The Effects of Household Asset Endowments on Agricultural Diversityamong Frontier Colonists in the Amazon

Stephen G. PerzDepartment of Sociology, 3219 Turlington Hall, University of Florida, PO Box 117330, Gainesville, FL32611-7330, USA; (phone: 352-392-0251, ext. 234; fax: 352-392-6568; e-mail: sperz@soc.ufl.edu)

Received 11 March 2003; accepted in revised form 14 March 2004

Key words: Brazil, Inequality, Land cover change, Land use, Livelihoods

Abstract

This paper focuses on agricultural diversity, a key property of agroforestry systems, and examines the influencesof household asset endowments. The analysis addresses a debate between ‘differential motivation’ and ‘differen-tial capacity’ arguments concerning the importance of asset inequality for agricultural diversification among ruralhouseholds in developing regions. I draw on data from a survey of small farm colonists in the Brazilian Amazonto assess components and measures of agricultural diversity, and to model those diversity measures using indi-cators of household asset endowments. The results indicate that agricultural diversification is modest in the studysite, but varies among households, as do asset endowments. Models of agricultural production and income di-versity indicate that agricultural diversity primarily reflects labor endowments, though certain types of capital arealso important. These findings bear implications for research on agricultural diversity in other contexts, and forpolicies aiming to promote ‘productive conservation’ by compatibilizing poverty reduction, economic develop-ment and environmental sustainability.

Introduction

A hallmark of agroforestry systems is the diversifica-tion of agricultural products at the level of the pro-ductive unit. While socioeconomic research onagricultural diversity and rural development has adistinguished pedigree �cf. Ellis 2000�, it has garneredbroader attention recently. There has been a conflu-ence of scholarship that suggests that agricultural di-versification may constitute a ‘win-win-win’ strategyby simultaneously promoting poverty reduction, eco-nomic development and environmental sustainabilityin poor regions �e.g., Lee and Barrett 2001�.Similarly, agricultural diversification in forestedregions has been held out as a means for achieving‘productive conservation,’ that is, the generation ofhigher and less variable incomes while conservingforest cover �Hall 1997�.

The potential of agricultural diversity for compati-bilizing different policy goals is indeed intriguing, butit raises numerous questions. In particular, discus-sions of the possibility for agricultural diversity toyield ‘win-win-win’ outcomes beg the question ofwho in fact manages to diversify their agriculture inthe first place. At the household level, the question ofwho diversifies requires a focus on the issue ofhousehold assets. If ‘asset rich’ households are betterable to diversify agriculturally, then socioeconomicinequality presents a challenge to efforts to promoteagricultural diversification, with implications for pro-ductive conservation in forested regions, and ‘win-win-win’ outcomes for rural households.

This paper focuses on the issue of household assetendowments and agricultural diversity by taking upthe case of the Brazilian Amazon, a frontier regionexperiencing rapid but uneven economic growth intropical forest ecosystems �e.g., Serrão and Homma

Agroforestry Forum �2005� 63: 263–279© Springer 2005

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1993�. I first discuss previous work on economic di-versification, theory on household production diver-sity, and a debate over whether asset-poor or asset-rich households are more likely to engage indiversified agriculture. The paper then introduces thestudy case, a frontier colony in the eastern BrazilianAmazon and site of a recent small farm survey. Theanalysis consists of a description of agriculturaldiversity in the study case households, followed bymodels of household agricultural production and in-come diversity using indicators of various types ofassets. The paper concludes with discussion of theimplications for research on agricultural diversity inother regions, policies seeking to promote agriculturaldiversification, and prospects for diversity as a meansof achieving productive conservation and ‘win-win-win’ rural development outcomes.

Background: Household Asset Endowments andAgricultural Diversification

Previous Work on DiversificationIn the context of agricultural economics and regionaldevelopment, diversification has motivated manylines of thinking and substantial research �e.g., Ellis2000�. There is a large literature with a macro-levelfocus on sectoral diversity in a region or country dur-ing the process of agricultural commercialization. Asa result, most work on rural diversification hasfocused on linkages between rural and urban areas,subsistence and commodity production, and farm-and off-farm employment.

There is however growing attention going to mi-cro-level work on rural livelihood diversity �reviewedin Ellis 2000�. Recent research also increasingly fo-cuses specifically on agricultural diversity �e.g.,Valdivia et al. 1996�. Further, there is interest in ag-ricultural diversity among farmers in frontier areasexperiencing incipient commercialization, where di-versity may persist despite the emergence of inputand product markets �e.g., Dorsey 1999�. Recent dis-cussions of ‘win-win-win’ scenarios of economic de-velopment, poverty reduction and environmentalsustainability have called attention to diversificationas a potentially efficacious agricultural strategy forrural households in developing regions �e.g., Lee andBarrett 2001; Hall 1997�. As a result of these discus-sions, interest in farm-level agricultural diversity inareas experiencing incipient commercialization hasgenerated theoretical discussion about the determi-nants of diversity.

Theory on Rural Household Diversification

Ellis �2000� provides a recent theoretical discussionof the motivations for diversification among ruralhouseholds in developing regions. Rural householdsmay diversify out of necessity due to vulnerability tounforeseeable crises, such as floods, droughts, illness,or market price swings, with the goal of ensuringfamily survival and reproduction. In addition, ruralhouseholds may diversify on their own initiative, in-vesting in additional enterprises, especially for mar-ket-oriented products, in order to spead risks whilegenerating returns for the sake of some householdgoal, such as educating children. Necessity andchoice combine to constitute more specific motiva-tions for diversification, including seasonality, riskmanagement, coping mechanisms, labor markets,credit markets, and asset endowments and strategies.

While all of these factors are important, the ques-tion of household inequality calls for a focus on assetendowments and strategies. For Ellis �2000�, assetendowments refer to production factor endowments�land, labor, and capital�, as well as local infrastruc-ture �roads, communications, etc.�. Household assetendowments shape household asset strategies, that is,how a household invests its resources for the sake ofachieving welfare goals. Asset endowments and strat-egies are particularly important to household liveli-hood diversity for several reasons. First, assetendowments are defined to a great extent at thehousehold level, and reveal inequalities amonghouseholds. Second, asset strategies constitute an in-terface between households and their context, includ-ing other factors important to diversity such asseasonality and markets. Third, asset endowments inpart reflect asset strategies, including the use of creditand labor markets, such that access to credit and useof hired labor constitute assets a household may de-ploy for diversification. And fourth, because inequali-ties in asset endowments may generate different assetstrategies among households, asset endowments andstrategies may yield household-level differences inagricultural diversity.

Interest in rural household assets and discussionsof the potential win-win-win consequences of diver-sification have stimulated debate concerning the abil-ity of asset-rich as opposed to asset-poor householdsto diversify their agricultural enterprises �Ellis 2000�.Some analysts have argued that asset-poor ruralhouseholds in regions with limited commercialopportunities have more diversified production sys-

264

tems �e.g., Haggblade et al. 1989�. This amounts to a‘differential motivation’ argument. In part, thisreflects the differentially high vulnerability of asset-poor households, which necessitates diversification asa coping mechanism or adaptation to seasonality. Di-versity by differential motivation may also result froma choice driven by the desire of particularly poorhouseholds, who may be impoverished due to a re-cent crisis, to improve their material well-being in or-der to ‘catch back up’ to better-off households, as viarisk management and hiring of extra labor.

Researchers have also found that in some instances,richer households have more diversified productionsystems �e.g., Reardon et al. 1992; Evans and Ngau1991�. I characterize this as a ‘differential capacity’argument. Given that asset endowments and strategiesinfluence livelihood decisions, one can view a house-hold’s asset endowments as constituting the house-hold’s capacity for adding new enterprises. In regionswith incipient commercialization and substantial vul-nerability among rural populations, all farminghouseholds will be motivated to adopt asset strategiesfor diversified production systems in order to reducevulnerability. But if households with greater asset en-dowments have greater capacity to add enterprises,richer households will differentially become more di-versified.

The differential motivation and differential capac-ity perspectives suggest that agricultural diversitymay not be equally feasible among unequal house-holds, which bears implications for policies seekingto promote ‘win-win-win’ development scenarios.Contrasting findings from a handful of previous stud-ies beg for more work on the determinants of agricul-tural diversity in different parts of the world, in orderto see whether specific linkages between householdassets and diversity can be generalized. Because mostwork on this question comes from long-settled areasof Africa and Asia �e.g., Ellis 2000�, I present a studycase from a frontier region in Latin America.

Study Case: Uruará, Pará, Brazil

The study case is the municipality of Uruará, a colo-nist community situated on the Transamazon highwaywith a township located at Lat. 03°42’54’ S, Long.53°44’24’ W in the Brazilian state of Pará. Uruarábegan in the early 1970s as a colonization project inthe central-eastern Brazilian Amazon to resettle land-less rural families from the impoverished BrazilianNortheast. The state demarcated land in lots of 100

ha, on which an initial wave of colonists began tosettle and implement small-scale farming systems�IDESP 1990�.

Farm households in Uruará began by cultivatingannual crops, later diversifying into perennials. Manyhouseholds also converted land to pasture for beefand dairy cattle. During the mid-1980s, some farmsgarnered substantial incomes from tree crops, whichthen commanded high prices �IDESP 1990�. But dur-ing the 1990s, farm households faced increasinglydifficult circumstances due to price declines in keyperennial crops, compounded by the spread of fungalattacks and other pests. By the mid-1990s, despite di-versification, farm households in Uruará faced theprospect of limited incomes from agricultural produc-tion �Perz 2001� as well as degradation of clearedland �Perz 2002�.

In June and July 1996, a nine-member researchteam consisting of North American and Brazilian so-cial and agricultural scientists administered a surveyquestionnaire to farm households in Uruará. Thequestionnaire was divided into two parts: the first ad-dressed household characteristics and the secondconcerned agricultural practices. The sample includes261 farm households, or 12% of all rural establish-ments in Uruará at the time �IBGE 1998�. Thesampled households owned 347 lots, and the samequestions were asked for each lot owned by a house-hold. The team sampled on the basis of ‘first oppor-tunity’ and employed a cadastral map from theBrazilian Amazon’s regional agricultural agency,EMBRAPA/CPATU, to ensure that cases were notclustered spatially or selective of households by so-cioeconomic status.1 Of the households sampled,81% earned more than half of their incomes from ag-riculture.

1‘First opportunity’ sampling raises questions about sampling bias.Brazil’s 1995/96 agricultural census allows for comparisons withthe 1996 survey to assess bias. The census indicated the followingland use allocation in Uruará: 65% in primary forest, 5.6% undercrops, 23% under pasture, 5.9% under secondary growth. The 1996farm survey yielded the following land use percentages: 65% inprimary forest, 6.6% under cropland, 22% under pasture, and 6.7%under secondary growth. The agricultural census also indicates thatrice, beans, corn and manioc were the three most important annualcrops in terms of land area planted, while cocoa, coffee, black pep-pers and bananas were the most important perennial crops in termsof number of trees; the survey showed that these same crops werealso the most important among the sampled households. I concludethat bias is minimal, though likely not absent.

265

Concepts, Measures, and Hypotheses

Agricultural Diversity: Conceptualization andMeasurementDiscussions relevant to the conceptualization andmeasurement of agricultural diversity are availablenot only from economics �Haughton and Mukerjee1995�, but also in ecology �Magurran 1988� and so-ciology �Gibbs and Poston 1975�. Conceptually,diversity has two components. First, diversity signi-fies the number of categories present �e.g., types ofagricultural products�, which I refer to as ‘structuraldiversity.’ Second, diversity encompasses the relativedistribution of units among those categories �e.g., kgor income from each agricultural product�, which Irefer to as ‘distributive diversity.’ In the case of agri-culture, a farming system with more products can besaid to be more diversified, and among two farmswith the same number of products, the one with moreevenly distributed production or income among thoseproducts can also be said to be more diversified.

There are numerous quantitative measures ofdiversity.2 Preliminary testing of over a dozen suchmeasures revealed many with disadvantages, such assensitivity to sample size, sensitivity to the distribu-tion of cases among categories, and/or undefined val-ues and loss of observations. In the end, I chose threemore or less complementary measures: the number ofproducts �S�, the 6th Gibbs-Poston index �M6�, andone type of inverse of the Herfindhal index �1-H�.3

The number of products S is calculated as

S � �i�1c a, �1�

where i represents a count variable for the categories,a is a dichotomous variable that takes a value of 1 if

a category is represented by a case and 0 otherwise,and c is the number of categories. S varies from 0 toc. For the case of agricultural production, S refers tothe number of products actually produced out of agiven set of possible products. S thus ignores the dis-tribution of production among products, making it apurely ‘structural’ measure of diversity. I use S be-cause it is easy to interpret and serves as a benchmarkfor assessing differences with other measures that alsocapture distributional aspects of diversity. Gibbs andPoston’s M6 is calculated as

M6 � c�1 ��i�1

c �xi � x̄� ⁄ 2

�i�1c xi

�, �2�

where xi is the number of units in a category, and xi

is the mean number of units across all categories. M6captures both structural and distributive aspects of di-versity, that is, it increases as the number of catego-ries with units rises as well as when the units are moreevenly distributed among categories. M6 varies froma minimum of 1.0 to a maximum of c, but unlike S,M6 often takes values other than integers, making in-terpretation somewhat less clear. The inverse of theHerfindhal index used here, 1-H, is calculated as

�3�

1 � H � 1 � �i�1c �xi

X�2

where X is the sum of the units in all categories. H isan index of ‘concentration,’ the inverse of diversity,so by taking 1-H one obtains a measure of diversity.1-H ranges from 0 to �1-�1/c��, so as diversityincreases, 1-H approaches 1, particularly when thereis a large number of categories. Like M6, 1-H is sen-sitive to both structural and distributive diversity. Iemploy 1-H because it is mathematically equivalentto but simpler to compute than several other diversitymeasures and because it accounts for distributive di-versity, though it is very sensitive to structural diver-sity, placing it somewhere between S and M6 in termsof sensitivity to evenness.4 To measure agriculturaldiversity in Uruará, I drew on household production

2Quantitative, unidimensional measures of diversity have beencriticized on the grounds that they do not distinguish betweenqualitatively different kinds of diversification. This critique iswell-taken, but there are also limitations to qualitative categoriza-tions of diversification types. Conclusions from qualitative distinc-tions are sensitive to the way in which distinctions are made, howmany distinctions are made, and whether the distinctions generatecategories that are not mutually exclusive.3MacIntosh’s U doesn’t account for proportional distribution; Ma-cIntosh’s D and E indexes have poor discriminant ability, especiallyat small sample sizes; Shannon’s index returns undefined values ifany category has a value of zero; Simpson’s index is very sensitiveat small and large sample sizes; another inverse of the Herfindhalindex, 1/H, exhibited non-normal distributions that yielded weakermodels; Gibbs and Poston identify various shortcomings with M1through M5 that are resolved with M6.

4Note that 1-H is mathematically equivalent to MacIntosh’s U ad-justed for sample size and squared as well as the Gibbs-Poston’sM1. However, 1-H avoids computational problems with U and M1,while offering a moderate degree of sensitivity to evenness com-pared to M6 �Gibbs and Poston 1975: 474-475�.

266

data from the 1996 survey and supplemental datafrom the 1995/96 Brazilian agricultural census �IBGE1998�. Because some households owned more thanone lot, I summed production values for all lots heldby each household. I gathered production data for allidentifiable annual and perennial crops and for cattle.5

This allowed for calculation of agricultural diversitybased on 18 products �see Table 1�: eight annual crops�rice, beans, corn, manioc, pineapples, sugar cane, to-matoes, and watermelons�, nine perennial crops �co-coa, coffee, black pepper, bananas, oranges, coconuts,cupuaçu, mangos, and guaraná� and beef. Productionvalues were given in �or converted to� kg.6 Iestimated beef production on the basis of the reportedcattle herd size and assumptions from state datasources about the annual off-take rate in Uruará in1996 and average kg per head.7 Hence, the 18 prod-ucts served as the categories and production in kgserved as the units used in calculating S, M6, and 1-Hfor each household in the Uruará sample.

In addition to calculating the diversity of agricul-tural production, I drew on additional informationabout product prices in Uruará in 1996 to estimate thediversity of gross agricultural incomes for the same18 products. This is important for several reasons.First, measuring diversity in terms of brute produc-tion of e.g., rice, coffee and beef does not captureprice-per-kg differences among the products. Second,

households sell some proportion of their production,and these proportions vary among products. Andthird, income from agriculture is of primary impor-tance for the livelihoods of most of the householdssurveyed.

Estimation of agricultural income diversity re-quired two additional steps. First, I obtained data in-dicating the proportion of a product sold. The Uruarásample provided such data for rice, beans, corn, andmanioc.8 State-level data for Pará from the 1995/96Brazilian agricultural census indicated the proportionof production sold for the other products �IBGE1998�.9 Second, I used the 1996 Uruará survey dataand 1995/96 agricultural census data for Uruará tocalculate prices per kg for each of the 18 products us-ing income generated, production figures, and esti-mates of production sold �IBGE 1998�.10 For each ofthe 18 products in every household, I then multipliedtotal production by the proportion sold and the priceper kg to obtain gross income values, and used theseincome values to again calculate S, M6, and 1-H foreach household in the sample.

Agricultural Diversity: Findings from the Study SiteTable 1 presents indicators of the importance of the18 agricultural products in terms of kg produced andincome generated among the farms in the 1996 Uru-ará sample. Taken together, agricultural diversifica-tion among annuals, perennials and cattle in Table 1appears somewhat limited, as only six out of 18products were produced by more than half of thehouseholds surveyed.

In terms of production, the most important annualswere rice, beans, corn and manioc, as most house-holds produced each of these. However, the few

5In a handful of cases �n�10�, crops were unidentified, and thesewere excluded from calculation of the diversity measures. The dataalso do not include non-bovine livestock, garden produce for sub-sistence consumption, or dairy products. The diversity estimatesreported here should therefore be taken as conservative.6In the cases of pineapples, bananas, cupuaçu, oranges and man-gos, production values were given in units other than kg �such asbunches or individual fruit�. I consulted publications on tropicalfruit to obtain estimates of kg per bunches/fruit for these products�e.g., Morton 1987�, and converted production values given into kgusing low estimates from those available, on the assumption thatfrontier produce will be of relatively low quality. In addition, insome cases, minor annual and/or perennial crops �e.g., sugar caneand mangoes� were indicated as productive, but production valueswere not indicated. In these cases, I used 1995/96 Brazilian agri-cultural census data for Uruará �IBGE 1998� on productivity �yieldper ha for annuals and yield per plant for perennials� and multi-plied this by the appropriate unit �ha or plants� to estimate produc-tion for that crop.7The 1995/96 Brazilian agricultural census indicates a cattle off-take and sale rate of 12% in Uruará �IBGE 1998�. Based on esti-mates by local agricultural extension agents in Uruará, I assumedan average off-take weight of 200 kg per head, and calculated beefproduction B as B = hrw, where h is reported herd size, r is theoff-take rate in Uruará in 1996, and w is the off-take weight in kgper head.

8The proportions sold in Uruará in 1996 were: rice 55%, beans40%, corn 39%, manioc 36%.9The proportions sold in Pará in 1996 were: pineapples 91%, sugarcane 91%, tomatoes 93%, watermelons 64%, bananas 73%, cocoa94%, coffee 65%, oranges 82%, black pepper 97%, coconuts 88%,cupuaçu 71%, mangoes 34%, guaraná 75%. The cattle off-take inUruará in 1996 was 12%.10I calculated prices as Pd = id / �pdsd�, where Pd refers to the priceper kg for a given product d, id is income from d in Uruará in 1996,divided by the product of municipal production pd in kg and theproportion of production sold sd. Prices per kg ran as follows: riceR$0.28, beans R$1.52, corn R$0.32, manioc R$0.43, pineapplesR$1.08, sugar cane R$0.30, tomatoes R$0.55, watermelonsR$2.16, bananas R$2.10, cocoa R$0.85, coffee R$1.01, orangesR$0.08, black pepper R$1.36, coconuts R$0.22, cupuaçu R$0.81,mangos R$0.26, guaraná R$3.86, and cattle, R$1.23. Prices aregiven in 1996 Brazilian Reais �R$�, at the time roughly equivalentto US$1.

267

Tabl

e1.

Com

pone

nts

ofA

gric

ultu

ral

Div

ersi

ty,

Farm

Hou

seho

lds,

Uru

ará,

Pará

,19

96.

Agr

icul

tura

lPr

oduc

tion

Agr

icul

tura

lIn

com

e

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enta

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ucer

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-tio

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g �Pe

rcen

tage

ofPr

oduc

ers

Ave

rage

Inco

me

�R$ �

aPe

rcen

tage

ofA

gric

ultu

ral

Inco

me

�R$ �

a

Ann

ual

Cro

psR

ice

(Ory

zasa

tiva

)78

.230

07.9

49.0

467.

89.

5B

eans

(Pha

seol

usvu

lgar

is)

55.2

284.

825

.317

4.6

3.6

Cor

n(Z

eam

ays)

77.0

1936

.428

.724

5.7

5.0

Man

ioc

(Man

ihot

escu

lent

a)53

.634

58.3

15.7

543.

911

.1Su

gar

Can

e(S

acch

arum

offı

cina

rum

)2.

212

0.3

2.2

33.2

0.7

Pine

appl

es(A

nana

sco

mos

us)

0.8

85.4

0.8

83.9

1.7

Tom

atoe

s(L

ycop

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con

escu

lent

um)

0.4

260.

50.

413

2.8

2.7

Wat

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itro

llus

vulg

arus

)0.

434

4.8

0.4

477.

49.

7Su

btot

al–

––

2159

.344

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Pere

nnia

lC

rops

Coc

oa(T

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rom

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cao)

33.7

816.

633

.765

1.7

13.3

Cof

fee

(Cof

fea

arab

ica)

32.6

394.

632

.625

8.6

5.3

Bla

ckPe

pper

s(P

iper

nigr

um)

55.9

658.

455

.986

5.4

17.6

Coc

onut

s(C

ocus

nuci

fera

)0.

00.

00.

00.

00.

0C

upua

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agr

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flora

)0.

81.

00.

80.

60.

0B

anan

as(M

usa

ssp.

)5.

418

3.6

5.4

280.

85.

7O

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itru

sau

rant

ium

)9.

229

.09.

21.

90.

0M

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s(M

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2.8

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0.2

0.0

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(Pau

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)0.

00.

00.

00.

00.

0Su

btot

al–

––

2059

.241

.9

Cat

tle68

.256

4.5

68.2

693.

414

.1

Tota

l–

––

4911

.910

0.0

n26

125

626

125

725

7

Sour

ces:

1996

Uru

ará

surv

eyan

dIB

GE

1998

;N

otes

:a A

tth

etim

eof

data

colle

ctio

n,R

$1ro

ughl

yeq

uale

dU

S$1.

268

households who produced sugar cane, pineapples, to-matoes and/or watermelons generated large quantitiesof these products, as indicated by the large averagekg produced relative to the small proportion of pro-ducers. Sugar cane, pineapples, tomatoes and water-melons account for 9% of the kg of annuals produced,but they comprised 34% of income from annuals.Overall, data for annuals suggest some diversificationfor consumption as well as sales, which generated onaverage over US$2100, or 44% of gross agriculturalincome.

Perennials show a somewhat distinct pattern. Onlyblack pepper was produced by most households,while large minorities produced cocoa and coffee, andsmaller minorities produced oranges and bananas.The income data reflect this pattern, and suggest lim-ited overall diversification among perennials, giventhe large number of relatively unimportant tree crops�including some yet to produce, such as guaraná�.That said, perennials were of comparable importanceto annuals in terms of income generated.

Cattle ranked among the most important products.Nearly 70% of households had cattle, and their off-take generated about 14% of gross agriculturalincome, more than any other single product exceptblack pepper.

The more diversified production systems in thestudy incorporate a commercial as well as a subsis-tence component and some combination of annuals,perennials and cattle. While it is possible that somehouseholds might diversify purely among subsistencecrops or focus entirely among cattle or a key peren-nial crop, this is rare. Only 6% of the households inthe survey reported no income from agriculture �Perz2001�, and 57% have farming systems that incorpo-rate one or more products in each of two or more

types of activities among annuals, perennials andcattle �Walker et al. 2002�.

Table 2 presents descriptive statistics for the threediversity measures of agricultural production andgross income for households in the Uruará survey.Given that 18 products were used in calculating thediversity measures, the results in Table 2 indicatelimited diversification. For agricultural production, Sindicates a maximum of 11 products and a mean ofless than five. When we take distributional evennessinto account with M6, diversification is lower, with amaximum value under 8.0 and a mean under 4.0.Similarly, 1-H indicates moderate diversification.

Because the three measures have different scales,it is useful to rescale them as proportions relative totheir maximum possible values.11 Rescaled measuresindicate where the diversity index means fall betweentheir minimum and maximum values, yielding pro-portional values between 0 and 1. Rescaled S, or Sr,equals 0.26; similarly, M6r�0.23, and �1-H�r�0.57.The relatively high �1-H�r value reflects this mea-sure’s high sensitivity to structural diversity.

Much the same finding emerges for the income di-versity measures. While S indicates that the maximumnumber of products sold was still 11, all three mea-sures exhibit lower mean values for income than forproducts, a reflection of some products not being soldin part or at all. Rescaled means for income diversitywere Sr=0.20, M6r�0.16, and �1-H�r�0.40.

11I rescaled the diversity measures using the following calculation:

Dr �D

maxD � minD, �4�

where Dr is a rescaled diversity measure, D is the ‘raw value’ di-versity measure �S, M6, or 1-H�, max D is that measure’s maxi-mum possible value given calculations with 18 products, and minD is it’s minimum possible value.

Table 2. Descriptive Statistics for Indicators of Agricultural Diversity, Farm Households, Uruará, Pará, 1996.

Min Max Mean Standard Deviation Skewness Valid n �n�261�

Agricultural ProductionNumber of Products �S� 0 11 4.75 2.07 � 0.17 261Gibbs/Poston �M6� 1.00 7.36 3.94 1.48 � 0.20 256Inverse Herfindhal �1-H� 0.00 0.81 0.54 0.22 � 1.27 256

Agricultural IncomeNumber of Income Sources �S � 0 11 3.30 1.89 0.65 261Gibbs/Poston �M6� 1.00 7.87 2.96 1.50 0.73 257Inverse Herfindhal �1-H� 0.00 0.85 0.43 0.26 � 0.52 257

Source: 1996 Uruará survey.

269

Despite the modest averages for agricultural diver-sity, the standard deviations indicate substantial vari-ation among households in the Uruará sample. Forexample, for S for agricultural production, a 95%confidence interval suggests that the households inthe sample produced between one and nine products,and sold between zero and seven. This variation raisessuspicions that, whether due to differential motivationor differential capacity, asset endowments influenceagricultural diversification among these farms.

Table 3 adds to the analysis by presenting correla-tions among the diversity measures. As one wouldhope, the diversity measures have fairly high correla-tions �up to r�0.93�. In addition, as one might expect,many correlation coefficients are far from perfect�down to r�0.59�, which confirms previous work in-dicating that S, M6, and 1-H capture structural anddistributive aspects of diversity to different degrees.

Determinants of Agricultural DiversityA key question that arises out of the debate over as-sets and household diversity is not just whether asset-poor or asset-rich households have more diversifiedagricultural systems, but what types of assets exhibitimportant effects on agricultural diversity. This sec-tion presents a set of household determinants of agri-cultural diversity, applied to the case of colonists inUruará in the Brazilian Amazon. I specify a model ofagricultural diversity by drawing on several theoreti-cal foundations.

My approach draws insights about farm householddecision-making from economic anthropology andhousehold economics in developing regions, whichare often characterized in terms of labor andespecially capital scarcities �Ellis 1993; Netting1993�. Uruará and other frontier colonies in the Ama-

zon fit such frameworks insofar as small producersthere face labor and especially capital scarcity rela-tive to land availability. I also draw on insights fromwork on commercialization and diversity in rural ar-eas of developing regions �e.g., Lee and Barret 2001;Dorsey 1999�. In frontier areas of the Amazon, manyproducers are engaged in diversified agricultural sys-tems involving commercial and subsistence compo-nents. This is also the case in the study site, as shownin Table 1. These considerations require discussion ofasset strategies in light of labor and capital scarcity,bearing in mind the importance of both subsistenceand commercial activities for diversity in agriculturalsystems.

With regard to labor scarcity, while households inrural areas of developing regions generally seek totake advantage of incipient commercialization to pro-duce for markets, they are also very concerned tominimize risks. Hence, there is a ‘subsistence-first’tendency among small producers. This in part reflectsthe domestic life cycle of households �e.g., Perz 2002;Walker et al. 2002�. Upon their arrival on the fron-tier, young families with dependent children prioritizefood security and invest heavily with their labor toplant annual crops to ensure household subsistence.Later, parents gain experience in locally appropriateagricultural practices, the household accumulates astock of cleared land, and children grow older, reduc-ing dependency while expanding the labor availablein the household. These processes reduce risk aver-sion in households, prompting families to invest incommercially-oriented activities. However, commer-cialization may reflect one of several specific assetstrategies, and thus have one of several impacts onagricultural diversity. Commercialization may 1.�supplement subsistence, diversifying a farming sys-

Table 3. Correlations among Indicators of Agricultural Diversity, Farm Households, Uruará, Pará, 1996.

Production Income

S M6 1-H S M6 1-H

Agricultural ProductionNumber of Products �S� 1.00Gibbs/Poston �M6� 0.91 ** a 1.00Inverse Herfindhal �1-H� 0.76 ** 0.85 ** 1.00

Agricultural IncomeNumber of Income Sources �S� 0.82 ** 0.73 ** 0.59 ** 1.00Gibbs/Poston �M6� 0.75 ** 0.75 ** 0.60 ** 0.93 ** 1.00Inverse Herfindhal �1-H� 0.65 ** 0.69 ** 0.66 ** 0.77 ** 0.86 ** 1.00

Source: 1996 Uruará survey; Notes: a � p � .15, * p � .05, ** p � .01.

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tem, as by adding perennial crops or cattle to a sys-tem featuring annuals; 2.� replace subsistence cropswith commercial products, which will have little ef-fect on diversity; 3.� specialize a farming system incattle or a single perennial, reducing diversity, or 4.�shift a household’s livelihood toward off-farm activi-ties, with variable effects on agricultural diversity.This returns us to the debate over whether greater as-sets yield more diversity, consistent with the ‘differ-ential capacity’ argument, or less diversity, consistentwith the ‘differential motivation’ argument. With re-spect to labor assets, given that 1.� most householdsin the study site commercialize some production�Table 1�, 2.� most households in the study site pro-duce one or more products from among two or moreactivities including annuals, perennials and cattle�Walker, et al. 2002�, 3.� the more diversified produc-tion systems have 5-10 products �Table 2�, and 4.�most of those products are labor-intensive �e.g., Ser-rão and Homma 1993�, my expectation is that largerlabor pools will be necessary for more diversified ag-ricultural systems, which corresponds to the ‘differ-ential capacity’ argument.

Strategies involving capital assets likely also influ-ence agricultural diversity. A key theme concerningthe commercialization of production among Amazo-nian colonists is the initial capital endowment house-holds bring to the frontier. Households with greaterinitial capital endowments are more likely to diver-sify beyond subsistence crops into commercial prod-ucts, as capital can substitute for or supplement laborassets �e.g., Perz 2001�. For the same reason, lateracquisition of capital, as via credit markets, can alsobe important for agricultural diversity. Given the de-bate over assets and diversity, and because assetstrategies vary, it is difficult to specify one impact ofcapital on agricultural diversity. Even more than la-bor, capital facilitates commercialization, which mayalso constitute a supplement, replacement, means ofspecialization or shift in asset strategies, with varia-ble implications for agricultural diversity. That said,for the reasons given for labor availability, andbecause capital assets can substitute for labor scarcityin some cases �as via hired labor�, my expectation isthat greater capital assets will facilitate more diversi-fied agricultural systems in the study site, consistentwith the ‘differential capacity’ argument.

This discussion however begs questions of thetypes of assets likely to be most crucial for agricul-tural diversity. At this point I draw from research onindigenous and other traditional agroforestry systems

in the Amazon, known for their diversity �e.g., Beck-erman 1987�. These agroforestry systems depend pri-marily on abundant land and heavy reliance on laborrather than capital inputs. This implies that in thecontext of the Amazon, where land is abundant andcapital is particularly scarce, labor availability may beparticularly important for agricultural diversificationin colonist farming systems like those of Uruará.

That said, it is also important to recognize thatcapitals of various sorts are available to frontier colo-nists. Bebbington �1999� provides a framework forunderstanding rural livelihood strategies that featuresdistinctions among various types of household capi-tals. I draw on that framework to consider not onlyfinancial and technological capitals, but also culturaland social capitals �and others�, which may also beimportant assets on which households draw in formu-lating their asset strategy. This elaboration takes intoaccount the highly variable and flexible use of house-hold assets in asset strategies, and allows for morerefined specification of the types of assets that aremost important for agricultural diversity.

These sources provide theoretical foundations forspecifying a model of agricultural diversity to assessthe effects of household asset endowments. If assetendowments affect diversity in agricultural produc-tion and incomes, then inequality among householdsin terms of those assets can be said to affect agricul-tural diversity, which bears implications for thedebate over assets and diversity, and agriculturalpolicies seeking ‘win-win-win’ scenarios for rural de-velopment. Table 4 presents four groups of explana-tory variables that should account for variation inagricultural diversity: infrastructure, land assets, laborassets, and various kinds of capital assets.

‘Infrastructure’ refers to the �average� distance inkm to Uruará town from a household’s lot�s� alongunpaved seasonal roads, as well as two binomial var-iables indicating if the road quality is ‘good’ or ‘fair’�as opposed to ‘bad’� as appraised by the interviewee.Farms closer to Uruará town should be more diversi-fied since they are better able to realize profits giventhe lower transportation costs, facilitating diversifica-tion via commercialization. Table 4 shows that farmswere on average nearly 30 km from Uruará town,though this varied substantially in the sample, andthat only 18% of households had ‘good’ roads, though52% had ‘fair’ roads.

‘Land Assets’ is operationalized as the number ofhectares �ha� held in a household’s lot�s�, transformedinto a natural log �ln� to obtain a normalized distri-

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bution. Though lots were generally about 100 ha, be-cause 25% of households held more than one lot, themean indicates an average total landholding of 117ha. Given the abundance of land in Uruará relative tolabor and capital, one would not expect land assets toaffect diversity. On the other hand, if larger proper-ties tend to foster the expansion of productionsystems via the addition of new enterprises, thengreater land assets will allow for greater agriculturaldiversity.

‘Labor Assets’ includes terms for the number ofadults in a household �which may include more thanone family�, the number of days of labor paid per lot

in the previous year �logged to obtain a normal dis-tribution�, and the number of children present. Thenumber of adults is of particular importance, becausefarm households in Uruará �like indigenous groupsand traditional populations in the Amazon� rely pri-marily on family labor, and because most of the ag-ricultural products included in the diversity measuresare labor-intensive. Given the conditions of incipientcommercialization and substantial vulnerability in thestudy case, larger family labor pools should allow formore diversified production systems because they fa-cilitate diversification via the addition of commercialproduction to ongoing subsistence-oriented activities,

Table 4. Descriptive Statistics for Explanatory Variables, Farm Households, Uruará, Pará, 1996.

Mean Std. Dev. Skew-ness Expected Effect on Agricultural Diversity

InfrastructureAverage Distance to Uruará Town �km� 29.35 14.04 0.26 �‘Good’ Road Quality �0�No, 1�Yes� 0.18 0.39 1.64 �‘Fair’ Road Quality �0�No, 1�Yes� 0.52 0.50 � 0.07 �

Land AssetsNatural Log �ln� Hectares �ha� Held 4.77 0.48 0.75 �

Labor AssetsNumber of Adults �Ages 15-65� 4.10 2.55 1.07 �Adults Squared 23.24 29.21 2.67 �Ln Days of Labor Hired 1.92 1.98 0.21 �Number of Children �Age � 15� 2.85 2.74 1.57 �/--?

Capital AssetsNatural Capital

Ln ha Cleared upon Acquisition 0.51 2.40 � 0.03 �Timber Extraction �0�No, 1�Yes� 0.18 0.38 1.71 �

Cultural CapitalRegion of Birth �0�Other, 1�Amazon� 0.17 0.38 1.78 �

Previous Agricultural Experience �0�No, 1�Yes� 0.66 0.48 � 0.66 �Years of Residence in Uruará 12.05 6.78 0.24 �Years of Residence Squared 191.06 179.60 0.88 �Years of Residence Squared 191.06 179.60 0.88 �

Technological CapitalAgricultural Capital upon Arrival �Factor Index� 0.00 1.21 4.90 �Labor-saving Capital �Factor Index� 0.00 1.57 1.32 �

Institutional CapitalCredit �0�No, 1�Yes� 0.59 0.49 � 0.37 �Extension Assistance �0�No, 1�Yes� 0.21 0.41 1.46 �

Social CapitalOff-farm Income �0�No, 1�Yes� 0.41 0.49 0.35 �Neighborhood Organization �0�No, 1�Yes� 0.32 0.47 0.79 �

Source: 1996 Uruará survey.

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as per the ‘differential capacity’ argument. That said,I also consider a squared term for the number ofadults, because households with particularly large la-bor pools may increasingly allocate labor to off-farmactivities, thus yielding a declining marginal increaseor even a decline in agricultural diversification. Thiswould provide partial support of the ‘differential mo-tivation’ argument by showing that moderate-sizedhouseholds have more diversified agricultural sys-tems than both large and small households. The daylabor variable is included to account for the effects ofUruará’s local labor market, which can offset house-hold labor scarcity, particularly at times when labordemand peaks. As a substitute or complement to fam-ily labor, more days of labor paid should lead to morediverse agricultural production and income. Finally, Iinclude a term for children not only because they mayalso contribute labor and foster agricultural diversity,but also because they constitute dependents who re-quire care that may withdraw adult labor from agri-culture, thereby reducing diversity. These two effectsare countervailing and inseparable, and the inclusionof children as a variable allows an assessment ofwhich effect dominates, if any. Table 4 shows thathouseholds on average had over four adults, hiredabout seven days of labor in the previous year per lotheld, and had nearly three children, though all threeindicators of labor assets vary among households inthe sample.

‘Capital Assets’ refers to a broad array of factorsthat may enhance agricultural diversity by injectingresources into a farming system. I consider indicatorsof natural, cultural, human, technological, institu-tional and social capital. ‘Natural capital’ is measuredas the ha of cleared land upon acquisition of the lot�s�held by a household and by whether the householdhad sold timber since arrival. I employ the first indi-cator because cleared land is considered a capital im-provement in Brazilian law �Alston et al. 1999� andbecause buying a lot with land already clearedreduces the costs of implementing a farming system.More cleared land upon acquisition should facilitateestablishment of more agricultural enterprises andthereby foster greater agricultural diversity. Timberextraction represents another source of value that canhelp finance new agricultural enterprises via the saleof natural resources �e.g., IMAZON 1996�. However,the effects of timber extraction may be limitedbecause of low prices paid at the time of the 1996survey �R$10-20 per tree�. Table 4 suggests thatcleared land upon acquisition averaged less than two

ha, though this varied among households, and that18% of households had sold timber, though this esti-mate may be low due to concerns among farmers thatthe research team was with the state forestry agency.

‘Cultural capital’ is operationalized as a binomialvariable indicating whether the head of the householdwas born in the Brazilian Amazon or not. Heads bornin the Amazon should on average be more acquaintedwith regional land use practices, disposing them toengage in more diversified systems traditionallyfound across the basin. At the same time, traditionalagricultural systems, which emphasize subsistenceproduction, may be less diversified than combinedsubsistence-commercial systems implemented by in-terregional migrants. Table 4 indicates that 17% ofhousehold heads were born in the Amazon, whichimplies a large contingent of interregional migrants inthe colonization zone.

‘Human capital’ refers to whether the head of ahousehold had previous agricultural experience, andhow long the household had resided in Uruará.12 Fol-lowing the ‘differential capacity’ argument, onewould expect that previous agricultural experiencewould be an asset that would foster greater agricul-tural diversification in Uruará; but following the ‘dif-ferential motivation’ argument, the opposite may betrue. Given the importance of commercialization fordiversity in the study site, I expect the former argu-ment to hold. Length of residence is included to cap-ture the effects of learning locally appropriateagricultural techniques by experimentation and expe-rience. Over time, as households experiment with ag-ricultural techniques and various crops, they shouldaccumulate a store of knowledge about what works,and this should translate into cultivation of moreproducts as that knowledge base grows �Moran 1989�.I consider a squared term for length of residence tosee if there are non-linearities in this process, whereinefficacious new knowledge becomes harder to pro-cure over time, such that residence duration shows adeclining marginal effect on diversity. Table 4 indi-cates that 66% of household heads did have agricul-tural experience before coming to Uruará, and thathouseholds averaged 12 years of residence in Uruará,though this varied substantially.

‘Technological capital’ is measured using two fac-tor-weighted indexes of agricultural capital. The first

12I consider previous agricultural experience rather than formaleducation since the latter had more missing values and the formerseemed more relevant to practical knowledge of agriculture.

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index reflects whether a household had, upon theirarrival in Uruará, a chainsaw, cocoa dryer, and a trac-tor, measured as 1 if so and 0 if not in each case.Principle components analysis generated factor load-ings used to weight the relative importance ofz-scores of these three indicators, which were thenadded together to form an index with a mean ofzero.13 If capital assets foster farm diversification viacommercialization, then the index of initial agricul-tural capital should exhibit a positive effect on agri-cultural diversity, as per the ‘differential capacity’argument. The other index focuses on labor-savingtechnological inputs used by a household in the yearprior to the survey. Given the labor-intensity of mostproducts in the study site, the use of labor-savingtechnologies should facilitate greater agricultural di-versity as via combined subsistence-commercial pro-duction systems. I drew on typologies of agriculturaltechnologies �e.g., Lee and Barrett 2001� to identifyfour labor-saving technologies: chainsaws, insecti-cides, fungicides, and herbicides. Each is measured as1 if not used and 0 if not. Another principle compo-nents analysis generated factor loadings, used toweight z-scores of the four indicators, which weresummed into an index with a mean of zero.14 Table 4shows that both indexes vary among households,suggesting inequalities in technological assets for ag-ricultural production.

‘Institutional capital’ not only refers to the use ofbank credit, but also whether a household had beenvisited by local extension agents. Both are binomialvariables, and both should be positively associatedwith agricultural diversity. Because credit injects ad-ditional funds into a farming system that can supple-ment or substitute for labor inputs, access to creditshould facilitate commercialization via adoption ofadditional agricultural enterprises. In the study site, akey goal of extension agents is to exchange informa-tion and expertise about appropriate and profitablefarming practices. As a result, visits by extensionagents should help households commercialize,

thereby increasing diversity. Table 4 indicates that59% of households had received agricultural credit,an indication of an established local credit market,while only 21% of households were visited by exten-sion agents, indicative of limited extension resources.

‘Social capital’ here refers to whether a householdreceived one or more sources of off-farm income �in-cluding remittances, pensions, small business in-comes, loans from third parties, and ‘other’ income�,and whether a household held one or more lots in anorganized neighborhood. Both of these are also bino-mial variables. Like credit, off-farm income can in-ject much-needed capital into a family farm that canpotentially be deployed for new agricultural enter-prises and thereby increase diversity. In Uruará andelsewhere, neighborhood organizations formed alongfeeder roads off the Transamazon highway corridor�Hall 1997�. Such organizations serve as mechanismsfor mutual aid among neighbors, who may establishproducer cooperatives to secure better prices, alerteach other when setting fires, and guard againstpoaching and incursions on established land claims.Neighborhood organizations thus help households inUruará to expand into commercial production, whileproviding a modicum of protection against crises inorder to manage risk, thereby fostering more diversi-fied agriculture. Table 4 shows that 10% of house-holds received remittances and 32% were in neigh-borhood organizations.

Models of Agricultural DiversityTo assess the effects of these determinants on house-hold agricultural diversity, Table 5 presents resultsfrom Tobit regression models of S, M6, and 1-H foragricultural production diversity.15 These models ac-count for the effects of one type of asset while con-trolling for the effects of others, in order to identifythe assets with the most important independentimpacts on agricultural diversity. As a reflection of thehigh correlations between S, M6, and 1-H, the mod-els tell broadly similar stories.16 The four groups of

13The factor weights from principal components analysis were:0.785 for chainsaws, 0.499 for cocoa dryers, and 0.588 for trac-tors. The factor dimension associated with these loadings had aneigenvalue of 1.21 and explained 40.4% of the common varianceof the three indicators.14The factor weights from principal components analysis were:0.604 for chainsaws, 0.654 for insecticides, 0.628 for fungicides,and 0.617 for herbicides. The factor dimension associated withthese loadings had an eigenvalue of 1.57 and explained 39.2% ofthe common variance of the four indicators.

15I opted for Tobit estimation because some diversity measures hadsizeable numbers of cases with minimum values, which can yieldbiased estimates of slope coefficients and their standard errors withOLS estimation �e.g., Maddala 1992�16While the results from the three models in Table 5 are broadlysimilar, there are some notable differences, and these are due to thedifferent aspects of diversity captured by S, M6, and 1-H. Theclearest example is for credit, which has a weak effect on S andmuch stronger effects on M6 and 1-H. This suggests that credit wasa particularly important stimulant of distributive diversification

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independent variables generate statistically significantmodels �p�Chi-square� � 0.01�, and several determi-nants exert significant effects �p � 0.05� in most or allof the models.

Indicators of labor assets exhibit the strongest ef-fects �p � 0.01�. The number of adults has a strong,positive, and non-linear effect on agricultural productdiversity. Households with more adult labor havemore diversified production systems, which is consis-tent with the ‘differential capacity’ argument. How-ever, especially large families also increasingly�evenness�, in addition to catalyzing the establishment of additional

product enterprises.

Table 5. Tobit Models of Agricultural Production Diversity, Farm Households, Uruará, Pará, 1996.

S M6 1-H

Likelihood Ratio Chi-square �df�19� 89.90 ** a 97.21 ** 79.51 **Valid Cases 251 246 246Left-censored Observations 9 20 20

Intercept 1.51 �1.38� 2.44 �1.02� * 0.44 �0.15� **

InfrastructureAverage Distance to Uruará Town �km� 0.00 �0.01� b � 0.01 �0.01� � 0.001 �0.001�‘Good’ Road Quality �0�No, 1�Yes� 0.04 �0.36� � 0.10 �0.27� � 0.02 �0.04�‘Fair’ Road Quality �0�No, 1�Yes� � 0.13 �0.27� � 0.13 �0.20� 0.01 �0.03�

Land AssetsNatural Log �ln� Hectares �ha� Held 0.03 �0.28� � 0.12 �0.20� � 0.04 �0.03� �

Labor AssetsNumber of Adults �Ages 15-65� 0.97 �0.15� ** 0.72 �0.11� ** 0.09 �0.02� **Adults Squared � 0.07 �0.01� ** � 0.05 �0.01� ** � 0.01 �0.001� **Ln Days of Labor Hired 0.22 �0.06� ** 0.17 �0.05� ** 0.02 �0.01� **Number of Children �Age � 15� 0.09 �0.05� � 0.02 �0.04� 0.005 �0.01�

Capital AssetsNatural Capital

Ln ha Cleared upon Acquisition � 0.09 �0.06� � � 0.08 �0.04� � � 0.01 �0.01� �Timber Extraction �0�No, 1�Yes� 0.04 �0.30� � 0.01 �0.22� � 0.01 �0.03�

Cultural CapitalRegion of Birth �0�Other, 1�Amazon� � 0.23 �0.33� � 0.37 �0.24� � � 0.04 �0.04�

Human CapitalPrevious Agricultural Experience �0�No, 1�Yes� � 0.14 �0.25� � 0.05 �0.18� 0.01 �0.03�Years of Residence in Uruará 0.06 �0.07� 0.03 �0.05� � 0.002 �0.01�Years of Residence Squared � 0.004 �0.003� � 0.002 �0.002� 0.00 �0.00�

Technological CapitalAgricultural Capital upon Arrival �Factor Index� � 0.06 �0.10� � 0.03 �0.07� � 0.001 �0.01�Labor-saving Capital �Factor Index� 0.05 �0.09� � 0.05 �0.07� � 0.01 �0.01�

Institutional CapitalCredit �0�No, 1�Yes� 0.48 �0.28� � 0.61 �0.21� ** 0.10 �0.03� **Extension Assistance �0�No, 1�Yes� � 0.20 �0.32� � 0.21 �0.24� � 0.01 �0.04�

Social CapitalOff-farm Income �0�No, 1�Yes� � 0.53 �0.24� * � 0.38 �0.18� * � 0.03 �0.03�Neighborhood Organization �0�No, 1�Yes� 0.56 �0.26� * 0.48 �0.19� * 0.04 �0.03�

Source: 1996 Uruará survey; Notes: a � p � .15, * p � .05, ** p � .01; b Values are unstandardized coefficients. Standard errors are inparentheses.

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allocate labor to off-farm activities rather than addi-tional agricultural enterprises. We get some sense ofthis effect by taking the results from the S model�since they are the most easily interpreted� and work-ing from the intercept with all other asset indicatorsset to their minimum values �since many arecategorical variables�. In this case, a household withone adult would have two agricultural products, andthis rises to nearly five products in households withnine adults, and thereafter declines. The decline inagricultural diversity among especially large house-holds provides partial support for the ‘differentialmotivation’ argument, as moderate-sized householdshave the most diversified agricultural systems. Thefindings indicate that neither labor-poor or labor-richhouseholds have the most diversified agricultural sys-tems, which provides a wrinkle in discussions ofhousehold assets and diversity.

Given the importance of family labor, it is intrigu-ing that hired labor is also important. For a one-per-cent increase in days of labor paid, S rises by 0.22products. This confirms the importance of labor mar-kets for diversity. Household and hired labor are un-correlated �r�0.001, p�0.99�, so one is not system-atically used to complement or substitute for theother. While more hired labor allows for more diver-sified production systems, consistent with the ‘differ-ential capacity’ argument, households hiring labor arenot necessarily also those with abundant labor,implying that an important source of labor capacity isfinancial capital used to hire labor.

Some capitals are also important, as credit,off-farm income, and neighborhood organizations ex-ert strong effects �generally p � 0.05�. These findingsconfirm the importance of institutional capital viacredit markets as well as social capital understood associal ties and organizational membership. Given thefocus of credit lines on adoption of commercial prod-ucts, this finding suggests that households add theseto an existing portfolio of subsistence crops, therebyincreasing agricultural diversity. Off-farm incomeshows an interesting negative impact on agriculturaldiversity, which indicates that households withoff-farm income sources are less focused on lookingafter many agricultural enterprises. Instead, off-farmincome is likely being used to pay for consumptiongoods, as in the case of retirees buying rather thangrowing their food, or as investments in existing ag-ricultural activities or in off-farm activities. The im-pact of membership in a neighborhood organizationon production diversity is positive, which suggests

that local organizations help households manage therisks of commercialization, thereby facilitating agri-cultural diversification.

Table 6 presents Tobit models of S, M6, and 1-Hfor agricultural income diversity, and affords the op-portunity to see if the significant determinants are thesame as for production diversity. Broadly speaking,Table 6 leads one to much the same conclusions asTable 5: the models are statistically significant, andlabor assets exhibit the most important effects,followed by credit and neighborhood organizationmembership.

That said, the findings for income diversity revealsome contrasts with those for production diversity.The size of the coefficients for adult labor is smallerfor income than production diversity, which suggeststhat household labor generates greater subsistencethan commercial diversification. Conversely, the co-efficient for hired labor is larger for income than pro-duction diversity, which implies that diversification ofcommercial enterprises relies more heavily on the la-bor market than subsistence diversification. Further,the equation for S income diversity reveals a signifi-cant positive effect for children, suggesting thatstructural diversification of income sources is facili-tated by child labor inputs in addition to othersources. Hence, the mix of labor inputs to generatesubsistence diversity differs somewhat from that forincome diversity. Turning to capitals, off-farm incomesources, which exerted significant negative effects onproduction diversity, have no significant effects onincome diversity. This suggests that off-farm incomereduces subsistence diversity, but not income diver-sity. Neighborhood organizations are also less impor-tant for income diversity, which suggests thatmembership in local mutual aid groups is more rel-evant for purposes of securing a diverse subsistencethan a diverse set of monetary income streams fromagriculture.

Discussion

The findings indicate that family farming systems inUruará exhibit variation in household asset endow-ments and agricultural production and income diver-sity, and that assets exert important effects ondiversity. On the whole, the findings provide moresupport for the ‘differential capacity’ argument byshowing that relatively asset-rich households – thosewith more adults, more hired labor, credit, and the

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social capital of membership in a neighborhood orga-nization – tend to have to higher agricultural diver-sity. Hence, asset inequality among households isrelevant when considering prospects for householdagricultural diversity. However, the most importantrespects in which ‘asset rich’ and ‘asset poorer’households differ in terms of agricultural diversity is

primarily in terms of labor, not capital. This calls formore attention to the specific assets that are likely tobe important for household diversity in rural areas ofdeveloping regions.

The findings return us to questions about the im-plications of agricultural diversification for compati-bilizing the goals of poverty reduction, economic

Table 6. Tobit Models of Diversity in Agricultural Income Sources, Farm Households, Uruará, Pará, 1996.

S M6 1-H

Likelihood Ratio Chi-square �df�19� 59.19 ** a 63.28 ** 63.13 **Cases 251 247 247Left-censored Observations 14 42 42

Intercept 0.73 �1.37� 1.32 �1.22� 0.29 �0.21�

InfrastructureAverage Distance to Uruará Town �km� � 0.001 �0.01� b � 0.004 �0.01� � 0.001 �0.001�‘Good’ Road Quality �0�No, 1�Yes� � 0.58 �0.36� � � 0.63 �0.32� � � 0.08 �0.06�‘Fair’ Road Quality �0�No, 1�Yes� � 0.13 �0.27� � 0.17 �0.24� � 0.03 �0.04�

Land AssetsNatural Log �ln� Hectares �ha� Held � 0.04 �0.27� � 0.17 �0.24� � 0.06 �0.04�

Labor AssetsNumber of Adults �Ages 15-65� 0.55 �0.15� ** 0.52 �0.13� ** 0.09 �0.02� **Adults Squared � 0.04 �0.01� ** � 0.03 �0.01� ** � 0.01 �0.002� **Ln Days of Labor Hired 0.26 �0.06� ** 0.23 �0.05� ** 0.03 �0.01� **Number of Children �Age � 15� 0.11 �0.05� * 0.05 �0.04� 0.003 �0.01�

Capital AssetsNatural Capital

Ln ha Cleared upon Acquisition � 0.06 �0.06� � 0.09 �0.05� � 0.01 �0.01� �Timber Extraction �0�No, 1�Yes� 0.10 �0.30� 0.09 �0.27� 0.02 �0.05�

Cultural CapitalRegion of Birth �0�Other, 1�Amazon� 0.17 �0.33� 0.18 �0.29� 0.003 �0.05�

Human CapitalPrevious Agricultural Experience �0�No, 1�Yes� 0.06 �0.24� 0.01 �0.22� � 0.004 �0.04�Years of Residence in Uruará 0.10 �0.07� 0.09 �0.06� 0.01 �0.01�Years of Residence Squared � 0.004 �0.003� � 0.004 �0.002� � 0.001 �0.0004�

Technological CapitalAgricultural Capital upon Arrival �Factor Index� � 0.02 �0.10� � 0.07 �0.09� � 0.02 �0.02�Labor-saving Capital �Factor Index� 0.002 �0.09� � 0.05 �0.08� � 0.01 �0.01�

Institutional CapitalCredit �0�No, 1�Yes� 0.46 �0.28� � 0.53 �0.25� * 0.15 �0.04� **Extension Assistance �0�No, 1�Yes� � 0.08 �0.32� � 0.12 �0.29� � 0.03 �0.05�

Social CapitalOff-farm Income �0�No, 1�Yes� � 0.28 �0.24� � 0.22 �0.21� � 0.02 �0.04�Neighborhood Organization �0�No, 1�Yes� 0.34 �0.26� 0.45 �0.23� * 0.06 �0.04�

Source: 1996 Uruará survey. Notes; a � p � .15, * p � .05, ** p � .01; b Values are unstandardized coefficients. Standard errors are inparentheses.

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development and environmental sustainability �Leeand Barrett 2001�, bound together in ‘productiveconservation’ efforts to raise incomes while maintain-ing forest cover �Hall 1997�. If researchers can docu-ment cases where agricultural diversity proves effica-cious for ‘win-win-win’ scenarios via productiveconservation, policy and community initiatives topromote diversity will have to deal with the issue ofhousehold asset inequality.

Given the importance of labor availability, the factthat it is tied to evolving household age structuresbears implications. First, it implies that the capacityfor households to diversify their agriculture changesover time as labor availability rises when childrengrow up and then declines if adult children move out.This means that households are ‘moving targets’ forpolicies to encourage agricultural diversity, andrespond differently over time to a given policy. Sec-ond, the scarcity of labor implies that a market forhired labor is crucial to offset household labor short-ages to sustain agricultural diversity over time. It isunclear how state policies might encourage a stablerural labor market for agricultural employment whilealso avoiding rising rural poverty. Community initia-tives, perhaps via neighborhood organizations, de-serve further attention with respect to systems oflabor reciprocity as a means of complementing wagelabor markets in order to supplement household labor�Hall 1997�.

The challenges of labor scarcity turn attention toinitiatives featuring capital as a means of reducinghousehold asset inequalities and broadening local ca-pacity for agricultural diversity. The importance ofcredit calls to mind an array of issues concerningcredit for smallholders. Credit policy in the Amazonhas at times been vilified for favoring large-scaleproducers and/or leading to wasteful forest clearingfor speculation rather than productive enterprises.Smallholders have mobilized to demand special creditlines, but successes have been partial, as access tocredit was ‘captured’ by powerful local interests, in-cluding in Uruará �Toni 1999�.

These difficulties call for wariness of unintendedeconomic and environmental consequences of policypackages and local initiatives in developing regions.It may only be feasible for ‘asset rich’ households totake advantage of supports for a specific crop and di-versify their production systems to incorporate thatenterprise. But if new supports are provided for a cropdeemed to be an emergent commodity and prices risefor another crop, many producers may happily diver-

sify and benefit. That said, market saturation oftenoccurs for new crops with high prices, leading toboom-bust dynamics and perhaps more forest clear-ing as producers seek to sustain their incomes bymaximizing their output volume. Conversely, if newsupports for a crop fail to generate much response todue a lack of market demand or due to persistent ob-stacles to adoption �such as unequal asset endow-ments�, producers may instead expand their existingenterprises �such as cattle�.

The findings and recent visits to Uruará �in 2002and 2003� bear implications for prospects in the studysite. Given the importance of perennials as a meansof commercializing and diversifying production inUruará, price fluctuations and pest attacks are daunt-ing challenges. Since the survey reported here, blackpepper prices dropped, prompting some producers toabandon the most widely grown perennial crop in1996. However, cocoa prices rose, and householdshave learned to manage cocoa pests by burning in-fected tree branches. Nonetheless, there is debate overthe future of agricultural diversity in the study site.One the one hand are those advocating pastureexpansion for specialized ranching systems, or alter-natively the establishment of mechanized agriculturefocused on one or two crops; on the other hand areadvocates of highly diversified agroforestry or agro-pastoral systems. Criticisms of both perspectives re-flect the central finding of the analysis presented here:‘differential capacity’ due to unequal asset endow-ments will hinder widespread household adoption ofthese systems.

At stake in this debate is the presumption that ag-ricultural diversification can yield win-win-win sce-narios of productive conservation, which have as yetat best only been partially documented in a few cases.While research on the economic and ecological im-plications of agroforestry systems has made greatstrides in the past two decades, including in the Ama-zon �e.g., Yamada and Gholz 2002; Browder andPedlowski 2000; Vosti et al. 1998�, there remains aneed for further agroforestry research that balancessocial and biophysical considerations �e.g., Mercerand Miller 1998�.

Acknowledgments

This research was supported by a grant from the USNational Science Foundation �SBR-9511965�. I thankAdilson Serrão and Alfredo Homma of EMBRAPA/

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CPATU for support in Brazil, Charles Wood andRobert Walker for support in the US, and researchteam members André Caetano, Roberto Porro, Fabi-ano Toni, Célio Palheta, Rui Carvalho, and LuizGuilherme Teixeira, as well as the people of Uruaráfor insights about the study site. This paper also ben-efited from comments by two reviewers and the edi-tors. Having said that, errors contained herein are theauthor’s responsibility.

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