic standpoint, each component implies alevel of investment, risk, and returns; from an environmental per-spective, each component carries different implications for forestconservation.

The first component, cultivation of annuals (such as rice, beans,corn, and manioc), involves limited land and capital inputs but re-quires substantial labor. Annuals command low prices; thus farmersgrow them primarily for subsistence, making this a risk-minimiza-tion strategy. Repeated cultivation of annuals degrades soils, neces-sitating the application of fertilizers or further deforestation.

Second, perennial crops (including bananas, cocoa, coffee, andblack pepper) also involve low land inputs, but require consider-able capital outlay for seedlings and high labor inputs for their careand for processing of produce. Perennial crops command higherprices; thus the strategy underlying their cultivation is maximiza-tion of returns, but price fluctuations create significant risk.

The third component, pasture formation for cattle breeding, re-quires substantial land and capital inputs, but relatively low laborinputs. Because beef commands high prices, cattle allow for lucra-tive commercial activity and constitute a capital reserve to cover un-foreseen expenses.

The fourth component concerns forest management throughconservation of mature timber or investments in reforestation. Ma-ture timber requires only land and functions as a capital reserve; re-forestation involves heavy capital investment and carries high risksbecause of the long time to maturity, but it affords a means of fu-ture wealth accumulation.


This explication of components reveals an array of opportunitiesand constraints regarding productive conservation. We may askwhether farming activities that bring larger returns are also thosewhich allow for forest conservation. In spite of theoretical argu-ments and a need for connections between agricultural productionand forest conservation, little empirical work on the Amazon hasconsidered such connections. Almeida et al. (1996) provide theonly comparative analysis of both the returns from different farmsystem components and the sustainability of those components, butthey scarcely analyze what determines who adopts a given compo-nent. Conversely, Ozório de Almeida (1992) and Murphy et al.(1997) present models of colonists’ income and assets with manyindicators, but they include no variables that might differentiatefarming systems.

In this paper I analyze agricultural income and assets amongsmall farm households, highlighting the role of farm system com-ponents. I explore whether agricultural incomes and assets aregreatest in households characterized by life cycle maturity, learning,differential wealth, or organization. The primary question of thispaper, however, is whether agricultural returns are greatest inhouseholds with farming systems characterized by intensification,diversification, or forestry, net of the effects of other factors.

An Empirical Example From the Amazon:The Case of Uruará, Pará, Brazil

The municipality of Uruará, in the Brazilian state of Pará, is an ap-propriate case for assessing the importance of farm system compo-nents for productive conservation. One reason for studying Uruaráis that it is located along the Transamazon highway, and has a rela-tively long history of land settlement for a frontier area in the Ama-zon (IDESP 1990). Until 1981, the area experienced rapid in-mi-gration by a “first wave” of colonists seeking land. Around 1985,Uruará experienced an economic boom due to high prices for twokey perennials, cocoa and black pepper. Many farmers had matureperennials by 1985, and were able to market their production andearn substantial incomes. The mid-1980s boom drew more coloniststo Uruará in a “second wave” of land settlement, which increasedthe population to 25,000 by 1988. Pepper and cocoa prices, how-ever, declined thereafter, leading to a recession and to farm fail-ures. During the mid-1990s, the decline in prices was comple-mented by the spread of crop diseases, which further reducedproduction. By 1996, farmers were shifting land use toward pasturefor cattle.

A second reason to study Uruará is that rural households thereexperienced relatively few external disruptions to small farming.Compared with the lack of support along the Belém-Brasília High-


way (Martine 1978), the land speculation in Rondônia (Martine1980), the rural violence in southern Pará (Schmink and Wood1992), and the dominance of large ranches in Mato Grosso (Lisan-sky 1990), the constraints faced by small farmers along the Trans-amazon appear minor. Uruará also contains many organizationsthat support small farmers, including the Catholic Church, state ex-tension agents, and neighborhood organizations.

In June and July 1996, a nine-member team composed of U.S.and Brazilian social and agricultural scientists conducted a surveyof small farm households in Uruará. The team gathered informa-tion about the heads of households, household composition, landuse, production of annual and perennial crops, land values, andother topics.2 The sample includes 261 rural establishments, or 12percent of all establishments in Uruará in 1996. Systematic sam-pling by distance proved unmanageable because houses on manylots were not visible, and sampling by the “nth” house was prob-lematic because proprietors often were absent. Instead the teamsampled on the basis of “first opportunity,” and employed a cadas-tral map from EMBRAPA/CPATU, the regional agricultural re-search agency, to ensure that samples were not clustered spatiallynor selective in regard to socioeconomic status. The resulting dataset constitutes as random and as representative a sample of Uru-ará’s rural households as possible (Walker and Wood 1998:6).

Operationalization of ConceptsTable 1 presents operational definitions for all variables used in theanalysis that follows. I focus on two outcomes: gross agricultural in-come and total property value. To account for variation in theseoutcomes, the analysis includes five groups of explanatory variables:characteristics of the household head, initial wealth, farm context,agricultural inputs, and farm system components. These indicatorsappraise the importance of the diverse arguments about produc-tion and conservation in Amazonian small farm agriculture.3

Household head characteristics are salient in existing models ofincome and wealth among Amazon farmers. In general, farmerswith greater exposure to commercial agriculture should generategreater incomes and assets. In Brazil, farmers from the more highlymechanized south, as well as those with urban backgrounds, more


2 Interviews elicited information primarily from male household heads (particu-larly concerning farming practices), but female heads and other family memberswere often present and contributed to responses (particularly concerning householdcomposition and items owned).

3 I considered but excluded other predictors: titling status, because titles are re-quired if one is to obtain credit, and credit exerted greater effects on the outcomes;years of fallowing between cultivation, because too many cases were missing (N =25); corrals and fencing, because they were multicollinear with pasture area; andbiophysical indicators, because none were collected in the Uruará survey.


Table 1. Operational Definitions and Hypothesized Relationshipsof Independent and Outcome Variables

Effects on

Agricultural PropertyVariable Operational Definition Income Value

Characteristics of household headRegion of birth 0 = Born in North, Northeast,

Center-West,1 = born in South, Southeast + +

Previous residence 0 = Urban, 1 = rural – –Years of school Years completed + +Age Years of age + +Age squared Age × age – –Previous farm 0 = None, 1 = previous farm + +Years of residence Years in Uruará + +

Initial wealthInitial agricultural Factor-weighted index:

capital chainsaw, tractor, cocoa dryer + +Initial wealth Factor-weighted index: urban

house, brick walls, electricity, generator, gas stove, sewing machine, refrigerator, radio, TV, satellite dish, bike, car 0 +

Farm contextFirst occupancy First occupant on first lot – –Km from highway Average distance of lot(s)

from Transamazon – –Poor road access 0 = Fair or good, 1 = bad – –Organized 0 = None, 1 = neighborhood

neighborhood association + +Fire damage Damage to crops, forest,

house, etc. – –Conflicts with 0 = None, 1 = Conflicts over

neighbors boundaries, fire, timberextraction – –

Farm inputsSize of property Natural log (ln) + +

of total hectares (ha) in all lots owned

Adults Sum of men and women ages 15–65 + +

Hired labor Ln of days of hired labor paid + +Technical assistance 0 = None, 1 = assistance from

extension agency + +Agricultural credit 0 = None, 1 = agricultural credit + +Agricultural inputs Factor-weighted index:

insecticides, chemical fertilizers,organic fertilizers, cattle vaccines, mineral salts + +

Remittance income 0 = None, 1 = remittances from 1+ members + +

Business income 0 = None, 1 = income from 1+ businesses + +

education, previous ownership of a farm, an older family (mea-sured by age),4 and longer residence in Uruará, should be betterprepared to realize monetary returns from agriculture in the Ama-zon. In particular, age serves as an indicator of a household’s lifecycle stage for assessment of the “life cycle” argument, and I in-clude length of residence to assess the “learning” argument byquantifying time since arrival.

Using two variables for initial wealth, I attempt to capture the im-portance of the “differential wealth” argument. Because agricul-tural capital reduces labor costs at the time of arrival in Uruará, itshould allow for greater income generation and asset accumula-tion.5 Initial wealth, which reflects housing quality and ownershipof durable goods, should not affect agricultural income, but shouldinfluence property values by facilitating investment in farm infra-structure.6


4Specification of age includes a squared term to capture the decline in monetaryreturns from experience, which reflects a shift in income sources later in life.

5 Initial agricultural capital is an index computed as the sum of the products of Z-scores for ownership of chain saws, tractors, and cocoa dryers, multiplied by theirrespective weights from a principal-components analysis. The respective factorweights employed are .79, .50, and .59.

6Initial wealth is a factor-weighted index computed in the same fashion as the pre-vious index. The variables and weights are urban house, .74; brick walls, .48; elec-tricity, .63, generator, .52; gas stove, .63; sewing machine, .54; refrigerator, .73; radio,.48; television, .77; satellite dish, .68; bicycle, .54; and car, .50.

Table 1. Continued

Effects on

Agricultural PropertyVariable Operational Definition Income Value

Farm system componentsAnnual crops Ln of ha under rice, beans,

corn, manioc, and others + +Cocoa trees Ln of young plus productive

cocoa trees + +Coffee trees Ln of young plus productive

coffee trees + +Pepper vines Ln of young plus productive

pepper vines + +Pasture Ln of ha under pasture – +Valuable timber 0 = No timber, 1 = valuable timber

on lot(s) 0 +Reforestation 0 = No reforestation,

1 = reforestation 0 +Outcome variables

Agricultural income Ln of gross agricultural income from annuals, perennials and cattle, in 1996 R$ 1 +

Property value Ln of summed values of all lots, in 1996 R$ + 1

It is also important to account for contextual factors that affectfarm performance.7 First occupancy of a lot may hinder incomeand asset generation because there is more forest to clear, whichdelays the onset of returns. Distance from the main highway alonga feeder road, as well as quality of the feeder road, also may reduceincome and property values because of higher transport costs.Neighborhood organizations along selected feeder roads may cre-ate communities of mutual aid, which may raise agricultural in-comes and property values. (I include this variable to evaluate the“organization” argument.) In addition, damage from fires startedby neighbors may curtail income and assets by destroying crops orcapital. Finally, a variable for conflicts with neighbors evaluates theimportance of violence as an inhibitor of incomes and assets.

The fourth group of variables, farming inputs, allows examina-tion of land, labor, and capital investments. The natural logarithmof total land area held by a household indicates land inputs, whichshould favorably influence incomes and especially property values.The number of men and women age 15 to 658 and the natural logof the number of days of labor hired in the previous year9 indicatethe labor inputs, which also should increase agricultural incomesand assets. In addition, I account for whether a household receivedstate extension aid or agricultural credit: both should raise valuesof both outcomes. The key variable in this group concerns the useof agricultural inputs: this index indicates the extent to whichhouseholds have “intensified” production.10 Use of agricultural in-puts should raise crop and pasture productivity and thereforeshould increase agricultural incomes and property values. Finally, Iinclude two indicators of nonagricultural income—remittancesfrom household members and household businesses—which mayincrease agricultural incomes and assets by providing funds for in-vestment in farming.


7 The farm context variables are based on questions asked about individual lotsrather than households per se. Where a household owned two or more lots, the re-sponse used refers to the first lot, usually the primary lot in multi lot farming sys-tems. For the distance variable, the value for a multi lot household is the mean dis-tance of all lots.

8The number of adults is understood here as a labor input, but it also reflects ahousehold’s life cycle stage. Preliminary models considered many specifications ofhousehold composition effects, including terms for all household members: adultmen and women, children, and the elderly. Terms for adult men and women werethe strongest. One weakness of this approach is that it misses potential effects of in-trahousehold dynamics, which cannot be assessed with data from this survey.

9For all logged variables with one or more zero values, I first added 1 (or .10 forvariables with a decimal digit) to all cases in order to avoid losing observations.

10Agricultural inputs is a factor-weighted index computed in the same fashion asprevious indexes. The variables and weights are insecticides, .58; chemical fertilizers,.48; organic fertilizers, .59; cattle vaccinations, .84; and mineral salts, .84.

The final group consists of indicators of farm system compo-nents.11 The natural log of land area under annual crops should af-fect agricultural incomes positively insofar as crops commandworthwhile market values, and should influence property values fa-vorably insofar as cropland represents investments in land. The nat-ural logs of three key perennials—cocoa, coffee, and black pep-per—should exert strong positive effects on agricultural incomesand assets in reflection of a return-maximization strategy. The nat-ural log of pasture area, on the other hand, should reduce agricul-tural incomes in that productivity per hectare is lower for pasturethan for crops (Almeida et al. 1996). Because cattle are valued sohighly as a capital reserve, however, pasture should raise propertyvalues. Finally, the presence of valuable timber and the practice ofreforestation should have no impact on agricultural incomes, giventhat forest resources serve as capital reserves to be realized over along period. For the same reason, both of these forestry variablesshould increase property values. The timber and reforestation vari-ables thus assess “forestry” arguments for productive conservation.Moreover, insofar as multiple farm components influence agricul-tural incomes and property values, “diversification” of farming sys-tems may foster productive conservation.

The two outcome variables are the natural logs of gross agricul-tural income and a self-reported estimate of the total sale value ofthe lot(s) owned by each household. Both are measured in 1996Brazilian reals (R$), where R$1.00 roughly equaled U.S.$1.00. Thetwo are related conceptually but do not represent the same con-cept, because income varies across years and may not, in 1996, re-flect accumulated property wealth.

Analysis of Descriptive FindingsTable 2 presents descriptive statistics for characteristics of house-hold heads, the initial wealth of households upon arrival, the con-text of farms, agricultural inputs, farm system components, andagricultural incomes and farm property values in the Uruará sam-ple. Among key variables that assess “production” arguments, themean age was high, indicating households at advanced stages of thelife cycle; average duration of residence was over 10 years, indicat-ing the possibility for learning; initial agricultural capital and over-all wealth both showed substantial standard deviations, indicating


11In an earlier paper (Perz 1998), I used time allocation by the head of householdas a means of distinguishing among farm system types. Although it worked well in amultiple classification analysis, addition of other controls in regressions led to weakmodels (R 2 < .30), as did a categorization scheme to measure the number of activi-ties and a factor-weighted index using the farm system components. Use of separateterms for farm system components allowed for assessment of distinct “productiveconservation” arguments, and the models were stronger.


Table 2. Characteristics of Household Heads, Initial Wealth andFarming Context, Farm Inputs, Farm System Components, Agricul-tural Incomes, and Farm Property Values, Rural Households in

Uruará, Pará, Brazil, 1996

StandardMean Deviation N

Independent variablesCharacteristics of household head

Proportion from Southern Brazil .32 .47 261Proportion from rural areas .85 .36 259Years of school completed 2.03 2.17 248Proportion with previous farms .31 .46 261Age 47.23 13.04 260Years of residence 12.05 6.78 260

Initial wealthInitial agricultural capital a .11 .29 260Initial wealth a .00 4.48 256

Farm contextProportion first occupants .33 .47 261Kilometers to main highway 17.52 14.20 261Proportion with poor roads .29 .46 261Proportion on organized roads .32 .47 261Proportion experiencing fire damage .28 .45 261Proportion with conflicts with neighbors .16 .37 258

Farm inputsHectares claimed in lot(s) b 4.77 .48 261Adult men and women 4.10 2.55 258Days of labor hired b 1.92 1.98 259Proportion with technical assistance .21 .41 256Proportion with agricultural credit .59 .49 259Agricultural inputs a .00 2.29 261Proportion with remittance income .10 .30 259Proportion with business income .08 .27 257

Farms system componentsHectares under annual crops b 1.26 1.48 259Cocoa trees b 3.15 4.14 261Coffee trees b 4.44 3.65 261Black pepper vines b 4.04 3.68 261Hectares under pasture b 2.86 1.45 261Proportion with valuable trees on lot(s) .85 .36 261Proportion practicing reforestation .07 .25 261

Outcome variablesAgricultural income sources (R$)

Rice 1,153 2,124 261Beans 86 232 261Corn 229 574 261Cocoa 694 2,157 260Coffee 442 1,461 260Black pepper 922 1,739 261Cattle off-take 517 843 260

Total agricultural income (R$) 4,033 4,497 258Total agricultural income b 7.38 2.15 258Total agricultural income c 1,604 n/a 258

Total property value (R$) 29,393 50,132 239Total property value b 9.71 1.04 239Total Property Value c 16,482 n/a 239

a Factor-weighted index.b Natural logarithm.c Antilog of the natural log mean.

differential wealth; and approximately one-third of householdslived in organized neighborhoods.

Among indicators derived from “conservation” arguments, Ifound variation in the use of agricultural inputs, indicating unevenintensification; farms on average had land under annual andperennial crops as well as pasture, suggesting diversification; andmany farms still held valuable timber while some practiced refor-estation, indicating some forestry.

Table 2 also describes the two outcome variables. Agricultural in-come reflects sales of four annuals (rice, beans, corn, and manioc),three perennials (cocoa, coffee, and black pepper), and cattle off-take. Estimates come from reported production in all lots, multi-plied by 1996 prices from EMATER, the Pará state extension agency(Walker and Wood 1998), multiplied by the proportion of produc-tion not consumed by the household. The total gross agriculturalincome from these sources averaged R$4,033, but the distributionis skewed, as indicated by the large standard deviation. I loggedthese values to reduce outlier effects, and obtained an alternativeaverage as the antilog of the mean log value, at R$1,604. Thoughthese estimates refer to gross rather than net income, and thoughthe lower estimate is more appropriate, both estimates suggest a siz-able cash flow from agriculture.12

The other outcome variable measures the total sale value of thelot(s) owned by a household. On average, landowners estimatedtheir property wealth at R$29,393.13 The antilog of the mean logfor land values was R$16,482. These findings indicate substantialproperty wealth among colonists surveyed.

Analysis of Multivariate Regression ModelsIn this section I discuss findings from OLS models of agriculturalincomes and property values.14 Table 3 presents six models of agri-


12Although not all smallholders in Uruará may be disposed to produce for mar-kets, data for production and consumption of three key annual crops (rice, beans,and corn) suggest that most households were market-oriented. Only a small minor-ity of households (15 out of 261, or 6 percent) consumed all of their food crops anddid not market any produce, leaving a large majority who did so (94 percent). Be-cause there were very few zeros for the income variable, I employ OLS rather thantobit estimation.

13It is possible that self-reported property values are biased or inconsistent. Whilestill in the field, the team discussed reported property values with knowledgeable localinformants such as extension agents. On the basis of distance from town, propertysizes, and hectares in pasture, the informants found the estimates to be reasonable, asdid colleagues at EMBRAPA/CPATU, the regional agricultural research agency.

14One might object that use of farm system components in models of agriculturalincome and property values amounts to explaining an outcome with itself. To thecontrary, I understand the farm component predictors as investments in agriculture,which is not the same as income from agricultural outputs or property values.


Table 3. Natural Logs of Total Agricultural Incomes Regressed onCharacteristics of Household Heads, Initial Wealth, Farm Context,

Inputs, and Farm System Components

Model

1 2 3 4 5 6

Intercept 1.89 2.01 3.45+ 4.38* 5.72** 5.74**Characteristics of household head

Born in South –.08 –.24 –.25 –.25 .14 –.04Rural previous residence –.37 –.34 –.45 –.35 –.37 –.52+

Years of school completed .13+ .11 .12 .09 .08 .05Previous farm .18 .18 .08 .05 .18 .09Age .17* .17* .15* .11 .03 –.03Age squared –.002* –.001* –.001+ –.001 .00 .00Years of residence in Uruará .07** .08** .04 .03 .05* .04*

Initial wealthInitial agricultural capital 1.29* 1.30* 1.01* .39 .66+

Initial wealth .01 –.01 .004 .04 .02Farm context

First occupancy –.15 –.29 –.36 –.14Kilometers from main highway –.04** –.003 –.01 –.01Poor road access –.15 –.15 –.07 –.06Organized neighborhood .68* .44 –.001 .17Experience of fire damage .48 .45 .17 .14Conflicts with neighbors .24 .11 .001 .15

Farm inputsHectares claimed in lot(s) –.36 –.16 .08Adult men and women .15* .01 –.02Days of labor hired .20** .10 .07Technical assistance .15 .40 .32Agricultural credit .83* .61+ .62*Agricultural inputs .10 .13+ .15Remittance income .04 .14 –.06Business income –.42 .07 –.02

Farm system componentsHectares under annual crops .59** .60**Cocoa trees .09** .09**Coffee trees .01 –.01Black pepper vines .15** .16**Hectares under pasture –.15 –.06Valuable timber –.72* –.35Reforestation –.23 –.49

InteractionsAgricultural inputs ×

ha under annuals –.21**Agricultural inputs ×

ha under pasture .08*Adjusted R 2 .08 .10 .13 .23 .49 .62F ratio 3.89** 3.89** 3.28** 3.88** 8.29** 12.74**Model, residual df 7, 219 9, 217 15, 211 23, 203 30, 196 32, 194

+ p < .10; * p < .05; ** p < .01.

cultural incomes among Uruará households, regressed on house-hold head, initial wealth, farm context, inputs, and farm compo-nent variables.15 Model 1 includes the characteristics of householdheads. All of the predictors exhibit the anticipated effects: educa-tion, age, and length of residence are statistically significant (at p <.10, adopted because of the small sample size), a finding that sup-ports the “life cycle” and “learning” arguments. Model 2 adds theinitial wealth variables. As expected, initial agricultural capital is sig-nificant; this point confirms the “differentiation” argument. Model3 adds the farm context variables and reveals an important distanceeffect. Neighborhood organization is also significant, confirmingthe “organization” argument. These models confirm all four “pro-duction” arguments, but the low R 2 values indicate that these ex-planations contribute little to our understanding of agricultural in-comes.

Models 4, 5, and 6 add variables pertaining to inputs and farmsystem components, and assess the importance of “conservation”arguments. Model 4 adds the input variables and indicates signifi-cant effects for the number of adults, day laborers hired, andwhether a household received agricultural credit. Although theagricultural inputs index has a positive effect, it is not significant;this suggests that “intensification”—a key argument for forest con-servation—does not contribute to higher agricultural incomes.Model 5 adds the indicators of farm system components, and showslarge and significant effects for annuals, cocoa, black pepper, andvaluable timber. The “diversification” argument is supported by thefinding that annuals and two perennials strongly influence agricul-tural incomes. The coefficient for remaining timber is negative, anindication that land left in forest reduces or limits agricultural in-comes. With the farm component variables, Model 5 is muchstronger than any previous equation (R 2 = .49). Model 6 adds twosignificant interaction terms; both involve agricultural inputs andincrease the variance explained (to R 2 = .62).16 Model 6 shows thatthe effect of intensification on income is curvilinear, with a largeand positive but declining effect via annuals and a smaller but in-creasing positive influence via pasture.

Table 4 presents six models of property values regressed on thesame five groups of predictors, plus one significant interactionterm. Model 1 shows the coefficients for the household head vari-ables, and verifies that education, age, and length of residence ex-


15 Correlations indicated virtually no multicollinearity among the independentvariables (r > .50). Examination of residuals in both the income and the land valuemodels revealed no heteroskedasticity.

16I tested all two-way interactions among variables that were significant or nearly soin Model 5 of the income and land value equations. Interactions in Model 6 arethose which were significant and increased the R 2 by at least .01.


Table 4. Natural Logs of Rural Property Values Regressed onCharacteristics of Household Heads, Initial Wealth, Farm Context,

Inputs, and Farm System Components

Model

1 2 3 4 5 6

Intercept 5.94** 6.19** 7.06** 4.11** 4.48** 4.64**Characteristics of household head

Born in South .16 .06 .07 .07 .10 .11Rural previous residence –.24 –.19 –.20 –.17 –.15 –.14Years of school completed .10** .07* .07* .05+ .05+ .05+

Previous farm .09 .09 –.02 –.08 –.11 –.12Age .12** .11** .10** .05* .05* .05+

Age squared –.001** –.001** –.001** –.001+ –.001+ .00Years of residence in Uruará .05** .06** .04** .02+ .02 .02

Initial wealthInitial agricultural capital .43+ .42+ .13 .04 .10Initial wealth .04** .04* .02 .02+ .02

Farm contextFirst occupancy –.15 –.08 –.16 –.15Kilometers from main highway –.02** –.02** –.02** –0.03**Poor road access –.34* –.24* –.25* –.26*Organized neighborhood .28+ .23+ .19 –.13Experience of fire damage .16 .11 .10 .10Conflicts with neighbors .10 .09 .06 .02

Farm inputsHectares claimed in lot(s) .85** .70** .71**Adult men and women .02 –.01 –.01Days of labor hired .05+ .02 .02Technical assistance .25+ .18 .17Agricultural credit .17 .13 .15Agricultural inputs .04 .03 .02Remittance income –.06 –.04 –.08Business income –.22 –.02 .004

Farm system componentsHectares under annual crops –.05 –.04Cocoa trees .04** .04*Coffee trees .03* .03*Black pepper vines .02 .02Hectares under pasture .12** .12**Valuable timber –.07 –.01Reforestation .33 .32

InteractionKilometers from highway ×

organized neighborhood .02*Adjusted R 2 .21 .27 .33 .54 .58 .59F ratio 9.16** 9.49** 7.96** 11.65** 10.71** 10.73**Model, residual df 7, 202 9, 200 15, 194 23, 186 30, 179 31, 178

+ p < .10; * p < .05; ** p < .01.

ert significant effects on property values; this finding confirms the“life cycle” and “learning” arguments. Model 2 adds the initialwealth variables; both are significant, but wealth based on housingand durable goods is more important than initial agricultural capi-tal. This finding confirms the “differentiation” argument. Model 3,which includes the farm context variables, reveals very strong ef-fects of distance and road quality, and a weaker influence by neigh-borhood organization.

Models 4, 5, and 6 again incorporate the input and farm compo-nent variables and yield greater explained variances. Model 4 showsa strong land area effect, along with weaker influences by hired daylabor and technical assistance. Model 5 reveals significant positiveeffects of cocoa, coffee, and pasture, but not of annuals, timber,nor reforestation. The combined effects of different perennials andpasture are quite large, and support the “diversification” argumentfor property values. Model 6 adds one significant interaction term,the product of distance and neighborhood organization, and showsa positive interaction. This suggests that property values are higherat a given distance on organized feeder roads than on unorganizedfeeder roads. Overall, Models 4 through 6 explain most of the vari-ance in property values (R 2 = .54).

DiscussionThese findings bear important implications for 1.) our expectationsabout wealth accumulation among small farm households in fron-tier areas, 2.) the role of activities that foster forest conservationalongside agricultural production, 3.) the recent history of frontiercommunities such as Uruará, and 4.) policies for environmentallycrucial regions such as the Amazon.

The descriptive analysis shows that small farm households in theBrazilian Amazon can generate appreciable incomes and assetsfrom agriculture. Farms in Uruará had average incomes ofU.S.$1,600 from agriculture in the harvest preceding the 1996 sur-vey, and they held properties with average sale values of U.S.$16,500.Moreover, the multivariate models provide empirical support foreach “production” argument in favor of wealth accumulation.Nonetheless, models that account only for the characteristics ofhousehold heads, initial wealth, and the farm context are incompleteinsofar as their explanatory power is much weaker than in modelsthat include agricultural inputs and farm system components. Al-though many background and contextual factors help to accountfor farm incomes and assets in the Amazon, future analyses of re-turns from frontier agriculture should focus on farm components.17


17Because the conclusions drawn here are based on findings from cross-sectionaldata, caution is required in interpreting the effects of farm system components in1996 on incomes and assets.

The central findings of the analysis provide an empirical founda-tion for arguments supporting “productive conservation” in theAmazon. The “intensification” thesis is confirmed by significant in-dependent and interaction effects of agricultural inputs on income;the “diversification” explanation is validated in that multiple farmsystem components exert significant effects on both income andproperty values. “Forestry” arguments are not supported, however:remaining timber exerted a negative effect on agricultural income,and reforestation showed only insignificant effects. Nevertheless, weshould not conclude hastily that productive conservation is flour-ishing in communities of the Amazon.

In Uruará, recent price declines and crop blights have reducedincomes from perennials, causing an economic crisis that may pre-vent future decisions on land use consistent with productive con-servation. The reduction of income from perennials limits fundsavailable for investment in pasture maintenance after purchasingcattle; this implies that the expansion of ranching may involve ex-tensive systems in land degradation. An alternative proposal sug-gests that proceeds from timber might fund investment in pasturemaintenance, allowing intensification of cattle breeding or diversi-fication into milk production (Almeida et al. 1996; Tourrand et al.1996). Timber prices in Uruará are low, however: R$10–20 per treefor farm households. Thus a large amount of timber must be ex-tracted to fund pasture maintenance, and little incentive is pro-vided to make the heavy investments required for reforestation.

Threatened with the prospect of rapid deforestation for eco-nomic activities of questionable sustainability, the community ofUruará mobilized a global development plan (PGDU) in 1995 andformed a foundation for sustainable development (FUNDASUR) in1996; this foundation highlights increased inputs, diversified mar-keting, and better forest management as keys to increasing incomeswhile maintaining the resource base (FUNDASUR 1996).

The dire situation in Uruará poses a serious dilemma for policydiscussions of productive conservation, and therefore of sustain-ability, in the Amazon. Given that Uruará faces this crisis even whileit already possesses extension assistance, tenure security, and com-munity organization, we must ask what additional measures can fos-ter productive conservation. Key factors limiting productive conser-vation in Uruará are low prices on agriculture and high inputprices and transport costs.

I suggest a two-pronged strategy that includes a role for state poli-cies, which recognizes current limitations on Brazilian state re-sources. First, local organizations in Uruará should form allianceswith nongovernment organizations and state agencies (Hall 1997) tosecure minimum agricultural prices, subsidies for timber plantingsand fertilizers, and road improvements. Such alliances can help com-


munities gain footholds in markets while fostering broader partici-pation in policy formation. Second, communities can improve theirchances of obtaining support if local organizations gather localknowledge about the best productive conservation options and dis-seminate that knowledge to all households (FUNDASUR 1996). This“coordinated” strategy of seeking external alliances while educatinghouseholds may improve the possibilities for sustaining productiveconservation in Uruará and perhaps elsewhere in the Amazon.

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Documents

From Sustainable Development to “Productive Conservation:” Forest Conservation Options and Agricultural Income and Assets in the Brazilian Amazon